Regional dimension of firm level productivity determinants: the case of manufacturing and service firms in Ukraine

Open access

Abstract

The main goal of this paper is to empirically investigate the regional dimension of productivity determinants for 24 regions of Ukraine using micro-level dataset for individual firms in 2013. The novelty of our analysis is the comparison of the determinants of productivity in the manufacturing and service sectors. We estimate both pooled regressions for all regions and separate regressions for particular regions. The estimation results obtained for the entire country demonstrate that the majority of our explanatory variables are statistically significant for the manufacturing sector and all are statistically significant for the service sector although at different levels of significance. At the same time, the estimation results obtained separately for each region show a large degree of heterogeneity across the regions and sectors and the lack of scale economies at the firm-level.

Abstract

The main goal of this paper is to empirically investigate the regional dimension of productivity determinants for 24 regions of Ukraine using micro-level dataset for individual firms in 2013. The novelty of our analysis is the comparison of the determinants of productivity in the manufacturing and service sectors. We estimate both pooled regressions for all regions and separate regressions for particular regions. The estimation results obtained for the entire country demonstrate that the majority of our explanatory variables are statistically significant for the manufacturing sector and all are statistically significant for the service sector although at different levels of significance. At the same time, the estimation results obtained separately for each region show a large degree of heterogeneity across the regions and sectors and the lack of scale economies at the firm-level.

1 Introduction

For over two decades, Ukraine has been struggling with economic and political transition. The recent changes in the political leadership and declarations for deeper economic reforms as well as joining the WTO and the recent signing of the free trade agreement (FTA) with the European Union (EU) have created new opportunities and prospects for economic recovery and improved performance of Ukrainian firms located in various regions of the country. Traditionally, there have been significant differences in the level of economic development reflecting productivity difference between firms located in the industrialized East and the agricultural West. The latter asymmetric trade liberalization episode may not equally affect the firms located in various regions of Ukraine.

Up to now, there have been relatively limited empirical evidence on the performance of the Ukrainian enterprises in general, and in particular regions. Therefore, we aim to fill at least a part of the existing gap in the literature by analysing the determinants of firm-level productivity in the manufacturing and service sectors across various regions of the country. In this paper, we empirically study the relationship between total factor productivity, intangible assets, capital intensity, firm size, competition in the industry, ownership status, and firm internationalization (exports and imports) of Ukrainian firms located in various regions, having controlled for industry-specific effects. The main contribution of this paper lies in its focus on Ukraine, which is still a country in transition, where not much empirical firm-level based analysis in the regional context has been conducted so far. Moreover, the novelty of our analysis is the identification of the differences in the productivity determinants in the manufacturing and service sectors across different regions of Ukraine.

In addition, unlike many other studies that used simple labour productivity measures, such as output per worker, in this paper as a productivity measure, we employ total factor productivity calculated by the Levinsohn-Petrin (2003) method. In particular, we study the role of several firm characteristics such as internationalization measured by foreign ownership, exports and imported inputs, firm size, private ownership, and the level of market concentration. We are also able to control for industry-specific effects. This paper is organized as follows. In the next section, we describe the research methodology and statistical data. Then, we discuss our empirical results. The final section summarizes and concludes.

2 Literature review

The economic literature has shown that there exist significant differences in productivity levels across firms. 1 This finding has shaped research agendas in a number of fields, including industrial organization, labour economics, and international trade. In the industrial organization literature, productivity levels are linked to demand, market structure and a number of features of technology. The frequently cited examples include the effects of competition (Syverson, 2004a; and Schmitz, 2005), sunk costs (Collard-Wexler, 2013), as well as the interaction of product market rivalry and technology spillover (Bloom et al., 2007). Another line of research has looked at the interaction between firm productivity levels and organizational structures (e.g., Maksimovic and Phillips, 2002; Schoar, 2002; Hortaçsu and Syverson, 2007).

Labour economics explores the impact of human capital of individuals on productivity differences (Abowd et al., 2005; Fox and Smeets, 2011). In other studies, the authors explore the productivity effects of incentive pay (Lazear, 2000), various human resources practices (Ichniowski and Shaw, 2003), managerial talent and practices (Bloom and Van Reenen, 2007), organizational forms (Garicano and Heaton, 2010), and social connections among co-workers (Bandiera et al., 2009). There are also studies on the role of productivity-driven reallocation on labour market dynamics (Haltiwanger et al., 2008).

The importance of productivity has been also analysed in the international trade literature. Theoretical models using heterogeneous productivity firms proposed by Eaton and Kortum (2002) and Melitz (2003) and their multiple extensions now became the dominant frameworks of the new trade theory through which economists study the relationship between productivity and international trade flows. In these models, the effects of trade liberalization vary across producers and depend on their productivity levels in particular. Aggregate productivity gains come from improved selection and increased competition that trade brings.

A large number of empirical studies confirmed many of the predicted patterns predicted by the new trade theories that accounted for firm heterogeneity (e.g., Pavcnik, 2002; Bernard et al., 2006; and Verhoogen 2008).

There have been a number of studies on the performance of enterprises in Ukraine. The majority of these studies focused on the whole country and only very few of them embraced a regional dimension. For example, in one of the early studies, Pivovarsky (2003) analysed the impact of ownership concentration on the firm performance. Subsequently, Brown et al. (2006) studied the effect of privatization on total factor productivity. Earle et al. (2014) showed that political favouritism combined with weak institutions had substantial redistributional impact on productivity, while Huynh et al. (2015) demonstrated that improvements in firm productivity in Ukraine’s manufacturing sector varied substantially across industries.

