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Corruption in Eastern Europe as Depicted by Popular Cross-Country Corruption Indicators

Posted on October 15, 2012 | Policy Brief

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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

[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.

Inter-Regional Convergence in Russia

Posted on October 1, 2012 | Policy Brief

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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

Note: For the EU and Western Europe the unit of observation is NUTS-2 region.[2]

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.

Figure 3. Russia’s Interregional Dispersion in GDP Per Capita in the International Context
 

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]
 
Note: results of semiparametric estimation

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.
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[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.

New Tools to Fight Corruption and the Need for Complementary Reform

Posted on June 20, 2012 | Policy Brief

High office buildings facing sky representing Institutions and Services Trade

Corruption remains a serious problem for most developing countries, undermining state capacity and incentives to invest besides social cohesion and democratic institutions. It is also an increasingly important problem for many highly developed ones. In Italy, for example, corruption has increased in the last decades and the parliament is now finally struggling to pass a (rather mild)”anti-corruption law”. Even in Sweden, a country constantly considered among the least corrupt ones in the world, the problem seems to be increasing according to a recent report by the Agency for Public Management (Statskontoret), which also suggests that the current legislation needs to be improved, for example by offering some form of protection to whistleblowers.

In most Central and Eastern European countries, however, the problem appears particularly serious. Corruption seems to have been rapidly increasing in the region this last decade (The Economist, April 11, 2011 ; Nations in Transit, editions 2001-2012), although there are some virtuous exceptions (for example Georgia and Estonia).

Corruption is often caused by, and at the same time, an instrument for political developments towards autocracy, such as those recently observed in some of these countries (limiting judicial autonomy, democratic participation and the free press). This suggests that in countries where these political developments are taking place we may expect a further worsening of the corruption problem in coming years.

A country that is apparently taking the fight against corruption seriously is India, where a strong grassroots anticorruption movement has developed. The issue has become central in recent political debates and several proposals have been put forward and debated in the parliament. Among these proposals is one by Kaushik Basu, the finance minister’s Chief Economic Advisor. He suggests – for a specific class of bribes paid to obtain a service to which one is entitled for – to treat bribe paying as legal while doubling the sanctions against bribe taking (Basu 2011). The logic behind this proposal is to create stronger incentives for bribe-paying individuals to report it to law enforcers and expose corrupt civil servants: reporting should lead to the restitution of the bribe, besides the conviction of the bribe taker.

Since this proposal was made last year, there has been a lively debate both at the Indian as well as the international level. The debate has however been rather informal, and involved some (voluntary and involuntary) misunderstanding of the proposal (see Dufwenberg and Spagnolo 2011 for a short account of this debate). The proposal has been deemed as “radical” by the proponent, and has sometime been treated and dismissed as a theoretical curiosity. In fact, the proposal is similar to existing legal provisions against corruption that have been in place for quite some time in several countries. The proposal is also related to other legal provisions widely used around the world to fight related forms of illegal transactions, in primis leniency policies now used by most antitrust authorities to fight price-fixing cartels, but also accomplice-witness amnesty and protection program against mafia-like criminal organization (see Spagnolo 2008 for an overview).

We know from academic research on these related revelation schemes that they can be very powerful if appropriately designed and administered, but they may fail or even be counterproductive if they are poorly designed or run (see e.g. Spagnolo 2004, Buccirossi and Spagnolo 2006, Apesteguia et al. 2007, Miller 2009, Bigoni et al. 2009). The exact details how these subtle mechanisms are designed and then actually implemented are crucial to their success.

Asymmetric Sanctions, Leniency and Whistleblowers

As earlier mentioned, the main idea behind Basu’s proposal for India, treating partners in corruption asymmetrically is not a theoretical curiosity. It is already present in milder form in the Russian, Japanese and German (violation-of-duty) legislation, where bribe payers face lower sanctions than bribe takers and in the way prosecutorial discretion is used in Anglo-Saxon countries. An analogous provision seems to have also been introduced in China in 1997, and its effectiveness has recently been questioned by some observers, although in a very superficial way. Unfortunately we have no serious evidence of how these legislations have affected corruption.

More generally, the idea of deterring a collaborative crime by shaping the incentives of criminal partners so that one of them has the incentive to betray the others and report information to law enforcers is well established. The Prisoner’s Dilemma story, where each among the partners in crime are promised a light sentence in exchange for cooperation to convict the other criminal partners is familiar to most countries’ standard law enforcement practice.

These schemes have been the main and most successful tool in the fight against mafia and political terrorism in Italy and other countries, and they are currently regarded as the most important and effective instrument in the hands of competition authorities in their fight against cartels (US Department of Justice, Spagnolo 2008, Acconcia et al. 2009).

Apart from law enforcement, analogous “divide and conquer” schemes have been widely used ever since the Roman Empire in war-related situations to break down enemies’ coalitions. They are tools that many do not like on moral grounds, because they induce distrust and betrayal of partners, which some people see as bad even when the betrayed partnership is a criminal one and distrust prevents the criminal activity.

Still related but somewhat different are the whistleblower protection (from retaliation) and reward schemes aimed at inducing innocent witnesses to report a crime. Reward schemes for whistleblowers have been used in the US since the civil war to limit corruption in federal procurement and to fight government fraud (through the False Claim Act, sometimes called the Lincoln Law from the president that introduced it). They have more recently been introduced by the IRS against tax evasion and by the Dodd-Frank Act against financial fraud.

When witnesses are working in the same organization as the wrongdoers, or when the latter are powerful individuals (besides being prone to commit illegal acts, like violent retaliation), blowing the whistle typically generates very harsh consequences for the witness; ranging from various forms of harassment in the organization, to the loss of job, isolation and directly or indirectly induced death.[1] Legal action is typically slow and uncertain but immediate, certain, and very costly, while whistleblower protection provisions are typically imperfect (if present). This is why, even with a relatively efficient legal enforcement system like the American, large rewards are seen as necessary and justified to induce more whistleblowing and compensation for its consequences.

Trust, Distrust and Corruption

In some sense, one can see Basu’s proposal of legalizing bribe paying for services one is entitled to (while doubling sanctions for bribe taking) as transforming potential accomplice-witnesses into potential innocent whistleblowers. The question is then whether this scheme will induce more people to blow the whistle and consequently fewer bureaucrats to demand/accept bribes. Some observers have suggested that this provision might instead induce more people to pay bribes because it makes it legal and thereby may erode moral norms against bribe paying.

In Dufwenberg and Spagnolo (2011), we argued that amending Basu’s proposal in a way resembling leniency programs used in antitrust, where immunity is awarded only if the wrongdoing is reported to the law enforcement agency, is one way to avoid sending the signal that bribe paying is now legal. The real problem for these schemes is therefore whether at the end they will really induce bribe payers to report.

The way these revelation mechanisms deter corruption is by generating “distrust” among potential partners in crime (Bigoni et al. 2012). By making it very attractive to report to law enforcers for one party and very costly to be reported for the others, these schemes may deter illegal cooperation by ensuring that the parties cannot trust each other.

However, for these schemes to generate distrust and produce their potentially strong deterrence effects, the risk that accomplice-witnesses and other potential whistleblowers report must be a real one. For this to be the case, whistleblowers must trust the law enforcement agency to which they report. The example of leniency policies in antitrust is illuminating. In the US, as long as competition authorities retained discretion, colluding firms rarely applied for reporting under the leniency program. It was only when the Department of Justice gave up discretion by making immunity “automatic” – subject to an explicit set of conditions being satisfied – and committed to this policy through published rules that firms started to again to report information on cartels.

Besides a high risk of being reported, for these schemes to elicit reports and produce deterrence it is also necessary that sanctions for convicted parties are sufficient. To continue the parallel with antitrust enforcement, even after the authorities gave up discretion on the programs, they are not inducing cartel members to report in other countries than the US.

Indeed, the most serious problem for the success of the Basu proposal, as well as for that of the leniency-based modification put forward in Dufwenberg and Spagnolo (2011), remains whether witnesses/bribe payers will trust the law enforcement agency to which they should report the crime. If the law enforcement agency is inefficient or also corrupt, reporting may lead to further harassment or worse, rather than protection and justice.

When protection programs are poorly administered and law enforcement agencies inefficient or corrupt, so that potential witnesses don’t trust law enforcement agencies, it becomes very difficult to induce whistleblowers to report, as well as dangerous for the whistleblower.

A second important reason why these schemes may fail to generate reports and to produce the intended deterrence effects is, as we mentioned, the low sanctions against bribe takers. Recent experimental results (in Bigoni et al. 2012) suggest that reporting incentives provided by leniency programs are only effective in deterring collusion if the sanctions for the convicted partners are sufficiently strong. If not, these schemes may have no effects or even perverse ones (they reduce the sum of expected sanctions, and because of their complexity, they could be manipulated; see e.g. Buccirossi and Spagnolo 2006). Basu did suggest doubling the sanctions for the bribe payers. This, however, may or may not be enough for the case at hand, and would require a more thorough evaluation.

Note than in the case of corruption, there is an additional reason for sanctions to be reinforced, in particular by the requirement to always remove from office the convicted bribe taker. The reason is that if the bribe taker is not removed from office after the report, bribe payers may fear that after whistleblowing the bribe taker may retaliate in future interactions.

Conclusions

Asymmetric sanctions as proposed by Basu (2011) and leniency conditional on reporting as proposed by Dufwenberg and Spagnolo (2011) have the potential to deter corruption in a systematic way.  Necessary conditions for this to happen, however, are that:

  1. Sanctions are sufficiently robust to ensure that the increased risk of being convicted because of a report by a whistleblower dominate on the lenient treatment offered to induce reports;
  2. Potential whistleblowers trust that the law enforcement institutions will act on the report and protect them from retaliation by the corrupt and their friends, rather than harass them.

