Project: FREE policy brief
The Economics of Russian Import Substitution
This policy brief discusses the economic mechanisms triggered by import substitution policies, associated losses and conditions that ensure positive economic effects. Numerical estimations of potential effects of Russian import substitution policies indicate a decline in GDP, decrease in output of unprotected sectors and consumers’ welfare losses. We conclude with a discussion of the role imports play in economic efficiency.
Import substitution: pro and contra
Two years after joining the WTO, in the new political reality, Russia began implementing a series of import substitution policies. Supported sectors range from agriculture and production of metal products, to computer equipment and special purpose vehicles. The potential economic effects of these policies are of substantial interest and importance both for researchers, policymakers and the general public. However, they have not yet been quantitatively assessed. This policy brief summarizes the results of a study of these effects conducted at CEFIR in 2016 (Volchkova and Turdyeva, 2016).
Import substitution can be implemented by a range of instruments aimed at creating preferential conditions for domestic producers of imported goods compared to foreign competitors. Barriers to trade are the most common and easily available policy tools. Trade barriers lead to price increase on domestic market relative to the world price of the good.
Domestic manufacturers in the protected industry enjoy higher prices on domestic market, thereby securing higher revenues at the same costs. The protected sector also is able to put into operation those capacities that were generating losses in the absence of protective measures. However, if the economy works at full employment in absence of import substitution, then in order to increase production in the protected sectors, factors should be reallocated there from the other sectors. As a result of the import-substituting policy, producers in unprotected sectors will decrease the scale of production, and some will exit the industry. That is, producers that were efficient enough before import substitution policies will be forced out by those that cannot compete at international prices. From the point of view of welfare economics, this maneuver is accompanied by a loss of economic efficiency.
Economic literature discusses several cases when import substitution can be justified, such as a presence of positive external effects from protected sectors to the economy; learning-by-doing effects in protected sectors; and an infant industry argument. All of these cases imply market failures in the absence of government intervention, leading to lower than socially optimal output of the sector in question. Then, government interventions aiming to increase output – such as import substitution – might bring additional welfare improvement to the economy. If any of these effects do take place then the gain brought by protected sectors may compensate for the loss by the unprotected. To validate any of these cases one needs to perform a thorough and independent analysis of the economy based on very detailed information.
Estimates of static and dynamic effects of import substitution
In order to illustrate the potential effects of import substitution policies in the current Russian situation, we use a static CGE model of the Russian Federation constructed at CEFIR.
Based on publicly available documents (Russian Government’s Decrees №2744-Р 29.12.2015 and № 2781-р 31.12.2015), we identify the sectors that are targeted by the import substitution policy: agriculture and four manufacturing sectors (metal production; machinery and equipment; cars; sea crafts, airplanes and spaceships).
To model the effects of import substitution, we calculate an ad valorem tariff equivalent, which ensures a 10% decline of the volume of import in each of five industries. In order to simulate proposed policy measures, we conduct six experiments: increase in import tariffs in each of five industries individually, and a comprehensive policy change with an increase in all five tariffs simultaneously.
If import substitution policy is implemented not by trade policy instruments but only through producer support measures then it will be accompanied only by changes in relative prices for producers while consumer prices will not be affected and will be determined solely by international prices. In this case, our estimates will represent an upper bound of possible consumers’ losses. Since the distortion of relative prices for producers do not depend on a particular instrument chosen to implement import substitution policy then the consequences for other sectors and for efficiency of the overall production will be the same under trade or domestic policy interventions.
Table 1 shows the results of our calculations. Columns (1) – (5) present the estimates of the effects of the import-substitution measures in the relevant sectors. Column (6) reports the results of the comprehensive policy reform.
Table 1. Consequences of the decline in imports by 10% in the protected sector (s).
| Agriculture | Metals | Machinery, and equipment | Cars | Sea crafts, airplanes and space ships | Tariff change in all industries | |
| (1) | (2) | (3) | (4) | (5) | (6) | |
| Ad valorem tariff equivalent, % | 2.9 | 3.9 | 6.1 | 6.7 | 5.6 | |
| Change in | ||||||
| CPI, % | 0.04 | 0.09 | 0.39 | 0.3 | 0.3 | 1.0 |
| Protected sectors’ output, % | 0.7 | 2.5 | 9.8 | 10.3 | 8.3 | 3.8 |
| All other production, % | -0.2 | -0.4 | -0.5 | -0.2 | -0.5 | -2.3 |
| GDP, % | -0.002 | -0.011 | -0.023 | -0.005 | -0.018 | -0.049 |
| Welfare, % | -0.015 | -0.020 | -0.074 | -0.041 | -0.080 | -0.215 |
Source: Authors’ own estimation.
Our results illustrate the anticipated effect of import substitution policy in economy with full employment. The protected industries increase their output at the expense of other industries. An increase in economic inefficiency is reflected by a fall in GDP.
In order to capture dynamic effects of the proposed import substitution policy, we simulate an import tariff increase in a Solow-type growth model calibrated for the Russian economy. The proposed policies result in a deeper economic decline in 2016 than in the baseline scenario (-0.76% in the baseline scenario and -0.79% in the import substitution scenario), followed by somewhat faster growth in subsequent years due to a lower base. The aftermath of the import substitution policy is still visible in 2020: GDP growth in 2020 relative to 2015 in the baseline equals 2.4365%, while the import restriction in all targeted industries will reduce economic growth in a five-year term by 0.007 percentage points, to 2.4295%. The numbers correspond to the expected reduction in economic efficiency as a result of the import substitution measures.
While numbers in terms of GDP do not look particularly large, the annual losses in GDP in nominal figures correspond to $650 million in value added, which is roughly equivalent to 30,000 jobs lost in Russia due to import substitution. Besides, effect on growth adds to 5,000 more jobs lost over 5 years.
As we mentioned above these losses might potentially be justified by the positive external effect from an increased output of the protected industries on the rest of economy. To ensure this, the selection of industries for protection should have been done through independent expertise based on a thorough analysis of sectoral interaction over time. However, the way the economic policy is formulated in modern Russia, with heavy influence of lobbying groups and very little contribution from independent economic research, we can hardly expect that the industries targeted for import substitution satisfy the objective criteria of positive external effects.
Imports as drivers of competitiveness
Classical trade theory shows that imports are a major cause of gains from trade integration. Modern trade theory complements the classical mechanism by selection effects among heterogeneous firms when only the most productive firms are able to sell in foreign markets (Melitz , 2003).
Keeping in mind that a substantial part of manufacturing trade flows consists of intermediate products that are used as inputs in subsequent production (in the case of Russia, the share of intermediates in imports is more than 60%) then the above reasoning implies that the competitiveness of domestic production is determined, among other things, by the availability of cheap imports.
Numerous empirical studies for many countries confirmed that industries with a higher share of imported intermediate goods are more productive than industries with a lower share (Feenstra, Markusen, and Zeile, 1992). Recent studies, analyzing data at the level of individual firms (Bernard at al., 2012; Castro, Fernandes, and Farolec, 2015; Feng, Li, and Swenson, 2016), confirm that the effect takes place at firm level: firms importing more intermediate goods have higher productivity than firms importing less, other things being equal, which suggests that imports of intermediate goods is an important source for the growth of firms’ competitiveness.
A study conducted for Russian firms showed that labor productivity in Russian companies which import intermediate goods is 20% higher compared to similar firms not importing intermediates (Volchkova, 2016).
On this basis, we have every reason to believe that import is one of the sources of economic competitiveness that enhances effectiveness of the economy. Thus import substitution policies in the absence of objective information and a profound selection procedure for protected sectors, are harmful to the economy. In an open economy, the effect of the firms’ selection and the availability of cheap imports ensure growth of sectoral productivity, but productivity declines in “protected” sectors. That is, while our estimates above assess the direct negative impact on Russian economic output and welfare from inefficient reallocation of factors of production, the implementation of import substitution policies also puts the Russian economy in a disadvantaged position relative to more liberal economies on the international markets due to forgone competitiveness. This creates additional obstacles for Russia on its way to export diversification and sustainable growth.
References
- Feenstra, Robert C, James R Markusen, and William Zeile. 1992. “Accounting for Growth with New Inputs: Theory and Evidence.” The American Economic Review 82 (2). American Economic Association: 415–21. http://www.jstor.org/stable/2117437.
- Feng, Ling, Zhiyuan Li, and Deborah L. Swenson. 2016. “The Connection between Imported Intermediate Inputs and Exports: Evidence from Chinese Firms.” Journal of International Economics 101: 86–101. doi:10.1016/j.jinteco.2016.03.004.
- Melitz, Marc J. 2003. “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity.” Econometrica 71 (6). Blackwell Publishing Ltd: 1695–1725. doi:10.1111/1468-0262.004
- Pierola Castro, Martha D., Ana Margarida Fernandes, and Thomas Farolec. 2015. “The Role of Imports for Exporter Performance in Peru.”
- Volchkova, Natalya A. 2016. “Prospects of the export diversification:” Dutch Disease “or the failures of economic policy?” in “Seven lean years: the Russian economy on the verge of structural changes: the round table materials” / ed. Rogov. -Moscow: Foundation “Liberal Mission” (in Russian)
- Volchkova, Natalya A., and Natalia A. Turdyeva 2016, “Microeconomics of Russian import substitution”, Journal of New Economic Association, forthcoming (in Russian)
The Economic Track Record of Pious Populists – Evidence from Turkey
In this policy brief, I summarize recent research on the economic track record of the Justice and Development Party (AKP) in Turkey. The central finding is that Turkey under AKP grew no faster in terms of GDP per capita when compared with a counterpart constructed using the Synthetic Control Method (SCM). Expanding the outcome set to health and education reveals large positive differences in both infant and maternal mortality as well as university enrollment, consistent with stated AKP policies to improve access to health and education sectors for the relatively poorer segments of the population. Yet, even though these improvements benefited women to a large extent, there are no commensurate gains in female labor force participation, and female unemployment has increased under AKP’s watch. Of further concern is the degree to which the SCM method applied to institutional measures fail to find any meaningful early improvements along this dimension, and more often than not reveals adverse institutional trajectories.
The Turkish political economy represents something of a puzzle. After a traumatic financial crisis in 2001, a series of political and economic reforms brought higher economic growth and a promise of EU membership. An authoritarian political elite, spearheaded by a military with a troubled past of controversial coups ousting democratically-elected governments, looked set to give way to a new cadre of political and economic elites who, despite a recent past as radical Islamists, seemed to favor free markets as well as democratic reform.
News media, as well as several international organizations, heaped praise on the Turkish government. In some cases, these represented optimistic interpretations of events, whereas in some cases they inadvertently served to spread a misleading picture of the strength of the Turkish economy. A recent World Bank report described Turkey’s economic success as “a source of inspiration for a number of developing countries, particularly, but not only, in the Muslim world” (World Bank, 2014).
Today, the state of Turkey’s political economy is represented very differently. Several international rankings of political institutions (Meyersson, 2016b) and human rights show Turkey spiraling ever lower, following years of stifling freedom of speech, recurring political witch hunts, and escalating internal violence. Lower GDP growth rates, falling debt ratings and exchange rates are evidence less of a rising new economic giant than a stagnating middle income country under increasingly illiberal rule. A recent IMF staff report (IMF, 2016) noted how Turkey remains “vulnerable to external shocks” and a labor market “marred by rapidly increasing labor costs, stagnant productivity, and a low employment rate, especially among women.”
What has been the AKP’s track record on economic growth in Turkey? While some has described it as an economic success (as noted above), others have pointed out that Turkey’s economic development has not been much more than middling (Rodrik, 2015).
Evaluating the economic track record of the AKP faces numerous challenges. The rise to power of the AKP government came in the wake of one of the worst financial crises in modern history and following a number of substantial economic and political reforms. Finding a candidate for the counterfactual, a Turkey without AKP rule, is challenging and looking solely at time series of Turkish development omits significant trends that likely shape its trajectory.
The focus of my new paper (Meyersson, 2016a) is thus to examine the economic and institutional effects of the AKP in a comparative case study framework. Using the Synthetic Control Method (SCM), developed by Abadie et al. (2010, 2015), I estimate the impact of the AKP on Turkey’s GDP per capita by comparing it to a weighted average of control units, similar in pre-intervention period observables. The construction of such a “synthetic control” avoids the difficulty of selecting a single (or a few) comparable country, and instead allows for a data-driven approach to find the best candidate as a combination of many other countries. This avoids ambiguity about how comparison units should be chosen, especially when done on the basis of subjective measures of affinity between treated and untreated units. The method further complements more qualitative research with a research design that specifically incorporates pre-treatment dynamics, which due to the financial crisis preceding the election of AKP to power, is essential. Similar to a difference-in-differences strategy, SCM compares differences in treated and untreated units before and after the event of interest. But in contrast to such a strategy design, SCM allocates different weights to different untreated units based on a set of covariates.
Figure 1. Results for Turkey’s GDP per capita
Note: Upper graph shows Turkey’s GDP per capita compared to a synthetic counterpart. The middle graph shows the difference between the former and the latter (black line) as well as placebo differences for untreated units (gray lines). The lowest graph plots the weights assigned to countries that constitute the synthetic control for Turkey. See Meyersson (2016a) for details.
As shown in Figure 1, I find that GDP per capita under the AKP in Turkey has not grown faster than its synthetic control. A “synthetic Turkey” (upper graph in Figure 1), which went through similar pre-2003 dynamics in its GDP per capita, also experienced an economic rebound very similar to that of Turkey.
This is robust to a range of specifications that in different ways account for the pre-AKP GDP dynamics. Restricting the set of control units to Muslim countries only, reveals Turkey to have actually grown significantly slower than the weighted combination of the Muslim counterparts. Moreover, a comparison of severe financial crises using SCM shows Turkey’s post-crisis trajectory in GDP per capita to be no faster than its synthetic control. The focus on post-crisis recoveries allows estimation of the composite effect, including both the financial crisis of 2001 as well as the election of AKP and, under the assumption that post-crisis – and pre-AKP – reforms were indeed growth enhancing, provides an upper bound for the effect of the AKP.
These results, however, hide some of the more transformative aspects of how the Turkish economy has changed during the AKP’s reign. Focusing on education outcomes, I instead find large positive effects on university enrollment for both men and women. These improvements are mirrored for key health variables such as maternal and infant mortality, and are likely responses to large-scale policy changes implemented by the AKP that are discussed in Meyersson (2016a). The policy changes include the extensive Health Transformation Program (HTP) implemented by the AKP government (Atun et al 2013), as well as mushrooming of provincial universities from 2006 and onward (Çelik and Gür, 2013).
As such, to the extent that the AKP has engaged in populism from a macroeconomic perspective, it has nonetheless also experienced a significant degree of social mobility, especially among the poorer segments of society. An exaggerated focus on economic output risks obfuscating the structural changes in key factor endowments that could very well prove beneficial in the long run. Still, the improved access to these areas has not been followed by improved outcomes in the labor markets, especially for women. The period under AKP has seen significant reductions in both female labor force participation as well as higher female unemployment. This raises concerns over to what extent the Turkish government has been able to put a valuable talent reserve to productive use, as well as allowing women meaningful labor market returns to education.
Figure 2. Results for Turkey’s gross enrollment in tertiary education
Note: Upper graph shows Turkey’s gross enrollment in tertiary education compared to a synthetic counterpart. The middle graph shows the difference between the former and the latter (black line) as well as placebo differences for untreated units (gray lines). The lowest graph plots the weights assigned to countries that constitute the synthetic control for Turkey. See Meyersson (2016a) for details.
An evaluation of the AKP’s institutional effect using multiple institutional indicators, measuring various aspects ranging from institutionalized authority, liberal democracy, and human rights results in a failure to find any durable early positive effects during AKP’s tenure. In the longer run, for all outcomes the overall effect seems to have been clearly negative. Finally, the significant reduction in military rents, whether measured in terms of expenditure or personnel, is illustrative of the degree to which the military’s political power diminished relatively early on, and posits concerns over lower economic rents as another source of friction between the civil and military loci of power in the country.
Overall, the results point to Turkey undergoing a transformative period during the AKP, socioeconomically as well as politically. Even though the initial years of higher GDP per capita growth under the AKP, in absolute terms, dwindle significantly in comparison to a synthetic counterpart, increased access to health and education provide reasons for political support of a government that has extended a socioeconomic franchise to a larger segment.
References
- Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Synthetic Control Methods for Comparative Case Studies: Estimating the Effects of California’s Tobacco Control Program,” Journal of the American Statistical Association, 105 (2010), 493-505.
- Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Comparative Politics and the Synthetic Control Method,” American Journal of Political Science, 2015, 59 (2), 495-510.
- Atun, Rifat, Sabahattin Aydin, Sarbani Chakraborty, Safir Sümer, Meltem Aran, Ipek Gürol, Serpil Nazlıoğlu, Şenay Özğülcü, Ülger Aydoğan, Banu Ayar, Uğur Dilmen, Recep Akdağ, “Universal health coverage in Turkey: enhancement of equity,” The Lancet, Vol 382 July 6, 2013.
- Çelik, Zafer and Bekir Gür, “Turkey’s Education Policy During the AKP Party Era (2002-2013),” Insight Turkey, Vol. 15, No. 4, 2013, pp. 151-176
- International Monetary Fund, “Staff Report for the 2016 Article IV Consultation: Turkey,” IMF Country Report No. 16/104
- Meyersson, Erik, 2016a, “’Pious Populists at the Gate’ – A Case Study of Economic Development in Turkey under AKP”, working paper.
- Meyersson, Erik, 2016b, “On the Timing of Turkey’s Authoritarian Turn”, Free Policy Brief, http://freepolicybriefs.org/2016/04/04/timing-turkeys-authoritarian-turn/
- Rodrik, Dani, 2015, “Turkish Economic Myths”, http://rodrik.typepad.com/dani_rodriks_weblog/2015/04/turkish-economic-myths.html
- “The World Bank, Turkey’s Transitions: Integration, Inclusion, Institutions.” Country Economic Memorandum (2014, December).
Does Product Market Competition Cause Capital Constraints?
At the very center of Schumpeter’s (1934, 1942) notion of creative destruction is firms’ access to bank capital, which helps to fund the innovation in competitive product markets that drives out less productive firms in favor of those with more profitable ideas. However, competition is a two-edged sword and may result in firms being unable to fund all of their otherwise economically profitable investments. Using unique survey data from 58 countries, Bergbrant, Hunter, and Kelly (2016) find that product market competition increases capital constraints and has a greater effect than banking sector competition. Further, we show that quantity-of-capital constraints negatively impact firm growth.
Capital and creative destruction
At the very center of Schumpeter’s (1934, 1942) notion of creative destruction is firms’ access to bank capital, which helps to fund the innovation in competitive product markets that drives out less productive firms in favor of those with more profitable ideas. While product market competition may be the fundamental driver of the innovation envisioned by Schumpeter, it may also impede access to the very source of capital that is supposed to fund that innovation. More intense product market competition can affect firms’ ability to finance their projects either by increasing the price of financing or by inducing capital constraints, whereby firms are unable to obtain the quantity of capital needed to fund all their positive net present value projects.
Recent research has focused on the price side of financing, showing that product market competition increases the cost of equity (Hou and Robinson, 2006) and the cost of debt (Valta, 2012). In this brief we examine the quantity side of financing; that is, whether product market competition increases capital constraints.
Isn’t it obvious that competition causes capital constraints?
Actually, no. There is a familiar argument that firms are reluctant to disclose commercially valuable information when competitors are more likely to exploit this information. Theory predicts that it is not optimal for creditors to respond to the resulting asymmetric information by raising interest rates; instead, restricting capital is more appropriate (Stiglitz and Weiss, 1981). However, competition may have the very opposite effect because a competitive environment lowers owners’ cost of monitoring and measuring managerial performance. Theory and recent empirical tests indicate that lower cost of monitoring managers induces greater disclosure by owners.
Whether or not product market competition makes banks restrict the supply of loans is arguably more important than whether it influences the cost of debt. Greenwald, Stiglitz, and Weiss (1984) show that firms’ investment behavior is not particularly sensitive to the interest rates they pay, consistent with the notion that increases in the cost of debt may reduce investment, but only at the margin; i.e., projects change from generating economic profits to generating economic losses (net present value changes from positive to negative). By contrast, increased capital constraints can lead to underinvestment by forcing firms to abandon projects which generate economic profits (net present values are positive), thus hindering investment and preventing firm innovation and growth (see Harford and Uysal, 2014).
What does the research tell us?
Recent research by Bergbrant, Hunter, and Kelly (2016) uses survey data obtained from the World Bank’s World Business Environment Survey, conducted among non-financial firms from around the world. Capital constraints are the response to a question about the extent of the obstacle to operations and growth posed by capital constraints that managers and owners rank from 1 (No Obstacle) to 4 (Major Obstacle). Competition is represented by an index constructed from eight individual forms of competition reported by firms.
The empirical evidence indicates that the intensity of product market competition significantly increases capital constraints. Table 1 shows the marginal effects of a change in the intensity of competition on capital constraints. For instance, the first row shows that a small (instantaneous rate of) increase in product market competition leads to an increase in the likelihood that capital constraints are a “major obstacle” (4 on a four- point scale) at a rate of 18.9%. Similar results hold when competition is assessed at a one-standard-deviation (3rd row) increase or when competition changes from 0 to 1 on a version of our competition index which ranges from 0 to 1 (5th row).
Table 1: Effect of competition on capital constraints
| For a change of:
|
No obstacle
(1) |
Minor obstacle
(2) |
Mod. obstacle
(3) |
Major obstacle
(4) |
| Marginal | -0.147 | -0.052 | 0.010 | 0.189 |
| p-value | (0.000) | (0.000) | (0.062) | (0.000) |
| +SD | -0.042 | -0.017 | 0.000 | 0.059 |
| p-value | (0.000) | (0.000) | (0.925) | (0.000) |
| 0 to 1 | -0.145 | -0.059 | 0.008 | 0.196 |
| p-value | (0.000) | (0.000) | (0.165) | (0.000) |
Note: The table reports the marginal effects “for a change of” product market competition of varying amounts on firms responding that capital constraints pose one of the four levels of “obstacle” for their operations.
The above results are qualitatively similar when the competition index is replaced by any one of its eight individual components. In addition, competition increases not only a measure of general capital constraints, as employed in the above analysis, but also specific forms of capital constraints. These include the credit constraints that firms experience when, as a precondition for lending, banks require that borrowers have special connections in the banking sector, pledge collateral, satisfy banks’ bureaucratic need for business documents, and pay bribes to corrupt bank officials. Further, the evidence is not unique to domestic bank capital as more intense product market competition also impedes firms’ access to nonbank equity, foreign bank capital, special export financing, and lease financing.
To further validate our main result we account for two well-established strands of research that contend that banking sector competitiveness is among the most important determinants of access to credit and that banking sector structure can also affect the competiveness of non-financial firms’ industries. The evidence reported in Table 2 shows that while (one of three measures of) banking sector competition and the degree of bank freedom affect capital constraints, in general the regulatory structure of the banking sector does not. More important, our main finding is unchanged when controlling for banking sector structure. Finally, it is important to note that in all our models we control for any cost-of-debt (higher-interest-rate) effects.
Table 2: Accounting for banking sector structure
| Competition (10 separate models) | +ve signif. |
| Lerner bank competition index | +ve, signif. |
| Bank concentration ratio | insignif. |
| Boone indicator of banking sector | insignif. |
| private credit as a fraction of GDP | insignif. |
| restrictions on nonbank activities | insignif. |
| fraction of bank applications denied | insignif. |
| bank freedom from gov’t interference | -ve, signif. |
| existence of a credit registry | insignif. |
| foreign bank share of banking system | insignif. |
| government share of banking system | insignif. |
Note: We augment our main model with the above banking sector variables, one at a time, to determine their impact on the significance (signif. or insignif.) of product market competition.
Capital constraints hurt firms’ growth and so we expect our measure of capital constraints to be negatively associated with growth. We confirm this in the data, after controlling for the direct impact of competition on growth. We also find that the quantity-of-capital effect has a greater impact on expected firm growth than the cost-of-capital effect.
Conclusion
Our research indicates that the intensity of product market competition increases capital constraints even in the presence of controls for banking sector competition. Our work suggests several policy recommendations. First, the implementation of a product-market competition policy, for instance by several Central and Eastern European countries in the 1990s (Fingleton et al., 1996; Dutz and Vagliasindi, 2000), should contemplate the possibility that such action is likely to have negative externalities for firms’ access to capital. Second, banking sector reforms aimed at creating a more competitive banking system in order to improve access to capital should not be pursued in isolation and should take into consideration the existing competitiveness of the product market. Third, given that the quantity-of-capital effect has a greater impact on firm growth than the cost-of-capital effect, policymakers should exert at least as much effort in easing quantity constraints as they do to reduce the cost of capital.
References
- Bergbrant, M.; D. Hunter; and P. Kelly, 2016. “Product Market Competition, Capital Constraints and Firm Growth”. Available at SSRN: https://ssrn.com/abstract=2594218.
- Dutz, M. A.; and M. Vagliasindi, 2000. “Competition policy implementation in transition economies: An empirical assessment”. European Economic Review 44, 762-772. Fingleton, J.; E. Fox; D. Neven; and P. Seabright, 1996. “Competition policy and the transformation of central and eastern Europe”. Working paper. CEPR, London.
- Greenwald, B.; J.E. Stiglitz; and A. Weiss, 1984. “Informational imperfections in the capital market and macroeconomic fluctuations”. American Economic Review 74(2), 194-199.
- Harford, J.; and V. B. Uysal, 2014. “Bond market access and investment”. Journal of Financial Economics 112, 147-163.
- Hou, K.; and D. Robinson, 2006. “Industry concentration and average stock returns”. Journal of Finance 61, 1927-1956.
- Schumpeter, J.A., 1934. “The Theory of Economic Development”. Harvard University Press, Cambridge, MA.
- Schumpeter, J. A., 1942. “Capitalism, Socialism and Democracy”. Harper and Brothers, New York, NY.
- Stiglitz, J.; and A. Weiss, 1981. “Credit rationing in markets with imperfect information”. Amer. Econ. Review 71, 393-410.Valta, P., 2012. “Competition and the cost of debt”. Journal of Financial Economics, 105(3), 661-682.
What Does Ukraine’s Orange Revolution Tell Us About the Impact of Political Turnover on Economic Performance?
Political turnover is a normal, even desirable, feature of competitive politics, yet turnover in a context of weak institutions can create policy uncertainty, disrupt political connections, and threaten the security of property rights. What is the impact of political turnover on economic performance in such an environment? We examine the behavior of over 7,000 enterprises before and after Ukraine’s Orange Revolution—a moment of largely unanticipated political turnover in a country with profoundly weak institutions. We find that the productivity of firms in regions that supported Viktor Yushchenko increased after the Orange Revolution, relative to that of firms in regions that supported Viktor Yanukovych. Our results illustrate that the efficiency consequences of turnover can be large when institutions are weak.
Introduction
Politics in much of the world is a winner-take-all contest. When Viktor Yanukovych fled Kyiv in February 2014, for example, he was joined by a close group of associates overwhelmingly drawn from the country’s Russian-speaking East, including Yanukovych’s home region of Donetsk. The governors who ran Ukraine’s regions under Yanukovych fared no better. Oleksandr Turchynov, who served as acting president from February to June of that year, did what all Ukrainian presidents do: he fired the existing governors and replaced them with figures friendly to the new regime.
What is the impact of such political turnover on economic performance? In principle, replacement of political elites can have profound consequences for enterprise owners and managers, who rely on the support of patrons in government for government contracts, direct and indirect subsidies, the security of property rights, and permits to do business. In a system without effective checks and balances, economic policy can also swing widely as power passes from one group to another. Yet little is known about the impact of such changes on firm productivity, a major driver of economic welfare.
We examine the impact of political turnover on productivity and other aspects of firm performance in “The Productivity Consequences of Political Turnover: Firm-Level Evidence from Ukraine’s Orange Revolution” (Earle and Gehlbach, 2015). Our main finding is that the productivity of firms in regions that supported Yushchenko, the eventual winner of the 2004 presidential election, increased after the Orange Revolution, relative to that of firms in regions that supported Yanukovych, the chosen successor of incumbent President Leonid Kuchma. These results demonstrate that political turnover in a context of weak institutions can have major efficiency consequences as measured by differences in firm productivity.
Ukraine in 2004
Three factors make Ukraine in 2004 an appropriate setting for identifying the effect of political turnover on economic performance. First, Ukraine under Kuchma was a paradigmatic case of “patronal presidentialism,” in which the president “wields not only the powers formally invested in the office but also the ability to selectively direct vast sources of material wealth and power outside of formal institutional channels” (Hale 2005, p. 138). Who won the presidential contest had enormous implications for economic activity.
Second, economic and political power was regionally concentrated in Ukraine’s Russian-speaking East—Yanukovych himself was closely affiliated with oligarchs in Donetsk—while the political opposition represented by Yushchenko had its base in the ethnically Ukrainian and less industrialized West. Voting in Ukraine’s 2004 presidential election reflected this regional divide.
Third, few gave Yushchenko much chance of winning the presidency until the presidential campaign was well underway. In the end, it took not only a highly contested election, but also sustained street protests to wrest power from the existing elite.
Together, these considerations imply not only that political turnover in Ukraine could have an impact on firm performance, but also that any such effect could be observed by comparing the performance of enterprises in regions supportive of the two candidates before and after Yushchenko’s unexpected election victory.
The Orange Revolution and Firm Performance
To analyze the impact of political turnover, we use data on over 7,000 manufacturing enterprises that we track over many years, both before and after the Orange Revolution. We compare the evolution of productivity across firms in regions by vote in the 2004 election that was won by Yushchenko, while controlling for any shocks to particular industries in any year, for constant differences across firms in the level or trend of their productivity, and for regional differences in industrial structure. This design avoids many of the other influences on firm-level productivity that might have coincided with the Orange Revolution.
