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The Shadow Economy in Russia: New Estimates and Comparisons with Nearby Countries
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
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
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
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?
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.
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.
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.
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.
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.
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
- Atkinson, Anthony B., Alessandra Casarico and Sarah Voitchovsky (2018). “Top incomes and the gender divide”, The Journal of Economic Inequality. 16 (2), 225–256.
- Azmat, Ghazala and Barbara Petrongolo, (2014). “Gender and the labour market: what have we learned from field and lab experiments?” Labour Economics. 30, 32–40.
- Blau, Francine D., Lawrence M. Kahn (2017). “The gender wage gap: Extent, trends, and explanations”, Journal of Economic Literature 55(3), 789-865.
- Bertrand, Marianne, Jessica Pan and Emir Kamenic (2015). “Gender identity and relative income within households”. The Quarterly Journal of Economics, 130 (2), 571–614.
- Bertrand, Marianne, (2018). “Coase Lecture – The Glass Ceiling”. Economica 85: 205–231.
- Bobilev, Roman, Anne Boschini, Jesper Roine (2019). Women in the Top of the Income Distribution –What Can we Learn from LIS-Data?, Forthcoming Italian Economic Journal.
- Boschini, Anne, Kristin Gunnarsson, Jesper Roine (2020). “Women in top incomes – Evidence from Sweden 1971–2017”. Journal of Public Economics, 181, January 2020.
- Brainerd, Elizabeth (2000). “Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union”, ILR Review, 54(1): 138-162.
- Campa, Pamela, Merve Demirel, Jesper Roine (2018). “How Should Policy-Makers Use Gender Equality Indexes?”. FREE Policy Paper, November 2018.
- Campa, Pamela and Michel Serafinell (2019). “Politico-Economic Regimes and Attitudes: Female Workers under State-Socialism.” The Review of Economics and Statistics, 101 (2). 233 – 248.
- Einhorn, Barbara (1993). “Cinderella Goes to Market: Citizenship, Gender and Women’s Movements in East Central Europe”. London/ New York: Verso.
- Eriksson, Karin Hederos and Anders Stenberg (2015). “Gender Identity and Relative Income within Households: Evidence from Sweden”, IZA Discussion paper No. 9533.
- Fisman, Raymond, Sheena S. Iyengar, Emir Kamenica and Itamar Simonson (2006). “Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment”. The Quarterly Journal of Economics, 121 (2), 673–697.
- Folke, Olle and Johanna Rickne (2020). “All the Single Ladies: Job Promotions and the Durability of Marriage”. forthcoming American Economic Journal: Applied Economics.
- Fortin, Nicole, Brian Bell, Michael Böhm (2017). “Top earnings inequality and the gender pay gap: Canada, Sweden, and the United Kingdom.” Labour Economics, 47, 107–123.
- ILO (N.D.). “Gender Equality”. Accessed February 2020.
- Myck, Michal and Kajetan Trzciński (2019). “From Partial to Full Universality: The Family 500+ Programme in Poland and Its Labour Supply Implications”, FREE Policy Brief, December 16, 2019.
- Pollert, Anne (2003). “Women, work and equal opportunities in post-Communist transition”, Work, Employment and Society, 17(2): 331-357.
- Roine, Jesper, and Daniel Waldenström (2015). “Long-Run Trends in the Distribution of Income and Wealth”, chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
- Roine, Jesper (2016),“Global Inequality – What Do We Mean and What Do We Know?”, FREE Policy Brief, April 24, 2016.
- UNICEF (1999). “Women in Transition”, Regional monitoring Report 6. UNICEF ICDC.
- Wolchik, Sharon L. and Alfred G. Meyer, eds. (1985). “Women, State, and Party in Eastern Europe”. Durham, NC.
- Zinovyeva, Natalia and Marina Tverdostup (2018). “Gender Identity, Co-Working Spouses and Relative Income within Households”. IZA Discussion Paper No. 11757.
Removing Obstacles to Gender Equality and Women’s Economic Empowerment – What Can Policy Makers Learn from Global Research on Gender Economics?
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
- Shahra Razavi “Policies for Gender Equality in an Unequal World: Challenges and Opportunities”, keynote presentation.
- Vardan Baghdasaryan and Gayane Barseghyan “Child Care Policy, Maternal Labor Supply and Household Welfare: Evidence From a Natural Experiment”.
- Michal Myck and Kajetan Trzcinski “From Partial to Full Universality: the Family 500+ Programme in Poland and its Labour Supply Implications”.
- Karen Mumford, Antonia Parera-Nicolau, Yolanda Pena-Boquete “Labour Supply and Childcare: Allowing Both Parents to Choose”.
- Norberto Pignatti, Karine Torosyan “Employment vs. Homestay and Happiness of Women in the South Caucasus”.
- Davit Keshelava et al. ISET Policy Institute Report “Social Economic Policy Analysis with Regard to Son Preference and Gender-biased Sex Selection”.
- Izabela Wowczko, Michał Myck and Monika Oczkowska “Gender Preferences in Central and Eastern Europe as Reflected in Family Structure”.
- Mridula Goel, Nidhi Ravishankar “Has Public Policy Succeeded in Enhancing Women Autonomy and Empowerment in India Over the Last Decade?”.
- Maria Perrotta Berlin, Evelina Bonnier and Anders Olofsgård “The Donor Footprint and Female Empowerment”.
- Dagmara Nikulin & Joanna Wolszczak-Derlacz “Gender Wage Gap and the International Trade Involvement. Evidence for European workers”.
- Marie-France Paquet, 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”.
A Decade of Russian Cross-Domain Coercion Towards Ukraine: Letting the Data Speak
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.
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
- Boschee, Elizabeth et al. (2019). “ICEWS Automated Daily Event Data.” (November 12, 2019).
- Clarivate Analytics (2019). “Web of Science Core Collection.” Web of Science Group. (January 20, 2020).
- Dow Jones (2019). “Factiva – Global News Monitoring & Search Engine.” Dow Jones. (December 2, 2019).
- Elsevier (2019). “Scopus.” (December 3, 2019).
- Google (2018). “Google Trends – The Homepage Explained – Trends Help.” (January 20, 2020).
- Holynska, Khrystyna, Yevhen Sapolovych, Mikhail Akimov, and Stephan De Spiegeleire (2019). “Events Datasets and Strategic Monitoring: Method Piece” (Forthcoming). The Hague Centre For Strategic Studies.
- Levada-Center (2019). “Levada Center.” (December 3, 2019).
- Moyer, Jonathan D., Tim Sweijs, Mathew J. Burrows, and Hugo Van Manen (2018) “Power and Influence in a Globalized World.” Atlantic Council. (November 26, 2019).
- OU Event Data (2018). “Terrier (Temporally Extended, Regular, Reproducible International Event Records)”. (January 29, 2020).
- The GDELT Project (2015). “GDELT 2.0: Our Global World in Realtime.” GDELT Blog. (October 11, 2018).
- Zimmerman, William, Sharon Werning Rivera, and Kirill Kalinin. (2019). “Survey of Russian Elites, Moscow, Russia, 1993-2016”. Version 6.” (November 26, 2019).
- Президент России (2019). “Президент России.” Президент России. (November 26, 2019).
Indexation Formula for Natural Gas Procurement in Ukraine
Due to the high volatility of natural gas market prices, it is almost impossible to adequately plan the purchases for the year ahead, so contract prices need to be regularly updated. This fact creates uncertainty for the contracting authorities, as well as room for unfair competition and corruption. We offer an indexation formula which uses the European gas prices as a benchmark for procurement prices and calculate the potential economic effect of this formula on the Ukrainian gas procurement market.
Problems with the Public Procurement of Natural Gas
Natural gas procurement poses a number of challenges for the contracting authorities (CAs), suppliers and controllers. Due to price volatility it is almost impossible to adequately plan the purchases for the year ahead, so prices need to be regularly adjusted. After the heating season starts, CAs find themselves in a weak position in price negotiations since they almost never have storage for accumulating stocks, and if the contract is cancelled the new procurement would take at least one month due to the existing public procurement regulations. The new version of the Law on Public Procurement, which was recently adopted by the Ukrainian parliament, addresses this problem by allowing CAs to have a new contract fast in case the previous contact was cancelled because of the supplier.
CAs often lack reliable data on market dynamics. There are cases when unreliable price references are provided by specialized agencies to support higher price claims of suppliers. As a result, CAs bear administrative responsibility if they do not have proper justification for changing contract prices when controlling agencies initiate an audit.
Natural gas suppliers may also find themselves in a situation of unfair competition. Since it is possible to win an open auction (i.e. by quoting a considerably lower than market price) and later raise the price to the market level with an additional contract, honest businesses might feel demotivated to participate in the procurement process. They cannot be sure if the contract price can be changed later because there is no proper legal mechanism to assess the need of such an adjustment.
Previous research shows that every third contract of natural gas purchase was amended with an additional contract at least once, usually raising the price for the customer (Shapoval, Memetova, 2017). Additional contracts are indeed used 1) as a tool for price overstatement by the supplier, 2) as a loophole for corruption, and 3) as a way to get a market price for a supplier who used dumping to win the auction (Gribanovsky, Memetova, 2017).
International Drivers of Gas Prices in Ukraine
Since 2016, the EU has been the only official exporter of natural gas to Ukraine. According to PwC, Ukraine imported 14.1 billion m3 in 2017, which is 44% of total gas consumption – the remaining 56% are extracted locally. In Ukraine, the prices for industrial consumers are not regulated, while the household prices are set by the government. Today, the average price on the unregulated gas market is in line with the prices in neighbouring countries – the Baltic states, Poland, Slovakia and Hungary (PricewaterhouseCoopers Advisory LLC, 2018).
European prices are formed on the large marketplaces. The two biggest hubs, Dutch TTF and British NBP, by far outweigh their competitors (ACER Market Monitoring Report, 2017). However, the third-biggest hub, German NCG, is the closest to the Ukrainian border, so its prices often become the benchmark for private traders. In some cases, NCG is the official benchmark for gas price – for instance, the purchase parity import price in Ukraine in 2017-2018 was based on this hub’s index.
In order to assess the impact of the European natural gas prices on procurement prices in Ukraine, we used the Month+1 futures hub prices from TTF, NCG and Austrian VTP CEGH. Procurement prices were extracted from the analytical module of ProZorro (the Ukrainian e-procurement system which CAs are obliged to use at all levels). We excluded irrelevant procurements and selected the contracts which had information on the volume procured. We calculated the average daily prices weighted by volume. Our dataset covers the time period from January 1, 2017 to December 31, 2018.
Figure 1: Natural gas prices at ProZorro and European hubs
As one can see in Figure 1, hubs prices are highly correlated, so they cannot be used as independent variables within a single model. Thus, we decided to take the NCG Month+1 price as a benchmark for explaining the relation between internal procurement prices and international market prices.
NCG Impact on Procurement Prices in ProZorro
In the period of low business activity on natural gas markets, especially in summer, few contracts are awarded. One might have noticed from Figure 1 that this leads to higher variance in daily prices caused by random factors. Therefore, in our model we decided to use the weighted average of weekly prices instead.
Figure 2: Weekly gas price fluctuations in ProZorro and NCG
Our econometric estimation shows that the NCG Month+1 price influences procurement prices with a lag of 7 weeks. In other words, the price at the German hub becomes relevant for the Ukrainian procurement market after almost 2 months on average.
Figure 3: Correlations between procurement prices and NCG Month+1 with different lags
According to the model, the weighted average gas price in ProZorro is more dependent on the NCG Month+1 gas price than on the reservation price in ProZorro. Thus, a UAH 1 increase in the reservation price adds UAH 0.41 to the final price, while each additional hryvnia of the NCG price leads raises the final price by UAH 0.63 in 7 weeks if the price growth trend is not taken into account.
Potential Cost-Saving Using the Price Indexation Formula
The Price Indexation Formula
As European gas prices strongly influence prices on the internal Ukrainian market, it is obvious that they should be included into the indexation formula, as well as exchange rate fluctuations. After consultations with stakeholders, the Ministry of Economic Development and Trade of Ukraine (MEDT) decided to adjust the initial formula proposed by the KSE and included price fluctuations on the Ukrainian Energy Exchange (UEEx) with a small weight into the formula in order to stimulate UEEx development.
The final formula was officially published in December 2018. This formula is not compulsory for any contract authorities, though it is recommended for use by the smaller public entities who do not have the in-house analytical capacity to make a realistic price assessment during negotiations with the suppliers.
where:
- CP – new price in UAH for 1000 m3 of natural gas (including value-added tax, VAT)
- PCP – current price in UAH for 1000 m3 of natural gas (including VAT) before adjustment
- K(cur) – average National Bank of Ukraine (NBU) UAH/EUR exchange rate for 5 days before the price change
- K(base) – average NBU UAH/EUR exchange rate on the day of the previous price adjustment (contract signed)
- NCG(avg) – average of daily NCG Month+1 index during 20 previous trading days before the day of price amendment, EUR per MW-hour
- NCG(base) – NCG Month+1 index on the day of the previous price amendment (contract signed), EUR per MW-hour
- VAT – rate of value-added tax, which is currently 20% in Ukraine
- CV – heating value of natural gas in MW-hour/1000 m3 on the date of the price adjustment
- UEEx(avg) – weighted monthly average natural gas price of UEEx (including VAT) on the day of price amendment
- UEEx(base) – weighted monthly average natural gas price on the UEEx (including VAT) on the day of the previous price amendment (contract was signed)
Thus, the formula includes current gas price, exchange rate changes, changes in NCG index and UEEx index.
