Author: Anna Pluta

Piedāvāto pabalstu un nodokļu reformu ietekme uz iedzīvotāju ienākumiem: kas būs galvenie ieguvēji?

Nesen publiskajā telpā parādījās informācija par nodokļu un pabalstu reformām, kuras Latviju varētu sagaidīt nākamajos gados. Šajā rakstā mēs apskatam dažas no piedāvātajām reformām no ienākumu sadalījuma un nevienlīdzības viedokļa.

Atbalsts ģimenēm ar bērniem

– Ģimenes valsts pabalsta paaugstināšanas rezultātā visvairāk iegūst trūcīgākās mājsaimniecības: viņu ienākumi pieaug vidēji par aptuveni 3.5%, bet turīgāko mājsaimniecību ienākumi – mazāk kā par 0.5%;

– Ja prioritāte ir atbalstīt trūcīgas mājsaimniecības un budžeta līdzekļi ir ierobežoti, IIN atvieglojuma par apgādājamiem bērniem aizstāšana ar jaunu pabalstu strādājošajiem vecākiem ir efektīvāks risinājums nekā ģimenes valsts pabalsta palielināšana.

Diferencētā neapliekamā minimuma (DNM) reformas

– Tās samazinās darbaspēka nodokļu slogu, bet neviena no šīm reformām būtiski neietekmēs ienākumu nevienlīdzības rādītājus, jo lielākie ieguvēji ir mājsaimniecības ar vidējiem rīcībā esošajiem ienākumiem. Trūcīgākās un turīgākās mājsaimniecības iegūs salīdzinoši maz.

IIN likmju izmaiņas un diferencētā neapliekamā minimuma aizstāšana ar fiksētā apmēra neapliekamo minimumu

– No piedāvātajām IIN izmaiņām iegūs visas iedzīvotāju grupas, bet lielākās IIN reformu ieguvējas ir salīdzinoši turīgas Latvijas mājsaimniecības, kas atspoguļosies arī ienākumu nevienlīdzības pieaugumā;

– Šis augsto algu saņēmēju ieguvums galvenokārt nāk no neapliekamā minimuma palielinājuma, jo piedāvātais fiksētais neapliekamais minimums pārsniedz maksimālo DNM (EUR 300), kas tiek piemērots 2020. gadā.

Call for papers for the 2nd conference “Corruption, Tax Evasion and Institutions” is now open

The conference will take place on 1-2 October, 2020, at the Stockholm School of Economics in Riga (SSE Riga), following the initial event that took place in May 2017 (see the 2017 conference program here). The conference aims to promote and diffuse high quality economic research on the mechanisms driving corruption and tax evasion, their relationships with institutions and their consequences on economic outcomes. Submit your papers until 14 June. More information

Fertility enhancing policies in Latvia

Economics of Childbearing and Pronatalist Policies. A new FROGEE policy brief on the situation in the region, containing an overview of the situation in Latvia written by Nicolas Gavoille (SSE Riga, BICEPS), Anna Pļuta (BICEPS) and Anna Zasova (BICEPS)

Latvia is a country with a relatively low fertility rate. In the a late nineties and the first half of the 2000s, a persistently negative net migration ratio and a declining population made  the fertility rate a particularly sensitive political issue and Latvia introduced a range of fertility enhancing programs, most of which are available to parents with children nowadays. In this brief, we argue that while these programs are likely to have played an important role in encouraging fertility, the exact impact is hard to identify. The reason is that the government spending on  family and children-related measures in recent years had a strong procyclical pattern , which makes it practically impossible to disentangle the effect of the policies from the effect of the economic cycle.

The full Report is available here.

Reports from Armenia, Belarus, Georgia, Poland, Ukraine and Russia are available in English and the national languages here.

About FROGEE Policy Briefs

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

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

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

Gender Gaps in Wages and Wealth: Evidence from Estonia

This policy brief introduces two related papers examining two types of gender gaps in Estonia. First, it presents the work of Vahter and Masso (2019), who study the wage gender gaps in foreign-owned firms and compare this gap with the situation in domestic ones. Then it summarizes a paper of Meriküll, Kukk, and Rõõm (2019), who focus on the wealth gender gaps and highlight the role of entrepreneurship in this gap.