Our primary finding is that the productivity of firms in regions that supported Yushchenko in 2004 increased after Yushchenko took power, relative to the productivity of firms in regions that supported Yanukovych (and, implicitly, his patron Kuchma, whom Yushchenko succeeded as president). This effect is most pronounced among firms that had the most to gain or lose from presidential turnover: firms in sectors that rely on government contracts; private enterprises, given Ukraine’s weak property rights; and large enterprises. Other measures of economic performance suggest that these results are driven by favorable treatment of particular firms, either before or after the Orange Revolution, rather than by broad changes in economic policy.
Conclusion
Political turnover is often desirable. Nonetheless, our results suggest that the distributional consequences can be profound when institutions are weak, that is, when access to those in power is the primary guarantee of market access, contract enforcement, and property-rights protection. Oscillation of privilege from one region or sector to another is inefficient, as firms initiate or postpone restructuring based on who is in power. The optimal solution, of course, is not to restrict turnover, but to make turnover safe for economic activity. This requires that institutions be reformed to guarantee equal treatment for all economic actors—a difficult process that has proceeded with fits and starts in post-Yanukovych Ukraine.
References
- Earle, John S.; and Scott Gehlbach, 2015. “The Productivity Consequences of Political Turnover,” American Journal of Political Science, 59(3), 708–723.
- Hale, Henry E, 2005. “Regime Cycles: Democracy, Autocracy, and Revolution in Post-Soviet Eurasia,” World Politics, 58(1), 133–65.
Russia and Oil — Out of Control
Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.
In a recent working paper (Becker, 2016), I take a careful look at the statistical relationship between Russian GDP and international oil prices. This brief summarizes this analysis and its policy conclusions.
Russia and oil, the basics
Although Russia’s oil dependence is discussed every time international oil prices drop, it is not uncommon to hear that oil is not really so important for the Russian economy. The argument is that the oil and natural resource sector only accounts for some 10 percent of Russian production. This is indeed consistent with the official sectoral breakdown of GDP that is shown in Figure 1 where the minerals sector indeed only has a 10 percent share.
Figure 1. Structure of GDP in 2015
Source: Federal State Statistics Service, 2016
However, this static picture of production shares does not translate into a dynamic macro economic model that allows us to understand what is driving Russian growth. Instead a careful analysis of the time series of Russian GDP is required to understand how important oil is for growth.
Russian GDP can be measured in many different ways: nominal rubles, real rubles, U.S. dollars, or in purchasing power parity (PPP) terms to mention the most common. Here we focus on GDP measured in real rubles and U.S. dollars since we want to get rid of Russian inflation, which has been quite high for most of the studied time period. The PPP measure generates figures and numerical estimates that are in between the real ruble and U.S. dollar measures and are not included here to conserve space.
The first evidence of the importance of international oil prices as a major determinant of Russian income at the macro level is presented in Figures 2 and 3 where the first figure shows dollar income and the second real ruble income. In both cases it is obvious that there is a strong correlation and that the correlation is higher for income measured in dollars.
Figure 2. U.S. dollar GDP and the oil price
Source: IMF, 2016
Figure 3. Real ruble GDP and the oil price
Source: IMF, 2016
However, it is also clear that all the time series have some type of trends or in econometric language, are non-stationary. This means that simple correlations of the time series shown in Figure 2 and 3 may not be statistically valid (or “spurious” as it is called in the literature). This is not a critical issue but can be handled by regular econometric methods.
Russia and oil, the econometrics
When time series are non-stationary they need to be transformed to some stationary form before we can do regular regressions (in Becker, 2016 I also address the issue of using a framework that allows for co-integration).
Two transformations that make the variables stationary are to use first differences or percent growth rates. Both are used before we run simple regressions of growth or first differences of GDP on growth or first difference in international oil prices. The full sample starts in 1993, but since the early years of transition were subject to many different shocks at the same time, a shorter sample starting in 2000 is also used.
A number of observations come from the estimates that are presented in Table 1: Oil prices are always statistically significant; the adjusted R-squared is higher for dollar income than real rubles (with one exception due to a large outlier in 1993); overall the explanatory power of these simple regressions are very high (42-92 percent) and the explanatory power increases in all specifications when going from the full sample (1993-2015) to the more recent sample (2000-2015). Note that the latter sample perfectly overlaps with the current political leadership so contrary to some wishes; the oil dependence has not been reduced under Putin/Medvedev.
Table 1. Russian macro “models”
Source: Becker 2016
Russia and oil, the forecasts
The strong correlation between international oil prices and Russian GDP provides a very simple econometric model for explaining past variations in Russian GDP. Unfortunately it does not imply that it is easy to forecast Russian GDP since international oil prices are very hard to predict. There are many models that have been used to forecast oil prices, but the IMF and many others now use the market for oil futures to generate its central forecast of oil prices.
The IMF also provides confidence intervals around the central forecast, and the uncertainty surrounding the forecast is substantial: In the latest forecast the 68 percent confidence interval goes from around 20 dollars per barrel to 60 one year ahead, while the 98 percent interval ranges from 10 dollar per barrel to around 85. With oil currently around 45 dollars per barrel, these variations imply that oil prices could either halve or double in the next year, not a very precise prediction to base economic policy on for Russia since the estimates for real ruble growth in the later sample in Table 1 imply that Russian GDP growth in real ruble terms could be anywhere from minus 5 to plus 10 percent, or a fifteen percentage point difference!
If we look at past IMF forecasts of oil prices and Russian GDP and see how much they deviate from actual values a year later we can compute one year ahead forecast errors. We can do this calculation for the last 16 years for which the IMF data is available. Figures 4 and 5 show how the forecast errors in oil prices correlate with the forecast errors for dollar income and real ruble income, respectively. Similar to the regressions presented in Table 1, the correlations are very high for both measures of GDP: 82 percent for dollar GDP, and 65 percent for real ruble GDP.
In other words, a very large share of the uncertainty surrounding Russian GDP forecasts can be directly attributed to variations in international oil prices, a variable that (again) Russia does not control. The fact that the variations in oil prices explain somewhat more of the variation in dollar income compared to real ruble income is a result of a policy change that in later years allowed the exchange rate to depreciate much more rapidly when oil prices fall.
Figure 4. Forecast errors
Source: Becker 2016
Figure 5. Forecast errors
Source: Becker 2016
Policy conclusions
The depth of Russia’s oil dependence is much greater than what casual observers of the mineral sectors share of GDP would suggest. At the macro level, variations in international oil prices explain at least two thirds of actual Russian growth and even more of the one-year ahead forecasts errors.
The experience of the 2008/09 global financial crisis provided an important lesson to Russian policy makers, which is that exchange rate flexibility is required to dampen the real impact of falling oil prices and to protect both international reserves and the fiscal position. In the more recent years, the currency has been allowed to depreciate in tandem with falling oil prices and the drop in real ruble income was therefore less severe in 2015 than in 2009. Income in dollar terms, instead, took a greater hit, but this was a necessary corollary to protecting reserves and the budget. A flexible exchange rate and gradual move to inflation targeting in combination with accumulating fiscal reserves in times of high oil prices are key to Russia’s macro economic stability.
Nevertheless, these policies are not sufficient to remove the long-run impact that low or declining oil prices will have on growth, measured both in real ruble terms or dollar terms. It is nice to have fire insurance when your house burns down, but when you rebuild the house you may want to consider not building another straw house. For Russia to build a strong economy that is not completely hostage to variations in international oil prices, fundamental reforms that encourage the development of alternative, internationally competitive, companies are needed. This includes reforms that initially will reduce policy makers control over the economy and legal system, but over time it will provide the much needed diversification away from exporting oil that puts the fate of the Russian economy squarely in the hands of international oil traders. Losing some control today may provide a lot more control in the future for the country as a whole, but perhaps at the expense of less control for the ruling elite.
References
- Becker, T, 2016, “Russia’s oil dependence and the EU”, SITE Working paper 38, August.
- Federal State Statistics Service (or Goskomstat), 2016, data http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/en/figures/domestic/
- IMF, 2016, World Economic Outlook, April data from http://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx
Will New Technologies Change the Energy Markets?
With an increasing world demand for energy and a growing pressure to reduce carbon emissions to slow down global warming, there is a growing necessity to develop new technologies that would help addressing demand and carbon footprint issues. However, taking into account the world’s dependence on hydrocarbons the question remains – can new technologies actually change the energy markets? In this policy brief, we highlight challenges and opportunities that new technologies will bring for energy markets, in particular wind energy, smart grid technology, and electromobility, that were discussed during the 10th SITE Energy Day, held at the Stockholm School of Economics on October 13, 2016.
The expanding world population and economic growth are considered the main drivers of the global energy demand. Up to 2040, total energy use is estimated to grow by 71% in developing countries and by 18% in the more mature energy-consuming OECD economies (IEA, 2016). In parallel, many countries (including the world’s biggest economies and largest emitters: USA and China) have signed the Paris agreement – the first-ever universal, legally binding global climate deal that aims to reduce emissions and to keep the increase in global average temperature from exceeding 2°C above pre-industrial levels.
Meeting a growing global energy demand, and at the same time reducing CO2 emissions, cannot be achieved by practicing ‘business as usual’. It will require some fundamental changes in the way economic activity is organized. In this context, the development of new technologies and how it will affect the energy sector is a crucial element.
Wind power, smart grid, and electromobility
With technological progress and support schemes to decrease CO2 emissions, wind energy is now a credible and competing alternative to energy produced from coal, gas and oil. In 2015, wind accounted for 44% of all new power installations in the 28 EU member states, covering 11.4% of Europe’s electricity needs (see here).
This new technology has triggered a downward pressure on energy prices because of a “Merit order effect” (i.e. a displacement of expensive generation with cheaper wind). While consumers may appreciate this development, Ewa Lazarczyk Carlson, Assistant professor at the Reykjavik University (School of Business) and IFN, stressed that the increasing importance of wind energy challenges the functioning of electricity exchange. First, a lower price has reduced the incentives to invest in conventional power plants necessary when the wind is not blowing or when it is dark. Moreover, with the renewable energy intermittency, the probability of system imbalance and price volatility has increased. In turn, this has led to an increase of maintenance costs for conventional generators due to their dynamic generation costs (i.e. start-ups and shut-down costs).
Digital technology has gradually been used in the energy sector during the last decades, changing the way energy is produced and distributed. With smart grid (i.e. an electricity distribution system that uses digital information) energy companies can price their products based on real time costs while customers have access to better information, allowing them to optimize their energy consumptions. Sergey Syntulskiy, Visiting Professor at the New Economic School in Moscow, stressed that smart grids have had at least two effects. They have made the integration of renewable energy to the system easier and have allowed for prosumers, i.e. entities that both consume and produce energy. The next step is to develop new regulatory incentives to optimize energy systems as well as to provide a legal framework for the exchange of information in the energy sector.
One of the main pollutants has long been the transport sector that accounts for 26% energy-related of CO2 emission (IEA, 2016). Electromobility – that is, use of electric vehicles – is often considered the solution for this problem. When this technology is widely adopted, a major switch from oil to electricity is expected for the transportation sector. Mattias Goldmann, CEO of Fores, argued that even if electromobility will improve air quality and reduce noise levels in cities, its positive impact relies on smart grids and locally produced energy. Moreover, the environmental benefits will be ensured only if electric energy is produced from renewable and clean sources.
Toward a carbon-neutral energy system?
The Nordic countries are currently pushing for a near carbon-neutral energy system in 2050. Markus Wråke, CEO at the Swedish Energy Research Centre, emphasized that the Nordic Carbon-Neutral Scenario is only feasible if new technologies allow for a significant change of energy sources and a better interconnected market (see report by IEA 2016 b).
To cut emissions, a decrease in oil and gas consumption in energy production and within the transport sector is needed (see Figure 1). The adoption of electric vehicles (EVs) and hybrid cars is very likely to drastically increase in the next decades (EVs may have a share of 60% of the passenger vehicle stock in 2050, IEA 2016b).
Figure 1. Nordic CO2 emissions in the CNS
Source: IEA, 2016.
There are currently limited technology options to reduce emissions for big industrial energy consumers. Moreover, there is a concern that those industries may choose to relocate if the Nordic emission standards are too strict. It is therefore important to have low and stable electricity prices. This can only be achieved if cross-border exchanges are improved (which means that the electricity trade in the Nordic region will have to increase 4-5 times by 2050). It is unclear however how policy makers will create a regulation that incentivizes energy companies to build interconnections and increase trade both between the Nordic countries, and the Western and Eastern European countries.
Figure 2. Electricity trade 2015 and 2050
Source: IEA, 2016.
Energy producers
Another concern is that energy-exporting and energy-importing countries may have opposing attitudes towards investing and developing new energy technologies. Countries among the biggest energy producers and exporters depend on a stable demand and price for energy. For example, Russian GDP growth depends between 50-92% on the oil price, depending on the variables used for calculations, as mentioned by Torbjörn Becker, Director of SITE. For large exporters of hydrocarbon, new energy technologies may be seen as a threat because of a potentially reduced energy demand and an increased price volatility that will, in turn, create fundamental issues to balance state budgets and improve living standards.
Figure 3. The Relationship between Russian GDP and oil price
Source: Calculations by Torbjörn Becker, October 13, 2016
The challenge of security of supply
To summarize, new energy technologies will drive energy companies towards optimizations and cost cutting, bring previously unseen connectivity to energy markets and make energy markets more complex. Samuel Ciszuk, Principal Advisor at the Swedish Energy Agency, stressed that interconnected, more complex and interdependent energy systems might increase the vulnerability of energy systems to external threats and intimidates to decrease the security of supply. Technological change and increased competition with lower profit margins will force companies to minimize their expenditure on energy production, storage and transmission and to find cheaper financing options. Optimization and searches for cheaper financing instruments will push energy companies towards selling some of the company assets to financial investors. These changes will create a more decentralized energy market, with more players. Such energy systems will become harder to govern in times of an energy crisis and external threats. Policy makers will have to design new and more complex regulations to fit the needs of the transforming energy markets.
References
- Fogelberg, Sara and Ewa Lazarczyk, 2015. “Wind Power Volatility and the Impact on Failure Rates in the Nordic Electricity Market”, IFN Working Paper 1065.
- IEA, Annual Energy Outlook, 2016a.
- IEA/OECD/Norden, 2016b. “Nordic Energy Technology Perspectives” (see here)
- Speaker presentation from the 10th Energy day, 2016 (see here)
Pay-for-Performance and Quality of Health Care: Lessons from the Medicare Reforms
Health care attracts major attention in terms of hospital and physician reimbursement, owing to the large share of public expenditures and the presence of welfare issues demanding regulation. The focus of this policy brief is quality adjustments of prospective payments in the health sector. Using the data on the 2013 reform in Medicare, we show differential effects of value-based purchasing, where price setting is related to benchmark values of quality measures. The theoretical and empirical evidence indicates that unintended effects appear for acute-care U.S. hospitals at the best percentiles of quality. The findings provide insights into benchmarking within pay-for-performance schemes in health care.
Overview
The Russian national project “Health”, which was started by the federal government a decade ago and has expanded to regionally financed hospitals, is an example of a public remuneration scheme targeted at increasing health care efficiency. The project emphasized the role of the primary sector and raised salaries of general practitioners. A part of salaries was linked to patients’ assessment of the quality of health care. The reimbursement was seen as a means to stimulate higher quality.
However, cautiousness is required in introducing such payment mechanisms. Indeed, international experience shows that quality-related pay in health care may lead to heterogeneous effects across different groups of providers. A recent CEFIR working paper uses administrative panels of the U.S. hospitals to analyze the changes in quality owing to the introduction of the quality-pay.
The U.S. Health Care Sector
Pilots of pay-for-performance
In the early 2000s, numerous private and public programs linking quality and reimbursements in health care existed in the U.S., mostly at employer or state level (Ryan and Blustein, 2011; Damberg et al., 2009; Pearson et al., 2008). A nationwide pilot of quality-performance reimbursement started with the Hospital Quality Incentive Demonstration, where quality measures for five clinical conditions (heart failure, acute myocardial infarction, community-acquired pneumonia, coronary-artery bypass grafting, and hip and knee replacements) were accumulated from voluntarily participating hospitals. Some of these quality-reporting hospitals opted for the pay-for-performance project (initially established for 2003-2006, and later extended to 2007-2009). The project provided respectively 2% and 1% bonus payments for hospitals in the top and second top deciles of each quality measure (as of the end of the third year of the project). Hospitals in the bottom two deciles, on the other hand, were to receive 1-2% penalties (Kahn et al., 2006). Overall, the financial incentives helped improving the quality of the participating hospitals, but the improvement was inversely related to baseline performance (Lindenauer et al., 2007). Moreover, low-quality hospitals required most investment in quality increase; yet, they were not financially stimulated (Rosenthal et al., 2004).
The accumulation of the measures within the Hospital Quality Incentive was followed by the launch of the Surgical Care Improvement Project (SCIP) and Hospital Consumer Assessment of Healthcare Providers (HCAHPS). HCAHPS was the first national standardized survey with public reporting on various dimensions of patient experience of care. The measures of the clinical process of care domain are collected within the Hospital Inpatient Quality Reporting (IQR) program. These are measures for acute clinical conditions stemming from the Hospital Quality Incentive (i.e. acute myocardial infarction, heart failure, pneumonia), as well as measures from the Surgical Care Improvement Project and Healthcare Associated Infections.
The 2013 reform of Medicare
The success of the pilot project in the U.S. in terms of average enhancement of hospital quality has resulted in the nationwide introduction of these reimbursement policies. Namely, a value-based purchasing reform started at Medicare’s acute-care hospitals in the fiscal year of 2013. The reform decreased Medicare’s prospective payment to each hospital by a factor α and redistributes the accumulated fund. As a result of this rule, all hospitals performing below the mean value of the aggregate quality are financially punished, as their so-called adjustment coefficient is less than unity. At the same time, hospitals above the mean value are rewarded (See details in the Final Rule for 2013: Federal Register, Vol.76, No.88, May 6, 2011.)
The aggregate quality – called the total performance score – is a weighted sum of the scores of the measures in several domains: patient experience of care, clinical process of care, outcome of care, and efficiency. The scores on each measure are based on the hospital’s position against the nationwide distribution of all hospitals. In short, positive scores are given to hospitals above the median, and higher scores correspond to performance at the higher percentiles. The scores are a stepwise function, assigning flat values of points to subgroups within a given percentile range. Hospitals above the benchmark (the 95th percentile or the mean of the top decile) are not evaluated according to their improvement relative to the performance in the previous year.
If one assumes that hospitals are only maximizing profit, then such a linear payment schedule should stimulate quality increases across all spectrums of hospitals. However, the theoretical literature generally separates the hospital management, interested in profits, from the physicians who make decisions affecting the level of quality. In particular, physicians are treated as risk-averse agents, who have a decreasing marginal utility of money; that is, their valuation of monetary gains of a certain size decreases as their income increases. In such behavioral model (Besstremyannaya 2015, CEFIR/NES WP 218) physicians’ decisions about the quality of care is shaped by the trade-off between the potential losses they may incur if fired in case of hospital budget deficit and/or bankruptcy and their own costly effort to maintain and improve quality.
In this respect, the reform introduced two mechanisms: (1) it decreased the level of reward for low-quality hospitals and increased it for high-quality hospitals; and (2) it established a positive dependence of reward on quality. We show that the two forces compete, and the first one may outweigh the second for physicians at hospitals with high quality. Indeed, in these hospitals improved budget financing makes the bankruptcy, and probability of firing, less likely. As a result, physicians may be satisfied with a given sufficient level of a positive reward and not willing to exert any further efforts to raise the amount of this reward. Furthermore, physicians may even become de-stimulated. As a result, in these higher quality hospitals, the quality of care stabilizes or even goes down after the reform.
To sum up, we hypothesize that quality scores increase at the lowest tails of the nationwide distribution, while it may stay stable or fall among the highest quality hospitals. The sign of the mean/median effect is ambiguous.
Empirics
Data on quality measures and hospital characteristics such as urban/rural location and ownership come from Hospital Compare. The panel covers the period from July 2007 to December 2013, and consists of 3,290 hospitals (12,701 observations). We exploit first-order serial correlation panel data models – longitudinal models where the value of the dependent variable in the previous period (lagged value) becomes one of the explanatory variables (see notations and definitions of analyzed measures in Tables 1-2.) The empirical part of the study evaluates the impact of the reform on changes of the quality scores of hospitals belonging to different percentiles of the nationwide distribution of each quality measure.
Table 1. Patient experience of care
| Comp-1-ap | Nurses always communicated well |
| Comp-2-ap | Doctors always communicated well |
| Comp-3-ap | Patients always received help as soon as they wanted |
| Comp-4-ap | Pain was always well controlled |
| Comp-5-ap | Staff always gave explanation about medicines |
| Clean-hsp-ap | Room was always clean |
| Quiet-hsp-ap | Hospital always quiet at night |
| Hsp-rating-910 | Patients who gave hospital a rating of 9 or 10 (high) |
Notes: Score on each measure is the percent of patients’ top-box responses to each question.
Table 2. Clinical process of care
| AMI-8a | Primary PCI received within 90 minutes of hospital arrival |
| HF-1 | Discharge instructions (heart failure) |
| SCIP-Inf1 | Prophylactic antibiotic received within 1 hour prior to surgical incision |
| SCIP-Inf3 | Prophylactic antibiotics discontinued within 24 hours after surgery end time |
| SCIP-Inf4 | Cardiac surgery patients with controlled 6 a.m. postoperative blood glucose |
| SCIP-VTE2 | Surgery patients who received appropriate venous thromboembolism prophylaxis within 24 hours prior to surgery to 24 hours after surgery |
Notes: Score on each measure is the percent of percent of cases with medical criteria satisfied.
The results of the estimates offer persuasive evidence for a non-rejection of our hypotheses: quality goes up at 1-5th deciles and falls at the 6-9th deciles (see Figures 1-2).
Figure 1. Mean change of scores owing to value-based purchasing across percentile groups of hospitals
It should be noted that the hypotheses concerning differential effects also rely on the fact that there is a certain population of hospitals to which each of the step-rates apply (Monrad Aas, 1995). Hence, the threshold and/or benchmark value in the national schedule may be worse than the value in a given hospital. Therefore, reimbursement with benchmarking becomes an additional cause of undesired effects.
Figure 2. Mean change of scores owing to value-based purchasing across percentile groups of hospitals
Conclusion
Our analysis confirms the presence of adverse effects of quality performance pay in health care. A remedy may be found in establishing benchmark at the value of the best performing hospital or employing ‘episode-based’ payment, which rewards a hospital for treating each patient case with corresponding criteria satisfied (Werner and Dudley, 2012; Rosenthal, 2008).
While the above results are based on the US data, they suggest that cautiousness is required in applying the pay-for-performance schemes to healthcare financing also in transition countries, and much attention should be paid to the potential adverse effects.
References
- Besstremyannaya, Galina, 2015. “The adverse effects of incentives regulation in health care: a comparative analysis with the U.S. and Japanese hospital data” (2015) CEFIR/NES Working Papers, No.218, www.cefir.ru/papers/WP218.pdf
- Damberg, Cheryl L, Raube, Kristiana, Teleki, Stephanie S and dela Cruz, Erin, 2009. ”Taking stock of pay-for-performance: a candid assessment from the front lines”, Health Affairs, Volume 28, pages 517-525.
- Kahn, Charles N, Ault, Thomas, Isenstein, Howard, Potetz, Lisa and Van Gelder, Susan, 2006. “Snapshot of hospital quality reporting and pay-for-performance under Medicare”, Health Affairs, Volume 25, pages 148-162.
- Lindenauer, Peter K, Remus, Denise, Roman, Sheila, Rothberg, Michael B, Benjamin, Evan M, Ma, Allen and Bratzler, Dale W, 2007. “Public reporting and pay for performance in hospital quality improvement”, New England Journal of Medicine, Volume 356, pages 486-496.
- Monrad Aas, I., 1995. Incentives and financing methods, Health policy, Volume 34, pages 205-220.
- Pearson, Steven D, Schneider, Eric C, Kleinman, Ken P, Coltin, Kathryn L and Singer, Janice A, 2008. “The impact of pay-for-performance on health care quality in Massachusetts, 2001-2003”, Health Affairs, Volume 27, pages 1167-1176.
- Rosenthal, Meredith B, Fernandopulle, Rushika, Song, HyunSook Ryu and Landon, Bruce, 2004. “Paying for quality: providers’ incentives for quality improvement”, Health Affairs, Volume 23, pages 127-141.
- Ryan, Andrew M and Blustein, Jan, 2011. “The effect of the MassHealth hospital pay-for-performance program on quality”, Health Services Research, Volume 46, pages 712-72.
- Werner, Rachel M and Dudley, R Adams, 2012. “Medicare’s new hospital value-based purchasing program is likely to have only a small impact on hospital payments”, Health Affairs, Volume 31, Number 9, pages 1932-1940.
Spatial Wage Inequality in Belarus
This policy brief summarizes the results of an analysis of wage inequality among the districts of Belarus over the period 2000-2015. The developments in wage inequality varied noticeably by sub-periods: wage disparity decreased in 2000-2005, stayed stable in 2006-2012, and increased again during the last three years. I find evidence for spatial dependency in wages between districts, and increasing separation within districts (between rural and urban population). A decomposition of wage inequality by different quantiles of districts shows that the real wage increase rate in the lower percentiles exceeds the real wage increase rate in the higher percentiles. From a theoretical point of view, my results reject the inverted U-shaped relationship between spatial inequality and economic development for Belarus, and support the hypothesis made by the French economist Thomas Piketty that slow growth rates lead to rise in inequality.
In Belarus, wages make up approximately 60% of household income and account for 46% of GDP. The equality of the wage distribution therefore affects the scale and degree of socio-economic disconnect in the country. On the one hand, too much inequality may dampen long-term growth. On the other hand, too much equality may reduce incentives for productivity improvements.
This policy brief outlines a study (Mazol, 2016), where I examine the wage inequality concern of Belarus using annual Belstat data on district average monthly nominal wages (excluding large cities) from year 2000 to 2015, corrected by the country’s CPI index (using 2000 as the base year).
Characteristics of district wages
According to the Belarusian statistical definitions by the end of 2015, Belarus has 118 districts with an overall population of 4.9 million (excluding large cities), which corresponds to approximately 50% of total population. Average district wages relative to the national mean has increased from 74% in 2000 to 82% in 2005, indicating a catching-up process in wage income between districts and large cities (see Figure 1).
Figure 1. Decomposition of district real wages at the regional level of Belarus
Source: Author’s own calculations.
However, from 2013, the convergence of wages reverted to divergence (79% in 2015) suggesting that the relatively poor district population have become even poorer in recent years.
District wages differed by 2.8 times in 2000 and by 2.4 times in 2015. The largest number of districts with the lowest wages concentrate in the northern part of Belarus, represented by Vitebsk region with a mostly rural population, whereas districts with the highest wages are mostly in the Minsk and Gomel region, which are the central and most industrialized parts of Belarus (Minsk, Zhlobin, Mozyr and Soligorsk) (see Figure 2).
Figure 2. Map of Belarus’ districts by levels of real wages in 2015
Source: Author’s own calculations.
However, the common feature in the allocation of different levels of district wages is that the higher/lower wage districts tend to concentrate with similar districts, indicating presence of spatial dependence in the wage distribution.
Spatial interdependencies of district wages
The spatial characteristics are tested using the Global Moran’s I statistic (Moran, 1950). A positive coefficient means that neighboring districts have similar wages and a higher value indicates an increase in the relationship.
The results show that the values of the Global Moran’s I statistic are positive and significant at the 5 percent level for the periods 2000-2008 and 2014-2015 (see Figure 3). This suggests that districts with similar high or low levels of wages tend to concentrate geographically.
Figure 3. Global Moran’s I statistic and GDP growth in Belarus
Source: Author’s own calculations.
Additionally, starting from 2012, the substantial increase in positive spatial interdependencies in wages between districts coincides with a significant decrease in economic growth. This suggests that the districts of Belarus tend to cluster more closely with each other during economic recessions, indicating a more profound formation of rich and poor clusters of districts. Such a trend could be caused by a lack of public financial resources, which restricts administrative redistribution of financial support in favor of poor districts. As a result, such districts tend to become even poorer (for example, districts in Vitebsk region).
Wage inequality in the districts of Belarus
Overall, the level of wage inequality among the districts of Belarus remains low for the studied period. Moreover, the growth rates of wages in districts with low wages are higher than in the richer districts, indicating presence of a convergence process (see Figure 4). Yet, the differences between these two groups continue to be large. In 2015, the 10th and 90th percentiles of district wages were 4.6 and 6.1 million Belarusian rubles, respectively.
Figure 4. Indexed real wage
Source: Author’s own calculations.
Regarding inequality dynamics, the country experienced a decline in wage disparity 2000-2005, but from 2013, the inequality in wages started to rise (see Figure 5) and this coincides with an economic slowdown and subsequent recession.
Figure 5. Measures of wage inequality
Note: CV – coefficient of variation. Source: Author’s own calculations.
Thus, during 2000-2015, Belarus’ accelerating levels of economic growth first led to a decrease in district wage inequality. During a time of high and stable economic growth, the level of district wage inequality was constant. But, during the last years’ negative economic growth, the district wage inequality in Belarus has started to increase again. From a theoretical point of view, these results reject the hypothesis of an inverted-U-shaped relationship between spatial inequality and economic development stated by Kuznets (1955), and confirms the hypothesis stated by the French economist Thomas Piketty (2014) that declining growth rates increase inequality.
Conclusion
My results suggest that spatial wage inequality in Belarus is a persistent phenomenon that has increased in recent years. I found evidence for a spatial dependency in wages between districts and an increasing separation within districts (between rural and urban population). These may lead to a socio-economic instability, growth of shadow economy, and even an emergence of depressed regions (for example, Vitebsk region).
In order to decrease spatial wage inequality and increase overall economic efficiency in the districts of Belarus, the government needs to implement specific policies aimed at facilitating regional drivers of economic growth through the formation of new economic centers at the district level.
References
- Barro, Robert J.; and Xavier Sala-i-Martin, 1992. “Convergence”. Journal of Political Economy, 100(2), 223-251.
- Kuznets, Simon, 1955. “Economic growth and income inequality”. American Economic Review, 45(1), 1-28.
- Mazol, Aleh, 2016. “Spatial wage inequality in Belarus”. BEROC Working Paper Series, WP no. 35, 37 p.
- Moran, Patrick, 1950. “Notes on continuous stochastic phenomena”. Biometrika, 37(1/2), 17-23.
- Piketty, Thomas, 2014. “Capital in the Twenty-first Century”. Cambridge, Massachusetts: Harvard University Press, 696 p.
- Smith Neil, 1984. “Uneven development”. New York, NY: Blackwell, 198 p.
- World Bank. 2009. World Development Report 2009. “Reshaping economic geography”. Washington, D.C.: The International Bank for Reconstruction and Development, 372 p.
Expanding Leniency to Fight Collusion and Corruption
Leniency policies offering immunity to the first cartel member that blows the whistle and self-reports to the antitrust authority have become the main instrument in the fight against cartels around the world. In public procurement markets, however, bid-rigging schemes are often accompanied by corruption of public officials. In the absence of coordinated forms of leniency for unveiling corruption, a policy offering immunity from antitrust sanctions may not be sufficient to encourage wrongdoers to blow the whistle, as the leniency recipient will then be exposed to the risk of conviction for corruption. Explicitly introducing leniency policies for corruption, as has been recently done in Brazil and Mexico, is only a first step. To increase the effectiveness of leniency in multiple offense cases, we suggest, besides extending automatic leniency to individual criminal sanctions, the creation of a ‘one-stop-point’ enabling firms and individuals to report different crimes simultaneously and receive leniency for all of them at once if they are entitled to it.
Leniency provisions to fight corruption
It has been noted that leniency policies and other schemes that encourage whistleblowing — such as reward and protection policies — should work in the fight against corruption as it does in the fight against collusion (Spagnolo, 2004; Spagnolo 2008; Buccirossi and Spagnolo, 2006). Cartels, corruption, and many other types of multi-agent offenses depend on a certain level of trust among wrongdoers, which is precisely what leniency programs aim to undermine by offering incentives for criminals to betray their partners and cooperate with the authorities (Bigoni et al., 2015; Leslie, 2004).
Of course, for offenses not covered by antitrust law, such as corruption, relevant authorities may have their own ways of granting leniency and encourage reporting, such as plea bargaining, whistleblower reward programs, deferred prosecution agreements (DPAs) and non-prosecution agreements (NPAs). On the other hand, some countries have recently introduced explicit leniency programs for corruption (for example, Brazil and Mexico). Yet, we observed that those instruments do not always cover all types of sanctions, are seldom integrated with antitrust leniency, and are often under the responsibility of multiple law enforcement agencies. Hence, improvements in the legal frameworks seem to still be necessary.
Leniency in a multi-offense scenario: the case of corruption cartels
Cartel offenses may be connected to other infringements. A particularly frequent and deleterious example of a multiple offense situation is the simultaneous occurrence of collusion (bid rigging) and corruption in public procurement (OECD, 2010). While cartels are estimated to raise prices by 20% or more above competitive levels (Connor, 2015; Froeb et al., 1993), corruption may add 5–25% to total contract values (EU, 2014; OECD, 2014b). Since public procurement is a market amounting to 13–20% of GDP in developed countries (OECD, 2011), it is clear that collusion and corruption represent a serious waste of public funds, negatively impacting the quality of public infrastructure and services provided by a state to its citizens.
Authorities face then two distinct, yet inter-related, challenges to guarantee the effectiveness of public procurement: ensuring integrity in the procurement process and promoting effective competition among suppliers (Anderson, 2010). Considering that success in deterring cartels and corruption depends largely on the incentives provided to infringers to self-report, the interaction between leniency provisions for cartels and the legal treatment of corruption adds a powerful new channel to the above-noted interdependence and thus should be — and already is — a concern to antitrust and anti-corruption authorities (OECD, 2014a).
A member of a corrupting cartel that blows the whistle on the cartel and applies for leniency to the antitrust authority will likely have to disclose information on the other infringement. Such information may then be used by the relevant law enforcement authority to prosecute and punish the applicant. Thus, the risk of prosecution for other cartel-connected offenses (corruption in this case) may reduce the attractiveness of reporting the cartel (Leslie, 2006). This kind of uncertainty works against the leniency policy’s deterrence goals and may even stabilize the cartel by providing its members with a credible threat to be used to prevent betrayal among them.
Existing leniency provisions for corrupting cartels
Antitrust leniency provisions are very similar worldwide, differing mainly in terms of whether cartels are only considered administrative infringements or are also criminally liable offenses. Where there is individual criminal liability, leniency programs should cover it. Surprisingly, Austria, France, German and Italy, where cartel, or at least bid rigging, is a criminal offense, do not follow this guideline. In these jurisdictions the co-operation of an individual with the antitrust authority during the administrative proceedings may be considered a mitigating circumstance, reducing imposed penalties or even allowing a discharge, but at the discretion of the court or the prosecution, which is likely to greatly reduce the propensity of wrongdoers to blow the whistle.
On the other hand, countries do not usually have specific leniency programs for corruption. Nonetheless, self-reporting and cooperation in bribery cases are usually given great importance by authorities and may lead to leniency and even immunity, through other mechanisms such as plea agreements, no-action letters, NPAs or DPAs, but those instruments rely on prosecutorial or judicial discretion. Brazil and Mexico do have formal leniency programs for corruption, providing more certainty and thus being more attractive to an applicant, although restricted to administrative liability. Individual corruption-related criminal provisions are laid down in each country’s criminal code and follow the recommendations made by the United Nations, in the 2003 Convention against Corruption, and by the Organization for Economic Co-operation and Development, under its 1997 Convention against Corruption of Foreign Public Officials in International Business Transactions.
Since enforcement authorities for collusion and corruption differ in most cases, such an arrangement demands that the infringer seek non-prosecution through at least two separate agreements, one with the antitrust authority and the other with the anti-corruption agency. The difficulty in coordinating such agreements is an obvious issue and will vary according to the number of authorities involved and to the proximity among them, that range from divisions of the same agency, in the case of the United States (Antitrust and Criminal Divisions of the Justice Department), to organizations from different government branches (Executive and Judiciary) in most jurisdictions.
In Brazil and the United States, antitrust leniency programs can provide protection for non-antitrust violations, committed in connection with an antitrust violation. While in Brazil, this provision does not currently include corruption infringements, in the United States it does, but only binds the Antitrust Division and not any other federal or state prosecuting agencies, i.e. leniency agreements may not prevent other authority from prosecuting the applicant for the non-antitrust violation.
How to improve the current legal framework
Countries should follow Brazil and Mexico’s example and create ex ante, non-relying on prosecutorial or judiciary discretion leniency programs for corruption infringements. Unlike these programs, leniency should also cover individuals, especially in terms of criminal liability for bid rigging and corruption. The protection from lawsuits for managers and directors could then become a primary incentive for them to blow the whistle on their and their companies’ illegal acts.
Additionally, it is advisable not to depend on collaboration between law enforcement groups, but to establish clear legal provisions to allow wrongdoers to report all illegal acts simultaneously and to be confident that they will escape sanctions upon co-operation with the authorities and presentation of evidence, i.e. the creation of a ‘one-stop point’.
This ‘one-stop point’ should be available for applicants at every law enforcement agency and must prevent other agencies from prosecuting the leniency applicant. In other words, when someone approaches—as an individual or as a representative of a legal person—any authority to report crimes he is involved in, it is important to allow him to report any other crimes that he knows about in exchange for lenient treatment. In order to prevent conflicts among agencies, the authority first contacted by the wrongdoer must be obliged to immediately involve any other one who may be competent over other possible reported infringements. The self-reporting wrongdoer must be reasonably certain that he will be granted leniency for all reported wrongdoings, provided that he fulfills the legal requirements for each infringement, obviously. Failing to report all known involvement in infringements may be a reason to reduce or even revoke leniency altogether, creating a penalty plus-like provision over different areas of law and a more powerful incentive to a thorough self-report.
Information about the possibility of reporting several illegal acts at the same time, and of obtaining leniency for each one, must be consistently disseminated to minimize detection and prosecution costs, as well as to contribute to the deterrence of future criminal behavior.
Finally, we note that companies and individuals from jurisdictions where leniency provisions for corruption are highly discretionary or non-existent would be less inclined to report cartel behavior abroad when bribing foreign public officials. Despite existing confidentiality rules on leniency programs, they might not want to risk being prosecuted for corruption at home. This would possibly block antitrust leniency agreements by removing the incentives to self-report, undermining the ability to catch international corrupting cartels. To prevent that, laws should be amended to allow leniency for a company or someone that self-reports abroad, and further coordination and collaboration between agencies from different countries would be necessary to avoid stabilizing criminal collusion and undermining the effectiveness of leniency programs.
Conclusion
The fight against cartels and bribery requires efforts on a national level as well as multilateral co-operation.
Creating leniency policies to fight corruption, including foreign, and coordinating them with antitrust leniency policies, emerges as an important priority. The absence of formal leniency programs for corruption, besides hindering anti-corruption enforcement, reduces wrongdoers’ incentives to blow the whistle and collaborate in corrupting cartel cases through the risk of criminal prosecution for the corruption offense. These programs must be carefully designed, however, to avoid opportunistic behavior and thus to achieve their goal of deterrence.
In order to increase the effectiveness of leniency programs in multiple offenses cases, we suggest the creation of a ‘one-stop point’, enabling firms and individuals to report different crimes simultaneously and obtain leniency, provided that they offer sufficient information and evidence for their partners in crime to be prosecuted.
References
- Anderson, R. D.; Kovacic, W. E.; Müller, A. C., 2010. Ensuring integrity and competition in public procurement markets: a dual challenge for good governance, in The WTO Regime on Government Procurement: Challenge ond Reform (Sue Arrowsmith & Robert D. Anderson eds.).
- Bigoni, M., Fridolfsson, S.O., Le Coq, C., Spagnolo, G., 2015. Trust, Leniency and Deterrence, 31 J. LAW ECON. ORGAN., 663.
- Buccirossi P.; Spagnolo, G., 2006. Leniency policies and illegal transactions, 90 J. PUBLIC ECON., 1281.
- Connor, J. M., 2014. Cartel overcharges, in The Law And Economics Of Class Actions (James Langenfeld ed.).
- European Commission, 2014. Report from the Commission to the Council and the European Parliament—EU Anti-Corruption Report 2014.
- Froeb, L. M.; Koyak, R. A.; Werden, G. J., 1993. What is the effect of bid rigging on prices?, 42 ECON. LETT., 419.
- Leslie, C. R., 2004. Trust, Distrust, and Antitrust, 82 TEX. L. REV. 515.
- Leslie, C. R., 2006. Antitrust Amnesty, Game Theory, and Cartel Stability, 31 J. CORP. L. 453.
- OECD, 2010. Global Forum on Competition Roundtable on Collusion and Corruption in Public Procurement.
- OECD, 2011. Public Procurement for Sustainable and Inclusive Growth – Enabling reform through evidence and peer reviews.
- OECD, 2012. Improving International Co-Operation in Cartel Investigations.
- OECD, 2014a. 13th Global Forum on Competition Discusses the Fight Against Corruption, Executive Summary.
- OECD, 2014b. OECD Foreign Bribery Report: An Analysis of the Crime of Bribery of Foreign Public Officials.
- Spagnolo, G. 2004. Divide et Impera: Optimal Leniency Programs, CEPR Discussion Paper nr 4840, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=716143
- Spagnolo, G., 2008. Leniency and Whistleblowers in Antitrust, in Handbook of Antitrust Economics (Paolo Buccirossi ed.), Cambridge MA: MIT Press.
- Stephan, P. B., 2012. Regulatory Competition and Anticorruption Law, 53 VA. J. INT. LAW 53.
- Waller, S. W., 1997. The Internationalization of Antitrust Enforcement. 77 BOSTON U. LAW REV. 343.
Russia’s State Armament Plan of 2010 – The Macro View in mid-2016
Russian defense spending has increased significantly in recent years and reached over 4 percent of GDP in 2015 according to estimates. If the Russian state armament program for 2011-2020 is fulfilled, further large investments will be made in the years to come to modernize the military forces. However, the macro economic realties have change dramatically since the original plans were drawn up in 2010. This brief provides an analysis of what the new macro economic reality means for the armament plans that were made in 2010. In short, the major issue is not that spending as a share of GDP has increased dramatically but rather that the nominal ruble amounts that make up the plan amount to significantly less real purchasing power both in real ruble and dollar terms according to the most recent forecasts. In other words, it is not necessarily the trade off between different government spending areas that will be the main issue in this new macro economic environment, but rather what the priorities will be regarding different types of military equipment within the existing plan.
A 2016 study by Julian Cooper details Russia’s state armament plans for 2011 to 2020, “GPV-2020” (in Russian, State armament program is Gosudarstvennaia Programma Vooruzheniia), to the extent that is possible by using open source information. He makes a special point of discussing the non-transparent structure of Russian defense spending, which makes more precise calculations and statements regarding this expenditure area difficult or even impossible. Nevertheless, he provides broad numbers for the state armament plans that are publically available and this is used in this brief.
The plans of 2010
The state armament plans for 2011-2020 that were made in 2010 were stated in nominal ruble terms. The full path of the plan has not been announced but a total of 19 trillion rubles has been mentioned.
Figure 1. Armament and defense spending
Source: Author’s calculations based on Cooper (2016)
Cooper’s study details amount until 2015 and in Figure 1, the remaining years have been guesstimated by a smooth trend that delivers a cumulative plan of 19 trillion rubles.
The armament plans were very ambitious and it is noteworthy that they were almost fully implemented during the years for which we have actual numbers from Cooper’s study (the blue and red lines almost overlap perfectly). The other rather remarkable feature is how high these spending are compared to the national defense spending reported in his report, with the GPV plan peaking at 70 percent of defense spending.
Changing macro environment
The armament plans were not made in a vacuum but decided based on the economic outlook at the time, i.e., what policy makers projected in 2010.
Figure 2. IMF forecasts and actual GDP
Source: Author’s calculations based on IMF (2010, 2016). Note: The IMF’s 2010 forecast only goes to 2015 and for the remaining years a constant growth rate based on the last year is used.
Figure 2 shows what the IMF’s growth forecasts back in 2010 implied for the development of nominal GDP (dotted blue line); what actually happened until 2015 (solid red line); and what is projected to happen between 2016 and 2020 according to the latest IMF World Economic Outlook forecast of April 2016 (dotted red line). As is pointed out in Becker (2016), international oil prices are key for Russia’s growth performance and any forecast of it is no better than the forecast of oil prices. This implies that also the IMF’s April 2016 projection is highly uncertain, but this is true for any other forecast of Russian GDP as well.
There are two important observations that follow from Figure 2; first, nominal GDP at the start of the program was underestimated; and second, the growth rate was overestimated. As coincidence some times has it, two wrongs make close to a right for 2016; i.e., the forecast of 2010 almost perfectly coincides with what is expected to be the nominal GDP level in 2016 and 2017 in the latest IMF forecast. However, since the slowdown in expected growth is rather significant, in later years the IMF now expects nominal GDP to be less than what it thought it would be in 2010.
Implications for the GPV
The fact that nominal GDP in 2016 and 2017 is almost exactly the same as projected in 2010 implies that the GPV plan as a share of GDP based on the 2010 forecast compared with the 2016 forecast is almost the same in 2016 and 2017. This may be viewed as a peculiar circumstance but it can also have real implications. If the plan in 2010 was developed with a greater view of priorities in different government spending areas, the fact that the plan is still not absorbing more as a share of GDP suggest that the plan may not necessarily be a contentious issue at the level of the government.
However, this is expected to change after 2017 when nominal GDP will be lower than originally thought, and therefore the GPV share of GDP would be higher as seen in Figure 3.
Figure 3. GPV plan as share of GDP
Source: Author’s calculations based on Cooper (2016) and IMF (2010, 2016)
A more immediate concern would be what the nominal spending plan from 2010 actually buys in real terms in 2016. This is a more fundamental issue than changes in nominal GDP that will affect how quickly the armed forces can modernize their equipment. Figure 4 compares how the real purchasing power of the plan has changed from the 2010 to the 2016 forecasts, both in terms of constant (or real) ruble terms (green and purple lines) and in nominal U.S. dollar terms (red and blue lines).
Figure 4. The real spending power of GPV
Source: Author’s calculations based on Cooper (2016) and IMF (2010, 2016)
It is clear that there has been a significant reduction in real purchasing power both in real ruble and dollar terms. The cumulative change in real ruble terms is a loss of 12 percent in purchasing power, while the loss in dollar terms is 45 percent. Since most of the loss in spending powers is from 2014 forward, the impact in the remaining years is even higher than what these cumulative numbers indicate.
The actual impact on the spending plan will crucially depend on how much of what is planned needs to be imported but it is nevertheless clear that there has been a significant reduction in purchasing power if the initial plan in nominal ruble is implemented. This is without any consideration of the impact of sanctions or reallocating government resources to other spending areas that may be considered and would affect this calculation.
Policy conclusions
Although the precision of the discussion in this brief is no better than the accuracy of the available numbers, the general trends and qualitative conclusions made here are most likely still relevant. And without any claim of being able to assess the quality of military equipment or the ability Russia’s military industrial complex to make the right priorities (see instead Rosefielde, 2016 for such discussion), it is clear from a pure economics standpoint that the changing macro environment will have serious real implications for how quickly the modernization process of equipment can go.
It is also highly likely that the worsening of the economic outlook in 2016 compared with 2010 will lead to more general discussions of government spending priorities. Spending on producing arms by the military industrial complex could in principle be a Keynesian type of demand injection that can raise growth in the short run if there are idle resources that are put to use and generate income to workers that in turn spend more of consumption. However, it is not likely that the resources required to build sophisticated new military equipment is idle even in an economic downturn, so this effect is likely not very significant. Instead, more spending in areas that are already in short supply will generate inflation or put pressure on the exchange rate depending on how much is produced domestically and how much is imported of the demanded goods and services.
Long-term growth can also be affected if the GPV plan crowd out resources from other spending areas. The effect will of course depend on what the spending alternatives are and how this is linked to future growth; if military spending does not generate growth by itself while reducing spending on education, research and health care that we think promote long-term growth, prioritizing military spending will have an additional price in terms of reduced future growth. There could be cases where spillovers from military production are significant and spur new businesses and thus generate economic growth, but this does not seem to have been the case in the past in Russia.
In short, it will be hard for policy makers to avoid making tough decisions on what spending areas to prioritize given the new macro outlook for Russia. And even if the spending in nominal rubles in the GPV-2020 plan does not change, there will be new trade-offs to be made within the plan given how higher inflation and a depreciated currency has reduced the purchasing power of the original 2010 plan.
References
- Becker, T, 2016, “Russia’s oil dependence and the EU”, SITE Working paper 38, August.
- Rosefielde, S., 2016, “Russia’s Military Industrial Resurgence: Evidence and Potential”, Paper prepared for the conference on The Russian Military in Contemporary Perspective Organized by the American Foreign Policy Council, Washington DC, May 9-10, 2016.
- Cooper, J., 2016, “Russia’s state armament programme to 2020: a quantitative assessment of implementation 2011-2015”, FOI report, FOI-R-4239-SE.
- IMF, 2010, World Economic Outlook, October 2010 data, http://www.imf.org/external/pubs/ft/weo/2010/02/weodata/index.aspx
- IMF, 2016, World Economic Outlook, April 2016 data, http://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx