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How Generative AI is Going to Affect the Georgian Labor Market

Posted on October 27, 2025 | Policy Brief, Policy Papers

Modern glass office building with employees working late, representing the impact of generative AI on the labor market.

This policy paper investigates the potential impact of generative artificial intelligence (GenAI) on the Georgian labor market, identifying which occupations and demographic groups are most affected. Drawing on the International Labor Organization’s (ILO) 2025 exposure scores and detailed 2023 Georgian Labor Force Survey data, our findings reveal that 26% of Georgian workers are in occupations where part of their tasks could potentially be performed, fully or partially, by GenAI, with over a third of those in medium- to high-exposure roles. Compared with the broader Europe and Central Asia region, Georgia has fewer workers in occupations vulnerable to full automation, while a larger share of the workforce is engaged in roles with potential for task augmentation. The analysis also reveals that GenAI’s impacts are uneven, with women, urban workers, younger individuals, and those with higher education being disproportionately represented in high-exposure occupations. Importantly, exposure scores measure technological feasibility rather than actual displacement risk – actual outcomes will depend on adoption rates, regulatory frameworks, and organizational decisions. Thus, timely and active policy involvement – from targeted upskilling to addressing digital disparities – is crucial to turn AI challenges into opportunities and fully harness its benefits by strengthening workers’ capacity to complement AI in their tasks.

Introduction

Generative artificial intelligence is playing an increasingly prominent role in workplaces worldwide. Tools such as ChatGPT, Midjourney, Google Gemini, and others are transforming how tasks are performed, changing work routines, and creating new forms of collaboration between humans and machines.

GenAI represents both a challenge and an opportunity. While different GenAI tools offer productivity gains and cost savings, their implementation may deepen regional disparities and pressure vulnerable groups – especially if targeted upskilling and digital-inclusion measures are not in place. At the same time, GenAI can reshape occupations by creating AI-specific and complementary roles. Balancing these opportunities and challenges is therefore critical to harness the full potential of GenAI.

A structured way to understand the effects of AI in the workplace is by considering two distinct channels: automation and augmentation. Automation refers to the complete substitution of human-performed tasks that can now be executed independently by AI without human involvement. Typically, such tasks are routine and cognitive, such as basic content generation or data classification. In contrast, augmentation captures scenarios where GenAI acts as a complementary tool, enhancing human performance without replacing the worker. The distinction between automation and augmentation is crucial for evaluating the implications of GenAI: while the former may lead to job displacement, the latter suggests changes in task composition, potential shifts in skill demand, and enhancements in labor productivity.

Consequently, the way GenAI shapes the new labor market reality will be affected by the composition of these two effects. In particular, in developing countries like Georgia, the extent of automation and resulting job displacement might be limited, as a large share of employment is in manual, physical sectors largely insulated from AI. At the same time, developing countries might underutilize the benefits of augmentation because many workers lack digital skills or access to GenAI tools, limiting productivity gains.

This policy paper aims to investigate the potential impact of generative artificial intelligence on the Georgian labor market through the automation-augmentation lens. In doing so, we utilize the approach used in the International Labor Organization’s (ILO)  Global Index of Occupational Exposure to Generative AI (Gmyrek et al., 2025). Rather than treating automation and augmentation potentials as two opposing categories with a large area of uncertainty in between, Gmyrek et al. (2025) apply a more refined classification that captures a spectrum of AI exposure. The ILO’s task-based framework categorizes occupations into six distinct groups by evaluating the extent to which their tasks can be automated by Generative AI. These groups are constructed considering both the average exposure score of tasks within each occupation and the standard deviation of exposure scores across those tasks. This enables the differentiation of jobs not only by their average exposure to GenAI but also by distinguishing between occupations where exposure is relatively evenly distributed across tasks and those where it is concentrated in only a subset of tasks. Aligning closely with the notions of augmentation and automation potential in the earlier ILO framework described in Gmyrek et al. (2023), an occupation is considered to have an “augmentation potential” when the average exposure score is low, but deviation across tasks is high. In other words, while some tasks in these jobs may have high automation potential, many others continue to require human involvement (Gmyrek et al., 2025). Conversely, an occupation is said to have an “automation potential” when the average exposure score is high and there is a high consistency of exposure across tasks (i.e., there is low standard deviation). Occupations that fall in between these two categories can be viewed as being in transition, slowly shifting from augmentation toward automation potential.

By using the ILO’s 2025 exposure scores and detailed Georgian Labor Force Survey data from 2023, we identify which occupations and demographic groups have the highest exposure to GenAI. Findings suggest that a significant share (26%) of Georgian workers face some level of exposure to generative AI, ranging from low to high, with over a third of them (100,584 individuals) falling into the medium- to high-exposure categories. The remainder are either not exposed (59%) or minimally exposed (15%). In comparison to other countries in Europe and Central Asia, Georgia is less affected by the threat of job displacement coming from automation, while the potential for augmentation is higher.

The exposure trends vary markedly by gender, age, and region. Urban workers, particularly in Tbilisi, are the most exposed, while rural workers face lower immediate exposure, reflecting existing digital divides and regional occupational characteristics. Gender disaggregated analysis shows that women, who are slightly overrepresented in AI-exposed clerical and administrative roles, account for 63% of workers in medium- and high-exposure occupations. Age-wise, younger and more digitally skilled workers tend to occupy the roles most affected by generative AI.

This paper proceeds as follows. First, we describe the methodology and data used to assess exposure to Generative AI in the Georgian labor market. Next, we present the ILO’s global estimates of GenAI-exposed occupations. The subsequent sections provide the results for Georgian workers, disaggregated by occupation, education, demographic groups, and region. The final section summarizes the findings and offers some policy insights.

Methodology and Data

This section outlines the methodology and data sources used to assess occupational exposure to Generative AI in Georgia, combining international GenAI exposure scores with nationally representative labor market information.

Several studies have developed indices or measures of occupational automation/AI exposure, often using different methods and classifications. A pioneering study by Frey and Osborne (2013) labeled occupations as automatable or not by applying a machine-learning classifier to the Occupational Information Network (O*NET) database of tasks – a comprehensive database of occupational information maintained by the U.S. Department of Labor. Their approach effectively treated each occupation as a unitary risk category, which has been critiqued for ignoring within-job task differences. Arntz et al. (2016), an OECD analysis, relaxed Frey and Osborne’s (2013) assumptions by using PIAAC survey data to allow task variation within occupations. Felten et al. (2019, 2021) developed the AI Occupational Exposure (AIOE) index, which uses overlaps between occupations’ O*NET skill and task requirements and known AI capabilities to compute exposure scores.  Using AI exposure metrics sourced from Felton (2021) and the IMF Complementarity Index, PwC Global AI Jobs Barometer (2025) classifies jobs into “augmented” or “automated” categories. Specifically, occupations with high AI exposure (>0.5 on a 0–1 scale) are split by an AI-complementarity threshold: those with high complementarity are deemed “augmented” (AI enhances tasks), while low complementarity jobs are “automated” (AI replaces tasks).

The exposure framework proposed by Gmyrek et al. (2025) builds on this literature and captures the occupational exposure to generative AI by combining algorithmic prediction with extensive large-scale survey data, supplemented by expert validation and iterative revisions to arrive at a refined global index of exposure. The process begins by considering each job title as a composite of tasks, each with varying susceptibility to automation. This approach aligns with contemporary labor market research emphasizing the mixed nature of task automation potential within occupations rather than assuming uniform automation across entire job categories.

The initial steps used to develop exposure scores involve an algorithmic assessment of automation potential for 2,861 detailed tasks derived from the Polish 6-digit occupational classification system. Utilizing three advanced Large Language Models (LLMs) – GPT-4, GPT-4o, and Gemini Flash 1.5 –  Gmyrek et al. (2025) assign a synthetic automation score on a continuous scale from 0 to 1, where 0 indicates no potential for automation, a score from 0 to 1 means augmentation by GenAI, and 1 signifies full automation potential without human involvement. This phase leverages sequential Application Programming Interfaces calls wherein each LLM is provided with contextual information regarding the task’s occupational classification and is instructed to assign the scores and justify them. Repeated scoring by multiple LLMs for the same task enabled triangulation and helped identify inconsistencies between model outputs. The distribution of synthetic scores revealed GenAI exposure in cognitive-intensive occupational groups and lower exposure where physical tasks dominate.

Subsequently, the researchers conduct a large-scale human survey utilizing the Computer-Assisted Web Interview (CAWI) technique to capture workers’ perceptions of task automation potential. The sample included respondents from all ISCO-08 1-digit occupational groups. Each respondent evaluates the automation susceptibility of a randomized set of 35 tasks from their occupation on a 0-100 scale.

To reduce biases related to uneven task familiarity and varying levels of GenAI knowledge, Gmyrek et al. (2025) supplement the survey with an expert validation stage. A smaller group of international experts from the ILO, National Research Institute of the Ministry of Digital Affairs in Poland, and the Polish Ministry of Family, Labor and Social Policy – each with extensive labor market expertise – assess a sample of tasks across occupational groups. Their evaluations focus on practical feasibility and workplace realities, helping to correct potential over- or underestimations from the general survey and ensuring that results reflect grounded, pragmatic perspectives on automation potential.

Moreover, two independent AI models then reconcile differences between survey respondents and experts, analyzing task-level scores and generating adjusted automation potentials with justifications.

The final stage constructs the Adjusted Global Index of GenAI Exposure, classifying occupations at the ISCO-08 4-digit level into six categories based on the mean and standard deviation of task-level automation scores. These range from Not Exposed and Minimal Exposure to four ascending Exposure Gradients:

  • Gradient 1 (Low exposure, high task variability)
  • Gradient 2 (Moderate exposure, high task variability)
  • Gradient 3 (Significant exposure, high task variability)
  • Gradient 4 (Highest exposure, low task variability)

Occupations composed of tasks with low average GenAI exposure scores and substantial variability of these scores across tasks (high task variability) are more closely associated with augmentation, whereas occupations with high average exposure scores and low task variability are more closely associated with automation. This classification advances beyond the binary automation–augmentation lens of prior studies by incorporating task variability within occupations, thus reflecting the heterogeneous automation risks embedded in different tasks.

ILO’s global estimates of GenAI-exposed occupations

This section presents the ILO’s global estimates of occupations exposed to Generative AI, illustrating potential impacts on labor markets worldwide.

As noted by Gmyrek et al. (2025), approximately 24% of the global workforce is engaged in jobs that involve some level of exposure to GenAI (Figure 1). This exposure is more prevalent in higher-income nations, where 34% of total employment is affected, compared to just 11% in low-income countries (Gmyrek et al., 2025).

Figure 1. Global estimates of occupations potentially exposed to GenAI (% of employment by sex)

Source: Figure 20 from Gmyrek et al. (2025).

As Figure 1 shows, women are disproportionately overrepresented in higher-exposure roles, while men are slightly more concentrated in lower-exposure jobs. Among male employees, approximately 21% of positions fall into one of the exposure levels, with 3.1% classified in gradient 3 and 2.4% in the highest exposure level, gradient 4. Conversely, the proportion of female employment in potentially exposed roles is significantly greater, particularly in the upper two gradients, where 5.7% of female workers are in gradient 3 and an additional 4.7% are in gradient 4. These gender disparities also widen with country income levels, increasing to 9.6% for women in gradient 4 versus 3.5% for men in high-income countries (Gmyrek et al., 2025).

When it comes to Europe and Central Asia, as Figure 2 shows, in this region, about 32% of employees (136 million jobs) are exposed to GenAI, with 5.7% considered highly exposed.

Figure 2. Estimates of occupations potentially exposed to GenAI for the Europe and Central Asia region (% of employment by sex)

Source: Figure 20 from Gmyrek et al. (2025).

This region exhibits a notable gender disparity: 26% of men compared to 39% of women are exposed, and 3.3% of male-held jobs (61 million) versus 8.6% of female-held jobs (75 million) are highly exposed to GenAI.

Results for the Georgian Labor Market

For the current analysis, we applied the above-described exposure gradients to 361 ISCO-08 4-digit occupations from the 2023 Georgian Labor Force Survey (LFS), covering a workforce of 1,313,636, of whom 45% (594,064) were women. Occupations lacking sufficient detail or unclassified under ISCO-08 were excluded.

As shown in Figure 3, overall, about 26% of Georgian workers fall within one of the four exposure gradients, with over a third (7.6%) of those falling in upper (Gradient 3 and Gradient 4) exposure categories.

Figure 3. Estimates of occupations potentially exposed to GenAI in Georgia (% of employment by sex)

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

Notably, female workers in Georgia are more concentrated in higher-exposure occupations compared to men. Among female workers, 30% of positions fall within the exposure gradients, compared to 23% among male employees. The gap is particularly evident in the upper gradients – 9.1% of women are in gradient 3 and 1.5% in gradient 4, versus 4.1% and 1.1% of men, respectively.

Compared to the broader Europe and Central Asia region, where 32% of employees are exposed to GenAI (versus 26% in Georgia), Georgia has a lower overall AI exposure. Moreover, the distribution across gradients also differs. Among those exposed to GenAI, higher gradients (Gradient 3 and Gradient 4) represent a smaller share of total employment in Georgia, while lower gradients (Gradient 1 and Gradient 2) represent a larger share. This indicates that, compared to the Europe and Central Asia region, fewer workers in Georgia are employed in jobs prone to automation, whereas a larger portion of workers are in jobs with potential for augmentation. One likely explanation of low overall exposure to AI in Georgia is the structure of the Georgian labor market, where a substantial share (16.5%) of employment is in agriculture, which is largely insulated from AI and automation. As for the higher share of Gradient 1 and Gradient 2 in Georgia, it is primarily driven by employment in the service industries, as shown in the sectoral analysis below. Following agriculture, a significant portion (15.5%) of Georgian workers are employed in the trade sector, with a large share of employment falling within the first two exposure gradients, aligning closely with the notion of augmentation (Figure 4).

Exposure by Occupations

Table 1 represents the top 20 most exposed occupations, disaggregated by gender. The occupations with the highest automation scores are predominantly clerical, administrative, and routine office roles (e.g., Data Entry Clerks; Typists and Word Processing Operators; Statistical, Finance, and Insurance Clerks, Financial Analysts, Payroll Clerks, and others). Among the occupations most exposed to AI, where gender disaggregation is feasible based on representativeness criteria, many are observed to be female-dominated in Georgia (Table 1).

Table 1. Top 20 AI-exposed occupations (by Gender)

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure. Note: The table presents gender disaggregation only for occupations with more than 25 survey participants to ensure data representativeness

According to Webb (2020), sectors with high shares of routine cognitive tasks, such as public administration, finance, education, and clerical services, typically feature a lot of communication-heavy, document-based, or repetitive analysis that is becoming increasingly feasible for generative AI tools such as ChatGPT and Copilot to automate or assist in. Especially clerical and administrative roles demonstrate a strong concentration of high exposure categories, which indicates a large share of activities – e.g., scheduling, documenting, and internal correspondence – can be effectively executed by AI systems.

Exposure by Sector

The sector-level exposure of Georgia’s labor force to GenAI reveals stark sectoral differences in exposure to AI-driven transformation. This analysis highlights NACE Rev.2 section-level economic activities that face the most significant automation risks or the potential for augmentation, as well as those largely insulated from GenAI.

As Figure 4 below illustrates, at the high-exposure end, Financial and Insurance Activities stand out.

Figure 4. AI-exposure by Sector

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure. Note: A – Agriculture, forestry and fishing, B – Mining and quarrying, C – Manufacturing, D – Electricity, gas, steam, E – Water supply and waste management, F – Construction, G – Wholesale and retail trade, H – Transportation and storage, I – Accommodation and food services, J – Information and communication, K – Financial and insurance activities, L – Real estate activities, M – Professional, scientific and technical, N – Administrative and support services, O – Public administration, P – Education, Q – Human health and social work, R – Arts, entertainment and recreation, S – Other service activities, T – Households as employers, U – Extraterritorial organizations.

Although only 6.2% of jobs (1,660 out of 26,606 workers) fall into Gradient 4 (the very high exposure category to GenAI), this is the highest share among service sectors. When Gradient 3 is included, the combined high-exposure share is even larger, further distinguishing this sector. Close behind Financial and Insurance Activities, Professional, Scientific, and Technical Activities also register elevated risk, with 2.2% of workers in Gradient 4 and 19.4% in Gradient 3. The Information and Communication sector – often seen as being at the forefront of digital transformation – has 19% of its workforce in Gradients 3 and 4. However, the majority of workers fall under Not Exposed (33.0%) or Minimal Exposure (36.1%).

The lowest exposure is found in sectors dominated by manual labor and physical tasks. Agriculture, Forestry and Fishing, Georgia’s one of the largest employing sectors with around 225 thousand workers, is overwhelmingly shielded: 86.8% are not exposed, and only 0.1% fall into Gradient 4. Construction shows a similar pattern, with 60.9% (71,721 of 117,770) unexposed and just 1.2% (1,412) in Gradient 4. Transportation and Storage, and Mining and Quarrying also report high insulation, with 51.1% and 65.6% of their workforces not exposed, respectively.

Furthermore, as Figure 4 shows, the higher prevalence of Gradient 1 and Gradient 2 roles, aligning closely with the notion of augmentation, is largely attributable to employment in Georgia’s service industries. After agriculture, a substantial portion of the workforce is employed in the trade sector, where most jobs fall within the first two exposure gradients, indicating strong potential for augmentation.

Exposure by Gender

The analysis uncovers stark gender differences in exposure to generative AI in the Georgian labor market. A larger share of the women falls in the middle to high exposure categories as compared to men. This is especially important in domains like clerical work, customer service, and administrative support, fields where women are typically overrepresented.

As Figure 5 shows that more men hold occupations with little or no exposure, while a larger share of women hold jobs in the Gradients 3 and 4 categories.

Figure 5. AI-exposure by Gender

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

In particular, women make up more than 60% of workers in the highest-exposure occupations, while men represent the large majority of workers in low-gradient occupations where AI-driven task substitution is less likely to have significant effects.

Exposure by Age and Education

Traditionally, education has been viewed as a means of shielding workers from technological displacement (e.g., Acemoglu and Autor, 2011; Autor, Levy, and Murnane, 2003). However, as Webb (2020) shows, artificial intelligence will affect the labor market very differently than previous automation and computerization in earlier waves (technologies like software and industrial robots), primarily by impacting high-skilled, high-wage occupations rather than low- or middle-skilled ones. As the author claims, highly educated workers (with college degrees, including Master’s degrees), higher-wage earners, and more experienced workers are most exposed to AI. Many tasks that require higher education – such as drafting legal documents, producing code, preparing reports, analyzing data, or writing content – are precisely the kinds of tasks GenAI can now perform or augment.

Analysis of Georgian labor market data demonstrates that a significant proportion of Georgian workers with bachelor’s or master’s level degrees are employed in occupations (administrative, legal, or financial services) with mid to high exposure gradients (Figure 6). Less educated workers tend to take occupations that are shielded from GenAI exposure.

Figure 6. GenAI-exposure by Educational Level

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

Analysis by age categories reveals that younger, more digitally skilled workers generally occupy occupations currently more affected by generative AI (Figure 7). In contrast, older workers tend to be more represented in the “Not Exposed” categories. This reflects that individuals aged 55 and above are less likely to work in roles requiring computer use or modern digital technologies, with many in this group being pensioners who remain active in agriculture and other physically demanding jobs.

Figure 7. GenAI-exposure by Age Group

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

Regional Disparities in Exposure

Exposure to generative AI varies significantly across regions of Georgia, with urban areas – particularly Tbilisi – exhibiting higher levels of exposure. Considering only urban areas, approximately 35% of Georgian workers face some level of exposure (from Gradient 1 to Gradient 4), of which 11% (86,852 individuals) fall into the medium- to high-exposure categories.

In contrast, rural and mountainous regions generally exhibit lower exposure. This is primarily due to the occupational pattern in these regions, which includes agriculture, manual labor, and low-digital service industries. Such jobs are less susceptible to generative AI as they heavily rely on physical labor that currently remains beyond the capabilities of current GenAI technologies. Figure 8 and Figure 9 below illustrate this distinction.

Unsurprisingly, among the urban areas, Tbilisi – home to roughly 32% of the workers – has the highest share of workers in Gradients 3 and 4 (moderate to high exposure) at 13%, followed by Kvemo- and Shida Kartli, with 12% and 10% of exposed workers, respectively. These two regions together employ around 17% of all workers in Georgia. Regions such as Kakheti and Samtskhe-Javakheti have the highest proportion of workers in the “Not Exposed” category, both in rural and urban areas, but these regions are hosting only 9% and 5% of all Georgian workers, respectively.

Figure 8. GenAI Exposure in Urban Areas

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

Figure 9. GenAI Exposure in Rural Areas

Source: Authors’ calculations based on Geostat’s LFS data and ILO’s estimates of occupations’ AI exposure.

Conclusion

Our analysis, based on ILO exposure scores and Georgian labor market data, reveals that compared to other European and Central Asian countries, where highly GenAI-exposed occupations are more prevalent, Georgia faces a lower feasibility of AI-driven displacement, with greater opportunities for task augmentation. This pattern reflects the structure of the Georgian labor market, with a large share of employment in agriculture – largely insulated from GenAI – and significant employment in service industries, particularly trade, which predominantly falls within the lower exposure gradients closely aligned with the notion of augmentation. At the same time, the observed impact of Generative AI is uneven across gender, age, region, and education. Women, urban workers, and individuals with higher education in Georgia are disproportionately represented in high-exposure roles. In contrast, rural and older workers are less engaged in occupations exposed to GenAI. This is likely due to factors such as limited connectivity, lower levels of digital literacy, fewer training opportunities, and the nature of jobs available in rural areas. Interestingly, higher education increases exposure in some cases, as graduates tend to cluster in cognitively routine jobs that are more vulnerable to automation. Regional disparities are also pronounced, with Tbilisi showing the highest concentration of high-exposure occupations.

While the above raises a serious concern, it is important to remember that the exposure scores measure technological feasibility rather than actual displacement risk; the latter will be influenced by adoption rates, regulatory frameworks, and organizational decisions.

Further, GenAI does not only substitute for existing tasks; it is actively reshaping task composition and creating new roles. These include AI-specific occupations such as machine-learning engineers, prompt engineers, AI product managers, and AI ethics/compliance officers, as well as complementary roles that augment human work through human-AI collaboration and oversight. Evidence from Acemoglu et al. (2022) demonstrates rapid growth in AI-related job postings and shifts in hiring patterns, showing that AI adoption can simultaneously reduce demand in some occupations while boosting it in others. The overall impact depends critically on the approach to technology adoption and investments in complementary skills and institutional frameworks. At this stage, no research has systematically examined these trends in Georgia. However, some examples can be observed in practice. For instance, there are services offering training on the use of AI in labor relations, including the ethical application of AI in human resources. In addition, ICT-sector vacancies often list familiarity with new technologies among the required tasks and emphasize that employees should be aware of and actively follow technological developments, including advances in artificial intelligence. Taken together, these examples suggest that AI-related specialization may gradually expand in the Georgian labor market.

Taken together, these findings highlight that generative AI presents both a challenge and an opportunity for the Georgian labor market. While certain occupations face high exposure to GenAI that could disrupt existing employment patterns, other sectors may experience productivity gains and/or new job creation. The lower overall exposure and higher prevalence of lower-gradient, augmentation-aligned roles suggest that Georgia is positioned to leverage AI for complementarity rather than face widespread automation. Acting early is crucial to turn challenges into opportunities and to fully harness the augmentation potential across all occupations. For this purpose, investing in targeted upskilling and reskilling programs is important not only for workers in high-exposure roles, enabling them to adapt and transition, but also for those in lower-gradient occupations, to strengthen their ability to complement GenAI in their tasks and enhance productivity. At the same time, the analysis highlights that exposure is unevenly distributed across regions and demographic groups, therefore underscoring the need to address digital skill and gender gaps, connectivity challenges, and regional disparities.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Towards European Union Membership: Poland’s EU Pre-accession Funds and Infrastructure Development

Posted on October 6, 2025 | Policy Brief, Policy Papers

European Union flag waving during a public demonstration, symbolizing support and integration efforts related to EU Pre-Accession Funds.

In advance of formal membership, candidate countries are offered three pillars of EU assistance: trade concessions, stabilization and association agreements and financial support. These instruments aim both to prepare candidates economically, politically and administratively, and to signal accession’s benefits to their populations. In this paper we describe the channels in which the third pillar – the EU pre-accession funds – affected Poland’s economic and institutional development ahead of its 2004 membership. The funds were designed to accelerate institutional transformation, modernize agriculture, strengthen rural communities, improve transport networks, and promote environmental protection. In Poland, between the mid-1990s and 2003, they supported extensive investments that produced unprecedented improvements in technical infrastructure. Poland’s accession referendum in 2003 turned decisively in favor of EU membership, despite strong regional variation in support. While no causal evidence is available, we argue that without the EU-funded infrastructural transformation, its outcome would have been less certain. For current EU candidate countries, Poland serves as an excellent example of how targeted external financial assistance can support structural transformation ahead of integration with the EU.

Introduction

Seven countries are currently eligible to receive financial support through the European Union’s Instrument for Pre-Accession Assistance (IPA III): Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, Serbia, and Türkiye. The funding allocated within the program for the 2021–2027 period amounts to 14.162 billion EUR (in 2021 prices; European Commission, 2024). IPA III is the successor to the former two IPA editions, which have provided support exceeding 24 billion EUR since 2007 to countries in the then EU enlargement region. IPA aims to support countries that have entered a pathway to EU membership, expected in the foreseeable future, to facilitate progressive alignment with EU rules, values, and various standards and policies enforced in the European Union before they become full members. It constitutes one of the pillars of assistance offered by the EU to countries with a prospect of membership, with trade concessions and stabilization and association agreements (SAAs) serving as the other two.

Next in line to obtain financial help through the pre-accession funding are Moldova and Ukraine, both of which were granted candidate status by the European Council fairly recently. While they have already started their accession negotiations and may benefit from trade concessions and SAAs, they still need to fulfill certain requirements to be eligible for IPA. Though formally also a candidate since late 2023, the accession process of Georgia is currently suspended due to concerns about democratic backsliding, implementation of controversial laws and disputed parliamentary elections.

In this paper, we examine Poland’s experience in utilizing the funding available prior to the 2004 EU enlargement to undergo important structural and systemic changes. Given the goals of the funding, we discuss the evolution of a number of economic indicators which can serve as evidence of the socio-economic advancement that occurred in Poland in the years leading to its EU accession. These examples illustrate different dimensions of development that societies in countries embarking on the EU accession process could benefit from on their way towards full integration.

EU Pre-accession Funding Options in the 1990s

Together with nine other countries, mainly from the Eastern European region and the former communist bloc (Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Slovakia, and Slovenia), Poland joined the EU in 2004. It was the largest enlargement of the European community both in terms of the number of new countries and population-wise.

On the pathway to EU membership, these candidates benefited from a coordinated set of financial instruments designed to accelerate their political, economic, and institutional development. During the 1990s and early 2000s, three programs offered financial assistance: Phare, SAPARD, and ISPA. Each addressed a different strategic challenge that candidates faced during their accession period – many of which underwent the transition from centrally planned to free market economies.

From the pool of soon-to-be EU members, Hungary and Poland were the first among the post-communist Central and Eastern European countries to formally start the accession process as early as 1994 (Cyprus and Malta applied in 1990). These two countries also inaugurated the distribution of financial assistance among the EU applicants. They became the first beneficiaries of the Phare program, which concentrated on supporting public administration reform, improving institutional capacity, and preparing regions for effective absorption of EU structural funds. It also helped modernize local infrastructure and provided targeted assistance to sectors undergoing major restructuring. Phare was soon extended to cover all other candidate countries.

The second initiative – SAPARD, concentrated on the needs of the agricultural sector and rural communities. The goal was to raise the competitiveness of local farming and modernize food production.

The third program, ISPA, funded major environmental and transportation initiatives.

These three programs helped close the gap between the candidate countries and older EU member states by improving infrastructure and enhancing the functioning of their institutions. Formally, they also helped ensure that the new members met EU strict standards and legal directives and built the foundations for their long-term cohesion. More detailed descriptions of the objectives of each program, with a special focus on Poland, are included in Box 1.

Figure 1 presents the annual expenditures between 1990 and 2003 within each of the three analyzed instruments provided by the European Union to Poland (bars, left axis). With connected lines, we show the scope of each program in cumulative amounts over time (right axis). During the 1990s, the budget spent on Poland under the Phare program was kept under 200 million EUR annually (in the last year of the decade, it increased to almost 300 million EUR). However, after the program’s restructuring since the beginning of the 2000s, annual spending through this instrument doubled. Among the three, Phare was the major funding source for Poland, as the country received a total of 3.5 billion EUR until 2003 (equivalent to 1.9% of the Polish GDP in 2003) – almost five times more than under the SAPARD program. Poland also obtained the highest total amount of funding of all candidate countries at the time, corresponding to 30% of the overall provided financial assistance (Kawecka-Wyrzykowska & Ambroziak 2006).

Figure 1. Values of  EU pre-accession funds in Poland

Source: Own compilation based on Tables 3, 4, 6 from Kawecka-Wyrzykowska & Ambroziak (2006). Note: in 2003 prices.

In 2000, ISPA and SAPARD were introduced to further support specific areas identified during the 1990s as critical and requiring targeted funding – the agricultural sector, initiatives to enhance the transportation network, and environmental protection. Through SAPARD, projects related to farming and rural infrastructure received approximately 150 million EUR per year in Poland, accumulating to 700 million EUR over the four-year period until 2003. Since one of the prerequisites in SAPARD was national co-funding of ca. 25% of the public contribution in the investments, overall 1.1 bn EUR (0.6% of the 2003 GDP) of public money was committed to different projects in Poland through this instrument (ARiMR 2025; investments consisted in 50% of private resources).

Projects supported within ISPA on average obtained 300 million EUR annually in Poland, with total spending reaching 1.4 billion EUR until 2003 (0.8% of the 2003 GDP). Poland was still the major beneficiary of these two types of financial support, though the total share of the funding received within each of them was much lower than in the Phare program, respectively 32% in SAPARD and 34% in ISPA (Kawecka-Wyrzykowska & Ambroziak 2006).

 

Box 1. Financial instruments offered in the 1990s on the pathway to EU membership: Phare, SAPARD, ISPA

Originally known as Poland and Hungary Assistance for Restructuring of the Economy, Phare was launched in 1989 at a pivotal moment in European history. Initially designed to support the two countries in their transition from communism to democracy and a market economy, Phare quickly expanded to cover other parts of Central and Eastern Europe. Its mission was not only to help rebuild economies, but also to support political democratization. At first, it operated through national programs, but as regional cooperation gained importance, Phare introduced international initiatives to foster cross-border collaboration. The evolving challenges faced by the transforming countries led to a significant change in the program’s operation in the late 1990s. Financial support was now focused on two main pillars: investment in essential infrastructure, which consumed about 70 per cent of resources, and institutional development, which received the remaining 30 per cent. Poland benefited from several specialized initiatives within Phare. Socio-Economic Cohesion focused on modernizing regional infrastructure and preparing Polish regions to efficiently absorb EU structural funds. Cross-Border Cooperation strengthened ties between Poland and its neighbors. Institutional Building contributed to more efficient and transparent public administration.

The Special Accession Program for Agriculture and Rural Development, SAPARD, was established in 1999 to help transform the agricultural sectors and rural economies of ten countries aspiring to join the EU at the time. The goal was to prepare farmers and food processors to meet strict EU sanitary and veterinary standards. In Poland, SAPARD played a major role given the country’s vast rural landscape and the important role of agriculture in the economy – accounting for 7% of the GDP in 1995 (CSO 2014). Around 75% of the total budget was allocated from EU funds, with the remainder covered by national co-financing. However, the rules required an own contribution from each beneficiary, thus around half of the total value of all investments realized through SAPARD was private capital (Supreme Audit Office, 2002). SAPARD in Poland focused on, on the one hand, the modernization of agriculture and, on the other, on rural development. A large part of the program went into modernizing agricultural holdings, supporting farmers in buying new machinery, improving farm buildings, and upgrading agricultural production to meet EU standards. Equally important was the modernization of food processing industries, like meat, dairy, fruits and vegetables. Another significant part of the program concentrated on infrastructure in rural communities — building roads, sewage systems, and improving basic services. To encourage economic diversification, assistance was provided to develop non-farming businesses and create new job opportunities outside of agriculture (EU Council, 1999a).

Created in 1999, the main goal of ISPA was to finance large-scale projects in two critical sectors: transportation and environmental protection. Projects selected for funding were typically expensive, exceeding 5 million EUR, and had a strategic, national or at least regional impact (EU Council, 1999b). From the society’s perspective, these initiatives improved living standards, protected public health and the natural environment and promoted sustainable development. In the environmental sector, ISPA focused mainly on critical areas, including improving the quality of drinking water, building modern sewage treatment plants, managing waste more efficiently, and reducing air pollution. Given the EU’s strict environmental directives, addressing these issues was a fundamental condition for accession. ISPA concentrated also on modernizing and expanding major roadways and railway lines, especially those which were signified as part of the Trans-European Transport Network. Improved transport connections facilitated trade, mobility, and regional development, essential for increasing economic competitiveness and tightening of physical linkage with the rest of Europe.

The total amount of received funding was only one of the factors that may have played a role in the scope and pace of overall socio-economic changes in Poland. Importantly, the spatial distribution of investments provided a unique opportunity to reduce the geographical inequalities deeply rooted in Polish history and related, in particular, to the partitions of Poland lasting from the late 1700s till the end of World War I (Becker et al. 2016; Grosfeld & Zhuravskaya 2015). The eastern regions of Poland were historically much less developed, with the agricultural sector maintaining a critical position in economic activity and employment.

To illustrate the differences in regional distribution of the funding, we use a number of indicators related to investments realized with the help of the SAPARD instrument – which was specifically targeted at supporting infrastructure in rural areas and advancements in the agricultural sector. In Figure 2, we present three measures of investment allocation – the total (public+private) value of investments completed in each region (a), total value of investments per capita (b), and per hectare of agricultural land (c). Depending on the analyzed indicator, we obtain a slightly different picture of the distribution of the investments in SAPARD throughout the country. It appears that the Western regions of Poland received the least funding from SAPARD, whereas the Eastern and most rural regions were less successful in securing the funding. In all three cases, though, the Wielkopolskie Voivodship – a region in the Central-Western part of Poland – stands out as the one that collected the highest funding not only overall, but also when calculated per inhabitant or, most crucially, per area of agricultural land.

Figure 2. Spatial distribution of the SAPARD investments in Poland, total amount (public+private) for the period 2000-2003

Source: Own compilation based on Table 7.2 from Rudnicki (2008). Note: Converted from PLN to EUR using 4PLN/EUR exchange rate; c) per hectare of agricultural land. As compared to Fig. 1 the amounts for SAPARD include private resources spent

The most likely reason behind the particular allocation of the funding is related to the application process. The total amount of the funding was granted to Poland with limited distributional guidelines, and the funds were allocated on the first-come, first-served basis (ARiMR 2003). The maps in Figure 2 suggest that farmers, agricultural producers and manufacturers, and rural municipalities in Wielkopolskie region were quick and efficient when it came to funding applications. The scale and scope of the investments, though – looking at the three different measures – shows the flow of substantial benefits to all central and eastern regions.

Infrastructural Metamorphosis of Poland in the 1990s

As described above, an exceptional stream of additional funds from the EU was directed to Poland from the early days of its transition. The funding programs evolved with time during the 1990s and became more specialized closer to EU accession to address the specific needs of the candidate countries. While causal evidence of the impact of EU pre-accession funds on evolving infrastructure remains scarce and is methodologically challenging (with just a few exceptions on more recent pre-accession funding schemes, like Denti 2013), a simple overview of a number of key indicators might serve as strong suggestive evidence that the funds actually made a significant difference. In this part of the paper, we take a closer look at some examples of Polish infrastructure that underwent enormous progress in the late 1990s and early 2000s. We stipulate that the EU funding played a crucial role in the acceleration of this development.

All three analyzed EU instruments – Phare, SAPARD and ISPA – shared some common objectives, for instance, increasing access to clean water in the population, reducing pollution in lakes, rivers, and the sea, and improving road conditions, especially the low-rank ones in remote, rural areas. In Figures 3-5, we present the scale of improvement observed in these three areas on the lowest level of regional disaggregation, namely, in Polish municipalities. We compare the three selected indicators over almost a decade, between 1995, the initial year of data availability, and 2004.

We begin with Figure 3, which depicts the expansion of the water pipe network measured in kilometers per 1,000 inhabitants in each municipality. As specified in the legend, the darker the green category, the higher the density of the water pipe network. The rapid expansion of the network between 1995 and 2004 is evident, especially in some parts of the country. Most often, the upgrade to the top category happened in regions that lagged well behind the rest of the country in 1995. Here, the notable examples are the central regions of Poland (Kujawsko-Pomorskie and Lodzkie Voivodships, including the northern part of the Mazowieckie Voivodship) and the north-eastern frontiers (Podlaskie and Warminsko-Mazurskie Voivodships).

Figure 3. Length of the water pipe system (in km) per 1000 inhabitants in Polish municipalities in 1995 and 2004

Source: Own compilation based on the statistics from the CSO Local Data Bank (BDL); Geodata: National Register of Boundaries (PRG). Note: The legend is based on 2004 data: the two top and bottom categories in the legend cover 10% of observations each, and the rest of the categories cover 20% of observations each. Municipality borders marked in white, voivodship borders in yellow. Poland underwent an important administrative reform in 1999, when 49 voivodships were aggregated into the current 16. For the year 1995, we use the post-reform voivodship division of the country. Between 1995 and 2004, only negligible administrative changes took place at the municipal level.

In Figure 4, we show the share of the population enjoying access to sewage treatment plant services. The progress over time in this respect was related, on the one hand, to the construction of new treatment facilities and, on the other, to the concurrent expansion of the sewage pipeline network, which resulted in a higher share of users for the existing wastewater treatment plants. The increase in the usage of the treatment plants over time is striking, especially given that at the starting point, in 1995, only a limited number of municipalities had a wastewater treatment plant in operation. These municipalities were mainly concentrated in the northwestern corner of Poland and in the southwestern region of Silesia.

In comparison to the water pipe system in Figure 3, the development of sewage treatment plant access was concentrated in regions that were already ahead of the rest of Poland in 1995 – specifically, the northwestern and southwestern ones. However, a substantial increase in access to sewage treatment services is also visible in central and eastern parts of Poland, where in 1995 plants offering these services were extremely rare. This particular type of development can also be viewed from the perspective of the extent of pollution reduction in Poland’s internal waters. The number of scientific reports documented a sharp decline in biochemical factors of industrial, agricultural and household origin, hazardous to both humans and the environment, commonly polluting Polish rivers and lakes in the 1990s (Gorski et al, 2017; Marszelewski & Piasecki, 2020).

Figure 4. Number of residents connected to sewage treatment plants per 1000 inhabitants in Polish municipalities in 1995 and 2004

Source: see Figure 3. Note: The legend is based on 2004 data: due to high prevalence of zeros the bottom category in the legend covers 30% of observations, the rest of categories cover 10% of observations each. Municipality borders marked in white, voivodship borders in yellow (see Notes in Figure 3 for details).

The third pair of maps (Figure 5) illustrates the development of the country’s road network. The Figure shows the expansion and modernization of the lower rank roads administered by municipalities, which seem particularly important from the point of view of day-to-day transportation and quality of life of local populations.

Figure 5. Length of the municipality road network (in km) per 1000 inhabitants in Polish municipalities in 1995 and 2004

Source and Note: see Figure 3.

The data in Figure 5 cover both paved or hard-surfaced roads and dirt roads. One point to keep in mind here is that with an overall development of a municipality and of the neighboring region, the status of the municipality’s small-scale road may be updated to a higher rank, administered by the county or even by the voivodship. Figure 5 does not account for such an update of rank (in the Figure of roads), so the numbers presented are likely to represent a lower bound of the actual advancement. The maps in Figure 5 compare the length of municipal roads per 1000 inhabitants in 1995 and 2004. While a significant improvement in the road system is visible almost all over the country, the central regions seem to have gained the most, at least when it comes to this particular type of roads.

Investments and Development vs. Public Perception

Overall, all three figures above demonstrate that during the decade before Poland integrated with the EU, significant progress was achieved in terms of improving the quality of life, increasing accessibility of public utilities, reducing environmental degradation and capturing sustainable urban development. Substantial investments in rural areas had an important impact on reducing regional disparities.

Another important observation when examining all three figures together is that, while advancement occurred throughout the country, the bulk of improvement in each of the considered aspects was concentrated in slightly different parts of it, and almost all Polish municipalities recorded an important inflow of investments related to the pre-accession funding. While again we cannot provide any causal evidence, below we confront the spatial distribution of infrastructural modernization from Figures 3-5 with public support for joining the EU expressed in the referendum organized in 2003, a year before accession.

Figure 6. Support for the EU accession in the referendum in 2003

Source: Own compilation based on the statistics from the National Electoral Commission; Geodata: National Register of Boundaries (PRG). Note: The bottom category in the legend covers municipalities that voted against EU integration (12.3% of observations), the rest of the categories cover 25% of the remaining observations each. Municipality borders marked in white, voivodship borders in yellow.

In Figure 6, we present the results of the vote on the municipal level, with darker blue shades indicating higher support for EU membership. The map clearly highlights high geographical variation in support for European integration, with much stronger proportions of votes in favor of EU membership in western and northern Poland. In contrast, the support in central and eastern Poland was substantially lower, reflecting a higher degree of skepticism towards the benefits of the EU. Clearly, many factors influenced people’s choices at the time of the referendum. They depended on their economic conditions, the degree of exposure to relations with Western European countries, the level of awareness of the potential gains from integration, as well as fears concerning the future of local economies and those related to cultural influences.

Just by looking at the map of support, it is impossible to say much about the degree to which the EU pre-accession funds affected the outcome of the referendum. For that, we would need to know more about the dynamics of support across regions. Yet, while the share of votes in favor of integration in many eastern municipalities was below 50%, people in a substantial majority of localities expressed overwhelming support for joining the EU. The result of the referendum was 77,45% in favor. Although no causal analysis linked the results to EU pre-accession funds, the scale of investment and its visibility, as well as its tangible effects – the direct translation of EU funds into daily quality of life all across Poland, are very likely to have turned many people’s votes in the EU’s favor.

Conclusion

Since the early 1990s, on the path to EU membership in 2004, Poland, like other candidate countries, received generous European pre-accession financial assistance. The combination of three financial instruments in operation at the time – Phare, SAPARD, and ISPA – enabled Poland to make substantial investments in key economic sectors, including public administration, agriculture, environmental protection, and physical infrastructure. The early launch of the Phare program prepared Poland to follow various EU standards and prerequisites, and contributed to the implementation of the cohesion policy. Initiation of assistance within SAPARD and ISPA instruments since 2000 strengthened the rural economy and competitiveness of Polish agriculture, and allowed for modernization of the transportation and environmental infrastructure. In pre-accession assistance, Poland received a total of 5.5 billion euro (over 3% of the 2003 GDP), by far the highest support provided to the candidate countries at the time.

Substantial investments made during the 1990s and early 2000s, largely covered by pre-accession financial aid, had a remarkable impact on the quality of existing infrastructure in Poland. Kilometers of roads were built and renovated in Polish municipalities, thousands of households acquired a connection with the water pipe network, and hundreds of wastewater treatment plants were constructed. This is only a small subset of selected advancements that can be demonstrated using quantitative data collected in a comparable way over time. Numerous other types of infrastructure received substantial investments to support development, modernization or enhancement. On top of that, all these improvements have likely contributed to further spill-over effects through higher levels of regional growth, a boost in the labor market with the creation of new jobs, a reduction of unemployment, or enhanced labor productivity. All these changes, taken together, played a key role in determining the overall quality of life for the Polish population, reducing regional economic inequalities, and improving the quality of the local natural environment, etc.

The distribution of support for Poland’s accession to the EU, as reflected in the 2003 referendum results, differed significantly by region. Enthusiasm for the EU was significantly lower in the eastern parts of the country, while residents of many western municipalities voted overwhelmingly in favor of membership. Yet, even at a very fine geographical distribution, we see only a relatively small group of municipalities – 12.3% – where less than 50% of residents voted in favor of EU membership, and the overall outcome across the country was a decisive “YES”. Thus, although the substantial advancement in infrastructural development all across the country did not convince the majority of residents in each and every locality, the number and geographical scope of those voting in favor was very decisive. It is impossible to say how high/low the support would have been without the received support. Yet, given the scale of the resulting changes in various basic dimensions of quality of life, it seems safe to say that, thanks to the funds, many voters looked at the future integration with a higher degree of appreciation. Naturally, other factors played a role in determining people’s decisions in the referendum, with economic conditions and prospects for socio-economic development being just one factor, albeit a likely important one.

Pre-accession funds in the current candidate countries, how they are used, distributed, and how they change people’s daily lives, will again prove important in showcasing the benefits of integration. At the same time, to secure the kind of support that the Polish population expressed in the 2003 referendum, it will be important to also highlight the broader benefits of integration and address fears and concerns of various population groups.

The experience of Poland and other member countries from Central and Eastern Europe can serve not only as an example of the benefits of pre-accession funds, which we studied in this policy paper. The countries’ socio-economic success and the changes in the quality of life, both before and after accession, should be seen as a clear case of fundamental changes, which would have been highly unlikely had the countries decided to stay out of the European Union.

Acknowledgement

The authors acknowledge the support from the Swedish International Development Cooperation Agency, Sida. We are grateful to Patryk Markowski for his assistance in preparing this analysis and detailed background research.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Georgia’s Growth Dilemma: Structural Transformation, Inequality, and the Future of Inclusive Development

Posted on May 19, 2025 | Policy Papers

Georgia Growth Dilemma Image

This policy paper examines Georgia’s economic growth and labor market developments between 2017 and 2023, with a focus on structural transformation and income distribution. While growth has been strong and absolute poverty has declined, challenges related to the economic inequality of Georgia remain, including labor reallocation, wage disparities, and regional gaps. Redistribution mechanisms, particularly social transfers, have played an important role in mitigating poverty; however, the pace of inclusive structural change has been limited. Key policy priorities include fostering productivity growth, expanding employment opportunities, and addressing inequality through targeted labor market and social policies.

Georgia’s Economic Rebound: Growth Beyond Recovery or Superficial Expansion?

In recent years, Georgia’s economy has demonstrated remarkable resilience and dynamism. Following a sharp contraction of real GDP by approximately 6.8 percent in 2020 due to the Covid-19 pandemic, the economy rebounded strongly, posting a growth rate exceeding 10 percent in 2021. This robust performance was maintained also in 2022 and 2023. The recovery was among the fastest in the region, fueled by a combination of strong private consumption, booming external demand, and exceptional inflows of remittances and capital.

According to national accounts data, output in 2023 not only recovered the losses sustained during the pandemic but significantly surpassed pre-pandemic levels. Georgia’s real GDP is on a trajectory above its previous trend, with potential output estimates also being revised upward (see Figure 1). Potential output — the level of economic activity the country can sustain without triggering inflation — is not directly observable and was estimated using a statistical technique known as the Kalman filter. This method uses historical GDP data and economic patterns to produce a smooth estimate of potential output over time, allowing policymakers to assess the gap between actual and sustainable growth. The economy’s output gap, which turned sharply negative during the pandemic, gradually closed by 2022 and moved into slightly positive territory by 2023, indicating that output had surpassed its estimated potential level as the recovery matured.

Figure 1. Output Growth, Output Gap, and Potential Output in Georgia

Figure showing Georgia’s output growth, output gap, and potential output from 2017–2024, highlighting post-pandemic recovery, strong GDP growth above potential, and implications for Georgia Economic Inequality

Source: Geostat. Author’s calculations.

Georgia’s recent economic growth is largely propelled by geopolitical factors linked to the Russo-Ukraine war. The sharp increase in remittance inflows from Russia — which soared by 403 percent to $2.1 billion in 2022 — provided a major boost to domestic consumption. At the same time, the relocation of capital, businesses, and skilled individuals intensified, with over 30,000 Russian-owned firms registered and nearly 115,000 Russian migrants arriving between 2022 and 2023, significantly stimulating investment and labor market activity. In addition, the rapid expansion of re-exports — particularly of motor vehicles and machinery — further accelerated external trade, with re-exports to Russia, Armenia, Kazakhstan, and Kyrgyzstan rising 6.6 times in 2024 compared to 2019, which strengthened the logistics and transport sectors.At the same time, substantial fiscal stimulus during the pandemic years, followed by a gradual normalization of fiscal policy, helped support aggregate demand without triggering immediate macroeconomic instability. Monetary policy remained broadly accommodative, while inflationary pressures — although pronounced — were largely kept under control relative to peer economies.

Despite these positive developments, the underlying composition of growth raises important questions. High headline growth figures do not automatically translate into widespread improvements in living standards or reductions in inequality. Growth that is driven by consumption booms, remittance surges, or temporary trade redirection may lack the deep, productivity-enhancing underpinnings necessary for long-term sustainability. Without broad-based sectoral upgrading and inclusive labor market outcomes, there is a risk that economic expansion will remain concentrated in a few dynamic sectors while leaving large parts of the population behind.

Moreover, the post-pandemic growth episode coincided with rising concerns about external vulnerabilities, including Georgia’s dependence on remittances (17.5 and 13.5 percent of GDP in 2022 and 2023, respectively (NBG, Geostat)) and commodity prices, as well as regional political risks. If these external drivers were to weaken, maintaining high growth rates without deeper structural changes could become increasingly difficult.

Therefore, while Georgia’s recent economic record is impressive by regional standards, it is crucial to assess whether this growth has been accompanied by meaningful structural transformation — namely, a reallocation of labor from low to high-productivity sectors, rising labor incomes, and reduced inequality. A deeper analysis of sectoral dynamics and employment patterns is required to determine whether Georgia’s economy is evolving toward a more inclusive and sustainable growth model.

“Structural Shifts of the Economy: Georgia Economic Inequality and Progress Without Transformation?”

The structural transformation of Georgia’s economy between 2017 and 2023 (see Figure 2) reveals both encouraging and concerning trends. The analytical framework used to assess structural transformation in Georgia between 2017 and 2023 draws on the methodology developed by McMillan and Rodrik (2011). This influential study emphasizes the role of labor reallocation across sectors as a key driver of aggregate productivity growth in developing and transition economies.

The analysis of the change in sectoral employment shares against relative sectoral productivity shows that, broadly speaking, labor reallocation has followed a growth-enhancing direction. The fitted trend line across sectors exhibits a positive slope, suggesting that workers have been moving, though slowly, away from low-productivity sectors and toward relatively higher-productivity activities. However, the shallow gradient of the trend line indicates that the pace and quality of this transformation have been modest. The most significant dynamic during this period is the continued exit of labor from agriculture. Agriculture remains a highly unproductive sector, with a large share of labor in subsistence agriculture, and experienced a substantial decline in its employment share—a positive signal for economic modernization. Nevertheless, much of the displaced labor appears to have been absorbed by mid-productivity services such as construction, retail trade (wholesale and retail trade; repair of motor vehicles and motorcycles), accommodation and food service activities, and transportation. These sectors have seen rising employment shares but remain relatively close to or below the economy-wide average in terms of labor productivity. Consequently, while labor mobility is evident, the migration has largely been into sectors that do not substantially enhance overall economic efficiency.

Figure 2. Structural Transformation and Productivity Alignment in Georgia, 2017–2023

Bubble chart of Georgia’s structural transformation and productivity alignment from 2017–2023, showing sectoral labor shifts and productivity disparities, with modest progress toward higher-value activities, highlighting links to Georgia Economic Inequality.

Source: Geostat. Author’s calculations. Note: Each bubble represents a sector, with its position determined by the change in its employment share (horizontal axis) and its relative productivity level (vertical axis). Bubble size corresponds to the sector’s share of total employment in 2017. The positive slope of the fitted trend line indicates modest progress in reallocating labor toward more productive activities, although the shift remains shallow and incomplete.

In contrast, high-productivity sectors, notably information and communication, financial and insurance activities, and real estate, have exhibited strong relative productivity but absorbed very limited shares of the labor force. These findings point to persistent barriers in accessing higher-value employment opportunities, likely stemming from skill mismatches, limited educational attainment, and structural rigidities in the labor market.

Public administration, education, and health — traditionally labor-intensive sectors — have maintained relatively large shares of employment while operating below average productivity levels, reflecting a lack of dynamism in these essential public service areas.

Overall, the observed pattern suggests that Georgia’s recent economic growth has been accompanied by low-quality structural transformation. Although there has been a reallocation of labor from extremely low-productive sectors, the transition has not been sufficient to significantly lift aggregate productivity or to broaden access to high-wage, high-skill employment.

These trends underscore the critical need for policy interventions aimed at improving the quality of labor mobility. Investment in education and vocational training to match labor supply with demand in high-productivity sectors is essential. Moreover, facilitating entrepreneurship, promoting innovation in services and manufacturing, and supporting labor market flexibility could help ensure that future structural changes generate more inclusive and sustainable economic growth.

Labor Market Dynamics: Recovery in Numbers, Challenges in Structure

Following the period of sectoral shifts and structural transformation outlined above, it is equally important to examine how the Georgian labor market has evolved in recent years. Labor market dynamics provide critical insight into the inclusiveness and sustainability of economic growth. Between 2017 and 2023, the number of employed individuals exhibited a general upward trajectory, particularly after the sharp decline during the Covid-19 pandemic in 2020 (see Figure 3). Employment numbers, which fell significantly in 2020 and 2021, recovered steadily in the following years, reaching approximately 1.33 million in 2023. Preliminary assessments suggest that this recovery is expected to continue into 2024, further consolidating the labor market’s post-pandemic rebound.

Figure 3. Employment and Unemployment Trends in Georgia

Bar and line chart showing employment and unemployment trends in Georgia from 2018–2024, with rising employment, falling unemployment, and implications for Georgia Economic Inequality.

Source: Geostat. Author’s calculations.

This recovery has been accompanied by notable improvements in Georgia’s wage dynamics. After a period of stagnation in nominal and real wages between 2018 and 2020, the economy experienced a sharp acceleration in wage growth, beginning in 2021. Average nominal wages rose rapidly, while real wage growth, which accounts for inflation, also turned positive after years of stagnation (see Figure 4). These wage dynamics indicate that the labor market tightening contributed to upward pressure on wages, suggesting better bargaining conditions for workers in certain sectors.

Figure 4. Nominal and Real Wage Dynamics in Georgia

Bar and line chart showing Georgia’s nominal and real wage dynamics from 2017–2024, with employment growth, wage fluctuations, and implications for Georgia economic inequality.

Source: Geostat. Author’s calculations.

At the same time, unemployment rates declined significantly. The unemployment rate, which had hovered between 17 and 20 percent prior to the pandemic, began a gradual downward trend from 2021 and onwards, falling to approximately 14 percent by 2023 (see Figure 3). This suggests that job creation in the recovery phase was relatively strong. However, the persistently high levels of unemployment earlier in the period underscore structural issues that remain unresolved.

Nevertheless, despite these positive headline trends, several deeper challenges persist within Georgia’s labor market. Employment growth has been concentrated primarily in low- and mid-productivity service sectors, as indicated previously. Informality remains high, particularly in agriculture and small-scale services, and labor underutilization (32.1 percent) — including underemployment and discouraged workers— continues to affect a significant segment of the workforce.

Moreover, the share of labor income relative to GDP provides important additional context (see Figure 5). As shown in Figure 5, the wage share of GDP fluctuated throughout the period, declining during the pandemic years and rebounding sharply in 2023. While this recovery is promising, it may however partially reflect inflationary effects or nominal wage adjustments rather than deep, productivity-driven gains.

Figure 5. Share of Wages in GDP and Economic Growth

Bar chart showing the share of wages in Georgia’s GDP from 2017–2023, highlighting fluctuations in labor income share and its relevance to Georgia economic inequality.

Source: Geostat. Author’s calculations.

Taken together, recent labor market developments in Georgia reflect a cyclical recovery reinforced by external demand and domestic consumption. Yet, the underlying structure of employment, informality, and the relatively slow reallocation of labor into high-productivity sectors suggest that the labor market’s transformation remains incomplete. Sustainable improvements in labor incomes and employment quality will require addressing skills mismatches, boosting formal sector job creation, and ensuring that future growth translates into widely shared labor market opportunities.

Despite the improvements in employment and wage dynamics, it remains critical to assess whether these labor market gains have translated into broader improvements in income distribution and living standards. Strong aggregate indicators do not necessarily imply that growth has been inclusive or that inequality has diminished. The following section examines how the distribution of income has evolved in recent years, focusing on wage structures, inequality measures, and the extent to which economic growth has been shared across different segments of the population.

Georgia Economic Inequality and Distributional Shifts: A Fragile Middle and Growing Extremes

Following the developments observed in the labor market, it is crucial to assess how economic growth and labor dynamics have translated into income distribution outcomes. Understanding the evolution of income inequality provides deeper insight into whether the benefits of growth have been broadly shared or have remained concentrated.

Wage income distribution patterns between 2017 and 2023 show a complex trajectory. The wage distribution curve (see Figure 6) reveals that while average earnings have increased over time, the overall shape of the distribution has also evolved significantly. The peak of the distribution has become sharper and shifted toward higher income brackets, particularly around the 1200–2400 GEL range. However, the right-hand tail of the distribution has simultaneously elongated, suggesting the emergence of a relatively small group of very high-income earners.

Figure 6. Wage Income Distribution Curves, 2017–2023

Line chart showing wage income distribution curves in Georgia for 2017, 2019, 2021, and 2023, illustrating shifts in earnings patterns and disparities relevant to Georgia economic inequality.

Source: RevenueServices. Author’s calculations.

This dual movement — a general upward shift combined with increasing dispersion — is captured through key statistical measures:

First, the standard deviation of incomes, which reflects the average distance of individual incomes from the mean, increased notably (57 percent) between 2017 and 2023. An increasing standard deviation indicates that income differences within the population have become more pronounced, with greater dispersion around the average wage. In simple terms, not only are people earning more on average, but the spread between low and high earners has widened substantially.

Second, the changes in skewness and kurtosis provide further depth to this picture. Skewness measures the asymmetry of the income distribution. In 2017, the distribution had a skewness of about 1.74, indicating a moderate right-skew, with income values concentrated around the lower and middle ranges and a gradual tapering toward higher incomes. By 2023, skewness had increased to 3.81, suggesting that the income distribution has become much more asymmetrical. A few individuals or households are earning disproportionately high incomes compared to the rest of the population. The visible downward shift in the 2023 wage distribution curve at the lower income ranges likely reflects two reinforcing trends. First, persistent inflation during the post-pandemic recovery years eroded the real value of wages, effectively pushing nominal incomes upward and compressing the lower tail of the distribution. Many workers who previously occupied the lowest wage brackets may have moved into higher nominal categories without corresponding improvements in purchasing power (average real wages declined until 2021, after which they began to recover, rebounding steadily through 2023 following the Covid-19 shock (See Figure 4)). Second, due to low rate of job creation, Georgia has experienced a high rate of labor emigration in recent years, especially to the EU and the U.S., particularly among low-skilled workers. This outflow of labor likely reduced the number of individuals earning wages in the lowest income ranges, further contributing to the observed contraction of the left side of the distribution curve. Together, these factors help explain why the red curve in 2023 dips below previous years in the lowest wage intervals.

Kurtosis, meanwhile, captures the “tailedness“ or the concentration of extreme outcomes. Georgia’s wage distribution moved from a kurtosis of about 5.16 in 2017 to 15.75 in 2023. Higher kurtosis indicates that extreme deviations (very low or very high incomes) have become more common relative to a normal distribution. In Georgia’s case, it points to an increasingly peaked distribution with fat tails: most people are concentrated around a modal income, but extreme earnings at the high end have become more significant.

In parallel, the Lorenz curves for 2017 and 2023 (see Figure 7) offer a graphical representation of these dynamics. The curve for 2023 lies slightly closer to the line of equality than that of 2017, reflecting a marginal improvement in income equality in the early part of the period. However, the difference is modest, and the curve’s arched shape hints at a persistent structural inequality. It should be mentioned that the calculations are based on household expenditures and therefore reflect the redistributive effects of government fiscal policy on income inequality. The Gini index, a summary statistic derived from the Lorenz curve, plots the cumulative share of income held by cumulative shares of the population. It measures income inequality on a scale from 0 (perfect equality) to 1 (perfect inequality) and thus provides a concise way to track changes in distributional dynamics over time.

In the case of Georgia, the index supports the discussion regarding the Lorenz curve. Between 2017 and 2021, the Gini index declined from 0.3711 to 0.3090, suggesting a reduction in income inequality during these years, likely influenced by pandemic-driven wage compression, social transfers, and temporary labor market adjustments. However, from 2021 onwards, inequality began creeping upward again, reaching 0.3271 in 2023. This suggests that the initial equalizing effects of crisis responses were short-lived and that structural disparities in income distribution have reasserted themselves as the economy recovered.

Figure 7. Lorenz Curves (2017 and 2023 comparison)

Lorenz curves comparing Georgia’s income distribution in 2017 and 2023.

Source: Geostat. Author’s Calculations.

Together, these metrics paint a coherent picture: the Georgian economy experienced nominal wage growth and a partial strengthening of middle-income segments during the recovery phase, however, this was accompanied by greater income dispersion, higher asymmetry (favoring high-income earners), and an increased concentration of wealth at the very top.

Thus, although the average worker is better off in absolute terms compared to 2017, the relative disparities within the labor force have widened, especially after 2021. Growth has been unevenly distributed, increasingly favoring highly skilled, capital-intensive, or well-connected sectors and workers.

Poverty trends during 2017–2023 (see Figure 8) add an important additional dimension to the analysis of income distribution. Over this period, Georgia achieved a substantial reduction in absolute poverty, with the share of the population unable to meet basic consumption needs falling from 21.9 percent in 2017 to 11.8 percent in 2023. This sharp decline reflects real improvements in living standards for a significant portion of the population and indicates that economic growth, combined with social policy interventions, had a meaningful impact in alleviating extreme deprivation. However, developments in relative poverty tell a more nuanced story.

Relative poverty, which measures economic disadvantage in relation to the median income, declined only modestly from 22.3 percent to 19.8 percent over the same period. The persistence of relative poverty, despite improvements in absolute poverty, suggests that while more people were able to meet basic needs, the income distance between lower-income households and the median population did not significantly narrow. This implies that, although economic growth has lifted many out of extreme poverty, underlying income inequality has remained largely intact. Crucially, redistribution plays a central role in shaping these poverty outcomes. Although Georgia’s tax system is largely regressive and heavily reliant on indirect taxes, fiscal transfers — particularly the old-age pension — play a powerful equalizing role. The pension system alone is estimated to reduce the national poverty rate by up to 18 percentage points, highlighting the critical role of targeted redistribution in mitigating deprivation. These fiscal tools are especially important in the absence of highly progressive taxation, and they help explain why absolute poverty declined so markedly despite ongoing structural labor market weaknesses. At the same time, the relatively limited change in relative poverty underlines the limits of redistribution in addressing inequality in the absence of more inclusive labor market outcomes and equitable income growth.

Figure 8. Evolution of Absolute and Relative Poverty Rates in Georgia, 2017–2023

Line chart showing absolute and relative poverty rates in Georgia from 2017–2023, with a sharp decline in absolute poverty and modest change in relative poverty, highlighting links to Georgia economic inequality.

Source: Revenue Services. Author’s calculations.

An important part of the labor market story is the role of public employment initiatives, particularly those targeting the labor market. While government employment programs have had a visible impact on official labor statistics (see Figure 9), particularly in peripheral regions, a closer examination reveals critical concerns regarding the quality and sustainability of the jobs created. Many of the positions facilitated through this program are characterized by very low wages, limited or no skills development opportunities, and lack of formal career advancement paths. Rather than serving as a bridge to stable, productive employment, these jobs often appear to fulfill administrative or social functions, providing basic income support without contributing meaningfully to workers’ long-term economic mobility.

Figure 9. Share of Social Service Agency (SSA) Program Employment in Total Regional Employment (2023)

Bar chart showing the share of Social Service Agency program employment in total regional employment across Georgia’s regions in 2023.

Source: Social Service Agency, Geostat, Author’s Calculations. Note: Bars represent regions in Georgia.

Moreover, there is evidence that the expansion of employment facilitated by the government program has, in some regions, been used as a tool for political management, particularly at the municipal level. By creating a network of publicly funded low-skill jobs tied to local government structures, employment program may inadvertently reinforce patronage systems and political dependence, particularly around electoral cycles.

This raises important concerns about the role of public employment programs in structural transformation. While temporary income support can be vital for vulnerable populations, overreliance on low-productivity public sector jobs can entrench regional stagnation and undermine incentives for private sector dynamism. For employment programs to contribute positively to structural transformation, it is essential that they be reoriented toward genuine skills development, pathways into productive sectors, and integration with broader labor market activation strategies.

Taken together, these trends suggest that while Georgia’s economic growth has lifted many individuals out of absolute poverty and improved average incomes, it has also led to greater wage dispersion, increasing concentration of incomes at the top end, and significant concerns about the depth and quality of labor market transformations. Without deliberate policy efforts to promote inclusive structural change, Georgia risks entrenching a dualistic economy, where prosperity coexists with persistent inequality and regional marginalization.

Conclusion and Policy Recommendations

In light of the findings discussed above, a set of targeted policy actions is proposed to foster a more inclusive, sustainable, and equitable economic transformation in Georgia. These recommendations aim to address the structural weaknesses identified in the labor market, income distribution, and regional economic development, ensuring that future growth benefits a broader cross-section of the population.

The findings of this policy paper highlight the need for a more deliberate approach to structural transformation in Georgia. While recent economic growth has been strong, labor reallocation toward high-productivity sectors remains incomplete, income distribution dynamics point to persistent inequalities, and improvements in living standards have not been evenly shared. To ensure that growth translates into broad-based prosperity, the following policy actions are recommended:

Georgia must accelerate the movement of labor from low-productivity sectors, particularly agriculture and informal services, into higher-productivity activities. Policymakers should prioritize sectoral strategies that support emerging dynamic industries — such as ICT, logistics, manufacturing, and modern services — while also upgrading traditional sectors like agriculture and tourism to enhance productivity and employment absorption.

Addressing skill mismatches is essential to enable labor mobility toward productive sectors. Investment in vocational education, digital skills training, and sector-specific workforce programs must be intensified, especially targeting young people, rural workers, and displaced labor from declining industries.

While public employment programs can play a stabilization role, the primary engine of sustainable job creation must be the private sector. Georgia should focus on creating an enabling environment for small and medium sized enterprises, supporting entrepreneurship, streamlining business regulations, and improving access to finance, especially outside of the capital Tbilisi.

Reducing regional disparities is critical for equitable growth. Targeted infrastructure investment, decentralized support for business development, and place-based labor market policies are needed to ensure that structural transformation benefits all regions, not only urban centers.

Promoting formal labor relations through fiscal incentives, simplified compliance procedures, and stronger enforcement can raise job quality and income security. At the same time, reinforcing social protection systems can mitigate inequality without discouraging labor market participation, helping to build a more resilient workforce.

Ongoing reforms must be informed by robust, high-frequency data. Strengthening labor market information systems, expanding coverage of income and employment surveys, and integrating administrative data into policymaking will enhance the government’s ability to monitor progress on structural transformation and distributional outcomes.

References

  • McMillan, M. S., & Rodrik, D. (2011). Globalization, structural change and productivity growth. NBER Working Paper No. 17143. National Bureau of Economic Research.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Three Years On – Ukrainians in Poland after Russia’s 2022 Invasion

Posted on February 21, 2025 | Policy Papers

Ukrainians in Poland rallying in Kraków, waving Ukrainian and Polish flags.

The wave of Ukrainian refugees that followed the full-scale Russian invasion on February 24th, 2022, was met in Poland with unprecedented levels of support and solidarity. According to data from the Polish Household Budget Survey, 70 percent of households offered some help to Ukrainians in Poland, and over 10 percent (1.3 million households) provided direct personal assistance. Overall, by early 2025, 1.9 million refugees had registered in the dedicated social security registry (PESEL-UKR system), and 1 million continue to be registered as residing in Poland. Drawing on other data sources, we argue in this policy paper that the latter figure is highly overstated, giving rise to unjustified criticisms of low school enrolment among Ukrainian children and low rates of labour market activity among adult refugees. We highlight the risks that these critical voices may become prominent in the ongoing campaign ahead of the Polish presidential elections. During the crucial months of prospective peace negotiations, when presidential candidates are appealing for voters’ support, we argue that the public debate in Poland concerning Ukraine and Ukrainian refugees ought to be grounded in reliable evidence.

Polish Support for Ukraine: Shifts in Public Attitudes, and Policy Challenges Amid War and Elections

The dramatic events of late February 2022 shook the populations across Ukraine, Europe and the world. The objective of the massive, full-scale Russian aggression was clear – to rapidly take over Kyiv, force Ukraine to surrender and take over full control of the country thus subjugating it into Kremlin’s rule. Three years later, while thousands of Ukrainian soldiers and civilians have lost their lives, and while Russia has imposed a massive economic and social burden on Ukraine, its key objective has badly failed and remains far from being realised. This thanks to the commitment of the Ukrainian government, the country’s army and the mobilisation of the Ukrainian population. In turn, the country’s resistance would not have been possible without substantial support from the outside, primarily from countries in the European Union and the U.S. International aid from governments to Ukraine between February 2022 and October 2024 amounted to over €230 billion (bn) with the largest part contributed by the US (€88 bn), the European Commission and European Council (€45 bn) and Germany (€16 bn). Proportional to 2021 GDP levels, the highest support came from Estonia (2.20 percent), Denmark (2.02 percent) and Lithuania (1.68 percent) (Kiel Institute, 2024). Support for Ukraine has come in many forms – military, material, financial, political and diplomatic. The international community has also imposed substantial economic and political sanctions against Russia, and has excluded it from many international forums, marginalising its voice in international discussions and meetings.

On top of that, Ukraine’s neighbours and many Western countries opened their borders and welcomed a massive wave of refugees escaping the immediate military invasion in the east and north of Ukraine, seeking safety from continued bomb and drone attacks on the entire country, and running away from the risk of a complete Russian take-over. It is estimated that up to 8 million Ukrainians left the country in the first months after the full-scale war started, initially moving mainly to Poland, Romania and Slovakia (Polish Economic Institute, 2022; UNCHR, 2022). At the same time the Russian aggression resulted in internal displacement of more than 3.6 million Ukrainians (IOM UN Migration, 2024). While many of the international and internal refugees have since returned, over 6.8 million Ukrainians still reside outside of Ukraine’s borders (UNCHR, 2025).

The wake of the war was met with an unprecedented wave of support among the Polish population (Duszczyk and Kaczmarczyk, 2022). We use data from one of the largest representative Polish surveys – the Household Budget Survey 2022 and 2023 – to show the degree of involvement among Polish households in direct and indirect support to Ukrainian refugees. We also show that declarative general sympathy towards Ukrainians reached over 50 percent in 2023 –  twice as high compared to 16 years earlier. This support has by now fallen close to the levels from just before the full-scale war (40 percent). As the immediate need for help has become less urgent, and the refugees have organised their lives in Poland, the involvement of Polish households in supporting the Ukrainian population has also declined. At its peak at the beginning of the war the proportion of Polish households that were actively involved in helping the Ukrainian population reached nearly 70 percent, with over 10 percent (i.e. more than 1.3 million) of the households providing direct assistance to the refugees.

In this policy paper we call into question some of the official data on the number of Ukrainian refugees who continue to reside in Poland (almost 1 million) (EUROSTAT, 2025). We argue that inconsistency across different sources with regard to precise numbers – such as likely inflated refugee count in the official social security register – may be used  to build unfavourable claims against the refugees and the Ukrainian cause overall, as arguments and narratives develop based on marginal anecdotal evidence and incorrect statistics. As the new U.S. administration tries – in its own way – to bring an end to the war, Ukraine will need continued strong support from all Western allies to end the war on favourable terms for Ukraine and to get significant additional help to rebuild the country. Ukraine’s safety and economic security will depend on Western military guarantees and closer integration with the EU. All of this requires the support of populations in these countries, which gets increasingly undermined by internal disputes and external political interferences.

As negotiations to end the war begin to take shape, Poland enters a crucial electoral campaign ahead of its May 2025 presidential elections. This combination is likely to place the Ukrainian question among the top issues on the local agenda. At the same time, there is a risk  that the extent of support towards Ukraine and Ukrainian residents in Poland will be used in the battle for electoral votes. We argue that any debate around this topic should draw on reliable, up to date data sources. In this regard, the  government should provide more information to clarify data inconsistencies, to shed more light on the situation among Ukrainian citizens currently residing in Poland, and to ensure that any doubtful narratives raised in the public debate are quickly addressed.

Ukrainian sovereignty, its peaceful development and prosperity are very much in the interest of both Poland and the rest of Europe. Therefore, the Polish government must provide arguments to reinvigorate the support for Ukraine among its population. This will be fundamental to ensure Ukraine’s military success and stability, to guarantee the mutual benefits of integration of the Ukrainian population in Poland, and for the future economic cooperation with Ukraine in the prospective enlarged European Union.

The Outbreak of the Full-Scale War: Ukrainians in Poland

In the first couple of months after the full-scale Russian invasion of Ukraine on February 24th  2022, over 2 million refugees fled to Poland through the common land border, with as many as 1.3 million people crossing the border during the first two weeks of the war (Figure 1a). The exact number of refugees who arrived in Poland is difficult to gauge as some people left Ukraine via the border with Romania or Slovakia and could have entered Poland across the uncontrolled borders of the Schengen area.

BOX 1. Ukrainian citizens in Poland before the war in 2022

Before February 24, 2022, the migration of Ukrainian citizens to Poland was regulated by existing legal mechanisms concerning all foreigners coming from non-EU countries (European Parliament, 2010). Migrants could apply for a temporary residence permit for a maximum of three years, most often in connection with prearranged employment or education (Sejm RP, 2013). Since 2017 Ukrainian citizens with biometric passports could travel to Poland and other EU countries without a visa, but their stay was limited to 90 days (European Parliament, 2017). Access to the Polish social transfer system for migrants and their families was strictly regulated and limited. Labor migrants and temporary visitors under the visa-free regime had no right to public benefits or healthcare (Sejm RP, 2003).

At the time, application for refugee status was possible, but required undergoing a lengthy and burdensome asylum procedure. Those with refugee status granted had access to public transfers and healthcare (Sejm RP, 2003).

In accordance with the European regulations of Council Directive 2001/55/EC of 20 July 2001, the Polish government responded to the refugee crisis by establishing a special residence status for those fleeing the war. The regulations were introduced as early as  March 12, 2022, and as a result, all Ukrainian refugees who arrived in Poland since 24 February could register themselves (and their family members) in a special social security registry, the so-called PESEL-UKR (Sejm RP, 2022). This registration immediately provided the refugees with an official status of temporary protection and legalized their stay in Poland until a specified date, which – as the war continued – has been regularly extended. In comparison to other, non-EU migrants, the PESEL-UKR status grants the refugees simplified access to the Polish labour market and gives them access to public healthcare and social transfers – including general support available to all legal residents, as well as special financial and non-monetary aid targeted specifically at refugees (Duszczyk and Kaczmarczyk, 2022). The registration process was streamlined and widely accessible in all municipality offices throughout Poland and resulted in rapid registration of the majority that had arrived to Poland since February 24, 2022. By the end of June 2022, 1.2 million individuals had registered for the PESEL-UKR status. The number grew to 1.4 million by October 2022 and continued to grow to 1.9 million registrations by January 2025. As evident from Figure 1b not all of those who crossed the Polish border (or arrived in Poland having left Ukraine through a different country) stayed in the country. Some continued their journey to other EU countries and beyond, while some decided to return to Ukraine. It is worth noting though that of all the registrations carried out by the end of 2024, nearly half happened in the first 8 weeks following the invasion.

Figure 1. Number of Ukrainian citizens crossing the border between Poland and Ukraine and registering for PESEL-UKR, 2021-2024

Note: Weekly data on crossings via all land borders with Ukraine.
Source: Open Data Portal (2025a, 2025b).

A notable and important legal change was introduced in October 2022, whereby individuals are automatically withdrawn from the PESEL-UKR registry after a period of 30 days when they (1) leave Poland, (2) apply for a residence permit, or (3) apply for international protection status (Sejm RP, 2022). This change is the reason for the substantial drop in the number of registered refugees at the end of 2022, with over 400 000 individual withdrawals (Figure 1b). This change in legislation was aimed at estimating more precisely the number of Ukrainian refugees currently residing in Poland. However, since withdrawals from the system require that departures from the territory of Poland are officially recorded at the border, or follow a parallel registration in another EU country, or are recorded as departures from the Schengen area through another country, the numbers in the system may still be far from the actual number of refugees currently residing in Poland.

Since late 2022 the number of registered Ukrainian refugees in Poland has been fairly stable at slightly below 1 million. Similarly, the shares of different age cohorts have not changed. In Figure 2 we show the split of those in the PESEL-UKR registry by age. Children under the age of 18 account for about 40 percent of all refugees, of which 30 percent are in schooling age (7-17). 7 percent of the refugees are aged 62 years or older. Among those aged 18-61 years old, 70 percent are women. It is worth noting that out of about half a million children recorded in the first 7 months, almost 400 000 are still registered in the PESEL-UKR registry, a number that has been stable since the end of 2022. As we show below, these values are significantly higher compared to the number of refugee children reported by two other administrative sources. This in turn casts doubt on the reliability of the estimates of the total number of Ukrainian refugees in Poland.

Figure 2. Ukrainian citizens registered with PESEL-UKR, by age group

Note: Based on registered year of birth, age as of 2025.
Source: Open Data Portal (2025b).

Where Are All the Registered Children?

To check the reliability of the PESEL-UKR registry data, we match the information from the registry with information from school registers provided by the Ministry of National Education, and the number of children benefitting from social transfers provided by the Social Insurance Institution (ZUS). As evident in Figure 3, the number of registered school-age children in the PESEL-UKR registry and the number of those who are officially registered in Polish schools significantly differ, and the difference seems stable over time. According to school records, most of the Ukrainian parents promptly enrolled their children in schools right after their arrival in Poland – about 120 000 pupils joined Polish schools as early as March 2022. The numbers grew in September 2024, which followed the introduction of obligatory schooling for all Ukrainian children aged between 7 and 17  (Sejm RP, 2024), with online classes in Ukraine permitted only for those in their final year. When we compare data for late 2024 and early 2025, we see that while about 270 000 children aged 7-17 were registered in the PESEL-UKR database, only 152 000 attended Polish schools – resulting in a very low enrolment rate of about 56 percent – raising legitimate concerns over the children’s academic and social development (see for example CEO, 2024).

Figure 3. Number of school-age children among Ukrainian refugees

Note: School registrations: all school types except preschool education, post-secondary schools, schools for adults and grades in which children are at least 18 years old. Ukrainian refugees only. Child benefit data points as reported in June, October and December.
Source: Open Data Portal (2025b, 2025c); information on 800+ benefit recipients: unpublished data from the Social Insurance Institution (ZUS).

As evident from Figure 3 though, from late 2023 all the way until early 2025, the ‘800+ benefit’ (which is a universal child benefit paid to all children aged 0-17) was paid to around 150 000 Ukrainian refugee children aged 7-17. Given the ease of claiming the benefit, and the relatively high value of the transfers (about 23 percent of net minimum wage per child per month), it seems very unlikely that so many families would opt out of the support. Looking at the close match between the numbers from ZUS and from the Ministry of Education, the more likely interpretation of the figures is not that children stay away from school and fail to claim social transfers, but rather that far fewer children continue to reside in Poland.

An additional argument supporting the inaccuracy of the PESEL-UKR data comes from a report published by the Narodowy Bank Polski (the Polish Central Bank) (NBP, 2024). Using information from a large survey conducted among Ukrainians living in Poland the report shows that 83 percent of school-age children in refugee families were enrolled in either a Polish or a Ukrainian school physically based in Poland. This is very far from the 56 percent rate calculated with reference to administrative data, again suggesting that the PESEL-UKR numbers of school-age children are highly inflated. If that is the case, not only the number of refugee children but the overall PESEL-UKR numbers (992 000 by January 2025) should be called into question.

How Many of the Registered Adults Are Active on the Labor Market?

The accuracy of the overall number of refugees is important because it is one of the key references for policy discussions. While international regulations specify that victims of war and conflict are granted the same basic rights and privileges as other legal residents, including access to the labour market, healthcare and other public services (Duszczyk et al., 2023), negative sentiments towards Ukrainian citizens have recently grown in Poland. Further, various restrictions on access to public support for Ukrainian refugees have already been publicly discussed and proposed in Parliament. These sentiments feed on the claims of fraudulent behaviour, unwillingness to engage in official employment and crowding out of public services for Polish nationals. Such claims about Ukrainians are spread more easily if not met by accurate numbers.

Figure 4. Number of Ukrainian men and women contributing to pension insurance in Poland

Note: ‘Other countries’ refers to other registered foreigners.
Source: Social Insurance Institution ZUS (2024).

Looking at labour market activity, the number of Ukrainians who were officially active on the Polish labour market (as employees, self-employed or receiving unemployment benefit) and who thus paid pension contributions to social security in December 2023 stood at 759 000 (see Figure 4). Of those 396 000 were men and 363 000 were women. While ZUS, the Social Insurance Institution, does not distinguish between migrants (those with the right to stay before February 24th, 2022) and refugees (with PESEL-UKR status) it seems safe to assume that those who registered in the ZUS database in 2022 and 2023 belong to the latter group. The difference between the number of Ukrainians contributing to social security in December 2021 and December 2023 is 132 000 and, as seen in Figure 4, the additional numbers of those registered differ only for Ukrainian women. New Ukrainian male refugees certainly also appear in the database in 2022 and 2023, but their number is difficult to estimate as some earlier migrants returned to Ukraine after the outbreak of the war, and as a result the net effect of men between 2021 and 2023 is essentially zero. Focusing on women, we can compare the number of new registrations in the ZUS database to the total number of women aged 18-59 (excluding students) in the PESEL-UKR database (about 335 000 in December 2023). Such a ratio would suggest that only about 40 percent of female Ukrainian refugees are formally contracted on the Polish labour market (on contracts paying social security contributions). This is much lower than the values presented in the NBP report (2024), suggesting that in July 2024, around 70 percent of the adult war refugees were working and further 19 percent were looking for a job. This comparison once again suggests that the PESEL-UKR numbers are significantly inflated.

