Tag: GDP

The Impact of Technological Innovations and Economic Growth on Carbon Dioxide Emissions

Posted on April 8, 2024 | Policy Brief

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This policy brief offers an examination of the interplay between economic growth, research, and development (R&D), and CO2 emissions in different countries. Analysing data for 83 countries over three decades, our research reveals varying impacts of economic and R&D activities on CO2 emissions depending on country income level. While increased economic growth often leads to higher emissions due to greater industrial activity, our model indicates that increased GDP levels, when interacted with enhanced investments in R&D, is associated with reduced CO2 emissions. Our approach also recognizes the diverse economic conditions of countries, allowing for a more tailored understanding of how to tackle environmental challenges effectively.

Technological Innovation and CO2 Emissions

Human activity has over the past few decades significantly contributed to environmental problems, in particular CO2 emissions. The consequences from increased CO2 emissions, such as global warming and climate change, have motivated extensive research focused on understanding their impact and finding potential solutions to associated issues.

Economic growth, and research and development (R&D) can serve as differentiating factors between countries when it comes to their pollution levels, specifically measured by CO2 emissions per capita. Higher levels of economic growth are associated with increased industrial activity and energy consumption, which may lead to increased CO2 emissions. At the same time, countries that invest more in R&D often focus on developing cleaner technologies and implementing sustainable practices, which may result in reduced CO2 emissions.

In this policy brief, we analyse CO2 emissions’ dependencies on technological innovation and economic growth. For our analysis we group the considered 83 countries into three wealth levels: High, Upper Middle, and Lower Middle income levels. This grouping facilitates a better understanding of the complex interplay between wealth, innovation and growth and their projection into emissions. Considering each wealth level group separately also allows us to account for varying economic and developmental contexts.

Data

Based on data availability, we analyse 83 countries, spanning from 1996 to 2019, inclusive. We follow current research trends and use R&D intensity as a proxy for technological innovation (see Chen & Lee, 2020; Petrović & Lobanov, 2020; Avenyo & Tregenna, 2022).

Data on energy use originate from Our World in Data. R&D data from after 2014 are based on figures from the UNESCO Institute for Statistics. All other indicators come from World Development Indicators (WDI).

Table 1 presents an overview of the variables considered in our empirical model. Our response variable is CO2 emissions per capita. We include several covariates (i.e. urban population, renewable energy, trade), found to be significant in previous studies where CO2 emissions was considered the dependent variable (Avenyo & Tregenna, 2022; Wang, Zeng & Liu, 2019; Petrović & Lobanov, 2020; Chen & Lee, 2020).

Table 1. Variable description.

Additionally, we include quadratic terms for GDP and R&D to account for nonlinearity and non-monotonicity. Also, we incorporate the interaction term between GDP and R&D (see Table 3). This allows us to evaluate whether the impact of technological innovations on CO2 emissions is dependent on the GDP level, or vice versa.

Wealth Level Classification

Existing literature highlights significant variation between countries in terms of economic growth and income levels, particularly in relation to R&D expenditure and CO2 emission levels (see Cheng et al., 2021; Chen & Lee, 2020; Petrović & Lobanov, 2020; Avenyo & Tregenna, 2022). Given this we deployed the Mclust method (Scrucca et al., 2016; Fraley & Raftery, 2002), and classified our considered countries into three distinct groups based on their median Gross National Income (GNI) over a specified range of years for each country. Following this methodology, we obtained three groups of countries: High, Upper Middle and Lower Middle. The list of countries categorized by their respective wealth level is presented in Table 2.

Table 2. Countries within each wealth group.

Low-income countries, (as categorized by the World Bank in 2022) were not included in the analysis as the study focuses on the impact of technological innovations on CO2 emissions, innovations which are less frequent in such economies. Limited infrastructure, financial resources, and access to technology often result in lower levels of R&D activities in low-income countries, which reduces the number of measurable innovations.

The Hybrid Model

Our leading hypothesis is that country income levels (measured by GDP) mediates the relationship between innovation (measured by R&D expenditures) and CO2 emissions. To test this, one could estimate this relationship for each group of countries separately. This policy brief instead estimates the relationship for the whole sample of countries accounting for group differences via interaction effects. Specifically, our estimation allows for interaction terms between some or all covariates and the wealth level. This approach, which we refer to as the hybrid model, thus combines elements of both pooled and separate models. It is a great alternative to separate models as it allows for estimation of both group-specific and sample-wide effects, and as it contrasts differential impacts across wealth level groups.

We test two versions of the hybrid model, one full and one reduced. The full model incorporates interactions with all covariates while the reduced model includes some indices without interactions, resulting in a relationship shared across all wealth levels. The reduced model assumes that the variables Renewable energy consumption, Energy use and Trade exhibit the same relationship with CO2 emissions across all wealth levels.

Both the reduced and full hybrid models have similar coefficients for the variables and interactions that they share. While the coefficients share signs in both the full and reduced hybrid models, they are smaller, in absolute values, in the reduced hybrid model. In Table 3 we present the estimates from the reduced hybrid model.

Table 3. Results from the reduced hybrid model with CO2 emissions as dependent variable, by wealth group level.

Note: The upper part of the table (denoted “interaction variables”) depicts the coefficients for the interaction term between the variable in the respective row and the income group in the respective column. * denotes a 0.05 significance level. ** denotes a 0.01 significance level. ***denotes a 0.001 significance level.

Several things are to be noted from Table 3. First, for High and Upper Middle wealth level countries there is a significant positive association between innovation (as proxied by R&D) and CO2 emissions. However, the significance levels of the interaction term for R&D and GDP reveal that the relationship between R&D and CO2 is not constant across wealth levels even within each group. Specifically, it appears that relatively high values of GDP and R&D are associated with a decrease in CO2 emissions in High and Upper Middle wealth level countries. This suggests that in wealthier countries, advancements in technology and efficient practices derived from R&D are likely contributing to reduced emission levels. Interestingly, GDP has no direct effect on emissions for countries in these two wealth groups. Rather, GDP only affects emissions through the interaction term with R&D.

In turn, for the Lower Middle wealth level countries, R&D has no impact on CO2 emissions, whether directly or via interaction with GDP. Instead, higher GDP leads to a significant increase in emissions. This suggests that for these countries economic growth entail CO2 emissions while R&D activities are too small to have a mediating effect.

Second, medium and high-technology industry value added manufacturing is only significant for countries within the Upper Middle wealth level. This is in line with previous literature (see Avenyo & Tregenna, 2022, Wang, Zeng & Liu, 2019). A higher proportion of medium and high-technology industry value added is often negatively associated with CO2 emissions due to the adoption of cleaner and more environmentally sustainable technologies and practices within these industries. Additionally, these industries are often subject to stringent environmental regulations. As a result, these industries can contribute to reduced emission levels, becoming key drivers of sustainable economic growth and environmental protection (Avenyo & Tregenna, 2022). Interestingly, in our estimation, this result is evident only for Upper Middle wealth level countries.

Third, urban population is only significantly increasing emissions for High wealth level countries. Such positive relationship can be attributed to several factors. There is often a higher concentration of industrial and manufacturing activities in urban areas, leading to increased emissions of pollutants as urbanization increases (Wang, Zeng & Liu, 2019). Additionally, urban areas tend to have higher energy consumption and transportation demands, further contributing to higher emission levels.

When it comes to the factors jointly estimated across wealth groups, the positive relationship between renewable energy consumption and CO2 emissions is well-documented within the literature (Chen & Lee, 2020) which emphasizes the need for sustainable energy practices and efficient resource management to mitigate adverse environmental impacts. In line with this, the significant negative relationship between renewable energy consumption and CO2 emissions suggests that an increase in renewable energy usage is associated with a reduction in CO2 emissions. This is in line with previous findings demonstrating that technological progress helps reduce CO2 emissions by bringing energy efficiency (Akram et al., 2020; Sharif et al., 2019).

Conclusion

This policy brief analyses the effects of GDP and technological innovations on CO2 emissions. The theoretical channels linking economic development (and technological innovations) and CO2 emissions are multifaceted, warranting the need for an econometric assessment. We study 83 countries between 1996 and 2020 in a setting that allows us to disentangle the effects across countries with different income levels.

Our findings underscore the importance of considering the various income levels of the considered countries and their interplay with R&D expenditures in environmental policy discussions. Countries with Lower Middle income levels exhibit insignificant effects from R&D expenditures on CO2 emissions, while for Upper Middle and High wealth level nations, increased R&D expenditures incurs higher emissions.

The moderating role of GDP adds complexity to this relationship. At sufficiently high wealth levels, GDP weakens the effect of R&D on emissions. This alleviating effect becomes stronger as GDP increases until reaching a turning point, at which the impact reverses and R&D expenditures instead decrease emissions.

Our results on the significant nonlinear relationship between R&D, GDP and CO2 emission levels highlights the complexity of addressing environmental challenges within the context of macroeconomics. It suggests that policies promoting both R&D and economic growth simultaneously can foster more sustainable development paths, where economic expansion is accompanied by a more efficient and cleaner use of resources, leading to lower CO2 emissions. This decoupling of economic growth from emissions is likely to be further enhanced by governments incentivising research and development focused on improved energy efficiency and emission reduction.

References

  • Akram, R., Chen, F., Khalid, F., Ye, Z., & Majeed, M. T. (2020). Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: Evidence from developing countries. Journal of cleaner production, 247, 119122.
  • Avenyo, E. K., & Tregenna, F. (2022). Greening manufacturing: Technology intensity and carbon dioxide emissions in developing countries. Applied energy, 324, 119726.
  • Chen, Y., & Lee, C. C. (2020). Does technological innovation reduce CO2 emissions? Cross-country evidence. Journal of Cleaner Production, 263, 121550.
  • Cheng, C., Ren, X., Dong, K., Dong, X., & Wang, Z. (2021). How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression. Journal of Environmental Management, 280, 111818.
  • Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association, 97/458, pp. 611-631.
  • Petrović, P., & Lobanov, M. M. (2020). The impact of R&D expenditures on CO2 emissions: evidence from sixteen OECD countries. Journal of Cleaner Production, 248, 119187.
  • Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R journal, 8(1), 289.
  • Sharif, A., Raza, S. A., Ozturk, I., & Afshan, S. (2019). The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations. Renewable energy, 133, 685-691.
  • Wang, S., Zeng, J., Liu, X., (2019). Examining the multiple impacts of technological progress on CO2 emissions in China: a panel quantile regression approach. Renew. Sustain. Energy Rev. 103, 140–150.

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.

Alcohol-Related Costs and Potential Gains from Prevention Measures in Latvia

Posted on February 26, 2024 | Policy Brief

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Latvia has the highest per capita registered alcohol consumption rate among EU and OECD countries (OECD, 2024). In this brief, we show that the total budgetary (direct) and non-budgetary (indirect) costs associated with alcohol consumption in Latvia in 2021 amounted to 1.3–1.8 percent of the GDP. Non-financial costs from alcohol abuse amounted to a loss of nearly 90 thousand years spent in good health and with a good quality of life. We assess the potential effects of five alcohol misuse prevention measures, all recognized by the World Health Organization (WHO) as effective in reducing harmful alcohol consumption – especially when implemented together. Our analysis focuses on the individual effects of each measure and shows that raising the minimum legal age for alcohol purchases and enforcing restrictions on alcohol advertising and marketing are likely to yield the largest reductions in alcohol-related costs, although these effects will take time to fully materialize.

Introduction

Alcohol consumption is an important risk factor for morbidity and premature death worldwide. It is associated with over 200 diagnoses recorded in the International Statistical Classification of Diseases and Related Health Problems (CDC, 2021), including liver diseases, injuries, malignancies, and diseases of the heart and circulatory system (WHO, 2018). Alcohol consumption at any level is considered unsafe (Burton & Sheron, 2018).

Globally, an average of 3 million people die each year due to alcohol-related harm, accounting for 5.3 percent of all deaths (Shield et al., 2020). In 2019, alcohol consumption was the main risk factor for disease burden in people between 25 and 49 years of age and the second most important risk factor in people aged 10-24 years (GDB, 2019).

Alcohol use is associated not only with health problems but also with social issues, posing risks to people’s safety and well-being. It causes harm not only to the individual but also to family members and society at large (Rehm & Hingson, 2013). Various sectors, including health, justice, home affairs, and social care agencies, are involved in preventing the consequences of alcohol misuse and reducing the harm this causes. This demonstrates the multiple negative impacts of alcohol use on public health and well-being (Flynn & Wells, 2013).

Latvia has the highest per capita registered alcohol consumption rate among the EU and OECD countries (OECD, 2024), and no clear trend of declining levels has been observed in recent years. Moreover, the consumption of spirits, which can potentially cause more harm than other alcoholic beverages (Mäkelä et al., 2011), is steadily increasing. According to WHO data (WHO, 2024), the high per capita consumption of registered absolute alcohol in Latvia, compared to other countries, is largely due to the consumption of spirits. In Latvia, the share of spirits in total consumption is around 40 percent. By comparison, in the Czech Republic and Austria, where total per capita alcohol consumption is similar to Latvian levels, spirits account for only 25 and 16 percent of total consumption, respectively, while the proportions of beer and wine are higher.

This policy brief reports the estimated costs related to alcohol use in Latvia in 2021, based on the study Alcohol Use, its Consequences, and the Economic Benefits of Prevention Measures (Pļuta et al., 2023). It also provides an overview of the expected benefits from implementing preventive measures, such as raising the minimum legal age for buying alcohol and restricting alcohol advertisements.

Costs of Alcohol Use in Latvia

We estimate three types of costs associated with alcohol consumption:

  • Direct costs: These include budgetary costs related to alcohol consumption, such as healthcare, law enforcement and social assistance costs, as well as expenses for public education.
  • Indirect costs: These costs represent unproduced output in the economy and arise from the premature deaths of alcohol users, as well as their reduced employment or lower productivity.
  • Non-financial welfare costs: This type of cost arises from the compromised quality of life of alcohol users, their families, and friends.

We estimate direct costs by utilizing detailed disaggregated data on alcohol-related budget costs in the healthcare sector, law enforcement institutions (including police, courts, and prisons), costs of public education (e.g., educating schoolchildren about the consequences of alcohol consumption), costs of awareness-raising campaigns, and social assistance costs. For cost categories that are only partially attributable to alcohol consumption, we classify only a fraction of these costs as attributable to alcohol use (e.g., liver cirrhosis is attributable to alcohol usage in 69.8 percent of the cases, so only this fraction of the budget costs on compensated medicaments is attributable to alcohol use). To estimate social assistance costs, including public expenditure on social services, sobering-up facilities, social care centres, orphanages, and specialized care facilities for children and out-of-family care, we conduct a survey among social assistance providers.

To estimate non-budgetary costs, we construct a counterfactual scenario where alcohol is not being overly consumed, ensuring higher productivity, a lower rate of unemployment, and lower mortality within the labour force. Finally, non-financial welfare costs are estimated by measuring the reduction in quality of life or QALYs lost (quality-adjusted-life-years) (for details, see the methodology section in Pļuta et al. (2023)).

The total direct and indirect costs of alcohol abuse in 2021 amounted to 1.3–1.8 percent of Latvia’s GDP. In comparison, revenues from the excise tax on alcoholic beverages in 2021 accounted for 0.7 percent of the GDP.

Direct costs, which entail expenses directly covered by the state budget, comprised 0.45 percent of the GDP. Among these costs, healthcare expenses were the largest component, constituting 37.8 percent  of total direct costs and 2.7 percent of general government spending on healthcare. Nearly half of these healthcare costs were attributed to the provision of inpatient hospital treatment for patients diagnosed with alcohol-related conditions. Another significant component of budgetary costs is associated with addressing alcohol abuse and combating illicit trade through law enforcement, accounting for 31.9 percent of total direct costs and 6.5 percent of general government spending on public order and safety.

Alcohol-related indirect costs amount to 0.9-1.3 percent of Latvia’s GDP. Despite not being directly covered by the state budget, they represent unproduced output and thus entail economic losses. The primary components of these indirect costs are linked to decreased output resulting from higher unemployment and reduced economic activity (0.6-0.8 percent of the GDP), as well as decreased output due to premature death among heavy drinkers (0.2-0.4 percent of the GDP). Notably, indirect costs attributed to alcohol misuse by males constitute almost two-thirds of the total indirect costs.

Finally, the non-financial costs from alcohol abuse in 2021 are estimated to reach 88 620 years spent in good health and with a good quality of life. These losses primarily stem from the distress experienced by household members from alcohol users, the decline in the quality of life among alcohol users themselves, and the premature mortality of such individuals.

The Effects of Preventive Measures

We consider five alcohol misuse preventive measures, all of which are included in the list of WHO “best buys” policies that effectively reduce alcohol consumption (WHO, 2017):

  • Reducing the availability of retail alcohol by tightening restrictions on on-site retail hours
  • Raising the minimum legal age for alcohol purchase from 18 to 20 years
  • Increasing excise tax on alcohol
  • Lowering the maximum allowed blood alcohol concentration limit for all drivers from 0.5 to 0.2 per mille (currently 0.2 for new drivers and 0.5 for all other drivers)
  • Restricting alcohol advertising and marketing

Our estimates of the expected reduction in alcohol-related costs resulting from these measures are based on two main components:

  • (1) our own estimates of alcohol-related costs in Latvia, as described above, and
  • (2) external estimates of the impact of the five misuse preventative measures on alcohol consumption derived from existing literature on other countries.

We then apply these external estimates to the calculated alcohol-related costs and Latvian data on alcohol consumption to determine the estimated impact for Latvia (for further details, see the methodology outlined in Pluta et al. (2023)).

Our findings indicate that the most substantial reduction in direct costs attributed to alcohol misuse is anticipated through raising the minimum alcohol purchase age to 20 years (yielding an 11.4-15.8 percent estimated cost reduction). Previous literature has shown that early initiation of alcohol use significantly increases the likelihood of risky drinking, and that risky drinking during adolescence significantly increases the risk of heavy drinking in adulthood (Betts et al., 2018; McCarty, 2004). Hence, raising the minimum legal age for alcohol purchase represents an effective tool to reduce alcohol consumption also among the adult population.

Another highly effective measure to reduce alcohol consumption is imposing restrictions on advertising, which results in a 5.0-8.0 percent estimated reduction of direct costs. There is a large body of literature indicating that alcohol advertising increases alcohol consumption among young people, as well as significantly increases the likelihood of alcohol initiation among adolescents and young adults (Noel, 2019; Jernigan et al., 2017). Also, among the adult population, alcohol consumption decreases with stricter advertising restrictions (see Casswell, 2022; Rossow, 2021).

However, it is important to emphasize that the full impact of both above discussed preventative measures will only manifest in the long run.

The Effect of Illicit Markets

It is often argued that illicit alcohol markets, which provide access to cheaper alternative alcohol than registered commercial markets, can limit the effectiveness of preventive measures on overall alcohol consumption (Rehm et al., 2022).

To explore the interplay between illicit alcohol circulation and alcoholism prevention measures we conduct semi-structured interviews with experts regarding the prevalence of illicit alcohol circulation in Latvia and strategies to mitigate it.

While our main findings emphasize the inherent challenge of precisely quantifying the size of the illicit alcohol market, our analysis suggests that the share of illicit alcohol in total alcohol consumption in Latvia is relatively low. We also conclude that the size of the illicit alcohol market has been diminishing in recent years, and that public interest in engaging with illicit alcohol is declining. Given these findings, the current scope of the illicit market is unlikely to substantially undermine the efficacy of alcohol control measures. This is especially true as the consumers of illicit alcohol represent a specific group minimally affected by legal alcohol control measures in the country.

Conclusion

Our findings underscore the substantial costs associated with the large alcohol consumption in Latvia. In 2021, budgetary (direct) and non-budgetary (indirect) costs reached 1.3–1.8 percent of Latvia’s GDP. Furthermore, non-financial costs from alcohol abuse represent a loss of nearly 90 thousand years spent in good health and with a good quality of life.

Furthermore, non-financial costs from alcohol abuse represent a loss of nearly 90 thousand years spent in good health and with a good quality of life. This stems primarily from the distress experienced by alcohol users’ household members, and the decline in life quality and premature mortality among users themselves.

Latvia stands out as a country with exceptionally high levels of absolute alcohol consumption per capita compared to other countries. Policy makers should implement effective preventive measures against alcohol consumption to maintain the sustainability of a healthy and productive society in Latvia.

Acknowledgement

This brief is based on a study Alcohol Use, its Consequences, and the Economic Benefits of Prevention Measures completed by BICEPS researchers in 2023, commissioned by the Health Ministry of Latvia (Pļuta et al., 2023).

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.

Belarusian Business in Turbulent Times

Posted on October 23, 2023 | Policy Brief

20231023 Belarusian Business Challenges

In the past three years, the Belarusian private sector appears to have been caught between a hammer and an anvil, experiencing domestic repressions and de-liberalization as well as collateral damage from sanctions and a deterioration of the country’s image. This policy brief discusses the challenges that Belarusian businesses have been facing since the onset of the Covid-19 pandemic and argues that the private sector may be the last hope for sovereignty and transformation of the country.

The years that have passed since the onset of the Covid-19 pandemic and the subsequent economic shocks have significantly altered the entrepreneurial landscape in Belarus. This period has seen the emergence of private businesses’ social and political activation during the pandemic, as well as during the 2020 election campaign and post-election protests (Bornukova & Friedrich, 2021). Businesses have also had to adapt to reactionary government policies, cope with sanctions against Belarus and deal with issues related to the Russian invasion of Ukraine. In the face of these challenges, the reactions and responses from small and medium-sized businesses signals that the private sector still has the potential to remain a driving force for socio-economic development in Belarus – despite the current political forces in power.

Private Sector Development; Liberalization and Regulation

The liberalization of the business environment, which lasted more than a decade and ended in 2020, allowed the private sector (enterprises without any state ownership share) to become the most dynamic part of the economy (see Figure 1).

From 2012 through 2020, the share of the private sector in employment increased by 7.7 percentage points. Similarly, the contribution from the private sector to the export of goods and services, as well as to GDP, exceeded the contribution from state-owned commercial enterprises. Moreover, even in the absence of significant privatization and restructuring of state-owned enterprises, the private sector took over the “social” function as an “employer of last resort”, absorbing workers released from the public sector (including from fully and partly state-owned enterprises) (IPM Research Center, 2020).

In addition, the development of the private sector increased the diversification of Belarus’ foreign trade. Private companies in the IT sector, advanced instrument manufacturing, electronics, and other high-value-added industries shifted their focus to developed countries’ markets, which reduced the dependency on Russian resources and markets. This increased Belarus’ economic sovereignty and its resilience to political tensions and other external shocks. The year 2020 however marked the end of the liberalization of entrepreneurial activities, as private businesses and private capital started to be seen as a threat to the political system (Bornukova & Friedrich, 2021).

Figure 1. Contributions from the Belarusian private sector to main economic indicators.

Source: Own elaboration based on Chubrik (2021) and IPM Research Center (2020).

Although there are no uncontestable figures describing business’ attitudes and activities during the political crisis in 2020, several non-academic projects documented that 58 percent of people protesting the fraud elections in 2020 worked within the private business sector (Devby.io, 2020). Dozens of businesses also openly supported the anti-regime strikes (The Village Belarus, 2020). As a consequence, legislation and law enforcement have since been steadily tightened, the tax burden has increased, and the possibility for using simplified taxation and accounting systems by small-scale businesses, in particular for sole proprietors, have been substantially reduced.

Against this backdrop, the government has also suppressed the publication of detailed statistical data including those on entrepreneurial activity. Since 2020, the Belarusian Research and Outreach Center (BEROC)’s quarterly enterprise surveys have become the main source of information and analysis on the business development situation.

In general, BEROC’s surveys demonstrate that, despite a reduced safety cushion and the lack of substantial state support during the pandemic, Belarusian businesses had, by the end of 2021,  adapted to the shocks from the post-election crisis and harsh de-liberalization, by realizing  their ability to cope, and finding creative solutions in the turbulent environment (Marozau, Akulava and Panasevich, 2021). Before Russia’s aggression against Ukraine, Belarusian entrepreneurs’ optimism about overcoming external barriers – i.e., factors that are out of a firm’s control such as macroeconomic instability, etc. – was the highest since 2015. However, increased uncertainty forced Belarusian businesses to focus primarily on maintaining the achieved scale of activity, halting investments (Kastrychnicki Economic Forum (KEF) & BEROC, 2022).

Optimism In Challenging Times

In general, the institutional environment for doing business in Belarus has deteriorated in recent years, both due to actions such as changes in tax legislation, price regulation and pressure on disloyal businesses, and due to negligence from the state, such as lack of significant support measures for private business, an outflow of businesses due to sanctions and an increasingly negative image of the country (KEF & BEROC, 2022). The Business Confidence Index (BCI, ranging from 0 – “extremely negative” to 100 – “extremely positive”), developed by BEROC and the Kastrychnicki Economic Forum based on OECD methodology, documented that at the end of 2020, the confidence level of business representatives regarding future developments was in the negative zone – arguably due to the political unrest and the Covid-19 pandemic. As firms accepted a new normality and adjusted their businesses, the BCI steadily grew before comfortably settling in the neutral zone at the end of 2021 (see Figure 2).

In March-April 2022, however, macroeconomic instability, disruption of supply chains, and shortages of raw materials and/or components following the Russian war on Ukraine became serious external barriers for Belarusian businesses. This lowered the BCI and businesses’ perception of their economic situation.

Quite surprisingly, the risks of doing business in Belarus in the second half of 2022, until early 2023, were estimated to be lower than in 2021 (see Figure 3). This may be explained by the fact that (for companies remaining in Belarus) many of the potential risks (inflation, exchange rate instability, sanctions, counter-sanctions, disruption of supply chains, tightening of price regulation, etc.) had already realized (BEROC, 2023).

Figure 2. Business Confidence Index and GDP growth rate, October 2020-March 2023.

Source: KEF & BEROC (2023) and the National Statistical Committee of the Republic of Belarus.

Figure 3. Risk perception by Belarusian Businesses.

Source: BEROC (2023).
Note: Risks were scored on a five-point scale, 1-5, where 1 denotes “very low” and 5 “very high”. Dotted lines denote the 95 percent confidence intervals.

The New Reality

The reaction from most Belarusian businesses to both pandemic- and war-related challenges has manifested in their search for new business models, an introduction of new products/services, and the entry into new export markets. Despite a bundle of powerful shocks to the economy stemming from the Russian war on Ukraine and related sanctions, some factors have dampened the anticipated drop in the economy: in particular, the increase in Russian support, export re-orientation to Russia and developing markets, alongside monetary stimuli, and interference with the activity of state-owned enterprises as well as artificial price controls (Kruk & Lvovskiy, 2022). As a result, the standard of living has remained at pre-war levels: in January-April 2023, real household disposable income and real salary grew by 1.6 percent and 3.8 percent respectively. With sanctions on Belarus being comparatively softer than those on Russian businesses, Belarusian businesses may have gained a comparative advantage and additional opportunities in both the domestic and Russian markets (BEROC, 2022). This caused optimism among entrepreneurs and in March 2023 – for the first time in the considered period – the composite BCI turned out to substantially exceed the neutral zone (see Figure 2). These positive spillovers are however likely to be bound in time – they will end both if the state of the Russian economy worsens (as this would reduce Russian support and decrease export revenues for Belarusian firms), and in the unlikely scenario that Russia’s current isolation is reduced. Whether Belarusian businesses will withstand the current protracted crisis depends on the ability of state authorities (current or new) to restore a constructive dialogue with the business community, return to the rule of law and create a business environment conducive to entrepreneurship.

According to business, the key factor needed to expand business activity is a reduction of external barriers (such as disruptions to supply chains, shortages of raw materials and/or components), rather than government support (e.g., financial, informational, etc.) (KEF & BEROC, 2022). Thus, “We do not need state support, but need the state not to worsen legal conditions for doing business” has become a motto of Belarusian entrepreneurs. Even in the context of war and political instability in the region, it allows looking at the prospects of the private sector in Belarus with some positive expectations.

At the same time, factors such as political repressions, sanctions against Belarus, problems with logistics, and the refusal of business partners to work with Belarusian companies due to the Russian aggression towards Ukraine have forced many Belarusian businesses, especially in high-tech sectors, to relocate. While the scale and direction of Belarusian business emigration is still difficult to assess (Krasko & Daneyko, 2022), these processes devastate entrepreneurship capital in Belarus and jeopardize the prospect of entire sectors such as the IT sector. In addition, the popular opinion about the lack of business opportunities implies that, unless conditions improve in terms of state policy and public confidence in the future, the socio-economic effects (employment, value added, tax revenue, innovations) from entrepreneurial activity in Belarus will diminish (GEM-Belarus 2021/2022). With operations severely affected by external barriers and restrictive legislation, halted investments and limited, if any, commercial contacts with Western countries and individual businesses, Belarusian private enterprises can hardly be seen as a source of stability for the current regime.

Conclusion

To promote an increased role of the private sector in the Belarusian economy, and to ensure high-quality and sustainable growth of the same, two prerequisites are critically necessary.

Firstly, a resolution of the political crisis and a restoration of authorities’ and state institutions’ legitimacy will significantly increase the populations’ confidence in state policy on business and economics. The principle of rule of law must be recognized and public and private actors must be treated equally in all spheres. It is also necessary to ensure the stability of tax legislation and economic law and the mitigation of excessive state control of business activities. All the above would lower external barriers and create stimuli for long-term business investments that, in turn, would facilitate economic transformation.

Although the sanctions’ packages imposed on Belarus by most developed countries due to domestic repressions, and complicity in the aggression against Ukraine, were directed towards the public sector, the private business suffered substantial macroeconomic and reputational consequences in their wake. The refusal of many foreign partners (suppliers, customers, banks, transport companies etc.) to work with Belarusian businesses – regardless of their affiliation with the state and attitude towards Lukashenko’s regime as well as towards the war on Ukraine – also substantially undermine businesses’ potential and Western soft power in Belarus. Such refusal is often driven by the argument that, by paying taxes, private businesses in Belarus support the current regime, when they should instead undermine the regime by halting operations (and thus tax revenues). At the same time, with the complete liquidation of civil society organizations and the termination of international projects and initiatives, the Belarusian private business may serve as the last resort in the hope of achieving independent, decentralized, and autonomous decision-making – all cornerstones of modern democracy (Audretsch & Moog, 2022).

From this perspective, the preservation of the private sector in Belarus may be of decisive importance in the future political processes, necessary to take into account by policymakers and business elites alike in developed countries.

In addition, relocated Belarusian businesses can play an important role in transforming the country by developing social ties between entrepreneurs and civil society, by providing support when solving problems related to doing business outside of Belarus and by investing in the Belarusian economy in the future. In this regard, establishing non-partisan Belarusian business associations abroad creates preconditions for consolidation of the most active part of the Belarusian community and its involvement in the envisaged economic transformation of the country.

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. 

What Drives Belarus to Be One of the Most Optimistic Nations in Europe?

Posted on March 6, 2023 | Policy Brief

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War in Ukraine, imposed sanctions on Belarus and the worst yearly GDP drop since the 1990s. Despite these challenges, Belarusian households were the third most optimistic in Europe in late 2022, following Lithuania and Montenegro. The Belarusian Consumer Confidence Index, calculated on the basis of four household surveys conducted in Belarus by BEROC’s Belarus Monitoring Project in 2021 and 2022, shows surprising resilience among Belarusians – especially in Q3 and Q4, 2022.  This brief shortly describes the components of the index and their evolution and discusses what factors might have been driving this high index. The brief argues the found optimism among Belarusians could have been driven by a state-owned media influence and by the Belarusian economy performing better than expected.

Optimism Without Grounds?

In 2022, Belarus experienced a 4.7 percent yearly GDP drop, the worst since the 1990s. The main reasons behind the decline is the Russian war on Ukraine and Belarus’ involvement in it, and, consequently, the severe sanctions imposed on Belarus and its main trade and economic partner: Russia. A surge of exports to Russia to counter the sanctions helped prevent the severity of the drop, although it still remains large. Forecasts for 2023 are also not encouraging. The World Bank expects the Belarusian economy to shrink by 2.3 percent. The European Bank for Reconstruction and Development’s forecast is -1 percent, the International Monetary Fund (IMF)’s is +0.2 percent, and the Eurasian Development Bank’s +0.3 percent, whereas the announced official target is +3.8 percent. In total, the GDP decrease could be as large as -6.9 percent in the two coming years, following the World Bank’s worst prognosis. The question is; Is this a lot?

The last GDP decline occurred in 2020 and amounted to a moderate -0.7 percent, despite the apex of Covid-19 related shutdowns, the decrease in the world economy, and the political crisis following the rigged elections in August. The most recent severe GDP drop happened between 2015 and 2016 with a decline of 3.8 percent in 2015 and 2.5 percent in 2016.

Figure 1. A comparison of GDP changes and the CCI values in Belarus in 2021 and 2022.

Note: Based on Eurostat methodology. Source: Belstat, BEROC.

Surprisingly, the lower the GDP, the higher the consumer confidence, as measured by the Consumer Confidence Index (CCI). For example, the CCI was -18.7 percent in Q4 2021, while the GDP increased by 2.3 percent in 2021. On the contrary, the CCI in Q4 2022 was -15.0 percent, while the GDP dropped by massive -4.7 percent (see Figure 1).

The experience from numerous financial crises in the 2010s may play an important role here by moving the expectation baseline and conclusively undermining confidence in the country’s economic institutions. However, even if this is the case, it would not explain the dynamics of consumer confidence in Belarus in relation to the country’s economic performance. In this brief we dig deeper into the determinants of this seemingly ungrounded consumer optimism.

The Consumer Confidence Index

The Consumer Confidence Index (CCI) used for this brief is based on four household surveys conducted in Belarus by the Belarusian Economic and Outreach Center (BEROC)’s Belarus Monitoring Project. The online surveys were conducted in December 2021, and in April, August and November in 2022. The surveys are representative for the urban population aged 18-64 (approximately 5 million people). They have also been weighted by region, sex and age.

The index is designed to measure consumer confidence from -100 percent to + 100 percent (0 being neutral). Consumer confidence is defined as the degree of optimism regarding the state of the economy which consumers express through their saving and spending patterns.

A few approaches for calculating the index can be used. One of them is the Eurostat methodology, which includes answers to four questions about households previous and expected financial position, the expected economic situation in the country, as well as the propensity to buy durable goods. Another approach is the Rosstat methodology, which, in addition to the Eurostat approach, includes one extra question on the previous economic situation in the country. We considered both methodologies to allow for a comparison of Belarus to countries in Europe as well as to Russia.

Belarus Compared to Russia

The CCI value, applying the Rosstat methodology, was -19.4 percent in Belarus in November 2022 (a 3.6 percentage point growth as compared to August 2022), while the index value in Russia was -22.7 percent (a 0.3 percentage point growth).

It is worth mentioning that there was a sharp drop in Q2 2022 in both countries. However, the index values recovered in Q3 2022 to Q4 2021 values, i.e., to the index values prior to the introduction of large-scale economic sanctions and prior to the war.

The pattern is somewhat similar to that during Covid-19-related restrictions, displaying a sharp drop and then a strong recovery. The magnitude of the drop was however much higher in 2020: 20.3 percent in 2020 compared to 10.3 percent in 2022 for Russia. No data is available for Belarus prior to Q4 2021 but the trajectory was likely similar. Apparently, households in neither country appear be desperate (see Graph 1).

Graph 1. The CCI in Belarus and Russia.

Note: Q1 2022 data not available for Belarus. Source: BEROC, Rosstat.

Belarus Compared to Europe

The Belarusian CCI, when excluding the component of the past state of the economy (i.e. applying the Eurostat methodology), was -15.0 percent in November 2022. This was 3.4 percentage points higher than the value in December 2021 and the third highest value in Europe, following Lithuania (-9.2 percent) and Montenegro (-8.6 percent). Moreover, the index was the highest observed for the entire period of observations by BEROC (from December 2021), as depicted in Graph 2.

Graph 2. The CCI in Belarus and the EU.

Source: BEROC, Eurostat.

The index values of the European Union and the Eurozone have not changed significantly from Q2 2022 and currently stand at -26.3 and -24.9 percent, respectively. Naturally, some countries have faced slight reductions, while others have seen slight increases, for instance, the indices for Italy, Croatia and Cyprus had all increased by more than 4 percentage points in Q4, 2022.

As evident from Graph 2, Belarus has since Q4 2021, moved from a below average position to become a leader in optimism on the European continent.

The Past and the Future

Throughout all four surveys, evaluations of the current state of the country and of personal wellbeing contrasted the projections for the future (see Graph 3). The projections for the future are much more positive, which is evident if we compare question 4 and 2 to question 3 and 1. At the same time, the share of negative answers is higher than the share of positive answers for all questions, and the term “optimism” should therefore be taken as the lack of strong negative views on the past and future.

A higher share of “difficult to say or do not know” answers is unsurprisingly found for questions regarding the future.

Graph 3. The composition of the CCI in Belarus for Q4 2022.

Note: All answers to the questions are distributed along a Likert scale from “will improve (has improved)” or “very good” to “will decline (has declined)” or “very bad”. For question 1 (Q1) and question 2 (Q2), the answer options range from “has improved” and “has declined”; and for question 5 (Q5), the answer options range from ”very good” to “very bad”. Source: BEROC.

The largest negative contribution to the index was the question on the current assessment of the country’s economic situation in relation to the previous year (question 1). The share of negative answers was 72 percent in December 2021, and it decreased only to 63 percent in November 2022, even though the economic performance prior to those periods was a 2.3 percent GDP growth and 4.7 percent GDP decline, respectively. Apparently, the worse the economy performed, the better was the perception of the past.

This is however not the case regarding the state of the household’s financial position. The share of negative answers was 48 and 47 percent, and the share of positive answers was 13 and 14 percent in December 2021 and November 2022, respectively.

The answers concerning the future standing of the economy and one’s personal financial position follow the same logic, with large disparities between the evaluation of the country’s economy – which one is negative about – and personal finances – where respondents are more optimistic.

What could influence the changes? We hypothesize that there are at least four possible explanations for the improvement in the CCI from Q1 to Q4, 2022:

a) a stabilization of the situation on the foreign exchange market
b) a slowing GDP decline, reaching a “local minimum”
c) an influence from Belarusian and Russian state-owned mass media outlets
d) failed negative expectations in previous periods

As discussed in a previous FREE Network Policy Brief by Luzgina (2022), the Belarusian currency market has stabilized since April, 2022. The Belarusian exchange rate is somewhat of a “Holy Grail” and a crucial factor for Belarusians after numerous financial crises in 2010s, so its stabilization could act as a positive signal for households. Indeed, when asking respondents about the factors influencing their income, the share of those who attributed this to the exchange rate had in August 2022 decreased by 25 percentage points, as compared to April the same year (from 45 to 20 percent, respectively).

The GDP decline slowed in the second half of 2022, from -4.9 percent in August to -4,7 percent in November. An additional positive development for Belarusians was that the inflation declined in November.

Media consumption is another essential factor in understanding consumer confidence. State-owned and independent media consumers showed significant differences in their assessments of the economy. Only 22 percent of state-owned media consumers rated the economy as “bad” or “very bad” compared to 68 percent of independent media consumers.

In April 2022, the World Bank estimated a possible Belarusian GDP change at -6.5 percent, the IMF
-6.4 percent and S&P -15 percent. The CCI in April was also at the lowest throughout BEROC’s observations at -23.0 percent. Despite these extremely negative forecasts for Belarus’ GDP, the actual outcomes were less catastrophic than expected. This might have improved respondents’ assessment of the future economic situation.

Conclusion

Data from the online household surveys show that imposed sanctions, the Russian war on Ukraine, and a declining economic growth in 2022 have not yet significantly affected the sentiments of Belarusians on a large scale. Rather, Belarusians’ expectations have improved despite serious current and future challenges to the Belarusian economy. In fact, Belarus is among the most optimistic nations in Europe, according to the surveys.

This is arguably due to a financial stabilization and an economic performance above expected, as well as exposure to state-owned media.

With this in mind, we may see an increase in households’ consumption in the following months, which will contribute to a slowdown in the GDP decline or even a slight economic recovery in 2023 – pending no new shocks occur.

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.

How to Sustain Support for Ukraine and Overcome Financial and Political Challenges | SITE Development Day 2022

Posted on January 9, 2023 | Policy Brief

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The Russian war on Ukraine has turmoiled Europe into its first war in decades and while the effects of the war are harshly felt in Ukraine with lives lost and damages amounting, Europe and the rest of the world are also being severely affected. This policy brief shortly summarizes the presentations and discussions at the SITE Development Day Conference, held on December 6, 2022. The main focus of the conference was how to maintain and organize support for Ukraine in the short and long run, with the current situation in Belarus and the region and the ongoing energy crisis in Europe, also being addressed. 

War in Ukraine, Oppression in Belarus

Starting off the conference, Sviatlana Tsikhanouskaya, Leader of the Belarusian Democratic Forces, delivered a powerful speech on the necessity of understanding the role of Belarus in the ongoing war in Ukraine. Tsikhanouskaya argued that Putin’s war on Ukraine was partly a result of the failed Belarusian revolution of 2020. The following oppression, torture, and mass arrestations of Belarusians is a consequence of Lukashenka’s and Putin’s fear of a free Belarus, a Belarus that is no longer in the hands of Putin – who sees not only Belarus but also Ukraine as colonies in his Russian empire. Amidst the fight for Ukraine, we must also fight for a free Belarus, Tsikhanouskaya added. Not only Belarusians fighting alongside Ukrainians against Russia in Ukraine, but also other parts of the Belarusian opposition need support from the free and democratic world and the EU. The massive crackdowns on opponents of the Belarusian regime today and the war on Ukraine are not only acts of violence, but they are also acts against democracy and freedom. The world must therefore continue to give support to those fighting in both Belarus and Ukraine. Ukraine will never be free unless Belarus is free, Tsikhanouskaya concluded.

Johan Forssell, Minister of Foreign Trade and International Development Cooperation continued Tsikhanouskaya’s words on how the Russian attack must be seen and treated as a war on democracy and the free world. Belarus, Moldova and especially Ukraine will receive further support from Sweden, Forssell continued, adding that the Swedish support to Ukraine has more than doubled since the invasion in February 2022. Support must however not be given only in economic terms and consequently Sweden fully supports Ukraine on its path to EU-membership, which will be especially emphasized during Sweden’s upcoming EU-presidency.  Support for the rule of law, democracy and freedom will continue to be essential and, in the forthcoming reconstruction of Ukraine, these aspects – alongside long term sustainable and green solutions – must be integrated, Forssell continued. Forssell also mentioned the importance of reducing the global spillover effects from the war. In particular, Forssell mentioned how the war has struck countries on the African continent, already hit with drought, especially hard with increased food prices and increased inflation, displaying the vital role Ukrainian grain exports play.

