Author: Admin
Latvian Unemployment is Cyclical
In terms of output decline and increase in unemployment, the economic recession in Latvia that started during the 2008-09 financial crisis was one of the most severe in the world. Using modern methods of statistical analysis, we demonstrate that the changes in unemployment should be attributed primarily to cyclical, rather than structural factors. This answer brings important implications for anti-crisis policy in Latvia and elsewhere in the world: it suggests that the surge in unemployment was largely a consequence of Latvia’s austerity policy, and that today, broader economic measures to support further economic recovery can be effective.
During the 2008-2009 recession Latvia experienced the EU’s largest and fastest increase in unemployment. This is illustrated in Figure 1 where it can be seen that the unemployment rate rose by approximately 14 percentage points from a low of 6.2% in early 2008 to 20.4% at the end of 2009. However, labour market recovery has not been equally rapid, with unemployment in 2011 and the first half of 2012 settling at around 16%. This corresponds to a decline of less than 5 percentage points from the peak. The most recent quarter has seen an improvement with the unemployment rate falling to 13.5%. Partly, the decline can be attributed to seasonal factors (seasonally adjusted unemployment rate declined by less; from 15.7% to 14.2%). However, if discouraged workers are counted, the reduction in unemployment was smaller and the rate of unemployment still stood at 16.8% in the 3rd quarter.
This observed persistence in unemployment is seen by many as a signal of the structural nature of the shocks that hit the economy during the recession and of the further intensification of structural problems.
Figure 1. Unemployment Rate (Age Group 15-74), Seasonally Adjusted, (%)[1] 
Note: Discouraged workers are those economically inactive who mentioned loss of hope to find a job as the main reason for not looking for a job.
Source: Central Statistical Bureau of Latvia, authors’ calculations.
For example, Krasnopjorovs (2012)[2] argues that there is a structural mismatch in the Latvian labour market, which mainly takes the form of a skills mismatch and concludes that the “employment rate now is similar to that observed in “normal times” of 2002-2004, [which] suggests a rather small [if any] negative output gap and a large share of structural unemployment in total unemployment”. Likewise, the Ministry of Finance of Latvia (2012)[3] argues that in the medium term, supply and demand mismatches will intensify. Thus, raising the risks of structural unemployment and, while not explicitly reporting their NAIRU estimates, the reported estimate for the output gap in 2012 is just -0.2% of potential GDP, but for 2013, a positive output gap of 0.7% is forecast.
The European Central Bank (2012)[4], when discussing inflation prospects in Latvia, identifies the situation in the labour market as a potential source of risk, as “labour shortages in certain sectors have appeared, suggesting that unemployment is likely to be close to its natural rate”. The European Commission’s (2012)[5] estimate for the NAIRU in 2012 is 14.6%, which is very close to the actual unemployment rate. The IMF (2012)[6] is the least categorical in characterising the nature of Latvian unemployment, arguing that “lack of skilled labor could become a constraint to growth and put pressure on wages unless the long-term unemployed re-enter the labor market”, at the same time forecasting that “[a] negative output gap and high unemployment should keep core inflation (…) low, and contribute to a gradual decline in headline inflation”.
Other commentators, e.g. Krugman[7] have argued that Latvian unemployment is largely explainable by cyclical factors.
Which explanation is correct is important both for current policy purposes and for the interpretation of past policy. Thus, “if cyclical factors predominate, then policies that support a broader economic recovery should be effective in addressing long-term unemployment as well; if the causes are structural, then other policy tools will be needed”.[8] On the other hand, “higher structural unemployment alters the role of short-run stabilization policies, including monetary policy, by increasing the possibility that expansionary policies will trigger inflation at higher rates of unemployment than otherwise”.[9]
In what follows, we evaluate the extent to which the recent evolution of Latvian unemployment can be interpreted as structural and provide some policy implications. We use three alternative approaches and all three point in the same direction: overwhelmingly both the increase in unemployment and its recovery are explainable by cyclical factors.
Decomposition of the Unemployment Rate into Structural and Cyclical Components
Our first approach is to directly decompose unemployment into structural and cyclical components. This is based on the following intuitive reasoning: when structural change occurs, unemployment is a result of changes in the composition of the labour market, i.e. the skill requirements of the jobs available today no longer match the skillset of the workers who are searching for jobs. On the other hand, when cyclical factors dominate, we would expect similar increases in unemployment across all sectors and locations. Using a formalised version of this approach, we conclude that changes in Latvian unemployment during the recession can be explained by changes in the unemployment rates in particular sectors and occupations, while the shares of the sectors and occupations in labour supply have been practically unchanged.
Following Lazear and Spletzer (2012)[10], we decompose the changes in the unemployment rate into structural and cyclical components, where the first component comes from changes in unemployment rates in a particular group assuming an unchanged structure, while the second component represents compositional changes in the structure of labour supply.
In order to implement this analysis, we use the most disaggregated categories of the sector of previous employment and occupations, which are obtainable from quarterly micro level LFS data. This covers 10 sectors of production and 9 occupations. We use a broad definition of unemployment and include discouraged workers to account for the nominal reduction in unemployment, which occurs just because people stop looking for a job. At the time of writing, data is only available for 2007-2011; hence, our analysis does not cover 2012.
Figures 2 and 3 show the decomposition of unemployment rate changes by sectors of production and by occupations.
Figure 2. Decomposition of Year-on-Year Changes in Unemployment Rate by Sectors of Production, Including Discouraged Workers, (% points)
Note: Includes only those unemployed who stopped working less than 8 years ago, for those who stopped working more than 8 years ago data on the previous sector of employment is not available; includes only those who indicated the sector of previous employment.
Source: Central Statistical Bureau of Latvia, authors’ calculations.
The sectoral decomposition suggests that the increase in unemployment in 2009-2010 can be fully attributed to cyclical factors – the structural component was small and even negative. The negative structural component is explained mainly by a reduction in the share of industry and construction in labour supply, which were sectors characterised by relatively high rates of unemployment.
Figure 3. Decomposition of Year-on-Year Changes in Unemployment Rate by Occupations, Including Discouraged Workers, (% points)
Note: Includes only those unemployed who stopped working less than 8 years ago, for those who stopped working more than 8 years ago data on the previous occupation is not available; includes only those who indicated previous occupation.
Source: Central Statistical Bureau of Latvia, authors’ calculations.
The occupational decomposition also suggests that changes in the rate of unemployment have been largely cyclical. The positive structural component in 2010Q1 can be explained by an increase in the share of civil servants, service workers, as well as shop and market sales workers. The positive structural component in 2010Q4 and 2011Q2 is a result of an increased share of craft and related trades workers, and elementary occupations.
In sum, the shares of both sectors and occupations in the economy have remained largely unchanged with unemployment changes explained by sectoral or occupational changes in unemployment rates.
Evaluating mismatch
A second approach is to directly estimate labour-market mismatch. Structural unemployment is usually defined as resulting from a mismatch between the labour demand and the skillset and locations of those looking for jobs. “[M]ismatch is defined as a situation where industries differ in their ratio of unemployed to vacancies”.[11] Using this approach our estimates show no significant mismatch between available vacancies the skills of workers.
To assess changes in the matching during the crisis, we calculate relative standard deviation of the number of unemployed per vacancy across sectors:
where x(i) is number of unemployed per vacancy in sector[12] i (including discouraged workers) and x¯ is average number of unemployed per vacancy across sectors.
Figure 4. Relative Standard Deviation of Unemployed per Vacancy across Sectors
Source: Central Statistical Bureau of Latvia, authors’ calculations.
Figure 4 presents the results of the relative standard deviation estimation. RSD increased in the beginning of the recession, but it has been declining since early 2009 indicating no increase in the degree of mismatch.
Estimating the Beveridge Curve
The third method uses the search and matching approach as developed by Pissarides (2000)[13] where the emergence of structural unemployment is signalled by deterioration in the efficiency of labour-market matching. Again, the conclusion is that except during the boom, when matching appears to have improved, Latvian unemployment cannot be explained by changes in the efficiency of matching.
We follow the Beveridge curve approach proposed by Barlevy (2011)[14] who follows Petrongolo and Pissarides (2001)[15] in assuming that matches in the labour market can be described by a Cobb-Douglas function, in which the number of matches depends on the unemployment rate, the vacancy rate, the productivity of the matching process, and elasticity of the number of matches with respect to the unemployment rate. The flow into unemployment is defined by the separation rate into unemployment; while the flow out of unemployment is given by the matching function. Equating the two flows yields the Beveridge curve which, given a constant separation rate, defines a negative relationship between vacancies and the unemployment rate.
Figure 5 plots the Beveridge curve for Latvia over 2005 – 2012Q2. We first observe that the Beveridge curve appears to have shifted downwards in 2007, pointing to an improvement in matching (an increase in the productivity parameter) as the economy approached the top of the boom. This is consistent with the idea that employers facing labour shortage became less “picky” in their hiring decisions. Starting from 2010, as the unemployment rate gradually declined there appears to have been a movement back along the Beveridge curve though perhaps with a minor outward shift.
Figure 5. Unemployment Rate (incl. Discouraged Workers) vs. Vacancy Rate in 2005-2012q2, Seasonally Adjusted
Source: Central Statistical Bureau of Latvia, authors’ calculations.
Estimating the parameters of the Beveridge curve permits assessment of changes in matching. To estimate A, we divide the sample into three periods and fit the Beveridge curve for these three periods: 2005-2006 (beginning of the boom), 2007-2009 (the peak and the recession) and 2010-2012 (the period of gradual reduction in unemployment). Apart from data on unemployment and the vacancies, we need to know the separation rate. Barlevy (2011)[16] argues that the relevant separation rate is likely to be fairly stable over the cycle – he assumes a constant separation rate of 0.03 for the U.S. (one can think of this separation rate as the flow of people from employment to unemployment in “normal” times). In the absence of concrete evidence to the contrary, we also assume a constant separation rate. However, this assumption is not crucial for our analysis, since we are interested in the change in A and not the level of A.
Figure 6 shows the fitted Beveridge curves, as well as the seasonally adjusted data over the period ranging from 2005 up to the second quarter of 2012.
Figure 6: Fitted Beveridge Curves and Actual Unemployment Rate (incl. Discouraged Workers) vs. Vacancy Rate in 2005-2012q2, Seasonally Adjusted
Source: Central Statistical Bureau of Latvia, authors’ calculations.
Our estimates of the parameters are presented in Table 1. The results show that A declined in 2010-2012, suggesting a slight deterioration in matching, yet A estimated on 2010-2012 data is slightly higher than A estimated on 2005-2006 data, the period which probably comes closest to the definition of “normal” times in our sample.
Table 1. Estimated Parameters of the Beveridge Curve
Source: Authors’ calculations.
Using estimated and the formula for the steady-state vacancy rate, we are able to calculate implied changes in A over the whole period under consideration. To do this, we employ two alternative estimates of : (1) , the estimate on 2005-2006 data, which can be viewed as estimate for “normal” times and (2) , average of estimates for the three periods.
Figure 7 illustrates the results of the estimation. These suggest that A declined from its peak in the beginning of 2008, in turn suggesting that matching has deteriorated as compared to the boom years. However, A started to grow in the end of 2011 and is currently above its level in 2005-2006. More importantly, our results suggest that there was no notable deterioration in matching since mid-2009, i.e. neither the increase in unemployment in the recession nor the subsequent recovery have been accompanied by significant intensification of labour market mismatches.
Figure 7: Implied A estimate
Source: Authors’ calculations.
Finally, our estimates of the Latvian Beveridge curve imply that changes in matching efficiency have been practically absent (except in the boom). Hence, changes in unemployment can largely be explained by cyclical factors.
Conclusion
Our analysis indicates no significant change in structural unemployment in Latvia during the 2008-2009 recession and afterwards. First, decomposition of the unemployment rate into structural and cyclical components illustrates the dominant role of the cyclical component. Second, direct estimation of mismatches also shows no evidence to support a structural explanation of the change in the Latvian unemployment rate. Finally, our estimates of the Beveridge curve during the period suggest that the efficiency of matching did not deteriorate during the recession and afterwards.
Accordingly, we conclude that in the course of the crisis not only did Latvia fall well below its long-term output trend, but Latvia is still operating below potential. This has implications for the assessment of Latvia’s internal devaluation policy. To put it in Blanchard’s (2012)[17] words: “Is it a success? The economic and social cost of adjustment has been substantial. Output further contracted by 16% in 2009, and is still 15% below its 2007 peak. Unemployment increased to more than 20% and still stands at 16% today, far higher than any reasonable estimate of the natural rate. Was there another, less costly, way of adjusting, through floating, and a slower fiscal consolidation? The truth is we shall never know”. The evidence presented here does not directly help to evaluate alternatives – still, it confirms that the chosen course was extremely costly and that today broader economic measures to support further recovery can be effective.
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References
- Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011
- Bernanke (2012), “Recent Developments in the Labor Market,” remarks to the National Association for Business Economics, March 26, 2012
- Blanchard (2012), “Lessons from Latvia”, June 2012
- Daly, Hobijn, Sahin, and Valletta (2012), “A Search and Matching Approach to Labor Markets: Did the Natural Rate of Unemployment Rise?,” Journal of Economic Perspectives 26(3), Summer 2012, pp. 3-26
- Daly, Hobijn, and Valletta (2011), “The Recent Evolution of the Natural Rate of Unemployment,” IZA Discussion Paper No. 5832, July 2011
- European Central Bank (2012), “Convergence report”, May 2012
- European Commission (2012), Autumn 2012 Forecast Exercise, Estimates of output gap and of potential output and their determinants, https://circabc.europa.eu, November 2012
- IMF (2012), “Republic of Latvia: First Post-Program Monitoring Discussions”, July 2012
- Krasnopjorovs (2012), “What is missing in Krugman’s structural unemployment story?”, blog on Bank of Latvia website, June 2012.
- Krugman, The Conscience of a Liberal, blog on New York Times, http://krugman.blogs.nytimes.com/?s=latvia
- Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012
- Ministry of Finance of Latvia (2012), “Convergence programme of the Republic of Latvia 2012-2015”, April 2012
- Petrongolo and Pissarides (2001), “Looking into the Black Box: A Survey of the Matching Function,” Journal of Economic Literature, 39(2), June 2001, pp. 390–431
- Pissarides (2000), Equilibrium Unemployment Theory (Second Ed.). Cambridge, MA: MIT Press
[1] Figure 1 uses data unadjusted for the results of the census carried out in Latvia in the first half of 2011 which showed that the population and the workforce was less than previously thought. This has implications for the calculation of all labour market statistics but the official statistics not yet been revised for years before 2011. Accordingly, for consistency over time, we use unadjusted data.
[2] Krasnopjorovs (2012), “What is missing in Krugman’s structural unemployment story?”, blog on Bank of Latvia website, June 2012
[3] Ministry of Finance of Latvia (2012), “Convergence programme of the Republic of Latvia 2012-2015”, April 2012
[4] European Central Bank (2012), “Convergence report”, May 2012
[5] European Commission (2012), Autumn 2012 Forecast Exercise, Estimates of output gap and of potential output and their determinants, November 2012
[6] IMF (2012), “Republic of Latvia: First Post-Program Monitoring Discussions,” July 2012
[7] Krugman, The Conscience of a Liberal, blog on New York Times
[8] Bernanke (2012), “Recent Developments in the Labor Market,” remarks to the National Association for Business Economics, March 26, 2012
[9] Daly, Hobijn, and Valletta (2011), “The Recent Evolution of the Natural Rate of Unemployment,” IZA Discussion Paper No. 5832, July 2011
[10] Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012
[11] Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012
[12] Here we use data on vacancies from the Central Statistical Bureau (data from enterprise surveys), since this data is more representative of the whole economy than the data on registered vacancies from the State Employment Agency. The latter is likely to be biased towards vacancies for low-qualified workers, as employers opt for different search methods for higher level positions. This is supported by the fact that, e.g. in 2012 vacancies for craft and related trades workers, plant and machine operators, and assemblers, as well as elementary occupations accounted for 50-60% of all vacancies registered with the State Employment Agency, while in the Statistical Bureau data these vacancies accounted for only about 20% of all vacancies.
[13] Pissarides (2000), Equilibrium Unemployment Theory (Second Ed.). Cambridge, MA: MIT Press
[14] Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011
[15] Petrongolo and Pissarides (2001), “Looking into the Black Box: A Survey of the Matching Function,” Journal of Economic Literature, 39(2), June 2001, pp. 390–431
[16] Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011
[17] Blanchard (2012), “Lessons from Latvia”, June 2012
Natural Resources, Intangible Capital and Sustainable Development in a Small, Oil-Rich Region
“Where scientific enquiry is stunted, the intellectual life of a nation dries up, which means the withering of many possibilities of future development.” – Albert Einstein, 1934 The rampant unemployment rates and the general contraction of economic activity in many western countries rekindled the fear of emigration and brain drain, which for a while seemed to be exclusively a developing-world problem. This brief illustrates a potential new approach to the issue, through a recent experience in a small but oil-rich region of Southern Italy.
Economic Growth and Brain Drain
Since the times of Solow, economic theory represents growth as the result of a process not unlike some sort of portfolio management. Just like any individual investor, countries own and need to manage certain assets, characterized by different properties and returns: some are exhaustible, others are renewable or living, and ensure a sustained stream of income. In the original formulations, the economy’s productive assets were identified in land, capital and labor, to which human capital was soon added. In 2006, the World Bank published estimates of 120 countries’ total wealth, in an attempt to introduce a broader view of what these assets really are [1]. The report classified a country’s capital into three main types: natural, produced (physical) and intangible. A striking pattern emerged. While the share of produced assets in total wealth is virtually constant across income groups of countries, the share of natural capital tends to fall with income, and the share of intangible capital rises. This means that rich countries are largely rich because of the skills of their populations and the quality of the institutions supporting economic activity.
There is an important relation between the different types of assets. In order to avoid illusory and temporary growth based on consuming the readily available natural capital, efficient management through saving and investment can transform one type of asset into another, achieving sustainability over time. Although this may sound as no big news, the analysis of the actual savings and rates of growth in the different form of capital reveals far from ideal situations all over the world. In many resource-rich developing countries, savings rates have been negative for many decades, meaning that resource rents have been at best used for consumption. In the worst cases, they have fueled corruption and private enrichment of small elites, as highlighted by the extensive literature on the “resource curse”.
Also, renewable natural resources are often exploited in an unsustainable fashion. One case in point is the thorny issue of fish stocks, but many more examples are discussed in the literature on ecosystem services. Even the intangible capital is under stress in many places. In the wording of the 2006 World Bank report, “intangible assets include the skills and know-how embodied in the labor force; social capital, that is, the trust among people in a society and their ability to work together for a common purpose; all those governance elements that boost the productivity of labor: an efficient judicial system, clear property rights, and an effective government.” Probably the first component in the list, what is traditionally indicated with the term human capital, is the most tangible, observable and relatively controllable part of it.
Controlling the Brain Drain?
Although there are many arguments in favor of international careers and general workforce mobility,[2] some regions experienced negative and prolonged net outflows – emigrants minus immigrants – to the extent that they now face a real risk of hold ups in their economic development. This, due to shortages of vitally needed high-skilled personnel. Even the economic sustainability of many basic services and businesses is in doubt due to the shrinking customer base.
Southern Italy is one of these regions. The net outflow of people with a bachelor or higher degree is negative[3] even at the national level, -2% over the latest ten years. In southern Italy, with a population of just above 13 million, the net balance of emigrants and immigrants over the same period amounts to -630,000. 70% of these people are aged between 15 and 34, and 25% hold at least a bachelor degree. To this figure, which is based on changes in official residence and therefore grossly underestimates the real size of the phenomenon, must be added the 150,000 that on average every year join the flow of internal migrants or long-distance commuters from the south to Northern Italy. Among these people, 47% are aged between 15 and 34, and almost 30% hold a bachelor or higher degree. The reason for these massive outflows can be identified in the labor market dynamics. If we break down the average 22% decline in job creation for youth between 2008 and 2011, new hires declined by 30% for youth with a bachelor degree and 14% for higher degrees, against 11% decline for youth with only secondary education.[4]
As opposed to physical capital, recent research shows that loss of human capital can have long lasting crippling consequences for economic growth (Waldinger, 2012). Among the policies that have been tried in order to stop or counterbalance the brain drain, a first set targets human capital as embodied in the workforce, i.e. tries to attract highly trained people. Probably the most popular are economic incentives in the form of tax rebates, higher wages or other job-related benefits and amenities. This kind of incentive regime exists in Italy since December 2010, though only targeting Italian nationals. However, for many high-skilled professionals, the important factors are others, such as a generally innovative and creative environment, a network with a critical mass, a transparent and competitive labor market not contaminated by politics, high quality support services, and other conditions that are not as easy and cheap to modify. Some countries have played the card of instead attracting prestigious foreign schools to their national territory to prevent their brilliant youth from leaving in the first place. Many famous western universities have already initiated partnerships with or lent their names to schools and universities in these countries and even built replicas of themselves – mostly in Asia – so as to get a toehold in the world’s largest education market, or in the Gulf States, where financial resources abound. There are successful examples of such partnerships in Italy, too.
A different approach has been taken by the new government, with the realization that the country can benefit from the pool of expatriated talents without moving them permanently back. A program of facilitation for visiting scholars and exchange students was thus launched in September 2012. But a step even further is actually possible. A network of scholars and high-skilled professionals that want to contribute to the development of a particular country or region, for example their place of origin, does not require physical presence on the territory, and not even any formal or institutional bond. The only needed ingredient is the Internet. Not removed from the environment and the conditions where they achieved success, these people can actually contribute even more. This is the idea behind, for example, Innovitalia.net and other smaller independent initiatives inspired by the concept of crowd-sourcing.[5]
The Experience of Basilicata
I recently witnessed (what I hope is) the birth of one such network in the region where I am from. Basilicata, also known as Lucania, is a small, poor region of less than 600,000 inhabitants scattered across 131 different municipalities on a territory of barely 10,000 squared kilometers, between the heel and the toe of the boot that the Italian peninsula resembles. Here, the crisis hit especially hard and migration outflows are since then even stronger, especially among youths. According to SVIMEZ (a think tank focused on entrepreneurship and economic activity in Southern Italy), Basilicata has lost 10% of its regional GDP since 2007, much more than the national average of -4.6%. Compared to other large European economies, Spain is currently at -2.7, while Germany and France, notwithstanding the low annual growth rates, are now back at the same level as in 2007. The youth employment rate (with the generous definition of 15-34) is alarmingly low at 30%, down by 15% since 2007, and only 24% for women. As a result, the consumption level of 27.5% of families is now below the poverty threshold, compared with 11% of families at the country level.[6]
Enter Europe’s largest onshore oil and gas reservoir; about 150,000 oil barrels are extracted in Basilicata every day, covering 12% of the national oil demand. The exploitation started in the late 1990s, although the reservoir has been known since at least the 1970s. It is expected that these oil fields will be operational until 2022, but at least one more reservoir with about the same estimated capacity remains unexploited. The regional government has for the time being blocked any new concession, hoping perhaps to negotiate better conditions. The truth is, there have been strong concerns – related to lack of transparency and in some cases to alleged corruption – voiced at the actual quantities of extracted oil and what is a fair distribution of revenues. After more than 10 years, it is hard to claim any major social impact of the project: there is a clear lack of funds to invest in local small and medium size businesses and, as observed above, unemployment in the area remains a problem while the regional population has plummeted.
Is this a case of “resource curse”? Not really. There is no clear evidence of corruption, or elite capture – the problem seems to be mostly poor management and a lack of ideas, mixed with the deeply rooted penchant of local politics for populism and the clientela system (patronage). To give an idea, creativity in using the oil money did not go much beyond the restoration of many of the small town’s pavements and facades. In 2009, in line with the so called “Development Action Plan” of the Berlusconi government, an 80 euro lump sum was distributed to all residents. After the crisis hit harder, the royalties have also been used to cover holes here and there in the current account. Data from the Ministry for Economic Development shows that capital investment in the region went down by 8.5% per year between 2008 and 2011, while current expenditure went up by 3%. Going back to the importance from the growth perspective of savings and investment versus consumption, it is worth remarking that current expenditure is (in most part) consumption.
Can this bounty instead become an answer to Basilicata’s troubles? This was the question driving the first Sustainable Development School, held at the end of October in Viggiano, a small town in the center of the oil field, hosting 23 oil wells. Sponsored by a number of institutions and associations, local or national,[7] the event attracted a group of 45 economists, sociologists, managers and entrepreneurs, engineers and culture sector specialists, in most part born in Basilicata and working or studying abroad. Seven of these participants were instead citizens of various countries in the Middle East and North Africa region, working or studying in Basilicata. This heterogeneous group worked together for two days on concrete proposals to be put on the administrator’s tables, in five main areas: Regional Economy in the new Euro-Mediterranean context, Energy and natural resources, Environmental protection, Infrastructure for environmental protection, Promotion of the historical, cultural and social heritage. Given the context, most projects focused on alternative proposals for how to use the royalties. The keyword was, however, sustainability. Everybody was well aware of the fact that for them to last longer than oil itself, these resources must be saved and earmarked to some productive use that, leveraging on other locally abundant resources, can start off a process of self-sustained development. The projects highlighted the stimulation of local small-scale entrepreneurship and the creation of employment opportunities as necessary ingredients for a fairer sharing of the revenues but most importantly for long-term sustainability.
Many local resources, not fully utilized at present, were brought in as examples: the abundant wood, the underexploited waterways, even the wastewater from bigger agricultural and animal farms, connected to the potential for small-scale generation of energy from renewable sources. On a slightly different note, the list continued with the historical and cultural heritage, natural beauty and the religious and culinary traditions that could support a much more developed tourism industry than what it does today. All of this, in the proposals of the participants, has the potential to support profitable businesses that bring employment to the community. This ingredient is considered crucial, in the perspective that the long-term survival of any (business) initiative requires tying its success to the welfare of the local communities. The focus was thus overwhelmingly on private initiative, with the public confined to the role of investing partner and provider of supportive infrastructure (material and immaterial) and services.
Overarching is undeniably the question of institutional quality, needed as the underlying canvas to support whatever initiative we hope to see blooming. A proposal that did not make it to the finals, though, involved the creation of a stable watchdog, either on local policies in general (and in particular on the use of the royalties) or more specifically focused on the environmental and health impact of the extractive activity. According to the more politically experienced participants, no administration would agree to finance an independent body with the explicit mandate to criticize them. Never mind that this type of institutions is common in other places. In Italy, the one body that currently operates with a watchdog function on the public administration, although limited to the financial aspect,[8] is facing threats of limitations of its powers. A lot remains to be learned. However, the perhaps most valuable outcome of this experience was, if not yet policy change at least a promising method to produce change, by mobilizing a latent ‘local’ resource and really transform oil rents in durable intangible capital.
References
- Where Is the Wealth of Nations? Measuring Capital for the 21st Century. Washington, DC: The World Bank, 2006
- The brain drain in Spain is mainly to Spain’s gain, The Economist, April 2012
- The Inclusive Wealth Report 2012, Cambridge University Press, 2012
- Rapporto sull’economia del Mezzogiorno, SVIMEZ, 2012
- Peer effects in science: evidence from the dismissal of scientists in Nazi Germany, Waldinger, F., The Review of Economic Studies, 2012
[1] Updates on these figures for a subset of 20 countries can be found in the newly released Inclusive Wealth Report 2012 , sponsored by a number of UN agencies, the first of what is intended to be an annual report looking at a broad measure of wealth. From the report: “Wealth is the social worth of an economy’s assets: reproducible capital; human capital; knowledge; natural capital; population; institutions; and time.”
[2] The Economist recently pointed out that “[w]hat some call “brain-drain” may in fact be a win-win situation for Europe’s economies. […I]n the short run, migration takes away pressure from budgets as the unemployed don’t claim benefits but move [abroad] instead. In the long run, there is a pool of highly skilled workers who have not fallen victim to hysteresis effects and can be re-activated for the [home] economy once the crisis is over.” However, it is not at all obvious that this migration is short-run, i.e. that these high-skilled workers will eventually go back. A survey of Italian scientists working aboard reveals, for instance, that the overwhelming majority excludes ever going back to Italy.
[3] The “import” of such people generally more than compensates the “export” in other big European countries.
[4] Source: SVIMEZ, 2012.
[5] A recent paper analyzing the experience of New Zealand (Davenport, 2040) reviews the waves of brain-drain response policies and calls this latest generation diaspora policies: “Diaspora policies are based on an assumption that many expatriates are not likely to return, at least in the short term, but represent a significant resource wherever they are located. This resource is not just embodied in the individual expatriate but also potentially includes their socio-professional networks. A key advantage of any diaspora option is that such connectivity initiatives do not require a large infrastructural investment in order to potentially mobilize this latent ‘national’ resource.”
[6] Source: ISTAT.
[7] Sponsors and partners included the municipal and regional administration, the Italian Institute for Asia and Mediterranean (ISIAMED) and its local branch, CeBasMed, the Val d’Agri National Park, the Regional Environmental Protection Agency, SVIMEZ and the University of Basilicata.
[8] The Corte dei Conti tribunal.
For Some Mothers More Than Others: How Children Matter for Labor Market Outcomes When Both Fertility and Female Employment Are Low
Authors: Krzysztof Karbownik and Michal Myck, CenEA.
Wide spread entry of women into the labor force has been one of the most pronounced socio-economic developments in the 20th century, and high levels of female employment are crucial from the point of view of continued economic growth and financial stability of many welfare systems (Galor and Weil, 1996). At the same time, demographic changes determined by the current and future fertility levels will play a vital role in shaping these developments and will affect the costs of social programs. Given the potentially strong link between female employment and family size, it seems that understanding the relationship between the two ought to be at the heart of policy discussions, especially in countries that are characterized by both low fertility and low female employment. In particular, in light of rising unemployment in low-fertility countries, which have been most severely affected by the economic crisis such as Greece, Spain and Latvia, our findings may serve as a guide with respect to the relationship between fertility and labor supply in an environment, which will be more common in Europe in the near future.
Becoming Entrepreneur in Belarus: Factors of Choice
This policy brief summarizes two papers by Maryia Akulava on entrepreneurship development in Belarus and outlines which factors affect the choice of becoming self-employed in Belarus. While one of the papers, “Choice of Becoming Self-Employed in Belarus: Impact of Monetary Gains”, focuses on the role of pecuniary benefits, the other paper, “Portrait of Belarusian Entrepreneur”, adopts a broader perspective by accounting for individual, sociological, and institutional factors.
Although the Belarusian government has repeatedly declared the importance of private entrepreneurship for the national economy, its role remains rather modest. In terms of private sector development, Belarus lags severely behind other post-socialist countries. Yet, over the last decade, some positive dynamics have been recorded. In particular, the number of small and medium enterprises (SMEs) per 1,000 people increased from 2.5 in 2003 to 7.2 in 2010. Still, this ratio is rather small in comparison with other post-socialist economies (Table 1) [3; 4; 5; 6].
Table 1. Number of Small Enterprises (SEs) per 1,000 People
| Number of SEs per 1000 people | |
| Belarus | 7.2 |
| Russia | 11.3 |
| Ukraine | 17 |
| Kazakhstan | 41 |
| United Kingdom | 46 |
| Germany | 37 |
| Italy | 68 |
| France | 35 |
| EU countries | 45 |
| United States | 74.2 |
| Japan | 49.6 |
Regarding the growth rates of SEs and individual entrepreneurs (IEs), the numbers leave much to be desired. Specifically, in 2009, the number of SMEs and IEs amounted to 62,700 and 216,000 respectively, while in 2011 – to 72.200 and 232,000. Therefore, despite the efforts of the authorities to encourage the development of private initiative, the number of SEs and IEs only increased by 15.2 and 7.4%, respectively.
Next, private sector employment remains rather low. It amounts to approximately 13%, while in the developed economies this figure varies between 60 and 70%. For instance, in the U.S., it amounts to 60%, in Germany and in France – around 65-70%, and in Japan – 85%. On the other hand, transition economies have smaller shares, including Russia – 17%, Kazakhstan – 20.6%, and Ukraine – up to 28.8%, [7].
Some important indicators are provided in Table 2 [8].
Table 2. Share of Small and Medium Business in Economic Indicators of Belarus
| Share of small sector | 2003 | 2008 | 2009 | 2010 |
| GDP | 8.2 | 11.2 | 11.4 | 12.4 |
| Volume of industrial production | 8.4 | 8.3 | 9.2 | 9.4 |
| Exports | 18.2 | 31.4 | 34.3 | 38.9 |
| Retail trade turnover | 9.2 | 27.8 | 29.5 | 28.2 |
| Economically active labor force | 13 | 13 | 13 | 13.1 |
Table 2 reveals an increased contribution of private entrepreneurs to the national economy. At the same time, the share of labor employed in the private sector remains unchanged at the level of 13%. This fact suggests that self-employment remains relatively unattractive for salaried workers.
So, what are the drivers of people’s choice? On the one hand, people might be reluctant to become entrepreneurs because of the prevailing social and cultural attitudes, or the lack of necessary experience. Post-socialist economies all share the legacy of planning and suppression of private initiative. On the other hand, government’s policies and regulations might ‘cool down’ enthusiasm or people simply have had or heard of some bad experiences. Thus, it is important to think of the reasons behind people’s choice and formulate policies to encourage entrepreneurship development in Belarus.
Who Is a Belarusian Entrepreneur?
In Belarus, entrepreneurs are active mainly in the non-manufacturing sector, including trade (30% of all entrepreneurs), provision of different services (16.5%), construction (13%), logistics (7%), and real estate (7%). The most common reasons to start your own business include a sudden, but attractive, business opportunity (66%), and the availability of funding for project implementation (33%).
As for the gender and age profiles of Belarusian entrepreneurs, 64% are men and 36% are women, with an average age of around 40-42 years. The majority of entrepreneurs is religious (54%), married (69%), and has children (75%). Around 65% have higher education, and about one third of them were among the top 10% students of their classes. Entrepreneurs report a good health status: 64% of them consider themselves as ‘healthy’. This is not surprising, given that entrepreneurship in Belarus is ‘survival for the fittest’. An entrepreneur has to be ready to take risks, be energetic, active and to continuously search for new business opportunities. Moreover, entrepreneurs are optimists, who evaluate themselves as successful (77%) and happy (81%) people.
Sociological characteristics reveal strong reliance on social networks. In general, the number of relatives or friends involved in the business activities is about two times larger than for salaried workers. Besides that, a much larger share of entrepreneurs consider their parents wealthy and successful (45% and 82%), compared with employees (34% and 37%, respectively).
Belarusian entrepreneurs stay in business because they like what they do (53%), and think that their work is important for society (29%). Profits and income remain a strong, but are not a decisive reason (25%).
Although entrepreneurs and employees do not differ substantially in terms of their attitudes towards family, friends, health, financial stability, religion, and so on, there is still a notable distinction. Specifically, entrepreneurs tend to praise work, power and influence over other people, and also like political freedom. In addition, they value their function of a service provider to other people.
Moreover, entrepreneurs have more trust to colleagues, other business people and subordinates than salaried workers. This is not surprising, given the importance of horizontal networks mentioned above. It is important to note that more than 30% of respondents expressed their trust to political authorities despite the government-induced difficulties for entrepreneurship development in Belarus.
Analysis of institutional infrastructure for doing business detects a negative relationship between a publicly-stated favorable attitude of authorities towards entrepreneurs and their decision to work in the private sector. This can be explained in following way: a priori, the government’s stance on entrepreneurship is evaluated positively, or at least considered as not harmful. Moreover, a person considers himself as being too small to attract the ‘extractive attention’ of the authorities. However, a posteriori, entrepreneurs revise their initial views. Their experience tells us that the government’s attitude is far from welcoming.
As for corruption, the attitude is ambiguous. On the one hand, entrepreneurs generally disfavor corruption. On the other hand, those who seek to expand their businesses consider corruption a way to avoid ‘unnecessary troubles’ and to overcome barriers created by the excessive ‘red tape’ in the economy.
What Are The Obstacles For Doing Private Business In Belarus?
Belarusian entrepreneurs consider the following factors as barriers to business development: (i) inflation and macroeconomic instability (55%), (ii) lack of financing (31%), (iii) high taxes (27%) and complexity of tax system (18%), (iv) legal vulnerability (23%), and (v) toughness of state administrative regulation inspections, licensing and certification requirements (19%). These barriers are largely of macroeconomic and regulatory nature. Moreover, authorities conduct a policy of close-to-full formal employment. This policy is aimed at securing jobs for people even at loss-making and poorly performing companies, which are kept afloat by subsidizes and directed loans. As a result, employees prefer to trade risks of working in the private sector, for a stable employment in the sector of state-owned enterprises.
As for the main barriers, which impede business start ups financial constraints are the most common factor (33%), followed by high risks (25%), the lack of necessary business skills, a clear understanding what to do in the market (15% and 13% respectively), and unwillingness to work a lot (16%). In other words, financial constrains along with the lack of business education are the two most important domestic barriers.
These findings correspond to the results of the research on the impact of pecuniary benefits on entrepreneurs. In that study, education does not appear to have a significant influence on the level of earnings by entrepreneurs. The latter are ‘self-trained’ by the experience of starting a business in the uncertain environment of the 1990s and matured in the course of doing their business in unfriendly conditions. However, as the economy evolves, activities and contracts become more sophisticated. To survive in the changing environment, entrepreneurs have to acquire new skills and learn new methods and concepts of doing business.
So far, it appears that the quality of education obtained by the entrepreneurs does not match the skills required in the Belarusian economy. Thus, it is important to organize seminars, to hold training and to run business education programs for the future and current entrepreneurs in order to upgrade their skills and thus to contribute to their improved performance on the market.
Conclusion
An efficient development of the private sector in Belarus requires a drastic improvement of the domestic business environment. In order to encourage domestic entrepreneurship, the authorities should improve macroeconomic management and cut much of the ‘red tape’. Entrepreneurship possesses a great potential to contribute to growth and development. Surveys reveal that government policies constrain the development of the domestic private sector. Moreover, the high tax burden should be reduced, and some fiscal ‘sweeteners’ could be offered for business startups. In addition, a somewhat higher priority should be given to the improvement of the quality of business education, and make it more accessible for the current and future business people. If implemented, all these measures would supposedly have a fostering impact on the development of a dynamic private sector in Belarus.
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References
Akulava M. 2012. “Choice of Becoming Self-Employed in Belarus: Impact of Monetary Gains”.
Akulava M. 2012. “Portrait of Belarusian Entrepreneur”. Work in progress.
Djankov S., Miguel E., Qian Y., Roland G. and Zhuravskaya E. 2005. “Who are Russia’s Entrepreneurs?” Journal of the European Economic Association, MIT Press. Volume 3 (2-3), 04/05.
Djankov S., Miguel E., Qian Y., Roland G. and Zhuravskaya E. 2006. “Entrepreneurship in China and Russia Compared” Journal of the European Economic Association, MIT Press. Volume 4 (2-3), 04/05.
http://netherlands.mfa.gov.by/_modules/_cfiles/files/sme_belarus_2011_1670.pdf
http://www.tambov-rosnou.ru/monograf/files/ind4.htm
http://www.erce.ru/internet-magazine/magazine/27/389/
http://www.mspbank.ru/files/documents/Ukraine.pdf
Sulakshin S. “State Economic Policy and Economic Doctrine of Russia. To Smart and Ethic Economy”. Т. II.
http://netherlands.mfa.gov.by/_modules/_cfiles/files/sme_belarus_2011_1670.pdf
Recent Dynamics of Returns to Education in Transition Countries
While, in an international comparison, transition countries spend a relatively large share of their GDP on education, and the population in transition countries is fairly highly educated, the returns to education in transition countries have been found to be relatively low, especially in comparison to other developing countries. In our paper, ‘Recent Dynamics of Returns to Education in Transition Countries’, we investigate whether the economic boom that transition countries experienced up to the 2008 financial crisis, increased the returns to education in these countries. Theories of skilled-biased technical change typically predict that periods of fast economic growth go together with an increase in the relative demand for skilled labor and hence an increase in the returns to education.
Using data from the 2007 wave of the International Social Survey Program (ISSP), the estimated return to an additional year of schooling in transition countries varied between a low 5.2 percent in Ukraine to a high of about 10 % in Poland (see Figure 1). Returns in transition countries were relatively low compared to developing countries in the ISSP sample, and on average not unlike OECD countries.
Figure 1. Returns to Education by Countries, 2007 Wave – Basic Specification
Note: Coefficients of the years of schooling variable in earning regressions. Dependent variables are monthly earnings. Specification includes: potential experience (linear and squared), dummy for gender. Source: Ukraine – ISSP 2008, all other countries – ISSP 2007.
The estimated dynamics in returns to education in the period 2002-2007 further suggest that the economic boom that took place in that period did not affect people with different amounts of education in different ways. Returns to education increased slightly in some transition countries and decreased slightly in others, but overall returns to education remained relatively moderate. More specifically, from table 2 we can see a decrease in returns in Bulgaria, Latvia and Poland, and an increase in the Czech Republic, Russia, Slovakia and Slovenia. Both increases and decreases are small in size however.
Table 1. Dynamics of Returns, Basic Specification
Note: Coefficients of the years of schooling variable in earning regressions with few controls as specified in the text.Source: Estimates for 1991-2002 are from Flabbi et al. (2008); estimates for 2007 and for Ukraine are by the authors.
A more detailed analysis for Ukraine using data from the Ukrainian Longitudinal Monitoring Survey, confirmed that economic growth did not have a major impact on the returns to education. The analysis for Ukraine however does suggest that, while in 2003 a secondary degree resulted in a somewhat higher wage, just having secondary education was no longer a differentiating factor in 2007.Moreover, only academic education made a difference, possibly because less and less people were paid very small wages (i.e. less than the official minimum wage).
The relatively limited importance of education for success on the labor market does not only show itself in the low estimated returns to education, it is also clear from the opinions people express about the factors that are important to get ahead. Table 3 gives the percentage of people who say a given factor is essential, important or fairly important to get ahead in a given country (based on the 2009 ISSP).
Table 2. To get ahead, it is essential, important or fairly important to
In most transition countries in the sample, most people think that hard work and ambition is the key to get ahead. Ukraine is no exception with hard work being thought to be essential, important or fairly important by about 94 percent of the respondents. Having a good education is thought to be at least fairly important by only about 73 percent of the respondents, with four other factors, besides hard work, scoring better on this criterion: having political connections, having ambition, having a wealthy family and knowing the right people. Also for the other transition countries in our sample, good education ranks only 5th, 6th or 7th.
Optimists could interpret these results as implying that at least education does not create the same social inequalities in the transition countries as it does in some other countries. Pessimists, on the other hand, who see education as an important driver of economic growth, will argue that low returns to education mean there is a low incentive for people to invest in education and that it is better to have education as a source of inequality rather than political or social connections, or having a wealth family.
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The Eurasian Customs Union among Russia, Belarus and Kazakhstan: Can It Succeed Where Its Predecessor Failed?
In 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union and imposed the Russian tariff as the common external tariff of the Customs Union. This resulted in almost doubling the external average tariff of the more liberal Kazakhstan. Russia has benefited from additional exports to Kazakhstan under the protection of the higher tariffs in Kazakhstan. However, estimates reveal that the tariff changes have resulted in substantial transfers from Kazakhstan to Russia since importers in Kazakhstan now purchase lower quality or higher priced Russian imports which are protected under the tariff umbrella of the common external tariff. Transfers from the Central Asian countries to Russia were the reason the Eurasian Economic Community (known as EurAsEC) failed, so this bodes badly for the ultimate success of the Eurasian Customs Union. What is different, however, is that the Eurasian Customs Union and its associated Common Economic Space aim to reduce non-tariff barriers and improve trade facilitation, and also to allow the free movement of capital and labor, liberalize services, and harmonize some regulations. Estimates by my colleagues and I show that if substantial progress could be made in trade facilitation and reducing non-tariff barriers, this could make the Customs Union positive for Kazakhstan and other potential Central Asian members. Unfortunately, so far the Customs Union has made these matters worse. On the other hand, Russia’s accession to the World Trade Organization will eventually substantially reduce the transfers from Kazakhstan to Russia, but this will need a strong political commitment from Russia which we have not yet seen. If that Russian political leadership is forthcoming, the Eurasian Customs Union could nonetheless succeed where its predecessor has failed.
In January 2010, Russia, Belarus and Kazakhstan formed the Eurasian Customs Union. Two years later, the three countries agreed to even closer economic ties, by signing the agreement to form a “common economic space.” Regarding tariffs, the key change was that the three countries agreed to apply the tariff schedule of the Customs Union as their common external tariff for third countries. With few exceptions, the initial common external tariff schedule was the Russian tariff schedule. Kazakhstan negotiated exceptions from the common external tariffs for slightly more than 400 tariff lines, but was scheduled to phase out the exceptions over a period of five years (World Bank, 2012). In addition, the members agreed to have the Customs Union determine the rules regarding sanitary and phyto-sanitary standards (SPS) and standards on good. Fearing transshipment of goods from China through Kazakhstan and from the European Union through Belarus, Russia negotiated and achieved agreement on stricter controls on the origin of imports from countries outside of the Customs Union. The common economic space (CES) stipulates that, in principle, there will be free movement of labor and capital among the countries, there will be liberalization of services on the CES and coordination of some regulatory policies such as competition policy.
In February 2012, the Eurasian Economic Commission began functioning. It is intended to act as the regulatory authority for the Customs Union in a manner similar to the European Commission for the European Union.
The Economics of Tariff Changes — Gains for Russia and Losses for Kazakhstan
Some proponents of the Eurasian Customs Union have argued that as a result of the Customs Union firms in the three countries will have improved market access through having tariff free access to the markets in all three countries. Prior to 2010, however, along with other countries in the Commonwealth of Independent States (CIS), the three countries had agreements in place that stipulated free trade in goods among them. Thus, the Customs Union could not provide improved market access due to reducing tariffs on goods circulating among the three countries.
Since the common external tariff was essentially the Russian tariff, there was little change in incentives regarding tariffs in Russia. The big change occurred in Kazakhstan, who had a much lower tariff structure than Russia prior to implementing the Customs Union tariff. Despite the exemptions, Kazakhstan almost doubled its tariffs in the first year of the Customs Union (see World Bank, 2012). The increase in tariffs on many items which were not produced in Kazakhstan but produced in Russia, led to a substantial increase in imports from Russia and displacement of imports from Europe. Many of Russia’s manufacturing firms, which were not competitive in Kazakhstan prior to the Customs Union, were now able to expand sales to the Kazakhstani market. This represents gains for Russian industry. Given the deeper manufacturing base in Russia compared with most of the CIS countries and the resulting uneven benefits of the common external tariff in favor of Russia, acceptance of the common external tariff has been a fundamental negotiating position of Russia regarding acceptance of members in the Customs Union.
Some cite the expanded Russian exports in Kazakhstan as evidence of success of the Customs Union. But the displacement of European imports, to higher priced or lower quality imports from Russia, represents a substantial transfer of income from Kazakhstan to Russia and is an example of what economists call “trade diversion”. Moreover, it is the reason the World Bank (2012) has evaluated the tariff changes of the Customs Union as a loss of real income for Kazakhstan.
Furthermore, the three countries together (and even a broader collection of CIS countries) constitute too small a market to erect tariff walls against external competition. They would lose the benefits of importing technology from advanced countries and would rely on high priced production from within the Customs Union. Some would argue that there are political benefits of trade to be taken into account, but experience has shown that when a customs union is inefficient and the benefits and the costs of the customs union are very unequal, the customs union can inflame conflicts (see Schiff and Winters, 2003, 194-195).
Non-Tariff Barriers — Extremely Costly Methods of Regulating Standards Worsened by the Customs Union
Non-tariff barriers, in the form of sanitary and phyto-sanitary (SPS) conditions on food and agricultural products and technical barriers to trade (TBTs) on goods, are a very significant problem of the Customs Union. There are standards based trade disputes between Belarus and Russia on several products, including milk, meat, buses, pipes and beer (see Petrovskaya, 2012). Anecdotal evidence indicates that Kazakhstani exporters complain bitterly regarding the use by the Russian authorities of SPS and TBTs measures, either to extract payments or for protection.
If the Customs Union could make substantial progress on reducing these barriers, it would be a significant accomplishment. My colleagues and I have estimated that progress on the non-tariff barriers and trade facilitation could outweigh the negative impact of the tariff changes for Kazakhstan (see World Bank, 2012). Unfortunately, so far the Customs Union has taken a step backward on both non-tariff barriers and trade facilitation.
A big problem in reducing standards as a non-tariff barrier is that standards regulation, in all three countries, is still primarily based on the Soviet system. As a holdover from the Soviet era, mandatory technical regulations are employed where market economies allow voluntary standards to apply. This regulatory system makes innovation and adaption to the needs of the market very costly as firms must negotiate with regulators when they want to change a product or how it is produced. Legislation in both Russia and Kazakhstan calls for conversion to a system of voluntary standards, but this is happening too slowly in all three countries. The problem is that the Customs Union has worsened the situation. Technical regulations are now decided at the level of the Customs Union, so firms that previously negotiated with their national standards authority, have had to now get agreement from the Customs Union. This has reportedly caused further delays, impeding innovation and the ability of firms to meet the demands of the market.
A second problem with efforts to reduce the non-tariff barriers is that the Customs Union is trying to harmonize standards of the three countries by producing mandatory technical regulations. The alternative is to use Mutual Recognition Agreements (MRAs). Experience has shown that no customs union has been able to broadly harmonize standards based on mandatory technical regulations, with the exception of the European Union. In fact, even in the European Union, they have had to use MRAs and only harmonized technical regulations after decades of work. While each member of the Customs Union is expected to create a system of mutual recognition of certificates of conformity, these certificates are not presently recognized in the other countries of the Customs Union. There is little hope for a significant reduction in standards of non-tariff barriers unless the system of mutual recognition is more widely recognized and adopted.
Trade Facilitation —Participation in International Production Chains Made More Difficult by the Customs Union
Customs posts between the member countries have been removed and this has reduced trade costs for both exporters and importers in the three countries. Russia’s concerns regarding transshipment have, however, led to an opposite impact on trade with third countries, i.e., the costs of trading with countries outside the Customs Union have increased. Participation in international production chains has become a key feature of modern international production and trade. If goods cannot move easily in and out of the country, multinational firms will look to other countries to make their foreign direct investment and for international production sharing. Addressing this significant problem will take a change of emphasis on the part of Russia.
Russian WTO Accession —Liberalization That Will Significantly Reduce Transfers to Russia
It has apparently been agreed by the Customs Union members that the common external tariff of the Customs Union will change to accommodate Russia’s WTO commitments. As a result, the applied un-weighted average tariff will fall in stages from 10.9 percent in 2012 to 7.9 percent by the year 2020 (see Shepotylo and Tarr, forthcoming).[1] This will have the effect of lowering the trade diversion costs of Kazakhstan. In addition, the Customs Union will be expected to adapt its rules on standards to conform to commitments Russia made as part of its WTO accession commitments. In the case of Belarus, it remains to be seen if it will implement the changes, as this will increase competition for its industries.
Conclusion — the Need to Russia to Exercise Political Leadership for Standards and Trade Facilitation Reform for Success of the Customs Union
In 1996, the same three countries formed a customs union. Later the same year, they were joined by Kyrgyzstan, then by Tajikistan and in 2005 by Uzbekistan. As Michalopoulos and I (1997) anticipated, the earlier Customs Union failed because it imposed large costs on the Central Asian countries, which had to buy either lower quality (including lower tech goods) or higher priced Russian manufactured goods under the tariff umbrella. The present Customs Union also started with the Russian tariff, which protects Russian industry and suffers from the same problem that led to the failure of the earlier Customs Union. Nonetheless, the present Customs Union could succeed. Crucially, due to Russia’s accession to the WTO, the tariff of the Customs Union will fall by about 40 to 50 percent.[2] This will make the Customs Union a more open Customs Union, very significantly reduce the transfers from Kazakhstan to Russia, and thereby reduce the pressures from producers and consumers in Kazakhstan on their government to depart from enforcement of the tariffs of the Customs Union. Further, the present Customs Union aims to reduce non-tariff barriers and improve trade facilitation, as well as it has “deep integration” on its agenda, i.e., services liberalization, the free movement of labor and capital and some regulatory harmonization. Although, to date, the Customs Union has moved backwards on non-tariff barriers and trade facilitation, one could optimistically hope for substantial progress. In the important area of non-tariff barriers, given the common history of Soviet mandatory standards, Russia will have to take the lead in moving the Customs Union toward a system of voluntary standards where no health and safety issue are involved, and toward a system of mutual recognition agreements and away from commonly negotiated technical regulations. On trade facilitation, Russia will have to reverse its pressure and find a way to allow the freer movement of goods with third countries while addressing its transshipment concerns.
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References
- Michalopoulos, Constantine and David G. Tarr (1997), “The Economics of Customs Unions in the Commonwealth of Independent States,” Post-Soviet Geography and Economics, Vol. 38, No. 3, 125-143.
- Petrovskaya, Galina (2012), “Belarus, Rossia, Ukraina. Obrechennye na torgovye konflikty” (Belarus, Russia, Ukraine. Doomed for trade conflicts), Deutsche Welle, June 14. www.dw.de/dw/article/0,,16023176,00.html.
- Schiff, Maurice and L. Alan Winters (2003), Regional Integration and Development, Washington DC: World Bank and Oxford University Press.
- Shepotylo, Oleksandr, and David G. Tarr (2008), “Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001–2005,” Eastern European Economics, 46(5):49–58.
- Shepotylo, Oleksandr, and David G. Tarr (forthcoming), “Impact of WTO Accession on the Bound and Applied Tariff Rates of Russia,” Eastern European Economics.
- Shymulo-Tapiola, Olga (2012), “The Eurasian Customs Union: Friend or Foe of the EU?” The Carnegie Papers, Carnegie Endowment for International Peace, October. Available at: www.CarnegieEurope.eu,
- World Bank (2012), Assessment of Costs and Benefits of the Customs Union for Kazakhstan, Report Number 65977-KZ, Washington DC, January 3, 2012. Available at: http://documents.worldbank.org/curated/en/2012/01/15647043/assessment-costs-benefits-customs-union-kazakhstan
[1] The final “bound rate” of Russia is higher at 8.6 percent on an un-weighted average basis; but there are about 1,500 tariff lines where the applied rate of Russia is below the bound rate. The applied weighted average tariff will fall from 9.3 percent in 2012 to 5.8 percent in 2020.
[2] Russian tariffs fall more on an un-weighted average basis than they do on a weighted average basis. See Shepotylo and Tarr (forthcoming).
Corruption in Eastern Europe as Depicted by Popular Cross-Country Corruption Indicators
In recent years, variously defined indicators of corruption from different sources have aimed at raising awareness about corruption and to provide researchers with better data for analyzing the causes and consequences of corruption. Most of them have achieved spectacular popularity, and are regularly cited in news reports on corruption around the world. However, in a 2006 study for the World Bank, Stephen Knack warns that the particular properties and limitations of these indicators are often neglected by data users, often leading to wrong interpretations and sometimes puzzling disagreements about the actual situation in a country or a region and its changes over time. The first part of this brief summarizes the main conclusions of this study; the second part presents updated data from different sources on recent corruption trends in the new EU members and the neighbors to the east, as a clear exemplification of the issues discussed.
Existing corruption indicators differ in many ways: where the original information or evaluation comes from, how they are built, who are their constituencies or audiences, as well as which of the many aspects of corruption they intend to capture. For these reasons, no single indicator or data source is best for all purposes.
The corruption indicators can be subdivided into three main groups: those based on surveys, either of firms or households, those reporting expert assessments, and finally, the recently popular composite indexes.
Two examples of firms’ surveys that will be presented below are the Business Environment and Enterprise Performance Survey (BEEPS) and the World Economic Forum (WEF) “Executive Opinion Survey”. Similar enterprise surveys have been conducted by the World Bank and in the IMD World Competitiveness Yearbook. However, BEEPS and WEF are more systematic and better comparable across countries and years, have broader coverage and disclose more information about their definitions and methodology, which makes them, in a sense, more research-friendly.
Surveys are relatively well-suited for evaluating the administrative corruption since they measure the prevalence of corruption as experienced by users of government services. They can also measure some aspects of state capture by asking about perceived undue influence over laws and regulations that affect business. However, surveys are definitely less effective in assessing the prevalence of corrupt transactions that occur entirely within the state, for example when politicians bribe bureaucrats or when funds are illegally diverted. Many types of conflict of interest are also not easily captured by surveys. For example, the equity stakes of public officials or employment promises to them by the firms (World Bank, 2000).
Expert assessments of corruption have been most widely used for comparisons across countries and over time because of bigger coverage in both dimensions. A large and growing number of organizations provide such assessments. Some examples are Freedom House’s Nations in Transit (NIT), the International Country Risk Guide (ICRG), the World Bank’s Country Policy and Institutional Assessment (CPIA). Corruption ratings from these sources are based on the assessment by a network of correspondents with country-specific expertise. In some cases, the final ratings are subsequently determined centrally by a smaller group of people. The organizations that are behind these indicators may be very different, with potential implications for what their ratings are measuring. Some are advocacy NGOs. Others are for-profit companies marketing their product to multi-national investors and paying subscribers. Most subscribers to the ICRG, for example, are more interested in conditions faced by foreign investors than in those faced by local residents. Corruption ratings produced by development agencies are also potentially influenced by their constituents (if for example they take into account the consequences for funds allocation decisions or relations with local partners).
An important difference as compared to the firms or households surveys is that corruption assessments place less emphasis on experience and more on perceptions. Moreover, the respondents in a firms’ survey can be asked more specific and objective questions because they comprise a more homogeneous group. For example, a typical question can be “Was an informal gift or payment expected or requested to this establishment, in reference to the application for an electrical connection?” (from the BEEPS 2009 questionnaire). Instead, a questionnaire directed to a group that includes public officials, academics, journalists, etc. must frame questions in such a way that they can be answered meaningfully by all of them, which necessitates broader questions.
More recently, composite indexes have gained popularity. Well known examples include Transparency International’s widely-cited “Corruption Perceptions Index” and the World Bank Institute (WBI) “Control of Corruption” index (Kaufmann, Kraay and Mastruzzi, 2008). Although the statistical methods used to produce them vary somewhat, both indexes standardize several corruption indicators such as ICRG, CPIA and even survey outcomes, to place them on a comparable scale, then aggregate them, so as to obtain a single value for each country. As a result, composite indexes suffer from the same problem as the corruption measures from individual sources such as ICRG, NIT or CPIA: if any component of a composite index is constructed in an opaque manner, the composite index will be opaque as well. Further limitations are introduced by the process of aggregation. Composite indexes have no explicit definition, but instead are defined implicitly by what goes into them. The sources used in constructing these composite indexes change over time, and from country to country in a given year. For any pair of countries the index values are very likely to reflect differing implicit definitions of corruption.
The standardization procedure used to place different indicators on a common scale precludes the ability to track changes meaningfully over time. A final issue with the composite indexes is the interdependence of expert sources. If expert assessments display high correlations driven by the fact that they consult each other’s ratings – or that they all base their ratings on the same information sources – this can undermine the main premise of the aggregation methodology that more sources produce more accurate and reliable estimates. The addition of another expert-based source containing little new information – relying on the same information sources as its competitors, or even checking their ratings – can actually reduce the accuracy of the composite index.
A general caveat in the use of corruption indicators, beyond the weaknesses of individual types discussed above, concerns the importance of their intended use. For some purposes, broader measures may be preferable: for example, a researcher studying the relation between corruption and economic growth may have no particular view on exactly which aspects of corruption most impair growth, and is hence content with a general measure. For other purposes, however, narrower measures may be required. For example, a donor funding projects in a country may be interested in a measure of corruption in public procurement, while a donor providing budget support might prefer a measure of the likelihood of funds diversion to unintended purposes. The design of effective anti-corruption reforms requires narrow measures to identify specific problem areas and track progress over time, and so on.
Finally, it is important to remember that some indicators are more suitable than others for measuring changes over time. Broad, multi-dimensional indicators are potentially problematic in this respect, because there is no way to ensure that the implicit weights given to the various dimensions do not change over time. Some indicators have no fixed and explicit criteria provided for each ratings level, so there is no way of ensuring that the same numerical rating means the same corruption level from one year to the next.
With this background in mind, it is easy to understand why, while it is often possible to form a broad assessment on the general situation and trends in corruption, different sources might often disagree markedly on specific countries, and in particular on which countries have improved and which have not. The evidence from different sources on recent corruption trends reported below provides a clear example in this respect. We are going to focus on the new EU members (Estonia, Bulgaria, Romania, Slovenia, Slovakia, Czech Republic, Hungary, Poland, Lithuania, Latvia), indicated as EU-group, and the non-Baltic former Soviet Republics (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan), indicated as CIS-group [1].
Levels and Trends in Corruption for the New EU-Members and the Eastern Neighbors
The Business Environment and Enterprise Performance Survey (BEEPS) is a nationally-representative survey of business firms assessing corruption and other problems faced by businesses in the ECA region. The BEEPS is sponsored by the European Bank for Reconstruction and Development (EBRD) and the World Bank, and has covered almost every country in the region since 1999. The two most recent waves with a good coverage of our countries are 2005 and 2009. The surveys typically contain multiple questions pertaining to narrower aspects of corruption, and so do the BEEPS.
Looking at the outcomes of the BEEPS, the most dramatic change between 2005 and 2009 is in the “bribe tax”, the share of annual sales paid in “informal payments or gifts to public officials to get things done”. The average in the new EU members increased more than four-fold from .72% to 3.11% of firm revenues. A positive value for the bribe tax was reported by 28.12% of firms in 2005, increasing to 62.1% in 2009, although this might simply reflect an increasingly open attitude in answering the survey. The corresponding increases for the CIS-group are more moderate, from 1.31% to 4.26% bribe tax and from 40.7% to 60.1% of firms declaring positive values. The only country where the bribe tax did not increase is Poland, although data for 2009 are not available for Belarus, Georgia, Tajikistan, Ukraine and Uzbekistan. The biggest increases are reported in Estonia and Slovenia, although they started from the lowest levels within the EU-group (0.29 and 0.17 respectively). These are two countries that, as we will see later, are consistently singled out as the best performers by the other indicators. This apparent contradiction might be due to a different reporting attitude in these countries. Similarly, the lowest level of bribe tax in the CIS-group for 2009 is reported in Russia (1.31), while the highest levels (8.8) in Azerbaijan, a country that according to other indicators is doing relatively well.
Besides the bribe tax, among the numerous other questions on corruption issues in the BEEPS, most show evidence of a modest improvement. For example, in 2005 about 13.4% (24%) of firms in the EU- (CIS-) group reported that paying bribes was frequently, usually or always necessary to get things done, and this figure is down to 6.75% (18.8%) in 2009. Most questions about specific public services also show evidence of a decline in the incidence of bribe paying, e.g. when paying taxes, dealing with customs and the courts.
The assessment on the fairness of the courts got worse in both areas, but at the same time it is considered a big obstacle by fewer businesses as compared to 2005. Also, the share of businesses that admit to paying a kickback payment to obtain a government contract, and the share of sales required for this payment, decreased over this period, markedly for the new EU members, though only slightly for the former Soviet Republics.
Slovenia and Estonia are the champions also in this respect, as well as Armenia in the CIS-group. Kickback payments are most expensive in Latvia (3.06% of sales) and Russia (4.65%). Corruption was however cited as the biggest obstacle to doing business by an increasing share of firms everywhere [2]. In the CIS-group as a whole, the share of firms that consider corruption the biggest obstacle to business increased from 6.4% in 2008 to 8.16% in 2009. The individual countries with the biggest shares are Romania (9.5%) and Azerbaijan (17.8% of firms), respectively in the first and second group. Ironically, the biggest increase between 2005 and 2009 is in Poland, the only country where the reported bribe tax actually decreased. This highlights how tricky it is to aggregate the information from these sources, given that very different aspects of the situation in a country are captured by each item.
More difficulties emerge with respect to evaluating change over time since different measures often move in opposite directions for a given country. For example, both Hungary and Azerbaijan experienced the biggest increases in bribe tax, but also a sharp decrease in kickback payments for government contracts and it is hard to balance the one against the other. This is also a reason why the picture emerging from these data does not necessarily agree with the aggregate indicators discussed below, although they are in part based on the very same outcomes of the surveys.
The World Economic Forum (WEF) “Executive Opinion Survey” is another cross-country survey of firm managers. The sample in each country is selected with a preference for executives with international experience, who tend to be from larger and exporting firms. The questions are designed to elicit “the expert opinions of business leaders” on corruption and other issues, and focus much less on direct firms’ experiences. Moreover, the aim is solely to produce country-level measures of the business climate, and not firm-level analyses. Cross-country rankings on several corruption questions from this survey are published in WEF’s annual Global Competitiveness Report. Ratings are computed as the simple average of all executives’ responses.
The 2011 WEF data include 7 variables related to corruption, all scaled from a low value of 1 to a high value of 7: Diversion of public funds, Irregular payments and bribes, Judicial independence, Favoritism in decisions of government officials, Burden of government regulation, Transparency of government policymaking and Ethical behavior of firms. The sample includes a total of 142 countries, including many developed countries, and covering most of the countries we have been addressing above. Both the average rating and the average rank are slightly higher for the EU-group, but the similar average hides quite a bit of variation between different countries and in different dimensions.
In particular, the CIS-group ranks higher with respect to both the extent to which government regulation is perceived as a burden for business, and the perceived transparency of policymaking, and the two averages are extremely close when it comes to the assessment of favoritism in officials’ decisions. The largest difference between the two groups seems to be the prevalence of irregular payments and bribes, in accordance with the evidence from the BEEPS.
Nevertheless, some countries in the second group, like Georgia and Tajikistan, have a higher average ranking than most of the new EU members and position themselves extremely well even in global terms in some dimensions. For example, Georgia is number 7 in the world with respect to the (absence of) burden of regulation, although not many more reach the upper quartile or even the upper half of the ranking. On the other hand, some of the new EU countries do very poorly in some respects, like the Slovak Republic ranking 135th (of 142) in terms of favoritism by public officials and the Czech Republic being 124th in diversion of public funds.
Compared to 2010, the EU-group saw a slight worsening in their rating, while the CIS-group improved. More in detail, half of the countries in the first group went down, including some quite substantial drops (Estonia, Poland and Slovenia, down by more than 1 point) while the others improved, though not spectacularly. All but one country (Georgia) in the second group improved their average rating from 2010, the biggest progress taking place in Azerbaijan with 1.5 points.
As opposed to the surveys discussed above, the NIT, CPIA and ICRG each provide a single measure of corruption, intended to reflect a mix of various aspects of corruption.
The NIT index is mostly concerned with the impact of corruption on business. It measures the corruption with on a 1-7 scale, 1 being the best possible rating and 7 being the worst, with quarter-point increments allowed.
The ranges of variation in the ratings during the last five years for the two regions do not overlap at all: all of the new EU countries positioned themselves always below a score of 4, while all the countries in the CIS-group stayed well above this threshold. This implies that the best performers within this group (Georgia and Armenia) have a consistently lower rating than the worst performing EU countries (Bulgaria and Romania). However, the trend over time in this period is very similar across the two regions. Both the averages are very flat, with a slight upward trend (i.e. to the worse). In the EU-group, this reflects the fact that five countries saw worsening in their rating, three saw no change at all and only two (Estonia and Lithuania) a slight progress. The lowest (and hence best) score is Estonia and Slovenia’s 2.25. Also in the other group only two countries – Armenia and Georgia – improved their rating. They also have the lowest scores in the region, 5.25 and 4 respectively. Six countries kept a stable rating while four got worse. The highest (and hence worst) score, 6.75, goes to Turkmenistan and Uzbekistan.
The CPIA question “Transparency, Accountability and Corruption in the Public Sector”, is assessed on a 1-6 scale, where a lower level corresponds to a worse situation in terms of corruption. This index focuses on less developed countries, so the EU-group is not covered. The most recent available data are for the period 2008-2011, during which four out of the six developing regions in the world improved.
In contrast to the stagnation with slight worsening described by the NIT, the ECA region is the one that sees the steepest improvement in the CPIA rating, increasing to 2.87 in 2011. This contrasting assessment can be explained by the fact that only six of our CIS-group countries are included in the CPIA sample: Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova and Uzbekistan. If we look at the average only in those six, also the NIT rating improved by about the same relative amount (1.5% of the value range). The two indexes do not agree, though, on the individual countries that they reward with a higher or punish with a lower score. In particular, only Georgia improved in both ratings, while Uzbekistan, for example, got a better CPIA score but a worse NIT score; Armenia and Azerbaijan, that respectively improved and worsened in the NIT assessment, are completely stable in the CPIA, and the opposite is true for Moldova.
Unlike the CPIA, the ICRG sample includes most developed countries. The focus of the ICRG is to establish the relative incidence of corrupt transactions. Its corruption ratings range from a minimum value of 0 to a maximum of 6, where higher rating corresponds to a better situation.
The latest available data are however not as recent as for the other indicators discussed here. In the three years up to 2007, the mean rating remained stable in both groups, around 2.5 in the new EU members and 1.8 in the former soviet countries, although only three of eleven countries from this group are included. Also in this case, the two ranges of values for the two regions do not overlap.
In the EU, Lithuania’s rating went down while Poland’s went slightly up. Estonia and Slovenia are again the best performers together with Hungary. The lowest rating in the region goes to Bulgaria, just as in the NIT evaluation, together with Latvia. All the three countries in the CIS-group made improvements, although from dismally low levels. This is not in contrast to the other assessments, since the data refer to an earlier period. The highest score of the three is Moldova’s (low) 1.5.
Both of the widely-known composite indexes of corruption (TI and WBI index) show large differences between the EU members and their eastern neighbors. The average score, varying from 1 to 10 and from -2.5 to 2.5, respectively, are much higher for the first than for the second group. Similarly the ranks – from 1 (best) to 182 (worst) for TI, reversed scale from 0 (worst) to 100 (best) for WBI – reflect a much worse situation in the CIS-group. However, the former Soviet countries improved their WBI rank between 2009 and 2010, as opposed to the new EU members which saw a slight drop. Although changes over time for these indexes should be taken with caution, this is coherent with the 2010-2011 comparison in the WEF.
The two indexes also agree on best and worst performer, respectively; Estonia and Bulgaria in the first group (Slovenia was best performer in 2009 according to WBI) and Georgia and Turkmenistan (on par with Uzbekistan according to TI) for the second. Both the largest improvement (Lithuania) and the largest backslide (Slovenia) from 2009 happened in the EU-group, but a larger share of the CIS-group countries experienced improvements, which is reflected by a smaller drop in the average score. The main difference between the two indexes is that WBI uses more sources and reports a value even for cases when only one source is available (TI requires a minimum of three sources), obtaining as a consequence a broader coverage. Otherwise, the two indexes are quite correlated, and subject to the same problems.
Summing up, all the indicators agree, not surprisingly, that the situation looks much brighter in the EU-group than in the CIS-group. Although, it is not clear that they are keeping up the good work in the most recent years. There is relatively more evidence of improvement over time in the CIS-group, despite the dismal starting point. Only few countries emerge unequivocally as good or bad performers. One example being the coherently positive performance of Georgia; for most of the other countries, the picture is mixed.
Given the variety and breadth of indicators, this conclusion was very much expected. Corruption is such a broad and multidimensional phenomenon that different indicators and different assessments are bound to result in different, often contrasting pictures. Unless one is very clear on which specific aspect is in focus, and sticks consequently with one particular measure, any conclusion based on general comparisons of corruption indicators both between countries and over time should be taken with serious cautiousness.
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References
- Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. (2008). “Governance Matters VII: Aggregate and Individual Governance Indicators, 1996-2007.” World Bank Policy Research Working Paper 4654. Washington: World Bank (June 24).
- Knack, Stephen. (2006). Measuring Corruption in Eastern Europe and Central Asia: A Critique of the Cross-Country Indicators. World Bank Policy Research Working Paper No. 3968, Washington,D.C.
- World Bank (2000). Anticorruption in Transition: a Contribution to the Policy Debate. Washington DC: World Bank.
- European Bank for Reconstruction and Development, Business Environment and Enterprise Performance Survey (BEEPS), http://www.ebrd.com/pages/research/economics/data/beeps.shtml
- World Economic Forum (WEF), Executive Opinion Survey, https://wefsurvey.org/index.php?sid=28226&lang=en&intro=0
- Freedom House, Nations in Transit, http://www.freedomhouse.org/report-types/nations-transit
- PRS Group, International Country Risk Guide, http://www.prsgroup.com/icrg.aspx
- Transparency International, Corruption Perception Index, http://www.transparency.org/research/cpi/overview
- World Bank Group, Country Policy and Institutional Assessment, http://www.worldbank.org/ida
[1] Turkmenistan and Uzbekistan are only unofficial members of the official Commonwealth of Indipendent States (CIS), and Georgia is not a member any longer since 2009.
[2] Bigger obstacles in the EU-group are the level of tax rates (19% of firms), access to finance and an inadequately educated workforce (11% each), along with political instability (10%). The biggest concern for most firms in the CIS-group is instead market practices from competitors in the informal sector.
Monetary Policy in Belarus since the Currency Crisis 2011
In the second half of 2010, the National Bank of Belarus carried out a soft monetary policy to stimulate domestic demand. Until March 2011, the country experienced strong economic growth. There was an increase in real incomes with a parallel increase in the negative trade balance and the reduction of international reserves. Stimulating policy became one of the reasons for the formation of a multiplicity of exchange rates on the foreign exchange market. Beginning of March and until the end of October 2011, there was an official and gray currency market in the country. High domestic demand and rapid devaluation processes led to the deployment of an inflationary spiral, which in turn meant a decrease in the growth of real incomes.
Inter-Regional Convergence in Russia
There was no inter-regional convergence in Russia during the 1990s but the situation changed dramatically after 2000. While interregional GDP per capita gaps still persist, the differentials in incomes and wages decreased substantially. Interregional fiscal redistribution has never played a major role in Russia, so understanding interregional convergence requires an analysis of internal capital and labor mobility. The capital market in Russia’s regions is integrated in a sense that local investment does not depend on local savings. Also, the barriers to labor mobility have come down. The situation is very different from the 1990s when many poor Russian regions were in a poverty trap: potential workers wanted to leave those regions but could not afford to finance their move. After 2000 (especially later in the first decade), these barriers were no longer binding. Overall economic development, as well as the development of financial and real estate markets, allowed even the poorest Russian regions to grow out of the poverty trap. This resulted in some convergence in the Russian labor market; the interregional gaps in incomes, wages and unemployment rates are now comparable to those in Europe.
Russia’s Regions are Finally Converging
Large interregional differences have always been an important feature of Russia’s transition to a market economy. This has been explained by the pre-transition geographical allocation of population and of physical capital that was determined by non-market forces. Soviet industrialization policies often pursued political or geopolitical goals. Even when they reflected economic realities, the economic decision-making was distorted substantially by central planning, price-setting and subsidies. In addition, the allocation of production was intended to serve a different country – the Soviet Union (or even the whole Council for Mutual Economic Assistance countries) rather than Russia alone. Moreover, believing in economies of scale rather than in competition, Soviet planners created many monotowns.[1] These towns, cities or even regions relied on a single industry. Therefore economic restructuring and inter-sectoral reallocation implied not only moving workers or capital between employers in one town, but also required moving workers or capital between cities.
Despite the need for geographical reallocation during the transition to a market economy, the differentials between Russian regions remained high (and even increased!) throughout the 1990s. However, after 2000 (especially later in the first decade) there was substantial convergence in incomes and wages (Figure 1). By 2010, this resulted in reduction of the inter-regional differences in incomes in line with European levels. In Figure 2, while inter-regional differences in Russia are still substantially above those in the US and Western Europe, they are comparable to those in the EU.
Figure 1. Differences among Russian Regions in Terms of Logarithms of Real Incomes, Real Wages, Unemployment, Real GDP Per Capita
Source: Guriev and Vakulenko (2012). Note: All variables measured as population-weighted standard deviations.
Figure 2. Income Differentials in Russia, Europe and the US
Interestingly, despite income convergence, there was no convergence in GDP per capita among Russia’s regions. Inter-regional dispersions in GDP per capita remain high not only by European standards, but also by standards of less developed countries. Indeed, in Figure 3, Russia is placed in the international context using the data recently developed by Che and Spilimbergo (2012).
Che and Spilimbergo calculate interregional differences for 32 countries in a compatible way and plot them against GDP per capita (averaged out for 1995-2005, in real PPP-adjusted dollars). Their main finding is that that there is a negative correlation between interregional differences and GDP per capita.
Since Russia was not in Che and Spilimbergo’s dataset, Guriev and Vakulenko (2012) reproduced their calculations for Russia, both for the 1995-2005 average (as they do for the other countries) but also for the individual years 1995, 2000, 2005 and 2010. It turns out that while Russia was “abnormally uniform” in the early 1990s, it did experience substantial divergence in the late 1990s. There was continuing, albeit weaker, divergence even in the early 2000s – so Russia became “abnormally unequal” given its GDP level. Even though there was some convergence late in the first decade, Russia is still “abnormally unequal”. Given the fast economic growth since 2000, Russia should have become substantially “more uniform” – at least given the downward-sloping relationship between income and inter-regional inequality in Che-Spilimbergo’s data.
Source: Che and Spilimbergo (2012). Note: The trend line is calculated without Russia.
Why didn’t income convergence happen in the 1990s and only start after 2000? Why hasn’t GDP convergence taken place? Large interregional differences are consistent with reduced income, wage, and unemployment differentials if the factors of production (labor and capital) have become more mobile while the productivity differences (due to geography, political and economic institutions, and inherited differences in infrastructure) remain in place. Therefore, in order to understand income convergence, an understanding of labor and capital mobility is needed.
Interregional Labor Mobility in Russia
Andrienko and Guriev (2004) studied internal migration flows in Russia in the 1990s and showed that the lack of convergence was explained by a “poverty trap”. In general, Russians did move from poorer to richer regions. However, in Russia’s very poor regions (in about 30% of the regions hosting about 30% of Russia’s population) the potential outgoing migrants wanted, but could not afford, to leave; so for these regions, an increase in income would have resulted in higher rather than lower outmigration.
What changed since 2000? Why did barriers to mobility come down? There are multiple potential explanations: (i) economic growth simply allowed most of Russia’s regions to grow out of the poverty trap; (ii) the development of financial and real estate markets reduced the transactions costs of moving therefore reducing the importance of the poverty trap; (iii) the development of capital markets increased capital mobility; (iv) federal redistribution reduced interregional differences.
According to Guriev and Vakulenko (2012), federal redistribution played a very minor role, while the other three explanations are consistent with the data. Our analysis of capital flows is, however, limited by the lack of detailed data, but our study of panel data on net capital inflows and investment shows that, first, capital does flow to regions with higher returns to capital and with lower wages and incomes, thus contributing to convergence. Second, investment in Russia’s regions is not correlated with savings which suggests that Russia’s capital market is not regionally segmented. As our data on capital are limited to the period after 2000, we cannot compare the recent years to those during the 1990s, but at least we can argue that recently, the capital market was functioning well and was contributing to convergence.
It is striking to what extent the poverty trap and liquidity constraints used to be, but are no longer, binding for labor mobility. Figure 4 is a graphical illustration of the poverty trap. Based on a semiparametric estimation with region-to-region fixed effects it shows the relationship between income in the origin region and migration (both in logarithm). Each dot on this graph represents migration from one region to another in a given year (during 1995-2010). As discussed above, the relationship is non-monotonic. If the sending region is poor, an increase in income results in higher out-migration; for richer regions, a further increase in income results in lower migration. The peak is at log income equal to 8.7 which amounts to average income equal to exp(8.7) ≈ 6003 in 2010 rubles and 1.02 of the Russian average subsistence levels in 2010. The regions to the left of the peak are in the poverty trap while the regions to the right are in a “normal mode” where liquidity constraints are not a substantial barrier to migration.
While in the 1990s tens of regions were below this threshold (and therefore were locked in the poverty trap), by 2010 only one region was below this threshold. In this sense, overall economic growth allowed Russian regions to overcome liquidity constraints by simply growing out of the poverty trap. We ran additional tests to show that financial development also contributed to relaxing liquidity constraints.
Figure 4. Income in the Origin Region and Migration[3]
What Next?
Should we be worried about high interregional differentials in GRP per capita? Not necessarily. In order to ensure inter-regional convergence in incomes and wages, convergence in GDP per capita is not required. As long as barriers to labor and capital mobility are removed, mobility (or even a threat of mobility) protects workers. Therefore, the very fact of remaining large inter-regional dispersion in GDP per capita should not serve by itself as a justification for government intervention (e.g. region-specific government investment).
As reducing barriers to mobility is important for convergence, this is exactly where policies can contribute the most. Developing financial and housing markets and improving investor protection are better policies for reducing inter-regional differences in income; these factors have already reduced income differentials among Russian regions.
We should, however, provide an important caveat. Our analysis was done at the regional level. We therefore do not address the sub-regional level and have nothing to say on the need for town-level government interventions. There may well be many cases where individual towns (e.g. so called mono-towns) are locked in poverty traps. In those cases government intervention may be justified and desirable. Our results show that poverty traps did exist in Russia in the 1990s at the regional level. These may well still exist at the town level even now. We cannot extrapolate the quantitative value of the income threshold we identified for the poverty traps from regional level to the town level but our analysis provides very clear qualitative criteria for government intervention. If the average citizen of a town would benefit from moving out but cannot finance the move (e.g. because his/her real estate is worthless), then the government can and should step in through supporting financial intermediaries that could finance the move. Therefore our analysis is fully consistent with the rationale for the government’s mono-towns restructuring program.
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References
- Andrienko, Yuri, and Sergei Guriev (2004). “Determinants of Interregional Mobility in Russia: Evidence from Panel Data.” Economics of Transition, 12 (1), 1-27.
- Che, Natasha, and Antonio Spilimbergo (2012). “Structural reforms and regional convergence.” CEPR Discussion Paper No. 8951.
- Guriev, Sergei and Elena Vakulenko (2012). “Convergence among Russian regions.” Background paper for the World Bank’s Eurasia Growth Project.
[1] Russian law defines monotowns as town where at least 25% employment is in a single firm. Even now, the Russian government’s Program for the Support of Monotowns lists 335 monotowns (out of the total of 1099 Russia’s towns and cities) with the total of 25% of Russia’s urban population. [2] EU (19): Belgium, Czech Republic, Germany, Estonia, Ireland, Greece, Spain, France, Italy, Latvia, Lithuania, Netherlands, Austria, Poland, Portugal, Slovakia, Finland, Sweden, United Kingdom. For EU (19) we consider only those NUTS-2 units for which there is data for each year. Western Europe: Austria, Belgium, Germany, Ireland, Greece, France, Italy, Netherlands, Norway, Portugal, Finland, Sweden, United Kingdom. [3] The graph shows the relationship between the logarithm of the real income in the sending region and the logarithm in migration controlling for income in the receiving region, unemployment and public goods in both sending and receiving, year dummies and other factors influencing migration. Moscow and Saint Petersburg are excluded.
Political Instability in Fragile Democracies: Political Cycles Kyrgyz Style
Democratization is rarely a straight and predictable process. Freedom House data from the Central and Eastern European Countries (CEEC) and the countries of the Commonwealth of Independent States (CIS) since 1991 reveals two distinct patterns. In one set of countries, democratization took root quite quickly and the transformation of political institutions seems quite deep and sustainable. In the other countries, the road to democratization, if ever started, has been much more partial and full of reversals. Among the CIS countries, none is regarded as free by Freedom House in 2012, four are regarded as partly free (Armenia, Kyrgyz Republic, Moldova and Ukraine), while the remaining seven countries (Azerbaijan, Belarus, Kazakhstan, Russia, Tajikistan, Turkmenistan, and Uzbekistan) are regarded as non-free. There has also been volatility over time within countries. Russia and Belarus have seen their score steadily deteriorating, while countries on the Balkan and south-east Europe have seen gradual improvements. With the lack of consolidated democratic institutions has also typically followed much political instability. Frequent changes in power, civil unrest, popular revolutions and military conflicts have pervaded countries like Ukraine, Georgia, and the Kyrgyz Republic. In other nations, repressive leaders have put a lid on visible instability, but at the cost of political rights and a fair judiciary system. In both cases, the economy has suffered as instability has deterred investors looking for a predictable environment guided by transparent rules of the game implemented equally for all. Corruption has flourished and political connections and nepotism has determined the opportunities for economic success.
