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How to Intensify and Diversify Ukrainian Exports? The Case of Bilateral Trade with Germany

Policy Brief

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This policy brief focuses on trade relations between Ukraine and Germany. In particular, it analyses bilateral trade in goods and examines the possibilities for increasing Ukrainian exports to Germany, in both the extensive and the intensive margins. The brief identifies prospective product groups for such increases and discusses potential obstacles to trade intensification. Finally, it provides recommendations for the further trade development.

German-Ukrainian Trade

Germany has recently become one of the most important trading partners for Ukraine. In 2018, Germany was fifth in terms of Ukrainian export destinations and third in terms of its import source countries.  While Ukraine, not surprisingly, is less important for German international trade (in 2018, Ukraine ranked 42nd in terms of Germany’s export and 45th in terms of its import), bilateral trade between Ukraine and Germany showed positive dynamics over the last five years.

Since Germany is a member of the European Union, its trade relations with Ukraine are regulated by legislation common for all EU member states. The EU’s political and economic cooperation with Ukraine is stipulated by the Association Agreement (AA). The AA is a comprehensive agreement provisioning the Deep and Comprehensive Free Trade Area (DCFTA) between Ukraine and the EU. While the provisional application of the AA began in the fall of 2014, the document fully entered into force on September 1, 2017. The abovementioned intensification of trade relations between Ukraine and Germany was to a significant extent driven by the signing of the DCFTA and a loss of a significant share of the Russian market.

The main Ukrainian exports to Germany include ignition wiring sets used in vehicles, aircraft and ships; low erucic acid, rape or colza seeds, iron ores agglomerated, maize, electrical switches etc. (see Table 1). Together, the top-15 product groups at a 6-digit level of the Harmonised System (HS) give 57% of the total exports from Ukraine to Germany.

Table 1. Top-15 Ukrainian product groups by export to Germany as of 2018

Source: UN Comtrade

This brief argues that both countries are likely to gain additional benefits from further intensifying bilateral trade relations. It summarizes the results of the research (Iavorskyi P. at al., 2019) on how to further expand and diversify Ukrainian exports to Germany, it identifies the prospective product groups and obstacles to their exports, and provides policy recommendations for trade development.

Promising Products

In order to find the most promising ways for increasing Ukrainian exports to Germany, this study employs a two-step approach. First, using a normalized revealed comparative advantage (NRCA) index (Run Yu et al, 2009) we distinguish goods, which Ukraine has world-wide comparative advantage in and Germany does not. A positive (negative) NRCA indicates that country’s actual share of a product in national exports is higher (lower) than the world average, – so that the country has a comparative advantage (disadvantage) in this commodity. According to this criterion, product groups with a negative NRCA for Germany and a positive NRCA for Ukraine were selected.

At the second stage, for the goods identified during the first step, a gravity model was estimated. A gravity model predicts bilateral trade flows based on the size of the economy and trade costs between them (such as distance, cultural differences, free trade agreements, tariffs, etc.). Being a general equilibrium model, it captures not only immediate impact of economic and political changes on trade between two countries, but also how it influences trade with other countries. A gap between current and potential export volumes predicted by the model is a potential for exports increase (which we refer to as undertrade).

The gravity model estimates the total undertrade between Ukraine and Germany at $ 500 million in 2016, or 35% of the total exports from Ukraine to Germany in the same year. Moreover, Ukraine has the potential to increase trade in both goods already exported to Germany as well as goods not yet supplied by Ukrainian companies to this market.

As for the structure of our findings, agricultural and mining commodities, as well as products of traditional Ukrainian export industries, such as metallurgy, are widely represented on the top of the undertraded commodity list. For example, more than a half of the estimated undertrade falls on primary food and primary industrial supplies, such as soybeans, barley, tomatoes, grain sorghum, iron ore, zirconium ores, etc. These categories already account for a large share of the current exports composition, and production in these sectors provides for a significant share of employment. Foreign currency inflow stipulated by exporting these products is also important for the Ukrainian economy.

At the same time, the undertrade in categories of final consumption, capital goods and transport is much lower. However, these product groups are important for exports diversification. These, for example, include liquid dielectric transformers, refrigerator cabinets, telescopes, tugs and pusher craft in capital goods, rail locomotives, railway cars, gas turbine engines in transport; automatic washing machines, electric space heaters, fans, coffeemakers, synthetic curtains, and leather apparel in consumer goods. Despite the complex regulation and relatively small amount of estimated undertrade, export diversification from primary to manufactured goods is important for overcoming export instability and long-term economic growth (Cadot at al. 2013), which is why promotion of trade in such areas is important.

Figure 1. Estimated undertrade according to broad economic categories

Source: Own calculations based on UN Comtrade data

Obstacles to Trade

Following the abolition or reduction of EU import duties between Ukraine and the EU under the DCFTA, tariffs do not significantly restrict exports of Ukrainian goods to the EU. Instead, technical regulations, sanitary and phytosanitary measures, geographical indications, licensing, etc. create significant barriers to bilateral trade. Thus, “non-tradability” can be explained, for instance, by the negative effects of various non-tariff barriers (both at European and national levels) or other factors, such as low competitiveness (in terms of price or quality) of Ukrainian goods compared to similar goods supplied by other countries, taste preferences of German consumers, peculiarities of importers’ associations, specific requirements of retailers, etc. Thus, harmonization of Ukrainian regulations with those of the European Union in accordance with the AA will help reduce customs barriers and existing divergences in regulations, and thus simplify the export of Ukrainian goods to the EU and Germany in particular.

Policy Recommendations

Based on the findings of the qualitative and quantitative research carried out, Ukrainian policy makers are advised to:

  • Timely and effectively align Ukrainian legislation, standards and practices with those of the EU, in line with the Action Plan and Commitments undertaken by Ukraine under the DCFTA within the framework of the AA with the EU, in particular in such areas as technical barriers to trade, sanitary and phytosanitary measures, customs, and protection of intellectual property rights.
  • Accelerate preparations for the signing of the ACAA (the Agreement on Conformity Assessment and Acceptance for Industrial Products) for the top three priority sectors of Ukrainian industry, which Ukrainian authorities agreed with European side, namely in the areas of low-voltage equipment, electromagnetic compatibility and machine safety, which will boost industrial technological exports to the EU and other countries.
  • Conduct government level negotiations with the EU and Germany regarding the removal of those barriers to the single market faced by the promising Ukrainian goods that will not be lifted as a result of harmonization of regulations with the European ones.
  • Take advantage of the Regional Pan-Euro-Mediterranean Preferential Rules of Origin Convention (the Pan-Euro-Med Convention), which establishes identical rules of origin for goods between its member-states under free trade agreements, and will facilitate the opening of new production facilities and involvement in regional and international value chains.
  • Provide information and consulting support to local manufacturers and exporters regarding the most promising destination markets, help them find partners on such markets, advise on the best ways to penetrate such markets by organizing trade missions, etc.

Another push to the German-Ukrainian trade promotion may arise from facilitating German FDIs to Ukraine. German entrepreneurs and investors are interested in localizing German production facilities in Ukraine and establishing joint German-Ukrainian enterprises, STIs, in particular in such areas as agriculture, light industry (including textiles), civil engineering, renewable energy, and circular economy (GTAI 2018a, 2018b, 2018c). This form of cooperation also boosts Ukrainian exports, since such enterprises often produce intermediate inputs for German production. In order to promote joint enterprises setup Ukraine should:

  • Establish effective mechanisms for protecting foreign investments, including export-oriented ones.
  • Ensure the rule of law and effective protection of property rights.
  • Create favorable macroeconomic conditions to ensure access to financing for both Ukrainian and foreign businesses.

References

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

The Russian Food Embargo: Five Years Later

Posted on October 14, 2019 | Policy Brief

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In this brief, we report the results of a quantitative assessment of the consequences of counter-sanctions introduced by the Russian government in 2014 – Russian food embargo. We consider several affected commodity groups: meat, fish, dairy products, fruit and vegetables. Applying a partial equilibrium analysis to the data from several sources, including Rosstat, Euromonitor, UN Comtrade, industry reviews etc. as of 2018, we obtain that consumers’ total loss amounts to 445 bn Rub, or 3000 Rub per year for each Russian citizen. This is equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line. Out of this amount, 84% is distributed towards producer gains, 3% to importers, while the deadweight loss amounts to 13%. Based on industry dynamics, we identify industries where import substitution policies led to positive developments, industries where these policies failed and group of industries where partial success of import substitution was very costly for consumers.

The full text of the underlying paper is forthcoming in the Journal of the New Economic Association in October 2019.

In August 2014, in response to sectoral sanctions against Russia, the national government issued resolution No. 778, which prohibited import of processed and raw agricultural products from the United States, the EU, Ukraine and a number of other countries (Norway, Canada, Australia, etc.).  The goal was to limit market access for countries, which supported sectoral sanctions. The other rhetoric of the counter-sanctions was to support domestic producers via trade restrictions, or by other words – import substitution.

This brief provides an update of welfare analysis of counter-sanctions based on partial equilibrium  model of domestic market. The initial estimations based on 2016 data can be found in another FREE Policy Brief here. This time we compare the consumption, outputs and prices of the counter sanctioned goods as of 2018 relative to 2013. The estimated consumer surplus changes, producer gains and prices are reported in Table 1.

Table 1. Welfare effects of counter-sanctions in 2018 relative to 2013.

Data sources: Rosstat, Euromonitor, UN COMTRADE
* Negative losses correspond to gains
** Negative gains correspond to losses
Green color was used to mark the commodity groups with a noticeable consumption growth in 2013-2018 and red color those with consumption decrease.

Effect on production

From the point of view of price dynamics, on the one hand, and consumption and output, on the other, the studied products can be divided into three groups.

The first group which we call “Success of import substitution”  includes goods for which real prices (in 2013 level) increased by 2016 but afterwards, the growing domestic production ensured that by 2018 prices fell below the level of 2013 with a corresponding increase in consumption. This group includes tomatoes, pork, poultry and, with some reservation, beef. For beef, growing domestic production pushed prices down after 2016, but the level of consumption and prices have not yet reached the pre-sanction level.

For the second group, import substitution has not resulted in a price decrease, we call this group “Failure of import substitution”. For products in this group, the initial increase in prices by 2016 was not reverted afterwards. Their consumption decreased significantly compared to 2013, and domestic production either continued to fall after 2016, or its growth turned out to be fragile. This group includes apples, cheese, fish, as well as condensed milk and processed meat.

We call the third group “Very expensive import substitution”. It includes fromage, sour milk, milk and (to a lesser extent) butter. This group is characterized by increase in consumption and output in the period 2016–2018, but real prices over this period still remain very high.

Effect on consumers

By comparing the losses and gains of consumers in different categories of goods due to changes in real prices and real consumption, our analysis provides the following monetary equivalents. For all considered counter-sanctioned product groups, with the exception of poultry, pork and tomatoes, consumer losses are around 520 billion rubles per year (in 2013 prices). In three product groups (poultry, pork, tomatoes), in which there was a decrease in prices and a significant increase in consumption, the consumer gains are equivalent to 75 billion rubles per year. Thus, the total negative effect from counter-sanctions for the consumers amounted to 445 billion rubles a year, or about 3000 rubles for a person per year.

Given the cost of the minimum food basket, defined in Russia as 50% of the subsistence level, the impact of counter-sanctions on the budgets of Russian consumers can be estimated as follows. 3000 rubles account for approximately 4.8% of the annual cost of the minimum food basket. The minimum food basket is a set of food products necessary to maintain human health and ensure its vital functions that is established by law. In other words, one can say that 3000 rubles a year are equivalent to a 4.8% increase in food expenditure for those who are close to the poverty line.

Consumer surplus losses were significantly redistributed in favor of domestic production, totaling 374 billion, or 2500 rubles per year per person. Another 56 billion rubles (or 390 rubles per person) correspond to the deadweight loss, i.e., reflect the inefficiency increase of the Russian economy, and 16 billion rubles (110 rubles per person) is the equivalent of redistribution in favor of foreign producers, who get access to Russian market with higher priced products than before counter-sanctions.

Effect on foreign partners

As a result of the selective embargo, the geography of Russian imports of the affected goods has changed. Traditional suppliers of these goods, primarily from Europe, were replaced by suppliers from other countries due to trade diversion. Given the changes in the composition of importers after the imposition of sanctions, we single out countries that have lost and countries that have gained access to the Russian market. We use the change in trade volumes from the respective countries as indicators of growth and decrease in share of these importers in the Russian market. Below we consider in detail the three groups of goods with the largest gains for importers in 2018 compared with 2013: cheese, apples, butter.

Cheese imports decreased significantly after the imposition of counter-sanctions, in 2018 accounting for only 42% of their dollar value in 2013. The total gain of importers due to the growth of domestic prices in 2013-2018 amounted to 17.3 billion rubles (Table 1) and was distributed among following importing countries: Belarus (78%), Argentina (6%), Switzerland (4%), Uruguay (3%), Chile (3%), other countries (6%). Countries that lost their shares of the Russian cheese market included Ukraine, Holland, Germany, Finland, Poland, Lithuania, France, Denmark, Italy and Estonia. As mentioned earlier, domestic production and Belarusian imports were not able to fully compensate for imports from countries on the counter-sanctions list, and in 2016-2018 cheese consumption in Russia decreased significantly.

Apple imports after the initial drop in 2016 partially recovered in 2018, amounting to 66% of their dollar volume in 2013. The total gain of importers in 2018 compared to 2013 amounted to 15.0 billion rubles (Table 1); it was distributed between Serbia (22%), Moldova (19%), China (13%), Turkey (10%), Iran (10%), Azerbaijan (7%), South Africa (4%), Chile (3%), Brazil (3%) and other countries (9%). Poland suffered the most from the ban on apple imports; it accounted for about 80% of all losses. Other losers from counter-sanctions include Italy, Belgium and France. The reorientation of trade flows did not completely replace Polish imports, so apple consumption in 2016-2018 was significantly lower than in 2013.

Imports of butter in 2018 was also below the level of 2013 (67% of dollar value). The gain of importers in 2018 compared to 2013 amounted to 11.2 billion rubles and was distributed among the following  trading partners: Belarus (90%), Kazakhstan (4%), Kyrgyzstan (3%) and other countries (3%). Among the countries bearing most of the negative burden of the diversion of trade, one should mention Finland and Australia.

Conclusions

Five year after counter-sanctions were put in place Russian consumers continue paying for them out of their pockets. While few industries have demonstrated a positive effect of import substitution policies, most are not effective enough to revert the price dynamics.

References

Short-Run and Long-Run Effects of Sizeable Child Subsidy: Evidence from Russia

Posted on October 7, 2019 | Policy Brief

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How to design the optimal pro-natalist policy is an important open question for policymakers around the world. Our paper utilizes a large-scale natural experiment aimed to increase fertility in Russia. Motivated by a decade-long decrease in fertility and population, the Russian government introduced a sequence of sizable child subsidies (called Maternity Capitals) in 2007 and 2012. We find that the Maternity Capital resulted in a significant increase in fertility both in the short run and in the long run. The subsidy is conditional and can be used mainly to buy housing. We find that fertility grew faster in regions with a shortage of housing and with a higher ratio of subsidy to housing prices. We also find that the subsidy has a substantial general equilibrium effect. It affected the housing market and family stability. Finally, we show that this government intervention comes at substantial costs.

In all European and Northern American countries the fertility is below the replacement level (United Nations, 2017). Following this concern, most of the developed countries have implemented various large scale and expensive pro-natalist policies. Yet, the effectiveness of these policies is unclear, and the design of the optimal pro-natalist policy remains a challenge.

There are several important open research questions on the evaluation of these programs. The first is whether these programs can induce fertility in the short-run and/or in the long-run horizon. Indeed, very few of these expensive and large-scale policies are proved to be an effective tool to increase fertility (Adda et al, 2017). The next set of questions deals with further evaluation of the programs: What are the characteristics of families that are affected by this policy? How costly is the policy, i.e. how much is the government paying per one birth that is induced by the policy? Finally, what are the non-fertility related effects of these policies? While most of the studies that analyze the effect of pro-natalist policies concentrate on fertility and mothers’ labor market outcomes, these, usually large-scale, policies may have important general equilibrium and multiplier effects that may affect economies both in the short run and long run (Acemoglu, 2010).

In our paper we utilize a natural experiment aimed to increase fertility in Russia to address these questions.

Motivated by a decade-long decrease in fertility and depopulation, the Russian government introduced a sizable conditional child subsidy (called Maternity Capital). The program was implemented in two waves. The first wave, the Federal Maternity Capital program, was enacted in 2007. Starting from 2007, a family that already has at least one child, and gives birth to another, becomes eligible for a one-time subsidy. Its size is approximately 10,000 dollars, which exceeds the country’s average 18-month wage and exceeds the country’s minimum wage over a 10-year period. The recipients of the subsidy can use it only on three options: on housing, the child’s education, and the mother’s pension. Four years later, at the end of 2011, Russian regional governments introduced their own regional maternity programs that give additional – on the top of the federal subsidy – money to families with new-born children.

In our paper, we document that the Maternity Capital program results in a significant increase in fertility rates both in the short run (by 10%) and in the long run (by more than 20%). This effect can be seen from both within-country analysis and from comparing the long-term growth of fertility rates in Russia with Eastern and Central European countries that face similar economic conditions and had similar pre-reform fertility trends. Like Russia, Eastern European countries experienced a drop in fertility rates right after the collapse of the Soviet Union and had similar trends in fertility up until 2007. Our results show that while having similar trends in fertility before 2007, afterward Russia significantly surpassed all the countries from this comparison group.

Figure 1 illustrates the effect of the Maternity Capital on birth rates. The top two panels show monthly birth rates (simple counts and de-seasoned); the bottom panels show total fertility rates in Russia versus Eastern European countries, and versus the European Union and the US.

Figure 1. Total Fertility Rate, Russia, Eastern European countries, USA and EU.

Source: Sorvachev and Yakovlev (2019), and http://www.fertilitydata.org/.

The effects of the policy are not limited to fertility. This policy affects family stability: it results in a reduction in the share of single mothers and in the share of non-married mothers.

Also, the policy affects the housing market. Out of three options (education, housing and pension), 88% of families use Federal Maternity Capital money to buy housing. We find that the supply of new housing and housing prices increased significantly as a result of the program. Confirming a close connection between the housing market and fertility, we find that in regions where the subsidy has a higher value for the housing market, the program has a larger effect: the effect of maternity capital was stronger, both in the short run and long run, in regions with a shortage of housing, and in regions with a higher ratio of subsidy to price of apartments (i.e. those regions where the real price of subsidy as measured in square meters of housing is higher).

Figure 2 below shows the effect of Federal Maternity Capital on birth rates in different regions. It shows no effect on fertility in Moscow, small effect in Saint-Petersburg; whereas the sizable effect of maternity capital in other Russian regions.

Figure 2. Effect of Federal Maternity capital, by regions

Source: Sorvachev and Yakovlev (2019), and http://www.gks.ru/.

These results suggest that cost-benefit analysis of such policies should go beyond the short-run and long-run effects on fertility. Ignoring general equilibrium issues may result in substantial bias in the evaluation of both short-run and long-run costs and benefits of the program.

While there are many benefits of the program, we show that this government intervention comes at substantial costs: the government’s willingness to pay for an additional birth induced by the program equals approximately 50,000 dollars.[1]

For more detailed evaluation of the results see Evgeny Yakovlev and Ilia Sorvachev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper # 254, 2019.

References

  • Acemoglu, Daron 2010 “Theory, General Equilibrium, Political Economy and Empirics in Development Economics”, Journal of Economic Perspectives, 24(3), pp. 17-32. 2010
  • Adda, Jérôme, Christian Dustmann and Katrien Stevens 2017. “The Career Costs of Children”. Journal of Political Economy, 125, 2, 293-337.
  • Ilia Sorvachev and Evgeny Yakovlev, 2019, “Short-Run and Long-Run Effects of Sizable Child Subsidy: Evidence from Russia”, NES working Paper #254 and LSE IGA Research Working Paper Series 8/2019

[1] Roughly, the WTP (US$50,000) exceeds nominal US$10,000 subsidy because the government pays for all (100%) families that give birth to a child to induce additional (20%) increase in fertility. See paper for more accurate elaboration.

The Gender Wage Gap in Belarus: State vs. Private Sector

Posted on September 30, 2019 | Policy Brief

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This brief is based on research that studies gender difference in wages in Belarus using survey data from 2017. According to the results, the unconditional gender wage differential equals 22.6%. The size of the wage gap is higher in the state sector than in the private sector. Additionally, it increases in the state sector throughout the wage distribution and accelerates at the top percentiles, indicating the presence of a strong glass ceiling effect.

Introduction

The causes and consequences of the gender wage gap in the labor market, that is the difference between the wages earned by women and men, continue to attract increasing attention in empirical studies worldwide.

Belarus’ labor market is not an exception and faces the problem of wage inequality like other neighboring and transition countries. According to the National Statistical Committee of the Republic of Belarus (Belstat), the average gender wage gap in terms of monthly wages was 19% in 2000, it increased up to 23.8% in 2015, and reached 25.4% in 2017.

In this regard, this brief updates the estimates of the gender wage gap in Belarus. And it summarizes the results of the study on what the role of the state and private sectors are in the distribution of gender wage differences in Belarus (Akulava and Mazol, 2018).

Data and methodology

The data used in the research is from the Generations and Gender Survey (GGS) conducted in Belarus in 2017. This survey is a nationally representative dataset that is based on interviews of about 10,000 permanent residents of Belarus, aged 18–79, covering the whole country disaggregated by regions. The GGS contains information on a range of individual (age, gender, marital status, educational attainment, employment status, hours worked, wages earned etc.) and household-level characteristics (household size and composition, land holding, location, asset ownership etc.).

The analysis is based on the typical Mincer model of earnings that estimates individual wage income as a function of various influencing factors using the OLS approach (Mincer, 1974). Specifically, the Mincerian wage equation is defined where the log of the hourly wage rate is regressed on a set of male and female workers’ personal and job characteristics (educational level, working experience, occupational type, organization type, family characteristics, and region).

Next, we use the Oaxaca-Blinder (OB) methodology (Oaxaca, 1973; Blinder, 1973) to identify and quantify the contribution of personal characteristics and the unexplained component (which is referred to as differences in returns) to the wage difference between males and females.

Finally, we apply the Machado-Mata (MM) technique (Machado and Mata, 2005) to look into the nature of the wage gap at various points of the income distribution and also to test the difference for individuals employed in the state or private sectors. For the Machado-Mata procedure, we estimate our specifications at the 10th, 25th, median, 75th and 90th percentiles of the wage distribution.

Results

The analysis shows that women’s wages are lower than men’s wages all over the wage distribution. The average raw gender wage gap equals 22.6% and it increased substantially compared with 9.0% in 1996 and 17.8% in 2006, the numbers obtained in the study conducted by Pastore and Verashchagina (2011).

Figure 1. Gender differential by quantile of the wage distribution

Source: Authors’ estimates based on GGS.

The level of female earnings is lower than the male regardless of the occupational type, educational background, work experience and organizational type. Moreover, the underpayment of women is lower for low earning workers, but increases up to the end of the wage distribution (see Figure 1).

The OB decomposition shows that female educational attainment and job-related experience help to decrease the level of the wage gap slightly (see Table 1).

Table 1. Oaxaca-Blinder decomposition results

Source: Authors’ estimates based on GGS.

However, the occupational choice is leading to an expansion of the difference in earnings. However, its effect is also small, indicating that occupational segregation plays a minor role in explaining the gender wage gap. The major share of the gender wage gap is formed by the unexplained part, which is likely to be attributed to discrimination.

Next, the level of remuneration is higher among private companies. However, contrary to other countries in transition, the average gender wage gap in Belarus in the private sector is lower than in the public sector.

Moreover, the MM decomposition estimates presented in Table 2 demonstrate that the gender wage gap in the state sector shows evidence of the glass ceiling effect (the size of the total wage gap expands at the top of the wage distribution), while no evidence of either glass ceiling or sticky floor (the size of the total wage gap increases at the bottom of the wage distribution) in the private sector.

The negative coefficient near the characteristics part in the private sector shows that female endowments outweighs their male counterparts. Thus, controlling for personal characteristics, if the labor market rewards males and females equally, the wages of females in the private sector should be substantially higher (see Table 2).

Table 2. Machado-Mata decomposition of the observed gender wage gap by organization type

Source: Authors’ estimates based on GGS.

Finally, the results also suggest that female workers are better off being in the private sector at the lowest and the highest percentiles (i.e. the size of the gender wage gap is lower there compared to the 25th and 50th percentile).

A possible explanation for all the above is that institutional differences seem to play a crucial role here. First, Belarusian private firms work under stronger regulation than in other transition economies which makes it harder for them to set low wages. Second, they also operate under stronger competition (compared to state companies), which force them to identify individual productivity more correctly, narrowing the gender difference in pay. In contrast, the paternalistic attitude to women left as a legacy from the Soviet Union further increases the gender wage gap in the public sector.

Conclusion

In this brief, we present new evidence on the existence of a gender wage gap in the Belarusian labor market and analyze the differences in its distribution between the state and private sectors.

Our results show that the unconditional gender wage gap in terms of hourly wages equals 22.6%. Thus, jointly with a previous study (see Pastore and Verashchagina, 2011) and recent official indicators, all these indicate that the pace towards gender equality in Belarus seems to be sluggish. For the moment, all institutional changes accomplished by the Belarusian government to reduce gender discrimination are not enough and require additional efforts to cope with that problem.

However, the gender wage gap is shown to be much wider in the public sector than in the private sector. At the same time the private sector appears to be more attractive than the public sector in the country in terms of the level of remuneration. Therefore, additional structural shifts of the economy accompanied by the growth of competition are needed to induce a further reduction of the gender wage gap.

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.

Social Media and Xenophobia

Posted on June 17, 2019 | Policy Brief

Man with a cell phone in hands spending time on social media

We study the causal effect of social media on hate crimes and xenophobic attitudes in Russia, using variation in social media penetration across cities. We find that higher penetration of social media leads to more ethnic hate crimes, but only in cities with a high baseline level of nationalist sentiment prior to the introduction of social media.  Consistent with a mechanism for the coordination of crimes, the effects are stronger for crimes with multiple perpetrators. We show that social media penetration also had a persuasive effect on young and uneducated individuals, who became more likely to have xenophobic attitudes.

In recent years, the world has witnessed a large increase in expressions of hate, particularly of xenophobia. Candidates and platforms endorsing nationalism and views associated with intolerance toward specific groups have also gathered increased popular support both in the U.S. and across Europe. There is a lot of speculation about the potential drivers of this increase in the expression of hate. In our recent paper (Enikolopov et al, 2019) we study the role of social media in this process. This brief introduces the topic and offers a short outline of our findings.

Conceptually, social media could foster hate being expressed through different channels. First, social media reduces the cost of coordination. For example, there is evidence that it facilitates political protest (Enikolopov, Makarin, Petrova, 2018). Coordination facilitated through social media might be particularly relevant for illegal and stigmatized activities, such as hate crime: social media might make it easier to find like-minded people (through targeted communities and groups); it might also reduce the cost of asking or exposing oneself by providing a more anonymous forum for social interactions. Social media might also influence people’s opinions: tolerant individuals might be more exposed to intolerant views, while intolerant individuals might end up in an “echo chamber” (Sunstein 2001, 2017, Settle 2018) that make their views even more extreme. In our paper, we study the causal effect of social media exposure on xenophobic crimes and xenophobic attitudes in Russia and provide evidence on the particular mechanisms behind these effects.

The challenge in identifying a causal effect of social media is that access and consumption of social media are not randomly assigned. To surmount this challenge, we follow the approach of Enikolopov et al. (2018) and exploit a feature of the introduction of the main Russian social media platform – VKontakte (VK). This social media, which is analogous to Facebook in functionality, was the first mover on the Russian market and secured its dominant position with a user share of over 90% by 2011. VK was launched in October 2006 by Pavel Durov, its founder, who at that time was an undergraduate student at St Petersburg State University (SPbSU). Initially, users could only join the platform by invitation, through a student forum of the University, which was also created by Durov.

As a result, the vast majority of the early users of VK were students of SPbSU. This, in turn, made their friends and relatives more likely to open an account. And since SPbSU attracted students from around the country, this sped up the development of VK in the cities, from which these students were coming from. Network externalities magnified these effects and, as a result, the idiosyncratic variation in the distribution of the home cities of Durov’s classmates had a long-lasting effect on VK penetration. Following this logic, we use fluctuations in the distribution of student of SPbSU across cities as an instrument for the city-level penetration of VK. We then evaluate the effect of higher VK penetration on hate crimes and hate attitudes, combining data on hate crimes for the period between 2007 and 2015 collected by a reputable Russian NGO SOVA with survey data on hate attitudes.

Previous findings indicate that whether information from media induces people to be involved in the active manifestation of xenophobic attitudes or not depends on predispositions of the population. For example, Adena et al (2015) demonstrate that radio propaganda by the Nazis in the 1930’s was effective only in areas with a historically high levels of anti-Semitism. The role of the underlying level of nationalism is likely to be even stronger for social media, in which the content of the media itself directly reflects the attitudes of the population. This is particularly relevant for hate crimes committed by multiple perpetrators, in which social media can facilitate the coordination of such crimes.

Thus, we test whether the effect of social media depends on the pre-existing level of nationalism. To get at this underlying sentiment, we break cities by their level of support for the Rodina (“Motherland”) party, which ran in the national 2003 elections (the last parliamentary elections before the creation of VK) on an explicit nationalistic, xenophobic platform.

We find that penetration of social media leads to more ethnic hate crimes, but only in cities with a high baseline level of nationalist sentiment prior to the introduction of social media. For example, in cities with a maximum level of support of Rodina an increase in the number of VK users by 10% lead to an increase in ethnic hate crimes by 20%, while it had no significant effect on hater crime in cities with minimal support of Rodina. There is also no evidence that future social media penetration is related to ethnic hate crimes before the creation of social media, regardless of the level of pre-existing nationalistic attitudes.

Further evidence is consistent with social media playing a coordination role in hate crimes. The effect of social media is stronger for crimes perpetrated by multiple individuals (as opposed to crimes committed by a single person), where coordination is more important. These heterogeneous effects are also not consistent with results being simply driven by a higher likelihood of hate crime in places with higher social media penetration, unless this effect were present precisely in cities with higher support for Rodina and for crimes with multiple perpetrators, for example – which we find unlikely.

Having found evidence of a causal effect of social media on ethnic hate crimes, consistent with a mechanism of coordination, we turn next to the impact of social media on xenophobic attitudes. We designed and organized an online survey, and launched it in the summer of 2018, reaching 4,327 respondents from 64 cities. To measure xenophobic attitudes, we examined answers to the question “Do you feel irritation of dislike for individuals from some other ethnicities?” Note that, unlike the coordination of hate crimes, the persuasive effects of social media are not necessarily expected to be strongest in cities with higher baseline nationalistic sentiment since individuals on social media can get as easily connected to people outside their city. In fact, it is conceptually possible that the persuasion would be stronger in cities with lower baseline nationalistic sentiment: individuals might have previously been less aware of and less exposed to these types of views before the introduction of social media.

Since there might be a stigma in reporting xenophobic attitudes even in anonymous surveys, we use a “list experiment” to approximate “truly-held” xenophobic attitudes. In particular, the list experiment works as follows: first, respondents are randomly assigned either into a control group or a treatment group. Respondents in all groups are asked to indicate the number of policy positions they support from a list of positions on several issues. Support for any particular policy position is never indicated, only the total number of positions articulated on the list that a respondent supports. In the control group, the list includes a set of contentious, but not stigmatized, opinions. In the treatment group, the list includes all the contentious opinions from the control list, but also adds the opinion of interest, which is potentially stigmatized. The degree of support for the stigmatized opinion can be assessed by comparing the average number of issues supported in the treatment and control groups. The question of interest, randomly added to half of the questionnaires, was “Do you feel irritation of dislike for individuals from some other ethnicities?”.

The results indicate that the average share of people who agree with the statement is 37%. While there is no significant effect of social media penetration on xenophobic attitudes for the whole sample, there is a significant effect for important subsamples, which are at a higher risk of being involved in hate crime, such as respondents with lower levels of education or young respondents. Of course, the individuals that became more likely to engage in hate crime are not necessarily the same that have been persuaded to have more xenophobic attitudes (especially given the question used to assess attitudes) – though it is possible that some individuals who would have been close to committing crimes in the absence of social media might have been persuaded enough to switch their behavior in the presence of social media.

At the same time, we do not find that social media leads to an increase in xenophobic attitudes when measured with a direct question. The results are confirmed if we use a much larger, nationally representative survey of more than 30,000 respondents conducted by one of the biggest Russian survey companies FOM in 2011. In principle, it is possible that social media not only changed real attitudes but also the perception of the social acceptability of expressing these attitudes. However, we do not find any evidence that social media reduces the stigma of admitting xenophobic attitudes. The fact that we find the effect of social media on actual attitudes, but not on the expressed ones suggests, that if anything the stigma increased, at least for the respondents who acquired xenophobic attitudes as a result of social media influence. This highlights the importance of using a survey method that reduces concerns with social acceptability, such as list experiments.

Overall, our results indicate that social media lead to an increase in both ethnic hate crimes and xenophobic attitudes in Russia. However, the effect on hate crime is observed only in cities in which there was already a high level of nationalism. Additional evidence indicates that this effect is driven both by facilitating the coordination of nationalists and by persuading people to become more xenophobic. These findings contribute to a growing body of evidence that social media is a complex phenomenon that has both positive and negative effects on the welfare of people (see also Allcott et al, 2019), which has to be taken into account in discussing policy implications of the recent changes in media technologies.

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.

Russia’s Real Cost of Crimean Uncertainty

Posted on June 10, 2019 | Policy Brief

Blue sky with fighter jets flying across the sky representing Russias Real Cost of Crimean Uncertainty

The annexation of Crimea has real costs to the Russian economy beyond what is measured by some items in the armed forces’ budget; social spending in the occupied territories; or the cost of building a rather extreme bridge to solve logistics issues. Russia’s real cost of the annexation of Crimea is also associated with the permanent loss of income that the entire Russian population is experiencing due to increased uncertainty, reduced capital flows and investment, and thus a growth rate that is significantly lower than it would have been otherwise. Since the years of lost growth are extremely hard to make up for in later years, there will be a permanent loss of income in Russia that is a significant part of the real cost of annexing Crimea and continuing the fighting in Eastern Ukraine. It is time to stop not only the human bleeding associated with Ukraine, but also the economic.

Estimating the real cost of Russia’s annexation of Crimea and the continued involvement in Eastern Ukraine is complicated since there are many other things going on in the Russian economy at the same time. In particular, oil prices fell from over $100/barrel in late 2013 to $30/barrel in 2016 (Figure 1). Becker (2016) has shown that 60-80 percent of the variation in GDP growth can be explained by changes in oil prices, so this makes it hard to just look at actual data on growth to assess the impact of Crimea and subsequent sanctions and counter sanctions.

Figure 1. Russian GDP and oil price

Source: Becker (2019)

The approach here is instead to focus on one channel that is likely to be important for growth in these circumstances, which is uncertainty and its impact on capital flows and investment.

From uncertainty to growth

The analysis presented here is based on several steps that link uncertainty to GDP growth. All the details of the steps in this analysis are explained at some length in Becker (2019). Although this brief will focus on the main assumptions and estimates that are needed to arrive at the real cost of Crimea, a short description of the steps is as follows.

First of all, in line with basic models of capital flows, investors that can move their money across different markets (here countries) will look at relative returns and volatility between different markets. When relative uncertainty goes up in one market, capital will leave that market.

The next step is that international capital flows affect investment in the domestic market. If capital leaves a country, less money will be available for fixed capital investments.

The final step is that domestic investments is important for growth. Mechanically, in a static, national accounts setting, if investments go down, so does GDP. More long term and dynamically, investments have a supply side effect on growth, and if investments are low, this will affect potential as well as actual growth negatively.

These steps are rather straightforward and saying that uncertainty created by the annexation of Crimea leads to lower growth is trivial. What is not trivial is to provide an actual number on how much growth may have been affected. This requires estimates of a number of coefficients that is the empirical counterparts to the theoretical steps outlined here.

Estimates to link uncertainty to growth

In short, we need three coefficients that link: domestic investments to growth; capital flows to domestic investments; and uncertainty to capital flows.

There are many studies that look at the determinants of growth, so there are plenty of estimates on the first of these coefficients. Here we will use the estimate of Levine and Renelt (1992), that focus on finding robust determinants of growth from a large set of potential explanatory variables. In their preferred specification, growth is explained well by four variables, initial income, population growth, secondary education and the investments to GDP ratio. The coefficient on the latter is 17.5, which means that when the investment to GDP ratio increases by 10 percentage points, GDP grows an extra 1.75 percentage points per year. Becker and Olofsgård (2018) have shown that this model explains the growth experience of 25 transition countries including Russia since 2000 very well, which makes this estimate relevant for the current calculation.

The next coefficient links capital flows to domestic investments. This is also a subject that has been studied in many empirical papers. Recent estimates for transition countries and Russia in Mileva (2008) and Becker (2019) find an effect of FDI on domestic investments that is larger than one, i.e., there are positive spillovers from FDI inflows to domestic investments. Here we will use the estimate from Becker (2019) that finds that 10 extra dollars of FDI inflows are associated with an increase of domestic investments of 15 dollars.

Finally, we need an estimate linking uncertainty with capital flows. There are many studies looking at risk, return and investment in general, and also several studies focusing on international capital flows and uncertainty.  Julio and Yook (2016) look at how political uncertainty around elections affect FDI of US firms and find that FDI to countries with high institutional quality is less affected by electoral uncertainty than others. Becker (2019) estimates how volatility in the Russian stock market index RTS relative to the volatility in the US market’s S&P 500 is associated with net private capital outflows. The estimate suggests that when volatility in the RTS goes up by one standard deviation, this is associated with net private capital outflows of $30 billion.

These estimates now only need one more thing to allow us to estimate how much Crimean uncertainty has impacted growth and this is a measure of the volatility that was created by the annexation of Crimea.

Measuring Crimean uncertainty

In Becker (2019), the measure of volatility that is used in the regression with net capital outflows is the 60-day volatility of the RTS index. Since we now want to isolate the uncertainty created by Crimea related events, we need to take out the volatility that can be explained by other factors in order to arrive at a volatility measure that captures Crimean induced uncertainty. In Becker (2019) this is done by running a regression of RTS volatility on the volatility of international oil prices and the US stock market as represented by the S&P 500. The residual that remains after this regression is the excess volatility of the RTS that cannot be explained by these two external factors. The excess volatility of the RTS index is shown in figure 2.

It is clear that the major peaks in excess volatility are linked to Crimea related events, and in particular to the sanctions introduced at various points in time. From March 2014 to March 2015, there is an average excess volatility of 0.73 standard deviations with a peak of almost 4 when the EU and the USA ban trade with Crimea. This excess volatility is our measure of the uncertainty created by the annexation of Crimea.

Figure 2. RTS excess volatility

Source: Becker (2019)

From Crimean uncertainty to growth

The final step is simply to use our measure of Crimean induced uncertainty together with the estimates that link uncertainty in general to growth.

The estimated excess volatility associated with Crimea is conservatively estimated at 0.7 standard deviations. Using this with the estimate that increasing volatility by one standard deviation is associated with $30 billion in capital outflows, we get that the Crimean uncertainty would lead to $21 billions of capital outflows in one quarter or $84 billions in one year. If this is in the form of reduced FDI flows, we have estimated that this means that domestic investments would fall by a factor of 1.5 or $126 billions.

In this period, Russia had a GDP of $1849bn and fixed capital investments of $392bn. This means that $126 billions in reduced investments correspond to a reduction in the investments to GDP ratio of 7 percentage points (or that the investments to GDP ratio goes from around 21 percent to 14 percent).

Finally, using the estimate of 17.5 from Levine and Renelt, this implies that GDP growth would have been 1.2 percentage points higher without the estimated decline in investments to GDP.

In other words, the Crimean induced uncertainty is estimated to have led to a significant loss of growth that has to be added to all the other costs of the annexation of Crimea and continued fighting in Eastern Ukraine. Note that recent growth in Russia has been just barely above 1 percent per year, so this means that growth has been cut in half by this self-generated uncertainty.

Of course, the 1.2 percentage point estimate of lost growth is based on many model assumptions, but it provides a more sensible estimate of the cost of Crimea than we can get by looking at actual data that is a mix of many other factors that have impacted capital flows, investments and growth over this period.

Policy conclusions

The annexation of Crimea and continued fighting in Eastern Ukraine carry great costs in terms of human suffering. In addition, they also carry real costs to the Russian economy. Not least to people in Russia that see that their incomes are not growing in line with other countries in the world while the value of their rubles has been cut in half. Some of this is due to falling oil prices and other global factors that require reforms that will reorient the economy from natural resource extraction to a more diversified base of income generation. This process will take time even in the best of worlds.

However, one “reform” that can be implemented over night is to stop the fighting in Eastern Ukraine and work with Ukraine and other parties to get out of the current situation of sanctions and counter-sanctions. This would provide a much-needed boost to foreign and domestic investments required to generate high, sustainable growth to the benefit of many Russians as well as neighboring countries looking for a strong economy to do trade and business with.

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.

Institutions and Comparative Advantage in Services Trade

Posted on June 3, 2019 | Policy Brief

High office buildings facing sky representing Institutions and Services Trade

Recent studies have highlighted the role of human capital and good economic institutions in establishing a comparative advantage in trade in complex institutions-intensive goods. We show that the effect of institutions on comparative advantage in services trade is quite different: in fact, countries with bad institutions rely significantly more on services exports. More specifically, as the quality of institutions deteriorates, information technology sector (ICT) services exports as a share of total ICT exports increase significantly and countries with worse institutions get a substantial comparative advantage in the provision of ICT services. This is especially applicable to transitional economies characterized by high, arguably exogenous, human capital at the level of most advanced countries.

Introduction

Recent research in international trade has demonstrated that institutions influence the determination of comparative advantage in the trade of goods. Countries with strong domestic institutions have a significant comparative advantage in producing complex, institutions-intensive goods while countries with weak institutions tend to specialize in less complex goods. Through this channel, weak institutions can hinder growth and development (Nunn and Trefler, 2014).

We argue that the role of institutions in services trade can differ significantly from the one in trade in goods. The intuition behind it is that services provision often relies less on institution-driven factors, such as public infrastructure, availability of large number of inputs, property rights and capital investments than the production of complex goods.

We show, in the case of the information technology sector (ICT), that countries with bad institutions rely significantly more on services exports even after controlling for human capital input requirements and availability. We focus on the ICT sector to isolate the differences in the role of institutions in determining comparative advantage in goods and services. Both ICT goods and services provision are equally intensive in human capital and thus present a good opportunity to study differences between goods and services provision.

Our study is motivated by high ICT services exports (e.g. software development) and low ICT goods exports (e.g. computers, phones, etc.) of transition countries which are known to have high human capital and low institutional indicators.

Institutions and ICT Services Exports

Figure illustrates the high human capital availability of transitions economies and weak domestic institutions relative to other countries. Specifically, we categorize countries into four groups: 23 most developed economies (e.g. USA, Canada, Japan and Western European economies); new members of the European Union (a group of 13 countries including Poland, Slovakia, and Baltic countries); transition economies group consists of 17 mostly post-Soviet countries including Russia, Ukraine, Belarus; the most numerous fourth group includes more than hundred other developing countries.

Figure 1. Institutions quality and schooling by country groups

1a

Source: Authors’ calculations, schooling data from Barro and Lee (2013)

1b

Source: Authors’ calculations, institutional indicators data from the World Bank World Governance Indicators

Figure 1a presents an average number of years of schooling, our measure of human capital, for each country group in 2000 and 2010 (the years are chosen based on data availability). The human capital is at a similar level in the most developed economies, EU-13 and transition economies, but significantly lower in other developing countries. Figure 1b illustrates the average institutional quality for each group in 2000 and 2010. Institutional quality for each country is calculated as an average of six indicators, distributed approximately from -2.5 to 2.5: control of corruption, government effectiveness, political stability, rule of law, regulatory quality, voice and accountability, with a lower value corresponding to worse institutional quality. In contrast to education, the average institutional quality of transition economies, although improving from 2000, remains on average lower than the institutional quality of other developing countries.

Consistent with the literature on institutions and comparative advantage in relationship and investment-intensive goods production, ICT goods export from transition economies is significantly lower than in other countries. In contrast, ICT services exports is at a higher level and faster growth in transition economies than in other countries.

Belarus presents a good motivating example. On the one hand, fundamental education in Belarus is at a level of the most advanced countries, which allows 21 universities in the country to educate about 7,000 graduates in IT industry in a year. On the other hand, ICT services exports in Belarus is thriving: over the last 10 years, the growth of ICT services is an eightfold increase (it was 150M USD in 2008 and 1.2B USD in 2017). Nowadays, Belarus is one of the world leaders in ICT services exports per capita. At the same time, ICT goods export is not growing even close to the level of ICT services exports. Over the same time period, it has grown only by about 30 percent: in 2008 ICT goods export was 105M USD, in 2016 – 140M USD (BELARUS.BY, 2019).

The importance of ICT services exports in transition economies is seen in Figure 2. The figure presents ICT services exports as a share of total exports of ICT goods and services. To obtain values for each country group, we average ICT services shares across countries within each group.

Figure 2. ICT services exports as share of total ICT exports

Source: Authors’ calculations, ICT services export data from Trademap, ICT goods export data from WDI

As Figure 2 shows, the average share of ICT services exports in transition economies is higher than the share of ICT services exports in all other groups of countries. Transition economies, characterized by high human capital and weak institutional quality, specialize in exports of services over goods in their ICT exports. This descriptive evidence suggests that abundant human capital, inherited from the USSR and arguably exogenous, shifts to services within the human capital intensive ICT sector when facing weak institutions.

Empirical panel analysis confirms the descriptive evidence. To test our hypothesis, we use the share of ICT services in total ICT exports as a dependent variable and we show that quality of institutions is a significant determinant. Our regressions show that the higher the quality of institutions is, the lower will the share of ICT services in total ICT exports be. Moreover, regression analysis allows us to quantify this dependence: as the quality of institutions increases by 1, which is approximately the difference between Belarus and Georgia (as can be seen in figure 3 below), the share of ICT goods in total ICT services increases by about 20%.

Institutions as a source of comparative advantage in services

To explore the role of institutions in the relative services provision within a sector further, we look at comparative advantage in exporting ICT services. We incorporate a measure similar to Relative Share measure used in Levchenko (2007) for the analysis of comparative advantage in goods export. The measure effectively compares the share of ICT services export for a given country with the world average. The index of revealed comparative advantage in ICT services over ICT goods is computed for country in the following way:

where  is share of ICT services exports in total ICT exports for country,  is the export of ICT services for all countries, and  is the total ICT export (goods plus services) for all countries.

We look at the revealed comparative advantage index across our group of transition economies in figure 3 and see that even within this group, there is a negative correlation between institutions quality and revealed comparative advantage in ICT services.

Figure 3. Revealed Comparative Advantage and Institutions Quality

Source: Authors’ calculations

Countries with high institutional quality, like Georgia, export relatively more goods compared to services. Countries with low institutional quality, like Ukraine and Belarus, have a comparative advantage in ICT services exports.

We hypothesize that the main mechanism responsible for this is as follows. Poor institutional quality, resulting in, for example, corruption and the impossibility to create binding contracts does not allow the countries to produce complex goods in the ICT industry, while the presence of high human capital in these countries allows them to produce ICT services that much less depend on corruption and contracting inefficiencies but are as intensive in human capital as ICT goods.

For a better understanding of the relationship between institutions and comparative advantage determination, we run panel regressions analysing the probability of having a comparative advantage in ICT services in exports of ICT goods and services as a function of institutional quality. Following Balassa (1965), a country has a comparative advantage in ICT services if the share of services in overall ICT exports is higher than the world average, in other words, revealed comparative advantage index is greater than 1. We find that one unit increase in institutional quality reduces the probability of having a comparative advantage in services by about 25%, which means that a country with institutional quality similar to Georgia is about 25% less likely to have comparative advantage than a country with institutional quality similar to Belarus.

Conclusion

In this brief we have discussed the role of institutions in determining comparative advantage in services. Our study argues that, given high human capital, low quality institutions create comparative advantage in services provision. Since low quality institutions act as an implicit tax on the production of complex goods, rational agents reallocate most resources to the production of services that are less sensitive to the institutional quality, while still requiring high level of human capital. We showed that transition economies are characterized by low institutions quality and high human capital. At the same time, transition economies have the highest share of ICT services export in total ICT export. We also showed that institutions negatively affect comparative advantage in ICT services export. Our results suggest that services exports can be a novel development channel for countries with weak institutional, capital investments and infrastructure. Specialization in high-value added services exports provides opportunity for fostering high human capital.

References

  • Arshavskiy, Victor, Arevik Gnutzmann-Mkrtchyan and Aleh Mazol, 2019. “Institutions and Comparative Advantage in Service Trade”, Working paper
  • Balassa, B. (1965). Trade liberalisation and “revealed” comparative advantage 1. The Manchester School of Economics and Social Studies, 33(2), 99-123.
  • Barro, Robert J. and Jong Wha Lee, 2013. “A new data set of educational attainment in the world, 1950–2010”, Journal of Development Economics, vol. 104, pp 184-198
  • Levchenko, Andrei A., 2007. “Institutional Quality and International Trade”, Review of Economic Studies, vol. 74, pp 791-819.
  • Nunn, Nathan and Daniel Trefler, 2014. “Domestic Institutions as a Source of Comparative Advantage”, Handbook of International Economics, Volume 4, Chapter 5, pp 263-315.
  • BELARUS.BY, 2019. “ИТ в Беларуси”, it-belarus, accessed on May 19, 2019

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 Learning Crisis: Combining Incentives and Inputs to Raise Student Achievement

Posted on May 27, 2019 | Policy Brief

20190527 The Learning Crisis

As school enrolment in low- and middle-income countries has increased substantially in the last couple of decades, attention has instead turned to the poor quality of education. This ”learning crisis” (UNESCO 2013) manifests itself in primary school students without basic skills in language and mathematics, and high school students being vastly outperformed by their peers in high-income countries (World Bank 2018). In this policy brief, I give a very brief background to the learning crisis and report on a research project we have implemented and evaluated in the Democratic Republic of Congo (DRC) with the aim of improving student learning in primary education. The intervention consisted of an incentivized program to stimulate more usage of existing textbooks for self-study, and the impact was evaluated through a randomized experiment (Falisse, Huysentruyt and Olofsgård 2019).

Education systems in many low- and middle-income countries fail to deliver actual learning at the level necessary for people and societies to thrive. According to leading international assessments of literacy and numeracy, the average student in low-income countries performs worse than 95 percent of the students in high-income countries. According to an assessment of second-grade students in India, more than 80 % could not read a single word from a short text or conduct two-digit subtraction. Students perform poorly also in some European middle-income countries; more than 75 % of students in Kosovo and the Republic of North Macedonia perform worse than the 25th percentile in the average OECD country (World Bank 2018). The reasons behind the learning crisis are of course many, ranging from poorly trained and absent teachers, lack of financial resources for infrastructure and learning material, malnutrition and lacking early childhood development, and sometimes weak demand.

Textbooks for Self-Study in the DRC

The learning crisis is particularly evident in fragile, low income countries. This is also where the major challenge to achieve the 2030 Sustainable Development Goal 4 of quality education to all lies (World Bank, 2018). Yet, very few interventions targeting student achievement have been evaluated in the most fragile countries of the world (Glewwe and Muralidharan 2016). This is a concern, since interventions that work in poor but stable environments may not be feasible or effective in even more resource constrained and violent environments (Burde and Linden 2013). In particular, there is an extra value in identifying interventions that are not only cost efficient, but also low cost in absolute terms and simple and transparent.

Projects focusing on school inputs have often yielded surprisingly disappointing results (Glewwe and Muralidharan 2016). One example is interventions focusing on textbook distribution despite belief in their effectiveness and investments from donors and governments (Glewwe, Kremer and Moulin 2009; Sabarwal et al. 2014). One major challenge with textbooks is that they for different reasons are often not used by teachers or pupils, and certainly not to their potential (e.g. Sabarwal et al. 2014). This raises the question of whether the potential of textbooks can be leveraged through incentives on their usage. A couple of recent papers have found that it is indeed the combination of inputs (including textbooks) and incentives that is critical to yield a significant impact on student test scores (Mbiti et al. 2019; Gilligan et al. 2018).

Following up on this idea we collaborated with the Dutch NGO Cordaid that is running a program in primary education in South Kivu, in eastern DRC, in 90 schools. We designed an intervention that encouraged 5th and 6th grade students from 45 randomly selected schools to regularly take home textbooks and use them for self-study. We used a mix of financial and non-financial incentives focused on the students, such as a public display of stars assigned to each student that brought math and French textbooks home and back in good condition, and an in-kind gift of pens and pencils for all students in classes regularly participating in the routine. We also offered participating schools a small flat compensation to compensate for lost and damaged books. The main goals of the intervention were to increase student achievement and to affect their aspirations for further study and more qualified careers.

To measure student achievement, we rely on self-conducted tests in the French language and math, but also high stakes national exam scores that determine eligibility to secondary education. Following the literature, we analyze test results using a model that assumes that baseline test scores capture student learning up to that point, so once this is controlled for end line results capture cleanly the added value of the intervention introduced. We also carefully address potential statistical problems due to slight unbalance between treatment and control groups, students from baseline not present at end line and poor compliance with the intervention in a small set of schools. The results are generally robust across different specifications of the details of the model.

We emphasize three main sets of results. First, we find that the students in the treatment schools (those selected to receive the books) scored significantly better than those in control schools on the French language tests. The estimated improvement was 1/3 of a standard deviation, which compares favourably with other interventions in developing countries targeting student test scores (Kremer et al. 2013). On the other hand, we found no significant impact on math scores. We cannot tell for sure why we observe this difference between French and math, but it should be noted that both textbooks were in French, suggesting that language could be learned from both books. It has also been suggested that math requires more supervision than language and that math is more ”vertical” in terms of skills progression while language is more ”horizontal”. That is, if students are far behind the curriculum in the textbook, they don’t have the necessary basic building blocks to understand the math problems. But for language, this matters less, as progress can be made in different areas more independently.

Secondly, students in treatment schools were more likely to sit and pass the national exam. This is important as this is a requirement for the continuation of schooling at a higher level. More qualified jobs, and jobs that require more French language skills, typically require at least secondary schooling. This is also consistent with the finding that students exposed to the intervention were more likely to aspire to non-manual jobs. Finally, the intervention was low cost and cost-efficient. In particular in fragile environments with very limited resources, this is essential. The intervention is also easy to implement and transparent and does not give raise to incentives to cheat as has been the case in some interventions linking incentives directly to student test performance.

Conclusions

The current key challenge in education policy in low- and middle-income countries is to improve student achievement while continuing the successful increase in enrolment despite often serious constraints in complementary inputs in the education production function. Financial resources for school infrastructure and material are limited, competent and motivated teachers are in short supply, and weak parental support and little early childhood development leaves children unprepared for sometimes too ambitious curricula. In such circumstances simple and low-cost interventions that make better use of existing resources are particularly valuable. In this project we designed and evaluated such an intervention, using incentives to stimulate more usage of existing textbooks, in a particularly challenging environment, Eastern DRC. We find a positive impact on French language skills and higher student aspirations as shown through greater participation in national exams required for continued education. On the other hand, we find no impact on math test scores. Serious sustainable improvement in student learning in a country like the DRC requires wholesale reforms to the education sector and substantially increased financial resources. Realistically, this is a long-run ambition. In the meanwhile, small low-cost interventions that match incentives with existing resources can significantly increase student achievement also in the short run.

References

  • Burde, Dana and Leigh L. Linden, 2013. “Bringing Education to Afghan Girls: A Randomized Controlled Trial of Village-Based Schools.” American Economic Journal: Applied Economics, 5(3), 27-40.
  • Falisse, Jean-Benoit, Marieke Huysentruyt and Anders Olofsgård, 2019. “Incentivizing Textbooks for Self-Study: Experimental Evidence on Student Learning from the Democratic Republic of Congo”, Working Paper.
  • Gilligan, Daniel O., Naureen Karachiwalla, Ibrahim Kasirye, Adrienne M. Lucas, Derek Neal, 2018. “Educator Incentives and Educational Triage in Rural Primary Schools.” NBER WP 24911.
  • Glewwe, Paul, Michael Kremer, and Sylvie Moulin, 2009. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” American Economic Journal: Applied Economics, 1(1): 112-35.
  • Glewwe, Paul and Karthik Muralidharan, 2016. “Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications”, in Handbook of the Economics of Education, pp. 653-743. Elsevier.
  • Kremer, Michael, Conner Brannen, and Rachel Glennerster, 2013. “The Challenge of Education and
  • Learning in the Developing World.” Science 340, 297-300.
  • Mbiti, Isaac, Karthik Muralidharan, Mauricio Romero, Youdi Schipper, Constantine Manda, Rakesh Rajani, 2019. “Inputs, Incentives, and Complementarities in Education: Experimental Evidence from Tanzania.” NBER WP 24876.
  • Sabarwal, Shwetlena, David K. Evans, and Anastasia Marshak, 2014. “The permanent input hypothesis: the case of textbooks and (no) student learning in Sierra Leone”, Policy Research working paper, no. WPS 7021. Washington, DC: World Bank Group.
  • UNESCO, 2013. “The Global Learning Crisis: Why every child deserves a quality education”, UNESCO, Paris.
  • World Bank, 2018. “World Development Report 2018: Learning to Realize Education’s Promise”, 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.

Gender and the Agency Problem

Posted on May 20, 2019 | Policy Brief

20190520 Gender and the Agency Problem Image 02

Is it good for a firm to have a female CEO? Are countries with more female politicians less corrupt? An increasing attention to female representation in key roles in society has called for research exploring the outcomes and implications of such representation. A useful approach to investigate the impact of gender in such contexts is the so-called principal-agent framework which studies situations in which one party acts on behalf of another party. The idea is that the gender of participating parties is likely to affect motives, behavior and outcomes, predicted by the principal-agent framework. This brief reviews the use of the principal-agent framework for analyzing the effect of gender in two important areas of research: corporate finance and corruption. It outlines postulated theoretical channels for gender to matter, summarizes empirical findings and points to some of the policy challenges.

Increasingly, arguments in favor of more women in key positions are being put forth in society. Many European countries have by now introduced gender quotas for corporate board participation, with Norway being the first one to mandate a quota of 40% female board membership in late 2003. The United States joined the trend in 2018, with California being the first state to require women on corporate boards. The 2019 share of female CEOs in Fortune 500 companies is 5 %; while this number sounds very low, it is twice as high as a decade ago. Women’s presence in politics and bureaucracy is also increasing in many countries worldwide.

This tendency is clearly positive news in the fight for more gender equality, and it is likely to improve the position of women in the society. However, its implications for other economic and societal outcomes are not immediately clear. For example, is a more gender-balanced board or a female CEO good news for company performance? How would female politicians affect policy and societal outcomes?

One useful approach for answering such questions is based on the so-called principal-agent framework (developed to study what is known as “agency problems”). This framework, widely used in economics, political science and other related disciplines in the last half century, addresses the problem of incentivizing one person (referred to as an “agent”) to act on behalf of another person or entity (referred to as a “principal”). Many situations in real life are well described by this basic framework and it has been used in a wide range of different contexts, from relationships within a firm, or between a lawyer and her client, to insurance, real estate, policy choices by elected officials or appointed bureaucrats, and even situations involving corruption.

The relevant question is then whether, and if so, how, the gender of the agents can affect motives, behavior and outcomes, predicted by the principal-agent framework. This brief will focus on two main areas of studies within gender economics that use agency theory to motivate their findings: the role of gender in corporate governance, and in corruption. The brief will outline the theoretical channels through which the gender of the actors may act in these contexts, summarize the empirical findings of this literature, and shortly comment on policy implications. While the focus on two areas only may seem to be relatively narrow, it will allow identifying a number of common gender effects across the contexts, which may suggest implications for the other potential applications of the approach.

The basic principal-agent framework

Effectively any situation in which one party acts on behalf of another party for monetary or non-monetary compensation can be analyzed within an agency framework. A typical feature of such situations is that the parties have different objectives: for example, the board of the firm (the principal in this case) would be interested in maximizing the firm value, while the CEO (the agent) would probably be more concerned about her personal compensation. This difference is not necessarily problematic per se as long as the principal can get the agent to act as the principal wants. However, if parties do not have the same information – which is typically the case in the reality – the misalignment of their objectives becomes an issue.

Two main problems may arise in such situations. The first one is referred to as the problem of hidden action (moral hazard) – that the agent is likely to act in line with her own objectives, rather than in the principal’s ones. This is likely to occur as long as her effort cannot be perfectly monitored by the principal. For example, shareholders typically cannot directly attribute the evolution of the firm’s value to the actions of the CEO, which may result in the CEO making decisions that are, for instance, too risky from the firm’s value maximization perspective. The second one is the problem of hidden information – when the agent is better informed about the issues at stake than the principal, which again may result in the agent not acting in the best interest of the principal. For example, shareholders may have a poorer knowledge of the market than CEO, which may result in the CEO making decisions maximizing her own compensation rather than the firm’s value.

To lessen the extent of these problems, one needs to think of the spectrum of tools/decisions under the agent’s control, as well as of the design of her compensation schemes so as to align her private objectives with those of the principal. For example, to motivate a CEO to behave in the interests of shareholders, his/her compensation package typically includes company stock options. In some cases, the way to provide better incentives for the agent is to delegate more decisions, allow her more discretion and link her compensation closely to the outcome of her actions. One possible example of such a mechanism is franchising: on average franchisees retain about 94% of franchise profits, which would make them very motivated to achieve good franchise performance. However, the cost of high incentivization is the potential misuse of decision power, especially if the set of the decisions for an agent to have control over is not chosen wisely and if sufficient alignment (or intrinsic motivation) is not achieved. Another obstacle when implementing the principal’s preferred outcome is the trade-off between agent’s incentivization and risk aversion. The agent is typically seen as more risk-averse than the principal (for example, firms’ shareholders would typically diversify their risks by investing in a number of companies, while the CEO’s main source of income would be associated with the company she manages). As a result, the agent may avoid undertaking the principal’s value-maximizing actions because of the risks associated with them.

The bottom line of this discussion is that the task of incentivizing the agent may be difficult, and the principal’s best-preferred outcome may not be achievable.

Gender and the agency problem

There are many twists and modifications of the basic framework described above aimed at better modelling the specific problem at hand. One particular feature of the principal-agent relationship that has received increasing attention in the literature is the gender of the participating parties. The main strands of this literature have studied the relevance of gender for corporate governance and corruption.

Gender and corporate governance

The corporate governance part of the literature focuses on the impact of the gender composition of the board of directors or of the gender of the CEO on firms’ (or banks’) performance, risk-taking, capital allocation decisions, firm reputation etc. One standard approach to this set of questions is to consider the principal-agent relationship between the agent – the CEO – and the principal(s) – the board of directors (and sometimes other firm stakeholders) – and ask how, and why, the gender of either party may affect the relationship between them and the outcomes of this relationship.

There are several channels suggested by the literature. First, women and men may have different personal characteristics – such as risk aversion, level of confidence or ethical values (though there is not necessarily agreement on the direction of the difference: while most studies argue that, on average, men are typically more overconfident than women (e.g., Barber and Odean, 2001; Lundeberg et al., 1994), there is no consensus about risk attitudes – e.g., Jianakoplos and Bernasek (1998) or Croson and Gneezy (2009) show that women are more risk-averse than men, while Adams and Funk (2012) document the opposite). These differences in personal traits may affect the decision-making of a board/CEO in an incomplete-information environment and ultimately the firm’s performance.

Second, women and men may face different employment opportunities in case they lose their job, which, again, is likely to affect their decision-making and risk-taking (e.g., Faccio, Marchica and Mura, 2016).

Third, more gender-diverse boards may better reflect the preferences of (gender-mixed) firm stakeholders; in terms of the agency theory this would imply more aligned interests between the principal and the agent. It may matter because mixed-gender groups (and, by implication, boards) may exhibit different decision-making processes than same-gender groups, which, again, may introduce frictions into the agency relationship (e.g., Amini et al., 2017 or Van Knippenberg and Schippers, 2007).

Finally, the gender composition of the board may matter because female board members may improve monitoring over the actions of the CEO, since they are more independent not being part of the same “old boys’” social networks as the male members of the board and the (male) CEOs (Adams and Ferreira, 2009).

Empirically, this literature is largely inconclusive: while the majority of studies does find that the gender of the firm’s decision-maker(s) matters, the sign of the effect differs between studies, datasets and specifications. For example, based on a US sample of firms, Bernile, Bhagwat and Yonker (2018) find that more gender-diverse boards lead to lower firm risk, and better performance. In turn, Adams and Ferreira (2009) document negative effects of more diverse boards on performance. Sila et al. (2016) find no relation between board gender diversity and risk. Similarly ambiguous are the findings on the effect of CEO’s gender on firms’ performance, as measured by risk exposure, capital allocation, propensity to acquire, business strategies etc.

One possible reason for this variability of findings is the endogeneity of the presence of female CEOs/board members and firms’ outcomes, which is difficult to account for empirically (Hermalin and Weisbach, 1998; Adams et al., 2010). For example, female CEOs may self-select into firms with lower risks due to their own risk-aversion. Alternatively, corporate culture may affect the relationship between the gender of the CEO/board members and firm performance, etc. (see Adams, 2016 for an overview of this problem). There has been a number of attempts to address the causality/endogeneity issues in this context. For example, Bernile, Bhagwat and Yonker (2018) and Alam et al. (2018) exploit variation in the gender composition of boards created by the diversity of potential directors residing a non-stop flight away from the firm headquarters. Their motivation is that the personal travel costs of directors decrease with the availability of non-stop flights. Faccio et al. (2016) attempt to resolve the endogeneity issue by proxying the likelihood of hiring a female CEO by a measure of how many other firms that share board members with the firm in question have female CEOs. The idea there is that working with female CEOs in other firms may make board members more familiar with working with female executives, and more willing to hire a female CEO in the firm in question. A subset of the literature exploits reforms introducing gender quotas in corporate boards. These studies argue that the reforms are introducing an exogenous variation in the proportion of mandated changes in board gender composition – firms with more women in the board prior to the reform would need less adjustments to comply with the reform (see, e.g., Bertrand et al., 2018 for a state-of-the-art example of such an approach). Still, the endogeneity concern remains very valid for this literature. A recent literature overview by Kirsch (2018) or somewhat more dated, but still be relevant one by Terjesen et al. (2009) can be a good starting point for more detailed information on this field.

Gender and corruption

Similarly, there is a sizeable literature of gender aspects of corruption. This literature addresses a variety of topics, including the impact of corruption on women and gender inequality, gender-associated forms of corruption, and most importantly for us in the current context, gender attitudes and behavior towards corruption. One of the predominant theoretical mechanisms in this literature, again, uses agency theory. The main difference to the version of agency theory applied in the corporate governance case above is, perhaps, that in the case of corruption there is not always a clear pattern of subordination between the principal and the agent. More specifically, the principal for a (potentially corrupt) agent official may be either a higher-level official, or the direct recipient of her services or the electorate in general (of the agent official is elected). However, just as in the corporate governance literature, the gender vs. corruption literature asks the question how the outcome of an interaction between the principal and the agent would be altered by the gender of either party. It argues that women may behave differently from men in a corrupt environment through a number of channels, most of which resemble the ones in the corporate governance literature outlined above.

For example, gender differences in behavior and attitudes to corruption may be due to of personal traits, such as risk aversion or gender-specific conformity with social norms (e.g., Esarey and Chirillo, 2013 suggest that women are more likely to conform to the local social norms, so they are less likely to engage in corruption in an institutional environment where corruption is condemned, than in the societies when it is more accepted).

These differences may be due to differences in outside options of the corrupt official in case corruption gets detected (such as alternative employment opportunities). They may also be due to women not being part of business/political network(s), or having less experience in how things are done in decision-making positions. This could make them better monitors when they are in a principal role, or less able (or willing) to engage in corruption when in the role of agent. Thereby, it may result in a negative link between women in government and corruption, but only a short-term one (e.g., Pande and Ford, 2011). However, Afridi et al. (2017) argues for an opposite view, that a newly appointed female bureaucrat’s lack of experience may increase corruption due to inability to handle matters efficiently. Their empirical results indeed support it: in India newly appointed female council heads are less efficient than male ones due to lack of experience; this efficiency gap also includes higher corruption levels in female-led villages. With time, as the female council heads gain experience, the difference disappears.

As can be expected, empirically this field is again not entirely conclusive. The early empirical research suggested a negative link between gender and corruption, or, more specifically, found that a higher presence of women in government is associated with lower levels of corruption (e.g., Dollar, Fisman, and Gatti, 2001 or Swamy et al., 2001). However, there has since been a wide discussion about the causal mechanisms of this relationship. One of the arguments has been that this correlation is due to institutional mechanisms: greater representation of women in power is observed in a more developed institutional environment, which is also providing more effective checks on corruption (e.g., Sung, 2003). Still, the discussion is ongoing, as other scholars argue that the relationship is still in place even after controlling for the institutional factors, though not in all power positions (e.g., Jha and Sarangi (2018) show that female presence in parliament decreases corruption while other measures of female participation in economic activities have no effect). There is certain evidence of female bureaucrats being less aggressive in extracting bribes (Dabalen and Wane, 2008) or female business owners paying less bribes (Breen et al., 2017), but the determinants and the causal relationship of these findings are again, unclear.

There has been a number of attempts to resolve the causality issue of the gender-corruption link. Similarly to the corporate governance literature, researchers have used an instrumental variable approach (e.g., Jha and Sarangi (2018) use number of genders in a country’s language to instrument for female labor force participation, as it has been shown that gender discrimination is higher in countries where the dominant language has two genders as opposed to countries where it has no gender or three or more genders. The same authors use the year of universal suffrage to instrument the female participation in parliament). Unlike in corporate governance literature, a large part of this literature uses experimental approach, relying both on lab experiments to study gender attitudes to corruption (e.g., Rivas, 2013), and natural experiments (Afridi et al., 2017 study the reform in India that randomly allocated a third of council headship positions to women) and quasi-experiments (Brollo and Troiano (2016) look into close elections in Brazil and use a regression discontinuity design to show that female mayors are less likely to be corrupt). A useful overview of the literature is offered in Rheinbay and Chêne (2016).

Summing up and policy implications

There is an active public and academic debate about the greater involvement of women in key positions in society, its implications and outcomes, and potential policies to achieve it. A natural way of analyzing the implications of having more women in strategic positions utilizes the principal-agent modelling approach, with the presumption that the gender of the parties is likely to affect the model’s predictions and outcomes. A substantial attention in this literature has been devoted to the impact of gender in corporate governance and corruption. Importantly, these two strands of literature outline several common channels through which gender is likely to have an impact, such as risk aversion, outside opportunities in case of losing employment, etc. This similarity suggests that the same channels are likely to play a role in other gender-relevant agency contexts.

Another similarity between these two areas of research is the ambiguity of the results in terms of both theoretical predictions and empirical findings. One possible source of this ambiguity is, likely, suboptimality of the empirical methods used, which might not allow to adequately establish the causal relationship between the characteristics and outcomes of the agency relation and gender of its participants. Differences of the contexts of the empirical studies are another probable contributor to the variation in predictions and results.

However, this ambiguity obviously does not mean that policies to empower women should not be undertaken at all. First, even if the results of a particular narrowly-targeted policy are so far found to be ambiguous, it may still be highly useful in changing social norms, with all the benefits attached to it. For example, there is no sufficient evidence that establishing gender quotes in corporate boards would improve firms’ performance. For example, Ahern and Dittmar (2012) find that introduction of quota in Norway had a negative effect on Tobin’s Q. However, a quota reform in Norway resulted in the appointment of better qualified female board members and raised the career expectations of younger women post-reform (Bertrand et al., 2018). Second, this ambiguity stresses that there is no universal “silver bullet” policy applicable to all countries and contexts: the design of policies that address gender inequalities, as any other policy, needs to carefully account for the local institutional and cultural context. Further, recent contributions to this literature has become much more informative for the policy makers. An active development of this field and its methods suggests that we are about to learn much about the role of gender and other compounding factors in the above contexts. In other words, modern informed gender policy is just around the corner.

References

  • Adams, R. B., (2016). Women on boards: The superheroes of tomorrow? Leadership Quarterly, 27 (3). pp. 371-386.
  • Adams, R. B., Hermalin, B. E., & Weisbach, M. S. (2010). The role of boards of directors in corporate governance: A conceptual framework and survey. Journal of economic literature, 48(1), 58-107.
  • Adams, R. B., & Ferreira, D. (2009). Women in the boardroom and their impact on governance and performance. Journal of financial economics, 94(2), 291-309.
  • Adams, R. B., & Funk, P. (2012). Beyond the glass ceiling: Does gender matter?. Management science, 58(2), 219-235.
  • Afridi, F., Iversen, V. & Sharan, M.R. (2017), Women political leaders, corruption, and learning: evidence from a large public program in India. Econ. Dev. Cult. Change, 66 (1) pp. 1-30.
  • Ahern, K. R., & Dittmar, A. K. (2012). The changing of the boards: The impact on firm valuation of mandated female board representation. The Quarterly Journal of Economics, 127(1), 137-197.
  • Alam, Z. S., Chen, M. A., Ciccotello, C. S. & Ryan, H. E., (2018). Gender and Geography in the Boardroom: What Really Matters for Board Decisions? Mimeo. Available at SSRN: https://ssrn.com/abstract=3336445
  • Amini, M., Ekström, M., Ellingsen, T., Johannesson, M., & Strömsten, F. (2016). Does gender diversity promote nonconformity?. Management Science, 63(4), 1085-1096.
  • Barber, B. M., and Odean T.  (2001). “Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment.” The Quarterly Journal of Economics 116, no. 1: 261-92.
  • Bernile, G., Bhagwat, V., & Yonker, S. (2018). Board diversity, firm risk, and corporate policies. Journal of Financial Economics, 127(3), 588-612.
  • Breen, M., Gillanders, R., McNulty, G., & Suzuki, A. (2017). Gender and corruption in business. The Journal of Development Studies, 53(9), 1486-1501.
  • Brollo, F., & Troiano, U. (2016). What happens when a woman wins an election? Evidence from close races in Brazil. Journal of Development Economics, 122, 28-45.
  • Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic literature, 47(2), 448-74.
  • Dabalen, A., & Wane, W. (2008). Informal payments and moonlighting in Tajikistan’s health sector. The World Bank Policy Research working paper 4555, https://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-4555
  • Dollar, D., Fisman, R., & Gatti, R. (2001). Are women really the “fairer” sex? Corruption and women in government. Journal of Economic Behavior & Organization, 46(4), 423-429.
  • Esarey, J., & Chirillo, G. (2013). “Fairer sex” or purity myth? Corruption, gender, and institutional context. Politics & Gender, 9(4), 361-389.
  • Faccio, M., Marchica, M. T., & Mura, R. (2016). CEO gender, corporate risk-taking, and the efficiency of capital allocation. Journal of Corporate Finance, 39, 193-209.
  • Hermalin, B. E., & Weisbach, M. S. (1998). Endogenously chosen boards of directors and their monitoring of the CEO. American Economic Review, 96-118.
  • Jha, C. K., & Sarangi, S. (2018). Women and corruption: What positions must they hold to make a difference?. Journal of Economic Behavior & Organization, 151, 219-233.
  • Jianakoplos, N. A., & Bernasek, A. (1998). Are women more risk averse?. Economic inquiry, 36(4), 620-630.
  • Kirsch, A. (2018). The gender composition of corporate boards: A review and research agenda. The Leadership Quarterly, 29(2), 346-364.
  • Lundeberg, M. A., Fox, P. W., and Punccohar, J. (1994). Highly confident but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86( 1), 114
  • Pande, R., & Ford, D. (2011). Gender Quotas and Female Leadership. Background Paper for World Development Report, World Bank.
  • Rheinbay J. & Chêne, M. (2016). Gender and corruption topic guide, Transparency International, https://www.transparency.org/files/content/corruptionqas/Topic_guide_gender_corruption_Final_2016.pdf
  • Rivas, M. F. (2013). An experiment on corruption and gender. Bulletin of Economic Research, 65(1), 10-42.
  • Sila, V., Gonzalez, A., & Hagendorff, J. (2016). Women on board: Does boardroom gender diversity affect firm risk?. Journal of Corporate Finance, 36, 26-53.
  • Sung, H. E. (2003). Fairer sex or fairer system? Gender and corruption revisited. Social Forces, 82(2), 703-723.
  • Swamy, A., Knack, S., Lee, Y., & Azfar, O. (2001). Gender and corruption. Journal of development economics, 64(1), 25-55.
  • Terjesen, S., Sealy, R. & Singh, V. (2009). Women Directors on Corporate Boards: A Review and Research Agenda. Corporate Governance: An International Review, 17(3), pp.320–337.
  • Van Knippenberg, D., & Schippers, M. C. (2007). Work group diversity. Annu. Rev. Psychol., 58, 515-541.

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.

Effects of Civil Confrontation in Social Media

Posted on May 13, 2019 | Policy Brief

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This paper describes the practices of civil confrontation which can be found in social media (analyzing the cases of the Ukrainian segment of Facebook). The research shows that such practices can be used by interest groups to deliberately affect target audiences in certain ways and thus exacerbate civil confrontation or to expand its scope. Psychological effects of such practices for the society include monotony, ambivalence, desensitization and alertness. These effects can be used either to distract the attention from a certain issue or to enhance social mobilization, to reduce protest potential or to push large groups into impulsive actions, to impose contradictory ideas or to stimulate society to rethink values.

Civil Confrontation in Ukraine

Considering the warfare going on in Ukraine and the consequent state of society, it is important to clearly define what is going on between large social groups.

Figure 1. Continuum of Conflicting Sociopolitical Processes

A useful way to structure our thinking about these processes may be to use an approach to sociopolitical conflict presented in Iarovyi (2019), which suggests that the continuum of conflicting sociopolitical processes has 4 stages, as illustrated by Figure 1. In what follows we concentrate on the second stage, which is civil confrontation. Civil confrontation is defined as a form of intra-group confrontation in the society marked by a crystallization of value conflicts between opposing sides. It has the potential to escalate into other forms of conflict interaction as indicated by Figure 1. Unlike social tension, which is the earliest stage, the confrontation has an articulated ‘enemy’ image and identity. However, it is not as deep as a social conflict which has systematic and deep roots and exists in the framework of problems connected with values. It is also far from being a civil war since it does not include a military component and does not assume a dehumanization of the opponent.

Nevertheless, differences between the stages are rather vague. Within Ukraine one can observe social tensions between certain groups (such as civil servants of the “old generation” and new employees), social confrontation (e.g., between supporters of certain presidential nominees) and social conflict (e.g., between the believers of Russian and Ukrainian Orthodox Churches). The aggravation within this continuum occurs as a gradual buildup of the counteraction and change of the conflict gradient to a deeper one. For the society it might be beneficial to minimize the aggravation and identify the conflicts in their early stages. A simple way to identify conflicts is by studying communications in social media. In my dissertation, which is the basis for this policy brief, I perform this exercise.

Communication of Civil Confrontation

Sociopolitical conflicts are developing via communication, which is the linguistic representation of a conflict. The latter thrives via group polarization – transforming heterogeneous opinions of people into mutually exclusive opposing positions. To define the conflict of discourses in the Ukrainian segment of social media, it is necessary to consider both features of the modern Ukrainian political discourse in general and specific features of communication in social media.

Markers of Civil Confrontation in Ukraine

The overview of political conflicts in Ukraine allows me to define the general characteristics of Ukrainian political discourse which influence the growth of confrontation. They include (1) the exploitation of ethnic and civic identities; (2) the impact of the external (overseas) interest groups; (3) difficulties with defining the stage and type of the ongoing conflicts and (4) a lack of proactive work of the government on reducing the risks of conflict. These markers were taken into account during the research as the defining framework of the practices of civil confrontation, and they are attributed to a smaller or larger extent to the cases which were studied.

Characteristics of the Discourse in Social Media

In the context of competing discourses, communication in social media needs to be pragmatic and focused on broadcasting the own agenda of writers, otherwise a user who is overwhelmed with information from different sources will be distracted. Moreover, this communication should be interactive and cooperate with the audience in real-time to improve its impact (Westcott, 2008).

Communication in social media is often much more intense than in the real life. While people do not normally enter discussions in social media to “wage wars” (Whiting and Williams, 2013), the environment of the Internet itself is characterized by a weaker level of censorship and self-censorship, the absence of limits that restrict participants, quick responsiveness, scattering attention, a lack of real contact, interruption of public communication of two people by third parties, anonymity etc. Thus, communication in social media is less restricted for negative reactions of participants, less productive and at the same time more aggressive.

Psychological Practices of Civil Confrontation on Facebook

The psychological practices of civil confrontation are defined as a set of established methods and techniques within the community which allow an individual to engage in interactions of social institutions and change one’s own psychological states and processes. In the process of reproducing such practices in communication, the emotions, settings, stereotypes, and value orientations of the communicator are changed.

The research of such practices in Ukrainian social media used the critical discourse analysis (CDA) model by Norman Fairclough (1992), with the selection of 6 cases that differ in the intensity of verbal confrontation, the intensity of the discourses’ struggle in the virtual environment and the spread of discourses outside the virtual environment.

The source of empirical material are Facebook accounts of users who take active role in political life and communication in Ukraine. We select Facebook firstly since this platform in Ukraine is highly politicized and represents various views of political communicators who are often absent on YouTube, Twitter etc., and secondly, it publishes large texts, sometimes with a strong visual component, which allows to utilize the CDA comprehensively.

Effects of the Civil Confrontation

Monotony

The effect of monotony, or the reduction of motivation to control the activities and participate in social life, is reproduced due to excessive exploitation of some discourses in society.

The first case in which this effect is present is the story of Ukrainian boxer Oleksandr Usik who took part in a fight in Moscow, the capital of the aggressor state according to Ukrainian legislation. Some parts of the Ukrainian community met this event with strong condemnation. Sports and culture are traditionally considered the elements of “soft power”. Thus they are often used (or believed to be used) for political purposes. However, citizens who are less politically motivated often tend to doubt the political ideologies and put their personal sympathies to a certain person in the first place. The social media communication regarding this case was characterized by a segregation of community members depending on their belief in the statement “sport/art is outside of politics”, and caused numerous arguments between communicators. At the time, this very situation made more and more people voice their tiredness of the war (which is subconsciously perceived as the reason for the argument). It leads to the gradual implantation of the idea that “the war is the case of the politicians, and the peoples of Ukraine and Russia are friendly”, and could strengthen the position of pro-Russian politicians in Ukraine. The implantation of this idea is beneficial for Russia, as it lowers the loyalty of Ukrainians to their own state and discredits the authorities.

The second case relates to public protests of the Ukrainian opposition in 2017-2018 which never caused a really strong reaction of the ordinary citizens. Discursive instruments used to involve more people into protests (the famous phrase “Kyiv, get up!” which was used during Euromaidan in 2014) did not work since the society was tired of regular protests in 2016-2017 on every slightest occasion, each of them labelled “a Third Maidan” by the organizers. The monotony “filled” the public discourse with unnecessary information, people became tired of protests and manipulations, and the protests became marginalized. Thus, the monotony effect could be used for the diversion of attention, the reduction of the protest potential or the formation of the social “fatigue” (sharp decline of the ability and motivation to perform the social roles and functions or stand for the position). Getting out of this state is possible if the rhythm of the information supply changes and its foci are shifted, which will lead to new reactions and roll the discourse out, making it topical once again.

Ambivalence

The ambivalence, or the duality of the attitude of the same person to the same object/phenomenon, instead of monotony, leads to the production of public anxiety and nervousness. It was identified on the case of “derusification”, when one prominent Ukrainian official labelled Soviet and pre-Soviet poets and writers (V. Vysotskiy, V. Tsoi, M. Bulgakov) as the “tentacles of the Russian World” (i.e. Pax Russia).

The discussions over this case not only intensified contradictions among participants, they also led to the expansion of civil confrontation. While in the previous case with the boxer, the incitement of the hostility on the everyday level failed as the issues are rather unimportant, in case of famous poets and singers the incitement affects deeply rooted notions and nostalgia of the communicators and is much more efficient. With the growing hostility between communicators of opposing sides, it leads to disorganization of thoughts of hesitant people (e.g. those who have warm feelings about the Soviet culture and sub-culture despite supporting Ukraine in its war with Russia). As a result, communicators tend to be more nervous when making decisions and taking actions (physical actions or discursive). Thus the ambivalence effect could be used to push people to commit impulsive actions and diminish their rational thinking. Reducing its negative effects is possible via engaging the society into a dialogue, promoting compromise proposals and sticking to the principles of mutual respect in the process of communication.

Desensitization

The effect of desensitization, or diminishing the emotional responsiveness of the society to violent actions, arises from the practices of discourse discreditation and determining the boundaries of what is permitted, and is connected primarily with the loss of sensuality by the communicators. It was identified in the case of attacks on Roma people in Ukraine which were widely criticized on the official level but considered quite normal by a large amount of “ordinary people” in social media. The justification of the violence and lack of mass condemnation of the aggressive actions raise the threshold of sensuality in the society which leads to tolerating violence against certain groups (in this case – ethnic groups).

The toleration of violence could be further extended to other groups (such as political opponents). If this effect is implemented gradually, the negative consequences may not be visible until it is too late. Minimization of the negative impact is possible via disclosure of information about such practices, drawing attention to them and articulating the importance of preserving the universal human values.

Alertness

The effect of alertness, or the state of being highly aware and ready to face confrontation, arises as a result of communicators’ reaction to actions of their opponents. It was traced in the cases of “Euro-plates” (massive importation into Ukraine of not-cleared cars with European license plates) and “Night on Bankova” [the street where the Presidential Office is located] (demands of civic activists for investigation of the allegedly political murder). The first case demonstrates a self-organized non-political platform of owners of such cars, which without the support of any recognizable politician managed to effectively protect their economic interests through communication of their idea to the masses. The second case suggests that due to the use of moderate and non-violent methods of communication and action by civil activists, as well as the high authority and recognizability of communicators, their ideas are attractive: the public accepts them and the authorities demonstrate readiness for the dialogue. It works much better than pushing people to radical actions, as in the case of monotony of street protests. In both cases described above in the context of alertness, a minority conversion takes place, where the discursive impact of the self-organized group is being spread to a broader public. Due to reassessment of the values this effect can potentially be used by interest groups to achieve their political goals and mobilize groups of supporters.

Conclusion

The above described effects can be used to distract public attention, to change (increase or decrease) the level of protest potential, to push people towards impulsive actions, to impose contradictory ideas or to stimulate the society to rethink values – both in a positive or negative way. These effects can be utilized by interest groups to draft the agenda and establish domination of their own discourse in the public sphere.

Thus, the actions to be taken by governmental decision makers who want to deal with negative consequences of such effects are: (1) engaging in the dialogue with the society, (2) responding to the mobilization of large groups of people with policy actions, (3) drawing attention to the importance of human rights (and actually pursuing this policy on the state level instead of only declaring it). One of the major activities here is monitoring aimed at a timely detection of dangerous trends and handling communication in a proper way.

Further research in this direction could be focused on assessing the impact of psychological effects on various target groups in the society in the short- and long-term perspective.

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.