Tag: Financial crisis

Does Political Illegitimacy in Belarus Imply New Economic Risks?

Large group of peaceful demonstration in Belarus that represents Belarus and people who seek to avoid economic risks

Today’s political crisis in Belarus has given rise to the phenomenon classified in political science as political illegitimacy. However, this is not a pure political phenomenon. It causes adverse and severe economic adjustments. In a short-term perspective, it gives rise to numerous risks of financial destabilization. Moreover, it is likely to deepen the current recession and make it protracted. In the long-term, political illegitimacy causes adverse institutional adjustments and erosion of human capital, which is likely to lead a country into a long-lasting depression. We argue that resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.

Short-term Economic Effects of Political Illegitimacy

Since August 9, 2020, Belarus has been widely discussed worldwide in mass media because of the country’s political crisis. Political scientists classify the current situation in Belarus as a case of political illegitimacy, i.e. there is no consensus in the Belarusian society concerning the recognition and acceptance of a new term for the governing regime.

In turn, the governing regime prefers to ignore the illegitimacy issue. There is an implicit assumption behind this:  illegitimacy is an intangible issue that can hardly result in any tangible threat to the sustainability of the governing regime.

We oppose this view and argue that, at least in an economic dimension, there are numerous channels through which illegitimacy transforms into tangible problems. Inasmuch as the stance of the economy affects political sustainability, it will undermine the latter.

From a short-term perspective, the issue of political illegitimacy has become part of the information accounted for in the decision-making of economic agents in Belarus. Hence, in their economic decisions they either try to struggle against it, or at least to hedge against corresponding adverse effects.

Most evident, the adjustments in decision-making has already visualized in households’ savings behavior. Directly, illegitimacy considerations gave rise to deposit withdrawals from the banking system and enlarged demand for hard currency. Consequently, this led to a rise in depreciation-/inflation-expectations and lowered public trust in the banking system, which in turn has amplified these patterns of the households’ behavior.  In August, Belarus experienced historical peaks in deposit outflows and international reserves were depleted as a result. This has substantially amplified the risks of financial turmoil.

So far, the authorities have curbed the financial stress by implementing a restrictive monetary policy. However, this does not suppress adverse patterns in households’ behavior. It only somewhat allows for a shift of adverse adjustments from financial markets towards the real economy. Moreover, it weakens but does not completely remove the threat of full-fledged financial turmoil, taking into account the systemic financial fragility in Belarus.

In addition to the illegitimacy issue itself, other adverse expectations are likely to give rise to unfavourable trends in households’ consumption behaviour as well. First, household consumption is likely to be dampened as a result of poor consumer confidence and sentiment. Second, additional losses in consumption are likely to occur due to tightening access to credit and progressing financial fragility.

Similar mechanisms are likely to be in place with respect to investment demand. First, poor confidence and sentiment undermine the investment activity of businesses. In Belarus, this channel is likely to be more powerful for private businesses, as investment plans of SOEs (due to their directive nature) are less sensitive to confidence and expectations. Second, investment activity is likely to decline due to deteriorating financial conditions and consequent contraction of credit. This linkage is especially important for the SOEs and housing investments.

The power of adverse consumption and demand trends is still questionable. However, preliminary estimates (introducing negative shocks in addition to scenarios in Kruk, 2020) show that they will reduce the output growth rate by at least 1.5-2.0 percentage points in 2020 Q3-Q4. In other words, they are expected to deepen the current recession and are likely to make it more long-lasting.

Deteriorating payment discipline is one more expected outcome from political illegitimacy. Being amplified by deteriorating financial conditions and economic activity, it can turn into a full-fledged payment crisis and fiscal instability.

Adverse Institutional Adjustments and Effects on Labor Market

Human-to-human interactions based on mutual benefit and trust are the core of a modern market-based economy. Key institutions created to support this interpersonal trust are laws and law-enforcement agencies. If a person does not trust her counterpart in a deal and does not think that she can take him to court to defend her rights, no deal will be signed. When an individual observes unrightful and politically-motivated court decisions in criminal cases, the distrust is also passed on to her beliefs that she would be able to defend her economic rights in the same court. As we observe police violence, tortures, and criminal charges of protesters with no attempt to prosecute those responsible, public trust in the law-enforcement system fades away, and thus all kinds of deals previously supported by a contract-enforcement system cease to exist.

The quality of a judicial system is widely recognized as a powerful determinant to overall institutional quality and the business environment. Hence, poor trust in it would likely undermine business activity directly. Existing businesses are to re-orient towards shorter-term strategies, being reluctant to initiating long-term and risky projects. Moreover, their inclination to geographical diversification of their business activity or even full migration is likely to rise. New entrants – that are extremely important to achieve productivity gains (Foster, Haltiwanger, and Syversen, 2008) – are less likely to start business in the country.

An increase in emigration is a usual consequence of political crisis, especially if it is accompanied by violence and politically-motivated incarcerations. What is unique about the current Belarus crises is that the list of potential emigrees include not only individuals but also firms, especially those working in the IT sector. After 11 August 2020, many IT companies found their employees detained, beaten and tortured. The offices of Yandex, Google and PandaDoc were searched and four top managers working at the latter were detained on tax evasion charges which are likely to be politically-inspired. As of the 18th of September, around 200 IT companies are considering relocation from Belarus and many more are considering partial relocation of their employees to already established foreign offices (Dev.by(2020a)). Results from a recent survey show that 33% of IT specialists have already decided to leave Belarus and the rest indicated that they will leave if the situation worsens (Dev.by(2020b)).

There are several major reasons for why the IT-sector is affected more by the current crises compared to traditional sectors of the Belarusian economy. Firstly, IT companies rarely own physical capital and thus can change their location in a matter of days by simply relocating their employees and laptops. Secondly, the IT labor market is global and mobile, and companies compete for the workers. Therefore, if many workers hold similar strong views on a particular situation, employers are bound to support them to a certain extent. As a result of the latter, many IT companies have openly voiced their disagreement with the election results and the politically motivated violence following the election. High-level employees and owners of major companies have participated in various opposition initiatives and as a result, now face retribution from Lukashenko’s government.

In addition to politically-motivated emigration, we can expect an increase in economically-driven emigration rates as the economy is expected to shrink (Bornukova and Lvovskiy, 2020).

What Is the Way Forward?

The political crisis in Belarus has triggered multidimensional adverse economic adjustments. Nevertheless, the authorities prefer to ignore the links between politics and economics. Hence, they try to overcome the problems with economic policy tools only. However, the room to maneuver with these tools is considerably restricted, and in some cases completely ineffective in suppressing adverse trends.

With respect to the short-term agenda, the authorities cannot offset the adverse trends. They can just mitigate challenges in one dimension and try to re-direct it to another one. For instance, currently the authorities focus on mitigating the probability of a full-fledged financial crisis. This consideration requires restricting monetary conditions. Otherwise, the exchange rate is likely to depreciate, which would be problematic from a corporate debt sustainability perspective. Although being somewhat effective in this regard, this policy mix dampens economic activity. From a financial dimension, the challenge is being re-directed to the real economy.

A similar picture might soon emerge in a fiscal sphere as well. An economic downturn and political crisis can result in a widening income gap. At the same time, the room for maneuver on the expenditure side is constrained. The funds accumulated from the previous periods have to a large extent already been spent to support SOEs. Hence, a further expansion of expenditures is hardly possible, as it would undermine fiscal and public debt sustainability. Therefore, fiscal stimulus is likely to fade away and can gradually even become negative.

Based on estimations in Kruk (2020), before the issue of illegitimacy appeared, the economy was developing according to a scenario of about a 3% drop in GDP in 2020 and a meagre recovery (if any) in 2021. Adding the assumptions associated with adverse adjustments due to the illegitimacy issue into the Kruk (2020) estimates, we show that the recession is likely to deepen by at least 1 percentage point in 2020. In 2021, output losses are likely to expand considerably. In regard to the long-term agenda, the situation is even worse. Conceptual decisions on economic activity by firms and households are closely linked with the issues of trust and legitimacy (Bornukova et al., 2020). Having lost them, the authorities are unlikely to have any effective tools for standing against adverse institutional adjustments and the erosion of human capital. Hence, we may expect that today’s poor growth potential of the Belarusian economy – up to 2.5% of per annum growth (Kruk, 2020) – is likely to weaken further and could even become negative. This means that the stagnation over the recent decade is likely to turn into a long-term depression.


The political crisis and the arising issue of political illegitimacy in Belarus impose severe economic challenges for the country. In a short-term perspective, there are numerous channels that are likely to deepen the recession and make it long-lasting. Moreover, risks to financial stability are progressing rapidly. Hence, there is little room for securing macro stabilization in the near future.

In a long-term perspective, the country is likely to suffer from the disruption of productivity enhancers. It will stem from lower business initiatives and the erosion of human capital. This is a way to a long-term depression.

Standard economic tools are mainly ineffective against both the short-term and long-term challenges. Resolving the political crisis in a way that revives trust and legitimacy is the only ‘good’ solution.


  • Bornukova, K. and Lvovskiy, L. (2020). Demography as a Challenge for Economic Growth, Bankauski Vesnik, 680 (3), PP. 31-35.
  • Bornukova, K. Godes, N., and Shcherba, E. (2020). Confidence in the Economy: What is It, How it Works and Why We Need it?, Bankauski Vesnik, 680 (3), PP. 95-99.
  • Foster, L., Haltiwanger, J., and Syversen, Ch. (2008). Reallocation, Firm Turnover, and Efficiency: Selection on Productivity of Profitability? American Economic Review, 98(1), PP. 394-425.
  • Kruk, D. (2020). Short-term Perspective for the Belarusian Economy, BEROC Policy Paper No. 92.
  • Dev.by. (2020a). https://dev.by/news/pochti200-relocate
  • Dev.by. (2020b). https://dev.by/news/opros-relocate-september2020.

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 Macroprudential Policy Instruments Reduce the Procyclical Impact of Capital Ratios on Lending? Cross-Country Evidence

20190506 Do Macroprudential Policy Instruments Image 01

In this brief, we ask about the capacity of macroprudential policies to reduce the procyclical impact of capital ratios on bank lending. We focus on aggregated macroprudential policy measures and on individual instruments and test whether their effect on the association between lending and capital depends on bank size. We find that macroprudential policy instruments reduce the procyclical impact of capital on bank lending during both crisis and non-crisis times. This result is stronger in large banks than in other banks. Of individual macroprudential instruments, only borrower-targeted LTV (loan-to-value) caps and DTI (debt-to-income) ratios weaken the association between lending and capital and thus act countercyclically. With our study, we are able to support the view that macroprudential policy has the potential to curb the procyclical impact of bank capital on lending and therefore, the introduction of more restrictive international capital standards included in Basel III and of macroprudential policies in general are fully justified.

Macroprudential policy after the GFC

The Global Financial Crisis (GFC) highlighted the need to go beyond a purely microprudential approach (i.e. focusing on the health of individual firms) to regulation and supervision of the banking sector. The empirical literature supports the view that macroprudential policies (i.e. those addressing the general condition of the whole financial system) are able to decrease the vulnerability of the banking sector (see Claessens et al., 2013 for a review, and Cerutti et al., 2015). The increased resilience of the banking sector means that banks are able to absorb losses of greater magnitude – due to higher capital buffers (or provisions) or better access to funding sources, thus reducing the likelihood of a costly disruption to the supply of credit (CGFS, 2012), in particular during crises or recessionary periods. Considering this, macroprudential policies are expected to reduce the procyclical impact of capital ratios on loan supply.

Lending activity of banks and capital ratio nexus

It is a well-known tenet in the banking literature that capital adequacy rules have an impact on the behaviour of banks (Borio & Zhu, 2012). They are expected to protect banks from economic death, i.e. from insolvency or going bankrupt. Previous literature stresses the importance of capital ratios for lending behaviour, during both good economic conditions and in crisis or recessionary periods, in particular in banks with thin capital ratios, and thus insufficient buffers needed to cover loan-losses, (see Beatty & Liao, 2011; Carlson, Shan, & Warusawitharana, 2013) or in large banks (Beatty & Liao, 2011). The problem of the effect of capital ratios on bank lending has been studied extensively since the 1990s, when the first Basel Accord was introduced as an international capital standard (see Jackson et al., 1999). In the wake of the recent GFC, the topic has attracted renewed attention as concerns have arisen that large losses at banks would hinder their capital adequacy and restrain their lending. Capital is found to affect lending behaviour in large publicly-traded banks by Beatty and Liao (2011) and in US commercial banks by Carlson et al. (2013). Additionally, in a cross-country study, Gambacorta and Marqués-Ibáñez (2011) show that publicly traded banks tend to restrict their lending more during recessions or crisis periods due to insufficient capital ratios. Such an effect is referred to as a procyclical capital ratio on bank lending (Beatty & Liao, 2011; Peek & Rosengren, 1995a).

However, previous literature on the link between lending and capital can be roughly subdivided into two groups: The studies that considered macroprudential policy instruments have been limited to individual countries (United States by Beatty & Liao, 2011 and Carlson et al., 2013; France by Labonne & Lame, 2014; United Kingdom by Mora and Logan, 2011), so that all banks were equally affected by the country’s banking policy and regulations. In turn, the studies that focused on the link between lending and capital across countries, have not accounted for macroprudential policy and its instruments (Gambacorta & Marqués-Ibáñez, 2011).

In our recent paper (Olszak, Roszkowska, and  Kowalska, 2019) we extend the existing research by exploring the countercyclical effects of macroprudential policy factors on the association between loan growth and capital ratios on a large cross-country panel.

Why can macroprudential policy affect the link between lending and capital ratios of banks?

While policy standard-setters argue that the new macroprudential approach to regulation and supervision should reduce procyclicality in banking, and in particular by increasing banks’ resilience, it should diminish the effect of capital ratio on loan supply, the empirical evidence on this subject is not available.

In our paper, we employ a cross-country data-set to examine whether the application of macroprudential policies affects the link between loan supply and capital ratios, before and during the 2007/2008 crisis period in a sample of over 4500 banks from 67 countries. The main purpose of the paper is to examine whether macroprudential policy instruments, which were in use before the GFC, had a significantly negative impact on the positive association between lending and capital ratios, during the crisis and in the non-crisis period. If we identify such a negative effect, we will be able to empirically test the view that macroprudential policy is effective in increasing the resilience of banks and thus affects the procyclicality of bank capital regulation.

Based on the previous evidence, we first hypothesize that the link between lending and capital is positive, and is reduced in countries which applied macroprudential policies in the pre-crisis period. Following the capital crunch theory (see Peek & Rosengren, 1995a; and Beatty & Liao, 2011), we expect that the link between lending and capital is strengthened in the crisis period, and is reduced in countries in which the use of macroprudential instruments was more extensive in the pre-crisis period and continued to be used during the crisis. As the association between loan growth and capital ratios, in particular during crisis periods, was found to be stronger in large banks (see Beatty & Liao, 2011), we also examine whether macroprudential policy effects on the association differ between large and other banks (i.e. medium and small).

