Project: FREE policy brief

Quota or not Quota? On Increasing Women’s Representation in Politics

Posted on May 4, 2020 | Policy Brief

20200504 FREE Network Policy Brief image with dozen people standing at the seaside in sunset representing Women’s Representation in Politics Image 01

All over the world, politics remains one of the most male-dominated spheres in society, in spite of the substantial progress made in achieving more gender balance in the last decades. A large number of countries worldwide have adopted some form of electoral gender quotas to accelerate this progress, but the empirical evidence on the effectiveness of such policy tools is mixed.

In this policy brief, we first discuss the potential impacts of gender quotas. Quotas may (a) increase women’s representation in political positions, or decrease it, if there are backlash effects; (b) improve or worsen the quality of selected politicians; and (c) bring about important policy changes, given the wealth of empirical evidence of gender differences in policy preferences, with, for instance, women appearing more concerned about health and the health system than men. We then provide an overview of the empirical evidence on quota impacts in the economics literature, and contextualize these findings with a special focus on the countries of the FREE (Forum for Eastern Europe and Emerging Economies) network. We end with policy advice on the design of gender quotas in the domain of politics.

Quotas in the World and in the FREE Network Region

According to the International Institute for Democracy and Electoral Assistance (IDEA), 127 countries worldwide currently use quotas with the goal of increasing the presence of women in governmental institutions. Broadly speaking electoral gender quotas can be classified into seat reservation and candidate lists quotas. The former limit the competition for a governmental seat to women, whereas the latter prescribe a minimum representation of women in electoral lists. Candidate quotas can be legislated, i.e. they constitute a legal requirement, or voluntary, whereby parties adopt quotas in their internal statute.

Table 1: Share of women in national parliaments (in %) FREE Network countries

Source: World Bank Data (2020).

The popularity of gender quotas is, however, not uniformly distributed across the globe. For example, while political gender representation is far from equal in most countries of FREE network region (see e.g. table 1), out of these countries only Armenia, Poland and Sweden dispose of electoral gender quotas (see figure 1).

Figure 1: Gender quotas in the FREE Network region

Note: the FREE Network region is marked in light red, the countries in the region with quota are marked in dark red. Source: SITE, 2020.

Since 2011, Armenia has had a legislated candidate quota of 40% for its National Assembly. This quota replaced a previous quota of 15%, passed in 2005 – one of the requirements to enter the Council of Europe (Itano 2007). Poland has also had a legislated candidate quota of 35% for the Lower House (the Sejm) as well as for subnational elections since 2011 (IDEA 2020; World Bank 2019). Sweden, the fourth most gender equal country worldwide according to the 2020 ranking of the World Economic Forum, and ninth in the women’s political empowerment sub-index, does not have legislated quotas. However, political parties themselves have decided to adopt voluntary quotas: the ruling Social Democrats use a zipper system in which the two sexes alternate on party lists; the Left Party has a minimum 50% quota for women, while the Green Party has a 50% gender quota (IDEA 2020). The Swedish Moderates, Liberals, Center parties and the extreme-right Swedish Democrats currently do not have gender quotas. The Swedish Democrats entered the parliamentary elections in 2018 with the highest share of male candidates observed among the Swedish parties – 70% (SVT 2020; SVT 2018).

In spite of their popularity among policy-makers worldwide, the merits of quotas are still largely debated. Opponents of gender quotas are often concerned about their effects on the meritocratic selection of politicians. Another common criticism is that nominating more female candidates may not automatically translate into more women in powerful positions. For instance, the shares of women in the Armenian and the Polish Parliament are 24 and 29% respectively (World Bank 2019), well below the national legislated candidate quota (it bears noting, however, that these shares have been growing over the last ten years, as shown in Figure 3). The respective shares of female ministers are 7% and 23% (Government of the Republic of Armenia 2020; OECD 2020,).

Figure 2: Share of women in national parliaments (in %)

Source: The authors’ own rendering of World Bank Data (2020).

Why is increasing women’s political participation considered a policy objective of utmost importance in many countries worldwide, and how can gender quotas help achieving it? In this brief we contribute to the ongoing debate on the merits of gender quotas, by offering an overview of their potential effects and by critically reviewing the empirical evidence from the most recent academic literature.

Which Effects Can We Expect From Quotas?

The primary objective of electoral quotas is to reduce gender gaps in representation in electoral lists and in the targeted representative institutions. Quotas can also activate trickle-up mechanisms, whereby gender gaps decrease in positions that are not directly targeted by the quota. The trickle up effect occurs, for instance, if women’s networks within parties or in governmental organizations help the promotion of female leaders. Furthermore, gender quotas may help to improve the quality of politicians. As noted by, among others, Bertrand (2018), a society likely improves the quality of its leaders when it enlarges the pool where those leaders are chosen from. A critical underlying assumption in this line of argument is that there are no major differences in the distribution of “political talent” between women and men. However, even with equal distribution of political talent, if the supply of women willing to enter politics is very limited and there are not enough qualified women to fill the quota positions, the average quality of a “quota” politician may end up being lower than that of her colleagues – and quotas may have the unintended consequence of reinforcing stereotypes against female politicians. This, in turn, may ultimately imply lower promotion rates of women to key positions and/or worse electoral support of female politicians, thereby undermining women’s political empowerment at various levels.

One of the most popular arguments in favor of the adoption of gender quotas is that women’s political preferences may not be adequately represented by male-dominated political bodies. Gender quotas, by increasing female representation among politicians (and possibly among voters), can thus help closing a potential gap in substantial representation. A large body of literature has documented gender differences in policy preferences, by considering, e.g. the size and composition of government spending after the expansion of suffrage to women (Kenny and Lott 1999), voting records in referenda (Funk and Gathmann 2015), survey data (see, e.g. Bagues and Campa 2020), or women’s contributions to legislative amendments (Lippmann 2020). In this historical moment when the world is plagued by a pandemic, the most important gender difference to emphasize seems to be in the area of health. Exploiting the federal referenda held between 1981 and 2001 in Switzerland, Funk and Gathmann (2015) show that Swiss women are more likely to be in favor of health, unemployment and social security spending than men, and less likely to be in favor of military spending. Similarly, based on survey data from a sample of nearly 60,000 Spanish residents, Bagues and Campa (2020) find that women are significantly more likely than men to report that the health system is one of the problems that affects them the most. Likewise, Lippmann (2020) analyzes the contribution of French legislators to amendments and finds that women are 25% more likely than men to initiate at least one amendment related to health issues. This gender difference regarding health policy is also visible in the European Social Survey (ESS), which covers a representative sample of the population of 19 European countries. When asked to give a general opinion on the current state of health services in their country, female respondents turn out to be significantly less satisfied than male respondents on average. The difference is statistically significant, albeit not particularly large (12% of a standard deviation) and holds in most of the countries included in the ESS. One potential reason behind this noticeable difference in satisfaction with health services is that women also report lower health status than men (10% of a standard deviation and statistically significant).

Figure 3: Self-reported satisfaction with the current state of national health services

Source: The authors’ own rendering of the ESS (2018).

A natural question to ask in spring 2020 is whether a world with more women among political leaders would have had health systems better equipped to face a pandemic. While we will never have a definite answer to this question, studies of the impacts of gender quotas can help assessing whether the gender of political leaders matters for policy decisions.

What is the Empirical Evidence on the Effects of Quotas?

Quotas increase women’s representation in electoral lists, but only when they are binding and appropriately enforced (i.e. the cost for parties of not complying with the quota must be high enough). Yet, when quotas are limited to the composition of electoral lists, the strategic positioning of female candidates in “not-winning” positions tends to undermine the quota effect on the election of women (see Esteve-Volart and Bagues, 2012, and Bagues and Campa, 2020). This seems to be the case of Poland: According to Gwiazda (2017), the lack of a placement mandate obliging parties to put women in the top positions of a party list, is indeed one reason why the Polish quota has not translated into a higher share of female representatives.

The evidence on the spill-over of quotas to higher positions is mixed. Two studies find that candidate quotas in Italy and Sweden increased the probability that women reach leadership positions, above and beyond the quota mandate (De Paola et al. 2010, O’Brien and Rickne, 2016). Bagues and Campa (2020), however, fail to establish similar evidence in Spain.

In studies of developing countries, Beaman et al. (2009) find that seat reservation in India improved male voters’ perception of female leaders, as well as women’s probability of being elected once the reservation was removed. Conversely, experimental evidence from Lesotho suggests that, if anything, a quota-mandated female representative reduces women’s self-reported engagement with local politics (see Clayton, 2015).

An increasing number of studies also examine the quota impact on the quality of the elected politicians, proxied by different measures. Baltrunaite et al. (2014) find that a gender quota improved the average education of elected politicians in Italy, and Besley et al. (2017) provide similar evidence looking at a measure of labor market performance in Sweden. Bagues and Campa (2020), studying candidate quotas in Spain, fail to find an improvement in the quality of politicians, measured by their education and electoral performance; however, their assessment is that the quota did not decrease quality either, contrary to the expectation of many quota opponents. However, Chattopadhyay and Duflo (2004) find that, in the context of seat reservations in rural India, quota candidates are less educated.

Finally, the evidence on whether gender quotas bring about policy change is scarce. Chattopadhyay and Duflo (2004) show that the reservation of the most important seat in Indian villages brought policy choices closer to women’s preferences. In Spanish municipalities, Bagues and Campa (2020) fail to find significant increases in the share of “female expenditures” (issues women have been found to care more about than men, based on surveys) over two legislatures when candidate quotas were used.

Conclusion

Gender quotas are a popular policy tool used to close existing gender gaps in political empowerment, which are large in many countries in the FREE Network. A growing economics literature on the impacts of gender quotas helps assessing what objectives policy-makers may be pursuing when they adopt them, and under which conditions these objectives can be achieved. There is a number of lessons to be learned from this literature.

First, the design of the quota is crucial for it to achieve its primary objective, which is to increase women’s presence in the targeted political positions. Placement mandates, for instance, are particularly important in the design of candidate quotas to avoid that women are strategically placed at the end of the ballot. Second, policy-makers need to take the local context into account. Whether a candidate quota can generate spill-overs to higher-level positions likely depends on the degree of centralization of political parties for instance; where party leaders are very powerful, we may be less likely to see an increase in the share of female leaders following the adoption of a candidate quota. Third, the question when gender quotas successfully bring about policy change needs additional investigation. Different factors likely play a role, such as: the type of position targeted by the quota (legislative or executive, local or national, etc.); the extent of the increase in representation achieved; the magnitude of the gender difference in preferences; the type of decision-making process prevailing (majority voting or unanimity); how the selection of politicians is affected by the quota; and how women’s influence on policy is measured. Studies that systematically vary some of these factors will improve our understanding of this area of research. Fourth, there is no overwhelming evidence of negative effects of gender quotas in a number of dimensions, at least over a medium-term horizon.

The case for adopting and testing different forms of gender quotas, perhaps in combination with additional measures, is therefore relatively strong. Overall, our assessment is that quotas will have to remain in policy-makers’ toolbox for some time if the worldwide effort to close the persisting gender gaps in political empowerment is to continue.

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 Swedish Exceptions: Early Lessons From Sweden’s Different Approach to COVID-19 – Insights From a SITE-LSE Webinar

Posted on April 27, 2020 | Policy Brief

Image of sunrise in Stockholm street representing Sweden's different approach to COVID-19 response

Sweden’s policy in the Corona crisis has been subject to a lot of discussion in international media recently. Some point to the country portraying “the Swedish way” as a valid policy alternative to the forced lock-down of society, others criticize the Swedish government for being imprudent. Given the pace with which the virus spreads and considering the volatility of current events, it is pre-mature to draw any definite conclusions. But it is certainly time to start an informed policy discussion. The webinar “The Swedish Exceptions: Early Lessons from Sweden’s different approach to COVID-19, jointly organized by the Stockholm Institute of Transition Economics (SITE) and the London School of Economics (LSE) on April 22, 2020” brought together academics from different relevant disciplines from Scandinavia, the UK and the US . The webinar allowed to discern a few of the motivations behind the Swedish policy choices as well as a number of criteria which will serve to measure the success of governments’ responses to the Covid-19 pandemic in the future.

Understanding the Swedish Approach to Covid-19

Much has been written and said about the Swedish reluctance to impose a strict lock-down on the country: the Swedish government has so far relied mostly on expert recommendations, avoiding from more stringent policies such as the strict lock-downs imposed by for instance Sweden’s neighboring countries Norway and Denmark (more on Sweden in the Covid-19 crisis here). The majority of the speakers in the webinar agree that the Swedish policy in the Corona crisis has been an outlier, even with respect to traditional Swedish policy: Peter Baldwin, historian and professor at the University of New York and the University of California, Los Angeles, argued that Sweden has had an interventionist tradition with respect to social and health policy in the past. “Native policy traditions” therefore do not explain why Sweden has chosen this policy course in his view.

While it seems difficult to pin down historical or ideological reasons behind the Swedish policy stance with respect to Covid-19, Lars Trägårdh, professor of social history at Ersta Sköndal Bräcke University College in Stockholm, pointed out that even though the legal differences may seem stark, the difference in the policy impact may be smaller than expected, the crucial factor being the degree of compliance with a certain measure or recommendation and not its legal force. Trägårdh further argued that, since it may take many months to develop a vaccine, the sustainability of a given policy strategy is essential. According to him, a policy relying on voluntary compliance as the Swedish one rather than legal obligation, may therefore yield comparable effects in the short and medium run and could even turn out to be more successful in the long run.

Trägårdh argued that the true exceptionality of the Swedish response to the global pandemic has been the choice to not close elementary schools. This policy choice can be explained above all by the concern for children’s rights: for smaller children, digital learning simply is not a valid option. As declared by the government on several occasions, another reason is that parents working in professions such as healthcare may be induced to stay at home if schools are closed. Finally, Trägårdh cited a recent study from Iceland which suggests that the effect of closing schools on limiting the spread of the virus may be relatively small.

Later in the discussion, another potential argument in favor of the Swedish strategy emerged: Professor Sara Hagemann from the LSE School of Public Policy described the difficulty of leaving a lock-down, which Denmark is currently experiencing. The question which measures are to be lifted and which sectors of the economy are to be opened first has caused considerably more controversy than imposing the initial lock-down. In contrast, the public debate in Sweden can immediately focus on dealing with the long-term consequences of the crisis according to Trägårdh.

The significance of the concept of “herd immunity” (meaning the protection from disease arising from large percentage of the population having developed immunity) for the Swedish strategy is unclear. Baldwin pointed out that even though Swedish authorities have declared not targeting herd immunity, many measures implicitly seem to be aiming for this outcome.

Results of the Swedish Approach Until Today

Tom Britton, professor of mathematics at Stockholm University, agreed that the Swedish response to the Covid-19 crisis came late and that there has been too little testing. However, he argued that the government’s policy has been consistent, focusing on reducing the spread of the virus and protecting risk groups and especially the elderly. Whether Sweden has achieved the latter goal is still up to discussion, though. As of April 2020, reported infections and deaths in nursing homes had increased, which according to Trägårdh has been the major failure of the Swedish policy response up until today. Yet, the speakers agreed that the Swedish government’s measures have received a lot of public support within Sweden so far, which is a non-negligible factor for the long-term success of the strategy.

General Policy Conclusions

Professor Ole Petter Ottersen, president of the Karolinska Institute in Stockholm, Sweden’s largest centre of medical research, stressed the speed with which the virus has been spreading: the rapid development forces policymakers to quickly take decisions based on limited information. Given the lack of data, Ottersen called for politicians to practice humility and acknowledge the uncertainty surrounding policy choices. According to him, it will take years to evaluate whether the Swedish model or the Norwegian model of a quick and strict lock-down is better suited to fight the pandemic.

Policymakers around the globe face a dilemma: for sustainable crisis management and given countries’ interdependency, measures meant to fight the spread of Covid-19 should be aligned internationally and taken cooperatively. Yet, as Hagemann pointed out, it is clear that one policy cannot fit all: countries differ for instance with respect to their socio-economic structure, health care quality and availability, demographics, and with respect to the point in time when they were hit by the virus. This is not only the case between countries, but even within countries, which could justify a differentiated approach between rural and urban areas in some instances. In other words, all models and policy recommendations have to be adapted to the specific local setting. A strategy which allows for making local adjustments while maintaining a global perspective will be a major challenge for policymakers in the coming months and, likely, years.

Britton stressed the importance of understanding the limits of the models being used. Their predictions depend on a lot of assumptions regarding for instance how individuals behave and to what extent rules and regulations are being respected. Anti-body tests will soon provide more data on the actual spread of the virus, but even then, major questions, such as how to treat a potential trade-off between preventing deaths from Covid-19 vs. the socio-economic and health costs caused by a lock-down, will remain unanswered. This trade-off is country specific as well: Hagemann argued that Sweden and the other Nordic countries have quite successfully implemented remote working and learning options. This, however, will not be feasible in most developing countries, for instance, which necessarily affects the cost-benefit analysis of the available policy options.

Further, data collection and availability undoubtedly need to improve. As long as no better instruments of analysis are available, both scientists and politicians should be transparent about the simplifying assumptions and models they base their policy recommendations and decisions on.

Finally, despite their different academic backgrounds, all experts agreed on the need to take into account the indirect consequences of both the spread of the virus and the policy measures implemented to fight it. Covid-19 is likely to reinforce social inequities. For instance, it has been shown that in Stockholm, immigrant communities have been hit the hardest. As soon as the imminent health crisis is under control, the policy focus, therefore, has to shift towards the socio-economic consequences of the crisis.

Acknowledgements

The Stockholm Institute of Transition Economics wishes to express its appreciation to the speakers for their contributions to the policy debate, to the London School of Economics for the successful cooperation in organizing the event, and to the audience for its engaging questions and interest in the topic.

List of Speakers:

  • Peter Baldwin, New York University and University of California, Los Angeles
  • Tom Britton, Stockholm University
  • Sara Hagemann, London School of Economics
  • Ole Petter Ottersen, Karolinska Institute, Stockholm
  • Lars Trägårdh, Ersta Sköndal Bräcke University College, Stockholm
  • Erik Berglöf, London School of Economics (moderator)

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.

Money as an Economic Category and Its Relationship With Crypto Assets

Posted on April 20, 2020 | Policy Brief

Image with two bitcoin coins representing money and crypto assets

This brief discusses money in its general definition and describes new types of money arising in the modern era of digitalization, such as electronic money, cryptocurrencies, Central Bank Digital Currencies (CBDC), etc. It provides an overview of some of the legislative approaches trying to deal with new types of money and outlines the benefits and shortcomings arising from allowing for financial operations with digital currency. It also stresses the necessity of a new integrated approach in national and international regulation of cryptocurrencies.

Introduction

Cryptocurrencies have existed for more than 10 years. During this period the interest towards this type of digital money has seen its ups and downs. However, by now, they have become part of modern financial markets. Today, more and more central banks consider the possibility of introducing national digital cash and try to create easy-to-understand and clear regulation for new payment methods. We can observe the rapid transformation of the traditional monetary system. At the same time, there is no clear understanding of how the new monetary system should look like. An essential step towards this understanding is developing a clearer systematization and definition of money, financial funds, cryptocurrencies, fiat money in the traditional and the modern sense. Explaining these concepts is necessary to facilitate effective regulation, the development and supervision of financial markets. Indeed, during rapid financial markets transformation, well-developed regulation is necessary to avoid excessive financial risks and speed up financial sector development.

The Place of Money in the Modern Financial System

Financial resources play an extremely important role in the economy: Monetary systems are like the blood circulation for the body. While there is a common understanding of what money is in the traditional sense, this concept does not take into account the recent development of the financial sector, the penetration of IT technologies, the entry of new non-financial institutions into the financial sector as well as the creation of new products at the intersection of finance and IT. As argued above, a clear and encompassing definition of money, reflecting these developments, is necessary for regulatory purposes both at the national and international level.

Typically, money is defined through its functions, such as a measure of value, means of circulation, means of payment and savings. For example, the Large Economic Dictionary suggests that “Money is the universal equivalent, a special product, used to form expressions of the value of all other goods. Money functions as a medium of exchange and of payments, as a measurement of value, wealth accumulation and world money” (Borisov, 2003). As can be seen, one of the most important characteristics of money is its universality. Money can be exchanged against different goods and services almost without any limitations. At the same time, Tarasov mentioned that money is “legal payment funds, usually consisting of banknotes and coins that are constantly circulating as a medium of exchange in accordance with government rule” (Tarasov, 2012). There are other definitions of money, but they usually describe traditional money.

Along with traditional fiat money, there are other payment methods and electronic money is the most common of them. According to the Belarusian legislation, electronic money is “units of value stored in electronic form, issued in exchange against cash and monetary funds and accepted as a means of payment […]”

Electronic money cannot be described as traditional cash or money on bank accounts. It is not included in the money supply and can be issued only by commercial banks. At the same time, electronic money can perform the same functions as traditional fiat money. Whether or not electronic money can be considered full-fledged money is essentially a legal issue.

Another very important question is dedicated to cryptocurrencies. Cryptocurrencies are usually issued based on blockchain technology (distributed ledger) and can be created (“mined”) by anybody. Hence, electronic money is representative of traditional money, but cryptocurrencies are not.

Taking into account the penetration of information technologies into finance as well as the appearance of electronic money and cryptocurrencies, we can define money as the universal equivalent (measure) of value constituting a legal means of circulation, payment and savings on certain territories within a particular jurisdiction, with a legal status guaranteed by the government (Luzgina, 2018). In this definition, the emphasis is placed on the legitimacy of money because in some countries, operations with digital currencies can be legally interpreted as operations with securities, equity etc., rather than money in the legal sense.

Belarus was one of the first countries that legalized operations with crypto assets. But this does not mean that cryptocurrencies have become the equivalent of national or foreign currencies. According to the Belarusian legislation, people can mine cryptocurrencies, exchange them against Belarusian rubles, foreign currencies, buy, sell and exchange against other tokens (Decree #8, 2018). There is no official permission to use crypto money as a measure of value, means of circulation or payment method. In other words, people cannot use bitcoins for purchasing goods and services. At the same time, cryptocurrencies can be used as traditional financial assets.

It is necessary to emphasize here that the digitalization of the financial sector is an ongoing process. It is very hard to be the leader in the sphere. Despite Belarus being an early mover in the legalization of crypto assets and notwithstanding the existence of a strong IT sector and attractive crypto assets regulation, Belarus is only the 59th among 65 countries in the Fintech Index 2020. Based on the experience of other countries, sustained progress in this area can be achieved by government support, the existence of a well-developed ecosystem and access to financing (Global FinTech Index 2020).

Belarus is not the only country in the world that has limitations on cryptocurrencies’ circulation as fiat money; restrictions differ depending on the jurisdiction. Many central banks consider cryptocurrencies as disruptive technologies with high risks. Regulatory bodies usually cannot control operations with crypto money. That is why cryptocurrencies can be attractive for payments in the grey economy. Moreover, exchange rate fluctuations of cryptocurrencies are very unpredictable. Owners of cryptocurrencies can become very rich as well as very poor within a short period of time.

Central banks can implement limitations to avoid or decrease risks. For example, operations with cryptocurrencies are prohibited in Bangladesh and strongly restricted in India. There are central banks (including the central banks of Malaysia and Austria) that take a neutral position with regards to crypto operations but inform the society about possible risks, including risks of high fluctuations (Luzgina, 2018). At the same time, Japan permits the circulation of cryptocurrencies as a means of payment within its current regulation. That is, the Japanese authorities legalized these digital assets and, supposedly, can keep risks under control.

It is important to understand that these, and other, differences in the approach to crypto assets regulation create barriers for international payments and investment transactions. At the same time, a unification of regulation would contribute to transparency and mitigate the risk of cybercrimes.

Central Bank Digital Currencies: Main Aspects

There is an intense political and academic debate about the future of crypto markets. At the same time, more and more countries begin to think about the introduction of Central Bank Digital Currency (CBDC). Countries like Ukraine, China, Sweden, Canada, Thailand and some others have announced their plans of issuing CBDC. CBDC can be compared with digital cash; it can reduce operational costs and make all money transactions more transparent. But there are some uncertainties: The technology is new and may cause confusion and even disapproval among the population who prefers to use only cash.

One of the most interesting examples of the introduction of CBDC is the case of Uruguay. In 2017-2018, this country realized a pilot project of CBDC (the e-peso). A limited amount of digital currency was issued and only 10,000 citizens joined the project. There was a limited list of stores and businesses that were allowed to work with digital currency and all transactions on the base of mobile phones were done only between registered users. This project has demonstrated several advantages of e-peso circulation. First, the system could work without Internet and provided anonymity but at the same time controllability of all operations. Second, security was the main concern: The person could get access to his/her digital resources even if he/she forgot the password of the digital wallet or lost the mobile phone, but non-authorized access was effectively avoided. Finally, the last but not the least advantage of the system was the exclusion of double charge or falsification during payment transactions. The project lasted half a year and finished successfully. However, transition to the digital currency did not follow.

As of now, many countries only consider or are going to realize pilot studies in this area. The only country that is going to implement CBDC in the foreseeable future is China. The cautious position of many central banks is understandable because CBDC is an analogue of digital cash. The population distrusts such forms of money. Another challenge is that senior citizens often prefer cash for payments and other financial transactions.

Tokens vs. Cryptocurrencies

Bitcoin and other cryptocurrencies present only one kind of digital tokens. According to the Belarussian legislation, a token is an entry in the register of transaction blocks (blockchain), or another distributed information system certified that the owner of a digital sign (token) has rights to civil law objects and (or) presents cryptocurrency. All cryptocurrencies are tokens but not all tokens can be defined as cryptocurrencies. Tokens are issued for multiple purposes. Governments in many countries try to identify all types of operations with tokens for the creation of clear regulation. For example, the Central Bank of Lithuania highlights the differences between issuing tokens in the framework of ICO (Initial Coin Offering) and STO (Security Token Offering). According to the Lithuanian regulation, ICO usually provides for presenting discount programs or using tokens as payment instruments. At the same time, STO includes the issuance of tokens that have features of bonds or other traditional financial instruments and is subject to regulation. In other countries, central banks do not highlight STO and operations regulation with tokens depends on the characteristics and specifics of each project.

Many countries have developed unique principles and rules of tokens regulation. But there are no unified approaches at the international level which makes it difficult for conscientious market participants to work with financial crypto assets over different jurisdictions. Moreover, there are uncertainties and risks that have to be investigated more in detail. Authorities in many countries are afraid of cybercrimes and increasing money laundering operations.

At the same time, many advantages are apparent. For example, in Belarus, crypto platforms get more popular, because they offer attractive financial instruments for the population and companies. On such platforms, companies can attract necessary resources and citizens invest in financial tools with regulated risks.

Figure 1 – Structure of digital, electronic money, tokens and financial means (Luzgina, 2018)

Comment: Fiat electronic money is an electronic analogue of fiat currency. In this case, if we put 100 euros in an electronic wallet, we should see 100 electronic euros after the transaction. At the same time, non-fiat electronic money differs from fiat currency. For example, we can exchange Belarusian ruble against electronic money – V-coin, which is issued by Belgazprombank in cooperation with the mobile operator – A1.

The above discussion results in a number of policy-relevant implications:

  1. The legal definition of money, financial funds and electronic money should be updated taking into account innovative forms of financial instruments development and the appearance of new financial market participants.
  2. Old rules and regulatory approaches hinder market development and unregulated space can create additional risks and uncertainties.
  3. The transition from cash to CBDC is possible but has limitations.
  4. A unified regulation for cryptocurrencies and other tokens should be developed at the international level for decreasing risks and further developing financial markets.

Conclusion

Financial market transformation is happening very rapidly. The penetration of information technologies in the financial sector created a huge number of new innovative products and simplified financial operations. All these changes have affected the payment system. The creation of electronic and digital currencies makes it necessary to reconsider the future of the traditional monetary system. But even the current regulation has to become more flexible and take into account the rapid growth of new types of financial market participants and products. The development of financial technologies creates additional risks, such as money laundering, money theft or uncontrolled financial operations which go beyond the borders drawn by national jurisdictions very often. Many central banks treat payments with cryptocurrencies and ICO with caution. At the same time, the process cannot be stopped because alternative methods of financial transactions are often more attractive compared with traditional financial services. But the low level of financial and digital literacy among the population combined with outdated legislation can slow down innovative processes in the financial sphere and augment the risks.

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 Economic Update — Brace for the Covid-19 Impact!

Posted on April 15, 2020 | Policy Brief

A view of central Moscow City at dawn representing the sudden stop of Russian economy

Russia’s oil dependence will once again contribute to an economic downturn that most certainly will follow the Covid-19 outbreak in Russia as in other countries. The decline in oil prices alone could lead to a drop in GDP of more than 8 percent. On the positive side, Russia manages its macro economy well. However, its fiscal reserves are not unlimited and the recent massive fall in oil prices has not been matched by a similar decline in the ruble exchange rate which means potential extra problems for the budget. Furthermore, monetary policy will have less of a role to play in dealing with this type of crisis. This means that Russia like other countries will face difficult trade-offs in dealing with the crisis at a time when some of the previously announced economic policy changes have not been well received by the public.

Introduction

The corona virus crisis will destroy both lives and economies as it spreads across the globe. Fortunately, the corona virus death toll in Russia so far is relatively modest compared to many other countries, but the economy is most certainly heading for very difficult times. This is (again) due to the fact that the Russian economy is too dependent on the developments of international oil prices (see e.g. Becker, 2016a,b). In recent years, Russia had to deal with two severe declines in oil prices that hit its economy, first in connection with the global financial crises 2008/09, and second, in 2014/15, when there was a fall in oil prices simultaneously with Russia being hit by international sanctions after the illegal annexation of Crimea. Although these episodes were very costly for the Russian economy, they also provided important lessons for policy makers on fiscal, monetary and exchange rate policies that come in handy today. They also contributed with data on the relationship between large movements in oil prices and the effects they had on GDP growth in Russia. This is useful at this stage to assess what can happen with the economy after the significant decline in oil prices that has followed in the course of the corona outbreak.

Dramatic Decline in Oil Prices

We still do not know when this crisis will be over, but when it comes to the fall in international oil prices the start has been far more severe than the two crises referred to above. Since the beginning of 2020, oil prices have fallen from around $60/barrel to around $15/barrel or as Figure 1 shows, a barrel is now worth around 25 percent of what it was worth three months ago. Furthermore, prices are rather volatile and will continue to be so and there will most certainly also be periods of sharp increases in oil prices going forward – but the overall result for the year compared to the previous year is most likely a very sharp fall in prices. This decline in oil prices has so far been much more dramatic than the two previous crisis episodes the Russian economy has experienced under Putin as president or prime minister.

Figure 1. Oil price developments in recent crises

Note: This graph is based on the European Brent spot price FOB published by the U.S. Energy Information Administration and the axis shows trading days, so that the graph covers the period from January 1 to March 30. Different qualities of oil of course have different prices, but the patterns shown here are similar for other oil prices as well.

Exchange Rate and Stock Market

As in previous crises, the Russian stock market and exchange rate are following the evolution of oil prices. However, neither the stock market, nor the exchange rate has fallen as rapidly as oil prices. This can be due to many factors, but one likely explanation is that investors think that the decline in oil prices will not last for as long as it has in past crises. Whether this assumption is correct remains to be seen of course, but if oil prices stay low for an extended period, we can expect to see further declines in both the exchange rate and stock market.

Figure 2. Oil prices, exchange rate and stock market

Sources: Oil prices as in Figure 1, the exchange rate from Central Bank of Russia, RTS index from Moscow Stock Exchange.

The fact that the exchange rate this time has “only” depreciated by 20 percent when oil prices have fallen by 70-80 percent means that the oil price measured in rubles has fallen much more dramatically in this crisis compared to the previous ones. In the 2008/09 global financial crisis, the oil price in ruble terms was, in the end, unchanged compared to the start of the crisis. In 2014/15 this was not the case, but the decline in the ruble oil price was a more modest 25 percent compared to the 60 percent drop right now. This has serious implications for the government’s budget which is ruble-based and highly dependent on oil revenues.

Economic Policy

The Russian government now has plenty of experience in dealing with crises. The first lesson after the crisis at the end of the 90s was to have enough fiscal resources to deal with a crisis without having to go to the IMF again. The second lesson came in the global financial crisis when the fixed exchange rate had to be abandoned to avoid depleting the central bank’s international reserves. A prudent fiscal policy backed by the National Wealth Fund and a flexible exchange rate is still the backbone of the macroeconomic policies that can help mitigate the impact of lower oil prices.

The central bank is pursuing inflation targeting and uses a 4 percent inflation rate as the target that guides its policy decisions. The main tool is setting the key interest rate at a rate that will achieve the inflation target. The key interest rate is currently 6 percent, significantly down from the high of 17 percent in January 2015. The central bank states clearly in its monetary policy documents that “Monetary policy lays the groundwork for economic development; however, it cannot be a source of a sustainable rise in economic potential” (see page 6 in Central Bank of Russia, 2020). This implies that the central bank will only lower the key interest rate if inflation falls, not to support growth or try to achieve other, potentially conflicting goals. This is good news for macroeconomic stability but may become an issue of political tension if there is a serious downturn in the economy while inflation remains higher than the target rate.

In mid-2019, the National Wealth Fund was doubled and went from $60 billion to just over $120 billion (Ministry of Finance, 2020). This was done as a one-off transfer of surplus funds from the government’s budget. However, at its peak in the global financial crisis, the combined reserve fund and wealth fund that existed then had assets of over $220 billion but by the start of 2011, the assets were down to $111 billion. In other words, a year and a half into that crisis episode, the government had used an amount from the funds that roughly corresponds to the total amount available in the National Wealth Fund today. The fiscal space is, therefore, less impressive than it may look at a first glace and just burning through the cash in the National Wealth Fund is not a sustainable fiscal policy if this crisis continues a few more months.

Instead, the government will have to plan other measures as soon as the most immediate spending to deal with the crisis is done. This will entail difficult trade-offs since the health system will need increased resources at the same time as households and companies will need support to mitigate the impact from lost jobs and closed businesses in the wake of corona-induced shut-downs rather than the decline in oil prices, so adding to the pressure coming from declining oil prices. Increasing taxes in a time of already depressed purchasing power and profits is also not an appealing option and although there are still tax increases in the pipeline, the government has announced that these will not come in effect this year. Like in many other countries, the Russian government is proposing several measures to support the economy that will be discussed in more detail in a forthcoming FREE policy brief. However, these measures will add to the costs of the government at a time of falling revenues. From an economic perspective, reallocating resources from the military and security sectors to other parts of the economy seems like an obvious choice under these circumstances, but most likely not the outcome of this process given the government’s geopolitical and domestic power ambitions. Again, the fiscal reserves will allow postponing these harder decisions, but if the crisis goes on for some time, alternative measures such as borrowing domestically or internationally will most certainly be discussed also in Russia. However, many governments will be in need of borrowing on international markets going forward and the rates required to access this type of funding may not be very attractive and still force domestic budget reallocations.

Growth Impact of the Oil Price Fall

It is of course too early in the crisis to make very precise forecasts on how the economy will fare in 2020. This will in the end crucially depend on how the Covid-19 pandemic develops and on government responses to the crisis not only in Russia but also in the rest of the world. A partial analysis of the impact of falling oil prices can however be done with the models presented in Becker (2016a) which link changes in oil prices to growth. This paper shows a few alternative specifications that differ in the GDP measure being in dollars or real rubles, and in some other dimensions. All specifications are highly statistically significant and able to explain between 60 and 90 percent of variations in GDP growth in the period 2000-2015. Focusing on the relationship between the percentage change in oil prices and growth in real ruble GDP, the estimated coefficient is 0.14. This implies that for every 10-percentage point drop of oil prices, GDP growth goes down by 1.4 percent. Currently, oil prices have declined by 75 percent since the beginning of the year. However, the model estimates are based on comparing how average oil prices change between years so this is the numbers we need to compute and compare. The average price of Brent oil (which is used in this model) was $64/barrel in 2019 but we obviously do not know what the average oil price will be this year. We therefore need to first “forecast” oil prices for the rest of the year before we can compute the impact on growth. If we make the simple assumption that oil prices stay at the current level and take into account that they were significantly higher the first couple of months this year, the average price would end up being $25/barrel. That would amount to a 60 percent decline in average oil prices between 2019 and 2020. The partial effect of this oil price decline would therefore make Russian real GDP drop by 8.5 percent in 2020. Again, this is the partial effect based on the estimated coefficient in a linear relationship between oil price changes and real GDP growth. In plainer English, we are not looking at the first order effect of closing stores etc. to avoid the virus from spreading but only the additional effect that we think will come from falling oil prices. In addition, the effect this massive decline in oil prices is assumed to have on GDP is derived by a coefficient that is estimated on smaller changes in oil prices and real GDP. Nevertheless, this exercise provides a first, and rather daunting, assessment of what can happen to GDP given the decline in oil prices alone.

Concluding Remarks with OPEC and IEA update

This brief has provided a first assessment of how the Russian economy may be impacted by the massive decline in oil prices that has followed in the course of the corona pandemic. It has shown that the economic downturn this time can be significantly worse than both the 2008/09 and the 2014/15 crises. A base line estimate suggests that GDP may fall by more than 8 percent only because of the fall in oil prices. The above calculation obviously includes neither the impact the health situation will have on companies or households, nor the government’s ability to mitigate the negative consequences. If the other problems the economy is facing as a direct result of the health crisis also lead to a significant decline in supply and demand, Russia could easily see real GDP declining by more than 10 percent in 2020.

Our estimate is an important reminder that Russia’s continued oil dependency is a risk to the economy and its citizens. Now is not the time for ambitious structural and institutional changes to generate growth, but hopefully the urgent crisis period passes without policy makers forgetting the risks the country’s oil dependence entails. They learnt the fiscal and monetary lessons well from past crises, now is the time to learn something new. The most appealing road to sustainable economic growth is still building credible property rights institutions and rule of law in a framework that would make Russia the innovative business-oriented superpower it could be.

A few days after the first version of this brief was published, oil prices started to rise as the OPEC together with Russia started discussions to cut production to support oil prices. A tentative agreement was reached which is supposed to cut production by 10 million barrels per day in May and June, the largest cut in OPEC’s history. Had this movements in prices continued, the forecast for the Russian economy would have been affected. However, this recovery in prices was soon reversed and oil prices started to fall again. The decline continued on April 15 as the International Energy Agency presented a dire forecast of oil demand and stated that this year may be the worst year ever in terms of declining demand. All in all, the price movements that have followed the OPEC meeting and the statements of the IEA do not change the baseline prediction this brief has provided.

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. This brief was first published on April 6, 2020 and then revised on April 15, 2020.

Recipient Type and the Effectiveness of Informational Campaigns: The Case of Meat

Posted on March 30, 2020 | Policy Brief

A picture of meat on the table table, indoor, sitting representing Informational campaigns that affect meat consumption

While global population growth has been accelerating during the last decades, the number of humans currently living on the planet is dwarfed by the amount of farm animals alive at any time, and even more by the quantity we slaughter for meat every year. According to the latest FAO statistics, this latter number is estimated at around 75 billion. Even ignoring animal welfare, this is severely affecting the health of the planet and our own. What should be done about this?

Externalities of Meat Consumption

Mankind has been butchering and eating animals for at least 3,4 million years (McPherron et al., 2010). Evolutionary biology theories claim that complementing our diet with meat contributed to the spectacular growth of our brain (Fonseca-Azevedo et al., 2012). Anthropological theories suggest that the necessity of hunting drove the development of tool building, language and social structures. The domestication of animals (and plants) around 10,000 years ago led to a jump in the history of civilization. In other words, eating meat is a large part of what made us human. However, during the last century, we took this to unsustainable levels. All in all, the agricultural sector accounts for 25 to 30% of global CO2 emissions, second only to the energy and transport sector, and 60% of non-CO2 emissions, in particular methane, which is much more efficient than CO2 at warming up the planet. A third to half of these emissions, depending on whether or not we include the share related to land use, comes from livestock production. Large scale factory farms, which cater to the ever-increasing global demand for cheap meat, are also responsible for other externalities, including distorted resource use (in particular of water and fertile land); local pollution of air and waterways, with consequences for neighbouring ecosystems and human health; abuse of antibiotics, which threatens their effectiveness with dramatic implications for the whole spectrum of modern medicine. The cheap and overabundant animal products with worsened nutritional properties, which result from these production methods are also behind the epidemic of “welfare diseases” such as diabetes, cardiovascular conditions and some types of cancer (Mozaffarian, 2016).

So, what should we do about this? Economics is very clear on this point. In the presence of externalities, market prices do not reflect social costs; therefore, the market mechanism fails, and decisions taken on the basis of these prices are suboptimal. If applied to meat consumption, this principle implies that, first of all, consumers and producers must pay for the emissions (and other externalities) they cause. Today’s carbon pricing systems, whether in the form of a tax like in Sweden or tradable emission permits like in EU, exempt the agricultural sector for various reasons. Moreover, as already mentioned there is more to meat than carbon emissions. Another FREE brief (Perrotta, 2011) makes the case for a meat (consumption) tax. Multiple teams of researchers (Wirsenius, Hedenus, and Mohlin, 2011; Edjabou and Smed, 2013; Gren, 2015; Andersson, 2019) have come as far as to compute the optimal level of such a tax, in different contexts and under different assumptions. There are also drawbacks to this approach, though. Climate-change curbing policy is in general an area where policy makers at all levels find it hard to converge to policies of strong incentives, such as taxes and regulation. Interventions targeting food production or dietary choices, in particular, are likely to face strong opposition from producers and consumers alike. It is therefore worth considering the alternative – or at least complementary – strategy of information and awareness campaigns.

The Power of Information

Given that a climate policy agenda of strong incentives is so fraught with obstacles, the potential for information to spark voluntary action would be very valuable. There is a catch here, however. Information about the benefits of an action often fails to encourage that action. Consider the case in point: for decades now, we have observed a persistence and increase of meat eating despite mounting evidence and widespread information on the ills of meat production and consumption. Indeed, this well-known weakness of informational interventions has contributed to the rising importance and application of alternative approaches. One example is the popularity of the so-called nudges (Thaler and Sunstein, 2009), modifications in the choice architecture that can subtly push agents towards an action without actually limiting the available alternatives. There is ample research on where and why the chain from information to action might get interrupted, and established evidence that the effectiveness of information depends on a variety of factors such as recipients’ prior beliefs, the sender’s credibility, and the non-informative content of the message, such as the emotional evocativeness of imagery (see a survey in DellaVigna and Gentzkow, 2010). Taking a step back to the stage before, namely the question whether information does reach the intended beneficiaries in the first place, at least three aspects of this have been investigated: limited attention, active avoidance, and selective retaining of information on the part of the recipients. In a new working paper (Berlin and Mandl, 2020), we investigate the role of individual type for selective information retention. We ask whether certain types of agents, in our case vegetarians, retain more of the information they are exposed to, even when exposed to a similar context and the same incentives to retain information as everyone else (so that hopefully the competing channels of limited attention and active avoidance can be neutralized). This has relevance for the possibility of tailoring the policy message, similar to the marketing theories of market segmentation. In contrast to well-developed marketing practices in the private sector, this potential has so far not been exploited in policy design. To the best of our knowledge, this mechanism has not been investigated in a real-life incentivized setting outside the lab before.

Natural Experiment in Class

We exploit a natural experiment in the context of higher education. A class of college students was assigned an essay about their plan for a Christmas dinner menu, after being exposed to a lecture and reading materials on the externalities of meat production, so that they could decide to make use of this information. The essays were to be written in randomly assigned groups of three, making the type combination, i.e. the presence of one or more vegetarian group members, a random group characteristic. We hypothesize that there is a difference in how carnivores and vegetarians deal with the provided information about the food industry. In particular, we test whether groups that include a vegetarian student recall a larger share of the information than groups made up only of carnivores. The essay was mandatory, and moreover it awarded study credits toward the final grade of the course (10/100 points). This constitutes a sizeable incentive and possibly provides a stronger motivation for information retention as compared to the average monetary rewards which lab experiments rely on. To measure the share of information retained, we preregistered a list of 30 words in both English and Swedish related to the learning outcomes of the lecture. We then used a script to measure how many of the 30 words appear in each essay. We call this number the essay’s score, separate and independent from the teacher’s assigned grade, which is of relevance for the student. The teacher-assigned grade, reflecting general comprehension of the topic rather than just the presence of keywords, is expected to be correlated with the score, but not perfectly. We also expect the grade to capture the ability of the students to a higher degree compared to the score, as the automatized word count fails to consider the context in which the words are mentioned.

Results

Figure 1. Group score by treatment status

Source: Berlin and Mandl (2020).

On average, groups including a vegetarian student scored higher (4.8) than groups with all meat-eaters (4.3), but not significantly so. The estimated Cohen’s d (0.347), a standard measure of effect size used to indicate the standardized difference between two means, is much smaller than the minimum detectable effect in our sample, which we estimated at 0.8. In other words, we do not have the statistical power to either accept or reject the null hypothesis. The reason is that the treated group displays larger variation in score outcomes, possibly due to the smaller than anticipated sample size: only 11 students out of almost 300 identified themselves as vegetarians or vegan (non meat-eaters), which is a much smaller proportion than what the latest survey of young adults in Sweden estimates (17%, Djurens Rätt, 2018).

Looking beyond the mean at the details of our data reveals an interesting pattern. As the Figure shows, the distribution of achieved scores among the vegetarian groups is bimodal: a lower-level concentration of scores is close to the mode of the control distribution, but there is an almost as large mass at a higher level. This might suggest that, quite understandably, (attention and) performance, in terms of recall, is affected by several factors beyond the type. In other words, not all the individuals with the relevant type display increased retention of information. While many vegetarians remain close to the mode for the meat-eating type, a large fraction obtains double the score, suggesting a substantial though heterogenous increase in the retention of information.

We also use regression analysis in order to control for potential omitted variables and net out some of the variation in the score data that is not related to our variable of interest (such as group size and ability). Robustness checks were performed with different specifications and alternative outcome variables, but the main conclusion remains the same: mean performance, in terms of information retention, is higher for the vegetarian type but not significantly so. However, these results should not be interpreted as a rejection of our original hypothesis about the importance of type for information retention, as our analysis is empirically underpowered due to the low number of vegetarians in the sample. More importantly, the method we propose is highly appropriate, easily replicable and cheap.

Conclusion

Information interventions are low-cost and can be effective. Understanding how they can be tweaked for best effect is an area of crucial research interest, in particular for such an area as climate-change curbing policy. We provide an easy and cheap method to investigate this further and hope that more future research will pursue this avenue.

References

  • Andersson, Julius J., 2019. “The ‘meatigation’ of Climate Change: Environmental and Distributional Effects of a Greenhouse Gas Tax on Animal Food Products.” London School of Economics
  • Berlin, Maria P. and  Benjamin Mandl , 2020. “Selective attention and the importance of types for information campaigns”, SITE Working Paper Series. 53.
  • DellaVigna, Stefano and Matthew Gentzkow, 2010. “Persuasion: empirical evidence.”, Annu. Rev. Econ. 2 (1), 643–669.
  • Djurens Rätt, 2018. “Opinionundersökning, Våren 2018.” Novus.
  • Fonseca-Azevedo, Karina and Suzana Herculano-Houzel, 2012. “Tradeoff between brain and body mass.” Proceedings of the National Academy of Sciences. 109 (45), 18571-18576
  • McPherron, Shannon P. et al., 2010. “Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia.” Nature 466, 857–860.
  • Mozaffarian, Dariush, 2016. “Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review.” Circulation. 133(2), 187–225.
  • Perrotta, Maria, 2011. “Tax Meat to Save the Baltic Sea.” FREE Policy Brief Series.
  • Säll, Sarah and Ing-Marie Gren, 2015. “Effects of an environmental tax on meat and dairy consumption in Sweden.” Food Policy. 55, 41-53.
  • Thaler, Richard H. and Cass R. Sunstein, 2009. “Nudge: Improving Decisions About Health, Wealth, and Happiness.” Penguin Group.

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 Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries

Posted on March 16, 2020 | Policy Brief

Image of a metro elevator going upwards representing shadow economy of Russia

We apply a new method to measure the shadow economy in Russia during the period 2017-2018 and provide evidence on the main factors that influence involvement in the shadow economy. Drawing on a methodology developed by Putnins and Sauka (2015), we estimate that the size of the shadow economy in Russia is 44.7% of GDP in 2018. This is similar to the size of the shadow economy in countries such as Kyrgyzstan, Kosovo, Ukraine, and Romania, but higher than the level of the Baltic countries. Our findings are largely consistent with other less direct approaches for estimating the shadow economy. An advantage of our approach is that it can provide more detailed information on the components of the shadow economy.

Introduction and Approach to Measuring the Shadow Economy

The aim of the Shadow Economy Index, which has now been estimated in a number of countries, is to measure the size of shadow economies and explore the main factors that influence participation in the shadow economy. We use the term “shadow economy” to refer to all legal production of goods and services produced by registered firms that is deliberately concealed from public authorities (OECD, 2002; Schneider, Buehn and Montenegro, 2010).

The Shadow Economy Index draws on a survey-based methodology developed by Putnins and Sauka (2015). It combines estimates of business income that has been concealed from authorities, unregistered employees, and ‘envelope’ wages. The approach exploits the fact that entrepreneurs and business leaders are in a unique position in that they have knowledge about the amount of business income that is concealed from authorities, the number of employees that work for them unofficially, and the extent to which they pay wages informally to avoid taxes.

The challenge for such methods is to elicit maximally truthful responses about these sensitive issues, otherwise, the size of the shadow economy will be underestimated. To address this challenge, we use a number of survey and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g. Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004). While the full details can be found in Putnins and Sauka (2015), they include confidentiality with respect to the identities of respondents, framing the survey as a study of satisfaction with government policy, phrasing misreporting questions indirectly about “similar firms in the industry” rather than the respondent’s actual firm, gradually introducing the most sensitive questions after less sensitive questions, excluding inconsistent responses, and controlling for factors that correlate with a potential untruthful response such as tolerance towards misreporting.

The Index measures the size of the shadow economy as a percentage of GDP. Computing the Index involves three steps:

  • (i) estimate the degree of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses;
  • (ii) estimate each firm’s shadow production as a weighted average of its underreported employee remuneration and underreported operating income, with weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and
  • (iii) calculate a production-weighted average of shadow production across firms.

The survey about shadow activity in Russia from 2017 to 2018 was conducted between February and March 2019. We use random stratified sampling to construct samples that are representative of the population of firms in Russia drawing on the official company register and covering all regions in Russia. In total, 500 phone interviews were conducted with owners, directors, and managers of companies in Russia. We use the same methodology to collect data in other countries, which we compare with Russia, conducting a minimum of 500 interviews in each country.

Size of the Shadow Economy in Russia and Nearby Countries

The estimated size of the shadow economy in Russia is 44.7% of GDP in 2018. Our estimates suggest that the year before, in 2017, the shadow economy was slightly larger with 45.8% of GDP, although the annual change is not statistically significant. For comparison with nearby countries, using the same approach, high levels of shadow economy are also found in Kyrgyzstan (44.5% of GDP in 2018), Kosovo (39.5% of GDP in 2018), Ukraine (38.2% of GDP in 2018), and Romania (33.35% of GDP in 2016), but considerably lower levels are found in the Baltic countries, especially Estonia (16.7% of GDP in 2018). See Table 1 for the full set of estimates.

The estimates using our direct micro-level approach to measuring the shadow economy are largely consistent with other less direct approaches for estimating the size of the shadow economies, such as Schneider (2019). An advantage of the direct micro-level approach is that it is able to provide more detailed information on the components of the shadow economy, which we turn to next.

Components and Determinants of the Shadow Economy in Russia

We find that envelope wages and underreporting of business profits stand out as the two largest components of the Russian shadow economy. Underreporting of salaries or so-called ‘envelope wages’ in Russia are approximately 38.7% of the true wage on average in 2018, whereas approximately 33.8% of business income (actual profits) are underreported. Unofficial employees in Russia as a percentage of the actual number of employees are estimated 28.2% in 2018.

Some companies in Russia, rather than simply concealing part of the income or employees, are completely unregistered and therefore also contribute to the shadow economy. We estimate that such companies make up 6.1% of all enterprises in Russia.

Our findings also suggest that there is a very high level of bribery in Russia: the magnitude of bribery (percentage of revenue spent on ‘getting things done’) is estimated to be 26.4%, whereas the percentage of the contract value that firms typically offer as a bribe to secure a contract with the government in Russia is 20.6% in 2018. We also find that more than one-third of companies in Russia pay more than 25% of the revenue or contract value in bribes.

We find that the size of the shadow economy in all sectors of the Russian economy is close to 40% with somewhat higher levels in the construction and wholesale sectors, controlling for other factors. Using regression analysis, we find that entrepreneurs that view tax evasion as a tolerated behaviour tend to engage in more informal activity, as do entrepreneurs that are more dissatisfied with the tax system and the government. This result offers some insights into why the size of the shadow economy in Russia is so large – it is at least in part due to relatively high dissatisfaction of entrepreneurs with the business legislation and the government’s tax policy. We also find some evidence that higher perceived detection probabilities and, in particular, more severe penalties for tax evasion reduce the level of tax evasion, suggesting increased penalties and better detection methods as possible policy tools for reducing the size of the shadow economy.

Finally, while firms of all sizes participate in the shadow economy, we find that younger firms tend to do so to a greater extent than older firms. The results support the notion that young firms use tax evasion as a means of being competitive against larger and more established competitors.

Acknowledgments

This research was supported by a Marie Curie Research and Innovation Staff Exchange scheme within the H2020 Programme (grant acronym: SHADOW, no: 778118).

References

  • Gerxhani, K. (2007). “Did you pay your taxes?” How (not) to conduct tax evasion surveys in transition countries. Social Indicators Research 80, pp. 555-581.
  • Hanousek, J. and Palda, F. (2004). Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries. Kyklos 57, pp. 237-252.
  • Kazemier, B. & van Eck, R. (1992). Survey investigations of the hidden economy. Journal of Economic Psychology 13, pp. 569-587.
  • Lechmann, E. and D. Nikulin (2017). Shadow Economy Index in Poland. Gdansk University of Technology, Poland: Gdansk.
  • Lysa, O. et al. (2019) Shadow Economy Index in Ukraine. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. Kyiv International Institute of Sociology, Ukraine: Kyiv.
  • Mustafa, I., Pula J.S., Krasniqi, B., Sauka, A., Berisha, G., Pula, L., Lajqui, S. and Jahja, S. (2019) Analysis of the Shadow Economy in Kosova. Kosova Academy of Sciences and Arts, Kosova: Pristina.
  • OECD, 2002. Measuring the Non-Observed Economy: A Handbook. OECD, Paris, France.
  • Putnins, T.J. and Sauka, A. (2019). Shadow Economy Index for the ‘Baltic Countries 2019-2018. SSE Riga: Riga, Latvia.
  • Putnins, T.J., A. Sauka and A. Davidescu (2020, forthcoming). Shadow Economy Index for Moldova and Romania, 2015-2018. SSE Riga, National Scientific Research Institute for Labour and Social Protection.
  • Putnins, T.J. and Sauka, A. (2015). Measuring the shadow economy using company managers. Journal of Comparative Economics 43, pp. 471-490.
  • SIAR (2019). Shadow Economy Index for Kyrgyzstan. SHADOW: an exploration of the nature of informal economies and shadow practices in the former USSR region. SIAR research and consulting, Kyrgyzstan: Bishkek.
  • Schneider, F. (2019) Calculation of the Size and Development of the Shadow Economy of 35 Mostly OECD Countries up to 2018. Unpublished manuscript.
  • Schneider, F., Buehn, A. and Montenegro, C. (2010). New estimates for the shadow economies all over the world. International Economic Journal 24, pp. 443-461.

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 Expectation Boom: Evidence from the Kazakh Oil Sector

Posted on March 9, 2020 | Policy Brief

Image of Kazakhstan with skyscraper in the middle of the capital city representing oil price

This policy brief shows that an oil price boom may trigger dissatisfaction with one’s income and that this dissatisfaction is independent of the effect of the boom on real economic conditions. Unique data from Kazakhstan allows us to quantify the impact of the recent oil price boom on satisfaction with income. Compared to other households in the country, households related to the oil sector suffer a marked drop in their satisfaction with their income during the period of high oil prices. Based on our results, we argue that an oil price boom creates a gap between people’s expectations of the benefits from the boom and the observed economic conditions. Our results call for researchers, policy makers and companies to devote more attention to the dynamics of satisfaction, not only during resource busts but also during resource booms.

Local Impact of Natural Resources

Often, resource wealth is associated with a curse, slowing economic growth in resource-rich developing countries (Venables, 2016). While traditionally, this relationship has been explored across countries, more recently, the literature started exploiting plausibly exogenous spatial variation in resource wealth within countries (Cust and Polhekke, 2015). We now know that resources can generate local economic wealth (Aragon and Rud, 2013), while also attracting corrupted individuals to power (Asher and Novosad, 2018) and triggering local conflicts (Berman et al. 2017; Rigterink, 2018). But, up to now, we know very little about the impact of resource booms on individuals’ perceptions. Since perceptions and behavioral biases may also drive actions, understanding whether and how resources affect perceptions is key in understanding the local impact of natural resources (Collier, 2017).

In a new working paper (Girard, Kudebayeva and Toews, 2020) we use Kazakhstan as a case study to shed more light on the importance of such perceptions. We document the conditions that preceded and presumably contributed to the violent conflicts in the oil rich districts of Kazakhstan in 2011. We show that periods of high oil prices can actually lead to a drop in reported satisfaction with income. This implies that due to mere changes in perceptions, which are not reflected in economic conditions, a large number of people may experience a significant drop in satisfaction with income, creating a fertile ground for conflicts.

The Zhanaozen Conflict

Our attention to the case of Kazakhstan is driven by the extreme events that took place in 2011 in the city of Zhanaozen, a booming oil town in the west of the country’s desert. In May 2011, after several years of high oil prices, private sector workers in Zhanaozen demanded amendments to the pre-existing collective bargaining agreement asking in particular for a raise in wages. Difficulties in negotiating an agreement resulted in local oil companies dismissing more than 2000 employees in the summer of 2011 and oil production dropping by 7% in the first three quarters of 2011 relative to the same period in the previous year. At the conflict climax, the police tried to clear the central square of Zhanaozen for the upcoming preparations of the Independence Day, resulting in the killing of 17 and the injuring of over 100 people (Satpayev and Umbetaliyeva, 2015).

Oil Price Boom in Kazakhstan

Kazakhstan offers an ideal case study for our research question for two reasons. First, the government of Kazakhstan closely monitored citizens’ satisfaction with income throughout most of the 2000s using a representative household panel survey. Using this data allows us to link variation in the price of oil to within household variations in satisfaction with income – conditional on household income, thus, capturing the changing perceptions of household heads regarding their income. Secondly, Kazakhstan is a small open resource rich economy, with sparsely populated and remote districts, whose economic activity nearly exclusively depends on the extraction of oil and gas. The fact that Kazakhstan is a small open economy implies that changes in the oil price may be treated as exogenous to households located in Kazakhstan. The spatial isolation of the oil rich districts allows us to consider the group of household heads employed in the private sector in the oil rich districts as either directly or indirectly involved in the extraction of oil and gas.

Figure 1. Kazakhstan

Source: Resource rich districts are indicated as treated. The information on the spatial identification of oil and gas rich district is taken from the Petroleum Encyclopedia of Kazakhstan and captures more than 90% of total oil and gas production in Kazakhstan (Munayshy Public Foundation, 2005).

Satisfaction With Income

To identify the effect of oil price fluctuations on satisfaction with income, we exploit three sources of variation: location of the household, sectoral employment and time. The group affected by the price of oil is the group of oil-related households. Oil-related households consist of households whose head is employed in the private sector of the oil rich districts of Kazakhstan, and who are thus the closest to the oil sector (by nature of their activity and place of residence). The differential evolutions in satisfaction of household heads employed in other sectors and households located in other districts – in other words, households which are more remote from oil and gas extraction than the oil-related households provide a plausible counterfactual.

The main results are depicted in Figure 2 which represents the relationship between income and satisfaction for 8 groups based on the three sources of variation: oil price (which was low between the years 2001 and 2004, and high between 2005 and 2009), place of residence as indicated in Figure 1, and sector of activity.

First, we note that the relationship between income and satisfaction is upward sloping: reported satisfaction with household income increases with income. This is intuitive. Focusing on oil poor districts that appear in the bottom panel, we observe that the relation between satisfaction and income is virtually the same across sectors and time periods of low and high prices of oil. This is, however, not true for oil-rich districts, which are depicted in the top panel. Here, the relationship between income and satisfaction only remains unaffected across time for household heads who are not employed in the private sector. The picture changes if we turn to household heads employed in the private sector, who are the oil-related household heads. The satisfaction with the income of oil-related household heads shifts downwards, compared to other households, in the period of high oil prices (years 2005-2009). This downward shift is even more striking since oil-related household heads valued their income relatively higher than other households during the period of low oil prices (2001-2004).

Figure 2. Satisfaction with Income

Source: Authors’ calculations based on satisfaction with income and household income as reported in the Household Survey of Kazakhstan. The figures above depict the relationship between logged household income per family member and reported satisfaction with income by the head of the household on a scale from 1 to 5. The relationship is depicted conditional on household fixed effects and year fixed effects. The former account for time-invariant household specific characteristics such as individual biases. The latter account for Kazakhstan specific shocks affecting households in oil poor and oil rich districts simultaneously due to political and economic business cycles. As a result, the relationship between logged income per family member and satisfaction is normalized to zero in both dimensions.

Lastly, we document that the negative variation in satisfaction is related to the contemporaneous change in the price of oil. The satisfaction with income is not persistent, it is unrelated to past and future levels of the oil price.

Conclusion

Our results suggest that oil prices fluctuations can be linked to the individual’s perception of income. The fact that oil-related household heads express a strong dissatisfaction compared to other household heads may help to understand what made December 2011 possible, when 17 people were killed and over 100 people were wounded in Zhanaozen. If generalizable, such dynamics of perceived satisfaction with income should be kept in mind by both policy makers and extractive companies not only during resource busts but also during resource booms.

References

  • Aragon, Fernando M. and Juan Pablo Rud (2013). “Natural resources and local communities: evidence from a Peruvian gold mine.” American Economic Journal: Economic Policy 5(2):1–25.
  • Asher, Sam and Paul Novosad (2018). Rent-seeking and criminal politicians: Evidence from mining booms. Working Paper.
  • Berman, Nicolas, Mathieu Couttenier, Dominic Rohner and Mathias Thoenig (2017). “This Mine Is Mine! How Minerals Fuel Conflicts in Africa.” American Economic Review 107(6):1564–1610.
  • Collier, Paul (2017). “The institutional and psychological foundations of natural resource policies.” The Journal of Development Studies 53(2):217–228.
  • Cust, James and Steven Poelhekke (2015). “The local economic impacts of natural resource extraction.” Annu. Rev. Resour. Econ., 7(1):251–268.
  • Girard, Victoire, Alma Kudebayeva and Gerhard Toews (2020). “Inflated Expectations and Commodity Prices: Evidence from Kazakhstan.“ GLO Discussion Paper Series 469.
  • Rigterink, Anouk S. (2020). “Diamonds, rebel’s and farmer’s best friend: Impact of variation in the price of a lootable, labour-intensive natural resource on the intensity of violent conflict.” Journal of Conflict Resolution 64(1):90–126.
  • Munayshy Public Foundation (2005). “Petroleum Encyclopedia of Kazakhstan.”
  • Girard, Victoire, Alma Kudebayeva and Gerhard Toews (2020). “Inflated Expectations and Commodity Prices: Evidence from Kazakhstan” Working Paper.
  • Satpayev, Dossym and Umbetaliyeva, Òolganay (2015). “The protests in Zhanaozen and the Kazakh oil sector: Conflicting interests in a rentier state.” Journal of Eurasian Studies 6(2):122–129.
  • Venables, Anthony J. (2016): “Using Natural Resources for Development: Why Has It Proven So Difficult?” Journal of Economic Perspectives, 30, 161 – 84.

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.

Women at the Top of the Income Distribution: Are Transition Countries Different?

Posted on March 2, 2020 | Policy Brief

Shadows of women walking down the road in a sunset representing women at the top of income distribution

This policy brief reviews recent research on women at the top of the income distribution. The overall trend across a number of countries is that, while women are still a minority (and more so the closer to the top one moves), their share in top income groups has steadily increased since the 1970s. Detailed data from Sweden suggests that most of this rise is due to women increasingly earning high labor incomes (rather than capital becoming more important). It also shows that there are important differences between top income men and women, especially with respect to family circumstances. Comparing preliminary results from former Soviet and Eastern European countries indicates that there are, on average, more women at the top of the income distribution in these countries. On the other hand, the average time trend indicates that the share of women in top groups is falling. The preliminary results also indicate considerable heterogeneity across countries. These preliminary results require more detailed study, as does the question to which extent the relatively strong representation of women at the top of the income distribution reflects the “economic power” of women in the region.

The Gender Aspect of Rising Top Shares

Rising inequality has received a lot of attention in the policy debate as well as in the academic literature over the past decade. A particular feature of this discussion has been the increased concentration of both wealth and income in top groups. The summary of the World Inequality Report 2018 starts by stating that “The top 1% has captured twice as much of global income growth as the bottom 50% since 1980”. Such facts have, in turn, brought a lot of attention to the characteristics of top groups. What is driving their income growth? What is their income composition? Why have top shares increased so much in recent decades? (see, e.g., Roine and Waldenström, 2015, for an extensive overview, or Roine, 2016, for a brief summary).

However, one aspect which has received relatively little attention is that of gender. This may seem a little surprising. In a time when gender dimensions are often acknowledged as being important, one would expect that questions about the gender composition of top groups would also be of interest. If we know that top income shares are increasing, what is the gender composition of these groups? How has this changed over time?

This brief outlines some recent results on these questions and also points to some preliminary findings about a potential contrast between Western countries and (former) transition countries.

Evidence from Sweden, 1971-2017

Sweden is one of the few countries having had independent taxation of all taxpayers for a long period of time, allowing for a thorough analysis of the gender composition of top income groups. After having had joint taxation for married couples for most of the 20th century, and a short period of the option to be taxed independently even if married, Sweden switched to fully independent taxation in 1971. In a recent paper Boschini et al. (2020) study developments of men and women in top income groups in Sweden using detailed registry data on the full population for the almost 50-year period since.

The study finds a number of interesting results. First, it is evident that the share of women in top income groups has increased significantly, yet women remain clearly underrepresented, and more so the higher up in the distribution one moves. Figure 1 below shows the basic development over time for three top groups: the top 10 (P90-100), the top 1 (P99-100), and the top 0.1 group (P99.9-100) in the total income distribution and the labor income distribution respectively.

Figure 1. Share women in top groups in Sweden.

Source: Boschini et al. (2020)

Besides showing the general development comparing the two panels also reveals a subtler point: especially in the earlier decades and in the very top group (the top 0.1 group), there were substantially more women at the top of the total income distribution than at the top of the labor earnings distribution. In the 1970s and 1980s, the share of women in the top 0.1 group of the total income distribution is about two to three times as large as in the labor earnings distribution. Put differently, this means that in the past, to the extent that there were any women at the very top, they were mainly there thanks to capital incomes. Over time this changes and detailed analysis in the paper shows that the growth of the share of women in top groups is driven by an increasing share of high-income women in the labor income distribution.

While it seems that top income men and women have converged in terms of income composition and observable individual characteristics, the one area that still stands out as being markedly different is partner income. Figure 2 shows that top income women are much more likely to have partners who are also in the top of the income distribution. Even if the trend indicates convergence, large differences remain. Out of the top 1 women who are married, 70% have a partner who is at least in the top 10 (and about 30% are also in the top 1). For married top 1 men, only 30% have a partner who is in the top 10, and only a couple of percentage points are in the top 1. Part of this is, of course, a reflection of there being fewer women in top groups, but this is far from explaining all the difference (See Boschini et al., 2020 for more details).

Figure 2. Share of top income partners in Sweden.

Source: Boschini et al. (2020)

This is of course far from conclusive, but it points in the direction of family circumstances being a potential factor for explaining the relative absence of women in top income groups. Having a partner with a top (income) career is likely to be more demanding (for both parties) and such couples are much more common among top income women than men.

Several strands of research connect to this: for example, Fisman et al. (2006) find, among other things, that men are significantly “less likely to accept a woman who is more ambitious than he”. Also, work by Bertrand et al. (2015), on the impact of gender identity suggest that there is a social norm prescribing that men should earn more than women, which creates a discontinuity in the distribution of women’s contribution to total household income at 50 % (although Hederos Eriksson and Stenberg (2015) and Zinovyeva and Tverdostup (2018) find alternative explanations for this observation). Folke and Rickne (2020) find that women who are elected to high political office in Sweden face a higher probability of divorce (while this is not the case for men). Furthermore, according to the World Values Survey, close to 40% of Americans as well as Europeans agree with the statement “(i)f a woman earns more money than her husband, it’s almost certain to cause problems”. Taken together, findings like these suggest that, even in relatively progressive countries, social norms may contribute to women shying away from entering career paths leading to top incomes.

What About Other Countries?

Even though the Swedish data is unusually detailed, it is certainly not the only country where individual tax data exist. Atkinson et al. (2018) calculate the share of women in top groups for eight countries over time periods when individual tax data exist. Figure 3 puts their results next to those from Sweden. The resulting picture shows a remarkably similar development across countries and over time. The share of women in the top 10 has approximately tripled since the 1970s, from around 10% to around 30%. For the top 1 group, the level is slightly lower, but the relative increase is similarly large, from slightly above 5% to around 20%.

Figure 3. International comparison.

Source: Atkinson et al. (2018) and Boschini et al (2020).

Bobilev et al. (2019) explore the extent to which Luxemburg Income Study (LIS) data can be used to shed light on the presence of women at the top of the income distribution. Their findings point to a similar trend across a broader set of countries. Even though the main analysis has to be limited to the share of women at the top of the labor income distribution (since the possibilities to separate out individual capital incomes is limited), the picture in terms of the share of women in top groups is surprisingly similar across the 28 countries for which sufficient data exists from around 1980 until today. The overall finding is that the share of women in the top 10 group increases from about 10% around 1980 to just below 30% today.

To the extent that LIS data allows us to look at partners and family circumstances, the data shows a consistent pattern of asymmetries between top income men and women similar to that in Sweden found by Boschini et al. (2020). Having a partner and having children are positively associated with being in top income groups for men, but negatively associated for women (even though these differences have decreased over time). Also, top income men are likely to have partners who are not in the top of the income distribution, while this is not the case for top income women. Understanding patterns like these and the underlying channels is likely to contribute to our comprehension of the remaining differences in top income shares between men and women.

Are There Differences Between “East and West”?

A particularly interesting pattern in the LIS data is the difference that emerges when contrasting transition countries to Western countries.

As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2019). This was mainly due to the high participation of women in the labor market as well as the (officially) universal access to basic health care and education.

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

Looking just at average values (in the labor income distributions), there are clear differences between East and West in top groups. The share of women among the top earning groups was considerably higher in some former Soviet countries during and after transition. However, the shares of women in top income groups have been converging in East and West.

Figure 4. Share of women in the top 10 / top 1 income groups, East vs. West.

Data source: Own calculations based on LIS data. West: unweighted average for Australia, Canada, Denmark, Italy, Norway, New Zealand, Spain, Great Britain. East: unweighted average for the Czech Republic, Estonia, Georgia, Hungary, Lithuania, Poland, Russia, Serbia, Slovenia and the Slovak Republic.

An analysis of the situation at the country level, provides a more complex picture. Figure 5 clearly indicates that the total representation of women in the top 10 income group has been higher in Eastern European countries than in the West (the pattern is similar for the top 1). However, while the share of women in top income groups has consistently increased in Western countries, the developments for women are much less homogenous in Eastern Europe (being below the diagonal indicates a higher share of women in the top 10 in 2005-2020 as compared to 1990-2005).

In Estonia, Slovakia and Poland, women are less likely to be part of the top income group in the period from 2005 to 2020 than they were in the years directly following transition. Considering that the most recent family policies in Poland have been shown to discourage female labor supply (Myck, Trzciński, 2019), this is maybe not so surprising.

Figure 5: Share of women in top 10 income group by country.

Data source: Own calculations based on LIS data. Eastern and Western countries defined as if Figure 4.

The share of women in the top 10 income group in Estonia declined from an astonishingly high 53% in 2000 to about 31% in 2013, which, admittedly, is still high compared to the corresponding average rate for Western countries (28%). Women in Russia, Hungary, Slovenia and the Czech Republic, by contrast, are more likely to be among the top earners in the period from 2005 to 2020 than they were between 1990 and 2005. Moreover, among all the countries in our sample, more recently, Slovenia is the country with the highest share of women in the top 10 of income earners (44% in 2007); Slovenian women seem to have gained grounds even after transition.

How come the representation of women in top income groups remains high (or even increases) in some transition countries but decreases in others? What is the role played by policy and regulation and what role can be attributed to social norms, family circumstances and institutions such as childcare? May economic growth have led to women dropping out of the labor force or never entering it to do care work, even when they had been or potentially could have been part of top income groups? What would be the impact of adding capital incomes to the picture?

Conclusion

Looking across a large number of countries, women seem to have increased their presence in top income groups since the 1970s. This has mostly been driven by women increasingly having high paying jobs. A preliminary look at LIS data indicates that former Soviet and Eastern European countries on average had higher shares of women in top groups around 1990, probably reflecting high labor market participation as well as relatively high education levels for women. But it also indicates that in some Eastern European countries, the share of women in top groups has dropped since the 1990s. As noted by Campa, Demirel, and Roine (2018) there seems to be an overall convergence in some dimensions of gender equality in transition countries, but there is also considerable variation across countries. More detailed studies of how men and women fare in terms of reaching top positions in incomes – but also in other areas like politics – are much needed and likely to be an interesting research area for years to come.

References

Removing Obstacles to Gender Equality and Women’s Economic Empowerment – What Can Policy Makers Learn from Global Research on Gender Economics?

Posted on February 24, 2020 | Policy Brief

20200224 Removing Obstacles to Gender Equality FREE Network Policy Brief Image 01

On November 15-16, 2019, the FREE Network and the ISET Policy Institute organized and conducted an international gender economics conference in Tbilisi, Georgia. The conference was organized as part of the FROGEE initiative – the Forum for Research on Gender Economics – supported by the Swedish International Development Agency (SIDA) and coordinated by the Stockholm Institute of Transition Economics (SITE). The conference brought together researchers, policymakers, and the broader development community to discuss obstacles to gender equality and women’s economic empowerment, as well as policies to remove existing constraints, with a particular focus on Eastern Europe and Emerging Economies. This policy brief provides an overview of the main takeaways from the presentations, with a special focus on policy-relevant lessons.

Introduction

In November 2019, Tbilisi welcomed its first international academic conference on gender economics, “Removing Obstacles to Gender Equality and Women’s Economic Empowerment”. The conference focused on the state of economic policy and gender issues around the world and more specifically in the ECA (Europe and Central Asia) region. The opening remarks were offered by two prominent keynote speakers – Dr. Caren Grown, Senior Director for Gender at the World Bank Group, Washington D.C, and Dr. Shahra Razavi, Chief of Research and Data at UN Women HQ in New York. The key addresses offered a global perspective on the current state of gender equality and progress made during the last 20 years. The global overview was followed by a policy panel discussion featuring prominent members of the policy-making community in Georgia. The panel participants reflected on how various policies have impacted gender (in)equality in the South Caucasus and in Georgia in particular. Later in the day, plenary presentations offered a preview of the South Caucasus Gender Equality Index, which is being developed by the ISET Policy Institute, and new research in gender economics done by academics in Georgia, Armenia, Belarus and Sweden.

The second day of the conference showcased research conducted by academics from over 15 countries covering 4 continents. It presented a range of diverse topics in gender economics, including, most prominently the links between childcare policies and labor supply decisions of women, female labor force participation (LFP) and happiness, evolving family structure and gender-selection preferences, the impact of economic, financial and public policies on women’s empowerment, the male-female earnings gap and gender aspects of international trade.

Below, we summarize the results and policy lessons that emerge from the body of work presented at the conference.

Gender Equality Progress in the ECA Region and Worldwide: Key Takeaways

First, as recent global data shows, the progress in women’s access to resources, in particular their access to the labor market, has on average stalled worldwide in the last 20 years. The labor market participation rate of women in 2018 stood at 63% globally, which is largely the same as in 1998, with some notable progress observed only in Latin America and the Caribbean (increase from 57% to 67% between 1998 and 2018), Australia and New Zealand (70 to 79%), as well as Northern Africa and West Asia (29 to 33%). The labor force participation gap between men and women is most pronounced for women who are married or in unions (44% gap, as opposed to 20% for single/never married or 17.9% for divorced/separated women).

Second, the ratio of time spent on unpaid care work by females was about 3-4 times that of males in most countries in the world, with some notable outliers: 11 times in Pakistan, 10 times in Cambodia and 9 times in Egypt. Only in Australia and New Zealand, the ratio of female to male time spent on unpaid work was slightly below 2. Thus, around the world, family responsibilities and unpaid work at home have clearly disproportionately burdened women, potentially preventing them from having an independent source of labor income, and generally weakening their financial position and bargaining power within the family unit. The recent UN Women report on Families in the Changing World (2019) argues for implementing a comprehensive package of family and women-friendly policy measures, which would include, among others, universal childhood education and care, universal healthcare coverage, long-term care for the elderly, etc. Such a comprehensive package would cost between 2-4% of GDP for most countries covered by the study. At the same time, the report argues that it would generate jobs, new investments and be a sizeable source of new tax revenue to the economies. Hence, the costs of such a program would be partially offset by the economic and tax benefits of formalizing the informal care economy. The study also details the ways in which countries could mobilize resources to pay for such packages, including improving tax collection, eliminating illicit financial flows, and leveraging aid and transfers.

For the South Caucasus in particular, the state of gender equality has not systematically been tracked until now. While there exists a number of thematic studies, surveys and narratives, as well as a more general Gender Inequality Index (GII) compiled by UNDP for all countries, a deeper systematic approach has recently been pioneered by the ISET Policy Institute, which started the ambitious project of developing a Gender Equality Index for the South Caucasus and, going forward, for the broader region of transition economies. The methodology behind the index is similar to the one adopted by the European Institute for Gender Equality, which tracks the Gender Equality Index for 28 European countries across a number of dimensions. Obviously, issues of data availability make it more challenging to build such an index in the context of transition economies. Thus, ISET-PI is working to construct some of the measures for the transition economies, using country-level data and household-level databases.

Childcare Policies and Labor Supply

One of the key messages emerging from the academic research in the area of childcare policies and labor supply was that gender-focused social policies need to be crafted carefully, with a focus on the binding constraints of the specific country context. A paper by Vardan Baghdasaryan and Gayane Barseghyan looked at how child-care service availability (affordability) affected the female labor force participation on the intensive and extensive margins in Armenia. The stage for a natural experiment in economic policy was set at the time when the Municipality of Yerevan unexpectedly decided to abolish childcare services fees (roughly 15% of average wage). The researchers hypothesized that such an intervention would have resulted in increased female LFP, as was the case in other (mostly developed) regions and countries around the world (e.g. Quebec in Canada). In the context of Armenia, however, the authors observe that there was no significant effect on female LFP rate on the extensive margin, meaning there was no evidence of inactive women entering the labor force. One possible explanation is that in the context of a developing country such as Armenia, the limiting factor to female participation in the labor force is the lack of market demand for the skills profile of non-active mothers. In such an environment, as the authors conclude, the monetary incentives do not suffice to lift the binding constraint on female LFP.

Yolanda Pena-Boquete presented a study on the case of Australia which analyzed how the labor hours and LFP of both women and men in the family are affected when either the mother’s or the father’s wages increase or when the price of childcare changes. The study finds that the mothers’ working hours respond positively and much stronger to a change in hourly wage than the fathers’. The policy implication is that an increase in mothers’ hourly wage would potentially result in a significant increase in their working hours and labor force participation. The wage effect on women’s working hours and LFP is much more pronounced even compared to the scenario when childcare prices decline.

Overall, the studies in this area demonstrated the need for a careful, multi-faceted approach in designing effective and cost-efficient labor market policies aimed at increasing labor force participation by married women with children.

Labor Force Participation and Happiness: Evidence from the South Caucasus

The paper by Norberto Pignatti and Karine Torosyan looked at the differences in the reported happiness levels between women of different labor market status in the three South Caucasus countries. The intriguing finding of the study is that while in Georgia, there is no difference in the reported happiness level between working women and housewives, in Armenia and Azerbaijan, working women with similar characteristics are much less likely to report being “very happy” than housewives. The interesting finding is that the overall results for Georgia also apply to the Armenian and Azerbaijani minority women in the country, implying that “cultural factors” may play a minor role in the reported differences between countries.

Family Structure and Gender-Selection Preferences

Gender-biased sex selection (GBSS) has been on the forefront of gender policy issues in the South Caucasus, as Armenia, Azerbaijan and, until recently, Georgia struggled with skewed sex ratios at birth (SRB). Understanding the driving forces behind GBSS, and in particular son-preference as a socio-economic phenomenon, is especially important. One of the recent studies on the issue was presented by Davit Keshelava of the ISET Policy Institute. The study “Social Economic Policy Analysis with Regard to Son Preference and Gender-biased Sex Selection” looked at the factors underlying GBSS rise and fall in Georgia over the last 15 years. The study also gleaned facts about the changing attitudes towards GBSS and son-preferences in different regions of Georgia. One of the study’s main findings is that the fall in the sex ratio at birth has been statistically significantly correlated with real income growth in the regions, reduction in poverty, and female employment. Among other factors significantly affecting the reduction in sex ratio at birth, was, surprisingly, the level of male education, while female education was statistically insignificant. The study documented a persisting son preference in Georgia, but also high awareness and strong negative attitudes towards gender biased sex selection in those regions that showed the sharpest improvement in sex ratio at birth over time.

Looking at the issue of gender preferences in the context of transition economies in Europe, Izabela Wowczko presented joint work with Michał Myck and Monika Oczkowska which investigated how preferences for the gender composition of children in the family might have changed in Central and Eastern European (CEE) countries after the fall of communism. The results showed that gender-neutrality was observed in almost all CEE countries before the transition. After the transition of the 1990s, many of the same forces which operated in the South Caucasus have affected the countries of Central and Eastern Europe – namely, decline in incomes, decimated traditional social safety nets and better access to ultrasound and family planning technologies. However, in the post-transition CEE countries, the authors observe a clear preference for a mix (boy/girl) or possibly boys at parity three (i.e. having two boys or a boy and a girl in the family reduced the likelihood of having a third child significantly, as opposed to having two girls). It was also observed that in most CEE countries (except Romania), there was an increased likelihood of having a second child if the first child is a boy – thus demonstrating a girl preference at parity two.

Policy Impact on Women’s Empowerment

A study from India by Mridula Goel and Nidhi Ravishankar looked at the impact of policy interventions on the long-term indicators of women empowerment. It shows that public policies were responsible for improving the so-called “power enablers”, such as literacy rates, financial access, property rights, political voice, etc. However, there is some evidence that not all traditional power enablers, e.g. having a bank account or working for money, are correlated with higher indicators of empowerment, measured by a woman’s autonomy in decision-making within the family. For example, working for money (receiving cash compensation) or having a bank account was found to be negatively correlated with a woman’s ability to decide how her own money is spent – possibly pointing to the existence of prejudice or negative attitudes within the household in such cases.

Another interesting study on this topic by Maria Perrotta Berlin, Evelina Bonnier and Anders Olofsgård looked at whether foreign aid projects foster female empowerment in the surrounding community using data from Malawi. It finds support for a small positive impact of aid on men’s and women’s attitudes related to domestic violence and sexual rights. There is, however, little systematic difference in the impact of gender-targeted aid versus general aid – with exceptions being the impacts on women’s experience of violence and women’s participation in decision-making.

Male-Female Earnings Gap and Gender Aspects of International Trade

The male-female earnings gap is a recurring topic in gender economics. Whether the gap is driven by differences in education and skills of men and women, labor market discrimination, choices of working hours, the “glass ceiling” or “sticky floor” phenomena, the gap is evident and persistent in both developed and developing countries. One of the papers presented by Dagmara Nikulin looked at the impact of trade liberalization on the gender wage gap in Europe. Generally, the economic literature does not provide conclusive evidence in this regard, and the link remains ambiguous. The paper, examining evidence from Europe, finds in particular that participation in global value chains (GVC), which the authors measure by foreign value added in exports, is correlated with reduced wages overall, but the negative effect on wage is lower for men than for women.

Echoing the results of the previous study, the paper by Marie-France Paquet and Georgina Wainwright-Kemdirim, “Since the effects of trade liberalization are not gender neutral, how can we improve its gender outcome? – Crafting Canada’s Gender Responsive Trade Policy” focuses on the problem of identifying and addressing potentially negative impacts of trade on female jobs. The study details a diagnostic modelling approach, which is to use CGE modeling combined with sectoral employment data (a labour module within CGE). The proposed model uses an overlapping generation framework and includes an occupational matrix to allow movements between occupations. This approach allows for specific potential impacts of generic FTAs by gender, age group and occupation.

Conclusion

To sum up, the first international academic conference on gender economics issues in Tbilisi highlighted the diversity and complexity of gender issues around the world and in the South Caucasus region in particular. It also became a powerful catalyst for new research and collaboration ideas among participating institutions and individual researchers. Finally, it demonstrated how policy-oriented research can help inform the policy-making community about the areas where intervention is most needed, design the most effective policies, and calculate the associated costs and benefits of interventions.

References to Selected Presentations

  1. Shahra Razavi “Policies for Gender Equality in an Unequal World: Challenges and Opportunities”, keynote presentation.
  2. Vardan Baghdasaryan and Gayane Barseghyan “Child Care Policy, Maternal Labor Supply and Household Welfare: Evidence From a Natural Experiment”.
  3. Michal Myck and Kajetan Trzcinski “From Partial to Full Universality: the Family 500+ Programme in Poland and its Labour Supply Implications”.
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A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak

Posted on February 17, 2020 | Policy Brief

20200217 A Decade of Russian Cross-Domain Coercion FREE Network Policy Brief Image 01

Russia’s coercion towards Ukraine has been a topic of major international events, meetings and conferences. It regularly makes the headlines of influential news outlets. But the question remains open – do we really understand it? We diligently collect and analyze data to make informed decisions in practically all domestic issues but is the same done for international relations? This research paper introduces a number of tools and methods that could be used to study Russia’s coercion towards Ukraine beyond its most visible manifestation, looking into latent trends and relations that could reveal more.

Introduction

For the past decade, Ukraine has been in the headlines of the major world news outlets more frequently than ever before. Ukrainian-Russian relations have been and still remain the topic of international summits and other events. The occupation of a part of Ukraine’s territory has been denounced and Russia’s coercion towards Ukraine is now generally accepted as a fact. But what do we really know about the underlying empirics and dynamics and how can this multi-domain assertiveness be measured and tracked? This paper presents a number of data-driven approaches that allow looking beyond the headline stories to identify and track various dimensions of Russia’s coercion towards Ukraine and the dynamics of its development.

Academic Interest

Mapping the landscape of scholarly literature reveals a number of interesting results. First, the body of works studying Russia’s coercion towards Ukraine remains relatively modest. It quintupled in 2014 but afterwards the interest started tapering off. A search for papers on this topic in Scopus and Web of Science with a very precise query (to increase the accuracy of search) and publication time of 2009-2019 returned 155 papers most of which were published in or after 2014.

Figure 1. Scholarly publications on Russian-Ukrainian relations.

Source: WoS and Scopus, 2009-2019

A closer look at the content of these works with the use of a bibliometric software called CiteSpace shows that the majority of papers focus on Putin, once again emphasizing the significant role of his personality in Russia’s coercion towards Ukraine. The second largest cluster has the “great power identity” as its main theme and presumably looks beyond actions of Russia to identify its ideological grounds. Another group of publications is devoted to sanctions, pointing to their important role in studying Russian-Ukrainian relations.

Figure 2. The landscape of topics in scholarly publications on Russian-Ukrainian Relations.

Expressions of Coercion

The “practical” side of Russia’s coercion towards Ukraine is also frequently associated with the personality of Vladimir Putin and his attitudes towards Ukraine. To analyze this perception further, we created a corpus of speeches of Russian presidents published on the Kremlin website, filtered them to keep only those that mention Ukraine, divided them into pre-2014 and 2014 and after, and then analyzed them using an LDA topic modeling algorithm. This algorithm is based on the assumption that documents on similar topics use similar words. So, the latent topics that a certain document covers can be identified on the basis of probability distributions over words. Each document covers a number of topics that are derived by analyzing the words that are used in it. In simple terms, the model assigns each word from the document a probabilistic score of the most probable topic that this word could belong to and then groups the documents accordingly.

Figure 3. Speeches of Russian presidents before 2014, LDA topic modeling.

Figure 4. Speeches of Russian presidents in 2014 and after, LDA topic modeling.

Quite surprisingly, we discovered that the overall rhetoric of speeches is very similar for the two periods. Although some speeches do differ and the later corpus includes new vocabulary to reflect some changes (i.e “Crimea”, “war”) the most common words remain practically the same. Thus, regardless of the apparent shift in relations between the two countries, Russian leadership still relies on the same notions of collaboration, interaction, joint activities, etc. The narrative of “brotherhood” between the nations persists despite and beyond the obvious narrative of conflict.

To include a broader circle of Russia’s leadership we also looked at the surveys of the Russian elite conducted regularly by a group of researchers led by William Zimmerman and supported by various funders over the years (in 2016 – the National Science Foundation and the Arthur Levitt Public Affairs Center at Hamilton College). Seven waves of the survey already took place; the most recent one in 2016. The respondents were the representatives of several elite groups (government, including executive and legislative branches, security institutions, such as federal security service, army, militia, private business and state-owned enterprises, media, science and education; for practical reasons from Moscow only).

The survey revealed a number of interesting observations. For instance, while the prevailing Russian opinion on Russia’s occupation of Crimea had been that it was not a violation of international law, a closer look at the demographic characteristics of respondents shows that they were not as coherent as it might seem from the outside. While the “green” answers from respondents with backgrounds such as media or private business may have been anticipated, the number of members of the legislative and especially executive branch and the military that had at least some doubt on the legality was surprisingly quite sizable, and they even demonstrated some support of the “violation of law” interpretation.

Figure 5. Elite and public opinion on Russia’s annexation of Crimea.

Comparing these elite opinions to the public opinion poll by Levada Center conducted in the same year shows that even the general public is slightly more likely to choose the most extreme “full legality” option than the respondents from the executive branch.

Beyond the elite or general opinion polls, we tried to develop a metric that might allow us to track Russian sensitivities towards Ukraine. For that, we examined two different ways of expressing “in Ukraine” in Russian language: ‘на Украине’ (the ‘official’ Russian expression) vs. ‘в Украине’ (the version preferred by Ukrainians). [In English, this can be compared so saying ‘Ukraine’ vs ‘the Ukraine’.]

Our first visual plots how many search queries were done on Google Search with both versions over the last decade.

Figure 6. Search queries for “в Украине” (green) versus “на Украине” (red), Google Trends, 2009-2019.

We can clearly observe that during less turbulent times the more politically sensitive version is much more common. This however drastically changes during the peaks of Russia’s coercion towards Ukraine when the number of searches with the less politically correct term increases significantly.

A different trend can be observed if we look at official media publications stored in the Factiva database. We estimated the ratio of search volumes for each term and observed that until the beginning of 2013, about a third of articles and news reports used “in Ukraine”. This changed around January 2013 when the ratio starts to decrease for “in Ukraine” searches and plummets to a mere 10% of outlets still preferring this term.

Figure 7. The ratio of “в Украине” to “на Украине” occurrences in large Russia media (2009 – 2019), Factiva.

Tracking Coercion Itself

What is the track record of Russia’s actual coercion over this decade? For this, we turn to a few recent datasets that try to systematically capture verbal and material actions (words and deeds): the automated event datasets. The largest one of those, called GDELT (Global Database of Events, Language, and Tone), covers the period from 1979 to the present, and contains over three quarters of a billion events. It is updated every fifteen minutes to include all “events” reported in the world’s various news outlets. To exclude multiple mentions of the same event by one newswire, the events are “internally” deduplicated. The events are not compared across newswires.

An event consists of a “triple” coded automatically to represent the actor (who?), the action (what?) and the target (to whom?) as well as a number of other parameters such as type (verbal or material; conflict or cooperation; diplomatic, informational, security, military, economic), degree of conflict vs cooperation etc. Other similar datasets include ICEWS (Integrated Crisis Early Warning System) and TERRIER (Temporally Extended, Regular, Reproducible International Events Records). For this analysis, we filtered out only those events in which Russia was the source actor and Ukraine was the target country. We present two metrics: (1) the percentage of all world events that this subset of events represents and (2) the monthly averages of the Goldstein score, which captures the degree of cooperation or conflict of an event and can take a value from -10 (most conflict) to +10 (most cooperation). Also, to add a broader temporal perspective, we looked beyond the last decade. It can be clearly seen that the number of events before 2013 was significantly lower, especially in “material” domains. Some verbal assertions from Russia towards Ukraine happened during the Orange Revolution and so-called “gas wars”.

The situation changes radically starting from 2013. The proportion of events increases with some especially evident peaks (i.e. during the occupation of Crimea). The verbal events remain quite neutral while the actions towards Ukraine move from some fluctuations to steadily conflictual.

Figure 8. Russia’s negative assertiveness towards Ukraine, 2000-2019.

Measuring Influence

We have seen that the past decade was exceptional in the scale of Russian assertiveness towards Ukraine. But what do we know about Russia’s influence on Ukraine and Ukraine’s dependence on Russia? Influence measures the capacity of one actor to change the behavior of the other actor in a desired direction. In an international context this often concerns the relations between countries. Influence can be achieved by various means, one of which is to increase the dependence of the target country upon the coercive one. This strategy is frequently employed by Russia willing to regain and/or increase control over the former post-Soviet countries. The Formal Bilateral Influence Capacity (FBIC) Index developed by Frederick S. Pardee (Center for International Future) looks at several diplomatic (i.e. intergovernmental membership), economic (trade, aid) and security (military alliances, arms import) indicators allowing to identify the level of dependence of one country upon another. This is especially interesting from a comparative perspective. Figure 9 shows that countries such as Armenia and Belarus remain highly dependent on Russia. For half of the decade, Ukraine was number three on this list. Today the situation has changed. Ukraine’s dependence on Russia has gradually decreased and has become even smaller than Moldova’s, moving closer to the steadily low level of dependence of Georgia. This may signify a positive trend and a break of a decade-long relationship of dependence.

Figure 9. Dependence of post-Soviet countries on Russia, FBIC.

Conclusion

Consequently, Russia and Ukraine have become much more visible in the international academic and policy research efforts. This can be measured through a number of instruments, including a comprehensive mapping of the academic landscape itself with regard to salience and topics that are being studied, analysis of the word choice (that could be represented by the use of the terms to describe events in Ukraine by the government media and Google search users (“на Украине” versus “в Украине”); speeches of Russian presidents that use the same rhetoric of collaboration when talking about Ukraine despite the obvious change in relationships) and material coercion (significant increase in number of assertive conflictual Russia’s actions towards Ukraine). Some findings do give hope for change: the opinions of the Russian elite on recent Russian actions towards Ukraine while remaining generally unfavourable are not as cohesive as it might appear and Ukraine’s dependence on Russia has decreased significantly.

Disclaimer

This research is a part of a larger research effort titled RuBase funded by the Carnegie Foundation of New York and implemented jointly by The Hague Centre for Strategic Studies and Georgia Tech with the help of the Kyiv School of Economics StratBase team in Ukraine. The ‘Ru’ part of the title stands for Russia; and ‘base’ has a double meaning – both the knowledge base built during the project, and the (aspirationally) foundational nature of this effort. The project intends to look beyond the often-shallow traditional understanding of coercion and apply innovative tools and instruments to study coercion in its multifaceted form. This is only a small selection of the tools that have been successfully tested in the course of this (ongoing) research project and applied to the study of Russia’s coercion in different domains. The prospects of any progress in resolving the Russian-Ukrainian conflict are currently slim, thus further work that would allow identifying patterns and trends that the human eye may oversee to understand Russia better and develop an informed foreign policy strategy both for Ukraine and the West is crucially important.

References