Tag: GDP
Political Responsibility for Economic Crises
This brief summarizes the results of research on the political costs of large-scale economic crises. In a large historic sample of countries, we study the impact of different types of crises, such as sovereign and domestic defaults, banking crises and economic recessions, on political turnover of top politicians: heads of the state and central bank governors. According to the findings, only default on domestic debt increases the probability of politicians’ turnover but not the default on external debt. As argued, this is due to the fact that the latter is not directly felt by the voters. In addition, we find that although currency crises increase chances of head of central bank turnover, it does not affect tenures of heads of state. Presumably, this is the case since currency crises are in the eyes of the public the responsibility of CB governors. These findings are relevant for both developed and transition economies, but are especially important for the latter as political turmoil and economic recessions are more prevalent in developing nations.
Overview and Key Findings
Large-scale economic crises are associated not only with the economic downturns, but also with political turnover. When the national economy is in a critical state, a default declaration often turns the economy back to growth as it is typically viewed as an act of acknowledging a problem and showing readiness for changes. However, politicians responsible for the economy and leaders of the states are often reluctant to declare default and try to postpone it, which worsens the situation. One of the reasons behind such unwillingness to act is a fear of a political turnover following the open acknowledgement of a problem.
This brief summarizes the findings Lvovskiy and Shakhnov (2018). We investigate the statistical evidence of political costs related to different types of economic crises.
We find that the effects of a crisis depend on the crisis type and on whether it was in the area of responsibility of a given politician. For example, external sovereign defaults have no effect on political turnover, which we interpret as external sovereign default having a small impact on the general public. On the contrary, domestic sovereign defaults have a large impact on the country population and often lead to the replacement of the top executive. In turn, banking crises are followed by the downfall of the government at the level of chief executive as well as the governor of the central bank.
While there is large literature on career concerns of politicians and political turnover, the majority of papers either focus on the regular changes through elections in democratic regimes (Treisman, 2015) or study a particular non-democratic country, like China (Li and Zhou, 2005). However, throughout history, crises have often happened in transition, non-democratic or not fully democratic countries. Furthermore, even in democratic countries many changes of government have been irregular. Since a delay in default declaration usually harms economies it is important to understand the mechanisms behind it in different institutional settings. Our paper contributes to this understanding by analyzing the impact of economic crises on political survival in a wide set of countries and regimes. Better understanding of the political costs that the top executives face while making such decisions is crucial for the prediction of these decisions as well as for international default negotiations and consultations.
Below we describe our finding in some more detail.
Statistical Analysis and Results
Our analysis consists of two main parts. We start with the political turnover for heads of state, who are in charge of the performance of the whole economy, which we measure by the GDP growth. Then, we look at central bank (CB) governors, who are in charge of the monetary policy, price stability, stability of the financial sector and banking supervision.
Table 1. Head of state changes
Table 1 presents the estimated linear probability regression models for the head of state turnover. As expected, elections have a strong impact on the probability of the turnover of the head of state. Further, as Column 1 in Table 1 shows default on external debt has no significant impact on the head of state tenure while default on domestic debt increases the yearly chances of being displaced by 34 %. This supports the idea that voters care more about their own savings than about the general situation with the state’s budget. When we look at the effect of past crises (the predictor variable in this case is whether a crisis took place last year), Column 2 coefficients for both external and domestic defaults appear to no longer be statistically significant. Instead, banking crises become significant. This situation could be due to the fact that one of the common consequences of domestic defaults is an ongoing distortion of savings which often leads to deposit runoffs, so the effect of the previous year’s domestic default now acts through a banking crisis.
Table 2. Central bank governor changes
Table 2 presents similar results but this time the left hand side variable is CB governor turnover. Similarly to the case with the head of state turnover, only default on domestic debt has a significant effect on the CB’s governor tenure and not the one on external debt. The main differences with Table 1 are that elections do not statistically predict turnover of CB heads while currency crises do. The former result is expected since in most countries there are no direct elections of central bank governors and central banks often have some degree of independence from the government. The latter result, that currency crises have a significant impact on CB governors’ tenures, implies that since currency control is one of the roles of a CB, its head is held accountable for currency crises and not the head of a state.
Conclusion
We examine the political cost of different types of economic crises, and find non-uniform effects of different types of crises on the political survival of various key officials. Domestic defaults, and recent banking crises are shown to be costly both for heads of states and central bank governors, while currency crises only have an impact on the political survival of the latter.
Interestingly and importantly, we find no evidence of the impact of (external) sovereign default on political turnover of the head of state or central bank governors. In other words, contrary to Yeyati and Panizza’s (2011) suggestion, it seems that there is no immediate political cost at the top associated with (external) sovereign default. One possible explanation is that the public does not punish a politician for defaults because by defaulting, the politician makes the optimal decision. In a modern world, many developing nations experience rapid growth of their sovereign debt. The presented evidence brings partial optimism that even if economic mistakes have already been made, top politicians would understand that acknowledging a problem and making steps toward its solution may not always be as costly for them as has previously been thought.
References
- Li, Hongbin; Li-An Zhou, 2005. “Political turnover and economic performance: the incentive role of personnel control in China,” Journal of Public Economics, 89 (9), 1743 – 1762.
- Lvovskiy, Lev; Shakhnov, Kirill, “Political Responsibility for Different Crises”, BEROC working paper #50, 2018
- Treisman, Daniel “Income, Democracy, and Leader Turnover”, American Journal of Political Science, 2015, 59 (4), 927–942.
- Yeyati, Eduardo Levy and Ugo Panizza, “The elusive costs of sovereign defaults,” Journal of Development Economics, January 2011, 94 (1), 95–105.
The Anatomy of Recession in Belarus
After impressive growth in the 2000s, Belarus’ economy has since the currency crisis of 2011 stalled. Structural issues – dominance of the state sector and directed lending practices – have made growth anemic. Recession for Belarus’ main trading partner and the decline of oil prices has aggravated the long-run problems. We perform growth diagnostics to separate the effects of total factor productivity (TFP) growth from capital accumulation over the recession. We show that, as in the 2000s, capital accumulation had the largest positive effect on growth in Belarus, but TFP gains were very low, or even negative in the years of recession.
During the 2000s, Belarus experienced extraordinarily high growth rates, despite a lack of economic reforms and low performance in the EBRD transition indicators. In Kruk and Bornukova (2014) we show that the growth was extensive in its nature, and mainly driven by capital accumulation. The total factor productivity (TFP) contribution to growth was low. After the currency crisis of 2011 in Belarus, however, growth rates have stagnated. Despite a high investment rate (which declined dramatically only after 2015) the growth rates were below 2 per cent per annum, which is a non-satisfactory performance for a developing economy (see Figure 1). In 2015, Belarus entered its first recession in the last 20 years with GDP declining by 3.9 per cent, and the recession has continued in 2016.
Figure 1. GDP Growth Rates and Investment Rates in Belarus (%), 2005-2015.
Source: Belstat
In the 2000s, the Belarusian government relied on directed-lending programs, and subsidized the interest rates for state-owned enterprises’ (SOE) loans. After the currency crisis of 2011, which many blamed on the loose monetary policies connected to directed-lending programs, the government switched to a so-called modernization policy that underlined the need to invest in new equipment and introduce new technologies. So far this policy have not bear fruits in terms of economic growth, but did it increase efficiency?
Growth Decomposition 2011-2015
Using the standard capital services approach modified for the Belarusian data in Kruk and Bornukova (2014), we decompose Belarusian economic growth in 2011-2015 into the growth of factors (capital and labor) and growth of TFP. We find that the lack of growth in TFP explains the lack of GDP growth and GDP decline over these years.
Figure 2. Gross Value Added Growth Decomposition in Belarus, 2006-2015.
Source: Author’s calculations based on Belstat data. Note: K stands for capital, L for labor, TFP for total factor productivity, and CU for capacity utilization.
A noteworthy fact about the Belarusian growth decomposition is that the direction of growth rate of capital and TFP has been persistently opposite in 2012-2015. Presumably, accelerated capital accumulation vs. stagnating/lowering TFP could be explained by initially insufficient levels of it (i.e. less than steady state). However, this explanation seems to be improper for the Belarusian path. According to our assessments, a capital stock has passed its steady state level at the turn of 2013-2014. Despite this, capital kept growing rapidly, while productivity contracted. An alternative explanation – a growth of the capital stock was secured by specific directed instruments; this artificial capital accumulation caused an endogenous contraction of TFP, as confirmed by the data.
Indeed, a TFP decline could accompany capital accumulation due to expanding allocation and technical inefficiencies. This explains the meltdown of economic growth in Belarus by 2013-2014 and its transition to the negative spectrum later on. In late 2014-2015, this was supplemented by exogenous negative shocks affecting TFP – deteriorating terms of trade and a shrinking energy subsidy from Russia – which caused a rapid dip into recession, which should be classified as structural adjustment.
In 2015-2016, lack of TFP growth and excessive capital accumulation caused further adjustments: firms reduced capital investments radically and contracted capacity utilization. These mechanisms amplified structural recession by a cyclical component.
Sectoral dimension: manufacturing
Out of all the manufacturing industries, only one – manufacturing of electrical, electronic and optical equipment – had positive TFP growth in 2011-2015. On average, manufacturing has lost 4.1% of TFP over this period, with the highest TFP losses in the industries that have always been hallmark for Belarus: manufacturing of machinery (-7.6%) and transport equipment and vehicles (-8.8%). The wood-processing industry has notoriously obtained huge financial aid during the modernization campaign (over 1 billion USD – but Belta (2015) lost 5.6% of TFP over 2011-2015.
We also find that the capital market continues to be distorted by the government interventions, leading to inefficient allocations in the sense that investment is not going to the most efficient industries. On the contrary, there is a negative relationship between the capital growth rate and the TFP growth rate in manufacturing industries. The labor market, which faces less government intervention, functions more efficiently. Labor growth is higher in the industries with higher initial labor productivity.
International comparisons
While comparing the TFPs of Belarusian industries to each other makes little sense (like comparing apples and oranges), comparing them to the TFPs of corresponding industries in other countries might shed some light on the comparative efficiency and competitiveness of the Belarusian economy. Table 1 lists the industries and sectors of the Belarusian economy that are the most and least competitive in a relative TFP sense.
Table 1. TFP winners and losers in Belarus
| 2014 TFP relative to | ||
| Czech Republic | Sweden | |
| Winners | ||
| Petroleum products | 1.98 | – |
| Transport services/communications | 1.67 | 0.70 |
| Trade and repair | 1.37 | 1.77 |
| Financial activities | 1.33 | – |
| Chemicals manufacturing | 1.17 | – |
| Losers | ||
| Transport vehicles | 0.72 | – |
| Machinery and equipment | 0.70 | 0.34 |
| Textiles | 0.68 | 0.26 |
| Woodworking | 0.56 | – |
| Electricity, gas and water | 0.41 | 0.22 |
| Agriculture | 0.40 | – |
Source: Author’s calculations.
The majority of the industries in the “winners” category are non-tradable (services like communications, finance, trade and repair). Coincidentally, trade, transport and finance also have relatively high shares of private ownership. Another group of winners are rent industries (petroleum benefitting from cheap Russian oil; and chemical industry built on potassium salts extraction).
As for the most of the manufacturing industries, where the government dominates, and where extensive financing was available at subsidized rates, TFP levels are relatively low. While the TFP performance of the manufacturing of transport vehicles, machinery and other equipment was also reported as low in 2010 (Kruk and Bornukova, 2014), the woodworking industry reached high levels of inefficiency after 2010, when the “modernization” program of this industry received a huge influx of capital.
The relative levels of TFP are good predictors of the future exports performance: higher-TFP industries are more competitive in the international markets. The current low relative TFP of the manufacturing sectors suggests that manufacturing exports will not recover in the coming years.
Conclusion
As in the 2000s, Belarus relies on capital accumulation to generate economic growth. In recent years, however, more investments have not generated growth and rather led to losses in TFP, aggravated by external factors. The current recession in Belarus is mainly a structural adjustment, driven by distortive policies of capital accumulation and allocation; and only partially driven by external shocks.
Lack of TFP growth leads to loss of international competitiveness, causing a collapse of exports. Deep structural reforms are necessary to revive growth and recuperate the lost export potential.
References
- Belta (2015) http://eng.belta.by/president/view/bellesbumprom-group-to-increase-exports-to-1.4-1.5bn-by-late-2017-2860-2014/
- Kruk, Dzmitry; and Kateryna Bornukova, 2014. “Belarusian Economic Growth Decomposition”, BEROC working paper series, WP no. 24
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).
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
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
Traces of Transition: Unfinished Business 25 Years Down the Road?
This year marks the 25-year anniversary of the breakup of the Soviet Union and the beginning of a transition period, which for some countries remains far from completed. While several Central and Eastern European countries (CEEC) made substantial progress early on and have managed to maintain that momentum until today, the countries in the Commonwealth of Independent States (CIS) remain far from the ideal of a market economy, and also lag behind on most indicators of political, judicial and social progress. This policy brief reports on a discussion on the unfinished business of transition held during a full day conference at the Stockholm School of Economics on May 27, 2016. The event was organized jointly by the Stockholm Institute of Transition Economics (SITE) and the Swedish Ministry for Foreign Affairs, and was the sixth installment of SITE Development Day – a yearly development policy conference.
A region at a crossroads?
25 years have passed since the countries of the former Soviet Union embarked on a historic transition from communism to market economy and democracy. While all transition countries went through a turbulent initial period of high inflation and large output declines, the depth and length of these recessions varied widely across the region and have resulted in income differences that remain until today. Some explanations behind these varied results include initial conditions, external factors and geographic location, but also the speed and extent to which reforms were implemented early on were critical to outcomes. Countries that took on a rapid and bold reform process were rewarded with a faster recovery and income convergence, whereas countries that postponed reforms ended up with a much longer and deeper initial recession and have seen very little income convergence with Western Europe.
The prospect of EU membership is another factor that proved to be a powerful catalyst for reform and upgrading of institutional frameworks. The 10 countries that joined the EU are today, on average, performing better than the non-EU transition countries in basically any indicator of development including GDP per capita, life expectancy, political rights and civil liberties. Even if some of the non-EU countries initially had the political will to reform and started off on an ambitious transition path, the momentum was eventually lost. In Russia, the increasing oil prices of the 2000s brought enormous government revenues that enabled the country to grow without implementing further market reforms, and have effectively led to a situation of no political competition. Ukraine, on the other hand, has changed government 17 times in the past 25 years, and even if the parliament appears to be functioning, very few of the passed laws and suggested reforms have actually been implemented.
Evidently, economic transition takes time and was harder than many initially expected. In some areas of reform, such as liberalization of prices, trade and the exchange rate, progress could be achieved relatively fast. However, in other crucial areas of reform and institution building progress has been slower and more diverse. Private sector development is perhaps the area where the transition countries differ the most. Large-scale privatization remains to be completed in many countries in the CIS. In Belarus, even small-scale privatization has been slow. For the transition countries that were early with large-scale privatization, the current challenges of private sector development are different: As production moves closer to the world technology frontier, competition intensifies and innovation and human capital development become key to survival. These transformational pressures require strong institutions, and a business environment that rewards education and risk taking. It becomes even more important that financial sectors are functioning, that the education system delivers, property rights are protected, regulations are predictable and moderated, and that corruption and crime are under control. While the scale of these challenges differ widely across the region, the need for institutional reforms that reduce inefficiencies and increase returns on private investments and savings, are shared by many.
To increase economic growth and to converge towards Western Europe, the key challenges are to both increase productivity and factor input into production. This involves raising the employment rate, achieving higher labor productivity, and increasing the capital stock per capita. The region’s changing demography, due to lower fertility rates and rebounding life expectancy rates, will increase already high pressures on pension systems, healthcare spending and social assistance. Moreover, the capital stock per capita in a typical transition country is only about a third of that in Western Europe, with particularly wide gaps in terms of investment in infrastructure.
Unlocking human potential: gender in the region
Regardless of how well a country does on average, it also matters how these achievements are distributed among the population. A relatively underexplored aspect of transition is to which extent it has affected men and women differentially. Given the socialist system’s provision of universal access to education and healthcare, and great emphasis on labor market participation for both women and men, these countries rank fairly well in gender inequality indices compared to countries at similar levels of GDP outside the region when the transition process started. Nonetheless, these societies were and have remained predominantly patriarchal. During the last 25 years, most of these countries have only seen a small reduction in the gender wage gap, some even an increase. Several countries have seen increased gender segregation on the labor market, and have implemented “protective” laws that in reality are discriminatory as they for example prohibit women from working in certain occupations, or indirectly lock out mothers from the labor market.
Furthermore, many of the obstacles experienced by small and medium-sized enterprises (SMEs) are more severe for women than for men. Female entrepreneurs in the Eastern Partnership (EaP) countries have less access to external financing, business training and affordable and qualified business support than their male counterparts. While the free trade agreements, DCFTAs, between the EU and Ukraine, Georgia, and Moldova, respectively, have the potential to bring long-term benefits especially for women, these will only be realized if the DCFTAs are fully implemented and gender inequalities are simultaneously addressed. Women constitute a large percentage of the employees in the areas that are the most likely to benefit from the DCFTAs, but stand the risk of being held back by societal attitudes and gender stereotypes. In order to better evaluate and study how these issues develop, gendered-segregated data need to be made available to academics, professionals and the general public.
Conclusion
Looking back 25 years, given the stakes involved, things could have gotten much worse. Even so, for the CIS countries progress has been uneven and disappointing and many of the countries are still struggling with the same challenges they faced in the 1990’s: weak institutions, slow productivity growth, corruption and state capture. Meanwhile, the current migration situation in Europe has revealed that even the institutional development towards democracy, free press and judicial independence in several of the CEEC countries cannot be taken for granted. The transition process is thus far from complete, and the lessons from the economics of transition literature are still highly relevant.
Participants at the conference
- Irina Alkhovka, Gender Perspectives.
- Bas Bakker, IMF.
- Torbjörn Becker, SITE.
- Erik Berglöf, Institute of Global Affairs, LSE.
- Kateryna Bornukova, Belarusian Research and Outreach Center.
- Anne Boschini, Stockholm University.
- Irina Denisova, New Economic School.
- Stefan Gullgren, Ministry for Foreign Affairs.
- Elsa Håstad, Sida.
- Eric Livny, International School of Economics.
- Michal Myck, Centre for Economic Analysis.
- Tymofiy Mylovanov, Kyiv School of Economics.
- Olena Nizalova, University of Kent.
- Heinz Sjögren, Swedish Chamber of Commerce for Russia and CIS.
- Andrea Spear, Independent consultant.
- Oscar Stenström, Ministry for Foreign Affairs.
- Natalya Volchkova, Centre for Economic and Financial Research.
Intermediate and Capital Goods Import and Economic Growth in Belarus
This policy brief presents estimation results of the influence of intermediate and capital goods (ICGs) imports on GDP growth taking into account changes in the exchange rate. The Belarusian economy substantially relies on ICGs imports, and my research indicates that imports of intermediate inputs negatively contribute to Belarus’ economic growth. The findings suggest that a devaluation of national currency can negatively influence both GDP growth and imports of intermediate goods. The negative influence on GDP growth is caused by a lower price competitiveness of the export sector, and the negative influence on imports of intermediate goods is due to a significant increase in the costs of imports.
According to endogenous growth theory technological progress is a key factor that enhances long-run economic growth (Grossman and Helpman, 1994). However, in developing countries scarce commercial activities in R&D limit technological progress (Grossman and Helpman, 1991). From this point of view, imports of ICGs play the same role in the development of the Belarusian economy (taking into account the nature of Belarusian manufacturing, which is mostly to assemble finished goods) as R&D activities in developed countries by transferring foreign technology and innovations (Coe et al., 1997; Mazumdar, 2001). In turn, Belarusian economic policy related to imports of ICGs is seriously conditioned by the foreign exchange constraint.
Imports of ICGs and GDP Growth
Imported ICGs (excluding energy goods) account for approximately 55% of all Belarus’ imports. Starting from 2001 up to 2010 high levels of GDP growth (7-8% on average) were associated with even higher growth levels of ICGs imports (see Figure 1).
Figure 1. Imports of ICGs in 2001-2014
Source: Belstat.
However, from 2011, average growth rate of GDP has decreased significantly from 7% in 2006-2010 to 2% in 2011-2014. This was coupled with a substantial drop in the average growth rates of ICGs imports. All these may indicate an insolvency of the current import-led growth (ILG) strategy of Belarus.
Moreover, using an Autoregressive-Distributed Lag (ARDL) approach (Pesaran et al., 2001) to study the long-run relationship between ICGs imports and GDP growth, it was found that a 1% growth in imports of intermediate goods caused a 2.7% decrease in real GDP (Mazol, 2015). The effect of capital goods imports is statistically insignificant.
The Toda-Yamamoto (TY) causality test (Toda and Yamamoto, 1995) clarifies this result, indicating unidirectional causality running from economic growth to imports of intermediate goods, and further to imports of capital goods (see Figure 2).
Figure 2. TY Causality Test
Note: * 10% level of significance; ** 5% level of significance; *** 1% level of significance. Source: Author’s own estimations.
Thus, instead of an ILG hypothesis, the findings establish presence of a GLI hypothesis for Belarus, supporting the view that for developing countries, trade is more a consequence of the rapid economic growth than a cause (Rodrik, 1995).
What is the intuition behind these results? The ILG strategy aims to improve efficiency and productivity, and can be appropriate only under two crucial conditions: first, it is necessary to acquire preferably advanced technology from abroad; and, second, there have to exist enough domestic technological capabilities and skilled human capital in order to successfully adapt new technologies from R&D intensive countries.
In Belarus, a violation of the first condition was caused by an ineffective industrial policy aimed to modernize state-owned enterprises (SOEs) (Kruk, 2014). In many cases, capital accumulation was accomplished without appropriate investment appraisal and efficient marketing strategies.
Furthermore, there is serious evidence against the second condition being fulfilled: the share of innovative goods of all shipped goods in the past 4 years have dropped by 5.5 percentage points – from 17.8% to 12.3% (Belstat); and the «brain drain» is still a big problem (mostly due to low salary levels in research areas).
Influence of Exchange Rate Policies
Through the cost of imported intermediates, the exchange rate has an important influence on the price competitiveness of the Belarusian economy. However, the Belarusian exchange rate has fluctuated widely since 2000s (see Figure 3). For example, between 2000 and 2014, the annual percentage change in the nominal effective exchange rate (NEER) has varied from approximately 135% to -2%, and the real effective exchange rate (REER) fluctuated between 23% and 11% annually.
Figure 3. The Exchange Rate 2000-2014
Source: Belstat, IFS.
The results from estimated ARDL models (Mazol, 2015) show that while a depreciation of the Belarusian currency negatively influences both the imports of intermediate goods and GDP growth, it does not have a statistically significant effect on the imports of capital goods.
Concerning the influence on intermediate inputs, the explanation is that there are two effects of exchange rate policy on trade. On the one hand, depreciation of national currency leads to growth in the domestic currency price of exports, which motivates national companies to expand production of exports – the derived demand effect. On the other hand, it increases the domestic currency price of imported intermediate inputs, decreasing the quantity of intermediate imports domestics companies can buy – the direct cost effect. The direct cost effect and the derived demand effect have opposite signs (Landon and Smith, 2007).
Additionally, devaluations in Belarus occur in most cases both to import source and export destination countries (first of all Russia). Thus, in the case of imports of intermediate goods, the impact of the direct cost effect is greater than the impact of the derived demand effect, leading to a negative effect on imports of intermediate goods.
Furthermore, the substantial reliance of the Belarusian export sector on imported inputs, combined with above-presented side effects, cause cost-push inflation in the export sector, which decreases its price competitiveness and, overly, the economic growth. This statement is confirmed by the fact that in the period 2002-2011, intermediate inputs were imported both under the permanent expansionary monetary policy and the fixed exchange rate policy (see Figure 3). As a result of such twin strategies, intermediate imports have become more and more expensive, while the price competiveness of Belarusian export goods have steadily declined (taking into account that most of its industrial part is shipped to Russia).
The reason why the exchange rate policy do not seem to have had an effect on capital goods imports is that machinery and equipment were typically imported in accordance with the government’s modernization plans. The realization of these plans often disregarded the current macroeconomic situation in Belarus, and the imports were made just for the sake of importing (to accomplish the plan).
Finally, starting in 2012, depreciation of the Belarusian ruble coincided with the economic recession caused primarily by structural problems that hit the country (Kruk and Bornukova, 2013). Therefore, the increase in flexibility of exchange rate policy had no additional effect on ICGs imports and economic growth in Belarus.
Conclusion
The findings presented here indicate that trade (in terms of ICGs imports) is more a consequence of the rapid economic growth in Belarus rather than a cause. The influence of imports of intermediate goods on GDP growth in the long run is negative. Additionally, the depreciation of the national currency has had a large negative effect on both intermediate imports and economic growth, while its effect on capital goods imports was statistically insignificant.
Thus, Belarusian economic policy based on imported technologies seems ineffective especially in recent years, most probably due to decreasing skills and the ability to imitate and innovate using foreign inputs. Therefore, policy should focus on abolishing the directive industrial management, which has led to a negative influence of ICGs imports on economic growth in Belarus.
Additionally, the country’s export strategy should be refined so that export destinations are different from import sources of intermediate goods that are used for export production. Moreover, the imports of capital goods should contribute to the development of new export markets, and monetary and fiscal policies should be refined in order to promote positive effects of currency valuation changes.
References
- Kruk D., Bornukova K. 2013. Decomposition of economic growth in Belarus. FREE Policy Brief Series, October 2013.
- Coe D., Helpman E., Hoffmaister A. 1997. North-south R&D spillovers. The Economic Journal 107(440): 134-149.
- Grossman G., Helpman E. 1991. Innovation and growth in the global economy. The MIT Press, Cambridge MA.
- Grossman G., Helpman G. 1994. Endogenous innovation in the theory of growth. Journal of Economic Perspectives 8: 23–44.
- Kruk, D. 2014. Stimulating growth in Belarus: Selecting the right priorities. FREE Policy Brief Series, November 2014.
- Landon S., Smith C.E. 2007. The exchange rate and machinery and equipment imports: Identifying the impact of import source and export destination country currency valuation changes. North American Journal of Economics and Finance 18: 3–21
- Mazumdar J. 2001. Imported machinery and growth in LDCs. Journal of Development Economics 65: 209-224.
- Mazol, A. 2015. Exchange Rate, imports of intermediate and capital goods and GDP growth in Belarus, BEROC Working Paper Series, WP no. 32.
- Pesaran M.H., Shin Y, Smith R.J. 2001. Bounds testing approaches to the analysis of level relationships. Applied Econometrics 16: 289–326.
- Rodrik, D. 1995. Getting interventions right how South Korea and Taiwan grew rich. Economic Policy 10: 53-107.
- Toda H.Y., Yamamoto, T. 1995. Statistical inference in vector auto regressions with possibly integrated processes. Econometrics 66: 225–50.
Global Inequality – What Do We Mean and What Do We Know?
Concerns about global economic inequality have become central in today’s policy debate. This brief summarizes what is known about the development of inequality globally, emphasizing the difference between the developments within countries and between countries. In the former sense, inequality has risen in most countries in the world since the 1980s, but in the latter sense inequality, has (most probably) dropped. To ensure future progress in terms of continued decreasing global inequality, fighting increasing inequality within countries is likely to be central.
In recent years, the distribution of income and wealth has emerged as one of the most widely discussed issues in societies everywhere. US President Barack Obama has called rising income inequality the “defining challenge of our time”, the topic has been on the agenda at meetings of the World Economic Forum in Davos, and studies by the IMF and the OECD (e.g., OECD, 2014, and IMF, 2014) have associated income inequality with lower economic growth. Thomas Piketty’s best-selling book “Capital in the Twenty-First Century” (2014) has placed the topic center-stage well outside academic and expert circles. At the same time, some have argued that all the talk about increasing inequality is in fact wrong and that it misses what they perceive as the more important story, namely, the decreasing global inequality. So, which is it, and what conclusions can be drawn?
Different Ways of Viewing the Facts About Global Inequality
When people talk about global income inequality there are a number of things that could be referred to. First, one might think of the inequality within countries across the world. From this perspective, the question in need of an answer would be: “How has inequality within individual countries changed globally in recent decades?” The short answer is that it has increased in most places. This is certainly the case in most of the developed world since the 1980s, while in emerging markets and developing countries (EMDCs) there are greater differences across time and regions. Looking at disposable incomes at the household level (the most commonly used measure in international comparisons) most countries in Asia and Eastern Europe have seen marked increases of inequality, while the trend seems to have been the opposite in Latin America and in large parts of Africa. In level terms, the development has been one of convergence since, on average, the countries in Eastern Europe and Asia started at much lower levels than those in Latin America and Africa. The development has resulted in that inequality levels are today on average at similar levels, with a Gini coefficient of between 0.4 and 0.45, in Africa, Asia, and Latin America (see figure 1 below and IMF, 2015) The same is true for the average across OECD countries where inequality has increased the most in percentage terms in countries starting at low levels, with the US being an exception in that inequality has increased even though the level has always been at the higher end among developed economies (e.g., OECD, 2015). The European average is today around 0.3 while the household disposable income Gini in the US is just below 0.4.
Figure 1. Change in the net Gini Index, 1990-2012
Source: IMF, 2015.
Looking at other income inequality measures, such as top income shares, the picture is similar: inequality has increased in most countries for which we have data since the 1980s. While it is important to recognize that top income shares are a very different measure of inequality, it has been shown that there is a close relationship between top income shares and the Gini coefficient in terms of capturing both level differences across countries and trends in the development (e.g., Leigh, 2007 and Morelli, Smeeding and Thompson, 2015). This together with one of the main strengths of the top income measure, namely, the length of the time series, allows us to put the recent developments in a historical perspective.
Figure 2 shows the income share of the top decile group for a number of mainly developed countries over the 20th century, illustrating the surprisingly common trends over the past 100 years (but also important level differences). On average, top shares (driven mainly by what happened in the top 1 percent) dropped from the beginning of the century until about 1980 after which it has risen in a fanning-out fashion. The point of the figure is clearly not to illustrate any individual country but rather to illustrate the overall long-run trend. For details of the historical development of income as well as wealth distribution, see Roine and Waldenström (2015).
Figure 2. Top 10 percent income share over the 20th century
Source: World top income database (WTID).
While the overall picture of rising inequality in most countries over the past decades is pretty clear, the development between countries is less so. There are two main reasons for this. First, it depends on what is considered the unit of observation and how these units are weighted. Second, it depends on what one assumes about the vast gaps in data availability, in particular in EMDCs (see e.g., Lakner and Milanovic, 2013, for more details).
As explained by for example Milanovic (2012) there are essentially three different ways in which one might think about the global distribution of income: 1) Treat every country as one observation and use a country’s GDP per capita as the measure of income; 2) do the same as in 1) but give different weight to each country according to its population; 3) Treat individuals (or households) as the unit of observation regardless of where people live. In all three cases it is possible to line up all observations from the poorest to the richest (and, hence, also to calculate a Gini coefficient). In the first way of looking at the world, we treat everyone in each country as being represented by the country’s average income and we also give the same weight to Luxemburg and India. In the second case, we recognize that more people live in India and weight it accordingly but we still, by construction, force everyone in each country to have the country average, thus ignoring within country inequality. Only in the last approach do we actually take into account both relative population size and differences in development within countries. This clearly seems the most satisfactory way to look at what has happened, but it is also the most demanding in terms of data.
In terms of the first two approaches, inequality in the world has fallen in the past decades. This is especially clear when weighting countries by population size. Rapid growth in China and India has caused average incomes in the world’s most populous and initially poor countries to increase faster than the global average, implying a reduction in global inequality. Some may think that this is not surprising and only to be expected since these countries start at such low levels, but in fact, this development marks the reversal of a 200-year trend toward increasing global inequality. Even “catch-up growth” is certainly not to be taken for granted.
Now the real question is this: What has happened to the global income distribution if we take into account the recent increasing inequality within many countries, including China and India? The answer turns out to complicated and uncertain (see Lakner and Milanovic, 2013 for details) but in the end most of the evidence points to decreasing global inequality in this sense too. As François Bourguignon puts it in a recent article in the Foreign Affairs: “…the increase in national inequality has been too small to cancel out the decline in inequality among countries” (Bourguignon, 2016, p. 14).
To understand both of these counteracting forces it is illustrative to look at real income growth across the global income distribution. Figure 3 below is taken from a presentation by Branko Milanovic, organized by SITE in 2014 (and available online here). It shows the real income growth for different percentile groups in the global distribution over the period 1988-2008. Moving from left to right the figure shows positive but modest growth for the very poorest individuals in the world, and much higher growth for the groups just above, with rates increasing toward the middle of the global distribution. In the range of about 5 dollars/day (in PPP adjusted terms) growth has been the highest. By developed-country standards, these people are still very poor, but globally they are truly the “middle class” in the sense that they make up the middle of the global income distribution. Moving further right we see a sharp drop in real income growth at a level around the 80th percentile. This part of the distribution is mainly populated by the lower middle classes of the developed world, and here income growth has been essentially zero over the past decades. Moving further right we again see a sharp increase in real income growth illustrating the large gains going to individuals in the top of the global income distribution.
Figure 3 summarizes much of what has happened: the left part showing the rapid growth of income among most of the world’s relatively poor, while the right shows the increasing inequality in the developed world, with the top of the distribution gaining the most.
Figure 3. Real income growth at various percentiles of the global income distribution, 1988-2008 (in 2005 PPPs).
Source: Lakner and Milanovic (2013).
Why This Matters and What Should Be Done About Global Inequality?
The forces that explain what has happened are of course complex and differ over time and across countries but one thing seems clear, the growth of real incomes in developing countries as well as the relative decline of incomes in the lower end of the income distribution in developed countries have at least in parts been shaped by the same intertwined processes of globalization and technological development. Overall, these processes are powerful positive developments, but at the same time it is easy to see how those who perceive themselves as losers in these developments may try to resist them using their political voice. It is important to remember that globalization is the result of a combination of technology and political decisions, and consequently not an inevitable process. After all, the globalization backlash in the period 1914-1945 did not happen because the technological feasibility of the process suddenly disappeared.
The appropriate government responses are of course also likely to be different across countries, but here there are also some common factors that stand out. In the developing world, the most challenging aspects will have to do with maintaining state capacity and the ability to tax increasingly mobile tax bases. In many developing countries taxation will also be key, but here the challenge is more about creating a capable and accountable state in the first place. As succinctly and, I think, correctly put by Nancy Birdsall in a review of Thomas Piketty’s “Capital in the Twenty-First Century”: “(I)n the developing world, the challenge is not, at least not yet, the one Piketty outlines — that an inherent tendency of capitalism is to generate dangerous inequality that if left unchecked will undermine the democratic social state itself. The challenge is the other way around: to build a capable state in the first place, on the foundation of effective institutions that are democratically accountable to their citizens.”
References
- Atkinson, Anthony B. 2015. “Inequality – What can be done?” Harvard University Press.
- Birdsall, Nancy. 2014. “Thomas Piketty‘s Capital and the developing world”
- Ethics & International Affairs / Volume 28 / Issue 04 / Winter 2014, pp 523-538.
- Bourguignon, François, and Christian Morrison. 2002. “Inequality among World Citizens: 1820-1992”, The American Economic Review, Vol. 92, No. 4. (Sep., 2002), pp. 727-744.
- Bourguignon, François. 2016. “Inequality and Globalization. How the rich get richer as the poor catch up”, Foreign Affairs, Volume 95, Number 1, pp. 11-16
- Lakner, Christoph, and Branko Milanovic. 2013. “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession.” WB Policy Research Working Paper 6719, World Bank, Washington.
- Leigh, Andrew. 2007. “How closely do top income shares track other measures of inequality?”, The Economic Journal, 117 (November), 589–603.
- OECD (2015), “Growth and income inequality: trends and policy implications”, OECD Economics Department Policy Notes, No. 26 April 2015.
- OECD. 2011. Divided We Stand: Why Inequality Keeps Rising. Paris: OECD Publishing.
- OECD. 2012. “Reducing Income Inequality While Boosting Economic Growth: Can It Be Done?” In Economic Policy Reforms: Going for Growth. Paris: OECD Publishing.
- Ostry, Jonathan David, Andrew Berg, and Charalambos G. Tsangarides. 2014. “Redistribution, Inequality, and Growth”, IMF SDN, February 17, 2014
- Milanovic, B. 2013. “Global Income Inequality by the Numbers: in History and Now.” Global Policy 4 (2): 198–208.
- Morelli, Salvatore, Smeeding, Timothy, and Jeffrey Thompson. 2015. “Post-1970 Trends in Within-Country Inequality and Poverty: Rich and Middle Income Countries”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
- Piketty, Thomas. 2014. “Capital in the Twenty-first Century”. Cambridge, Massachusetts: Harvard University Press.
- Pritchett, Lant. “Divergence, Big Time.” Journal of Economic Perspectives, Summer 1997, 11(3), pp. 3-17.
- Roine, Jesper, and Daniel Waldenström. 2015. “Long-Run Trends in the Distribution of Income and Wealth”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Russia: Increasing Concentration of the Economy and Low Investment
Author: Oleg Shibanov, New Economic School and Corporate University of Sberbank.
The Russian economy became more concentrated in 2014. The new RBC-500 rating shows that the 643 largest companies in Russia produce 77% of the country’s GDP. Moreover, 94% of the net profit of these companies was generated in the oil and gas sector. This is up from 71% in 2013. This increasing concentration appears unstable at times of huge external shocks on commodity prices.