We use the Bankscope database and data-set on macroprudential policies available in Cerutti et al. (2015) to test our hypotheses. We analyse the effects of macroprudential policies on the association between lending and capital ratio using individual commercial bank data from 67 countries over the period of 2000–2011.


We find a consistent and strong effect of macroprudential policies on the association between loan growth and capital ratios.

Further, unlike previous studies on the link between bank vulnerability and macroprudential policy, we differentiate between large, medium and small banks, because previous evidence shows that capital ratios affect bank lending with a different magnitude, depending on the bank size (see Beatty & Liao, 2011). Indeed, we find evidence in favour of the expectation that bank size matters for the impact of macroprudential policies for the link between lending and capital.

Analysis of the role of individual macroprudential policy instruments shows that only loan-to-value caps and debt-to-income ratios weaken the positive effect of capital ratios on lending. This means that in countries which apply such instruments, bank lending is not prone to shortages in capital buffers, in particular during financial crisis. Thus, the banking sector does not add to business cycle fluctuations.

We also identify which instruments are better at curbing the procyclicality of capital standards. In particular, we find that borrower targeted macroprudential instruments (such as loan-to-value caps) or restrictions on balance sheets of financial institutions (such as dynamic provisions or leverage ratios), are more effective in reducing the procyclicality of capital standards.

Policy implications

Our finding that macroprudential policies are able to alleviate the impact of capital ratio on lending, in particular during the crisis, may have certain implications for policy makers in the area of implementation of commonly recognized standards targeted at the reduction of borrower risk-taking. Our results suggest that more frequent use of these instruments may create additional buffers in large banks and in emerging and closed-capital-account economies, thus making large banks’ lending and lending of banks in emerging markets and closed economies less affected by capital ratios during crisis periods. Therefore, in the current work aimed at creating macroprudential regulations, more attention should be focused on instruments which have the potential to reduce borrower risk.


  • Beatty, A., & Liao, S. (2011). Do delays in expected loss recognition affect banks’ willingness to lend? Journal of Accounting and Economics, 52, 1-20.
  • Borio, C., & Zhu, V.H. ( 2012). Capital regulation, risk-taking, and monetary policy: A missing link in the transmission mechanism? Journal of Financial Stability, 8, 236–251. doi:10.1016/j.jfs.2011.12.003
  • Carlson, M., Shan, H., & Warusawitharana, M.(2013). Capital ratios and bank lending: A matched bank approach. Journal of Financial Intermediation, 22, 663–687. doi:10.1016/j.jfi.2013.06.003
  • Cerutti, E., Claessens, S., & Laeven, L. (2015). The use and effectiveness of macroprudential policies: New evidence. IMF Working paper WP/15/61.
  • Claessens, S., Ghosh, S., & Mihet, R. (2013). Macro-Prudential policies to mitigate financial system Vulnerabilities. Journal of International Money and Finance, 39, 153–185.
  • Committee on the Global Financial System. (2012). Operationalising the selection and application of macroprudential instruments. CGFS Papers No 48. Bank for International Settlements. 2012.
  • Gambacorta, L., & Marqués-Ibáñez, D. (2011). ‘The bank lending channel. Lessons from the crisis.’ Working paper series No 1335/May 2011. European Central Bank.
  • Jackson, P., Furfine, C., Groeneveld, H., Hancock, D., Jones, D., Perraudin, W., Yoneyama, M. (1999). Capital requirements and bank behaviour: The impact of The Basle Accord. Basle: Bank for International Settlements.
  • Labonne, C., & Lame, G. (2014). Credit growth and bank capital requirements: Binding or not? Working Paper.
  • Mora, N., & Logan, A. (2012). Shocks to bank capital: Evidence from UK banks at Home and Away. Applied Economics, 44(9), 1103–1119.
  • Olszak, M., Roszkowska, S. & Kowalska, I. (2019). Do macroprudential policy instruments reduce the procyclical impact of capital ratio on bank lending? Cross-country evidence, Baltic Journal of Economics, 19:1, 1-38, DOI: 10.1080/1406099X.2018.1547565
  • Peek, J., & Rosengren, E. (1995a). The capital crunch: Neither a borrower nor a lender be. Journal of Money, Credit and Banking, 27, 625–638.

Acknowledgement: This Policy Brief is based on a recent article published in the Baltic Journal of Economics (Olszak, Roszkowska, and Kowalska, 2019).

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.

Political Responsibility for Economic Crises

20180422 Political Responsibility for Economic Crises IMAGE 02

This brief summarizes the results of research on the political costs of large-scale economic crises. In a large historic sample of countries, we study the impact of different types of crises, such as sovereign and domestic defaults, banking crises and economic recessions, on political turnover of top politicians: heads of the state and central bank governors. According to the findings, only default on domestic debt increases the probability of politicians’ turnover but not the default on external debt. As argued, this is due to the fact that the latter is not directly felt by the voters. In addition, we find that although currency crises increase chances of head of central bank turnover, it does not affect tenures of heads of state. Presumably, this is the case since currency crises are in the eyes of the public the responsibility of CB governors. These findings are relevant for both developed and transition economies, but are especially important for the latter as political turmoil and economic recessions are more prevalent in developing nations.

Overview and Key Findings

Large-scale economic crises are associated   not only with the economic downturns, but also with political turnover. When the national economy is in a critical state, a default declaration often turns the economy back to growth as it is typically viewed as an act of  acknowledging a problem and showing readiness for changes. However, politicians responsible for the economy and leaders of the states are often reluctant to declare default and try to postpone it, which worsens the situation. One of the reasons behind such unwillingness to act is a fear of a political turnover following the open acknowledgement of a problem.

This brief summarizes the findings Lvovskiy and Shakhnov (2018). We investigate the statistical evidence of political costs related to different types of economic crises.

We find that the effects of a crisis depend on the crisis type and on whether it was in the area of responsibility of a given politician. For example, external sovereign defaults have no effect on political turnover, which we interpret as external sovereign default having a small impact on the general public. On the contrary, domestic sovereign defaults have a large impact on the country population and often lead to the replacement of the top executive. In turn, banking crises are followed by the downfall of the government at the level of chief executive as well as the governor of the central bank.

While there is large literature on career concerns of politicians and political turnover, the majority of papers either focus on the regular changes through elections in democratic regimes (Treisman, 2015) or study a particular non-democratic country, like China (Li and Zhou, 2005). However, throughout history, crises have often happened in transition, non-democratic or not fully democratic countries. Furthermore, even in democratic countries many changes of government have been irregular. Since a delay in default declaration usually harms economies it is important to understand the mechanisms behind it in different institutional settings. Our paper contributes to this understanding by analyzing the impact of economic crises on political survival in a wide set of countries and regimes. Better understanding of the political costs that the top executives face while making such decisions is crucial for the prediction of these decisions as well as for international default negotiations and consultations.

Below we describe our finding in some more detail.

Statistical Analysis and Results

Our analysis consists of two main parts. We start with the political turnover for heads of state, who are in charge of the performance of the whole economy, which we measure by the GDP growth. Then, we look at central bank (CB) governors, who are in charge of the monetary policy, price stability, stability of the financial sector and banking supervision.

Table 1. Head of state changes

Table 1 presents the estimated linear probability regression models for the head of state turnover. As expected, elections have a strong impact on the probability of the turnover of the head of state. Further, as Column 1 in Table 1 shows default on external debt has no significant impact on the head of state tenure while default on domestic debt increases the yearly chances of being displaced by 34 %. This supports the idea that voters care more about their own savings than about the general situation with the state’s budget. When we look at the effect of past crises (the predictor variable in this case is whether a crisis took place last year), Column 2 coefficients for both external and domestic defaults appear to no longer be statistically significant. Instead, banking crises become significant. This situation could be due to the fact that one of the common consequences of domestic defaults is an ongoing distortion  of savings  which often leads  to deposit runoffs, so the effect of the previous year’s domestic default now acts through a banking crisis.

Table 2. Central bank governor changes

Table 2 presents similar results but this time the left hand side variable is CB governor turnover. Similarly to the case with the head of state turnover, only default on domestic debt has a significant effect on the CB’s governor tenure and not the one on external debt. The main differences with Table 1 are that elections do not statistically predict turnover of CB heads while currency crises do. The former result is expected since in most countries there are no direct elections of central bank governors and central banks often have some degree of independence from the government. The latter result, that currency crises have a significant impact on CB governors’ tenures, implies that since currency control is one of the roles of a CB, its head is held accountable for currency crises and not the head of a state.


We examine the political cost of different types of economic crises, and find non-uniform effects of different types of crises on the political survival of various key officials. Domestic defaults, and recent banking crises are shown to be costly both for heads of states and central bank governors, while currency crises only have an impact on the political survival of the latter.

Interestingly and importantly, we find no evidence of the impact of (external) sovereign default on political turnover of the head of state or central bank governors. In other words, contrary to Yeyati and Panizza’s (2011) suggestion, it seems that there is no immediate political cost at the top associated with (external) sovereign default. One possible explanation is that the public does not  punish a politician for defaults because by defaulting, the politician makes the optimal decision.  In a modern world, many developing nations experience rapid growth of their sovereign debt. The presented evidence brings partial optimism that even if economic mistakes have already been made, top politicians would understand that acknowledging a problem and making steps toward its solution may not always be as costly for them as has previously been thought.


  • Li, Hongbin; Li-An Zhou, 2005. “Political turnover and economic performance: the incentive role of personnel control in China,” Journal of Public Economics, 89 (9), 1743 – 1762.
  • Lvovskiy, Lev; Shakhnov, Kirill, “Political Responsibility for Different Crises”, BEROC working paper #50, 2018
  • Treisman, Daniel “Income, Democracy, and Leader Turnover”, American Journal of Political Science,  2015, 59 (4), 927–942.
  • Yeyati, Eduardo Levy and Ugo Panizza, “The elusive costs of sovereign defaults,” Journal of Development Economics, January 2011, 94 (1), 95–105.

Financial Stress and Economic Contraction in Belarus

20180211 Financial Stress and Economic Contraction in Belarus Image 01

This brief summarizes the results of an analysis of financial stress episodes in the Belarusian economy. Based on a principal component analysis, I construct a financial stress index for Belarus (BFSI) that incorporates distinctive indicators for the banking sector, exchange market and external debt risks covering the period January 2004 to September 2016. Next, I identify episodes of financial turmoil in Belarus using the BFSI and assess the consequences for the real economy. Finally, I investigate the long-run relationship between financial stress and economic activity in Belarus.

It has become conventional wisdom that a well developed and smoothly operating financial system is critically important for economic growth (see Levine, 2005). It helps in overcoming frictions in the real sector, influencing economic agents’ savings and investment behavior, and therefore enabling the real economy to prosper (Beck, 2014).

In contrast, financial stress to financial system can be defined as the force that influences economic agents through uncertainty and changing expectations of loss in financial markets and financial institutions. It arises from financial shocks such as banking or currency crises (Iling & Ying, 2006). Consequently, the current stress level in the financial system can be quantified by combining a number of key individual stress measures into a single composite indicator – the Financial Stress Index (FSI).

In practice, such indices are already widely used, and allow regulators to maintain financial stability and help investors to assess the overall riskiness of investments in financial instruments of the country. The FSI for Belarus (BFSI) has been estimated for the first time and can be used as an early warning signal of systematic risk in the Belarusian financial sector (Mazol, 2017). In the financial context, systematic risk captures the risk of a cascading failure in the financial sector, caused by inter-linkages within the financial system, resulting in a severe economic downturn.

Construction of the FSI for Belarus

Based on a principal component analysis, the calculated index incorporates distinctive indicators for banking-sector risk estimated by the Banking Sector Fragility Index (BSFI), currency risk assessed by the Exchange Market Pressure Index (EMPI), and the external debt risk proxied by the growth of total external debt.

The BFSI reflects the probability of a crisis (episode of financial stress) – the smaller is the indicator, the better. The stability regime ends, when the BFSI exceeds a predetermined threshold. In particular, episodes of financial stress are determined as the periods when the BFSI is more than one standard deviation above its trend, which is captured by the Hodrick–Prescott filter. The identified episodes of financial stress show that one or more of the BFSI’s subcomponents (banking, external debt or foreign exchange) has changed abruptly.

Episodes of financial stress

During 2004—2016, two episodes of financial stress were detected in the economy of Belarus (see Figure 1). In both cases, there were large devaluations of the Belarusian currency, caused by the need to adjust its real exchange rate.

Figure 1. Episodes of financial stress in Belarus 2004—2016

Source: Author’s own calculations.

The first episode began in December 2008 and ended in May 2009. This episode was mainly a consequence of the global economic and financial crisis that caused a deep recession in Russia, reducing Russia’s demand for import of products from Belarus, further loss of competitiveness due to the sharp depreciation of the Russian ruble and deterioration of the current account balance and the depletion of foreign exchange reserves.

The second episode of financial stress began in December 2011 and ended in May 2012. It was caused by the renewed unbalanced macroeconomic policy aimed primarily at boosting aggregate demand by increasing government spending and accelerating economic growth; and monetary policy aimed at targeting the exchange rate. All this has led to problems in the foreign exchange market that eventually encompassed issues in the banking sector and caused a sharp reduction in foreign exchange reserves.

Financial stress and recessions

Figure 2 shows the contribution of each of the sub-indices to the increase in the BFSI.

Figure 2. The dynamics of components of BFSI during 2004-2016

Source: Author’s own calculations.

The main feature of the graph is that the currency stress is the prevailing factor in the two identified stress episodes. However, while the origins of the second episode were in the currency market, by early 2012, the stress had become much more broad based – the banking stress and the external debt stress contributed significantly to BFSI growth at the same time.

In contrast, since the beginning of 2016 until the end of the observation period, an upward movement in the BSF sub-index was detected indicating that the National Bank of Belarus (NBB) had to be worried about instability in the banking sector, which was mostly related to a loans crisis of state-owned enterprises (SOEs). A loans crisis of SOEs in Belarus means the inability of these enterprises to repay their debts and the need for budget coverage of their obligations and investments in fixed capital (see Figure 3). This happened due to a significantly higher cost of capital for SOEs after the second episode of the financial stress had begun.

Figure 3. Sources of investment financing and overdue loans of Belarusian enterprises

Source: Belstat.

Correspondingly, in the late 2016, the above problems have amplified the external debt stress (lack of external financing) in the economy of Belarus (see Figure 2).

Next, the results showed that financial stress negatively influences economic activity proxied by the index of composite leading indicators (CLI). In particular, an increase by one standard deviation (s.d.) in the BFSI leads to the contraction in the CLI index by 0.5 s.d. (see Mazol, 2017).

Moreover, financial stress has caused significant real output losses. The first episode of financial stress has resulted in the contraction of GDP by 5.9%. Second one has pushed Belarusian economy into a severe recession, which lasted 52 months with cumulative output losses about 12.9% of GDP (see Table 1).

