Location: Baltic countries
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 Economics, 13(2), 157-183.
- Blau, F. D., & Kahn, L. M. (2000). Gender differences in pay. Journal of Economic perspectives, 14(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 Economics, 131(2), 633-686.
- Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review, 104(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 Economics, 155(1), 105-148.
Liberal Democracy in Transition – The First 30 Years
This year marks 30 years since the first post-communist election in Poland and the fall of the Berlin Wall. Key events that started a dramatic transition process from totalitarian regimes towards liberal democracy in many countries. This brief presents stylized facts from this process together with some thoughts on how to get this process back on a positive track. In general, the transition countries that joined the EU are still far ahead of the other transition countries in terms of democratic development.
The recent decline in democratic indicators in some EU countries should be taken seriously as they involve reducing freedom of expression and removing constraints on the executive, but should also be discussed in light of the significant progress transition countries entering the EU have shown during the first 30 years of transition. The brief shows that changes in a democracy can happen fast and most often happen around elections, so getting voters engaged in the democratic process is crucially important. This requires politicians that engage the electorate and have an interest in preserving democratic institutions. An important question in the region is what the EU can do to promote this, given its overloaded political agenda. Perhaps it is time for a Greta for democracy to wake up the young and shake up the old.
This brief provides an overview of political developments in transition countries since the first post-communist elections in Poland and the fall of the Berlin Wall 30 years ago. It focuses on establishing stylized facts based on quantitative indices of democracy for a large set of transition countries rather than providing in-depth studies of a small number of countries. The aim of the brief is thus to find common patterns across countries that can inform today’s policy discussion on democracy in the region and inspire future studies of the forces driving democracy in individual transition countries.
The first issue to address is what data to use to establish stylized facts of democratic development in the region. By now, there are several interesting indicators that describe various aspects of democratic development, which are produced by different organizations, academic institutions and private data providers. In this brief, three commonly used and well-respected data providers will be compared in the initial section before we zoom in on more specific factors that make up one of these indices.
The big picture
The three indicators that we look at first are: political rights produced by Freedom House; polity 2 produced by the Polity IV project; and the liberal democracy index produced by the V-Dem project. Figures 1-3 show the unweighted average of these indicators for two groups of countries. The EU10 are the transition countries that became EU members in 2004 and 2007 and include Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland, Romania, Slovakia, and Slovenia. The second group, FSU12, are the 12 countries that came out of the Soviet Union minus the three Baltic countries in the EU10 group, so the FSU12 group consists of Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.
Figure 1. Freedom House
Source: Freedom House and author’s calculations
Note: Scale inverted, 1 is best and 7 worst score
Figure 2. Polity IV project
Source: Polity IV project and author’s calculations
Note: Scale from -10 (fully autocratic) to 10 (fully democratic)
Figure 3. V-Dem
Source: V-Dem project and author’s calculations
Note: Scale from 0 to 1 where higher is more democratic
All three indicators convey the message that the democratic transformation in the EU10 group was very rapid in the early years of transition and the indicators have remained at high levels since the mid-90s only to show some decline in the most recent years for two of the three indicators. The FSU12 set of countries have made much less progress in terms of democratic development and remain far behind the EU10 countries in this regard. Overall, there is little evidence at the aggregate level that the democratic gap between the EU10 and FSU12 groups is closing. While the average EU10 country is more or less a full-fledged democracy, the average FSU12 country is at the lower end of the spectrum for all three democracy measures.
The average indicators in Figures 1-3 obviously hide some interesting developments in individual countries and in the following analysis, we will take a closer look at the liberal democracy index at the country level. We will then investigate what sub-indices contribute to changes in the aggregate index in the countries that have experienced significant declines in their liberal democracy scores.
For the first part of the analysis, it is useful to break down the democratic development in two phases. The first phase is from the onset of transition (1989, 1991 or 1993 depending on the specific country) to the time of the global financial crisis in 2009 and the second phase is from 2009 to 2018 (the last data point).
Figure 4. Liberal democracy, the first phase
Source: V-Dem project and author’s calculations
Figures 4 and 5 compare how the liberal democracy indicator changes from the first year of the period (measured on the horizontal axis) to the last year of the period (on the vertical axis). The smaller blue dots are the individual countries that make up the EU10 group while the red dots are the FSU12 countries. The 45-degree line indicates when there is no change between start and end years, while observations that lie below (above) the line indicate a deterioration (improvement) of the liberal democracy index in a specific country.
In the first phase of transition (Figure 4), all of the EU10 countries increased their liberal democracy scores and the average increase for the group was almost 0.5, going from 0.26 to 0.74. This was a result of many of the countries in the group making significant improvements without any countries deteriorating. The FSU12 group had a very different development with the average not changing at all since the few countries that improved (Georgia and Ukraine) were counterbalanced by a significant decline in Belarus and a more modest decline in Armenia.
Figure 5. Liberal democracy, the second phase
Source: V-Dem project and author’s calculations
The very rapid improvement in the liberal democracy index in the EU10 countries in the first phase of transition came to a halt and also reversed in several countries in the second phase of transition. Of course, as they had improved so much in the first period, there was less room for further positive developments, but the rapid decline in some of the countries was still negative news. However, it does point towards that reform momentum was very strong in the EU accession process, but once a country had entered the union, the pressure for liberal democratic reforms has faded.
Overall, the EU10 average fell by 0.1 from 2009 to 2018. This was a result of declining scores in several countries. The particularly large declines in this period have been seen in Hungary (-0.28), Poland (-0.27), Bulgaria (-0.14), the Czech Republic (-0.14), and Romania (-0.12). Again, the average FSU12 score did not change much, although Ukraine (-0.2) put its early success in reverse and lost as much in this period as it had gained earlier.
Country developments
Since much of the current discussion centers on how democracy is being under attack, the figures name the countries that have seen significant declines in the liberal democracy score in the first or second phase of transition. Figures 6 and 7 show the time-series of the liberal democracy index in the countries with significant drops at some stage of the transition process.
Figure 6. FSU12 decliners
Source: V-Dem project and author’s calculations
In many countries, the drop comes suddenly and sharply, with the first and most prominent example being Belarus. There, it only took three years to go from one of the highest ranked FSU12 countries to fall to one of the lowest liberal democracy scores. In Poland, Romania, Bulgaria and Armenia, the process was also very rapid and significant changes happened in 2-3 years.
Figure 7. EU10 decliners
Source: V-Dem project and author’s calculations
In the Czech Republic and Hungary, the period of decline was much longer and in the case of Hungary, the drop was the most significant in the EU10 group. Ukraine stands out as more of an exception with a roller-coaster development in its liberal democracy score that first took it up the list and then back down to where it started. For those familiar with politics in these countries, it is easy to identify the elections and change in government that have occurred at the times the index has started to fall in all of these countries. In other words, the democratic declines have not started with coups but followed election outcomes where in most cases the incumbent leaders have been replaced by a new person or party.
How democracy came under attack
We will now take a closer look at what has been behind the instances of decline in the aggregate index by investigating how the sub-indices have developed in these countries. The sub-indices that build up the liberal democracy index are: freedom of expression and alternative sources of information; freedom of association; share of population with suffrage; clean elections; elected officials; equality before the law and individual liberty; judicial constraints on the executive; and legislative constraints on the executive (the structure is a bit more complex with mid-level indices, see V-Dem 2019a).
Table 1 shows how these indicators have changed in the time period the liberal democracy indicator has fallen significantly (with shorter versions of the longer names listed above but in the same order). The heat map of decline indicated by the different colours is constructed such that positive changes are marked with green, smaller declines are without colour, declines greater that 0.1 but smaller than 0.2 are in yellow and larger declines in red. Note that the liberal democracy index is not an average of the sub-indices but based on a more sophisticated aggregation technique (see V-Dem 2019b). Therefore, the Czech Republic and Bulgaria can have a greater fall in top-level liberal democracy index that what is indicated by the sub-indices.
Table 1. Changes in liberal democracy indicators at times of democratic decline
Source: V-Dem project and author’s calculations
For the countries with the largest changes in the liberal democracy index, it is clear that both freedom of expression and alternative sources of information have come under attack together with reduced judicial and legislative constraints on the executive. Among the EU10 countries, Hungary and Poland stand out in terms of reducing freedom of expression, while Romania has seen most of the decline coming from reducing constraints on the executive. Not surprisingly, Belarus stands out in terms of the overall decline in liberal democracy coming from reducing both freedom of expression and constraints on the executive in the most significant way.
On a more general level, the attack on democracy does differ between the countries, but in the cases where serious declines can be seen, the attack has been particularly focused on information aspects and constraints on the executive. At the same time, all countries let all people vote (suffrage always at 1) and let the one with the most votes get the job (elected officials).
Policy conclusions
This brief has provided some stylized facts on the first 30 years of liberal democracy in transition and some details on how democracy has come under attack in individual countries. It leaves open many questions that require further studies and some of these are indeed ongoing in this project and will be presented in future briefs and policy papers here.
Some observations have already been made here that can inform policy discussions on liberal democratic developments in the region. The first is that changes can happen very rapidly, both in terms of improvements but also in terms of dismantling important democratic institutions, including those that provide constraints on the executive or media that provides unbiased coverage before and after elections. What is also noteworthy is that these changes have almost always happened after an election where a new person or party has come to power, so the democratic system is used to introduce less democracy in this sense.
It is also interesting that in all of the countries, the most easily observed indicators of democracy such as suffrage and having the chief executive or legislature being appointed by elections are given the highest possible scores. In other words, even the most autocratic regime wants to look like a democracy; but as the old saying goes, “it is not who votes that is important, it is who counts”.
The regime changes at election times that have led to declining liberal democracy scores have also in many cases come as a result of the incumbents not doing a great job or voters not turning up to vote. It was enough for Lukashenko in Belarus to promise to deal with corruption and rampant inflation that was a result of the old guard’s mismanagement to turn Belarus into an autocracy. In Hungary, the change of regime came after the Socialist leader was caught on tape saying he had been lying to voters. While in Romania, only 39% voted in the 2016 election. And in Bulgaria, around half of the voters stayed at home in the presidential election the same year.
In sum, both incompetent and corrupt past leaders and disengaged or disillusioned voters are part of the decline in a liberal democracy that we have seen in recent years. It is clearly time for policy makers that are interested in preserving liberal democracy in the region and elsewhere to think hard about how democracy can be saved from illiberal democrats. Part of the answer clearly will have to do with how voters can be engaged in the democratic process and take part in elections. It also involves defending free independent media and the thinkers and doers that contribute to the liberal democracy that we cherish. The question is if the young generation will find a Greta for democracy that can kick-start a new transition to liberal democracy in the region and around the world.
For those readers that want to participate more actively in this discussion and have a chance to be in Stockholm on November 12, SITE is organizing a conference on this theme which is open to the public. For more information on the conference, please visit SITE’s website (see here).
References
- Freedom house data downloaded on Oct 4, 2019, from https://freedomhouse.org/content/freedom-world-data-and-resources
- Freedom house methodological note available at https://freedomhouse.org/report/methodology-freedom-world-2018
- Polity IV project data downloaded on Oct 4, 2019, from http://www.systemicpeace.org/inscrdata.html
- Polity IV project manual available at http://www.systemicpeace.org/inscr/p4manualv2018.pdf
- V-Dem project data downloaded on Sept 24, 2019, from https://www.v-dem.net/en/data/data-version-9/
- Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Lührmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Steven Wilson, Agnes Cornell, Lisa Gastaldi, Haakon Gjerløw, Nina Ilchenko, Joshua Krusell, Laura Maxwell, Valeriya Mechkova, Juraj Medzihorsky, Josefine Pernes, Johannes von Römer, Natalia Stepanova, Aksel Sundström, Eitan Tzelgov, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2019a. “V-Dem [Country-Year/Country-Date] Dataset v9”, Varieties of Democracy (V-Dem)
- Pemstein, Daniel, Kyle L. Marquardt, Eitan Tzelgov, Yi-ting Wang, Juraj Medzihorsky, Joshua Krusell, Farhad Miri, and Johannes von Römer. 2019b. “The V-Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data”, V-Dem Working Paper No. 21. 4th edition. University of Gothenburg: Varieties of Democracy Institute.
“New Goods” Trade in the Baltics
We analyze the role of the new goods margin—those goods that initially account for very small volumes of trade—in the Baltic states’ trade growth during the 1995-2008 period. We find that, on average, the basket of goods that in 1995 accounted for 10% of total Baltic exports and imports to their main trade partners, represented nearly 50% and 25% of total exports and imports in 2008, respectively. Moreover, we find that the share of Baltic new-goods exports outpaced that of other transition economies of Central and Eastern Europe. As the International Trade literature has recently shown, these increases in newly-traded goods could in turn have significant implications in terms of welfare and productivity gains within the Baltic economies.
New EU members, new trade opportunities
The Eastern enlargements of the European Union (EU) that have taken place since 2004 included the liberalization of trade as one of their main pillars and consequently provided new opportunities for the expansion of trade among the new and old members. Growth in trade following trade liberalization episodes such as the ones contemplated in the recent EU expansions could occur because of two reasons. First, because countries export and import more of the goods that they had already been trading. Alternatively, trade liberalization could promote the exchange of goods that had previously not been traded. The latter alternative is usually referred to as increases in the extensive margin of trade, or the new goods margin.
The new goods margin has been receiving a considerable amount of attention in the International Trade literature. For example, Broda and Weinstein (2006) estimate the value to American consumers derived from the growth in the variety of import products between 1972 and 2001 to be as large as 2.6% of GDP, while Chen and Hong (2012) find a figure of 4.9% of GDP for the Chinese case between 1997 and 2008. Similarly, Feenstra and Kee (2008) find that, in a sample of 44 countries, the total increase in export variety is associated with an average 3.3% productivity gain per year for exporters over the 1980–2000 period. This suggests that the new goods margin has significant implications in terms of both welfare and productivity.
In a forthcoming article (Cho and Díaz, in press) we study the patterns of the new goods margin for the three Baltic states: Estonia, Latvia and Lithuania. We investigate whether the period of rapid trade expansion experienced by these countries after gaining independence in 1991—average exports grew by more than 700% between 1995 and 2008 in nominal terms, and average imports by more than 800%—also coincided with increases in newly-traded goods by quantifying the relative importance of the new goods margin between 1995 and 2008. This policy brief summarizes our results.
Why focus on the Baltics?
The Baltic economies present an interesting case for a series of reasons. First, along a number of dimensions, the Baltic countries stood out as leaders among the formerly centrally-planned economies in implementing market- and trade-liberalization reforms. Indeed, those are the kind of structural changes that Kehoe and Ruhl (2013) identify as the main drivers of extensive margin increases. Second, unlike other transition economies, as part of the Soviet Union the Baltics lacked any degree of autonomy. Thus, upon independence, they faced a vast array of challenges, among them the difficult task of establishing trade relationships with the rest of the world, which prior to 1991 were determined solely from Moscow. Lastly, as former Soviet republics, the Baltic states had sizable portions of ethnic Russian-speaking population, most of which remained in the Baltics even after their independence. At least in principle, this gave the Baltic economies a unique potential to better tap into the Russian market.
Defining “new goods”
We use bilateral merchandise trade data for Estonia, Latvia and Lithuania starting in 1995 and ending in 2008, the year before the Global Financial Crisis (GFC). The data are taken from the World Bank’s World Integrated Trade Solution database. The trade data are disaggregated at the 5-digit level of the SITC Revision 2 code, which implies that our analysis deals with 1,836 different goods.
To construct a measure of the new goods margin, we follow the methodology laid out in Kehoe and Ruhl (2013). First, for each good we compute the average export and import value during the first three years in the sample (in our case, 1995 to 1997), to avoid any distortions that could arise from our choice of the initial year. Next, goods are sorted in ascending order according to the three-year average. Finally, the cumulative value of the ranked goods is grouped into 10 brackets, each containing 10% of total trade. The basket of goods in the bottom decile is labeled as the “new” goods or “least-traded” goods, since it contains goods that initially recorded zero trade, as well as goods that were traded in positive—but low—volumes. We then trace the evolution of the trade value of the goods in the bottom decile, which represents the growth of trade in least-traded goods.
Findings
For ease of exposition, we present the results for the average Baltic exports and imports of least-traded goods, rather than the trade flows for each country. Results for each individual country can be found in Cho and Díaz (in press). We report the least-traded exports and imports to and from the Baltics’ main trade partners: the EU15, composed of the 15-country bloc that constituted the EU prior to the 2004 expansion; Germany, which within the EU15 stands out as the main trade partner of Latvia and Lithuania; the “Nordics”, a group that combines Finland and Sweden, Estonia’s largest trade partners; and Russia, because of its historical ties with the Baltic states and its relative importance in their total trade.
Least-traded exports
Figure 1 shows the evolution over time of the share in total exports of the goods that were initially labeled as “new goods”, i.e., those products that accounted for 10% of total trade in 1995. We find that the Baltic states were able to increase their least-traded exports significantly, and by 2008 such exports accounted for nearly 40% of total exports to the EU15, and close to 53%, 49% and 49% of total exports to Germany, the Nordic countries, and Russia, respectively. Moreover, we find that the fastest growth in least-traded exports to the EU15 and its individual members coincided with the periods when the Association Agreements and accession to the EU took place. Finally, we discover that the rapid increase in least-traded exports to the EU15 during the late 1990s and early 2000s is accompanied by a stagnation of least-traded exports to Russia. This suggest that, as the Baltics received preferential treatment from the EU, they expanded their export variety mix in that market at the expense of the Russian. Growth in least-traded exports to Russia only resumed in the mid 2000s, when the Baltics became EU members and were granted the same preferential treatment in the Russian market that the other EU members enjoyed.
Figure 1. Baltic least-traded exports
Source: Cho and Díaz (in press).
Least-traded imports
Figure 2 plots the evolution of Baltic least-traded imports between 1995 and 2008. We find that new goods imports also grew at robust rates, but their growth is about half the magnitude of the growth in the least-traded exports—the least-traded imports nearly doubled their share, whereas the least-traded exports quadrupled it. The least-traded imports from the EU15 and its individual members exhibited consistent growth throughout. On the other hand, imports of new goods from Russia—which had also been growing since 1995—started a continuous decline starting in 2003. This change in patterns can be attributed to the Baltics joining the EU customs union. Prior to their EU accession, the average Baltic tariff was in general low. Upon EU accession, the Baltics adopted the EU’s Commercial Common Policy, which removed trade restrictions for EU goods flowing into the Baltics, but—from the perspective of the Baltic countries—raised tariffs on non-EU imports, in turn discouraging the imports of Russian new goods.
Figure 2. Baltic least-traded imports
Source: Cho and Díaz (in press).
Are the Baltics different?
Figure 1 shows that the Baltic states were able to increase their least-traded exports by a significant margin. A natural question follows: Is this a feature that is unique of the Baltic economies, or is it instead a generalized trend among the transition countries?
Table 1: Growth of the share of least-traded exports (percent, annual average)
Source: Cho and Díaz (in press).
Table 1 reveals that the new goods margin played a much larger role for the Baltic states than for other transition economies such as the Czech Republic, Hungary and Poland (which we label as “Non-Baltics”), for all the export destinations we consider. Moreover, we find that while until 2004—the year of the EU accession—both Baltic and Non-Baltic countries displayed high and comparable growth rates of least-traded exports, this trend changed after 2004. Indeed, while there is no noticeable slowdown in the Baltic growth rate, after 2004 the Non-Baltic growth of least-traded exports to the world and to the EU15 all but stops, with the only exception being the Nordic destinations.
Conclusion
The Baltic states, and in particular Estonia, are usually portrayed as exemplary models of trade liberalization among the transition economies. Our results indicate that the Baltics substantially increased both their imports and exports of least-traded goods between 1995 and 2008. Since increases in the import variety mix have been shown to entail non-negligible welfare effects, we expect large welfare gains for the Baltic consumers experienced due to the increases in the imports of previously least-traded goods. Moreover, the literature has documented that increases in export variety are associated with increases in labor productivity. Our findings reveal that the Baltics’ increases in their exports of least-traded goods were even larger than their imports of new goods, thus underscoring the importance of the new goods margin because of their contribution to labor productivity gains.
References
- Broda, Christian; and David E. Weinstein, 2006. “Globalization and the gains from variety,” Quarterly Journal of Economics, Vol. 121 (2), pp. 541–585.
- Chen, Bo; and Ma Hong, 2012. “Import variety and welfare gain in China,” Review of International Economics, Vol. 20 (4), pp. 807–820.
- Cho, Sang-Wook (Stanley); and Julián P. Díaz. “The new goods margin in new markets,” Journal of Comparative Economics, in press.
- Feenstra, Robert C.; and Hiau Looi Kee, 2008. “Export variety and country productivity: estimating the monopolistic competition model with endogenous productivity,” Journal of International Economics, Vol. 74 (2), pp. 500–518.
- Kehoe, Timothy J.; and Kim J. Ruhl, 2013. “How important is the new goods margin in international trade?” Journal of Political Economy, Vol. 121 (2), pp. 358–392.
Higher Competition in the Domestic Market – A Way to Boost Aggregate Productivity
Competition is a good thing not only because of lower prices and larger variety. Higher competition in the domestic market also shifts necessary labour and capital resources from less productive domestic-oriented firms to export-oriented productivity champions. Such firms will make better use of production factors and generate larger output. Thus, simply increasing the level of competition in the domestic market can boost the aggregate productivity of a country.
The aggregate productivity of a country can be boosted even without changing the productivity of individual enterprises. This can be achieved by improving the allocation of resources – the redistribution of labour and capital towards more productive firms. These firms will make better use of production factors and generate larger output. But how can one affect the allocation of resources? Economic theory says that allocation depends on the productivity of individual firms: more productive enterprises attract more labour and capital. However, there exists another factor behind allocation: distortions.
Distortions affect the allocation of resources
A model developed by Hsieh and Klenow (2009) – one of the most popular frameworks to study the allocation of resources – has a very important and realistic feature: it acknowledges that firms are not treated equally. Some firms may face lower supply of banking loans ending with higher capital costs. Other firms could confront with trade unions and higher wages. Tax rates may also differ across firms. These are all examples of distortions. Firms facing larger distortions are forced to underuse respective production factor, while firms that enjoy more favourable conditions tend to overuse capital and labour, generating more output.
While it is virtually impossible to imagine an economy without any distortions (the one where all firms face the same taxes, costs of labour, capital etc.), not all distortions damage the allocation of resources. Only distortions to productive firms create misallocation of resources by shifting labour and capital towards unproductive firms. Thus, removal of such distortions can improve the efficiency of allocation and raise the aggregate output of the country.
According to Hsieh and Klenow (2009) the distortions faced by every individual firm can be quantified from the balance sheets and profit/loss data. For example, observing lower-than-usual ratio of capital to intermediate inputs (comparing with other enterprises in a narrowly defined industry) indicates a capital distortion, possibly related with limited access to banking loans. Similarly, lower-than-usual share of wages in total production costs implies high labour distortions. Finally, the size of the distortion can be detected as a case of abnormally low share of intermediate inputs in total output, and signals about the restrictions to total output (e.g. due to higher taxes for large enterprises).
Misallocation of resources is small in Latvia
In my recent research (see Benkovskis, 2015), I use anonymised firm-level dataset for 2007–2013 and apply the Hsieh and Klenow (2009) model to study the allocation of resources in Latvia – a unique example of a small and open economy facing extreme structural shifts during the financial crisis. According to my estimates, the negative contribution of misallocation to aggregate productivity was close to 27% in 2013 (see Figure 1). In other words, it suggests that actual aggregate productivity could be boosted by 27% if all distortions were removed!
This may seem large but in fact 27% is a comparatively low figure. Hsieh and Klenow (2009) argue that full liberalisation would boost aggregate manufacturing productivity by 86–115% in China, 100–128% in India, and 30–43% in the US. Dias et al. (2015) show that removing distortions would lead to a 30% gain in output of Portugal in 2011. Thus, misallocation of resources is relatively small in Latvia. Even more important: the misallocation of resources decreased after the crisis in Latvia (contrary to the case of Portugal), adding more than 10 percentage points to aggregate productivity growth between 2010 and 2013.
Figure 1. Contribution from misallocation of resources to aggregate total factor productivity, %
Source: Benkovskis (2015). Note: shows the contribution of misallocation comparing with the counterfactual case of no distortions.
The finding that allocation of resources improved after the crisis is interesting per se, but uncovering the reasons behind the improvement is even more important. Figure 1 provides a decomposition, which shows that labour distortions are minor in Latvia due to high flexibility of labour market (in line with recent findings by Braukša and Fadejeva, 2016). The capital distortions, while being minor in 2007–2008, increased afterwards, pointing to some credit supply constraints faced by the highly productive enterprises after the financial crisis. However, by far largest contribution comes from the misallocation of intermediate inputs – the turnover of the most productive firms face some constraints. And it was the ease of constraints to turnover for the most productive firms that determined the improvements in aggregate productivity since 2010.
The level of competition matters for misallocation
My research stresses the importance of the competition level on the market, since higher competition serves as a natural constraint for the firm to increase its turnover. What if the most productive Latvia’s firms systematically come up against higher competition? I found that indeed this is the case. First, recent results by Fadejeva and Krasnopjorovs (2015) show that Latvia’s domestic market has lower competition level comparing with external markets. Second, it is widely acknowledged that exporters tend to be more productive comparing with domestically oriented firms (see e.g. Bertou et al., 2015, who report positive export premiums for EU countries, while Benkovskis and Tkačevs, 2015, find higher productivity of exporters in Latvia). Thus, Latvia’s productive export-oriented firms are subject to higher competition and cannot enlarge their turnover as easy as other entities. This shifts labour and capital towards small and less productive firms working solely on domestic market, creating the misallocation of resources.
The domestic competition factor can also explain the improving allocation of resources after 2010. The study by Fadejeva and Krasnopojorovs (2015) reveals that the competition gap between domestic and foreign markets narrowed after the financial crisis (see Table 1). Namely, life was too easy on the local Latvia’s market during the boom time, allowing unproductive firms to survive and drain away resources from more productive firms. But conditions became tougher after the crisis (although the competition level still remained lower than abroad). We can view this as a “cleansing effect of the crisis”: some of the least productive domestic oriented firms went bankrupt (or decreased their turnover), freeing the necessary capital and labour resources for productive exporters.
Table 1: Change in the competitive pressure on main product in domestic and foreign markets compared to the situation before 2008, %
| Domestic market | Foreign market | |||
| 2008–2009 | 2010–2013 | 2008–2009 | 2010–2013 | |
| Strong decrease | 2.9 | 2.2 | 0.9 | 1.0 |
| Moderate decrease | 11.8 | 3.8 | 7.6 | 5.9 |
| Unchanged | 33.8 | 24.7 | 45.7 | 51.5 |
| Moderate increase | 30.0 | 28.1 | 25.2 | 19.7 |
| Strong increase | 18.7 | 38.5 | 11.2 | 8.8 |
| Does not apply | 2.8 | 2.8 | 9.4 | 13.1 |
Source: Fadejeva and Krasnopjorovs (2015), Table A.102. Notes: based on the sample of 557 Latvia’s firms; results are weighted to represent firm population.
Conclusion
This research has an important policy conclusions applicable to any country that seeks to increase aggregate productivity. The competition level in the domestic market is important not only for consumers, who enjoy lower prices and higher variety. Higher competition in the domestic market also shifts necessary resources from less productive domestic-oriented firms to export-oriented productivity champions.
References
- Benkovskis, Konstantins; 2015. “Misallocation of resources in Latvia: did anything change during the crisis?”, Latvijas Banka Working Paper No.5/2015.
- Benkovskis, Konstantins; and Olegs Tkacevs, 2015. “Everything you always wanted to know about Latvia’s service exporters (but were afraid to ask)”, Latvijas Banka Working Paper No.6/2015.
- Berthou, Antoine; Emmanuel Dhyne; Matteo Bugamelli; Ana-Maria Cazacu; Calin-Vlad Demian; Peter Harasztosi; Tibor Lalinsky; Jaanika Meriküll ; Filippo Oropallo; and Ana Cristina Soares, 2015. “Assessing European Firms’ Exports and Productivity Distributions: The CompNet Trade Module”, ECB Working Paper, No. 1788.
- Braukša, Ieva; and Ludmila Fadejeva, 2016. “Internal labour market mobility in 2005–2014 in Latvia: the micro data approach”, Baltic Journal of Economics, 16(2), 152–174.
- Dias, Daniel A.; Carlos Robalo Marques; and Christine Richmond, 2015. “Misallocation and Productivity in the Lead Up to the Eurozone Crisis“, International Finance Discussion Papers 1146.
- Fadejeva, Ludmila; and Olegs Krasnopjorovs, 2015. “Labour Market Adjustment during 2008–2013 in Latvia: Firm Level Evidence”, Latvijas Banka Working Paper, No. 2/2015.
- Hsieh, Chang-Tai; and Peter J. Klenow, 2009. “Misallocation and manufacturing TFP in China and India“, The Quarterly Journal of Economics, 124(4), 1403–1448.
Traces of Transition: Unfinished Business 25 Years Down the Road?
This year marks the 25-year anniversary of the breakup of the Soviet Union and the beginning of a transition period, which for some countries remains far from completed. While several Central and Eastern European countries (CEEC) made substantial progress early on and have managed to maintain that momentum until today, the countries in the Commonwealth of Independent States (CIS) remain far from the ideal of a market economy, and also lag behind on most indicators of political, judicial and social progress. This policy brief reports on a discussion on the unfinished business of transition held during a full day conference at the Stockholm School of Economics on May 27, 2016. The event was organized jointly by the Stockholm Institute of Transition Economics (SITE) and the Swedish Ministry for Foreign Affairs, and was the sixth installment of SITE Development Day – a yearly development policy conference.
A region at a crossroads?
25 years have passed since the countries of the former Soviet Union embarked on a historic transition from communism to market economy and democracy. While all transition countries went through a turbulent initial period of high inflation and large output declines, the depth and length of these recessions varied widely across the region and have resulted in income differences that remain until today. Some explanations behind these varied results include initial conditions, external factors and geographic location, but also the speed and extent to which reforms were implemented early on were critical to outcomes. Countries that took on a rapid and bold reform process were rewarded with a faster recovery and income convergence, whereas countries that postponed reforms ended up with a much longer and deeper initial recession and have seen very little income convergence with Western Europe.
The prospect of EU membership is another factor that proved to be a powerful catalyst for reform and upgrading of institutional frameworks. The 10 countries that joined the EU are today, on average, performing better than the non-EU transition countries in basically any indicator of development including GDP per capita, life expectancy, political rights and civil liberties. Even if some of the non-EU countries initially had the political will to reform and started off on an ambitious transition path, the momentum was eventually lost. In Russia, the increasing oil prices of the 2000s brought enormous government revenues that enabled the country to grow without implementing further market reforms, and have effectively led to a situation of no political competition. Ukraine, on the other hand, has changed government 17 times in the past 25 years, and even if the parliament appears to be functioning, very few of the passed laws and suggested reforms have actually been implemented.
Evidently, economic transition takes time and was harder than many initially expected. In some areas of reform, such as liberalization of prices, trade and the exchange rate, progress could be achieved relatively fast. However, in other crucial areas of reform and institution building progress has been slower and more diverse. Private sector development is perhaps the area where the transition countries differ the most. Large-scale privatization remains to be completed in many countries in the CIS. In Belarus, even small-scale privatization has been slow. For the transition countries that were early with large-scale privatization, the current challenges of private sector development are different: As production moves closer to the world technology frontier, competition intensifies and innovation and human capital development become key to survival. These transformational pressures require strong institutions, and a business environment that rewards education and risk taking. It becomes even more important that financial sectors are functioning, that the education system delivers, property rights are protected, regulations are predictable and moderated, and that corruption and crime are under control. While the scale of these challenges differ widely across the region, the need for institutional reforms that reduce inefficiencies and increase returns on private investments and savings, are shared by many.
To increase economic growth and to converge towards Western Europe, the key challenges are to both increase productivity and factor input into production. This involves raising the employment rate, achieving higher labor productivity, and increasing the capital stock per capita. The region’s changing demography, due to lower fertility rates and rebounding life expectancy rates, will increase already high pressures on pension systems, healthcare spending and social assistance. Moreover, the capital stock per capita in a typical transition country is only about a third of that in Western Europe, with particularly wide gaps in terms of investment in infrastructure.
Unlocking human potential: gender in the region
Regardless of how well a country does on average, it also matters how these achievements are distributed among the population. A relatively underexplored aspect of transition is to which extent it has affected men and women differentially. Given the socialist system’s provision of universal access to education and healthcare, and great emphasis on labor market participation for both women and men, these countries rank fairly well in gender inequality indices compared to countries at similar levels of GDP outside the region when the transition process started. Nonetheless, these societies were and have remained predominantly patriarchal. During the last 25 years, most of these countries have only seen a small reduction in the gender wage gap, some even an increase. Several countries have seen increased gender segregation on the labor market, and have implemented “protective” laws that in reality are discriminatory as they for example prohibit women from working in certain occupations, or indirectly lock out mothers from the labor market.
Furthermore, many of the obstacles experienced by small and medium-sized enterprises (SMEs) are more severe for women than for men. Female entrepreneurs in the Eastern Partnership (EaP) countries have less access to external financing, business training and affordable and qualified business support than their male counterparts. While the free trade agreements, DCFTAs, between the EU and Ukraine, Georgia, and Moldova, respectively, have the potential to bring long-term benefits especially for women, these will only be realized if the DCFTAs are fully implemented and gender inequalities are simultaneously addressed. Women constitute a large percentage of the employees in the areas that are the most likely to benefit from the DCFTAs, but stand the risk of being held back by societal attitudes and gender stereotypes. In order to better evaluate and study how these issues develop, gendered-segregated data need to be made available to academics, professionals and the general public.
Conclusion
Looking back 25 years, given the stakes involved, things could have gotten much worse. Even so, for the CIS countries progress has been uneven and disappointing and many of the countries are still struggling with the same challenges they faced in the 1990’s: weak institutions, slow productivity growth, corruption and state capture. Meanwhile, the current migration situation in Europe has revealed that even the institutional development towards democracy, free press and judicial independence in several of the CEEC countries cannot be taken for granted. The transition process is thus far from complete, and the lessons from the economics of transition literature are still highly relevant.
Participants at the conference
- Irina Alkhovka, Gender Perspectives.
- Bas Bakker, IMF.
- Torbjörn Becker, SITE.
- Erik Berglöf, Institute of Global Affairs, LSE.
- Kateryna Bornukova, Belarusian Research and Outreach Center.
- Anne Boschini, Stockholm University.
- Irina Denisova, New Economic School.
- Stefan Gullgren, Ministry for Foreign Affairs.
- Elsa Håstad, Sida.
- Eric Livny, International School of Economics.
- Michal Myck, Centre for Economic Analysis.
- Tymofiy Mylovanov, Kyiv School of Economics.
- Olena Nizalova, University of Kent.
- Heinz Sjögren, Swedish Chamber of Commerce for Russia and CIS.
- Andrea Spear, Independent consultant.
- Oscar Stenström, Ministry for Foreign Affairs.
- Natalya Volchkova, Centre for Economic and Financial Research.
The Economic Complexity of Transition Economies
‘Diversification’ is a constant concern of policy-makers in resource rich economies, but measurement of diversification can be hard. The recently formulated Economic Complexity Index (ECI) is a promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system. The ECI is based on the diversity and ubiquity of a country’s exports. This brief uses ECI to discuss the economic diversity of transition economies in the post-Soviet decades, and the relationship between economic diversification and per capita income.
The search for and construction of appropriate predictors of economic development are among the main goals of economists and policy-makers. Education, infrastructure, rule of law, and quality of governance are all among the commonly used indicators based on inputs. The recently formulated Economic Complexity Index (Hidalgo and Hausmann, 2009) is a new promising predictor of economic development characterizing the overall complexity and diversity of the economy as a system.
Indeed, the importance of production and trade diversification for economic development has been highlighted by the economic literature. Numerous studies have found a positive relationship between diversified and complex export structure, income per capita and growth (Cadot et al., 2011; Hesse, 2006; Hausmann et al., 2007). In line with this, Hausmann et al. (2014) demonstrate the predictive properties of the ECI for economic development and GDP per capita, which implies that the ECI can serve as a useful complement to the input-based measures for policy analysis by reasoning from current outputs to future outputs.
This brief uses the ECI to discuss the evolution of economic diversification, its relationship to per capita income in transition economies in the post-Soviet decades, and its policy implications.
How is economic complexity measured?
The economic complexity index (ECI) is a novel measure that reflects the diversity and ubiquity of a country’s exports. The index considers the number of products a country exports with revealed comparative advantage and how many other countries in the world export such goods. If a country exports a high number of goods and few other countries export these products, then its economy is diversified (a wide range of exports products) and sophisticated (only a few other countries are able to export these goods). Thus, the measure tries to capture not a specific aspect of the economy, but rather its overall sophistication.
For example, Japan, Switzerland, Germany and Sweden have been in a varying order at the top of the ranking of the Economic Complexity Index from 2008 until 2013. This means that these countries export a large number of highly sophisticated products.
In contrast, Tajikistan is among the countries at the bottom of the world ranking by the ECI with raw aluminum, raw cotton and ores making up 85% of all Tajikistan’s exports in 2013. However, not only are Tajikistan’s exports concentrated among very few narrow products, these products are also ubiquitous and the ability to export them does not require knowledge and skills that can be used in the production and exports of many other products.
As the index for each country is constructed relative to other countries’ exports, it is comparable over time.
What can we learn from the economic complexity of transition economies?
The economic complexity index can serve as a useful indicator for understanding transition economies in the post-Soviet period. A strong relationship between GDP per capita and economic complexity is found in the sample of transition economies in Figure 1. This figure presents the relationship for the last year for which data is available for the sample of 13 post-Soviet states and Poland. As can be seen in Figure 1, the economic complexity is positively related to income per capita. This is especially true for Poland, Estonia, Lithuania, Latvia and Russia, who all have higher than average economic complexity and high levels of per capita income. While Belarus and Ukraine also have diverse and complex economies, they have somewhat lower income per capita than the first group.
Figure 1. Economic Complexity and GDP per capita
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Natural resource-rich, or rather, oil-rich countries are the exception from the abovementioned correlation. Most transition countries with below than average economic complexity are characterized by low income per capita levels, except for Kazakhstan and Azerbaijan, which are oil-rich countries. Still, the overall picture is straightforward: countries with a complex export structure have a higher level of income.
One of the advantages of a systemic measure like export complexity is its straightforward policy application. The overall diversity and sophistication of the economy can thus be a complementary measure for the assessment of economic progress and development to GDP and GDP per capita, which are more susceptible to the volatile factors such as commodity prices.
Figure 2 shows the development of economic complexity for 14 post-Soviet countries and Poland between 1994 and 2013 (due to data availability issues, only one year is available for Armenia).
First, we see that the economic complexity has diverged over time, although there is some similarity in the rankings among countries over time. The initial closeness is likely related to the planned nature of the Soviet economy that aimed to distribute production among Soviet Republics. In the post-Soviet context, however, the more complex economies (Estonia, Belarus, Lithuania, Ukraine, Latvia, Russia) kept or increased their sophistication and diversity of exports. Poland is the leading economy in terms of complexity, both in the beginning and towards the end of the sample period. Belarus, the second most complex economy in 2013 and the most complex economy in several years prior, shows an increasing trend in its sophistication of exports. Although its GDP per capita is noticeably lower than what would be expected from such a sophisticated economy, the complex production structure may explain its ability to withstand a permanent high inflation and external macroeconomic shocks. Some others, e.g., Tajikistan and Azerbaijan, saw a decreasing trend in economic complexity; Georgia and Kazakhstan, notably, lost in economic complexity but also in their ranking among their peers.
Figure 2. Economic Complexity of Transition Economies
Source: Data on GDP per capita is from the World Bank, and the data on the Economic Complexity Index is from the Observatory of Economic Complexity.
Conclusion
This brief revisited the economic complexity of transition economies and its evolution since the 1990s. The post-Soviet and other transition countries have had diverging economic development paths: Some have managed to build complex production economies, while others’ comparative advantage remains in raw materials. These differences are also reflected in their income levels.
Across the world, economic diversification is associated with higher per-capita income. As the brief showed, this relationship also holds for the post-Soviet countries; policy-makers should take economic diversification seriously. Increasing economic complexity may well pave the path to higher income levels.
References
- Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What’s behind the hump?. Review of Economics and Statistics, 93(2), 590-605.
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The atlas of economic complexity: Mapping paths to prosperity. Mit Press.
- Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of economic growth, 12(1), 1-25.
- Hesse, H. (2006). Export diversification and economic growth. World Bank, Washington, DC.
- Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. proceedings of the national academy of sciences, 106(26), 10570-10575.
Latvia Stumbling Towards Progressive Income Taxation
The 2016 budget includes measures aimed at increasing the progressivity of the Latvian income tax system. In this brief we report some exercise on the impact of these measures using the Latvian EUROMOD tax-benefit microsimulation model. We show that by their design, the reforms are aimed at a reduction in income inequality and an increase in the progressivity of the tax system. However, there are risks that the behavioural response of the tax payers will subvert the intended impact of the reforms.
Ever since it was introduced in 1994 the Latvian personal income tax has been applied at a flat rate, albeit varying over time, mitigated only by a small untaxed personal allowance. Partly as a result of this, the Latvian tax-benefit system redistributes less original income than most other EU countries. Is this all about to change? The 2016 budget currently being debated in the Parliament contains two proposals aimed at introducing more progressivity in the personal income tax. These are the introduction of a “solidarity tax” aimed at high earners and the introduction of an earnings differentiated non-taxable allowance. The stated aims of these measures are to reduce inequality and help low wage-earners.
Description of the Reforms
Solidarity Tax
The solidarity tax foresees that income above 48,600 EUR per year will be taxed at a rate of 10.5% (employee’s part), plus 23.59% (employer’s part). The new tax will affect a very small share of wage earners. According to Finance ministry’s estimate, this tax will affect 4.7 thousand persons, whose income in 2015 exceeded this threshold, or 0.59% of all employed individuals (Finance Ministry, 2015).
Differentiated Non-Taxable Personal Allowance
The differentiated non-taxable personal allowance will be introduced gradually between 2016 and 2020. The basic idea is to make the allowance dependent on income: individuals receiving income below a certain threshold are eligible for the maximum possible allowance, then the allowance gradually declines with income until it is zero. The system will be introduced gradually in the sense that the minimum allowance will not reach zero until 2020 – it will be gradually reduced from 85 EUR in 2016 to 0 EUR in 2020.
The way the system will be implemented foresees that during a fiscal year, all individuals will be taxed applying the minimum non-taxable allowance (e.g., 85 EUR in 2016). At the beginning of the next year, people eligible for a higher tax allowance will have the opportunity to apply for a tax refund, by making an income declaration, and to get the overpaid tax back.
Simulations of Reforms: Inequality
Below we present simulation results from EUROMOD, which is an EU-wide tax-benefit microsimulation model (for more details see Jara and Leventi, 2014). The results show the first-round effect of the simulated policies, i.e., they show the pure effect of the proposed reforms abstracting from any behavioural responses that these reforms might induce. We simulate the effect of five reform scenarios: two scenarios of differentiated non-taxable allowance (one scenario reflects the system that is planned to be introduced in 2016, the second scenario represents the system that is planned to be introduced in 2020), one scenario that simulates introduction of the solidarity tax, and two scenarios that combine the solidarity tax with the new non-taxable allowances. We compare these reforms with the baseline system, which describes the tax-benefit rules that are in place in 2015.
It is important to note that we assume in the simulations that everyone who is eligible for a tax refund under the new non-taxable allowance rules does in fact apply for the refund, which means that we estimate the maximum possible effect from the introduction of the higher tax allowances.
Table 1 summarizes the effect of the proposed reforms on income inequality as measured by the Gini coefficient. All the proposed reforms reduce income inequality, but the solidarity tax achieves higher equality by reducing incomes in the top decile. The non-taxable allowance mainly affects people in the middle of the income distribution, as the bottom deciles contain proportionally fewer employed individuals, while in the top deciles the allowance, which is set in absolute terms, makes a smaller share of the income – hence, a weaker effect. Pensioners, who mainly belong to the lower deciles of the income distribution, do not gain from a higher allowance, because of a special taxation regime for pensions that already provides for a higher personal allowance. All major benefits (unemployment benefit, social assistance, child-related benefits) are not subject to personal income tax, hence benefit recipients also do not gain from the proposed changes (see Figure 1).
Table 1. Gini Coefficient Associated with the Reforms
| Baseline | ST* | 2016 allowance | 2020 allowance | ST + 2016 allowance | ST + 2020 allowance | |
| Gini | 0.361 | 0.358 | 0.360 | 0.357 | 0.357 | 0.355 |
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax
Figure 1. Deviation of Equivalised Disposable Income from the Baseline Scenario, %
Source: authors’ calculations using EUROMOD
Figure 1 also shows that the losers from the solidarity tax are in the highest decile, though it should be borne in mind that enterprises are also losers because they now have to pay part of the solidarity tax. The solidarity tax generates no direct gainers.
Impact on Progressivity
The progressivity of a tax or system is typically measured by the Kakwani index. The Kakwani index (Kakwani, 1977) can vary between −1 and 1 and the larger the index, the more progressive is the tax. A positive index indicates that the tax is progressive and a negative index indicates it is regressive. Table 2 shows the calculated Kakwani index for all major direct taxes (which include personal income tax, social contributions and the newly introduced solidarity tax) and separately for personal income tax (PIT) for each of the postulated scenarios. The results suggest that all of the proposed reforms increase the progressivity of the tax system.
Table 2. The Kakwani Index for the Six Scenarios
| Baseline | ST* | 2016 allowance | 2020 allowance | ST + 2016 allowance | ST + 2020 allowance | |
| All income taxes* | 0.034 | 0.040 | 0.048 | 0.058 | 0.054 | 0.064 |
| PIT | 0.07 | 0.07 | 0.10 | 0.12 | 0.10 | 0.12 |
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax; income taxes include personal income tax, social contributions and the newly introduced solidarity tax
Qualifications and Risks
The above results capture the so-called first round impact of the tax changes. In practice people will react to the changed incentives by changing behaviour and thereby changing the impacts. For example, the higher net reward for working in low wage jobs may increase the supply of workers willing to work in such jobs thereby possibly having a bigger positive effect on the incomes of low income households than implied by the simulations.
Perhaps more significant is the potential effect of the solidarity tax on the behaviour of high earners and of the enterprises that employ them. This effect is captured by the concept of the elasticity of taxable income – defined as the change in taxable income in response to a change in the marginal tax rate. The taxable income elasticity concept takes into account all the behavioural aspects of the taxpayer in response to a change in the tax rate. As well as labour supply responses it includes other responses e.g. switching the form in which income is received as well as simple tax evasion (Saez et al., 2012). It is the switching of the form in which income is received, away from wage income towards other less-taxed forms of income that can be expected here. Thus according to an internal Latvian Employers Confederation employer survey, if the solidarity tax is implemented one third of employers will consider using legal tax optimization tools such as dividends or the microenterprise tax to avoid paying the tax. Here, employers are important as well as employees, because employers will pay the larger share of the tax. If this happens on a significant scale (high elasticity of taxable income) then the intention of the solidarity tax will be subverted.
There are also risks with the differentiated personal allowance. If the burden of annual reporting of income is too high then many may simply not do it and suffer the loss of income or find a way of recouping through shadow earnings.
Concluding Remarks
The Latvian authorities should be applauded for grasping the nettle of progressive taxation but perhaps only with one hand for the way they have chosen to do it. Thus, the solidarity tax creates an incentive for both employers and employees to find ways of avoiding it and find they surely will. A tax accountant once said of the 80% supertax applied to high earnings in pre-Thatcher UK that it was a ‘voluntary tax’. This is also the likely fate of Latvia’s solidarity tax.
The differentiated personal allowance will clearly benefit low earners, if they claim it. In fact it will also benefit people earning well over the average wage. But will the low earners claim? Very few people in Latvia have ever filed an income declaration and we fear that many low earners will not do so now.
Thus at the top end progressivity is likely to be largely avoided and at the bottom end may not be fully claimed.
References
- Finance Ministry (2015). “Solidaritātes nodokli maksās tikai personas ar algu virs 48 600 eiro gadā,” available at http://www.fm.gov.lv/lv/aktualitates/jaunumi/nodokli/51253-solidaritates-nodokli-maksas-tikai-personas-ar-algu-virs-48-600-eiro-gada
- Kakwani, Nanak C. (1977). “Measurement of Tax Progressivity: An International Comparison”. Economic Journal 87 (345): 71–80
- Jara, X. and Leventi, C. (2014). “Baseline results from the EU27 EUROMOD (2009-2013),” EUROMOD Working Papers EM18/14, EUROMOD at the Institute for Social and Economic Research.
- Saez, E., J. Slemrod, and S. H. Giertz, (2012). “The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review.” Journal of Economic Literature, 50(1): 3-50
The Political Economy of the Latvian State Since 1991: Some Reflections on the Role of External Anchors
This brief discusses the role of external anchors or goals such as WTO accession, NATO and EU accession in Latvia’s development strategy since 1991. On the one hand the external goals ‘depoliticised’ many potentially contentious areas of Latvian life. On the other hand, some developments would not have happened or would not have happened as fast without the constraints imposed by the external goals. For example liberalisation of the citizenship laws was prompted by NATO accession and the balance was tipped when the rejection of Latvia from fast-track EU accession talks in December 1997 led Latvia to abandon its quota or ‘windows’ naturalisation system. Most recently, Eurozone accession was an externally defined exit strategy from the austerity episode induced by the economic and financial crisis. Today there are no big external goals left to guide policy making. Home grown problems such as inequality require home grown solutions. But even now an external dependency persists. For example a long needed reform of the financing model of higher education has had to wait for a World Bank report published in September 2014 for action to be taken.
On January 1st, 2015 Latvia assumed the Presidency of the European Union. This milestone represents a certain level of maturity of the Latvian state and offers an opportunity for reflection on some aspects of how politics and political economy have evolved in Latvia between 1991 and today.
After Latvia regained independence in 1991, it faced (at least) two political economy challenges: one was to disentangle the economy from the Soviet system in which it had been deeply integrated, and the second, perhaps more difficult challenge, was to create an independent nation state. At a formal level, the solution to the latter challenge appeared straightforward – assume continuity of the Latvian state. Effectively this meant reinstating the pre-war constitution, which was indeed done for the most part. Symbolically this continuity was signalled by, for example, calling the first post-Soviet parliamentary elections held in June 1993 the elections for the 5th Saeima (parliament). The elections for the 4th Saeima had taken place more than 60 years earlier in October 1931.
At a practical level the challenges were more complex – Latvia had had no practical experience of statehood for nearly fifty years and mistakes were made. For example, Latvia initially diplomatically recognised Taiwan rather than the Peoples Republic of China.
There was a presumption that newly independent Latvia should become a market economy but little consensus on how this should be achieved. This is in contrast to Estonia where a group of ‘young market economy Turks’ were able to implement a kind of zero option i.e. zero tariffs, fast privatisation, etc. In Latvia there were strong protectionist sentiments and the initial privatisation was a muddled process.
Advice and advisers were abundant in post-independence Latvia. In the early 1990s, Latvia was awash with international advisers: the IMF and the World Bank were both present, the Germans were advising on a constitution for the Bank of Latvia, the British were active in public administration reform, the Danish advised on research and higher education and so on. Advice was often conflicting with different advisers promoting their own visions of structures as models that Latvia should adopt e.g. on legal and education systems. Today, we see something akin to this in the Eastern Partnership countries such as Moldova and Ukraine.
There was a general sense of the desirability of a ‘return to Europe’ but no plan or strategy. Nevertheless, even without a conscious plan a strategy emerged – namely a strategy of external anchors.
The external goals or anchors that emerged included the following:
- World Trade Organisation, 1998
- NATO, 29 March 2004
- European Union, 1 May 2004
- Eurozone, 1 January 2014
The most important effect of the external anchors was that they ‘depoliticised’ many potentially contentious areas of Latvian life. This has been particularly important given the fragmentation that has historically dominated Latvian politics. Thus, in the interwar period, no less than 32 different political parties were represented in the Saeima. In the early post-Soviet parliaments, similar tendencies were observed with newly created parties being the winners in terms of the number of seats in the first four elections. The election of 2006 was the first in which the previously largest party returned as the largest party. Between the first post-Soviet election in 1993 and the 2014 election, there have been no less than 17 governments which mostly have been uneasy coalitions of 3 or 4 partners with divergent views and interests. In this context the benefit of external anchors is self-evident.
The external anchors each contributed in different ways: WTO accession contributed to modify the protectionist sentiments that were rife in the early years of independence. Rather curiously, Estonia, which adopted a radical free trade policy right from the first days of independence, had more difficulties in achieving their WTO membership than ‘protectionist’ Latvia. Estonia was obliged to implement additional economic regulations in order to conform to the rules of the WTO and the EU (to which it was committed to join as its WTO application proceeded), and as a consequence, Estonian WTO accession was delayed to 1999. The WTO accession process also gave Latvia’s fledgling Foreign Ministry invaluable experience of multi-lateral negotiation.
Apart from the obvious security benefit, NATO membership was conditional on the creation of the Latvian anti-corruption Bureau (KNAB) and on the liberalisation of citizenship legislation, the latter because NATO was concerned about the prospect of a member state with a large number of non-citizen residents.
EU accession represents the biggest and most significant anchor. The requirement of candidate countries to accept the EU acquis communautaire took huge swathes of economic and social legislation out of the political arena. While the economic criteria for accession presented few difficulties of principle for Latvia – most people were in favour of a market economy – the requirement of respect for and protection of minorities presented problems for many Latvian politicians and liberalisation of the citizenship law was resisted until after 1997 when the rejection of Latvia from fast-track EU accession talks in December 1997 prompted a rethinking of Latvia’s intransigent position on the quota or ‘windows system’.
It is hard to over-estimate the impact of EU accession on Latvia. What would Latvia be like today if it were not a member state of the EU? There are sufficient tendencies even now in Latvia to suggest we would observe something like a tax-haven, off-shore economy, probably with weak democratic institutions. EU accession has saved the Latvian people from something like such a fate.
Even later in Latvia’s largely self-inflicted financial and economic crisis of 2008-10 it was the ‘Holy Grail’ of accession to the Eurozone that politically anchored Latvia’s famous austerity programme.
What of today? The ‘big’ external anchors are used up, and Latvia today:
- Is the fourth poorest country in the EU with GDP per capita in 2013 at 67% of the EU average (only Croatia, Romania and Bulgaria are poorer);
- Is a particularly unequal society – Latvia has some of the worst poverty and inequality indicators in the EU;
- Has a shadow economy at 23.8% of GDP (data on 2013; Putniņš and Sauka (2014)); and
- Has an internationally uncompetitive higher education system.
These and other problematic aspects of Latvian life and society are home grown and it is hard to imagine external anchors that can improve poverty or inequality, that can reduce the size of the shadow economy, or which can improve the quality of the Latvian higher education system.
Nevertheless, Latvian policy makers seem to be addicted to the external anchor concept and often find difficult to progress without it. The recent experience of reform of the financing of higher education illustrates. Latvia has historically had a funding mechanism for universities and other higher education institutions based entirely on student numbers. The lack of a link between funding and quality has resulted in a Latvian higher education system that is strong on enrolment but low on quality e.g. as measured by peer-reviewed publications. At some level this has been understood and there has been much talk of reform. Although various reports and evaluations have been published, there has been little progress on concrete reform until the Ministry of Education commissioned the World Bank in December 2013 to produce a report on funding models for Latvia. The final report was delivered in September 2014 and action has now been taken to adopt the World Bank recommended three-pillar model where the funding criteria will now include performance and innovation.
Of course, the new model will not solve all the problems of Latvian higher education – far from it – but it illustrates the pervasive nature of policy makers seeming dependency on external anchors.
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References
- Putniņš, Tālis & Arnis Sauka (2014). “Shadow Economy Index for the Baltic Countries. 2009-2013,” The Centre for Sustainable Business at SSE Riga, May 2014.
Equity and Efficiency in the Latvian Tax-Benefit System
There is a trade-off between two major objectives of a tax-benefit system: equity and efficiency. The tax-benefit systems that redistribute a lot of income tend to generate disincentives to work. The tax-benefit systems that create good incentives to work and earn, are less effective in mitigating poverty, social exclusion and deprivation. In this brief we argue that, when contrasted to other EU countries, the Latvian tax-benefit system is less effective in achieving either of the objectives.
Equity-Efficiency Trade-Off
There is a fundamental trade-off between the two principal objectives of a tax-benefit system – income redistribution and efficiency. On the one hand, income redistribution is desirable as it helps to mitigate socially undesirable market outcomes such as poverty and deprivation. On the other hand, more income redistribution is often associated with higher distortions to labour supply and work effort.
There is no universal prescription as to how much a government should redistribute. The answer to this question depends, among other factors, on the relative value that society (government) assigns to the welfare of different population groups, and on the individuals’ labour supply elasticity.
However, a given degree of income redistribution can be achieved at a different cost of efficiency. In this brief, we analyse the degree of income redistribution generated by the tax-benefit system and work incentives in Latvia in the context of other EU countries. In our analysis, we use the European microsimulation tax-benefit model EUROMOD (Sutherland and Figari, 2013) version G2.0, EU-SILC data, and the analysis framework developed by Jara and Tumino (2013).
Income Redistribution in the EU
EU countries differ substantially in terms of inequality of original income and in terms of the degree of redistribution generated by the tax-benefit system (see Figure 1, data on 2007 and 2013). The Gini coefficient of equivalised household original income (which consists of income from employment and self-employment, property income, private pensions, private transfers and other relatively minor components) ranges from around 0.4 (Cyprus, Netherlands) to almost 0.55 (Romania in 2007, Ireland in 2013).
Inequality of original income in Latvia in 2007 was at the EU average level (Gini coefficient of 0.47), but the degree of income redistribution generated by direct taxes, benefits and pensions was the lowest in the EU. As a result, the inequality of disposable income in Latvia in 2007 was the highest in the EU (Gini coefficient of 0.37). Part of the answer as to why the degree of income redistribution in Latvia is so low is a relatively small contribution of pensions to redistribution – it is almost half of that observed in the EU on average, despite the fact that the share of public pension recipients in the total Latvian population in 2007 was above the EU average. Another important factor was the very minor role of means-tested benefits: in the EU on average, means-tested benefits generate a reduction in Gini coefficient by about 0.02, while in Latvia the corresponding figure is just one tenth of this.
Figure 1. Gini coefficients of original equivalised household income and degree of redistribution generated by tax-benefit systems in the EU in 2007 and 2013
Source: EUROMOD statistics, authors’ calculations.
In the course of the crisis and the following recovery, the degree of redistribution in Latvia increased (see lower panel of Figure 1). An important factor behind the increase was growing number of pension recipients and an increase in the average size of pensions (both in absolute terms and relative to employment income). The increase in the number of pension recipients was not a result of changes in eligibility criteria, but was due to population ageing and the fact that more people applied for other types of pensions. The growth in the average size of pension was due to generous indexation of pensions in 2008 and compositional changes, as pensions of new pensioners until 2012 were larger than the average pension. Another reason for a growing degree of redistribution was an increase in the size and the number of recipients of means-tested benefits (mainly Guaranteed Minimum Income (GMI) benefit). This was a result of reforms in the provision of the means-tested benefits and of falling incomes from employment, which made more people eligible for the social assistance programmes. Nevertheless, despite the increase in recent years, the degree of income redistribution in Latvia remains one of the lowest in the EU.
Work Incentives
The existence of a trade-off between income redistribution and better work incentives suggests that tax-benefit systems that ensure less income redistribution are likely to generate better work incentives. Jara and Tumino (2013) have demonstrated the existence of this trade-off in the EU countries in 2007-2010 by identifying a negative and statistically significant correlation between Gini coefficients and Marginal Effective Tax Rates (METR). The METR is a measure that is commonly used to quantify work incentives at the intensive margin. It shows what proportion of a small increase in earnings (which results from e.g. an increase in the supplied hours of work) is lost as a result of extra tax payments or foregone benefits that the person is no longer eligible for after the increase in earnings. The negative correlation identified in Jara and Tumino (2013) suggests that countries with less income redistribution (i.e., higher Gini coefficients) tend to have better work incentives (lower METRs).
In Latvia, the mean METR in 2013 was 32.2%, only slightly below the EU average (34.5%), and much higher than the average in Estonia (22.8%) and Lithuania (27.4%), despite a lower degree of income redistribution (EUROMOD statistics). Another feature of the Latvian tax-benefit system is that it is characterised by especially high METRs for poor individuals. Thus, in 2013, 94% of individuals who faced METRs in excess of 50% belonged to the two bottom deciles of distribution of equivalised disposable income. This is different from many other European countries, where distribution of high METRs is either more even across deciles or rising towards the top end of income distribution (Jara and Tumino (2013), data for 2007).
The main reason for high METRs faced by the poorest population groups in Latvia is the design of means-tested benefits (GMI and housing benefits), which generates 100% METRs for the recipients of these benefits. Namely, for each additional euro earned, the amount of benefit is reduced by one euro, which leaves the net income unchanged. This adversely affects employment incentives for the poorest individuals and increases the poverty risk.
Figure 2 illustrates mean METRs by deciles of equivalised disposable income in Latvia and shows the contribution of taxes, benefits and social insurance contributions (SICs) to the mean METRs. It clearly demonstrates that high METRs in the bottom deciles result mainly from the contribution of benefits, which disappears in the fourth decile. The contribution of SICs is slightly smaller in the bottom decile, which is due to the fact that the proportion of employed individuals is smaller in the bottom decile. For the same reason, and also because of basic tax allowances, the contribution of direct taxes is smaller in the bottom deciles, but then the contribution of taxes levels off, reflecting the Latvian flat tax rate.
Figure 2. The contribution of direct taxes, benefits and social insurance contributions (SIC) to METRs in Latvia by deciles of equivalised disposable income in 2013
Source: authors’ calculations using EUROMOD-LV
In their study on the incentive structure created by the tax and benefit system in Latvia, the World Bank (2013) pointed out the problem of bad work incentives generated by Latvian means-tested benefits. Our results, which are based on a population-representative database of incomes, also identify means-tested benefits as the major contributor to high METRs in the lowest deciles of the income distribution. Another concern expressed by the World Bank (2013) was that the problem of informal employment (either in the form of undeclared wages or work without a contract) can be exacerbated by high participation tax rates and METRs.
Conclusion
The Latvian tax-benefit system is characterized both by a relatively low degree of income redistribution and relatively weak work incentives, as measured by METRs. Recipients of means-tested benefits (GMI and housing benefits) are faced with 100% METRs, as benefits are withdrawn at the same rate as household income rises. This creates disincentives to increase labour supply for low-paid/low-skilled individuals, and hence creates a risk of poverty traps. Evidence from the literature suggests that the labour supply of low paid workers is particularly sensitive to the incentives generated by the tax-benefit system, hence reforms that would bring down METRs in the bottom deciles could yield positive results in terms of employment of low paid/low skilled workers.
A potential reform is to introduce either a gradual phasing out of the means-tested benefits, or to exclude a certain amount of employment income from the income test for the means-tested benefits. Such reforms would be targeted at the bottom end of the income distribution, help combat poverty, improve the incentive structure of the Latvian tax-benefit system, and positively affect the labour supply of low-skilled/low-paid workers.
References
- EUROMOD statistics on Distribution and Decomposition of Disposable Income, accessed at http://www.iser.essex.ac.uk/euromod/statistics/ using EUROMOD version no. G2.0, retrieved on October 14, 2014
- Jara, H. Xavier & Alberto Tumino (2013). “Tax-benefit systems, income distribution and work incentives in the European Union,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 27-62.
- Sutherland, Holly & Francesco Figari (2013). “EUROMOD: the European Union tax-benefit microsimulation model,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 4-26.
- World Bank (2013). “Latvia: “Who is Unemployed, Inactive or Needy? Assessing Post-Crisis Policy Options”. Analysis of the Incentive Structure Created by the Tax and Benefit System. Financial Incentives of the Tax and Benefit System in Latvia,” European Social Fund Activity “Complex support measures” No. 1DP//1.4.1.1.1./09/IPIA/NVA/001
Entrepreneurship in Latvia and Other Baltic States: Results from the Global Entrepreneurship Monitor
This policy brief summarises the results and implications of an upcoming Global Entrepreneurship Monitor (GEM) 2012 Latvia Report: a study on the entrepreneurial spirit and the latest trends in entrepreneurial activity in Latvia. The results suggest that Latvia is a rather entrepreneurial country (it rates second out of all EU countries by the share of population in early-stage entrepreneurial activity). GEM also finds that Latvian early-stage entrepreneurial activity is counter-cyclical. Early-stage entrepreneurship and self-employment have been important supports for those who were hit by the crisis in 2008-2009. Latvian entrepreneurs are measured to have strong international orientation and growth ambitions. The majority of them are young and middle-age males; in turn, females and the older age group (55-64) represent an “untapped entrepreneurial resource” potential to be addressed by policymakers.