Location: Georgia

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.

Would a Higher Minimum Wage Meaningfully Affect Poverty Levels Among Women? – A Simulation Case from Georgia

Posted on February 27, 2023 | Policy Brief

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In economic literature the effect of minimum wage on the labour market and its relevance as an anti-poverty, equality-enhancing policy tool, is a matter of vigorous debate. The focus of this policy brief is a hypothetical effect on poverty rates, particularly among women, following an increase in the minimum wage in Georgia. A simulation exercise (Babych et al., 2022) by the ISET-PI research team shows that, in Georgia, a potential increase in the minimum wage is likely to result in an overall positive albeit small reduction in poverty rates in general. At the same time, women are likely to gain more from such minimum wage policy than men. The findings are consistent with the literature claiming that a minimum wage increase alone may not result in meaningful poverty reduction. Any minimum wage increase should thus be enhanced by other policies such as training programs increasing labor force participation among women. 

Many countries around the world have enacted minimum wage laws. According to the International Labour Organization (ILO) “Minimum wages can be one element of a policy to overcome poverty and reduce inequality, including those between men and women” (ILO, 2023). In economic literature, the minimum wage debate has been particularly acute, with pros and cons of the minimum wage increases, their effect on the labor market, and their relevance as an anti-poverty and equality-enhancing policy tool fiercely contested in empirical studies and simulation studies. In this policy brief, we focus on the effect of a minimum wage increase in Georgia on poverty rates, and in particular poverty rates among women.

Minimum Wage Effects

According to the European Commission (2020) a number of benefits is associated with the introduction of minimum wage. These benefits include a reduction in in-work poverty, wage inequality and the gender pay gap, among others.

International evidence, however, cautions against considering an increase in minimum wage as the silver bullet to end poverty. A 2019 report by the International Labour Organization (ILO, 2019) shows that the incidence of poverty among the working poor is comparable to the incidence of poverty among individuals outside of the labor market. Therefore, even if an increase in minimum wages would lift all working poor out of poverty, a substantial number of poor would remain.

Moreover, minimum wage can have a potential adverse effect on employment of the most vulnerable by deterring firms from hiring low-wage, low-skilled labor (Neumark, 2018).  The adverse employment effect will be stronger if current wages correspond more closely to the real productivity of labor. In such scenario companies would lose by retaining low-productivity workers and, likely respond to the increase in minimum wage by laying off workers, resulting in the loss of wages, rather than in their increase. On the other hand, if salaries are lower than the real productivity of the less productive workers, companies might still be able to profit from employing them and will not be forced to lay them off, resulting in a wage increase for low-wage workers.

Whether – and to what extent – the introduction of a minimum wage reduces poverty and/or assists low-income households then depends on how many individuals are going to lose their jobs, how many workers will maintain their jobs and receive a higher wage, and where these winners and losers are positioned along the distribution of family incomes.

With regard to employment effects, the results are not perfectly homogeneous. On the one hand, a large body of evidence suggests that minimum wages do lower the number of jobs accessible to low-skill employees (Sabia, Burkhauser and Hansen, 2012; Sotomayor, 2021; Neumark, 2018) On the other hand, some scholars argue that once the study design is changed to take into account the non-random distribution of minimum wage policies in different parts of the country in question, the “disemployment effect” of minimum wage policies (considering the example of United States) largely disappear (Allegretto et al., 2013; Dube et al., 2010).

With regards to poverty, a number of studies look at minimum wage as an anti-poverty policy tool for developing countries and consider its effectiveness in reducing poverty and/or inequality. For example, a study by Sotomayor (2021) suggests that poverty and income inequality in Brazil decreased by 2.8 and 2.4 percent respectively within three months of a minimum wage increase. Effects diminished with time, particularly for bottom-sensitive distribution measures, a process that is consistent with resulting job losses being more frequent among poorer households. The fact that the subsequent yearly increase in the minimum wage in Brazil resulted in a renewed drop in poverty and inequality shows that possible unemployment costs might be outweighed by benefits in the form of higher pay among working persons and – potentially – by positive spillover effects such as increased overall consumption.

Minimum Wage and Female Poverty

As in the case of poverty in general, there is some discrepancy in the literature on whether a minimum wage increase would help reduce poverty among women. Single mothers have been the focus of research in this regard since they are typically the most vulnerable low-wage workers, likely to be hurt by the loss of employment following an increase/ introduction of a minimum wage. Burkhauser and Sabia (2007) argue that the minimum wage increases in the U.S. (1988-2003) did not have any effect on the overall poverty rates, on the poverty rates among the working poor, or on poverty among single mothers. They argue that an increase in Earned Income Tax Credit (EITC), which provides a wage subsidy to workers depending on income level, tax filing status, and the number of children, would have a higher impact on poverty, in particular among single mothers.

In the meantime, Neumark and Wascher (2011) find that EITC and minimum wage reinforce each other’s positive effect for single women with children (boosting both employment and earnings), but negatively affects childless single women and minority men. Another study on the U.S. (Sabia, 2008) looked at the effect of minimum wage increases on the welfare of single mothers, finding that most of them were unaffected as they earned above-minimum wage. Single mothers with low-education levels did not see an increase in net incomes due to the negative effect on employment and hours worked: for low-skilled individuals, a 10 percent increase in minimum wage resulted in an 8.8 percent decline in employment and an 11.8 percent reduction in hours worked.

Yet another study (DeFina, 2008) focus on child poverty rates and show that minimum wage increases have a positive (reducing) impact on child poverty in female-headed families. The effect is small but significant (a 10 percent increase in the minimum wage decreases child poverty rates by 1.8 percentage points), controlling for other factors.

Ultimately, the effect of minimum wage on poverty among women or female-headed households is somewhat ambiguous. It depends on the poverty threshold used, other policy instruments (such as the EITC), existing incentives to enter employment and how, in the specific country of interest, labor laws may affect the employer’s cost of hiring (e.g. for France, see Laroque and Salanie, 2002).

The discussion is however relevant for countries like Georgia, where the wage gap between men and women is quite large, and where more women than men tend to work in low-wage and vulnerable jobs. While the overall poverty gap between men and women in Georgia is insignificant (mainly because poverty is measured at the household level), the gap becomes apparent when comparing female-headed households to male-headed ones. The poverty rates in the former case are nearly 2 percentage points higher in Georgia (20 percent vs. 18.3 percent in 2021). The poverty rates are the highest among households with only adult women (39.3 percent for all-female households vs. 20.1 percent overall in 2018).

A Simulation of a Minimum Wage Raise in Georgia

The Georgian minimum wage legislation dates back to 1999. The presidential decree N 351 from June 4, 1999 states that the minimum (monthly) wage that is to be set in Georgia is equal to 20 GEL (with some specific exceptions in the public sector). This is a non-binding threshold.  Therefore, one has to think carefully what consequences might arise from raising the minimum wage to a much higher level. In addition to previously discussed aspects, one issue to keep in mind is the different average wages across different regions in Georgia. For example, a national minimum wage increase might have more of an impact in poorer regions, where both wages and incomes are lower, while it may still be non-binding in Tbilisi.

The ISET-PI research team (Babych et al., 2022) use Georgian micro data from the Labor Force Survey (LFS) and the Household Integrated Expenditure Survey (HIES), to simulate the effect of instituting a nation-wide minimum wage on both employment and poverty rates in different regions of Georgia. One focus area of the study was to analyze the effects of a minimum wage increase on female poverty. As with any exercise using a simulation approach, this study is subject to limitations imposed by the assumptions used, e.g. how much labor demand would respond to changes in the minimum wage, etc. The study considered two hypothetical thresholds of the minimum wage; 250 and 350 GEL respectively.

Figure 1. Share of private sector employees earning below certain thresholds, by gender, 2021.

Source: Authors’ calculations based on the Labor Force Survey (Geostat, 2021).

The expected household income after the minimum wage increase was calculated and then compared to the poverty threshold (for each household in a standard way, using the “adult equivalence” scale). According to this methodology, any person who lives in a household which falls below the poverty threshold is considered to be poor. A “working poor” household is defined as a household below the poverty threshold where at least one adult is working.

Figure 1 shows that there is a substantial share of both men and women whose monthly wage income falls below the hypothetical minimum wage thresholds. In addition, women are more than two times as likely to be earning below these thresholds. However, the possible impact from an increased minimum wage on female vs. male poverty is not clear-cut. Since many women are part of larger households which include adult males, their possible income losses/gains may be counterbalanced by income gains/losses of male family members, leaving the overall effect on household income ambiguous.

In addition, poverty rates are not likely to be much affected by a minimum wage increase if most poor households are “non-working poor” (where adult family members are either unemployed or outside of the labor force), a consideration particularly relevant for Georgia. The share of poor individuals who live in “working poor” households (with at least one household member employed) is just 41 percent nationally (and 35 percent in rural areas), meaning that close to 60 percent of poor individuals nationwide (and 65 percent in rural areas) are not likely to be directly affected by minimum wage increases.

Female vs. Male Poverty: Scenarios Following a Minimum Wage Increase

As one can see in Figure 2, increased minimum wages tend to reduce poverty, but the impact is not larger than one percentage point. Not surprisingly, females benefit more than males (0.3 and 0.8 percentage points vs. 0.2 and 0.9 percentage points poverty reduction for men and women respectively, under different threshold scenarios).  The maximum positive impact on poverty reduction is observed under a higher minimum wage threshold.

Figure 2. Estimated impact on poverty rates, based on the national subsistence minimum.

Source: Authors’ calculations based on the Household Integrated Expenditure Survey (Geostat, 2021).

The impact of an increased minimum wage on the expected median consumption of households doesn’t exceed a few percentage points either, as illustrated in Figure 3.

Figure 3. Median monthly consumption per “equivalent adult” in the household under the status quo and minimum wage scenarios, 2021.

Source: Authors’ calculations based on the Household Integrated Expenditure Survey (Geostat, 2021).

The impact is greatest in urban areas other than Tbilisi (between a 2.5 percent and a 4.2 percent increase in median consumption relative to the status quo). The lower impact in Tbilisi is most likely driven by relatively higher wages, while the low impact in rural areas is likely driven by lower participation in wage employment.

Conclusions

In the hypothetical case of Georgia, an impact of a minimum wage increase on poverty rates is expected to be limited, in line with the literature. In our study this finding is mostly driven by the fact that only a relatively small share of poor individuals live in “working poor” households (about 40 percent, nationally). The remaining 60 percent of poor individuals will be unaffected by the reform.

The quantitative impact on female and male poverty is estimated to be low, although the female poverty rate reduction is somewhat larger than among males.

It is important to note that the analysis doesn’t consider possible differential impacts on different groups of vulnerable families, such as families with small children and single mothers with small children. Some reasons to why groups of households may or may not be affected by the hypothetical minimum wage increase, based on their employment status and other factors, have been discussed above.

Another important point is that our exercise should not be seen as an argument against an increase of the minimum wage in Georgia. Instead, it suggests that such a reform would not have much of an impact if done in isolation. Indeed, the existing literature on minimum wage seems to be in consensus on the fact that minimum wage policies would be more impactful if supplemented by the following measures:

  • Maintain and expand targeted social assistance to groups that do not benefit or that are losing jobs/incomes as a result of the minimum wage changes
  • Have job re-training programs in place to help laid-off workers
  • Have human capital investment programs in place to increase workers’ productivity, in particular for low-productivity sectors
  • Consider other support instruments targeted toward the most affected groups of the population such as single working mothers etc.

These recommendations should be incorporated in the policy making regarding minimum wages in Georgia.

Acknowledgement

We are grateful to Expertise France for financially supporting the original report (Babych et al., 2022), which features some of the results and points raised in this policy brief.

References

Disclaimer: Opinions expressed during events and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

An Overview of the Georgian Wine Sector

Posted on November 21, 2022 | Policy Brief

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Georgia has an 8000-year-old winemaking tradition, making the country the first known location of grape winemaking in the world. In this policy brief we analyze and discuss major characteristics of the wine sector in Georgia, government policies regarding the sector and major outcomes of such policies. The brief provides recommendations on how to ensure sustainable development of the sector in a competitive, dynamic environment.

Introduction

The Georgian winemaking tradition is 8000 years old, making Georgia the world’s first known location of grape winemaking. There are many traditions associated with Georgian winemaking. One of them is ‘Rtveli’ – the grape harvest that usually starts in September and continues throughout the autumn season, accompanied with feasts and celebrations. According to data from the National Wine Agency, the annual production of grapes in Georgia is on average 223.6 thousand tones (for the last ten-years), with most grapes being processed into wine (see Figure 1).

Figure 1. Grape Processing (2013-2021)

Source: National Wine Agency, 2022. Note: Some producers do not participate In Rtveli and the total annual quantity of processed grape in the country might therefore be higher than the numbers presented in the figure.

Wine is one of the top export commodities for Georgia. It constituted 21 percent of the total Georgian agricultural export value in 2021 (Geostat, 2022). Since 2012 wine exports have, on average, grown 21 percent in quantitative terms, and by 22 percent in value (Figure 2). The average price per ton varies from 3 thousand USD to 3.9 thousand USD (Figure 2). Exports of still wine in containers holding 2 liters or less constitute, on average, 96 percent of the total export value.

Figure 2. Georgian Wine Exports (2012-2021)

Source: Geostat, 2022.

The main destination market for exporting Georgian wine is the Commonwealth of Independent States (CIS) countries which account for, on average, 78 percent of the export value (2012-2021). The corresponding share for EU countries is 10 percent. As of 2021, the top export destinations are Russia (55 percent), Ukraine (11 percent), China (7 percent), Belarus (5 percent), Poland (6 percent), and Kazakhstan (4 percent).  While Russia is still a top market for Georgian wine, Russia’s share of Georgian wine exports declined after Russia imposed an embargo on Georgian wines in 2006. The embargo forced market diversification and even after the reopening of the Russian market and Georgian wine exports shifting back towards Russia, its share declined from 87 percent in 2005 to 55 percent in 2021.

While there are more than 400 indigenous grape varieties in Georgia, only a few grape varieties are well commercialized as most of the exported wines are made of Rkatsiteli, Mtsvane, Kisi, and Saperavi grape varieties (Granik, 2019).

Government Policy in the Wine Sector

The Government of Georgia (GoG) actively supports the wine sector through the National Wine Agency, established in 2012 under the Ministry of Environmental Protection and Agriculture (MEPA). The National Wine Agency implements Georgia’s viticulture support programs through: i) control of wine production quality and certification procedures; ii) promotion and spread of knowledge of Georgian wine; iii) promotion of export potential growth; iv) research and development of Georgian wine and wine culture; v) creation of a national registry of vineyards; and vi) promotion of organized vintage (Rtveli) conduction (National Wine Agency, 2022).

During 2014-2016, the GoG’s spending on the wine sector (including grape subsidies, promotion of Georgian wine, and awareness increasing campaigns) amounted to 63 million GEL, or 22.8 million USD (As of November 1, 2022, 1 USD = 2.76 GEL according to the National Bank of Georgia). Out of the spending, illustrated in Figure 3, around 40-50 percent was allocated to grape subsidies implemented under the activities of iv) (as mentioned above).

There are two types of subsidies used by the GoG– direct and indirect. Direct subsidies imply cash payments to producers per kilogram of grapes. As for indirect subsidies, they entail state owned companies purchase grapes from farmers.

Starting from 2017, the GoG decided to abandon the subsidiary scheme and decrease its spending on of the wine sector.  The corresponding figure reached a minimum of 9.2 million GEL (3.3 million USD) in 2018. Meanwhile, the grape production has been increasing, reaching its highest level in 2020 (317 thousand tons). In 2020, the GoG resumed subsidizing grape harvests to support the wine sector as part of the crisis plan aimed at tackling economic challenges following the Covid-19 pandemic. The corresponding spending in the wine sector increased from 16.7 million GEL (around 6 million USD) in 2019 to 113.4 million GEL (41 million USD) in 2020, out of which the largest share (91 percent) went to grape subsidies. In 2021, the GoG continued its extensive support to the wine sector and the corresponding spending increased by 44 percent, compared to 2020. The largest share again went to grape subsidies (90 percent).

Figure 3. Grape Production and Government Spending on the Wine Sector (2014-2021)

Source: Ministry of Finance of Georgia, National Statistics Office of Georgia, Author’s Calculations, 2022.

In 2022, the GoG have continued subsidizing the grape harvest to help farmers and wine producers sell their products. During Rtveli 2022, wine companies are receiving a subsidy if they purchase and process at least 100 tons of green Rkatsiteli or Kakhuri grape varieties grown in the Kakheti region, and if the company pays at least 0.90 GEL per kilogram for the fruit. If these two conditions are satisfied, 0.35 GEL is subsidized from a total of 0.9 GEL per kilogram of grapes purchased (ISET Policy Institute, 2022). Moreover, the GoG provides a subsidy of 4 GEL per kilogram for Alksandrouli and Mujuretuli grapes (unique grape varieties from the Khvanchkara “micro-zone” of the north-western Racha-Lechkhumi and Kvemo Svaneti regions), if the buying company pays at least 7 GEL per kilogram for those varieties (Administration of the Government of Georgia, 2022). Overall, about 150 million GEL (54.2 million USD), has been allocated to grape subsidies in 2022.

Policy Recommendations

Although the National Wine Agency is supposed to implement support programs in various areas like quality control, market diversification, promotion and R&D, these areas lack funding, as most of the Agency’s funds are spent on subsidies. Given that the production and processing of grapes have increased over the years, subsidies have been playing a significant role in reviving the wine sector after the collapse of the Soviet Union (Mamardashvili et al., 2020).  However, since the sector is subsidized as of 2008, the grape market in Georgia is heavily distorted. Prices are formed, not on the bases of supply and demand but on subsidies, which help industries survive in critical moments, but overall prevent increases in quality and fair competition. They further lead to overproduction, inefficient distribution of state support and preferential treatment of industries (Desadze, Gelashvili, and Katsia, 2020). After years of subsidizing the sector, it is hard to remove the subsidy and face the social and political consequences of such action.

Nonetheless, in order to support the sustainable development of the sector, it is recommended to:

  1. Replace the direct state subsidy with a different type of support (if any), directed towards overcoming systemic challenges in the sector related to the research and development of indigenous grape varieties and their commercialization level.
  2. Further promote Georgian wine on international markets to diversify export destination markets and ensure low dependence on unstable markets like the Russian market. Although wine exporters have in recent years entered new markets, to further strengthen their positions at those markets, it is vital to:
    • ensure high quality production through producers’ adherence to food safety standards.
    • promote digitalization – e-certification for trade and distribution, block chain technology for easier traceability and contracting, e-labels providing extensive information about wine etc. – enabling producers to competitively operate in the dynamic environment (Tach, 2021)
    • identify niche markets (e.g. biodynamic wine) and support innovation within these sectors to ensure competitiveness of the wine sector in the long-term (Deisadze and Livny, 2016).

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.

Understanding the Economic and Social Context of Gender-based and Domestic Violence in Central and Eastern Europe – Preliminary Survey Evidence

Posted on May 11, 2022 | FROGEE PROJECT, Policy Brief

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This brief presents preliminary findings from a cross-country survey on perceptions and prevalence of domestic and gender-based violence conducted in September 2021 in eight countries: Armenia, Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. We discuss the design and content of the study and present initial information on selected topics that were covered in the survey. The collected data has been used in three studies presented at the FROGEE Conference on “Economic and Social Context of Domestic Violence” and offers a unique resource to study gender-based violence in the region.

While the COVID-19 pandemic has amplified the academic and policy interest in the causes and consequences of domestic violence, the Russian invasion of Ukraine has tragically reminded us about the gender dimension of war. There is no doubt that a gender lens is a necessary perspective to understand and appreciate the full consequences of these two ongoing crises.

The tragic reason behind the increased attention given to domestic violence during the COVID-19 lockdowns is the substantial evidence that gender-based violence has intensified to such an extent that the United Nations raised the alarm about a “shadow pandemic” of violence against women and girls (UN Women on-line link). Already before the pandemic, one in three women worldwide had experienced physical or sexual violence, usually at the hands of an intimate partner, and this number has only been increasing. The tragic reports from the military invasion of Ukraine concerning violence against women and children, as well as information on the heightened risks faced by war refugees from Ukraine, most of whom are women, should only intensify our efforts to better understand the background behind these processes and study the potential policy solutions to limit them to a minimum in the current and future crises.

The most direct consequences of gender-based and domestic violence – to the physical and mental health of the victims – are clearly of the highest concern and are the leading arguments in favour of interventions aimed at limiting the scale of violence. One should remember though, that the consequences and the related social costs of gender-based and domestic violence are far broader, and need not be caused by direct acts of physical violence. Gender-based and domestic violence can take the form of psychological pressure, limits on individual freedoms, or access to financial resources within households. As research in recent decades demonstrates, such forms of abuse also have significant consequences for the psychological well-being, social status, and professional development of its victims. All these outcomes are associated with not only high individual costs, but also with substantial social and economic costs to our societies.

This policy brief presents an outline of a survey conducted in eight countries aimed at better understanding the socio-economic context of gender-based violence. The survey, developed by the FREE Network of independent research institutes, has a regional focus on Central and Eastern Europe, with Sweden being an interesting benchmark country. The data was collected in September 2021 in Armenia, Belarus, Georgia, Latvia, Poland, Russia, Sweden and Ukraine. The socio-economic situation of all these countries irrevocably changed with the Russian invasion of Ukraine on 24 February 2022, the ongoing war, and its dramatic consequences. The world’s attention focused on the unspeakable violence committed by the Russian forces in Ukraine, the persecution in Belarus and Russia of their own citizens who were protesting against the invasion, and the challenges other neighbouring countries have faced as a result of an unprecedented wave of Ukrainian refugees. This change, on the one hand, calls for a certain distance with which we should judge the survey data and the derived results. On the other hand, the data may serve as a unique resource to support the analysis of the pre-war conditions in these countries with the aim to understand the background driving forces behind this dramatic crisis. In as much as the gender lens is necessary to comprehend the full scale of the consequences of both the COVID-19 pandemic and the war in Ukraine, it will be equally indispensable in the process of post-war development and reconciliation once peace is again restored.

Survey Design, Countries, and Samples

The survey was conducted in eight countries in September 2021 through as a telephone (CATI) survey using the list assisted random digit dialling (LA-RDD) method covering both cell phones and land-lines, and the sampling was carried out in such a way as to make the final sample representative of the respective populations by gender and three age group (18-39; 40-54; 55+). The collected samples varied from 925 to 1000 individuals. The same questionnaire initially prepared as a generic English version was fielded in all eight countries (in the respective national languages). The only deviations from the generic version were related to the education categories and to a set of final questions implemented in Latvia, Russia and Ukraine with a focus on the evaluation of national IPV legislation.

Table 1 presents some basic sample statistics, while Figure 1 shows the unweighted age and gender compositions in each country. The proportion of women in the sample varies between 49.4% in Sweden and 55.0% in Belarus, Russia and Ukraine. The average sample age is between 43 (Armenia) and 51 (Sweden), while the proportion of individuals with higher education is between 29.3% in Belarus and 55.4% in Georgia. The highest proportion of respondents living in rural areas could be found in Armenia at 62.9%, while the lowest was in Georgia at 24.1%. Figure 1 illustrates good coverage across age groups for both men and women.

Table 1. FROGEE Survey: samples and basic demographics

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Figure 1. FROGEE Survey: gender and age distributions

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Socio-economic Conditions and Other Background Characteristics

To be able to examine the relationship between different aspects of domestic and gender-based violence to the socio-economic characteristics of the respondents, an extensive set of questions concerning the demographic composition of their household and their material conditions were asked at the beginning of the interview. These questions included information about partnership history and family structure, the size of the household and living conditions, education and labour market status (of the respondent and his/her partner) and general questions concerning material wellbeing. In Figure 2 we show a summary of two of the latter set of questions – the proportion of men and women who find it difficult or very difficult to make ends meet (Figure 2A) and the proportion who declared that the financial situation of their household deteriorated in the last two years, i.e. since September 2019, which can be used as an indicator of the material consequences of the COVID-19 pandemic. We can see that the difficulties in making ends meet are by far lowest in Sweden, and slightly lower in the other EU countries (Latvia and Poland). The differences are less pronounced with regard to the implication of the pandemic, but also in this case respondents in Sweden seem to have been least affected.

 Figure 2. Making ends meet and the consequences of COVID-19

a. Difficulties in making ends meet


b. Material conditions deteriorated since 2019

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Perceptions and Incidence of Domestic and Gender-Based Violence and Abuse

Frequency of differential treatment and abuse

The set of questions concerning domestic and gender-based violence started with an initial module related to the different treatment of men and women, with respondents asked to identify how often they witnessed certain behaviours aimed toward women. The questions covered aspects such as women being treated “with less courtesy than men”, being “called names or insulted for being a woman” and women being “the target of jokes of sexual nature” or receiving “unwanted sexual advances from a man she doesn’t know”, and the respondents were to evaluate if in the last year they have witnessed such behaviours on a scale from never, through rarely, sometimes, often, to very often. We present the proportion of respondents answering “often” or “very often” to two of these questions in Figure 3A (“People have acted as if they think women are not smart”) and 3B (“A woman has been the target of jokes of a sexual nature”). We find significant variation across these two dimensions of differential treatment, and we generally find that women are more sensitive to perceiving such treatment. It is interesting to note that the proportion of women who declared witnessing differential treatment in Sweden is very high in comparison to for example Latvia or Belarus, which, as we shall see below, does not correspond to the proportion of women (and men) witnessing more violent types of behaviour against women.

Figure 3. Frequency of differential treatment (often or very often)

a. People have acted as if they think women are not smart


b. A woman has been the target of jokes of a sexual nature

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Questions on the frequency of witnessing physical abuse were also asked in relation to the scale of witnessed behaviour. Here respondents were once again asked to say how often “in their day-to-day life” they have witnessed specific behaviours. These included such types of abuse as: a woman being “threatened by a man”, “slapped, hit or punched by a man”, or “sexually abused or assaulted by a man”. The proportion of respondents who say that they have witnessed such behaviour with respect to two of the questions from this section are presented in Figure 4. In Figure 4A we show the proportion of men and women who have witnessed a woman being “slapped, hit or punched” (sometimes, often or very often), while in Figure 4B being “touched inappropriately without her consent”. Relative to the perceptions of differential treatment the incidence of a woman being hit or punched (4A) declared by the respondents seems more intuitive when considered against the overall international statistics of gender equality. The proportions are lowest in Sweden and Poland, and highest in Armenia and Ukraine. However, the perception of inappropriate touching by men with respect to women (Figure 4B) shows a similar extent of such actions across all analysed countries.

Figure 4. Frequency of abuse (sometimes, often or very often)

a. A woman has been slapped, hit or punched by a man


b. A woman has been touched inappropriately, without her consent, by a man

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Perceptions of abuse

The questions concerning the scale of witnessed behaviours were complemented by a module related to the evaluation of certain behaviours from the perspective of their classification as abuse and the degree to which certain types of gender-specific behaviours are acceptable. Thus, for example respondents were asked if they consider “beating (one’s partner) causing severe physical harm” to be an example of abuse within a couple (Figure 5A) or if “prohibition to dress as one likes” represents abuse (Figure 5B). This module included an extensive list of behaviours, such as “forced abortion”, “constant humiliation, criticism”, “restriction of access to financial resources”, etc. As we can see in Figure 6, with respect to the clearest types of abuse – such as physical violence – respondents in all countries were pretty much unanimous in declaring such behaviour to represent abuse. With respect to other behaviours the variation in their evaluation across countries is much greater – for example, while nearly all men and women in Sweden consider prohibiting a partner to dress as he/she likes to be abusive (Figure 5B), only about 57% of women and 36% of men in Armenia share this view.

The questionnaire also included questions specifically focused on the perception of intimate partner violence. These asked respondents if they knew about women who in the last three months were “beaten, slapped or threatened physically by their intimate partner”, and the evaluation of how often intimate partners act physically violent towards their wives.

Figure 5. Perceptions of abuse: are these examples of abuse within a couple?

a. Beating causing severe physical harm


b. Prohibition to dress as one likes

Source: FROGEE Survey on Domestic and Gender-Based Violence.

A further evaluation of attitudes towards violent behaviour was done with respect to the relationship between a husband and wife and his right to hit or beat the wife in reaction to certain behaviours. In Figure 6 we show the distribution of responses regarding the justification for beating one’s wife in reaction to her neglect of the children (6A) or burning food (6B). The questions also covered such behaviour as arguing with her husband, going out without telling him, or refusing to have sex. As we can see in Figure 6, once again we find substantial country variation in the proportion of the samples – both men and women – who justify such violent behaviour within couples. This was particularly the case when respondents were asked about justification of violent behaviour in the case of a woman neglecting the children. In Armenia as many as 30% of men and 22% of women agree that physical beating is justified in those cases. These proportions are manyfold greater than what can be observed in countries such as Latvia, where 3% of men and women agreed that abuse was justifiable under these circumstances, or Sweden, where only 1% of men and women agreed.

Figure 6. Perceptions of abuse: is a husband justified in hitting or beating his wife

a. If she neglects the children


b. If she burns the food

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Seeking help and the legal framework

The final part of the questionnaire focused on the evaluation of different reactions to incidents of domestic and gender-based violence. Respondents were first asked if a woman should seek help from various people and institutions if she is beaten by her partner – respondents were asked if she should seek help from the police, relatives or friends, a psychologist, a legal service or if, in such situations, she does not need help. In Figure 7 we show the proportion of people who agreed with the last statement, i.e. claimed that it is only the couple’s business. The proportions of respondents who declare such an attitude is higher among men than women within each country, and is highest among men in Armenia (48%) and Georgia (25%). Again, these proportions are in stark contrast to men in Sweden, or even Poland, where only 4% and 8% of men agreed, respectively. Nevertheless, looking at the total survey sample, a vast majority believe that a woman who is a victim of domestic violence should seek help outside of her home, indicating that at least some forms of institutionalised support for women are popular measures with most people.

Figure 7. Proportions agreeing that domestic violence is only the couple’s business

Source: FROGEE Survey on Domestic and Gender-Based Violence.

The interview also included questions on the need for specific legislation aimed at punishing intimate partner violence and on the existence of such legislation in the respondents’ countries. The latter questions were extended in three countries – Latvia, Russia and Ukraine – to evaluate the specific sets of regulations implemented recently in these countries and to facilitate an analysis of the role IPV legislation can play in reducing violence within households. Legislation on domestic violence is relatively recent. During the last four decades, though, changes accelerated in this respect around the world. Legislative measures have been introduced in many countries, covering different aspects of preventing, protecting against and prosecuting various forms of violence and abuse that might happen within the marriage or the family. Research strives to offer evaluations on what legal provisions are most effective, in a setting in which statistics and information are still far from perfect, and as a consequence of the dearth of strong evidence the public debate on the matter is often lively. For legislation to have an effect on behaviour through shaping the cost of committing a crime, on the one hand, and the benefit of reporting it or seeking help, on the other, or more indirectly through changing norms in society, information and awareness are key. For how can deterrence be achieved if people do not know what the sanctions are? And how can reporting be encouraged if victims do not know their rights? The evidence on legislation awareness is unfortunately quite scarce. A survey of the criminology field (Nagin, 2013) concludes that this is a major knowledge gap.

Figure 8 shows the proportions of answers to questions concerning the need for and existence of legislation specifically targeted towards intimate partner violence. We can see that while support for such legislation is quite high (Figure 8A), it is generally lower among men (in particular in Armenia, Russia and Belarus). Awareness of existence of such laws, on the other hand, is much lower, and it is particularly low among women. It should be pointed out that all countries have in fact implemented provisions against domestic violence in their criminal code, but only around half of the population, sometimes much fewer, are aware of that.

Figure 8. Need for and awareness of IPV legislation

a. State should have specific legislation aimed at punishing IPV


b. Country has specific legislation aimed at punishing intimate partner violence

Source: FROGEE Survey on Domestic and Gender-Based Violence.

Recent reforms of DV legislation that were implemented in Russia in 2017, in Ukraine in 2019 and in Latvia just a few months ago (at the time of the survey, the changes were at the stage of a proposal) were the subject of the final survey questions in these countries. We find that awareness of these recent reforms is very low in all three countries, and knowledge about the reform content (gauged with the help of a multiple-choice question with three alternative statements) is even lower. Our analysis suggests that gender and family situation are the two factors that most robustly predict support for legislation, while education and age are associated with awareness and knowledge of the reforms. Minority Russian speakers are less aware of the reforms in both Ukraine and Latvia, in Ukraine are also less likely to answer correctly about the content of the reform, and in Latvia are less supportive of DV legislation in general.

Analyses of this type are useful for policy design, to better understand which groups lack relevant knowledge and should be targeted by, for example, information campaigns to combat DV, such as those many governments around the world implemented during the covid-19 pandemic.

Future Work Based on the Survey

The above is just a small sample of the rich source of information that has resulted from conducting the survey. Already from this simple overview we can see some interesting results. There are, for example, clear differences between men and women in perceptions of how common certain types of abusive behaviour are. However, for many questions differences between countries are larger than those between men and women within a country. Interestingly such differences are also different depending on the severity of the abuse or violence. In Sweden the perception of women being victims of less violent abuse is higher than in some other countries where instead some more violent types of abuse are reported as being more common. This could, of course, be due to actual differences in actual events but it is also possible that there are differences in what types of behaviour are considered to represent harassment and abuse in different societies. More careful data work is needed to try to answer questions like this and many others. Currently there are a number of ongoing research projects based on the survey results, three of which will be presented at the FREE-network conference on “Economic and Social Context of Domestic Violence” in Stockholm on May 11, 2022. Our hope is that this work will help in taking actions to prevent gender-based abuse and domestic violence based on a better understanding of underlying cross-country differences in social norms and attitudes and their relation to socio-economic factors.

About FROGEE Policy Briefs

FROGEE Policy Briefs is a special series aimed at providing overviews and the popularization of economic research related to gender equality issues. Debates around policies related to gender equality are often highly politicized. We believe that using arguments derived from the most up to date research-based knowledge would help us build a more fruitful discussion of policy proposals and in the end achieve better outcomes.

The aim of the briefs is to improve the understanding of research-based arguments and their implications, by covering the key theories and the most important findings in areas of special interest to the current debate. The briefs start with short general overviews of a given theme, which are followed by a presentation of country-specific contexts, specific policy challenges, implemented reforms and a discussion of other policy options.

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.

Assessing a Model for the Implementation of an Equal Pay Review and Reporting (EPRR) Methodology in Georgia

Posted on November 3, 2021 | Policy Brief

20211102 Assessing a Model of Equal Pay

Georgia’s gender pay gap has started to attract the attention of the population and policymakers alike. The gap persists despite working women generally reporting better labor-market skills and personal characteristics. It has been argued that this could be the result of systematic gender-based workplace wage discrimination, resulting in unequal pay for equal work. The discussion that ensued highlights how the fight to guarantee equal pay for equal work could benefit from establishing an Equal Pay Review and Reporting Mechanism. In response, the ISET-PI team – after reviewing the best international practices – devised and tested an excel based tool that could help companies and governmental agencies identify, monitor, and fight gender discrimination in Georgia. The main quantitative result of the exercise identified that, should reporting be made mandatory, extending the obligation to companies that employ up to 50 people would make the administrative costs for companies and public administration up to twenty times higher; thus, the usefulness of the tool was found to be substantially limited when applied to smaller companies. Finally, the exercise emphasized the reluctance of companies to provide the data required, leading to the conclusion that the successful implementation of such an initiative would require the enforcing agency to have the legal authority to sanction failures to provide the necessary data.

Introduction

One of the key gender inequality indicators is the gender pay gap – or gender wage gap – calculated as the average difference between the remuneration for men and women in the labor market. Its evolution is monitored worldwide, and closing this gap is considered a key step towards more inclusive and prosperous economies and societies. According to the World Economic Forum, as of 2020, no country (including the top-ranked ones) had yet achieved gender parity in wages.

In Georgia, the unadjusted hourly gender pay gap amounts to 17.7 percent of the average male hourly wage (UN Women, 2020). Moreover, when controlling for personal characteristics of men and women, the adjusted hourly gender pay gap in Georgia is estimated to be 24.8 percent (UN Women, 2020). This implies that women, on average, have better observable labor-market characteristics but are still paid less than men.

These findings prompted a core discussion within the Georgian society on the presence of unequal pay for equal work in Georgia as one of the possible reasons for the gap and how to tackle the problem. The idea of equal pay for equal work entails that individuals in the same workplace are given equal pay if they perform the same type of work. Consequently, this potential source of the pay gap can only be verified at the individual employer level. This is accomplished by calculating the unexplained gender pay gap at the organizational/employer level and validating whether, and why, these differences exist.

Given the attention the topic holds in the national discourse, ISET Policy Institute created and tested an excel tool, built in line with the international best practices and adapted to the Georgian context, to help employers and government offices identify and measure the differences in wages between men and women performing equal work. During this process, the team learned several noteworthy lessons, as summarized in this policy brief.

International Experience

There is growing consensus that transparency is critical when dealing with pay inequality and, therefore, gender pay reporting should become the norm. Since 2010, several (mostly developed) countries have introduced reporting schemes to monitor gender pay gaps, promote awareness about gender equality issues throughout society (particularly among employees), and increase organizations’ accountability to address gender inequalities (Equileap, 2021).

However, the gender pay gap is a key issue for which the disclosure of information remains particularly low. Equileap’s 2021 report revealed that 85 percent of organizations worldwide did not publish information on remuneration differences between female and male workers in 2020.

Three countries, according to Equileap, lead the way in gender pay gap reporting: Spain, the UK, and Italy (Figure 1). In each of these top three countries, reporting is mandatory.

Figure 1. Percentage of organizations publishing gender pay information, per country

Source: Equileap, 2021. The figure only includes countries for which more than 49 surveyed organizations were included in the Equileap dataset.

However, even in these countries, and, more generally, in all countries scrutinized by Equileap but Iceland, firms with 50 or fewer employees are not required to report on gender pay gaps.

The Case of Georgia

Georgian legislation clearly establishes the principle of equal pay for equal work for all employees. The requirement applies to both public and private organizations. Nevertheless, enforcement of the law remains a significant challenge.

At present, Georgia has no reporting requirements regarding employee salaries for private organizations. It has not yet designed a reporting scheme for equal pay for equal work, nor has it assigned the task of collecting this information to any governmental body.

Moreover, Labour Inspectorate representatives state that few wage discrimination cases are currently being filed in the country. The main reason behind this is that norms regarding equal pay for equal work have never been properly specified. In addition, there are no explicit criteria defining the concept of ‘equal work’. Thus, employers and employees alike do not seem to fully understand the phrase – equal pay for equal work.

The Excel Tool

After a careful review of the three tools presently utilized to calculate gender pay inequality (the Swiss Logib, the German Logib-D, and the Diagnosis of Equal Remuneration (DER) tool developed by UN Women), ISET-PI built a Georgian model as a modified version of the DER tool that is adapted to the Georgian context and includes some variables from the Swiss tool.

The tool itself is an excel file with several worksheets. The two main facets are the inputted data sheet and the results sheet. Companies may input information on their employees in the data sheet, and the findings will then be demonstrated in the results sheet. The tool first identifies people performing the same work, and classifies jobs based on their official titles, alongside managerial responsibilities and skill requirements. After individuals are grouped by job, the tool calculates the average salary within each group separately for men and women. Thereafter, the pay gap is calculated based on the average salary for the two gender groups.

With the support of the Employers’ Association, several companies of all sizes were approached to test the tool. Unfortunately, only a few agreed to participate, and just two completed the trial: one small-sized enterprise (with 50 or fewer employees) and a large-sized enterprise (with 250 or more employees).

While low participation rates have significantly limited our analysis, we still obtained several important insights which are discussed in the next subsection.

Findings

Firstly, it is important to note that companies’ willingness to share anonymized salary data was very low, even among the companies that completed the test.

Secondly, the usefulness of the tool for obtaining a comprehensive view of equal pay for equal work in small companies (with 50 or fewer employees) appeared fairly limited as few people within the same firm perform the same job.

Thirdly, we performed a simple cost assessment exercise to evaluate the compliance costs – to both companies and the government – of collecting and reporting the gender pay gap. We found that extending the data collection requirement to small companies would increase the compliance costs by up to 20 times (high-cost scenario) compared to an example where small companies are exempt. This is because there are many more small companies in Georgia (146,802), than those classified as medium or large ones (2,752 and 609, respectively).

In addition, during the implementation of the exercise, we became aware of the following:

  • Under the existing legal provisions, it would be extremely difficult to introduce the EPRR in a mandatory format – no governmental agency could sanction companies for failing to comply.
  • Opting for the mandatory option and sanctioning the emergence of unequal pay in certain job categories could incentivize companies to manipulate the data input. In this case, therefore, it would be ill-advised to provide the full tool to companies, as they could more easily adjust data inputting to obtain more favorable indicators through successive iterations.

Conclusion

Setting up an EPRR system is one way to contribute to the implementation of the equal pay for equal work principle.

Designing the Georgian Model for the Implementation of an Equal Pay Review and Reporting Methodology generated several useful insights that might prove valuable for policymakers in Georgia and other developing countries:

1) The EPRR instrument can be utilized for the analysis of gender pay gaps within companies with more than 50 employees. Within smaller companies, evaluating the gender pay gap significantly increases the costs to society, while providing rather limited additional information.

2) The decisions about whether to provide the analytical part of the tool to companies, and whether reporting should be voluntary or mandatory should be taken jointly. If the goal is to provide an instrument to the agency enforcing the equal pay for equal work principle and to facilitate appeals from workers, the tool should be made mandatory. However, in this case, companies should only provide the input data, without having access to the part of the tool that assesses pay gaps at the job level. On the other hand, if the goal of the reform is to support willing companies in their efforts to eliminate unequal pay for equal work conditions, a non-mandatory form may be preferable. In this instance, companies should have access to the full version of the tool. This would allow them to better understand the dynamics that lead to unequal pay and thus put in place internal remedial actions.

3) If the goal is to provide a tool to the agency enforcing the equal pay for equal work principle, it is crucial that any gaps in the associated legislation are closed. As such, the enforcing agency should be capable of sanctioning failures to provide the required data, and prosecuting violations of the equal pay for equal work principle.

Finally, it is important to note that testing the application of the equal pay for equal work principle at the company level through an EPRR system, while useful for identifying potential causes of the gender pay gap and the existence of gender disparities within companies, is just a first step in a longer and more complex process. Once disparities are identified, both companies and enforcing agencies should follow up with additional research and analysis to determine whether these disparities are linked to discriminatory practices, and what type of remedial options could be adopted.

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.

Vaccination Progress and the Opening Up of Economies

Posted on July 5, 2021 | Policy Brief

20210622 Reopening Soon Webinar Image 01

In this brief, we report on the FREE network webinar on the state of vaccinations and the challenges ahead for opening up economies while containing the pandemic, held on June 22, 2021. The current state of the pandemic in each respective country was presented, suggesting that infection rates have gone down quite substantially recently in all countries of the network, except in Russia which is currently facing a surge in infections driven by the delta-version of the virus. Vaccination progress is very uneven, limited by lacking access to vaccines (primarily Ukraine and Georgia) and vaccine scepticism among the population (primarily in Russia and Belarus but for certain groups also in Latvia, Poland and to some extent Sweden). This also creates challenges for governments eager to open their societies to benefit their economies and ease the social consequences of the restrictions on mobility and social gatherings. Finally, the medium to long term consequences for labour markets reveal challenges but also potential opportunities through wider availability of work–from-home policies. 

Background

In many countries in Europe, citizens and governments are starting to see an end to the most intense impact of the Covid-19 pandemic on their societies. Infection and death rates are coming down and governments are starting to put in place policies for a gradual opening up of societies, as reflected in the Covid-19 stringency index developed by Oxford University. These developments are partially seasonal, but also largely a function of the progress of vaccination programs reaching an increasing share of the adult population. These developments, though, are taking place to different degrees and at different pace across countries.  This is very evident at a global level, but also within Europe and among the countries represented in the FREE network. This has implications for the development within Europe as a whole, but also for the persistent inequalities we see across countries.   

Short overview of the current situation

The current epidemiological situation in Latvia, Sweden, Ukraine, and Georgia looks pretty similar in terms of Covid-19 cases and deaths but when it comes to the vaccination status there is substantial variation.

Latvia experienced a somewhat weaker third wave in the spring of 2021 after being hit badly in the second wave during the fall and winter of 2020 (see Figure 1). The Latvian government started vaccinating at the beginning of 2021, and by early June, 26% of the Latvian population had been fully vaccinated.

Sweden, that chose a somewhat controversial strategy to the pandemic built on individual responsibility, had reached almost 15 thousand Covid-19 deaths by the end of June of 2021, the second highest among the FREE network member countries relative to population size. The spread of the pandemic has slowed down substantially, though, during the early summer, and the percentage of fully vaccinated is about to reach 30% of the population.

Figure 1. Cumulative Covid-19 deaths 

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

Following a severe second wave, the number of infected in Ukraine started to go down in the winter of 2020, with the total deaths settling at about 27 thousand in the month of February. Then the third wave hit in the spring, but the number of new daily cases has decreased again and is currently three times lower than at the beginning of the lastwave. However, a large part of the reduction is likely not thanks to successful epidemiological policies but rather due to low detection rates and seasonal variation. 

In June 2021, Georgia faces a similar situation as Ukraine and Latvia, with the number of cumulative Covid-19 deaths per million inhabitants reaching around 1300 (in total 2500 people) following a rather detrimental spring 2021 wave. At the moment, both Georgia and Ukraine have very low vaccination coverage relative to other countries in the region(see Figure 5).

In contrast to the above countries, Russia started vaccinating early. Unfortunately, the country is now experiencing an increase in the number of cases (as can be seen in Figure 2), contrary to most other countries in the region. This negative development is likely due to the fact that the new Covid-19 delta variant is spreading in the country, particularly in Moscow and St. Petersburg. Despite the early start to vaccinations, though, the total number of vaccinated people remains low, only reaching 10.5% of the population.

Figure 2. New Covid-19 cases

Source: Aggregated data sources from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, compiled by Our World in Data.

In some ways similar to Sweden, the government of Belarus did not impose any formal restrictions on individuals’ mobility. According to the official statistics, in the month of June, the rise in the cumulative number of covid-19 deaths and new daily infections has declined rapidly and reached about 400 deceased and 800 infections per one million inhabitants, respectively. Vaccination goes slowly, and by now, around 8% of the population has gotten the first dose and 5% have received the second.

There were two major waves in Poland during the autumn 2020 and spring 2021. In the latter period, the country experienced a vast number of deaths.  As can be seen in Figure 3, the excess mortality P-score – the percentage difference between the weekly number of deaths in 2020-2021 and the average number of deaths over the years 2015-2019 – peaked in November 2020, reaching approximately 115%. The excess deaths numbers in Poland were also the highest among the FREE Network countries in the Spring of 2021, culminating at about 70% higher compared to the baseline. By mid-June, the number of deaths and cases have steeply declined and 36% of the country’s population is fully vaccinated.

Figure 3. Excess deaths

Turning to the economy, after a devastating year, almost all countries are expected to bounce back by the end of 2021 according to the IMF (see Figure 4). Much of these predictions build on the expectations that governments across the region will lift Covid-19 restrictions. These forecasts may not be unrealistic for the countries where vaccinations have come relatively far and restrictions have started to ease. However, for countries where vaccination rates remain low and new variations of the virus is spreading, the downside risk is still very present, and forecasts contain much uncertainty.

 Figure 4. GDP-growth

Vaccination challenges

Since immunization plays such a central role in re-opening the economy and society going back to normal, issues related to vaccinations were an important and recurring topic at the event. The variation in progress and speed is substantial across the countries, though.

Ukraine and Georgia are still facing big challenges with vaccine availability and have fully vaccinated only 1.3% and 2.3% of the population by the end of June, respectively. Vaccination rates have in the recent month started to pick up, but both countries face an uphill battle before reaching levels close to the more successful countries.

Figure 5. Percent fully vaccinated

Other countries a bit further ahead in the vaccine race are still facing difficulties in increasing the vaccination coverage, though not so much due to lack of availability but instead because of vaccine skepticism. In Belarus, a country that initially had bottleneck issues similar to Ukraine and Georgia, all citizens have the opportunity to get vaccinated. However, Lev Lvovskiy, Senior Research Fellow at BEROC in Belarus, argued that vaccination rates are still low largely because many Belarusians feel reluctant towards the vaccine at offer (Sputnik V).

This vaccination scepticism turns out to be a common theme in many countries. According to different survey results presented by the participants at the webinar, the percentage of people willing or planning to get vaccinated is 30% in Belarus and 44% in Russia. In Latvia, this number also varies significantly across different groups as vaccination rates are significantly lower among older age cohorts and in regions with a higher share of Russian-speaking residents, according to Sergejs Gubins, Research Fellow at BICEPS in Latvia.

Webinar participants discussed potential solutions to these issues. First, there seemed to be consensus that offering people the opportunity to choose which vaccine they get will likely be effective in increasing the uptake rate. Second, governments need to improve their communication regarding the benefits of vaccinations to the public. Several countries in the region, such as Poland and Belarus, have had statements made by officials that deviate from one another, potentially harming the government’s credibility with regards to vaccine recommendations. In Belarus, there have even been government sponsored disinformation campaigns against particular vaccines. In Latvia, the main problem is rather the need to reach and convince groups who are generally more reluctant to get vaccinated. Iurii Ganychenko, Senior Researcher at KSE in Ukraine, exemplified how Ukraine has attempted to overcome this problem by launching campaigns specifically designed to persuade certain age cohorts to get vaccinated. Natalya Volchkova, Director of CEFIR at NES in Russia, argued that new, more modern channels of information, such as professional influencers, need to be explored and that the current model of information delivery is not working.

Giorgi Papava, Lead Economist at ISET PI in Georgia, suggested that researchers can contribute to solving vaccine uptake issues by studying incentive mechanisms such as monetary rewards for those taking the vaccine, for instance in the form of lottery tickets. 

Labour markets looking forward

Participants at the webinar also discussed how the pandemic has affected labour markets and whether its consequences will bring about any long-term changes.

Regarding unemployment statistics, Michal Myck, the Director of CenEA in Poland, made the important point that some of the relatively low unemployment numbers that we have seen in the region during this pandemic are misleading. This is because the traditional definition of being unemployed implies that an individual is actively searching for work, and lockdowns and other mobility restrictions have limited this possibility. Official data on unemployment thus underestimates the drop in employment that has happened, as those losing their jobs in many cases have left the labour market altogether. We thus need to see how labor markets will develop in the next couple of months as economies open up to give a more precise verdict.

Jesper Roine, Professor at SITE in Sweden, stressed that unemployment will be the biggest challenge for Sweden since its economy depends on high labor force participation and high employment rates. He explained that the pandemic and economic crisis has disproportionately affected the labor market status of certain groups. Foreign-born and young people, two groups with relatively high unemployment rates already prior to the pandemic, have become unemployed to an even greater extent. Many are worried that these groups will face issues with re-entering the labour market as in particular long-term unemployment has increased. At the same time, there have been more positive discussions about structural changes to the labour market following the pandemic. Particularly how more employers will allow for distance work, a step already confirmed by several large Swedish firms for instance.

In Russia, a country with a labour market that allowed for very little distance work before the pandemic, similar discussions are now taking place. Natalya Volchkova reported that, in Russia, the number of vacancies which assumed distance-work increased by 10% each month starting from last year, according to one of Russia’s leading job-search platforms HeadHunter. These developments could be particularly beneficial for the regional development in Russia, as firms in more remote regions can hire workers living in other parts of the country.

Concluding Remarks

It has been over a year since the Covid-19 virus was declared a pandemic by the World Health Organization. This webinar highlighted that, though vaccination campaigns in principle have been rolled out across the region, their reach varies greatly, and countries are facing different challenges of re-opening and recovering from the pandemic recession. Ukraine and Georgia have gotten a very slow start to their vaccination effort due to a combination of lack of access to vaccines and vaccine skepticism. Countries like Belarus and Latvia have had better access to vaccines but are suffering from widespread vaccine skepticism, in particular in some segments of the population and to certain vaccines. Russia, which is also dealing with a broad reluctance towards vaccines, is on top of that dealing with a surge in infections caused by the delta-version of the virus.

IMF Economic Outlook suggests that most economies in the region are expected to bounce back in their GDP growth in 2021. While this positive prognosis is encouraging, the webinar reminded us that there is a great deal of uncertainty remaining not only from an epidemiological perspective but also in terms of the medium to long-term economic consequences of the pandemic.

Participants

  • Iurii Ganychenko, Senior Researcher at Kyiv School of Economics (KSE/Ukraine)
  • Sergejs Gubins, Research Fellow at the Baltic International Centre for Economic Policy Studies (BICEPS/ Latvia)
  • Natalya Volchkova, Director of the Centre for Economic and Financial Research at New Economic School (CEFIR at NES/ Russia)
  • Giorgi Papava, Lead Economist at the ISET Policy Institute (ISET PI/ Georgia)
  • Lev Lvovskiy, Senior Research Fellow at the Belarusian Economic Research and Outreach Center (BEROC/ Belarus)
  • Jesper Roine, Professor at the Stockholm Institute of Transition Economics (SITE / Sweden)
  • Michal Myck, Director of the Centre for Economic Analysis (CenEA / Poland)
  • Anders Olofsgård, Deputy Director of SITE and Associate Professor at the Stockholm School of Economics (SITE / Sweden)

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.

For a Better Budget Management of Infrastructure Investments

Posted on April 19, 2021 | Policy Brief

Aerial photo of buildings and roads representing infrastructure investments

Many developing countries rely on investment-to-GDP metrics as a sign of progress towards their development goals. Unfortunately, too often the focus on investment pushes aside the issues of adequately maintaining existing infrastructure. The result could be disastrous to human lives, health, and well-being. Lack of maintenance of existing infrastructure is a well-known problem, not only in developing economies but also in some developed countries. However, how much the government should plan to spend on maintenance over the lifetime of infrastructure assets is neither a simple nor straightforward question. In this policy brief, we examine the cases of two transition economies – Georgia and Estonia – and provide a more general discussion of the challenges and possible solutions to infrastructure maintenance issues. We argue that relevant research along with properly aligned incentives could help the countries overcome these problems and optimize infrastructure spending.

Introduction

The efficiency of infrastructure investment has gotten quite some attention in the past years. A recent book by G. Schwartz et al. (2020) shows that countries waste about 1/3 (and some even more) of their infrastructure spending due to inefficiencies. With poor management, the major budgetary efforts undertaken to make room for infrastructure investments go to waste. The question of how much the country should plan to spend on maintenance over the lifetime of infrastructure assets is neither simple nor straightforward. In two recent ISET-PI blog posts, Y. Babych and L. Leruth (2020a, b) stress the importance of striking the right balance between new infrastructure investments and the rehabilitation and maintenance of existing infrastructure. Without this balance, the up-keep of public infrastructure could either be too expensive for the budget to handle, or, at the other extreme, would quickly deteriorate to the point where it is no longer operational and needs to be rebuilt from the ground up (which is the case in many developing countries, including Georgia, Armenia, Ukraine, and others). This policy brief focuses on the reasons why developing (and even some developed) countries tend to invest too little in public infrastructure maintenance and what can be done to solve this problem. We first examine the cases of Georgia and Estonia, two post-Soviet transition economies with different approaches to infrastructure maintenance financing. This analysis is then followed by a more general discussion about the infrastructure maintenance challenges and potential solutions.

Maintenance vs. Investment: the Cases of Georgia and Estonia

Developing countries tend to use investment (public or private) as a share of GDP to measure their economic progress and prospects. Georgia is one of the countries that has invested a lot in public infrastructure. Public investment grew sharply between 2003-2007 to 8% of GDP and settled at 6% of GDP after 2017 (PIMA GEO 2018).  The capital stock is about 90% of GDP. In comparison, in Estonia, another post-Soviet economy, public investment was about 4% of GPD, whereas the capital stock was 57% of GDP in 2015. Yet, the quality of Georgia’s public infrastructure is much lower than in Estonia (Georgia is in 69th place globally according to Global Competitiveness Index 2017-2018, while Estonia is in 32nd place).  The reason for this is quite simple:  management, especially the maintenance of public infrastructure. Both countries recently went through a Public Investment Management Assessment (PIMA), a comprehensive framework developed by the IMF to assess infrastructure governance. The results suggest that Georgia is much weaker than Estonia in planning, budgeting, and maintenance. (A complete summary of the assessment results can be found here).

Georgia’s case is far from unique. The country belongs to the vast majority of emerging economies that have not efficiently linked their medium- and long-term infrastructure plans within a sustainable fiscal framework. Moreover, infrastructure planning deficiencies spread way beyond the emerging markets: Allen et al. (2019) estimate that 56% of all world countries do not have a proper Public Investment Program.

Why is Infrastructure Maintenance a Challenge for Many Countries?

Even though maintenance, rehabilitation, and new investments are intrinsically linked, the practical process of integrating these three infrastructure components is complex. Blazey et al. (2019), for example, identify the following reasons:

  • Political economy reasons—governments will opt for a ribbon-cutting rather than maintaining existing assets;
  • Fiscal reasons—budget funding for operations and maintenance is prone to be cut when fiscal space is limited;
  • Institutional reasons—in many countries, separate agencies still prepare investment and current expenditure budgets;
  • Capacity reasons— up-to-date information on the state of assets may not be readily available.

A number of international studies (usually sectorial) point to the high cost of neglecting maintenance. A study on the upkeep of bridges and roads in the US shows that 1$ of deferred maintenance will cost over 4$ in future repairs. The same holds for airports. In Africa, the World Bank estimates that timely road expenditure of $12 billion spent in the 80s would have saved $45 billion in reconstruction costs during the next decade. It is not only rehabilitation costs that increase with poor maintenance: user costs can increase dramatically (Escobal and Ponce, 2003); health costs in terms of injuries or deaths; and ecological costs (the water lost daily because of leaks could satisfy the needs of 200 million people according to the World Bank, 2006).

Conceptually, however, the link between maintenance, rehabilitation, and new investments is simple to understand. Figure 1 below, adopted from Thi Hoai Le et al. (2019), clarifies this point. As discussed in Babych and Leruth (2020b), when planned maintenance activities (such as planned repair, upkeep, etc.) are insufficient, then the rate at which infrastructure is deteriorating will be high, and the unplanned maintenance costs will increase as well. This response would, in turn, result in a higher total cost. If the amount of planned maintenance activities is excessive, then the unplanned costs may be low, but the total cost is higher than optimal. In order to strike the optimal balance, there need to be just enough planned maintenance activities. 

Figure 1. Optimal zone of maintenance.

Source: Thi Hoai Le et al., (2019).

Conceptually simple maybe, but the devil(s) is (are) in the details. We have already listed above some of the reasons why integration is complex. Data availability is another issue raised by numerous Public Investment Management Assessments made by the IMF. The reporting standards are simply not built in a way that would allow for the compilation of maintenance and rehabilitation data (although aggregate estimates of investment data are available). In any case, the Government Finance Statistics Manual of the IMF (2014) does not separate maintenance expenditure, which is undoubtedly an area that requires further deepening.  More fundamentally perhaps, as pointed out long ago by Schick (1966), there is an additional issue relating to governance philosophy: “planning and budgeting have run separate tracks and have invited different perspectives, the one conservative and negativistic, the other innovative and expansionist …”. Finally, with governments looking for the ‘cheap’ route through public-private partnerships (PPPs) to finance infrastructure development, fiscal risks have increased in advanced and emerging economies in the early 2000s (IMF, 2008). To our knowledge, there have been no systematic assessments of PPP-related fiscal risks since IMF’s report in 2008, but as fiscal positions have deteriorated with the Covid-19 pandemic, PPP projects are likely even riskier today.

What Can Be Done to Improve Infrastructure Maintenance?

Leaving the data, PPPs, and inter-departmental culture issues aside, several considerations that emerge from a closer look at Figure 1 can feed the policy discussions. Let us first consider the notion of planned maintenance (the orange line). In principle, as a project is developed, the cost of maintenance is projected over its life cycle. If the infrastructure is maintained accordingly, its life span may even exceed the projections. At the time the project is conceived, a schedule of maintenance expenditure is also planned and integrated into the analysis. In the figure above, one would expect that these cost assumptions are located in the ‘optimal maintenance zone’ with a limited amount to be spent on unplanned maintenance later on. This level of planned maintenance should then be integrated as a ‘given’ in all subsequent budgets. Usually, as we have already mentioned, it is not.

If we now move to ‘unplanned’ maintenance (the line in blue), we are really referring to situations when infrastructure must be brought back to shape after months (or even years) of neglect. In some cases, this can no longer be labeled as maintenance, and it becomes rehabilitation. Reduce regular maintenance a bit more and the authorities must start over.

Finally, the continuity of the curves is misleading: it is wrong to say that things are necessarily smooth even in the optimal zone.

Let us look more closely at the leading causes and the ways to overcome the problems that arise when optimizing maintenance expenditure.

Setting benchmarks: One explanation for the shortage of maintenance planning outlined above is the lack of information on the practical implementation of such planning.  There are too few studies on maintenance expenditure for policymakers to set benchmarks and develop reliable estimates. The existing studies in this area tend to focus on OECD countries (where data availability is less of a constrain) and on the transportation sector (roads, rail, etc.) perhaps because the private sector is more often involved (see, for example, the American Society of Civil Engineers from 2017, that concluded that 9 percent of all bridges are structurally deficient). Some studies have looked at buildings (e.g., Batalovic et al., 2017 or the Ashrae database, 2021) and unsurprisingly concluded that the age of the construction and its height are significant variables to explain maintenance outlays. However, we are not aware of studies that would, for example, distinguish between different types of maintenance in order to limit overall costs. We are neither aware of studies investigating which organizational arrangements are the most efficient (as discussed by Allen et al., 2019). The bottom line is that there is not much to use as a benchmark, and an effort must be made to build reliable estimates.

Policy dialogue on maintenance is needed:  The abovementioned considerations of the consequences of delayed, unplanned, and sometimes unexpected maintenance bring us to our next point. Things break down when they are not maintained (and sometimes break down when they are maintained too), and such long-term aspects must be more present in the policy dialogue with developing countries. Clearly, delaying maintenance increases fiscal costs in the short- and longer-term (Blazey et al., 2019).

The smoothness of the curves in Figure 1 can be misleading because insufficient maintenance may suddenly trigger a major problem (a bridge or a dam can collapse, as it happened in Italy and in India recently,)  and this will entail high costs, even disasters involving in human lives. The major collapses of nuclear plants (as in Chornobyl, Ukraine, and more recently in Fukushima, Japan) are other examples of the same problem. In addition, studies estimate that poor maintenance of transmission lines could be one of the reasons for electricity blackouts (Yu and Pollitt, 2009). In fact, the lack of maintenance increases the speed at which the value of the existing capital of infrastructure is eroding. While politicians may well hope that this will not happen during their tenure, the probability of a failure increases as maintenance decreases.

On top of the above, inefficiency in maintenance expenditures can be aggravated by wrongly set incentives, both for domestic actors and foreign donors. Indeed, the latter play an important role in infrastructure investment in many developing countries. In Georgia, for example, 40% of infrastructural projects are funded by foreign donors. Setting the right incentives for both parties, as well as their interplay, are thus of immense importance.

Aligning the incentives: Incentives are against maintenance. As pointed out by Babych and Leruth (2020a), capital investment and rehabilitation look good on paper. Maintenance, on the other hand, is considered a current expenditure item in the Government Finance Statistics (GFS) (IMF, 2014). Spending more on maintenance will therefore not look good since 1) more maintenance will reduce government savings in the short term; 2) spending less on maintenance will increase the need for virtuous-looking investment expenditure in the medium and long term. Yet, in spite of the lack of clear benchmarks, donors can play an essential role by stressing the need to systematically integrate maintenance in the budget and in the Medium-Term Expenditure Framework (MTEF). To some extent, it is already the case. In Georgia, projects that are funded by donors tend to follow better appraisal procedures. However, ex-post audits are irregular – e.g., no individual projects audits were completed by State Audit Office during 2015-2017 (PIMA GEO, 2018). If donors could include these audits in their dialogue, it would clearly be helpful. Training subnational governments in proper maintenance management would be even more critical as capacities tend to be weaker than in the center.

Overcoming a potential moral hazard problem of donor involvement: Excessive donor involvement in new investments could also be counterproductive. Donors should carefully examine the need to build new infrastructure and first consider the possibility of performing some rehabilitation while holding the authorities accountable for the maintenance of existing ones. If the authorities are expecting a donor to eventually replace a piece of infrastructure that does not function, the incentives to maintain it are greatly reduced.

Conclusion

  • Developing economies, but also emerging ones like Georgia, as well as Armenia, Ukraine and others, would benefit from proper incentives and support from the international donors to integrate maintenance into the infrastructure planning framework;
  • This is especially important for local governments, who lack the financial and human capital resources to maintain local infrastructure properly, making regions outside of the capital city less attractive places to invest or live in;
  • Given the absence of transparent and comparable sources of information about the composition of maintenance expenditures – for example, the Government Finance Statistics (IMF), which does not distinguish between maintenance and rehabilitation expenditures, – donors could insist that governments compile these expenditures and report on them, at least for the major projects;
  • The culture of maintaining rather than rehabilitating or replacing is directly linked to the sustainable development goals and the circular economy concept. In light of their commitment to Agenda 2030, the international community and the national governments in countries like Georgia should consider prioritizing and implementing the set of reforms suggested in their respective PIMAs.

References

  • Allen, R., M. Betley, C. Renteria and A. Singh, “Integrating Infrastructure Planning and Budgeting,” in Schwartz et al. (2020), pp. 225-244 (2019).
  • American Society of Civil Engineers, Infrastructure Report Card, Reston, Va, (2017).
  • ASHRAE, Purpose of The Service Life and Maintenance Cost Database, available at., (2021).
  • Babych, Y., and L. Leruth, “Tbilisi: a Growing City with Growing Needs,” ISET-PI Blog available at, (2020a).
  • Babych, Y., and L. Leruth, “To Prevent, to Repair, or to Start Over: Should Georgia Put’ Maintenance’ Ahead of ‘Investment’ in Its Development Dictionary?,” ISET-PI Blog available at, (2020b).
  • Batalovic, M., K. SokolijaM. Hadzialic, and N. Batalovic, “Maintenance and Operation Costs Model for University Buildings,” Tehnicki Vjesnik, 23(2), pp. 589-598, (2017).
  • Blazey, A., F. Gonguet, and P. Stokoe, “Maintaining and Managing Public Infrastructure Assets,” in Schwartz et al. (2020), pp. 265-281 (2019).
  • Escobal, J. and C. Ponce, “The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor,” Working Paper 40, Grupo de Analysis Para el Desarrollo (GRADE), Lima, Peru, (2003).
  • IMF, “Fiscal Risks—Sources, Disclosure, and Management,” Fiscal Affairs Department, Washington DC,(2008).
  • IMF, GFS, Government Finance Statistics Manual, IMF, Washington DC, (2014).
  • PIMA EST, Republic of Estonia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2019).
  • PIMA GEO, Republic of Georgia: Technical Assistance Report-Public Investment Management Assessment, IMF, Washington DC, (2018).
  • Rozenberg, J., and M. Fay, eds, “Beyond The Gap: How Countries Can Afford The Infrastructure They Need While Protecting The Planet,” Sustainable Infrastructure Series, The World Bank, Washington DC, (2019)
  • Schick, A., “The Road to PPB: The Stages of Budget Reform,” Public Administration Review, 26(4), pp. 243-258, (1966).
  • Schwartz, G., M. Fouad, T. Hansen, and G. Verdier, Well Spent : How Strong Infrastructure Governance Can End Waste in Public Investment, IMF, Washington DC, (2020).
  • Thi Hoai Le, A., N. Domingo, E. Rasheed, and K. Park, “Building Maintenance Cost Planning and Estimating: A Literature Review,” 34th Annual ARCOM Conference, Belfast, UK (2019).
  • World Bank, The Challenge of Reducing Non-Revenue Water in Developing Countries – How The Private Sector Can Help,” Water Supply and Sanitation Board Discussion Paper Series No 8, Washington DC, (2006).
  • Yu, W., and M. Pollitt, “Does Liberalization Cause More Electricity Blackouts?,” EPRG Working Paper 0827, Energy Policy Research Group, University of Cambridge, United Kingdom, (2009).

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.

Food Security in Times of Pandemic in Georgia

Posted on November 16, 2020 | Policy Brief

An image of the wheat field with with grain harvester representing food security

The lockdowns and trade restrictions related to the COVID-19 pandemic resulted in shortages of some major food commodities on international and local markets. In this policy brief, we discuss and analyze Georgia’s response to the crisis in terms of food security and agricultural policy. Furthermore, we provide recommendations to ensure fewer disruptions in food supply chains and low volatility in food prices.

Background

COVID-19 has posed significant risks to the food security of many countries including Georgia. Lockdowns and pandemic-related trade restrictions across the world have resulted in shortages of some major food commodities on international and local markets (e.g. sunflower oil shortage in Russia). As of October 16, 2020, according to a World Bank report, 62 jurisdictions have executed a total of 62 export controls in food commodities since the beginning of 2020 (Table 1).

Table 1. Total number of new export controls and import reforms in the food sector globally since January 2020, by month.

Source: World Bank Group, Global Alert Team, 2020

Most of the interventions have involved import reforms with the largest number of new regulations imposed in March-April.  On August 18, 2020, the Eurasian Economic Commission announced an EAEU import tariff quota on certain agricultural goods, valid for 2021. Turkey has also conducted a price stabilization policy by announcing purchasing prices for apricots, paddy, and dried raisin. On August 5, 2020, the government of Turkey introduced additional customs duties on certain agricultural products including chocolate, pasta, and some food preparations. It also eliminated import duties on wheat and barley in October.

Given that Georgia is a net importer of food, and in light of the trade restrictions imposed by its major trade partners, food security moved up on Georgia’s agricultural policy agenda. In order to weaken the adverse impact of the pandemic, keep food prices stable, and reduce input prices for farmers, the state designed the following set of measures:

  • 10M Georgian lari (GEL) from the Ministry of Environmental Protection and Agriculture (MEPA) budget were allocated to subsidize imports of 9 food products: pasta, buckwheat, vegetable oil, sugar, wheat, wheat flour, milk powder, and beans (Legislative Herald of Georgia, 2020). The program subsidized importers’ additional costs resulting from exchange rate fluctuations and was implemented between March 15-May 15;
  • Additional 16M GEL were allocated for purchasing sugar (5,000 tons), vegetable oil (1,500 thousand liters), and pasta (500 tons) stocks from private companies;
  • An anti-crisis plan, “Caring for Farmers and Agriculture”, was presented by the state on March 12. The plan entailed two forms of aid: direct assistance to farmers and sectoral support. Some of the support measures included the distribution of so-called “agricultural cards”– subsidies for cattle-breeding and land cultivation services for smallholder farmers (registered farms with plots in the range of 0.25-10 ha); provision of cheap diesel fuel for farmers; nullification of costs of land reclamation services; provision of agricultural loans and insurance; grants for machinery, equipment, and cooperatives.

Results of Government Interventions

As of October 9, 2020, state support schemes had the following results:

  • Up to 165,000 farmers had been granted agricultural cards. The size of the subsidy exceeded 28.9M GEL;
  • Under the agro-diesel program (which subsidized fuel prices for agro-producers) 122,000 beneficiaries received discount cards on 32,000 tons of agro-diesel;
  • More than 17,000 policies had been issued and 18,000 hectares (around 2% of agricultural land) had been insured under the agro-insurance program. The value of the insured crop exceeded 160M GEL;
  • Across different regions of Georgia, 255 applications for modernization of the dairy sector were approved. In total, 12.4M GEL were spent on this program;
  • 2,215 agro-loans had been issued with a 6-month interest rate covered by the state. The total amount of loans exceeded 40M GEL, including the co-financing of interest rates, which exceeded 3.3M GEL.

While many farmers have benefited from state support programs, these programs were not directly focused on the main consequences of the pandemic. The major threats posed by the pandemic – disruptions in food supply chains leading to decreased sales of agricultural products and price volatility – were not sufficiently addressed by the state support programs. According to the Georgian Farmers’ Association (GFA), 55% of surveyed farmers and agricultural business representatives encountered complications with product realization due to pandemic-related restrictions. Most farmers depend on the HoReCa (hotels, restaurants, and cafés) and hospitality sector, and their products are largely procured for accommodation and food facilities. 60% of those surveyed claimed that they were simply unable to sell their products due to the closure of hotels, restaurants, and cafés.

Food Price Dynamics

During March-May 2020 – the first months of the pandemic – food prices in Georgia showed upward trends on both a month-on-month and year-on-year basis (Figure 1).

Figure 1. Month-on-month and year-on-year changes in food prices

Source: GeoStat, 2020

The main explanation is likely the depreciation of the GEL against the US dollar: during March-May 2020, the GEL depreciated against the USD by 15.8% from 2.71 to 3.14 compared to March-May 2019 (National Bank of Georgia, 2020). As Georgia is a net importer of food commodities, the depreciation of the GEL put upward pressure on food prices. To limit the GEL depreciation and its impact on food prices, the Government of Georgia subsidized additional costs of importers of major food commodities arising from exchange rate fluctuations. The price restraint mechanism involved negotiating with food importers to not increase prices of their commodities and setting the exchange rate of the GEL against the USD at 3, while the Government of Georgia subsidized the corresponding difference between the actual and fixed exchange rates. Despite minimizing the effects of GEL depreciation, food prices in Georgia experienced a significant increase during the observed period: disruptions in supply chains associated with the COVID-19 pandemic led to food shortages that further increased food prices.

In April, annual food price inflation marked its highest level at 16.1% during March-August 2020.  Since then, annual food price inflation has been decreasing as farming activities resumed after COVID-19-related restrictions were relaxed and seasonal (locally produced) agricultural products appeared on the market. Accordingly, food prices started to decrease on a monthly basis.

However, with very few exceptions, prices for major food commodities that were subsidized by the state during March-May increased for both month-over-month and year-on-year comparison (Table 2). On a monthly basis, the biggest price changes were observed for sugar; while on annual basis prices for buckwheat increased the most.

Table 2. Year-on-year changes in prices of major food commodities, March-September 2020

Source: GeoStat, 2020

While food prices could have increased even more in the absence of subsidies, it appears that the state measures did not fully reach their objectives and could not fully overshadow the adverse impact of the pandemic and GEL depreciation.

Recommendations

The pandemic has shown the need for increasing the level of food security in Georgia. Given the multidimensional nature of food security, a longer-term policy should consider not only an increase in domestic production of key food commodities but also a diversification of import markets to ensure low volatility in food supply and prices. As an immediate response to the pandemic, it is recommended to:

  • further subsidize farm inputs in order to reduce the current costs of production;
  • support farmers in selling their produce;
  • develop state programs that strengthen local producers;
  • focus on diversification of import markets for food commodities which constitute a high share of households’ consumption basket.

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 Social Impacts of Covid-19 – Case for a Universal Support Scheme?

Posted on May 28, 2020 | Policy Papers

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Beyond its impact on the healthcare system, the Covid-19 pandemic has already reached labor markets throughout every economy via economic shocks. As of 1 April 2020, ILO estimates indicate a substantial rise in global unemployment, leading to a 6.7% decline in working hours in the second quarter of 2020, which is equivalent to 195 million full-time workers.[1] In this policy note we will draw the reader’s attention to the potential scale of the impact on the labor market and the respective social consequences in Georgia. We will identify a wide variety of groups affected by the Covid-19 crisis, with a special emphasis on the labor market, and provide our judgement on the possible extent of the repercussions. The current crisis affects almost every segment of the population, including members of the following large social groups:

  • Labor market participants face high risk of job loss. Fewer employment opportunities and broad scale layoffs force a large section of self-employed and salaried workers into challenging circumstances.
  • Recipients of Targeted Social Assistance (TSA) are at great risk of slipping deeper into poverty. While members of this group mostly rely on social assistance layouts, the supplementary income that they receive, often from informal sources, could be cut. In addition, the increased prices on food and other essential goods could be particularly detrimental to this group of people.
  • Senior citizens are extremely exposed to the danger of the virus and struggle with greater health risks.

Our analysis starts with an overview of the Georgian labor market and the short-term impacts of Covid-19 on workforce displacement throughout the various sectors. The impact is not gender neutral, as it affects men and women differently depending on the sector. Therefore, we will further provide the decomposition of the impacts on the labor market and propose gender-responsive solutions to the pandemic. To mitigate adverse effects across various vulnerable groups, we will review the existing theoretical and practical evidence on targeted and universal support schemes. An overview of international social support programs is moreover provided in this note. We will further analyze the relative merits and drawbacks of our pre-defined policy options based on a multi-criteria assessment in the context of the Covid-19 crisis and thereafter provide recommendations for policy implementation.

Covid-19 – Impact Across Sectors and an Overview of the Labor Market

Unemployment in Georgia is expected to experience a large-scale increase in the short-term, leading to massive social problems. Workers have been told to remain at home because of the broad virus containment measures taken during the outbreak. Those with the opportunity to work from home are relatively well-off, unlike the large variety of vulnerable groups affected by the lockdown. Low levels of economic activity impact almost all industries, and the most vulnerable sectors include accommodation and food services, most wholesale and retail trade and entertainment and recreation. These difficulties place hundreds of thousands at risk, either by downward adjustments to income or working hours, or by completely losing their jobs.

In order to evaluate Covid-19’s potential short-run effect on employment across various economic sectors, we have qualitatively assessed the strength of the impact at the sub-sectoral level,[2] taking into account the following: (1) list and scale of economic activities prohibited during the ‘lockdown’; (2) restrictions imposed on transportation; (3) drop in consumer demand; (4) fall in intermediate input use.

In Table 1 we present our assessment of the Covid-19 impact across sectors, coupled with the corresponding labor market statistics.[3]

Table 1: Covid-19 impact on possible workforce displacement across sectors.

Source: Authors’ sectoral assessments and calculations based on Geostat Labor Force Survey (LFS 2018).

The key findings from the labor market assessment include:

  • Close to 30 percent of hired workers face a high risk of job displacement, mostly driven by an expected fall in economic activity in the trade, construction, manufacturing, and accommodation and food services sectors;
  • The least impacted industries are projected to be education, public administration and defense, utilities, and health;
  • The majority of self-employed are active in the agricultural sector, which faces a moderate impact for several reasons: the closedown of open food markets, restrictions on transportation, and a partial decline in demand (mostly from the food service sector). Although agriculture is not projected to be severely affected, a substantial number of the self-employed (mostly subsistence farmers) in this sector, considering their significantly lower than average baseline earnings, may require special policy emphasis within this group.

Finally, it should be emphasized that the severity of impacts across sectors will further depend on the longevity of the lockdown measures and the sequence in which they may be lifted for different economic activities.

In addition to the assessments in Table 1, Annex 1 presents a correlation between our estimates weighted by sub-sectors and the ILO’s assessment of the current global impact of the crisis on economic output across the sectors. It should be further noted that, in most cases, the scale of impacts coincide, and the remaining differences are due to: (1) our approach being based on more detailed sub-sectoral data; (2) the ILO looks at the global impact, whereas we focus solely on Georgia.

Short-term Workforce Displacement Risks in Vulnerable Sectors

To alleviate social problems stemming from the labor market shock during the strict, short-term quarantine measures, the clear need for safety net programs has raised the important questions of how they should be designed and who the recipients of support should be.

The discussion of social program designs requires a thorough analysis of the potential target groups. As mentioned in the previous section, after drastic quarantine and lockdown measures, many people in Georgia are at risk of finding themselves without jobs or with decreased salaries and earnings, which, in turn, is a main cause of social problems, like the inability to provide food and other necessities. The highly affected groups, as outlined in Table 1, can be clustered across the following sectors of economy:

  • The accommodation and food service sector is currently the most directly and highly affected sector. Hotels and restaurants are completely closed for an uncertain period, except for the food delivery business. However, even this is constrained to certain periods of the day, since according to the state’s emergency rules after 21:00 all movement, including delivery, is forbidden.

Most people hired within accommodation businesses face temporary job loss. This group includes hotel administration staff, housekeeping staff, people working in hotel restaurants, etc. Similarly affected are employees in restaurants and cafés, faced with cutbacks in salaries, if not complete job loss.

Another significant group within this sector are the self-employed. Owners of small family hotels and restaurants, typically dependent on tourism expenditure, now find themselves without any cashflow.

  • A significant portion of the wholesale and retail trade sector also faces major shutdowns. To begin with, employees of trade centers and individual stores are now out of work for an indefinite period. These include consultants in clothing stores, hardware stores, household appliance stores, etc. A very limited number of shops that continue to work via online sales have retained several employees on decreased salaries.

The reality is also harsh for the self-employed in retail trade. Open marketplaces, including construction materials shops and farmers’ markets have been shut, and such people are left without a vital income source. It should also be noted that most of these workers are members of a lower social strata and are less likely to have enough, if any, savings for the quarantine period.

  • As for the relatively small, but equally affected, arts, entertainment and recreation sector, art galleries, museums, night clubs, theatres, movies, and sports and spa facilities, have all been closed down due to their ‘non-vital’ function. Salaried as well as self-employed workers in these sectors found themselves without employment soon after the state emergency was announced.
  • Additional highly affected groups are those hired and self-employed in the transportation sub-sectors. The closing of public transportation has left hired bus, metro, and minibus drivers entirely without work.

Other than hired employees, self-employed drivers for intercity transportation are now left without work since intercity commuting is now forbidden under the state of emergency. Comparatively less affected are self-employed taxi drivers, who are still allowed to work, however only between 06:00-21:00. The fact that many drivers previously worked night shifts, combined with declined daytime demand, results in significant cutbacks in daily earnings for taxi drivers.

  • Another significantly affected group are those workers employed in households. These include housemaids, nannies, private tutors, handymen, etc. Since everyone is being cautious and following social distancing instructions, many households have dismissed their hired help for an indeterminate period, and even those still employed have a hard time getting to work due to the suspension of public transport, and are therefore left without vital daily income.
  • The agriculture, forestry, and fishing sector, the largest in terms of employment, remains less affected relatively, though it is facing restrictions since restaurants and cafés require fewer agricultural products than before. Moreover, as farmers’ markets have closed, their access to marketplaces has become significantly constrained. Farmers are now supplying only supermarket chains and restaurants with delivery services, a significant economic decrease compared to the normal environment. It should also be noted that self-employed small farmers are in the majority in the sector. Such workers are likely without strong links to supermarket chains or restaurants, and therefore, they will be more noticeably affected by the economic impact.

An important specificity of self-employed and domestic workers is that many are also informally employed, thus their identification by official sources (i.e. in tax returns or small business registers) is extremely problematic. Thus, the existence of a large variety of potentially affected groups, as well the inability to correctly estimate the severity of impacts across groups, highlights the need for a temporary social protection mechanism that will cover all affected parties, particularly since the people included in the groups above are not typically the main recipients of social assistance programs.

Decomposition of Labor Market Impacts by Gender

In this section, we present the gender decomposition of labor market impacts, and conclude that unemployment-driven assistance may benefit men considerably more than women.

Chart 1 summarizes the distribution of self-employed and salaried men and women across low, medium, and highly affected industries, based on the sub-sectoral assessments previously described and using gender-disaggregated employment data.

Figure 1: COVID-19 impact on possible workforce displacement, by gender

Source: Authors’ sectoral assessments and calculations based on Geostat’s Labor Force Survey (LFS, 2018).

It is evident that the proportion of employed men is significantly higher in the most vulnerable sub-sectors. Such a picture is highlighted by the high male-employment ratios in construction, transportation, and parts of manufacturing, as well as the high female-employment in the minimally affected education and healthcare industries[4].

To summarize, during the current crisis men are more susceptible to job displacement, and if a social assistance policy is solely based on labor market outcomes, they will yield higher benefits. Such social support mechanisms will deepen existing gender inequalities[5] in the country as women face disproportionate and increasing burden of care work (in situation of lockdown).

Social Assistance Policy Objectives in a Crisis

Considering the diversity of groups influenced by the lockdown, any assistance program should have several main policy objectives:

  1. Maximizing the reach of a policy to those in need and minimizing their risk of impoverishment – a large part of the population is affected by the lockdown, thus there is a substantial risk of increasing poverty directly from job loss and indirectly via job losses within families. Social assistance should, in a best-case scenario, reach the maximum number of disadvantaged people, while avoiding providing assistance to the affluent.
  2. Minimizing fiscal pressure – social assistance can create substantial pressure on the budget, especially in the current situation as revenues have decreased due to the lockdown. Furthermore, people who do not require support should not receive assistance, thus, decreasing unjustified pressure on the budget.
  3. Progressivity and gender responsiveness – an assistance program should provide proportionally larger support to those in greater need and aim to balance support by gender.

To mitigate the negative social impact of the economic lockdown, the government will have to provide significant and effective social assistance. And this is where all governments face a key dilemma, as they decide between providing targeted versus universal assistance.

An Overview of Targeted vs. Unconditional Universal Assistance

Targeted assistance is based on the methodology to define target groups, this could be under a points-based system (similar to current targeted social assistance available in Georgia) or a certain criterion defining affected groups. Under any targeting approach, two major challenges exist: (i) missing certain affected people (exclusion error), where defining an ideal criterion is impossible; and (ii) supporting those who do not require any assistance (inclusion error). Hanna and Olken (2018)[6] show that targeted programs have the potential to maximize welfare, however, they require a substantial amount of data and effort to minimize errors in the inclusion and exclusion of recipients. They further illustrate that, under normal circumstances, to reach 80% of poor people, the inclusion error will be around 22-31%. Deciding on a targeting methodology can also be costly and time consuming. Klasen and Lange (2016)[7] highlight that there is little difference between simple targets, such as demography or geography, and more complex asset-based measures, and both make poor proxies as they do not capture poverty effects in great enough detail.

In contrast, a universal support scheme can also be considered; defined as an unconditional transfer to every member of society. From the administrative perspective it is substantially easier to organize and administer, as it will not require the formation of targeting methodology or identification of target groups. Compared to targeted assistance, universal support will simply not have exclusion errors. However, the universality of the scheme would be associated with large inclusion errors. Nevertheless, considering the current situation in Georgia, with a large variety of affected groups, the inclusion error need not be as high as in normal circumstances. As previously noted, due to the lockdown, the number of vulnerable groups will have increased substantially.

Unlike targeted support schemes, there is limited practical evidence behind the implementation of universal programs (Banerjee et al., 2019).[8] However, some of the impacts can be identified from existing pilot case studies, impact assessments of existing targeting schemes, and an analysis of theoretical knowledge. The key here is that the expected impacts depend substantially on the duration and type of the support scheme (i.e. direct cash transfers, provision of vouchers or coupons, tax credit).

For our purposes we assume that the duration of the support scheme will be relatively short-term (related to the length of the lockdown). Furthermore, there is nearly no practical evidence on the impact of the long-lasting universal support schemes (Banerjee et al. 2019). Theoretically, long-lasting universal support can have a negative impact on labor force participation. Moreover, Banerjee et al. (2017)[9] finds no evidence that unconditional transfers discourage work. Considering the characteristics of the crisis, labor market participation is already limited because of the lockdown.

In addition, direct unconditional cash transfers could serve the progressivity purpose well, as households in greater need will receive a larger portion of their income, compared to those who require less assistance. Progressivity will depend on whether the recipient of a cash transfer is a household or an individual. Providing a cash transfer to households might have a disproportionate impact on larger households, requiring them to sustain themselves with less money per capita. Another important point to consider is whether money should be provided to everyone or only to the working age population (those above 15 years of age).

Coupons and Vouchers vs. Direct Cash Transfer

The type of support scheme can have a substantial influence on its impacts from the welfare and macroeconomic perspectives. One form of support scheme is the provision of vouchers or coupons to help households with utility payments or to purchase essential goods. Utility vouchers will disproportionally support more well-off households that use more appliances. The universality of such vouchers is also questionable, as some households are not connected to the utility networks (for instance the natural gas network), and thus will not benefit at all from vouchers. Considering the situation, the positive impact of vouchers is that during such a lockdown utility companies will not face liquidity problems that may otherwise arise from increased delinquency rates.

On the other hand, cash transfers allow recipients to rationalize between the consumption of different types of goods. As opposed to the provision of coupons and vouchers, transfers could further increase welfare by allowing individuals to self-rationalize (Ghatak & Maniquet, 2019).[10]

A Review of Social Support Programs Internationally

In this section, we discuss various governments’ (Table 2) social protection measures during the Covid-19 crisis. The actions taken cover the different functions of social protection, such as unemployment benefits; special social assistance or direct cash transfers; wage subsidies; deferrals of tax payments; pensions and pension fund adjustments; sickness and childcare benefits; etc.

In order to promote income security and stimulate aggregate demand, several countries have introduced either universal or quasi-universal direct cash payments (e.g. Australia, Hong Kong, Singapore, Serbia, Greece, the US). In order to further ease liquidity constraints on individuals and enterprises, some countries have announced the deferral of certain tax payments, social security contributions, rent, and utility payments (e.g. Bulgaria, Estonia, Spain, Canada). In addition, several governments are providing grants and wage subsidies to SMEs, start-ups, and other hard-hit businesses to avoid the drop in revenues and safeguard employment. In most cases, these measures were supplemented by extended unemployment benefits.

Table 2: Covid-19 social protection measures, by country

Central, South, and Eastern European Countries Certain Social Protection Measures Taken

                                 

Estonia
  • Suspended payments to the Pillar II pension fund;
  • Support the Unemployment Insurance Fund to cover wage reductions.
Poland
  • Wage subsidies for employees of affected businesses and self-employed persons;
  • Self-employed and employees working on civil contracts will receive a one-time benefit.
Latvia
  • Covering 75% of employees’ wages (in sectors suffering losses as a result of the coronavirus crisis) from the state budget, with a maximum monthly payment per employee set at €700;
  • Exempting covered wages from personal income tax and social contributions.
Serbia
  • A universal cash transfer of 100 EUR to each citizen over 18 years old;
  • Wage subsidies, including a payment of minimum wages for all SME employees and entrepreneurs for three months;
  • Payment of 50% of the net minimum wage for three months for employees in large private sector companies and for employees who are currently not working.
Bulgaria
  • Government-backed payment of 60% of the salaries of employees working in affected sectors who might otherwise be laid off;
  • Deferral of various tax and utility payment deadlines;
  • Offering interest-free loans to workers put on leave;
  • The possibility for the registered unemployed to sign labor contracts with agriculture producers, without losing their unemployment benefits.
Albania
  • Government support of small businesses/self-employed that are forced to close activities due to the Covid-19 pandemic by paying their minimum salaries;
  • Doubling social assistance and unemployment payments;
  • Part of defense spending reallocated toward humanitarian relief for the most vulnerable.
Ukraine
  • Adopting legislation that allows households to deduct the expense of Covid-19 medicine from personal income tax;
  • Introducing a one-off pension increase to low-income pensioners of 1,000 UAH and a regular monthly 500 UAH pension top-up for retirees aged 80 years and over;
  • Canceling payment of the Single Social Contribution for several categories of payer between March-May 2020.
Asia-Pacific  
Hong Kong, China
  • A one-off universal cash transfer of 1,280 HKD (165 USD) for 7 million adult residents;
  • A one-off extra allowance for 1.33 million recipients of the standard Comprehensive Social Security Assistance Payment, Old Age Allowance, Old Age Living Allowance, or Disability Allowance.
Australia
  • A one-off payment of 750 AUD (431.9 USD) for social security, veterans and other income support recipients and eligible persons, assisting around 6.5 million lower income Australians.
New Zealand
  • A permanent increase in social spending to protect vulnerable people (over the next four years);
  • Wage subsidies for affected businesses in all sectors and regions;
  • An income support package for the most vulnerable, including a permanent 25 NZD per week benefit increase and a doubling of the Winter Energy Payment for 2020;
  • Covid-19 leave and self-isolation support.
Singapore
  • A one-off payment as quasi-Universal Basic Income. All Singaporeans aged 21 and above will receive a one-off cash transfer of 300 SGD (205.38 USD), 200 SGD (136.9 USD), or 100 SGD (61.5 USD), depending on their income. Cash-payouts will also be given to families with children and elderly parents;
  • Providing a 100 SGD (61.5 USD) supermarket voucher to lower-income households;
  • Taxi and private-hire car drivers affected by the Covid-19 outbreak (around 40,000 eligible drivers in Singapore) will receive up to 20 SGD (13.7 USD) per vehicle per day for three months;
  • Enhanced Wage Credit Schemes – (co-funding wage increases for Singaporean employees);
  • For low-wage workers, the government will provide a Workfare Special Payment. Singaporeans on Workfare will receive 20 per cent more for work completed in the past year, with a minimum cash pay-out of 100 SGD (61.5 USD).
Western Countries  
United States of America
  • Direct cash payments of $1,200 for those earning up to $75,000 and $500 per child;
  • One-time tax rebates for individuals;
  • Expanding unemployment benefits.
Canada
  • Temporary income support to workers staying at home without access to paid sick leave;
  • Support to individuals and families with low and modest incomes with a special top-up payment under the Goods and Services Tax (GST) credit;
  • An increase in childcare benefits;
  • Support to businesses through income and sales tax deferrals.
Germany
  • Providing $55 billion to help small businesses and the self-employed avoid bankruptcies, with cash payments of up to $16,225;
  • Offering $8.5 billion safety-net programs for the self-employed.
Greece
  • Transfers to vulnerable individuals, including cash stipends;
  • Full coverage of pension and health benefit payments for employees working in hard-hit firms and self-employed people;
  • Extension of unemployment benefits by two months, and paid leave for parents who have children not attending school;
  • Liquidity support for hard-hit businesses through subsidized loans, loan guarantees, interest payment subsidies, and deferred payments of taxes and social security contributions.
Spain
  • A temporary subsidy for household employees affected by Covid-19;
  • A temporary monthly allowance of around 430 EUR for temporary workers whose contract (of at least two months) expires during the state of emergency and who are not entitled to collect unemployment benefits;
  • Tax payment deferrals for small and medium enterprises and the self-employed for six months;
  • An allowance for self-employed workers affected by economic activity suspension.
Norway
  • Larger wage subsidies for temporary lay-offs and more generous unemployment benefits;
  • Expanded sickness, childcare, and unemployment benefits;
  • A compensation scheme for heavily affected but otherwise sustainable businesses;
  • Grants for start-ups.

Source: Policy Responses to Covid-19, IMF policy tracker, April 2020; Social protection responses to the Covid-19 crisis, ILO, March 2020; Countries’ public announcements of Covid-19 economic responses.

Alternative Policy Options

Considering the existing social challenges, policy objectives, and possible alternatives implemented around the world, we propose the following five policy options:

Option 1 – Targeted Assistance

Considering the current situation in Georgia, the state’s capacity to implement a targeted exercise is extremely limited. This is largely due to the lockdown and the complexity of matching the current economic challenges and general characteristics of target groups. One way for the government to target different groups would be to use its administrative resources and revenue service databases to identify affected unemployed people no longer receiving salaries. However, using these resources, it will be hard to identify the majority of self-employed and informal workers who have also lost their income (fully or partially) and are facing hardships; examples of these individuals may include a small business owner working at the Eliava construction materials market, a self-employed tourism sector worker, a domestic worker – a nanny or cleaning lady, etc. Under normal circumstances, such individuals do not require any social assistance, however due to the lockdown they may not have enough cash inflow to sustain their families.

Furthermore, targeted assistance can create perverse incentives for some employees. Depending on the amount of the assistance, employees (that are still allowed to work) whose net salaries are close to the assistance threshold, might be discouraged from work. For example, if targeted assistance is 200 GEL, a grocery store worker with a gross salary of 300 GEL might prefer to leave their job temporarily (as unpaid leave for example).

Furthermore, the government could target following socially vulnerable groups that are easier to identify, such as:

  • Receivers of targeted social assistance, adults – 297,094 individuals;
  • Receivers of targeted social assistance, under 18 – 161,374 individuals;
  • Pensioners – 765,911 individuals.

Providing additional support to these groups will mean indirectly covering some self-employed individuals and informal workers. Many of such socially vulnerable groups work informally or are self-employed. Furthermore, some individuals could potentially have family members that are either informally or self-employed.

To calculate the total number of people subject to the targeted scheme, we consider the above listed individuals and add the group of hired employees that may lose the job or may have to take unpaid leave. Based on our estimates, around 200,000 hired workers may lose their income. Adding this to the number of TSA recipients (458,468) and pensioners not receiving TSA payments (692,431) brings the total number of beneficiaries of a targeted assistance scheme to 1,350,899 individuals. Assuming, 150 GEL in assistance per adult, and 75 GEL for under 18s, this will bring the cost of targeted assistance to approximately 191 mln. GEL per month.

Option 2 – Income Tax Breaks

The second policy option to consider is a variation on a tax break (tax credit, lowering income, or other taxes)[11]. Such an assistance mechanism will not be universal and only benefit the taxpayers. Furthermore, it is not a fact that tax relief will be transferred from employers to employees. Thus, essential social assistance may not be provided to a large proportion of the population. In addition, due to the lockdown, opportunities for investments have shrunk and hence, most tax saving will not influence economic growth. Finally, a decrease in tax rates will create additional pressure on government revenues, already negatively influenced by the lockdown, which may potentially create fiscal problems.

Aside from the costs of tax breaks, one should also bear in mind that this policy option is only intended for income tax payers who managed to retain their jobs. In an optimistic scenario, about 200,000 of hired employees will be left jobless, thus, about 640 thousand people will be aided by tax breaks. If income tax for all these employees would be reimbursed, the cost of tax breaks would amount to approximately GEL 136 mln. (monthly). It should also be mentioned that if companies are not paying income tax to the government, they might fail to reimburse this money to their employees, leaving some people without any assistance.

Option 3 – Unconditional Universal Cash Transfers

The third policy option is unconditional universal cash transfers. In this case, the government would make an unconditional cash transfer to every member of society. From a practical perspective there are two important questions to be answered: (i) should cash transfers be provided to individuals or to households?; and, (ii) should cash transfers only be made to the working age population or to children as well?

To minimize the potential negative consequences stemming from the possible negative gender impacts, individual payments are the preferred system. This may be as men are more often than not considered to be heads of their households, and if assistance is household-based women may not be able to take full advantage of it.

Furthermore, to ensure the progressivity of a universal cash transfer, it should not be limited to the working age population. A common approach would be to give guardians of children a decreased amount of a standard Universal Basic Income (UBI) payment (Ghatak & Maniquet, 2019). The progressivity of such a scheme is an important advantage, as it ensures support to those people who are not participants of the labor market and dependents of employed family members. Thus, the universal system helps mitigate the substantial indirect impacts on poverty resulting from job losses.

A major drawback of the unconditional universal cash transfer is its expense. This is primarily due to the large inclusion error, which accompanies this system by its very definition. However, alternatively, in a targeted program the vast majority of the affected self-employed and domestic workers (in total, close to 50% of all employment) are nearly impossible to identify. Furthermore, due to the lockdown, the potential group under risk of impoverishment is greater than under normal conditions. Consequently, compared to a perfectly targeted system (without any inclusion or exclusion errors) an unconditional universal cash transfer would be only marginally costlier.

However, with imperfect targeting, an unconditional cash transfer would be substantially costlier compared to targeted assistance. Assuming 150 GEL assistance for all working age population (2,968,964 individuals) and 75 GEL for children (754,500 individuals), the total cost of unconditional universal cash transfers would be 502 mln. GEL per month.

Option 4 – An Opt-out/Opt-in Unconditional Universal Transfers

As previously mentioned, the significant cost of a universal support scheme is a notable challenge, particularly because budgetary fiscal pressure is already high due to decreased economic activity and tax revenues. Thus, implementing a potentially costly assistance program will be hard from a public finance perspective. To partially alleviate this problem and decrease the inclusion error of universal cash transfers, the government could implement it in the following ways:

  1. The government could offer unconditional transfers to all individuals whose income is impossible to identify, while providing an opt-out option in case they do not deem the assistance necessary (for example, individuals and their families with savings or those unaffected by non-labor income);
  2. The government may assist employed workers based on their income using the following two principles:
    1. Offer assistance using an opt-out option to everyone whose income is below a certain threshold (for example, 700 GEL gross salary for the month of March);
    2. Offer assistance using an opt-in option to everyone whose income is above the threshold.

Opt-out/opt-in universal cash transfers have the potential for governmental savings. To evaluate the expected cost of this option we assume that half of all employees (i.e. 430,000) with a salary of over 700 GEL gross would opt-in into the system. In this case, the total cost of opt-out/opt-in universal cash transfers would be up to GEL 470 mln. Furthermore, in the better-case scenario, where no employees with a gross salary over 700 GEL would opt-in into the system, the total cost of the cash transfer scheme would be up to GEL 437 mln. Thus, our expected cost of the opt-out/opt-in universal cash transfer will be an average of GEL 454 mln[12].

Option 5 – Conditional Cash Transfers

To decrease the fiscal pressure associated with unconditional universal cash transfers, the government could use relatively simpler methods to minimize inclusion errors in the system. In this case, the government could potentially exclude employees who may not face an urgent need for assistance. Firstly, the government could exclude individuals who received an income of over 40,000 GEL in 2019 from the program. Secondly, those workers with an average monthly income of 1,200 GEL in 2020 could also be left outside the assistance scheme. This will allow the government to limit the inclusion error of the cash transfer system, while keeping similar overall impacts.

We evaluate the expected cost of the conditional cash transfer assuming 30% of the hired workers (258,048) having monthly income above 1,200 GEL. Based on the same population data, as for calculation of the cost of the unconditional cash transfer, the expected cost for conditional cash transfer will be roughly GEL 463 mln.

Multi-Criteria Analysis of Policy Options

To summarize these options, we have created a multi-criteria assessment of the different possibilities for social assistance using our pre-defined policy objectives. We assess each policy option on a 5-point scale, with 1 representing the worst performance, while 5 showing perfect performance. The overall efficiency of the policy option is a simple average of points in each criterion.

Table 3: Multi-Criteria Assessment of different social assistance systems during Covid-19

Assessment Criteria Option 1 – Targeted Assistance Option 2 – Income Tax Break Option 3 – Unconditional Universal Cash Transfer Option 4 – Opt-out/opt-in Unconditional Universal Cash Transfer Option 5 – Conditional Cash Transfer

 
Monthly Cost of the assistance Scheme (mil. GEL) 191 136 502 454 463
1. Minimization of Exclusion Error (minimization of impoverishment risk) 3 1 5 5 4
2. Minimization of Inclusion Error (minimization of fiscal cost) 4 2 2 3 3
3. Ease of implementation 2 5 5 4 4
4. Progressivity 4 1 4 5 5
5. Gender responsiveness 3 2 5 5 5
 

Overall Efficiency

 3.2 2.3 4.2 4.4 4.2

Summary and Recommendations

In this policy note, we have summarized the potential social impacts of Covid-19 and the subsequent lockdown caused by the pandemic. Our assessment of the sub-categories of employment show that there is a large group of mid to highly affected individuals among the employed populace. Around 30% of hired employees will be significantly influenced, while 22% will suffer a medium impact. The impact on the self-employed will also be substantial, roughly 15% of the group will be highly affected, where 84% of self-employed individuals will feel a medium impact from the lockdown. The impacts are also disproportionate from a gender perspective, posing a risk of unemployment-driven assistance benefitting men more so than women.

Having reviewed international responses to the Covid-19 crisis from 17 selected countries, the evidence compiled has helped to form possible designs for a social assistance program. We believe that direct cash transfers to individuals are preferable to providing assistance for the purchase of specific goods or services, as individuals can self-rationalize.

Our multi-criteria assessment shows that an opt-out/opt-in unconditional universal cash transfer is marginally better compared to other universal cash transfer schemes. It has the best performance in minimizing the risk of impoverishment. Furthermore, our analysis shows that under the current conditions, the government’s ability to correctly design a targeted program that is able to reach all affected individuals is limited. This is primarily due to the relatively high percentage of self-employed on the Georgian labor market. Consequently, a targeted program would have a limited impact on minimizing the risk of impoverishment. This is even more true for possible tax breaks. The greatest merit of a targeted program is that it imposes less fiscal pressure and is thus substantially less costly compared to a universal support scheme.

Annex 1 – Comparison of Sectoral Impact Assessments by ILO (globally) and ISET-PI (for Georgia)

Annex 2 – Summary of the assumptions used for calculating costs of different support schemes

  Indicator Amount
Population
A Working Age Population (>15)  2,968,964
B Population Below Working Age (<15)  754,500
C Total Population  3,723,464
Hired Workers  
D Total Hired Workers  860,161
E Hired Workers with salary above GEL 700  430,081
F Share of hired workers with salary above GEL 1,200 30%
G Total number of hired workers who lose labor income 200,000
H TSA Recipients (>18)  297,094
I Pension Recipients 692,431
J TSA Recipients (<18) 161,374
Cash Transfer
K Cash transfer per adult (GEL) 150
L Cash transfer per child (GEL) 75
  • [1] ILO Monitor 2nd edition: COVID-19 and the world of work, April 2020.
  • [2] NACE 2 classification system, 4-digit level
  • [3] Based on the Labor Force Survey, Geostat (2018)
  • [4] One has to note that the working environment for frontline health workers has changed and they are exposed to higher health risk and psychological stress, which regardless of relatively stable labor market positions makes them more vulnerable physically and psychologically.
  • [5] For example, more women live in poverty as demonstrated by the fact that 55% of social assistance recipients are women.
  • [6] Hanna, R. & Olken, B. (2018). Universal basic incomes vs. targeted transfers: anti-poverty programs in developing
  • countries. J. Econ. Perspect. 32(4):201–26.
  • [7] Klasen, S. & Lange, S. (2016). How narrowly should anti-poverty programs be targeted? Simulation evidence from Bolivia and Indonesia. Discuss. Pap. 213, Courant Res. Cent., Göttingen, Ger.
  • [8] Banerjee, AV., Niehaus, P. & Suri T. (2019). Universal basic income in the developing world. Annu. Rev. Econ. 11:961–85.
  • [9] Banerjee, AV., Hanna, R., Kreindler, G. & Olken B. (2017). Debunking the stereotype of the lazy welfare recipient: evidence from cash transfer programs. World Bank Res. Obs. 32:155–84
  • [10] Ghatak M. & Maniquet F. (2019). Some theoretical aspects of a universal basic income proposal. Annu. Rev. Econ.11.
  • [11] For the purposes of this policy option we will concentrate solely on income tax breaks.
  • [12] These scenarios do not consider additional potential saving from individuals with an opt-out option utilizing this opportunity.

Disclaimer

This policy brief was first published as an ISET policy note on April 17, 2020 under the title “The Social Impacts of COVID-19 – Case for a Universal Support Scheme?”.

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.