Estimation of Potential Cost-Saving for Contract Authorities
The simplest yet time-efficient way to empirically verify the hypothesis of potential cost-saving after the introduction of the price indexation formula in the gas market is a retrospective analysis of the contracts which had already been signed.
The basic principle of estimation is comparing actual prices with the potential prices calculated based on the price indexation formula. For this, we collected a dataset of natural gas procurement contracts covering the time period from August 2017 to the end of August 2018. This period includes both short-term contracts signed for the heating season or its part (usually signed in August-September, sometimes in January-February) and middle-term contracts which are active for at least one year (usually signed in December-March). We took into account all the additional contracts to these contracts signed before January 1, 2019.
Supply schedules and prices of additional contracts are not readily available in a machine-readable format, so we kept only contracts with the total value higher than UAH 1 million. These are 27.5% of all contracts but they cover 79.3% of the total value of natural gas procurement in Ukraine. The final dataset contains prices of additional contracts and monthly supply schedules.
Our earlier analysis of all the contracts on the shorter time scale showed no correlation between prices and volumes in gas procurement contracts (Shapoval, Memetova et al., 2017), therefore our results can be extrapolated to all the gas contracts.
The biggest gap between actual and indexation prices would be in November 2017, averaging UAH 623. However, until the end of the year the gap reduced threefold to UAH 170.
Figure 4: Monthly increase of gas procurement prices
We combined the supply schedules with the prices found in the additional contracts in order to estimate potential savings. Obviously, the highest savings were observed during the heating season. However, in September they were negative (see Figure 6). Thus, while the market prices of natural gas started rising in August, actual procurement prices lagged behind until the end of September-October.
Figure 5: Monthly cost savings in case of applying price indexation formula
In total, for contracts of over UAH 1 million, potential cost savings from applying the price indexation formula would have been equal to UAH 120.25 million. If these estimations are extrapolated to all the contracts, this figure would reach UAH151.6 million. This is a rather modest sum in relative terms – only 2.7% of the total contract value. However, using the formula is expected to assist smaller CAs who often lack the knowledge of market dynamics to negotiate the optimal price more effectively and limit their dependence on the suppliers’ estimates.
Besides, the parties concluded the contracts without taking into account the opportunity of using the indexation formula. Therefore, actual cost savings might be lower, first of all because the suppliers’ auction strategy would be different. In particular, the dumping strategy with subsequent price increase through additional contracts would become useless. If the formula is used, a lower starting price would mean a lower increase in absolute terms (UAH per 1000 m3), because the formula calculates the change in relative terms (in per cent). For example, if the market price grows by 15% during the indexation period, the starting price can also be raised by only 15%.
Conclusion
The application of the price indexation formula for natural gas procurement may have a positive impact on the public procurement market. We recommend taking into account the prices of the European hubs adjusted by exchange rate fluctuations.
Had the price indexation formula been used for additional contracts in gas procurement in 2017-2018, the average price would have declined by UAH 623, potentially allowing CAs to save UAH 151.6 million.
Formula pricing would raise the negotiation power of customers (CAs) before the start of the heating season. This is especially true for the smaller ones which are not able to professionalize procurement processes. Natural gas price indexation within clearly defined boundaries will create more favourable conditions for fair competition by eliminating the stimuli for dumping at the auction stage.
References
- ACER Market Monitoring Report, 2017 .
- Gribanovsky Olexiy, Memetova Inna, 2017. “Additional contracts as a result of price dumping” (in Ukrainian).
- Order of CMU 187 of March, 22 2017, http://zakon.rada.gov.ua/laws/show/867-2018-%D0%BF#n127 (in Ukrainian).
- PricewaterhouseCoopers Advisory LLC, 2018. “Ukrainian gas market: Discovering investment potential and opportunities”.
- Shapoval Natalia, Memetova Inna, 2017. “Additional contacts in Ukraine: causes and methods of prevention” (in Ukrainian).
- Shapoval Natalia, Memetova Inna, Gribanovsky Olexiy, Tchmil Olexandra, 2017. “Three Sources of Losses in Natural Gas Public Procurement” (in Ukrainian).
Data Sources
- CEP, 2019. Source code of the project [github repository] https://github.com/cep-kse/natural_gas_formula.
- EUStream, Energy content at Budince. [web page] https://tis.eustream.sk/TisWeb/#/?nav=bd.gcv.
- PowerNext, 2017. Futures market data. [web page]. Retrieved from https://www.powernext.com/futures-market-data.
- ProZorro, 2014-2020. Ukrainian public procurement data. [bi system] http://bi.prozorro.org.
Does Gender Diversity Actually Matter?
Measuring the effects of gender diversity on performance is important to understand the impact of gender quotas. However, the effects of gender diversity remain understudied. We need data with a reliable assessment of team member quality to disentangle the effects of diversity from compositional effects (when higher-quality women replace mediocre men). We use the unique database of the trivia game “What? Where? When?” which has information on both the performance and gender composition of the team and allows to track each player individually. We find that the gender diversity of the team has no statistically significant effect once we control for the quality of each player. In this particular environment, with little evidence of gender discrimination, instruments like gender quotas have no merit. This result does not apply to discriminatory environments where gender quotas could bring benefits through compositional effects.
Introduction
As gender quotas have been widely introduced in politics and in the corporate world, the effects of gender diversity have become the center of attention of many economists. Many observational studies find positive effects of gender diversity on corporate boards’ performance (Desvaux, Devillard, & Sancier-Sultan, 2010). Other studies, using the introduction of gender quotas in boards as a natural experiment, find negative effects on stock valuation, which disappear in the longer run (Ahern and Dittmar, 2012; Matsa and Miller, 2013; Eckbo et al., 2019).
The effect of gender diversity on team performance may run through two different mechanisms. One mechanism is compositional effects due to discrimination: if women face a glass ceiling, only the best women get into teams/boards, and they are on average of higher quality than men. Hence, boards with female representatives perform better. The discrimination mechanism has been shown to be at work in the political setting, for example: gender quotas in parties lead to higher-quality women replacing mediocre men (Besley et al., 2017). The other mechanism is the true effect of gender diversity through complementarity between men and women: if they differ substantially in some dimensions, these differences might become the source of better team decisions, or, on the contrary, inefficiencies in decision-making.
To separate between the two mechanisms – compositional effects and diversity effects – we need data with reliable quality measurement for each team member. Controlling for team member quality would take care of the compositional effect, and the gender composition would be significant only if there is a true gender diversity effect.
We use the What? Where? When? trivia game dataset to measure the effects of gender diversity on team performance with and without control for a player’s quality.
The What? Where? When? Game
What? Where? When? (WWW) is a team-played trivia game popular in post-Soviet countries. Teams of six players are asked questions and have one minute to come up with an answer. Typically, in order to find the correct answer, a team needs to combine both logical thinking and knowledge. A tournament usually consists of 36-90 questions. The team with the most correct answers wins the first place. In 2003, a unified database of the game was created. This database contains records of more than 218,000 individuals who have played in at least one of the 6,000 recorded tournaments.
The What? Where? When? Dataset
A unit of observation in our dataset is one game played by a team. It contains the unique ID of the team, the ID of each player, information about the number of games played by the team and by each player, the tournament date, the difficulty of the tournament and the number of teams. We identify the gender of the players through their names and patronymic names. Overall, we use 74,475 team-game observations which were played by 2,854 teams (23,000 single players) from 2013 to 2018.
Performance Measure
The measure of a team’s performance in a tournament is the percentage of correct answers normalized by the average percentage of correct answers in this tournament. We use player’s individual fixed effects as a measure of their quality in our regression analysis.
Gender Aspects in What? Where? When?
Only 31.5% of the players in the sample are female, however, other than that, we fail to find any significant evidence indicating gender discrimination or segregation. Table 1 presents the actual shares of team-game observations by gender composition as well as the predicted shares if assignment to teams was random. The difference between the actual shares and predicted shares does not appear to be economically significant.
Table 1: The actual distribution of women across teams is not different from random
Results
The basic model of our analysis, Model 1 examines the association between the performance of a team, normalized by tournament difficulty, with dummy variables for gender diversity (defined as the number of minority gender players in the team, i.e. diversity_1 is true if there is only one woman or only one man on the team). We also include the individual fixed effects of each player in the second specification (Model 2), to control for the quality of players and rule out possible composition effects.
Table 2. Effect of diversity on performance with and without the individual quality controls
The coefficients of Model 1 and 2 are shown in Table 2. While diversity is significant in the first specification, after accounting for the individual quality of players, we cannot reject the hypothesis of insignificance of gender diversity. These results hold under different specifications: with controls for player experience, with different player experience cutoffs, or including the neural network-generated predictions of performance.
Figure 1. The distribution of individual coefficients (proxy for player quality) for female and male players
Figure 1 presents the distributions of individual coefficients of female and male players. In our sample, the female distribution centers slightly to the left of the male one. It explains the negative diversity coefficients in the specification without the individual fixed effects – in this case, the diversity dummies capture the lower average quality of female players.
Conclusion
Our study aimed at disentangling compositional and pure effects of gender diversity by using a novel dataset of a team played trivia game. Our main finding is that after accounting for the individual quality of team members, the gender composition of a team does not appear to be significant for a team’s performance.
Although it is always dangerous to extrapolate findings obtained in specific settings, we believe that the positive gender diversity effects found in other studies are often manifestations of the change in the average quality of team/board members i.e. compositional effects rather than gender diversity effects per se. From a policy point of view, this means that while we need gender quotas in areas suffering from gender discrimination, once we reach equal opportunities such instruments may no longer have any positive effects.
References
- Ahern, Kenneth R., and Amy K. Dittmar, 2012. “The changing of the boards: The impact on firm valuation of mandated female board representation.” The Quarterly Journal of Economics 127.1: 137-197.
- Besley, Timothy, Olle Folke, Torsten Persson, and Johanna Rickne, 2017. “Gender quotas and the crisis of the mediocre man: Theory and evidence from Sweden.” American Economic Review 107, no. 8 : 2204-42.
- Desvaux, Georges, Sandrine Devillard, and Sandra Sancier-Sultan, 2010. “Women at the top of corporations: Making it happen.” McKinsey & company : 7-8.
- Eckbo, B. Espen, Knut Nygaard, and Karin S. Thorburn, 2019. “Board Gender-Balancing and Firm Value.” Dartmouth College working paper.
- Matsa, David A., and Amalia R. Miller, 2013. “A female style in corporate leadership? Evidence from quotas.” American Economic Journal: Applied Economics 5.3: 136-69.
Ethnic Geography: Measurement and Evidence
The effects of ethnic geography, i.e. the distribution of ethnic groups across space, on economic, political and social outcomes, are not well understood. We develop a novel index of ethnic segregation that takes both ethnic and spatial distances between individuals into account. Importantly, we can decompose this index into indices of spatial dispersion, generalized ethnic fractionalization, and the alignment of spatial and ethnic distances. We use ethnographic maps, spatially disaggregated population data, and language trees to compute these four indices for 161 countries. We apply these indices to study the relation between ethnic geography and current economic, political and social outcomes. We document that country level quality of government, income and trust increase with the alignment component of segregation.
Ethnic Geography: Key Idea
There is a vast literature on how a country’s ethnic diversity affects economic, political and social outcomes. This literature provides evidence for negative effects of ethnic diversity on e.g. peace, public goods provision, redistribution, the quality of government, and economic development in general. In these studies, ethnic diversity is typically quantified by indices based on the different ethnic groups’ country-wide population shares. By definition, these indices ignore ethnic geography, i.e. the distribution of ethnic groups across space.
Alesina and Zhuravskaya (2011) make an important first step towards taking ethnic geography into account. They construct an index of ethnic segregation that is based on the various ethnic groups’ population shares in different subnational units such as regions or provinces. We contribute to the literature on ethnic diversity by proposing a set of indices that capture important aspects of ethnic geography.
Theory
We derive a new segregation index that is based on both spatial and ethnic distances between pairs of individuals. Starting from a general class of indices that are expressions of the relation between a randomly selected pair of individuals, we uniquely characterize an index via a set of axioms. Our index avoids the standard problems of the so-called a-spatial segregation measures (based on population shares in administrative units), in particular the border dependence mentioned by Alesina and Zhuaravskya (2011) and the checkerboard problem (White 1983, Reardon and O’Sullivan 2004). Both problems are potentially very severe and are illustrated in detail in the paper. Importantly, our index can be decomposed into three (sub)indices: an index of spatial dispersion, a well-known index of generalized ethnic fractionalization (see below), and a measure of the alignment of spatial and ethnic distances between individuals (i.e. ethno-spatial alignment or, simply, alignment hereinafter). Interested readers can also find a stylized illustration of what each component stands for in the paper.
Data and Illustration
We compute these four indices of ethnic geography for 161 countries from all over the world using a combination of digital maps showing the distribution of ethno-linguistic groups all over the world and the current population at a very high resolution. As robustness check, we also compute our measure using historical population maps as well as a simpler map based on global land cover data that should proxy for the exogenous component of the spatial distribution of a country’s population.
This provides us with real world examples of countries that differ in, for example, alignment, but are otherwise similar. For example, Togo and Benin are neighboring countries located in West Africa with comparable climatic, geographic and demographic characteristics. Moreover, they were both French colonies after WWI, became independent in 1960, and started their post-colonial history in tumultuous ways that culminated in coups. The ruling autocrats both managed to stay in power for many years.
Benin and Togo are also comparable in terms of generalized ethnic fractionalization (between the median and the third quartile of our sample) and spatial dispersion (above the third quartile). Ethno-spatial alignment is however considerably higher in Benin (1.35, which is above the third quartile) than in Togo (1.11, which is below the median). Figure 1 shows the different ethnic homelands and the main language groups which these ethnic homelands belong to.
Ethno-spatial alignment is relatively high in Benin as there is a relatively clear divide between Kwa speaking groups in the South, Defoid speaking groups in the Center, Gur speaking groups in the North, and some smaller groups speaking very different languages in the North East. As a result of this divide, linguistically distant individuals tended to live far apart from one another. In contrast, ethno-spatial alignment is relatively low in Togo, mainly because there are Gur and Kwa speaking groups in the country’s South, its Center and its North. As a result of these large and widespread language groups, linguistically distant individuals often lived relatively close to one another.
Figure 1:
Empirical Analysis
We use our indices in cross-country regressions to improve our understanding of the role that ethnic geography plays in economic, political and social outcomes around the globe. We first focus on the associations between our index of ethnic segregation on the one hand, and the quality of government, incomes and generalized trust on the other hand. We find a negative relation between ethnic segregation and the quality of government, similar to Alesina and Zhuravskaya (2011) with their index of a-spatial segregation in their sample of 97 countries. We further find that our index of ethnic segregation tends to be negatively associated with incomes, too, but unrelated to generalized trust. See also Ejdemyr et al. (2018) and Tajima et al. (2018) for recent contributions on Malawi and Indonesia.
More importantly, we study the relation between the three components of our index of ethnic segregation – ethnic fractionalization, spatial dispersion and ethno-spatial alignment – and these outcome variables. Strikingly, we find a positive and statistically significant association between the alignment of ethnic and spatial distances between individuals, and the quality of government, incomes and trust. Hence, societies perform better when ethnically diverse people live far apart, relative to what they would have, had all ethnic groups been represented in all locations with population shares equal to their country shares.
Conclusion
To better understand the role of ethnic geography and to mitigate well-known problems of a-spatial segregation measures, we have developed a new segregation index that is based on ethnic distances between groups and spatial distances between locations rather than categorical data on ethnic groups and administrative units.
The decomposition of our segregation index reveals that it corresponds to the product of generalized ethnic fractionalization, spatial dispersion, and the alignment between ethnic and spatial distances. This ethno-spatial alignment is a novel concept that captures, broadly speaking, whether ethnically different individuals tend to live far from each other, relative to the situation where all groups appeared in each location with population shares equal to their country ones.
Using these indices in cross-country regressions suggests, among other things, that countries with higher ethno-spatial alignment tend to be better governed, richer, and more trusting.
Of course, the indices we have developed can also be applied to measure the ethnic geography of cities. For example, one could use our segregation index instead of a-spatial measures to compare segregation across Russian metropolitan areas or within metropolitan areas over time. Finally, we would like to stress that our theoretical framework is not specific to the ethnic dimension. Instead of categorizing individuals by ethnic groups and measuring linguistic distances, future research could focus on other social or socio-economic cleavages that are believed to be salient in a particular setting.
References
- Alesina, Alberto, and Ekaterina Zhuravskaya, “Segregation and the Quality of Government in a Cross Section of Countries,” American Economic Review, 101 (2011), 1872–1911.
- Ejdemyr, Simon, Eric Kramon, and Amanda Lea Robinson, “Segregation, Ethnic favoritism, and the Strategic Targeting of Local Public Goods,” Comparative Political Studies, 51 (2018), 1111–1143.
- Hodler, Roland, Michele Valsecchi and Alberto Vesperoni, “Ethnic Geography: Measurement and Evidence,” CEFIR Working Paper No. 253, June 2019. http://cefir.ru/download.php?id=4681
- Reardon, Sean F., and David O’Sullivan, “Measures of Spatial Segregation,” Sociological Methodology, 34 (2004), 121–162.
- Tajima, Yuhki, Krislert Samphantharak, and Kai Ostwald, “Ethnic Segregation and Public Goods: Evidence from Indonesia,” American Political Science Review, 112 (2018), 637–653.
- White, Michael J., “The Measurement of Spatial Segregation,” American Journal of Sociology, 88 (1983), 1008–1018.
Human Trafficking, Prostitution Legislation, and Data
This report is the compilation of exploratory research work conducted within a project led by Giancarlo Spagnolo (SITE and EIEF), together with Maria Perrotta Berlin (SITE–SSE) and Ina Ganguli (UMass Amherst). Whilst investigating the effects of asymmetric punishment in the regulation of prostitution, the interaction of markedly different legislations for this along the Franco-German border was of interest. In this report, we present gathered information and data regarding human trafficking and sex work in Germany. We begin by broadly outlining both topics and continue with presenting points that should be considered in future research. The results from a limited survey, where sex workers and counsellors were interviewed, are also presented.
Exploring the Interaction Between Sex Work Regulation and Human Trafficking along the Franco-German Border
In this report, we present data regarding human tracking and the forced prostitution with which it is often connected, as well as data regarding voluntary and consensual sex work. Though we present these topics alongside one another because of the many possible ways in which they may be connected, we do not make any correlating assumptions.
There are different claims regarding the existence of a correlation between sexual exploitation and prostitution policies. Working under the assumption that a correlation does exist, it is still unclear what it would look like (Sonnabend and Stadtmann, 2018). While some argue that legalising sex work leads to an increased social acceptance of the phenomenon and thereby also increased demand for voluntary sex work. It also makes it easier and cheaper for criminals to track people and find customers that, unwittingly or not, pay for sex from victims of human tracking for sexual exploitation (Sonnabend and Stadtmann, 2018; Cho et al., 2013; Jakobsson and Kotsadam, 2013).
On the other hand, restricting or criminalising sex work may make it less likely for such victims, as well as buyers of sexual services of any kind, to collaborate with police forces to report illicit activities related to human tracking (Bisschop et al., 2017; Cunningham and Shah, 2014). Thus, when sex work is criminalised, any that remains will move into the dark, and hence become much more difficult to control (Scoular, 2010).
Sonnabend and Stadtmann (2018) found that different empirical studies have given rather contradictory results. For instance, whilst Cho et al. (2013) found a positive correlation between legal prostitution and tracking flows comparing existing data from over 150 countries, they also acknowledged that this needed to be considered with caution due to the lack of consistent data on human tracking across countries.
On the other side of the spectrum, a report by the New Zealand Government (2008) and a study on human tracking in Europe (Hernandez and Rudolph, 2015) suggest that no links between the sex industry and human tracking can be made. Instead, Hernandez and Rudolph would argue that human tracking in Europe stems mostly within already existing migratory and refugee corridors and is more likely to happen where host countries have weaker institutions, higher general crime rates and more liberal border controls. Host countries’ rates of asylum seekers, however, do not seem to play a role in the extent of human tracking.
Sonnabend and Stadtmann (2018) also endeavoured to calculate the effects of the Nordic model on sex work. They found that the prohibition of sex work is likely to create more loopholes and worse conditions for voluntary prostitution, and thus conditions that facilitate sexual exploitation and human tracking. As can be deduced from this brief introduction, any pre-existing view on the interplay between sex work and human tracking can be easily reinforced as virtually every standpoint can find some support in research. Throughout this report, we attempt to present the information we have gathered bearing these disparate previous findings in mind.
This report consists of six sections. This brief introduction is followed by a section that elaborates on the connections that can be made between human tracking and prostitution legislation. Section 3 presents current issues regarding human tracking internationally, as well as a compilation of the available data for Germany. Section 4 focuses on Germany and the legislative and regulatory environment under which lawful sex work is carried out there. Thereafter, we present some findings from a limited field survey of actors within the prostitution scene along the Franco-German border. We round this report off with a conclusion, briefly summarising and discussing our findings.
Regulatory Efforts
Regardless of how sex work and human tracking may be related, the likelihood of discovering tracking victims can be affected by the policies in place surrounding prostitution. Opponents of laws restricting prostitution often argue that when sex trade is outlawed, even if only for one party (as in the case of the Nordic model) the activity does not cease to exist, but is simply moved into the black market. Getting a full grasp of the extent of human tracking within the already restricted environment of prostitution will be considerably more difficult than if it had been within a legalised setting that allowed for regulatory oversight. However, as will discussed in this report, getting the regulatory oversight right is a challenge and there are considerable legitimate criticisms of this liberal stance too.
Varieties of Legislation
Across Europe, we find different kinds of prostitution legislation. Countries have chosen to combat the negative elements often associated with sex trade in ways that differ greatly in mechanisms and outcomes. These can be grouped into four overarching groups, all working in somewhat different ways, presented in Table 1 above.
Historically, prostitution has been a highly sensitive issue. Traditional moral and religious values all over Europe have condemned extramarital sex of any kind. Laws have ranged from outlawing prostitution to barely touching the topic. This becomes apparent when looking at the four overarching types of legislation that we will cover in this section. Abolitionist and prohibitionist policies present the two dominant legal traditions.
The four groups do not only differ from one another, but there is great variation between countries of each respective group, perhaps with the exception of countries that have neo-abolitionist policies. For instance, while brothels are legal in Germany and the Netherlands, Latvia has chosen to regulate prostitution from an abolitionist standpoint by making sex work a licensed profession but largely does nothing more (Cabinet of Ministers (Latvia), 2008). In Turkey, prostitution was legalised already in 1923. In recent years, however, Turkey has been a lot more restrictive in issuing new licenses (Sussman, 2012).
The prohibitionist group differs less in terms of legislation and more when it comes to enforcement. The enforcement of these policies tends to be weak, with measures needed to ensure that no prostitution is carried out behind closed doors largely lacking. We find the greatest in-group variation amongst the abolitionist countries. This approach is also the one that is least easily defined. In a sense, it, therefore, becomes a catch-all term for countries that fall outside of the scope of the other three groups.
The reasons for the differences in legislation on this matter is that countries, for varying reasons, have chosen to prioritise different goals. This is obvious when looking at the mechanisms of each type in Table 1. What they all have in common, however, is that without diligent enforcement of the laws, illegal prostitution is likely to persist.
Project and Intentions
As part of a research project at the Stockholm Institute of Transition Economics (SITE) in which researchers Giancarlo Spagnolo (SITE), Maria Perrotta Berlin (SITE) and Ina Ganguli (UMass Amherst) are looking into the effects of asymmetric punishment in legislation, the neo-abolitionist ‘Nordic model’ for prostitution is of interest.
Though the focus is placed on Scandinavia, France came into the picture as similar legislation was adopted there in April 2016. As a result of the change in France, which presumably made it harder for people to purchase sexual services, it became interesting to see how potential consumers may take advantage of the open border to Germany, where prostitution is legal. Before the law changed, the exchange of sex for money had been legal, with some restrictions on solicitation. Running a brothel, pimping or paying for sex with a minor had, however, never been allowed. With the change towards asymmetric punishment, selling sex for money remained legal whereas purchasing it was now considered a crime.
This put the two most recently developed types of prostitution legislation, regulationst in Germany and neo-abolitionist in France, next to one another along the Franco-German border. The effects of the cross-border dynamics are what we, as research assistants to the team at SITE, tried to map. We assumed that the regulationist approach of Germany would enable us to more easily investigate the effects of the change in legislation in France across the border.
For this reason, we investigated how customers and working conditions, among other things, changed in areas of Germany neighbouring France. As our research progressed, this underlying assumption would prove to be less straightforward than we had thought originally. Though we cannot give a conclusive answer to how the change in regulation came to affect the market for sex in Germany, we gained insights and gathered information that we have compiled in this report.
The State of Human Tracking
. . . the recruitment, transportation, transfer, harbouring or receipt of persons, by means of the threat or use of force or other forms of coercion, of abduction, of fraud, of deception, of the abuse of power or of a position of vulnerability or of the giving or receiving of payments or benefits to achieve the consent of a person having control over another person, for the purpose of exploitation. Exploitation shall include, at a minimum, the exploitation of the prostitution of others or other forms of sexual exploitation, forced labour or services, slavery or practices similar to slavery, servitude or the removal of organs.
— DEFINITION OF ‘TRAFFICKING IN PERSONS’, ARTICLE 3, PARAGRAPH (A) OF THE UN PROTOCOL TO PREVENT, SUPPRESS AND PUNISH TRAFFICKING IN PERSONS, ESPECIALLY WOMEN AND CHILDREN, SUPPLEMENTING THE UNITED NATIONS CONVENTION AGAINST TRANSNATIONAL ORGANIZED CRIME
The international community has long been struggling with the prevalence of human tracking. Initially, under the heading of slavery, human tracking has, through multiple conventions over the past 150 years, been explicitly restricted and outlawed by most countries. Though what we regard today as human tracking and modern slavery might not resemble stereotypical ideas from the past, at its core, it is still very much the same. We consider a situation as human tracking when a person is involuntarily under the control of another and forced to commit acts against his or her own will.
As the definition above is read, we see that human tracking can encompass many different criminal acts. However, there are three basic elements that are needed to legally define a situation as an instance of human tracking: an act, a means, and a purpose of exploitation. Because of its often hidden nature, the full extent of human tracking is difficult to map. Nonetheless, many countries across the world acknowledge the seriousness of the issue and are making efforts to combat it. Policymakers are further aware that the fight against human tracking is wholly dependent on international cooperation given that perpetrators often exploit vulnerable people by removing them from their countries of origin.
Once again, when discussing sexual exploitation in tracking, it is inevitable that regulations regarding prostitution will have to be discussed. Though sexual exploitation in trafficking does not necessarily entail prostitution, it is a straightforward way for trackers to monetise an already illicit activity. However, this does not mean that tracking for sexual purposes will be the underlying cause for all sex work, or even related to it in general, especially in jurisdictions where it is permitted. The relationship between prostitution and human tracking is complex and multifaceted and is deserving of thoughtful analysis.
A Lack of Reliable Information
There is uncertainty surrounding the prevalence of human tracking. Due to its illicit nature, it is hard to grasp really how widespread it is. It is only possible, as with any other black market good or criminal activity, to observe the number of detected instances of tracking. Currently, 173 of 193 UN member states have ratified the Palermo protocol on tracking in persons (UN Treaties Collection, 2019). In 160 of those, human tracking has been explicitly criminalised, and the numbers we have to rely on come from those countries (Chatzis, 2018). In the period 2012-2014, the United Nations Oce on Drugs and Crime (2016), UNODC, reported a total of 63,251 victims worldwide. Compared to estimates ranging in the tens of millions from the ILO (2012) among others, the number of detected victims is minuscule (US State Department, 2017).
There is also an under-reporting of different kinds of tracking. Especially organ removal is yet to be sufficiently mapped (Chatzis, 2018). Using any currently existing data involves an inherent bias stemming from this under-reporting that should be kept in mind. There are, however, some obvious tendencies shown in the data available to us, that help us in understanding human tracking at large.
A clear majority of reported victims are women, though the share of men has increased over time and is substantial. Data from 2014 published by the UNODC (2016) puts men at 21 per cent of victims. Men are particularly under-represented among victims of sexual exploitation; women make up 97 per cent of those exploited for sexual purposes (Chatzis, 2018). Men are considerably more likely to be exploited as forced labour. Namely, 85 per cent of detected male victims were exploited for labour. Overall, the victims of forced labour are in 63 per cent of all cases male (Chatzis, 2018).
Reasons for Persistence
Regardless of many efforts to combat this problem, human tracking persists. According to Chatzis (2018), the reasons for this lie not only in the complex nature of the crime, but also (i) in the widespread use of the internet in facilitating it, (ii) in an international trend to deregulate labour markets, and (iii) in increased flows of migration. Especially in Europe, labour markets have seen a push from politicians asking for more flexibility after the most recent recession. Conflicts around the world, particularly in the Middle East, saw the nationalities of tracking victims mirror those of increased outward migration (Chatzis, 2018).
The sheer number of refugees over the most recent years, and the strain this has put on countries, agencies and organisations, has also added to a likely increase in the undetected cases of tracking. European countries have not been capable of effectively screening for likely victims of tracking (Chatzis, 2018). One could even claim that the incapability in understanding the mechanisms of tracking has caused some European countries to induce it, albeit inadvertently. For instance, the Italian government’s agreement with Libyan authorities to stem the flow of migrants across the Mediterranean has been reported to create slave-like conditions for predominantly African migrants in Libya (Kirchgaessner and Tondo, 2018). This was also reflected in our study, which will be further discussed below, counsellors working in Germany that we interviewed had personally met Nigerian women, whose experience as victims of sex tracking began in Libya.
Public Official Figures
Available data allow us to make some general statements concerning the types of tracking around the world. A majority of detected tracking victims globally have been victims of sexual exploitation. However, the most prevalent kind of tracking changes with geography. In Africa, victims are more commonly detected in circumstances defined as forced labour, rather than as sexual exploitation. However, this information comes with the caveat that it could instead be a reflection of what kind of tracking rapporteurs in different parts of the world are able to detect.
When looking at Europe, the most recent figures from 2016 reported by the UNODC show that the share of detected tracking victims that had suffered sexual exploitation is substantial, reportedly more than two-thirds of victims were tracked for sex (UNODC, 2018b). Similar numbers were reported for Western and Southern Europe, a region largely made up of the countries that were part of the Western Bloc, in the preceding report from the UNODC (2016). Throughout Western and Southern Europe, twelve (thirteen) countries reported the 12,226 (12,775) victims detected there come from all over the world in 2016 (2014), with citizenships in 124 (137) different countries.
There are, however, some clear trends that can be gleaned from the numbers. For Western and Southern Europe, victims more frequently come from outside the country they are detected in or nearby countries. A third of victims had their origin in Central and South-Eastern Europe, an area largely corresponding to countries of the former Eastern Bloc that have now become part of the European Union (UNODC, 2018b). This share was the same in 2014 (UNODC, 2016). The remainder is largely made up of victims from Africa and East Asia.
German Data
The Federal Criminal Police Oce (Bundeskriminalamt), BKA, is the government agency that compiles data for all sorts of criminal activities across the country. The BKA annually publishes a report on the state of human tracking in Germany, currently under the name of ‘Bundeslagebild Menschenhandel’. Again, when viewing these figures, we need to keep in mind that the numbers presented here only concern the detected instances of human tracking.
These reports, from 1999 until now, are available online at the BKA’s website. The most recent report for 2018 was made public in September 2019. Though the structure of the report differs slightly from year to year, there are some tables that are available throughout the period. For this report, we have primarily used the disclosure of nationalities of victims of human tracking for sexual exploitation. As could perhaps be expected, the vast majority of reported victims over the past 20 years come from Europe. Over time, we see a decreasing trend in the number of victims of human tracking. However, due to the relatively low levels of human tracking from non-European countries, this trend is really only noticeable for victims of European origin.
Ideally, we would want to disaggregate the data and look at individual countries instead of continents. However, the BKA does not publicly disclose the figures for all countries. What it does, though, is feature numbers for a selection of countries of origin each year. Though not explicitly stated, it is likely to be the most frequent origin countries for each year. In Table 2 below, we show which countries appear and when. An important change in the report is the inclusion of German victims from 2003 (BKA, 2003).
Throughout the period and not subject to any major change, is that most victims of human tracking are citizens of Central and Eastern European countries. However, over the course of 18 years, there are some changes regarding which countries are more and less prominent.
At the turn of the millennium, countries of the former Soviet Union (FSU) constituted a majority of all non-German human tracking victims. This can be said despite having explicit data for only five of those countries, Russia, Ukraine, Belarus, Lithuania and Latvia, in the period 1999-2002. For 2003-2007, it is possible to observe a shift where the share and number of these countries decrease in conjunction with them being less and less mentioned in BKA’s reporting.
Bulgaria and Romania first appear in the 2001 report and the number of victims rises sharply until 2003. Since then, there have been fluctuations in the number of reported victims from these two countries, but they have overall been somewhat stable at a considerably higher level than before. Since 2008, they have made up more than 55 per cent of the European non-German victims, and the majority of all non-German nationalities for all but three years.
Prostitution in Germany
In the past 30 years, prostitution laws in Germany have undergone numerous changes. Not only German law is likely to have affected the prevalence of prostitution within the country, though. The expansion of the EU and domestic as well as EU-wide policies in relation to it, policies in neighbouring countries, and major geopolitical events might have all contributed to the current state of prostitution and human tracking in Germany.
However, the greatest change is arguably the 2001 law, the Prostitutionsgesetz (ProstG), which institutionalised prostitution in Germany, taking the exchange of sex for money from a legal grey area into a legally recognised occupation. In principle, this regulationist approach could bring illegal and criminal acts often related to the sex trade, such as human tracking, to the surface, thereby creating a safer prostitution market for both sex workers and consumers through the possibility of regulatory oversight. However, with time, polarised opinions have been raised about this policy. Some have praised the ProstG as a milestone for sex workers’ rights. Others have proclaimed that Germany has become an exploitative ‘battery cage’ (Conrad and Felden, 2018). There have been several previous investigations into the ways in which the ProstG has impacted the state of prostitution, as well as reports on human tracking in Germany reaching different conclusions (e.g. Tavella, 2008; Czarnecki et al., 2014; Gunderson, 2012; Kavemann and Steffan, 2013).
With time, it became clear that legalisation without regulation may be fertile soil for the uncontrolled growth of prostitution activities. For this reason, the German government enacted the Prostituiertenschutzgesetz (ProstSchG), or the ‘Prostitute Protection Act’, in July 2017. The act added a number of statutory requirements on sex workers, which we will cover shortly. Germany’s approach to prostitution represents an interesting case in the European context, where prostitution has typically been very differently conceptualised and thus, legally dealt with.
The Evolution of German Law
The most significant shift for Germany is arguably the recently mentioned ProstG, which created the occupational status for sex work in Germany. It was enacted after extensive debate. Sex workers had voiced their misgivings with the then-current legislation, where prostitution was not illegal but without a legitimate position in society. Brothels and sex workers were perceived to be prevented from achieving acceptable standards in their working conditions because of the regulations in place. From the early 1980s to the mid-90s, several debates on the topic were held in the Bundestag. Draft legislation was rejected in June 1998 by the governing centre-right CDU/FDP coalition. The following centre-left SPD/Greens government brought the proposals back to parliament, which then later passed them with support from all parties bar the CDU/CSU group on 20 December 2001. The law came into force on January 1, 2002 (Kavemann and Steffan, 2013).
With ProstG, sex work was set on an equal legal footing to any other kind of profession. Those practising it were now entitled to social insurance and were given the legal means to demand payment from customers (Kavemann and Steffan, 2013). However, there are geographical restrictions on prostitution, which vary between states. The 1974 Einfu ̈hrungsgesetz zum Strafgesetzbuch, EGStGB, contains one article (number 297), which is of particular relevance.
The EGStGB article gives states the right to restrict the areas and times in which prostitution is allowed through decrees. For municipalities (Gemeinde) with a population above 20,000 inhabitants, a part of the municipality can be set off-limits for prostitution, with the option to forbid it completely in municipalities with up to 50,000 inhabitants. This law has been used as a justification for instance in Baden-Wu ̈rttemberg and Saarland, where prostitution has been forbidden in municipalities with less than 35,000 inhabitants since 1979 and 1982 respectively. The law also allows for restrictions regarding which times of the day prostitution is permitted.
In October 2016, the Bundestag passed the ProstSchG, effective as of 1st July 2017. introducing new regulations on the trade of sex. To lawfully work as a prostitute, one would now have to register as such and thereafter carry a work ID. Registration requires valid ID documents, a health check-up and a yearly health examination to maintain the status. Furthermore, the registration needs to be renewed every two years. In order to protect sex workers’ right to anonymity, one may be registered under a pseudonym if requested.
Additional provisions in ProstSchG include barring registrations if there is evidence of the registration being induced by third parties, or when in the last six weeks of pregnancy. When registering, the responsible agency is required to inform sex workers of their rights and responsibilities, including what the ProstSchG entails, such as consultation opportunities in relation to health and pregnancy, and how to get help in emergencies. Additionally, all prostitution-related businesses, such as brothels and Laufhauser (establishments where sex workers rent rooms), need to register and get permits as well.
Lastly, the ProstSchG also introduced a statutory condom requirement during intercourse. Not following this requirement could result in a fine of up to 50,000 Euros. Though the law as a whole was welcomed by most states, especially because of the statutory permission requirement (Erlaubnispflicht), many of the other requirements are related to significant implementation costs.
