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Higher Competition in the Domestic Market – A Way to Boost Aggregate Productivity

Posted on February 27, 2017 | Policy Brief

20170227 Higher Competition in the Domestic Market - FREE Policy Brief

Competition is a good thing not only because of lower prices and larger variety. Higher competition in the domestic market also shifts necessary labour and capital resources from less productive domestic-oriented firms to export-oriented productivity champions. Such firms will make better use of production factors and generate larger output. Thus, simply increasing the level of competition in the domestic market can boost the aggregate productivity of a country.

The aggregate productivity of a country can be boosted even without changing the productivity of individual enterprises. This can be achieved by improving the allocation of resources – the redistribution of labour and capital towards more productive firms. These firms will make better use of production factors and generate larger output. But how can one affect the allocation of resources? Economic theory says that allocation depends on the productivity of individual firms: more productive enterprises attract more labour and capital. However, there exists another factor behind allocation: distortions.

Distortions affect the allocation of resources

A model developed by Hsieh and Klenow (2009) – one of the most popular frameworks to study the allocation of resources – has a very important and realistic feature: it acknowledges that firms are not treated equally. Some firms may face lower supply of banking loans ending with higher capital costs. Other firms could confront with trade unions and higher wages. Tax rates may also differ across firms. These are all examples of distortions. Firms facing larger distortions are forced to underuse respective production factor, while firms that enjoy more favourable conditions tend to overuse capital and labour, generating more output.

While it is virtually impossible to imagine an economy without any distortions (the one where all firms face the same taxes, costs of labour, capital etc.), not all distortions damage the allocation of resources. Only distortions to productive firms create misallocation of resources by shifting labour and capital towards unproductive firms. Thus, removal of such distortions can improve the efficiency of allocation and raise the aggregate output of the country.

According to Hsieh and Klenow (2009) the distortions faced by every individual firm can be quantified from the balance sheets and profit/loss data. For example, observing lower-than-usual ratio of capital to intermediate inputs (comparing with other enterprises in a narrowly defined industry) indicates a capital distortion, possibly related with limited access to banking loans. Similarly, lower-than-usual share of wages in total production costs implies high labour distortions. Finally, the size of the distortion can be detected as a case of abnormally low share of intermediate inputs in total output, and signals about the restrictions to total output (e.g. due to higher taxes for large enterprises).

Misallocation of resources is small in Latvia

In my recent research (see Benkovskis, 2015), I use anonymised firm-level dataset for 2007–2013 and apply the Hsieh and Klenow (2009) model to study the allocation of resources in Latvia – a unique example of a small and open economy facing extreme structural shifts during the financial crisis. According to my estimates, the negative contribution of misallocation to aggregate productivity was close to 27% in 2013 (see Figure 1). In other words, it suggests that actual aggregate productivity could be boosted by 27% if all distortions were removed!

This may seem large but in fact 27% is a comparatively low figure. Hsieh and Klenow (2009) argue that full liberalisation would boost aggregate manufacturing productivity by 86–115% in China, 100–128% in India, and 30–43% in the US. Dias et al. (2015) show that removing distortions would lead to a 30% gain in output of Portugal in 2011. Thus, misallocation of resources is relatively small in Latvia. Even more important: the misallocation of resources decreased after the crisis in Latvia (contrary to the case of Portugal), adding more than 10 percentage points to aggregate productivity growth between 2010 and 2013.

Figure 1. Contribution from misallocation of resources to aggregate total factor productivity, %

Source: Benkovskis (2015). Note: shows the contribution of misallocation comparing with the counterfactual case of no distortions.

The finding that allocation of resources improved after the crisis is interesting per se, but uncovering the reasons behind the improvement is even more important. Figure 1 provides a decomposition, which shows that labour distortions are minor in Latvia due to high flexibility of labour market (in line with recent findings by Braukša and Fadejeva, 2016). The capital distortions, while being minor in 2007–2008, increased afterwards, pointing to some credit supply constraints faced by the highly productive enterprises after the financial crisis. However, by far largest contribution comes from the misallocation of intermediate inputs – the turnover of the most productive firms face some constraints. And it was the ease of constraints to turnover for the most productive firms that determined the improvements in aggregate productivity since 2010.

The level of competition matters for misallocation

My research stresses the importance of the competition level on the market, since higher competition serves as a natural constraint for the firm to increase its turnover. What if the most productive Latvia’s firms systematically come up against higher competition? I found that indeed this is the case. First, recent results by Fadejeva and Krasnopjorovs (2015) show that Latvia’s domestic market has lower competition level comparing with external markets. Second, it is widely acknowledged that exporters tend to be more productive comparing with domestically oriented firms (see e.g. Bertou et al., 2015, who report positive export premiums for EU countries, while Benkovskis and Tkačevs, 2015, find higher productivity of exporters in Latvia). Thus, Latvia’s productive export-oriented firms are subject to higher competition and cannot enlarge their turnover as easy as other entities. This shifts labour and capital towards small and less productive firms working solely on domestic market, creating the misallocation of resources.

The domestic competition factor can also explain the improving allocation of resources after 2010. The study by Fadejeva and Krasnopojorovs (2015) reveals that the competition gap between domestic and foreign markets narrowed after the financial crisis (see Table 1). Namely, life was too easy on the local Latvia’s market during the boom time, allowing unproductive firms to survive and drain away resources from more productive firms. But conditions became tougher after the crisis (although the competition level still remained lower than abroad). We can view this as a “cleansing effect of the crisis”: some of the least productive domestic oriented firms went bankrupt (or decreased their turnover), freeing the necessary capital and labour resources for productive exporters.

Table 1: Change in the competitive pressure on main product in domestic and foreign markets compared to the situation before 2008, %

Domestic market Foreign market
2008–2009 2010–2013 2008–2009 2010–2013
Strong decrease 2.9 2.2 0.9 1.0
Moderate decrease 11.8 3.8 7.6 5.9
Unchanged 33.8 24.7 45.7 51.5
Moderate increase 30.0 28.1 25.2 19.7
Strong increase 18.7 38.5 11.2 8.8
Does not apply 2.8 2.8 9.4 13.1

Source: Fadejeva and Krasnopjorovs (2015), Table A.102. Notes: based on the sample of 557 Latvia’s firms; results are weighted to represent firm population.

Conclusion

This research has an important policy conclusions applicable to any country that seeks to increase aggregate productivity. The competition level in the domestic market is important not only for consumers, who enjoy lower prices and higher variety. Higher competition in the domestic market also shifts necessary resources from less productive domestic-oriented firms to export-oriented productivity champions.

References

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Gaming the System: Side Effects of Earnings-Dependent Benefits

Posted on January 30, 2017 | Policy Brief

20170130 Gaming the System FREE Policy Brief Image

Today policy makers in developing and middle-income countries face tremendous challenges in combating various forms of tax evasion. Increasingly it is proposed to tie social security benefits to the reported income and in this way increase tax compliance incentives. We use administrative data from Latvia to study generous childcare benefits, which depend on the reported wages in the pre-childbirth period. Our analysis reveals pronounced wage growth shortly before the childbirth, which we rationalize by the legalization of previously undeclared wages. Obtained results show that the wage growth is temporary and lasts only until the end of the period, which is taken into account when calculating parental benefits.

Today policy makers around the world are increasingly preoccupied with reducing various forms of tax evasion. To provide tax compliance incentives it is often proposed to tie social security benefits to declared wages. For example, Kumler et al. (2013) show that a reform tying future pension benefits to the payroll tax in Mexico increased tax payments after the reform. Similarly, Cruces and Bergolo (2013) and Bergolo and Cruces (2014) demonstrate that a reform tying health care insurance of children to the reported earnings of parents increased “legal” labor supply in Uruguay.