Gender inequality is not only a moral issue. An extensive literature has highlighted the cost of gender inequality in terms of economic (in)efficiency. Most of the academic work has, however, focused on either the US and Western Europe or developing countries. Research focusing on systematic gender disparities in Eastern Europe is rather scarce. Yet, there is much to be learned from this region. The purpose of the FROGEE (Forum for Research on Gender in Eastern Europe) project is to study several issues related to gender inequality in former socialist countries.

This policy brief summarizes two papers presented at the 2nd Baltic Economic Conference at the Stockholm School of Economics in Riga, on June 10-11, where a special session on gender economics was held with the support of the FROGEE project. The event, organized by the Baltic Economic Association (see balticecon.org), gathered more than 85 researchers from the Baltics and all over the world. These two papers focus on Estonia, one of the most successful economies among the transition countries, where however the gender wage gap is among the largest in the European Union.

Firm ownership and gender wage gap

An important source of wage inequality originates in firm-specific pay schemes (see for instance Card et al. 2016). Understanding the characteristics of firms associated with a gender pay gap is thus a necessary step to design relevant policy responses. In a paper entitled “The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap”, Jaan Masso and Priit Vahter, both at the University of Tartu, compare the situation in foreign-owned firms with domestic ones. The fact that foreign-owned firms provide on average higher wages to their employees is well documented. However, the question of whether this premium differs between men and women remains largely overlooked.

A potential channel linking firm ownership and gender wage gap is the transfer of management practices from the home country of the investor to the affiliate. The great majority of FDI in Estonia originates from Finland and Sweden, two countries that regularly top international rankings on gender equality and that have set the fight against gender inequality as a top priority. Observing a lower level of gender wage gap in firms owned by Swedish and Finnish capital would suggest the existence of such a mechanism, even if there is evidence that Scandinavian countries do not stand out in a positive way when it comes to women in the top of the distribution (see for instance Boschini et al., 2018, and Bobilev et al., 2019).

On the other hand, Goldin (2014) has shown that a large part of the gender wage gap in the US can be explained by differences in job “commitment”: firms disproportionately reward workers willing to be available 24/7, more flexible regarding business trips, spending longer hours in the office, etc. Such workers happen to be more often men than women. Multinational firms may require such commitment and flexibility to a larger extent than domestic firms, due for instance to their higher exposure to international competition. This would imply a larger gender pay gap in foreign-owned firms compared to local firms.

To investigate this issue, Masso and Vahter (2019) rely on Estonian administrative data, providing information on the whole universe of workers and of firms in the country between 2006 and 2014. This matched employer-employee dataset allows to track the wage of individuals over the years, but also to compare wages both across and within firms. It thus becomes possible to estimate the gender wage gap at the firm level (controlling for relevant individual-level factors affecting wages, such as age and experience), and then to check whether this measure systematically differs between domestic and foreign-owned firms.

However, simply comparing the gender pay gap between these two types of firms could lead to spurious conclusions. Foreign-owned firms have on average different characteristics than domestic ones: they do not operate in the same sectors, they do not have the same size nor the same productivity. To overcome this issue, the authors rely on a matching method: for each foreign-owned firms, they match a domestic firm with similar (observable) characteristics.

They find that in domestic firms, women are on average paid 19% less than men, even after accounting for many other factors associated with wage. In foreign-owned companies, both men and women are better paid. However, both genders do not benefit from the same premium: men are paid roughly 15% more in foreign-owned firms, whereas the premium for women is only 5.4%. This difference implies an even larger gender wage gap in multinational firms. To illustrate the economic significance of these results, for a man and a woman earning a monthly wage of 1146 euros (the average gross wage in Estonia in 2016), the premium for switching from a domestic to a foreign-owned firm is respectively 171 and 62 euros. Further, they provide some evidence that lower “commitment” is associated with a stronger wage penalty in foreign-owned firms. All in all, these results suggest that there is not necessarily a relationship between a multinational wage policy (especially in its gender wage-gap dimension) and the gender norms prevailing in its country of incorporation.