In connection to the introduction of ProstSchG, the German Federal Statistical Oce, Destatis, was tasked with gathering statistics on several of the registration requirements now statutorily demanded (law ProstStatV, 2017) from state and local authorities. As of yet, there have been significant difficulties in gathering this data. At the time of writing this report, only ten of Germany’s sixteen states have provided any data. The data provided is also incomplete, with several Landkreise and even some major cities unable to successfully roll out the new legislation (Destatis, 2019). Hopefully, many of the current data issues will be mitigated in the future.
The Extent of Prostitution in Germany
Though a country with regulationist policies, Germany has little publicly available statistics concerning the state of prostitution in Germany. A central issue is and has long been the actual size of the sex trade market. One figure that is often referred to in many newspaper articles is that of 400,000 prostitutes. It has been circling around in the media for at least the past 15 years and we have not been able to identify the original source.
However, the estimates vary widely depending on the paper reporting the number, and there is generally no reference to how estimates were created or by whom. One extensive, though not exhaustive search by us found the total number of prostitutes in Germany over the past 20 years to reportedly range from a lower bound of 60,000 (Stephani, 2017) to an upper bound of 700,000 (Junge, 2001). The most recent official number of issued licenses (from 31st December 2017), however, are 1,350 across Germany (See Table 3 below)4 and a total of 7,000 having reported to relevant authorities (Destatis, 2019).
Today, prostitution is commonplace across Germany. In German and international media, the country is often referred to as one of the prostitution hubs of Europe. One figure that is commonly referred to in the media is that of 1.2 million transactions a day (Junge, 2001). Though again, this is only an estimate with unclear foundations. Three surveys conducted between 2012 and 2015, one by bi-weekly women’s magazine Brigitte and two by the German edition of Playboy, had German men responding to if and how often they buy sex. There were considerable differences in their results, indicating that between 10 and 88 per cent of German men had bought sex at some point (Crocoll, 2013; FOCUS Online, 2012; 2015).
By moving away from sex workers having the option to register as such on their social insurance and instead of making it mandatory to register to get a permit, the German government hopes to tackle the difficulties it has had in understanding the full extent of the prostitution market. In 2013, the Federal Employment Agency reported that the number of women registered as sex workers on their social insurance was 44 (Meyer and Nagel, 2013). Beyond doubt, this figure did not correspond to reality.
In a study included in the 2007 governmental evaluation of ProstG, 305 sex workers completed written interviews to explore some of the reasons why the number of registered sex workers was so low. Only 1 per cent of respondents had a formal employment contract as sex workers, while some had other professions outside of prostitution. A clear majority (roughly 70 per cent) said they were freelancing. Responses from brothel operators also indicated that sex workers were given the option to be registered as “employees of an artists’ agency or as a prostitute” (BMFSFJ, 2007). This demonstrates the failure to turn sex work into a profession like any other, which might be related to the common stigma associated with this profession, and likely spurred on the introduction of the ProstSchG measures.
Changes Outside of Germany
Apart from domestic reforms in regulation, the German market for sex is likely to have been affected by multiple outside factors since the enactment of the 2001 ProstG. Together with our initial point of interest, the French reform in 2016, we have listed some of the most prominent events in Table 4 above.
EU Enlargement
On 1 May 2004, the European Union gained ten new member states: Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. Roughly two and a half years later, in 2007, Bulgaria and Romania also joined. In this period, the European Union saw the number of member states rise from 15 to 27 and its total population increased by roughly a quarter (Eurostat, 2018). All these countries were and still are, below the EU-28 GDP per capita average, together with only four ‘old’ members (Eurostat, 2017).
Since their accession, there has been a considerable movement of labour across Europe from these countries (European Commission, 2011). However, there were certain provisions in place for both accession groups, restricting the free movement of labour from those countries to Germany at first. Full freedom was not granted until seven years after joining (Andor, 2014; European Commission, 2018).
Ukraine
Following the annexation of Crimea by the Russian Federation and the subsequent war in Donbas, around 1.6 million people have been displaced according to the UN (2018). The majority of those are registered as internally displaced within Ukraine, but around 1 million people have sought asylum in neighbouring countries. In the period from 2011 to 2015, a report commissioned by the International Organisation for Migration (IOM) and written by the market research institute GfK estimated that the share of the population vulnerable to human tracking rose from 14 to 21 per cent, a 50 per cent increase (GfK, 2015). In 2017, that share had not changed (GfK, 2017).
At the same time, the number of registered tracked persons has followed a bit of a U-curve. The OSCE (2016) reported that the number was 380 in 2006, and in 2015 it was 111 and during the first ten months of 2016 only 96. The UNODC (2018a), however, presents slightly different numbers from the Ukrainian Ministry of Social Policy, totalling 83 in 2015. From 2014 to 2017 they report the number of victims rising from only 27 to 198. According to the UNODC, a majority of victims were tracked for labour exploitation, but the share of those exploited for sexual purposes increased from a few percents initially to around a quarter in 2017. The discrepancy in the numbers reported by the UNODC and the OSCE is an issue, though.
Additionally, the change in visa rules for Ukrainian (and Georgian) citizens in 2017 eased access to the Schengen area. If in possession of a biometric passport, Ukrainian citizens could now go to Schengen countries without a visa (European Commission, 2019). In a small survey, conducted in December 20175, we found through interviews with sex workers and counsellors in Germany that there has been a perceived increase in sex workers from Ukraine recently. Again, we need to acknowledge that the origin countries of sex workers do not necessarily indicate activities surrounding human tracking. There are, however, reasons to assume that with an increased migratory flow from Ukraine, human tracking activities may possibly take advantage of the same corridor.
Migration Crisis
As mentioned in the case of the war in eastern Ukraine, displaced people and refugees are exposed to an increased risk of being tracked (IOM, 2015). In armed conflict, fighting groups not uncommonly abduct and recruit men, women and children to be forcibly used as combatants, sexual and domestic slaves, forced labour or coerced into marriages (UNODC, 2016). Chatzis (2018) also stressed conflict as one of the main reasons for the persistence of tracking.
Though the number of reported cases of tracking from especially Syria initially went up following the outbreak of the Syrian Civil War, absolute levels remained relatively low (UNODC, 2016). As noted by Chatzis (2018) though, it is not safe to say that this is an accurate reflection of reality as the crisis created circumstances where screening for tracking was, and in part still remains, neglected.
France
On the 6th of April 2016, France implemented the ‘Nordic model’, designed to discourage buyers of prostitution and ease pressures on prostitutes. Those caught buying sex could face up to 3500 Euros in fines for repeat offences and fines of up to 1500 Euros for first-time offences (McPartland, 2016). Before the law changed, France would have been categorised as a country with abolitionist legislation. It had in place restrictions on pimping, brothels, and solicitation, while the passive solicitation of customers had been banned in 2003 (Chrisafis, 2012). However, with regard to the fundamental act of paying and being paid for sex, there was no statutory ban (RFI, 2015).
EU Directive
The 2011 EU Directive ‘on preventing and combating tracking in human beings and protecting its victims’ (Directive 2011/36/EU) was agreed by the Council to homogenise the varying national regulations of this cross-country problem. It replaced a 2002 Council Framework Decision. For Germany, it included new penal provisions, which would ease the implementation against trackers and tracking (CBSS, 2016).
In the directive, member states were requested to transpose it by April 6th, 2013. By that date, Germany was the sole member state not to have transposed the directive into law (European Parliament, 2016). Not until roughly three years later in July 2016 did the Bundestag pass a bill proposed by the federal government to turn it into law. Even after the decision, implementation would wait until 1st July 2017.
Though Germany today has the legislative tools to combat tracking, critics lament the lenient application of laws. Only 26 per cent of the trackers convicted receive prison sentences, leaving potential trackers less than deterred (US State Department, 2017).
Data Gathering Mission
As any publicly available data on prostitution in Germany has been fraught with issues, the research team at SITE decided to get in touch with those most familiar with the subject: sex workers, brothel owners, as well as people working closely with sex workers and potential victims of human tracking, this last group henceforth referred to as counsellors. These counsellors worked in the public sector and for non-profit organisations as either health care professionals or social workers. By reaching out to all three groups, we intended to get closer to finding out the answers to the questions at the core of this effort: What were the effects on sex work and human tracking in the German regulationist environment caused by a change from abolitionist to neo-abolitionist legislation in France? More specifically, would sex buyers become more likely to go from France to Germany than they had been before to circumvent the risk of sanctions?
Method
To uncover the dynamics of the prostitution market in Germany, we deployed a mixed-method approach to create a holistic picture of the sex work market at the German border (Plowright, 2011), combining quantitative and qualitative methods. Specifically, we held semi-structured interviews with counsellors working with sex workers and victims of human tracking for the purpose of sexual exploitation active in the region along the border. This allowed the interviewees to share relevant information. Through our interactions, we could also gain vital knowledge of how to improve our plan for approaching and surveying sex workers.
One crucial constraint when interacting with sex workers in the field was time. As we could not offer any financial incentives, we developed a questionnaire (see Appendix) that would be able to capture the information we were searching for, without taking up too much of their time. Questions ranged from asking the sex workers about the extent to which they felt safe, liked or disliked their working conditions, to what the perceived national backgrounds of their clients were. The questionnaire aimed particularly at asking about possible changes in the nationalities they served, their profits, and prices they could charge over the preceding two years.
Most questions did not mention the law change in France or the possible difference in the number of French customers, although it was asked at the end of the survey in case this had not been mentioned by the interviewees. With the help of our counsellor contacts, we created a questionnaire that could be answered quickly, within 5-10 minutes from when we first approached them. It enabled us to gather mostly quantitative data with some minor scope for open answers, allowing some data of a qualitative nature as well.
When working with a sensitive subject such as sex work, it is crucial to respect the personal integrity of everyone involved. This meant that we had to ensure the sex workers’ anonymity, not record the conversations and even limit the sharing of raw data between researchers. One counsellor emphasised that many sex workers typically hide their occupation from their wider social circle, often even close family, due to the stigma and fear of social exclusion.
There is also a tendency for many sex workers to be reluctant about reporting the sometimes precarious conditions they live or work under, due to the stigma they face. Our efforts tried to mitigate this issue, committing ourselves to not share information that could identify them afterwards even within the research team. Of course, this did not apply to the experts we interviewed. These latter interviews were recorded and transcribed to ensure the completeness of the information provided.
Data
Geographically, we endeavoured to interview sex workers and brothel owners as close to the French border as possible. Specifically, this meant that the interviews we carried with these actors were in the cities of Saarbrucken, Saarlouis, Offenburg, Trier, and Freiburg and the village Rilchingen- Hanweiler outside of Saarbrucken. In order to get a more general understanding of sex work and human tracking across Germany, as well as more border-specific insights, we primarily interviewed counsellors exposed to the German-French border dynamics, based in Kehl, Strassbourg, Heilbronn, Freiburg, and Trier, but also some farther away in Berlin and Dortmund.
When approaching sex workers in the field, we learned quickly the truth of what counsellors had already warned us of. Out of 44 sex workers, only 17 accepted to be interviewed when approached. The survey was conducted in October and November 2017, generally late at night and either in a street setting or in a brothel. More than half of the sex workers (around 60%) had at that point been working in the sector for more than 1.5 years and almost a quarter for more than 8 years. Three-quarters of the sample was judged to have a good of comprehension of the questions, whereas language proved to be a considerable problem with the remainder of our interviewees.
Nine semi-structured interviews with counsellors working directly with either sex workers and/or victims of human tracking working along the Franco-German border were conducted. These interviews took place in person or over the phone. In both cases, they were recorded and later transcribed. These experts were working, as previously mentioned, either in publicly run health institutions that directed their services specifically towards the needs of sex workers or at NGOs aiming to support sex workers or victims of human tracking in various concerns.
In contrast to the mostly quantitative data obtained from the interviews with sex workers, the interviews with counsellors were of a qualitative nature and allowed a more nuanced understanding of the complexities surrounding sex work and sexual exploitation across the German-French border.
Limitations and Difficulties
Initially, our plan had been to reach out to sex workers through health organisations that counsel them on a regular basis. However, we faced major obstacles to that. These organisations work for a long time to establish trust between them and this vulnerable and often stigmatised part of society. For this reason, they were very hesitant to arrange any contact between us and the sex workers they counsel. Research and health experts have repeatedly shown how hard it is to detect whether or not a sex worker is the victim of human tracking. It often takes social workers a long time of counselling until a sex worker decides to open up about the reasons that led them to pursue sex work.
For this reason, the options of gathering data were few, and sex workers had to be approached during their working time in brothels or street prostitution areas. This, in turn, caused several difficulties.
First, it highlighted the lack of resources at our disposal. Approaching subjects in the field is one of the most costly methods, particularly when dealing with a hidden population such as this. Whatever information we would be able to gather would not necessarily provide a representative sample of answers.