Andrij Plachotnjuk, Ambassador Extraordinary and Plenipotentiary of Ukraine to the Kingdom of Sweden, further talked about the need for rebuilding a better Ukraine, emphasizing the importance of involvement from Kiyv School of Economics (KSE) and other intellectuals and businesses in this process. Plachotnjuk also pinpointed what many others would come to repeat during the day; that resources, time and efforts devoted to supporting Ukraine must be maintained and persevered in the longer perspective.

Economic Impacts From the War and How the EU and Sweden Can Provide Support

During the first half of the conference, the Ukrainian economy and how it can be supported by the European Union was also discussed. On link from Kiyv, Tymofiy Mylovanov, President of the Kyiv School of Economics, shared the experiences of the University during wartime and presented the work KSE has undertaken so far – and how this contributes to an understanding of the damages and associated costs. Since the invasion, KSE has supported the government in three key areas; 1) Monitoring the Russian economy, 2) Analyzing what sanctions are relevant and effective, and 3) Estimating the cost of damages from the war. For the latter, KSE is collaborating with the World Bank using established methods of damage assessment including crowd sourced information on damages complemented with images taken by satellites and drones. According to Mylovanov, the damage assessment is crucial in order to counter Russia’s claims of a small conflict and to remind the international community of the high price Ukraine is paying to hold off Russia.

The economic impact from the war was further accentuated during the presentation by Yulia Markuts, Head of the Centre of Public Finance and Governance Analysis at the Kyiv School of Economics. Markuts explained how the Ukrainian national budget as of today is a “wartime budget”. Since February 2022, the budget has been reoriented with defense and security spending having increased 9 times compared to 2021, whereas only the most pressing social expenditures have been implemented. This in a situation where the Ukrainian GDP has simultaneously decreased by 30 percent. Although there has been a substantial inflow of foreign aid, in the form of grants and loans, the Ukrainian budget deficit for 2023 is estimated to 21 percent. Part of the uncertainty surrounding the Ukrainian budget stems from the fact that the inflow from the donor community is irregular, prompting the government to cover budget deficits through the National Bank which fuels inflation and undermines the exchange rate. Apart from the large budget posts concerning military spending, major infrastructural damages are putting further pressure on the Ukrainian budget in the year to come, Markuts continued. As of November 2022, the damages caused by Russia to infrastructure in Ukraine amounted to 135,9 billion US Dollars, with the largest damages having occurred in the Kiyv and Donetsk regions, as depicted in Figure 1.

Figure 1. Ukrainian regions most affected by war damages, as of November 2022.

Source: Kiyv School of Economics

The infrastructural damages constitute a large part of the estimated needed recovery support for Ukraine, together with losses to the state and businesses amounting to over one trillion US Dollars. However, such estimates do not cover the suffering the Ukrainian people have encountered from the war.

The large need for steady support was discussed by Fredrik Löjdquist, Centre Director of the Stockholm Centre for Eastern European Studies (SCEEUS), who argued the money needs to be seen as an investment rather than a cost, and that we at all times need to keep in mind what the consequences would be if the support for Ukraine were to fizzle out. Löjdquist, together with Cecilia Thorfinn, Team leader of the Communications Unit at the Representation of the European Commission in Sweden, also emphasized how the reconstruction should be tailored to fit the standards within the European Union, given Ukraine’s candidacy status. Thorfinn further stressed that the reconstruction must be a collective effort from the international community, although led by Ukraine. The EU is today to a large extent providing their financial support to Ukraine through the European Investment Bank (EIB). Jean-Erik de Zagon, Head of the Representation to Ukraine at the EIB, briefly presented their efforts thus far in Ukraine, efforts that have mainly been aimed at rebuilding key infrastructure. Since the war, the EIB has deployed an emergency package of 668 million Euro and 1,59 billion for the infrastructure financing gap. While all member states need to come together to ensure continued support for Ukraine, the EIB is ready to continue playing a key role in the rebuilding of Ukraine and to provide technical assistance in the upcoming reconstruction, de Zagon said. This can be especially fruitful as the EIB already has ample knowledge on how to carry out projects in Ukraine.

During a panel discussion on how Swedish support has, can and should continuously be deployed, Jan Ruth, Deputy Head of the Unit for Europe and Latin America at Sida, explained Sida’s engagement in Ukraine and the agency’s ambition to implement a solid waste management project. The project, in line with the need for a green and environmentally friendly rebuild, is today especially urgent given the massive destructions to Ukrainian buildings which has generated large amounts of construction waste. Karin Kronhöffer, Director of Strategy and Communication at Swedfund, also accentuated the need for sustainability in the rebuild. Swedfund invests within the three sectors of energy and climate, financial inclusion, and sustainable enterprises, and hash previously invested within the energy sector in Ukraine. Swedfund is also currently engaged in a pre-feasibility study in Ukraine which would allow for a national emergency response mechanism. Representing the business side, Andreas Flodström, CEO and founder of Beetroot, shared some experiences from founding and operating a tech company in Ukraine for the last 10 years. According to Flodström there will, apart from a huge need in investments in infrastructure, also be a large need for technical skills in the rebuild. Keeping this in mind, bootcamp style educations are a necessity as they provide Ukrainians with essential skills to rebuild their country.

A recurring theme in both panel discussions was how the reconstruction requires both public and private foreign investments. Early on, as the war continues, public investments will play the dominant part, but when the situation becomes more stable, initiatives to encourage private investments will be important. The potential of using public resources to facilitate private investments through credit guarantees and other risk mitigation strategies was brought up both at the European and the Swedish level, something which has also been emphasized by the new Swedish government.

Impacts From the War Outside of Ukraine – Energy Crisis and Other Consequences in the Region

The conference also covered the effects of the war outside of Ukraine, initially keying in on the consequences from the war on energy supply and prices in Europe. Chloé Le Coq, Professor of Economics, University Paris-Pantheon-Assas (CRED) & SITE, gave a presentation of the current situation and the short- and long-term implications. Le Coq explained that while the energy market is in fact functioning – displaying price increases in times of scarcity – the high prices might lead to some consumers being unable to pay while some energy producers are making unprecedented profits. The EU has successfully undertaken measures such as filling its gas storage to about 95 percent (goal of 80 percent), reducing electricity usage in its member countries, and by capping market revenues and introducing a windfall tax. While the EU is thus appearing to fare well in the short run, the reality is that EU has increased its coal dependency and paid eight times more in 2022 to fill its gas storage (primarily due to the imports of more costly Liquified Natural Gas, LNG). In the long run, these trends are concerning given the negative environmental externalities from coal usage and the market uncertainty when it comes to the accessibility and pricing of LNG. Uncertainties and new regulation also hinder investments signals into new low-carbon technologies, Le Coq concluded. Bringing an industrial perspective to the topic, Pär Hermerèn, Senior advisor at Jernkontoret, highlighted how the energy crisis is amplified by the increased electricity demand due to the green transition. Given the double or triple upcoming demand for electricity, Hermerèn, referred back to the investment signals, saying Sweden might run the risk of losing market shares or even seeing investment opportunities leave Sweden. This aspect was also highlighted by Lars Andersson, Senior advisor at Swedenergy, who, like Hermerèn, also saw the Swedish government’s shift towards nuclear energy solutions. Andersson stated the short-term solution, from a Swedish perspective, to be investments into wind power, urging policy makers to be clear on their intentions in the wind power market.

Other major impacts from the war relate to migration, a deteriorating Belarusian economy and security concerns in Georgia. Regarding the latter, Yaroslava Babych, Lead economist at ISET Policy Institute, Georgia, shared the major developments in Georgia post the invasion. While the Georgian economic growth is very strong at 12 percent, it is mainly driven by the influx of Russian money following the migration of about 80 000 Russians to Georgia. This has led to a surge in living costs and an appreciation of the local currency (the Lari) of 12,6 percent which may negatively affect Georgian exports. Additionally, it may trigger tensions given the recent history between the countries and the generally negative attitudes towards Russians in Georgia. Michal Myck, Director at CenEa, Poland, also presented migration as a key challenge. While the in- and outflow of Ukrainian refugees to Poland is today balanced, the majority of those seeking refuge in Poland are women and children and typically not included in the workforce. To ensure successful integration and to avoid massive human capital losses for Ukraine, Myck argued education is key, pointing to the lower school enrollment rates among refugee children living closer to the Ukrainian border. Apart from the challenges posed by the large influx of Ukrainian in the last year, the Polish economy is also hit by high energy prices, fuel shortages and increasing inflation. Lev Lvovskiy, Research fellow at BEROC, Belarus, painted a similar but grimmer picture of the current economic situation in Belarus. Following the invasion, all trade with Ukraine has been cut off, while trade with Russia has increased. Belarus is facing sanctions not only following the war, but also from 2020, and the country is in recession with GDP levels dropping every month since the invasion. Given the political and economic situation, the IT sector has shrunk, companies oriented towards the EU has left the country and real salaries have decreased by 5 percent. At the same time, the policy response is to introduce price controls and press banknotes.

Consequences of War: An Academic Perspective

The later part of the afternoon was kicked off by a brief overview of the FREE Network’s research initiatives on the links between war and certain development indicators. Pamela Campa, Associate Professor at SITE, presented current knowledge on the connection between war and gender, with a focus on gender-based violence. Sexual violence is highly prevalent in armed conflict and has been reported from both sides in the Donetsk and Luhansk regions since 2014 and during the ongoing war, with nearly only Russian soldiers as perpetrators. Apart from the direct threats of sexual violence during ongoing conflict and fleeing women and children risking falling victims to trafficking, intimate partner violence (IPV) has been found to increase post conflict, following increased levels of trauma and post-traumatic stress disorder (PTSD). While Ukrainian policy reforms have so far strengthened the response to domestic violence there is still a need for more effective criminalization of domestic violence, as the current limit for prosecution is 6 months from the date crime is committed. An effective transitional justice system and expertise on how to support victims of sexual violence in conflict, alongside economic safety measures undertaken to support women and children fleeing, are key policy concepts Campa argued. Coming back to the broader topic of gender and war, Campa highlighted the need for involvement of women in peace talks and negotiations, something research suggests matter for both equality, representativeness, and efficiency.

Providing insights into the relationship between the environment and war, Julius Andersson, Assistant Professor at SITE, initially summarized how climate change may cause conflict along four channels: political instability and crime rates increasing as a consequence of higher temperatures, scarcity of natural resources and environmental migration. Conflict might however also cause environmental degradation in the form of loss of biodiversity, pollution and making land uninhabitable. As for the negative impact from the war in Ukraine, Andersson highlighted how fires from the war has caused deforestation affecting the ecosystems, that rivers in conflict struck areas in Ukraine and the Sea of Azov are being polluted from wrecked industries (including the Azovstal steelworks) and lastly that there is a real threat of radiation given the four major nuclear plants in Ukraine being targeted by Russian forces. Coming back to a topic mentioned earlier during the day, Andersson also emphasized potential conflict spillovers into other parts of the world due to the war’s impact on food and fertilizer prices.

Concluding the session, Jonathan Lehne, Assistant Professor at SITE, reviewed how war and democracy is tied to one another, highlighting that while studies have found that democracies per se are not necessarily less conflict prone, it is still the case that democratic countries almost never fight each other. As for the microlevel takeaways from previous research, it appears as if individuals and communities having experienced violence and casualties actually reap a democratic dividend in some respects, such as greater voting participation. On the other hand, while areas with a large refugee influx also experience an increased voter turnout, voting for right-wing parties also increase with politicians exploiting this in their communication.

Book Launch – Reconstruction of Ukraine: Principles and Policies

The Development Day was also guested by Ilona Sologoub, Scientific Editor at VoxUkraine, Tatyana Deryugina, Associate Professor of Finance at the University of Illinois at Urbana-Champaign, and Torbjörn Becker, Director of SITE, who presented their newly released book “Reconstruction of Ukraine: Principles and policies”. Sologoub started off by giving an overview of the mainly economic topics covered in the book and pointing out that the main purpose of the book is to inform policy makers about the present situation and to suggest needed reforms and investments. Becker outlined the four key principles recommended to stem corruption during reconstruction; 1) Remove opportunities for corruption and rent extraction, 2) Focus on transparency and monitoring of the whole reconstruction effort, 3) Make information and education an integral part of the anti-corruption effort, and 4) Set up legal institutions that are trusted when corruption does occur. Deryugina focused on the energy sector and related back to what had previously been discussed throughout the day, the need to “build-back-better”. Deryugina mentioned that Ukraine, previously heavily reliant on coal and gas imports from Russia, now have the opportunity to steer away from low energy efficiency and bottleneck issues, towards becoming a European natural gas hub. The book is available for free here. There will also be a book launch on the 11th of January 2023 at Handelshögskolan.

Concluding Remarks

Via link from Kiyv, Nataliia Shapoval, Head of KSE Institute and Vice President for Policy Research at Kyiv School of Economics closed the conference by emphasizing the urgency of continued education of Ukrainians in Ukraine and elsewhere to avoid loss of Ukrainian human capital. Shapoval also stressed how universities can act as thinktanks, support policy makers in Ukraine and Europe to come up with effective sanctions against Russia and provide a deeper understanding of the current situation – a situation which will linger and in which Ukraine needs continued full support.

This year’s SITE Development Day conference gave an opportunity to discuss the need for continued support for Ukraine and the implications from the war in a global, European, and Swedish perspective. Representatives from the political, public, private and academic sectors contributed with their insights into the challenges and possibilities at hand, providing greater understanding of how the support can be sustained, with the goal of a soon end to the war and a successful rebuild of Ukraine.