Table 1. Descriptive statistics on episodes of financial stress and recessions in Belarus

Episodes of financial stress Duration (months) Output lossa

(% of GDP)

Number of months after start of financial stress to recession


December 2008 –

May 2009

6 12 -5.85 0
December 2011 –

May 2012

6 52 -12.89 6

Note: a) output loss is measured as GDP below trend during recession; b) a recession is occurred if there was a serious contraction in the economic activity (CLI) during six month or more. Source: Author’s own calculations.

Finally, a great reliance of Belarusian economy on external financing is associated with longer and sharper downturn in the aftermath of second episode of financial stress (see Figure 2).


The study has three policy implications. First, the BFSI may be considered as a comprehensive indicator that successfully determines the main episodes of financial stress in Belarusian economy and can be used to study their macroeconomic consequences.

Second, the BFSI identifies the most salient stress factors for Belarus, thereby showing which financial sectors need to be monitored carefully by national regulator to avoid a critical buildup of risks in the financial system.

Third, efforts to confine financial stress will support the country’s economic activity in the long run, which may include intervention in the foreign exchange market and build up of investor confidence in the economy.


  • Beck, Thorsten, 2014. “Finance, growth, and stability: lessons from the crisis”. Journal of Financial Stability, 10, 1-6.
  • Illing, Mark; and Ying Liu, 2006. “Measuring financial stress in a developed country: an application to Canada”. Journal of Financial Stability, 2, 243-265.
  • Levine, Ross, 2005. “Finance and growth: theory and evidence”. In: Aghion, P., Durlauf,S.N. (Eds.), Handbook of Economic Growth, vol. 1A. Elsevier, Amsterdam, 865-934.
  • Mazol, Aleh, 2017. “The influence of financial stress on economic activity and monetary policy in Belarus”. BEROC Working Paper Series, WP no. 40, 33 p.

Time to Worry about Illiquidity

At a time when central banks have injected unprecedented amounts of money, worrying about illiquidity may appear odd. However, if poorly understood and unaddressed, illiquidity could be the foundation of the next financial crisis. Market liquidity is defined as the ease of trading a financial security quickly, efficiently and in reasonable volume without affecting market prices. While researchers find that it has been positively correlated with central bank’s liquidity injection, it may no longer be the case. The combination of tightly regulated banks, loosely regulated asset managers, and zero (or negative) policy rates could prove toxic.

One recent volatile day on the markets, an investor called her bank manager asking to convert a reasonably small amount of foreign currency. The sales person was quick to respond: “I will hang up now and we will pretend this call never happened”. In other words, the bank was not ready to quote her any price. The typical academic measures of market liquidity, such as bid-offer spreads, remained tranquil on Bloomberg, there was no transactions taking place.

When the investor was finally forced to exchange, the result was messy: currency price gapped—fell discontinuously—causing alarm among other market participants and policymakers. All that due to a transaction of roughly $500,000 in one of the top emerging market currencies in the world according to the BIS Triennial Central Bank Survey at an inopportune moment.

Markets becoming less liquid

Post crisis, G-7 central banks have embarked on unconventional monetary policy measures to boost liquidity and ease monetary policy at the zero-lower-bound, while tightening bank regulation and supervision. On net, however, the ability to transact in key financial assets in adequate volumes without affecting the price has fallen across a range of markets, including the foreign exchange markets that are traditionally assumed to be the most liquid compared to bonds, other fixed income instruments and equities.

Financial market participants have reported a worsening of liquidity, particularly during periods of stress. Event studies include the 2013 “taper tantrum” episode, where emerging markets’ financial assets experienced substantial volatility and liquidity gapping that did not appear justified by the Fed’s signal to reduce marginally its degree of monetary policy accommodation, as well as the recent shocks to the US Treasury market (October 2014) and Bunds (early 2015).

Banks are retreating

Market-makers (international “sell-side” or investment banks as in the introducing example), which used to play the role of intermediators among buyers and sellers of financial assets, are now increasingly limiting their activities to a few selected liquid assets, priority geographies and clients, thus leading to a fragmentation of liquidity. Market-makers have also been reducing asset holdings on their balance sheets in a drive to reduce risk-weighted-assets, improve capital adequacy and curb proprietary trading. As a result, they are less willing to transact in adequate volumes with clients.

In the past, leverage by banks has been associated with higher provision of market liquidity. Loose regulation and expansionary monetary policy has been conducive to higher leverage by banks pre-2008. It is therefore puzzling that, now, at the time of unconventionally large monetary expansions by central banks, sell-side banks are unwilling to provide market liquidity. The answer may lay in tighter bank capital and liquidity regulation as more stringent definitions of market manipulation. Risk aversion by banks has also become harsher, a trader stands to lose a job and little to gain on a $2 million swing in her daily profit and loss, while in the past a swing of $20 million at a same bank would have hardly warranted a telling-off. Banks have become safer, but can that also be said about the financial system?

Asset managers growing in importance

Ultra-accommodative and unconventional monetary policies have compressed interest rates across all maturities. In a world where US Treasuries at two-year maturity do not even yield 1%, and Bunds are yielding negative rates even beyond 5 years, investors in search for yield are looking at longer (and less liquid) maturities and riskier assets. If banks are unable to meet this demand, others will: assets under management (AUM) by non-bank financial institutions, specifically real asset managers have expanded dramatically in recent years. Total size of top 400 asset managers’ AUM was EUR50 trillion in 2015, compared to EUR35 trillion in 2011 according to IPE research, with the largest individual asset manager in excess of EUR4 trillion. A fundamental problem arises when such asset managers are lightly regulated and very often have similar investment strategies and portfolios.

In the industry jargon, these asset managers are called long-only or real-money. Why the funny names? Long-only means they cannot short financial assets, as opposed to hedge funds. For every $100 collected from a range of individual investors’ savings via mutual funds, pension and insurance fund contributions, a small share (say 5%) is set aside as a liquidity buffer and the rest is invested in risky assets. Real money refers to the fact that these managers should not be levered. However, that is true only in principle as leverage is related to volatility.

Performance of real-money asset managers is assessed against benchmark portfolios. For emerging markets, the portfolio would typically be a selection of government bonds according a range of criteria, including size of outstanding debt, ease of access by international investors, liquidity, and standardization of bond contracts. Investors more often than not do not hedge foreign currency exposure. The benchmark for emerging markets sovereigns could have 10% allocated to Brazil, 10% to Malaysia, 10% to Poland and 5% to Russia, for example. India, on the contrary, would be excluded, as it does not allow foreign investors easy access to government bonds.

Benchmarks and illiquidity dull investor acumen

Widespread use of benchmarks among institutional asset managers can steer the whole market to position in “one-way” or herding, contributing to illiquidity and moral hazard risks. Benchmarks by construction reward profligate countries with large and high-yielding stocks of government debt.

While each individual portfolio manager may recognize the riskiness of highly-indebted sovereigns, benchmarking makes optimal to hold debt by Venezuela, Ukraine or Brazil as each year of missed performance (before default) is a risk of being fired, while if the whole industry is caught performing poorly, it is likely that the benchmark is down by as much.

Furthermore, real-money asset managers have become disproportionally large relatively to the capacity of sell-side banks (brokers) to provide trading liquidity. In fact some positions have de-facto become too large-to-trade. Even a medium-sized asset manager of no more than $200bn under management (industry leaders have $2-$4 trillion AUM) that attempts to reduce holdings of Ukraine, Venezuela or Brazil at the signs of trouble, is likely to trigger a disproportionate move in the asset price. This further reduces incentives to diligently assess each individual investment. In such environment, risk management has become highly complex, stop losses may no longer be as effective, while more stringent cash ratios would put an individual asset manager at a disadvantage to others.


Anecdotal and survey-based measures from the market demonstrate that liquidity is scarcer and less resilient during risk-off episodes. While regulation has made banks stronger, it may have rendered the financial system less stable. Lightly regulated real asset managers are increasing assets under management, are often positioned “one-way” and are becoming too-large-to-trade.

Nonetheless, systemic risk stemming from illiquidity in the new structure of the market remains little researched and poorly understood by policymakers and academics. Most models of the monetary transmission mechanism and exchange rate management do not incorporate complexities of market liquidity.

While regulatory changes have been largely driven by policy makers in the developed markets (naturally since they were at the epicenter of the global financial crisis), it is the emerging markets that in my view are most at risk. They tend to have less developed and less liquid domestic financial markets, and be even more prone to liquidity gaps with higher risks of negative financial sector-real economy feedback loops.


  • Sahay, R., et.al., “Emerging Market Volatility: Lessons from the Taper Tantrum”, IMF SDN/14/09, 2014 http://www.imf.org/external/pubs/ft/sdn/2014/sdn1409.pdf
  • Shek, J., Shim, I. and Hyun Song Shin, (2015), “Investor redemptions and fund manager sales of emerging market bonds: how are they related?” BIS Working Paper No. 509, http://www.bis.org/publ/work509.pdf
  • “Market-making and proprietary trading: industry trends, drivers and policy implications”, Committee on the Global Financial System, CGFS Papers, no 52, November 2014. www.bis.org/publ/cgfs52.pdf
  • “Fixed income market liquidity”, Committee on the Global Financial System, CGFS Papers, no 55, January 2016. www.bis.org/publ/cgfs55.pdf
  • Hyun Song Shin, “Perspectives 2016: Liquidity Policy and Practice” Conference, AQR Asset Management Institute, London Business School, 27 April, 2016. https://www.bis.org/speeches/sp160506.htm
  • Fender, I. and Lewrick, U. “Shifting tides – market liquidity and market making in fixed income instruments”, BIS Quarterly Review, March 2015. www.bis.org/publ/qtrpdf/r_qt1503i.htm
  • Tobias Adrian, Michael Fleming, and Ernst Schaumburg, “Introduction to a Series on Market Liquidity”, Liberty Street Economics, Federal Reserve Bank of New York, August, 2015.

Hedge Funds Non-Transparency: Skill of Risk-Taking?

20191231 Default Image 01

This policy brief raises the issue of whether the secretive nature of hedge funds allows funds to misbehave and take excess risks that may in turn be contagious for the whole economy. We use a novel dataset and a new methodology to argue that at least part of the excess performance of more secretive funds during the pre-crisis period was indeed due to higher risks taken.

Hedge Funds – the Secretive Investment Vehicles

In the modern era of delegated portfolio management, hedge funds constitute some of the most interesting and complicated investment vehicles, with a global industry size of over US$2.5 trillion and an overall number of funds of about 10,000 (according to Hedge Fund Research, Inc). The industry grew dramatically during the early 2000s, often providing investors with returns superior to those available in other financial sectors.

The natural question arising is then what exactly made hedge funds enjoy these superior returns. Historically, hedge funds have operated in a relatively secretive way that did not require them to disclose the details about their operations to regulators. Some have argued that it is this secretive nature of hedge funds that has allowed fund managers to employ superior trading strategies and effectively preserve the managerial know-how (in terms of stock-picking skill, market timing or faster trading technology) from being potentially replicated by others.

At the same time the secretive nature of hedge funds might simply allow the fund managers to hide the excessive risks their strategies are exposed too, thereby earning superior returns during relatively good periods (when risky strategies earn the risk premium), but having drastic collapses during relatively bad periods (when these risks realize).

Distinguishing between these two major explanations of superior performance is critically important for potential policy implications regarding hedge funds transparency and disclosure. If the secretive nature of hedge funds attracts more skillful managers that employ proprietary know-how strategies and invests into acquiring more information about the instruments they trade (i.e. generate so called “alpha”), more disclosure would not be necessarily good. This, since it would allow other funds or investors to free-ride on these more skillful managers, reducing their competitive advantage and incentives for providing superior performance. If on the other hand, secrecy allows hedge funds to misbehave and take more systematic risk than they claim they take (i.e. they have a higher “beta”), then there may be a rationale for increasing disclosure requirements, so that investors understand what they are being compensated for in the form of superior returns.

Is There More Risk in Secretive Hedge Funds?

The traditional approach to distinguishing between high-alpha and high-beta funds involves adopting a certain model of risk, i.e. selecting a set of observable risk factors that hedge funds may load on, and then adjusting their raw performance using the estimated exposures to these different factors. This would yield alpha – the risk-adjusted return – that can in turn be used as a measure of managerial skill.

In Gorovyy et al. (2014), we argue that the above methodological approach may sometimes be misleading in evaluating managerial performance. Indeed, in the absence of the true model (e.g. not knowing all factors or not being able to observe them) such alpha would be overestimated as long as these omitted or unobserved factors are earning positive returns during the estimation period (and underestimated, respectively, if the returns are negative). For practical purposes this means that if hedge funds load on unobservable factors, which during the estimation period happen to crash rarely, but deliver a positive return most of the time, we would erroneously attribute funds’ superior returns to managerial skill and not risk.

To tackle this issue, we offer a different approach and suggest that during relatively good times high-alpha and high-beta explanations may be observationally equivalent, but during relatively bad times, they are not. In particular, if during bad times the risks that funds have been loading on realize, we would observe relatively worse performance of funds that loaded more on such factors, ceteris paribus. Thus, in order to distinguish between high-alpha and high-beta funds, we need to look precisely at periods when we would be comfortable assuming that such unobserved factors are likely to crash.

In order to implement this idea, we use a novel proprietary dataset obtained from a fund-of-funds – that is, a hedge fund that invests in other hedge funds, and, hence, has a lot of information about these other hedge funds – and spans April 2006 to March 2009, to directly measure the secrecy level of a fund that is missing in public hedge-fund databases. This qualitative measure describes the willingness of the hedge-fund manager to disclose information about its positions, trades and immediate returns to fund investors. It is based on formal and informal interactions of the fund-of-funds with hedge funds it invests in, such as internal reports, meetings with managers and phone calls.

Figure 1. Performance of Secretive vs. Transparent Funds
Source: Author’s own calculations.

First of all, we document that secretive funds significantly outperform transparent funds during the relatively good times, as suggested, for example, by the period between April 2006 and March 2007 – a growth period according to NBER, and a period of rapid rise of the U.S. stock market indices. In particular, we find that the most secretive funds earned on average about 5% in annualized terms more than the most transparent funds during this period, even when we control for differential risk exposure of different strategies over time and various hedge-fund control variables.

In order to understand whether this superior performance of more secretive funds is due to managerial skill, or some other factors that may not be observable or not known in the model, we need to see what happened to these funds during the relatively bad period of time, i.e. during the period when we would feel comfortable assuming that risk factors on which hedge funds may have loaded did indeed realize. Although we may have in mind some of the omitted factors being potentially related to rare events and tail risk (as also supported by loadings on strategies associated with option-based returns as in Agarwal and Naik, 2004), they may well represent other risks that were likely to realize during the crisis period. We therefore label April 2008 to March 2009 as the “bad” period – a recession period according to NBER, highlighted by the bankruptcy filing by Lehman Brothers in September 2008 and some of the largest drops of stock market indices in history.

As we see from the graph in figure 1, the performance comparison between secretive and transparent funds largely reversed during this bad period. In particular, also supported by our more saturated regression results, transparent funds outperformed the secretive ones during the crisis by the magnitude of about 10-15% in annualized terms, depending on the exact specification. This explicit consideration of the bad period allows us to conclude that at least a part of the performance differential between secretive and transparent funds during good times can be attributed to a higher risk-taking by secretive funds, which earned a premium during good times but faced these realized risks during bad times.

Potential Policy Implications

As a response to the recent financial crisis, many developed economies have passed regulatory reforms considerably increasing the required disclosure levels, suggesting that the secretive nature of alternative investment vehicles has been considered to be something undesirable (e.g. for contagious effects on the economy, or the ex-post bailouts of the “too-big-to-fail” financial institutions). The examples of such policies include the U.S. Dodd-Frank Wall Street Reform Act passed in July 2010, the European Union Alternative Investment Fund Managers Directive 2011/61/EU that entered into force in July 2013, and the Regulation Guide 240 issued by the Australian Securities and Investments Commission in September 2012.

However, given that hedge funds receive money from relatively sophisticated and wealthy investors (i.e. generally having at least $1 million in net worth), whether more risk in hedge funds strategies is good or bad for them in particular, and the society in general becomes a somewhat debatable question. More importantly, the essence of many of the hedge-fund strategies lies in the so-called dynamic trading – with asset positions and risk exposures being adjusted daily or even more frequently. In such an environment, reporting these positions to the regulatory authorities even on a monthly basis may not adequately describe the exact risks taken by the hedge funds.

More relevant questions, on the other hand, may be about whether investors correctly perceive the exact risks faced by the fund, how large the degree of asymmetric information is within the hedge fund industry, and whether any action may be needed to correct it. These remain open questions and we hope that future research will address them.


  • Agarwal, V., and Naik N.Y., 2004, “Risks and portfolio decisions involving hedge funds,” Review of Financial Studies, 17(1), 63-98.
  • Gorovyy Sergiy, Patrick Kelly, and Olga Kuzmina, “Hedge Funds Non-Transparency: Skill of Risk-Taking?”, CEFIR Working paper.

Latvian Unemployment is Cyclical

20121210 Latvian Unemployment is Cyclical Image 01

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.


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.


  • 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

[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

Who Needs a Safety Net?

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.

The Distributional Impact of Austerity Measures in Latvia

20180114 Individual Retirement Timing Image 02

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.


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.


  • 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)

Shock “Therapy” the Market Way

Policy Brief Image with Tall Buildings Representing Transition, Market Economy and Shock Therapy

Twenty years after transition began and the merits of “shock therapy” were argued the most hotly, (former) transition countries are hard hit by global shocks originating in Western market economies. Although discussions now focus on troubles in Western developed countries, countries in Eastern Europe and the CIS were particularly hard hit in 2008/09. This should not come as a surprise given their pre-crisis vulnerabilities. As transition countries opened up to trade and capital flows—like other countries that want to reap the benefits of the global economy—they also became subject to the shocks that hit open economies. The very high current account deficits and/or reliance on commodity exports prior to the crisis exposed many countries in this region to two of the shocks that have been most costly to emerging markets and developing countries in the past, namely sudden stops in capital flows and terms of trade shocks. However, the lesson from the crisis should not be that opening up is bad in general, but that shock absorbers should be put in place to minimize the damage market-based “shock therapy” can do.

Shock Therapy and Transition

It is now twenty years since the failed 1991 coup that led to the breakup of the Soviet Union and started the transition from plan to market in (then) communist countries. A few years earlier, in 1989, the Stockholm Institute of East European Economies was set up at the Stockholm School of Economics. Its first director was Anders Åslund who was a strong proponent of shock therapy (see Åslund 1992). The basic idea was that a rapid transition from plan to market through changing institutions, privatizations and other liberal reforms would minimize the cost of transition. There are still different views on the merits of shock therapy as a reform strategy, but this brief will not address this debate.

In the wake of the academic debate of shock therapy, the broader research agenda was put under the heading “transition economics”, which became a new field in economics. This also lead Erik Berglöf, the new director of the Stockholm Institute of East European Economies, to change the institute’s name to the Stockholm Institute of Transition Economics, or SITE for short, the name we still use today. The economics of transition was also the title of the fifth Nobel symposium in Economics (Berglöf and Roland, 2007).

The use of the label “transition economics” may see a revival in the aftermath of the Arab spring. However, the economic issues that now face the (former) transition countries are much the same issues that emerging markets, developing countries and also advanced countries around the world grapple with. This is not least true when we look at what has happened in the current crisis.

Below this brief will argue that the shock therapy ex-communist countries were subject to in the early 90’s has been changed to the shock “therapy” open market economies around the world have been facing for a very long time. And just as there were heated debates on what the right therapy was then, the world is now debating what the “therapy” for the current shocks should be.

Output Drops Around the World

Significant drops in output have not only been observed in countries transitioning from plan to market but have occurred with regularity throughout modern history in countries around the world. The figure from Becker and Mauro (2006) shows the frequency of countries that entered into a major output loss event—defined as a cumulative loss of at least 5 percent of initial GDP in an event that last at least two years—during the 20th century. In the after-war period, a relative modest 5 to 10 percent of countries have entered into a period of significant output loss. However, in the great depression, almost half of the countries for which data is available entered into a period of significant loss of output.

Since the methodology used in Becker and Mauro follows countries until they return to pre-crisis levels of GDP, it is too early to provide a full account of what the number would be in the current crisis that started in 2008. Nevertheless, it is straightforward to compute how many countries have experienced output losses in 2008/09 (which is the first criteria to satisfy in the Becker and Mauro measure) and this number is close to fifty percent, on par with the great depression. This is not to say that the total output loss will be as high as in the great depression, but it clearly tells the story that this is the worst global crisis we have seen since then in terms of how many countries have been affected. The share of countries affected at the onset of this crisis varied greatly in different parts of the world and at different levels of development. The most surprising statistic from this crisis is that 90 percent of advanced economies experienced an output drop whereas “only” 40 percent of emerging market countries did. The regional differences between emerging markets are also striking; 85 percent of countries in Central and Eastern Europe (CEE) and more than half of CIS countries saw output decline, far above other regions.

Shocks 2.0

The Becker and Mauro paper also looks at the correlates of major output drops and focuses on a number of domestic and external macro, financial and political shocks as triggers of output collapses. A large dataset of shocks and output drops is used to compute the frequency of the different shocks; the likelihood that a particular shock leads to an output collapse (as defined above); and the output loss associated with such event. These numbers are then used to calculate how much different types of shocks have cost in terms of lost output for emerging markets and developing countries. The scale in the chart shows how much it would be worth in terms of GDP per annum to avoid a certain shock.

For emerging market countries, the worst shock has been sudden stops in capital flows that cost almost a percent of GDP per year. Unless countries have high levels of foreign exchange reserves, sudden stops in capital flows mean that (often large) current account deficits have to contract and even become surpluses quickly because there is no external financing available for a deficit. This, in turn, affects domestic production and demand and can have a serious effect on output. Add to this the financial uncertainty that is often associated with sudden stops and it makes it the number one shock to worry about for emerging market countries integrated in the global economy and financial markets.

Less developed countries are in several cases dependent on concentrated commodity exports to generate foreign exchange. This makes this group of countries more vulnerable to terms-of-trade shocks. The estimates of how costly these shocks are range from around half a percent of GDP per year, as is shown in the chart, to around 2.5 percent of GDP if a more extensive sample including very long-lasting output events is used.

Other shocks like currency, political and debt crisis have also been associated with significant losses in output, but tend to occur less frequently, which makes the cost per year smaller.

The 2008/09 Crisis

As was mentioned previously, not enough time has passed since the start of the crisis in 2008 to use the methodology in Becker and Mauro to compute cumulative output losses since many countries are still below their pre-crisis GDP levels. However, projected losses can be computed by using the IMF’s World Economic Outlook forecasts of GDP growth rates. If we then rank the countries according to worst output performance, CEE and CIS countries dominate the “top-ten” list with seven entries. Latvia at the top of the list is projected to lose a cumulative 40 plus percent of pre-crisis GDP during the 11 years it is projected to take the country to return to the GDP level it enjoyed in 2007. The other Baltic countries, Ukraine and Armenia have also been hit particularly hard in this crisis. Russia and Romania are close behind three advanced countries that had to seek IMF and EU assistance to deal with the crisis; Ireland, Iceland and Greece.

The forecasts used for these calculations are constantly being revised and the ranking of countries based on actual outcomes will certainly look different years from now. We can only hope that the current forecasts are too pessimistic although right now many revisions go in the wrong direction.

Could we have predicted what countries were hit in this crisis based on history? Based on the Becker and Mauro (2006) paper: Yes and no. “No” regarding the fact that overall, many advanced countries were hit this time. The paper found that in the past, the frequency of output collapses have decreased with the income level of countries, contrary to what has been the case this time. “Yes” for the fact that CEE and CIS countries were hit significantly more that other emerging markets since they were particularly vulnerable to the sudden stops and terms-of-trade shocks that the paper showed often are associated with severe output losses.

The signs of vulnerability in CEE and CIS countries were easy to see, but warnings ignored; the Baltic countries and Romania had double-digit current account deficits that where to a large part financed by external debt. For example, current account deficits in Latvia passed 20 percent of GDP in some years, far beyond the 4-5 percent deficits some Asian countries had prior to the Asian crisis in 1997. Ukraine also had large current account deficits and concentrated (metal) exports on top of that, exposing the country to both sudden stops and terms-of-trade shocks.

Russia’s dependency on energy and mineral exports was also well known, with 80 percent of export revenues coming from this source. However, the pre-crisis boom in oil and mineral prices had made Russian policy makers think this was a strength and not a vulnerability. On top of that, the strong external position of the government and central bank obscured the external financial vulnerabilities that had built up in the private sector. With the sharp decline in oil prices in the crisis, Russia was hit both by a terms-of-trade shock and a sudden stop in capital flows to the private sector.

There were of course individual countries in other regions that were showing vulnerabilities and were hit in the crisis, but the countries in the CEE and CIS region stood out as particularly vulnerable to the shocks that hit emerging market and developing countries in the past. The IMF’s Global Financial Stability Report from October 2008 summarizes these vulnerabilities in its Table 1.5 on macro and financial indicators for emerging markets very well. The shaded boxes that indicate potential problems in the table completely dominated the picture for CEE and CIS countries whereas other regions looked significantly less vulnerable. In short, history told us what shocks matter and the vulnerability indicators clearly showed where the shocks would do most damage.

Therapy 2.0

What are the policy lessons from this? Countries will always be subject to different types of shocks, and the question is how these shocks can best be absorbed to minimize the economic costs. In other words, what is the “therapy” needed to deal with these shocks? A key challenge is to find the policies and tools that strike the right balance between crisis prevention and high sustainable growth. Isolation to protect against external shocks is certainly not the answer.

Early warning systems (EWS) that identify vulnerabilities ahead of time and give policy makers time to reduce these vulnerabilities and thus avoid crisis is of course what everyone dreams of. The IMF and others have worked on various EWS models since the Mexican and Asian crises with mixed success (see Berg, Borensztein and Pattillo, 2005). With this crisis, the G20 and other groups of policy makers have made new calls for developing EWS, at times seemingly unaware of past efforts and the limited success in this area.

However, at the IMF the more formalized or mechanical EWS models are complemented with both bi-lateral and multilateral surveillance with the bulk of the findings made publicly available and communicated to relevant policy makers. These surveillance efforts contain much more information than more limited EWS models and also come with policy recommendation on how to deal with potential vulnerabilities.

There are of course limitations also with the surveillance by the IMF and other international and domestic organizations. First of all, they have to get it right and at the right time. This is far from trivial, not least because of our limited understanding of the links between the financial sector and the real economy. And even when the analysis gets it “right” in the sense of identifying vulnerabilities, it may take a long time before vulnerabilities translate into real problems and during this time, the analysis and recommended policies may seem misguided.

This is linked to the second major issue; to make relevant policy makers (politicians) listen to and take action on the advice. There is a reason Reinhart and Rogoff called their book “This time is different” since this is often the response to warnings from the IMF and others that suggest a party has been going on for too long and the punch bowl needs to be taken away.

Given the limitations of early warnings and surveillance more generally, there remains a strong need to reduce vulnerabilities in a systematic manner and develop tools to deal with the crises that were not prevented. This will require both domestic measures and a strong commitment to international cooperation. The latter part is of course extremely important right now in order to find appropriate solutions (read debt resolutions) to the problems cross-border banking and capital flows have created. It will also be key in setting up the rules for the future: what capital requirements should banks have; (how) should financial transactions or institutions be taxed; how can cross-border supervision be made more efficient; what type of crisis resolution mechanisms should be put in place both at the international and regional levels; etc, etc. Unless there is broad international agreement on these issues they will do little to address the weaknesses that were at the heart of this crisis.

On the domestic side, the usual IMF recommendation of creating a stable macroeconomic environment—with fiscal room to maneuver and a monetary policy that leads to stable prices—is always going to be part of a countries ability to absorb shocks. For countries that are integrated in international financial markets, exchange rate flexibility and a reasonable level of international reserves seem to be advisable. Jeanne and Rancière (2008) analyze optimal foreign exchange reserves for countries that are subject to sudden stops. Becker (1999) instead looked at accumulation of government assets as part of an optimal public debt and asset management strategy in a world with bailouts of the private sector which seems particularly relevant today.

The macro side should of course be combined with strong domestic supervision of the financial sector; structural policies that lead to sustainable growth in a competitive global environment; and strategies in commodity exporters to reduce the vulnerabilities associated with a narrow export base.

Although advanced countries get most of the attention in the international financial press today, emerging market and developing countries should not think that this is a new world were the shocks of the past do not matter to them. They do, so better get ready for “shock therapy” the market way while there still is time.


  • Becker, T., (1999), “Public debt management and bailouts”, IMF WP 99/103.
  • Becker, T. and P. Mauro, (2006), “Output drops and the shocks that matter”, IMF WP 06/172.
  • Berg, A., E. Borensztein, and C. Pattillo, (2005), “Assessing Early Warning Systems: How Have They Worked in Practice?”, IMF Staff Papers, 52:3, 462-502.
  • Berglöf, E. and G. Roland (2007), The economics of transition—The fifth Nobel symposium in economics, Stockholm Institute of Transition Economics (SITE) and Palgrave Macmillan.
  • IMF (2008), Global Financial Stability Report, “Financial stress and deleveraging”, October, Table 1.5 p.46.
  • Jeanne, O. and R. Rancière, (2008), “The optimal level of international reserves for emerging market countries: A new formula and some applications”, CEPR Discussion Papers 6723.
  • Reinhart, C. and K. Rogoff, (2009), “This time is different: eight centuries of financial folly”, Princeton University Press.
  • Åslund, A., (1992) “Post-Communist Economic Revolutions: How Big a Bang?”, Center for Strategic & International Studies, Washington D.C..

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.