Tag: Economic Growth
The Russian economy under Putin (so far)
Russians are heading to the polling booths on March 18, but where will the economy head after Putin has been elected president again? This brief provides an overview of the economic progress Russia has made since 2000 as well as an economic scorecard of Putin’s first three tenures in the Kremlin and uses this to discuss what can be expected for the coming six years. Although significant growth has been achieved since 2000, all of this came in the first two tenures of Putin in the Kremlin on the back of increasing oil prices. In order to generate growth in his upcoming presidential term, Putin and his team will need to address the significant needs for reforms in the institutions that form the basis for modern market economies. Otherwise, Russia will continue to be hostage to the whims of the international oil market and eventually lose most of its exports and government revenues as the world moves towards a carbon free future. Perhaps this is beyond the scope of Putin as president, but not beyond the horizon of young Russians that will be casting their votes on Sunday and in future elections.
Let’s assume that Putin will be elected president again on March 18 (for once a very realistic assumption made by an economist). What will this mean for the Russian economy in the coming six years given what happened during his previous and current tenures in the Kremlin? To assess the future as well as to understand Putin’s power and popularity, this brief starts by looking back at the economic developments in Russia since Putin first became president.
Although many different factors enter the power and popularity function of Putin, economic developments have a special role in providing the budget constrain within which the president can operate. A higher income level means more resources to devote to any particular sector, project, voting group or power base. This is not unique to Russia, but sometimes forgotten in discussions about Russia, that often instead only focus on military power or control of the security apparatus and media. These are of course highly relevant dimensions to understand power and popularity in Russia, but so is economic development, particularly in the longer run.
Russia’s economy in the world
The economic greatness and progress of a country is usually assessed in terms of the size of the economy, how much growth that has been generated, and how well off the citizens are relative to the citizens of other countries. So, by our common indicator gross domestic product (GDP), has Russia become a greater and more powerful country since Putin first became president? Table 1 shows two things, the absolute level of GDP measured in USD at market exchange rates and the rank this gives a country in a sample of 192 countries in the world that the IMF collects data on (this brief is too short for a long discussion of the most relevant GDP measure, but GDP at market exchange rates makes sense when comparing the economic strength of countries in a global context, Becker 2017 provides a discussion of alternative measures as well). When Putin become president for the first time in 2000, the value of domestic production was estimated at $279 billion, which implied a 19th place in the world rankings of countries’ GDP. In 2016, almost three presidential terms of Putin later, Russia’s GDP had increased by 4½ times to $1281 billion and its ranking improved to 12th place in the world. This clearly is an impressive record by most standards. However, the Russian economy is still the smallest economy of the BRIC countries and corresponds to only 7 percent of the US economy in 2016. In other words, impressive progress by Russia but the country is (still) not a global superpower in the economic arena.
Table 1. Russia in the world (GDP in USD bn)
Source: IMF (2017)
For the average Russian, income per capita is a measure more closely connected to consumption and investment opportunities or ‘welfare’. Progress in this area is also more likely to affect how individuals assess the performance of its political leaders. Of course, progress in terms of overall GDP and GDP per capita is closely linked unless something unusual is happening to population growth. Therefore, it is not surprising that GDP per capita also increased by around 4½ times between 2000 and 2016 (Table 2). This is the first order effect of the economic development in Russia, but in addition, citizens of Russia moved up from a world income rank of 92nd to 71st. This has implications when Russian’s compare themselves with other countries and can in itself provide a boost of national pride.
It also directly affects opportunities and status for Russians visiting other countries. Being at place 71 may not be fully satisfactory to many, but we should remember that due to the rather uneven income distribution in Russia, many of the people that travel abroad are far higher up on the global income ranking than what this table indicate. Nevertheless, Russia is far behind the Western and Asian high-income countries in terms of GDP per capita. And although the picture would look less severe if purchasing power parity measures are used, the basic message is the same; Russia has still a lot of catching up to do before its (average) citizens enjoy the economic standards of high-income countries.
Table 2. Russian’s in the world (GDP/capita)
Source: IMF (2017)
The macro scorecard of Putin
So what generated the impressive 4½ times increase in income in USD terms from 2000 to 2016 and can we expect high growth during Putin’s next six years in office? The short answer to the first question is the rise in international oil prices and to the second question, we don’t know. Table 3 provides a comparison of different economic indicators for Putin’s two first terms in office compared with his current term (where GDP data ends in 2016 so the sample is cut short by a year). It is evident that the impressive growth over the full period is entirely due to the strong growth performance in the first two presidential tenures. Rather than generating growth in the most recent period, the economy has shrunk. This is explained by the evolution of international oil prices, which quadrupled in the first eight years and instead halved in the more recent period. These swings in oil prices have also been accompanied by significant shifts in foreign exchange reserves, the exchange rate, and the value of the stock market.
In Becker (2017) I discuss in more detail the importance of international oil prices in understanding the macro economic development in Russia. In particular, it is important to note that it is changes in oil prices that correlate with GDP growth and other macro variables and that the problems with predicting oil prices makes it very hard to make good predictions of Russian growth.
Table 3. A macro scorecard of Putin in office
Source: Becker (forthcoming)
Policy conclusions
To break the oil dependence and take control of the economic future of Russia, the president will need to implement serious institutional reforms that constitute the basis for a modern, well-functioning market economy in his next term. Otherwise, Russia will continue to be hostage to unpredictable swing in international oil prices and nobody—including the president, the central bank, the IMF and financial markets—will be able to predict where the Russian economy is heading in the next couple of years.
Figure 1. Reforms (still) needed
Source: World Bank (2017)
In the longer run, the prediction is much easier. With the world moving towards a green economy, the price of oil will see a structural decline that will rob Russia (and other oil exporters) of most of its export and government revenues. The reforms which basically every economist agree are needed are related to market institutions and Figure 1 provides a clear illustration of key reform areas. The progress during Putin’s years in office has been modest at best. Swedish institutions in 2016 have been added to the figure as a comparison and it is clear that the institutional gap between Russia and Sweden is significant. Of course, all countries are different, but Russian policy makers that are interested in reforming its economy are most welcome to Sweden for a discussion of what we have done to build our institutions.
References
- Becker, T. (2017). ‘Macroeconomic Challenges’, in Rosefielde, S., Kuboniwa, M., Mizobata, S. and Haba K. (eds.) The Unwinding of the Globalist Dream: EU, Russia and China, Singapore: World Scientific Publishing.
- Becker, T. (forthcoming), ‘Russia’s economy under Putin and its impact on the CIS region’, Chapter 2 in T. Becker and S. Oxenstierna (eds.) Perspectives on the Russian Economy under Putin, London: Routledge.
- IMF (2017), World Economic Outlook database, April 2017 edition available at http://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx
- World Bank (2017), Worldwide Governance Indicators (WGI), 2017 update available at http://info.worldbank.org/governance/wgi/index.aspx#home
Stylized Facts from 25 Years of Growth in Transition
This brief summarizes the growth experience of transition countries 25 years after the dissolution of the Soviet Union. We divide our sample into two main groups: the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries (ex Baltics) that emerge from the Soviet Union (FSU12). The growth experiences of these two groups have been distinctly different. The magnitude of the initial transition decline in output was much more severe in the FSU12 group. Despite growing almost 2 percentage points faster than the average EU10 for the following fifteen years, the FSU12 group is still further behind the EU10 group than they were at the beginning of transition. This illustrates how hard it is for countries to recover from large negative income shocks and thus the importance for countries to avoid such negative events. However, there are no signs of transition countries being stuck in a low or middle-income trap or that natural resource wealth leads to lower growth during this period.
2017 marked the 25-years anniversary after the dissolution of the Soviet Union and the beginning of the transition for the economies in the region. In a recent paper, we explore the growth experience of transition countries over these 25 years (Becker and Olofsgård, 2017). The paper has four main parts: an overview of the transition literature focusing on growth; a part that provides a detailed description of growth in transition; an analytical section that investigate if we can explain growth in transition countries with a standard growth model; and finally an exploration of whether institutional and other variables that have been highlighted in the transition literature (but are excluded from the basic growth model) are correlated with growth in transition countries. This brief summarizes the descriptive part of the paper, while the more analytical sections will be the topic of future briefs.
For most of the paper, we divide our sample into two main groups; the 10 transition countries in Eastern Europe and the Baltics that became EU members in 2004 and 2007 (EU10); and the 12 countries that emerged from the Soviet Union (FSU12). In addition, we include three transition countries that are not part of either group (Croatia, Albania and Macedonia – Other3) and we also divide the FSU12 group into the four countries that export significant amounts of fuel (FSUF) and the eight countries that do not (FSUNF). There are of course remaining differences within these groups, but this aggregate analysis allows us to see certain patterns in the transition process more clearly.
Initial output collapses
The focus in economics is often on how to generate higher growth and not about protecting against significant drops in output. There are some exceptions, including Becker and Mauro (2006) and Cerra and Saxena (2007), where the focus is on output losses and how countries recover after crises. For transition countries, a very important feature of the economic development process is exactly the initial drop in income and the time it has taken countries to recover from the initial phase of transition. Table 1 shows how much income fell in the different country groups and the time it took to get back to the pre-transition income level.
Table 1. Output drops and recoveries
Source: Becker and Olofsgård (2017)
The initial collapse in the FSU12 group was enormous, with income cut in half. The EU10 countries also had massive output losses, but “only” lost a quarter of their income on average. This took over a decade to recover from, while the path back to pre-transition income levels in the average FSU12 country was almost twice as long. There have been many papers written on the economic chaos that was part of the initial transition process, and explanations for this decline has been attributed to, e.g., misleading data, lack of functioning markets, shock therapy and poor economic and legal institutions in general. All of these factors have likely played important roles in the process, but regardless of the explanation, this was a very unfavorable time in terms of economic outcomes for hundreds of millions of people in these countries. Avoiding such costly drops in output should be a top priority for economic policy makers in any country at all times, not just in transition.
From collapse to growth
In most transition countries, the initial phase of decline in transition lasted several years, but eventually the negative growth rates turned positive (Figure 1). Again, we can see that the EU10 group had fewer years of declining incomes with growth resuming in 1993, while for the FSU12 group, growth in transition only started in 1996/7.
Figure 1. Bust-Boom countries
Source: Becker and Olofsgård (2017)
What is less visible in Figure 1 due to the wide scale needed to capture the initial output drops is that the FSU12 groups has shown significantly higher growth than the EU10 group in the last 15 years. Over the more recent period, the average FSU12 country has grown by close to 6 percent, while growth for the EU10 has been around 4 percent per annum (Table 2).
Table 2. Real GDP/cap growth
Source: Becker and Olofsgård (2017)
The faster growth in FSU12 countries is particularly pronounced among the fuel exporters, which were growing by one and a half percentage point faster than the non-fuel exporters between 2000 and 2015. But the table also shows that the very negative growth experience during the first ten years of transition is hard to erase and the EU10 countries have grown faster over the full 25-year period compared to the FSU12 countries. In terms of understanding the growth experience of the different country groups and time periods, it is clear that the sharp increase in international oil prices during the last 15 years of the period generated high growth in the fuel exporting countries in the FSU12 group. Interestingly though, also the non-fuel exporters grew faster than the EU10 in this time period. This is likely linked to spillovers from Russia to the other countries in the region, but could also be related to some recovering after the massive initial declines in output. Such macro and external factors are not always stressed in discussions of growth in transition countries, which more often focus on the pace of reforms or strength of institutions, but seem to be relevant at this aggregate level when comparing the initial and later phases of transition.
Relative incomes in transition countries
Growth or the lack thereof is of importance in determining income levels, which is what we generally think is what influences welfare. The question is then what the growth processes we have analyzed imply for income levels in transition countries, and in particular, how the income levels in these countries compare with other countries.
Figure 2. Income relative to 15 old EU countries
Source: Becker and Olofsgård (2017)
The short story here is that the relative ranking of the different groups is largely unchanged from the start of transition until the end of 2015. The group of countries that eventually joined the EU has the highest income level while the non-fuel exporting FSU countries have the lowest. However, the leading group still only has around 60 percent of the income of the average “old” EU country while the average FSU12 country has half of that or around 30 percent of the income of the old EU countries. This puts the relatively high growth rates of the FSU12 group over the last 15 years in perspective; the road to reach old EU level incomes is long indeed. Also, within the FSU group, it is clear that there is a sharp dividing line between the fuel exporters and the rest. This is in stark contrast to the notion of a “natural resource curse” that is often blamed for poor growth in oil and mineral rich countries.
Growth traps in transition?
One issue that comes up with regards to both low and middle-income countries is if they are stuck at a certain level in the relative income rankings of the world. This is referred to as the low or middle-income trap and the question is if there are signs of transition countries being stuck in such traps.
Figure 3. Moving up the income ladder
Source: Becker and Olofsgård (2017)
Figure 3 shows how transition countries are classified into the World Banks income groups low income (1 in the Figures scale), lower middle income (2), higher middle income (3) and high income (4) groups.
It is clear that the FUS 12 group of countries was sliding down the scale initially, but since the beginning of the 2000’s, all of the transition countries have been climbing up the World Bank income ranking scale without any apparent signs of a low or middle-income trap.
Policy conclusions
There are of course country differences along all the dimensions discussed in this brief but grouping the transition countries together provides some interesting general observations of growth in transition. First of all, it is clear that it is very hard to fully recover from large drops in income. Even with the help of some extra growth following a crisis, it seems to take a long time for most countries to make up for lost ground. This suggests that policy makers in transition as well as other countries need to take measures to hedge the really bad outcomes and not only focus on how to generate an extra one percent of growth.
The other observation is that at the aggregate level, external factors and more mechanical macro boom-bust-boom type of growth factors may dominate what we generally think of as the long-run determinants of growth (such as institutions, education, and micro level reforms to make markets work better) over very long time spans. This does not mean that the focus on the more fundamental growth drivers should diminish, but it is important that reforms in these areas are complemented with a macroeconomic framework that reduces the risks of costly output collapses.
Finally, it is clear that the incomes generated by natural resources can produce growth at the macro level and that there is little evidence that transition countries should be stuck at any particular level in the global income rankings. Go transition countries!
References
- Becker, T, and A. Olofsgård (2017), “From abnormal to normal—Two tales of growth from 25 years of transition”, SITE Working paper 43, September.
- Becker, T., and P. Mauro, (2006). “Output Drops and the Shocks That Matter”. IMF Working Papers 06/172.
- Cerra, V., and S.C. Saxena (2008). ”Growth Dynamics: The Myth of Economic Recovery”. American Economic Review, 98(1), 439–457.
Remaining Challenges for Faster Growth in CESEE
Between 1995 and 2016, per capita GDP levels in Europe have converged, as countries that had lower income levels in 1995 on average have seen faster growth rates between 1995 and 2016 (Figure 1).
Figure 1
Income differentials between CESEE and Germany have narrowed significantly during this time. If we look at CESEE as a whole, in 1995 GDP per capita of CESEE was only a third of Germany. By 2016 it has increased to almost half. If we look at individual countries, all countries in CESEE have seen faster GDP growth than in Germany, but there have been important cross-country differences. For example, growth has been relatively rapid in the EU New Member States and very slow in Ukraine.
Nevertheless, CESEE is still much poorer than Germany. The richest country in CESEE – Slovenia – has the income level per capita Germany had in 1990 (Figure 2). Poland is as rich as Germany was in the late 1970s. And Ukraine, which in early transition had similar level of income to Poland, is now as rich as Germany was in the early 1950s.
Figure 2
CESEE is poorer both because labor productivity is lower and a smaller share of the population works. GDP per capita is the product of GDP per worker and the employment to population rate:
In 2015, labor productivity in CESEE was still well below that in Germany and the Netherlands (Figure 3, x-axis). Employment rates were also lower, but those differences were less pronounced (Figure 3, y-axis).
Figure 3
Differences in employment rates are, however, more pronounced if we take into account that in CESEE a higher share of the population is of working age. The employment to population rate is the product of the employment to working age population [1] rate:
The share of the working age population in CESEE is relatively high (Figure 4), although it is now declining. The employment to working age ratios in CESEE are well below those in Germany (Figure 5); only the Baltics come close.
Figure 4
Figure 5
It will be challenging to further increase the employment to total population rate, given the impact of aging and the already relatively low level of unemployment. The decline of the working age population will accelerate in the next decade (Figure 6) as the baby-boom generation is retiring; in a number of countries the working age population is set to decline by more than 1 percent annually. [2] If the share of the working age population that works remains constant, the share of the employment to total population rate will fall sharply. At the same time, the unemployment rate in many countries is already close to pre-crisis lows (Figure 7). It will therefore be key to increase labor force participation rates, which in most countries are still below those of Germany, particularly those of women (Figure 8).
Figure 6
Figure 7
Figure 8
A higher capital stock may be even more important than raising the employment rate. There is a strong correlation between the level of capital stock per capita and GDP per capita (Figure 9, left panel). The relationship between the employment rate and GDP per capita is much weaker (Figure 9, right panel). Further convergence of CESEE will thus require capital deepening. As of 2015, the capital stock per capita in CESEE region is on average only a quarter of that in Germany.
Figure 9
Figure 10
Figure 11
Figure 12
Unfortunately, the growth of the capital stock per capita has slowed (Figure 10), which reflects the decline in investment rates. Investment rates are low compared with other emerging market countries (Figure 11). Saving rates are low too (Figure 12), which suggests that a rebound of investment could lead to a re-emergence of high current account deficits, unless savings increases as well. Yet it may be challenging to boost saving. With labor markets tightening, wages shares are likely to increase, which is likely to reduce corporate profits. Indeed, in a number of countries this is already happening (Figure 13). Household savings are difficult to influence. Boosting public savings would help, yet even though unemployment rates are falling, few countries plan a meaningful fiscal tightening (Figure 14).
Figure 13
Figure 14
TFP growth has slowed as well. TFP growth has recovered somewhat in recent years, but it is still much slower than in the pre-crisis years (Figure 15). The TFP slowdown might be a result of both the decrease of productivity in main trading partners and unfinished post-crisis adjustment.
The IMF’s CESEE Regional Economic Issues have identified several factors that might restrain productivity and investment. The May 2016 and November 2016 IMF CESEE Regional Economic Issues [3] analyzed several areas where reforms are needed in CESEE, and recommended to improve institutions to boost productivity. The May 2016 REI suggested the largest efficiency gains might come from increasing protection of property rights, upgrading legal systems and other government services. In this context, the November 2016 REI discussed the need to improve public investment management and tax administration. Given the large gaps in infrastructure and capital stock to Western Europe, improving the efficiency of public investment by improving its allocation and the implementation of frameworks and procedures could boost potential growth significantly. Regarding tax administration, reducing compliance gaps, would help improve tax collection, which could generate more fiscal revenues and allow for higher public investment.
Figure 15
In short, further catch-up is possible but challenging. Labor force participation could be further increased, which would also help to offset declining share of working age population. A slowdown or even reversal of net emigration would also contribute. The capital stock is relatively low, and higher investment is needed especially in infrastructure, but raising the saving rate will be a challenge. Since the crisis the TFP has slowed considerably, and re-igniting TFP growth will be crucial for boosting growth. For all this, improving the quality of institutions and legal frameworks will help.
Bas Bakker is the IMF’s Senior Resident Representative for Central and Eastern Europe; Marta Korczak and Krzysztof Krogulski are economists in the IMF’s regional office for Central and Eastern Europe in Warsaw. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. Comments by [Jorg Decressin] on an earlier version are gratefully acknowledged.
[1] The working age population is the population ages between 15 and 64.
[2] In many countries, demographics pressures have been exacerbated by the net emigration. A reduction in emigration, or even reversal, would also help. See IMF Staff Discussion Note “Emigration and Its Economic Impact on Eastern Europe” available at https://www.imf.org/external/pubs/ft/sdn/2016/sdn1607.pdf
[3] In many countries, demographics pressures have been exacerbated by the net emigration. A reduction in emigration, or even reversal, would also help. See IMF Staff Discussion Note “Emigration and Its Economic Impact on Eastern Europe” available at https://www.imf.org/external/pubs/ft/sdn/2016/sdn1607.pdf
On Economics of Innovation Subsidies in Russia
Following the general agreement that innovation is a source of economic growth, the Russian government has provided various stimuli to foster domestic innovation. One of the mechanisms of innovation policy is research subsidies. This policy brief starts off with a discussion of the theoretical predictions and empirical evidence, which relates the economic incentives of research subsides to innovation and growth. We then address the potential adverse effects of focusing innovation subsidies mainly on large public companies in Russia. Finally, we attempt to establish a link between the innovation rate and market competition within Russian industries.
Overview
According to data from the Russian Statistical Agency, the R&D intensity – measured by R&D expenditure as percent of sales – increases with company size. Companies with 50 to 500 employees spend 1% of their sales on R&D, while the R&D intensity varies from 2 to 5% of sales for larger businesses (see Figure 1). The size non-neutrality of R&D in Russia contradicts the findings in the theoretical and empirical literature, which hold for companies in the developed countries (Cohen, 2010). An explanation may be the excessive government support to public companies in Russia, and in particular, to larger public corporations. A positive consequence of such policies is that public corporations come ahead of private companies, not only in R&D intensity, but also in innovation rates (see Figures 2–3).
However, government support towards innovation does not necessarily have a positive impact on overall economic activity. The purpose of this brief is to discuss the unwanted effects of the government policy in the form of research subsidies, both in theory and in an application to public companies and corporations in Russia. We base our analysis on the outcomes of the 2014–2017 micro surveys by the Analytical Center under the Government of the Russian Federation.
The role of government
Fighting under-provision of innovation
According to the seminal paradigm of the endogenous growth models with technological change, companies are engaged in quality competition, and their innovations are explained by a rational decision to raise profits through expanding the markets for existing products or entering markets for new products (Schumpeter, 1942; Romer, 1990; Grossman and Helpman, 1991; Kletter and Kortum, 2004). The innovation becomes one of the causes of economic growth, which is proved in empirical applications for developed countries, such as the U.S., Japan and the Netherlands (Akcigit and Kerr, 2010; Lentz and Mortensen, 2008; Grossman, 1990).
Figure 1. Innovation rate and R&D intensity by company size (number of employees)
Source: Indicators of Innovation in the Russian Federation: 2017. Tables 2.4, 2.16, Data for 2015. Innovative rate is % of companies involved in innovative activity.
However, the technological change is closely linked to knowledge disclosure, which means that new products become vulnerable to imitation, and that the non-rival character of knowledge causes an under-provision of innovation on the market (Arrow, 1962). The argument supports the cause for government policies through the system of intellectual property rights on the legal side, and research subsidies as an economic mechanism (Rockett, 2010; Hall and Lerner, 2010). Research subsidies are expected to have a positive effect on innovation rate, as is empirically shown for the U.S. in Acemoglu et al. (2016) and Wilson (2009). However, the impact on economic growth is ambiguous (Acemoglu et al., 2013; Grossman, 1990).
Figure 2. Innovation rate and R&D intensity by ownership
Source: Indicators of Innovation in the Russian Federation: 2017. Tables 2.6, 2.17, Data for 2015, public corporations are different from organizations by regional/federal government.
Figure 3. Share of public funds in R&D financing, % of company budget
Notes: Indicators of Innovation in the Russian Federation: 2017. Table 1.13; Innovation Development Programmes of Russian State-Owned Companies, Fig.4.
Unwanted effects of subsidies
Two concerns are associated with subsidization of innovation. First, while research subsidies may stimulate innovation among the targeted companies, the growth effect is likely to be heterogeneous across companies in the industry or economy, leading to a neutral or even negative overall effect. For instance, the increased innovation rate in subsidized large incumbents may curb entry of new (and more productive) firms, so the net outcome is deceleration of growth in the economy (Acemoglu et al., 2013). Research subsidies may even cause a shrinking of the high-tech sectors: if skilled labor moves from manufacturing to research labs, manufacturing may experience a shortage of labor, resulting in the net effect being a decrease in production (Grossman, 1990).
Another extreme of subsidizing entrants, in view of antitrust policies, occurs when former entrants change their market status to incumbents: now they face lower profits relative to newer entrants and hence, become less incentivized in their economic activity (Segal and Whinston, 2007).
Second, innovation policy (for instance, in the form of subsidies) may sometimes not even increase the innovation rate. Indeed, incumbents have no incentives to innovate in order to keep their market power or to prevent entry of higher quality firms in industries with non-perfect competition (Rockett, 2010; Qian, 2007).
Both mechanisms are likely to hold for Russian industries, where the protection of large public corporations has led to low competition, various forms of distortions on the market and hence, weak incentives to innovate.
Potential adverse effects in Russia
Large companies are likely to attract public attention owing to their obvious advantages in spreading fixed costs of innovations (Cohen,
2010). Russia is no exception to the phenomenon, so public corporations, which are commonly of a large size, received government subsidies. However, the subsidy is primarily used for acquiring new technologies and perfecting design, rather than conducting R&D (See Figure 4 with comparison available for communications and IT industry). The fact points to a possibility of a small effect of innovations on growth of public companies. Only if the research subsidy is spent on delegating the R&D research to specialized firms, with a subsequent acquiring of the resulting technology, the existing policy of supporting public corporations may induce their growth and/or growth of the corresponding industry.
Figure 4. Structure of spending the research subsidy in communications and IT in 2013, %
Notes: Indicators of Innovation in the Russian Federation: 2017. Table 1.134 Innovation Development Programmes of Russian State-Owned Companies, Fig.3.
In an attempt to formally assess the effect of innovation subsidies on company growth, we focus on the time profiles of the common proxies for company size: sales, profits and employment (Akcigit et al., 2017; Akcigit and Kerr, 2010; Acemoglu et al., 2013). The macroeconomic literature predicts that innovation becomes one of the channels for an increase of each of the three variables through a rise in quality. Motivated by this literature, the micro-data analysis “On the Interaction of the Elements of the Innovation Infrastructure”, conducted by the Analytical Center under the Government of the Russian Federation (2014), asked companies to assess their changes in sales, profits and employment in response to the innovation subsidy. As a result, the outcomes of the above analysis allow for a comparative assessment of the impact of the government’s innovation subsidy for public and private companies.
In particular, the results point to higher growth across private companies owing to research subsidies: the percent of private companies with new employees is higher than that of public companies. Similarly, the percentage of private companies that increased market share or raised profits/export due to subsidies exceed those of the public companies (see Figure 5). Here, we interpret new hires as employment growth and increase of market share as a potential indicator of sales growth.
Figure 5. Economic activity owing to research subsidies, % of companies
Source: Analytical Center under the Government of the Russian Federation, 2014. Fig.22
The innovation activity in private Russian companies lead to a higher prevalence of new products in comparison with public companies. The fact goes in line with a more important role of research and development in the innovative activity of private Russian companies (see Figure 4).
Finally, we attempt to establish a link between the innovation rate and market competition at the level of Russian industries. For this purpose, we use the results of the annual surveys “An assessment of the competitiveness in Russia”, conducted in 2015–2017 by the Analytical Center across 650–1500 companies from 84 Russian regions. The respondents were asked if they implemented R&D as a strategy for raising their competitiveness. We use the percentage of firms doing R&D as a proxy for the innovation rate. Competition in the industry was evaluated by respondents on a five-point scale (no competition, weak, median, high and very high), and we combine the prevalence of the two top categories as a proxy for competition in the industry.
Figure 6. Competition and R&D in Russian industries, % of firms
Source: Analytical Center under the Government of the Russian Federation, 2017, pp.8, 18.
The results show that innovative activity in the form of R&D or product modification is observed in industries with relatively high competition in Russia – for instance, in machinery and electric/electronic equipment (Figure 6). At the same time, industries where competition is not as high (e.g. woodworking, construction) show absence of either type of innovation. The findings go in line with the economic theory about market competition being a prerequisite for the rational choice of companies about innovation. Moreover, if the purpose of government subsidies is to foster innovation, the effective allocation of subsidies would imply the focus on Russian industries with high competition – here various forms of innovation do play a role in the company strategy on the market.
Conclusion
Our analysis outlines the theoretical foundations for the potential adverse effects of innovation policies in the form of research subsidies. The unwanted outcomes may relate to heterogeneity of companies and absence of the association between innovation activity and growth on non-competitive markets.
We offer the empirical evidence, which points to the undesired effects of subsidizing public companies in Russia. For instance, compared to the overall Russian sector of communications and IT, the innovative activity in public corporations has a weaker association with research and development. Additionally, compared to private companies, the innovations may result in smaller prevalence of increased exports, profits or new hires, as well as in a less frequent development of new products by public companies in Russia.
References
- Acemoglu, D., Akcigit, U., Bloom, N., Kerr, W. R., 2013. “Innovation, reallocation and growth”, National Bureau of Economic Research Working paper, No. 18993.
- Acemoglu, D., Akcigit, U., Hanley, D., Kerr, W. (2016). Transition to clean technology. Journal of Political Economy, Volume 124(1), pages 52-104.
- Akcigit, U., Kerr, W. R., 2010. “Growth through heterogeneous innovations” National Bureau of Economic Research Working Paper, No. 16443.
- Analytical Center under the Government of the Russian Federation, 2014. “On the Interaction of the Elements of the Innovation Infrastructure”, Analytical report, in Russian.
- Analytical Center under the Government of the Russian Federation, 2015-2017. “An Assessment of the Competitiveness in Russia”, Analytical reports, in Russian.
- Arrow, K., 1962. “Economic welfare and the allocation of resources for invention”, In The Rate and Direction of Inventive Activity: Economic and Ssocial Factors, Princeton University Press, pages 609-626.
- Cohen, W. M., 2010. “Fifty years of empirical studies of innovative activity and performance”, Handbook of the Economics of Innovation, Volume 1, pages 129-213.
- Grossman, G. M., Helpman, E., 1991. “Quality ladders in the theory of growth”, The Review of Economic Studies, Volume 58(1), pages 43-61.
- Grossman, G.M., 1990. ”Explaining Japan’s innovation and trade”, BOJ Monetary and Economic Studies, Volume 8(2), pages 75-100.
- Hall, B. H., Lerner, J., 2010. “The financing of R&D and innovation”, Handbook of the Economics of Innovation, Volume 1, pages 609-639.
- Indicators of Innovation in the Russian Federation: 2017. N. Gorodnikova, L. Gokhberg, K. Ditkovskiy et al.; National Research University Higher School of Economics, in Russian.
- Innovation Development Programmes of Russian State-Owned Companies: Interim Results and Priorities, 2015. M. Gershman, T. Zinina, M. Romanov et al.; L. Gokhberg, A. Klepach, P. Rudnik et al. (eds.), National Research University Higher School of Economics, in Russian.
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- Lentz, R., Mortensen, D.T., 2008. “An empirical model of growth through product innovation”, Econometrica, Volume 76(6), pages 1317–1373.
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- Wilson, D. J., 2009. Beggar thy neighbor? The in-state, out-of-state, and aggregate effects of R&D tax credits. The Review of Economics and Statistics, Volume 91(2), pages 431-436.
Cross-Country Differences in Convergence in CESEE
Since 1989, there have been large differences in the convergence of the income levels of the former communist countries in CESEE with those in the US. Most Central European countries have seen a sharp rise in relative incomes, but many countries in former Yugoslavia and the CIS have not—indeed, some countries, including Moldova and Serbia, are now poorer than they were in 1989 (Figure 1).
Figure 1. Transition outcomes
Source: Total Economy Database and IMF staff calculations.
Figure 2. GDP level in Poland and Ukraine
Source: Total Economy Database and IMF staff calculations.
The difference between Ukraine and Poland is particularly stark. In 1989, both had similar income levels, but Poland is now more than three times as rich (Figure 2). As a result, cross-country income differences in CESEE remain large. In 1989, the Czech Republic, Russia, Slovenia and Croatia had the highest income per capita in 1989, about 4 times as high as in Albania and Moldova, the poorest in the group. Twenty-six years later, the differences are even larger. GDP per capita in Slovenia is 6 times as high as in Moldova (Figure 3).
Figure 3. Cross-country income differences
Source: Total Economy Database and IMF staff calculations.
What Explains Convergence Differences?
These differences in convergence do not seem to reflect data problems. True, GDP statistics in 1989 were not very good. It is hard to measure value added when prices are not quite right. Moreover, GDP at that time was probably not a good indicator or of consumer welfare. Much of what was produced was not wanted by consumers (e.g. military expenditures) and/or of low quality. Nevertheless, these issues apply to all post-communist countries in the regions—it is not clear that some countries suffered from data problems more than others.
Indeed, more direct measures of economic activity also suggest large initial output falls and large cross-country differences. Between 1990 and 1995 electricity consumption per capita fell by almost 40 percent in Ukraine and Moldova. By then electricity consumption in Poland had nearly recovered to the 1990 level (Figure 4).
Figure 4. An alternative measure of decline in economic activity
Source: IFA Statistics and IMF staff calculations.
Instead, several factors seem to have a played a role:
- The speed of transition to a market economy
- War and conflicts
- Boom-busts
- EU Membership
- Whether transition has been completed
Countries that reformed early had a shorter and shallower post-transition recession. The lower the EBRD transition index in 1995 (i.e., the less the economy was reformed), the sharper the output decline between the beginning of the transition and 1995 (Figure 5).
Figure 5. Market reforms and post-transition recession
Source: Total Economy Database and IMF staff calculations.
Why was this? In late 1989, a fierce debate broke out over what came to be called gradualism versus shock therapy. Many gradualists argued that the structural flaws of the economy would frustrate attempts at liberalization, and therefore that reforms should be implemented in a gradual, sequenced way. But for others—including key figures such as Leszek Balcerowicz in Poland—understanding the nature of the problem meant the opposite: reform was a seamless web that could only succeed if all the changes happened together, because liberal prices, improved governance, and a stable economic and financial environment were needed to reinforce one another; little could be achieved with a partial reform. The evidence from the past 25 years has vindicated the seamless web theory of transition. There is no doubt that some reforms took much longer than anticipated, including privatization, both of banks and companies. But it seems clear that the countries that made sweeping changes, and that kept at reform and stabilization have done well.[2] Countries that followed a more gradual path suffered from the decline of the old industries and did not get the boost from the growth of new firms. And in some countries bouts of macroeconomic instability repeatedly undermined reforms and sapped political momentum.
Weaker growth in the early transition years was not compensated by faster growth later. Countries, where output declines were deeper in early 1990s, did not see more rapid growth in subsequent years (Figure 6).
Figure 6. Permanent output losses in the early transition
Source: Total Economy Database and IMF staff calculations.
Wars and conflicts also played an important role. It is striking that the five countries with the lowest growth all had a war or serious conflict between 1990 and 2015 (Figure 7).
Figure 7. Wars and conflicts impact on long-term growth
Source: Total Economy Database and IMF staff calculations.
Avoiding boom-busts helped boost longer-term growth. Steady growth rates seem to be more conducive to higher long term growth than booms followed by busts. Between 2002 and 2008, Romania had capital inflows fueled boom and grew much faster than Poland, but thereafter it suffered a deep bust, and between 2002 and 2015, Poland has grown faster (Figure 8).
Figure 8. The hare and the tortoise
Source: Total Economy Database and IMF staff calculations.
EU accession was a powerful catalyst for reforms and upgrading of institutional frameworks. CESEE countries that joined the EU were required to bring their regulations and institutions up to Western European standards. There is a striking difference in the level of EBRD transition indicators between EU countries and non-EU countries (Figure 9).
Figure 9. EU accession as a reform catalyst
Source: EBRD and IMF staff calculations.
Thus, prospects of EU Membership have led to more reforms and, as a consequence, to stronger growth (Figure 10).
Figure 10. Market reforms and changes in income levels
Source: EBRD, Total Economy Database and IMF staff calculations.
Countries that upgraded their institutions to EU standards saw a decline in cross-country income differences. Countries that joined the EU in 2000s show clear pattern of convergence. The difference between Bulgaria and Slovenia has narrowed by 15 percent of Slovenia’s GDP since the former begun EU accession negotiations in 2000 (Figure 11, right panel). Similarly, a group of candidate and potential candidate countries, including Croatia (which joined the EU only in 2013) have converged as well (Figure 11, left panel).
Figure 11. Convergence within CESEE regions
Source: Total Economy Database and IMF staff calculations. Note: The EU has recognized Bosnia and Herzegovina as potential EU candidate countries.
By contrast, there was no convergence among the European CIS-countries. Russia, the richest of CIS countries grew by only 0.6 percent annually since 1989, while output per capita declined in Moldova and Ukraine. Only Belarus achieved growth rates comparable to non-CIS countries, but its largely unreformed economy may have approached the limits of the current extensive growth model (Figure 12).
Figure 12. Convergence in the European CIS region
Source: Total Economy Database and IMF staff calculations.
Countries that have a more completed transition are richer. There is a strong correlation between progress in market reforms and a country’s income level (Figure 13).
Figure 13. Market reforms and income level
Source: EBRD, Total Economy Database and IMF staff calculations.
Similarly, richer countries have a more vibrant private sector (Figure 14).
Figure 14. Market reforms and private sector share in the economy
Source: EBRD, Total Economy Database and IMF staff calculations.
Correlation does of course not mean causality but is it telling that there is no highly reformed poor country.
Convergence Post-2009 Crisis
Post-2009, catch-up has slowed down. Pre-crisis, convergence was rapid and widespread. In some countries, the GDP per capita gap to the US narrowed by more than 12 percentage points in 2003-08. Since 2010 only two-thirds of countries in the region have continued to catch-up with the US, while Ukraine and Slovenia saw a widening of income differences (Figure 15). And if we include the 2009 crisis, which was deeper in CESEE than in Western Europe, convergence has been even less.
Figure 15. Convergence pace pre- and post-crisis
Source: WEO database and IMF staff calculations.
More recently, there have also been large differences across regions: while the CIS was in recession, the non-CIS countries doing much better.
- The CIS countries suffered from falling commodity prices, and from the impact of sanction on Russia.
- By contrast, the non-CIS countries saw a gradual acceleration of GDP growth, on the back of a pick-up of domestic demand in the euro area. Labor markets in many EU New Member States (NMS) are tightening rapidly, and unemployment is quickly approaching pre-crisis lows, though GDP growth rates are well below those in the pre-crisis years.
How can we boost Convergence going forward?[3]
GDP per capita is the product of GDP per worker (labor productivity) and the share of the population that works (the employment rate):
Low GDP per capita can thus be the result of both low labor productivity and a low employment rate. In CESEE, both factors play a role:
- In most CESEE countries, the employment rate is below that in Western Europe (Figure 18). Low employment rates are a particular problem in SEE and some CIS countries.
- The labor productivity gap with Western Europe is still large, even though it has declined in the past twenty years.
Figure 16. Big differences in growth among regions
Source: WEO database and IMF staff calculations.
Figure 17. Labor markets in EU new member states
Source: Eurostat.
Figure 18. Labor utilization and productivity
Source: Total Economy Database, UN population statistics and IMF staff calculations.
To raise labor productivity more investment is needed. The capital stock per worker in a typical CESEE economy is only about a third of that in advanced Europe. Domestic saving rare are too low in most the region; policies should, therefore, focus on institutional reforms that reduce inefficiencies and increase returns on private investment and savings.
Boosting total factor productivity (TFP) is important as well. CESEE countries have to address structural and institutional obstacles that prevent efficient use of available technologies or lead to an inefficient allocation of resources. The recent IMF CESEE report suggests the largest efficiency gains are likely to come from improving the quality of institutions (protection of property rights, legal systems, and healthcare); increasing the affordability of financial services (especially for small but productive firms), and improving government efficiency.
Conclusion
Since the fall of communism, there have been large differences in the convergence of income levels with the US among CESEE countries. Much of these differences reflect differences in policies. Countries that reformed more and earlier saw faster growth than countries that reformed less or later. Macro-stability also helped, and countries that avoided boom-busts tended to grow faster.
Continued convergence will require a higher investment, higher TFP, and higher employment rates. The capital stock per worker is still below that in Western Europe. Higher investment rates will require higher saving rates, lest large current account deficits emerge anew. Addressing structural and institutional obstacles would also help convergence, as it will support higher labor force participation and allow for a more efficient allocation of resources.
Notes and References
- [1] Bas B. Bakker is the Senior Resident Representative and Krzysztof Krogulski an economist in the IMF’s Regional Office for Central and Eastern Europe in Warsaw. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
- [2]This is not to say that the rapid and seamless approach was without problems, notably large losses of output and high unemployment in the short run. Thus, reform will always have to worry about the social safety net and, under some circumstances, may benefit from external assistance, which is where the IMF and others can come in.
- [3]The IMF addressed this question in depth in the spring 2016 issue of “CESEE Regional Economic Issues.”
Disclaimer: Opinions expressed in policy papers and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
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 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).
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.
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.