On the other hand, Kreiner et al. (2016) document inter-temporal wage shifting in Denmark to enjoy significantly lower marginal tax rates. In light of the results by Kreiner et al. (2016), it is possible that employees and employers collude to increase the wage during the period, which is taken into account when calculating social security benefits. If the wage increase is temporary then the result of tying social security benefits to wages might be a net loss to the government finances. Hence, the question of whether tying social security benefits to reported wages is a solution to the problem of payroll tax evasion is still open.

We demonstrate that tying social security benefits to the declared wages can backfire to the extent that it can lead to the excessive payments of social security benefits, while doing almost nothing to reduce payroll tax evasion, in this way producing net fiscal loss to government finances. More specifically, we show that if the contribution period that determines the size of the benefit is relatively short and social security benefits are generous, then by colluding, employees and employers can temporally increase the legal wage to extract generous benefits afterwards. This result can have implications for the design of social benefit systems in many countries, where relatively short contribution periods ensure generous long-lived benefits afterwards.

Institutional background and methodology

We illustrate this phenomenon by studying the childcare benefit in Latvia, which in 2005-2008 depended on parents’ declared wage in the pre-childbirth period. This system, introduced in 2005, replaced a universal (very modest in size) childcare benefit. The new rules foresaw that one of the parents could receive a benefit that was equivalent to the parent’s previous net wage until the child became one year old. The average wage that determined the size of the benefit was calculated over the 12-months period that ended three months before the childbirth (hereinafter – benefit qualification period) and therefore included 5 months of pregnancy. Initially the benefit was not compatible with employment but as of March 2007 it became possible to simultaneously work full-time and receive the benefit.

Presumably, the 2005 reform created incentives to report higher earnings before the childbirth, because of the generosity of the new benefit and because the benefit qualification period included pregnancy, i.e., the period when the mother knows if/when she will be eligible for the benefit. To uncover the effects of the incentives to report more income, we use administrative data on declared monthly wages and use three sources of identifying variation in a difference in differences setup.

First, we compare wage growth during pregnancy with wage growth of women who did not become pregnant. The identifying assumption is that, in the absence of pregnancy, the wages of women who became pregnant would follow the same trend as the wages of other women. Under this assumption, any difference in the wage growth can be interpreted as a legalization of previously undeclared wages. However, this assumption may not hold because pregnancy is not randomly assigned across women: women can anticipate a wage increase (e.g. anticipate a promotion) and adjust the decision to have a child. Therefore, we use a second source of identifying variation by comparing wage growth during pregnancy for women employed in the private sector with wage growth for women employed in the public sector, where tax evasion is presumably absent. Assuming that promotion anticipation effects in the private and the public sector are identical, this difference in wage growth can be interpreted as the growth of wages resulting from wage legalization.

Our previous assumption might be violated if promotions in the public sector can be easier to predict (which means that anticipation effects in the private and the public sectors are not necessarily identical). To address this challenge, we use a third source of identifying variation coming from the 2005 reform, which tied the childcare benefit to the previous earnings. Since this reform increased incentives to disclose higher earnings during pregnancy, the difference in wage growth in the private sector versus public sector should not be observed before the reform.

Estimations are based on a matched employee – employer administrative dataset, which covers monthly-declared earnings of all employed workers in Latvia from 1996 to 2010.

Results

There are three main findings. First, wage growth during the first five months of the pregnancy in the private sector is always higher than that in the public sector. If we use this observation to obtain an estimate of the wage growth due to the legalization of previously undeclared wages, we find, depending on the regression specification, that it varies between 5 and 7 percent.

Second, this effect is mainly driven by the time period after the reform of 2005 (see Figure 1). Thus, if we use the time period before the reform of 2005 only to difference out permanent differences in the anticipation effects between public and private sector, our preferred regression specifications provide us with an estimate that varies from 5 to 6 percent.

Figure 1. Difference-in-difference-in-difference estimate by year, %

Note: difference in difference in differences estimate for a given year is calculated by first comparing wages of pregnant women with those of not pregnant before and during first five months of the pregnancy. Then this estimate is compared between public and private sectors. Everything is compared with respect to one year before the reform announcement – 2003.

The final finding shows that the sharp jump in the wage growth in private sector versus the public sector starts to appear exactly in the first month of the pregnancy (see Figure 2). It is important to note that we do not see any differential wage growth between the public and the private sector before the date of conception, indicating that potential anticipation effects are limited.

Figure 2. Difference-in-difference-in-difference-in-differences estimate by pregnancy month, %

Note: difference in difference in difference in differences estimate for a given month is calculated by first comparing wages of pregnant women with those of not pregnant in a given month with respect to one month before the date of conception. Then this estimate is compared between public and private sectors and finally previously calculated difference is contrasted before and after the reform tying parental benefits to reported wages.

Due to the fact that many women do not return to the same employer after childbirth, it is problematic to make inferences about the wage a woman receives once she returns to the labor market. To overcome this challenge we use the same social security data for men for the time period covering January 2007 until August 2010.

As explained previously, starting in March 2007 the childcare benefit became compatible with full time employment. The outcome of this reform was that many men started to receive the benefit, while continuing to work. This allows us to perform the previous analysis for the sample of men.

Results presented in the Figure 3 show that similarly as in the sample of women we see a sharp increase in the wage during the qualification period. Additionally, we see a slowdown in the wage growth once the qualification period ends. It is important to mention that displayed coefficients describe the difference between public and private sector in the change in wages between men whose partners became pregnant and those who did not with respect to the reference period (here one month before the conception date). We also record a sharp growth in wages in the public sector in the months following the childbirth. On the contrary, wages in the private sector stay the same, hence the large difference in the months following the childbirth.

Figure 3. Difference-in-difference-in-differences estimate for men by month of partner’s pregnancy, %

Note: difference in difference in differences estimate for a given month is calculated by first comparing wages of men whose partner became pregnant with those men whose partner did not become pregnant with respect to one month before the date of conception. Then this estimate is compared between public and private sectors

Conclusion

Drawing on the example of the childcare benefit in Latvia, we show that declared wages sharply increase during the period that is taken into account when calculating social security benefits. This wage growth is temporary and does not continue once the benefit qualification period is over. We interpret this phenomenon as the legalization of previously undeclared wages: this temporary legalization of earnings is possible, because the benefit qualification period is relatively short (12 months), and includes 5 months of pregnancy, which makes the average wage during the qualification period relatively easy to affect. Such setting creates bad incentives – an employee and an employer can collude to increase the average wage that determines the size of the benefit.

Additionally, our research casts doubts on policies tying parental benefits to declared earnings with an aim to reduce opportunity costs of high earners and increase their fertility. Researchers analyzing such policies should be very cautious when interpreting their results because the effect that they capture might not come from high earning women, but rather from women who manage to increase their income during pregnancy. Absent monthly data, it might be challenging to disentangle the two.

Many countries implement earnings-dependent benefits. Our results show that even very well designed social security benefits can and will be abused if people are given wrong incentives. Thus to achieve the best outcomes policy makers when deciding whether to tie social security benefits to declared earnings should take into account side effects described in this brief.

References

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Socio-Economic Policy in Poland: A Year of Major Changes in Benefits, Taxes, and Pensions

Posted on January 23, 2017 | Policy Brief

Socio-Economic Policy in Poland - FREE Policy Brief Image

2016 was the first full calendar year of the new Polish government elected to power in October 2015. The year marked a number of major changes legislated in the area of socio-economic policy some of which have already been implemented and others that will take effect in 2017. In this policy brief, we analyse the distributional consequences of changes in the direct tax and benefit system, and discuss the long-term implications of these policies in combination with the policy to reduce the statutory retirement age.

The Law and Justice party (Prawo i Sprawiedliwość, PiS) won an absolute majority of seats in both houses of the Polish Parliament in the parliamentary elections of October 2015. Earlier that year, Andrzej Duda of PiS was elected President of the Polish Republic. In both cases, the electoral victories came on the wave of pledges of significant financial support to families with children and to low-income households, especially pensioners. The new president pledged to cut back the pension age to the levels prior to the 2012 reform, which introduced a gradual increase from 60 and 65 to 67 for both women and men, and to nearly triple the income tax allowance. Following Duda’s victory in May 2015, PiS reiterated these pledges in the parliamentary election campaign and added the promise to increase the total level of financial support for families with children by over 140% through a nearly universal benefit called “Family 500+” and to hike the minimum wage by over 8%.

Despite a rather tight budget situation, the government went ahead with the “Family 500+” and successfully rolled it out in April 2016 (Myck et al., 2016a). The new instrument directs support of 500 PLN per child per month (110 EUR) to all second and subsequent children in the family in the age group between 0 and 17. Benefits for the first child in the family in this age group are granted conditional on overcoming an income threshold of 800 PLN (180 EUR) per person per month. Since April 2016, over 2.7 million families have received the benefit and 60% of them received the means tested support (if they have more than one child this is paid out in combination with the universal benefit).

The second key electoral pledge – to increase the tax allowance from 700 to 1,850 EUR at an estimated cost of 4.8 billion EUR – has so far been postponed (CenEA, 2015a). Increases in the allowance became a major policy issue in October 2015 when the Constitutional Tribunal ruled that maintaining its level below minimum subsistence, as it was at the time, was unconstitutional. To satisfy the Tribunal’s ruling, the allowance would have to increase to ca. 1,500 EUR at a cost of nearly 15 billion PLN (3.4 billion EUR, and about 0.8% of GDP, CenEA 2015b). Instead of a simple increase in the allowance, the government decided to implement a digressive tax allowance for 2017. This raised the value to the required minimum subsistence level for the lowest income tax payers, but since it is rapidly withdrawn as taxable income rises, the allowance will be unchanged to a large majority of taxpayers and will cost the public purse only 0.2 billion EUR (CenEA, 2016). This policy will be more than paid for by the fiscal drag given the decision to freeze all other parameters of the tax system, which will cost the taxpayers 0.5 billion EUR (Myck et al., 2016b).

The policies that directly affect household budgets will in total amount to about 5.5 billion EUR in 2017 (1.3% of GDP and 6.2% of the planned central budget expenditures) and will include also an increase in the minimum pension to benefit about 1.5 million pensioners. The cost of the “Family 500+” reform makes up the large majority of this value (5.4 billion EUR). Households from the lower income decile groups will benefit the most from this reform package, with their monthly disposable income increasing on average by 15.1% (ca. 60 EUR). High-income households from the top income decile will see their income grow on average by only 0.5% (see Figure 1). Overall, nearly all of the gains will go to families with children, with single parents gaining on average about 95 EUR and married couples with children about 84 EUR per month. Other types of families will, on average, see negligible changes in their household disposable incomes (see Figure 2). Thus, the implemented package clearly has a very progressive nature and redistributes significant resources to families with children.

Figure 1. Distributional consequences of changes in direct tax and benefit measures implemented between 2016-2017

Source: calculations using CenEA’s microsimulation model SIMPL based on PHBS 2014 data.

The pension age and public finances in the years to come

The most recent major reform, legislated at the end of 2016 and which will come into effect in October 2017, represents an implementation of yet another costly electoral pledge. This policy has overturned gradual increases in the statutory retirement age, initiated by the previous government in 2012. Despite the very rapid ageing of the Polish population, the new government decided to return to the pre-2012 retirement ages of 60 and 65 for women and men, respectively. This comes at a time when, according to EUROSTAT (Eurostat, 2014), the old-age dependency ratio in Poland, i.e. the proportion of the 65+ population to the working-age population, will grow from the current 24% to 27% in 2020 and to 40% in 2040. With the defined contribution pension system, the shorter working lives resulting from this change will be reflected in significantly reduced benefits (Figure 2). For example, pension benefits of men retiring in 2020 will on average be 13.5% lower than the pre-reform value. For women that retire in 2040, the pension benefits will on average fall by 15.2%, which corresponds to a 43% lower benefit than the pre-reform value, and with consequences of the reform becoming more severe over time. The reform will also be very costly to the government budget. In 2017, it is expected to cost 1.3 billion EUR and its full effect will kick in after 2021, when the cost of the reform will exceed 3.9 billion EUR per year (Figure 2).

Figure 2. Reducing the statutory retirement age and its implications on pension benefits and public finances

Source: Based on data from Council of Ministers (2016).

Conclusion

Since coming to power in October 2015, the PiS government has implemented a majority of its costly electoral pledges. Direct changes in taxes and benefits will cost 5.5 billion EUR in 2017 and benefit primarily those in the lower end of the income distribution and in particular families with children. The reduced statutory retirement age will add an extra 1.3 billion EUR in 2017 and as much as 3.9 billion EUR four years later. The very generous “Family 500+” programme has significantly reduced child poverty and may have important positive long-term effects in terms of health and education for today’s beneficiaries. However, its fertility implications are still uncertain and the programme is expected to reduce the employment rate among mothers. While the government maintains that its financing is secured, it is becoming clear that maintaining the policy will not be possible without higher taxes.

The government came to power claiming that the implementation of this programme will be based on reducing tax fraud and that only a small fraction will be financed from tax increases. While it seemed likely at the time when these declarations were made, the expected major shift in the reduction of tax fraud has yet not materialised. The government have withdrawn from the pledge of reducing the VAT and from assisting those with mortgages denominated in Swiss Francs, while its income tax allowance reform was nearly thirty times less expensive compared to that announced in its electoral programme.

With a very tight budget for 2017 based on relatively optimistic assumptions, the key factors determining further realisations of the generous programme will be the rate of economic growth and related dynamics on the labour market. Developments of the labour market will also be essential for the longer-term economic success of the implemented reform package. This relates both to the future level of participation of women and to the success of extend working lives of people who will soon reach the new reduced retirement age.

References

  • CenEA (2015a) Konsekwencje prezydenckiej propozycji podwyższenia kwoty wolnej od podatku (Consequences of the presidential proposal to raise the incoem tax allowance), CenEA press release, 3 December 2015.
  • CenEA (2015b) Co z kwotą wolną od podatku po wyroku Trybunału Konstytucyjnego? (what will happen to the income tax allowance after the decision of the Constitutional Tribunal?), CenEA press release, 13 November 2015.
  • CenEA (2016) Zmiany w kwocie wolnej od podatku za 800 mln rocznie (Changes in the income tax allowance at the cost of 800m per year), CenEA press release, 29 November 2016.
  • EUROSTAT (2014) Eurostat – Population projections EUROPOP2013, access 21 December 2016.
  • Myck, M., Kundera, M., Najsztub, M., Oczkowska, M. (2016a) 25 miliardów złotych dla rodzin z dziećmi: projekt Rodzina 500+ i możliwości modyfikacji systemu wsparcia. (25bn for families with children: plans for the Family 500+ reform and other options to modify the system of support.), CenEA Commentaries, 18 January 2016.
  • Myck, M., Kundera, M., Najsztub, M., Oczkowska, M., 2016b, Zamrożony PIT i utrzymane wyższe stawki VAT – jak brak zmian w podatkach wpłynie na budżety gospodarstw domowych? (Frozen PIT and higher VAT – how lack of changes in taxees will affect househod budgets?), CenEA Commentaries, 05 October 2016.
  • Council of Ministers (2016) Position of the Council of Ministers on the presidential bill proposal, Warsaw, 25 July 2016.

The Anatomy of Recession in Belarus

Posted on December 12, 2016 | Policy Brief

FREE Policy brief Image | 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

The Economics of Russian Import Substitution

Posted on December 5, 2016 | Policy Brief

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This policy brief discusses the economic mechanisms triggered by import substitution policies, associated losses and conditions that ensure positive economic effects. Numerical estimations of potential effects of Russian import substitution policies indicate a decline in GDP, decrease in output of unprotected sectors and consumers’ welfare losses. We conclude with a discussion of the role imports play in economic efficiency.

Import substitution: pro and contra

Two years after joining the WTO, in the new political reality, Russia began implementing a series of import substitution policies. Supported sectors range from agriculture and production of metal products, to computer equipment and special purpose vehicles. The potential economic effects of these policies are of substantial interest and importance both for researchers, policymakers and the general public. However, they have not yet been quantitatively assessed. This policy brief summarizes the results of a study of these effects conducted at CEFIR in 2016 (Volchkova and Turdyeva, 2016).

Import substitution can be implemented by a range of instruments aimed at creating preferential conditions for domestic producers of imported goods compared to foreign competitors. Barriers to trade are the most common and easily available policy tools. Trade barriers lead to price increase on domestic market relative to the world price of the good.

Domestic manufacturers in the protected industry enjoy higher prices on domestic market, thereby securing higher revenues at the same costs. The protected sector also is able to put into operation those capacities that were generating losses in the absence of protective measures. However, if the economy works at full employment in absence of import substitution, then in order to increase production in the protected sectors, factors should be reallocated there from the other sectors. As a result of the import-substituting policy, producers in unprotected sectors will decrease the scale of production, and some will exit the industry. That is, producers that were efficient enough before import substitution policies will be forced out by those that cannot compete at international prices. From the point of view of welfare economics, this maneuver is accompanied by a loss of economic efficiency.

Economic literature discusses several cases when import substitution can be justified, such as a presence of positive external effects from protected sectors to the economy; learning-by-doing effects in protected sectors; and an infant industry argument. All of these cases imply market failures in the absence of government intervention, leading to lower than socially optimal output of the sector in question. Then, government interventions aiming to increase output – such as import substitution – might bring additional welfare improvement to the economy. If any of these effects do take place then the gain brought by protected sectors may compensate for the loss by the unprotected. To validate any of these cases one needs to perform a thorough and independent analysis of the economy based on very detailed information.

Estimates of static and dynamic effects of import substitution

In order to illustrate the potential effects of import substitution policies in the current Russian situation, we use a static CGE model of the Russian Federation constructed at CEFIR.

Based on publicly available documents (Russian Government’s Decrees №2744-Р 29.12.2015 and № 2781-р 31.12.2015), we identify the sectors that are targeted by the import substitution policy: agriculture and four manufacturing sectors (metal production; machinery and equipment; cars; sea crafts, airplanes and spaceships).

To model the effects of import substitution, we calculate an ad valorem tariff equivalent, which ensures a 10% decline of the volume of import in each of five industries. In order to simulate proposed policy measures, we conduct six experiments: increase in import tariffs in each of five industries individually, and a comprehensive policy change with an increase in all five tariffs simultaneously.

If import substitution policy is implemented not by trade policy instruments but only through producer support measures then it will be accompanied only by changes in relative prices for producers while consumer prices will not be affected and will be determined solely by international prices. In this case, our estimates will represent an upper bound of possible consumers’ losses. Since the distortion of relative prices for producers do not depend on a particular instrument chosen to implement import substitution policy then the consequences for other sectors and for efficiency of the overall production will be the same under trade or domestic policy interventions.

Table 1 shows the results of our calculations. Columns (1) – (5) present the estimates of the effects of the import-substitution measures in the relevant sectors. Column (6) reports the results of the comprehensive policy reform.

Table 1. Consequences of the decline in imports by 10% in the protected sector (s).

  Agriculture Metals Machinery, and equipment Cars Sea crafts, airplanes and space ships Tariff change in all industries
(1) (2) (3) (4) (5) (6)
Ad valorem tariff equivalent, % 2.9 3.9 6.1 6.7 5.6
Change in
CPI, % 0.04 0.09 0.39 0.3 0.3 1.0
Protected sectors’ output, % 0.7 2.5 9.8 10.3 8.3 3.8
All other production, % -0.2 -0.4 -0.5 -0.2 -0.5 -2.3
GDP, % -0.002 -0.011 -0.023 -0.005 -0.018 -0.049
Welfare, % -0.015 -0.020 -0.074 -0.041 -0.080 -0.215

Source: Authors’ own estimation.

Our results illustrate the anticipated effect of import substitution policy in economy with full employment. The protected industries increase their output at the expense of other industries. An increase in economic inefficiency is reflected by a fall in GDP.

In order to capture dynamic effects of the proposed import substitution policy, we simulate an import tariff increase in a Solow-type growth model calibrated for the Russian economy. The proposed policies result in a deeper economic decline in 2016 than in the baseline scenario (-0.76% in the baseline scenario and -0.79% in the import substitution scenario), followed by somewhat faster growth in subsequent years due to a lower base. The aftermath of the import substitution policy is still visible in 2020: GDP growth in 2020 relative to 2015 in the baseline equals 2.4365%, while the import restriction in all targeted industries will reduce economic growth in a five-year term by 0.007 percentage points, to 2.4295%. The numbers correspond to the expected reduction in economic efficiency as a result of the import substitution measures.

While numbers in terms of GDP do not look particularly large, the annual losses in GDP in nominal figures correspond to $650 million in value added, which is roughly equivalent to 30,000 jobs lost in Russia due to import substitution. Besides, effect on growth adds to 5,000 more jobs lost over 5 years.

As we mentioned above these losses might potentially be justified by the positive external effect from an increased output of the protected industries on the rest of economy. To ensure this, the selection of industries for protection should have been done through independent expertise based on a thorough analysis of sectoral interaction over time. However, the way the economic policy is formulated in modern Russia, with heavy influence of lobbying groups and very little contribution from independent economic research, we can hardly expect that the industries targeted for import substitution satisfy the objective criteria of positive external effects.

Imports as drivers of competitiveness

Classical trade theory shows that imports are a major cause of gains from trade integration. Modern trade theory complements the classical mechanism by selection effects among heterogeneous firms when only the most productive firms are able to sell in foreign markets (Melitz , 2003).

Keeping in mind that a substantial part of manufacturing trade flows consists of intermediate products that are used as inputs in subsequent production (in the case of Russia, the share of intermediates in imports is more than 60%) then the above reasoning implies that the competitiveness of domestic production is determined, among other things, by the availability of cheap imports.

Numerous empirical studies for many countries confirmed that industries with a higher share of imported intermediate goods are more productive than industries with a lower share (Feenstra, Markusen, and Zeile, 1992). Recent studies, analyzing data at the level of individual firms (Bernard at al., 2012; Castro, Fernandes, and Farolec, 2015; Feng, Li, and Swenson, 2016), confirm that the effect takes place at firm level: firms importing more intermediate goods have higher productivity than firms importing less, other things being equal, which suggests that imports of intermediate goods is an important source for the growth of firms’ competitiveness.

A study conducted for Russian firms showed that labor productivity in Russian companies which import intermediate goods is 20% higher compared to similar firms not importing intermediates (Volchkova, 2016).

On this basis, we have every reason to believe that import is one of the sources of economic competitiveness that enhances effectiveness of the economy. Thus import substitution policies in the absence of objective information and a profound selection procedure for protected sectors, are harmful to the economy. In an open economy, the effect of the firms’ selection and the availability of cheap imports ensure growth of sectoral productivity, but productivity declines in “protected” sectors. That is, while our estimates above assess the direct negative impact on Russian economic output and welfare from inefficient reallocation of factors of production, the implementation of import substitution policies also puts the Russian economy in a disadvantaged position relative to more liberal economies on the international markets due to forgone competitiveness. This creates additional obstacles for Russia on its way to export diversification and sustainable growth.

References

  • Feenstra, Robert C, James R Markusen, and William Zeile. 1992. “Accounting for Growth with New Inputs: Theory and Evidence.” The American Economic Review 82 (2). American Economic Association: 415–21. http://www.jstor.org/stable/2117437.
  • Feng, Ling, Zhiyuan Li, and Deborah L. Swenson. 2016. “The Connection between Imported Intermediate Inputs and Exports: Evidence from Chinese Firms.” Journal of International Economics 101: 86–101. doi:10.1016/j.jinteco.2016.03.004.
  • Melitz, Marc J. 2003. “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity.” Econometrica 71 (6). Blackwell Publishing Ltd: 1695–1725. doi:10.1111/1468-0262.004
  • Pierola Castro, Martha D., Ana Margarida Fernandes, and Thomas Farolec. 2015. “The Role of Imports for Exporter Performance in Peru.”
  • Volchkova, Natalya A. 2016. “Prospects of the export diversification:” Dutch Disease “or the failures of economic policy?” in “Seven lean years: the Russian economy on the verge of structural changes: the round table materials” / ed. Rogov. -Moscow: Foundation “Liberal Mission” (in Russian)
  • Volchkova, Natalya A., and Natalia A. Turdyeva 2016, “Microeconomics of Russian import substitution”, Journal of New Economic Association, forthcoming (in Russian)

The Economic Track Record of Pious Populists – Evidence from Turkey

Posted on November 21, 2016 | Policy Brief

FREE Network Policy Brief | A Case Study of Economic Development in Turkey under AKP

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

fig1Note: 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

fig2Note: Upper graph shows Turkey’s gross enrollment in tertiary education compared to a synthetic counterpart. The middle graph shows the difference between the former and the latter (black line) as well as placebo differences for untreated units (gray lines). The lowest graph plots the weights assigned to countries that constitute the synthetic control for Turkey. See Meyersson (2016a) for details.

An evaluation of the AKP’s institutional effect using multiple institutional indicators, measuring various aspects ranging from institutionalized authority, liberal democracy, and human rights results in a failure to find any durable early positive effects during AKP’s tenure. In the longer run, for all outcomes the overall effect seems to have been clearly negative. Finally, the significant reduction in military rents, whether measured in terms of expenditure or personnel, is illustrative of the degree to which the military’s political power diminished relatively early on, and posits concerns over lower economic rents as another source of friction between the civil and military loci of power in the country.

Overall, the results point to Turkey undergoing a transformative period during the AKP, socioeconomically as well as politically. Even though the initial years of higher GDP per capita growth under the AKP, in absolute terms, dwindle significantly in comparison to a synthetic counterpart, increased access to health and education provide reasons for political support of a government that has extended a socioeconomic franchise to a larger segment.

References

  • Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Synthetic Control Methods for Comparative Case Studies: Estimating the Effects of California’s Tobacco Control Program,” Journal of the American Statistical Association, 105 (2010), 493-505.
  • Abadie, Alberto, Alexis Diamond, and Jens Hainmueller, “Comparative Politics and the Synthetic Control Method,” American Journal of Political Science, 2015, 59 (2), 495-510.
  • Atun, Rifat, Sabahattin Aydin, Sarbani Chakraborty, Safir Sümer, Meltem Aran, Ipek Gürol, Serpil Nazlıoğlu, Şenay Özğülcü, Ülger Aydoğan, Banu Ayar, Uğur Dilmen, Recep Akdağ, “Universal health coverage in Turkey: enhancement of equity,” The Lancet, Vol 382 July 6, 2013.
  • Çelik, Zafer and Bekir Gür, “Turkey’s Education Policy During the AKP Party Era (2002-2013),” Insight Turkey, Vol. 15, No. 4, 2013, pp. 151-176
  • International Monetary Fund, “Staff Report for the 2016 Article IV Consultation: Turkey,” IMF Country Report No. 16/104
  • Meyersson, Erik, 2016a, “’Pious Populists at the Gate’ – A Case Study of Economic Development in Turkey under AKP”, working paper.
  • Meyersson, Erik, 2016b, “On the Timing of Turkey’s Authoritarian Turn”, Free Policy Brief, http://freepolicybriefs.org/2016/04/04/timing-turkeys-authoritarian-turn/
  • Rodrik, Dani, 2015, “Turkish Economic Myths”, http://rodrik.typepad.com/dani_rodriks_weblog/2015/04/turkish-economic-myths.html
  • “The World Bank, Turkey’s Transitions: Integration, Inclusion, Institutions.” Country Economic Memorandum (2014, December).

Does Product Market Competition Cause Capital Constraints?

Posted on November 14, 2016 | Policy Brief

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At the very center of Schumpeter’s (1934, 1942) notion of creative destruction is firms’ access to bank capital, which helps to fund the innovation in competitive product markets that drives out less productive firms in favor of those with more profitable ideas. However, competition is a two-edged sword and may result in firms being unable to fund all of their otherwise economically profitable investments. Using unique survey data from 58 countries, Bergbrant, Hunter, and Kelly (2016) find that product market competition increases capital constraints and has a greater effect than banking sector competition. Further, we show that quantity-of-capital constraints negatively impact firm growth.

Capital and creative destruction

At the very center of Schumpeter’s (1934, 1942) notion of creative destruction is firms’ access to bank capital, which helps to fund the innovation in competitive product markets that drives out less productive firms in favor of those with more profitable ideas. While product market competition may be the fundamental driver of the innovation envisioned by Schumpeter, it may also impede access to the very source of capital that is supposed to fund that innovation. More intense product market competition can affect firms’ ability to finance their projects either by increasing the price of financing or by inducing capital constraints, whereby firms are unable to obtain the quantity of capital needed to fund all their positive net present value projects.

Recent research has focused on the price side of financing, showing that product market competition increases the cost of equity (Hou and Robinson, 2006) and the cost of debt (Valta, 2012). In this brief we examine the quantity side of financing; that is, whether product market competition increases capital constraints.

Isn’t it obvious that competition causes capital constraints?

Actually, no. There is a familiar argument that firms are reluctant to disclose commercially valuable information when competitors are more likely to exploit this information. Theory predicts that it is not optimal for creditors to respond to the resulting asymmetric information by raising interest rates; instead, restricting capital is more appropriate (Stiglitz and Weiss, 1981). However, competition may have the very opposite effect because a competitive environment lowers owners’ cost of monitoring and measuring managerial performance. Theory and recent empirical tests indicate that lower cost of monitoring managers induces greater disclosure by owners.

Whether or not product market competition makes banks restrict the supply of loans is arguably more important than whether it influences the cost of debt. Greenwald, Stiglitz, and Weiss (1984) show that firms’ investment behavior is not particularly sensitive to the interest rates they pay, consistent with the notion that increases in the cost of debt may reduce investment, but only at the margin; i.e., projects change from generating economic profits to generating economic losses (net present value changes from positive to negative). By contrast, increased capital constraints can lead to underinvestment by forcing firms to abandon projects which generate economic profits (net present values are positive), thus hindering investment and preventing firm innovation and growth (see Harford and Uysal, 2014).

What does the research tell us?

Recent research by Bergbrant, Hunter, and Kelly (2016) uses survey data obtained from the World Bank’s World Business Environment Survey, conducted among non-financial firms from around the world. Capital constraints are the response to a question about the extent of the obstacle to operations and growth posed by capital constraints that managers and owners rank from 1 (No Obstacle) to 4 (Major Obstacle). Competition is represented by an index constructed from eight individual forms of competition reported by firms.

The empirical evidence indicates that the intensity of product market competition significantly increases capital constraints. Table 1 shows the marginal effects of a change in the intensity of competition on capital constraints. For instance, the first row shows that a small (instantaneous rate of) increase in product market competition leads to an increase in the likelihood that capital constraints are a “major obstacle” (4 on a four- point scale) at a rate of 18.9%. Similar results hold when competition is assessed at a one-standard-deviation (3rd row) increase or when competition changes from 0 to 1 on a version of our competition index which ranges from 0 to 1 (5th row).

Table 1: Effect of competition on capital constraints

For a change of:

 

 No obstacle

(1)

 Minor obstacle

(2)

 Mod. obstacle

(3)

 Major obstacle

(4)
Marginal -0.147 -0.052 0.010 0.189
p-value (0.000) (0.000) (0.062) (0.000)
+SD -0.042 -0.017 0.000 0.059
p-value (0.000) (0.000) (0.925) (0.000)
0 to 1 -0.145 -0.059 0.008 0.196
p-value (0.000) (0.000) (0.165) (0.000)

Note: The table reports the marginal effects “for a change of” product market competition of varying amounts on firms responding that capital constraints pose one of the four levels of “obstacle” for their operations.

The above results are qualitatively similar when the competition index is replaced by any one of its eight individual components. In addition, competition increases not only a measure of general capital constraints, as employed in the above analysis, but also specific forms of capital constraints. These include the credit constraints that firms experience when, as a precondition for lending, banks require that borrowers have special connections in the banking sector, pledge collateral, satisfy banks’ bureaucratic need for business documents, and pay bribes to corrupt bank officials. Further, the evidence is not unique to domestic bank capital as more intense product market competition also impedes firms’ access to nonbank equity, foreign bank capital, special export financing, and lease financing.

To further validate our main result we account for two well-established strands of research that contend that banking sector competitiveness is among the most important determinants of access to credit and that banking sector structure can also affect the competiveness of non-financial firms’ industries. The evidence reported in Table 2 shows that while (one of three measures of) banking sector competition and the degree of bank freedom affect capital constraints, in general the regulatory structure of the banking sector does not. More important, our main finding is unchanged when controlling for banking sector structure. Finally, it is important to note that in all our models we control for any cost-of-debt (higher-interest-rate) effects.

Table 2: Accounting for banking sector structure

Competition (10 separate models) +ve signif.
Lerner bank competition index +ve, signif.
Bank concentration ratio insignif.
Boone indicator of banking sector insignif.
private credit as a fraction of GDP insignif.
restrictions on nonbank activities insignif.
fraction of bank applications denied insignif.
bank freedom from gov’t interference -ve, signif.
existence of a credit registry insignif.
foreign bank share of banking system insignif.
government share of banking system insignif.

Note: We augment our main model with the above banking sector variables, one at a time, to determine their impact on the significance (signif. or insignif.) of product market competition.

Capital constraints hurt firms’ growth and so we expect our measure of capital constraints to be negatively associated with growth. We confirm this in the data, after controlling for the direct impact of competition on growth. We also find that the quantity-of-capital effect has a greater impact on expected firm growth than the cost-of-capital effect.

Conclusion

Our research indicates that the intensity of product market competition increases capital constraints even in the presence of controls for banking sector competition. Our work suggests several policy recommendations. First, the implementation of a product-market competition policy, for instance by several Central and Eastern European countries in the 1990s (Fingleton et al., 1996; Dutz and Vagliasindi, 2000), should contemplate the possibility that such action is likely to have negative externalities for firms’ access to capital. Second, banking sector reforms aimed at creating a more competitive banking system in order to improve access to capital should not be pursued in isolation and should take into consideration the existing competitiveness of the product market. Third, given that the quantity-of-capital effect has a greater impact on firm growth than the cost-of-capital effect, policymakers should exert at least as much effort in easing quantity constraints as they do to reduce the cost of capital.

References

  • Bergbrant, M.; D. Hunter; and P. Kelly, 2016. “Product Market Competition, Capital Constraints and Firm Growth”. Available at SSRN: https://ssrn.com/abstract=2594218.
  • Dutz, M. A.; and M. Vagliasindi, 2000. “Competition policy implementation in transition economies: An empirical assessment”. European Economic Review 44, 762-772. Fingleton, J.; E. Fox; D. Neven; and P. Seabright, 1996. “Competition policy and the transformation of central and eastern Europe”. Working paper. CEPR, London.
  • Greenwald, B.; J.E. Stiglitz; and A. Weiss, 1984. “Informational imperfections in the capital market and macroeconomic fluctuations”. American Economic Review 74(2), 194-199.
  • Harford, J.; and V. B. Uysal, 2014. “Bond market access and investment”. Journal of Financial Economics 112, 147-163.
  • Hou, K.; and D. Robinson, 2006. “Industry concentration and average stock returns”. Journal of Finance 61, 1927-1956.
  • Schumpeter, J.A., 1934. “The Theory of Economic Development”. Harvard University Press, Cambridge, MA.
  • Schumpeter, J. A., 1942. “Capitalism, Socialism and Democracy”. Harper and Brothers, New York, NY.
  • Stiglitz, J.; and A. Weiss, 1981. “Credit rationing in markets with imperfect information”. Amer. Econ. Review 71, 393-410.Valta, P., 2012. “Competition and the cost of debt”. Journal of Financial Economics, 105(3), 661-682.

What Does Ukraine’s Orange Revolution Tell Us About the Impact of Political Turnover on Economic Performance?

Posted on November 7, 2016 | Policy Brief

20161107 What Does Ukraines Orange Revolution Image 01

Political turnover is a normal, even desirable, feature of competitive politics, yet turnover in a context of weak institutions can create policy uncertainty, disrupt political connections, and threaten the security of property rights.   What is the impact of political turnover on economic performance in such an environment? We examine the behavior of over 7,000 enterprises before and after Ukraine’s Orange Revolution—a moment of largely unanticipated political turnover in a country with profoundly weak institutions. We find that the productivity of firms in regions that supported Viktor Yushchenko increased after the Orange Revolution, relative to that of firms in regions that supported Viktor Yanukovych. Our results illustrate that the efficiency consequences of turnover can be large when institutions are weak.

Introduction

Politics in much of the world is a winner-take-all contest. When Viktor Yanukovych fled Kyiv in February 2014, for example, he was joined by a close group of associates overwhelmingly drawn from the country’s Russian-speaking East, including Yanukovych’s home region of Donetsk. The governors who ran Ukraine’s regions under Yanukovych fared no better. Oleksandr Turchynov, who served as acting president from February to June of that year, did what all Ukrainian presidents do: he fired the existing governors and replaced them with figures friendly to the new regime.

What is the impact of such political turnover on economic performance? In principle, replacement of political elites can have profound consequences for enterprise owners and managers, who rely on the support of patrons in government for government contracts, direct and indirect subsidies, the security of property rights, and permits to do business. In a system without effective checks and balances, economic policy can also swing widely as power passes from one group to another. Yet little is known about the impact of such changes on firm productivity, a major driver of economic welfare.

We examine the impact of political turnover on productivity and other aspects of firm performance in “The Productivity Consequences of Political Turnover: Firm-Level Evidence from Ukraine’s Orange Revolution” (Earle and Gehlbach, 2015). Our main finding is that the productivity of firms in regions that supported Yushchenko, the eventual winner of the 2004 presidential election, increased after the Orange Revolution, relative to that of firms in regions that supported Yanukovych, the chosen successor of incumbent President Leonid Kuchma. These results demonstrate that political turnover in a context of weak institutions can have major efficiency consequences as measured by differences in firm productivity.

Ukraine in 2004

Three factors make Ukraine in 2004 an appropriate setting for identifying the effect of political turnover on economic performance. First, Ukraine under Kuchma was a paradigmatic case of “patronal presidentialism,” in which the president “wields not only the powers formally invested in the office but also the ability to selectively direct vast sources of material wealth and power outside of formal institutional channels” (Hale 2005, p. 138). Who won the presidential contest had enormous implications for economic activity.

Second, economic and political power was regionally concentrated in Ukraine’s Russian-speaking East—Yanukovych himself was closely affiliated with oligarchs in Donetsk—while the political opposition represented by Yushchenko had its base in the ethnically Ukrainian and less industrialized West. Voting in Ukraine’s 2004 presidential election reflected this regional divide.

Third, few gave Yushchenko much chance of winning the presidency until the presidential campaign was well underway. In the end, it took not only a highly contested election, but also sustained street protests to wrest power from the existing elite.

Together, these considerations imply not only that political turnover in Ukraine could have an impact on firm performance, but also that any such effect could be observed by comparing the performance of enterprises in regions supportive of the two candidates before and after Yushchenko’s unexpected election victory.

The Orange Revolution and Firm Performance

To analyze the impact of political turnover, we use data on over 7,000 manufacturing enterprises that we track over many years, both before and after the Orange Revolution. We compare the evolution of productivity across firms in regions by vote in the 2004 election that was won by Yushchenko, while controlling for any shocks to particular industries in any year, for constant differences across firms in the level or trend of their productivity, and for regional differences in industrial structure. This design avoids many of the other influences on firm-level productivity that might have coincided with the Orange Revolution.

Our primary finding is that the productivity of firms in regions that supported Yushchenko in 2004 increased after Yushchenko took power, relative to the productivity of firms in regions that supported Yanukovych (and, implicitly, his patron Kuchma, whom Yushchenko succeeded as president). This effect is most pronounced among firms that had the most to gain or lose from presidential turnover: firms in sectors that rely on government contracts; private enterprises, given Ukraine’s weak property rights; and large enterprises. Other measures of economic performance suggest that these results are driven by favorable treatment of particular firms, either before or after the Orange Revolution, rather than by broad changes in economic policy.

Conclusion

Political turnover is often desirable. Nonetheless, our results suggest that the distributional consequences can be profound when institutions are weak, that is, when access to those in power is the primary guarantee of market access, contract enforcement, and property-rights protection. Oscillation of privilege from one region or sector to another is inefficient, as firms initiate or postpone restructuring based on who is in power. The optimal solution, of course, is not to restrict turnover, but to make turnover safe for economic activity. This requires that institutions be reformed to guarantee equal treatment for all economic actors—a difficult process that has proceeded with fits and starts in post-Yanukovych Ukraine.

References

Russia and Oil — Out of Control

Posted on October 31, 2016 | Policy Brief

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Russia’s dependence on oil and other natural resources is well known, but what does it actually mean for policy makers’ ability to control the economic fate of the country? This brief provides a more precise analysis of the depth of Russia’s oil dependence. This is based on a careful statistical analysis of the immediate correlation between international oil prices — that Russia does not control — and Russian GDP, which policy makers would like to control. I then look at how IMF’s forecast errors in oil prices spillover to forecast errors of Russian GDP. These numerical exercises are striking; over the last 25 years oil price changes explain on average two thirds of the variation in Russian GDP growth and in the last 15 years up to 80 percent of the one-year ahead forecast errors. Instead of controlling the economic fate of the country, the best policy makers can hope for is to dampen the short-run impact of oil price shocks. A flexible exchange rate and fiscal reserves are key volatility dampers, but not sufficient to protect long-term growth. The latter will always require serious structural reforms and the question is what needs to happen for policy makers to take action to get control over the long-term fate of the economy.

In a recent working paper (Becker, 2016), I take a careful look at the statistical relationship between Russian GDP and international oil prices. This brief summarizes this analysis and its policy conclusions.

Russia and oil, the basics

Although Russia’s oil dependence is discussed every time international oil prices drop, it is not uncommon to hear that oil is not really so important for the Russian economy. The argument is that the oil and natural resource sector only accounts for some 10 percent of Russian production. This is indeed consistent with the official sectoral breakdown of GDP that is shown in Figure 1 where the minerals sector indeed only has a 10 percent share.

Figure 1. Structure of GDP in 2015

slide1Source: Federal State Statistics Service, 2016

However, this static picture of production shares does not translate into a dynamic macro economic model that allows us to understand what is driving Russian growth. Instead a careful analysis of the time series of Russian GDP is required to understand how important oil is for growth.

Russian GDP can be measured in many different ways: nominal rubles, real rubles, U.S. dollars, or in purchasing power parity (PPP) terms to mention the most common. Here we focus on GDP measured in real rubles and U.S. dollars since we want to get rid of Russian inflation, which has been quite high for most of the studied time period. The PPP measure generates figures and numerical estimates that are in between the real ruble and U.S. dollar measures and are not included here to conserve space.

The first evidence of the importance of international oil prices as a major determinant of Russian income at the macro level is presented in Figures 2 and 3 where the first figure shows dollar income and the second real ruble income. In both cases it is obvious that there is a strong correlation and that the correlation is higher for income measured in dollars.

Figure 2. U.S. dollar GDP and the oil price

slide2Source: IMF, 2016

Figure 3. Real ruble GDP and the oil price

slide3Source: IMF, 2016

However, it is also clear that all the time series have some type of trends or in econometric language, are non-stationary. This means that simple correlations of the time series shown in Figure 2 and 3 may not be statistically valid (or “spurious” as it is called in the literature). This is not a critical issue but can be handled by regular econometric methods.

Russia and oil, the econometrics

When time series are non-stationary they need to be transformed to some stationary form before we can do regular regressions (in Becker, 2016 I also address the issue of using a framework that allows for co-integration).

Two transformations that make the variables stationary are to use first differences or percent growth rates. Both are used before we run simple regressions of growth or first differences of GDP on growth or first difference in international oil prices. The full sample starts in 1993, but since the early years of transition were subject to many different shocks at the same time, a shorter sample starting in 2000 is also used.

A number of observations come from the estimates that are presented in Table 1: Oil prices are always statistically significant; the adjusted R-squared is higher for dollar income than real rubles (with one exception due to a large outlier in 1993); overall the explanatory power of these simple regressions are very high (42-92 percent) and the explanatory power increases in all specifications when going from the full sample (1993-2015) to the more recent sample (2000-2015). Note that the latter sample perfectly overlaps with the current political leadership so contrary to some wishes; the oil dependence has not been reduced under Putin/Medvedev.

Table 1. Russian macro “models”

slide4Source: Becker 2016

Russia and oil, the forecasts

The strong correlation between international oil prices and Russian GDP provides a very simple econometric model for explaining past variations in Russian GDP. Unfortunately it does not imply that it is easy to forecast Russian GDP since international oil prices are very hard to predict. There are many models that have been used to forecast oil prices, but the IMF and many others now use the market for oil futures to generate its central forecast of oil prices.

The IMF also provides confidence intervals around the central forecast, and the uncertainty surrounding the forecast is substantial: In the latest forecast the 68 percent confidence interval goes from around 20 dollars per barrel to 60 one year ahead, while the 98 percent interval ranges from 10 dollar per barrel to around 85. With oil currently around 45 dollars per barrel, these variations imply that oil prices could either halve or double in the next year, not a very precise prediction to base economic policy on for Russia since the estimates for real ruble growth in the later sample in Table 1 imply that Russian GDP growth in real ruble terms could be anywhere from minus 5 to plus 10 percent, or a fifteen percentage point difference!

If we look at past IMF forecasts of oil prices and Russian GDP and see how much they deviate from actual values a year later we can compute one year ahead forecast errors. We can do this calculation for the last 16 years for which the IMF data is available. Figures 4 and 5 show how the forecast errors in oil prices correlate with the forecast errors for dollar income and real ruble income, respectively. Similar to the regressions presented in Table 1, the correlations are very high for both measures of GDP: 82 percent for dollar GDP, and 65 percent for real ruble GDP.

In other words, a very large share of the uncertainty surrounding Russian GDP forecasts can be directly attributed to variations in international oil prices, a variable that (again) Russia does not control. The fact that the variations in oil prices explain somewhat more of the variation in dollar income compared to real ruble income is a result of a policy change that in later years allowed the exchange rate to depreciate much more rapidly when oil prices fall.

Figure 4. Forecast errors

slide5Source: Becker 2016

Figure 5. Forecast errors

slide6Source: Becker 2016

Policy conclusions

The depth of Russia’s oil dependence is much greater than what casual observers of the mineral sectors share of GDP would suggest. At the macro level, variations in international oil prices explain at least two thirds of actual Russian growth and even more of the one-year ahead forecasts errors.