Shepotylo and Vakhitov (2015) identified the effect of liberalization of services on total factor productivity (TFP) of manufacturing firms, while Cieślik et al. (2015) studied the relationship between productivity and exporting using the firm-level BEEPS survey data. This relationship has been recently re-examined by Cieślik et al. (2017a) using a broader firm-level Derzhkomstat (the State Committee of Statistics of Ukraine) dataset and a different measure of productivity. In both cases, the empirical results showed the positive link between the export status and productivity. In addition, it was found that firms located in western Ukraine were more export-oriented compared to their counterparts located in other regions of the country.

In another study based on the same dataset, Cieślik et al. (2017b) examined the determinants of total factor productivity of Ukrainian firms in manufacturing and services using the firm-level data for the years 2005 and 2013. Their empirical results showed a positive link between TFP, intangible assets, capital intensity, firm size, competition in the industry, ownership status and firm internationalization measured by exports and imports. However, so far, very few attempts were made to empirically study the relationship between productivity and firm characteristics of Ukrainian enterprises in the regional context.

The notable exception includes the recent study by Cieślik et al. (2017c) who studied the determinants of the firm-level productivity in the manufacturing sector only across the large Ukrainian macro-regions using micro-level dataset for 2013. They found that the link between the firms’ productivity, firm size, imports and the level of competition seemed to be valid for all Ukrainian macro-regions. At the same time, the effects of private ownership status, capital intensity and exports seemed to be region specific. Finally, they found no relationship between firm productivity and foreign ownership in any of the macro-regions.

Similar to the study by Cieślik et al. (2017c), the current study is based on the extensive firm-level data for the Ukrainian firms in the year 2013. However, in contrast to the aforementioned study for five large macro-regions in this paper, we evaluate whether the determinants of productivity in both manufacturing and service sectors are significantly different across 24 smaller regions (oblasts) of Ukraine. Therefore, the current study can be viewed as an extension and reconsideration of the results presented in that paper.

3 Research Methodology and Statistical Data

In this section, we discuss our research methodology and statistical data. First, we briefly explain what productivity conceptually is and how we measured it in practice. 2 Then we discuss our estimating equation and the data sources.

In the simplest way, productivity is defined as efficiency in production, that is, how much output is obtained from a given set of inputs (Syverson, 2004). In the case of single-factor production functions, productivity measures the number of units of output produced per unit of a given input. The most commonly used measure of this type is labour productivity, that is, the number of units of output per unit of labour. 3 However, in reality, single factor productivity levels may be affected by the intensity of use of the other inputs that are also used in the production process. For example, two firms having the same production function can have different labour productivity levels resulting from different intensity of use of capital.

Therefore, a productivity concept that is invariant to the intensity of use of observable factor inputs is often employed in the productivity measurement. This measure is called total factor productivity (TFP). The difference in TFP reflects variation in output produced from a fixed set of inputs. Firm with higher-TFP produce greater amounts of output with the same set of observable inputs than firms with lower-TFP. TFP is most easily seen in the formulation of a production function where output is the product of a function of observable inputs and a factor-neutral shifter. This means that TFP is a residual. Over the years, the economic literature has made considerable progress in explaining the effects on output that do not come from changes in observable inputs like labour or capital. 4

In this study, we use TFP calculated by the Levinsohn-Petrin method. This method enables the estimation of the production function on the basis of inputs of capital, labour and intermediate goods. The TFP-based measure of productivity is better as compared to the simple labour productivity measures used in the earlier studies, as it takes into account the productivity of other factors of production. Moreover, the TFP measure calculated by the Levinsohn-Petrin method is better as compared to the Olley-Pakes method, as it takes into account the role of intermediate inputs.

In explaining TFP differences between Ukrainian firms located in various regions in our estimating equation, we account for firm and industry characteristics that may affect firm productivity. Following the literature review presented in the previous section in our estimating equation, we refer to three previously discussed strands in the economic literature: industrial organization, international trade and labour economics. The firm characteristics include the firm’s capital to labour ratio, its size, the ownership status, the level of firm internationalization (exports and imported inputs) as well as the measure of competition within the industry in particular regions. We also control for the level of market concentration in the industry and industry-specific effects.

To study empirically, the relationship between firm productivity, measured by its TFP, and its determinants, we estimate the following empirical model:

lnTFPijr=a0+a1lnsizeijr+a2lnKLratioijr+a3importijr+a4exportijr+a5privateijr+a6foreignijr+a7lnHHIj+vj+vr+ ε ijr

where ln TFPijr denotes the natural logarithm of the level of productivity of firm i operating in industry j located in region r, ln sizeijr denotes the natural logarithm of the size of the firm measured in terms of its full-time employees, ln KLratioijr denotes the natural logarithm of the capital-labour ratio measured as the stock of firm’s fixed assets per a full time employee, importsijr is an indicator variable showing whether the firm is importing or not, exportijr is an indicator variable showing whether the firm is exporting or not, privateijr is an indicator variable showing whether the firm is privately owned or not, foreignijr is an indicator variable showing whether the firm has foreign ownership or not, and ln HHIj stands for the natural logarithm of the Herfindahl-Hirschman index calculated for the NACE 2-digit industry, vj is a dummy variable representing the industry-specific fixed effect, ur is a dummy variable measuring the region-specific fixed effect, and finally, εijr is the error term that is assumed to satisfy the standard properties (iid). The αs are the parameters of the empirical model that need to be estimated by the ordinary least squares (OLS).

The data used in the empirical study comes from several statistical sources and covers only the year 2013. This data comes mainly from Derzhkomstat (2015) and includes the balance and income statement indicators. These are related to fixed assets, total revenues, total labour cost, cost of materials, and so on. Similarly, the information on domestic and foreign ownership comes from the State Committee of Statistics of Ukraine. The foreign trade data (export and imports) comes from External Economic Activity Database of the State Committee of Statistics of Ukraine. Finally, the employment data (total number of full-time workers) has been received from the Ukrainian employment authorities.

The statistical data has been classified according to the KVED-2010 statistics that became effective from 1 January 2012. KVED is the Ukrainian national classification developed by the State Committee for Technical Regulation and Consumer Policy of Ukraine to collect information on economic activity. KVED-2010 includes 3 agricultural industries, 5 mining industries, 25 manufacturing industries, 56 services industries. In this study, we limit our attention only to the manufacturing and services industries.

The definitions of variables used in our empirical study and their summary statistics for the firms operating in the manufacturing and service industries are reported in Tab. 1a and Tab. 1b, respectively.

Tab. 1a

Definitions of variables and summary statistics, manufacturing sector

VariableDefinitionObsMeanStd. Dev.MinMax
TFPTotal factor productivity calculated based on Levinsohn-Petrin input shares11556188.81839.980.1752215.16
SizeTotal number of full-time employees11556130.03754.72135625
KLratioCapital to labour ratio calculated as the ratio of fixed assets in the end of period115562475.6840136.680.033762930
ImportDummy variable indicating if an enterprise imports or not115560.080.2801
ExportDummy variable indicating if an enterprise exports or not115560.100.3001
PrivateDummy variable indicating private ownership of an enterprise115560.820.3801
ForeignDummy variable indicating foreign ownership of an enterprise115560.010.0701
HHIHerfindahl-Hirschman index for NACE 2-digit industry11556323.85287.5496.081929.94
Note: the values in the above tables are reported in absolute terms.
Tab. 1b

Definitions of variables and summary statistics, services sector

VariableDefinitionObsMeanStd. Dev.MinMax
TFPTotal factor productivity calculated based on Levinsohn-Petrin input shares84475342.863015.440.01594673
SizeTotal number of full-time employees8447546.03689.11196477
KLratioCapital to labour ratio calculated as the ratio of fixed assets in the end of period844755538.6578420.530.017842810
ImportDummy variable indicating if an enterprise imports or not844750.060.2401
ExportDummy variable indicating if an enterprise exports or not844750.020.1501
PrivateDummy variable indicating private ownership of an enterprise844750.950.2201
ForeignDummy variable indicating foreign ownership of an enterprise844750.000.0601
HHIHerfindahl-Hirschman index for NACE 2-digit industry84475348.70797.3317.2910000
Note: the values in the above tables are reported in absolute terms.

The level of firm productivity measured by TFP has been calculated using the Levinsohn-Petrin (2003) methodology. TFP can be estimated in Stata using command “levpet” that implements the estimators of input shares on the basis of log data for total revenues, capital stock, number of employees and cost of materials (raw materials, electricity, fuel).

The input shares were computed using the data of total revenues, fixed capital, number of employees and cost of materials of enterprises for each of KVED-2010 sectors on the basis of the unbalanced sample of enterprises for the time period of 2005–2013. 5

The degree of competition between the firms within the industry has been measured by the Herfindahl-Hirschman Index (HHI). This is a frequently employed measure of market concentration in the industrial organization (IO) literature. It has been calculated for each of the available KVED-2010 industries so that

HHIj=i=1NTRisecTR100j2

where N denotes the total number of firms operating in industry j, TR is the total revenue of the enterprise i,

secTR represents the sum of total revenues of all firms in industry j. The higher value of HHI is associated with the higher degree of concentration within the industry and consequently less competition.

The correlations between our explanatory variables for the manufacturing and service sectors are reported in Tab. 2a and 2b, respectively. The analysis of correlations between particular explanatory variables does not reveal strong correlations.

Tab. 2a

Correlations between variables, manufacturing sectors

TFPSizeKLratioImportexportPrivateforeignHHI
TFP1
size0.1321
KLratio0.1130.0001
import0.0940.133-0.0041
export0.0210.120-0.0060.4521
private0.0390.0070.0060.0490.0551
foreign0.000-0.0010.0030.0540.0400.0351
HHI0.0280.0700.0030.0590.0550.053-0.00741
Tab. 2b

Correlations between variables, services sectors

TFPSizeKLratioImportexportPrivateforeignHHI
TFP1
Size0.0041
KLratio0.094-0.0021
Import0.0560.030-0.0091
Export0.0460.035-0.0010.2161
Private0.003-0.071-0.0130.0510.0271
Foreign0.0050.0070.0110.0500.0280.0081
HHI-0.0310.021-0.007-0.064-0.030-0.1210.0151

4 Estimation Results

In this section, we empirically investigate the firm-level determinants of productivity for particular regions of Ukraine and report two sets of estimation results for the manufacturing and service sectors, respectively. Our estimation results for manufacturing companies are reported in Tab. 3a, b, while for services firms in Tab. 4a, b.

Tab. 3a

Estimation results for the manufacturing sector across regions

All regionsKyivCherkasyChernihivChernivtsiDniproDonetskIvano-FrankivskKharkivKhersonKhmelnitskyiKirovogradLuhansk
InSize-0.0640.004-0.103-0.028-0.159-0.063-0.036-0.179-0.061-0.046-0.096-0.126-0.097
(9.64)**(0.22)(2.52)*(0.51)(2.54)*(2.71)**(1.50)(3.52)**(2.68)**(0.91)(2.19)*(2.32)*(2.82)**
InKLratio0.0100.0200.0380.1280.005-0.022-0.0030.0400.011-0.0400.0750.074-0.021
(2.14)*(2.05)*(1.23)(3.27)**(0.11)(1.26)(0.17)(1.11)(0.76)(1.01)(1.94)(1.84)(0.77)
Import0.5830.788-0.087-0.2140.3320.6870.3540.3110.5060.6800.4860.1250.382
(15.25)**(10.26)**(0.32)(0.65)(0.80)(4.95)**(2.26)*(1.15)(4.07)**(1.66)(1.86)(0.30)(1.76)
Export0.111-0.0930.5320.4140.3600.0660.1190.6380.1280.2770.3080.4130.230
(3.07)**(1.07)(2.43)*(1.68)(1.21)(0.47)(0.84)(3.00)**(1.03)(0.90)(1.31)(1.35)(1.26)
Private-0.0280.085-0.157-0.1480.043-0.0270.036-0.2420.066-0.247-0.229-0.0880.199
(1.08)(1.18)(1.08)(0.79)(0.19)(0.28)(0.34)(1.15)(0.77)(1.41)(1.49)(0.50)(1.52)
Foreign-0.064-0.145-0.6780.4030.068-0.9170.540-0.436-0.498-0.613
(0.51)(0.60)(0.67)(0.42)(0.27)(0.84)(1.29)(0.70)(0.71)(0.64)
InHHI0.6560.334-0.0620.1420.0790.8721.1050.8330.6520.3160.943-0.3450.475
(12.62)**(3.07)**(0.18)(0.30)(0.18)(5.04)**(7.27)**(3.44)**(3.40)**(0.73)(2.10)*(0.78)(2.04)*
Constant0.4951.9874.2072.5683.751-0.191-1.599-0.1160.6092.661-1.0965.5071.653
(1.78)(3.34)**(2.25)*(1.01)(1.66)(0.21)(1.98)*(0.09)(0.60)(1.13)(0.47)(2.38)*(1.33)
Industry- specific effectsyesyesyesyesYesyesyesyesyesyesyesyesyes
N115572515317207137895895223948169260226416
R20.510.530.480.580.510.500.460.510.570.660.560.490.55
(Absolute value of z-statistics in parentheses) Absolute value of t-statistics in parentheses * significant at 5%; ** significant at 1%.
Tab. 3b

Estimation results for the manufacturing sector across regions, continued

All regionsLutskLvivMykolajivOdesaPoltavaRivneSummyTernopolUzhgorodVinnytsiaZaporizhzheZhytomyr
InSize-0.0640.030-0.109-0.152-0.115-0.036-0.090-0.1260.010-0.003-0.055-0.083-0.065
(9.64)**(0.60)(3.94)**(2.86)**(3.27)**(1.07)(1.86)(2.93)**(0.18)(0.05)(1.39)(2.92)**(1.65)
InKLratio0.010-0.0720.008-0.034-0.0030.026-0.1370.0210.025-0.0550.060-0.0130.004
(2.14)*(1.91)(0.41)(0.83)(0.15)(0.99)(3.74)**(0.54)(0.57)(1.36)(2.05)*(0.59)(0.14)
Import0.5830.5620.8080.7360.4820.3680.6510.1220.7270.5070.5590.6070.197
(15.25)**(1.98)*(5.98)**(2.17)*(2.60)**(1.39)(2.80)**(0.47)(2.24)*(2.07)*(2.09)*(2.75)**(0.75)
Export0.111-0.143-0.0480.5490.1820.1710.3080.312-0.1300.175-0.049-0.0000.511
(3.07)**(0.65)(0.37)(1.56)(1.08)(0.69)(1.47)(1.29)(0.45)(0.88)(0.21)(0.00)(2.58)*
Private-0.028-0.004-0.012-0.132-0.0130.041-0.1930.004-0.542-0.171-0.2610.0030.016
(1.08)(0.02)(0.11)(0.74)(0.10)(0.30)(1.07)(0.03)(2.76)**(0.78)(1.88)(0.03)(0.12)
Foreign-0.0640.252-0.4980.513-0.788-0.665
(0.51)(0.47)(0.91)(0.93)(0.89)(1.18)
InHHI0.656-1.1390.942-0.6140.405-1.031-1.5580.8760.5390.3320.3620.761-0.023
(12.62)**(3.13)**(5.42)**(1.49)(1.50)(2.26)*(4.77)**(3.37)**(1.47)(1.13)(1.65)(3.20)**(0.09)
Constant0.4959.705-0.8807.8842.0288.99612.607-0.5830.5942.4111.5610.3043.828
(1.78)(4.98)**(0.94)(3.47)**(1.43)(3.84)**(7.25)**(0.39)(0.31)(1.43)(1.25)(0.24)(2.91)**
Industry-specific effectsyesYesyesyesYesyesyesYesyesyesyesyesyes
N11557183725241499347201193191199338529311
R20.510.600.580.380.540.570.670.660.610.590.490.490.53
(Absolute value of z-statistics in parentheses) Absolute value of t-statistics in parentheses * significant at 5%; ** significant at 1%.
Tab. 4a

Estimation results for the services sector across regions

All regionsKyivCherkasyChernigivChernivtsiDniproDonetstkIvano-FrankivskKharkivKhersonKhmelnytskyiKirovogradLuhansk
InSize-0.187-0.155-0.175-0.235-0.12-0.194-0.198-0.14-0.212-0.229-0.166-0.228-0.262
(51.81)**(24.32)**(6.55)**(6.39)**(3.00)**(15.93)**(14.97)**(5.08)**(14.62)**(7.57)**(6.44)**(6.33)**(12.74)**
InKLratio0.0410.0480.0390.0490.0250.0200.0490.0100.0470.0160.0360.0220.043
(22.05)**(15.42)**(2.63)**(2.67)**(1.10)(3.10)**(7.08)**(0.69)(6.34)**(1.07)(2.56)*(1.22)(3.78)**
Import0.930.9530.6051.0440.7640.990.7890.9210.7330.861.0770.7271.054
(48.14)**(33.51)**(3.40)**(4.19)**(3.75)**(14.01)**(8.24)**(5.77)**(9.58)**(3.85)**(7.27)**(2.54)*(6.67)**
Export0.4090.5730.3810.206-0.2450.2630.9400.0340.4780.610-0.2790.7230.549
(13.43)**(10.32)**(2.03)*(0.73)(0.88)(2.27)*(6.45)**(0.17)(4.33)**(2.48)*(1.19)(2.31)*(3.12)**
Private0.0620.1590.268-0.689-0.173-0.0430.0010.5690.2520.4830.137-0.2480.239
(2.79)**(3.13)**(1.80)(3.59)**(0.93)(0.51)(0.01)(3.57)**(2.79)**(3.34)**(1.12)(1.31)(2.20)*
Foreign0.2770.2220.2090.2721.7680.091-0.36-0.4670.4500.6490.7350.2700.341
(3.59)**(2.12)*(0.24)(0.19)(1.96)(0.45)(0.58)(0.57)(1.18)(0.76)(1.50)(0.41)(0.39)
InHHI-0.127-0.123-0.0580.004-0.011-0.112-0.162-0.044-0.122-0.039-0.034-0.118-0.108
(10.43)**(5.93)**(0.52)(0.03)(0.07)(2.52)*(3.34)**(0.48)(2.59)**(0.41)(0.38)(0.95)(1.36)
Industry-specific effectsyesyesyesyesyesyesYesyesyesyesYesyesyes
Constant-3.218-3.426-3.884-3.004-3.922-2.980-2.712-4.427-3.218-4.041-4.034-2.671-3.168
(34.50)**(21.25)**(4.95)**(3.34)**(3.63)**(8.89)**(7.52)**(6.63)**(9.03)**(5.84)**(6.18)**(2.92)**(5.35)**
Observations84475268381545115064567296882133554651254138710032399
R-squared0.860.850.870.860.910.850.830.900.850.900.910.860.86
(Absolute value of z-statistics in parentheses) Absolute value of t-statistics in parentheses * significant at 5%; ** significant at 1%.
Tab. 4b

Estimation results for the services sector across regions

All regionsLutskLvivMykolajivOdesaPoltavaRivneSummyTernopilUzhgorodVinnytsiaZaporizhzheZhytomyr
InSize-0.187-0.179-0.197-0.269-0.201-0.230-0.196-0.198-0.171-0.264-0.240-0.223-0.181
(51.81)**(6.69)**(13.09)**(11.34)**(11.63)**(10.29)**(7.43)**(6.11)**(4.82)**(7.85)**(9.15)**(11.29)**(5.83)**
InKLratio0.0410.0170.0620.0290.0390.0350.0340.0240.0450.0630.0470.0230.003
(22.05)**(1.25)(7.71)**(2.31)*(4.55)**(2.94)**(2.43)*(1.33)(2.54)*(3.55)**(3.47)**(2.16)*(0.20)
Import0.9301.2250.8590.8411.1420.8830.9080.6600.5950.7260.9550.8490.933
(48.14)**(9.15)**(12.66)**(3.71)**(11.46)**(5.49)**(5.85)**(2.92)**(2.97)**(4.89)**(6.32)**(6.87)**(4.71)**
Export0.409-0.2140.1880.7800.5080.6080.2700.3850.212-0.3360.7010.3770.169
(13.43)**(1.24)(1.68)(3.07)**(3.68)**(3.21)**(1.54)(1.36)(0.68)(1.89)(3.92)**(2.13)*(0.99)
Private0.062-0.1240.0070.219-0.272-0.011-0.135-0.0560.267-0.4690.4030.116-0.107
(2.79)**(0.70)(0.09)(1.63)(2.45)*(0.08)(1.08)(0.38)(1.60)(2.74)**(3.23)**(0.96)(0.79)
Foreign0.2771.7960.0400.7210.2850.453-0.2650.7840.129-0.0090.197-0.080
(3.59)**(2.21)*(0.08)(1.09)(0.85)(0.54)(0.43)(0.62)(0.17)(0.01)(0.16)(0.11)
InHHI-0.127-0.014-0.049-0.021-0.258-0.054-0.101-0.176-0.079-0.1280.017-0.127-0.099
(10.43)**(0.14)(1.06)(0.25)(4.65)**(0.74)(1.16)(1.48)(0.67)(1.15)(0.18)(1.89)(0.98)
Constant-3.218-3.833-3.817-4.018-1.594-3.533-3.232-2.465-3.651-2.581-4.396-2.950-2.853
(34.50)**(5.33)**(11.38)**(6.23)**(3.74)**(6.43)**(4.98)**(2.86)**(4.18)**(3.16)**(6.66)**(5.80)**(3.93)**
Industry-specific effectsyesyesyesYesyesyesyesyesyesyesyesyesyes
Observations8447512924444156844352078127711738591157168029051398
R-squared0.860.900.900.880.840.880.910.870.890.880.880.860.87
(Absolute value of z-statistics in parentheses) Absolute value of t-statistics in parentheses * significant at 5%; ** significant at 1%.

First, as useful benchmarks, we describe our estimation results for the manufacturing and service sectors obtained for the entire country, which are reported in the first columns of Tab. 3a and Tab. 4a, respectively. These results were obtained having controlled for industry specific and region specific effects. It turns out that only some of our explanatory variables are statistically significant for manufacturing firms while all the explanatory variables turned out to be statistically significant for the services firms. In both cases, the estimated coefficient on the firm size variable is statistically significant at the 5% level. It displays a negative sign for both manufacturing and services firms, which is in line with the results reported in Cieślik et al. (2017c).

The estimated coefficient on the capital to labour ratio variable displays an expected positive sign and is statistically significant at 10% level for manufacturing firms and at 5% level for services firms. As a higher capital-labour ratio is associated with the higher level of productivity of Ukrainian firms and the cost of capital remains quite high while the cost of labour remains quite low in Ukraine, there is a substantial potential to boost firm productivity by increasing their capital stocks.

The estimated coefficients on all the variables measuring internationalization of firms, including exports and imports, display expected positive signs and are statistically significant at 5% levels for both manufacturing and services firms. This confirms the positive relationship between firm productivity and international openness in Ukraine documented in Cieślik et al. (2017c). The estimated coefficient on the variable describing the foreign ownership is not statistically significant at all in the manufacturing sector; while in the service sector, it is significant at 5% level. Similarly, we find that the estimated coefficient on the private ownership variable is not statistically significant at all in the manufacturing sector; while in the service sector, it is significant at 5% level.

Finally, we find that the market structure also matters for firm productivity in both manufacturing and service sectors. In particular, we find that in both cases, the estimated parameters on this variable are statistically significant at 5% levels. However, in the case of manufacturing sector, the estimated parameter on this variable is positive, while in the case of the service sector it is negative. This means that firm productivity in the manufacturing sector increases with the higher concentration within the industry, while the opposite holds for the service sector.

In the remaining columns of Tab. 3a, b and Tab. 4a, b, we show the estimation results for each of the 24 regions in Ukraine. We find that in the majority of regions, the results remain generally consistent with the benchmark results, although not all explanatory variables are statistically significant in all regions. In particular, the capital to labour ratio is statistically significant for the manufacturing firms only in four regions: Kyiv, Chernihiv, Rivne and Vinnytsia, while for the services firms, it is significant in a majority of regions except Chernivtsi, Ivano-Frankivsk, Kherson, Krivograd, Lutsk, Summy and Zhytomyr.

The firm size contributes negatively to firm productivity; this result holds for most regions for the manufacturing firms except Kyiv, Chernihiv, Donetsk, Kherson, Lutsk, Poltava, Rivne, Ternopil, Uzhgorod, Vinnytsia and Zhytomyr, and for all 24 regions for the services firms.

The importance of internationalization for productivity of Ukrainian firms is quite limited in the manufacturing sector. Imports matter for firm productivity in Kyiv, Dnipro, Donetsk, Kharkiv, Lutsk, Lviv, Mykolajiv, Odesa, Rivne, Ternopil, Uzhgorod, Vinnytsia, and Zaporizhzhe. However, the export status is important for productivity of manufacturing firms only in a few regions: Cherkasy, Ivano-Frankivsk and Zhytomyr. At the same time, imports are important for productivity in the service sector in all regions and exports in the majority of regions including: Kyiv, Donetsk, Dnipro, Kharkiv, Cherkasy, Kherson, Kirovograd, Luhansk, Mykolajiv, Odesa, Poltava, Vinnytsia and Zaporizhzhe.

Foreign ownership is not significant in any of the regions in the case of the manufacturing sector, while in the case of the service sector, it is statistically significant in two regions: Kyiv and Lutsk. Private ownership is statistically significant only in a very limited number of regions. In the case of manufacturing sector, it is significant only in one region: Ternopol, while in the case of the service sector in nine regions: Kyiv, Chernigiv, Ivano-Franskivsk, Kharkiv, Kherson, Luhansk, Odesa, Uzhgorod and Vinnytsia.

Finally, the HHI for the manufacturing sector is statistically significant in several regions and displays a positive sign in ten regions: Kyiv, Dnipro, Donetsk, Ivano-Frankivsk, Kharkiv, Khmelnytskyi, Luhansk, Lviv, Summy, and Zaporizhzhe and the negative sign in three regions: Lutsk, Poltava, and Rivne. In the case of service sector, the estimated parameter on the HHI is always negative and statistically significant in five regions: Kyiv, Dnipro, Donetsk, Kharkiv, and Odesa.

5 Conclusions

The paper presented the study of micro-level determinants of total factor productivity of manufacturing and services firms in 24 Ukrainian regions in 2013. First, we discussed separately the benchmark results for manufacturing and services sectors obtained for the entire country and then separate results for each region. The estimation results obtained for the whole country demonstrated that the majority of firm productivity determinants were statistically significant for the manufacturing sector except foreign and state ownership variables and all of them for the service sector although at different levels of significance.

Then, we discussed the results obtained separately for each region. These results revealed a large degree of heterogeneity across regions with respect to the role of particular determinants of firm-level productivity for the manufacturing and the service sectors. In particular, the negative link between firm productivity and firm size turned out to be valid for a majority of regions in the case of manufacturing firms and for all regions in the case of services firms. The lack of evidence for economies of scale at firm-level, which is in contrast to the studies for other countries, requires further investigation. Moreover, the positive link between productivity and firm capital to labour ratio turned out to be valid for only a few regions in the case of manufacturing firms and for a majority regions in the case of services firms.

Similarly, the positive link between productivity and exports and imports turned out to be valid for only a few regions in the case of manufacturing firms, while in the case of services firms, it was significant for a majority regions for exports and all of them for imports. The indicator variable describing the private ownership was not statistically significant in all but one region in the case of manufacturing sector, while in the case of service sector, private ownership was significant only in a minority of regions. At the same time, the variable describing foreign ownership was not statistically significant in any region in the case of manufacturing sector, while in the case of service sector, foreign ownership was not significant in a majority of regions, in contrast to the studies for other countries. These unexpected results may reflect a still relatively low level of inward foreign direct investment in Ukraine. Therefore, this issue requires more attention in future studies.

Finally, the role of market concentration turned out to be different for manufacturing and services firms. In particular, the estimated coefficient on the HHI variable in the case of manufacturing sector turned out to be statistically significant for a majority of regions and mostly positive, while in the case of service sector, it was statistically significant only in a minority of regions and negative.

The comparison of estimated parameters across regions can be a starting point for future research on identification of groups of similar regions, decomposition of TFP variance into firm-level and regional-level determinants, or a closer evaluation of cross-regional heterogeneity. Moreover, the use of spatial statistics and spatial econometrics in future studies could broaden the scope of regional analysis by identifying possible spillover mechanisms. This could lead to specific regional policy guidelines.

Annex
Annex

The regions (oblasts) of Ukraine

Source: http://2001.ukrcensus.gov.ua/eng/regions/

Citation: Central European Economic Journal 5, 1; 10.1515/ceej-2018-0004

Acknowledgements

The paper has been presented during The Warsaw International Economic Meeting organised by the Faculty of Economic Sciences, University of Warsaw. WIEM 2018 is a project implemented with Narodowy Bank Polski under the economic education programme.

The paper does not reflect the official position of Narodowy Bank Polski.

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    Cieślik A. Gauger I. Michałek J.J. 2017a Firm Heterogeneity Location and Export Performance Empirical Evidence from Ukrainian Firm-Level Data Journal Transition Studies Review vol. 24(2) pp. 65–81.

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    Cieślik A. Gauger I. Michałek J.J. 2017b Determinants of Productivity of Ukrainian Firms Comparative Economic Research 20(1) 5–19.

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    Collard-Wexler A. (2013) Demand Fluctuations in the Ready-Mix Concrete Industry Econmetrica Vol. 81 (3) pp. 1003-1037.

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    Derzhkomstat 2015. Derzhavniy Komitet Statystyky Ukrainy. Available from www.ukrstat.gov.ua

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    Eaton J. and Kortum S. (2002) Technology Geography and Trade Econometrica vol. 70(5) pp. 1741–79.

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    Ericson R. and Pakes A. (1995) Markov-Perfect Industry Dynamics: A Framework for Empirical Work Review of Economic Studies vol. 62(1) pp. 53–82.

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    Foster L. Haltiwanger J. and Syverson Ch. (2008) Reallocation Firm Turnover and Efficiency: Selection on Productivity or Profitability? American Economic Review vol. 98(1) pp. 394–425.

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    • Export Citation
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    Fox J.T. and Smeets V. (2011) Does Input Quality Drive Measured Differences in Firm Productivity? National Bureau of Economic Research Working Paper 16853.

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    Garicano L. and Heaton P. (2010). Information Technology Organization and Productivity in the Public Sector: Evidence from Police Departments Journal of Labor Economics vol. 28(1) pp.167-201.

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    • Export Citation
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    Griliches Z. and Mairesse J. (1998) Production Functions: The Search for Identification In Econometrics and Economic Theory in the Twentieth Century: The Ragnar Frisch Centennial Symposium ed. Steinar Strřm pp. 169–203. Cambridge; New York and Melbourne: Cambridge University Press.

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    Haltiwanger J. Scarpetta S. and Schweiger H. (2008) Assessing Job Flows across Countries: The Role of Industry Firm Size and Regulations National Bureau of Economic Research Working Paper 13920.

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    Hopenhayn H.A. (1992) Entry Exit and Firm Dynamics in Long Run Equilibrium Econometrica vol. 60(5) pp. 1127–50.

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    • Export Citation
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    Hortaçsu A. and Syverson Ch. (2007) Cementing Relationships: Vertical Integration Foreclosure Productivity and Prices Journal of Political Economy vol. 115(2) pp. 250–301.

    • Crossref
    • Export Citation
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    Huynh K P Jacho-Chavez D T Kryvtsov O Shepotylo O Vakhitov V 2015 ‘The Evolution of Firm-Level Distributions for Ukrainian Manufacturing Firms’ Journal of Comparative Economics Available from: doi:10.1016/j.jce.2015.10.004. Forthcoming.

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    Ichniowski C. Shaw K. and Prennushi G. (1997) The Effects of Human Resource Management Practices on Productivity: A Study of Steel Finishing Lines American Economic Review vol. 87(3) pp. 291–313.

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    Jovanovic B. (1982) Selection and the Evolution of Industry Econometrica vol. 50(3) pp. 649–70.

    • Crossref
    • Export Citation
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    KVED-2005 classification 2005. Available from http://www.dkrp.gov.ua/info/842

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    KVED-2010 classification 2010. Available from http://minfin.com.ua/taxes/-/normdoc/kved2010.html [February 2015]

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    Lazear E.P. (2000) Performance Pay and Productivity American Economic Review vol. 90(5) pp. 1346–61.

    • Crossref
    • Export Citation
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    Levinsohn J. and Petrin A. (2003) Estimating Production Functions Using Inputs to Control for Unobservables Review of Economic Studies vol. 70(2) pp. 317–41.

    • Crossref
    • Export Citation
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    Levinsohn J Petrin A 2003 ‘Estimating Production Functions Using Inputs to Control for Unobservables’ Review of Economic Studies vol. 70(2) pp. 317-341. Available from: doi: 10.1111/1467-937X.00246

    • Crossref
    • Export Citation
  • [35]

    Maksimovic V. and Phillips G. (2002) Do Conglomerate Firms Allocate Resources Inefficiently across Industries? Theory and Evidence Journal of Finance vol. 57(2) pp. 721–67.

    • Crossref
    • Export Citation
  • [36]

    Melitz M.J. 2003. “The Impact of Trade on Intraindustry Reallocations and Aggregate Industry Productivity Econometrica vol. 71(6) pp.1695–1725.

    • Crossref
    • Export Citation
  • [37]

    Olley G. Steven and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry.” Econometrica 64(6): 1263–97.

    • Crossref
    • Export Citation
  • [38]

    Pavcnik N. (2002) Trade Liberalization Exit and Productivity Improvement: Evidence from Chilean Plants Review of Economic Studies vol. 69(1) pp. 245–76.

    • Crossref
    • Export Citation
  • [39]

    Pivovarsky A 2003 ‘Ownership concentration and performance in Ukraine’s enterprises’ IMF Staff Papers vol. 50 (1). Available from https://www.imf.org/external/pubs/.../Pivov.pdf [February 2015]

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    Schmitz J.A. 2005. “What Determines Productivity? Lessons from the Dramatic Recovery of the U.S. and Canadian Iron Ore Industries following Their Early 1980s Crisis.” Journal of Political Economy vol. 113(3) pp. 582–625.

    • Crossref
    • Export Citation
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    Schoar A. (2002) Effects of Corporate Diversification on Productivity Journal of Finance vol. 57(6) pp. 2379–2403.

    • Crossref
    • Export Citation
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    Shepotylo O Vakhitov V 2015 ‘Services liberalization and productivity of manufacturing firms’ Economics of Transition vol. 23 (1) pp. 1–44.

    • Crossref
    • Export Citation
  • [43]

    Syverson Ch. (2011) What Determines Productivity? Journal of Economic Literature vol. 49:2 pp. 326–365

  • [44]

    Syverson Ch. (2004) Market Structure and Productivity: A Concrete Example. Journal of Political Economy vol. 112(6) pp. 1181–1222.

    • Crossref
    • Export Citation
  • [45]

    Verhoogen E.A. (2008) Trade Quality Upgrading and Wage Inequality in the Mexican Manufacturing Sector Quarterly Journal of Economics vol. 123(2) pp. 489–530.

    • Crossref
    • Export Citation

Footnotes

2The extensive review of the theory of productivity indexes can be found in Caves et al. (1982) and the references therein.
3Occasionally, capital based productivity measures are also used.
4The detailed discussion on the productivity measurement issues can be found in the vast literature on the subject. The frequently cited examples include Olley and Pakes (1996), Griliches and Mairesse (1998), Blundell and Bond (2000), Levinsohn and Petrin (2003), and Ackerberg et al. (2007).
5Some industries were omitted from the analysis due to problems with calculating factor input shares. It was not possible to calculate the Levinsohn-Petrin input shares for the following manufacturing industries: production of ready-made garments, manufacture of leather, production of paper, manufacture of other mineral products, manufacture of transport equipment and tobacco industry, so they were not included in the sample.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1]

    Abowd J.M. Haltiwanger J. Jarmin R. Lane J. Lengermann P. McCue K. McKinney K. and Sandusky K. (2005) The Relation among Human Capital Productivity and Market Value: Building Up from Micro Evidence. In Measuring Capital in the New Economy ed. Corrado C. Haltiwanger J. and Sichel D. pp. 153–98 Chicago and London: University of Chicago Press.

    • Crossref
    • Export Citation
  • [2]

    Ackerberg D. Benkard C.L. Berry S. and Pakes A. (2007) Econometric Tools for Analyzing Market Outcomes In Handbook of Econometrics Volume 6A ed. James J. Heckman and Edward E. Leamer pp. 4171–276. Elsevier North-Holland.

  • [3]

    Asplund M. and Nocke V. (2006) Firm Turnover in Imperfectly Competitive Markets Review of Economic Studies vol. 73(2) pp. 295–327.

    • Crossref
    • Export Citation
  • [4]

    Bandiera O. Barankay I. and Rasul I. (2009) Social Connections and Incentives in the Workplace: Evidence from Personnel Data Econometrica vol. 77(4) pp. 1047–94.

    • Crossref
    • Export Citation
  • [5]

    Bernard A.B. J. Jensen B. and Schott P.K. (2006) Trade Costs Firms and Productivity Journal of Monetary Economics vol. 53(5) pp. 917–37.

    • Crossref
    • Export Citation
  • [6]

    Bloom N. and Van Reenen J. (2007) Measuring and Explaining Management Practices across Firms and Countries Quarterly Journal of Economics vol. 122(4) pp. 1351–1408.

    • Crossref
    • Export Citation
  • [7]

    Bloom N. Schankerman M. and Van Reenen J. (2007) Identifying Technology Spillovers and Product Market Rivalry.” National Bureau of Economic Research Working Paper 13060.

  • [8]

    Blundell R. and Bond S.R. (2000) GMM Estimation with Persistent Panel Data: An Application to Production Functions Econometric Reviews vol. 19(3) pp. 321–40.

    • Crossref
    • Export Citation
  • [9]

    Brown J. David J. Earle S. and Telegdy Á. (2006) ‛The productivity effects of privatization: longitudinal estimates from Hungary Romania Russia and Ukraine’ Journal of Political Economy vol. 114(1) pp. 61-99. Available from http://dx.doi.org/10.1086/499547 [February 2006]

    • Crossref
    • Export Citation
  • [10]

    Caves D.W. Christensen L.R. and W. Diewert E. (1982) The Economic Theory of Index Numbers and the Measurement of Input Output and Productivity Econometrica vol. 50(6) pp. 1393–1414.

    • Crossref
    • Export Citation
  • [11]

    Cieślik A. Michałek J.J. