Location: Latvia

There is a trade-off between two major objectives of a tax-benefit system: equity and efficiency. The tax-benefit systems that redistribute a lot of income tend to generate disincentives to work. The tax-benefit systems that create good incentives to work and earn, are less effective in mitigating poverty, social exclusion and deprivation. In this brief we argue that, when contrasted to other EU countries, the Latvian tax-benefit system is less effective in achieving either of the objectives.

Equity-Efficiency Trade-Off

There is a fundamental trade-off between the two principal objectives of a tax-benefit system – income redistribution and efficiency. On the one hand, income redistribution is desirable as it helps to mitigate socially undesirable market outcomes such as poverty and deprivation. On the other hand, more income redistribution is often associated with higher distortions to labour supply and work effort.

There is no universal prescription as to how much a government should redistribute. The answer to this question depends, among other factors, on the relative value that society (government) assigns to the welfare of different population groups, and on the individuals’ labour supply elasticity.

However, a given degree of income redistribution can be achieved at a different cost of efficiency. In this brief, we analyse the degree of income redistribution generated by the tax-benefit system and work incentives in Latvia in the context of other EU countries. In our analysis, we use the European microsimulation tax-benefit model EUROMOD (Sutherland and Figari, 2013) version G2.0, EU-SILC data, and the analysis framework developed by Jara and Tumino (2013).

Income Redistribution in the EU

EU countries differ substantially in terms of inequality of original income and in terms of the degree of redistribution generated by the tax-benefit system (see Figure 1, data on 2007 and 2013). The Gini coefficient of equivalised household original income (which consists of income from employment and self-employment, property income, private pensions, private transfers and other relatively minor components) ranges from around 0.4 (Cyprus, Netherlands) to almost 0.55 (Romania in 2007, Ireland in 2013).

Inequality of original income in Latvia in 2007 was at the EU average level (Gini coefficient of 0.47), but the degree of income redistribution generated by direct taxes, benefits and pensions was the lowest in the EU. As a result, the inequality of disposable income in Latvia in 2007 was the highest in the EU (Gini coefficient of 0.37). Part of the answer as to why the degree of income redistribution in Latvia is so low is a relatively small contribution of pensions to redistribution – it is almost half of that observed in the EU on average, despite the fact that the share of public pension recipients in the total Latvian population in 2007 was above the EU average. Another important factor was the very minor role of means-tested benefits: in the EU on average, means-tested benefits generate a reduction in Gini coefficient by about 0.02, while in Latvia the corresponding figure is just one tenth of this.

Figure 1. Gini coefficients of original equivalised household income and degree of redistribution generated by tax-benefit systems in the EU in 2007 and 2013

Source: EUROMOD statistics, authors’ calculations.

In the course of the crisis and the following recovery, the degree of redistribution in Latvia increased (see lower panel of Figure 1). An important factor behind the increase was growing number of pension recipients and an increase in the average size of pensions (both in absolute terms and relative to employment income). The increase in the number of pension recipients was not a result of changes in eligibility criteria, but was due to population ageing and the fact that more people applied for other types of pensions. The growth in the average size of pension was due to generous indexation of pensions in 2008 and compositional changes, as pensions of new pensioners until 2012 were larger than the average pension. Another reason for a growing degree of redistribution was an increase in the size and the number of recipients of means-tested benefits (mainly Guaranteed Minimum Income (GMI) benefit). This was a result of reforms in the provision of the means-tested benefits and of falling incomes from employment, which made more people eligible for the social assistance programmes. Nevertheless, despite the increase in recent years, the degree of income redistribution in Latvia remains one of the lowest in the EU.

Work Incentives

The existence of a trade-off between income redistribution and better work incentives suggests that tax-benefit systems that ensure less income redistribution are likely to generate better work incentives. Jara and Tumino (2013) have demonstrated the existence of this trade-off in the EU countries in 2007-2010 by identifying a negative and statistically significant correlation between Gini coefficients and Marginal Effective Tax Rates (METR). The METR is a measure that is commonly used to quantify work incentives at the intensive margin. It shows what proportion of a small increase in earnings (which results from e.g. an increase in the supplied hours of work) is lost as a result of extra tax payments or foregone benefits that the person is no longer eligible for after the increase in earnings. The negative correlation identified in Jara and Tumino (2013) suggests that countries with less income redistribution (i.e., higher Gini coefficients) tend to have better work incentives (lower METRs).

In Latvia, the mean METR in 2013 was 32.2%, only slightly below the EU average (34.5%), and much higher than the average in Estonia (22.8%) and Lithuania (27.4%), despite a lower degree of income redistribution (EUROMOD statistics). Another feature of the Latvian tax-benefit system is that it is characterised by especially high METRs for poor individuals. Thus, in 2013, 94% of individuals who faced METRs in excess of 50% belonged to the two bottom deciles of distribution of equivalised disposable income. This is different from many other European countries, where distribution of high METRs is either more even across deciles or rising towards the top end of income distribution (Jara and Tumino (2013), data for 2007).

The main reason for high METRs faced by the poorest population groups in Latvia is the design of means-tested benefits (GMI and housing benefits), which generates 100% METRs for the recipients of these benefits. Namely, for each additional euro earned, the amount of benefit is reduced by one euro, which leaves the net income unchanged. This adversely affects employment incentives for the poorest individuals and increases the poverty risk.

Figure 2 illustrates mean METRs by deciles of equivalised disposable income in Latvia and shows the contribution of taxes, benefits and social insurance contributions (SICs) to the mean METRs. It clearly demonstrates that high METRs in the bottom deciles result mainly from the contribution of benefits, which disappears in the fourth decile. The contribution of SICs is slightly smaller in the bottom decile, which is due to the fact that the proportion of employed individuals is smaller in the bottom decile. For the same reason, and also because of basic tax allowances, the contribution of direct taxes is smaller in the bottom deciles, but then the contribution of taxes levels off, reflecting the Latvian flat tax rate.

Figure 2. The contribution of direct taxes, benefits and social insurance contributions (SIC) to METRs in Latvia by deciles of equivalised disposable income in 2013

Source: authors’ calculations using EUROMOD-LV

In their study on the incentive structure created by the tax and benefit system in Latvia, the World Bank (2013) pointed out the problem of bad work incentives generated by Latvian means-tested benefits. Our results, which are based on a population-representative database of incomes, also identify means-tested benefits as the major contributor to high METRs in the lowest deciles of the income distribution. Another concern expressed by the World Bank (2013) was that the problem of informal employment (either in the form of undeclared wages or work without a contract) can be exacerbated by high participation tax rates and METRs.

Conclusion

The Latvian tax-benefit system is characterized both by a relatively low degree of income redistribution and relatively weak work incentives, as measured by METRs. Recipients of means-tested benefits (GMI and housing benefits) are faced with 100% METRs, as benefits are withdrawn at the same rate as household income rises. This creates disincentives to increase labour supply for low-paid/low-skilled individuals, and hence creates a risk of poverty traps. Evidence from the literature suggests that the labour supply of low paid workers is particularly sensitive to the incentives generated by the tax-benefit system, hence reforms that would bring down METRs in the bottom deciles could yield positive results in terms of employment of low paid/low skilled workers.

A potential reform is to introduce either a gradual phasing out of the means-tested benefits, or to exclude a certain amount of employment income from the income test for the means-tested benefits. Such reforms would be targeted at the bottom end of the income distribution, help combat poverty, improve the incentive structure of the Latvian tax-benefit system, and positively affect the labour supply of low-skilled/low-paid workers.

References

• EUROMOD statistics on Distribution and Decomposition of Disposable Income, accessed at http://www.iser.essex.ac.uk/euromod/statistics/ using EUROMOD version no. G2.0, retrieved on October 14, 2014
• Jara, H. Xavier & Alberto Tumino (2013). “Tax-benefit systems, income distribution and work incentives in the European Union,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 27-62.
• Sutherland, Holly & Francesco Figari (2013). “EUROMOD: the European Union tax-benefit microsimulation model,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 4-26.
• World Bank (2013). “Latvia: “Who is Unemployed, Inactive or Needy? Assessing Post-Crisis Policy Options”. Analysis of the Incentive Structure Created by the Tax and Benefit System. Financial Incentives of the Tax and Benefit System in Latvia,” European Social Fund Activity “Complex support measures” No. 1DP//1.4.1.1.1./09/IPIA/NVA/001

Authors: Daria Anosova (NES), Konstantin Sonin (CEFIR and NES), Alf Vanags (BICEPS), and Anna Zasova (BICEPS).

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.

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.

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.

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.

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.

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.

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.

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.

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