Location: Baltic countries

Fact or Fiction? The Reversal of the Gender Education Gap Across the World and the Former Soviet Union

Posted on March 4, 2013 | Policy Brief

In this policy brief, I discuss the reversal of the gender education gap in many countries around the world – a fact that is still not widely known, although is increasingly gaining attention. I describe recent studies that have documented this fact for both developed and developing countries and have provided evidence on the trend. As there has not been much analysis of the education gap in the former Soviet Union countries, I present some measures of the education gap in the USSR and FSU countries, and compare them to other countries around the world. Finally, I discuss the potential causes of the reversal identified in the literature and how the reversal of the gap is related to other gender disparities.

And Then There Were Eighteen? Will Latvia Join the Euro Zone in 2014?

Posted on February 4, 2013 | Policy Brief

20130204 And Then There Were Eighteen Image 01

Latvia’s government is zealously preparing for accession to the Euro Zone. Prime Minister Valdis Dombrovskis is expected to request the European Central Bank (ECB) and European Commission (EC) prepare their respective convergence reports on Latvia’s readiness to enter Economic and Monetary Union (EMU) within the next two months. The expectation is that Latvia will join on 1 January 2014. Indeed, the three-party coalition government has long been readying for the technical changeover to the euro. The Cabinet of Ministers adopted a detailed national euro changeover plan in September 2012 and appointed a high-level steering committee to manage the process. The government has launched a controversial multi-million euro advertising blitz aimed at winning over Latvia’s skeptical public.[1] Parliament passed the law on euro adoption in a 52-40 vote on 31 January 2013.

What could possibly go wrong? Although unlikely, a referendum or the collapse of the Dombrovskis coalition government could yet derail Latvia’s euro ambitions.

Latvia and Europe

All Latvian governments have steered a steady pro-Western course in the two decades since the fall of the Soviet Union. International recognition was followed by membership of the Council of Europe, World Bank and the other minor and major international organizations that make up the international community. However, the big attractions were the Western clubs – NATO and the European Union. Membership of both was achieved in the two ‘big bang’ enlargements of 2004. In all the giddy excitement of finally joining the Western world and seemingly slipping away from Russia’s bear-hug, Latvia initially aimed to quickly join the Euro Zone, setting a target of 1 January 2008.

However, the government proved half-hearted in its efforts, preferring to enjoy the low-hanging fruit of a cheap credit-driven booming economy rather than balance the budget. Both government and public entered a period of rabid consumption and spending that resembled nothing so much as sailors in a pub after a year at sea. Unsurprisingly, Latvia rapidly slipped far away from meeting the Maastricht criteria on inflation. Accession to the Euro Zone was quietly dropped from the political discourse.

However, euro adoption returned as a frontline government initiative after the dramatic economic collapse of 2008, and the advent to power of Valdis Dombrovskis, the Baltic Angela Merkel. Dombrovskis will soon have been in power for four years, a lifetime in Latvian politics where, prior to Dombrovskis, the average prime minister served for less than a year.[2] He has overseen harsh austerity measures of tax hikes and spending cuts, but remains surprisingly popular (not least because his party was in opposition during the post-2004 economic bubble years). He has twice been re-elected to office, proving once again that Latvians favour monochrome technocrats over colourful populists.

Despite a return to growth (in 2012 Latvia recorded the highest GDP growth in the EU), the government has maintained tight control over spending. Indeed, it has even perhaps been over-zealous, with both the IMF and EU recently chipping in with criticism of the social spending cuts that Latvia has made to its 2013 budget.[3] Nevertheless, Latvia is now applauded as a model of austerity and frequently used as a positive contrast to Greece.[4]

Moreover, Latvia is now on the cusp of meeting the Maastricht criteria for accession to the Euro Zone. A January 2013 IMF staff report argued that Latvia meets the public debt and budget deficit criteria, although inflation and interest rates may be a hurdle depending on the EU member states used for the reference value calculation (will Greece be treated as an outlier?).[5] The informal political signals from both the EC and ECB are clearly positive. However, euro accession could still be derailed by either a referendum or a change of government.

Let the People Decide?

The biggest potential hurdle remains the threat of a public referendum. The EC and ECB will not contemplate Latvia’s accession to the euro zone with the Damocles Sword of a referendum hanging over the process. Moreover, public support for the euro remains low, with just 8% of the public wanting the euro introduced quickly and 41% being absolutely opposed to the currency.[6] A vote would be a real throw of the dice.

A citizen’s initiative aiming to delay euro adoption, by demanding a vote on the timing of accession, was submitted to Latvia’s electoral authority (by the awkwardly named Latvia’s Social Democratic Movement for an Independent Latvia, a fringe party that has never been elected to parliament) in late 2012. The Central Election Commission must make a final decision on whether to allow the initiative to go ahead by February 3. However, the legal opinions provided by scholars, the Latvian ombudsman’s office and the Latvian parliament’s legal advisers indicate that the initiative is likely to be rejected because:

Latvians effectively voted to join the euro when voting on accession in 2003;
The Council of Ministers is the only institution authorized to choose the date of accession to the euro zone, thus any initiative specifying a date (or conditions that need to be met) is not legal;
The text of the initiative conflicts with the constitution.[7]

While the ruling could be challenged in Latvia’s Constitutional Court or a reworded initiative submitted to the Central Election Commission, the weight of the legal opinions already delivered indicates that these efforts would be unlikely to succeed. At worst, the uncertainty could delay euro adoption past January 1, 2014 (and the Latvian legal system can certainly be ponderous at times). The same is true of any parliamentary attempt to initiate a referendum by having a one-third minority of deputies force the president to sit on the euro adoption law while citizens sign an initiative.[8] Indeed, legal opinions cited by the President state that because euro introduction is a treaty obligation, a majority of parliamentarians (51 of 100) would need to sign any initiative attempting to call a referendum. The opposition will not be able to rustle up a majority of parliamentary deputies (although the legal haggling could delay the date of euro adoption).

Coalition Collapse?

The other risk is a collapse of the government coalition. While the Reform Party and the prime minister’s Unity Alliance are firm supporters of euro adoption, the third coalition member – the radical right populist National Alliance is more torn. Its rank and file membership is largely against the euro, primarily for nationalist reasons (they see the Latvian Lat as a symbol of sovereignty and national identity). One NA parliamentarian even broke coalition ranks and voted against euro adoption. A motley conglomeration of far right radical groups and nationalist intellectuals has begun speaking out against the ‘commercialization’ and ‘westernization’ of Latvia, and sees the euro adoption battle as the opportunity to draw a final line in the sand. They are likely to put the National Alliance’s ministers and parliamentary deputies under severe pressure.

Indeed, the National Alliance already played the ‘euro card’ in November 2012, successfully extracting budgetary concessions for pet projects from Prime Minister Dombrovskis. They may well play it again, as they seek a greater number of ministerial portfolios. However, as Dombrovskis pointed out, opening up of the coalition agreement could well lead to the collapse of a government already creaking at the edges.

Conclusion: After Dombrovskis

There is strong political resolve to lever Latvia into the Euro Zone. Moreover, the unusual confidence emanating from both government officials and the Bank of Latvia indicates that certain reassurances have been made in Brussels and Frankfurt. Indeed, Latvia’s glowing current reputation as the poster child of austerity gives it a once-in-a-decade political momentum. Latvia’s entry into the euro on schedule on January 1, 2014 is more likely than not.

However, looking to the future, one pertinent question needs to be addressed. Which Latvia will we see in the Euro Zone? The grey, serious, disciplined almost Teutonic Latvia of Valdis Dombrovskis? Or the reckless drunken sailor, that has marked much of Latvia’s post-communist era?

Naturally, Dombrovskis holds the key to this question. He is expected to leave domestic politics after the October 2014 parliamentary election, probably to cash in his international political capital with a well remunerated European post (the timing is right for a 2014-2019 European Commissioner portfolio). At best, if re-elected, he might be persuaded to stay on to oversee Latvia’s presidency of the European Union in 2015. In any case, while Latvia has been reborn as a paragon of economic virtue under his watch, these assets have not been institutionalized. Dombrovskis will leave behind the same old fractured, frail and quarrelsome parties, politicians and oligarchs that he inherited. Recent international criticism of disequilibrium in government welfare and tax policies hints that political backsliding has already begun.

Latvia is at its strongest when its political, economic and administrative elite units in pursuit of some concrete target. Independence from the Soviet Union, then NATO and EU accession, followed by harsh austerity measures and now even Euro Zone accession were achieved far quicker than many observers had believed possible. International conditionality has made up for the absence of ideology and ideas as moral and political compasses in Latvian politics. However, when left to their own devices, Latvian politicians have tended to run amok. After Latvia enters the Euro Zone it will be left without an all-encompassing political plan. Quite frankly, that is rather worrying.

▪

References

Aslund, Anders (2013) ‘Why austerity works and stimulus doesn’t’.
DNB Banka (2012), ‘Latvijas Barometrs: Eiro ieviešana Latvijā’.
Eglitis, Aaron (2013), ‘EU joins IMF in criticizing Latvian cuts to tax, social spending’. Bloomberg news.
IMF Staff Report No. 13/28 (2013). Available at: http://www.imf.org/external/np/sec/pn/2013/pn1311.htm
Pettai, Auers and Ramonaite (2011), ‘Political Development’ In Marju Lauristin (ed.), Estonian Human Development Report 2010/2011: Baltic Way(s) of Human Development: Twenty Years On. Tallinn: Eesti Koostoo Kogu. 144-163.
Swedbank (2012). ‘Fulfilling the Maastricht Criteria – mission possible for Latvia and Lithuania?’.

[1] See the Latvia euro changeover site. Available at: http://www.eiro.lv

[2] Pettai, Auers and Ramonaite (2011), ‘Political Development’ In Marju Lauristin (ed.), Estonian Human Development Report 2010/2011: Baltic Way(s) of Human Development: Twenty Years On. Tallinn: Eesti Koostoo Kogu. 144-163.

[3] Aaron Eglitis (2013), ‘EU joins IMF in criticizing Latvian cuts to tax, social spending’. Bloomberg news.

[4] Anders Aslund, an ardent cheerleader of Latvia’s austerity programme, puts the country’s success down to ‘front loading’ reforms, particularly fiscal adjustment . See Anders Aslund (2013) ‘Why austerity works and stimulus doesn’t’.

[5] IMF Staff Report No. 13/28 (January 2013). Also see Swedbank Analysis (1 August 2012). ‘Fulfilling the Maastricht Criteria – mission possible for Latvia and Lithuania?’

[6] Although another 42% had a positive attitude towards the euro, but did not want to see it hurriedly introduced. See DNB Banka (November 2012), ‘Latvijas Barometrs: Eiro ieviešana Latvijā’.

[7] The legal opinions can be found on the Central Election Commission’s homepage.

[8] See Article 1, paragraph 3 in the law on referendums and initiatives.

Latvian Unemployment is Cyclical

Posted on December 10, 2012 | Policy Brief

In terms of output decline and increase in unemployment, the economic recession in Latvia that started during the 2008-09 financial crisis was one of the most severe in the world. Using modern methods of statistical analysis, we demonstrate that the changes in unemployment should be attributed primarily to cyclical, rather than structural factors. This answer brings important implications for anti-crisis policy in Latvia and elsewhere in the world: it suggests that the surge in unemployment was largely a consequence of Latvia’s austerity policy, and that today, broader economic measures to support further economic recovery can be effective.

During the 2008-2009 recession Latvia experienced the EU’s largest and fastest increase in unemployment. This is illustrated in Figure 1 where it can be seen that the unemployment rate rose by approximately 14 percentage points from a low of 6.2% in early 2008 to 20.4% at the end of 2009. However, labour market recovery has not been equally rapid, with unemployment in 2011 and the first half of 2012 settling at around 16%. This corresponds to a decline of less than 5 percentage points from the peak. The most recent quarter has seen an improvement with the unemployment rate falling to 13.5%. Partly, the decline can be attributed to seasonal factors (seasonally adjusted unemployment rate declined by less; from 15.7% to 14.2%). However, if discouraged workers are counted, the reduction in unemployment was smaller and the rate of unemployment still stood at 16.8% in the 3rd quarter.

This observed persistence in unemployment is seen by many as a signal of the structural nature of the shocks that hit the economy during the recession and of the further intensification of structural problems.

Figure 1. Unemployment Rate (Age Group 15-74), Seasonally Adjusted, (%)[1]

Note: Discouraged workers are those economically inactive who mentioned loss of hope to find a job as the main reason for not looking for a job.

Source: Central Statistical Bureau of Latvia, authors’ calculations.

For example, Krasnopjorovs (2012)[2] argues that there is a structural mismatch in the Latvian labour market, which mainly takes the form of a skills mismatch and concludes that the “employment rate now is similar to that observed in “normal times” of 2002-2004, [which] suggests a rather small [if any] negative output gap and a large share of structural unemployment in total unemployment”. Likewise, the Ministry of Finance of Latvia (2012)[3] argues that in the medium term, supply and demand mismatches will intensify. Thus, raising the risks of structural unemployment and, while not explicitly reporting their NAIRU estimates, the reported estimate for the output gap in 2012 is just -0.2% of potential GDP, but for 2013, a positive output gap of 0.7% is forecast.

The European Central Bank (2012)[4], when discussing inflation prospects in Latvia, identifies the situation in the labour market as a potential source of risk, as “labour shortages in certain sectors have appeared, suggesting that unemployment is likely to be close to its natural rate”. The European Commission’s (2012)[5] estimate for the NAIRU in 2012 is 14.6%, which is very close to the actual unemployment rate. The IMF (2012)[6] is the least categorical in characterising the nature of Latvian unemployment, arguing that “lack of skilled labor could become a constraint to growth and put pressure on wages unless the long-term unemployed re-enter the labor market”, at the same time forecasting that “[a] negative output gap and high unemployment should keep core inflation (…) low, and contribute to a gradual decline in headline inflation”.

Other commentators, e.g. Krugman[7] have argued that Latvian unemployment is largely explainable by cyclical factors.

Which explanation is correct is important both for current policy purposes and for the interpretation of past policy. Thus, “if cyclical factors predominate, then policies that support a broader economic recovery should be effective in addressing long-term unemployment as well; if the causes are structural, then other policy tools will be needed”.[8] On the other hand, “higher structural unemployment alters the role of short-run stabilization policies, including monetary policy, by increasing the possibility that expansionary policies will trigger inflation at higher rates of unemployment than otherwise”.[9]

In what follows, we evaluate the extent to which the recent evolution of Latvian unemployment can be interpreted as structural and provide some policy implications. We use three alternative approaches and all three point in the same direction: overwhelmingly both the increase in unemployment and its recovery are explainable by cyclical factors.

Decomposition of the Unemployment Rate into Structural and Cyclical Components

Our first approach is to directly decompose unemployment into structural and cyclical components. This is based on the following intuitive reasoning: when structural change occurs, unemployment is a result of changes in the composition of the labour market, i.e. the skill requirements of the jobs available today no longer match the skillset of the workers who are searching for jobs. On the other hand, when cyclical factors dominate, we would expect similar increases in unemployment across all sectors and locations. Using a formalised version of this approach, we conclude that changes in Latvian unemployment during the recession can be explained by changes in the unemployment rates in particular sectors and occupations, while the shares of the sectors and occupations in labour supply have been practically unchanged.

Following Lazear and Spletzer (2012)[10], we decompose the changes in the unemployment rate into structural and cyclical components, where the first component comes from changes in unemployment rates in a particular group assuming an unchanged structure, while the second component represents compositional changes in the structure of labour supply.

In order to implement this analysis, we use the most disaggregated categories of the sector of previous employment and occupations, which are obtainable from quarterly micro level LFS data. This covers 10 sectors of production and 9 occupations. We use a broad definition of unemployment and include discouraged workers to account for the nominal reduction in unemployment, which occurs just because people stop looking for a job. At the time of writing, data is only available for 2007-2011; hence, our analysis does not cover 2012.

Figures 2 and 3 show the decomposition of unemployment rate changes by sectors of production and by occupations.

Figure 2. Decomposition of Year-on-Year Changes in Unemployment Rate by Sectors of Production, Including Discouraged Workers, (% points)

Note: Includes only those unemployed who stopped working less than 8 years ago, for those who stopped working more than 8 years ago data on the previous sector of employment is not available; includes only those who indicated the sector of previous employment.

Source: Central Statistical Bureau of Latvia, authors’ calculations.

The sectoral decomposition suggests that the increase in unemployment in 2009-2010 can be fully attributed to cyclical factors – the structural component was small and even negative. The negative structural component is explained mainly by a reduction in the share of industry and construction in labour supply, which were sectors characterised by relatively high rates of unemployment.

Figure 3. Decomposition of Year-on-Year Changes in Unemployment Rate by Occupations, Including Discouraged Workers, (% points)

Note: Includes only those unemployed who stopped working less than 8 years ago, for those who stopped working more than 8 years ago data on the previous occupation is not available; includes only those who indicated previous occupation.

Source: Central Statistical Bureau of Latvia, authors’ calculations.

The occupational decomposition also suggests that changes in the rate of unemployment have been largely cyclical. The positive structural component in 2010Q1 can be explained by an increase in the share of civil servants, service workers, as well as shop and market sales workers. The positive structural component in 2010Q4 and 2011Q2 is a result of an increased share of craft and related trades workers, and elementary occupations.

In sum, the shares of both sectors and occupations in the economy have remained largely unchanged with unemployment changes explained by sectoral or occupational changes in unemployment rates.

Evaluating mismatch

A second approach is to directly estimate labour-market mismatch. Structural unemployment is usually defined as resulting from a mismatch between the labour demand and the skillset and locations of those looking for jobs. “[M]ismatch is defined as a situation where industries differ in their ratio of unemployed to vacancies”.[11] Using this approach our estimates show no significant mismatch between available vacancies the skills of workers.

To assess changes in the matching during the crisis, we calculate relative standard deviation of the number of unemployed per vacancy across sectors:

where x(i) is number of unemployed per vacancy in sector[12] i (including discouraged workers) and x¯ is average number of unemployed per vacancy across sectors.

Figure 4. Relative Standard Deviation of Unemployed per Vacancy across Sectors

Source: Central Statistical Bureau of Latvia, authors’ calculations.

Figure 4 presents the results of the relative standard deviation estimation. RSD increased in the beginning of the recession, but it has been declining since early 2009 indicating no increase in the degree of mismatch.

Estimating the Beveridge Curve

The third method uses the search and matching approach as developed by Pissarides (2000)[13] where the emergence of structural unemployment is signalled by deterioration in the efficiency of labour-market matching. Again, the conclusion is that except during the boom, when matching appears to have improved, Latvian unemployment cannot be explained by changes in the efficiency of matching.

We follow the Beveridge curve approach proposed by Barlevy (2011)[14] who follows Petrongolo and Pissarides (2001)[15] in assuming that matches in the labour market can be described by a Cobb-Douglas function, in which the number of matches depends on the unemployment rate, the vacancy rate, the productivity of the matching process, and elasticity of the number of matches with respect to the unemployment rate. The flow into unemployment is defined by the separation rate into unemployment; while the flow out of unemployment is given by the matching function. Equating the two flows yields the Beveridge curve which, given a constant separation rate, defines a negative relationship between vacancies and the unemployment rate.

Figure 5 plots the Beveridge curve for Latvia over 2005 – 2012Q2. We first observe that the Beveridge curve appears to have shifted downwards in 2007, pointing to an improvement in matching (an increase in the productivity parameter) as the economy approached the top of the boom. This is consistent with the idea that employers facing labour shortage became less “picky” in their hiring decisions. Starting from 2010, as the unemployment rate gradually declined there appears to have been a movement back along the Beveridge curve though perhaps with a minor outward shift.

Figure 5. Unemployment Rate (incl. Discouraged Workers) vs. Vacancy Rate in 2005-2012q2, Seasonally Adjusted

Source: Central Statistical Bureau of Latvia, authors’ calculations.

Estimating the parameters of the Beveridge curve permits assessment of changes in matching. To estimate A, we divide the sample into three periods and fit the Beveridge curve for these three periods: 2005-2006 (beginning of the boom), 2007-2009 (the peak and the recession) and 2010-2012 (the period of gradual reduction in unemployment). Apart from data on unemployment and the vacancies, we need to know the separation rate. Barlevy (2011)[16] argues that the relevant separation rate is likely to be fairly stable over the cycle – he assumes a constant separation rate of 0.03 for the U.S. (one can think of this separation rate as the flow of people from employment to unemployment in “normal” times). In the absence of concrete evidence to the contrary, we also assume a constant separation rate. However, this assumption is not crucial for our analysis, since we are interested in the change in A and not the level of A.

Figure 6 shows the fitted Beveridge curves, as well as the seasonally adjusted data over the period ranging from 2005 up to the second quarter of 2012.

Figure 6: Fitted Beveridge Curves and Actual Unemployment Rate (incl. Discouraged Workers) vs. Vacancy Rate in 2005-2012q2, Seasonally Adjusted

Source: Central Statistical Bureau of Latvia, authors’ calculations.

Our estimates of the parameters are presented in Table 1. The results show that A declined in 2010-2012, suggesting a slight deterioration in matching, yet A estimated on 2010-2012 data is slightly higher than A estimated on 2005-2006 data, the period which probably comes closest to the definition of “normal” times in our sample.

Table 1. Estimated Parameters of the Beveridge Curve

Source: Authors’ calculations.

Using estimated and the formula for the steady-state vacancy rate, we are able to calculate implied changes in A over the whole period under consideration. To do this, we employ two alternative estimates of : (1) , the estimate on 2005-2006 data, which can be viewed as estimate for “normal” times and (2) , average of estimates for the three periods.

Figure 7 illustrates the results of the estimation. These suggest that A declined from its peak in the beginning of 2008, in turn suggesting that matching has deteriorated as compared to the boom years. However, A started to grow in the end of 2011 and is currently above its level in 2005-2006. More importantly, our results suggest that there was no notable deterioration in matching since mid-2009, i.e. neither the increase in unemployment in the recession nor the subsequent recovery have been accompanied by significant intensification of labour market mismatches.

Figure 7: Implied A estimate

Source: Authors’ calculations.

Finally, our estimates of the Latvian Beveridge curve imply that changes in matching efficiency have been practically absent (except in the boom). Hence, changes in unemployment can largely be explained by cyclical factors.

Conclusion

Our analysis indicates no significant change in structural unemployment in Latvia during the 2008-2009 recession and afterwards. First, decomposition of the unemployment rate into structural and cyclical components illustrates the dominant role of the cyclical component. Second, direct estimation of mismatches also shows no evidence to support a structural explanation of the change in the Latvian unemployment rate. Finally, our estimates of the Beveridge curve during the period suggest that the efficiency of matching did not deteriorate during the recession and afterwards.

Accordingly, we conclude that in the course of the crisis not only did Latvia fall well below its long-term output trend, but Latvia is still operating below potential. This has implications for the assessment of Latvia’s internal devaluation policy. To put it in Blanchard’s (2012)[17] words: “Is it a success? The economic and social cost of adjustment has been substantial. Output further contracted by 16% in 2009, and is still 15% below its 2007 peak. Unemployment increased to more than 20% and still stands at 16% today, far higher than any reasonable estimate of the natural rate. Was there another, less costly, way of adjusting, through floating, and a slower fiscal consolidation? The truth is we shall never know”. The evidence presented here does not directly help to evaluate alternatives – still, it confirms that the chosen course was extremely costly and that today broader economic measures to support further recovery can be effective.

▪

References

Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011
Bernanke (2012), “Recent Developments in the Labor Market,” remarks to the National Association for Business Economics, March 26, 2012
Blanchard (2012), “Lessons from Latvia”, June 2012
Daly, Hobijn, Sahin, and Valletta (2012), “A Search and Matching Approach to Labor Markets: Did the Natural Rate of Unemployment Rise?,” Journal of Economic Perspectives 26(3), Summer 2012, pp. 3-26
Daly, Hobijn, and Valletta (2011), “The Recent Evolution of the Natural Rate of Unemployment,” IZA Discussion Paper No. 5832, July 2011
European Central Bank (2012), “Convergence report”, May 2012
European Commission (2012), Autumn 2012 Forecast Exercise, Estimates of output gap and of potential output and their determinants, https://circabc.europa.eu, November 2012
IMF (2012), “Republic of Latvia: First Post-Program Monitoring Discussions”, July 2012
Krasnopjorovs (2012), “What is missing in Krugman’s structural unemployment story?”, blog on Bank of Latvia website, June 2012.
Krugman, The Conscience of a Liberal, blog on New York Times, http://krugman.blogs.nytimes.com/?s=latvia
Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012
Ministry of Finance of Latvia (2012), “Convergence programme of the Republic of Latvia 2012-2015”, April 2012
Petrongolo and Pissarides (2001), “Looking into the Black Box: A Survey of the Matching Function,” Journal of Economic Literature, 39(2), June 2001, pp. 390–431
Pissarides (2000), Equilibrium Unemployment Theory (Second Ed.). Cambridge, MA: MIT Press

[1] Figure 1 uses data unadjusted for the results of the census carried out in Latvia in the first half of 2011 which showed that the population and the workforce was less than previously thought. This has implications for the calculation of all labour market statistics but the official statistics not yet been revised for years before 2011. Accordingly, for consistency over time, we use unadjusted data.

[2] Krasnopjorovs (2012), “What is missing in Krugman’s structural unemployment story?”, blog on Bank of Latvia website, June 2012

[3] Ministry of Finance of Latvia (2012), “Convergence programme of the Republic of Latvia 2012-2015”, April 2012

[4] European Central Bank (2012), “Convergence report”, May 2012

[5] European Commission (2012), Autumn 2012 Forecast Exercise, Estimates of output gap and of potential output and their determinants, November 2012

[6] IMF (2012), “Republic of Latvia: First Post-Program Monitoring Discussions,” July 2012

[7] Krugman, The Conscience of a Liberal, blog on New York Times

[8] Bernanke (2012), “Recent Developments in the Labor Market,” remarks to the National Association for Business Economics, March 26, 2012

[9] Daly, Hobijn, and Valletta (2011), “The Recent Evolution of the Natural Rate of Unemployment,” IZA Discussion Paper No. 5832, July 2011

[10] Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012

[11] Lazear and Spletzer (2012), “The United States Labor Market: Status Quo or a New Normal?,” NBER Working Paper Series, No. 18386, September 2012

[12] Here we use data on vacancies from the Central Statistical Bureau (data from enterprise surveys), since this data is more representative of the whole economy than the data on registered vacancies from the State Employment Agency. The latter is likely to be biased towards vacancies for low-qualified workers, as employers opt for different search methods for higher level positions. This is supported by the fact that, e.g. in 2012 vacancies for craft and related trades workers, plant and machine operators, and assemblers, as well as elementary occupations accounted for 50-60% of all vacancies registered with the State Employment Agency, while in the Statistical Bureau data these vacancies accounted for only about 20% of all vacancies.

[13] Pissarides (2000), Equilibrium Unemployment Theory (Second Ed.). Cambridge, MA: MIT Press

[14] Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011

[15] Petrongolo and Pissarides (2001), “Looking into the Black Box: A Survey of the Matching Function,” Journal of Economic Literature, 39(2), June 2001, pp. 390–431

[16] Barlevy (2011), “Evaluating the Role of Labor Market Mismatch in Rising Unemployment,” Economic Perspectives, 35(3), July 28, 2011

[17] Blanchard (2012), “Lessons from Latvia”, June 2012

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:

the baseline scenario – simulation of 2011 tax-benefit policy system (with austerity measures implemented), and
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)

Baltic Shadow Economies

Posted on September 26, 2011 | Policy Brief

Policy Brief Image of Two Shadows from Walking Men Representing Shadow Economies

This policy brief summarises the results and implications of a recent study of the size and determinants of the shadow economies in Estonia, Latvia, and Lithuania. The results suggest that the shadow economy in Latvia in 2010 is considerably larger than in neighboring Estonia and Lithuania. While the shadow economy as a percentage of GDP in Estonia contracted from 2009 to 2010, it expanded in Latvia and Lithuania. An important driver of shadow activity in the Baltic countries is the entrepreneurs’ dissatisfaction and distrust in the government and the tax system. Involvement in the shadow economy is more pervasive among younger firms and firms in the construction sector. These findings have a number of policy implications, which are discussed at the end of this brief.

Background and Aims

Anecdotal evidence suggests that the shadow economies in the Baltic countries and other emerging Central and Eastern European countries are substantial in size relative to GDP. This is an important issue for these countries because informal production has a number of negative consequences.

First, countries can spiral into a ‘bad equilibrium’: individuals go underground to escape taxes and social welfare contributions, eroding the tax and social security bases, causing increases in tax rates and/or budget deficits, pushing more production underground and ultimately weakening the economic and social basis for collective arrangements. Second, tax evasion can also hamper economic growth by diverting resources from productive uses (producing useful goods and services) to unproductive ones (mechanisms and schemes to conceal income, monitoring of tax compliance, issuance and collection of penalties for non-compliance). Third, informal production can constrain entrepreneurs’ ability to obtain debt or equity financing for productive investment because potential creditors/investors cannot verify the true (concealed) cash flows of the entrepreneur. This can further impede growth. Finally, shadow activities distort official statistics such as GDP, which are important signals to policy makers.

The aim of our study is to measure the size of the shadow economies in Estonia, Latvia, and Lithuania, and to analyse the factors that influence participation in the shadow sector. We use the term ‘shadow economy’ to refer to all legal production of goods and services that is deliberately concealed from public authorities. The study also makes a methodological contribution by developing an index of the size of the shadow economies as a percentage of GDP. It is foreseen that the index will be published regularly.

Although an index invites comparisons, and maybe even ‘competitions’ between countries, the purpose here is not to create a ‘Baltic championship’ on shadow economies. The index should primarily be seen as a tool to promote discussion on the size and role of the shadow economy and to provide a metric which can be used to measure the degree of success in fighting the shadow economy.

Method of Measuring the Shadow Economies

Estimates the size of the shadow economies are derived from surveys of a stratified random sample of entrepreneurs in the three countries (591 in Latvia, 536 in Lithuania and 500 in Estonia). The rationale for this approach is that those most likely to know how much production or income goes unreported, are the entrepreneurs who themselves engage in the misreporting and shadow production.

Survey-based approaches face the risk of underestimating the total size of the shadow economy due to non-response and untruthful response given the sensitive nature of the topic. We minimise this risk by employing a number of surveying and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g., Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004).

These approaches include framing the survey as a study of satisfaction with government policy, gradually introducing the most sensitive questions after less sensitive questions, phrasing misreporting questions indirectly, e.g., asking entrepreneurs about the shadow activity among ‘firms in their industry’ rather than ‘their firm’, and, in the analysis, controlling for factors that correlate with potential untruthful response, such as tolerance towards misreporting. We aggregate entrepreneurs’ responses about misreported business income, unregistered or hidden employees, as well as unreported ‘envelope’ wages to obtain estimates of the shadow economies as a proportion of GDP.

There are three common methods of measuring GDP: the output, expenditure and income approaches. Our index is based on the income approach, which calculates GDP as the sum of gross remuneration of employees (gross personal income) and gross operating income of firms (gross corporate income). Computation of the index proceeds in three steps: (i) estimate the extent of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses; (ii) estimate each firm’s shadow production proportion as a weighted average of the two underreporting estimates with the weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and (iii) calculate a production-weighted average of shadow production across firms. Taking weighted averages of the underreporting measures rather than a simple average is important for the shadow economy index to reflect a proportion of GDP.

Size of the Shadow Economies

Table 1 indicates that the shadow economy as a proportion of GDP is considerably larger in Latvia (38.1%) compared to Estonia (19.4%) and Lithuania (18.8%) in 2010. Only Estonia has managed to marginally decrease the proportional size of its shadow economy from 2009 to 2010 – a statistically significant decrease of 0.8 percentage points. In contrast, the proportional size of the shadow economies in Lithuania and Latvia has increased by an estimated 0.8 and 1.5 percentage points, respectively.

Table 1. Shadow economy index for the Baltic countries

Note: This table reports point estimates and 95% confidence intervals for the size of the shadow economies as a proportion of GDP. The third column reports the change in the relative size of the shadow economies from 2009 to 2010.

Form of Shadow Activity

Figure 1 illustrates the average levels of underreporting (business profits, number of employees and salaries) in each of the countries in 2009 and 2010. The average levels of underreporting in all three areas are in the order of two to three times higher in Latvia compared to Lithuania and Estonia. In Latvia and Lithuania, the degree of underreporting of business profits and salaries (‘envelope’ wages) is approximately twice as large as the underreporting of employees. The exception to this trend is the relatively low amount of underreported business profits in Estonia, likely to be a result of low corporate tax rates. Bribery in Latvia and Lithuania constitutes a similar fraction of firms’ revenue, approximately 10%, whereas in Estonia bribery is less pervasive and constitutes around 6% of firms’ revenue.

Figure 1. Simple averages of underreporting and bribery among Estonian (EE), Lithuanian (LT) and Latvian (LV) firms in 2009 and 2010.

Determinants of Involvement in the Shadow Economy

The literature on tax evasion identifies two main groups of factors that affect the decision to evade taxes and thus participate in the shadow economy. The first set emerges from rational choice models of the decision to evade taxes. In such models individuals or firms weigh up the benefits of evasion in the form of tax savings against the probability of being caught and the penalties that they expect to receive if caught. Therefore the decision to underreport income and participate in the shadow economy is affected by the detection rates, the size and type of penalties, firms’ attitudes towards risk-taking and so on. These factors are likely to differ across countries, regions, sectors of the economy, size and age of firm, and entrepreneurial orientation (innovativeness, risk-taking tendencies, and pro-activeness).

Empirical studies find that the actual amount of tax evasion is considerably lower than predicted by rational choice models based on pure economic self-interest. The difference is often attributed to the second, broader, set of tax evasion determinants – attitudes and social norms. These factors include perceived justice of the tax system, i.e., attitudes about whether the tax burden and administration of the tax system are fair. They also include attitudes about how appropriately taxes are spent and how much firms trust the government. Finally, tax evasion is also influenced by social norms such as ethical values and moral convictions, as well as fear of feelings of guilt and social stigmatisation if caught.

Our study uses regression analysis to identify the factors that are statistically related to firms’ involvement in the shadow economy. The results indicate that the size of the shadow economy is smaller in Estonia and Lithuania relative to Latvia, after controlling for a range of factors.

Tolerance towards tax evasion is positively associated with the firm’s stated level of income/wage underreporting. Satisfaction with the tax system and the government is negatively associated with the firm’s involvement in the shadow economy, i.e. dissatisfied firms engage in more shadow activity, satisfied firms engage in less.

This result is consistent with previous research on tax evasion, and offers an explanation of why the size of the shadow economy is larger in Latvia than in Estonia and Lithuania; namely that Latvian firms engage in more shadow activity because they are more dissatisfied with the tax system and the government as illustrated in Figure 2. Analysing each of the four measures of satisfaction separately we find that shadow activity is most strongly related to dissatisfaction with business legislation, followed by the State Revenue Service, the government’s tax policy, and finally the government’s support for entrepreneurs.

Figure 2. Average satisfaction of firms with the tax system and government in 2010.

Note: These questions use a 5-point scale: 1=“very unsatisfied”; 2=“unsatisfied”; 3=“neither satisfied nor unsatisfied”; 4=“satisfied”; and 5=“very satisfied”. SRS is State Revenue Service.

Another strong determinant of involvement in the shadow economy is firm age, with younger firms engaging in more shadow activity than older firms. This effect dominates relations between firm size and shadow activity. A possible explanation for the relation is that young firms entering a market made up of established competitors use tax evasion as a means of being competitive in their early stages. The regression results also provide some evidence that after controlling for other factors, firms in the construction sector and firms that have a pro-active entrepreneurial orientation tend to engage in more shadow activity.

Policy Implications

First, the relatively large size of the shadow economies in the Baltic countries, and their different expansion/contraction trends, cause significant error in official estimates of GDP and its rates of change, because although statistics bureaus in each of the countries attempt to include some of the shadow production in GDP estimates they do not capture the full extent. Not only is GDP used in key policy ratios such as government deficit to GDP, debt to GDP, but also the rate of change is used as a key indicator of economic performance and therefore guides policy decisions. When the shadow economy is expanding (as in Latvia and Lithuania) official GDP growth rates underestimate true economic growth and when the shadow economy is contracting (as in Estonia) official GDP growth rates overstate true economic growth. At a minimum, policy makers need to be aware of these biases in official statistics, but ideally, statistical bureaus would implement more rigorous methods to estimate and incorporate shadow production in official statistics.

Second, our results suggest that to reduce the size of the shadow economies in the Baltic countries by encouraging voluntary compliance, a key factor that needs to be addressed is the high level of dissatisfaction with the tax system and with the government. Addressing this issue could involve actions such as making tax policy more stable (less frequent changes in procedures and tax rates), and increasing the transparency with which taxes are spent.

Finally, our estimates of the size of the shadow economies suggest that there is significant scope for all three governments to increase their revenues by bringing production ‘out of the shadows’. Investment in programs aimed at reducing the size of the shadow economies could be rather profitable for the Baltic governments, because even a small influence on entrepreneurial behaviour could result in significant revenue increases.

References

Gerxhani, K. (2007) “‘Did you pay your taxes?’ How (not) to conduct tax evasion surveys in transition countries”, Social Indicators Research 80, pp. 555-581.
Hanousek, J., and F. Palda (2004) “Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries”, Kyklos 57(2), pp.237-252.
Kazemier, B., and R. van Eck (1992) “Survey investigations of the hidden economy”, Journal of Economic Psychology 13, pp. 569-587.

Schneider, F., A. Buehn, and C.E. Montenegro (2010) “Shadow economies all over the world: New estimates for 162 countries from 1999 to 2007”, World Bank Policy Research Working Paper 5356.

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