The experience of the 2008/09 global financial crisis provided an important lesson to Russian policy makers, which is that exchange rate flexibility is required to dampen the real impact of falling oil prices and to protect both international reserves and the fiscal position. In the more recent years, the currency has been allowed to depreciate in tandem with falling oil prices and the drop in real ruble income was therefore less severe in 2015 than in 2009. Income in dollar terms, instead, took a greater hit, but this was a necessary corollary to protecting reserves and the budget. A flexible exchange rate and gradual move to inflation targeting in combination with accumulating fiscal reserves in times of high oil prices are key to Russia’s macro economic stability.

Nevertheless, these policies are not sufficient to remove the long-run impact that low or declining oil prices will have on growth, measured both in real ruble terms or dollar terms. It is nice to have fire insurance when your house burns down, but when you rebuild the house you may want to consider not building another straw house. For Russia to build a strong economy that is not completely hostage to variations in international oil prices, fundamental reforms that encourage the development of alternative, internationally competitive, companies are needed. This includes reforms that initially will reduce policy makers control over the economy and legal system, but over time it will provide the much needed diversification away from exporting oil that puts the fate of the Russian economy squarely in the hands of international oil traders. Losing some control today may provide a lot more control in the future for the country as a whole, but perhaps at the expense of less control for the ruling elite.

References

Will New Technologies Change the Energy Markets?

Posted on October 24, 2016 | Policy Brief

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With an increasing world demand for energy and a growing pressure to reduce carbon emissions to slow down global warming, there is a growing necessity to develop new technologies that would help addressing demand and carbon footprint issues. However, taking into account the world’s dependence on hydrocarbons the question remains – can new technologies actually change the energy markets? In this policy brief, we highlight challenges and opportunities that new technologies will bring for energy markets, in particular wind energy, smart grid technology, and electromobility, that were discussed during the 10th SITE Energy Day, held at the Stockholm School of Economics on October 13, 2016.

The expanding world population and economic growth are considered the main drivers of the global energy demand. Up to 2040, total energy use is estimated to grow by 71% in developing countries and by 18% in the more mature energy-consuming OECD economies (IEA, 2016). In parallel, many countries (including the world’s biggest economies and largest emitters: USA and China) have signed the Paris agreement – the first-ever universal, legally binding global climate deal that aims to reduce emissions and to keep the increase in global average temperature from exceeding 2°C above pre-industrial levels.

Meeting a growing global energy demand, and at the same time reducing CO2 emissions, cannot be achieved by practicing ‘business as usual’. It will require some fundamental changes in the way economic activity is organized. In this context, the development of new technologies and how it will affect the energy sector is a crucial element.

Wind power, smart grid, and electromobility

With technological progress and support schemes to decrease CO2 emissions, wind energy is now a credible and competing alternative to energy produced from coal, gas and oil. In 2015, wind accounted for 44% of all new power installations in the 28 EU member states, covering 11.4% of Europe’s electricity needs (see here).

This new technology has triggered a downward pressure on energy prices because of a “Merit order effect” (i.e. a displacement of expensive generation with cheaper wind). While consumers may appreciate this development, Ewa Lazarczyk Carlson, Assistant professor at the Reykjavik University (School of Business) and IFN, stressed that the increasing importance of wind energy challenges the functioning of electricity exchange. First, a lower price has reduced the incentives to invest in conventional power plants necessary when the wind is not blowing or when it is dark. Moreover, with the renewable energy intermittency, the probability of system imbalance and price volatility has increased. In turn, this has led to an increase of maintenance costs for conventional generators due to their dynamic generation costs (i.e. start-ups and shut-down costs).

Digital technology has gradually been used in the energy sector during the last decades, changing the way energy is produced and distributed. With smart grid (i.e. an electricity distribution system that uses digital information) energy companies can price their products based on real time costs while customers have access to better information, allowing them to optimize their energy consumptions. Sergey Syntulskiy, Visiting Professor at the New Economic School in Moscow, stressed that smart grids have had at least two effects. They have made the integration of renewable energy to the system easier and have allowed for prosumers, i.e. entities that both consume and produce energy. The next step is to develop new regulatory incentives to optimize energy systems as well as to provide a legal framework for the exchange of information in the energy sector.

One of the main pollutants has long been the transport sector that accounts for 26% energy-related of CO2 emission (IEA, 2016). Electromobility – that is, use of electric vehicles – is often considered the solution for this problem. When this technology is widely adopted, a major switch from oil to electricity is expected for the transportation sector. Mattias Goldmann, CEO of Fores, argued that even if electromobility will improve air quality and reduce noise levels in cities, its positive impact relies on smart grids and locally produced energy. Moreover, the environmental benefits will be ensured only if electric energy is produced from renewable and clean sources.

Toward a carbon-neutral energy system?

The Nordic countries are currently pushing for a near carbon-neutral energy system in 2050. Markus Wråke, CEO at the Swedish Energy Research Centre, emphasized that the Nordic Carbon-Neutral Scenario is only feasible if new technologies allow for a significant change of energy sources and a better interconnected market (see report by IEA 2016 b).

To cut emissions, a decrease in oil and gas consumption in energy production and within the transport sector is needed (see Figure 1). The adoption of electric vehicles (EVs) and hybrid cars is very likely to drastically increase in the next decades (EVs may have a share of 60% of the passenger vehicle stock in 2050, IEA 2016b).

Figure 1. Nordic CO2 emissions in the CNS

slide1Source: IEA, 2016.

There are currently limited technology options to reduce emissions for big industrial energy consumers. Moreover, there is a concern that those industries may choose to relocate if the Nordic emission standards are too strict. It is therefore important to have low and stable electricity prices. This can only be achieved if cross-border exchanges are improved (which means that the electricity trade in the Nordic region will have to increase 4-5 times by 2050). It is unclear however how policy makers will create a regulation that incentivizes energy companies to build interconnections and increase trade both between the Nordic countries, and the Western and Eastern European countries.

Figure 2. Electricity trade 2015 and 2050

slide2Source: IEA, 2016.

Energy producers

Another concern is that energy-exporting and energy-importing countries may have opposing attitudes towards investing and developing new energy technologies. Countries among the biggest energy producers and exporters depend on a stable demand and price for energy. For example, Russian GDP growth depends between 50-92% on the oil price, depending on the variables used for calculations, as mentioned by Torbjörn Becker, Director of SITE. For large exporters of hydrocarbon, new energy technologies may be seen as a threat because of a potentially reduced energy demand and an increased price volatility that will, in turn, create fundamental issues to balance state budgets and improve living standards.

Figure 3. The Relationship between Russian GDP and oil price

slide3Source: Calculations by Torbjörn Becker, October 13, 2016

The challenge of security of supply

To summarize, new energy technologies will drive energy companies towards optimizations and cost cutting, bring previously unseen connectivity to energy markets and make energy markets more complex. Samuel Ciszuk, Principal Advisor at the Swedish Energy Agency, stressed that interconnected, more complex and interdependent energy systems might increase the vulnerability of energy systems to external threats and intimidates to decrease the security of supply. Technological change and increased competition with lower profit margins will force companies to minimize their expenditure on energy production, storage and transmission and to find cheaper financing options. Optimization and searches for cheaper financing instruments will push energy companies towards selling some of the company assets to financial investors. These changes will create a more decentralized energy market, with more players. Such energy systems will become harder to govern in times of an energy crisis and external threats. Policy makers will have to design new and more complex regulations to fit the needs of the transforming energy markets.

References

  • Fogelberg, Sara and Ewa Lazarczyk, 2015. “Wind Power Volatility and the Impact on Failure Rates in the Nordic Electricity Market”, IFN Working Paper 1065.
  • IEA, Annual Energy Outlook, 2016a.
  • IEA/OECD/Norden, 2016b. “Nordic Energy Technology Perspectives” (see here)
  • Speaker presentation from the 10th Energy day, 2016 (see here)

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