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Leniency, Asymmetric Punishment and Corruption: Evidence from China

20150928 FREE Policy Brief Leniency, Asymmetric Punishment Image 01

Since coming into office two years ago, Chinese President Xi Jinping has carried out a sweeping, highly publicized anticorruption campaign. Skeptics are debating whether the campaign is biased towards Mr. Xi’s rivals, and even possibly related to the current economic slowdown. What is less debated is the next stage of Mr. Xi’s anti-corruption strategy, which is going to alter the legal statutes. Amendment IX, proposed in October 2014, includes heavier penalties, but two important tools in the fight of corruption – one-sided leniency and asymmetric punishment – became more limited and discretional. We argue that studying a 1997 reform and its effects can shed some light onto why the Chinese leadership seems dissatisfied with the current legislation and the likely effects of the proposed changes.

What We Know about Leniency

In our context, leniency can be defined as the concession of reduced sanctions (or full immunity) to wrongdoers that cooperate by self-reporting and providing information against former partners in crime. Formal and informal exchanges of leniency against information and collaboration are normal features of law enforcement in most countries. Policies of this kind have been extensively and quite successfully used to fight the Italian and American mafias, drug dealing and other organized crimes, and have become the main instrument to fight collusion in antitrust since the US reform in 1993 (see Spagnolo, 2008).

For crimes in which multiple offenders cooperate, one-sided leniency conditional on being the first to self-report can be a very powerful tool of law enforcement: by playing the partners in crime against each other, it may elicit information, greatly facilitate prosecution and generate deterrence at a very low cost. A conspicuous scientific literature with theoretical, experimental and empirical contributions shows the great potential of these policies, when properly designed and administered, for deterring collusive crimes (Miller 2009; Spagnolo 2008; Bigoni et al. 2012, 2015). On the other hand, Buccirossi and Spagnolo (2006) show specifically for the case of corruption that, when poorly designed or administered, these same policies may become ineffective or even counterproductive.

Asymmetric Punishment

A related way of using leniency towards one party (to play it against the other) in the fight against corruption has been at the center of a recent intense policy debate after the popular note “Why, for a Class of Bribes, the Act of Giving a Bribe Should Be Treated as Legal”, by Kaushik Basu (2011). Then chief economist of the Indian government and now of the World Bank, Basu advocated asymmetric depenalization of bribe giving, which can be thought of as a form of unconditional, one-sided leniency. More precisely, the note proposed to legalize bribe giving in the form of harassment bribes (also called extortionary, or discharge-of-duty bribes) paid to obtain something one is entitled to, while strengthening sanctions against bribe taking. As with other forms of leniency, the idea is to create a conflict of interests between the partners in crime by increasing the temptation for one party to betray and report the illegal act, leading to a severe punishment of the other.

In the debate sparked by this note many different arguments have been put forward, both against it and in favor of it. Dufwenberg and Spagnolo (2015) discuss formally some of the issues raised by critics of the proposal, while Abbink et al. (2014) provide (mixed) experimental evidence on its effectiveness. Later, a blogpost by a Chinese law scholar, Li (2012), attracted our attention to the case of China, where asymmetric punishment (bribe-giver impunity) has been in place since 1997. She argued, probably reflecting the political debate in the country rather than based on factual evidence, that the system had not been successful. We felt this claim granted a deeper investigation into the details of the Chinese legal reform and the changes it introduced, and of course a careful inspection of the data to back it.

A Study in Red

In a new working paper, Perrotta Berlin and Spagnolo (2015), we set out to understand the evolution of the anti-corruption legislation in China over the last decades, and then to evaluate the effects of the policy changes occurring in 1997. Two new elements were given the strongest legal status in 1997: leniency for wrongdoers that self-reported and cooperated with investigators, and asymmetric punishment (no charge for bribe givers) for bribes paid to obtain something one was entitled to. Concurrently, penalties were decreased, in particular for bribe-takers.

To understand the likely effects of this policy change we would ideally look at correspondent changes in corrupt transactions. Data on the prevalence of bribery, however, are notoriously hard to come by because of the secretive nature of this activity. Instead, we use several data sources which capture on the one hand actual corruption cases tried in courts, and on the other hand surveys of corruption perceptions. In particular, we have collected the number of arrests and public prosecutions on the counts of corruption and bribery from the Procuratorates’ Yearly Reports for each Chinese province since 1986.

It is not straightforward to infer changes in total corruption, which is unobserved, from changes in discovered cases tried in court. The data on prosecutions mix together corruption and anticorruption activities, as they fail to distinguish occurrence of the criminal activity from detection. A policy that deters crimes but at the same time increases the fraction of those that are successfully prosecuted will have an ambiguous effect on the number of prosecutions. We adapt for this purpose the testable predictions developed by Miller (2009): he models the occurrence of criminal activity (cartel formation, in this case) and derives predictions for how changes in the rate of occurrence and the rate of detection affect the time series of detection.

The preliminary evidence we have so far points to a substantial and stable reduction in the number of major corruption cases around the 1997 reform, a result consistent with a positive deterrence effect of the 1997 reform. The evidence is suggestive, and some alternative interpretations of the patterns in the data, shown in the plot below, cannot be excluded at the moment. While a peak-and-slump pattern as in Miller (2009) would have been much stronger evidence supporting the success of the reform at deterring corruption, we cannot exclude that the drop in prosecutions is simply due to a general worsening in detection. Although we deem this unlikely in the light of the general political climate of the time, we need more and better data to support our interpretation. Still, claims that the reform did not have an effect appear not supported by the data.

Figure 1. Change in Corruption Prosecutions before and after law reform in 1997

MariaGiancaPicSource: Perrotta-Berlin and Spagnolo (2015).

More to be done

A case study analysis is under way to corroborate and help the interpretation of these preliminary findings. We will analyze in depth a stratified random sample of prosecution case files between 1980 and 2010. Given that we sample a given number of cases, in this part of the analysis we cannot gain any insight about the incidence of bribery in general. We can instead observe the impact of the legislative reform on specific details of the corrupt behavior, and the mechanisms through which this behavior occurs or is deterred. In particular, we will be able to distinguish between cases of extortionary (harassment) bribes and bribes paid to obtain illegitimate benefits. Moreover, this will allow us to shed light on whether and how leniency and asymmetric punishment were applied in practice. The details of the case files might even allow us to gain insight into how the bribe-size and the value of corrupt deals evolved through the reform and even the selection into bureaucracy.

Conclusion

One-sided leniency, conditional on reporting an act first, or unconditional, as when bribe giving is depenalized, may be powerful corruption deterrence instruments if well designed and implemented in the right environment, but may also have negative effects. It has been argued that these instruments have been ineffective in China, after they were reformed in 1997, however, without data supporting the claim. Part of the reason lies in the difficulty to obtain good data on corruption. Another obstacle is the subtlety of interpreting them when they relate only to detected and convicted cases, rather than to the whole population of corruption cases.

We cannot solve completely the issue of data quality, as we also need to rely on official reports of counts of corruption cases. However limited, the exercise performed on aggregated data clearly shows that the 1997 Criminal Law reform did have an effect, consistent with increased corruption deterrence. To further support this finding we will collect and analyze micro-data from a randomized sample of these cases. This will allow us to isolate at a higher level of detail the changes in criminal behavior, reporting behavior and prosecution activity, and link them to the details of the legal reform to highlight the mechanisms at work.

China is home to a sixth of humanity, and currently undergoing a massive crackdown on corruption. Whatever we can learn about the effectiveness of their past and present anti-corruption policies is likely to have considerable welfare effects. Moreover, the 1997 reform was the object of a policy debate, and comments on its effectiveness came without data to support them. We believe our effort to use data to shed light on what this reform actually changed will be a valuable input to further research and policy discussion on this important topic.

References

  • Abbink, K., U. Dasgupta, L. Gangadharan, and T. Jain. “Let-ting the Briber Go Free: An Experiment on MitigatingHarassment Bribes.” Journal of Public Economics, 111,2014, 17–28.
  • Basu, K. “Why, for a Class of Bribes, the Act of Giv-ing a Bribe Should Be Treated as Legal.” WorkingPaper 172011 DEA, Ministry of Finance, Governmentof India, 2011
  • Bigoni, M., S.-O. Fridolfsson, C. LeCoq, and G. Spagnolo.“Fines, Leniency and Rewards in Antitrust.” RANDJournal of Economics, 43, 2012a, 368–90.
  • Bigoni, M., S.-O. Fridolfsson, C. LeCoq, and G. Spagnolo.. “Trust and Deterrence.”. Journal of Law, Economics, and Organization (2015)
  • Buccirossi, P., and G. Spagnolo. “Leniency Policies and Ille-gal Transactions.” Journal of Public Economics, 90,2006, 1281–97.
  • Buccirossi, P., Marvão, C. M. P., & Spagnolo, G. (2015). Leniency and Damages. Available at SSRN 2566774.
  • Dufwenberg, M. and Spagnolo, G., Legalizing Bribe Giving (April 2015). Economic Inquiry, Vol. 53, Issue 2, pp. 836-853, 2015.
  • Li, X. Guest post: bribery and the limits of game theory – the lessons from China. http://blogs.ft.com/beyond-brics/2012/05/01/guest-post-bribery-and-the-limits-of-game-theory-the-lessons-from-china/, 2012. Accessed: 2015-05-20.
  • Miller, N. H. Strategic leniency and cartel enforcement. The American Economic Review, pages 750–768, 2009.
  • Perrotta Berlin, M. and G. Spagnolo, Leniency, Asymmetric Punishment and Corruption: Evidence from China, SITE Working Paper, 2015 (forthcoming)

Evaluating the Political Man on Horseback – Coups and Economic Development

Image of a military man standing in the middle of the street representing coups and economic development

In a new paper (Meyersson, 2015) I examine the development effects of military coups. Coups overthrowing democratically elected leaders imply a very different kind of event than those overthrowing autocratic leaders, and these differences relate to the implementation of authoritarian institutions following a coup in a democracy. Although coups taking place in already autocratic countries show imprecise and sometimes positive effects on economic growth, in democracies their effects are distinctly detrimental to growth. Moreover, when coups overthrow democratic leaders, they fail to promote economic reforms, stop the occurrence of economic crises and political instability, as well as have substantial negative effects across a number of standard growth-related outcomes including health, education, and investment.  

Do military coups matter for economic development? After all, successful coups – i.e. where the military or state elites have unseated an incumbent leader – have occurred 232 times in 94 states since 1950 (see Figure 1). Moreover, around a quarter of these overthrew democratically elected governments (Powell and Thyne, 2012). The prevalence of military coups has not been lost on researchers, yet despite an abundance of research aiming to explain the occurrence of coups (see for example Acemoglu and Robinson, 2001; Collier and Hoeffler, 2006 & 2007; Leon, 2014; Svolik, 2012) much less research has focused on its economic effects (two exceptions are the papers on covert US operations during the Cold War by Dube, Kaplan, and Naidu, 2011 and Berger, Easterly, Nunn, and Satyanath, 2013). Olsen (1963), for example, claimed that coups “often bring no changes in policy.” Londregan and Poole (1990), in their panel-data analysis, find no effects of coups on income.

By now, there is mostly a consensus that significant military influence in politics is detrimental for democracy (Dahl, 1971; Huntington, 1965; Linz and Stepan, 1996). Nonetheless, military coups overthrowing democratically elected governments are often met with ambiguity. Western governments have a long history of tacit support for military coups overthrowing democratic governments, be it left-leaning governments in Latin America or Islamist governments in the Middle East and North Africa (Schmitz 2006). Commentators expressing support for coups often do so invoking extreme outcomes to represent the counterfactual to the military coup; if Pinochet had not overthrown President Allende, the latter would have created a Castro-style regime in Chile; if the Algerian army hadn’t annulled the elections in 1992, the Islamist FIS would have turned Algeria into an Islamist dictatorship in the Maghreb, and so on (Los Angeles Times 2006, Open Democracy 2013). Similarly, the fault for the coup and preceding problems fall invariably upon the ousted leader, with the coup constituting an unfortunate, but necessary, means to rid the country of an incompetent, if not dangerous, leader (Foreign Policy, 2013).

Other commentators have pointed out the risks of allowing a military to intervene and dictate post-coup institutions to their advantage; a “Faustian” bargain likely to bring regime stability but no solution to the real underlying problems behind the conflict in the first place. Yet others lament the human rights abuses following coups, and the inherent ineptitude of military leaders in running the economy (NYT, 2013; New Republic, 2013; Washington Post, 2013).

Figure 1. Successful and Failed Coup Attempts by Country and Year

fig1Notes: The graph shows successful (solid circles) and failed coup attempts (hollow circles) by country and year, and aggregated by country (right graph) as well as by year (top graph). A circle in blue means the political regime was classified by Cheibub et al 2010 as a democracy in the year before the attempt and a red circle means they classified the regime as an autocracy.

Military coups tend to be endogenous events, and establishing a causal relation between coups and development is therefore a challenge. The unobservable likelihood of a coup – often referred to as coup risk (Collier and Hoeffler, 2006 & 2007; Londregan and Poole, 1990; Belkin and Schofer, 2003) – may be driven by many factors also affecting a country’s development potential, such as weak institutions, the military’s political power, social conflict, and economic crises etc.

In order to address this problem, I employ several empirical strategies including comparing successful versus failed coup attempts, matching methods, as well as panel data techniques, using a dataset of coup attempts during the post-World War II era. These methods facilitate, in different ways, comparisons of development consequences of coups in situations with arguably more similar degrees of coup risk.

Of significant importance is distinguishing coups when they occur in clearly autocratic settings from those where they overthrow democratically elected governments. I show that a military coup overthrowing a regime in a country like Chad may have very different consequences than a military leader overthrowing a democratically elected president in a country like Chile. In the former, a coup appears to constitute the manner in which autocracies change leaders. In the latter, coups typically imply deeper institutional changes with long-run development consequences.

I find that, conditional on a coup-attempt taking place, the effect of coup success depends on the pre-intervention level of democratic institutions. In countries that were more democratic, a successful coup lowered growth in income per capita by as much as 1-1.3 percent per year over a decade. In more autocratic countries, I find smaller and more imprecisely estimated positive effects. This effect is robust to splitting the sample by alternative institutional measures, as well as to a range of controls relating to factors such as leader characteristics, wars, coup history, and natural resources. As Figure 2 illustrates, the economic effect of coups tend to worsen over time. Extending the analysis to matching and panel-data methods reveal these results to be highly robust.

Figure 2. Relationship between a Successful Coup and Growth in GDP per capita

fig2Notes: The three graphs represent the coefficient on a successful coups on growth in GDP per capita (PPP) between year t-1 and t+s with s given by the x-axis for all regimes(left), autocracies (middle), and democracies (right). Controls include period t-1 values of log GDP per capita, annual growth, log population, PolityIV index, annual change in the PolityIV index military expenditures as a share of GDP, annual change in military exp/GDP, military personnel as a share of population, years since the last coup, total number of previous coups, social unrest, leader tenure, as well as continent and year dummies respectively. See Meyersson (2015) for details.

A commonly held view is that coups overthrowing democratically elected leaders often provide an opportunity for engaging in unpopular but much needed economic reforms. Not only do I show that coups fail at this, but also that they tend to reverse important economic reforms, especially in the financial sector, while also leading to increased indebtedness and an overall deteriorating net external financial position, and an increased propensity to suffer severe economic crises. A documented reduction in social spending suggests a shift in economic priorities away from the masses to the benefit of political and economic elites.

Whereas coups occur mostly in dire situations, their prescriptions, as shown, rarely constitute adequate remedies to the underlying problems, as the institutional changes brought by these events show clear detrimental development consequences. Any short-lived benefit of regime stability a coup brings, comes at a steep economic, political, and human cost in the longer run.

References

  • Acemoglu, Daron and James A. Robinson, “A Theory of Political Transitions,” The American Economic Review, Vol. 91, No. 4 (Sep., 2001), pp. 938-963
  • Berger, Daniel, William Easterly, Nathan Nunn, and Shanker Satyanath. 2013. ”Commercial Imperialism? Political Influence and Trade during the Cold War.” American Economic Review, 103(2): 863-96.
  • Belkin, Aaron, and Evan Schofer, 2003,“Toward a Structural Understanding of Coup Risk”, Journal of Conflict Resolution, Vol. 47 No. 5, October 2003 594-620
  • Cheibub, Jos ́e Antonio, Jennifer Gandhi, and James Raymond Vreeland, 2010, “Democracy and dictatorship revisited,” Public Choice (2010) 143: 67-101.
  • Collier, Paul and Anke Hoeffler, 2006, “Grand Extortion: Coup Risk and the Military as a Protection Racket,” working paper
  • Collier, Paul and Anke Hoeffler, 2007, “Military Spending and the Risks of Coups d’ ́etat,” working paper.
  • Dahl, Robert A., Polyarchy: Participation and Opposition, Yale University Press 1971.
  • Dube, Arindrajit, Ethan Kaplan, and Suresh Naidu, “Coups, Corporations, and Classified Infor- mation”, Quarterly Journal of Economics, Quarterly Journal of Economics, 2011 (Vol. 126, Issue 3)
  • Foreign Policy, “Blame Morsy,” Michael Hanna, July 10 2013,
  • Huntington, Samuel P., 1965, “Political Development and Political Decay,” World Politics, 386- 429
  • Leon, Gabriel, 2014, “Loyalty for Sale? Military Spending and Coups d’Etat,” Public Choice 159, 363-383
  • Linz, Juan, and Alfred Stepan, Problems of Democratic Transition and Consolidation: Southern Europe, South America, and Post-Communist Europe, Johns Hopkins University 1996
  • Los Angeles Times, “Iraq needs a Pinochet”, Jonah Goldberg, December 14, 2006
  • Londregan, John B and Kenneth T. Poole, “The Coup Trap, and the Seizure of Executive Power,” World Politics, Vol. 42, No. 2 (Jan., 1990), pp. 151-183
  • Meyersson, Erik, 2015, Political Man on Horseback – Military Coups and Development, working paper, http://erikmeyersson.com/research/
  • Olsen, Mancur, “Rapid Growth as a Destabilizing Force,” The Journal of Economic History, Vol. 23, No. 4 (Dec., 1963), pp. 529-552
  • Open Democracy, February 11 2013, https://www.opendemocracy.net/arab-awakening/hicham-yezza/how-to-be-different-together-algerian-lessons-for-tunisian-crisis.
  • Powell, Jonathan M, and Clayton L Thyne, 2012, “Global instances of coups from 1950 to 2010: A new dataset,” Journal of Peace Research 48(2) 249-259
  • Schmitz, David F. “The United States and Right-Wing Dictatorships”, Cambridge University Press 2006
  • Svolik, Milan W., The Politics of Authoritarian Rule, Cambridge University Press 2012.
  • The New Republic, “Egypt Officially Declares What Is and Isn’t Important”, Nathan J. Brown, July 9 2013, http://www.newrepublic.com/article/113792/egypt-president-adli-mansour-makes-constitutional-declaration.
  • The New York Times, “A Faustian Pact: Generals as Democrats”, Steven A. Cook, July 5, 2013

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.

Non-Tariff Measures in the Context of Export Promotion Policies

20150511 Non-Tariff Measures FREE Network Policy Brief Image 01

This brief focuses on the role of non-tariff measures (NTMs) in international trade. While multilateral and bilateral trade negotiations have resulted in worldwide reductions in tariffs, we observe an increasing trend in the application of non-tariff measures. In this brief, we will discuss the evidence of the effect of such measures on exports. The brief also contributes to the discussion of export promotion policies: whether governments, especially in developing countries, should concentrate their efforts to remove only external barriers since there is empirical evidence that internal barriers are no less important for exports.

Economists, policy makers and international organizations are increasingly recognizing the importance of non-tariff measures (NTMs) as substantial impediments to international trade. A survey conducted by UNCTAD among exporters in several developing countries ranks SPS and TBT measures the top trade barriers with on average 73 percent of the respondents viewing them as the primary trade barrier (UNCTAD 2010). The World Bank published a book on NTBs where different authors contributed chapters addressing many aspects of the NTMs (World Bank, 2012). The World Trade Organization (WTO) itself devoted its entire 2012 World Trade Report to such measures with a particular focus on technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures. Availability of the new datasets on NTBs allowed researchers to study the effect of these measures on intensive (changes for existing exports) and extensive margins (changes due to entry and exit into exporting) of trade.

Even though trade theory does not specifically address the question of non-tariff barriers that include (but are not limited to) technical regulations, sanitary and phytosanitary measures, the logic of traditional models can easily be extended to these measures. In particular, they can be thought of as part of the fixed/additive costs for exporting firms as they impose compliance costs on exporters. These compliance costs are related to potential adjustments of production processes, and certification procedures needed to meet the requirements of countries imposing such regulations and standards (Schlueter et al., 2009). In a Melitz-type model, these costs are expected to have a negative impact on volumes of trade, number of exporters and number of goods exported. At the same time, average exports per firm may actually increase as the export market-shares are reallocated towards firms that are more efficient.

The existing empirical evidence of the impact of NTMs is mixed; researchers have found both positive and negative effects. The differences in results depend largely on the sector, country and type of NTM imposed. While the effect may overall be negative or null, for some sectors the effect is found to be positive (Moenius, 2004; Fontagné et al., 2005; Chen et al., 2006; Disdier et al., 2008; Medin and Melchior, 2015).

In a recent working paper, Besedina (2015) investigates the effect of introducing an NTM (either SPS or TBT) on export dynamics (in particular, exports concentration and entry and exit into exporting) using the World Bank Exporters database, with a special focus on trade in foodstuff. In particular, we examine how TBT and SPS measures affect export concentration and diversification (both at product and destination level) as well as entry and exit of firms into exporting. If introduction of an NTM increases costs of exporting, the ‘new’ trade theory started by Melitz (2003) predicts that some exporters will stop to export and thus the number of exported product varieties will fall as well (change in extensive margin).

The most important result from our analysis is that the introduction of a TBT or an SPS measure does not seem to affect sectoral export dynamics. Given the above discussion, this result may appear surprising at first. What can possibly explain this zero effect?