Countries with sufficiently independent and efficient law enforcement institutions should definitely consider introducing or reinforcing their revelation schemes, asymmetric treatment or leniency conditional on reporting, to counter the current widespread increase in corruption.

Simply introducing these schemes in countries with weaker institutions, in particular with a low level of independence of law enforcement agencies, may do more harm than good: after all they imply reduced sanctions and their complexity makes them easily manipulated.

These schemes can be very useful for these countries, but only if they are introduced as part of a broader set of complementary reforms that include increased judicial independence and the creation of a specialized law enforcement unit with particularly high levels of accountability and independence, able to credibly offer to whistleblowers at least confidentiality and protection from retaliation, if not monetary rewards.

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References

[1] The sad recent stories of Sergei Magnitsky in Russia and of S.P. Mahantesh in India clarify that this risks are real.

Putin and the Modernization of Russia – a Chimera?

Posted on June 11, 2012 | Policy Brief

20180315 The State of Russia Image 01

Vladimir Putin is once more the Russian President and a new government has been formed consisting of most of the same faces and mentality. Putin’s victory looks complete – yet there is a very real risk that it will be Pyrrhic. Even if the ‘managed’ political and economic system – rooted in a lack of competition and openness – that has been his defining project can remain stable, it will continue to sap the country’s vitality. In the election campaign, even Putin acknowledged the country’s lack of modern and competitive industries, as well as a business environment plagued by corruption, cronyism and excessive regulation. Yet, in calling for further modernisation of the economy, Putin has also called for more of the same policies, notably a central role for the Russian state in supporting new industries and technological leadership; a newly established State Corporation for Siberia and the Far East is a case in point. 

However, this very model has so far achieved very limited results. Oil and gas still account for nearly 70% of total merchandise exports and around half of the federal budget. While relying on publicly funded and managed entities – such as Rusnano – to shepherd the economy into more diversified and more productive spaces, particularly in high-tech activities, has also yielded a relatively meagre harvest. Rusnano itself has already acknowledged the limited portfolio of innovative projects to fund.

In the arena that provides the most compelling metric of competitiveness – export markets – relatively few Russian firms compete in international markets and very few do in higher value added trade. Ricardo Hausmann (2007) has argued that the products that a country exports also reflect the proximity of products and their reliance on similar sets of inputs, such as physical assets and knowledge or skills. Near the start of Russia’s transition it has been calculated that Russia had comparative advantage in only 156 out of 1242 product lines when using a 4-digit SITC classification. Most were natural resources. In contrast, China had comparative advantage in 479 product lines. And as regards proximity, few of Russia’s export products were closely connected to other products, meaning that there was limited scope for enhancing exports. Yet, by 2010 our research shows that there has been an increased concentration on natural resource exports. The contraction of manufacturing has, further, been associated with a fall in the number of Russian product lines with comparative advantage to 103. In contrast, the number for China increased in 2010 to 513. So, despite Putin’s rhetoric, the Russian export basket has become even more concentrated since the mid-1990s. Moreover, the ability to shift into proximate products, as well as diversify into new ones, remains very restricted. This is due to several factors.

A common diagnosis is that failings in the business environment are to be blamed. This is not a new complaint. While the options for limiting these constraints may not be straightforward but the broad policy direction and options are well understood. The challenge is in enforcement. In this – as also with improving governance and further reducing the role of public ownership – improvement is only likely to start with serious political commitment. That is still lacking.

But modernising the economy depends on much more than a good business climate. Critically, it depends on what sorts of skills and knowledge are available to the economy. Yet, even here where many have believed that Russia is relatively favourably situated, on closer inspection, the situation turns out to be far more problematic. In fact, our evidence indicates deterioration in the quality of both skills and education over time, including limitations on the supply of high quality management. Evidence from surveys suggests that Russian firms face problems in finding workers with the appropriate skill profile. While this may be the situation for existing firms, it seems likely that potential entrants to new, diversified activities may, if anything, face even steeper constraints. To understand whether this is indeed the case, the leading – 270 – recruitment firms in Russia were surveyed using face-to-face interviews in 23 locations in Russia, including Moscow and St. Petersburg. This included a small experiment looking at skills availability for work in more innovative activities, such as web technology aimed at social networking and marketing. The aim was to see whether innovative activities faced more binding constraints when trying to hire.

The results of this survey are unequivocal. Not only are there widespread skill gaps for all types of skills, but it takes firms a much longer time to fill vacancies for skilled personnel. This is particularly true for relatively innovative activities. Recruiting managers or high level professionals in the major Russian cities on average takes 3-5 times longer for innovative activity. Even in Moscow, recruiting a manager or high level professional would take between 3-4 times longer; the gap was yet greater in the Urals, Siberia and the Far East.

Moreover, looking at the sorts of skills that are lacking for each type of potential recruit (e.g., a manager); recruiters also report an absence of basic or essential skills. For example, lack of problem solving and management skills were overwhelmingly the most commonly cited limitations for managers, with high level professionals most commonly lacking both problem solving and practical skills. Among the consequences, many firms decide to postpone launching new products and/or modernizing plant.

In short, our evidence shows not only widespread skill shortages but also major barriers on the availability of personnel for firms wishing to establish new or relatively innovative activity. At the same time, anecdotal evidence also suggests that among the thin layer of top talent – likely to be essential for high tech and other innovative activities – many prefer to emigrate. In contrast, Russia fails to attract talent from other countries, not least because of a restrictive migration regime.

The last decade has seen an emphasis on modernising and diversifying Russia. The results have been depressingly limited. Yet Putin and his government propose more of the same. In effect, they are continuing to take a huge gamble by relying on a mix of energy prices and publicly funded industrial policy to paper over the structural weaknesses of the economy. As this article has shown, what Russia currently produces and exports – and the underlying skills and knowledge – provide a very weak base for achieving the goals of modernisation.

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References

  • Denisova, I., and S.Commander, S.Commander and I. Denisova (2012), ‘Are skills a constraint on firms? New evidence from Russia’, EBRD and CEFIR/NES, mimeo
  • Hausmann, R., and Klinger, B., (2007), “The Structure of the Product Space and the Evolution of Comparative Advantage”, CID Working Paper No. 146
  • Volchkova, N., Output and Export Diversification: evidence from Russia, CEFIR Working Paper, 2011

 

Buyer Power as a Tool for EU Energy Security

Posted on June 7, 2012 | Policy Brief

20120607 Buyer Power as a Tool for EU Energy Image 02

In this policy brief we address the recently revived idea of a common energy policy for the EU – an idea of the EU acting as a whole when dealing with energy security issues. We focus on a particular mechanism for such a common policy – the substantial “buyer power” of the EU in the natural gas market. We start by relating the “buyer power” mechanism to the current context of the EU energy markets. We then discuss the substitutability between “buyer power” and alternative energy security tools available to the EU.  In particular, we argue that two main energy security tools – the diversification of the gas sources and the liberalization of the internal gas market – may counteract such buyer power, either by decreasing the leverage over the gas supplier(s) or by undermining coordination. Thereby, investing both into diversification, market liberalization and energy policy coordination may be inefficiently costly. These trade-offs are often overlooked in the discussion of EU energy policy.

The security of energy supply has been part of the European political agenda for more than half a century – at least, since the creation of the European Coal and Steel Community (ECSC) in 1952. However, the Community’s view on the energy security policy and its desirable tools has been changing over time. In the early decades of European integration energy security issues were predominantly seen as belonging to the national competence level. Due to substantial variation in the energy portfolios and energy needs among the Member States, attempts to create a common energy policy were largely unsuccessful. The first large move towards a common energy policy came in the mid-1980s with the idea of developing a common internal energy market. The focus was on liberalization, privatization and integration of the internal markets, with an objective of achieving more competitive prices, improving infrastructure, and facilitating cooperation in case of energy supply shocks. In particular, the internal market was seen as a tool to (partially) overcome the disparity in the energy risk exposure among the Member States.  A considerable effort was put in this direction and a certain progress was accomplished.

The second half of 2000s has been characterized by a number of gas crises between one of the largest EU gas suppliers, Russia and the transit countries  – Ukraine (in 2006, 2007 and 2009) and Belarus (in 2004 and 2010).  These crises repeatedly caused reduction, and sometimes even complete halts, of Russian gas flows to the EU. As a result, the focus of the EU energy policy shifted towards measures ensuring the security of external energy supply. The policy debate has been stressing the dependency of the EU on large fuel suppliers, such as Russia in case of gas, and the need to lower this dependency. Suggested remedies included diversification of gas sources (in particular, away from Russian gas – such as construction of Nabucco pipeline or introduction of new LNG terminals), strengthening of the internal market, and more efficient energy use. The debate was further heated by the construction (and late 2011 launch) of the Nord Stream pipeline, which, according to popular opinion, would further increase the EU dependence on Russia.

In what follows, we address this external energy policy debate. We argue that the dependence per se is not necessarily dangerous for the EU and can be counteracted with due coordination between the Member States. Further, we argue that in dealing with large gas suppliers, there is certain substitutability between such coordination and other proposed energy policy measures, such as diversification of the energy routes or further market liberalization. Thereby, the EU would be better off by carefully choosing an appropriate mix of energy policy tools, rather than by getting all of them at once.

Indeed, the dependency of the EU on Russian natural gas is large. The share of Russian gas in the total EU gas consumption is around 20%,1 and for the group of EU Member States importing gas from Russia this share constitutes around one third.1  Furthermore, in a number of EU Member States – such as Austria, Bulgaria, Estonia, Finland, Lithuania and Slovakia – the share of Russian gas in total consumption is above 80%.3

However, it is important to remember that the dependency is mutual. The current share of gas exports to the EU of total Russian gas exports is around 55%,1 and these gas exports constitute around one fifth7 of Russian federal budget revenues. These observations suggests that the EU as a whole would also possess a substantial market power in the gas trade between Russia and the EU, and this market power can be exercised to achieve certain concessions.

More precisely, this situation could be viewed through a prism of what the economic literature refers to as “buyer power”. Inderst and Shaffer (2008) identify buyer power as “the ability of buyers (i.e., downstream firms) to obtain advantageous terms of trade from their suppliers (i.e., upstream firms)”.5 The notion of buyer power is typically used in the context of vertical trade relationship between a small number of large sellers and a few large buyers. As there are only a few agents, each with considerable market power, the outcome of such trade would typically be determined through some kind of bargaining procedure, rather than via a market mechanism. In such bargaining, the extent of buyer power depends on the seller’s outside option, or, in other words, on the ease for the seller to cope with a loss of a large part of its market.

Consider for example a single seller serving a few buyers. Intuitively, were there a disagreement between the seller and a small buyer, it should be relatively easy for the seller to reallocate the freed-up capacity to the remaining buyers, making each of them consume just a little bit more of a product. However, the larger is the freed-up capacity of the seller in case of a disagreement, the more difficult it is for the seller to reallocate this capacity to the rest of the market. Moreover, allocating this relatively large capacity to the remaining buyers is likely to suppress the price and lower the monopoly profits of the seller. Inderst and Wey (2007) show that, under some relatively standard modeling requirements, “the supplier’s loss from a disagreement increases more than proportionally with the size of the respective buyer”.6 In other words, an increase in the size of the buyer undermines the seller’s outside option, thereby weakening the seller’s bargaining position and allowing the buyer to negotiate a preferential treatment.

It is relatively straight-forward to see the parallels between this argument and the gas trade relation between the EU and Russia. In a sense, the buyer power theory provides an economic (rather than political) rational for the September 2011 European Commission proposal to coordinate the external energy policy in order to “exercise the combined weight of the EU in external energy relations”.2 At the same time, the large buyer mechanism also allows us to see more clearly, why such a coordination policy may come into conflict with the other proposed energy policy tools.

In particular, consider the diversification of the gas supplies across producers. The argument for the diversification is that it decreases the dependency on each particular supplier, thereby lowering the exposure to the idiosyncratic risks of these suppliers. However, lower volumes of gas imports from such suppliers imply a loss of the EU’s buyer power vis-a-vis these suppliers. This would worsen the terms of the respective gas trade deals or undermine the stability of the supply. Of course, this argument suggests by no means that a diversification strategy is useless or harmful for the EU energy security; however, one would need to account for the relative importance of lower dependency vs. lower buyer power in making the diversification decisions. In other words, the EU can achieve the same level of gas supply stability by investing either into further diversification of gas supply or into better coordination among the members. Trying to achieve both objectives at the same time may result in efficiency loss, at least from the gas supply security perspective. Importantly, this tradeoff has been largely overlooked in the discussion of the EU energy policy.

Another energy policy objective pursued by the EU in the last decades is the creation of an integrated and deregulated internal gas market. Again, the relationship between this energy policy objective and the buyer power is two-fold. On one hand, better integration of internal gas markets would help to even out the disparities in the gas supply risk exposure across the Member States, thereby facilitating cooperation and lessening the tensions between the energy security interests on the national vs. community-wide level. On the other hand, gas market liberalization and a push towards more competitive gas trade environment within the EU may come into conflict with the supranational coordination of buyer power. Once large state-run gas purchasing actors are dissolved and replaced by multiple private, not necessarily domestic, and possibly small market participants, it might be much more difficult, if at all possible, to achieve coordination in bargaining with the gas supplying side. As Finon and Locatelli (2007) argue, “if the major gas buyers are weakened in the name of the principles of short-term competition, their bargaining power and their financial capacity to handle large import operations would be reduced”.4 Moreover, there is a clear conceptual contradiction between coordination among gas buyers and the competitiveness principles of the European gas market. Again, this tradeoff needs to be taken into account in the common energy policy design.

Finally, it is important to mention that the “large buyer” argument is less relevant for the EU markets for other fuels, such as oil, liquefied natural gas, or coal. The key difference comes from the inherent structure of the gas market, as compared to the one of oil, coal, etc. Indeed, the EU imports most of its natural gas via pipelines, which makes it difficult for both sides of the deal to switch to an alternative partner. In other words, the natural gas market serving the EU is effectively a local market. Instead, fuels like oil, liquefied natural gas, or coal are traded more globally, and are much more fungible (that is, it is much easier to find an alternative supplier or a consumer). Global markets imply smaller market shares of the EU (indeed, the EU consumes only about 16 %1 of the world oil). This, coupled with better fungibility of oil, LNG, etc. undermines the power of the large buyer argument for other fuels.

To sum up, the EU has a noticeable potential for improving its position in the gas trade deals and enhancing the stability of its gas supplies. This potential comes from the large buyer power possessed by the EU in the gas market, and is in line with the long considered and recently revived idea of “one voice” common energy policy. At the same time, the extent to which the buyer power can be used as an energy policy tool may be limited by the other policy instruments, such as diversification of gas supplies, a shift towards LNG or alternative fuels, or internal market liberalization. This has to be taken into account in choosing the optimal energy security policy mix.

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References

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

Do Economic Sanctions Work?

Posted on March 19, 2012 | Policy Brief

North Korean rocket displayed under a glass dome, illustrating the resilience of sanctioned regimes—an image exploring the question: Do Economic Sanctions Work?

Analysts have interpreted the recent openings in Myanmar and North Korea as the finally successful result of years of international pressure and economic sanctions. At the same time, debate is hot on the scope for similar measures in Iran, Syria, and, closer to us, Belarus and Hungary. Does economics have anything to say on this? What can we learn from the analysis of past experiences?

On February 29th, after decades of frustrating attempts by the outside world with sticks and carrots, but mostly economic and diplomatic isolation, North Korea announced that it would suspend its enrichment of uranium and its tests of weapons and long-range missiles. It would even allow an inspection by the International Atomic Energy Agency, the first one since the country walked out of the Nuclear Non-Proliferation Treaty in 2003. The recently inaugurated leader, young Kim Jong Un, asked, in exchange, some tons of food aid and the promise of talks. Some believe this was inspired by another recent unexpected “opening”: the turn-of-the-year developments in Myanmar, where a cease-fire and the release of many of the political prisoners prompted a slow but sure thawing in the country’s diplomatic relations with the rest of the world. Some months on, the government’s intentions to move from a military dictatorship to greater pluralism still seem sincere enough. Many have interpreted these events as the finally successful result of years of international pressure and economic sanctions on the two countries. Is the tide turning for sanctions enthusiasts?

At the same time, though, concerns are rising that EU member Hungary is moving in quite the opposite direction, after a change in the constitution that endangers the independence of the media, the judiciary and the central bank. Hungarians protesting in the streets are openly talking about authoritarian evolution drawing parallels with the behavior of the government in Belarus, which only months ago attracted harsh criticism – and stringent sanctions. Hungary might follow suit in this respect as well: its credit line with the IMF is still hanging from a thread, and the EU threatened law suit over the constitutional changes, while a potential limitation of the country’s voting rights in Brussels is whispered as the “nuclear option”.

Although the situation looks increasingly, explosive both in Syria and Iran, even in these cases the hopes of the international community rest exclusively on economic coercion. Syria’s economy is now under severe pressure, after even the Arab League imposed sanctions. This is first time such a decision is taken against a fellow member. Near all trade and financial relations have been cut off, with the exception of some banks in Lebanon and perhaps a few business friends in China and Russia that might still offer assistance to Bashar Assad’s regime. But the country’s foreign reserves, already low one year ago at the offset of the crisis, should be running out by now, and inflation is rising as many consumption goods become scarce. At the same time, although Saudi Arabia is arming the rebel groups, a military intervention sanctioned by the international community seems unlikely, given the recent Libyan precedent.

The sanctions faced by Iran over its nuclear program are also growing to unprecedented severity, and also in this case military action does not seem to be considered an option – except by (understandably) jumpy Israel. Given the stage that the nuclear program has reached, and the level of protection built around it, bombing is not likely to stop it. Experts say that a successful US-lead operation could at most delay it some ten years. Arguably, this would only result in an even angrier Iran equipped with nuclear weapons, in ten years from now. Hence it would appear much more fruitful to try to change the population’s attitude, so that Iranians themselves can in turn affect their political leaders’ attitude, even if this needs replacing the regime altogether. This way the prospect of a nuclear Iran would not look as scary.

As the international community considers over and over its stance in all these thorny situations, a legitimate question in everybody’s mind is: What is the likelihood that the sanctions will work? Does the economic literature have anything to say on this matter?

Achieving the Goal

According to Richard Baldwin, Professor of International Economics at the Graduate Institute of Geneva, “[i]t would be difficult to find any proposition in the international relations literature more widely accepted than those belittling the utility of economic techniques of statecraft.” In other words, a prominent scholar’s synthesis of the literature is that economic sanctions do not work. The anecdote most widely cited by advocates of sanctions is of course South Africa. The economic pressure imposed on the country in the mid-1980s certainly contributed to the strain that the inefficient and costly apartheid regime was increasingly suffering, finally leading to its dismissal. At the opposite end of the spectrum stands Iraq, where neither the comprehensive sanctions nor the oil-for-food program, in principle a quite clever combination of sanctions and aid, could achieve anything. The success of the following military intervention is also a subject of debate, though not one I will address here. Some have drawn the conclusion that the discriminating factor lies in how important for the target regime is the recognition of and identification with the sanctioning part. Others argue the probability that the sanctions succeed is linked to the cost born by the target, or by the sanctioning part (also called the sender), or other observable factors. If truth be told, these are both quite special cases, hard to generalize. But then again, one could argue that every episode involving international disputes is a special case. It follows that the systematic study of economic sanctions with the evaluation of their effects is not a straightforward task at all.

The first step to evaluate the success of imposed economic sanctions is to establish what the goal is. In the most basic terms, there are two types of explicit goals. In some cases, the imposition of an economic sanction is purely punitive towards a policy or act of a regime, or towards the regime itself, and aims at expressing disapproval from the initiating part, when inaction can signal complicity. Hoffman [8] was one of the first to suggest that “sanctions are mostly adopted to alleviate cross pressure situations, resulting when a (foreign) government faces demands for action but war is undesirable”. In this case, it makes little sense to talk about success or failure, as the imposition of sanctions is a goal in itself.

In the extreme case, this type of sanctions aims at destabilizing the target regime, inducing political change. This seems to be part of the aim of actions taken against Syria, although an end to the Iranian theocracy, and Lukashenko’s regime in Belarus, for that matter, would certainly be welcome as well. An analysis of the historical records from 1914 to 1989 [4] reveals that the probability of success with this goal has been 38% when the regime was very stable to start with and up to 80% in “distressed” countries. The single most important factor of success is hence, not surprisingly, the pre-sanctions stability of the political system in the target country. In some cases, paradoxically the imposition of sanctions stimulated political cohesion in the target country – the so called rally-round-the-flag effect. This is what seems to be happening, at least at this stage, in Hungary. The evidence suggests that there is a threshold of political cohesion above which external intervention strengthens the target government.  According to Lindsay [13], three factors make it more likely that sanctions produce political integration rather than regime collapse:

  1. If they are seen as an attack on the whole country rather than on a specific faction
  2. If identification with the sanctioning part is weak or even negative
  3. If no alternative to the sanctioned course of action is available or perceived as better

In this light, measures that can be manipulated to punish only or prevalently the regime’s domestic supporters and political base are to be considered as superior. Travel bans and freezes of assets, foreign bank accounts and property of functionaries are examples of this type of measures. Financial restrictions, in addition to be perceived as comparatively fairer, have also been more effective in the past. Moreover, also to the point that the sanctions should not, if possible, hurt everyone indiscriminately, they are preferable to measures that hurt the productive sector, like trade restrictions.

Alternatively, sanctions are designed to compel a specific policy change in the target country. This is the case of Hungary and its new constitution, and formally of Iran, which is only required to drop its quest for nuclear weapons. The emerging consensus in the sanctions literature is that concessions are most likely at the threat stage [11]. Nevertheless, there are cases where the threat of sanctions fails and sanctions are then actually imposed. And, although the success rate becomes lower at this stage, there are examples where the target yields only after the sanctions are imposed. It might seem tempting then to investigate whether observable variables can predict the likelihood of success in these cases, because this would teach us something about the current crises around the world. However, trying to understand when and why sanctions have success based on the analysis of empirical data is complicated by a number of challenges.

First of all, there are at least two sources of censoring in the sample of imposed sanctions: because it is only a specific type of disputes that reach this stage, the evaluation based on them will be biased. The first reason why these are special cases is due to the fact that imposed sanctions have already failed at the threat stage. Hovi et al. [9] look at this situation from a game-theoretic perspective and argue that, if sender and target are rational, a threat of sanctions could fail because of one of three reasons: 1) it is not credible, so no actual sanctions will follow the threat; 2) it is not sufficiently potent, meaning that the target considers sanctions to be a lesser evil than yielding; 3) it is noncontingent, i.e. the target expects sanctions to be imposed regardless of whether it yields or not. If any one of these is true, then the target that did not yield at the threat stage will not yield after sanctions are imposed either (or no sanctions will be imposed if alternative 1 is true). Imposed sanctions will work only if at least one of these factors is initially not known with certainty, or wrongly perceived by the target: if the target believes the threat non credible, but then sanctions are actually imposed; if the target was wrong in judging the cost of the sanctions and realizes it only after sanctions are actually imposed; or if the target thought that sanctions would be imposed regardless of its behavior, but is subsequently persuaded that, in fact, the sanctions will cease if it yields. Otherwise, with perfect knowledge and rational decision-making, sanctions that are actually imposed are bound to fail precisely because they were imposed, i.e. because they failed at the threat stage.

Further selection occurs even earlier than the threat stage. The literature has examined thoroughly how strategic interaction during the sanction episode affects sanctions outcomes and duration (for example, [15], [7], [14], [5], [6], [12]). Much fewer studies have undertaken the possibility that states also act strategically before episodes, when choosing whether to challenge the status quo and how much to demand of the target. Theories around this stage of the “game” are referred to as endogenous demand theories. Krustev [11] proposes the idea that perhaps “strategic demands can account for the widely cited discrepancy between the frequent use of sanctions and the modest success rate of these instruments”. His game-theoretic model has the implication that oftentimes sender governments strategically choose hard cases, because “the uncertain prospects that the target agrees to a large demand might outweigh the certain prospects of receiving minor concessions”. This also results in a low observed success rate.

Beyond the difficulties related to selection, another challenge that the analyst faces is to isolate the effect of sanctions. Usually, sanctions are not adopted in a vacuum, but rather complement other types of actions (e.g. diplomatic pressure, military action), which interact with the success of the measures. Similarly, there is the issue of unintended consequences, that also affect the costs on both parts, and hence the likelihood of success. Most importantly, some of these unintended effects might change the situation so drastically that talking about success or failure does not make sense anymore.

Unintended Consequences

Besides the success or failure with the specific goals they are intended to obtain, economic sanctions bring about a host of more or less foreseeable unintended consequences as well. One especially undesirable outcome of trade sanctions has recently been brought to attention from the analysis of former Yugoslavia [2]. Under a regime of import restrictions, private and public actors might be pushed towards the use of unlawful methods in order to avoid the sanctions and reach the international market through unofficial ways. An unhealthy cooperation between politicians, organized crime and smuggling networks might then establish itself and persist even beyond the duration of the sanctions.

This consideration speaks against isolating the target country from trade flows. A case in itself concerns, though, trades which already lie on the boundary of lawfulness and little contribute to the productive sector, such as arms traffic. These can and should be decisively stopped. Aside from the security benefits to such a move, this also has the potential to dry up a significant source of revenue for the contested leadership.

Be it on credit or on trade, it goes without saying that any restriction will hurt the economy. The political consequences of an economic downturn caused by the sanctions are not easy to foresee. Recent research on fragile states [3] studies the relationship between national incomes and two types of political violence: repression, i.e. unilateral violence by the incumbent government, and civil conflict, two-sided use of violence on the part of the state as well as insurgent groups. The link with the national income prospects is given by the consideration that both parts, deciding whether to resort to violence, evaluate the cost and benefits of violent action. The incumbent government has a cost-advantage, being able to dispose of the state resources. The costs for potential insurgent factions go down with deteriorating economic conditions, for example in presence of high unemployment, because then those involved have less to lose. Insurgence then becomes more likely. This theory is consistent with the last century’s worth of evidence, including the recent wave of revolutions in the Arab world, suggesting that countries seeing a decline in incomes move towards democracy considerably faster. The evidence is anecdotal, though, and more rigorous empirical analysis [1] revealed no significant pattern.

Moreover, the step between opposition insurgence and the establishment of a new, possibly democratic, regime might not be rapid at all, as the Syrian tragedy is reminding us of every day. The question is then whether the leverage of economic measures from outside is likely to make any difference during this phase. As analysts push for the political and logistical backing of the international community to the revolt in Syria, and as Saudi Arabia is arming the rebels, we must consider that also measures aimed at supporting eventual opposition factions, or the democratic system in general, might have undesirable consequences. Comparative statics in the context of the same theoretical framework referred to above show that, for example, the promise of financial assistance conditional on free multi-party elections may raise the incumbent’s perception of instability and hence raise the risk of repression and increased looting, unless combined with reforms to strengthen executive constraints. Even pressure for the release of political prisoners might set out a ransom system, with perverse incentives to taking more and more prisoners to be exchanged with economic assistance – this might still be a risk in Myanmar, given the abundance of political prisoners still held by the government.

Another important difference between trade and financial restrictions is that the former are likely to result in accumulation of debt. The burden of this debt, that the sanctioned regime is responsible for, will weigh on the future growth of the country, hence on future generations of taxpayers and potentially on a future government, which ideally should not be held accountable for the course of action chosen today by a contested leadership.  Alternatively, in the case of a collapse of the economy, the debt could be defaulted. This risk is on the countries or financial institutions that today lend money to the sanctioned regime. In other words, interrupting trade without at the same time closing the lines of credit would put the sanctioning part or third part lenders in the least desirable situation.

In some cases, the target has the possibility to resort to alternative lenders in third countries. Although this is preferable to a situation where the sanctioning part itself bears the risk on the debt, it is not ideal because it frustrates the sanctioning effort. An innovative proposal has been put forward by Jayachandran and Kremer [10], related to the legal doctrine of odious debt. They propose that any debt incurred by a particular regime, that could be argued to be contracted without the consent of the people and not for their benefit, is declared by some supranational institution illegitimate and nontransferable to successor regimes. This would create disincentive for lenders in third countries, and potentially eliminate equilibria with illegitimate lending. Even this type of loan sanctions hurt the economy and hence ultimately the population; however they create a long-run benefit for the population by preventing the accumulation of an unjust debt that today finances mismanagement, looting or repression and tomorrow has to be repaid by someone who never agreed to incur it. It would be very interesting to see this solution implemented in practice!

Conclusion

In short, sanctions are difficult to implement so as to reach the intended goal and minimize the unintended effects, but are maybe even more difficult to study systematically. International disputes are often complicated matters, situations that evolve over long time horizons. The traditional research question of when sanctions work might not be the most relevant one. Including in the analysis the strategic behavior occurring at the threat stage, and even before that, is a first step, although basing policy on the prediction that threats work better than sanctions does not strike me as a very useful conclusion.

The fact that evaluation is problematic and generalization almost impossible does not mean, however, that the study of sanctions is useless altogether. Economic analysis may still be informative for decision-making, and produce innovative ideas on the design of supranational institutions for conflict management, like the proposal on odious debt illustrates.

Bibliography

  • [1] D. Acemoglu, S. Johnson, J.A. Robinson, and P. Yared. Reevaluating the modernization hypothesis. Journal of Monetary Economics, 56(8):1043–1058, 2009.
  • [2] P. Andreas. Criminalizing consequences of sanctions: Embargo busting and its legacy. International Studies Quarterly, 49(2):335–360, 2005.
  • [3] T. Besley and T. Persson. Repression or civil war? American Economic Review, 99(2):292–297, 2009.
  • [4] J. Dashti-Gibson, P. Davis, and B. Radcliff. On the determinants of the success of economic sanctions: An empirical analysis. American Journal of Political Science, pages 608–618, 1997.
  • [5] H. Dorussen and J. Mo. Ending economic sanctions. Journal of Conflict Resolution, 45(4):395, 2001.
  • [6] D.W. Drezner. The hidden hand of economic coercion. International Organization, 57(03):643–659, 2003.
  • [7] J. Eaton and M. Engers. Sanctions: some simple analytics. The American Economic Review, 89(2):409–414, 1999.
  • [8] F. Hoffmann. The functions of economic sanctions: A comparative analysis. Journal of Peace Research, 4(2):140–159, 1967.
  • [9] J. Hovi, D.F. Sprinz, and R. Huseby. When do (imposed) economic sanctions work? World Politics, 57(4):479–499, 2006.
  • [10] S. Jayachandran and M. Kremer. Odious debt. The American economic review, 96(1):82–92, 2006.
  • [11] V.L. Krustev. Strategic demands, credible threats, and economic coercion outcomes. International Studies Quarterly, 54(1):147–174, 2010.
  • [12] D. Lacy and E. Niou. A theory of economic sanctions and issue linkage: The roles of preferences, information, and threats. Journal of Politics, 66(1):25–42, 2004.
  • [13] J.M. Lindsay. Trade sanctions as policy instruments: A re-examination. International Studies Quarterly, pages 153–173, 1986.
  • [14] T.C. Morgan and A.C. Miers. When threats succeed: A formal model of the threat and use of economic sanctions. In Annual Meeting of the American Political Science Association, Atlanta, GA, 1999.
  • [15] G. Tsebelis. Nested games: Rational choice in comparative politics, volume 18. Univ of California Press, 1990.

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

Inflation Expectations and Probable Trap for Macro Stabilization

Posted on February 27, 2012 | Policy Brief

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As of today, a majority of the negative consequences of the deep Belarusian currency crisis of 2011 seem to have been realized. Hence, the Belarusian economy is now ‘purified’ from main macroeconomic distortions and has a chance for sustainable long-term growth. Nevertheless, there are signals that some nominal and real inertia may generate new shocks for the national economy. From this view, the money market is of great concern, while interest rates signal maintained high inflation expectations. High and unstable expectations may entrap monetary policy and generate new shocks for the Belarusian economy. In this policy brief, we deal with a visualization of inflation expectations and argue for the necessity of a new nominal anchor in order to stabilize expectations for future periods.

In 2011, Belarus experienced its highest inflation and devaluation in modern history. These were consequences of the automatic macroeconomic adjustment determined by a number of both long- and short-term distortions in the national economy. Changes in prices and exchange rate adjusted real parameters towards their long-run equilibrium level. Hence, from a long-run perspective, one may interpret these adjustments as favorable since they ‘purified’ the economy from the macroeconomic imbalances that may have hampered growth. Furthermore, shifting from exchange-rate (XR) targeting to a managed float is another essential aftermath of the currency crisis. Economic authorities had to recognize that accommodative monetary policy (MP) was not compassable with XR targeting since it resulted in a considerable overvaluation of the real XR, and correspondingly, an incredibly large current account deficit. Thus, the new exchange rate regime may be argued to be a new automatic stabilizer for Belarus, providing the level of current account balance consistent with other macroeconomic fundamentals. Overall, the current stance of the national economy might be treated as a chance to “begin again from the ground up”. In this sense, the Belarusian economy as of today is sometimes compared to the Russian economy after its crisis in 1998, which then performed particularly high growth rates.

In our opinion, realizing the opportunity for a strengthening of long-term growth through structural changes undoubtedly should become a policy priority of Belarus in the near future. However, it should be emphasized that despite “purification” from major macroeconomic imbalances, there are still a long list of short-term challenges. In particular, one may stress the risks of expansionary policy revival; increasing external debt burden; growth in non-performing loans, which may undermine the solvency of the banking system; reduction of foreign demand due to shocks in global economy. These risks are more or less observable and may be monitored. Hence, the realization of one or the other shocks from this list might not come as a surprise, and economic authorities seem to at least realize this, and when possible, take prevention measures.

At the same time, another challenge seems to be more adverse and urgent; namely, the question of inflation and devaluation expectations. In economic theory, expectations play a crucial role in affecting behavior of economic agents. Recognition of the role of expectations at the money market determined intention to “subject” and stabilize these within modern monetary policy frameworks.

In Belarus, given the recent history of high inflation and devaluation, corresponding expectations of Belarusian economic agents are likely to be rather high. Moreover, shifting from XR targeting to a managed float has not yet resulted in provision of a new nominal anchor for the public.

For instance, disinflation was declared to be a priority goal, but there are no strict commitments on its numerical value, as well as in respect to procedures and mechanisms to provide disinflation trends. As of today, the Belarusian MP regime can hardly be classified as a standard regime. The MP Guidelines for 2012 assume indicative targets on international reserves, refinancing rate and the growth rate of banks’ claims on the economy. The latter witnesses the propensity to monetary targeting. However, the instable relationship between the monetary aggregate to be targeted and the ultimate goal (inflation), as well as the indicative nature of this commitment give rise to doubts in respect to treating it as monetary targeting. Furthermore, commitment on bank claims on the economy can hardly be treated as a nominal anchor for the public. According to the taxonomy of MP regimes by Stone (2004), Belarus is currently closer to the weak anchor regime, which assumes “no operative nominal anchor…and central bank reports a low degree of commitment… and high degree of discretion”.

Thus, our hypothesis assumes that there has been an adverse shock in inflation expectations due to weak nominal anchor and recent experience of huge inflation. If that is the case, this may be an additional source of shock for the money market, which may cause a new wave of macroeconomic instability. In order to make policy recommendations, this hypothesis needs empirical support. However, it is difficult to identify expectations in empirical analyses since this variable is typically unobservable and cannot be univocally measured. Instead, expectations are most often treated indirectly through other variables. Many central banks deal with the results of sociological polls on this issue, but these approaches may suffer from different economic meanings and measurements of inflation expectations by economic agents.

An alternative approach was proposed by St-Amant (1996) and extended by Gotschalk (2001), who base on famous Fischer equation representing current nominal interest rate as the sum of ex-ante real interest rate and expected inflation. Further, based on the approach by Blanchard and Quah (1989), structural vector autoregression (SVAR) between nominal and real interest rate is identified with a number of restrictions, which allows decomposing changes in the nominal rate to those associated with ex ante real rate and inflation expectations. The latter may be used as a measure of inflation expectations. Such a measure of inflation expectations assumes explicit economic meaning referring to the money market, i.e. the rate of future inflation, which will provide the, by economic agents, expected level of interest rate. Taking the data from statistics (not polls) and international comparability of such estimates are important advantages of this approach.

We applied this methodology to Belarusian data (nominal and real interest rate on ruble households’ deposits with a term more than a year). The obtained time series measure changes in inflation expectations in the current period for a period of the next 12 months. However, our goal is to visualize the level of inflation expectation and not changes in expectation. Therefore, we use the series in levels, choosing January 2003 as the base period (when National Bank of Belarus actually shifted to XR targeting regime), and assigned a zero level (as starting one) to it. The obtained series of inflation expectations is provided in Figure 1.

Figure 1. Inflation Expectations in Belarus

The estimated series of inflation expectations show a decrease in 2003 – mid 2005, which may be explained by the effectiveness of the new nominal anchor (XR), and correspondingly the expected disinflation. The expectation of reflation in late 2005 till late 2007 may be explained by the more expansionary policy and changes in Russian preferences that took place during this period. After that, there was a period of stable expectation, which is likely to be explained by the credibility of the nominal anchor (nevertheless, there was a shock in late 2008 that is associated with the impact of the global crisis).

The most considerable shock took place in the beginning of 2010, which has a lack of intuitive explanation and might be associated with a phase of radically expansionary policy.

Finally, a new significant shock took place in late 2010 – beginning 2011 which might be associated with the visualized problems at the currency market at that time.

Currently, there is a very high level of inflation expectations and its increased volatility in the second half of 2011 seem to be of a great importance. It signals that economic agents do not treat price shocks as a single-shot, but mostly tend to consider it as a long-lasting process. Hence, the absence of a nominal anchor and the fresh memory of huge inflation seem to be responsible for the current high and instable inflation expectations.

Maintenance of high inflation expectations is a dangerous threat for the money market. Propagating inflation through expectations may be considered as a separate channel within the monetary transmission mechanism (along with interest rate, exchange rate and bank-lending channels). In other words, even without additional fundamental preconditions for inflation, inflation expectations may become a self-fulfilling prophecy.

However, during the last two months (December 2011 and January 2012) this adverse effect seems to have been suppressed by monetary authorities, as the monthly inflation rate reduced radically in comparison to average rate in May-November 2011. This is likely to be the outcome of the significant monetary policy tightening that has resulted in a sharp increase in nominal interest rates by banks. On the one hand, such nominal interest rate complies with the shocks in inflation expectations and real ex ante interest rate (the latter grew as well at the background of the crisis). In other words, current level of nominal interest rates will equalize ex post real rate with ex ante real rate if the actual inflation rate has been as high as current inflation expectations. But on the other hand, if actual inflation had been much lower than expected one (and it tends to be so, in case of keeping on conservative MP), ex post real rate would be much higher than the ex-ante one. For instance, such a situation has already been peculiar during December and January: according to our estimations, ex ante real interest rate in December was about 3.6% in annual terms (preliminary data on January shows that it in this month it is rather similar), but annualized ex post real rate for these months is about 30%.

This suggests that there is a trap for the monetary authorities. If they keep high interest rates, based on the expected inflation, the impact of expectations on actual inflation will be mitigated, but the losses, say in terms of output, will be high because of the extremely high ex post real interest rates. If the monetary authorities facilitated the rapid reduction of nominal interest rates, current nominal rates would not guarantee ex ante real interest taking into consideration the high inflation expectations, which would then constitute a severe shock for the money market. Hence, the mechanism of self-fulfilling prophecy would work.

Furthermore, the increased ex ante real rate (and high probability of even higher ex post real rate in national currency) could give speculative incentives for a number of economic agents. For example, many agents could increase the share of national currency in their savings portfolio, either avoiding buying hard currency (which took place during the peak of the currency crisis) for new deposits, or changing the nomination of their deposits to the national currency (i.e. selling the hard one). In a sense, this trend may be interpreted as the compensation of losses on ruble deposits in the last year, which is needed to revive the demand for such deposits. But in any case, these internal processes (along with restricting money supply by the National bank) influence the domestic currency market. Through this, the supply and demand are formed not only due to current and financial international flows. Hence, due to these incentives for hard currency supply and demand, the current value of the nominal rate may substantially deviate from the equilibrium rate. The latter may be defined as in Kruk (2011): the one that may clear the market immediately (given short-term trends in current account flows at the background of medium-term values of other fundamentals).

Figure 2. Actual and Equilibrium Exchange Rate

Note: For 2010Q1-2011Q1 official rate of the National bank is taken as actual nominal rate, for 2011Q2 the exchange rate at the ‘black market’ (used by internet shops), and for 2011Q3 ‘black market’ and later the exchange rate of the additional BCSE session are taken.

The assessments of the equilibrium exchange rate based on this methodology (Kruk (2011)) show that in the third quarter, the actual rate almost equals the equilibrium rate. For 2011Q4, all necessary data is not available yet, but an approximate assessment correction of the equilibrium rate of the Q3 for average inflation between Q3 and Q4 may be used (i.e. in real terms the rate should not have changed in order to sustain equilibrium). Such an assessment indicates that the actual rate in the Q4 is again overestimated by roughly 5-10% in comparison to the equilibrium rate.

At a first look, such an ‘overhang’ at the domestic currency market seems to not be a great problem. But along with the trap stemmed from the high and unstable inflation, this may contribute and propagate possible shock at the money market. Furthermore, this ‘overhang’ is due to speculative incentives, which in turn, are due to high inflation expectations. Hence, high and unstable inflation expectations are a prime cause of this ‘overhang’.

Finally, we may argue that unfavorable inflation expectations is a multidimensional problem, which generates grounds for shocks at the money market and entraps monetary policy at the current stage. Therefore, restraining inflation expectations must currently be an absolute and unconditional priority of economic policy.

This gives rise to the issue of which policy tools that are needed for solving this problem. Tight monetary policy alone may not be enough and/or its losses in terms of output may be unacceptably high, especially taking into account that keeping the Belarusian economy depressed is likely to cause huge migration and thus reducing the prospects for long-term growth.

Our view on the problem of inflation expectations supposes that they stem both from recent experience of very high inflation and the absence of nominal anchor. Inflation memory cannot easily be removed, but introducing a new nominal anchor seems to be worthwhile. Among possible options, given the desire to preserve autonomous monetary policy in Belarus, the introduction of inflation targeting (IT) is seen as inevitable. A shift to this regime is associated with plenty of obstacles and might not be realized immediately (Kruk (2008)). A gradual shift to IT through its intermediary phases (so called IT Lite) is more expedient and complies more with the requirement of obtaining new powers and capacities at the National Bank of Belarus.

Taking on more and more strict commitments in terms of inflation and implementing mechanisms and procedures peculiar for IT (the latter is even more important than commitments themselves) will increase credibility and public trust for the National bank. The other side of the coin involves decreasing and less volatile inflation expectations, which do not challenge monetary policy and facilitate low and stable inflation. Another advantage of IT is the possibility to mitigate price shocks.

Our main policy recommendation is therefore that it is necessary to shift to an IT framework as soon as possible, starting from exploiting the forms of IT Lite. The advantages of this step overweigh all the obstacles, including those associated with the reluctance of economic authorities to change institutional preconditions.

However, one important clause should be emphasized. Shifting to IT (especially gradually through IT Lite) does not guarantee that current high inflation expectations will be reduced automatically and immediately. In other words, it does not guarantee that the cost of reducing inflation in terms of output will decrease (though for the present Belarusian situation there are grounds to suspect that it would facilitate). For instance, Mishkin (2001) shows that “there appears to have been little, if any reduction, in the output loss associated with disinflation, the sacrifice ratio, among countries adopting inflation targeting… The only way to achieve disinflation is the hard way: by inducing short-run losses in output and employment in order to achieve the longer-run economic benefits of price stability”. However, an introduction of IT assumes that new shocks in inflation expectations may be prevented, and due to it, low and stable inflation will be more likely.

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References

  • Blanchard, O., Quah, D. (1989). The Dynamic Effects of Aggregate Demand and Supply Disturbances, American Economic Review, Vol. 79, No.4, pp.655-673.
  • St-Amant, P. (1996). Decomposing US Nominal Interest Rate into Expected Inflation and Ex Ante Real Interest Rates Using Structural VAR Methodology, Bank of Canada, Working Paper No. 96-2.
  • Gottschalk, J. (2001). Measuring Expected Inflation and the Ex Ante Real Interest Rate in the Euro Area Using Structural Vector Autoregressions, Kiek Institute of World Economics, Working Paper No.1067.
  • Mishkin, F. (2001). From Monetary Targeting to Inflation Targeting: Lessons from Industrialized Countries, World Bank, Policy Research Working Paper No. 2684.
  • Kruk, D. (2008). Optimal Instruments of Monetary Policy under the Regime of Inflation Targeting in Belarus, National Bank of Belarus, Materials of International Conference “Efficient Monetary Policy Options in Transition Economy”, pp. 305-322.
  • Kruk, D. (2011). The Mechanism of Adjustment to Changes in Exchange Rate in Belarus and its Implications for Monetary Policy, Belarusian Economic Research and Outreach Center, Policy Paper No. 004.

Is Regional Policy Effective in the Long Run? Learning from Soviet History

Posted on February 20, 2012 | Policy Brief

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Regional inequality has been a pressing issue in many countries, and also between the countries of the European Union.  Unequal economic development, where some regions develop successfully and prosper while other regions stagnate, is often viewed as a source of social instability and economic inefficiency. Many kinds of regional policy have been proposed in order to mitigate such a situation by promoting growth in lagging regions. The policies range from subsidies and favorable tax policies for business investment to large-scale government investment projects.  The ultimate goal of all regional policies is to create an environment for sustainable growth in regions that have fallen behind. In theory it might appear that a policy, which is implemented during a specific period of time, would be sufficient to achieve sustainable development:  subsidies or creation of infrastructure would lure firms into a region and create a favorable environment for economic agents (both firms and people). The temporary policy would create agglomeration externalities that would ensure sustainable development even after the policy is discontinued.

However, are such regional policies in fact successful?  Researchers often observe a short-run impact, but it is less clear whether regional policy can make a difference in the long run. From the literature on historical “natural experiments”, we know that spatial structures of economic activity are very resilient to temporary impact. For example, the wholesale destruction and loss of life in WWII seems to have had little or no effect on the regional shares of population and manufacturing in the long run. On the other hand, significant and permanent (or long-lasting) changes to market access, such as the division ofGermanyafter WWII, do reshape the spatial economy in the long run.

Our study looks at the long-run patterns of Soviet city growth in light of Stalin’s industrialization and WWII. The Soviet government’s investment decisions during that period were dictated to a large extent by military strategy and ideology. Massive relocation of productive resources from west to east before, during, and after WWII represents a unique natural experiment, in which production factors were destroyed in some parts of the USSR, while new production facilities and infrastructure were created in other regions of the country. Using a unique dataset, we test whether Gulag camps, wartime evacuation of industry, and location near the war front had a long-run effect on city size.

In the 1930s-1950s, Stalin’s system of penal labor camps (the Gulag) was widely used as a source of cheap labor, especially in remote locations where there was no other available labor force. Penal labor was used in a variety of sectors (logging, mining, manufacturing and construction). Presence of a camp near a city or town usually meant that this location was chosen by the Soviet government for an investment project. We trace the impact of having a camp nearby on city growth from 1930 to the present day.

Evacuation of enterprises from western to eastern regions of the USSR (to avoid their possible capture by the advancing German army) is traditionally named among factors that determined post-war growth of cities in the Urals andSiberia. Indeed, data show that the majority of evacuated enterprises never returned to their original location in the westernUSSR. Western cities that sent enterprises into evacuation often lost their significance in the immediate post-war period. We test whether evacuation affected the growth of cities in the longer run, ceteris paribus.

Unfortunately, no detailed data on deaths and destruction in Soviet cities during WWII are publicly available. We therefore measure the impact of wartime damage by constructing a set of indicators for cities that were occupied or were close to the front line during WWII.

The results show that (controlling for pre-war city size, rate of growth, and geographical location) occupation and location 30 km or 200 km from the front line do have a negative and statistically significant effect on city size by 1959. However, this effect disappears by 1970. This is consistent with findings forJapanandWestern Germany, where pre-war trajectories of city growth were restored after 25-30 years.

Surprisingly, the result is roughly the same for cities which hosted evacuated enterprises. Controlling for pre-war size and growth rate, geography and presence of Gulag camps, cities that received evacuated plants grow faster until 1959, but the difference is not statistically significant in 1970 and later. Thus, contrary to the commonly held belief, the effect of evacuation was only temporary.

By contrast, the presence of a Gulag camp increases city size in a long time horizon. Gulag cities grow faster not only in the 1930s-1950s when the Gulag system was operational, but also in the 1970s and 1980s. On average, the Gulag effect only disappears in the 1989 population census.

Specialization of the camp also makes a difference. Effect on city population from a camp where prisoners were involved in agriculture or logging is short-lived. Such camps were not used to build capital or infrastructure, so the nearby cities did not become more attractive for free labour. However, if a city had a camp where prisoners worked in manufacturing, mining, or construction of production facilities or housing, its population increased permanently. Compared with the best match from a control group (a city of similar characteristics, but without a Gulag camp), such a city accrued 50% more population, and this difference remains statistically significant even until the census of 2010.

Overall, the evidence on Soviet city growth supports the common finding: the direct effects of WWII were relatively short-lived. The experience of enterprise evacuation shows that one-shot relocation of production factors by the state also fails to produce robust changes in the geographical redistribution of economic activity in the long run. However, when the Soviet government established new industrial centers in the eastern parts of theUSSR, and made massive investments in production facilities and infrastructure using Gulag labor, it managed to permanently shift the geography of economic activity. This example illustrates the size and scope of impact that is required to affect economic geography in the long run.

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Who Needs a Safety Net?

Posted on February 13, 2012 | Policy Brief

One definition of safety net found on the internet is the following: “a net placed to catch an acrobat or similar performer in case of a fall”. This brings to my mind the thrilling performances I saw at the circus when I was a child and I have to admit in most cases there was a safety net. Only in some rare occasions it was removed and the increased tension became palpable. We knew that only the best acrobats could dare performing in those conditions since the slightest mistake or distraction could lead to disastrous consequences. Born in this context, the term safety net has soon been extended beyond circuses. The same internet source, right below the standard definition adds: “fig. a safeguard against possible hardship or adversity: a safety net for workers who lose their jobs”.

Imagine you are a European worker in a time of crisis. You are the only breadwinner in your family and you become unemployed. The situation of your family is going to worsen significantly, but you know that – at least for some time – you and your family will be able to survive thanks to your unemployment benefits and to other forms of social support. In the meantime, hopefully, you will be able to get a new job – maybe thanks to the help from a public employment agency – or will at least be admitted into some publicly sponsored training program increasing your probability to get a new job.

Imagine that, instead of being fired, you get sick. Luckily most of the costs for your care will be covered by the public healthcare system. You will continue receiving your salary (with a reduction as the length of the period of sickness goes beyond a certain number of days) for at least a few months, typically until you can go back to work. If your illness is really serious, at some point you will not receive compensation but you will keep your job unless you stay away from your workplace continuously for a very long period. Should you lose your job, you will still be able to rely for a while on unemployment benefits and on additional forms of social support. Your family will be suffering of course, but at least you will be able to “gain some time” to find a solution.

Now imagine a different scenario. You lose your job. You get one month severance pay but no unemployment benefits. The labor market is hardly creating new jobs, so you have a high probability of not finding a good job and will have either to accept to be unemployed for a long period of time or to work in badly paid temporary jobs, maybe in very dangerous working places (because nobody is in charge of checking working conditions). In case you choose not to risk and to try looking for safer jobs, most likely during your unemployment period you will not receive any training and certainly no support from (non-existing) public employment agencies.

Or, what if you are sick and all healthcare costs fall on you. If you have a private health insurance you get some assistance. If not, you have to dissave in order to get some treatment. You receive one month of salary, after which your employer is free to fire you without having to give you any compensation. So you suddenly find yourself sick and not only unable to help your family but being a burden for it, with no public support and no income. To be fair, you might receive some sort of assistance, after you have applied to the government for support as a needy household if your situation has deteriorated so much that you cannot ensure even your subsistence (maybe by selling assets). However, this support is typically not that high.

This second case is not that of a fictional country. It is a representation of the conditions of most workers in Georgia.

If you keep this in mind, you will not be surprised looking at the following pictures taken from the latest EBRD (European Bank for Reconstruction and Development) Transition Report, titled: “Crisis and Transition: the People’s Perspective”. The tables and pictures included in the report are based on a series of household surveys conducted by the EBRD in a number of transition countries plus a few selected countries of Western Europe. The aim of this study was to study how the crisis had affected household’s welfare in order to draw some conclusion about the potential vulnerability of countries and households to future crises.

Figure 1.


Source: EBRD Transition Report 2011

In this first picture Georgia (in red) stands out as very much above the regression line. It is what is defined as an “outlier”. In this case, being an outlier means exactly that Georgian households, despite having been themselves hit by a relative smaller number of negative events, appear to have suffered much more than households in similar situations in other countries. In other words, they were forced to cut their consumption much more than households in other countries.

The second picture (below) allows us to see where Georgian households had to cut their consumption. Of course, cutting the consumption of luxury goods is not the same as cutting the consumption of food or healthcare. Looking at the second picture, the situation in Georgia appears even worse. Most households have had to cut exactly where one would hope they had not to: staple food consumption and visits to doctors.

Neither of these cuts bode well for the future of Georgian households, as they are likely to have long lasting (negative) effects. Especially as a new world crisis seems approaching.

Figure 2.


Source: EBRD Transition Report 2011

Why this discussion about Georgia and safety nets? The reason is because for some time now Georgia has been presented consistently as a showcase country with an impressive reform track (including an extremely liberal labor market reform that has drastically reduced all forms of workers’ protection) and equally impressive growth rates.

Much less has been said about how Georgian people have been affected by these reforms. For sure the picture that emerges from the EBRD study is of a country where households are extremely vulnerable to any slowing down of the economy or worsening of the macroeconomic conditions, much more than in most other countries.

Again, looking at the EBRD study, we can see that this is related to at least two factors: on the one hand the extremely weak safety net provided by the state; on the other hand, the limited success (so far) in translating high growth rates into a substantial amount of new, “good quality” jobs. This is what led the EBRD, after presenting these results to suggest the following two key priorities for the Georgian government: “…to create a basis for export led growth… […] but also to establish an effective social safety net”.

I would like to conclude with my personal answer to the question: “who needs a safety net?” The answer is a lot of people, I would say, especially in times of crisis like the current. After all, not even the best acrobats would dare to perform all the time without it, especially when they are trying their most dangerous performances for the first time and when preconditions are less than perfect. Why? Because the cost of failure would be too high. Like in the case of acrobats – even more, as they are not risking their own lives – policy makers have the responsibility of taking into account in their evaluations what could go wrong and think of ways to minimize negative impacts on the population.

Most economists would agree that only a sustainable increase in the welfare of citizens (including the most vulnerable ones) is the true sign of development of a country in the long run. Assuring this, as someone sometimes seems to forget, requires also creating and maintaining – especially when markets are less than perfect, a solid social safety net.
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The Distributional Impact of Austerity Measures in Latvia

Posted on February 6, 2012 | Policy Brief

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For a country of its size, Latvia was mentioned in the last decade’s macroeconomic discourse remarkably often: first, for its exceptional growth up to 2007, then – for a dramatic GDP contraction in the aftermath of the 2008 financial crisis, and for the so-called “internal devaluation” policy that was the cornerstone of Latvia’s recovery strategy. Now, when GDP recovery is underway for 9 quarters, Latvia is held up as an example of a country that paved its way out of the crisis with decisive and timely budget austerity measures. The size of budget consolidation package was remarkable, reaching almost 17% of GDP in 2008-2011. Today, when there is so much talk about austerity in the context of the Eurozone debt crisis, Latvian consolidation experience is of particular interest. In this brief, we are looking at the distributional impact of selected implemented austerity measures, using a microsimulation tax-benefit model EUROMOD. Our results suggest that the impact of these measures is likely to have been progressive, meaning that rich population groups are bearing a larger part of the burden.

From Boom to Recession

The “Baltic Tigers” – a term coined to praise the Baltic countries for their dynamic development in the 2000s, especially after their accession to the EU in 2004. During 2004-2007, average annual GDP growth in the Baltics exceeded 8% (in Latvia average growth was 10%). The growth was to a large extent driven by an externally financed credit bubble, leading to overheating of the Baltic economies: inflation was skyrocketing, unemployment was at historically low levels, and current accounts posted double-digit deficits. Before the outbreak of the crisis, the Latvian economy was in the most vulnerable position: Estonia was better situated thanks to prudent fiscal policy implemented in the “good” times, whereas Lithuania was less exposed thanks to its private sector being relatively less indebted.

The growth slowdown in Latvia began in 2007 and was initially triggered by the government’s adopted “anti-inflation plan” and the two of the biggest banks’ actions aimed at restricting credit expansion. Altogether, this initiated a decline in real estate prices. By December 2007, the average price of a square metre in a standard-type apartment in Riga had fallen by 12% from its peak in July (Arco Real Estate, 2008). Construction, retail trade and industrial production growth slowed down in the second half of 2007. GDP quarter-on-quarter growth approached zero by end-2007 and turned negative in the 1st quarter of 2008. In August 2008, the second largest Latvian commercial bank, domestically owned Parex Bank, faced deposit run and was unable to finance its syndicated loans, and in November 2008, the Latvian government took the decision to nationalize the bank. By the 3rd quarter of 2008, GDP quarter-on-quarter contraction exceeded 6%. The budget revenues lagged behind the expenditures, resulting in a gradually growing budget deficit, which reached about 5.5% of GDP in the 3rd quarter of 2008 (see Figure 1).

Figure 1: Year-on-year growth of general government budget total revenues, tax revenues and expenditures, %; seasonally adjusted budget balance, % of GDP

Source: Eurostat, authors’ calculations

In circumstances where the fiscal position was quickly deteriorating but world financial markets were frozen, the Latvian government was forced to seek financial assistance from international lenders. After tough negotiations in November and December 2008, Latvia received a 7.5 billion euro (about 1/3 of GDP) bailout facility from the IMF, the European Commission, the World Bank and the Nordic countries. Latvia received the funding in a series of tranches, with the transfer of each tranche being subject to implementation of a strict reform package agreed with the lenders.Given that introduction of the euro in 2014 remained the Latvian government’s target, one of the key elements of the reform programme was maintaining the lat’s peg to the euro. Therefore, the Latvian government had to accept especially strict and wide-ranging budget consolidation measures.

Budget Consolidation

The total size of budget consolidation achieved in 2008-2011 was impressive: overall, the fiscal impact of the reforms is estimated at 16.6% of GDP (Ministry of Finance of Latvia, 2011). Under the pressure of international lenders, budget consolidation was front-loaded and was achieved astonishingly fast – the fiscal impact of the reforms implemented in 2009 reached almost 10% of GDP, whereas the impact of 2010 and 2011 year measures was much smaller – 4.1% and 2.6%, respectively (see Figure 2).

Figure 2: Size of the implemented consolidation measures and budget deficit outturn, % of GDP*

* Budget deficit in 2011 is the Bank of Latvia’s autumn forecast
Source: Ministry of Finance, Bank of Latvia, Eurostat

Yet the way the consolidation was done was rather chaotic. The 2009 consolidation was mainly implemented by expenditure cuts, including strong wage and employment reductions in the public sector (public pay and employment cuts were continued in the following years, wages were cut by 15-20% in each round and most bonuses were abolished). On the revenue side, the government stuck to the goal of shifting tax burden from labour to consumption, thus the consolidation was mainly achieved by raising indirect taxes, while the personal income tax was reduced. Another line followed by the government at the time was to strengthen support to those affected by the crisis, for example, the duration of unemployment benefits was increased.

Nevertheless, by the time preparation of the 2010 budget started, it became clear that in circumstances of continuing GDP fall and peaking unemployment (in 2009, GDP fell by 17.7%, and the rate of unemployment reached 17.1%), the reduction in labour taxes could not be sustained while the social budget could not bear the burden of growing expenditures. Consequently, the reduction in the personal income tax was reversed (the tax rate was raised even above the pre-crisis level). To consolidate the social budget, the government implemented an across the board cut by introducing ceilings on the size of many benefits. In 2011, the tax burden on labour was further increased by raising the rate of mandatory social security contributions.

Budget consolidation was done under the pressure of the crisis and the reform package was designed in a great rush. What also may not be disregarded, is that the three years – 2009, 2010 and 2011 – were election years in Latvia: in 2009, there were local government elections, in 2010 – parliamentary elections and in 2011 – parliamentary re-elections . Elections have arguably affected the composition of implemented austerity measures. Thus, in June 2009, just ten days after local government elections, amendments to the Law on State Pensions were passed, which stipulated that old-age pensions should be cut by 10%, but pensions to working pensioners should be cut by 70%. This decision caused a strongly negative public reaction and on December 21, 2009, the Constitutional Court ruled that the government’s decision was unconstitutional arguing that the state must guarantee peoples’ right to social security. In the following budget consolidation rounds, even in the face of convoluted IMF recommendations to find a constitutional way of ensuring sustainability of the pension system (IMF, 2010), the government remained strictly opposing any pension cuts.

The mix of implemented reforms is crucial not only because it determines the effectiveness with which the budget consolidation is achieved. What is equally important is that the mix of reforms affects the distribution of costs of the crisis and shapes the economic recovery path. The consequences of the crisis – the dramatic rise in unemployment and wage reductions in the private sector – had a strong impact on incomes, yet policy makers can do little to directly affect this process. On the other hand, policy makers can offset or aggravate those effects by implementing reforms, such as those that made up the austerity packages. In this brief, we assess the distributional impact of selected austerity measures, which were implemented in 2009 – 2011.

Modelling Approach and Limitations

We use the Latvian part of the tax-benefit microsimulation model EUROMOD and follow a similar approach as that taken by Callan et al (2011). We limit our analysis to reforms in direct taxes, social contributions, and cash benefits . In particular, the following austerity measures are included in the analysis:

  • removal of income ceiling for obligatory social insurance contributions (in 2009);
  • increase in the rate of social insurance contributions for employees, employers, and self-employed (June 30, 2011);
  • reduction of tax exemptions (July 1, 2009);
  • increase in the rate of personal income tax (2010);
  • introduction of benefit ceiling for unemployment benefits (2010), maternity, paternity, and parental benefit (November 3, 2010);
  • cuts in state family benefit (2010);
  • cuts in child birth benefit (2010);
  • reduction in the amount of parental benefit by limiting eligibility to non-working parents only (May 3, 2010);
  • making stricter income assessment criteria for guaranteed minimum income (GMI) and reducing amount of the GMI benefit for some groups (2010).

We assess the distributional impact of these austerity measures by comparing two alternative scenarios:

  1. the baseline scenario – simulation of 2011 tax-benefit policy system (with austerity measures implemented), and
  2. the counter factual scenario – simulation of tax-benefit policy system that would have emerged in 2011 in the absence of austerity measures.

If a policy was changed as a part of the austerity package (e.g. income tax increase), we implement a pre-austerity policy (e.g., reduce the income tax to its pre-austerity level). However, if the changes in the policies were regular (e.g. an increase in minimum wage that was planned long before the discussion of austerity measures had started) or not related to austerity measures (e.g. increase in duration of unemployment benefit) we include them in the counterfactual scenario, as well as in the austerity package scenario. By defining the counterfactual scenario in this manner we focus on the impact of austerity measures only holding other things equal.

Despite Latvia is one of the countries where the size of the austerity package was especially large, the distributional effect of the implemented measures has not been analysed neither before nor after the policies had been implemented. Until recently Latvia didn’t have a national microsimulation model which could be used to assess the impact of taxes and benefits on household income. This paper is the first attempt to do this.

However, our analysis is subject to some drawbacks. First, EUROMOD’s input data is based on the European Union Statistics on Income and Living Conditions 2008 (with the income data referring to 2007). We adjust 2007 incomes up to 2011 using updating factors based on the aggregate evolution of such incomes according to national statistics. However, we do not adjust for the changes in the labour market that happened during this period. Therefore, we estimate the effect of austerity measures on data that represent the population with pre-crisis labour market characteristics (e.g. relatively low number of unemployed people).

Second, the analysis is limited to the direct impact of the implemented measures, disregarding the secondary effects such as e.g. behavioural responses of people on the implemented policies.

Results

The simulation results suggest that the impact of the analysed austerity measures was progressive with top income groups being the most affected (see Figure 3). The six countries considered in Callan et al (2011) show different degrees of progressivity: Greece demonstrated a clearly progressive impact, while Portugal was the only country where the effect was regressive. The result for Latvia is likely to be a consequence of introduced ceilings on contributory benefits, as well as the increases in income tax and social insurance contributions. While income tax in Latvia is flat (except for a relatively small untaxed personal allowance), the lowest income deciles contain proportionately more unemployed people and pensioners.

Figure 3: Percentage change in household disposable income due to austerity measures by income deciles

Source: based on own calculation using EUROMOD

Higher progressiveness was observed for households with children (see Figure 4), which is explained by the introduction of ceilings on child-related contributory benefits. At the same time, the impact on the households with elderly was more even.

Figure 4: Percentage change in household disposable income due to austerity measures for different types of households by income quintiles



Source: based on own calculation using EUROMOD

While the introduction of austerity measures made all income groups poorer, progressivity of the impact reduced income inequality. The Gini coefficient of the counter factual scenario is 1 percentage point higher than that of the base scenario. After implementation of the austerity measures, the poverty line decreases because the median income decreases. As a result, poverty rates using relative poverty lines decreased. The poverty rate of the elderly was affected the most, because pension income was not cut and pensioners became relatively better off as compared to other population groups. However, if measured against the fixed poverty threshold, the poverty rate increased in all population groups (see Table 1).

Table 1: Poverty rates and Gini coefficient before and after implemented austerity measures

Source: based on own calculation using EUROMOD

Concluding Remarks

The austerity measures analysed in this paper have had a progressive impact, with the richest population groups likely to be bearing most of the costs. This result should be interpreted with caution. It should be taken into account that we do not model all of the austerity measures that were implemented in 2009-2011. E.g., we do not model the impact of changes in VAT rates, which is likely to have been quite strong and regressive.

Latvia is a society with extremely high income inequality. For example, the income quintile share ratio calculated by the Eurostat (S80/S20), which measures income inequality, in 2009 was the second highest in the EU (6.9 as compared with an EU average of 4.9). It is unlikely that the progressive impact identified in this paper will significantly reduce income inequality gap in Latvia relative to other European countries.

References

  • Arco Real Estate (2008). Real estate market overview (Sērijveida dzīvokļi, 2008. gada decembris)
  • Callan, Tim, Chrysa Leventi, Horacio Levy, Manos Matsaganis, Alari Paulus & Holly Sutherland (2011). “The distributional effects of austerity measures : a comparison of six EU countries”, Social situation observatory, Research note 2/2011.
  • International Monetary Fund (2010). Republic of Latvia: Second Review and Financing Assurances Review Under the Stand-By Arrangement, Request for Extension of the Arrangement and Rephasing of Purchases Under the Arrangement and Request for Waiver of Nonobservance and Applicability of Performance Criteria. IMF Country report No. 10/65, March 2010.
  • Ministry of Finance of Latvia (2011). Budget consolidation in 2008-2011 (Veiktā budžeta konsolidācija laika posmā no 2008.-2011. gadam)