List of Participants in Order of Appearance

  • Anders Olofsgård, Deputy Director at SITE
  • Sviatlana Tsikhanouskaya, Leader of the Belarusian Democratic Forces
  • Johan Forssell, Minister of Foreign Trade and International Development Cooperation
  • Andrij Plachotnjuk, Ambassador Extraordinary and Plenipotentiary of Ukraine to the Kingdom of Sweden
  • Tymofiy Mylovanov, President of the Kyiv School of Economics (on link from Kyiv)
  • Yuliya Markuts, Head of the Centre of Public Finance and Governance Analysis, Kyiv School of Economics
  • Jean-Erik de Zagon, Head of the Representation to Ukraine at the European Investment Bank
  • Cecilia Thorfinn, Team leader of the Communications Unit at the Representation of the European Commission in Sweden
  • Fredrik Löjdquist, Centre Director of the Stockholm Centre for Eastern European Studies (SCEEUS)
  • Jan Ruth, Deputy Head of the Unit for Europe and Latin America at Sida
  • Karin Kronhöffer, Director of Strategy and Communication at Swedfund
  • Andreas Flodström, CEO and founder of Beetroot
  • Chloé Le Coq, Professor of Economics, University Paris-Pantheon-Assas (CRED) & SITE
  • Lars Andersson, Senior advisor at Swedenergy
  • Pär Hermerèn, Senior advisor at Jernkontoret
  • Ilona Sologoub, VoxUkraine scientific editor (on link)
  • Tatyana Deryugina, Associate Professor of Finance at the University of Illinois at Urbana-Champaign (on link)
  • Torbjörn Becker, Director at SITE
  • Michal Myck, Director at CenEa, Poland
  • Yaroslava Babych, Lead economist at ISET Policy Institute, Georgia
  • Lev Lvovskiy, Research fellow at BEROC, Belarus
  • Pamela Campa, Associate Professor at SITE
  • Julius Andersson, Assistant Professor at SITE
  • Jonathan Lehne, Assistant Professor at SITE
  • Nataliia Shapoval, Head of KSE Institute and Vice President for Policy Research at Kyiv School of Economics (on link)

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 Effects of Sanctions

Posted on May 10, 2022 | Policy Brief

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Sanctions imposed on Russia after its invasion of Ukraine are argued to be the strongest and farthest-reaching imposed on a major power after WWII, more numerous and more comprehensive than all other measures currently in force against all other sanctioned countries. A question often asked, which is hard to answer, is whether sanctions are effective. In the present case, the effect most associate with success would be a swift end of the hostilities, perhaps accompanied by a regime change in Russia. But even when it seems these prizes are out of reach, sanctions certainly have effects, all too often glossed over by the debate but nonetheless of significance.

Why Are Sanctions Seen as Ineffective?

Sanctions are restrictions imposed on a country by one or more other countries with the intent to pressure in effect some desirable outcome, or conversely to condemn and punish some undesired action already taken. When evaluating sanctions, therefore, the focus is naturally on whether they succeed to discourage this particular course of action, or to remove the decision-makers responsible for it. And on this account, sanctions are overwhelmingly seen as unsuccessful. However, a few complications cloud this conclusion.

First of all, sanctions that are implemented already failed at the threat stage. If the threat of a well-specified and credible retribution did not deter the receiving part from pursuing the sanctioned course of action, it is because they reckoned that they can afford to ignore it. So, unless this punishment goes beyond what was expected, in scope or in time, its implementation will also fall flat. This implies that any effort to evaluate sanctions retrospectively suffers from the negative selection problem, when almost exclusively cases of failure, intended in this particular sense, are observed.

Second, sanctions are a rather blunt instrument, that often cannot be targeted with the precision one would desire. Even though sanctions have over time become “smarter”, in the sense that stronger efforts are made to target the regime, or elites that may have the clout to actually affect the regime (think the oligarchs in Russia), they often fail to reach or affect in a meaningful way those individuals that are the real objective, for various reasons. Instead, they can cause significant “collateral damage”, to groups of a population that often are quite far removed from any real decisional power, including those in the sending countries, and even third parties who are extraneous to the situation. The damage inflicted to those parties can only in very special circumstances be part of a causal link eventually impacting the intended outcome. For instance, citizens struggling in an impoverished economy could be led to a riot, or in some other way put pressure on their government – but this implies that the country is sufficiently free for riots to take place or for voters’ opinions to be taken into consideration.

To this, it should be added that, once a course of action has been taken, it might be not obvious how to change or undo it, notwithstanding the signaled displeasure from the sanctioning parties. Sanctions are therefore rarely working in isolation. When positive outcomes are achieved, it is often the case that diplomatic channels were kept open and clear incentives offered for a way out. But then it might be unclear whether it was the sanctions or something else that led to the success.

Other Effects of Sanctions

The pitfalls highlighted above, which make it tricky to answer whether sanctions are effective at reaching their aim, also apply when studying other effects that sanctions might have. There is of course a range of outcomes that might be affected: in this literature we find studies looking at inequality (Afesorgbor et al., 2016), exchange rates (Dreger et al., 2016), trade (Afesorgbor, 2019; Crozet et al. 2020), the informal sector (Early et al, 2019), military spending (Farzanegan, 2019), women’s rights (Drury, 2014), and many more. But as it often happens the most studied outcome is GDP, as this is a measure that efficiently summarizes the whole economy and correlates very nicely with many other outcomes we care about.

Suppose then that we would like to investigate what is the effect of sanctions on a target country’s GDP.  One problem is identifying an appropriate counterfactual; to observe what would have happened in the target country in the absence of sanctions. It is also an issue that the incidence of international sanctions is often a product of a series of events in the target or sender country (e.g. the Iraqi invasion of Kuwait or the apartheid system in South Africa), which also have impacts on the economy that would need to be isolated from the impact of sanctions themselves.

A variety of econometric techniques can be of help in this situation. One first idea is to use, as a reference, cases where sanctions were almost implemented. Gutmann et al. (2021) compare countries under sanctions to countries under threat of sanctions, while Neuenkirch and Neumeier (2015) contrast implemented sanctions to vetoed sanctions, in the context of UN decisions. Both studies find a relatively sizeable negative impact on GDP, in a large group of countries over a long period of time. In the first study, the target country’s GDP per capita decreases on average by 4 percent over the two first years after sanctions imposition and shows no signs of recovery in the three years after sanctions are removed. The second study estimates a reduction in GDP growth that starts at between 2,3 and 3,5 percent after the imposition of UN sanctions and, although it decreases over time, only becomes insignificant after ten years. It should be considered that a lower growth rate compounds over time: experiencing a slower growth even by only 1 percent over ten years implies a total loss of almost 15 percent. As a comparison, the average GDP loss due to the Covid-19 pandemic is estimated to be 3,4 percent in 2020.

These studies have limitations. Countries under threat of sanctions are probably making efforts to avoid punishment, which might imply that these countries are precisely the ones who would be most negatively affected by the sanctions. If so, the impact found by Gutmann et al. (2021) is probably underestimated. Neuenkirch and Neumeier (2015) only look at UN sanctions, which on one hand might give a larger impact because of the multilateral coordination. But on the other hand, the issue of an appropriate counterfactual emerges again: countries whose sanctions are vetoed might be larger, more influential, and better connected within the international community or to some of the major powers, which may also affect their economic success in other ways.

Kwon et al. (2020) adopt a different technique and come to a different conclusion. They use an instrumental variable (IV) approach and find that standard OLS overestimates the negative effect of sanctions, in other words, that sanctions’ effects are less negative than we think. They find an instantaneous effect on per capita GDP that becomes insignificant in the long run, just as if sanctions never happened.

Our confidence in these estimates hinges upon the validity of the IV used. In this case, the actual imposition of sanctions is replaced by its estimated likelihood based on sender countries’ variation in institutions and diplomatic policies (which are exogenous to the target country’s economic developments) and pre-determined country-pair characteristics (trade and financial flows, travels, colonial ties). Therefore, episodes where sanctions are imposed because the sender country happens to be in a period of hawkish foreign policy and because the target does not have strong historical relations with them are contrasted to episodes in which the opposite is true, and sanctions are therefore not implemented, everything else being equal.

The results also show that there is heterogeneity across types of sanctions, with trade sanctions having both a short and long run negative impact, while smart sanctions (i.e. sanctions targeted on particular individuals or groups) have positive effects on the target country’s economy in the long run.  This is quite an important point in itself. Often, sweeping statements about effectiveness of “sanctions” lump all the different measures together, and fail to appreciate that there may be substantial differences. However, the effect of one or another type of sanctions will vary depending on the structure of the economy that is hit.

A third approach is the synthetic control method. Here the researcher tries to replicate as closely as possible the path of economic development in the target country up to the point of sanctions’ implementation, using one or a weighted average of several other countries. In this way, evolution after sanctions’ inception can be compared between the actual country and its synthetic control. Gharehgozli (2017) builds a replica of Iran based on a weighted combination of eight OPEC member countries, two non-OPEC oil-producing countries and three neighboring countries, that match a set of standard economic indicators for Iran over the period 1980-1994. The study finds that over the course of three years the imposition of US sanctions led to a 17.3 percent decline in Iran’s GDP, with the strongest reduction occurring in 2012, one year after the intensification of sanctions (2011-2014) was initiated.

This is a stronger effect than those presented earlier. However, it only speaks to the special case of Iran, rather than estimating a broader global average effect. Another study focusing on Iran (Torbat, 2005) makes the important point that the effect of sanctions varies by type: financial sanctions are found to be more effective (in lowering Iran’s GDP) than trade sanctions – which contrasts with what is found to be true on average by Kwon et al. (2020).

Finally, the relation between economic damage and the effectiveness of sanctions in terms of reaching their goals is debatable. In a theoretical model, Kaempfer et al. (1988) suggest that this relation might even be negative and that the most effective sanctions are not necessarily the most damaging in economic terms. The sanctions most likely to facilitate political change in the target country are those designed to cause income losses on groups benefiting from the target country’s policies, according to the authors.

The Effect of Sanctions on Russia

Are these results from previous studies useful to form expectations about the effects of the current sanctions on Russia? The invasion of Ukraine which started at the end of February was a relatively unexpected event, at least in character and scale, in contrast to what can be said in the majority of situations involving sanctions. However, the context leading up to it was not one of normality either. Besides the global pandemic, Russia was already under sanctions following the Crimean Crisis in 2014. The impact of those economic sanctions, and of the counter-sanctions imposed by Russia as retaliation, is still unclear – and will be in all probability completely dwarfed by the current sanction wave as well as other exogenous shocks, such as significant changes in oil prices in this period. Kholodilin et al. (2016) estimated the immediate loss of GDP in Russia to be 1,97 percent quarter-on-quarter, while no impact on the aggregate Euro Area countries’ GDP could be observed. A Russian study (Gurvich and Prilepsky, 2016) forecasted for the medium term a loss of 2,4 percentage points by 2017 as compared to the hypothetical scenario without sanctions. This pales in comparison to the magnitude of consequences that are being contemplated now. Even the potentially optimistic, or at least conservative, assessment of the current situation by the Russian Federation’s own Accounts Chamber, in the words of its head Alexei Kudrin, suggests that: “For almost one and a half to two years we will live in a very difficult situation.” At the end of April, they published revised forecasts on the economic situation, among which the one for GDP is shown below. Russian Central Bank chief Elvira Nabiullina also sounded bleak, speaking in the State Duma: “The period when the economy can live on reserves is finite. And already in the second – the beginning of the third quarter, we will enter a period of structural transformation and the search for new business models.” The World Bank has forecasted that Russia’s 2022 GDP output will fall by 11.2% due to Western sanctions. These numbers do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo (see an earlier FREE Policy Brief on the dependency of Russia on oil export).

Figure 1. Revised forecasts of growth rates for the Russian economy

Source: Macroeconomic survey of the Bank of Russia, April 2022.

Are these estimates realistic, and what would have been the counterfactual development without sanctions? If we believe the studies reviewed in the previous section, and also taking into account the unprecedented scale and reach of the current sanctions, at least the time horizon, if not the size, of the consequences forecast by Russian authorities is, though substantial, certainly underestimated. But there is too much uncertainty at the moment, hostilities are still ongoing and sanctions are not being lifted for quite some time in any foreseeable scenario. One reason why these sanctions are not likely to be relaxed, and why their impact is expected to be more severe than in most cases, is that a very broad coalition of countries is backing them. Not only this but the sanctioning countries see Russia’s conduct as a potential threat to the existing world order, so their motivation to contrast it is particularly strong relative to, say, the cases of Iran, North Korea, or Burma.

Moreover, these loss estimates do not yet factor in the announcement of the sixth EU sanction package, which famously includes an oil embargo. Oil is a fundamental driver of growth in Russia. An earlier FREE Policy Brief shows how two-thirds of Russia’s growth can be explained by changes in international oil prices. This is not because oil constitutes such a large share of GDP but because of the secondary effect oil money generates in terms of domestic consumption and investment. Reducing export revenues from the sale of oil and gas will therefore have significant effects on Russia’s GDP, well beyond what the first-round effect of restricting the oil sector would imply.

In short, it is too early to venture an assessment in detail, however, the scale of loss that can be expected is clear from these and many other indicators. In the longer run, it will only be augmented by the relative isolation in which Russia has ended up, implying lower investments and subpar capital inputs at inflated prices, and by the ongoing brain drain (3,8 million people have already left the country since the war began).

Conclusion

In conclusion, the debate about economic sanctions as a tool of foreign policy is often restricted to a binary question: do they work or not? There is ample support in the literature studying sanctions to say that this question is too simplistic. Even if we do not see immediate success in reaching the main aim of the sanction policy, they do cause damage, in many dimensions, and such damage is non-negligible. The political will and the regime behind it may be unaffected, but the resources they need to continue with their course of action will unavoidably shrink in the longer run.