Second, whether in the context of a brothel or on the street, sex workers usually have a limited time in which they actually earn money. In most cases, street prostitution is not only limited to certain streets, but also to certain times of the day. For instance, in Saarbrucken, it is only allowed between 10 pm and 6 am. As for brothels, sex workers generally have to pay high rents for a limited number of hours there. Considering that we could not offer financial compensation for the interviews, many women felt disturbed by the request, even if it took only 10 minutes, as it could mean the loss of a potential client.
Third, most women working as sex workers often hide their occupation from friends and family members. When working, they generally use other names, thereby enacting the role of their sex worker persona. This makes it harder from a research perspective to ask questions that might touch upon their more private experiences outside their role as a sex worker. For instance, the probability of finding a robust indicator of their real-life satisfaction or health may be rather low.
Fourth, when visiting brothels, there was another complication that emerged for the researcher- in-field. Most people who go to brothels want to be guaranteed privacy. This, however, can be disturbed if it becomes clear that an observer who is not part of the milieu is in the brothel. For this reason, most attempts to talk to prostitutes within brothels failed.
Lastly, a major limitation in the data gathering process was the language barrier between us and many sex workers. The lacking language skills were an important indicator to understand that these women were less likely to have spent a long time in Germany. However, it also made it difficult to ensure that they understood the questions posed in the survey. In some cases, sex workers with better German skills translated on behalf of their colleagues, which in turn may have caused some inaccuracies.
Findings
On sex work conditions and the impact of changes in prostitution law in France
By surveying sex workers, we could not find any evidence for our working hypothesis. The change of the French prostitution law did not seem to increase the number of French people coming to Germany to buy sex. From the interviews with sex workers that we carried out, all but one assessed the number to be the same, while the last one, based in Offenburg, stated that there had been an increase in tourism from France. Neither had there been any noticeable difference in the number of customers from any of the countries listed in our questionnaire: the US, the UK, France, Italy, Spain, and Russia.
A possible reason for this seemingly minimal change in the influx of French customers was suggested by a health expert working both in France and in Germany with sex workers. According to her, there had been very few cases of prosecution of prostitution customers despite in France the law (Kehl/Straßburg BE0009). This can be disputed though, as the Coalition Against Prostitution (2017) reported that 937 sex buyers were arrested in the first year following the implementation of the law, rising to 4000 by early 2019 according to the Fondation Scelles (2019). Where most of these arrests have happened is not disclosed though. If there are geographical differences across France, it could mean that there are regions in France, possibly bordering Germany, where it may still be safe enough for sex buyers not feeling the need to go elsewhere. Should that be the case, then it could be a reason why no increase in the German sex work market can be detected.
Asked about more general changes taking place since 2016, sex workers and health experts reported that the economic conditions for prostitutes had worsened considerably. Five out of the 17 sex workers, based in different cities, reported that prices for their services had dropped. According to them, this was related to two recent developments within the market. Firstly, more and more women from Eastern European countries such as Bulgaria and Romania were working as sex workers. Considering the comparatively low salaries in these countries, these sex workers tend to be more willing to compete by lowering their prices. Secondly, according to sex workers, customers claim to have less money and thus bargain more.
From the questions posed on our questionnaire, we are unable to clearly identify whether the prices had been falling since 2016 or if it is a continuation of a more long-term trend. Although several women indicated that the decline in prices was recent, those who did had only worked for less than five years.
When responding to questions regarding health and overall life satisfaction, half of the interviewed sex workers claimed that they felt completely safe while working, against almost a quarter that did not. Looking more closely at the ten sex workers that said they had been working in milieu for at least five years, just three of them claimed to feel safe. Therefore, it seems that sex workers who have worked longer in the milieu feel less confident about their safety.
Overall, the sex workers who worked predominantly in the context of street prostitution reported feeling less safe, and also a lower overall life satisfaction, than those in brothels. When asked about their personal health, no sex worker reported to be unwell, but six of them refused to answer this question.
Counsellor Inputs
Migratory flows from Eastern Europe
Many of the counsellors we interviewed seemed to agree that the total number of active sex workers did not necessarily differ since the implementation of the ProstG in 2002. However, those who had been working in the field for many years said they had noticed the proportion of foreign-born prostitutes rising significantly with the implementation of the 2001 law. All but two of the seven counsellors we interviewed estimated that the share of non-German sex workers was above 50 per cent. Those of the other view still believed it to be above a quarter. An earlier policy report by the Institute for East and Southeast European Studies from August 2015 confirmed this belief (Petrungaro and Selezneva, 2015). The fact that most of our counsellors found prostitutes to be majority non-German corresponds with earlier findings that showed this share for female sex workers rising from 52% in 1999 to 63% in 2008 (TAMPEP, 2008).
What they did all agree on, however, was that Eastern Europe was the most common background of foreign sex workers, which is also the case in the data we gathered. More specifically, women from Bulgaria and Romania form a majority within the group of sex workers in Germany who recently took up this profession. Women from Asian countries, such as Thailand, as well as from Latin America were observed to be the second-largest group of people working as sex workers by counsellors. Though, they were far fewer than their Eastern European colleagues. For example, in Strasbourg the most frequent nationality among sex workers was Bulgarian (Straßburg/Kehl, BE0004). Around Saarbrucken, we got to talk predominantly to Romanian and Bulgarian sex workers.
Our survey of sex workers indicated that the high share of Bulgarian and Romanian sex workers was to be found especially in street prostitution contexts. In brothels, we found a more diverse ethnic composition (Saarbrucken, BE0003). Some counsellors, for instance, one in Kehl, claimed that this shift towards more and more women from Bulgaria and Romania is strongly correlated with the incorporation of Eastern European countries in the EU. For sex workers, this meant that they were particularly vulnerable in the transition phase that new member states had to go through after becoming an EU member but before being allowed unrestricted freedom of movement, as they were not eligible for the protections in the 2002 German law (Kehl/Straßburg, BE0009).
According to a counsellor working with sex workers in Trier, the socio-economic background of sex workers from Romania and Bulgaria varies. Some of the women are students in their home countries and thus highly qualified in the labour market. They often work in rather high-priced brothels. At the same time, there are also women who are living under very poor conditions (Trier, BE0008).
A further finding from our interviews was in contrast to a common belief that sex workers migrate to Germany. Counsellors reported that it is very common for sex workers to have a permanent base in their origin country and only travel to Germany, as well as Austria and Switzerland, in order to earn a living and support their families for shorter periods (Trier, BE0008). There are some women, however, who do not have the know-how to organise their own trips, and thus, stay at one place for longer and end up settling down in Germany (Trier, BE0008). Often, the families and close acquaintances back home know little about the occupation of the women.
From the interviews, we got to understand more about the mobility of sex workers. Sex workers that come to Germany commonly also travel throughout the country to pursue work. This makes them different from domestic sex workers, who are usually based in one place and do not travel around far away to pursue work. According to a social worker in Dortmund (BE0006), the police refers to the high mobility of foreign prostitutes within German borders as ‘prostitution tourism.’
Our survey showed a certain (albeit weak) relationship between sex workers’ country of origin and the number of years they had been working in Germany. Of the 17 respondents, those who stated to have worked in Germany for less than 3 years were exclusively Romanian and Bulgarian nationals. The over-representation of Romanian nationals also strengthens the view that it is among the most dominant origin countries in the current German prostitution market. For Bulgaria, our survey did not have a similar over-representation.
In Saarbrucken, counsellors reported that with the change of the visa-rules for Ukraine, more and more Ukranian women started working as sex workers in and surrounding Saarbrucken. However, according to one counsellor, none of them showed indications of being victims of human tracking (BE0003). In the tracking data from the BKA, there was an uptick in the number of Ukrainian victims in 2016, 22 compared to 2 for 2015. However, for the most recent report, Ukraine did not feature and we are therefore unable to say what the situation is like now.
Origin Countries of Victims
Of the information that we could gather from our interviews, there were some things that stood out to us. Repeatedly, Eastern Europe and West Africa were mentioned as the primary origins of tracking victims. Particularly one country was mentioned repeatedly: Nigeria.
One of our counsellors in Saarbrucken noted that women from Nigeria were more likely to be victims of human tracking. To get from Nigeria to Germany is generally rather difficult and they are therefore more exposed to be exploited by trackers (Saarbrucken, BE0003). In Freiburg we met with another counsellor who also emphasised the plight of Nigerian women, going so far as to say that since the refugee crisis, human tracking patterns have changed and Nigerian sex workers in Germany are mostly always victims of human tracking (Freiburg, BE0007). A counsellor working in Kehl and Strassbourg also noted how the refugee crisis has been used to bring vulnerable women from Nigeria to Germany. Summarising, it seems to be the case that the poorer the country, the more likely it is that the woman you encounter in this milieu is a victim of human tracking (Kehl/Straßburg BE0009).
In Dortmund, one counsellor noted that West African (and Nigerian) women have often been tracked elsewhere too (Dortmund, BE0006). A fellow counsellor working in Kehl and Strassbourg gave a similar account. Probably throughout the refugee crisis, women from non-EU states have been particularly over-represented. This does not mean that there are not any EU-citizens who are victims of human tracking, but right now, the biggest stream of human tracking victims are Nigerian women, who usually apply for asylum in Germany. They have come from Nigeria, often through Libya, then to Italy, and further on to other EU states. Once they reach Germany, some seem to attempt to save themselves from further exploitation, but sometimes they remain under the control of their trackers (Kehl/Straßburg BE0009).
In Heilbronn, one expert was keen to also discuss the German victims of tracking, who according to BKA statistics made up around a quarter of all victims. These usually fall prey to a partner who uses the ‘lover boy method’ making a woman, or usually young girl, fall in love with him and later forcing her into prostitution (Heilbronn, BE0002).
Changing characteristics of human tracking in Germany over time
In Saarbrucken, one counsellor also noted how the nature of human tracking has changed over the past 10–15 years, with trackers becoming more and more subtle. Where they before were much more violent towards their victims, they seem to have adopted strategies in order to hide the signs of violence and tracking better and instead apply more psychological violence. According to this person, it is also increasingly common for a family member or partner to be involved in the tracking. Overall, the lines drawn between prostitution and human tracking appear to become increasingly blurry and hard to detect. It makes it more difficult for victims to identify themselves as such, and in turn complicates legal processes as it is sometimes hard to prove whether a person working as a prostitute did it voluntarily or under coercion (Saarbrucken, BE0003).
As for the trend in human tracking, there appear to be differing opinions. In Heilbronn, one counsellor reported the number of cases rising. However, it is difficult to say if this is a reflection of an actual increase or of heightened awareness among the general public with the influx of West Africans (Heilbronn, BE0002). In Trier, however, the situation was perceived to have improved slightly, with one counsellor (BE0008) noting that stricter laws seem to lower the number of cases of human tracking.
Conclusion
Though interlinked, it is important that prostitution and human tracking for sexual exploitation are not associated by default. There are many sex workers that have practised their profession free from any undue influence by a third party. However, there is undoubtedly an abundance of evidence showing that this is not always the case. For the past few decades, however, policymakers have been attempting novel legislative approaches to create a clear delineation between these two phenomena.
The initiative behind this report was the interest in legislation introducing asymmetric punishment. As has been described above, the Nordic model’s approach in which only the party purchasing sex is committing an offence applies a similar sort of mechanism to the market for prostitution. Though this topic has been covered before, it still represents an area of rather novel research. Generating broad-brush findings applicable to all settings is unlikely, but findings that are relevant and robust for a specific time and place might be obtainable with the right methodology.
The introduction of new regulations regarding prostitution in France was therefore of great interest, as the proximity to other countries, especially Germany, where sex work has been a legal profession since 2002, could mitigate the issues on finding good data. The outset of our research efforts was a belief, now in hindsight perhaps rather naive, that data would be more readily available in the German regulationist institutional setting.
As was later discovered, data availability was an issue there too. Recent legislative efforts, namely the 2017 ProstSchG, might amend the most pressing concerns. However, it is more than likely that outright criminal activities are being perpetuated within the scope of a regulated market for sex. Finding methods that accurately track the effects of this law is an area of crucial research. In futures studies on this topic, remaining aware of unrelated events and changes that might possibly affect this policy is important. For larger countries, differences within a country also need to be considered. In the case of Germany, the federal structure allows Bundeslander to adopt slightly different policies.
We were not able to identify robust indicators that suggest a changing inflow of customers from France to Germany after the 2016 neo-abolitionist law in France. Assuming that the law has not been implemented and enforced sufficiently, this may also raise a question that affects the scope of this report. For instance, one may ask whether the ‘Nordic model’ of prostitution policy is easily implemented in different countries disregarding the cultural, institutional and social characteristics that originally brought it about and if it is reasonable to expect similar outcomes in each setting. Investigating which characteristics are important would improve any future changes of this kind elsewhere.
We can, however, confidently argue for additional research on cross-country sex work, as well as on the working conditions and financial situations those operating within the milieu face. Though we were unable to establish robust evidence on the interplay between sex work and human tracking, we were repeatedly told that general flows of migrant workers temporary working in Germany, mostly from Eastern Europe, affect conditions within the milieu. In this regard, sex work differs little from other business sectors that report similar concerns. This issue was particularly significant for street workers, especially since they already (and possibly because of this) reported to feel much less safe than those working in brothels.
On a practical level, our attempt to survey sex workers taught us just how difficult it is to gain the trust needed to obtain information from sex workers. Since prostitutes are only identifiable while they are working, remaining cautious of not being perceived as contributing to the experienced stigma sex workers face is imperative. For those not working in brothels and Laufhauser, there are generally also rather strict restrictions in place for when and where they may operate. Regardless of setting though, there tend to be time pressures whilst at work, meaning many approached to answer questions will decline that request. Building a dataset of any significant size will, therefore, require significant time and resources.
Having spent significant time working on this project, we have few clear-cut answers to give. Prostitution and human tracking may be intertwined. However, how they correlate and the causal relations between them remains a perplexing matter. By talking to people working in and around the milieu and improving the availability of data, the general understanding can be greatly improved. For instance, through the interviews we conducted in late 2017 with counsellors providing support to sex workers in Germany, the uptick in 2018 of detected Nigerian victims of human tracking for sexual exploitation was in part foreseen. This highlights the need for better and more data, as well as research covering sex work and human tracking so that both topics can be addressed appropriately and more effectively in the future.
References
- [Andor 2014] Andor, Laszlo: End of restrictions on the free movement of workers from Bulgaria and Romania. https://europa.eu/rapid/press-release_MEMO-14-1_en.htm. Version: January 2014
- [Bisschop et al. 2017] Bisschop, Paul; Kastoryano, Stephen; Klaauw, Bas van d.: Street Prostitution Zones and Crime. In: American Economic Journal: Economic Policy 9 (2017), Novem- ber, Nr. 4, 28–63. http://dx.doi.org/10.1257/pol.20150299. – DOI 10.1257/pol.20150299. – ISSN 1945–7731
- [BKA ] BKA: Bundeslagebilder Menschenhandel. https://www.bka.de/DE/ AktuelleInformationen/StatistikenLagebilder/Lagebilder/Menschenhandel/ menschenhandel_node.html
- [BMFSFJ 2007] BMFSFJ: Bericht der Bundesregierung zu den Auswirkungen des Gesetzes zur … Version:January2007.https://www.bmfsfj.de/bmfsfj/bericht-der-bundesregierung- zu-den-auswirkungen-des-gesetzes-zur-regelung-der-rechtsverhaeltnisse-der- prostituierten–prostitutionsgesetz—prostg-/80766. 2007. – Report
- [Cabinet of Ministers (Latvia) 2008] Cabinet of Ministers (Latvia): Regulations Regarding Restriction of Prostitution. https://likumi.lv/doc.php?id=169772. Version: 2008
- [CAP 2017] CAP: 937 sex buyer arrests: read how France has effectively shifted the burden in one year. Version:2017.https://mailchi.mp/bcac7356fd73/937-sex-buyer-arrests-read- how-france-has-effectively-shifted-the-burden-in-one-year?e=[UNIQID]
- [CBSS 2016] CBSS: Human Tracking – Baltic Sea Region Round-Up 2016. Version:2016. http://www.cbss.org/wp-content/uploads/2016/11/Human-Trafficking-Baltic-Sea- Region-Round-Up-2016.pdf. 2016. – Report
- [Chatzis 2018] Chatzis, Ilias: Human tracking – an overview. http://play.quickchannel. com/qc/play/ability543/26439/mainshow.asp?id=1tkisd. Version: April2018
- [Cho et al. 2013] Cho, Seo-Young; Dreher, Axel; Neumayer, Eric: Does Legalized Prostitution Increase Human Tracking? In: World Development 41 (2013), January, 67–82. http://dx. doi.org/10.1016/j.worlddev.2012.05.023. – DOI 10.1016/j.worlddev.2012.05.023. – ISSN 0305750X
- [Chrisafis 2012] Chrisafis, Angelique: How prostitution became France’s hottest social issue. In: The Guardian (2012), September. https://www.theguardian.com/society/2012/sep/24/ prostitution-france-hottest-social-issue. – ISSN 0261–3077
- [Conrad and Felden 2018] Conrad, Naomi; Felden, Esther: Inside the ’battery cage’: Prostitution in Germany. In: Deutsche Welle (2018), June. https://www.dw.com/en/inside-the- battery-cage-prostitution-in-germany/a-44350106
- [Crocoll 2013] Crocoll, Sophie: Und keiner will’s gewesen sein. In: Su ̈ddeutsche.de (2013), September. https://www.sueddeutsche.de/wirtschaft/peinlich-aber-erfolgreich-und- keiner-will-s-gewesen-sein-1.1782896
- [Cunningham and Shah 2014] Cunningham, Scott; Shah, Manisha: Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health / National Bureau of Economic Research. Version: July 2014. http://dx.doi.org/10.3386/w20281. 2014 (20281). – Working Paper
- [Czarnecki et al. 2014] Czarnecki, Dorothea; Engels, Henny; Kavemann, Barbara; Schenk, Wiltrud; Steffan, Elfriede; Tu ̈rnau, Dorothee: Prostitution in Germany – A Comprehensive Analysis of Complex Challenges. (2014). https://spi-research.eu/wp-content/uploads/ 2014/11/ProstitutioninGermanyEN_main.pdf
- [Destatis 2019] Destatis: Prostituiertenschutzgesetz: Verwaltungsvorg ̈ange noch im 32
Aufbau. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Soziales/
Prostituiertenschutz/prostituiertenschutzgesetz.html. Version:2019 - [European Commission 2011] European Commission: Frequently asked questions: The end of transitional arrangements for the free movement of workers on 30 April 2011. https://europa.
eu/rapid/press-release_MEMO-11-259_en.htm. Version: April2011 - [European Commission 2018] European Commission: Enlargement – transitional provisions.
https://ec.europa.eu/social/main.jsp?catId=466&langId=en. Version:2018 - [European Commission 2019] European Commission: Visa liberalisation with Moldova, Ukraine and Georgia. Version: 2019. https://ec.europa.eu/home-affairs/what-we-
do/policies/international-affairs/eastern-partnership/visa-liberalisation-
moldova-ukraine-and-georgia_en - [European Parliament 2016] European Parliament: Plenarsitzungsdokument A8- 0144/2016. http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+ REPORT+A8-2016-0144+0+DOC+PDF+V0//DE. Version:2016
- [Eurostat 2017] Eurostat: Volume indices of GDP and AIC per capita, 2016 (EU-28=100). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File: 2ChartVolume_indices_of_GDP_and_AIC_per_capita,_2016_(EU-28%3D100).png.
Version: 2017 - [Eurostat 2018] Eurostat: Population on 1 January. https://ec.europa.eu/eurostat/tgm/ table.do?tab=table&init=1&plugin=1&language=en&pcode=tps00001. Version:2018
- [FOCUS Online 2012] FOCUS Online: Jeder zehnte deutsche Mann geht ins Bordell. In: FOCUS Online (2012), October. https://www.focus.de/panorama/welt/umfrage-unter-deutschen- maennern-33-prozent-geben-geld-fuer-sex-aus_aid_834202.html
- [FOCUS Online 2015] FOCUS Online: Jeder vierte deutsche Mann hat schon fur Sex bezahlt. In: FOCUS Online (2015), January. https://www.focus.de/panorama/welt/laut-neuer- playboy-umfrage-jeder-vierte-zahlt-fuer-sex_id_4398001.html
- [Fondation Scelles 2019] Fondation Scelles: The abolition of prostitution, a French reality: a 3-year assessment of the 2016-444 Law. https://www.fondationscelles.org/en/news/270- the-abolition-of-prostitution-a-french-reality-a-3-year-assessment-of-the- 2016-444-law. Version: April2019
- [GfK 2015] GfK: Human tracking survey: Ukraine. http://iom.org.ua/sites/default/ files/pres_gfk_iom2015_ukraine_eng_fin_3_2.pdf. Version: June2015
- [GfK 2017] GfK: Survey on Migration and Human Tracking in Ukraine, 2017. 2017
- [Gunderson 2012] Gunderson, Constance: Human Tracking: The Tracking of Women in Northern Germany for the Purpose of Sexual Exploitation: Systemic Overview of Community Based Responses and Challenges. LIT Verlag Mu ̈nster, 2012. – ISBN 978–3–643–90263–4
- [Hernandez and Rudolph 2015] Hernandez, Diego; Rudolph, Alexandra: Modern day slavery: What drives human tracking in Europe? In: European Journal of Political Economy 38 (2015), Nr. C, 118–139. https://ideas.repec.org/a/eee/poleco/v38y2015icp118-139.html
- [Hubbard et al. 2008] Hubbard, Phil; Matthews, Roger; Scoular, Jane: Regulating sex work in the EU: prostitute women and the new spaces of exclusion. In: Gender, Place & Culture 15 (2008), April, Nr. 2, 137–152. http://dx.doi.org/10.1080/09663690701863232. – DOI
10.1080/09663690701863232. – ISSN 0966–369X - [ILO 2012] ILO: 21 million people are now victims of forced labour, ILO says. Version: June 2012. http://www.ilo.org/global/about-the-ilo/newsroom/news/WCMS_181961/lang-en/index.htm
- [IOM 2015] IOM: Addressing Human Tracking and Exploitation in Times of Crisis- Evidence and Recommendations for Further Action to Protect Vulnerable and Mobile Populations. https://publications.iom.int/books/addressing-human-trafficking-and-
exploitation-times-crisis-evidence-and-recommendations-0. Version: December2015 - [Jakobsson and Kotsadam 2013] Jakobsson, Niklas; Kotsadam, Andreas: The law and economics of international sex slavery: prostitution laws and tracking for sexual exploitation. In: European Journal of Law and Economics 35 (2013), February, Nr. 1, 87–107. http://dx.doi.org/10.
1007/s10657-011-9232-0. – DOI 10.1007/s10657–011–9232–0. – ISSN 0929–1261, 1572–9990 - [Junge 2001] Junge, B (signature B.: Prostitution: 1,2 Millionen M ̈anner am Tag. In: Der Tagesspiegel (2001), May. https://www.tagesspiegel.de/kultur/prostitution-1-2-millionen-maenner-am-tag/225870.html
- [Kavemann and Steffan 2013] Kavemann, Barbara; Steffan, Elfriede: Zehn Jahre Prostitutionsgesetz und die Kontroverse um die Auswirkungen | APuZ / Bundeszentrale fur politische Bil- dung. Version: 2013. http://www.bpb.de/apuz/155364/zehn-jahre-prostitutionsgesetz- und-die-kontroverse-um-die-auswirkungen. 2013 (9/2013). – Report
- [Kirchgaessner and Tondo 2018] Kirchgaessner, Stephanie ; Tondo, Lorenzo: Italy’s deal with Libya to ’pull back’ migrants faces legal challenge. In: The Guardian (2018), May. https://www.theguardian.com/world/2018/may/08/italy-deal-with-libya-pull- back-migrants-faces-legal-challenge-human-rights-violations. – ISSN 0261–3077
- [McPartland 2016] McPartland, Ben: Paying for sex in France: New law has been ’catastrophic’. https://www.thelocal.fr/20160928/prostitution-in-france-has-fining- clients-changed-anything. Version: September2016
- [Meyer and Nagel 2013] Meyer, S.; Nagel, L.-M.: 44 Huren in Deutschland – oziell – WELT. In: Welt am Sonntag (2013), November. https://www.welt.de/print/wams/article121482755/ 44-Huren-in-Deutschland-offiziell.html
- [New Zealand Government 2008] New Zealand Government: Report of the Prostitution Law Review Committee on the Operation of the Prostitution Reform Act 2003 / Ministry of Jus- tice. Version: May 2008. http://prostitutescollective.net/wp-content/uploads/2016/ 10/report-of-the-nz-prostitution-law-committee-2008.pdf. Wellington, New Zealand, May 2008. – Report. – ISBN 978-0-478-29052-7
- [OSCE 2016] OSCE: #LiveFree: OSCE Project Co-ordinator warns Ukrainians of human trafficking risks. https://www.osce.org/ukraine/285431. Version: December 2016
- [Petrungaro and Selezneva 2015] Petrungaro, Stefano; Selezneva, Ekaterina: Rights of Sex Workers in Germany: Shifting Focus from the Locals to the Migrants from Eastern and Southeastern Europe? In: IOC Policy Issues (2015), Nr. 8, S. 7
- [Plowright 2011] Plowright, David: Using Mixed Methods: Frameworks for an Integrated Methodology. Los Angeles: SAGE, 2011 https://trove.nla.gov.au/version/51348949. – ISBN 978–1–84860–108–6
- [RFI 2015] RFI: French Senate tears up controversial prostitution law. http://en.rfi.fr/ france/20150331-french-senate-overturns-controversial-law-against-prostitution. Version: March 2015
- [Scoular 2010] Scoular, Jane: What’s Law Got To Do With it? How and Why Law Matters in the Regulation of Sex Work. In: Journal of Law and Society 37 (2010), Nr. 1, 12–39. http:// dx.doi.org/10.1111/j.1467-6478.2010.00493.x. – DOI 10.1111/j.1467–6478.2010.00493.x. – ISSN 1467–6478
- [Sonnabend and Stadtmann 2018] Sonnabend, Hendrik; Stadtmann, Georg: Good intentions and unintended evil? Adverse effects of criminalizing clients in paid sex markets with voluntary and involuntary prostitution / European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics. Version: 2018. https://econpapers.repec.org/ paper/zbweuvwdp/400.htm. 2018 (400). – Discussion Paper
- [Stephani 2017] Stephani, Ilan: Jeder, der arbeitet, verkauft seinen K ̈orper. In: Die Zeit (2017), December. https://www.zeit.de/arbeit/2017-11/prostitution-arbeit- sexarbeit-gesellschaft. – ISSN 0044–2070
- [Sussman 2012] Sussman, Anna: Turkey: The Brothel Next Door. https://pulitzercenter. org/reporting/turkey-brothel-next-door. Version: May2012
- [TAMPEP 2008] TAMPEP: TAMPEP National Mapping Reports (Germany). Version:2008. https://webgate.ec.europa.eu/chafea_pdb/assets/files/pdb/2006344/2006344_d4_ deliverable_t8_annex_10_d_national_reports_mapping.pdf. 2008 (9). – Report. – 108–122 S.
