Resource Misallocation in Turkey

This paper examines resource misallocation within narrow industries in Turkey. It finds that resource misallocation in Turkey is substantial. The hypothetical gain from moving to "U.S. efficiency" is 24.5 percent of manufacturing total factor productivity in 2014. The evolution of resource misallocation over time and across disaggregated sectors is also examined. Improvement in allocative efficiency was sizable between 2003 and 2013, but significantly slower after 2007. However, the earlier trend reversed in 2014 and resource misallocation worsened in Turkey's manufacturing. The cross-sector analysis reveals that misallocation is most pronounced in textiles, transport, food, and leather.


Introduction
Turkey's economy expanded rapidly in the 2000s, and per capita income is now close to the threshold beyond which the World Bank classifies countries as high income. While part of this economic growth was attributable to human and physical capital accumulation, an important fraction was due to total factor productivity (TFP) growth .
Growth in TFP can be divided into three different sources. The first one is via growth in technology, which is the most traditional thinking about TFP. The second source is the movement of factors from sectors with lower productivity (such as agriculture) to sectors with higher productivity (such as manufacturing). About two-thirds of the overall productivity gains in Turkey came from the shift of labor out of agriculture and into higher-productivity manufacturing and service industries . The third source of improving a country's TFP is via better allocation of resources within industries. This takes place when resources from firms with lower productivity move to firms with higher productivity. Since the seminal work of Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), we know that across-firm resource misallocation within industries can lead to lower aggregate TFP. Across-firm resource misallocation is a consequence of highly productive firms not obtaining sufficient resources (in terms of capital and labor) to expand production, while firms with low productivity continue employing resources instead of shrinking and eventually exiting. This could be a result, for example, of politically connected firms having easier access to finance and therefore expanding production, despite their productivity being lower than that of less connected firms. This phenomenon could substantially reduce a country's total output and productivity because highly productive firms would be smaller and less productive ones would be larger than they should be at optimal allocation. Hsieh and Klenow (2009) estimate that if the problem of resource allocation in China and India is eliminated, that is, if capital and labor are hypothetically reallocated to equalize marginal products to the extent observed in the United States, manufacturing TFP can increase 30% to 50% in China and 40% to 60% in India.
In this paper, using Hsieh and Klenow's (2009) framework (HK, henceforth), we measure how much aggregate manufacturing TFP in Turkey could increase if capital and labor were reallocated to equalize marginal products across firms within each four-digit sector to the extent observed in the United States in 1997. For Turkey, moving to '1997 U.S. efficiency' could have boosted manufacturing TFP by 24.5% in 2014. Thanks to the availability of Turkish manufacturing data covering 11 years, from 2003 to 2014, we can track the improvement of resource allocation in Turkey over time. We find that resource allocation improved quite significantly. In 2003, the manufacturing TFP gap from the US caused by resource misallocation is 56.1%, while that in 2013 is only 18.4%. Finally, we break down resource misallocation 3 by industries and by regions. The results reveal that resource misallocation is larger among textile, transport, food, and leather industries.
The rest of the paper is organized as follows: section 2 provides some background on Turkish economy; section 3 reviews the related literature; section 4 explains the analytical framework; section 5 describes the data set; section 6 presents the results; and finally, section 7 concludes.

Background on Turkey's economy 2
Turkey is an upper middle income country and a member of G20. Turkey has the world's 17th largest nominal GDP (in PPP units) and stands at the threshold to high income today, with GNI per capita (Atlas method, current US$) at $10,830 in 2014. 3 Turkey's economic development after 2001 resulted in impressive economic achievements. After a banking crisis in 2001, the country embarked on a concerted path of structural reforms supported by strong fiscal consolidation, strengthened banking supervision, and a shift to a flexible exchange rate regime with an independent central bank responsible for inflation targeting. The pro-market reform process was further enhanced and anchored by the EU Accession process. In the following period, Turkey`s economy grew on average by 6.9% annually until the Global Economic and Financial crisis.
Turkey had already started to exhaust the benefits of the reform momentum of the early 2000s. However, the global crisis and subsequent rapid recovery in Turkey had diverted attention from remaining structural weaknesses and considerably complicated macroeconomic management . Following a swift rebound from the recession in the crisis in 2008-09, concerns over Turkey's vulnerability to tightening global liquidity and deteriorating political and regulatory environment caused a lack of private investment spending . The interventions in independent regulatory institutions suggest that the principles of arms' length regulation had not yet put down deep roots. Furthermore, there have been growing concerns over the transparency in public tenders and the allocation of land development rights in Turkey . The anchor provided by the EU Accession process had been weaker ever since the mid-2000s and Turkey had been losing market share in FDI to emerging markets as a whole since 2007.
Against this backdrop, private investment, one of the main drivers of growth in the pre-2008 period, dropped sharply in 2012 and has stagnated since then. As a result, economic growth slowed since 2012. 4

Literature review
This study is related to an overarching question on countries' TFP (total factor productivity): why are some countries' productivity levels higher than others? A traditional answer is the country's technology level.
For example, in some countries, firms adopt advanced technologies and are innovative in producing new ones. In other countries, because of several reasons such as lack of access to finance or low levels of human capital, firms are not as good in terms of learning and producing new technologies. Conventional policy implications hence focus on factors affecting a country's aggregate technological absorption capacity.
Examples of a country's technological absorption capacity include access to finance, education, FDI, and openness.
More recently, a new strand of literature has explored the role of resource misallocation for countries' aggregate TFP. The idea is that in addition to primitive technology, resource misallocation can hurt a country's aggregate productivity. As discussed in the introduction, resource misallocation refers to productive firms not being able to expand, and unproductive firms being larger than they should be. Because resources flow to the wrong firms, the country's total output is lower given the same input. This means the country has lower productivity. Policy implications for this approach are drastically unrelated to technology. The focuses are now competition policies and political economy. Restuccia and Rogerson (2008), in a standard neoclassical growth model with heterogeneous firms, provide the first framework to examine how resource misallocation can affect aggregate productivity. They consider distortions that generate differences in the prices faced by individual firms. For example, politically connected firms may have lower interest rates on loans than unconnected firms. Restuccia and Rogerson (2008) term these policies as "idiosyncratic distortions". They emphasize that the productivity losses due to misallocation would be larger if the "distortions" are positively correlated with the level of productivity of firms. For example, if highly productive firms happen to be politically unconnected and have to pay higher interest rates, they will not have sufficient resources to expand. As a result, the country's aggregate productivity is reduced. On the other hand, if unconnected firms happen to be unproductive as well, the negative impacts of misallocation on aggregate productivity is certainly not as large.
Drawing on the seminal work of Restuccia and Rogerson (2008), a growing number of studies have quantified the costs generated by resource misallocation. Hsieh and Klenow (2009), in their landmark study, examine the quantitative effect of resource misallocation. The basic underlying assumption in their paper is that if resource misallocation is completely removed, the marginal products of labor and capital for all firms should be equalized. Therefore any unequal marginal products of production factors are due to resource misallocation. With this assumption, they estimate that if the problem of resource allocation in China and India is improved, meaning, if capital and labor are hypothetically reallocated to equalize marginal products to the extent observed in the United States, manufacturing TFP can increase 30% to 50% in China and 40% to 60% in India. Subsequent research following the methodology of Hsieh and Klenow (2009) confirms the quantitative importance of misallocations for several countries. Examples are Camacho and Conover (2010) for Colombia, Busso et al. (2013) for Latin American countries, and Kalemli-Ozcan and Sorensen (2012) and Cirera et al. (2015) for African countries.

Framework
In this section, we present the HK framework which constitutes the theoretical background to measurement of within sector misallocation.
Consider an economy with many sectors, denoted s. A final output Y is produced in each country using a Cobb-Douglas production technology: where is the value added share of sector s, and ∑ 1.
Each sector's output is the aggregate of the individual firm's output , using the CES technology: where is the differentiated product by firm i in sector s.
Each firm produces a differentiated product with the standard Cobb-Douglas production function: , where stands for firm-specific productivity, and are the firm's capital and labor respectively, and is the industry-specific capital share. Note that the assumption in this framework is that firms in the same narrowly-defined sector (i.e. 4-digit NACE) have the same production function.
Each establishment maximizes current profits: where is the firm's value added (which is the firm's revenue minus the cost of intermediate inputs), w and R are the cost of one unit of labor and capital respectively. The term denotes firm-specific output distortions that reduce firms' revenues. Many factors could contribute to output distortions, ranging from transportation costs to harassment from authorities. These factors could reduce output for a given set of input. The firm-specific "capital" distortions, which raise the cost of capital (relative to labor), is denoted 6 with . Credit market imperfections (such as differential access to finance) and labor market frictions could contribute to different "capital" distortions across firms.
From Hsieh and Klenow (2009), we distinguish between two productivity measures, one expressed in physical units (TFPQ) and the other in monetary value (TFPR): It is important to note that it is normal that TFPQ differs across firms, different firms may have different productivity levels. However, in this framework, if there were no distortions, TFPR should be equalized across firms in the same industry. This is because of the assumption in the model that firms are monopolistically competitive. Without distortions, low productivity firms have less resources and produce less. Since their product is relatively scarce, they can charge a higher price, , which equates across firms i. In other words, in the absence of distortions, more capital and labor should be allocated to firms with higher TFPQ to the point where their higher output results in a lower price and the exact same TFPR as smaller firms. As a consequence, any dispersion of TFPR across firms within an industry is an indication of distortions. A firm with higher TFPR than the sector average is more "taxed", meaning, it suffers more obstacles, than other firms.
To empirically implement the HK framework, we follow HK and choose the elasticity of substitution 3, R=10 (assuming the real interest rate=5% and the depreciation rate of 5%). Follow HK, we use capital share, ,and labor share, 1 , from the U.S. manufacturing sectors. The underlying assumption is that capital and labor shares from sectors in the U.S. represent the least distorted environment. Any deviation of capital-labor share from the U.S.'s level is an indication of distortions.
The output and capital wedges can be computed as follows: Note that is firm i's wage bill; is the firm's value added. Both values are available in the census data. To understand the intuition of equation (8), we rewrite it as: Note that is the labor-capital ratio in the undistorted (U.S.) environment. If firm i's actual labor capital ratio is higher than the undistorted labor capital ratio, this indicates that the firms face difficulties accessing capital (relative to hiring labor), and as a result, use less capital than the optimal level. This is equivalent to stating the firm has a positive capital wedge .
Armed with and , HK shows that TFPR can be calculated as: Equation (10) implies that in the absence of distortions (i.e. = 0 and =0), TFPR is the same for all firms "i" within a sector "s". Using this equation, one can induce that a firm with higher and/or higher also has a higher TFPR.
In addition, the industry level is: Note that when there are no distortions (i.e. = 0 and =0) for all i, the right hand side of (11) equals the right hand side of (10), which means that TFPR are equalized for all i.
Firm i's productivity can be calculated as: and the efficient industry's productivity level (when all marginal products are equalized) is: From (10) to (13), we can calculate the ratio of the actual TFP in the economy to the efficient level of TFP: We calculate the ratio of actual TFP to the efficient level of TFP and then aggregate this ratio across sectors using the Cobb-Douglas aggregator. 8

Data
We use the Annual Industry and Services Statistics (AISS) to carry out the empirical exercise. AISS is a survey data set conducted annually by the Turkish Statistics Institute (TURKSTAT) since 2003. AISS covers all firms which employ more than 20 workers and draws a sample from firms employing 20 or fewer workers. Our analysis is based on the entire coverage period of 2003 -2014 with a particular focus on the manufacturing sector.
We exclude firms employing 20 or fewer workers, because only a subset of these firms are included in the data. We also observe some inconsistencies in sample weights of drawn firms in some years. 4 Therefore, we keep our focus on the firms that have more than 20 workers given that these firms constitute the whole population instead of a sample.
The data set covers a large set of variables including investment, sales, energy expenditures, material expenditures, number of employees, ownership type, location, and industry. The number of employees is available at the gender and paid/unpaid breakdown. Location is provided at the NUTS3 level (province), and industry classification is available in NACE Rev2 at 4-digit level. 5 Investment expenditures are reported in three categories, namely, computer and programing, machinery and equipment, and buildings and structure. We calculate a firm level capital series by using the disaggregated investment series and corresponding depreciation rates. Following Taymaz and Yilmaz (2009), depreciation rates of 5%, 10%, and 30% are used for building and structure, machinery and equipment, and computer and programing, respectively, to construct initial capital stock and to apply the perpetual inventory method. For the firms that report non-zero investment at their initial year, we calculate capital stock by dividing the firms' average investment with the depreciation rate of investment. For the firms that report zero investment at their initial year, it is assumed that they cannot be producing without capital. Therefore, the initial capital stock is calculated at the year that they report positive investment and this amount is iterated back to the beginning year by dividing capital stock by the value (1 -each year.
After calculating a capital stock series for building and structure, machinery and equipment, and computer and programming, these series are aggregated to form the capital stock series of the firm.
All the monetary variables are reported in Turkish Lira with current prices. We normalize the input expenditures with the corresponding 3-digit deflators. The firm level output is deflated by 3-digit output price deflators. 9 Our empirical exercise also requires sector-level capital/labor ratios in the US, which are obtained from NBER manufacturing database. Since US sectors are coded according to the SIC (Standard Industrial Classification), we implement the necessary conversion between SIC and NACE while merging the US capital/labor ratios with our firm-level data.
In the data cleaning process we dropped observations at a number of different steps, ending up with a smaller data set than the original one.   Table 2 presents the benchmark results of the paper. The first column shows the year of the data coverage.

Measuring distortions
The second column displays the number of firms in each year. Note that only firms with more than 20 workers are included in our data set. The third column shows the potential TFP gains if TFPR are equalized among firms within a sector (i.e. if the resource misallocation problem is completely removed). The fourth column shows the potential TFP gains if allocative efficiency in Turkey improves to the U.S. level (thus misallocation reduces to the U.S. level). The columns five through ten show the statistics for the distributions of TFPQ, log , and of TFPR, log ⁄ , over time. The final two columns show the standard deviations of capital and output wedges. The main take-away message of the paper is shown in columns 3 and 4. They display hypothetical aggregate TFP gains from removing misallocation over the years. Larger hypothetical gains imply that the resource misallocation across firms within a sector is more pronounced. The TFP gain of 78% in 2014 means that if resources are allocated efficiently across firms within a sector, i.e. more productive firms having more resources and less productive firms having less resources, Turkey's manufacturing TFP would increase by 78% (Table 2,  How is Turkey compared to other countries? To answer this question, we put these numbers in a crosscountry context to get a sense of the economic magnitude of these numbers. Table 3 compares the latest available year in each country: Turkey in 2014, United States in 1997, India in 1994, and China in 2005 There is more TFPQ dispersion in Turkey than in the United States and China, but less than India. The ratio of 75 th to 25 th percentiles of TFPQ in the latest year are 5.0 in India, 4.5 in Turkey, 3.6 in China and 3.2 in United States. 9 Table 3 also provides TFPR dispersion statistics for the same group of country-years. The TFPR dispersion in Turkey is significantly more than that in the United States, while is only slightly more than those in China and India. These numbers suggest that distortions are greater in Turkey than in United States, China and India. The ratio of 75 th to 25 th percentiles of TFPR in the latest year are 2.6 in Turkey, 2.3 in China, 2.2 in India, and 1.7 in United States. What is the potential for TFP growth from a reduction in misallocation? We calculate "efficient" output to compare it with the actual output level to measure the hypothetical gains from removing the within-sector misallocation of resources (Table 4) How do we explain the different results regarding TFPR dispersion and TFP gains between Turkey, India and China? The answer is with TFPQ dispersion. If country A's TFPQ is more dispersed than country B's, country A's TFPR could be more dispersed than country B's, even if the levels of misallocation in the two countries are similar. Applying the same argument to Turkey, China, and India, since Turkey's TFPQ is more dispersed than that in China, Turkey's TFPR can be more dispersed and at the same time Turkey's misallocation is less severe than China's.
Another explanation is with industry weights. It can be seen from equation (14), when calculating TFP gains, we take industry weights into account. Thus, one can consider TFPR dispersion as the measure of unweighted misallocation, while do TFP gains as the measure of weighted misallocation. The results show that TFPR dispersion is higher in Turkey, but TFP gain is lower in Turkey. This is most likely because the sectors that have high TFPR dispersion have lower weight in GDP in Turkey, making TFP gains from reallocation of resources within sectors smaller compared to peer countries.

Distortion and Productivity
In the absence of frictions, more capital and labor should be allocated to firms with higher TFPQ to the point where their higher output results in lower prices and the exact same TFPRs as those of smaller firms.
Hence, TFPR would not vary across firms within an industry unless firms face distortions. However, in reality, capital and labor distortions engender TFPR dispersion within industries, as shown in Figure 1.
Distortions would be particularly harmful if they are positively correlated with firm's physical productivity (i.e. they are higher for more productive firms). To see the relationship between productivity and distortions, Figure 3 plots TFPQ against TFPR. In the frictionless world, all firms would fall along the zero log ⁄ line. Along this undistorted equilibrium line firms would differ only on their physical productivity, TFPQ. However, figure 3 shows that TFPR is strongly increasing in TFPQ in Turkey, suggesting that more productive firms face larger distortions. In other words, the figure implies that high productivity firms are subject to higher implicit taxes that keep these firms smaller than their optimal levels.

17
Similarly, low productivity firms receive implicit subsidies that enable these firms to expand and lower the firms' marginal products. To better understand the sources of distortions, we decompose the overall distortion into the "capital" wedge, log 1 , and the "output" wedge, log . Figure 4 shows that there is no systematic relation between the capital wedge and productivity level. At almost every productivity level, capital wedge dispersion is quite high, indicating that at every given level of productivity, some firms have an easier access to capital markets while some firms have difficulties. However, the capital wedge does not  Figure 5 shows that output wedges are monotonically increasing in productivity. This suggests that compared to a frictionless equilibrium, productive firms are subject to a larger output wedge, causing them to produce less than their optimal output, while unproductive firms receive an implicit output subsidy and produce beyond their optimal level, resulting in an inefficient allocation of resources and thus lower aggregate TFP. Figure 4 and Figure 5 jointly suggest that the answers to resource misallocation in Turkey lies in the output markets, not in the factor markets. Thus, policy measures that focus on output markets and eliminate distortions can reduce misallocation and bolster aggregate TFP in Turkey.

Distortions and Firm Size
The relationship between distortions and size is an important dimension to examine to understand the costs of misallocation. Size is measured in terms of value added of the firms. Figure 6 plots the relationship between firm size and productivity distribution of firms with respect to their sector averages, while Figure 7 plots the relationship between firm size and distortions. It is clear that there is a strong positive relationship between firm size and productivity, suggesting that larger firms are on average more productive than their smaller counterparts. Similarly, large firms have higher TFPR compared to the sector averages, whereas small firms have smaller TFPR than the sector averages. This implies that small firms are less productive, but receive implicit subsidies that grow them larger than otherwise they would. Moreover, on average, medium, and especially large, firms are more productive than their competitors, but they face greater implicit taxes that raise their marginal products and constrain their growth. As a result, these more productive, large firms remain smaller than they would otherwise. Overall, Figures 6 and 7 together imply that small, unproductive firms operate at the expense of large, productive firms, as a result of idiosyncratic distortions, leading to a significant misallocation of resources within industries. In the absence of distortions, small firms would cut their production and increase their prices, ending up with higher TFPR, whereas medium and large firms would expand their production and lower their prices, ending up with lower TFPR. Large, more productive firms would expand, while small less productive firms would downsize until TFPR across all firms equalized within industries.

Misallocation by Industry
The results presented so far are for the manufacturing sector as a whole, weighted by the industry value added shares. By focusing on the aggregate outcome we might obscure important differences across industries. The finding will provide evidence for policymakers to focus on certain industries to address resource misallocation. Hence, it is instructive to investigate to what extent distortions vary across sectors.
We have grouped industries that are closely related under broader categories in order to reduce the number of industries. The food sector includes manufacturing of food products, beverages and tobacco products (NACE rev. 2 2-digit sector codes 10-12). The textiles sector includes manufacturing of textiles and wearing apparel (NACE rev. 2 2-digit sector codes 13-14). The chemicals sector includes manufacturing of chemicals and chemical products, rubber and plastic products (NACE rev. 2 2-digit sector codes 20 and 22   Table 6 presents the potential TFP gains if resource misallocation is removed in each of these stand-alone industries. Table 6 shows that potential TFP gains are above 80% in the Furniture and Chemicals sectors, and around 90% in the Textiles, Transportation, Food, and Leather sectors in 2014. Thus, the room for TFP growth by removing distortions and improving allocative efficiency is large in these sectors. Conversely, the potential TFP gains are about 50% in Machinery and Metals, implying that reforms can also yield meaningful TFP growth in these sectors.
The best performing sector is Electronics, where potential TFP gains are close to 0% in 2014. Also note that misallocation in Electronics was already low in 2003 in comparison to the other sectors. In 2011 and 2013, the potential TFP gains were negative in the Electronics sector, implying that equalizing TFPR within 4-digit industries would lead to lower aggregate TFP in this sector. This may be due to the relationship between distortions and productivity level. In section 6.2, we showed that TFPR is positively correlated with TFPQ in the manufacturing sector in Turkey, meaning that distortions favor less productive firms and punish more productive firms. However, if TFPR is negatively correlated with TFPQ, in other words, if distortions punish less productive firms and favor more productive firms, potential TFP gains can be negative. A closer look at the electronics sector in Turkey and a deeper analysis of market structure, infrastructure, rules, and regulations in this sector can provide valuable lessons for other sectors and policy makers.  Table 8 suggests that the misallocation within Tigers is smaller than West and Others. This is an interesting result because it indicates that business environment in Tigers treat firms relatively equally compared to a more developed region like West. Table 8 shows the hypothetical gains from equalizing TFPR within sectors for each region. The reallocation of resources from less productive firms to more productive firms within industries could increase TFP by about 81% in Others, 70% in West, and 46% in Tigers in 2012. The potential TFP gains are large in each region, thus new reform momentum can yield significant improvements in aggregate productivity in every region. Especially in West, where firms are disproportionately located, elimination of distortions could lead to a substantial rise in TFP not only in West, but also in aggregate manufacturing in Turkey, given that West has the largest weight among regions.