First, one may argue that the sector dynamic variables we use in our analysis may not capture changes in the behavior of economic agents (firms) well: while marginal firms may be affected by technical barriers and SPS, averaging across firms may actually conceal this. However, in our analysis we investigate exports at a relatively disaggregated level (4-digit product lines). So while averaging might be a concern, we believe it is not likely to be driving the zero effect.

Second, the concern is that the effect of introducing an NTM measure may not be felt immediately (within one year). In order to verify this, we include lagged trade-barrier variables two periods, but the results were unchanged. Third, it may be the case that it is the number of NTMs rather than the introduction of them that matters. In order to address this point, we performed the same type of analysis using the change in the number of measures introduced. The results were again not affected, and we still do not find any statistically significant relationship between NTMs and exports dynamics.

Despite the absence of an effect of NTMs, this paper reveals an important and policy-relevant finding: the home country’s business environment and institutional factors are important determinants of export performance. It is rather the monetary costs and more complicated exporting procedures imposed by the NTM measures that hamper product and market diversification of the country’s exporters. Hence, policy makers, especially in developing countries, should not only be concerned with removing external barriers to exports (like NTMs) but should also aim to reduce internal barriers and costs imposed on exporting firms by corrupt practices and burdensome regulatory procedures.

Another important dimension for domestic policies towards exporters stems from the work by Melchior (2015, forthcoming) who studies Norwegian exports to BRICS countries overtime and shows that export growth largely depends on the intensive margin (it explains 93 percent of the export growth). Using firm-level data for seafood exports, he finds that only 54% of “trades” – measured as firm/importing country/product combinations – survive from one year to the next. Hence, there is massive “churning” (entry and exit at the same time), and churning is relatively more important in small and in growing export markets. In other words, exporting companies constantly enter and exit foreign markets, add new products, or discontinue exporting some products. A policy implication from this finding is that export-promotion offices should help firms stay in export markets rather than focus on entering these markets. Hence, while it is important to enable domestic firms to enter foreign markets, it seems equally important to ensure their survival in foreign markets, which can be facilitated by a removal of both external and internal barriers.

References

  • Disdier, A-S, L. Fontagné and M. Mimouni (2008), “The Impact of Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements”, American Journal of Agricultural Economics 90(2): 336-350.
  • Fontagné, L., F. von Kirchbach, and M. Mimouni (2005). “An Assessment of Environmentally-related Non-tariff Measures”, The World Economy 28(10): 1417-1439.
  • Medin H. and A. Melchior (2015) ”Trade barriers or trade facilitators? On the heterogeneous impact of food standards in international trade”, NUPI mimeo
  • Melchior (2015) ” Non-tariff barriers, firm heterogeneity and trade: A study of seafood exports, with a particular focus on BRICs”, NUPI mimeo
  • Melitz, M. J. (2003), “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity,” Econometrica, 71(6): 1695–1725.
  • Moenius, J. (2004), “Information versus Product Adaptation: The Role of Standards in Trade”, Working Paper, International Business & Markets Research Center, Northwestern University mimeo.
  • UNCTAD (2010), Non-Tariff Measures: Evidence from Selected Developing Countries and Future Research Agenda (UNCTAD/DITC/TAB/2009/3). New York and Geneva.
  • World Bank (2012), Non-Tariff Measures – A Fresh Look at Trade Policy’s New Frontier, ed. O. Cadot and M. Malouche, The World Bank, Washington D.C.

Export Costs of Visa Restrictions

20150330 Export Costs of Visa FREE NETWORK Policy Brief Image 01

We study the role of visa restrictions in determining export flows between firms and countries, and find a significant negative impact of visa restrictions. Our results indicate that visa costs not only diminish the value of export, but also the probability of new firms to enter visa restricted foreign markets. We interpret these results as evidence that visa restrictions contribute to trade costs faced by exporting firms.

There is no doubt that policy decisions in the area of foreign relations influence economic links between countries. However, quantifying these effects is usually very difficult – not least because visa regimes are relatively stable over time, not allowing for sufficient variation to estimate the effect of a regime change. As a result, decision-making is often based on very limited quantitative grounds, and mostly driven by qualitative intuition and strong political preferences. However, these decisions might have very important redistributive effects and create unequal access to markets for producers from different countries. For example, while WTO emphasizes a nondiscrimination clause to be one of the main principles of trade policies for member countries, foreign policy might become a very important source of discrimination in international trade.

An example of such policy decisions is visa requirement for foreign visitors. The channel of the effect is rather intuitive – visa requirements on foreign nationals might affect the intensity and costs of business visits needed to establish trade relations between firms in different countries.

In Kapelko and Volchkova (2015) we test the impact of foreign visa requirements on the international trade based on the Russian case. The Russian economy represents a unique setting to study the effect of visas on trade flows. Over the first decade of 2000, there were more than 30 visa regime changes between Russia and foreign countries. Thereby, there is sufficient variation for quantifying the export costs of visa restrictions.

Evidence

Economists observe that when a pair of countries has visa restrictions – both bilateral and unilateral – their bilateral trade flows, tourist exchanges, and FDI flows are smaller compared to pairs of similar countries without these restrictions (Neumayer, 2011). The anecdotal evidence also indicates that business meetings, conferences and other interactions which involve people from different countries are often cancelled or delayed due to the failure of some participants to obtain visa stamps on time. Therefore, we can assume that costs of visas for international transactions include not only simple monetary costs associated with the visa fee but also less predictable components such as the risk of refusal, time costs, etc.

Economic research often relies on some intrinsic features of goods or industries as a way to test the hypothesis. Namely, if the extent of the studied effect depends on these features then one would compare the effects across goods or industries controlling for the features. In our case, if the effect of visas is due to risks associated with the inability of businessmen to attend meetings or negotiations, then we can expect a negative effect of visa restrictions on trade flows, which will be stronger for goods trade since it requires more interactions between the buyer and seller. For this study, we rely on Rauch’s (Rauch, 1999) definition of relation specific goods and compare the effect of visas across goods with different degrees of sensitivities to the relations.

Method

The recent developments in trade theory and empirical research provide a specification of structural relations between country-level bilateral costs of trade and firm level decision to export. The heterogeneous firms approach brought by Marc Melitz (Melitz, 2003) to the international trade framework emphasizes that fixed costs of exporting play an important role in shaping patterns of exports. The literature distinguishes between fixed and variable costs of exporting, but the empirical evidence on cost composition is very limited and very little is known so far about the fixed costs of exporting. We proxy both these costs with visa restrictions, and use heterogeneity in firms’ decisions whether to export or not, to various destinations, to estimate the effect of visas on market access and trade flows.

Data

We combine annual data on exporters, volume of export of each exporter to each destination from the Russian Customs Transaction Database with data on all bilateral visa constraints for the period 2003-2010 between Russia and 180 foreign export destinations.

First, we test whether Russian firms export less to countries which impose strict visa restrictions compared to countries with less restrictive visa regimes or visa waiver programs, other things being equal. We test these effects separately for trade in goods which are more specific to the parties involved in the transaction (relation-specific goods, such as manufactured goods, and equipment with specific technical requirements on part of buyer) and trade in goods that depend less on the parties involved in the transactions (non-relation specific goods, such as more homogeneous, standard goods) (Rauch, 1999). Then, we estimate the effects of visa restrictions on the value of trade to chosen destinations.

The obvious concern is that visa decisions are dependent on trade. Politicians might facilitate visa negotiations if the country’s economic interests expand toward some destinations. It might for example affect visa waivers between countries. To deal with this issue we use tourist flows between countries as an instrument to allow for more accurate measurement of visa effects.

Our empirical strategy is to use the two-stage least squares approach with weighing in the second step to eliminate the potential bias due to selection into exporters to particular destination (Imbens and Wooldridge (2009)).

Results

Our results indicate that visas have a strong negative effect on market access, and it is twice as high for export of relation-specific goods as for export of non-relation specific goods. Controlling for the choice of destination, visas have a significant negative effect on the value of exports of relation-specific goods as well.

More specifically, our estimations indicate that:

  • the probability of the firm to export to visa-restricted destinations is below the probability of export to visa-free destinations. The probability gap is estimated to be about 36 percent for the overall sample, 40% for relationship specific transactions and 26% for non-relationship specific export.
  • the value of exports for relation specific goods is negatively affected by visa restrictions while there is no effect of visa restrictions on the export of non-relation specific goods. Our estimations indicate that the effect of visa is quite substantial so the value of relation specific export is twice as low to visa restricted as to visa free destinations.

These results emphasize the economic importance of visa restrictions and they are consistent with the assumption that visa restrictions do, in fact, contribute to the costs of market access. The negative effect of visa restrictions on the value of exports of relationship specific goods indicates that they also contribute to the variable costs of export.

Conclusions

The implications of this analysis may be very important. It demonstrates that visa regimes play a role as a non-tariff restriction or as a barrier, and can have significant effects on the development of trade relations between countries. The losses in trade due to visa restrictions are both extensive and intensive in nature: fewer firms are engaged in trade between countries with strong visa restrictions and they trade less in terms of more sophisticated goods. Therefore, we document at least two types of distortions in trade flows due to visas: visa distorts trade relations across countries with different visa requirements, and visa distorts trade flows across different types of goods to destinations with different visa requirements. Given the substantial negative effects of visas on trade relations, it is worth accounting for these economic costs when Ministries of Foreign Affairs engage in negotiations toward visa waivers.

References

  • Helpman, E., M. Melitz, and Y. Rubinstein. 2008. Estimating Trade Flows: Trading Partners and Trading Volumes. Quarterly Journal of Economics, Vol. 123 No2, 441-487.
  • Imbens, G., and J. Wooldridge . 2009. “Recent developments in the econometrics of program evaluation”. Journal of Economic Literature, 47(1) pp5-86
  • Kapelko, N., and N. Volchkova. 2015. “Export costs of visa restrictions”, CEFIR Working Paper, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2243136
  • Melitz, M. J. 2003. “The impact of trade on intra-industry reallocations and aggregate industry productivity.” Econometrica 71(6).
  • Neumayer, E. 2011. “On the Detrimental Impact of Visa Restrictions on Bilateral Trade and Foreign Direct Investment.” Applied Geography 31 (3): 901–907.
  • Rauch, J. E. 1999. “Networks Versus Markets in International Trade.” Journal of International Economics 48 (1): 7–35.

The Role of Belarusian Private Sector

The development of a private sector and the expansion of its role in the economy is one of the key goals repeatedly announced by the Belarusian authorities. The reforms carried out in Belarus in 2006-2014 moved the country from 106th to 57th position in the World Bank Doing Business ranking. The official statement is that reforms boosted the rapid development of business initiatives and its impact on economic development. Unfortunately, there is no clear confirmation of this statement. The absence of a transparent and clear methodology in Belarusian statistics on how to evaluate the role of the private sector makes it difficult to evaluate the exact input of the Belarusian business in the economy and compare its role to other countries.

In the last 5 years, the Belarusian authorities have repeatedly highlighted the need to develop the private sector, perceiving it as the main source for sustainable economic growth and competitiveness of Belarus in the future.

However, it may be difficult to assess the real role of the private sector in the Belarusian economy. First, existing data do not allow a clear identification of the boundaries between the private and state-owned sectors in Belarus. Furthermore, there are certain methodological differences in identifying and evaluating the private sector between Belarusian official statistics, the World Bank approach and alternative methodologies. These methodological variations combined with data limitations result in significantly different estimates of the role of the private sector for the Belarusian economy. The problem concerns both the evaluation of the role of small and medium enterprises (SMEs) and the private sector in general.

Small and Medium Enterprises

One good example of the abovementioned data issue is the statistics for SMEs sector. Unlike the EU, Belarus does not include individual entrepreneurs to the micro organizations in the SME sector. This results in highly different estimates for the number of SMEs per 1000 inhabitants (Figure 1). If we follow the methodology of the National Statistical Committee of the Republic of Belarus (Belstat), the number is 9.7 firms per 1000 people. However, switching to the EU methodology (IFC report, 2013) raises the number significantly up to 35.9. Moreover, the inclusion of unregistered self-employed individuals involved in the shadow economy (which according to estimations of the authorities amount to at least 100,000 inhabitants) increases the number to 46.5 firms per 1000 people, which is above the level of many European countries.

Figure 1. SME density

figure1Source: own estimations from Belstat data, Eurostat.

Private Sector

As for the private sector in general, the problem here is that the official statistics counts enterprises with mixed form of ownership and state presence to the private sector. This makes it difficult, if at all possible, to obtain the exact input of the private sector to the economy and see the dynamics of its change.

More specifically, there are three potential ways to assess the contribution of the private sector. Unfortunately none of them provides reliable estimates of the role of business. The first method is to use official data. The main problem here is that the private sector according to official statistics includes enterprises with state presence as well as large private companies that are under state control and not totally independent. Thus, the contribution of the private sector calculated based on these figures is likely overestimated.

The second method is to look at enterprises that do not report to the Belarusian ministries, following the methodology of the World Bank used in their evaluation of Belarus machinery industry (Cuaresma et al., 2012). Here, non-ministry reporting enterprises work as a proxy for a private firm, as in this case it doesn’t have to report directly to Belarusian ministries and is independent from the state.

The problem is that the majority of large private enterprises, even though there is no state share in them, are not in this list. In Belarus these enterprises often form a part of state concerns on the one hand and are independent on the other. The example here is JSC “Milavitsa”, one of the largest lingerie producers in EE, which is a part of the Bellegprom concern. Therefore, this methodology likely underestimates the role of the private sector.

The third way is to try to exclude state presence from the official data of the private sector. According to official statistics, the private sector includes several groups of enterprises, such as individual entrepreneurs, legal entities with/without state/foreign presence, etc. However, the absence of a clear distinction between these sub-groups allows for only rough estimates, through the extraction of the state presence.

As a result, all obtained numbers are qualitatively different from each other and there is no clear answer if any of them reflects the real picture.

For example, the contribution of the private sector in total employment according to the three different methods (Figure 2) provides the following results. Officially, in 2013 around 53% of the active labor force worked in the private sector. However, the exclusion of state presence in private property changes the results significantly and the share of the active labor force involved in the private sector drops to a level of 31%, while the non-ministry reporting enterprises employ around 18% of the active labor force.

Figure 2. Private sector in employment (%)

figure2Source: own estimations from Belstat data.

The input of the private sector in the total production volume (Figure 3) is also very diverse depending on the method of evaluation. Official data show that the private sector is responsible for 80% of total production volume. However, the exclusion of state presence decreases the value to a level of just 26%, which is similar to the result demonstrated by the non-ministry reporting enterprises (25%).

Figure 3. Private sector in total production volume (%)

figure3Source: own estimations from Belstat data.

At the same time, the absence of a clear definition of the private sector does not allow for obtaining reliable information about its effectiveness. If we take the rate of return on assets (ROA), again, there is a significant gap in the results of the different methods of estimation (Figure 4). ROA of the private sector according to official statistics is significantly lower than similar indicators based on the data obtained by the other two methods (in 2013: 1.17 vs. 2.4 and 1.3 respectively). Thus, the lower the “measured” state presence, the higher is the productivity of the private sector, especially in comparison with the effectiveness of the state sector (0.25).

Figure 4. Return on Assets (BYR/BYR)

figure4Source: own estimations from Belstat data.

Conclusion

The above discussion has illustrated that diffuseness of data and the definition of the private sector is likely to create troubles for understanding the importance of the private sector in Belarus. This, in turn, may undermine the effectiveness of economic and political measures targeted towards this sector.

The implementation of a clear, unified and transparent methodology of how to estimate the role of business and what exactly can be treated as a private sector in statistics would allow for a better understanding of the obstacles and barriers that the private sector is dealing with, as well as to help developing effective measures of business support. Until then, the official statistics should not stick to just one definition of the private sector. Instead, it can use all three abovementioned gradations, as a better reflection of the realities of Belarusian business.

References

  • Cuaresmo, J., Oberhofer, H., Vincelette, G. (2012).‘Firm Growth and Productivity in Belarus: New Empirical Evidence in the Machine Building Industry’, World Bank, Policy Research Working Paper No. 6005.
  • ‘Business Environment in Belarus 2013.Survey of Commercial Enterprises and Individual Entrepreneurs’, IFC, Report.

Meeting Qualification Mismatch with Vocational Training

20141020 Meeting Qualification Mismatch with Vocational Training Image 01

While in an ideal world the qualification preferences of job seekers and employers would coincide, in reality this is often not the case. Besides informational asymmetries (job seekers not knowing which qualifications are demanded by employers) the reason is that employers may be in need of qualifications that are not considered attractive by the job seekers. In the country of Georgia, we want to address this problem through a “recommendation system” which will suggest vocational training to job seekers. There are two main problems to be tackled in this project: (1) How can we decide what would be the most useful qualification for a given job seeker, and (2) how can we incentivize the job seekers to follow our recommendations? This policy brief discusses our approach to this problem.

Introduction

Qualification mismatches are common in many labor markets around the world (see for example, Ghignoni and Verashchagina (2014) for Europe, McGuinness and Sloane (2011) for the UK, and Béduwé and Giret (2011) for France). It is well known that qualification mismatch is a relevant problem also in the country of Georgia, as was shown in various studies (see ISET (2012) and The World Bank (2013)).

The ISET Policy Institute (ISET-PI) was commissioned by the World Bank to assist the Social Service Agency (SSA) of Georgia, an agency of the Ministry of Labor, Health, and Social Affairs, in developing a system which will recommend vocational training to job seekers with the aim to reduce the qualification mismatch in Georgia.

Job Seekers’ Preferences Matter

Vocational training addresses the needs of two different groups. It is demanded by job seekers, who want to improve their human capital in a way that matches their preferences and, in the optimal case, maximizes their chances to get back into employment. At the same time, vocational training also addresses the needs of employers, whose businesses may face shortages in qualified personnel.

It is not enough to only include employers in the analysis if one wants to effectively fight the qualification mismatch. If one does not consider job seeker’s preferences, it may happen that people prefer to not participate in the vocational training system at all. Even if one can effectively incentivize job seekers to attend training programs, as is the case in Germany for example, where the refusal to participate in training is sanctioned by a reduction of unemployment benefits (cf. Neubäumer (2012)), it is likely that involuntary training will be less effective. Therefore, it is problematic that most studies which analyze the demand for qualifications in the job market, for example for the European Union (Lettmayr and Nehls (2012)), New Zealand (Earle (2008)), and Australia (Shah (2010)), exclusively focus on employers and neglect the preferences of the people who are to be trained. In Georgia, we will do it differently.

Why Would Job Seekers Follow Our Recommendations?

The objective of the recommendation system we develop is to maximize the impact the training has on the employment chances of the job seeker. Arguably, this is also the primary goal for most job seekers, as they often state that they want to receive training in an “employable” profession. Therefore, if the purpose of the recommendation system is communicated properly, and if it is transparent and trustworthy, the job seekers may want to voluntarily follow its advice.

Recommendation System vs. Matching Algorithm

One can think of two different ways of advising job seekers in their training choices: recommendation systems and matching algorithms.

Recommendation systems make suggestions to job seekers separately. These kinds of systems are ubiquitous on the Internet. For example, Amazon.com proposes books to its customers based on their purchasing history. In a similar way, a recommendation system for vocational training would suggest vocational training programs to job seekers based on relevant data about their characteristics and the job market situation. Yet its major shortcoming is that a recommendation system will not take into account what other job seekers do and what recommendations were given to them.

For that reason, in a recommendation system, it can happen that the number of people recommended to choose a certain program is larger than that program’s capacity (because the advice comes as a ranking, this does not cause the system to be useless, as the job seeker may then choose the program which is highest in the ranking and which has free places).

Likewise, if many job seekers follow the advice of the recommendation system, oversupply and undersupply of certain qualifications in the job market is not ruled out. This is again due to the fact that recommendations are made separately. If there is a huge demand for, say, plumbers, and many people receive the advice to receive training in plumbing, this may subsequently cause an oversupply of plumbers.

In contrast, a matching algorithm aims at an overall optimum for the whole group of job seekers. Genuine matching algorithms do not make separate recommendations, but propose a globally optimal assignment. In Western countries they are used, for example, to match interns to hospitals, students to universities, and kidneys to dialysis patients. Matching theory is one of the most successfully applied subfields of game theory, acknowledged through the award of the Economics Nobel Prize of 2012 to matching theorist Alvin E. Roth. The standard survey of matching theory is Roth and Sotomayor (1990).

In a matching algorithm, the abovementioned problems of a recommendation system would not occur (up to statistical uncertainty), because the matching algorithm would take into account how the suggestions made by the system affect the demand for a program. It would aim to keep the number of people, likely to choose a program, to remain below its capacity.

While a matching algorithm is more ambitious, it also has disadvantages compared to a simple recommendation system. First of all, the data requirements are higher, as the capacities of programs have to be taken into account. More importantly, in a matching algorithm the recommendations will be generated in a way that is not transparent to the job seeker (though it is possible to give some general explanations). This may reduce acceptance and willingness to participate. The recommendation system, on the other hand, can work in a relatively transparent way. Finally, a recommendation system can be adjusted and changed on an ongoing basis by Social Service Agency personnel without the help of external experts. Given its complexity, this is hardly possible with a matching algorithm.

Therefore, it was decided that the simpler option of a recommendation system is to be pursued. Later, the system may be upgraded to a full-blown matching algorithm.

The Technical Aspects of How Recommendations are made

Consider the situation of a job seeker looking for vocational training. Through the envisioned system, they will receive a recommendation of which qualification to pick in the vocational training system of the SSA.

The pieces of information used for making this recommendation are personal characteristics of the job seeker (like age, gender, preferences, skills, and other information obtained through the website worknet.ge which is operated by the SSA) and the current and future economic situation in different sectors. To this end, we will use value added tax data that can be decomposed into 45 sectors and updated on a monthly basis. For forecasts, we will draw on the Business Confidence Index of ISET, which allows decomposition into 5 sectors.

Given the information about the job seeker and the economic environment in different sectors, we will answer the question: “How many months do we expect the job seeker to be unemployed in the year after the training if the training was in qualification X?” Here, X can be whatever is offered in the vocational training system at the location of the job seeker, for example welder, mechanic, accountant, or IT expert. Alternatively, we could answer the question: “What is the salary we expect the job seeker to have in the year after the training if the training was in qualification X?”

The recommendation made to the job seeker will be: “Choose the training in field X if somebody with your personal characteristics, given the economic situation and outlook, has the lowest expected number of unemployed months (or the highest salary) in X in the year after training in X was received.” This recommendation is likely to be accepted by the job seeker if also the job seeker wants to maximize their employment chances (or maximize salary).

The forecast can be made using econometric regression analysis. Let i be a job seeker and xi be the number of months unemployed in the year after training was received. Then we have for each qualification one estimation equation

FPB_Oct20_fig1where alpha is the intercept and the betas are the coefficients for different personal and economic characteristics. When the alpha and beta coefficients are known, then one can enter the specific data for a job seeker and forecast how long it would take him to find a job if training would be received in a particular field.

For estimating the coefficients, no recommendations will be made for some time (like 3 months) after the system is launched and only information will be collected. The SSA or a specialized survey agency will call the job seekers every month after they received training and ask whether they found employment. Job seekers who received training through the SSA will be obliged to answer this question truthfully. Information about the characteristics of the job seeker is known through their participation in the worknet.ge system, which is a requirement for anybody who wants to receive vocational training through the SSA.

When the recommendation phase starts, further data will be collected. Errors in the estimation of the coefficients will be corrected “automatically” through the feedback (in terms of job market performance of the trainees) that the system gets on an ongoing basis. To increase this effect, the database used for the estimation of the coefficients will be “rolling”, i.e. people who recently received training will be added while those who received training a longer time ago (e.g. one year or more) will be removed from the database.

Conclusion

In Georgia, ISET will design and implement a recommendation system for vocational training, addressing the qualification mismatch in the labor market. As in many other areas, Georgia is willing to go for innovative policy solutions making use of advanced economic methods, very much in line with the country’s reputation as one of the top reformers in the world.

References

  • Béduwé, Catherine and Giret, Jean-Francois (2011): “Mismatch of vocational graduates: What penalty on French labour market?”, Journal of Vocational Behavior 78, pp. 68-79
  • Earle, David (2008): “Advanced trade, technical and professional qualifications: Matching supply to demand”, New Zealand Government Ministry of Education, Auckland.
  • Ghignoni, Emanuela and Verashchagina, Alina (2014): “Educational qualifications mismatch in Europe. Is it demand or supply driven?”, Journal of Comparative Economics, in press
  • ISET (2012): “National Competitiveness Report for Georgia”, Tbilisi.
  • Lettmayr, Christian F. and Nehls, Hermann (2012): “Skills supply and demand in Europe: Methodological framework”, CEDEFOP Working Paper No. 25
  • McGuinnes, Seamus and Sloane, Peter J. (2011): “Labour market mismatch among UK graduates: An analysis using REFLEX data”, Economics of Education Review 30, pp. 139-145
  • Neubäumer, Renate (2012): “Bringing the unemployed back to work in Germany: training programs or wage subsidies?”, International Journal of Manpower 33, pp. 159 – 177
  • Roth, Alvin E. and Sotomayor, Marilda (1990): “Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis”, Econometric Society
  • Shah, Chandra (2010): “Demand for qualifications and the future labour market in Australia 2010 to 2025”, Center for the Economics of Education and Training Working Paper, Monash University
  • The World Bank (2013): “Georgia: Skills Mismatch and Unemployment Labor Market Challenges”, World Bank Report No. 72824-GE

Is Cutting Russian Gas Imports Too Costly For The EU?

20140608 FREE Network Policy Brief

This brief addresses the economic costs of a potential Russian gas sanction considered by the EU. We discuss different replacement alternatives for Russian gas, and argue that complete banning is currently unrealistic. In turn, a partial reduction of Russian gas imports may lead to a loss of the EU bargaining power vis-à-vis Russia. We conclude that instead of cutting Russian gas imports, the EU should put an increasing effort towards building a unified EU-wide energy policy.

Soon after Russia stepped in Crimea, the question of whether and how the European Union could react to this event has been in the focus of political discussions. So far, the EU has mostly implemented sanctions on selected Russian and Ukrainian politicians, freezing their European assets and prohibiting their entry into the EU, but broader economic sanctions are intensively debated.

One such sanction high on the political agenda is an EU-wide ban on imports of Russian gas. Such a ban is often seen as one of the potentially most effective economic sanctions. Indeed the EU buys more than half of total Russian gas exports (BP 2013), and gas export revenues constitute around one fifth of the Russian federal budget (RossBusinessConsulting,2012 and our calculations). Thus, by banning Russian gas the EU may indeed be able to exert strong economics pressure on Russia.

However, the feasibility of such sanction is questionable. Indeed, in 2012 Russia supplied around 110 bcm of natural gas to EU-28 (Eurostat), which constitutes 22.5% of total EU gas consumption. There are a number of alternatives to replace Russian gas, such as an increase in domestic production by investing in shale gas, or switching to other energy sources, such as nuclear, coal or renewables. However, many of the above alternatives, e.g. shale gas or nuclear power, involve large and time-consuming investments, and thus cannot be used in the short run (say, within a year). Others, such as wind energy, are subject to intermittency problem, which again requires investments into a backup technology. The list of alternatives implementable within a short horizon is effectively down to replacing Russian gas by gas from other sources and/or switching to coal for electricity generation. Below, we argue that even if such a replacement is feasible, it is likely to be very costly for the EU, both economically and environmentally.

Notice that any replacement option will be automatically associated with a significant increase in economic costs. This is due to the fact that a substantial part of Russian gas exports to Europe (e.g., according to Financial Times, 2014 – up to 75%) are done under long-term “take-or-pay” contracts. These contracts assume that the customer shall pay for the gas even if it does not consume it. In other words, by switching away from Russian gas, the EU would not only incur the costs of replacing it, but also incur high financial or legal (or both) costs of terminating the existing contracts with Russia, with the latter estimated to be around USD 50 billion (Chazan and Crooks, Financial Times, 2014).

Due to this contract clause, own costs of replacement alternatives become of crucial importance. The coal alternative is currently relatively cheap. However, a massive use of coal for power generation is associated with a strong environmental damage and is definitely not in line with the EU green policy.

What about the cost of reverting to alternative sources of gas? First, in utilizing this option, the EU is bound to rely on external and potentially new gas suppliers. Indeed, the estimates of potential contribution within the EU – by its largest gas producer, the Netherlands – are in the range of additional 20 bcm (here and below see Zachmann 2014 and Economist 2014). Another 15-25 bcm can be supplied by current external gas suppliers: some 10-20 bcm from Norway, and 5 bcm from Algeria and Libya. This volume is not sufficient for replacement, and is not likely to be cheaper than Russian gas.

This implies that the majority of the missing gas would need to be replaced through purchases of Liquefied Natural Gas (LNG) on the world market, in particular, from the US. This option may first look very appealing. Indeed, the current gas price at Henry Hub, the main US natural gas distribution hub, is 4.68 USD/mmBTU (IMF Commodity Statistics, 2014). Even with the costs of liquefaction, transport and gasification – which are estimated to be around 4.7 USD/mmBTU (Henderson 2012) – this is way lower than the current price of Russian gas at the German border (10.79 USD/mmBTU, IMF).

However, this option is not going to be cheap. A substantial increase in the demand for LNG is likely to lead to an LNG price hike. Notice that, at the abovementioned prices, US LNG starts losing its competitive edge in Europe already at a 15% price increase. Just for a very rough comparison, the 2011 Fukushima disaster lead to 18% LNG price increase in Japan in one month after disaster. Some experts are expecting the price of LNG in Europe to rise as much as two times in these circumstances (Shiryaevskaya and Strzelecki, Bloomberg, 2014).

Moreover, it is not very likely that there will be sufficient supply of LNG, even at increased prices. For example, in the US, which is the main ”hope” provider of LNG replacement for Russian gas, only one out of more than 20 liquefaction projects currently has full regulatory approval for imports to the EU. This project, Cheniere Energy’s Sabine Pass LNG terminal, is planned to start export operations no earlier than in the 4th quarter of 2015 with a capacity of just above 12bcma (World LNG Report, 2013). Of course, there are other US and Canada gas liquefaction projects currently undergoing regulatory approval process, but none of them is going to be exporting in the next year or two. Another potential complication is that two thirds of the world LNG trade is covered by long-term oil-linked contracts (World LNG Report, 2014), which significantly restricts the flexibility of short-term supply reaction, contributing to a price increase. All in all, LNG is unlikely to be a magical solution for Russian gas replacement.

All of the above discussion suggests that it may be prohibitively expensive for the EU to do completely without Russian gas. Maybe the adequate solution is partial? That is, shall the EU cut down on its imports of natural gas from Russia, by, say, a half, instead of completely eliminating it?

On one hand, this may indeed lower the costs outlined above, such as part of take-or-pay contract fines, or costs associated with an LNG price increase. On the other hand, cutting down on Russian gas imports may lead to an important additional problem, loss of buyer power by the EU.

Indeed, the dependence on the gas deal is currently mutual – as outlined above, not only Russian gas is important for the EU energy portfolio; the EU also represents the largest (external) consumer of Russian gas, with its 55% share of the total Russian gas exports. In other words, the EU as a whole possesses a substantial market power in gas trade between Russia and the EU, and this buyer power could be and should be exercised to achieve certain concessions, such as advantageous terms of trade from the seller etc.

However, the ability to have buyer power and to exercise it depends crucially on whether the EU acts as a whole to exercise a credible pressure on Russia. That is, the EU Member States may be much better off by coordinating their energy policies rather than diluting the EU buyer power by diversifying gas supply away from Russia. This coordination may be a challenge given the Member States’ different energy profiles and environmental concerns. Also, such coordination requires a stronger internal energy market that will allow for better flow of the gas between the Member States. While demanding any of these measures would be double beneficial: they will improve the internal gas market’s efficiency, and at the same time reinforce the EU’s buyer power vis-à-vis Russia.

To sum up, the EU completely banning Russian gas imports does not seem a feasible option in the short run. In turn, half-measures are not necessarily better due to the loss of the EU’s buyer power. Thereby, the best short-term reaction by the EU may be to put the effort into working up a strong unified energy policy, and to place “gas at the very back end of the sanctions list” for Russia as suggested by the EU energy chief Gunther Oettinger (quoted by Shiryaevskaya and Almeida, Bloomberg, 2014).

 

References

More Commitment is Needed to Improve Efficiency in EU Fiscal Spending

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The member states of the European Union coordinate on many policy areas. The joint implementation of public good type projects, however, has stalled. Centralized fiscal spending in the European Union remains small and there exists an overwhelming perception that the available funds are inefficiently allocated. Too little commitment, frequent rounds of renegotiation and unanimous decision rules can explain this pattern.

Currently, the EU allocates only about 0.4% of its aggregate GDP to centralized public goods spending (European Commission (2014)). This is surprising given the fiscal federalism literature’s classic predictions of efficiency gains from coordinated public goods provision (see for example Oates (1972) and the more recent contributions of Lockwood (2002) and Besley and Coate (2003) for a discussion). Yet, recent proposals to expand centralized fiscal spending in the EU have been met with skepticism if not outright rejection. The most frequently cited argument claims existing funds are already being allocated inefficiently and any expansion of centralized spending would turn the EU into a mere transfer union (Dellmuth and Stoffel (2012) provide a review).

Centralized fiscal spending in the EU is provided through the “Structural and Cohesion Funds”, which are part of the EU’s so called “Regional Policy” and were initially instituted in 1957 by the Treaty of Rome for the union to “develop and pursue its actions leading to the strengthening of its economic, social and territorial cohesion” (TFEU (1957), Article 174). At that point, the six founding members agreed it was important to “strengthen the unity of their economies and to ensure their harmonious development by reducing the difference existing between the various regions and the backwardness of the less favoured regions” (stated in the preamble of the same treaty). A reform in 1988 has further emphasized this goal by explicitly naming cohesion and convergence as the main objectives of regional policy in the EU.

Today, actual fiscal spending in the EU is far from achieving this goal. The initially agreed upon contribution schemes are often reduced by nation specific discounts and special provisions as the most recent budget negotiations for the 2014-2020 spending cycle showed yet again. Moreover, the perception is that available funds are being spent inefficiently (see for example Sala-i-Martin (1996) and Boldrin and Canova (2001)).

Figure 1. 2011 EU Structural and Cohesion Funds
Figure1

Figure 1 shows the national contributions to the structural funds as well as EU spending from that same budget in each member nation in per capita terms (data published by the European Commission). If fiscal spending was efficiently structured to achieve the above mentioned goal of convergence, one would observe a strong negative correlation between contributions and spending. The data shows, however, that while some redistribution is clearly implemented, rich nations still receive large amounts of the funds meant to alleviate inequality in the union (see Swidlicki et al. (2012) for a detailed analysis of this pattern for the contributions to and spending of structural funds in the UK).

What prevents a group of sovereign nations from effectively conducting the basic fiscal task of raising and allocating a budget to achieve an agreed upon common goal? In a recent paper, we theoretically examine the structure of the bargaining and allocation process employed by the EU (Simon and Valasek (2013)). Our analysis suggests that efficiency both in terms of raising contributions and allocating fiscal spending cannot be expected under the current institutional setting. While poorly performing local governments, low human capital in recipient regions, and corruption might all play a role in creating inefficiency (see for example Pisani-Ferry et al. (2011) for a discussion of the Greek case), improving upon those will only solve part of the problem.

We demonstrate that the inefficiency of EU spending in promoting the goal of convergence can be explained by the underlying institutional structure of the EU, where sovereign nations bargain over outcomes in the shadow of veto. Specifically, we model the outcome of the frequent negotiation rounds employed by the EU as the so-called Nash bargaining solution, explicitly taking into account the possibility for each member nation to veto and to withdraw its contribution (as the UK threatened in the most recent budget negotiations). It turns out that it is precisely the combination of voluntary participation, unanimity decision rule and the lack of a binding commitment to contribute to the joint budget that generally prevents efficient fiscal spending. In such a supranational setting, the distribution of relative bargaining power arises endogenously from countries’ contributions and their preferences over different joint projects. This creates a link between contributions to and allocation of the budget that is absent in federations, where contributions to the federal budget cannot simply be withdrawn and spending vetoed. Since the EU members lack such commitment, this link will necessarily lead to an inefficient outcome.

Why Does the EU Have These Institutions?

If the currently employed bargaining process cannot lead to an efficient outcome, why then did the EU member nations not institute a different allocation process right from the start? Of course, agreeing on a binding contract without the possibility for individual veto is politically difficult. More complicated bargaining processes may also be much more costly in terms of administration than is relying on informal negotiations and mutual agreement. Our analysis suggests another alternative: If the potential members of the union are homogeneous with respect to their income and the social usefulness (or spillovers) of the projects they propose to be implemented in the union, then Nash bargaining will actually lead to the budget being raised and allocated efficiently. The intuition behind this result is simple: If all countries have the same endowment, their opportunity costs of contributing to the joint budget are the same. Moreover, symmetric spillovers do not give one country a higher incentive to participate in the union than the other. Consequently, all countries have the exact same bargaining position. Thus, equilibrium in the bargaining game must produce equal surpluses for all nations. At the same time, with incomes and spillovers perfectly symmetric, the efficient allocation also produces the same surplus for each nation, so that it coincides with the Nash bargaining solution. It is important to notice, though, that symmetric income and spillovers do not imply homogeneous preferences: Each nation can still prefer its “own” project to the others. Instead, symmetry leads to a perfectly uniform distribution of bargaining power in equilibrium. Moreover, our analysis shows that efficiency is achieved if the union budget is small relative to domestic consumption and member countries have similar incomes.

This resonates well with the history of the European Union. In fact, the disparities between the founding members were not large, so that the current bargaining institutions could reasonably have been expected to yield efficiency. Only the inclusion of Greece, Ireland, Portugal and Spain created a more economically diverse community (European Movement (2010)). Our model shows that as the asymmetries between member countries or the importance of the union relative to domestic consumption grow, Nash bargaining leads to increasingly inefficient outcomes. Figure 2 shows this effect for a union of two nations. Keeping aggregate income constant and assuming symmetric spillovers between the two nations’ preferred projects, we vary asymmetry in their domestic incomes. The graphs show the Nash bargaining outcome (marked with superscript NB) compared to the generally efficient solution. As country A’s income increases, so does its outside option (i.e. all else equal, the higher the income, the less a country would lose if the joint projects were not implemented). Thus, country A’s bargaining position relative to country B increases in equilibrium, leading to an inefficient outcome. The allocation of funds to the union projects (upper right panel) depicts this channel very clearly: While the efficient allocation is independent from the distribution of national incomes, the Nash bargaining solution reflects the changing distribution of power. Nation A is able to tilt the allocation more toward its own preferred project the higher its income. Moreover, it is able to negotiate a “discount” for its contribution. While its contribution (labeled xa) does increase with its income (labeled ya), country A still pays less than would be budgetary efficient given its higher income (upper left panel). As a result of the inefficiencies introduced by the bargaining process, aggregate welfare in the union declines as asymmetry grows. Again, it is worth noting, that the loss in aggregate welfare is relatively small when asymmetry is small, but grows more than proportionally as the countries become more and more unequal (lower right panel).

Figure 2. The Effect of a Union of Two Countries

Figure2

This has troubling implications for the EU, as income asymmetry has increased with every subsequent round of expansion while the bargaining procedure for the fiscal funds has essentially stayed the same. It is not surprising then that a larger and more asymmetric EU has resulted in supranational spending that is increasingly inefficient.

The EU as a “Transfer Union”

We go on to show that the level of redistribution inherent in the Nash bargaining solution depends crucially on the overall size of the budget the union intends to raise. Increasing the EU’s budget for centralized fiscal spending would indeed lead to more “transfers” to low income members (in terms of net contributions), bringing the EU closer to the original goal of convergence. In fact, the EU could pick a budget such that inequality in terms of total welfare between member nations is completely alleviated. Such an outcome necessarily implies that the net gain from being part of the union for high-income nations is lower (albeit still positive) than for low-income members. However, this in turn has consequences for the endogenous distribution of bargaining power: Richer nations would be able to assert even more power and push even further for their own preferred projects, rendering the allocation of funds across projects less efficient. This trade-off between equality and efficiency implies that complete convergence is not necessarily socially desirable.

Arguably, this trade-off might be more important for a transition period than in the long run. If fiscal spending does not only lead to convergence in instantaneous welfare, but also has a positive effect on long-run performance and GDP growth, income asymmetries across countries will decrease even if the allocation of spending across projects is not entirely efficient. Less inequality in turn will lead to a more efficient allocation process in the future and endogenously reduce the level of necessary transfers. However, whether the growth effect of the EU’s structural funds is indeed positive remains a much-debated empirical question (see for example Becker et al. (2012)).

Institutions Fit for a Diverse Union

As the EU has expanded from the original six nations to the current 27, there has been a concurrent evolution of decision-making rules. A qualified majority rule is now used in many areas of competency. We show that the allocation of fiscal spending could also benefit from the implementation of a majority rule. Efficiency would be improved as long as the low-income member nations endogenously select into the majority coalition while their contributions to the budget remain relatively low. In connection to this, the EU might benefit from enforcing rules specifying contributions as a function of national income (such rules exist, but are easily and often circumvented), forcing wealthier member nations to pay more. An exogenous tax rule without the possibility to negotiate a discount, for example, may indeed improve overall efficiency.

It is important to note, however, that a unanimous approval of such a change is unlikely. The institutional mechanism of Nash bargaining is an “absorbing state” after the constitution stage, in the sense that not all member nations can be made better off by switching to an alternative institution. Therefore, the discussion of alternative institutions and decision making processes is particularly relevant when considering new mechanisms that increase fiscal spending at the union level, such as the proposed EU growth pact. If the same bargaining process remains to be employed even for new initiatives, even though a majority rule is preferable and implementable relative to the status quo, the opportunity for the EU to achieve efficiency in its fiscal spending is lost.

References

  • Becker, S. O., Egger, P and von Ehrlich, M (2012) “Too Much of a Good Thing? On the Growth Effects of the EU’s Regional Policy”, European Economic Review 56: 648 – 668
  • Besley, T. and Coate, S. (2003) “Centralized versus Decentralized Provision of Local Public Goods: A Political Economy Approach” Journal of Public Economics 87: 2611 – 2637
  • Boldrin, M and Canova, F (2001) ”Europé’s Regions – Income DIsparities and Regional Policies” Economic Policy 32: 207 – 253
  • Delmuth, L.M. and Stoffel, M.F. (2012) “Distributive Politics and Intergovernmental Transfers: The Local Allocation of European Structural Funds” European Union Politics 13: 413 – 433
  • European Commission (2014) Data available at http://ec.europa.eu/regional_policy/what/future/index_en.cfm
  • European Movement (2010) “The EU’s Structural and Cohesion Funds” Expert Briefing, available at http://www.euromove.org.uk/index.php?id=13933
  • Lockwood, B. (2002) “Distributive Politics and the Cost of Centralization” The Review of Economic Studies 69: 313 – 337
  • Oates, W.E. (1972) “Fiscal Federalism” Harcourt-Brace, New York
  • Pisani-Ferry, J., Marzinotto, B. and Wolff, G. B. (2011) “How European Funds can Help Greece Grow” Financial Times, 28 July 2011.
  • Sala-i-Martin, X (1996) ”Regional Cohesion: Evidence and Theories of Regional Growth and Convergence”, European Economic Review 40: 1325 – 1352
  • Simon, J. and Valasek, J.M. (2013) “Centralized Fiscal Spending by Supranational Unions” CESifo Working Paper No. 4321.
  • Swidlicki, P., Ruparel, R., Persson, M. and Howarth, C. (2012) “Off Target: The Case for Bringing Regional Policy Back Home” Open Europe, London.
  • TFEU (1957) “Treaty Establishing the European Community (Consolidated Version)”, Rome Treaty, 25 March 1957, available at: http://www.refworld.org/docid/3ae6b39c0.html

Skill Structure of Demand for Migrants in Russia: Evidence from Administrative Data

20171022 Rewarding Whistleblowers to Fight Corruption Image 02

Authors: Simon Commander (IE Business School, EBRD and Altura Partners) and Irina Denisova (CEFIR, NES).