References

  • Afesorgbor, S. K. (2019). The impact of economic sanctions on international trade: How do threatened sanctions compare with imposed sanctions?. European Journal of Political Economy, 56, 11-26.
  • Afesorgbor, S. K., & Mahadevan, R. (2016). The impact of economic sanctions on income inequality of target states. World Development, 83, 1-11.
  • Crozet, M., & Hinz, J. (2020). Friendly fire: The trade impact of the Russia sanctions and counter-sanctions. Economic Policy, 35(101), 97-146.
  • Dreger, C., Kholodilin, K. A., Ulbricht, D., & Fidrmuc, J. (2016). Between the hammer and the anvil: The impact of economic sanctions and oil prices on Russia’s ruble. Journal of Comparative Economics, 44(2), 295-308.
  • Drury, A. Cooper and Dursun Peksen. “Women and economic statecraft: The negative impact international economic sanctions visit on women.” European Journal of International Relations 20 (2014): 463 – 490.
  • Early, B., & Peksen, D. (2019). Searching in the shadows: The impact of economic sanctions on informal economies. Political Research Quarterly, 72(4), 821-834.
  • Farzanegan, Mohammad Reza. (2019). “The Effects of International Sanctions on Military Spending of Iran: A Synthetic Control Analysis.” Organizations & Markets: Policies & Processes eJournal .
  • Gharehgozli, O. (2017). An estimation of the economic cost of recent sanctions on Iran using the synthetic control method. Economics Letters, 157, 141-144.
  • Gurvich E., Prilepskiy I. (2016). The impact of financial sanctions on the Russian economy.  Voprosy Ekonomiki. ;(1):5-35. (In Russ.) https://doi.org/10.32609/0042-8736-2016-1-5-35
  • Gutmann, J., Neuenkirch, M., and Neumeier, F., 2021. ”The Economic Effects of International Sanctions: An Event Study” CESifo Working Paper No. 9007
  • Kaempfer, W. H., & Lowenberg, A. D. (1988). The theory of international economic sanctions: A public choice approach. The American Economic Review, 78(4), 786-793.
  • Kholodilin, Konstantin A. and Netsunajev, Aleksei. (2016) Crimea and Punishment: The Impact of Sanctions on Russian and European Economies. DIW Berlin Discussion Paper No. 1569, SSRN: https://ssrn.com/abstract=2768622
  • Kwon, O., Syropoulos, C., & Yotov, Y. V. (2020). Pain and Gain: The Short-and Long-run Effects of Economic Sanctions on Growth. Manuscript.
  • Neuenkirch, M., & Neumeier, F. (2015). The impact of UN and US economic sanctions on GDP growth. European Journal of Political Economy, 40, 110-125.
  • Torbat, A. E. (2005). Impacts of the US trade and financial sanctions on Iran. World Economy, 28(3), 407-434.
  • World Bank. (2022). “War in the Region” Europe and Central Asia Economic Update (Spring), Washington, DC: World Bank.

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 Russia’s GDP Numbers in the COVID-19 Crisis

Posted on March 9, 2021 | Policy Brief

20210308 Understanding Russia GDP Numbers FREE Network Policy Brief Image 02

Russia’s real GDP fell by a modest 3 percent in 2020. The question addressed here is how a major oil-exporting country can go through the COVID-19 pandemic with a decline of this magnitude when oil prices fell by 35 percent at the same time as the domestic economy suffered from lock-downs. The short answer is that it is mainly a statistical mirage. The aggregate real GDP decline would have been almost three times greater than in the official statistics if changes in exports were computed in a way that better reflects their value. In particular, the real GDP calculation uses changes in volumes rather than values to omit inflation, but for exports, it thus ignores large changes in international oil prices. In the end, what the government, companies, and people in Russia can spend is much more closely related to how much money is earned on its exports than how many barrels of oil the country has sold to the rest of the world. More generally, this means that real GDP growth in Russia is not a very useful statistic in years with large changes in oil prices, as was the case in 2020, since it does not properly reflect changes in real income or spending power. When policymakers, journalists, and scholars now start to compare economic developments across countries in the covid-19 pandemic, this is something to bear in mind.

Introduction

The world is closing the books on 2020 and it is time to take stock of the damage done by the COVID-19 pandemic thus far. A year into the pandemic, over 100 million cases have been confirmed and almost 2.5 million people have died worldwide according to ECDC (2021) statistics. Russia has not been spared and Rosstat reported 4 million infected and over 160 000 dead in 2020.

Human suffering in terms of lost health and lives is certainly the main concern in the pandemic, but on top of that comes the damage done to economies around the world. Falling incomes, lost jobs, closed businesses, and sub-par schooling will create significant health and other problems even in a fully vaccinated world for years to come.

Understanding how real GDP has fared in the crisis does not capture all of these aspects, but some. With the IMF’s latest World Economic Outlook update on economic performance out in January 2021, it is easy to start comparing GDP growth across countries (IMF, 2021). GDP growth is a standard measure of past performances in general, but the numbers for 2020 may also enter various domestic and international policy discussions of what does and does not work in protecting economies in the pandemic. For countries that seem to have fared better than their peers, the growth numbers are likely going to be used by incumbent politicians to boost their ratings or by consumers and business leaders making plans for the future.

In short, real GDP numbers are important to most economic and political actors, domestically and globally, with or without a crisis unfolding. It is therefore important to understand how Russia, a major oil exporter with significant losses of lives and incomes in the pandemic, could report a real GDP decline of only 3 percent in 2020 (Rosstat, 2021). Although this is not far from the global average reported by the IMF (2021), it is far better than the 7.2 percent drop in the Euro area, 10 percent fall in the UK, or 7.5 to 8 percent declines of its BRICS peers, South Africa and India. This brief provides the details to understand that Russia’s performance is more of a statistical artifact than a fundamental reflection of the health of the Russian economy.

Oil prices, GDP growth, and the ruble

Russia’s dependence on exporting oil and other natural resources is well documented (see for example Becker, 2016a and 2016b) and often discussed by Russian policymakers and pundits. In particular, changing international oil prices is a key determinant of growth in the Russian economy. Even if the level of real GDP disconnected from oil prices somewhere between 2009 and 2014 (Figure 1), the link between real GDP growth and changes in oil prices persists (Figure 2).

Figure 1. Russia real GDP and oil prices

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

The empirical regularity that still holds is that, on average, a 10 percent increase (decline) in oil prices leads to around 1.4 percent real GDP growth (fall), see Becker (2016a). With a 35 percent decline in oil prices in 2020, this alone would lead to a drop in GDP of around 5 percent.

Figure 2. GDP growth and oil price changes

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat.

One factor that has a fundamental impact on how the relationship between oil prices and different measures of GDP changes over time is the ruble exchange rate. For a long period, Russia had a fixed exchange rate regime with only occasional adjustments of the rate. A stable exchange rate was the nominal anchor that should instill confidence among consumers and investors. However, when changes in the oil prices were too significant, the exchange rate had to be adjusted to avoid a complete loss of foreign exchange reserves. This was evident in the 90’s with the crisis in 1998 and later in the global financial crisis in 2008/09. Eventually, this led to a flexible exchange rate regime and in 2014, Russia introduced a flexible exchange rate regime together with inflation targeting as many other countries had done before it.

As can be seen in Figure 3, this has important implications for how changes in international oil prices in dollars are translated into rubles. Note that the figure shows index values of the series that are set to 100 in the year 2000 so that values indicate changes from this initial level. Starting in 2011, but more prominently since 2014, the oil price in rubles has been at a significantly higher level compared to the oil price measured in dollars, which is of course due to the ruble depreciating. This affects the government’s budget as well as different measures of income in rubles. However, if oil prices in dollars change, this affects the real spending power of Russian entities compared with economic actors in other countries regardless of the exchange rate regime. Moving to a flexible exchange rate regime was inevitable and the right policy to ensure macroeconomic stability in Russia when oil prices went into free fall. Nevertheless, it does not change the fundamental economic fact that falling oil prices affect the real income of an oil-exporting country. It also makes it even more important to understand how real GDP is calculated.

Figure 3. Oil prices and exchange rate indices

Source: Author’s calculations based on U.S. Energy Information Administration and Central Bank of Russia.

The components of real GPD

GDP is an aggregate number that can be calculated from the income or expenditure side. The focus in this brief is on the expenditure side of GDP. The accounting identity at play is then that GDP is equal to private consumption plus government consumption plus investments (that can be divided into fixed capital investments plus change in inventories) plus exports minus imports (where exports minus imports is also called net exports). Being an accounting identity, it should add up perfectly but in the real world, components on both the income and expenditure sides are estimated and things do not always add up as expected. This generates a statistical discrepancy in empirical data.

Another important note on real GDP (rather than nominal GDP measured in current rubles) is that the focus is on how quantities change rather than prices or ruble values. The idea is of course to get rid of inflation and focus on, for example, how many refrigerators are consumed this year compared to last year and not if the price of refrigerators went up or down. This may sound obvious, but it comes with its own problems concerning implementation and interpretations. For Russia, real GDP becomes problematic because its main export is oil (gas and its related products). The price of oil is just one of many drivers of Russia’s inflation but is an extremely important driver of its export revenues and growth as has been discussed above. On top of that, oil prices are volatile and basically impossible to control for Russia or even the OPEC.

So why does this matter for understanding Russia’s real GDP growth in 2020? The answer lies in how the different components of real GDP are computed. To make this clear, the evolution of the components between 2019 and 2020 is shown in Table 1.   

Table 1. Russia’s GDP components from the expenditure side

Source: Author’s calculations based on data from Rosstat

In short, private consumption fell by close to 9 percent in 2020 compared to 2019; government consumption increased by 4 percent; gross fixed capital formation declined by 6 percent while inventories increased by 26 percent; exports lost 5 percent, but imports went down by 14 so that net exports showed an increase of 65 percent! To calculate the impact these changes have on aggregate GDP growth, we need to multiply with the share of GDP for a component to arrive at the impact on GDP growth in the final column of Table 1.

Although there are some issues to resolve with both government consumption and inventory buildup, to understand real GDP growth in 2020, it is crucial to understand what happened to exports and imports in real GDP data. First of all, how does this data compare with the balance of payments data that measures exports and imports in dollar terms or the data that show the value of exports of oil, gas, and related products? Table 2 makes it clear that the numbers do not compare at all! Again, this is due to real GDP numbers being based on changes in volumes rather than values while the trade date reports values in dollars (that can be translated to rubles by using the market exchange rate).

In the real GDP statistics, net exports show growth of 66 percent in 2020, compared to declines of 37 to 44 percent if merchandise trade data is used. Going into more detail, real GDP data has exports declining by 5 percent, while other indicators fall by between 11 and 37 percent. It is similar with imports (that enter the GDP calculation with a negative sign); the import decline recorded in real GDP is 14 percent, while trade data suggest a 6 percent decline in dollar terms but an increase of 7 percent in nominal ruble terms.

Table 2. Trade statistics

Source: Author’s calculations based on Rosstat, Central Bank of Russia and BOFIT

What would it mean if we use some of these alternative growth rates for exports and imports (while keeping other components in line with official statistics) to calculate aggregate GDP growth in 2020? The rationale for keeping other components unchanged is that this provides a first-round effect of changing trade numbers on real GDP growth.

To make this calculation, the GDP shares of exports and imports (or net exports) in 2019 are needed. Table 1 shows that these numbers are 27 and 24 percent (or a net 3 percent) of total GDP. Multiplying the share of a GDP component with its growth rate gives the contribution of the component to overall GDP growth. The calculations based on different trade data are shown in Table 3. The last line of the table is what GDP growth would have been with these alternative trade data. Note that the real GDP growth number is -2.9 percent when we use the individual components of GDP decomposition (rather than the official headline number -3.1 real GDP growth when using aggregate GDP) so this is shown here to make the table consistent with the alternative calculations. In the last column of Table 3, oil and gas exports are assumed to make up for half of exports and this number disregards changes in other exports or imports to isolate the effect of changes in the value of oil and gas exports from other changes.

The summary of this exercise is that with more meaningful trade data used in calculating GDP growth, Russia would have recorded a decline of around 9 percent rather than 3 percent. This is of course a partial analysis focusing on the trade part of real GDP since this effect is very striking. Other components of the calculation may also have issues that need to be adjusted to arrive at a more realistic growth number. Still, even the current estimate is not unrealistic. For example,  household consumption fell by around 9 percent, which would be consistent with a GDP decline of 9 percent that is not recovered in the future in a permanent income model.

Table 3. GDP growth contributions from alternative trade data

Source: Author’s calculations based on U.S. Energy Information Administration and Rosstat

Conclusions

Real GDP growth numbers are important to understand economic developments in a country and provide the foundation for many types of economic decisions. The numbers are also used to compare the economic performance of different countries and evaluate policy responses in the COVID-19 pandemic we are currently part of.

The problem with Russia’s reported growth of minus 3 percent is not that the real GDP calculation is wrong per se, but it is clearly the wrong metrics to use for understanding how incomes and purchasing powers of Russian households, companies, and the government changed in 2020. If we instead use trade data that better reflect plummeting oil prices in international markets, alternative estimates of Russia’s real growth show a GDP decline of (at least) 9 percent.  This is a three times larger drop than the official number of minus 3 percent. This is important to keep in mind when Russia’s economic performance in the pandemic is compared with other countries or while discussing the economic realities of people living in Russia.

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.

How Should Policymakers Use Gender Equality Indexes?

Posted on November 13, 2018 | Policy Brief, Policy Papers

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. Extending the measure back to 1990, we look at the development of the overall index as well as that of its components. We show that, even though gender inequality in transition countries for the most part has decreased since 1990, once overall development is taken in account these countries appear to fare better in 1990 than today. We also caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations of the GII with other indexes (over years when multiple gender inequality indexes exist) as well as across sub-indexes suggests that such an approach has limitations. Finally, we emphasize the need to understand levels as well as trends and underlying mechanisms to better inform policy to improve gender equality.

On Measuring Progress

When studying economic development, or any issue really, one faces the challenge not only of finding the right way to identify and measure what are often complex changes, but also of communicating the bottom line efficiently. This naturally leads to the search for a single metric according to which we can rank progress and follow it over time. In the realm of economic development the standard measure is GDP growth. But, of course, focusing only on GDP leaves out many important dimensions of development, such as health and education.[1] In an attempt to capture these dimensions, while still arriving at a single number that measures development, the Human Development Index (HDI) was developed in the late 1980s. Since then, a number of alternative indexes capturing additional aspects of human wellbeing have been suggested; see the report by the “Commission on the Measurement of Economic Performance and Social Progress” (Stiglitz, Sen and Fitoussi, 2009).

Just as for overall development, there is great interest in single measures that capture the gender dimension of this development. Over the past decades a number of such “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few.

These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions. The various indexes proposed differ in what dimensions they include (as will be explained below) and, much as a consequence of this, in the time periods they can cover. In some cases (as will also be shown below) it is possible to extend the time coverage of the indexes, but most of the times it is hard to recover the underlying data.

In this brief we summarize what the most popular indexes tell us about the development of gender equality in transition countries, contrasting these to Western European countries.[2] Whenever we have been able to find the underlying data, we also add to publicly available measures by extending indexes back to early 1990s. We then comment on the development of gender equality in transition countries and, perhaps most importantly, on why an indexes-based analysis should be interpreted with some care.

Gender Equality Before 1990

As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018). This was mainly due to the high participation of women in the labor market as well as the (official) universal access to basic health care and education.

However, some scholars have suggested that not all aspects of gender equality were as advanced in the countries in the Soviet Union and in Central and Eastern Europe (Einhorn, 1993; Wolchik and Meyer, 1985). Even though women were highly integrated in the labor market, they were also still expected to take care of child rearing and house work (UNICEF, 1999). The gender pay gap and gender segregation in the labor market was also similar to levels found in OECD countries. In addition, despite the high number of women in representative positions in communist party politics, women were rarely found in positions of real power in the political sphere (Pollert, 2003).

Generally speaking, while the communist regimes succeeded in promoting women’s access to the labor market and tertiary education, they failed to eliminate patriarchy (LaFont, 2001). Such a dichotomy gives rise to a broad set of questions regarding gender equality in transition countries as well as the measurement of gender equality in this context. What happened to gender equality, in relation to economic growth, during the transition, when new governments often broke with the tradition of promoting women’s employment and education? Did gender equality enhanced by communism leave a legacy or did underlying patriarchic values characterizing many of the communist societies come to dominate? How should we regard developments of indexes that try to weight several components within a context, such as that of transition countries, where these components may move in different directions from each other, given the dichotomy characterizing gender relations?

The Different Indexes

There are several different indexes that are often quoted in policy discussions. Two important measures are the Gender Development Index (GDI) and the Gender Inequality Index (GII), both calculated by the UNDP and reported annually in the Human Development Report (HDR). A third, more recent index that has received increasing attention is the World Economic Forum’s global Gender Gap Index (GGI), which is published in the yearly Gender Gap Report. These three can serve as illustrations of what gender equality indexes typically try to capture.

The Gender Development Index (GDI) essentially measures gender differences in the Human Development Index (HDI). The HDI in turn aims to capture achievements in three basic dimensions of human development: health (measured by life expectancy), knowledge (measured by expected and mean years of schooling) and living standards (measured by GNI per capita). The GDI then basically tries to assess the relative performance in these three dimensions for men and women respectively. If health (or education, or income)  in the population on average goes up, this improves the HDI. But to the extent that the improvements are felt differently by men and women, this will show in the GDI. There are several potential problems with the measurement of this index, especially when it comes to dividing GNI per capita between men and women (see e.g. Dijkstra and Hanmer, 2000); on the other hand, the index offers a transparent way to connect gender inequality to the HDI measure.

The other UNDP measure, the Gender Inequality Index (GII), was reported for the first time in the 2010 Human Development Report. It was created to address some of the perceived shortcomings of its forerunner, the Gender Empowerment Index (GEM) which had been introduced together with the GDI in 1995 (see e.g., Klasen and Schuler, 2011 for problems with GDI as well as GEM). The GII measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation. As with the GDI, the areas of health, education, and economic empowerment are present, but the index also considers some aspects of health that are more directly relevant for women, and includes a component trying to capture political participation. The economic measure of labor force participation is also somewhat easier to interpret (and measure) than GNI divided between men and women. As for the GDI, GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of female seat share in parliaments in 1990[3]. We downloaded the latter from the World Bank indicators database[4]. We also added information on the share of women in the 1990 Polish Parliament, from the Inter-Parliamentary Union[5], and on the share of women in the 1990 Georgian “Supreme Council,” from Beacháin Stefańczak and Connolly (2015).

A third more recently developed index is the Global Gender Gap Index. This covers areas of political empowerment, health and survival, economic participation and educational attainment, as measured using 14 different variables. An indicator is available for each of the sub areas covered, which are then weighted together in an overall indicator of the gender gap. The Global Gender Gap Index is clearly more detailed and provides a more nuanced picture of existing gender gaps compared to the GDI or the GII. But this amount of detail also comes with potential costs; it is more difficult to interpret the overall index as there are more underlying components that may change simultaneously, and it is also more difficult to reconstruct the index back in time.

What Does the GII Index Tell Us About Gender Equality in Transition Economies?

Among the above mentioned indexes, we focus on the GII here. Extending this measure when possible allows us to study gender inequality starting from 1990 for a limited set of countries (we expand the sample of countries when looking at different dimensions of the GII separately below)[6]. Figure 1 reports values for the index in box plots, which show the index median, maximum, minimum, 75th and 25th percentile for two groups of countries: transition countries and Western-European countries. When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. Below we will show that this is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries[7].

This first piece of evidence from the data is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, two qualifications are in order here. First, transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their HDI ranking among all the countries with non-missing GII values in the years considered. The larger the difference, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Source: Own calculations based mainly on UNDP data.

The trends of transition countries and Western Europe are now opposite. In the former group, in 1990 the median standing in terms of gender equality was better than that in human development; this difference appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality in relation to their level of overall human development over the period studied. Put differently, the gains in human development made by former socialist countries since the transition have not translated into comparable gains in gender equality as measured by the GII index.

Second, it is also important to emphasize that, as noted above, according to several scholars the socialist push in favor of gender equality was directed only to certain spheres of women’s lives, namely their economic empowerment. This suggests that a composite index can mask important contrasting patterns among its components.

In Figures 3 to 5 we document that different variables indeed paint quite diverging pictures of gender inequality in transition countries.

Figure 3. Development of adolescent births and maternal mortality, 1990-2015

Figure 4. Development of secondary education and share of women in parliament, 1990-2015.

Figure 5. Labor force participation, 1990-2015

Source: Own calculations based mainly on UNDP data.

In each figure we display box-plots for the three areas covered by the GII: health (measured by teenage births and maternal mortality), empowerment (measured by secondary education and share of women in Parliament) and labor force participation. Looking at the different variables separately also allows us to increase the number of countries significantly, since for many countries only the seat share of women in parliaments is missing in 1990.

As the figures show transition countries in 1990 displayed relatively low levels of gender inequality in labor force participation and secondary education. Over the last 25 years, they have kept improving the latter, while the former has stalled, resulting in Western European countries displaying a higher median level of gender equality in labor force participation for the first time around 2010. Reproductive health, while improving since the transition, is still far from converging to Western European standards. Finally, political representation appears to be responsible for the increase in inequality immediately after the transition that we have noted in Figure 1. While it is hard to compare the meaning of representation in the context of 1990 totalitarianisms to that of the democratic regimes emerged later, during the regime change women de facto lost descriptive representation, which was sometime guaranteed in socialist times by gender quotas (Ostrovska, 1994).

In conclusion, breaking down the GII by its components shows that, while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: women maintained but did not improve their relative status in the labor force, they gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

What Does the GII Index (And Other Indexes) Not Tell Us?

The conclusion in the previous paragraph raises the question of which other areas of progress, stagnation or deterioration in gender equality in transition countries that might be overlooked in the GII index. Above, we have summarized two more indexes, the GDI and the Gender Gap Index, which focus on additional dimensions of gender inequality but are more limited in terms of time availability. For the time over which there is overlap between the available indexes, the correlation between the GII index and the GDI and the Gender Gap Index respectively, is roughly 0.60. Interestingly, such correlation is higher in the sample of western European countries (0.64 and 0.68 respectively); when the sample is limited to transition countries, the correlations are down to 0.40 and 0.50 respectively.

Several factors might account for the differences across indexes. Unlike the GII, both the GDI and the Gender Gap Index, for instance, include measures of income inequality. On the other hand, the GDI, as pointed out above, does not account for issues related to reproductive health and political representation. The Gender Gap Index is the only one to include, among the health measures, sex-ratios (typically defined as the ratio of male live births for every 100 female births). This turns out to be especially important for some of the transition countries: in the most recent Gender Gap Report, Georgia, Armenia and Azerbaijan remain among the worst-performing countries globally on the Health and Survival sub-index, due to some of the highest male-to-female sex ratios at birth in the world, just below China’s. This goes hand in hand with very high scores in terms of gender equality in enrolment in tertiary education, for which each of these countries ranks first (at par with a few other countries), having completely closed the gender gap. In fact, women are more likely to be enrolled in tertiary education than men.

The relatively low correlation among the different indexes for the group of transition countries also deserves special attention, because it might be a direct consequence of the peculiar history of women’s rights and empowerment in the region. Since some dimensions of gender equality were fostered through a top-down approach, rather than as the result of demands and needs expressed by an organized society, it is more likely that over the last thirty years elements of modernization coexisted with more traditional forms of gender inequality.

Finally, it is worth pointing out that none of the above indexes accounts for important dimensions of gender inequality such as,: gender violence, division of chores in the household, political representation at the local level, and the presence of women in STEM’s professions (where the largest job creation might happen over the next couple of decades). Once more, some of these measures might be particularly relevant for transition countries. Just to mention one example, gender violence is an urgent issue in a few of the countries in the area[8]. A case in point in this respect is Moldova: in 2017, the country ranked 30th out of 144 countries in the Gender Gap Index. Its rank for the sub-index called “Economic Opportunity and Participation” was 11[9]. The country performs especially well in terms of economic opportunity and participation because women not only participate in the labor market in almost equal rates as men, but they are also relatively fairly represented in professions traditionally less feminized elsewhere, such as “professional and technical workers” and “legislators, senior officials and managers.” At the same time, gender violence appears quite prevailing in Moldova: according to the UN, in 2014 “lifetime prevalence of psychological violence” in Moldova was of 60%. Official country statistics also report that the percentage of ever-partnered women aged 15-65 years experiencing intimate partner physical or sexual violence at least once in their lifetime in 2011 was 46%[10].

While limited in scope, the example above illustrates how some of the available indexes might not capture some important drivers of gender inequality in the region.

Conclusion

In this policy brief, we have reviewed some of the available gender inequality indexes that are commonly used in policy discussion as well as in policy-making.

We have then discussed gender inequality in transition countries focusing on one of these indexes, the Gender Inequality Index, whose span we have extended to the beginning of the transition period. Our analysis has highlighted some points to be mindful of when using comprehensive indexes to discuss gender inequality, especially in transition countries:

  • It can be fruitful to analyze gender inequality indexes in relation to levels of development. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment or gender violence, might require more targeted policy interventions, since they do not necessary go hand in hand with overall development.
  • While comprehensive indexes can be useful in terms of effective communication, it is often difficult to compress all the potential forms that gender inequality can take into a single index, especially over time. This is due to both conceptual issues and data limitations. Moreover, even when this is done, a comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions.
  • The previous point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. In the context of transition countries, for instance, it has been argued that low levels of female representation in political institutions can be the result of women’s large participation to the labor market while division of roles in the household remained traditional. In the words of anthropologist Suzanne LaFont, “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden.”[11] In such a context, average values of an index on gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.
  • Identifying policies to address gender inequality in transition countries might be especially difficult because, depending on the dimension that one focuses on, the challenge at hand is different: in terms of education and employment, the policy goal appears to be maintaining current levels of equality or increasing them from relatively high initial points; the type of policies to do so are likely different than those used in Western European countries in the last 30 years, where the challenge was rather how to increase equality from relatively much lower levels. Conversely, in other dimensions the challenge is how to make major leaps forward, which move transition countries closer to Western European standards: this is the case for sex-ratios, for instance, and reproductive health more in general. The importance of initial levels and trends for policy implications also showcases how crucial it is to acquire more historical knowledge of policies, institutions, and statistics.

Overall, policy discussions and policy-making should go beyond mere descriptions of what indexes and related international comparisons tell us about gender inequality. A better knowledge and understanding of all of the drivers of gender inequality, of their historical evolution, and of their connections both with overall development and among them, is crucial to give sound policy recommendations.

References

  • Beacháin Stefańczak, K.Ó. and Connolly, E.(2015),  ‘Gender and political representation in the de facto states of the Caucasus: women and parliamentary elections in Abkhazia’. Caucasus Survey, 3(3), pp.258-268.
  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labor Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • Dijkstra, A. and L. Hanmer (2000), ‘Measuring socio-economic gender inequality: towards an alternative for UNDP’s Gender-related Development Index’, Feminist Economics, Vol. 6, No. 2, pp. 41-75.
  • Einhorn, B. (1993), Cinderella goes to market: citizenship, gender, and women’s movements in East Central Europe, London: Verso.
  • Klasen, S. and Schuler, D. (2011) Reforming the Gender-Related Development Index and the Gender Empowerment Measure: Implementing Some Specific Proposals. Feminist Economics. (1) 1 – 30
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Ostrovska, I. (1994). Women and politics in Latvia. Women’s Studies International Forum 2, 301–303.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.
  • Stiglitz, Joseph, Amartya Sen, and Jean-Paul Fitoussi (2009). `The measurement of economic performance and social progress revisited.’ Reflections and overview. Commission on the Measurement of Economic Performance and Social Progress, Paris.
  • Tur-Prats, Anna (2018). Unemployment and Intimate-Partner Violence:  Gender-Identity Approach. GSE Working Paper No. 1564
  • Unicef. Women in transition. 1999.
  • UN. The World’s Women 2015.
  • Wolchik, S. L. and Meyer, A.G. (1985), Women, State and Party in Eastern Europe, Durham, NC: Duke University Press.

Footnotes

  • [1] In contrast to a common perception, economists are generally well-aware of the limitations of GDP as a measure of welfare. In fact, the reference manual of national accounts, the SNA 2008, makes this explicit in stating that there is “no claim that GDP should be taken as a measure of welfare and indeed there are several conventions in the SNA that argue against the welfare interpretation of the accounts”.
  • [2] By “transition countries,” we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Starting from this, we – as will be made clear below – sometimes limit the set of countries further depending on data availability.
  • [3] http://hdr.undp.org/en/data
  • [4] https://data.worldbank.org/indicator/SG.GEN.PARL.ZS
  • [5] http://archive.ipu.org/parline-e/reports/2255_arc.ht
  • [6] For Western Europe these countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, Russian Federation.
  • [7] The outlier among Western countries is Malta.
  • [8] While explaining the sources of gender violence in the region is beyond the scope of this report, incidentally we notice that, according to recent research, female economic empowerment in a context where patriarchal values are dominant might backfire against women in the form of increased gender violence. See Tur-Prats, 2018.
  • [9] http://reports.weforum.org/global-gender-gap-report-2017/dataexplorer/#economy=MDA
  • [10] UNFPA (2015). Combatting Violence against Women and Girls in Eastern Europe and Central Asia. https://eeca.unfpa.org/en/publications/combatting-violence-against-women-and-girls-eastern-europe-and-central-asia
  • [11] LaFont, Suzanne (2001). One Step Forward, Two Steps Back: Women in the Post-Communist States. Communist and Post-Communist Studies, Vol. 34, pp 208.

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 Gaps in Transition – What do we learn (and what do we not learn) from gender inequality indexes?

Posted on November 12, 2018 | Policy Brief

20181112 Gender Gaps in Transition Image 01

We look at the development of gender inequality in transition countries through the lens of the Gender Inequality Index (GII), which aims to capture overall gender inequality. By extending the measure back to 1990, we show that even though gender inequality in transition countries for the most part has decreased since the fall of the iron curtain, once overall development is taken into account, transition countries did better in relation to other countries in terms of rank differences before transition. We, however, caution against relying exclusively on composite indexes to understand patterns of gender inequality. While the desire of policy makers to get one number that captures gender inequality development is understandable, weak correlations across different overall indexes, as well as across different sub-indexes that make up each index, suggest that such an approach has limitations.

Indexes of gender inequality

In the public debate of socio-economic issues there is an understandable interest in single measures that summarize complex issues, describe historical developments and allow international comparisons. The use of GDP to measure economic development is the most immediate example of this way of proceeding. The same applies to gender inequality. Over the past decades a number of “gender equality indexes” have been developed by international organizations such as the UNDP, the EIGE (European Institute for Gender Equality) and the WEF (World Economic Forum), to name a few. These measures receive a lot of attention and in particular the reporting of country rankings tends to have an influence on political and policy discussions.

In this brief, we study the development of the Gender Inequality Index (GII) in transition countries, contrasting these to Western European countries.  By transition countries, we refer to all countries that were part of the Soviet Union plus the Central and Eastern European countries that were heavily influenced by the Soviet Union before 1990 (not including Albania and former Yugoslavia). Whenever we have been able to find the underlying data, we extend the GII measure back to the early 1990s. This extension allows us to measure the development of gender inequality through the lens of a single index since the beginning of the transition. We then discuss what the GII tells us about gender inequality in transition, but also – perhaps more importantly – what it does not tell us. Our analysis is discussed as well as shown in some more detail in our forthcoming companion FREE Policy Paper.

The Gender Inequality Index

The GII was reported for the first time in the 2010 Human Development Report. It measures gender inequalities in three dimensions of human development: 1) reproductive health, measured by maternal mortality and adolescent birth rates; 2) empowerment, measured by representation in parliament and secondary education among adults; and 3) economic status, measured by labor force participation.

GII country-values from 1995 are available on the UNDP website.  Conveniently for our purpose, most of the underlying data that the index is based on are also made available from the UNDP for the years 1990, 1995, 2000, 2005, and every year between 2010 and 2015, with the only exception of the female seat share in Parliament in 1990. Using the UNDP data, and data on the female seat share in Parliament in 1990 from additional sources (see the FREE Policy Paper for a list of sources), we obtain values for the GII from the beginning of the transition in 1990 until 2015.

What does the GII index tell us about gender equality in transition economies?

Figure 1 reports values for the GII index in box plots, which show the index 25th and 75th percentile (respectively bottom and top of the box), its median (horizontal line in the box), its maximum and minimum (whiskers), and outliers (dots) for two groups of countries: transition countries and Western-European countries. We have reconstructed the values of the GII index for a limited set of countries within these groups (see the note to Figure 1 for the list of countries). When interpreting Figure 1, recall that higher GII values imply more inequality.

Figure 1. The Gender Inequality Index in transition countries and Western Europe, 1990-2015

Nov122018_Figure1

Source: Own calculations based mainly on UNDP data. The transition countries are: Armenia, Bulgaria, Georgia, Hungary, Poland, Romania, and the Russian Federation. For Western Europe the countries are: Austria, Belgium, Cyprus, Denmark, Finland, France, Greece, Iceland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland.

Figure 1 shows that based on the GII, median gender inequality is larger in transition countries than in Western Europe and has been so throughout the entire period since 1990. In both regions, the index shows a decreasing trend, after an initial increase in 1995 in the transition countries. As we show in the Policy Paper, this decrease is mainly due to a drop in female representation in national parliaments. The variance of the index scores has declined over time in Western Europe, while it remained mostly unchanged in the transition countries.

The evidence from the GII is somewhat at odds with the common notion that transition countries enjoy relatively low level of gender inequality. However, it is important to notice that transition and Western European countries are generally at different levels of development. Figure 2 displays the country groups’ performance in relation to their level of human development. This is done by measuring the difference between their GII ranking and their Human Development Index ranking (HDI) among all the countries with non-missing GII values in the years considered. The HDI is an UNDP-developed measure of overall human development. See the policy paper for details about its measurement. The larger the difference between GII- and HDI-ranking, the worse the group performance in terms of gender inequality in relation to its level of development.

Figure 2. Difference between Gender Inequality Index ranking and Human Development Index ranking in transition countries and Western Europe, 1990-2015

Nov122018_Figure2

Source: Own calculations based mainly on UNDP data.

The trends between transition countries and Western Europe are now opposite. In 1990, the median standing in terms of gender inequality was better than that in human development for transition countries, and the relative level of gender inequality was lower than in Western Europe. The (negative) difference between GII and HDI ranking however appears to have narrowed over time, and it is close to zero in 2015. Western European countries have instead improved their gender equality ranking in relation to their ranking in terms of human development over the period studied. Put differently, the ranking improvement in terms of human development in former socialist countries since the transition have not translated into comparable gains in gender equality ranking as measured by the GII index.

It is also important to emphasize that, according to several scholars, a dichotomy in terms of gender relations existed in transition countries during the socialist period. This is because on one hand the socialists put substantial into effort to empower women economically (see e.g. Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2018), but on the other hand they failed to eliminate patriarchy (LaFont, 2001). This suggests that a composite index can mask important contrasting patterns among its components. In the Policy Paper we uncover such contrasting patterns. By looking separately at the different components of the GII index, we show that while Western European countries have invariantly improved their levels of gender equality since 1990, the trend in transition countries depends on the measure one looks at: Women maintained, but did not improve, their relative status in the labor force. They gained more equality in education and especially in terms of reproductive health, and lost descriptive political representation.

Conclusion

In this policy brief we have studied the development of gender inequality in transition countries through the lens of the Gender Inequality Index, whose span we have extended to the beginning of the transition period. We have shown that, based on this index, gender inequality has decreased since 1990 in transition countries, a trend which is common to that in Western Europe. However, once the changes in overall development during this period are taken into account, it appears that transition countries fared better in 1990 than today. Our analysis thus shows that analyzing gender inequality indexes in absolute terms and in relation to levels of development can deliver different conclusions. The factors that account for these differences should be kept in mind in policy discussions and policy-making. Some issues related to gender inequality, such as maternal mortality, are potentially addressed with a comprehensive strategy aimed at overall development. Conversely, other drivers of gender inequality, such as women’s political empowerment, do not necessary go hand in hand with overall development, and might therefore require more targeted policy interventions.

We have also cautioned the reader about the limitation of using comprehensive indexes to describe developments in gender inequality. A comprehensive index can overshadow important sources of gender inequality if it is composed of sub-indexes that move in opposite directions. This point can be especially relevant in the context of transition countries, which historically experienced a top-down approach to gender equality, the results of which in the long-term appear to be major advancements in some dimensions of women’s empowerment and contemporary potential backlash in other dimensions. It has been argued, for instance, that low levels of female representation in political institutions in transition countries can be the result of women’s large participation in the labor market while the division of roles in households remained traditional. In the words of anthropologist Suzanne LaFont (2001), “Women have been and continue to be overworked, and their lives have been over-politicized, the combination of which has led to apathy and/or the unwillingness to enter the male dominated sphere of politics. Many post-communist women view participation in politics as just one more burden”. In such a context, average values of an index of gender equality might mask high achievements in economic empowerment coexisting with lack of political representation.

References

  • Brainerd, E. (2000), ‘Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union’, Industrial and Labour Relations Review, 54 (1), pp. 138-162.
  • Campa, P. and Serafinelli, M. (2018), ’Politico-economic Regimes and Attitudes: Female Workers under State-socialism’, Review of Economics and Statistics, Forthcoming.
  • LaFont, Suzanne (2001), ‘One step forward, two steps back: women in the post-communist states.’ Communist and post-communist studies 34(2), pp. 203-220.
  • Pollert, A. (2003), ‘Women, work and equal opportunities in post-Communist transition’, Work, Employment and Society, Volume 17(2), pp. 331-357.

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.

Political Responsibility for Economic Crises

Posted on April 22, 2018 | Policy Brief

20180422 Political Responsibility for Economic Crises IMAGE 02

This brief summarizes the results of research on the political costs of large-scale economic crises. In a large historic sample of countries, we study the impact of different types of crises, such as sovereign and domestic defaults, banking crises and economic recessions, on political turnover of top politicians: heads of the state and central bank governors. According to the findings, only default on domestic debt increases the probability of politicians’ turnover but not the default on external debt. As argued, this is due to the fact that the latter is not directly felt by the voters. In addition, we find that although currency crises increase chances of head of central bank turnover, it does not affect tenures of heads of state. Presumably, this is the case since currency crises are in the eyes of the public the responsibility of CB governors. These findings are relevant for both developed and transition economies, but are especially important for the latter as political turmoil and economic recessions are more prevalent in developing nations.

Overview and Key Findings

Large-scale economic crises are associated   not only with the economic downturns, but also with political turnover. When the national economy is in a critical state, a default declaration often turns the economy back to growth as it is typically viewed as an act of  acknowledging a problem and showing readiness for changes. However, politicians responsible for the economy and leaders of the states are often reluctant to declare default and try to postpone it, which worsens the situation. One of the reasons behind such unwillingness to act is a fear of a political turnover following the open acknowledgement of a problem.

This brief summarizes the findings Lvovskiy and Shakhnov (2018). We investigate the statistical evidence of political costs related to different types of economic crises.

We find that the effects of a crisis depend on the crisis type and on whether it was in the area of responsibility of a given politician. For example, external sovereign defaults have no effect on political turnover, which we interpret as external sovereign default having a small impact on the general public. On the contrary, domestic sovereign defaults have a large impact on the country population and often lead to the replacement of the top executive. In turn, banking crises are followed by the downfall of the government at the level of chief executive as well as the governor of the central bank.

While there is large literature on career concerns of politicians and political turnover, the majority of papers either focus on the regular changes through elections in democratic regimes (Treisman, 2015) or study a particular non-democratic country, like China (Li and Zhou, 2005). However, throughout history, crises have often happened in transition, non-democratic or not fully democratic countries. Furthermore, even in democratic countries many changes of government have been irregular. Since a delay in default declaration usually harms economies it is important to understand the mechanisms behind it in different institutional settings. Our paper contributes to this understanding by analyzing the impact of economic crises on political survival in a wide set of countries and regimes. Better understanding of the political costs that the top executives face while making such decisions is crucial for the prediction of these decisions as well as for international default negotiations and consultations.

Below we describe our finding in some more detail.

Statistical Analysis and Results

Our analysis consists of two main parts. We start with the political turnover for heads of state, who are in charge of the performance of the whole economy, which we measure by the GDP growth. Then, we look at central bank (CB) governors, who are in charge of the monetary policy, price stability, stability of the financial sector and banking supervision.

Table 1. Head of state changes

Table 1 presents the estimated linear probability regression models for the head of state turnover. As expected, elections have a strong impact on the probability of the turnover of the head of state. Further, as Column 1 in Table 1 shows default on external debt has no significant impact on the head of state tenure while default on domestic debt increases the yearly chances of being displaced by 34 %. This supports the idea that voters care more about their own savings than about the general situation with the state’s budget. When we look at the effect of past crises (the predictor variable in this case is whether a crisis took place last year), Column 2 coefficients for both external and domestic defaults appear to no longer be statistically significant. Instead, banking crises become significant. This situation could be due to the fact that one of the common consequences of domestic defaults is an ongoing distortion  of savings  which often leads  to deposit runoffs, so the effect of the previous year’s domestic default now acts through a banking crisis.

Table 2. Central bank governor changes

Table 2 presents similar results but this time the left hand side variable is CB governor turnover. Similarly to the case with the head of state turnover, only default on domestic debt has a significant effect on the CB’s governor tenure and not the one on external debt. The main differences with Table 1 are that elections do not statistically predict turnover of CB heads while currency crises do. The former result is expected since in most countries there are no direct elections of central bank governors and central banks often have some degree of independence from the government. The latter result, that currency crises have a significant impact on CB governors’ tenures, implies that since currency control is one of the roles of a CB, its head is held accountable for currency crises and not the head of a state.

Conclusion

We examine the political cost of different types of economic crises, and find non-uniform effects of different types of crises on the political survival of various key officials. Domestic defaults, and recent banking crises are shown to be costly both for heads of states and central bank governors, while currency crises only have an impact on the political survival of the latter.

Interestingly and importantly, we find no evidence of the impact of (external) sovereign default on political turnover of the head of state or central bank governors. In other words, contrary to Yeyati and Panizza’s (2011) suggestion, it seems that there is no immediate political cost at the top associated with (external) sovereign default. One possible explanation is that the public does not  punish a politician for defaults because by defaulting, the politician makes the optimal decision.  In a modern world, many developing nations experience rapid growth of their sovereign debt. The presented evidence brings partial optimism that even if economic mistakes have already been made, top politicians would understand that acknowledging a problem and making steps toward its solution may not always be as costly for them as has previously been thought.

References

  • Li, Hongbin; Li-An Zhou, 2005. “Political turnover and economic performance: the incentive role of personnel control in China,” Journal of Public Economics, 89 (9), 1743 – 1762.
  • Lvovskiy, Lev; Shakhnov, Kirill, “Political Responsibility for Different Crises”, BEROC working paper #50, 2018
  • Treisman, Daniel “Income, Democracy, and Leader Turnover”, American Journal of Political Science,  2015, 59 (4), 927–942.
  • Yeyati, Eduardo Levy and Ugo Panizza, “The elusive costs of sovereign defaults,” Journal of Development Economics, January 2011, 94 (1), 95–105.