Gender and wealth gap

The vast majority of academic papers studying gender inequality focuses on the wage gap. But gender inequality can affect other types of economic outcomes, such as labor force participation, unemployment duration, or wealth. The latter is of particular interest since wealth can greatly contribute to empowerment. Merike Kukk, Jaanika Meriküll and Tairi Rõõm, all at the Bank of Estonia, extend the literature with a paper entitled “What explains the Gender Gap in Wealth? Evidence from Administrative Data”. This paper is one of the first to study the gender wealth gap in a post-transition country. The literature on the gender wealth gap is rather scarce because of a lack of suitable data: wealth measures are often computed at the household level, while individual-level data is necessary for such a study.

The main aim of this paper is to depict a precise portrait of this phenomenon in Estonia. In particular, the authors do not simply estimate the overall wealth gap but investigate the magnitude of the gap across the wealth distribution. In other words, is there a difference between the poorest men and the poorest women? Or on the other side of the distribution, are the richest men more wealthy than the richest women?

For this purpose, Kukk, Meriküll and Rõõm combine administrative individual-level data on wealth with survey results. The administrative data are generally considered of much better quality than the other, but they do not provide a lot of additional information on individuals. On the other hand, survey data provide a wealth of information about individual characteristics. Merging allows getting the best of both worlds. Regarding the methodology, the authors use unconditional quantile regression to track gender differences at different deciles of the wealth distribution. They further decompose this “raw” gender gap into two components: the “explained” part, i.e., the part of the gap resulting in differences in characteristics between men and women (demographics, education, etc.), and the “unexplained” part.

This study estimates the raw, unconditional gender wealth gap in Estonia to be 45%, which is of similar magnitude as in Germany. Interestingly, this difference is essentially driven by differences in the top of the distribution: there is a large gap between the richest men and the richest women. This “raw” difference is however explained by a single variable: self-employment, as men are much more likely to have business assets than women. Once controlling for the entrepreneurship status, the wealth difference between the richest Estonians becomes insignificant. This suggests the need to support policies encouraging female entrepreneurship and to remove barriers particularly affecting women. For instance, the literature has previously pointed out that women have less access to external sources of capital than men (e.g., Aidis et al., 2007). Such distortions can ultimately result in a wealth gap at the top of the distribution, as documented by this paper.

In addition, the literature has proposed several mechanisms that could result in gender-specific patterns of wealth accumulation. The simplest channel is through the wage gap, as it can be seen as the accumulation of the wage gap over time (e.g. Blau and Kahn, 2000). The authors thus compare the gender gaps in wealth and income. They uncover a strong wage gap, with men earning significantly more than women starting at the 6th decile: the higher we go in the income distribution, the larger the wage gap. How to reconcile this finding with the absence of a wealth gap conditional on entrepreneurship status? A possible explanation suggested by the authors is that women simply accumulate wealth better than men do.

Conclusion

These two papers illustrate two different mechanisms explaining gender-specific economic outcomes. The larger wage gap observed in multinational companies can be explained by a stronger commitment penalty for women, mostly because of childcare. This asks for two potential policy interventions. First, the development of childcare could facilitate the reduction in the “commitment gap” that disrupts women’s careers. Second, institutions could support a more flexible repartition of childcare responsibilities. Note however that Estonia already has the longest duration of leave at full pay (85 weeks), and that this leave can be freely split between parents. As for the wealth gap at the top of the wealth distribution, it can to a large extent be explained by the entrepreneurship status. This difference could partly be explained by differences in preferences and risk-aversion, which would require long-run policies to be mitigated. But in the short run, there is room for specific policies supporting female entrepreneurship and removing barriers particularly affecting women, such as a tighter credit constraint.

References

  • Aidis, R., Welter, F., Smallbone, D., & Isakova, N. (2007). Female entrepreneurship in transition economies: the case of Lithuania and Ukraine. Feminist Economics13(2), 157-183.
  • Blau, F. D., & Kahn, L. M. (2000). Gender differences in pay.  Journal of Economic perspectives14(4), 75-99.
  • Bobilev, R., Boschini, A., & Roine, J. (2019). Women in the Top of the Income Distribution: What Can We Learn From LIS-Data?. Italian Economic Journal, 1-45.
  • Boschini, A., Gunnarsson, K., & Roine, J. (2018). Women in Top Incomes: Evidence from Sweden 1974-2013. IZA Discussion Paper No. 10979 .
  • Card, D., Cardoso, A. R., & Kline, P. (2015). Bargaining, sorting, and the gender wage gap: Quantifying the impact of firms on the relative pay of women. The Quarterly Journal of Economics131(2), 633-686.
  • Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review104(4), 1091-1119.
  • Meriküll, J., Kukk, M., & Rõõm, T. (2019). What explains the gender gap in wealth? Evidence from administrative data. Bank of Estonia WP No. 2019-04.
  • Vahter, P., & Masso, J. (2019). The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap. Review of World Economics155(1), 105-148.

Development of a methodology for expanding the range of territorial statistics (2019–2020)

Duration: July 2019 – May 2020

Research team: Sergejs Gubins (principal investigator), Anna ZasovaMarija KruminaAnna Pluta

Funded by Central Statistical Bureau of Latvia

Research results were presented by Sergejs Gubins at the seminar “Territorial Statistics and Their Application” (latv. – “Teritoriālā statistika un tās izmantošanas iespējas”) on September 24, 2020.

 

Projects funded by the Central Statistical Bureau of Latvia

CURRENT PROJECTS

Digitalization issues in national accounts: digital intermediary platforms in Latvia

Duration: September 2021 – November 2022

Research team: Sergejs Gubins (principal investigator).

Project information: read more about the project.

 

RECENTLY COMPLETED PROJECTS

Development of a methodology for expanding the range of territorial statistics

Duration: July 2019 – May 2020

Research team: Sergejs Gubins (principal investigator), Anna Zasova, Marija Krumina, Anna Pluta.

Project information: read more about the project.

Sex, Drugs and Bitcoin: How Much Illegal Activity Is Financed Through Cryptocurrencies?

Using novel approaches that exploit the blockchain to identify illegal activity, we estimate that around $76 billion of illegal activity per year is financed through payments in bitcoin (46% of bitcoin transactions). This staggering number is close to the scale of the US and European markets for illegal drugs and suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.”

Cryptocurrencies have grown rapidly in price, popularity, and mainstream adoption. The total market capitalization of bitcoin alone exceeds $150 billion as of July 2018, with a further $150 billion in over 1,800 other cryptocurrencies. The numerous online cryptocurrency exchanges and markets have a daily dollar volume of around $50 billion. Over 170 ‘cryptofunds’ have emerged (hedge funds that invest solely in cryptocurrencies), attracting around $2.3 billion in assets under management. Recently, bitcoin futures contracts have commenced trading on the major US derivatives exchanges (CME and CBOE), catering to institutional demand for trading and hedging bitcoin. What was once a fringe asset is quickly maturing.

The rapid growth in cryptocurrencies and the anonymity that they provide users has created considerable regulatory challenges, including the use of cryptocurrencies in illegal trade (drugs, hacks and thefts, illegal pornography, even murder-for-hire), potential to fund terrorism, launder money, and avoid capital controls. There is little doubt that by providing a digital and anonymous payment mechanism, cryptocurrencies such as bitcoin have facilitated the growth of ‘darknet’ online marketplaces in which illegal goods and services are traded. The recent FBI seizure of over $4 million of bitcoin from one such marketplace, the ‘Silk Road’, provides some idea of the scale of the problem faced by regulators.

In a recent research paper (Foley, Karlsen, and Putnins, 2018), which is forthcoming in the Review of Financial Studies, we quantify the amount of illegal activity that involves the largest cryptocurrency, bitcoin. As a starting point, we exploit several recent seizures of bitcoin by law enforcement agencies (including the US FBI’s seizure of the Silk Road marketplace) to construct a sample of known illegal activity. We also identify the bitcoin addresses of major illegal darknet marketplaces. The public nature of the blockchain allows us to work backwards from the law enforcement agency bitcoin seizures and the darknet marketplaces through the network of transactions to identify those bitcoin users that were involved in buying and selling illegal goods and services online. We then apply two econometric methods to the sample of known illegal activity to estimate the full scale of illegal activity. The first exploits the trade networks of users to identify two distinct ‘communities’ in the data—the legal and illegal communities. The second exploits certain characteristics that distinguish between legal and illegal bitcoin users, for example, the extent to which individual bitcoin users take actions to conceal their identity and trading records, which is a predictor of involvement in illegal activity.

We find that illegal activity accounts for a substantial proportion of the users and trading activity in bitcoin. For example, approximately one-quarter of all users (26%) and close to one-half of bitcoin transactions (46%) are associated with illegal activity. The estimated 27 million bitcoin market participants that use bitcoin primarily for illegal purposes (as at April 2017) annually conduct around 37 million transactions, with a value of around $76 billion, and collectively hold around $7 billion worth of bitcoin.

To give these numbers some context, the total market for illegal drugs in the US (Kilmer et al, 2014) and Europe (EMCDDA, 2013) is estimated to be around $100 billion and €24 billion annually. Such comparisons provide a sense that the scale of the illegal activity involving bitcoin is not only meaningful as a proportion of bitcoin activity, but also in absolute dollar terms. The scale of illegal activity suggests that cryptocurrencies are transforming the way black markets operate by enabling ‘black market e-commerce’. In effect, cryptocurrencies are facilitating a transformation of the black market much like PayPal and other online payment mechanisms revolutionized the retail industry through online shopping.

In recent years (since 2015), the proportion of bitcoin activity associated with illegal trade has declined. There are two reasons for this trend. The first is an increase in mainstream and speculative interest in bitcoin (rapid growth in the number of legal users), causing the proportion of illegal bitcoin activity to decline, despite the fact that the absolute amount of such activity has continued to increase. The second factor is the emergence of alternative cryptocurrencies that are more opaque and better at concealing a user’s activity (e.g., Dash, Monero, and ZCash). Despite these two factors affecting the use of bitcoin in illegal activity, as well as numerous darknet marketplace seizures by law enforcement agencies, the amount of illegal activity involving bitcoin at the end of our sample in April 2017 remains close to its all-time high.

In shedding light on the dark side of cryptocurrencies, we hope this research will reduce some of the regulatory uncertainty about the negative consequences and risks of this innovation, facilitating more informed policy decisions that assess both the costs and benefits. In turn, we hope this contributes to these technologies reaching their potential. Our work also contributes to understanding the intrinsic value of bitcoin, highlighting that a significant component of its value as a payment system derives from its use in facilitating illegal trade. This has ethical implications for bitcoin as an investment. Third, the techniques developed in the paper this brief is based on can be used in cryptocurrency surveillance in a number of ways, including monitoring trends in illegal activity, its response to regulatory interventions, and how its characteristics change through time. The methods can also be used to identify key bitcoin users (e.g., ‘hubs’ in the illegal trade network) which, when combined with other sources of information, can be linked to specific individuals.

References

Acknowledgment: This Policy Brief is based on a recent research paper (Foley, Karlsen, and Putnins, 2018), which is forthcoming in the Review of Financial Studies, published by Oxford University Press and the Society for Financial Studies.

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.

Merging Latvian EUROMOD with CGE model (2017-2019)

Duration: 2017 – 2019

Research team from BICEPS: Anna Pļuta, Anna Zasova, Oļegs Matvejevs

Research team from the Bank of Latvia: Konstantins Benkovskis and Ludmila Fadejeva

Funded by the Bank of Latvia

 

Publication in The Annals of Regional Science

Congratulations to BICEPS research fellow Sergej Gubin on publishing article “Does new information technology change commuting behavior?” in The Annals of Regional Science! The article estimates the aggregate effect of the Internet and IT on commuting distances in the Netherlands. The results show that the effect is too small to be identified and likely to be absent.