Using Russian Ministry of Labor administrative data for all legal migrant applications in 2010 and matching the migrant to the sponsoring firm, we find that there is some – albeit limited – evidence of firms using migrants to address high skill shortages. However, the overwhelming majority of migrants are skilled or unskilled workers rather than qualified professionals; a reflection of the low underlying rates of innovation and associated demand for high skill jobs.

Migration policy continues to be a priority in Russian economic policy. This is driven both by a demand for labor – given the unfavorable demographic trends of the last decades – and the easily available supply from the CIS countries. It is still not clear, however, what is the skills structure of the demand for migrants. Relatively new administrative data on demanded permissions to employ migrants sheds however some light on the issue.

In particular, we use the 2010 nationwide dataset ‘Job positions filled by migrants’ published by the Russia Federal Employment Service. The dataset gives detailed information on the applications for permits for migrants, including the 4-digit occupation, firm ID and the offered wage. The Federal Employment Service’s role is to approve or reject an application. In almost all cases documented in this dataset, approval was granted. Moreover, in 99% of the cases, the duration of the permitted contract was one year.

The data allow us to study the skill composition of demand for migrants from the legal sector, with the sizeable illegal labor migration staying beyond the scope of the study. The total number of applications for all of Russia in 2010 was just over 890,000, of which nearly 250,000 or 28% originated from firms in Moscow. The analysis below uses the permission data for the 21 most developed Russian regions (a full version of the paper is available as Commander and Denisova, 2012).

A breakdown of the number of requests in 2010 by skill type using the one-digit ISCO-88 classification (Managers, High-level professionals, Mid-level professionals, Service worker, Skilled agricultural workers, Craft and trades workers, Plant and machine operators, Unskilled workers) shows that over 70% of the requests were for skilled and unskilled workers. At the same time, about 17% of the total migration requests were for higher-level professionals (7%) and managers (10%). Among managers, nearly nine out of ten requests were for production or department managers with no more than 12% of managerial migration requests being for top-level executives. Among the category of high-level professionals, architects and engineers accounted for over two-fifths of requests.

Is the situation any different in the main urban labor markets? In Moscow a lower proportion – around two thirds of the migrant applications – were for skilled and unskilled workers. The starkest difference was that professionals working in IT accounted for a minute share of total high-level skill applications in Russia, but nearly 9% in Moscow. Thus, while there are some differences in the migration profile between Moscow and the rest of the country, the broad picture that emerges is one where migration policy and practice seem to be responding mainly to the apparent bottlenecks at the lower-skill end of the labor market.

Legal requests for migrants are massively dominated by requests concerning low-skill groups; and illegal migrants, as shown by anecdotal evidence, are mainly low skilled. At the same time, there is a sizeable demand for qualified migrants, managers and professionals. There are two potential motives to issuing a request for a qualified migrant: to economize on the costs of labor by substituting a local laborer with a migrant; or to fill in the gap of the scarce qualification/skills hardly available domestically. The two motives could be distinguished by looking at the wage offers associated with the posted positions and comparing them with wages paid in comparable occupations in the same region. The aim of the exercise is to see – particularly within the categories of higher-skilled applicants – whether they command any wage premium that might reflect their scarcity value.

Figures 1-2 plot the reported (relative) wage offers for two migrant skill categories: Department Managers (ISCO code=122) and Computing Professionals (ISCO code=213). The figures depict distributions of relative (to the region average) wage in logs, thus implying that the points around 0 are the wage offers at the level of regional average, above 0 means positive wage premium, and below 0 means negative wage premiums (economizing on the costs). Each figure also gives the mean search wage from the EBRD survey of recruiting agencies in 2010 (relative to the regional average).

Figure 1. Relative Wage Distribution, Production and Operation Department Managers (ISCO-88 Code: 122)
Denisova1
Source: Authors’ calculations based on Rostrud 2010

It is clear from Figure 1 that the wage offers for migrants do not identify any clear positive selection effect, in that migrants’ wages mostly fall below the survey search mean comparators. In the majority of cases, the offered wages also fall below the regional average wage thus implying that the motive is to substitute for cheaper labor.

The demand for migrants with skills of IT professionals is more complicated: there are those who offer wages below regional average, but there is also a large group of those ready to pay a wage premium to attract migrants (with log wage above zero). The search through recruiting agencies (the survey wage) would still require offering higher wages.

Figure 2. Relative Wage Distribution, IT Professionals (ISCO-88 Code: 213)
Denisova2
Source: Authors’ calculations based on Rostrud 2010

For further analysis, the migration dataset was mapped to the ORBIS (a dataset assembled by Bureau van Dijk) firm observations using the unique national tax identification code (so called INN). The ORBIS data includes information on firms’ balance sheets and simple performance data such as output per employee.

When looking only at demand from firms that lie in the top 10-20% of the productivity distribution (productivity is calculated as output per worker in the narrowly defined industry), the picture looks somewhat different: wage offers tend to lie above the average (Figure 3). It is likely that the most productive firms tend to offer wages higher than both regional average for the occupation and the survey-based search wages. This implies that the scarcity of skills on the domestic labor market is one of the more important motives behind the demand for migrants from high-productivity firms.

 
Figure 3. Relative Wage Distribution, Production and Operation Department Managers (ISCO-88 Code: 122), 10% Most Productive Firms
Denisova3
Source: Authors’ calculations based on Rostrud 2010 and Orbis-Roslana

To control for other firm characteristics, we run regressions relating the relative wage of a migrant to a set of firm and region characteristics, including measures of size and ownership, a measure of recent growth in the region, as well as the level and change in foreign direct investment in a given region since 2007. We also control for the tightness of the local labor market, using a measure of search wages raised in the EBRD survey compared to average wages in a region. The estimates are run with and without region, industry and occupation controls. The results show that relatively high wages tend to be associated with large and/or foreign-owned firms. Growth in a region or the level of FDI per capita are not systematically associated with the relative wage once controls enter the regression, suggesting that the relative wage is largely determined by firm-level features. The measure of labor market tightness enters positively but is insignificant whencontrolling for industry, region and occupation.

Overall, the data from the Russian Ministry of Labor that documents all applications for migrants to Russia in 2010 and allows matching the migrant to the sponsoring firm, show that there is very limited evidence of firms using migrants to fill high-skill jobs. In fact, the overwhelming majority of migrants, skilled or unskilled workers, were mostly originating from other states of the CIS. Furthermore, most were hired at relatively low wages in comparison to the occupation/region averages or the wages reported in the EBRD survey of recruiting firms. At the same time, there is a sizeable portion of demand for skilled migrants, which are offered wage premiums. The demand originates mostly from highly productive firms. Migration policy should acknowledge these different motives behind the demand for migrants.

References

  • Simon Commander and Irina Denisova “Are Skills a Constraint on Firms? New Evidence from Russia”, IZA Discussion Paper No. 7041, November 2012

The Arab Spring Logic of the Ukrainian Revolution

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Motivated by the unusual patterns and dynamics of the Arab Spring, we construct a model explaining the vulnerability of the newly established incumbent to popular unrest. Using this model for the case of similar protests in Ukraine, we find that the current combination of availability of information, military capacity of the incumbent and his radicalization, together with the opportunity costs of participation in a protest, are likely to result in the formation of new government that is also vulnerable to popular protests. The persistence of the protests after the formation of a temporal government in Ukraine supports this hypothesis. Additionally, as the policy position of Viktor Yanukovych was relatively mild, his potential successor might be more radical. Exponential growth of social media users, reduction of military capacity, relatively high unemployment and the possible radicalization of the Ukrainian President might put the country into an “instability zone” with recurrent protests.

On the night of 21 November 2013 spontaneous protests erupted in Kiev, the capital of Ukraine, after the Ukrainian government suspended preparations for signing an Association Agreement and a Free Trade Agreement with the European Union in favor of agreements with Russia. The movement concentrated on Independence Square (Maidan Naseljenosti) soon took on the name “Euromaidan”. Soon the protest spread to other cities in the country. The initial agenda of closer relations with the EU was soon encompassed in the wider protest against Viktor Yanukovych, elected President in 2010. He fled the country on February 21 under the pressure of popular protests, exacerbating the leadership crisis. Temporary leadership was taken up by the Speaker of the Supreme Rada – Oleksander Turchinov, while new elections were scheduled to take place on May 25.

Despite the successful removal of Victor Yanukovich from power and a promise of new elections in May, the protests on Maidan did not cease. The major factor of uncertainty comes from the very nature of the protests. For many months, it ran without organizers or formal leadership so that the future course of action remains unclear. It is hard to comply with the demands of Maidan, as no clear set of demands are formulated. Though five figures of Maidan: Tymoshenko, Klitschko, Tyagnybok, Yatsenuk and Yarosh remain the most visible, none of them has sufficient support of Maidan. Whichever course prevails – resumed Eurointegration or an alliance with Russia (which became a less likely option)– the number of people who oppose the new course is likely to be enough to fill a new Maidan.

The swift happenings in Maidan are highly reminiscent of the events of the Arab Spring at its crux: it also was a leaderless protest, coordinated mainly with social media, and encompasses people of vastly different socio-economic, political and demographic characteristics.

Using social media technologies, Euromaidan has created an interactive map of logistics (http://maydanneeds.com/) that provides detailed information on and locations of where to eat, makeshift hospitals, information booths, and the barricades. Clicking on the icons of the map, one discovers not only the locations of the facilities but also their needs, which enables coordination of protesters’ efforts to contribute to the common cause. However, just as in case of Tahrir Square or the Tunisian unrest, the common cause is poorly defined: aside from dissatisfaction with Viktor Yanukovych, the protesters exhibited very different preferences for the future course of action, and the three most prominent figures of the protest – Klitschko, Tyagnybok and Yatsenuk – were shunned as they spoke about the common agenda.

The aftermath of the Arab Spring remains unclear for both protesters and the world. The Syrian social unrest has resulted in ongoing violent conflict, while Libyan society still experiences serious problems with the formation of a new government after the murder of Kaddafi and the end of civil war. Tunisia and Egypt were able to choose new Presidents and form new governments. The latter were themselves dismissed soon after they came to power: the first elected post-Mubarak government collapsed in mid-2013 after a year of almost uninterrupted protests. These two cases are especially interesting as constitutional exits of leaders who were in autocratic office for less than one year were generally caused by coups and not protests between 1945 and 2002 (Svolik, 2009).

Nevertheless, we can apply the knowledge acquired there to the new Ukrainian protest we observed on Maidan and try to predict its development by the means of stylized models suggested in Dagaev, Lamberova, Sobolev and Sonin (2013).

Our approach relies on four simple parameters that drive the dynamics of the protests. First, we consider the costs of collective action – the opportunity costs of spending time on Maidan. The second parameter is the military capacity of the incumbent that can be devoted to the suspension of the protest. The higher it is, the more numerous should the protest be to succeed. The third parameter we use is the degree of the radicalization of the incumbent (the difference between his position and the preferred policy of the majority of the population). Finally, we use an information availability parameter (how many people are aware of the place and time of the occurrence of the protest).

With the electric telegraph, a communication tool of the 19th century, information availability was low and many of those who would have been glad to pay the costs of collective actions to replace the incumbent stay at home as they are not aware of the protest taking place. With Facebook and Twitter, the availability of information is much higher. According to our findings, the crucial role in dynamics of contemporary mass actions is played by the ratio of military capacity to the information availability rather than their values per se.

Our framework assumes that each citizen’s decision of whether to participate in the protest or not is based on the difference between her position and the preferred policy of the incumbent. According to this decision, all citizens can be classified into two groups – those who participate in a protest against the incumbent, and those who do not. We define a person, who has the median position among the protesters, as the expected new incumbent. So if the elections were held among the protesters, he would receive the widest support. If the number of citizens participating in the protest is sufficient to overcome the military capacity of the current incumbent, the protest becomes successful, and the expected new incumbent of the protest becomes the new incumbent. The combination of military capacity, opportunity costs and costs of coordination determine the size of the stability zone – a segment of policy space where the incumbent is not vulnerable to mass protest.

The model allows us to predict the dynamics of the protests that is generated by different combinations of the parameters. For illustrative purposes, the availability of information about the protest is proxied by data on Facebook penetration and military capacity is described by the number of military personnel per capita in 2009 collected by the International Institute for Strategic Studies (Hackett, 2010). The incumbent policy position is proxied by the Legitimacy Index from Polity IV, where a higher index corresponds to lower legitimacy of the incumbent and his regime. Finally, the costs of participation in a protest are proxied by the employment rate. A recent study by Campante and Chor stresses unemployment as important determinants of opportunity costs of taking to the streets during Arab Spring (Campante & Chor, 2012). As unemployed individuals have fewer options of how to spend their time, one should expect that a substantial number of unemployed people corresponds to a relative ease of sparking unrest.

Using these parameters, we can explain success or failure of the protest, and predict some proprieties of its aftermath.

For example, high military capacity, opportunity costs and costs of coordination generate a broad stability zone, so that even a radical incumbent would not face a threat of revolution. The decline of any of three parameters can narrow the zone of stability and make the autocrat vulnerable to mass protest. As the incumbent is highly radical, a significant part of the population takes to the streets. As a result, the new incumbent’s position is sufficiently close to the one of the median voter and is, thus, inside the stability zone. An example of such a scenario is the overturn of Slobodan Milosevic after the fall of communism, when there were eight failed and one successful attempts to form a wide coalition of opposition parties (Spoerri, 2008). The process of finding a common ground started in 1990 with the emergence of the coalition of six parties, the Associated Opposition of Serbia, which broke shortly after a series of power struggles, policy disagreements, and personality clashes. It was only ten years later that the protest which facilitated Milosevic’s downfall took place, as the leader of the united opposition, the Democratic Opposition of Serbia, was able to ensure the non-involvement of the crucial military unit on behalf of Milosevic (Bujosevic & Radanovic, 2003).

In contrast, the events of the Arab Spring had different political dynamics. Low military capacity and high unemployment of Egypt and Tunisia determined a narrow stability zone ex ante. The absence of protests of these long-lived regimes can be explained by the relatively moderate position of the incumbent. In 2010, the Egyptian and Tunisian regimes had scores of 5 and 4 (out of 12) of Political Legitimacy from Polity IV, respectively. However the emergence of social media that enabled the users to coordinate their actions easily narrowed the relatively small stability zone. As the incumbent was less radical, fewer citizens benefit from its replacement and take to the streets. Thus, the new incumbent is defined by protesters, her policy position is more radical and now is out of the stability zone. The new incumbent immediately faces the new social unrest.

Table 1 presents the stylized results of our study. Locating the combination of parameters of the country in the table allows us to make the prediction about the dynamics of the protest. There are several possible courses of events: the protest can be weak and die out soon, with the incumbent staying in place; it can be significant, but yet not large enough to overthrow the incumbent; it can lead to the replacement of the incumbent, followed by the period of stability; and, finally, it can result in the replacement of the incumbent, but not cessation of the protest.

Table 1. Protest Outcome as a Function of Parameters
Table1

What do our findings tell us about Ukraine? The previously used proxy for the information index there is not a good choice, as the majority of users prefer the Russian version of Facebook – Vkontakte – as the major social network of the country. Thus, we rely on the Vkontakte penetration data (as of 2013, 18.5 million of people in Ukraine were using the network, constituting 40.6% of the population, see report of Ukranian IT-news agency AIN.UA: http://ain.ua/2013/11/28/503853).

The military parameter, that reduces the likelihood of successful protest, is low, compared to the countries of Arab Spring and constitutes 2.8 active military per 1000 people (see the Ukrainian law “Armed Forces of Ukraine for 2013”, http://w1.c1.rada.gov.ua/pls/zweb2/webproc34?id=&pf3511=49126&pf35401=283834), which is half of the one in Egypt at the beginning of the protests. The unemployment parameter fell to 8.6 during the incumbency of Victor Yanukovych, which corresponds to the pre-protest unemployment in Egypt in 2010.

The legitimacy measure presents a difficulty for comparison with the Arab Spring cases, as the Legitimacy Index has not yet been updated. However, the harsh actions of Victor Yanukovych during the 2013-2014 protests (including the suppression of the protest and the passage of laws denying freedom of assembly and freedom of expression, as well as the refusal to repeal his earlier changes of the constitution towards more presidential form of government) suggest that his legitimacy level fell by approximately 50% (and was about 20% right before he was ousted from power), which is corroborated by polls (“Ukraine’s future in peril under President Yanukovych”, The Washington Post, 2 December, 2013). Thus, the conservative estimate is that his legitimacy index shifted from 3 to 4 or 5, as shown on Figure 1.

For the purpose of comparison, we plot the Arab Spring countries and Ukraine in the space of our variables in Figure 1. The X-axis shows the incumbent’s departure from the median population-preferred policy (proxied by the Legitimacy Index from Polity IV). We use the value of the index in the year prior to the start of unrest, and the index value in 2010 for countries with no protests (Morocco, Oman, Djibouti). The Y-axis corresponds to the employment level. The size of the bubble corresponds to the ratio of military capacity and Facebook (or Vkontakte) penetration (data from the Arab Spring Social Media Report).

The shading of the bubble reflects the type that country belongs to: striped (no significant protest), light gray (continuing protest), and dark grey (multiple protests). Syria is excluded from the classification and is marked white, because of the civil war and international intervention.

Figure 1. Legitimacy index (X-axis), Employment (Y-axis), Military capacity / Social media penetration (size of the bubble) in Arab Spring countries and UkraineFigure 1

Figure 1 illustrates that countries appear in tight clusters in line with our theoretical predictions. The countries with continuing protests that did not lead to the downfall of the incumbent are divided into two groups. The first group (Kuwait, Jordan, Lebanon, and Bahrain) has relatively a moderate incumbent policy position and extremely high level of development of new media. The reasons why these countries are not «striped» is high military capacity of the government that could be employed against protesters, combined with high opportunity costs of protesting (low unemployment rates).

The second group of countries (Syria, Algeria, Iraq, Yemen, and Mauritania) has more radical incumbents and higher unemployment rates (so the incentives to protests are higher there), but is poor in terms of IT development. The ratio of military capacity and Facebook (Vkontakte) penetration is high, which is reflected by the size of bubbles that are much larger than in the first groups. That is why in a country with a small military capacity (such as Yemen), the protests did not lead to the incumbent’s replacement.

Two Arab Spring countries belong to the “multiple protests” group. Both Egypt and Tunisia had relatively mild incumbents in the pre-protest era, with Tunisia’s Bashar al-Assad being the milder of the two. Both countries had relatively high unemployment rates and wide Facebook coverage, both factors alleviating the problem of organizing a collective action. Despite the fact that before the start of the protests Facebook coverage in Egypt had close to average values among the countries of Arab Spring, they grew at exponential rates and Egypt attained leading positions in the region in usage of new media several months later. Moreover, low rates of military capacity made protest activity less risky in both Tunisia and Egypt. The remaining differences in Facebook coverage and employment rates in Egypt and Tunisia account for the different structure of recurrent protests, predicted by our model.

Comparison of the Arab Spring countries data with the Ukrainian case shows that high military capacity, new media penetration and unemployment generate even more narrow stability zones than one observes in the cases of Egypt and Tunisia. The reason why the incumbent was not vulnerable to mass protests can be explained by the political legitimacy of Yanukovych as the winner of relatively free elections in 2010.

But if the Arab Spring protests were triggered by rapid growth of new (cheap) communication technologies, the successful protest against Yanukovych can be explained by his radicalization. The radicalization took the form of parliamentary acts that put significant constraints on political rights and civil liberties and violent suppression of dissident actions.

Employing the proposed approach and contrasting the Ukrainian case with the countries of the Arab Spring allows us to draw several conclusions.

Firstly, the current combination of availability of information, military capacity of the incumbent and his radicalization, together with the opportunity costs of staying on Maidan, are likely to result in successful and recurrent protest. The persistence of the protests after the formation of a temporal government supports this hypothesis.
Secondly, it is worthwhile to note that as the policy position of Viktor Yanukovych was relatively mild, his potential successor might be more radical.

Thirdly, the exponential growth of social media, the reduction of military capacity and relatively high unemployment puts Ukraine into an “instability zone”. This implies that the 2004 scenario of the Orange Revolution is unlikely to repeat. The protest of 2004 resulted in a general election, and the elected president Viktor Yushchenko served his term without interruption. The protests of 2013 are more likely to result in a rapid change of incumbents and a period of instability.

One factor can strengthen the possible incumbent’s vulnerability. The external pressure of the Russian government reduces costs of collective resistance to the new Ukrainian authorities among pro-Russian citizens, while the promise of Western countries to support fast EU integration can incentivize politicians to accelerate reforms opposed by significant parts of the population.

References

  • Bujosevic, D., & Radanovic, I. (2003). The fall of Milosevic: the October 5th revolution. Palgrave Macmillan.
  • Campante, F. R., & Chor, D. (2012). Why was the Arab world poised for revolution? Schooling, economic opportunities, and the Arab Spring. The Journal of Economic Perspectives, 167–187.
  • Dagaev, D., Lamberova, N., Sobolev, A., & Sonin, K. (2013). Technological Foundations of Political Instability. Centre for Economic Policy Research Working Paper Series
  • Hackett, J. (2010). The Military Balance 2010: The Annual Assessment of Global Military Capabilities and Defense E conomics. London: The International Institute for Strategic Studies.
  • Spoerri, M. (2008). Uniting the opposition in the run – up to electoral revolution – Lessons from Serbia 1990 – 2000. Totalitarismus Und Demokratie, 5(2005), 67–85.
  • Svolik, M. (2009). Power sharing and leadership dynamics in authoritarian regimes. American Journal of Political Science, 53(2), 477–494