Addressing the public concerns with regard to school enrolment and labour market activity with correct figures could help counter the growing negative sentiments towards Ukrainians in Poland as well as towards the overall support for the process of securing peace in Ukraine and integrating it closer with Poland and the EU. In the next section we show that when the full-scale war started in February 2022, not only the sentiments were strongly in favour of supporting Ukraine. Additionally, the level of engagement of the Polish population in actively assisting Ukrainian refugees was truly unprecedented.

Individual Support in Response to the Outbreak of the War

In the first few weeks after the full-scale Russian invasion the Polish society almost uniformly united in providing help and assistance to Ukrainians affected by the war. The Polish Economic Institute estimated that during the first 3 months the financial, humanitarian and material help provided by the Polish society alone reached 9-10 billion PLN, which corresponded to 0.34-0.38 percent of Poland’s GDP (Baszczak et al. 2022). Polish private businesses were also quick to join the assistance efforts, donating money, food, medical and other specialized equipment, and providing services such as transportation, insurance, and education free of charge (WEI 2023). Until May 2022, 53 percent of Polish enterprises engaged in different kinds of relief or support.

The assistance to refugees has been documented in numerous anecdotes, formal reports and extensive media coverage. The scale of support is also reflected in the Polish Household Budget Survey, a regular household survey conducted by the Central Statistical Office. Already in the first quarter of 2022 the survey included several questions related to the assistance given by the interviewed households to Ukrainian refugees. These questions were then included in the survey throughout 2022 and 2023. As shown in Figure 5, when the inflow of refugees from Ukraine started in late February 2022, nearly 70 percent of Polish households offered some form of assistance. Most of this help took the form of gifts and money transfers, but 10.4 percent, i.e. over 1.3 million Polish households, offered direct help such as transport, providing an overnight stay, delivering goods to accommodation venues, etc. The fraction of those offering assistance stayed very high through the first half of 2022, and 23 percent of Polish households still provided some form of assistance in the last quarter of 2022 (Figure 5). As the war stalled, and the Ukrainian population settled and became more independent, and the Polish government took official responsibility of assisting those still in need, the level of direct support from households fell. However, in late 2023 9 percent of Polish households still continued to provide some form of assistance. What is really special about the initial wave of support is that the positive attitudes towards the refugees and the Ukrainian cause were nearly universal. As seen in Figure 6, assistance was offered by high and low educated households (79 and 59 percent), those living in large cities and in rural areas (73 and 68 percent), the young and the old (66 and 63 percent). Households who declared good material conditions were more likely to offer help (75 percent), but even among those who declared difficulties with their financial status 41 percent came forward to offer some assistance.

Figure 5. Polish households engaged in assisting Ukrainian refugees, 2022-2023 (by quarter)

Note: Help covers support and transfers to individuals and institutions in Ukraine as well as to Ukrainian refugees in Poland. “Personal assistance” – direct help to refugees (with job search, doctor’s visits, public matters, language lessons, translation, etc.), “Other help” – help at the border, in reception points, temporary accommodation points, gift collection points, transportation, hosting or subletting own housing free of charge, blood donation.
Source: own compilation based on the Polish Household Budget Surveys 2022-2023.

Figure 6. Polish households engaged in assisting Ukrainian refugees (any help) in the first quarter of 2022, by household characteristics

Notes: Urban status – A: rural area, B: city below 100 000 inhabitants, C: city over 100 000 inhabitants. Material situation (self-assessed) – D: bad or rather bad material situation, E: average material situation, F: good or rather good material situation. Age of head of household – G: 18-29, H: 30-59, I: 60 and older. Education of head of household – J: lower than secondary, K: secondary or postsecondary, L: tertiary. Source: own compilation based on the Polish Household Budget Survey 2022.

It is worth noting also that by the time the full-scale war broke out in February 2022 the sentiments among the Polish population towards Ukrainians had improved compared to attitudes in the 1990s and early 2000s. These sentiments have been regularly surveyed by the Public Opinion Research Center CBOS, and we summarize them in Figure 7. As evident, in the early 1990s the proportion of Poles declaring positive sentiments towards Ukrainians was very low. It steadily increased until  about 2017 and then grew rapidly from 2018 till 2020. In 2022 the sentiments towards Ukrainians reached their peak, with over 50 percent of Poles declaring fondness towards them – on par with nations such as Lithuania and Slovakia. At the same time positive attitudes towards Russians reached an all-time low of 6 percent. Positive sentiments towards Ukrainians declined in 2024 – the last year for which the data is available – but even after the drop they are still high when compared with attitudes before 2023.

While the general positive sentiments towards Ukrainians in Poland has improved over the years, 2022 was truly unique when it comes to attitudes toward Ukrainian refugees (see Figure 8). Between 2015 and 2018, i.e. after Russia’s annexation of Crimea in 2014, around 50-60 percent of Poles declared that refugees from the conflict areas in Ukraine should be welcomed in Poland. When the same question was asked again in March 2022, 95 percent agreed that Ukrainian refugees should be welcomed in Poland and nearly 60 percent declared that they ‘definitely’ agreed with such a policy. However, the proportion of Poles in support of welcoming Ukrainian refugees has decreased. In late 2024 the share was more or less back at the level prior to the full-scale war, i.e. at over 50 percent.

Figure 7. Share of survey participants declaring fondness towards foreigners of different origin

Source: The Public Opinion Research Center CBOS (2024a).

Figure 8. Opinion survey: If Poland should accept Ukrainian refugees coming from the conflict territories

Note: The surveys were discontinued between 2018 and 2022.
Source: Public Opinion Research Center CBOS (2024b).

Why Have Sentiments Shifted?

At the crucial time of a possible long-awaited end to the Russian invasion, when coordinated support of Western governments will be essential to secure a just and long-lasting solution, the willingness of these governments to firmly stand behind Ukraine will, to a large extent, depend on the sentiments among their voters. Thus, the wavering enthusiasm for the Ukrainian cause in countries such as Poland can be seen as a worrying sign, in particular given how high the level of support was in the early days of the invasion. This support will be particularly important over the next few months, given the likely period of intensive international negotiations and the battle for votes in the upcoming Polish presidential elections.

It is not unusual to try to put the blame for various unfortunate developments on external forces, including global trends, external conflicts and all things ‘foreign’. Thus, the fact that many people in various countries, including Poland, blame their perceived worsened economic conditions on the consequences of the war and the related influx of Ukrainian refugees is far from surprising. While some politicians might want to explain the complex broad context, others will take advantage of these sentiments and continue to fuel the negative discourse. With that in mind, three main topics have been particularly visible in the public debate in Poland:

  • access to social transfers, in particular to the ‘800+’ child benefit for Ukrainian refugees
  • Ukrainian refugees’ participation in the Polish labour market and tax contributions to the local budget
  • risks to particular groups of interest, most prominently reflected in Poland by the crisis surrounding imported Ukrainian grain (see Box 2)

The first two issues are strongly related to the general approach to immigration and integration of migrants in the Polish society. The popular media discourse – in traditional and social media – tends to focus on instances of abuse of social support and public services, and to build up negative sentiments along the lines of supposed unwillingness to engage in legal economic activity among those who have settled in Poland. While one can certainly identify anecdotes which selectively confirm all sorts of misbehaviour, the overall evidence would clearly reject such claims. As discussed, the surveys conducted by the NBP show that a significant majority of migrants and refugees from Ukraine find legal employment in Poland. Further research based on administrative data demonstrates that many Ukrainians establish and successfully run their businesses in Poland (Polish Economic Institute, 2024). Between January 2022 and June 2024 Ukrainian migrants and refugees established almost 60 000 enterprises in Poland, and as Vézina et al. (2025) argue, these firms did not crowd out Polish businesses, meaning they represent a true value added to the national and local economies.

Recent public discussions, however, have focused on the combination of employment and benefit claims. The debate started with two parliamentary initiatives by the right wing Konfederacja and Prawo i Sprawiedliwość opposition parties and was then picked up by the leading government party’s presidential candidate, Rafał Trzaskowski (money.pl, 2025). The proposed legislative changes are broadly similar, suggesting that access to the main child benefits – the ‘800+ benefit’ – should be limited to those refugee families where at least one of the parents is formally employed. Such conditionality does not apply to Polish families, and according to current legislation, to no other families legally residing in Poland (Konfederacja, 2025; Prawo i Sprawiedliwość, 2025). The supposed aim of the changes would be to, first of all, limit fraudulent claims among those who no longer reside in Poland, and secondly, to restrict access to the benefits to those who contribute with their taxes to the public budget only. On both counts the policy seems badly misconceived. As shown above, the ‘800+’ claims closely match the numbers of children officially registered in Polish schools, far below the numbers registered in the PESEL-UKR database. Moreover, such a policy is unlikely to lead to much higher employment among refugee parents. The benefit is universal and received by all families regardless of employment status or income; previous research has shown a similar benefit to have negligible effects on employment (see for example: Myck and Trzcinski, 2019). Therefore, the most likely reason for some refugee parents to not take up work is not unwillingness, but rather other constraints – constraints which will not change as a result of the proposed restrictions. Most Ukrainian families who fled the war are mothers whose partners could not join them due to military restrictions on the mobility of Ukrainian men. While many women settled and found jobs, family obligations may significantly limit some refugee’s options for regular employment. For these families, withdrawing the eligibility for the ‘800+ benefit’ would be a significant loss of income with potentially dire consequences for their children. It is thus difficult to understand the initiatives as anything other than attempts to address the growing critical sentiments towards the refugees to gain support among voters who are convinced by the anecdotal narrative. As argued above – with the exception of anecdotes – there is very little evidence in support of such legislative changes. Even from the point of view of potential budgetary gains, the proposed limitations on benefit claims would impose heavy administrative costs which would likely exceed any resulting savings. The politicians coming forward with such proposals would be well advised to consider data from various sources and avoid raising issues which have a clear potential to fuel negative sentiments towards refugees and migrants.

BOX 2. The dispute over the Ukrainian grain

In February 2022, Russia’s full-scale invasion destabilized the Ukrainian market, in particular the agricultural sector, due to blocked exports through the Black Sea. To enable exports, so-called Solidarity Lanes were established, including corridors crossing Poland (European Commission 2022). However, Poland was not prepared to handle and re-export large volumes of Ukrainian agricultural products, due to insufficient capacity of Polish sea ports (farmer.pl, 2023; for such quantities experts argue that road transport is unprofitable; Kupczak, 2023). This led to a surplus of grain in multiple storehouses throughout the country, especially in Southeastern Poland. Overall, Polish grain stocks increased by over 250 percent, from 3.8 to almost 10 million tones (Supreme Audit Office, 2023).

The drastic surplus of grain, together with much lower prices for Ukrainian crops, led to a dramatic price drop—one could buy mixed Polish-Ukrainian grain for half the price it cost the previous year (rp.pl, 2023). Apart from its impact on quantity and price, Ukrainian grain drew public attention also due to concerns regarding its quality (money.pl, 2023). Imported agricultural and food articles must undergo rigorous quality controls at the border, depending on their purpose – human consumption, animal fodder or cultivation, conducted by the respective state inspection office. Random controls held in 2022 by the Food Articles Inspection revealed that 2.4 percent of the grain samples were banned from entering the market (rp.pl, 2023).

According to a report by the Supreme Audit Office (2023), controls run by the Veterinarian Inspection were drastically limited as of May 2022 which allowed poor quality fodder grain to enter the Polish market (Supreme Audit Office 2023). Since technical grain – used in the production of biofuels, insulating materials or oils – is exempt from border quality controls, its imports and sale as consumable grain could be particularly profitable. Several incidents of such forgery were subject to investigation confirming that large quantities of technical grain originating from Ukraine were sold as consumable to Polish companies (gov.pl, 2024).

The tightened border controls that followed, resulted in multiday delays in the transportation of food products from Ukraine. To mitigate these constraints an agreement was reached, and, as of March 8, 2023, grain transit through Poland to other final destinations (within EU or to a third country via Polish ports) is exempt from border controls at the Polish-Ukrainian border and sealed by the National Revenue Administration. These seals can be removed only at the final destination (gov.pl, 2023a).

Throughout this period Polish farmers held demonstrations opposing the influx of Ukrainian grain. The border crossings with Ukraine were temporarily blocked by protests aimed at disrupting the flow of goods. The symbolic dumping of Ukrainian grain on the ground at the Medyka border crossing resulted in a famously cited statement by the Ukrainian President Volodymyr Zelensky that this event may be seen as evidence of the “erosion of solidarity” with Ukraine (BBC, 2024).

After the EU-level temporary embargo on four types of grains and oil seeds from Ukraine was lifted in mid-September 2023 (which was in effect since May 2023), Ukraine agreed to introduce export measures to avoid grain surges (European Commission, 2023). Nevertheless, Poland administered a unilateral ban on selected products and their derivatives (gov.pl, 2023b), which led Ukraine to file a complaint with the World Trade Organization (WTO, 2023). While the ban still applies (gov.pl, 2025), the Polish government has on multiple occasions actively sought to convince the EU to include wheat (and other grains) among the crops covered by the quotas under the EU-level 2022 regulation on temporary trade liberalization with Ukraine (the Autonomous Trade Measures Regulation; OKOpress, 2024; European Commission, 2024).

Conclusions

Considering the current approach by the U.S. administration under President Donald Trump, Ukraine’s position in the prospective negotiations will strongly depend on the support it can gather from its European allies. This in turn is likely to reflect the sentiments towards the Ukrainian cause among European voters. In Poland, where critically important presidential elections are scheduled for May 2025, the importance of these sentiments might be particularly salient. On the one hand, the candidates are likely to voice support for Ukraine to secure peace and stability in the region. On the other hand, they may appeal for support among voters who are critical of the generous approach of Polish public institutions towards Ukrainian refugees.

As shown in this policy paper, the critical voices highlighting instances of abuse of privileges granted to refugees are largely unfounded, and much of the critical discourse is linked to – in our view – highly inaccurate numbers of officially registered refugees with the PESEL-UKR status system. The government would do a service to the quality of the debate about Ukrainian refugees in Poland, and at the same time defuse some of the critical claims, by verifying the PESEL-UKR database.

Using administrative data on school enrolment and benefit claims we show that these match almost perfectly, with around 150 000 children aged 7-17 in both registries in late 2024. This is far less than the 270 000 children in this age group registered in the PESEL-UKR database and assumed to be residing in Poland. Similarly, survey data suggests that about 70 percent of Ukrainian refugees are active on the Polish labour market. This proportion is much lower when official data based on social security contributions is compared to the total number of adult refugees in the PESEL-UKR registry. The comparison once again suggests that the figures in the latter database are significantly overstated. It is thus very unlikely that the number of Ukrainian refugees in Poland is as high as the numbers officially reported in the registry (992 000 in January 2025).

The accuracy of the numbers is important for several reasons, and the ability to address various critical claims in the public debate is only one of them. At the time of an electoral campaign ahead of a highly significant presidential election, this reason, however, may prove fundamental to avoid further polarization of the debate about continued support for Ukrainian refugees in Poland. It is also crucial for securing strong support for Ukraine by the Polish government in the coming challenging months of peace negotiations. While it is likely impossible to restore the level of positive attitudes toward Ukrainian citizens seen in Poland in February and March 2022, that degree of solidarity should serve as a foundation for a deepened relationship between the two countries.

Acknowledgement

The authors acknowledge the support from the Swedish International Development Cooperation Agency, Sida. We are grateful to Patryk Markowski for helpful research assistance. The Polish Household Budget Survey data (2022, 2023) used in the analysis was provided by Statistics Poland (Główny Urząd Statystyczny). We are grateful to the Social Insurance Institution ZUS (Zakład Ubezpieczeń Społecznych) for providing us with unpublished data on child benefit recipients.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Reforming Financial Support in Widowhood: The Current System in Poland and Potential Reforms

Posted on April 22, 2024 | FROGEE PROJECT, Policy Papers

Woman in a blue jacket sitting on a bench with a cat in autumn, overlooking a city landscape representing Widowhood support Poland

In this policy paper, we discuss the material conditions of widows and widowers compared to married couples in Poland, and analyse the degree to which the current support system to those in widowhood in Poland limits the extent of poverty among this large and growing share of the population. The analysis is set in the context of a proposed reform recently discussed in the Polish Parliament. We present the budgetary and distributional consequences of this proposal and offer an alternative scenario which limits the overall cost of the policy and directs  additional resources to low income households.

Introduction

According to the National Census in 2021 there were about 2.2 million widows and 450 000 widowers in Poland. In the following year over 123 000 women and about 47 000 men became widowed. Apart from the severe consequences for mental health and psychological well-being, losing a partner typically has implications also for material wellbeing, in particular in cases of high income differentials between the spouses and in situations when the primary earner – often the man – dies first. Material conditions of the surviving spouse in widowhood depend on the one hand on the couple’s accumulated resources, and, on the other hand, on the available  support system. Many countries have instituted so-called survivors’ pensions, whereby the surviving spouse continues to receive some of the income of her/his deceased partner alongside other incomes. The systems of support differ substantially between countries and they often combine social security benefits and welfare support for those with the lowest incomes.

In this policy paper we discuss the material situation of widows and widowers versus married couples in Poland and analyse the degree to which the current Polish support system for  people in widowhood limits the extent of poverty within this group. We compare the current system of survivors’ pension with a proposed reform discussed lately in the Polish Parliament;the introduction of a ‘widow’s pension’. We present the budgetary and distributional consequences of the announced scheme and offer an alternative scenario which limits the overall cost of the policy and focuses additional resources on low income households. Our results show significant income gains for  widows/widowers from the implementation of the recently proposed widow’s pension. The policy however, would come at a substantial cost to the public purse, and the most significant benefits would be accrued by surviving partners at the top of the income distribution. Our proposed alternative scenario is better targeted at poorer households and achieves the objective of limiting poverty in widowhood at a substantially lower cost.

The Material Situation of Widows and Widowers in Poland

Numerous research papers show a strong impact of losing a spouse on mental health and overall well-being (Blanner Kristiansen et al., 2019; Lee et al., 2001; Ory & Huijts, 2015; Sasson & Umberson, 2014; Schaan, 2013; Siflinger, 2017; Steptoe et al., 2013). Adena et al. (2023) use a comprehensive dataset on older women observed a number of years before and after the death of their spouses. The study finds a sharp deterioration in mental health among widows after their partner’s death, displayed as a higher likelihood of crying (Figure 1a) or an increased probability of depression (Figure 1b). The authors provide evidence that, in comparison to similar women who remained partnered, widows suffer from poorer mental health and experience worsened quality of life for several years after their partners’ death.

Figure 1. Women’s mental health before and after their partners’ death.

Charts comparing the share of widowed women who cried and those at risk of depression over five years representing policy brief that covers widowhood support Poland.

Source: Adena et al. (2023). Notes: The control group consisted of women from statistical “twin” marriages with an identical distribution of selected characteristics; Figure 1b) Risk of depression defined as 4 or more depression symptoms according to the EURO-D scale. For methodological details see Adena et al. (2023).

While the impact of spouse’s death on widows mental health is largely undisputed, the impacts on their material situation are ambiguous (Ahn, 2005; Bíró, 2013; Bound et al., 1991; Corden et al., 2008; Hungerford, 2001).The differences across countries in the material situation of widowed versus partnered elderly people undoubtedly reflect countries’ various social security systems for those in widowhood. At the same time, these differences may also stem from variations in other factors that widows and widwers can rely on such as the prevalence of property ownership or accumulation of wealth and savings. It should be noted though, that in contrast to the immediate effects of spouse’s death on mental health, the consequences for widows’ and widowers’ material situation may unfold over a number of years. This is reflected in the results from poverty surveys which often point to the poorer material standing of widows and widowers (Panek et al., 2015; Petelczyc & Roicka, 2016; Timoszuk, 2017, 2021).

Similar conclusions can be derived from subjective evaluations of households’ material situation reflected in the Central Statistical Office’s Polish Household Budget Survey (HBS). In Figure 2a we present the percentage of people aged 65 and over who declared a ‘bad’ or ‘rather bad’ material situation of their household between 2010 and 2021, split between widows, widowers and married couples.. Throughout the analysed period, the share of both widows and widowers reporting a rather bad material situation was significantly higher than for married couples aged 65+. While in 2010 30 percent of widows and 20 percent of widowers reported a rather bad material standing, this share amounted to just above 10 percent among married couples. In all social groups the ratio of those in a rather bad material situation declined significantly over the analysed decade. A particularly significant drop was observed among widows; in 2021 the share of widows declaring a rather bad material situation declined to the level observed for married couples eleven years earlier.

Data capturing the risk of poverty from Eurostat, based on the EU Statistics on Income and Living Conditions Survey (EU-SILC), also display significantly worse material conditions of older individuals living alone compared to those living with another adult (Figure 2b). While this data does not explicitly allow us to divide the sample based on marital status, it is highly likely (and assumed hereafter) that the majority of single-person households 65+ cover widows or widowers, while two-person households aged 65+represent married couples. As compared to Figure 2a, the dynamics of the poverty levels among people aged 65+ in Figure 2b differ from the dynamics of the assessment of the overall material situation. Among two-person households, the risk of poverty in Poland declined between 2010 and 2013, and then remained relatively stable at about 15 percent until 2020. Among one-person households the poverty rate also declined during the first five years (from 33 percent in 2010 to 25 percent in 2015), however, it then increased to 37 percent in 2020. Consequently, the gap in poverty risk between two-person and one-person households increased substantially, from 8 percentage points in 2010 to 22 percentage points in 2020.

Figure 2. Material situation among households with individuals aged 65 and over.

Charts comparing material situation and poverty risk among widows, widowers, married couples, and households in Poland, Germany, Czech Republic, and Italy representing policy brief that covers widowhood support Poland.

Source: Own compilation based on: a) HBS; b) Eurostat. Notes: a) Widows and widowers aged 65+ living in one-person households; married couples living in two-person households with at least one spouse aged 65+; b) Eurostat data does not allow for division by gender or marital status. In two-person households both persons are adults, at least one is aged 65+. At-risk-of-poverty rate is defined as 60 percent of the median equivalized income of the entire population.

When analyzing poverty risk information, it should be noted that this indicator is based on income thresholds calculated separately for each year, accounting for the whole population. Poverty risk threshold may therefore increase as a result of income boosts among other groups and in consequence raise the risk of poverty of older people even if their real incomes are stable or grow. Thus the substantial increase in o the poverty risk share among Polish individuals 65+ and living alone after 2015, is related to the sharp rise in income of families with children and wage dynamics, which, in turn raised the poverty threshold considered in the analysis. Based on Figure 2b it is also worth noting that in comparison to Poland the risk of poverty among single-person households 65+ grew even faster in the Czech Republic (though the situation among two-person households 65+ was stable there). The  relative position of these households deteriorated also in Germany (the share at risk of poverty increased from 24 percent in 2010 to 31 percent in 2020). It is therefore clear that even though absolute material conditions may have improved among widowed households in Poland over the last decade, their relative position in the income distribution – as in many other countries – places them at a significantly greater risk of poverty compared to partnered older individuals. Questions regarding the level of state support directed towards widowed older individuals are therefore highly relevant for government policy.

Figure 3. The living situation of widows, widowers and married couples aged 65 and over, in Poland.

Charts showing share of owner occupiers and dwelling size in square meters per person among widows, widowers, and married couples in Poland representing policy brief that covers widowhood support Poland.

Source: Own compilation based on HBS. Notes: Widows and widowers aged 65+ living in one-person households. Married couples in two-person households with at least one spouse aged 65+.

To better understand the broader context of material conditions in widowhood, and to try to address the discrepancy between the trends in subjective evaluation and widows’ relative position in the income distribution, it is also worth examining other aspects of material well-being. In Figure 3a we present some statistics on property ownership. As we can see, the majority of individuals aged 65+ in Poland, both widowed and married, owned the house or flat they lived in. For example, in 2010 62 percent of all widows and 68 percent of all widowers owned their dwelling, and these shares increased to 72 percent for both groups by 2021. Moreover, among older owner occupiers, the size of the house or apartment per person living in it was on average two times larger for widows and widowers (50 m2) as compared to married couples (25 m2), as depicted in Figure 3b. The high share of widows and widowers owning housing assets may therefore be one of the most important explanations to the discrepancies between the dynamics of income poverty and the declarations about the overall material situation observed in recent years. Although the risk of relative income poverty among widows and widowers have increased since 2016 (after a period of decline between 2010 and 2015), widowhood in Poland is not unequivocally associated with poor material conditions. While some widowed individuals clearly face a challenging material situation, for many the current system of survivor’s pension seems to offer adequate protection against the risk of a significant financial deterioration following the loss of a spouse. This suggests that any additional support through a new social security instrument should be directed principally to a relatively narrow group of widows and widowers in order to help particularly those in a difficult financial situation.

Survivor’s Pension, Widow’s Pension and an Alternative Solution

In this part of the paper we present simulations of changes in the level of household income and the relative position in the income distribution among widows under different scenarios of support through the social security system. In the first step we use the 2021 HBS data (uprated to 2023 income levels) to calculate disposable incomes of the entire sample of nearly 31 000 households under the 2024 Polish tax-benefit system using the SIMPL tax and benefit microsimulation model (henceforth the ‘baseline’ system; more details on the SIMPL model: Myck et al., 2015, 2023a; Myck & Najsztub, 2014). Based on the baseline system, we divide the households into ten income decile groups according to their disposable income (equivalised, i.e. adjusted for household composition). In the second step we focus on the sample of 4188 married couples aged 65 and over, representing 1.7 million Polish households (almost 13 percent of the total population). 65 percent of these couples lived in two-person households and the remaining 35 percent cohabited also with other people. In the baseline system, the incomes received by these households placed 9.5 percent of them in the lowest (1st) income decile group and 4.4 percent in the highest (10th) group (see Table 1).

Table 1. Relative position of households with married couples aged 65+ in the income distribution.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: The baseline system for calculating the equivalised income thresholds was the January 2024 system; the thresholds for the income decile groups were calculated on the basis of a full sample of households.

Figure 4a shows a comparison of men’s and women’s gross retirement pensions in our sample of married couples 65+ in the baseline system. Every dot corresponds to one married couple and a combination of the spouses’ pensions. The greater concentration of combinations of these values above the 45-degree line indicates that in most marriages , the husbands’ retirement pensions are higher than the wives’. The differences are also apparent in Figure 4b, which presents the percentages of individuals receiving a pension benefit within the given value range of the pension. The share of women are greater than the share of men at lower benefit values (below 3000 PLN gross per month), and the opposite is true for higher pension amounts. Overall, for 65 percent of all couples, the husband received a higher retirement pension than his wife. There are also older people who did not receive retirement benefits – either because they continued to work or because they were not entitled to a retirement pension (this is the case for 9 percent of husbands and 10 percent of wives), as illustrated by the first column in Figure 4b. It is worth noting that for 2 percent of the couples only the husband received a retirement pension (the wife had never worked and was not eligible for retirement pension or she still worked). In the current Polish system of support for surviving spouses, the amount of own and spouse’s retirement pension is crucial for the choice of the benefit one makes when a spouse dies. A widowed person can choose to continue receiving their own full retirement pension or to receive a survivor’s pension, which is equivalent to 85 percent of the pension of the deceased spouse. Given the differences between men’s and women’s pensions, many women choose the latter option, either because their own retirement pension is significantly lower than the survivor’s pension or because they are not entitled to their own retirement pension.

Figure 4. Retirement pension amounts received by husbands and wives aged 65+

Comparison of men's and women's retirement pension gross amounts and percentage of individuals receiving retirement pensions in value brackets representing policy brief that covers widowhood support Poland.

Source: Own compilation based on HBS 2021. Notes: Both spouses aged 65 and over; gross monthly retirement pensions; in less than 1 percent of the marriages at least one spouse received a retirement pension higher than 10000 PLN (not included in the Figure). 1PLN~0.23EUR.

We treat the sample of married couples aged 65 years or more as a reference sample in our analysis of the consequences from the implementation of various support schemes within the social security system, in the case of widowhood. The calculations presented below reflect the financial situation of the analyzed sample after the hypothetical death of husbands. We focus on widows, as they represent the vast majority of widowed individuals (due to, e.g., longer life expectancy of women and age differences between spouses). We simulate four support scenarios:

I) a system with no support for widowed individuals – this would be the situation without the current survivor’s pension, in which widows would need to rely fully on their own social security incomes (pensions);

II) the current system of survivor’s pension: in which the widow must choose between 100 percent of her own pension or the survivor’s pension (85 percent of her deceased husband’s gross pension)

III) a system with the widow’s pension (currently debated in the Polish Parliament): the widow must choose between: a) 100 percent of her own pension + 50 percent of the survivor’s pension (42,5 percent of the deceased husband’s gross pension), b) 50 percent of her own pension + 100 percent of the survivor’s pension (85 percent of her dead husband’s gross pension);

IV) an alternative system in which the widow chooses between: a) 100 percent of her own pension + 50 percent of a minimum pension if her husband received at least minimum retirement pension (50 percent of the husband’s pension if it was lower than the minimum pension), b) 100 percent of the survivor’s pension (85 percent of the husband’s pension) increased to the minimum pension if the husband received at least minimum retirement pension.

While the simulations are based on a hypothetical death of a husband, they provide a realistic picture of the financial situation of households in which women face widowhood. It is also important to note that the simulations of the financial conditions of ‘widowed’ households take into account other potential forms of public social support such as housing benefits and social assistance for low-income households. The results thus include the most relevant forms of financial support individuals might receive from the Polish government.

Figure 5 shows the results of the four aforementioned scenarios in the form of flow charts between income decile groups. The starting point (the left-hand side of each chart) are the income groups of households with married couples aged 65+, i.e. before the simulated widowhood. The transition to the income deciles on the right hand side of each chart is the result of a change in equivalised disposable income in the widowhood simulation, under different support scenarios (I – IV). Thus, on the right hand side we observe the income groups in which the women would find themselves after the death of their husbands, conditional on the assumed system of support: without the survivor’s pension (system I, Figure 5a), with the survivor’s pension (system II, figure 5b), with the widow’s pension (system III, Figure 5c) and under the alternative system (system IV, Figure 5d).

Figure 5a shows that without any additional support the financial situation of older women would significantly deteriorate in the event of the death of their spouses (Figure 5a). The share of women whose income would place them in the lowest two decile groups would be as high as 54.7 percent (compared to 17.5 percent of married couples), and 82.8 percent of the widows would be in the bottom half of the income distribution (compared to 57 percent of married couples). The current survivor’s pension seems to protect a large proportion of women (Figure 5b), although the proportion of those who find themselves in the lowest two income decile groups still more than doubles relative to the situation of married couples, to 38.3 percent. Further, 74.9 percent of the widows would find themselves in the bottom half of the distribution. The proposed widow’s pension (Figure 5c) offers much greater support with a very high share of new widows remaining in the same decile or even moving to a higher income group. For example, with the widows’ pension 8.0 percent of women would be in the 9th income decile group and 5.3 percent in the 10th group, while, in comparison, 7.0 percent and 4.4 percent of married couples found themselves in these groups, respectively. 

Figure 5. Change in income decile among women aged 65+, following a hypothetical death of their husbands.

Comparison of income decile groups under different widowhood pension systems in Poland, highlighting income shifts across four scenarios and representing policy brief that covers widowhood support Poland.

Source: Own calculations based on HBS 2021 using SIMPL model; graphs were created using: https://flourish.studio/

The proposed alternative system (Figure 5d) raises widows’ incomes compared to the current survivor’s pension system, but it is less generous than the system with the widow’s pension. Importantly however, it increases the incomes of widows in the lower income groups, which means that, compared to the current system, the number of women dropping to the poorest income groups following their husband’s death would be significantly reduced (24.0 percent would be in the lowest two deciles). At the same time 4.6 percent and 3.4 percent of the widows would be placed in the 9th and the 10th decile groups, respectively.

Table 2 shows the change in the poverty risk among the women in five considered scenarios, i.e. before they become widowed and after the hypothetical death of their husband under the considered four systems of support. 10.5 percent of married couples aged 65+ had equivalised disposable incomes which placed them below the poverty line calculated in the baseline system. After the simulated death of a husband, in a scenario without the survivor’s pension, the poverty rate among widows would increase to 35.3 percent, while the current survivor’s pension limits it to 20.7 percent. Poverty would be further reduced in the two systems with considered reforms: to 11.0 percent the widow’s pension system and to 11.8 percent in the alternative system.

Table 2. At-risk-of-poverty rates in the analysed scenarios.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: The at-risk-of-poverty threshold is set at 60 percent of median equivalised disposable income in the baseline system.

Total Costs of the Considered Schemes

As mentioned above, the presented simulations take into account the conditions of current older couples. Therefore, we cannot directly calculate the consequences of the two suggested systems (the widow’s pension system and the alternative system) for those who are already widowed. This applies in particular to the present-day cost from the suggested changes to the widowhood support schemes to the public budget . In order to accurately estimate the changes in already widowed people’s incomes, we would have to have the information on the values of widow’s pensions and of pensions that their deceased spouses received when they were still alive, information that is not available in the HBS.

Nevertheless, our simulations allow us to compare the aggregated costs of support for women in the simulated widowhood scenarios under different support systems. Such calculations suggest that an implementation of the widow’s pension would increase the gross benefits received by widows by 34.2 percent compared to the current survivor’s pension system., while the alternative system would raise them by 14.7 percent. Applying these growth rates to the social security benefits currently received by widows and widowers (from the HBS data) implies additional annual costs of 24.1 bn PLN (5.6 bn EUR) under the widow’s pension system, and 10.5 bn PLN (2.5 bn EUR) under the alternative system.

Who Gains the Most?

From a distributional perspective, the simulated outcomes of the two suggested systems of support in widowhood can be compared to the baseline situation. In Figure 6 we show average changes in widowed women’s disposable income resulting from a change from the current system with survivor’s pension to the system with widow’s pension, and to our alternative design. Gross monthly survivor’s pensions of the widows are divided into seven groups, starting from 0-500 PLN up to 5501 PLN and more. One can clearly see that women who would, on average, gain the most from the implementation of the widow’s pension are those who already have a relatively high survivor’s pension in the current system. The average rise in disposable income (net) among those with gross monthly pensions between 4501 and 5500 PLN would be 1200 PLN, if widow’s pension was implemented. In contrast, women who receive 501-1500 PLN (gross) per month under the current survivor’s pension, would see a net monthly gain of about 350 PLN. These women would benefit slightly more under the alternative system – on average about 390 PLN, while much lower increases (on average about 220 PLN per month) would be faced by women in the 4501-5500 PLN group. Women in the last group, with gross monthly pensions of 5501 PLN and more under the current survivor’s pension system, would additionally gain even less in the alternative system – on average about 170 PLN. Thus overall, greater gains would accrue to those with lower current benefits in the alternative system.

Figure 6. Average increase in disposable income among widows by current survivor’s pensions’ value group.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR.

In Figure 7 we categorise the sample of widows in terms of the range of their gains resulting from the two analysed reforms. The gains are calculated as changes in disposable income between the current system of support and the modelled reforms. We see that 20 percent of widows would gain over 1000 PLN extra per month as a result of the widow’s pension’s reform, while a further 24 percent would gain between 801 to 1000 PLN and 28 percent could expect to see a gain of between 601-800 PLN per month. The reform would leave the incomes of only about 12 percent of the widows unchanged – most of them are women who are not eligible for their own retirement pensions. In the alternative system the incomes of 34 percent of the analysed widows would remain unaffected. This group of women includes not only those without their own retirement pensions, but also those whose husbands received much higher pensions than themselves. This means that even if a widow’s retirement pension were to increase by 50 percent of the minimum pension, it would still be lower than 85 percent of her spouse’s retirement pension (see Figure 4a). In the alternative system about 17 percent of women in the sample would increase their disposable income by less than 400 PLN per month. For 28 percent, the increase would be in the range of between 400 and 600 PLN per month. While 21 percent would receive increased benefits under the alternative system, none of the hypothetical widows would receive more than 800 PLN per month.

Figure 7. Share of women by ranges of increases from the widow’s pension and the alternative scenario.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR.

Figure 8 presents the average effect of the modelled reforms on disposable incomes of women in the sample, divided by income decile groups. Households were assigned to one of ten income groups based on their equivalised disposable income in the baseline system (i.e. according to the joint income of the couples). Figure 8 reflects the distribution of gains from the implementation of the widow’s pension or the alternative system. In the first case, the highest gains would be concentrated among the richest households. While women in the 8th and 9th income decile would, on average, receive an increase in their disposable income of about 1100 PLN per month, those in the 2nd decile group would, on average, receive only an additional 470 PLN per month. The distribution under the alternative system is far more concentrated on low income households. The highest average additional gain of about 420 PLN per month would be granted to widows from the 3rd income decile group, and benefits to women in the upper half of the income distribution would be significantly lower. Women in the top decile would gain, on average, only about 280 PLN per month. In many of the poorest households in our sample of couples, neither partner qualifies for a retirement pension. As a result, widows in this group would experience significantly lower average gains under both analyzed systems compared to those in higher income brackets.

Figure 8. Average gains due to the implementation of widow’s pension and the alternative system, by income decile group.

Source: Own calculations based on HBS 2021 using the SIMPL model. Notes: Change in the disposable income with respect to the current system with survivor’s pension. 1PLN~0.23EUR. Assignment to the income group was done prior to the hypothetical death of husbands.

Conclusion

In 2021 only 10 percent of the Polish widows and 8 percent of the Polish widowers aged 65 and more evaluated their material situation as rather bad, percentages that had dropped significantly since 2010. According to the HBS the majority of widowed individuals in Poland are also owners of the dwelling they live in. At the same time, income poverty among older persons living alone has increased in Poland since 2015, suggesting that despite the subjective evaluations, incomes of these older individuals – many of whom are widowed – have not managed to keep up with the dynamics of earnings and social transfers aimed at other demographic groups in Poland. As showed in our simulations, the current widowhood support system in Poland substantially limits the risk of poverty following the death of one’s partner. However, while the current survivor’s pension decreases the poverty risk from 35.3 percent (in a system without any support) to 20.7 percent, the risk of poverty among widows is still significantly higher compared to the risk faced by married couples.

The simulations analysed in this Policy Paper has covered the proposal of a support system reform, thewidow’s pension, which is currently discussed in the Polish Parliament. The simulations also covered an alternative alternative proposal putting more emphasis on poorer households. Both of these reforms would provide additional support to individuals affected by widowhood. In the case of the widow’s pension the average value of social security benefits would increase by 34.2 percent, whereas the alternative scenario would increase these benefits by 14.7 percent. If the pensions of current widows and widowers were to be increase by these proportions, the total annual cost to the public sector would amount to 24.1 bn PLN (5.6 bn EUR) and 10.5 bn PLN (2.5 bn EUR) per year, respectively. As shown above, the impact of these two reforms on poverty levels among widowed individuals would be very similar – the reforms would reduce it to 11.0 and 11.8 percent, respectively. The substantial difference in the total cost of these two alternatives is mainly due to the fact that the bulk of the additional benefits from the implementation of the widow’s pension is concentrated among high-income widows and widowers, while the highest profits in the modelled alternative system are targeted at households at the bottom of the income distribution.

If the aim of the potential legislative changes is to support widows and widowers in a difficult material situation and to reduce the extent of poverty, the widow’s pension currently discussed in the Polish Parliament seems to be far from ideal. As demonstrated in this Policy Paper, additional support addressed to widows and widowers in Poland can be designed in a way that substantially reduces the risk of poverty, with limitations on benefit increases to those already in a favourable financial situation. Our proposed alternative system would generate higher incomes for the poorest widows and widowers similar to the widow’s pension, while its cost to the public budget would be less than half of the cost of the discussed widow’s pension reform.

References

  • Adena, M., Hamermesh, D., Myck, M., & Oczkowska, M. (2023). Home Alone: Widows’ Well-Being and Time. Journal of Happiness Studies. https://doi.org/10.1007/s10902-023-00622-w
  • Ahn, N. (2005). Financial consequences of widowhood in Europe: Cross-country and gender differences.
  • Bíró, A. (2013). Adverse effects of widowhood in Europe. Advances in Life Course Research, 18(1), 68–82. https://doi.org/10.1016/j.alcr.2012.10.005
  • Blanner Kristiansen, C., Kjær, J. N., Hjorth, P., Andersen, K., & Prina, A. M. (2019). Prevalence of common mental disorders in widowhood: A systematic review and meta-analysis. Journal of Affective Disorders, 245, 1016–1023. https://doi.org/10.1016/j.jad.2018.11.088
  • Bound, J., Duncan, G. J., Laren, D. S., & Oleinick, L. (1991). Poverty Dynamics in Widowhood. Journal of Gerontology, 46(3), S115–S124. https://doi.org/10.1093/geronj/46.3.S115
  • Corden, A., Hirst, M., Nice, K., University of York, & Social Policy Research Unit. (2008). Financial implications of death of a partner. Social Policy Research Unit, University of York.
  • Hungerford, T. L. (2001). The Economic Consequences of Widowhood on Elderly Women in the United States and Germany. The Gerontologist, 41(1), 103–110. https://doi.org/10.1093/geront/41.1.103
  • Lee, G. R., DeMaris, A., Bavin, S., & Sullivan, R. (2001). Gender Differences in the Depressive Effect of Widowhood in Later Life. The Journals of Gerontology: Series B, 56(1), S56–S61. https://doi.org/10.1093/geronb/56.1.S56
  • Myck, M., Król, A., Oczkowska, M., & Trzciński, K. (2023a). Komentarze Przedwyborcze CenEA 2023: Druga kadencja rządów Zjednoczonej Prawicy: Wsparcie rodzin w czasach wysokiej inflacji. https://cenea.org.pl/2023/09/13/wybory-parlamentarne-2023-w-polsce-komentarze-przedwyborcze-cenea/
  • Myck, M., Król, A., Oczkowska, M., & Trzciński, K. (2023b). Komentarze Przedwyborcze CenEA 2023: Materiały metodyczne. https://cenea.org.pl/2023/09/13/wybory-parlamentarne-2023-w-polsce-komentarze-przedwyborcze-cenea/
  • Myck, M., Michał Kundera, Najsztub, M., & Oczkowska, M. (2015). Przedwyborcze miliardy: Jak je wydać i skąd je wziąć (II; Raport Przedwyborczy CenEA 2015). CenEA. http://cenea.org.pl/Badania/Research/raportvat.html
  • Myck, M., & Najsztub, M. (2014). Data and Model Cross-validation to Improve Accuracy ofMicrosimulation Results: Estimates for the Polish Household Budget Survey. International Journal of Microsimulation, 8(1), 33–66. https://doi.org/10.34196/ijm.00111
  • Myck, M., Najsztub, M., Oczkowska, M., & Trzciński, K. (2019). Pakiet podatkowo-świadczeniowych rozwiązań rządu Zjednoczonej Prawicy. Raport Przedwyborczy CenEA 12/04/2019. https://cenea.org.pl/wp-content/uploads/2019/05/raportcenea12042019.pdf
  • Ory, B., & Huijts, T. (2015). Widowhood and Well-being in Europe: The Role of National and Regional Context. Journal of Marriage and Family, 77(3), 730–746. https://doi.org/10.1111/jomf.12187
  • Panek, T., Kotowska, I., & Sączewska-Piotrowska, A. (2015). Sytuacja materialna gospodarstw domowych osób starszych. W Rynek pracy i wykluczenie społeczne w kontekście percepcji Polaków. Diagnoza Społeczna 2015. Raport tematyczny. (s. 107–137).
  • Petelczyc, J., & Roicka, P. (2016). Sytuacja kobiet w systemie emerytalnym. Instytut Spraw Publicznych. https://www.isp.org.pl/pl/publikacje/sytuacja-kobiet-w-systemie-emerytalnym
  • Sasson, I., & Umberson, D. J. (2014). Widowhood and Depression: New Light on Gender Differences, Selection, and Psychological Adjustment. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69B(1), 135–145. https://doi.org/10.1093/geronb/gbt058
  • Schaan, B. (2013). Widowhood and Depression Among Older Europeans—The Role of Gender, Caregiving, Marital Quality, and Regional Context. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 68(3), 431–442. https://doi.org/10.1093/geronb/gbt015
  • Siflinger, B. (2017). The Effect of Widowhood on Mental Health—An Analysis of Anticipation Patterns Surrounding the Death of a Spouse. Health Economics, 26(12), 1505–1523. https://doi.org/10.1002/hec.3443
  • Steptoe, A., Shankar, A., Demakakos, P., & Wardle, J. (2013). Social isolation, loneliness, and all-cause mortality in older men and women. Proceedings of the National Academy of Sciences, 110(15), 5797–5801. https://doi.org/10.1073/pnas.1219686110
  • Timoszuk, S. (2017). Wdowieństwo a sytuacja materialna kobiet w starszym wieku w Polsce. Studia Demograficzne, nr 2(172), 121–138. http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ekon-element-000171500466
  • Timoszuk, S. (2021). Wdowieństwo w starszym wieku. O sytuacji finansowej wdów w Polsce.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Closing the Gender Data Gap

Posted on March 7, 2024 | FROGEE PROJECT, Policy Brief

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High-quality data plays a crucial role in enhancing our comprehension of evolving social phenomena, and in designing effective policies to address existing and future challenges. This particularly applies to the gender dimension of data, given the profound impact of the pervasive so-called “gender data gap”. In recent decades, data recovered from archives, high quality surveys, and census and administrative data, combined with innovative approaches to data analysis and identification, has become pivotal for the progress of documenting structural gender differences. Nonetheless, before we can close the gender gaps on the labour market, within households, in politics, academia and other areas, researchers and policy-makers must first ensure a closure of the gender data gap.

Policy Brief | EN langauge version

Policy Brief | GE language version

Closing Gender Data Gap: Advancing Research and Policy Through Better Evidence

Any progress in our understanding of social phenomena hinges on the availability of data, and there is no doubt that recent advances in economics and other social sciences would not have been possible without countless high quality data sources. As we argue in this policy brief, this applies also, and perhaps particularly, to the documentation of different dimensions of gender inequalities and the analysis to identify their causes. Over the last few decades innovative ways to document historical developments, combined with improvements in the access to existing data, as well as new approaches to data collection, have become cornerstones in the progress made in our understanding of the various expressions of gender inequality. In the economic sphere this has covered themes such as labor market status,  earning and income levels, wealth accumulation over the life course, education investments, pensions, as well as consumption patterns and time allocation – in particular caregiving and household work. Researchers have also been able to empirically study gender inequalities in politics, culture, crime, the justice system and in academia itself.

Groundbreaking studies in gender economics, including those by Claudia Goldin, the recent Nobel Prize laureate, would not have been possible without high quality data and innovative ways aimed at closing the “gender data gap”, a term coined by Caroline Criado Perez, in her bestseller “Invisible women” (Criado Perez, 2020). In the introduction to the book she notes that “(…) the chronicles of the past have left little space for women’s role in the evolution of humanity, whether cultural or biological. Instead, the lives of men have been taken to represent those of humans overall.” (p. XI). The gender data gap is the result of deficits of informative data sources on women, which has been augmented by frequent lack of differentiation of information by sex/gender in available sources. Closing the gender gaps along the dimensions already identified in existing studies will require a continuous monitoring of evidence, thus closing the gender data gap in the first place. New studies focused on greater equality and on the effectiveness of various implemented policies will continue to rely on good data. Thankfully, few new datasets currently ignore the gender of the respondents. However  as our understanding of the biological and cultural aspects of sex and gender grows, the way data is collected will need to be modified.

As we prepare for the new challenges ahead of those designing data collection efforts and examining the data, we believe it is important to give credit to the authors of some of the groundbreaking studies that paved the way to the current pool of evidence on gender inequality. Around the time of the International Women’s Day, we recall several empirical studies in gender economics that, in our opinion, merit special attention due to either their innovative approaches to data collection, their unique access to original data sources, or their methodological novelty. These studies bring valuable insights into specific dimensions of gender inequality. This short list is naturally a subjective choice, but we believe that all of these studies deserve credit not only among researchers within gender economics, but also among those more broadly interested in the recent progress in the understanding of different aspects of gender inequality.

From Data to Policy Recommendations

Over the last few decades substantial efforts have been made to provide empirical evidence concerning historical trends in inequalities between men and women on the labor market. Seminal work in this field was conducted by Claudia Goldin in the 1970s and 80s, culminating in the publication of the path-breaking book Understanding the Gender Gap: An Economic History of American Women (Goldin, 1990). The book fundamentally changed the view of women’s role in the labor market. Empirically Goldin shows that female labor force participation has been significantly higher in historical times than previously believed. Before Goldin, researchers mainly studied twentieth century data. Based on this it looked as if women’s participation in the labour market is positively correlated with economic growth. Goldin’s work showed instead that women were more likely to participate in the labour force prior to industrialization, and that early expansion of factories made it more difficult to combine work and family. Seen over the full 200 year period, from before industrialization to today, the pattern of women’s labour market participation is in fact U-shaped, pointing to the importance of various societal changes that alter incentives and possibilities for women’s work. Goldin’s contribution is however not just about getting the empirical picture right. At least equally important is the recognition of women as individual economic agents, who make forward looking decisions under various institutional constraints and limitations related to social norms about identity and family, as well as education opportunities and labor market options. While some decision can be modeled as taken by “the economic man”, others by households, it may seem surprising that studying women’s decisions was for so long neglected.

Institutional, cultural and economic factors behind historical trends have become the focus of much of the literature trying to identify the forces driving gender disparities. Some of the most original work considers the role that “chance” plays in determining individual decisions related to gender – how having a first-born son (e.g. Dahl and Moretti, 2008) or having twins (Angrist and Evans, 1998), both of which can be considered random, – affect choices related to partnership, future fertility and the labor market. Others examin the influence of gender imbalances caused by major historical events. Brainerd (2017) investigates the consequences of extremely unbalanced sex ratios in cohorts particularly affected by the massive loss of lives during World War II in the Soviet Union. By exploiting a unique historical data source derived from the first postwar census, combined with statistics registry records from archives, Brainerd provides evidence that the war-induced scarcity of men profoundly affected women’s outcomes on the marriage market. Women were more likely to never get married, give birth out of wedlock and get divorced. On top of that, unbalanced sex ratios affected married women’s intrahousehold bargaining power and resulted in lower fertility rates and a higher rate of marriages with a large age gap between spouses. The post-war institutional setup increased the cost of divorce and withdrew legal obligations to support children fathered out of wedlock, which exacerbated the consequences from the shortage of men by further reducing the rates of registered marriages and increasing marital instability.

The examples above highlight how conditions beyond individuals’ control can contribute to social gender imbalances, or shed light on existing gender biases. How these ‘exogenous’ circumstances translate into economic inequalities and what additional factors drive disparities has been the focus of much academic work on gender inequalities. One of the most challenging questions has been that of demonstrating that discrimination of women, rather than women’s characteristics or choices, are behind the growing body of evidence on economic gender inequality. In this respect Black and Strahan (2001) provide important convincing conclusions by using significant changes in the level of regulation in the US banking sector. Increasing competition between banks lowered banks’ profits, and led to a reduced ability of managers to ‘divide the spoils’, and thus to discriminate between different types of employees. The authors used information on wages within specific industries (including banking) from one of the oldest ongoing surveys in the world – the US Current Population Survey (CPS). By exploiting detailed individual data covering a period of several decades the authors show that higher levels of banking sector regulations (prior to deregulation) facilitated greater premia paid out to male compared to female employees. Thus, increased competition in the banking sector brought favorable changes to women’s pay conditions as well as their position in banks’ management.

While long running surveys such as the CPS continue to serve as invaluable sources of information on the relative conditions of men and women, the growing availability of administrative data has opened new opportunities for documentation of inequalities and identification of the reasons behind these. For instance, the ability to track individuals throughout their work history before and after the arrival of their first child has allowed researchers to compare the trajectories of women’s and men’s earnings, wages and working hours. This comparison has revealed the existence of the so-called “child penalty”, with women experiencing a drop in their labor market position relative to their male partners after the birth of their first child, and with the gap persisting for many years. Strikingly, this penalty has been estimated in some of the most gender-equal countries in the world, such as Sweden (Angelov et al., 2016) and Denmark (Kleven et al., 2019), two countries which have spearheaded collecting and making rich administrative data available to researchers.

Another area where individual register data has proven invaluable is in the study of the so-called “glass ceiling”, i.e., the sharply increasing differences between men and women when it comes to pay as well as representation in the very top of the income distribution. In a seminal study by Albrecht et al. (2003), individual earnings for men and women were compared and differences were found to be markedly higher (with men earning much more) when comparing men in the top of the male income distribution with women in the top of the female income distribution. Also making use of Swedish registry data, Boschini et al. (2020) study a related question, namely the evolution of the share of women in the top of the income distribution. In line with other glass-ceiling results, they demonstrate that the share of women in the top is small, and that it gets smaller the higher one looks, , although it has increased over time. Decomposing incomes into labor earnings and capital income they also show that while women seem to be catching up in the labor income distribution, they clearly lag in the capital income distribution. Also, the income profile of the partners of high-income men and high-income women are strikingly different. Most high-income women have high-income partners, while the opposite is not true for high-income men.

Differences in the economic position of men and women reflected in the above examples can have their origin much before the time individuals enter the labor market. They can be driven by differences in schooling opportunities, as well as other forms of early life investments, to the extent that even much of what is perceived as choices or preferences later in life are in fact results of these subtle early life disadvantages for women. While these have largely diminished in the global North, there is a growing number of studies documenting these differences in the global South. Jayachandran and Pande (2017) examine the impact of son preference, a widespread cultural practice for example in India, on child health and development. The study leverages a simple, standardized, and broadly available indicator – the height of children – which is measured at routine health checks and included in many population surveys, such as the Demographic and Health Surveys (DHS). Additionally, their use of a natural experiment, based on the birth order of children, helps to establish a causal relationship between eldest son preference and nutritional disparities that have long-term developmental consequences among subsequent children, not only for girls but for Indian children on average. Findings like these underscore the importance of gender equality not only as a fundamental value but also as a crucial factor in promoting growth and development at the societal level.

The social costs of gender inequality have also motivated the growing research interest in gender-based violence and crime. Given the specific challenges associated with these topics – such as the clandestine and underreported nature of these acts but also the consideration for victims’ confidentiality and safety – studies in this area has required researchers to develop and apply innovative tools and data collection methods. In this framework list experiments have emerged as a methodology allowing respondents to disclose sensitive or socially undesirable attitudes indirectly, reducing the likelihood of the so-called social desirability bias in survey reporting. In a list experiment, respondents are presented with a set of statements or behaviors and asked to indicate their agreement or engagement with these. Among listed items, one is considered “sensitive” and is included only for a randomly selected subset of respondents. By comparing the average number of items agreed with by the entire sample to a control group that did not get the sensitive item, researchers can estimate the proportion of respondents who agreed with or engaged in the sensitive behavior or opinion. Kuklinski et al. (1997) is one of the pioneering contributions in this area, estimating the proportion of voters who harbored racial prejudices but who may have been unwilling to admit it in a direct survey question. List experiments have since become a widely used tool in political science and economics and have helped in the advancement of our understanding of gender-based violence (Peterman et al., 2018). Given the strong assumptions underlying the analysis the method has not become the ”statistical truth serum” it was at some point considered to be. However, list experiments have broadened the analytical opportunities in an area plagued by significant informational and data challenges.

While worldwide gender gaps in economic opportunities and especially in education and health have rapidly declined (and sometimes reversed) in the last decades, larger differences remain in political empowerment (see e.g., WEF Gender Gap Report 2023). Another Nobel Prize laureate in economics, Esther Duflo, in her joint work with Raghabendra Chattopahyay (2004), have pioneered a highly prolific area of research on the impacts of women as policymakers. In their study, they leverage a unique policy experiment in India  that randomized the gender of the leader of Village Councils, and a detailed dataset based on extensive surveys administered to both Village Council leaders and villagers. The surveys allowed for estimation of the investments in different public goods in 265 Village Councils, as well as the preferences over each of these public goods among female and male villagers. Combining the randomization and this rich dataset, the authors establish that political leaders prioritize public goods that are more relevant to the needs of their own gender, suggesting that women’s under-representation in politics might result in women’s and men’s preferences being unequally represented in policy decisions.

Conclusions and Recommendations

The narrowing gender gap in political representation across various levels of government, the growing influence of women in other areas such as public institutions, administration etc., and the heightened awareness of the crucial role gender equality plays in socio-economic progress all bode well for improvements in access to high-quality gender-differentiated data sources. Before we can recognize and close gender gaps identified from high-quality data, the gender data gap needs to firstly be closed. Governments and public institutions should make their  increasing amounts of digitized information available for research purposes. Funding should be available to collect data through surveys, and these could in turn be combined with details available in administrative sources to take advantage of the breadth of survey data and the precision of official statistics. Information needs to be collected on a frequent and regular basis to make sure that the consequences of various major developments, such as legal changes, conflicts or natural disasters, can be identified. Innovative data sources, for instance information from mobile apps or social media, can provide additional useful insights into socio-economic trends, old and new dimensions of inequalities and regular timely updates on different aspects of gender disparities. These new data sources can become the basis for future innovative studies on gender inequalities, contributing to a better understanding of the mechanisms behind these inequalities, and providing evidence for policies and other efforts to effectively close the remaining gaps. Already now there is enough evidence to conclude that closing these gaps is not only just but that it also constitutes a fundamental basis for continued inclusive economic development.

Post Scriptum

Contributing to the existing pool of data sources we are happy to share a regional dataset with information on gender norms and gender-based violence: the FROGEE Survey 2021. The data was collected using the CATI method (phone interviews) in autumn 2021 in Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. In each country interviews were conducted with between 925 and 1000 adults. The survey covered areas such as: basic demographics, material conditions, labor market status, gender norms, attitudes towards harassment and violence, awareness of violence against women and awareness of legal protection for gender violence victims.

The data collection was funded by the Swedish International Development Cooperation Agency (SIDA) as part of the FREE Network’s FROGEE project. The dataset and supporting materials are freely available for research purposes. For more information see: FROGEE Survey on Gender Equality.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

The Impact of Rising Gasoline Prices on Households in Sweden, Georgia, and Latvia – Is This Time Different?

Posted on December 1, 2023 | FREECE Brief, Policy Papers

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Over the last two years, the world has experienced a global energy crisis, with surging oil, coal, and natural gas prices. For European households, this translates into higher gasoline and diesel prices at the pump as well as increased electricity and heating costs. The increase in energy related costs began in 2021, as the world economy struggled with supply chain disruptions caused by the Covid-19 pandemic, and intensified as Russia launched a full-scale invasion of Ukraine in late February 2022. In response, European governments have implemented a variety of energy tax cuts (Sgaravatti et al., 2023), with a particular focus on reducing the consumer cost of transport fuel. This policy paper aims to contextualize current transport fuel prices in Europe by addressing two related questions: Are households today paying more for gasoline and diesel than in the past? And should policymakers respond by changing transport fuel tax rates? The analysis will focus on case studies from Sweden, Georgia, and Latvia, countries that vary in economic development, energy independence, reliance on Russian oil, transport infrastructure, and transport fuel tax rates. Through this study, we aim to paint a nuanced picture of the implications of rising fuel prices on household budgets and provide policy guidance.

Record High Gasoline Prices, Historically Cheap to Drive

Sweden has a long history of using excise taxes on transport fuel as a means to raise revenue for the government and to correct for environmental externalities. As early as in 1924, Sweden introduced an energy tax on gasoline. Later, in 1991, this tax was complemented by a carbon tax levied on the carbon content of transport fuels. On top of this, Sweden extended the coverage of its value-added tax (VAT) to include transport fuels in 1990. The VAT rate of 25 percent is applied to all components of the consumer price of gasoline: the production cost, producer margin, and excise taxes (energy and carbon taxes).

In May 2022, the Swedish government reduced the tax rate on transport fuels by 1.80 SEK per liter (0.16 EUR). This reduction was unprecedented. Since 1960, there have only been three instances of nominal tax rate reductions on gasoline in Sweden, each by marginal amounts in the range of 0.04 to 0.22 SEK per liter. Prior to the tax cut, the combined rate of the energy and carbon tax was 6.82 SEK per liter of gasoline. Adding the VAT that is applied on these taxes, amounting to 1.71 SEK, yields a total excise tax component of 8.53 SEK. This amount is fixed in the short run and does not vary with oil price changes.

Figure 1. Gasoline Pump Price, 2000-2023.

Source: Drivkraft Sverige (2023).

Figure 1 shows the monthly average real price of gasoline in Sweden from January 2000 to October 2023. The price has slowly increased over the last 20 years and has been historically high in the last year and a half. Going back even further, the price is higher today than at any point since 1960. Swedish households have thus lately been paying more for one liter of gasoline than ever before.

However, a narrow focus on the price at the pump does not take into consideration other factors that affect the cost of personal transportation for households.

First, the average fuel efficiency of the vehicle fleet has improved over time. New vehicles sold in Sweden today can drive 50 percent further on one liter of gasoline compared to new vehicles sold in 2000. Arguably, what consumers care about the most is not the cost of gasoline per se but the cost of driving a certain distance, as the utility one derives from a car is the distance one can travel. Accounting for vehicles’ fuel efficiency improvement over time, we find that even though it is still comparatively expensive to drive today, the current price level no longer constitutes a historical peak. In fact, the cost of driving 100 km was as high, or higher, in the 2000-2008 period (see Figure 2).

Figure 2. Gasoline Expenditure per 100 km.

Source: Trafikverket (2023) and Drivkraft Sverige (2023).

Second, any discussion of the cost of personal transportation for households should also factor in changes in household income over time. The Swedish average real hourly wage has increased by more than thirty percent between 2000-2023. As such, the cost of driving 100 km, measured as a share of household income, has steadily declined over time. Further, this pattern is consistent across the income distribution; for instance, the cost trajectory for the bottom decile is similar to that of all wage earners (as illustrated in Figure 3). In 1991, when the carbon tax was implemented, the average household had to spend around two thirds of an hour’s wage to drive 100 km. By 2020, that same household only had to spend one third of an hour’s wage to drive the same distance. There has been an increase in the cost of driving over the last two years, but in relation to income, it is still cheaper today to drive a certain distance compared to any year before 2013.

Figure 3. Cost of Driving as a Share of Income, 1991-2023.

Source: Statistics Sweden (2023).

Taken all together, we see that on the expenditure side, vehicles use fuel more efficiently over time and on the income side, households earn higher wages. Based on this, we can conclude that the cost of travelling a certain distance by car is not historically high today.

Response From Policymakers

It is, however, of little comfort for households to know that it was more expensive to drive their car – as a share of income – 10 or 20 years ago. We argue that what ultimately matters for households is the short run change in cost, and the speed of this change. If the cost rises too fast, households cannot adjust their expenditure pattern quickly enough and thus feel that the price increase is unaffordable. In fact, the change in the gasoline price at the pump has been unusually rapid over the last two years. Since the beginning of 2021, until the peak in June 2022, the (nominal) pump price rose by around 60 percent.

So, should policymakers respond to the rapid price increase by lowering gasoline taxes? The perhaps surprising answer is that lowering existing gasoline tax rates would be counter-productive in the medium and long run. Since excise taxes are fixed and do not vary with the oil price, they reduce the volatility of the pump price by cushioning fluctuations in the market price of crude oil. The total excise tax component including VAT constitutes more than half of the pump price in Sweden, a level that is similar across most European countries. This stands in stark contrast with the US, where excise taxes make up around 15 percent of the consumer price of gasoline. As a consequence, a doubling of the price of crude oil only increases the consumer price of gasoline in Sweden by around 35 percent, while it increases by about 80 percent in the US. Households across Sweden, Europe, and the US have adapted to the different levels of gasoline tax rates by purchasing vehicles with different levels of fuel efficiency. New light-duty vehicles sold in Europe are on average 45 percent more fuel-efficient compared to the same vehicle category sold in the US (IEA 2021). As such, US households do not necessarily benefit from lower gasoline taxation in terms of household expenditure on transport fuel. They are also more vulnerable to rapid increases in the price of crude oil. Having high gasoline tax rates thus reduces – rather than increases – the short run welfare impact on households. Hence, policymakers should resist the temptation to lower gasoline tax rates during the current energy crisis. With imposed tax cuts, households will, in the medium and long run, buy vehicles with higher fuel consumption and thus become more exposed to price surges in the future – again compelling policymakers to adjust tax rates, creating a downward spiral. Instead, alternative measures should be considered to alleviate the effects of the heavy price pressure on low-income households – for instance, revenue recycling of the carbon tax revenue and increased subsidies of public transport.

Conclusion

To reach environmental and climate goals, Sweden urgently needs to phase out the use of fossil fuels in the transport sector – Sweden’s largest source of carbon dioxide emissions. This is exactly what a gradual increase of the tax rate on gasoline and diesel would achieve. At the same time, it would benefit consumers by shielding them from the adverse effects of future oil price volatility.

The most common response from policymakers regarding fuel tax rates however goes in the opposite direction. In Sweden, the excise tax on gasoline and diesel was reduced by 1.80 SEK per liter in 2022 and the current government plans to further reduce the price by easing the biofuel mandate. Similar tax cuts have been implemented in a range of European countries. Therefore, the distinguishing factor in the current situation lies in the exceptional responses from policymakers, rather than in the gasoline costs that households are encountering.

Gasoline Price Swings and Their Consequences for Georgian Consumers

The energy crisis that begun in 2021 has also made its mark on Georgia, where the operational expenses of personal vehicles, encompassing not only gasoline costs but also maintenance expenses, account for more than 8 percent of the consumer price index. The rise in gasoline prices sparked public protest and certain opposition parties proposed an excise tax cut to mitigate the gasoline price surge. In Georgia, gasoline taxes include excise taxes and VAT. Until January 1, 2017, the excise tax was 250 GEL per ton (9 cents/liter), it has since increased to 500 GEL (18 cents/liter). Despite protests and the suggested excise tax reduction, the Georgian government chose not to implement any tax cuts. Instead, it initiated consultations with major oil importers to explore potential avenues for reducing the overall prices. Following this, the Georgian National Competition Agency (GNCA) launched an inquiry into the fuel market for motor vehicles, concluding a manipulation of retail prices for gasoline existed (Georgian National Competition Agency, 2023).

The objective of this part of the policy paper is to address two interconnected questions. Firstly, are Georgian households affected by gasoline price increases? And secondly, if they are, is there a need for government intervention to mitigate the negative impact on household budgets caused by the rise in gasoline prices?

The Gasoline Market in Georgia

Georgia’s heavy reliance on gasoline imports is a notable aspect of the country’s energy landscape. The country satisfies 100 percent of its gasoline needs with imports and 99 percent of the fuel imported is earmarked for the road vehicle transport sector. Although Georgia sources its gasoline from a diverse group of countries, with nearly twenty nations contributing to its annual gasoline imports, the supply predominantly originates from a select few markets: Bulgaria, Romania, and Russia. In the last decade, these markets have almost yearly accounted for over 80 percent of Georgia’s total gasoline imports. Furthermore, Russia’s share has substantially increased in recent years, amounting to almost 75 percent of all gasoline imports in 2023. The primary reason behind Russia’s increased dominance in Georgia’s gasoline imports is the competitive pricing of Russian gasoline, which between January and August in 2023 was almost 50 percent cheaper than Bulgarian gasoline and 35 percent cheaper than Romanian gasoline (National Statistics Office of Georgia, 2023). Given the dominance of Russian gasoline in Georgia, the end-user (retail) prices of gasoline in Georgia, are closer to gasoline prices in Russia than EU gasoline prices (see Figure 1).

Figure 1. End-user Gasoline Prices in Georgia, Russia and the EU, 2013-2022.

Source: International Energy Agency, 2023.

However, while the gasoline prices increased steadily in 2020-2022 in Russia, gasoline prices in Georgia increased sharply in the same period. This more closely replicated the EU price dynamics rather than the Russian one. The sharp price increase in gasoline raised concerns from the Georgian National Competition Agency (GNCA). According to the GNCA one possible reason behind the sharp increase in gasoline prices in Georgia could be anti-competitive behaviour among the five major companies within the gasoline market. Accordingly, the GNCA investigated the behaviour of major market players during the first eight months of 2022, finding violations of the Competition Law of Georgia. Although the companies had imported and were offering consumers different and significantly cheaper transport fuels compared to fuels of European origin, their retail pricing policies were identical and the differences in product costs were not properly reflected in the retail price level. GNCA claims the market players coordinated their actions, which could have led to increased gasoline prices in Georgia (National Competition Agency of Georgia. (2023).

Given that increased gasoline prices might lead to increased household expenditures for fuel, it is important to assess the potential impact of recent price developments on household’s budgets.

Exploring Gasoline Price Impacts

Using data from the Georgian Households Incomes and Expenditures Survey (National Statistics Office of Georgia, 2023), weekly household expenditures on gasoline and corresponding weekly incomes were computed. To evaluate the potential impact of rising gasoline prices on households, the ratio of household expenditures on gasoline to household income was used. The ratios were calculated for all households, grouped in three income groups (the bottom 10 percent, the top 10 percent and those in between), over the past decade (see Figure 2).

Figure 2. Expenditure on Gasoline as Share of Income for Different Income Groups in Georgia, 2013-2022.

Source: National Statistics Office of Georgia, 2023.

Figure 2 shows that between 2013 and 2022, average households allocated 9-14 percent of their weekly income to gasoline purchases. There is no discernible increase in the ratio following the energy crisis in 2021-2022.

Considering the different income groups, the upper 10 percent income group experienced a slightly greater impact from the recent rise in gasoline prices (the ratio increased), compared to the overall population. For the lower income group, which experienced a rise in the proportion of fuel costs relative to total income from 2016 to 2021, the rate declined between 2021 and 2022. Despite the decline in the ratio for the lower-level income group, it is noteworthy that the share of gasoline expenditure in the household budget has consistently been high throughout the decade, compared to the overall population and the higher-level income group.

The slightly greater impact from the rise in gasoline prices for the upper 10 percent income group is driven by a 4 percent increase in nominal disposable income, paired with an 8 percent decline in the quantity of gasoline (Figure 3) in response to the 22 percent gasoline price increase. Clearly, for this income group, the increase in disposable income was not enough to offset the increase in the price of gasoline, increasing the ratio as indicated above.

For the lower 10 percent income group, there was a 23 percent increase in nominal disposable income, paired with a 9 percent decline in the quantity of purchased gasoline (Figure 3) in response to the 22 percent gasoline price increase . Thus, for this group, the increase in disposable income weakened the potential negative impact of increased prices, eventually lowering the ratio.

Figure 3. Average Gasoline Quantities Purchased, by Household Groups, per Week (In Liters) 2013-2022.

Source: National Statistics Office of Georgia, 2023.

Conclusion

The Georgian energy market is currently fully dependent on imports, predominantly from Russia. While sharp increases in petrol prices have been observed during the last 2-3 years, they do not seem to have significantly impacted Georgian households’ demand for gasoline. Noteworthy, the lack of impact from gasoline price increases on Georgian households’ budgets, as seen in the calculated ratio (depicted in Figure 2), can be explained by the significant rise in Georgia’s imports from the cheap Russian market during the energy crisis years. Additionally, according to the Household Incomes and Expenditures survey, there was in 2022 an annual increase in disposable income for households that purchased gasoline. However, the data also show that low-income households spend a high proportion of their income on gasoline.

Although increased prices did not significantly affect Georgian households, the extremely high import dependency and the lack of import markets diversification poses a threat to Georgia’s energy security and general economic stability. Economic dependency on Russia is dangerous as Russia traditionally uses economic relations as a lever for putting political pressure on independent economies. Therefore, expanding trade and deepening economic ties with Russia should be seen as risky. Additionally, the Russian economy has, due to war and sanctions, already contracted by 2.1 percent in 2022 and further declines are expected (Commersant, 2023).

Prioritizing actions such as diversifying the import market to find relatively cheap suppliers (other than Russia), closely monitoring the domestic market to ensure that competition law is not violated and market players do not abuse their power, and embracing green, energy-efficient technologies can positively affect Georgia’s energy security and positively impact sustainable development more broadly.

Fueling Concerns: The True Cost of Transportation in Latvia

In May 2020, as the Latvian Covid-19 crisis began, Latvia’s gasoline price was 0.99 EUR per liter. By June 2022, amid the economic effects from Russia’s war on Ukraine, the price had soared to a record high 2.09 EUR per liter, sparking public and political debate on the fairness of fuel prices and potential policy actions.

While gas station prices are salient, there are several other more hidden factors that affect the real cost of transportation in Latvia. This part of the policy paper sheds light on such costs by looking at some of its key indicators. First, we consider the historical price of transport fuel in Latvia. Second, we consider the cost of fuel in relationship to average wages and the fuel type composition of the vehicle fleet in Latvia.

The Price of Fuel in Latvia

Latvia’s nominal retail prices for gasoline (green line) and diesel (orange line) largely mirror each other, though gasoline prices are slightly higher, in part due to a higher excise duty (see  Figure 1). These local fuel prices closely follow the international oil market prices, as illustrated by the grey line representing nominal Brent oil prices per barrel.

The excise duty rate has been relatively stable in the past,  demonstrating that it has not been a major factor in fuel price swings. A potential reduction to the EU required minimum excise duty level will likely have a limited effect on retail prices. Back of the envelope calculations show that lowering the diesel excise duty from the current 0.414 EUR per liter to EU’s minimum requirement of 0.33 EUR per liter could result in approximately a 5 percent drop in retail prices (currently, 1.71 EUR per liter). This at the cost of a budget income reduction of 0.6 percent, arguably a costly policy choice.

In response to recent years’ price increase, the Latvian government opted to temporarily relax environmental restrictions, making the addition of a bio component to diesel and gasoline (0.065 and 0.095 liters per 1 liter respectively) non-mandatory for fuel retailers between 1st of June 2022 until the end of 2023. The expectation was that this measure would lead to a reduction in retail prices by approximately 10 eurocents. To this date, we are unaware of any publicly available statistical analysis that verifies whether the relaxed restriction have had the anticipated effect.

Figure 1. Nominal Retail Fuel Prices and Excise Duties for Gasoline and Diesel in Latvia (in EUR/Liter), and Nominal Brent Crude Oil Prices (in EUR/Barrel), January 2005 to August 2023.

Source: The Central Statistical Bureau of Latvia, St. Louis Federal Reserve’s database, OFX Monthly Average Rates database, The Ministry of Finance of Latvia, The State Revenue Service of Latvia.

The True Cost of Transportation

Comparing fuel retail prices to average net monthly earnings gives insight about the true cost of transportation in terms of purchasing power. Figure 2 displays the nominal net monthly average wage in Latvia from January 2005 to June 2023 (grey line). During this time period the average worker saw a five-fold nominal wage increase, from 228 EUR to 1128 EUR monthly. The real growth was two-fold, i.e., the inflation adjusted June 2023 wage, in 2005 prices, was 525 EUR.

Considering fuel’s share of the wages; one liter of gasoline amounted to 0.3 percent of an average monthly wage in 2005, as compared to 0.12 percent in 2023, with diesel displaying a similar pattern. Thus, despite recent years’ fuel price increase, the two-fold increase in purchasing power during the same time period implies that current fuel prices may not be as alarming for Latvian households as they initially appeared to be.

Figure 2. Average Nominal Monthly Net Wages in Latvia and Nominal Prices of One Liter of Gasoline and Diesel as Shares of Such Wages (in EUR), January 2005 to June 2023.

Source: The Central Statistical Bureau of Latvia.

Another factor to consider is the impact of technological advancements on fuel efficiency over time. The idea is simple: due to technological improvements to combustion engines, the amount of fuel required to drive 100 kilometers has decreased over time, which translates to a lower cost for traveling additional kilometers today. An EU average indicator shows that the fuel efficiency of newly sold cars improved from 7 liters to 6 liters per 100 km, respectively, in 2005 and 2019. While we lack precise data on the average fuel efficiency of all private vehicles in Latvia, we can make an informed argument in relation to the technological advancement claim by examining proxy indicators such as the type of fuel used and the average age of vehicles.

Figure 3 shows a notable change in the fuel type composition of the vehicle fleet in Latvia. Note that the decrease in the number of cars in 2011 is mainly due to a statistical correction for unused cars. At the start of the 21st century, 92 percent of Latvian vehicles were gasoline-powered and 8 percent were diesel-powered. By 2023, these proportions had shifted to 28 percent for gasoline and 68 percent for diesel. Diesel engines are more fuel efficient, usually consuming 20-35 percent less fuel than gasoline engines when travelling the same distance. Although diesel engines are generally pricier than their gasoline counterparts, they offer a cost advantage for every kilometer driven, easing the impact of rising fuel prices. A notable drawback of diesel engines however, is their lower environmental efficiency – highlighted following the 2015 emission scandal. In part due to the scandal, the diesel vehicles growth rate have dropped over the past five years in Latvia.

Figure 3. Number of Private Vehicles by Fuel Type and the Average Age of Private Vehicles in Latvia, 2001 to 2023.

Source: The Central Statistical Bureau of Latvia, Latvia’s Road Traffic Safety Directorate.

Figure 3 also shows that Latvia’s average vehicle age increased from 14 years in 2011 to 15.1 years in 2023. This is similar to the overall EU trend, although EU cars are around 12 years old, on average. This means that, in Latvia, the average car in 2011 and 2023 were manufactured in 1997 and 2008, respectively. One would expect that engines from 2008 have better technical characteristics compared to those from 1997. Recent economic research show that prior to 2005, improvements in fuel efficiency for new cars sold in the EU was largely counterbalanced by increased engine power, enhanced consumer amenities and improved acceleration performance (Hu and Chen, 2016). I.e.,  cars became heavier, larger, and more powerful, leading to higher fuel consumption. However, after 2005, cars’ net fuel efficiency started to improve. As sold cars in Latvia are typically 10-12 year old vehicles from Western European countries, Latvia will gradually absorb a more fuel-efficient vehicle fleet.

Conclusion

The increase of purchasing power, a shift to more efficient fuel types and improvements in engine efficiency have all contributed to a reduction of the overall real cost of transportation over time in Latvia. The recent rise in fuel prices to historically high levels is thus less concerning than it initially appears. Moreover, a growing share of cars will not be directly affected by fuel price fluctuations in the future. Modern electric vehicles constitute only 0.5 percent of all cars in Latvia today, however, they so far account for 10 percent of all newly registered cars in 2023, with an upward sloping trend.

Still, politicians are often concerned about the unequal effects of fuel price fluctuations on individuals. Different car owners experience varied effects, especially when considering factors like income and location, influencing transportation supply and demand.

First, Latvia ranks as one of the EU’s least motorized countries, only ahead of Romania, with 404 cars per 1000 inhabitants in 2021. This lower rate of vehicle ownership is likely influenced by the country’s relatively low GDP per capita (73 percent of the EU average in 2022) and a high population concentration in its capital city, Riga (32 of the population lives in Riga city and 46 percent in the Riga metropolitcan area). In Riga, a developed public transport system reduces the necessity for personal vehicles. Conversely, areas with limited public transport options, such as rural and smaller urban areas, exhibit a higher demand for personal transportation as there are no substitution options and the average distance travelled is higher than in urban areas. Thus, car owners in these areas tend to be more susceptible to the impact of fuel price volatility.

Second, Latvia has a high Gini coefficient compared to other EU countries, indicating significant income inequality (note that the Gini coefficient measures income inequality within a population, with 0 representing perfect equality and 1 indicating maximum inequality. In 2022, the EU average was 29.6 while Latvia’s Gini coefficient was 34.3, the third highest in the EU). With disparities in purchasing power, price hikes tend to disproportionately burden those with lower incomes, making fuel more costly relative to their monthly wages.

These income and location factors suggest that inhabitants in rural areas are likely the most affected by recent price hikes. Distributional effects across geography (rural vs urban) are often neglected in public discourse, as the income dimension is more visible. But both geography and income factors should be accounted for in a prioritized state support, should such be deemed necessary.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Georgian Economy and One Year of Russia’s War in Ukraine: Trends and Risks

Posted on March 13, 2023 | News

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Russia’s invasion of Ukraine profoundly impacted the global economy, immediately sending shockwaves across the globe. The attack of a country that was once a major energy supplier to Europe on the country which was one of the top food exporters in the world, sent food and fuel prices spiralling, causing major energy shortages and the prospect of protracted recession in the United States and the European Union.

The unprovoked and brutal aggression resulted in nearly universal condemnation and widespread sanctions placed on Russia by the United States, the EU, and other Western allies. Financial sanctions were perhaps the most unexpected and significant with the potential for immediate impact on Russia’s neighbours, including those that did not formally join the sanctions regime. In addition to sanctions, the major consequence of the war was mass migration waves, particularly from Ukraine, but also from Russia and Belarus to neighbouring countries.

At the start of the war, it was expected that the Georgian economy would be severely and negatively impacted for the following reasons:

  • First, as a former Soviet republic, Georgia historically maintained close economic trade ties with both Russia and Ukraine. The ties with Russia have weakened considerably in the wake of the 2008 Russo-Georgian war but remained significant. Russia was the primary market for imports of staple foods into Georgia, such as wheat flour, maize, buckwheat, edible oils, etc. Russia and Ukraine were both important export markets for Georgia. Russia was absorbing about 60 percent of Georgian wine exports and 47 percent of mineral water exports, while Ukraine was one of the leading importers of alcohol and spirits from Georgia (46 percent of Georgia’s exports). Tourism and remittances are other areas where Georgia is significantly tied to Russia and somewhat weaker to Ukraine. Before the pandemic, in 2019 Russia accounted for 24 percent of all tourism revenues, while Ukraine for 6 percent. Remittances from Russia accounted for 16.5 percent of total incoming transfers in 2021.
  • Second, while the Georgian government chose to largely keep a neutral stance on the war (announcing at one point that they would not join or impose sanctions against Russia), the main financial and trade international sanctions were still in effect in Georgia due to international obligations and close business ties with the West. These factors were reinforced by strong support for Ukraine among the Georgian population, where the memory of the Russian invasion of Georgia in 2008 remains uppermost.
  • In addition, Georgia is a net energy importer, and while the dependence on energy imports from Russia is not significant, the rising prices would have affected Georgia profoundly.

Original publication: This policy paper was originally published in the ISET Policy Institute Policy Briefs section by Yaroslava Babych, Lead Economist of ISET Policy Institute. To read the full policy paper, please visit the website of ISET-PI. 

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Rebuilding Ukraine: The Gender Dimension of the Reconstruction Process

Posted on March 8, 2023 | FROGEE PROJECT, Policy Brief, Policy Papers

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The post-war reconstruction of Ukraine will have to comprehensively address a number of objectives to set the country on a path of stable, sustainable and inclusive growth. In this Policy Paper we argue that the principles of “building-back better” need to take the gender dimension under consideration. While the war has exposed women and men to different risks and challenges, various types of gender inequality were also pervading the Ukrainian society prior to it. Gender responsiveness in the preparation, design and execution of reconstruction programs is essential to ensure fair and effective allocation of the coming massive inflow of resources in the reconstruction effort. We argue that the principles and implementation mechanisms developed under the gender responsive budgeting (GRB) heading are suitable to apply in the process. We also document that the principles of GRB have in recent years become well established in Ukrainian public finance management and point out areas where the application of a GRB approach will be of particular importance.

Introduction

In August 2022, in the midst of the full-scale Russian invasion, the Ukrainian government adopted the State Strategy for ensuring equal rights and opportunities for women and men for the period until 2030 and approval of the operational plan for its implementation for 2022-2024 (Cabinet of Ministers of Ukraine, 2022), reaffirming its commitment to promote gender equality in Ukraine with a focus on empowering women and eliminating gender-based discrimination in all areas of life. The Strategy follows a number of earlier legislative initiatives that had placed gender equality at the center of Ukrainian public policy and included a comprehensive approach to the design of fiscal policy at the central and local government level, adopting the principles of gender responsive budgeting (GRB). Given substantial gender gaps in numerous areas of life in the Ukrainian society these principles will have to be considered in the future reconstruction process to address such disparities. Following the overall guidance presented by the authors of the CEPR Report published in late 2022, titled “Rebuilding Ukraine: Principles and policies” (Gorodnichenko et al., 2022), this Policy Paper examines some key dimensions of the future reconstruction of Ukraine from the perspective of gender equality with a focus on consistent and effective adoption of the principles of GRB.

Gorodnichenko et al. (2022) noted the critical importance of thinking already today about how Ukraine will rebuild after the war is over – “advanced planning and preparations now will save lives and increase chances of success (…) these steps will give hope to millions of Ukrainians that after the horrors of the war there is light at the end of the tunnel”. We argue, that if the reconstruction is to result in stable, sustainable development and bring tangible benefits to all Ukrainians, the principles of “building-back better” need to take the gender dimension under consideration. This is important for efficiency as well as equality reasons. Such an approach is fully consistent with the 2022 State Strategy which recognizes that gender equality is not only a human right but also a driver of economic growth and social development. The Strategy also provides a framework for mainstreaming gender into government policies and programs, including the budget, and recognizes the importance of gender budgeting as a tool for promoting gender equality and ensuring that public resources are allocated in a fair and equitable manner. Different forms of gender inequality permeated Ukrainian society before the war: while women were more educated than men, they were less likely to participate in the labor force, were severely under-represented in senior positions in business and politics as well as in fast-developing sectors such as information and communication technology, were earning lower wages, and were more likely to be victims of gender-based violence (see, e.g. World Economic Forum, 2021). The war has also exposed women and men to different risks and challenges (see, e.g., Berlin Perrotta and Campa, 2022). Gender responsiveness in the preparation, design and execution of the reconstruction programs is crucial to ensure fair and effective allocation of the vast amount of resources that will be mobilized through the reconstruction effort, providing a unique opportunity to address pre-war and war-related gender inequalities. We argue that the principles and implementation mechanisms developed under the heading of gender responsive budgeting are suitable tools to apply in the process. There are numerous examples from various post-disaster reconstruction experiences showing how sensitivity along the gender dimension can determine the success or failure of specific initiatives, and how thinking in advance along gender equality lines can help address the change from an ineffective and unfair status quo, to successfully “build-back better” (see Box 1).

The dimensions of post-war reconstruction of Ukraine covered in Gorodnichenko et al. (2022) range from necessary changes in governance, through reforms in the business and finance environment, energy and transportation infrastructure, as well as the labor market, the education and the healthcare system, to a discussion of the structure most efficient to deliver international aid. The Report offers an invaluable blueprint for peace-time reconstruction and development of Ukraine and constitutes a crucial reference point for the discussion about the efficient use of resources necessary to ensure rapid and sustainable development of the country. Below we build on its main principles, examine them through a gender lens and apply a gender responsive budgeting approach to highlight the areas where it can be used at different stages of the reconstruction process.

In what follows we draw on the growing literature in the fields, among others, of political economy, development, education and labor economics, that examines the importance of gender diversity and identifies implications of gender inequalities for socio-economic outcomes at the micro and the macro level. On the basis of this literature, we point out the dimensions of the reconstruction process where a gender responsive approach can be particularly beneficial, and specify the stages of the process where the principles of gender responsive budgeting can be effectively applied to ensure efficient and fair distribution of recovery resources. The paper begins with a brief introduction to gender budgeting (Section 2), followed by three sections focusing on key categories of the reconstruction. First, in Section 3, we discuss how a gender responsive approach can shape governance reforms in the post-war period. In Section 4 we examine how gender sensitivity combined with the principles of GRB can influence the allocation of recovery funds in the process of physical rebuilding after the war, as well as the design of the physical environment. Finally, Section 5 highlights the crucial role of human capital in post-war development and points out a number of areas where reconstruction policies might have to be carefully drafted, taking into consideration the specific needs and requirements of women and men. We stress throughout that the concept of gender budgeting and gender responsiveness has been exercised in Ukraine for some time and that it is well rooted in Ukrainian public policy making. These principles should thus come naturally to representatives of key institutions in the discussion of plans for the country’s reconstruction and their execution.

2. Applying Gender Responsive Budgeting Principles to the Process of Post-war Reconstruction

At the heart of gender responsive budgeting lies the recognition of the potential of financial and fiscal policies to influence gender disparities. Gender budgeting integrates “a clear gender perspective within the overall context of the budgetary process through special processes and analytical tools, with a view to promoting gender-responsive policies” (Downes et al. 2017). It is aimed at ensuring that fiscal policies and public financial management practices and tools are formulated and implemented with a view to promote and achieve gender equality objectives, and that adequate resources for achieving them are allocated (IMF, 2017). For GRB to be effective, gender considerations ought to be included in all the stages of the budget cycle, including:

  1. the setting of fiscal policy goals and targets
  2. the preparation of the annual budget and its approval by the legislature
  3. the control and execution of the approved budget
  4. the collection of revenues, the preparation of accounts, and financial reports
  5. the independent oversight and audit of the budget

At each stage of the process, different tools have been developed to ensure that discussion on the gender impact of a specific fiscal policy will constitute an integral part of budget decision-making, execution and reporting. These tools include documents ensuring that spending ministries and agencies are fully briefed on the legal and administrative procedures to be followed in implementing gender responsive budgeting as well as on the requirements to include gender-relevant indicators in budget requests, to provide data disaggregated by sex, or to request specific budgetary allocations for gender-related programs or projects (Budlender, 2015). Moreover, gender budget statements can be published with the budget document as strategic tools to implement gender-responsive policies by allocating adequate resources to reach strategic goals and measuring impact and results. Gender budgeting also includes requirements for gender-impact assessment of the potential direct and indirect effect of policy proposals on gender equality and more broadly on different groups in the society. The regulations may require such assessments to be made prior to implementation (ex-ante assessment) as well as after the roll out of the policies (ex-post evaluation).

The principles of GRB originated in the 1980s in the Australian government in the form of the so-called ‘Women’s Statement’. The principles were applied more broadly in transition and developing countries with support of UN Women and numerous NGOs and research institutions. In recent years, mainly as a result of recognition of the effectiveness of GRB from international financial institutions, such as the IMF, the World Bank and the OECD, the approach has been more firmly integrated with other existing budget tools. It has thus become much more common as a standard technical budget instrument in numerous developed and developing countries (For more details on the development of GRB theory and practice see for example: Budlender et al. 2002; O’Hagan and Klatzer 2018, and Kolovich 2018). Currently over ninety countries around the world apply some form of GRB. While in most of them its use has not been systematized and fully integrated in the overall budget process, countries such as Australia, Austria, Canada and the Spanish province of Andalusia apply GRB consistently across all levels of government and systematically monitor its execution. Ukraine is also among the countries that in recent years have made rapid progress towards comprehensive integration of GRB in its public policy (see Box 2).

The Ukrainian government firmly upheld the principles of GRB after the Russian invasion in February 2022, at a time when one might think that gender equality considerations would lose priority in the management of public finances. Throughout the war the Ministry of Finance has continued to ask line ministries to provide gender responsive budget requests, and fiscal policy has been monitored to ensure informed policies with regard to the distribution of the limited crisis-budget funds among different groups in society. These policies together with the State Strategy for ensuring equal rights and opportunities for women and men for the period until 2030 and approval of the operational plan for its implementation for 2022-2024 (Cabinet of Ministers of Ukraine, 2022), adopted in August 2022, reaffirm the Ukrainian government’s commitment to gender responsive policy making and lay the foundations for the application of such an approach during the post-war recovery process. Effective implementation of GRB principles requires specific knowledge and expertise, and the lack of which has often been a key challenge in meaningful integration of gender analysis in financial processes and documents. Competence in finance among civil servants in line ministries and the Ministry of Finance needs to be combined with gender expertise in sector budget analysis. Development of the combination of these competencies in Ukraine in recent years bodes well for integrating the GRB principles in the process of recovery and reconstruction.

At different stages of the reconstruction process the needs of various social groups along the gender dimension as well as others such as age, disability or religion, ought to be taken into account. To ensure fair and effective use of recovery funds the process should consider the following principles:

  • Participation: consultation with different population groups by gender, age, disability, profession, and other characteristics should enable assessment of the priority objectives for reconstruction in specific localities.
  • Equity: there is always a risk of neglecting the needs of different categories of people (e.g. people with disabilities) while focusing on the needs of the majority of the population.
  • Addressability: it is important to realize that a reconstruction program aimed at “everyone” risks significant misallocation of funds, reaching “no one”. A careful approach needs to consider different economic, cultural, recreational, educational and service needs of well-specified groups of individuals.

The planning and execution of the reconstruction process could follow the lines of intersectional gender budgeting analysis which focuses on the analysis of how different budget measures impact different groups of citizens – women and men – taking into account their disability status, age, place of residence and other variables. Taking as an example a foot bridge reconstruction, a gender responsive analysis would enable information on the citizens in the area, their needs, and their use of the infrastructure. The reconstructed bridge should benefit pedestrians, often women who might sell their products at the marketplace, or whose access to various services requires to cross the river. The analysis would also consider employment levels among women in the reconstruction of the bridge, etc. Considering the example of a school reconstruction, the process needs to consider if there are children in the area and/or whether they will return to that area with their families; whether there is/will be sufficient access to transportation and whether – in case the school is not reconstructed – the children can conduct their education in other schools in the area. Reconstructed educational institutions should consider gender-sensitive infrastructure and account for design of facilities, such as ramps, to address the needs of individuals with disabilities.

The Ukrainian government is strongly committed to supporting gender equality trough, among other means, gender mainstreaming processes with well-established legal frameworks for gender budgeting. Reconstruction efforts shall acknowledge and use the existing analytical tools in Ukraine to ensure that donor funds, projects and initiatives achieve their objective of sustainable and equitable development. Effective and fair distribution of the reconstruction funds will require that substantial care is paid to the analysis of the beneficiaries at the stages of planning and during reconstruction.

3. The Gender Perspective on Governance in Post-war Reconstruction

The institutional arrangements adopted both at the national level in Ukraine and at the international level for the administration and distribution of reconstruction funds will be of crucial importance to the success of recovery efforts and their translation into rapid and sustainable development of the country. In this Section we take the gender perspective on these two dimensions of governance. First, we argue that, at the national level, improvements could be made in the Ukrainian electoral system to extend women’s access to elected political positions in order to increase women’s influence in the overall process of policy-making. Drawing on international evidence we argue that this would not only further ensure support for the application of the gender budgeting approach, but it would also help selecting more competent and non-corruptible politicians. Second, we build on the proposal in Mylovanov and Roland (2022) to create an EU-affiliated agency that would manage the funds from multilateral donors (the “Ukraine Reconstruction and European Integration Agency” – UREIA) and examine how the GRB principles should be applied to efficiently integrate them with other dimensions of such an agency’s activities.

3.1 Increasing Women’s Representation in Ukrainian Political Institutions

In international comparisons, Ukraine lags behind in terms of women’s representation in politics, with gender gaps persisting in national as well as local institutions – in spite of some recent progress. It is likely that a large presence of women in political institutions would help addressing concerns regarding the effective implementation of the gender budgeting principles.  Local and central politicians could promote ex-post evaluations of local and national projects to verify that the intended gender-breakdown of beneficiaries were reached, and they could consider and implement corrective measures when unintended balances were found. In this respect we note, once again, that key decision-makers in Ukraine have shown strong commitment to the principles of gender-budgeting, by supporting and prioritizing its implementation – even during the dramatic circumstances of the Russian invasion (see Box 2). However, the commitment to gender-budgeting among policy-makers in Ukraine would likely become even stronger with a larger presence of women among them. The gender composition of political institutions has been shown to affect the allocation of public funds. For example, Chattopadhyay and Duflo (2004) find that female village chiefs in India tend to spend more money in budgetary areas that appear to be especially important for female villagers. Similarly, an analysis of the bills proposed by French legislators shows that women tend to work more on so called “women’s issues” (Lippmann, 2022). We would therefore expect female politicians to be more likely to support an effective implementation of gender-budgeting principles. Moreover, we expect project proposals crafted by more gender equal groups to be more representative of both women and men’s needs and priorities, which in turns should make the reconstruction process more balanced across different areas and allow it to address numerous inefficiencies of the pre-war status quo (see Box 1).

It is also worth noting that some literature in economics and political science documents that, as more women are elected to political institutions, the average “quality” of elected politicians tends to increase (Besley et al., 2020; Baltrunaite et al., 2018). Moreover, female policy-makers are less likely to engage in corruption and patronage (Brollo and Troiano, 2018; Dollar et al., 2001; Swamy et al., 2001), a dimension which will certainly be closely monitored at an international level, and one which is key in ensuring international public support for the reconstruction.  Policies that increase women’s representation in politics could thus also help improve the quality of democratic institutions, a development that is of utmost importance in the face of Ukraine’s ambition to join the EU. While the existing empirical evidence does not unanimously link women’s representation in politics to more women-friendly budgetary expenditures or better institutions, it is worth noting that there is also no evidence of any major drawback from policies that help women accessing political institutions. Increasing women’s representation in Ukrainian political institutions would also be in line with the argument that bringing a critical mass of new people in politics will help counteracting “oligarchizing” tendencies (Mylovanov and Roland, 2022) in the development of Ukrainian democracy. Numerous options are available in terms of changes in the political ‘rules of the game’ to help address the current underrepresentation of women in Ukrainian political institutions. In Box 3 we list a few of these options.

3.2 Gender Budgeting in the Work of UREIA

Gender-budgeting in the reconstruction process requires an ex-ante gender-analysis of the different projects being financed, which relies on the availability of sex-disaggregated data and specialized skills. Given that gender-budgeting has been part of Ukraine public finance system for a number of years, there is likely a good supply of trained personnel who can work together with international experts right from the beginning of the reconstruction.  Conducting the ex-ante work of gender assessment within the reconstruction agency should speed up the process that we envision, as the tasks involved will be routinely sourced to the same teams of skilled individuals who will analyze different projects through the gender-budgeting lens. The agency should then also be in charge of a centralized evaluation of the various gender-analysis results. This work of overview will provide a comprehensive picture of who is reached by the entire pool of available reconstruction funds, thus allowing to distinguish project-specific gender differences – which can be justified by specific needs being targeted at project-level – from a systematic bias toward one of the genders in the overall reconstruction process. A clear picture of who are the beneficiaries of specific reconstruction initiatives, including statistics disaggregated by gender and potentially by other characteristics, may play a key role in reassuring the Ukrainian society that the recovery funds are used to benefit a broad spectrum of the population, as well as in legitimizing the use of these funds in the eyes of the international donor community.

The conclusions of the international literature on the implications of women’s representation in political institutions for the scope of realized public initiatives mentioned in Section 3.1, pertain also to the functioning of the UREIA. The very design and composition of the agency’s staff ought to ensure gender diversity in its ranks at all levels of seniority to safeguard both the highest quality of the work being carried out by UREIA, as well as the appropriate scope of projects undertaken by the agency, most preferably supported by the principles of GRB. Recent empirical studies indicate that the personal traits of public procurement actors, such as their abilities or competencies, may play a key role in influencing procurement practices and outcomes (see, e.g., Best, Hjort and Szakonyi, 2022 or Decarolis et al., 2020), and gender-based variations in personal characteristics such as risk aversion, ethical values, and others have been demonstrated to be significant, including in the context of corruption (see a review in Chaudhuri, 2012).

4. Post-war Reconstruction: the Gender Perspective on Rebuilding the Physical Environment

The physical environment provides the background for the functioning of societies and at the same time, through its physical durability, imposes a long-lasting legacy that may determine the dynamics of social processes well beyond the time of construction. It shapes the organization of cities, the location and efficiency of public infrastructure, as well as the transport networks and it is also an influential precondition and determinant of behavior and outcomes. There is plenty of examples of how the physical environment affects economic outcomes, both at the individual and societal level. The presence of large infrastructures such as ports or highways determined the process of agglomeration (Ganapati, 2021; Faber, 2014), while paved roads and irrigation canals affect local development and structural transformation of rural areas (Aggarwal, 2018; Asher et al., 2022). Availability of urban green spaces has implications for health outcomes and violence (Kondo et al., 2018) and the safety of commuting routes affects girls’ college choices (Borker, 2021). Moreover, elements of the built environment may also affect social norms (Josa and Aguado, 2019; Baum and Benshaul-Tolonen, 2021).

The post-war reconstruction of the physical environment will shape the structure of Ukrainian cities and villages for decades to come, and hence the process ought to consider very broad aspects of influence of the built environment, with a clear focus on the identity of its users and beneficiaries. We firmly believe that the application of the principles of GRB will facilitate effective use of recovery resources and at the same time help address the inefficiencies of the pre-war status quo to create an environment which fairly takes into consideration the interests of both men and women. With respect to the physical environment in particular, obvious path dependencies limit swift changes to benefit women and other marginalized groups (Hensley, Mateo-Babiano, and Minnery 2014) and from this perspective the post-war recovery process can be thought of as a unique opportunity to address a number of imbalances.

4.1 Gender Mainstreaming in Urban Planning

It has been pointed out that gender mainstreaming in urban planning remains inadequate, which has been linked to the gender bias in the planning industry, both in terms of representation – who plans the cities affects how the cities are planned (Beall, 1996) – and the dominant culture (Sahama et al., 2012). It seems intuitive that a planning approach which takes into account how beneficiaries of the design are disaggregated by gender, and how the design affects the functioning of different groups, would result in an environment much more suited to the needs of these groups. The design should take into consideration different preferences with regard to employment, leisure, housing, open spaces, transportation, and the environment. Gender is relevant across all these issues in urban planning. Including more women in planning and decision-making might be the easiest way to ensure that such perspective is accounted for.

As we argue in Section 5, the effective use of Ukraine’s human capital will be essential for the success of its recovery process and further development. The built environment has important consequences in this realm and so, when rethinking cities, questions such as zoning, connectivity and mobility, as well as the quality of sidewalks and lighting need to be considered in relation to the necessity to juggle work, care for household members, and other daily duties (Grant-Smith, Osborne, and Johnson 2017). The rebuilt physical infrastructure will affect the lives of those who are particularly limited by safety concerns, and it will affect the quality of life of those who walk pushing a pram or supporting elderly relatives. These aspects have been shown to be particularly important for women, increasing their actual and perceived vulnerability when they travel around the city, cutting them off from after-dark activities (Ceccato et al., 2020), but also affecting life choices with a long-lasting impact (Borker, 2021). Utilizing Geographic Information Systems (GIS), satellite imagery and open data sources holds the promise of creating more effective methods for observing patterns of utilization of the city and incorporating a gender responsive approach along these lines in urban planning of reconstructed areas of Ukraine (Carpio-Pinedo et al., 2019).

4.2 Gender Sensitivity in the Design of Transport Infrastructure

Transport infrastructure is crucial to the development of society. When a large share of the infrastructure capital needs to be rebuilt or updated, as will be the case in Ukraine, this opportunity may be used to lay new foundations for both economic and social development. To make the most of such an opportunity, attention ought to be paid to a number of identified risks. Unequal resource distribution has been observed both in connection with new construction of infrastructure (MacDonald, 2005) and relocation of the same (Chandra, 2000; Unruh and Shalaby, 2012). The large stakes inherent in these projects can generate high incomes and rent-seeking leading to a deepening of inequalities and further marginalization of those already vulnerable from the conflict. As women have been particularly strongly affected by the war and the resulting internal displacement (Obrizan, 2022a), the reconstruction process ought to pay particular attention to the risks of exacerbating some unequal developments that emerged with the war. Women’s representation in budgeting, procurement, and decision-making might make these aspects more salient and facilitate their integration into the recovery process.

Mobility is connected with social inclusion, more general well-being and a higher quality of life (this literature is reviewed in Josa and Aguado, 2019). The transport infrastructure is particularly important from the point of view of gender equality as usage of transportation and transport mode preferences significantly vary across socio-economic groups, including by gender (Grieco and McQuaid, 2012; Ghani et al., 2016). In the reconstruction planning and rebuilding process the prioritization of public funding for roads, highways, and railways compared to slow modes, such as walking and cycling, should be put in relation to usage and preferences in different groups of the population. One way through which women are excluded, from mobility itself and from other economic outcomes that mobility would help to reach, such as education (Borker, 2021) and employment (Das and Kotikula, 2019), are safety concerns. In dozens of cities around the world, lack of safety and prevalence of sexual harassment in public transit has resulted in the creation of safe spaces to facilitate safer travel conditions for women (Kondylis et al., 2020). The reconstruction could put significant emphasis on the safety of public transportation which would benefit women in particular and facilitate their effective integration in the future aspects of socio-economic development.

4.3 The Gender Perspective in Increasing Energy Efficiency

One of the key focus points of post-war reconstruction will be rebuilding the energy infrastructure, which has, over the course of the war increasingly been a target of Russian bombing. This process will have to be accompanied by considerations of reorientation, in terms of the energy mix, with a focus on self-sufficiency and environmental sustainability, but also most likely of relocation. At the same time the country should pay significant attention to energy efficiency, which may significantly influence both the energy self-sufficiency of Ukraine and the environmental aspects of power and heating.

It is worth noting at this point that natural resources and their exploitation have significant implications for local communities with consequences from projects often spilling over to local attitudes, leading to gender inequalities through channels such as labor and marriage markets, environmental quality and health, fertility and violence (see a review in Baum and Benshaul-Tolonen, 2021). Both exploitation and new energy infrastructure projects – similar to other aspects of the build environment – will have to consider effective connection to the new urban and production mix, so that the energy infrastructure serves the new cities and the updated geographic distribution of various productive sectors, but also the impact that infrastructure positioning can have on surrounding communities. The presence of infrastructure may generate rents and inequality, and the same is true also for energy infrastructure.

The post-war reconstruction will also present a chance to substantially improve energy self-sufficiency through increased efficiency in energy consumption. Ukraine currently has an energy intensity in production that exceeds the EU average by a factor of 2.5. Although energy efficiency in industry and buildings represents the lion share of such gains, households’ consumption behavior has the potential to contribute substantially, both directly through the consumption of fuel and electricity, and indirectly through the consumption of goods and services (Bin and Dowlatabadi, 2005), as well as through the support for a green policy agenda (Douenne and Fabre, 2022). In this area women and gender-related attitudes might be particularly important. Recent literature claims that women tend to be more environmentally friendly than men, partly due to individual characteristics and attitudes considered more prevalent among women, such as risk aversion, altruism, and cooperativeness – important for environmental behaviors (Cárdenas et al., 2012 and 2014; Andreoni and Vesterlund, 2001). There is also empirical evidence that households where women have more decision power display higher energy-efficiency and energy savings (Li et al., 2019), while firms with more women in their board source significantly more energy from renewables (Atif et al., 2020). It might therefore prove instrumental that energy-efficiency policies directed to households (nudges, information/education, financial incentives) and firms respectively (including gender quotas in boards) take these aspects into account.

5. Post-war Reconstruction: the Gender Perspective on Rebuilding and Strengthening Ukraine’s Human Capital

The human cost of the Russian invasion of Ukraine, including the implications from the Russian occupation of Ukrainian territories since 2014, is immeasurable. The loss of lives, as well as the consequences of disabilities, physical injuries and mental trauma will scar the Ukrainian future for decades to come. The invasion has resulted also in massive displacement and emigration, as well as in the loss of numerous aspects of individual capacities. From the point of view of Ukraine’s reconstruction and future development, all these losses, apart from demonstrating dramatic individual human tragedies, need to be perceived as loss of an essential building block of socio-economic growth – human capital.

Successful post-war reconstruction of Ukraine and its long-term sustainable development can only be ensured if sufficient care is taken of areas which are key to the development and effective utilization of human capital. These cover, in particular but not exclusively, the areas of healthcare, education, research and the labor market and all of them have been extensively covered and discussed in Gorodnichenko et al. (2022, see chapters: 10, 11, 12, 13). Drawing on their general conclusions, we particularly focus on some of the gender aspects of human capital development in the context of planning Ukraine’s reconstruction. Highlighting gender aspects is sometimes misunderstood as being focused on achieving gender equality in numbers across domains. This is not our focus here. The starting point is to look at a number of empirical facts about actual conditions and, based on this, point to the importance of taking the gender dimension into account to achieve efficiency in the reconstruction process. Gender sensitivity seems particularly important in the area of human capital development, and given the fundamental role of human capital for growth (e.g., Barro, 2001; Squicciarini and Voigtländer, 2015; Goldin, 2016) it is essential for an effective use of reconstruction resources as well as for ensuring a cost-efficient, sustainable and fair process of redevelopment.

The reconstruction interventions we address in this Section are those in which the gender aspect is particularly salient. We categorize these under three broad overlapping headings: 5.1; supporting internally displaced individuals, returning international migrants, war veterans and other victims of conflict, 5.2; providing effective education and training to younger generations, and 5.2; reducing institutional constraints on labor market participation.

5.1 Supporting Internally Displaced, Returning International Migrants, War Veterans and Other Victims of Conflict

Forced internal displacement and international migration – apart from the resulting direct consequences for physical and mental health – comes with separation from family and local social networks, from jobs and schools as well as loss of physical and financial assets. According to UNWomen 7,9 million Ukrainians have been forced to leave the country and 90 percent of them are women with their children. Of the more than 5 million internally displaced 68 percent are women (as of Jan 2023; UNWomen, 2023). Many of those forced to move will either not be able to return home or will return to their localities devastated by the war along a number of dimensions.

Effective rebuilding and reconstruction will strongly rely on the input from these hundreds of thousands of individuals. We ought to bear in mind that a great majority of international war migrants are women, and supporting them in returning to Ukraine and in reintegration – often in places other than those they had left – will be of vital importance to the process of reconstruction. Significant care will also have to be taken of returning war veterans – most of whom are men, as well as victims of war related sexual violence – mostly women. Ukraine already counts more than 300,000 veterans from different armed conflicts on Ukrainian territory since 1992 – including 18,000 women or about 6 percent (Ministry of Veterans Affairs of Ukraine, 2022). According to the head of the Armed Forces of Ukraine, about 1 million are currently mobilized, with roughly 5 percent being women (Boyko, 2022). The Ministry of Social Policy of Ukraine (2022b) expects the number of veterans and their families to amount to 5 million. To support their involvement in the reconstruction process, short run interventions ought to address the following critical areas: housing and safety, physical and mental health, and active labor market policies. All these areas involve significant gender considerations.

a) Housing and safety

As many of the internally displaced and those returning to Ukraine from abroad will not be able to return to their homes, provision of safe and good quality housing will represent a major challenge in the reconstruction efforts. While ‘roof over your head’ is equally important for everyone, some aspects of the housing infrastructure, especially local safety and safe connectivity with other key locations, are of particular relevance to the wellbeing of women. Although already mentioned in in our discussion of reconstruction of the physical environment in Section 4, it is important to bear in mind that good quality housing and access to critical infrastructure and effective transportation networks have substantial implications for the effective ways of participation of different members of the society in its socio-economic activities. If the human capital of men and women is to be efficiently engaged in the reconstruction process and further developed, the physical context in which it will happen must be adjusted with the objectives of different groups in mind. Housing, neighborhood conditions, and safe transportation translate into access to jobs, training, education, and local services. The design of the physical reconstruction after the war ought to take these different perspectives into account along the lines of gender responsive budgeting to clearly delineate and correctly identify priorities for the allocation of recovery funds.

b) Physical and mental health support

It is clear that experiences from threat to one’s life and safety, the need to flee one’s home and search refuge, continued experience of insecurity, the direct exposure to terror and violence – including sexual violence – and war atrocities will leave a significant proportion of the Ukrainian population traumatized and in need of specialized mental health support. Additionally, numerous individuals will come out of the war with life-changing physical injuries, while to countless people the period of war will result in substantial neglect of common health problems which otherwise would have been taken care of. These dramatic consequences of war will have to be comprehensively addressed as part of the reconstruction effort to support the affected and vulnerable groups, with the aim to address both their physical and mental health deficiencies. The issues involved are too complex for a Policy Paper to deal with in detail – we can only highlight health as an area to be prioritized in the allocation of recovery funds. With that in mind it is important to stress that there are numerous examples in the public heath literature showing the significance of the gender perspective with regard to the efficient use of public resources and appropriate design of health interventions, taking into account the specific requirements of men and women both in physical and mental health (Abel & Newbigging, 2018; Chandra et al., 2019; Diaz-Granados et al., 2011; Judd et al., 2009; Oertelt-Prigione et al., 2017).

War veterans – primarily men – will be a group in need of particular concern and a comprehensive approach with regard to physical and mental health. Specific specialized support will have to be offered also to victims of conflict-related sexual violence – mostly women. The direct health support will often need to go along with education and training as well as assistance in such areas as housing and material conditions.

Already before the full-scale Russian invasion Ukraine had rolled out several programs in support of veterans from the ongoing 2014 conflict. These included establishing private or publicly co-funded therapy centers for treating posttraumatic stress disorder (Colborne, 2015) and creating organized groups of psychological and psychiatric specialists providing psychological assistance (Quirke et al., 2020). They also included conducting special trainings for general practitioners to provide mental health consultations to increase the overall capacity of Ukraine’s health care system to address mental health issues (Kuznetsova et al., 2019), and broadcasting national TV/social media awareness campaigns such as ‘Mental Health Awareness Week’ (Quirke et al., 2021). Since 2017, as part of the broader healthcare reform program, a thorough reform of the mental health services provision has been underway. The key identified challenges targeted with the reform were: securing human rights protection in mental health legislation, improving regulation of the mental healthcare sector and expanding delivery of mental health services outside of the institutionalized settings (The Ministry of Health of Ukraine, 2018; Weissbecker et al., 2017).

c) Active labor market policies (ALMP)

In precarious conditions in particular, women tend to be those responsible for care of elderly and children, which additionally contributes to disconnecting them from the labor market. It seems that large scale ALMP programs for displaced individuals and returning migrants will be essential to improve the match between skills and the local post-war labor market conditions.

With greater war time labor market disconnect among women, many of whom will have spent months without employment or in various forms of war-time subsistence work, ALMPs will be critical for many in the process of post-war reconstruction. Overview studies show that effectiveness of labor market interventions is generally positive for men and women (e.g. Card et al., 2010). These are often similar in size even though in settings with high employment gaps – such as in the case of Ukraine – the programs tend to be more effective for women (Bergman and van den Berg, 2008). Appropriate identification of skill shortages and provision of training can be an effective way of supporting the post-war Ukrainian labor market and the integration of women in particular. The design of these programs ought to pay special attention in order to avoid labor market stereotyping, to provide broad and integrated routeways to deliver the greatest pool of talent, and to ensure that men and women are appropriately matched to jobs suitable to their skills and abilities. Significant training programs should also be directed towards war veterans.

The skills training aspect of ALMPs has other important gender dimensions – women represent a large majority of Ukrainian teachers, and their skills can be utilized not only in schools but also in adult education and retraining, taking particular advantage of the extensive network of vocational education institutions. Similarly, around 83 percent of the country’s healthcare workers are women, and skills upgrading in the healthcare sector – especially focused on increasing the competence and skills of nurses to take over greater responsibilities for primary care – will constitute an important reform element in the Ukrainian healthcare sector (see Gorodnichenko et al., 2022, chapter 12).

5.2 Providing Effective Education and Training to Younger Generations

Ukrainian youth have in recent years faced a double blow to their educational development. The first one in the form of numerous Covid-19 pandemic related restrictions, followed by the disruption in their education process due to the Russian invasion. The latter especially affected those who had to flee their homes and leave their local schools, as well as those whose schools have been destroyed and rendered dysfunctional. However, many Ukrainian schools opted for or were forced to limit the extent of provided classes and/or provided some of the instruction online. According to UNICEF, the war in Ukraine has disrupted education for more than 5 million children (UNICEF, 2023). 60 percent of children have experienced different traumatic events such as separation from family and friends, moving to another region, shelling and bombing, having witnessed the death of relatives or loved ones, etc. In early 2023, 42 percent of children aged 3-17 studied online, 29 percent both online and in school/kindergarten, 26 percent attended educational institutions while 3 percent studied at home (Sociological Group Rating, 2023). As mounting evidence from the Covid-19 pandemic shows, such disruptions accumulate in the form of significant human capital losses (e.g., Gajderowicz et al., 2022, Contini et al., 2021) and post-war recovery will have to address these to minimize the losses to the pool of skills of the future Ukrainian work force.

Home schooling and school routines disrupted in various ways might, in particular in communities characterized by traditional gender norms, impose additional limitations on the education of girls who may be tasked with greater home and care responsibilities. Thus, while emphasis on catching up on effective learning will be of utmost importance for all students, from the point of view of gender equality, it will be particularly important to closely monitor the school coverage and return to standard school attendance among girls. As post-pandemic evidence from developing countries suggests this may be of particular relevance with regard to teenage students (Kwauk et al., 2021). Post-war recovery initiatives aimed at financial support for households ought to ensure that households with older children in particular do not need to trade off material conditions and schooling opportunities. This might call for programs designed to incentivize school attendance in particular among children in displaced families and for returning international migrants (Aygün et al., 2021).

The post-war reconstruction initiatives in education might also be a chance for the education system to be more forthcoming in promoting high skilled occupations among female students. The 2018 PISA study demonstrated that while Ukrainian 15-year-old girls and boys do equally well in mathematics and science, their objectives with regard to occupation – in particular in STEM areas – differ significantly (OECD, 2019).

5.3 Reducing Institutional Constraints on Labor Market Participation

In order to make most of the potential of the Ukrainian labor force in the process of post-war reconstruction, the plans ought to target various institutional constraints to labor market participation. In this respect the gender equality literature has stressed in particular the provision of early and pre-school childcare to facilitate employment of parents, and in particular of mothers (Addati et al., 2018; Attanasio et al., 2008; Azcona et al., 2020; Gammarano, 2020). Although much has been done during the past decades to improve women integration in the labor market, attitudes in the home and in the family care realm remain traditional and unbalanced (Babych et al., 2021; Obrizan, 2022b). This translates into an unequal division of care and work at home as well as participation in the labor market.

While childcare facilities have been shown to play a key role in supporting female participation in numerous contexts, they are going to be of particular importance to displaced families and returning international migrants, who may lack family support and social networks to organize informal care. Before the full-scale invasion, a relatively high proportion of children aged 3-5 and 5-6 (88 and 97 percent, respectively) were covered by institutional childcare (Ministry of Education and Science of Ukraine, 2021). Returning to such high levels of coverage will be an important element of the reconstruction process. Additionally, authorities should extend the coverage of childcare available to younger children, which in 2019 was much lower (18 percent).

Similarly, welfare arrangements in a broader sense are important to facilitate employment of all working age individuals, men as well as women. It is well established that in situations where government support is cut in various ways, it is typically the women who withdraw from the labor market to manage not just childcare but elderly care and other welfare functions (Mateo Díaz and Rodriguez-Chamussy, 2016). While a high proportion (54 percent) of people in Ukraine before the 2022 invasion declared that care duties should be equally divided between spouses, as many as 41 percent thought that it is the woman’s responsibility (Babych et al., 2021). This implies that it is still likely that, when faced with institutional and informal care constraints, it will be women who will be more likely to drop out of the labor market.

To facilitate effective reconstruction, high participation rates among both men and women will be of utmost importance. To achieve this, substantial reconstruction funding ought to be committed to ensure adequate care support directed both to parents of young children as well as to those with care responsibilities of older family members. Such support will be particularly important in localities with high numbers of internally displaced and returning international migrants. These needs should be correctly accounted for when planning the reconstruction process and allocation of funds, and the GRB approach is likely to be an essential instrument to ensure that objectives of different groups of the Ukrainian society are appropriately addressed.

Conclusions

Over the last few years, the Ukrainian government has introduced substantial reforms in the management of public finances with the aim of developing gender responsive procedures to ensure greater gender equality in the delivered outcomes. The government’s commitment was confirmed in August 2022 with the adoption of the State Strategy for ensuring equal rights and opportunities for women and men for the period until 2030 and approval of the operational plan for its implementation for 2022-2024 (Cabinet of Ministers of Ukraine, 2022). The implemented legislation and the experience from practicing gender responsive budgeting at different levels of government can prove to be an invaluable platform to be utilized in the post-war reconstruction process. Pre-war statistics from many areas of life in Ukraine demonstrated a high degree of inequality along the gender dimension. Gender gaps were high in employment, pay levels, the allocation of home and care responsibilities, and it could also be seen in senior positions in politics, company management, and academia. One of the many tragic consequences of the full-scale Russian invasion and the ongoing war is that these gaps are likely to grow.

If the post-war reconstruction process is to take the principles of “building-back-better” seriously, then, apart from many other dimensions which need to be considered (see Gorodnichenko et al., 2022), recovery planning and execution will also have to address various social inequalities, especially that along the gender dimension. As argued in this Policy Paper, to ensure fair and effective use of recovery funds, the reconstruction process should pay close attention to the identity of its beneficiaries, as well as the way decisions are being made. The authorities, including the central agency responsible for the reconstruction (e.g., UREIA, see Gorodnichenko et al., 2022), should take full advantage of existing tools and instruments of the gender responsive budgeting approach, as well as of an equitable representation within their ranks, and build on the basis of existing Ukrainian legislation and practice of gender budgeting (see Box 2). The reconstruction process will offer a unique chance to set Ukraine on the path of inclusive, stable and sustainable development. We have pointed out a number of areas in which the gender dimension will be particularly important – these include both the reconstruction and rebuilding of the physical environment as well as support and recovery of the full potential of Ukrainian citizens – old and young, men and women. The reconstruction of Ukraine will be a hugely challenging task, and it will have to involve massive resources. International support for channeling those funds to Ukraine and their effective use will depend on how effectively and how fairly they will be used. The application of gender responsive budgeting can help both in ensuring efficiency of allocation of the funds, and in strengthening the legitimacy for the provision of support by the international community.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Gender Gap Widens During COVID-19: The Case of Georgia

Posted on April 11, 2022 | Policy Papers

20220411 Gender Gap Widens Image 01

Gender inequality has been a persistent (albeit steadily improving) problem for years. The COVID-induced crisis put women in a disproportionately disadvantaged position, jeopardizing decades of progress achieved towards equality between men and women. However, these effects of the pandemic were not universal across countries. This policy brief aims to evaluate the gender-specific effects of the COVID-19 crisis in Georgia, looking at labor market outcomes and entrepreneurial activities. As expected, the impact of the pandemic was not gender-neutral in this regard, being especially harmful for women. As the Georgian economy rebounds after the crisis, we show that the widened gender gaps are partially offset only in certain aspects. In order to countervail the disproportionate effects of the pandemic, targeted policy measures are needed to stimulate women’s economic activity.

Introduction

Past economic recessions, including the COVID-induced crisis, have never been gender-neutral (e.g., Liu et al., 2021; Ahmed et al., 2020). While economic crises are usually associated with disproportionate negative impacts on labor market outcomes of men compared to women, the impact of the crisis is, debatably, more severe for women-led businesses as compared to their male-led counterparts (e.g., Torres, 2021; Nordman and Vaillant, 2014; Grimm et al.,2012).

The disproportionate labor market outcomes of economic crises are claimed to be due to the fact that men are predominantly employed in cyclical sectors such as construction or manufacturing; therefore, women have to increase their employment during economic downturns as a means of within-family insurance (Alon et al., 2021). The recent COVID-induced crisis, due to its unique nature, turns out to be an exception in this regard. The pandemic and the subsequently-adopted measures primarily adversely affected contact-intensive sectors (where the worker is required to perform tasks in close physical proximity to other people) that predominantly employ women (Mongey, Pilossoph, and Weinberg 2020; Albanesi and Kim 2021). Moreover, large-scale lockdowns increased the burden of unpaid care, which is generally shouldered by women disproportionately (Babych, 2021), leaving less available time for them to work. It should be noted that gender gaps in the labor market were a persistent (albeit steadily improving) problem even before the pandemic (Eurofound, 2016). Therefore, COVID-19 poses a threat jeopardizing the progress achieved in this direction and worsening gender inequality.

COVID-19 brought unprecedented adverse consequences for not only employed workers but entrepreneurs as well. Increased unpaid care and housework pose additional burdens on female top managers, making women-led businesses more vulnerable to the crisis.

The unequal gender implications of the COVID-19 crisis have been widely debated. Growing evidence (Albanesi and Kim 2021; Torres et al., 2021; Alon et al., 2020; Caselli et al., 2020, Fabrizio et al., 2021) attests that, on average, the effects of the pandemic put women in a disproportionately disadvantaged economic position. However, the extent of this effect varies across countries and is absent in some cases (Campa et al., 2021; Torres et al., 2021).

This policy brief aims to examine the gender-specific nature of the COVID-19 crisis in Georgia. With this aim, we study the differential effects of the pandemic on the economic activity of women in terms of labor market outcomes and entrepreneurship. First, we contrast labor market outcomes for Georgian men and women during the COVID-19 crisis. Secondly, we try to assess the magnitude of the disproportionate impact on women-led businesses compared to men-led ones. We calculate gender gaps across different measures of firm-level performance, such as sales revenue, liquidity and owners’ expectations of falling into arrears. Finally, we examine whether there are any signs of recovery yet in 2021 and draw policymakers’ attention to emerging issues.

Labor market highlights

The adverse effects of the pandemic on female employment were conditioned by both supply and demand-side factors. The latter include decreased economic activity, mainly in service-related sectors (hospitality, personal care, etc.) that are dominated by women (Eurofound, 2021). In Georgia, as of 2019, women constituted the majority of workers in sectors such as hospitality (56%), education (83%) and activities of households as employers of domestic personnel (99%) that experienced some of the sharpest declines in employment during 2020. Moreover, women are more likely to be employed in part-time and temporary jobs (14% of women, as opposed to 11% of men, were employed part-time as of 2019, Geostat Labor Force Survey 2019), leaving them more vulnerable during times of crisis.  Supply-side factors were triggered by the unequal burden of unpaid work generally undertaken by women in Georgia, mainly due to cultural reasons as well as the higher opportunity cost of time for men (women in Georgia on average earned 64% of men’s salaries in 2019, Geostat). School and daycare closures and decreased childcare involvement of grandparents increased household responsibilities for women. A UN Women survey-based study showed that in the midst of the pandemic in Georgia, around 42% of women reported spending more time on at least one extra domestic task as opposed to 35% of men (UN Women, 2020). This would naturally lead to more women than men leaving the labor force. Indeed, looking at the data, we see that in one year after the COVID-19 outbreak, women contributed to 98% (48,000 individuals) of the decrease in the Georgian labor force in 2020 (Geostat). Moreover, a close look at the percentage point difference between the labor force participation rates of Georgian men and women reveals a notable growth in the gender gap starting from 2020. The same can be said about employment rates (Figure 2).

Figure 1. Difference between male and female labor force participation and employment rates

Source: Geostat

To further elaborate on the tendencies in employment, Bluedorn et al. (2021) look at the differences between employment rate changes among male and female workers in 38 advanced and emerging economies. Replicating the exercise with the Georgian data, we can observe results similar to those obtained in Bluedorn et al. (2021). In Figure 2, we see differences between female and male employment rate changes. For each gender group, the latter is computed as an absolute difference between the quarterly employment rate and its annual average level from the previous year. Once the difference takes a negative value, implying that the drop in employment was sharper for women, one could say that we observe a “She-cession” phenomenon as termed by Bluedorn et al. (2021). As we can see, in 2020, the employment rate of women fell more than that of men. This widened gender gap was partially offset in 2021.

Figure 2. Employment rate changes by gender (deviation from the previous year average)

Source: Geostat

Remote work: a burden or a blessing for women?

One important aspect of the COVID-19 crisis was a wide-scale switch to remote work. This development had some gender-specific implications as well. The evidence shows that the prevalence of the switch to remote work was higher among women compared to men (41% vs. 37%) in the EU (Sostero et al., 2020). This tendency also holds in Georgia, where 11% of women as opposed to only 3% of men reported usually working from home in the last three quarters of 2020 (Julakidze and Kardava, 2021). It is not clear whether this tendency can be explained by gender-related occupational differences of male and female jobs (Dingel and Neiman, 2020; Boeri and Paccagnella, 2020; Sostero et al., 2020) or, rather, different personal choices of men and women working in the same occupations. Interestingly, across different countries, we observe a positive correlation between gender inequality (as measured by the Gender Inequality Index) and gender differences in the switch to remote work (measured by the ratio of the share of remote workers among female and male workers). To account for this observation, we can stipulate that gender differences in switching to remote work might be explained by differing gender roles in households, and in society at large, across countries (as proxied by the gender inequality index).

Figure 3. Relative prevalence of remote work among female and male workers

Source: Eurostat, Statistics Sweden, Statista, Geostat, UNDP Human Development Reports

Regardless of the reason, remote work is likely to have some important implications on gender roles. However, the directionality of these implications is not straightforward. On the one hand, remote work offers flexibility for women to juggle household and work responsibilities. On the other hand, since women compared to men have been shown to be more likely to use the time saved from commuting to engage in housework, the switch to remote work might increase their “total responsibility burden” (Ransome, 2007) and lead to time poverty (Peters et al., 2004; Hilbrecht, Shaw, Johnson and Andrey, 2008). Indeed, according to CARE International South Caucasus (2020), around 48% of female survey participants in Georgia placed additional effort into housework and childcare in the midst of the pandemic. Moreover, as women are more likely and expected to use remote working as a means of balancing work-life responsibilities (Moran and Koslowski, 2019) their bargaining power at work decreases relative to their male counterparts. This could have some adverse career implications for female workers. Recent enforced lockdowns might pose an opportunity in this regard, as once-remote work becomes something close to a “new normal” employers will likely decrease the penalty for remote workers.

Spotlight on women-led business performance during the COVID-19 crisis

Calamities brought by the pandemic worsened financial outcomes for enterprises, affecting their ability to operate and have stable financial income. Similar to other crises, the pandemic has not been gender-neutral (Liu et al., 2021; Ahmed et al., 2020) in terms of the effect on business performance.

Gaps in the performance of women- and men-led businesses have been prevalent beyond any economic crisis as well, and have been documented in a number of studies (e.g., Amin, 2011; Bardasi et al., 2011), registering gender differences in sales and productivity in favor of men-owned enterprises. As suggested by Campos et al. (2019), these performance gaps may be due to lower levels of capital owned by women as opposed to men, a smaller number of employees hired by women-owned firms, as well as different practices in using advanced business tools and innovation. In addition, the existence of these gender gaps has also been explained as stemming from the prevailing social norms that assign certain obligations to women. Nordman and Vaillant (2014) and Grimm et al. (2012) suggest that unpaid housework and family-care led to a constrained number of hours women could afford to spend on the work and management of firms, negatively affecting their productivity.

According to the Women Entrepreneurship Report (Global Entrepreneurship Monitor (GEM), 2021), the pandemic imposed an additional burden in terms of increasing family-care duties on women. The GEM survey (2021) conducted in 43 countries worldwide shows that the likelihood of enterprise closure is 20% higher for women-led compared to men-led businesses. The higher likelihood of closure reflects the adverse factors that may have hindered the operating capacity of firms. For example, a survey conducted by UNIDO (2020) suggests that, as a result of the Coronavirus crisis, African and Middle Eastern women-led firms experienced diminished revenues. In addition, 41% of women-led firms were short of cash flow and unable to fulfill financial obligations, while only 32% of male entrepreneurs were exposed to the same problem.

More rigorous analysis on this matter has been conducted by Torres et al. (2021) and Liu et al. (2021). They try to examine the asymmetric effects of the COVID-19 crisis on women-led firms in several dimensions utilizing new datasets from the World Bank: COVID-19 Follow-up Enterprise Survey and the World Bank Business Pulse Survey. The findings of Liu et al. (2021) for 24 countries from Central Europe & Central Asia and Sub-Saharan Africa confirm that during the pandemic women-led businesses are subject to a higher likelihood of closure than men-led businesses and that female top managers are more pessimistic about the future than their male counterparts. Finance and labor factors were mentioned to be the major contributors to these disadvantages; for example, women-led businesses were found to be less likely to receive bank loans compared to men-led businesses. Lastly, the disadvantages experienced by women-led firms were claimed to widen in highly gender-unequal economies and developing countries. Torres et al. (2021) study the impact of the early phase of the COVID-crisis on gender gaps in firm performance for 49 mostly low- and middle-income countries. The results demonstrate that women-led businesses experienced a greater reduction in sales and lower liquidity compared to their male counterparts, which has been reflected in a higher likelihood for women-led companies in several sectors to fall into arrears. On the other hand, as a response to changing circumstances, women-led firms were found to be more likely to increase the utilization of online platforms and make product innovations. Nevertheless, they struggled to obtain any form of public support.

The impact of the pandemic on firms was not gender-neutral in Georgia

The pandemic-induced fragile environment had an adverse impact on entrepreneurs in Georgia– the effects of the shock were significantly more severe for female entrepreneurs than for their male counterparts. In order to assess the gender differences in the impact of the pandemic on firms, we utilize firm-level data on Georgian enterprises from the second round of the World Bank COVID-19 Follow-up Enterprise Survey, conducted in October – November 2020.

Following the methodology as presented in Torres et al. (2021), we assess whether there are differences in the magnitude of reduction in sales revenue (self-reported percentage change in sales revenue one month before the interview as compared to the same period of 2019) and available liquidity for women- and men-led businesses, and whether falling into arrears in any outstanding liabilities is more expected by female top managers (in the next six months from the interview).

Depending on the type of dependent variable, continuous or binary, either Ordinary Least Squares (OLS) or Probit models are estimated, respectively. Along with the gender of the top manager of firms, we also control for sector and firm size. The Georgian database contains a total of 701 enterprises (581 SMEs and 120 micro-businesses).

Table 1. Magnitude of the disproportionate impact of COVID-19 on women-led businesses in Georgia, October-November 2020

Source: The World Bank COVID-19 Follow-up Enterprise Survey, Second Round. Author’s calculations. ***Significant at the 1% significance level; ** significant at the 5% significance level.

Table 1 presents the results of the regression analysis of gender differences among Georgian enterprises in terms of the impact of the pandemic. As observed, women-led businesses reported larger declines in sales, revenues, and liquidity. The predicted drop in sales was 18 percentage points (pp) higher for enterprises with a female top manager than for men-led firms. The larger drop in sales should have been reflected in the reduced cash flow availability and in hardship to cover operating costs. Indeed, as the results demonstrate, women-led enterprises are on average 12.9 pp more likely to have reduced availability of liquidity. This may explain women’s negative future expectations. Moreover, the average predicted probability of expecting to fall into arrears is 11.3 pp higher for women-led firms in Georgia as compared to men-led businesses.

The unequal effect of the COVID-19 crisis on women-led businesses might have been fueled by the disproportionate burden of unpaid care and housework shouldered by women in Georgia, leaving less time available for work and managing enterprises. On the other hand, as Torres et al. (2021) claim, female business owners tend to employ more female workers (the social group more exposed to the unequal burden of the pandemic) than male owners. This, in turn, could further hamper the productivity of women-led businesses and increase their vulnerability to economic shocks.

On the road to recovery

2021 has been characterized by a rather rapid recovery for the Georgian economy, as evidenced by the 10.6% (preliminary estimate) annual growth rate of real GDP. Signs of recovery can also be observed in the labor market – the labor force increased by 4% (YoY) in the 3rd quarter of 2021, while employment was also characterized by a growing trend (1%, YoY).

Along the lines of economic recovery, the gender gap in the labor market also seems to be narrowing. For instance, the steadily growing gap between male and female labor force participation rates seems to stagnate over 2021 (Figure 1). Moreover, as is illustrated in Figure 2 above, the difference between women’s and men’s employment rate changes is positive in 2021, meaning that the employment rate was increasing more (or decreasing less) for women. If this tendency persists, we might stipulate that the disproportionate effects of the COVID-19 crisis on female employment are on the way to recovery.

To examine whether Georgian firms have experienced concurrent movement in their performance along with the economic recovery, we utilize third-round data (from September 2021) of the World Bank COVID-19 Follow-up Enterprise Survey and scrutinize whether the gender differences have narrowed since the previous round of the survey (Table 2).

Table 2. Magnitude of the disproportionate impact of COVID-19 on women-led businesses in Georgia, September 2021.

Source: The World Bank COVID-19 Follow-up Enterprise Survey, Third Round. Author’s calculations. ***Significant at the 1% significance level.

Although the third-round survey data suggests that the predicted percentage drop in sales sharply declined for both men- and women-led businesses, the findings are not statistically significant and therefore cannot claim any signs of recovery in the gender gap in this respect. No signs of recovery are observed in terms of average predicted probability of reduced liquidity of firms and expectations of falling into arrears, either. Gender gaps in these two indicators still persist and are as strong in magnitude as in the second-round survey estimates (from October-November 2020). It seems that despite the economic rebound, not all traces of the pandemic crisis for firms have been eradicated from a gender perspective.

Conclusion

The pandemic came with high economic costs. It hit women disproportionately harder, adversely affecting their employment and entrepreneurial prospects. The unequal burden of the COVID-crisis shouldered by women in Georgia could be one of the reasons for the massive labor force dropouts among female workers and poor performance of women-led businesses. Georgian enterprises with female owners experienced a significantly larger decline in sales compared to their male-owned counterparts, consequently suffering from a shortage of cash flow and fears of falling into arrears.

Despite the great rebound in growth after the initial COVID-19 shock, the pandemic-associated increase in the gender gap seems to have been only partially offset in Georgia. In particular, there is a larger positive upsurge in women’s employment rate, as well as a diminishing difference between male and female labor force participation and employment rates. Following the ongoing recovery in sales revenue of Georgian enterprises (though the predicted gender difference was statistically insignificant), the gender gap in sales is shrinking too. But, in spite of the economic rebound, differences in available liquidity and expectations of falling into arrears have not yet been eradicated, indicating that the adverse influence of the pandemic on women still persists. It leaves female entrepreneurs a still more vulnerable group, which could be of special interest to policymakers to ease their liquidity problems.

Policies should also be directed towards encouraging women to become more economically active. In this regard, remote work seems to pose an opportunity if coupled with affordable childcare support policies.

References

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.