- [Tavella 2008] Tavella, Anne M.: Sex Tracking and the 2006 World Cup in Germany: Concerns, Actions and Implications for Future International Sporting Events. In: Northwestern Journal of International Human Rights 6 (2008), Nr. 1, 23. http://scholarlycommons.law. northwestern.edu/cgi/viewcontent.cgi?article=1071&context=njihr
- [UN 2018] UN: Humanitarian response. http://www.un.org.ua/en/resident-coordinator- system/humanitarian-response. Version:2018
- [UN Treaties Collection 2019] UN Treaties Collection: 12 a Protocol to Prevent, Suppress and Punish Tracking in Persons, Especially Women and Children, supplementing the United Nations Convention against Transnational Organized Crime. https://treaties.un.org/Pages/ ViewDetails.aspx?src=TREATY&mtdsg_no=XVIII-12-a&chapter=18&lang=en. Version:2019
- [UNODC 2016] UNODC: Global Report on Tracking in Persons 2016. United Nations Pubns, 2016. – ISBN 978–92–1–130339–1. – OCLC: 1045553483
- [UNODC 2018a] UNODC: Country Profiles – Eastern Europe and Central Asia. Version:2018. https://www.unodc.org/documents/data-and-analysis/glotip/2018/GLOTIP_2018_ EASTERN_EUROPE_AND_CENTRAL_ASIA.pdf. 2018. – Report
- [UNODC 2018b] UNODC: Global report on tracking in persons 2018. 2018. – ISBN 978–92–1– 130361–2. – OCLC: 1102388905
- [US State Department 2017] US State Department: Tracking in Persons Report. Version: 2017. https://www.state.gov/reports/2017-trafficking-in-persons-report/. 2017. – Report
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 Political Economics of Long Run Development in Eastern Europe: Insights from the 2019 SITE Academic Conference
Thirty years after the fall of communism, many assume that the economic transition of Eastern Europe and the former Soviet States towards a system of market economy is complete. But the region faces new challenges, of both economic and political kind, which renders a thorough understanding the past even more important. This policy brief is based on the scientific contributions presented at the 7th SITE Academic Conference held at the Stockholm School of Economics from December 16th to December 17th, 2019. Organized by the Stockholm Institute of Transition Economics (SITE), the conference brought together academics from all over Europe and the United States to share and discuss their research on economic and political development in Eastern Europe.
The Imperial and Soviet Periods
In the first section of the conference, papers with a focus on the long-term history of Eastern Europe and its implications for more recent events were presented. Marvin Suesse presented his research on how the Russian State Bank financed Tsarist Russia´s belated industrialization, a question that had been discussed by historians, but never thoroughly analyzed quantitatively. By geo-coding historical manufacturing censuses around the turn of the century and using distance between bank branches and factory location, the causal impact of the expansion of the State Bank is estimated, revealing large effects on firm revenues and productivity. These effects are largest in areas where alternative means of financing were least available and where human capital was more abundant.
Natalya Naumenko presented her findings on the economic consequences of the 1933 Soviet famine, which in terms of casualties was extremely devastating. She uses the meteorological conditions a year earlier as an instrumental variable and finds that the famine, which was mostly a rural phenomenon, had a persistent negative effect on the urban population while the rural population recovered relatively quickly.
Gerhard Toews discussed the long-term consequences on regional development of the displacement of an estimated 3 million “enemies of the people”, political prisoners typically belonging to the elite of the society, into the gulags in the early years of the Soviet Union. Using archival data, he has constructed a large database describing the gulag population in terms of the shares of “enemies” relative to other prisoners and taking into account their socio-economic characteristics i.e. the much higher levels of education of the former group. Exploiting variation within gulags, the results suggest that a historically higher density of “enemies” means higher economic prosperity today as measured by nightlight intensity.
Taking another angle, Christian Ochsner investigated the effects of the Red Army´s occupation on post-war Europe, using the demarcation line crossing the Austrian state of Styria as a natural experiment. His conclusion is that even the temporary occupation affected the region’s long-term development, the main channel being age-specific migration.
Finally, Andreas Stegman offered an analysis of the effects of the 1972 East German Extended Visitors Program. The program reduced travel restrictions for West German visitors traveling to certain districts of East Germany. Using a geographic regression discontinuity design comparing similar districts with and without the program, he shows that included districts indeed received much more visits from West Germany and that their citizens were more likely to protest against the Communist government and less likely to vote for the ruling party. This suggests that face-to-face interaction can influence beliefs and attitudes in non-democratic regimes, in turn influencing individual behavior and societal outcomes during transition.
Corruption, Conflict and Public Institutions
Another topic of the conference was the current role of corruption, conflict, electoral fraud and public sector effectiveness for the region. Scott Gehlbach presented his most recent research on the ownership patterns and strategies of Ukrainian oligarchs before and after the Orange revolution. By mapping oligarchs to changing political leadership, he shows how firm owners in Ukraine take actions to protect their property depending on their connections with the current government. He finds that obfuscation of ownership behind holding companies and complicated structures is a potentially valuable strategy in this environment in general but becomes particularly important when an oligarch loses direct connections to the ruling regime.
Likewise, Timothy Frye analyzed election subversion by employers in Russia, Argentina, Venezuela, Turkey and Nigeria. He finds that in Russia, public sector employers and especially state-owned firms are more likely to influence their employees’ decision to vote than private companies. Furthermore, work place mobilization by employers in Russia is clearly negatively associated with the freedom of the press. Election subversion is more likely to be successful when the degree of dependence of the employee is high and the employer’s potential threats are credible. Among Russian firm officials, the most frequently named motivations for them to practice election subversion are the desire to improve their relationship with the authority and the intention to help their party.
Michal Myck studied the impact of the transition experience on economic development around the Polish-German border. Polish communities close to the border were economically backward at the beginning of the transition but could potentially benefit from trade opportunities with an opening towards the West. Using similar methods to those of Stegman above, and nightlight intensity as a measure of economic activity as for instance Toews, Myck finds significant evidence for economic convergence both between Germany and Poland, and between Polish border regions and the rest of Poland.
Vasily Korovkin presented his research on the impact of the conflict in Eastern Ukraine on trade in non-conflict areas in Ukraine, hypothesizing that the conflict may cause a trade diversion away from Russia, particularly so in areas with many ethnic Ukrainians. Using variation in the share of the Russian speaking population at the county level as well as detailed firm level export and import data, he finds that the decrease in trade with Russia is negatively correlated with the share of the Russian speaking population. Potential mechanisms include a decline in trust at the firm level and changes in local attitudes including consumer boycotts.
Finally, Tetyana Tyshchuk analyzed the effects of a Ukrainian public sector reform on civil servants’ capacity and autonomy. The reform created public policy directorates parallel to the regular bureaucracy in 10 ministries. Members of the directorates were hired based on a different procedure and different merits relative to regular public servants and received significantly higher salaries. Tyshchuk finds that the better paid civil servants indeed score higher on many, though not all, indicators of capacity and autonomy.
Information, Populism and Authoritarianism Today
The final important theme of the conference was the role of information and media, old and new, in today’s politics. In the event´s first keynote speech, Ruben Enikolopov analyzed the political effects of the Internet and social media whose low entry barriers and reliance on user-generated content make them decisively different from traditional media channels. On the one hand, this represents a chance for opposition leaders and whistleblowers to make their voice heard and may improve government accountability. On the other, these media may also become a platform for extremists. Enikolopov presented some of his work analyzing to what extent social media has contributed to fighting corruption in Russia. Using the timings of blog posts by the famous Russian opposition leader Alexei Navalny on corporate governance violations in state-owned companies, he shows that revelations resulted in an immediate drop in the price of the traded shares of the respective companies. He also finds evidence suggesting that Navalny´s blog posts resulted in management changes in these companies. In related papers, he exploits the spread of VKontakte (VK), the Russian version of Facebook, to better understand the influence of social networks on political activism, voting and the occurrence of hate crime. He finds that the spread of VK is indeed causally related to political protests, though not because it nurtures opposition to the government, but rather because it facilitates protest co-ordination. With respect to hate crime, he finds that social media only has an effect in areas where it falls on fertile grounds and where there already are high levels of nationalism. The tentative conclusion is that in Russia – as in Western countries – social media seems to have increased political polarization.
On a similar topic but taking a more theoretical approach, Galina Zudenkova investigated the link between information and communication technologies (ICT), regime contestation and censorship. In a game theoretical framework, where citizens use ICT both to learn about the competency of the government and to coordinate protests, governments can use different tools to censor information to increase their chances of survival. Zudenkova finds that less competent regimes are more likely to censor coordination, whereas intermediate regimes are more likely to focus on censoring content. These theoretical predictions are then tested using country level data.
The targeted use of information has also played a key role in Putin’s Russia according to Daniel Treisman. In his keynote speech, he argued that while the 20th century dictatorships were mainly based on violence and ideology, the 21st century has been characterized by a sizeable shift towards what he calls “informational autocracy”. Constructing a dataset on the methods used by authoritarian regimes to maintain power between 1946 and 2015, he shows that the use of torture and violence peaked among those dictators who took power in the 1980s and has declined since. Furthermore, he highlights a remarkable shift from topics of violence towards topics of economic competency in dictators’ speeches. However, Treisman finds that by instrumentalizing information, dictators fool the public “but not the elite”. In democratic regimes, those with tertiary education tend to rate their political leaders higher than people without tertiary education. In the new informational authoritarian regime, the opposite seems to be the case. According to Treisman, this is because the “informed elite” has a better understanding of the political reality in places where the media is censored, Putin’s Russia being a good example. Treisman concluded that this new model of authoritarianism has become the prevalent model outside of Europe and today also has its advocates inside the European Union.
The conference ended with a final keynote speech by Sergei Guriev on the political economy of populism. Using existing definitions, he first confirmed that Europe has seen a rise in right-wing populism in the last 20 years. Secular trends, such as globalization and new communication technology, but also the recent global financial crisis, are driving factors behind the rise of populist parties. For instance, analyzing regional variation in voting patterns suggests that the Brexit vote was primarily driven by economic motives rather than by anti-immigrant sentiments. Ironically, though, most evidence suggests that populist governments have a below-average economic performance once in office, the US and Poland being notable exceptions. A key point of Guriev’s presentation was that populism seems to be a good method to obtain power, but, once in power, populists tend to be less successful in promoting citizen welfare. These findings seem to be of high importance given the increasing public support for populist parties around the world and in parts of Eastern Europe
The conference was very well received and on behalf of SITE, the authors would like to express their appreciation to all speakers and participants for sharing their knowledge and to Riksbankens Jubileumsfond for financial support. For those interested to learn more about the papers summarized very briefly above, please visit the conference website and the presenters’ websites as indicated in the text and here below.
Speakers at the Conference
Andreas Stegman, briq – Institute on Behavior and Inequality
Christian Ochsner, CERGE-EI and University of Zurich
Daniel Treisman, University of California, Los Angeles
Galina Zudenkova, TU Dortmund University
Gerhard Toews, New Economic School Moscow
Marvin Suesse, Trinity College
Michal Myck, CenEA
Natalya Naumenko, George Mason University
Ruben Enikolopov, New Economic School Moscow
Scott Gehlbach, University of Chicago
Sergei Guriev, Sciences Po Paris
Tetyana Tyshchuk, Kyiv School of Economics
Timothy Frye, Columbia University
Vasily Korovkin, CERGE-EI