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Is Cutting Russian Gas Imports Too Costly For The EU?

Posted on June 9, 2014 | Policy Brief

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

Posted on May 26, 2014 | Policy Brief

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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.

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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

Posted on May 5, 2014 | Policy Brief

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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.

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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

Posted on March 31, 2014 | Policy Brief

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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

Liquidity and Monetary Policy in Belarus

Posted on March 24, 2014 | Policy Brief

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High inflation and devaluation expectations after the 2011 currency crisis force Belarusian monetary authorities to seek non-conventional policy measures. Instead of using the refinancing rate as an instrument on the money and credit markets, the National Bank of Belarus resorts to liquidity squeezes, which drive up the rouble interbank rates. The banks have to raise deposit and loan rates in response. As a result, households continue to keep savings in the national currency deposits, while firms struggle to keep up with the payments. This situation, however, will have to end soon.

Belarusian economy is characterized by state ownership domination and various (including political) constraints. This often makes it tempting for the Belarusian authorities to resort to untraditional policy measures, or use the conventional policies in unexpected ways. A good example is Belarusian monetary policy in 2012-2013. In 2011 Belarus experienced a severe currency crisis: the exchange rate of the Belarusian rouble (BYR) crumbled from 3011 BYR per USD in January 2011 to 8470 BYR in December 2011. Prices followed the currency and doubled: in 2011 the inflation rate was 108%. Due to high government influence on the labor market and competition from the Russian labor market, real incomes quickly recuperated (Bornukova, 2012). But the owners of the deposits in Belarusian roubles took a hit – their savings lost almost a half of real value. More and more people converted their deposits into USD or other foreign currency. Inflation and devaluation expectations were soaring (Kruk, 2012).

The National Bank of Belarus clearly realized that the proper response would be to increase the interest rates: this policy measure would partially compensate the losses of rouble deposit holders, make rouble deposits attractive again and curb the growth in lending, one of the major causes of the currency crisis.

However, there is a catch. Formally, the main monetary instrument of the National bank is the refinancing rate. Yet, despite the name, this is not the rate at which the National bank is refinancing the commercial banks. Officially, it is only a “basis for setting interest rates on the operations involving liquidity provision to banks”. The problem is that most of the floating rates, especially those on concessional loans, have the refinancing rate as its basis rate. Very high refinancing rate would hurt debt-financed organizations, in particular in agriculture and construction. And the National bank found a compromise: the refinancing rate would remain relatively low; but the National bank would regulate the money market through liquidity squeezes: it would offer liquidity to the commercial banks only at a much higher collateral loan and overnight rates. The lack of liquidity due to a squeeze would drive up the interest rates on the interbank market.

Figure 1: Main interest rates in Belarus in 2012-2014
Figure1
Source: The National Bank of the Republic of Belarus.
 

Figure 1 shows the main interest rates in Belarus in 2012-2014. The refinancing rate was steadily decreasing throughout the whole period. The overnight rate (which moves together with the collateral loan rate), also set by the National bank, for some period was almost two times higher than the refinancing rate, reaching 70 percent  at  its peak. The overnight rates mostly exceeded the rate set in the interbank market. The interbank rate reflects the market price of liquidity. The National bank influences this rate by offering (or not) liquidity to the state-owned commercial banks.

The National bank has successfully used liquidity squeezes as an instrument of stabilization on the currency market. As Figure 2 shows, the two major spikes in the interbank rate coincided with the higher rates of currency devaluation. The first major devaluation episode began in the autumn of 2012. At that time the market reacted to the increased lending and the news about the ban on the exports of “solvents”, which meant Belarus would have to pay back to Russia the customs duties on oil. On the other hand, the periods of high liquidity and low interbank rates were usually followed by devaluation episodes.

Figure 2: Changes in the exchange rate and the interbank rate, 2012-2014
Figure2
Source: The National Bank of the Republic of Belarus.
 

In the summer 2013 devaluation speeded up once again, fueled by the potassium scandal. The National bank responded with lower liquidity and higher rates, which reached peak values of 50% and higher in September 2013.

Of course, this policy had other effects besides calming the currency market. As Figure 3 demonstrates, deposit and credit rates mainly reacted to the changes in the interbank rate, with peaks in the autumn of 2012 and summer-autumn of 2013. Enormously high deposit rates (often higher than 40 percent) delivered a hefty real rate of return given inflation of 22 percent in 2012 and 16 percent in 2013. Rouble deposits were growing throughout the period. But someone had to pay those rates.

Figure 3. Short-run deposit and loan rates for firms and individuals
Figure3
Source: The National Bank of the Republic of Belarus.
 

High real rates became a burden for firms and households. The commercial banks had to stop many of their long-term individual lending programs (mainly those financing housing purchases). Instead, the banks put their efforts into the development and promotion of short-term consumer credit, which was virtually non-existent just a couple of years before.  Many firms switched to cheaper loans in U.S. dollar, but the National bank quickly shut down these practices by introducing restrictions on foreign currency loans. Credit growth slowed down, and did not decline only due to the government-sponsored lending programs and a boom in consumer credit.

High loan rates together with the growing wages and low sales suffocated the firms. Average profitability across the country is declining since summer 2012, reaching the record low profit margin and negative aggregate net profits in December 2013 (see Figure 4). The lack of liquidity lead to the crisis of payments: accounts receivable and accounts payable on the 1st of February 2014 were 24.7 and 31.6 percent higher than a year before.

Figure 4. Average profit margin in Belarus, 2012-2014
Figure4
Source: The National Statistical Committee of the Republic of Belarus
 

Today Belarus experiences high pressure for devaluation. The international currency reserves are depleted; the current account balance is in the red for a long time. The exporting enterprises quickly lose competitiveness due to low productivity. For the first time since 2009 GDP growth is virtually non-existent (and even negative in the first months of 2014). Some of the main trading partners – Russia, Ukraine and Kazakhstan – have already devaluated their currencies and face uncertain prospects for growth. It looks like the successful practice of fighting devaluation with liquidity squeezes at the cost of the real economy will have to end soon.

References

The crisis in Ukraine and the Georgian economy

Posted on March 17, 2014 | Policy Brief

High office buildings facing sky representing Institutions and Services Trade

We analyze how the crisis in Ukraine will likely impact the Georgian economy and distinguish between short-run and long-run effects. We argue that the short-run effects are transmitted through trade and capital flows and that they are rather negative for Georgia and can hardly be bolstered. In the long-run, however, the crisis could improve the competitiveness of the Caucasus Transit Corridor, an important trading route between Europe and Central Asia Georgia participates in. We give recommendations how political decision makers could support such a development in the wake of an impairment of the northern Ukrainian transit routes.

Introduction

When Ukrainian President Victor Yanukovich decided not to sign the association agreement with the European Union and instead opted for a Russian package of long-term economic support, many Ukrainians perceived this not to be a purely economic decision.  Rather, they feared this to be a renunciation of Western cultural and political values, and – to put it mildly – were not happy about this development.

The Russian political system, characterized by a prepotent president, constrained civil rights, and a government controlling important parts of the economy through its secret service, is not exactly the dream of young Ukrainians. Russia can offer economic carrots, but these do not count much against the soft power of Europe that comes in the form of political freedom, good governance, and economic development to the benefit of not just a small group of oligarchs.

Hence, it was all but surprising when many young Ukrainians took their anger about Yanukovich to the streets. After protests that lasted for nearly three months, President Yanukovich fled the country, a temporary government took over, and chaos broke out on the Crimean peninsula.

The dispute about the Crimea has the potential to impede the relations between Russia and the West for a long time to come, in particular if Russia enforces an annexation of the territory. Moreover, the tensions could quickly turn into a military conflict. The aircraft carrier USS George H.W. Bush was moved into an operational distance to the Crimea, accompanied by 20 smaller U.S. warships, and 12 additional fighter planes will be stationed in Poland. Yet even if there will be no direct confrontation between official Russian and U.S. forces, Ukraine could become the battleground of a proxy war, a kind of conflict that was common in the Cold War era. In this respect, one can already read the writing on the wall: the new Ukrainian government begs the U.S. for supplying arms and ammunition, and while the Obama administration is still reluctant to give in to such requests, the call is supported by hawkish U.S. congressmen who might finally prevail.

Ukraine is a country that is geographically close to Georgia and, like Georgia, has vital economic stakes in the Black Sea area. Georgia will not be unaffected by whatever happens in Kiev and Simferopol. In this policy brief, we will inform policy makers about the likely short-run and long-run economic consequences of the turmoil in Ukraine, discuss the challenges and opportunities that may arise, and derive some policy recommendations.

Short-run economic consequences

The crisis in Ukraine will almost instantaneously affect trade and capital flows between Georgia, Ukraine, and Russia. The effects will likely be negative and hit Georgia in a situation of economic recovery.

The Georgian real GDP growth rates were 6.3% in 2010, 7.2% in 2011, and 6.2% in 2012, and the real GDP per capita evolved from about 2,600 USD to about 3,500 USD in this time, but the upsurge discontinued in 2013 (if no other source is mentioned, figures presented in this policy brief (including those in the graphs) come from the Georgian statistical office GeoStat). ISET-PI, in its February 2014 report on the leading GDP indicators for Georgia, estimates the GDP in 2013 to be 2.6%, while GeoStat, the statistical office of Georgia, believes it to be 3.1%.

The unsatisfactory performance of the Georgian economy in 2013 was arguably caused by political uncertainties resulting from the government change that took place in late 2012, and as these uncertainties are largely overcome, most economists believe that Georgia will get back to its remarkable growth trajectory in 2014. The IMF, in its Economic Outlook, predicts a real GDP Growth of 6% in 2014, and the government of Georgia expects this number to be 5%. With an escalating crisis in Ukraine, it is questionable whether these rosy forecasts are still realistic.

Effects on imports

In 2013, Ukraine and Russia were the 3rd and the 4th largest importers to Georgia, respectively. Graph 1 shows the top five importers to Georgia, which together make up about 50% of total imports. The imports from Ukraine and Russia are mainly comprised of consumption goods: of all goods that were imported between 2009 and 2013 from Ukraine and Russia, about 30% were foodstuff. The ten main import goods in this time (in order of monetary volume) were cigarettes, sunflower oil, chocolate, bread, cakes, meat other than poultry, poultry, and sugar.

If the supply of these goods would be reduced through a breakdown of production and logistics, roadblocks, damaged infrastructure etc., the consequences for Georgia would not be utterly severe. From Ukraine and Russia, Georgia receives few goods that are (1) needed for investment projects and (2) cannot be produced domestically (an example of sophisticated investment goods that need to be imported would be ski lifts for tourism projects). Moreover, as Ukraine and Russia supply primarily standard goods that are produced almost everywhere, it is unlikely that a cutback in their imports would lead to sharp price rises in Georgia. Very quickly, increased imports from other countries would close any supply gaps. In addition, many imported consumption goods, like Ukrainian orange juice, are but luxury for ordinary Georgians, who buy their food in cheap domestic markets that sell almost exclusively local products.

Graph01

Effects on exports

A small anecdote may illustrate the status of Georgian products in the Russian market. In the late 1940s and early 1950s, Stalin used to invite his comrades to his Kuntsevo dacha almost every night. At these occasions, he drank only semi-sweet Georgian red wine. His clique, usually preferring Russian vodka, adopted this habit out of fear to displease the dictator. Yet the real highlight of these nightly gatherings took place after midnight, when an opulent feast began, featuring all the delicacies of the Georgian cuisine. Through Stalin (and the fact that Georgia was a preferred destination of Soviet tourism), Georgian food obtained an excellent reputation in most countries of the former Soviet Union, and, to the dismay of Georgians, some younger Russians even do not know that Khinkali is not an originally Russian dish.

As can be seen in Graph 2, Russia and Ukraine are among the top 5 destinations for Georgian produce, together absorbing about 14% of total Georgian exports in 2013. In 2006, two Georgian products that are traditionally highly popular in Russia, namely wine and mineral water (the famous “Borjomi” brand), were banned from the Russian market. Yet in the wake of the diplomatic thaw that set in after the new government assumed power last year, this ban was lifted, and in 2013, the export of these goods regained momentum. In 2013, 68% of all wine exported from Georgia was sold in Russia and Ukraine (44 and 24 percentage points, respectively). In both countries, Georgian wines are sold at the higher end of the price range and are typically consumed by people with middle and high income. It is likely that these exports, in particular those to Ukraine, will be affected considerably by the crisis. This may happen through decreased demand for luxury foods and through a possible depreciation of the Ukrainian hryvna and the ruble vis-à-vis the Georgian lari.

Another sector that may be affected by the situation in Ukraine is the car re-export business. Georgia imports huge numbers of used cars from the U.S., Europe, and Japan, and passes them on to countries in the region. While this business hardly yields potential for real economic progress, it accounts for roughly 25% of Georgian exports! Of these 25%, about 7 percentage points go to Russia and Ukraine. Moreover, many cars are imported to Georgia on the land route from Europe through Ukraine and Russia (often driven by private, small-scale importers). If it will become more difficult to cross the border between Russia and Ukraine, this business, providing income to many low-skilled Georgians, may be at risk.

It should also be noted that Ukrainians and Russians make up an ever-increasing share of the tourists coming to Georgia (though the biggest group of tourists are Israelis). Also through this channel, an economic downturn in Ukraine and Russia will have unpleasant consequences for Georgia.

Graph02

Effects on capital flows

According to the National Bank of Georgia, in 2013 a total of 801 mln USD was flowing in from Russia (see Graph 3). Ukraine contributed 45 mln USD to the money inflows, still significant for an economy as small as Georgia’s. An economic downturn in Russia and Ukraine would hit many Georgian citizens, often pensioners and elderly people, who depend on remittances of their children and other family members sent from these countries. This may aggravate a trend that already exists: in January 2014, money inflows decreased by 4% from Russia and by 5% from Ukraine (compared to January 2013).

Graph03

Long-run economic consequences

Most of the economic dynamics Georgia experienced since 2003 was “catch up growth”. A country permeated by corruption, with a dysfunctional police and judicial system, without protection of property rights and contract enforcement, will grow almost automatically when the government restarts to fulfill its basic functions. Yet once this phase of returning to normal economic circumstances is over (Georgia probably is already in this situation), high growth rates can hardly be achieved without a strong export orientation of the economy, in particular when an economy is as small as Georgia’s. Most economists concerned with Georgia are therefore struggling to identify economic sectors where Georgia is in a good position to develop export potential. The National Competitiveness Report for Georgia, written in 2013 by the ISET Policy Institute on behalf of USAID, therefore extensively discusses the question what Georgia can deliver to the world. Though not related to export in a classical sense, the report points out that one of the advantages Georgia has is its geographical location, providing for possibilities to transform Georgia into a logistics hub.

There are three main routes to transport goods from Europe to the Central Asian countries (e.g. from Hamburg to Taraz in Kazakhstan). One route goes via the Baltic ports of Klaipeda or Riga, and then through Ukraine and Russia, and another route goes overland through Ukraine. A third one, the so called Caucasian Transit Corridor, has the Georgian port city of Poti and Turkey as its Western connection points, then goes through Georgia, Azerbaijan, and the Caspian Sea, and further east it splits up into a Kazakhstan and a Turkmenistan branch.

According to the Almaty based company Comprehensive Logistics Solutions, the fastest and cheapest route is the one through the Baltic ports. The transport from Hamburg to Taraz takes around 33 days and costs 6,220 USD per standard container. The overland transport via Ukraine takes around 34 days and costs 7,474 USD. Finally, transport through the CTC currently takes the longest time, namely around 40 days, and costs 6,896 USD.

Unlike many other economic activities, competition for transportation is more or less a zero-sum game played by nations. If transport through Ukraine and Russia will be restrained due to closed borders and political and economic instability, the total transport volume will not change substantially. Rather, instead of going through the northern routes, the goods will flow through the CTC. A similar development could be observed when the embargo against Iran was tightened and shipping goods through Iranian ports became increasingly difficult for Armenia and Azerbaijan. As a result, Azerbaijan, traditionally importing through Iran and exporting through Poti, now facilitates both its imports and exports through Poti.

This is a great chance for Georgia if it wants to become serious about transforming into a logistics hub. In our policy recommendations, we will speak about how to utilize on this opportunity.

Policy recommendations

Georgia can do little to bolster the short-run effects that are transmitted through the trade and capital flow channels. Political decision makers should be aware of problems that might arise for particularly vulnerable groups in the population, like pensioners who lose income in case remittances from Russia and Ukraine run dry, and help out with social support if necessary.

Regarding the long-run impact, Georgia should use this opportunity for gaining ground in the competition with northern transit routes. The Caucasus Transit Corridor can become much faster and cheaper if (a) a deepwater port and modern port facilities with warehouses will be built in Poti, (b) the road and train infrastructure will be improved, and (c) it will be easier to bring cargo over the Caspian Sea. Regarding the latter point, it would be important to assist Azerbaijan in improving the port management at Baku (in particular reducing corruption), and in reforming the monopolistic Azerbaijani State Caspian Sea Shipping Company.

Azerbaijan invests 775 mln USD into the Georgian part of the Baku-Tbilisi-Kars railway, proving their serious interest to upgrade CTC. Given this impressive commitment of Azerbaijan, Georgia should not stand back.

Conclusion

The crisis in Ukraine yields short-run risks and long-run opportunities for the Georgian economy. While there is little that can be done about the risks, the opportunities call for courageous steps to improve the Caucasus Transit Corridor. If the countries that hold stakes in the CTC are now further reducing the cost of transportation and make the route faster and more customer-friendly, the CTC may establish itself as the main trading route connecting Europe and Central Asia. Once critical investments have taken place, CTC’s advantage could be sustained beyond the current crisis. It is a competitive route that simply needs upgrading, which can happen now as a fallout of the conflict between Ukraine and Russia.

References

Macroeconomic Performance and Preferences for Democracy

Posted on February 16, 2014 | Policy Brief

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This policy brief summarizes the results of our research on factors influencing preferences for democracy in transition countries. The aim of this work was to detect which macroeconomic and individual factors impact the choice of supporting democracy. The results showed that the performance of the country, described by level of GDP, unemployment, level of corruption and economic growth, has a serious impact on an individual’s perception of democracy. At the same time, individual factors like education and age also influence people’s choice of support of democratic authorities.

Individual perception of democracy is a question that attracts the attention of policymakers.  The macroeconomic instability that has been observed worldwide lately is likely to impact individual attitude toward democratic values and political institutions. The recent economic crisis brought a deterioration of the economic situation around the world and provided new challenges to cope with. It is likely that macroeconomic indicators have an impact on how a person perceives democracy. Literature studying similar questions has shown that GDP growth, unemployment and inflation all affect personal attitude to democratic institutions (Clarke et. al., 1994; Barro, 1999; Papaioannou and Siourounis, 2008). As for individual characteristics, the level of education is revealed by the literature as a very important factor in the context of the individual’s propensity of democracy approval.

The literature on the determinants of political support and attitudes to democracy was mostly focusing on exploring stable world economies with long-formed and steady-functioning democracies. We tried to look at a similar question in the context of transition economies, where democratic institutions are still under development.

We intend to estimate individuals’ propensity to favor democratic values. The specification of our econometric model was based on the literature addressing the same topic. The estimation procedure used probit econometric techniques, which allows for the calculation of the propensities of interest while taking into account the influence of both macroeconomic factors and individual characteristics. The paper used two sources of data: macroeconomic information was collected from the World Development Indicators of the World Bank, and individual-level cross-sectional data was obtained from Life in Transition Survey (LITS) 2010, which initially covered 38864 individuals from 35 countries. However, as the paper focuses on countries in transition, the final set only included individuals from 30 countries, most from Eastern Europe, Baltics and CIS, and excluded representatives of Western Europe. This data allowed for substantial data variation in the context of economic development vs. perception of democratic values (Graph 1).

Figure 1. Support of Democracy and GDP Per Capita
 brief1
Source: WDI and LITS 2010

Inclusion of different macroeconomic variables together with individual factors allowed for an evaluation of their importance and level of impact on the perception of democratic values (Table 1). The results show that GDP per capita has a positive and significant effect on individuals’ perception of democratic values, which is in line with the literature claiming that standard of living in countries with not so high level of GDP is positively correlated with satisfaction with their life and the political system (Easterlin, 1995; Clark et al., 2008; Stevenson and Wolfers, 2008). Inflation rates are not significant and do not influence individuals’ attitude to democracy. On the other hand, economic growth is strictly positive and significant, and an increase of the economic growth rate raises propensity of democratic support by around 1.6 percentage points. The possible explanation here is that the growth rate of GDP works as a proxy of expectations for improvements of the standard of living in the future.

Table 1. Influence of Macroeconomic and Individual Factors on Perception of Democracy
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Unemployment works as an indicator of a country‘s economic performance and has an expected negative sign in terms of individuals’ satisfaction with life and political institutions, which is also in line with the results in the literature (Di Tella et al., 2001; Wagner and Schneider, 2006). Impact of unemployment was tested using a cross product of unemployment and the Freedom House Index (this latter indicator shows the level of political and civil rights from 1 (most free) to 7 (least free)). The sign on this cross product is positive, which captures their mutual positive impact on the support for democracy. Thus, the higher the unemployment in a country with a low level of democratization is, the larger the probability of democratic support by individuals in these countries is.  The indicator for the level of corruption in a country was also taken into account, via the Corruption Perception Index. This index ranks countries on a scale from 0 (highly corrupt) to 10 (effectively, corruption-free). The results show that the less corrupt a country is, the higher the propensity that an individual in that country will support democracy is. In fact, one additional point in the index increases the propensity of support by almost 4 percentage points. Military expenditures negatively affect the support of democratic values, and so does the existence of oil in the country. Here, military expenditures may be seen as a proxy for a less democratic regime, so that the leaders there have higher incentives to rule using suppressive measures with a support of military force in the country (Mulligan, Gil and Sala-i-Martin, 2004).

As for the individual factors, both secondary and higher education appear to be very important factors with a positive impact on the satisfaction with democracy. This finding follows the literature (Barro, 1999; Przeworski et al., 2000; Glaeser et al., 2004). In our results, people with secondary or higher education degree showed 10 and 18 percentage points higher propensity of support, respectively. Age also seem to matter: positive perception of democracy is specific to those aged 18-54, compared to the older generation, which goes in line with the explanation that senior citizens are more conservative than younger citizens. We also observe a negative significant coefficient on female gender, which may, perhaps, be related to women being more conservative than men.

Subjective relative income measure (answer to the question “to which income quintile do you think you belong to?”) has a positive impact on the support for democracy. Surprisingly, individuals from middle-income group have a more positive attitude than those who regard themselves as rich. Employment status is positively correlated with the support for democracy. Moreover, self-employment and employment in the public sector have a larger effect on the propensity of positive attitude to democratic values than employment in the private sector.

Divorced and widowed people expressed less support for democracy than single individuals, which might signal some dissatisfaction that impacts on personal attitude. Urban residency is positively correlated with the support of democracy. The same relationship is present for the risk tolerance of an individual. Finally, inclusion of a subjective measure of life satisfaction brought some changes to the general picture. It appeared that those who are satisfied with life strongly support the democratic values and such mentality raises the propensity of support by 7 percentage points. Moreover, inclusion of this variable makes the effect of being rich insignificant.

To sum up, the results showed that economic performance of the country described by various macroeconomic indicators has a serious impact on individual’s perception of democracy and, most probably, of other forms of government. At the same time individual factors also influence people’s satisfaction with the authorities. Thus, individual support of a political system is based on the results of performance of both the individual and the country.

References

  • Barro R. 1999. “Determinants of Democracy.”Journal of Political Economy 107, #S6.
  • Clark A. and Oswald A.J. 1994.“Unhappiness and Unemployment.”EconomicJournal104.
  • Clark A., FrijtersP. and Shields M. 2008. “Relative Income,Happiness and Utility: An Explanation for the Easterlin Paradox and Other Puzzles.” Journal of Economic Literature46,# 1.
  • DiTellaR., MacCulloch R.J., Oswald A.J. 2001. “Preferences over inflation and unemployment: Evidence from surveys of happiness.”American Economic Review91.
  • Easterlin R. 1995. “Will Raising the Incomes of All Increase the Happiness of All?”Journal of Economic Behavior and Organization27, # 1.
  • Glaeser E., La PortaR., Lopez-de-SilanesF. and ShleiferA. 2004.“Do Institutions Cause Growth?” Journal of Economic Growth.9, #3.
  • Mulligan C.B., Gil R. and Sala-i-Martin X. 2004. “Do Democracies HaveDifferent Public Policies than Nondemocracies?” Journal of Economic Perspectives18, #1.
  • Papaioannou, E. and Siourounis G. 2008.“Economic and Social Factors Drivingthe Third Wave of Democratization.” Journal of Comparative Economics36, #3.
  • Prezworski A., Alvarez M., Cheibub J. and LimongiF. 1996. “WhatMakes Democracy Endure?” Journal of Democracy 7, #1.
  • Stevenson, B. and Wolfers, J. 2008. “Economic Growth and SubjectiveWell-Being: Reassessing the Easterlin Paradox.” Brookings Papers on Economic Activity  1.
  • Wagner A.F. andSchneider F. 2006. “Satisfaction with Democracy and the Environment in Western Europe: A Panel Analysis.” IZA Discussion Papers 1929, Institute for the Study of Labor (IZA).

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.

The Charity of the Extremely Wealthy

Posted on January 27, 2014 | Policy Brief

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Analyzing data from the Giving Pledge (a public pledge to give away at least half of one’s fortune during one’s lifetime, launched by Bill Gates and Warren Buffett in 2010) and the Forbes billionaires’ list, I find that self-made billionaires are substantially more likely to give away large amounts of money, than do billionaires who inherited their money. Policy makers in many emerging markets with ‘new’ billionaires thus better quickly modernize their charity laws.

In 2010, two billionaires Bill Gates and Warren Buffett launched the Giving Pledge, a public pledge to give away at least half of one’s fortune during one’s lifetime (http://givingpledge.org/), which by now has been signed by 114 people. 114 are not much, you might think, and you might want to add your own name to the list. But, unfortunately, not everybody is invited to make this pledge. Gates and Buffet focus only on the extremely wealthy people: 85 of the signatories of the pledge are among the 1426 billionaires identified by Forbes in 2013, and most of the others were on Forbes’ billionaire list in earlier years. Of these 1426, 135 billionaires come from Central and Eastern Europe or the Former Soviet Union (see table I)

Worldwide, about 6% of billionaires (85/1426) have made this pledge. Among the signatories is one Russian billionaire, Vladimir Putanin, and one Ukrainian billionaire, Victor Pinchuk, which makes Ukraine score above average, with one out of ten, or 10% of Ukrainian billionaires signing.

Table 1. Number of 2013 Forbes Billionaires from the Former Soviet Union

# 2013 Forbes Billionaires

#  of Selfmade

Giving Pledge

Name of Signatory

Russia

110

110

1

Vladimir Potanin

Ukraine

10

10

1

Victor Pinchuk

Kazakhstan

5

4

0

Czech Republic

4

4

0

Poland

4

4

0

Romania

1

1

0

Georgia

1

1

0

In my most recent working paper, Claire Monteiro of Georgetown University and myself investigate whether it is possible to explain why these 6 % have signed, and the other 94% have not (yet) signed the Pledge. Or to put it in a more interesting way, why Putanin and Pinchuk signed but the other CEE/FSU oligarchs have not.

We investigate this question by analyzing whether generous billionaires have specific characteristics in common, characteristics that not so generous billionaires do not have. Doing this is possible because Forbes publishes not only a ranking of billionaires, it also provides background information about each billionaire like the billionaire’s education, age, how many children (s)he has and so on.

My analysis shows that three factors have a significant effect on the chance that a billionaire will be generous. First, a billionaire who is self-made is about three to four times more likely to sign than a billionaire who inherited his/her billion(s). This finding that how one earned one’s money affect how one spends this money is consistent with University of Chicago professor Richard Thaler’s ‘mental accounting’ theory and with earlier research showing that the propensity to consume is bigger if income received is framed as a bonus rather than if it is framed as a rebate, and the research showing that windfall gains (money won in a lottery) is more readily consumed than non-windfall gains (money for which one had to work). Note that all but one billionaire from the CEE/FSU are categorized by Forbes as self-made.

Second, billionaires with more money are more likely to sign the Giving Pledge and promise to give away half their fortune – for example, compared to an average billionaire who has about 4 billion dollar in estimated net worth (like Victor Pinchuk), a billionaire with an estimated net worth of about 15 billion dollars (like Vladimir Potanin) is roughly 50% more likely to promise to give away half of her/his fortune. Third, billionaires whose fortune comes from the technology/telecommunications industry are about twice as likely to announce that they will give away at least half of their fortune, compared to billionaires from other sectors.

The influence of other factors is small and less precisely estimated: older billionaires tend to be more likely to sign (possibly because being closer to the end of one’s life makes one think more about what one wants to leave behind), as do those who have more children (maybe because having more children makes it more likely that the inheritance will lead to fights among family members) or those having a Ph.D. Moreover, billionaires from the food and retail industry tend to be less likely to sign than those from the metallurgy industry.

Taken together my model predicts for Ukraine that Victor Pinchuk is the Ukrainian billionaire who is most likely to sign (4% probability), being 10 times more likely to sign than Yuriy Kosiuk (the Ukrainian billionaire who is least likely to sign with 0.4% probability). The difference in estimated net worth (3.8 billion versus 1.6 billion), age (52 versus 44), the number of children (4 versus 1) and education (Ph.D versus bachelor), and the sector in which they are active (metals and mining versus food and retail) explain this difference in probability. Victor Pinchuk is also about 30% more likely to sign than Rinat Akhmetov – while the latter has a higher estimated net worth (15.4 billion versus 3.8 billion), the effect of education (bachelor versus Ph.D), age (46 versus 52) and children (2 versus 4) play in favor of Victor Pinchuk, outweighing the wealth effect.

While it is definitely fun to do these kinds of computations, my research also has serious implications. The fact that inherited billionaires are much less charitable than the self-made billionaires means that academics should not assume that ‘all money is equal’ as they typically do – how you acquire money affects what you will do with it. It also implies that the countries from CEE/FSU with lots of ‘new’ wealth should modernize their charity laws quickly – once the self-made billionaires pass their wealth on to their children, it will become much more difficult to turn this massive wealth into charity.

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References

 

* A version of this policy brief has been published in Russian at Forbes.ua.

Putting the “I” Back in Team: The Rise of International Teams in Science

Posted on January 20, 2014 | Policy Brief

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In this policy brief, I discuss the increasing prevalence of international teams in the production of scientific knowledge.  I outline several potential factors that may explain these trends and discuss recent evidence from an original survey of coauthors on scientific papers regarding their collaboration behavior.  Finally, as a notable example of increased international collaboration, I discuss the increase in scientific collaboration between Russia and the US after the end of the Cold War.

The Increase in Collaboration and Internationalization of Teams

Teams are becoming more prevalent in science.  Both the share of papers produced by teams and the number of scientists working on scientific papers has increased in recent decades (Wuchty, Jones and Uzzi, 2007).  Economic theory suggests that scientific research is becoming increasingly collaborative since the frontier of scientific knowledge has become more complex and specialized so that more researchers are needed to combine their expertise to make advances (Jones, 2009).  Team members are also becoming more geographically dispersed: the share of papers resulting from international collaborations has increased, and within the US, scientists today are more likely to have coauthors located in a different city than before (Freeman, Ganguli and Murciano-Goroff, 2014).

These trends can be seen clearly in the graph below from the National Science Board’s Science and Engineering Indicators 2012.  It shows the share of both world papers and US papers from 1990-2010 that are coauthored, coauthored with domestic coauthors only, and coauthored with at least one international coauthor.  Collaboration in general and international collaboration have been increasing steadily since 1990 both in the world and in the US.  However, for the US, the share of domestic-only collaborations has plateaued, while it is increasing in the rest of the world.  In a recent Nature article, Adams (2013) shows that this trend similarly holds for other Western countries (United Kingdom, Germany, France, the Netherlands, Switzerland), while for emerging economies (China, India, South Korea, Brazil, Poland), domestic collaborations are also increasing.

Figure 1. World and US Trends in Scientific Collaboration, 1990-2010
fig1
Source: From National Science Board (2012)

Why has Science Become More International?

There are many potential reasons for the recent increases in international collaboration.  An important factor has likely been the spread of the scientific workforce and R&D activities throughout the world (Freeman, 2010). The growing number of science and engineering PhDs in developing countries, some of whom are international students and post-docs returning to their home countries has expanded the supply of potential collaborators around the world (Scellato, Franzoni, and Stephan, 2012).  Another factor is funding that has shifted scientific production towards international teams, as increased government and industry R&D spending in developing countries and grant policies by the European Union and other countries have supported international cooperation.

The lower cost of travel and communication in recent decades has also reduced the cost of collaborating with people in different locations.  For example, Agrawal and Goldfarb (2008) show how the expansion of Bitnet, the precursor to the Internet, led to increased collaboration between institutions within the US.  Finally, the location of scientific equipment and materials, such as the CERN Large Hadron Collider, telescopes, or climatological data available only in certain parts of the world, have increased international collaboration, and in some fields, has made international collaboration a necessity.

Survey Evidence on Scientific Collaborations

In a recent paper, my coauthors and I present the results of an original survey we conducted of scientists regarding collaboration (Freeman, Ganguli and Murciano-Goroff, 2014).  In August 2012 we conducted a web-based survey of the corresponding authors of scientific papers with at least one US coauthor published in 2004, 2007, and 2010 in the fields of Nanotechnology, Biotechnology, and Particle Physics.

We customized each survey to ask the corresponding author about the collaboration and individual team members.  The survey questions asked about how the team formed, how it communicated and interacted during the collaboration, the contribution of each coauthor, types of research funding, and the advantages and disadvantages of working with the team.  We received 3,925 responses, so that our response rate was approximately 20%.

The survey also asked the respondent which country each coauthor was “primarily based in during the research and writing” of the article. This gives us a more accurate measure of whether teams are international than can be typically gleaned from publication data, which are based on author affiliations at the time of publication.  Defining international teams from author affiliations alone can produce errors if affiliations change between the time the research was undertaken and the time of publication, or because some people have affiliations from more than one country.

Our analysis of the survey data uses the respondents’ information to define US collocated, US non-collocated and international teams. One of our key results is that face-to-face meetings continue to play an indispensible role in collaborations: most collaborators first met while working in the same institution.  Teams also reported that while carrying out the research, they communicated often through face-to-face meetings, even with coauthors from distant locations.

Figure 2 below displays how the corresponding author responded about how they first met their team members.  It shows that former colleagues play a very important role in the formation of international teams, followed by former students, conferences and institution visits, which equally contribute.  The graph also shows the similarity between international teams and US non-collocated teams in how coauthors met.  For other survey questions, our analysis also shows similarities between international teams and US non-collocated teams, suggesting that the salient issues are more about geography in general rather than necessarily about national borders.

Figure 2. How Coauthors First Met
fig2
Source: From Freeman, Ganguli and Murciano-Goroff (2014)

 

Another key finding from our survey is that the main reason for most collaborations, whether domestic or international, is to combine the specialized knowledge and skills of coauthors. We also asked the corresponding authors their views of the advantages and challenges of their collaboration.  The most often cited advantage for all types of collaborations was “Complementing our knowledge, expertise and capabilities” and “learning from each other”.  For the challenges, US non-collocated and international teams tended to agree more that there was “Insufficient time for communication”, “Problems coordinating with team members’ schedules”, and “Insufficient time to use a critical instrument, facility or infrastructure”, but international teams did not report these problems more often than US non-collocated teams. Where international teams differed is that these teams were the most likely to agree that their “research reached a wider audience”.

International Collaboration After the End of the USSR

A small but significant part of the increase in international collaboration since the 1990s can be attributed to the end of the Cold War.  In “Russian-American Scientific Collaboration” (Ganguli, 2012), I examine trends in international collaboration by Russian and US scientists since the end of the USSR.  Given the nature of the Cold War and restrictions on travel and communication with the West, I show that there was a dramatic increase in the number of publications with at least one Russian and a US coauthor from 1985 to 2005.

In addition to the lifting of travel and communication restrictions, there are several factors that contributed to the surge in collaborations between American and Russian scientists after the end of the USSR.  First, at the level of the Russian government, there was a switch to a more open and collaborative approach to science. Part of this effort included establishing international centers for research in Russia aimed at integrating Russia into the global science community. Another important factor facilitating collaborations with Western researchers were foreign grant programs. The large increase in the emigration of Russian scientists in the 1990s to the West also contributed to international collaboration.  After emigrating, many Russian scientists maintained close links to their colleagues in Russia, and coauthored papers with their former colleagues, which are counted as internationally coauthored publications.

While many of these factors have aided international cooperation after the end of the USSR, there have also been significant challenges that made cooperation difficult.  Some of these challenges in the early 1990s included the political instability, organizational turnover making long-term funding agreements difficult to implement, difficulty transferring funds due to the underdeveloped banking system, high taxation and customs duties, lack of effective intellectual property rights, poor infrastructure, lack of a shared language (both linguistic and cultural), and external regulations (see further discussion in OECD, 1994).  However, many of these challenges have now been overcome, leading to the continued increase in international collaboration between Russian and US scientists.

My analysis in Ganguli (2012) shows that the increase in Russian-American collaboration was more pronounced in some fields of science versus others, particularly in Physics.  Figure 3 shows that the bulk of the articles published with Russian and American coauthors were Physics articles, with a sharp increase occurring immediately after 1991.

Figure 3. Russia-United States Publications By Field, 1985-2005
fig3
Source: From Ganguli (2012)

 

While some of the differences across the fields can be attributed to the number of scientists active in these fields, there are also other potential contributing factors.  For example, it may be that there was greater emigration of scientists from certain fields abroad, and links between emigrants and those who remained in Russia persisted. Graham and Dezhina (2008: 24) suggest that over 50 percent of emigrants were physicists and mathematicians. Another reason may be that international collaboration was more important in some fields due to the knowledge or resources needed to conduct research during the economic crisis of the 1990s.  As Wagner Brahmakulam, Peterson, Staheli, and Wong (2002) point out, physics research received significant amounts of US government funding for international collaboration, partly because expensive equipment that is needed and through collaboration, countries could share costs.  Also, physicists from many countries often meet and work together at international research centers like CERN.  Moreover, in some fields, the US and Russian governments shared priorities in funding international cooperation, like biomedical and health sciences, energy, physics, while there were gaps in some areas where Russia devoted resources and the US did not, like chemistry (Wagner et al. 2002: 24).  Graham and Dezhina (2008: 141) also discuss how Western colleagues benefited from working with Russians especially in fields like zoology, botany and the earth sciences, since the Russian colleagues provided access to data from unique regions not available previously.

Support for International Teams?

This policy brief has discussed some reasons for the increase in international scientific collaboration and related empirical evidence, including insights from collaboration after the end of the USSR.  The growth in collaboration and the geographic dispersion of teams is likely to continue; the frontier of scientific knowledge will become more complex and specialized, so that an even greater numbers of researchers will be needed to combine their expertise, and they are likely to be spread across increasingly distant locations.

These trends raise many complex issues for policymakers.  For some countries, international collaboration may be the only way to sustain the science sector as the frontier of knowledge becomes more complex and resource-intensive.  For some, international collaborations may increase the emigration of home-grown talent to wealthier countries.  To what extent international collaboration should be supported, and how, will be important policy questions going forward. Typically, funding for international projects has been the main policy lever, and the Russian experience suggests that grant programs did play a critical role in that case.  As our survey evidence in Freeman, Ganguli and Murciano-Goroff (2014) suggests, face-to-face meetings are especially important in forming and sustaining international collaborations.  Thus, funding mechanisms that include provisions for research stays and face-to-face meetings may be the most effective means for fostering international collaborations.

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References

  • Adams, J. (2013). “Collaborations: The Fourth Age of Research.” Nature, 497(7451), 557-560.
  • Agrawal A, Goldfarb A (2008). “Restructuring Research: Communication Costs and the
  • Democratization of University Innovation,” American Economic Review, 98(4):1578-1590.
  • Freeman, Richard B. (2010). “Globalization of Scientific And Engineering Talent: International Mobility of Students, Workers, and Ideas and The World Economy.” Economics Of Innovation And New Technology, Volume 19, issue 5, 201 pp. 393-406.
  • Freeman, Richard B., Ina Ganguli and Raviv Murciano-Goroff (2014).  “Why and Wherefore of Increased Scientific Collaboration,” NBER Working Paper No. 19819, Issued in January 2014.
  • Ganguli, Ina (2012).  “Russian-American Scientific Collaboration” in Y.P. Tretyakov (ed), Russian-Аmerican Links: Leaps Forward and Backward in Academic Cooperation. St. Petersburg, Russia: Nestor-Historia, pp. 120-135.
  • Graham, Loren and Irina Dezhina (2008).  Science in the New Russia: Crisis, Aid, Reform. Bloomington and Indianapolis: Indiana University Press, 2008.
  • Jones, Ben (2009). “The Burden of Knowledge and the ‘Death of the Renaissance Man’: Is
  • Innovation Getting Harder?” Review of Economic Studies, 76:283-317.
  • National Science Board (2012). Science and Engineering Indicators 2012. Arlington VA: National Science Foundation (NSB 12-01).
  • National Science Board (2006). Science and Engineering Indicators 2006. Arlington VA: National Science Foundation (NSB 06-01).
  • OECD (1994).  Science, Technology, and Innovation Policies. Federation of Russia. Paris: Organisation for Economic Co-operation and Development, 1994.
  • Scellato, G., Franzoni, C., & Stephan, P. (2012). “Mobile Scientists and International Networks,” NBER Working Paper 18613.
  • Wagner, Caroline, Irene Brahmakulam, D.J. Peterson, Linda Staheli, and Anny
  • Wong (2002).  U.S. Government Funding for Science and Technology Cooperation with Russia.
  • Santa Monica, CA: RAND Corporation, 2002.
  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). “The Increasing Dominance Of Teams In Production Of Knowledge.” Science, 316(5827), 1036-1039.

Academic Inbreeding in Ukraine

Posted on December 9, 2013 | Policy Brief

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In Ukraine, having a university degree only provides a noisy signal of one’s productivity, which means social ties and personal relations play a relatively more important role in the Ukrainian economy in general. Therefore it should not come as a surprise that inbreeding is very common in Ukrainian academia; for example, about 50% of faculty obtained their university degree from the university that employs them. Given the absence of clear “quality signs” for fresh university graduates, inbreeding can be viewed as a second-best option for hiring decisions. Our econometric analysis shows that inbred faculty does not differ in its (observable) quality from non-inbred faculty. At the same time, ceteris paribus, inbred faculty has somewhat lower salaries. We also find that the extent of inbreeding is slightly higher in universities with a “national” status and lower in very small universities (of less than 1000 students).

Academic inbreeding is the practice of universities hiring their own graduates to academic positions. Inbred faculty is thus faculty employed at the same university from which they graduated.  Inbreeding implies a low level of competition for faculty vacancies possibly resulting in low quality hires. However, inbred faculty can be cheaper, reduce the chance of a mismatch between university and faculty member, and can be better “tailored” to the needs of a certain university or discipline. For some specific narrow disciplines inbreeding can be the only way to hire faculty (for example, if only one university in a region provides courses in a certain discipline, teachers of that discipline most probably will be inbred). In research, inbreeding can help to pass on tacit knowledge but it can also prevent “fresh blood” and new ideas from entering into the university. In developed countries, universities usually try to limit inbreeding in order to first, “disseminate” their graduates and earn a good reputation, and second, hire the best graduates on the market through an open competition. In less developed countries, inbreeding is more common because of the higher role of personal relations in hiring decisions in general.

Although very widespread, academic inbreeding in Ukraine has received little or no attention from researchers or policy makers. Data on inbred faculty is similarly scarce. There is only one recent exception – in the summer of 2013, the Centre for Social Research surveyed about 400 university professors. The survey contains information on a wide range of aspects of faculty employment, such as working hours, publications, participation in conferences, income size etc., including the question on whether a person works at the same university from which (s)he graduated. We used this data to do an econometric analysis of the factors that determine inbreeding and the impact of inbreeding. We complemented the survey data by data from an online questionnaire we distributed among KSE graduates whom we know work in academia, their acquaintances and among the network of KSE partners who work in academia (a total of 59 responses).

Causes of Inbreeding

Besides providing a person with knowledge and skills necessary for a white-collar job, education has several other functions. One of them is signaling, i.e. people who successfully graduate from an educational institution should have higher abilities (ceteris paribus) than those with lower grades or dropouts. This function of education is almost entirely lost in Ukraine because of widespread corruption. In Ukraine, good students can obtain good skills and knowledge together with good grades. However, “bad” students can obtain the same grades for money: besides paying professors for exam grades, students can buy a course paper, a diploma thesis and even a doctoral dissertation. Cheating and plagiarism are also very widespread; not only in students’ work, but also in academic research. Hence, based on the diploma alone, a potential employer will have difficulties telling apart a “good” student from a “bad” one. Therefore, other screening mechanisms are relatively important in Ukraine.

Many private-sector employers, for example, will pay more attention to previous work experience and personal recommendations than formal education. For example, the ULMS-2007 survey shows that from 48% to 68% of people found a job through relatives or friends, which is comparable to the extent of inbreeding found by this study in academics (48.6% in the CSR-2013 survey, 68% in our online survey). This situation pushes students, who do not expect to be hired by relatives or friends, to find a full-time job already in the first or second year of studies, providing them with both incentives and funds to “buy” a diploma. This creates a “vicious circle” – the low value of a diploma makes employers looking at previous work experience, and the need to gain that experience further devalues diplomas.

For universities, “previous work experience” is the student’s performance during their studies. Hence, by inbreeding their own students, universities reduce uncertainty, which they would be facing if they looked for needed candidates on an open market. As the academic career of a person develops, (s)he can develop additional signals of his/her “quality”; first of all, scientific degrees (Candidate of Sciences, Doctor of Sciences) and/or ranks (Docent or Professor) and connected to them publications in Ukrainian and foreign journals (with the last ones being much more valuable). Therefore, as we show, younger and less distinguished faculty (with shorter teaching experience and without a Doctor degree or Professor rank) is more likely to work at a university from which they graduated.

Estimation Results

Our econometric estimation showed that the extent of inbreeding does not depend on the quality of a university as measured by its rank in Ukraine. Inbreeding is less common in very small universities (of less than 1000 students), and is independent of the university size after this threshold. Universities with a “national” status have slightly higher level of inbreeding.

We also show that inbred faculty does not differ in “quality” (measured as the number of publications in Ukrainian and foreign journals and the probability to get a foreign fellowship) from other faculty, although, ceteris paribus, inbred faculty do get lower salaries.

Results from both the CSR-2013 survey and our online questionnaire indicate that personal connections are very important both for entering a university and for further promotion. Usually an academic career starts when a person begins his/her Ph.D. studies; at the same time, (s)he starts working as an assistant or a lecturer (when admitting students to Ph.D. studies, universities prefer their own MA graduates). To move up the career ladder, a person should earn scientific degrees or ranks, have certain duration of teaching experience and a minimal required number of publications (all the formal requirements for certain academic positions are stipulated in a Decree of the Cabinet of Ministers). According to the law, currently there is no tenure system, and faculty is hired with one- three- or five-year contracts (the longest contracts can last up to seven years, but only in the universities with a “national” status).

Hiring Procedures at Universities

When a vacancy is open (e.g. a contract expires), a university should make an announcement in a pedagogical journal and/or on its website; then candidates should be interviewed at a chair meeting, and a selected candidate should be approved by the faculty dean. A candidate should have a required teaching experience and publications. There are about 1500 journals on the list of Higher Attestation Commission (the body that organizes the dissertations defense), which means that practically all universities issue at least one journal, and very few of them are refereed. This means that publishing in the home university’s journal is the cheapest and easiest way for a faculty member to get the needed number of publications. Therefore, publications are very often of very poor quality and do not contain any real research, especially in social sciences. To mitigate this problem, the Ministry of Education and Science introduced a new requirement for scientific degrees – since 2013, 20% of publications should be in foreign-refereed journals.

When hiring, all formal requirements and procedures are typically observed – a competition is announced, the chair meeting held, the candidate has the required duration of work experience and the number of publications (their quality is discussed above). However, in reality there is very often just one candidate “for” whom the vacancy is opened, and outside people, even if they apply for a vacancy, are ignored. Usually a chair meeting supports the opinion of a chair head, but either way, a dean could overturn a chair meeting decision, so despite seemingly open procedures, in reality a person’s employment depends on his/her relations with a chair head and/or a faculty dean. Studying at a university is the most common but not the only way to establish these relations. A person can get acquainted with a chair head or a faculty dean at a conference, be his/her relative or friend, or be recommended by his/her relative or friend.

Such a widespread reliance on personal connections is a legacy from the Soviet times when personal ties replaced market mechanisms, and students were allocated to their first workplaces rather than hired on a competitive basis. Since universities were situated in cities, staying at a university implied a better living environment, and salaries were also good. Therefore many students tried to stay at their alma mater by establishing good relations with a chair head or a faculty dean. Nowadays, university salaries are not competitive so students staying at universities are not necessarily the best ones. However, they are not the worst ones either because otherwise they would not be offered a position.

Concluding Remarks

In Ukraine, academic inbreeding provides universities with a relatively cheap and well-prepared workforce. On the other hand, it also fosters isolation of universities and conservation of existing “traditions” – whether good or bad. Given low academic mobility of both students and professors, this situation prevents dissemination of knowledge and lowers competition, which necessarily leads to degradation.

Currently, inbreeding is not on the agenda of either researchers or policy makers. In fact, no one seems to have considered it as a problem. Perhaps, it will not be discussed as a problem any time soon because there are many other “bigger” problems in Ukrainian higher education. To name a few, these are:

  • high centralization and insufficient level of university autonomy;
  • low salaries and high teaching workload of professors;
  • low extent of university research and very low quality of the existing research, especially in humanities and social sciences;
  • high corruption and low standards of studying and research work (ubiquitous cheating and plagiarism);
  • low sensitivity of educational programs to the needs of modern economy.

Perhaps, introduction of formal limits on inbreeding (setting a quota for both MA graduates admitted to Ph.D. programs and for Ph.D. graduates hired to teaching positions at the same university) could bring some “fresh air” into the system. This measure would extend the pool of candidates available to a university and introduce an element of competition between them. It would also create incentives both for universities to improve their Ph.D. programs and for students to put greater effort into studies.

References

  • Bilyk, Olga and Iuliia Sheron (2012) Do Informal Networks Matter in the Ukrainian Labor Market? EERC Working paper No 12/11E.
  • Coupe, Tom and Hanna Vakhitova (2010). Recent Dynamics of Returns to Education in Transition Countries, KSE/KEI Working paper.
  • Osipian, Ararat (2009). Corruption and Reform in Higher Education in Ukraine, Canadian and International Education, vol. 38, pp. 104-122.
  • Shaw, Marta, Chapman, David and Nataliya Rumyantseva (2011). The Impact of the Bologna Process on Academic Staff in Ukraine, Higher Education Management, vol. 23, pp. 71–91.
  • Stephens, Jason, Romakin, Volodymyr and Mariya Yukhymenko (2010). Academic Motivation and Misconduct in Two Cultures: A Comparative Analysis of US and Ukrainian Undergraduates, International Journal for Educational Integrity, vol. 6, pp. 47–60.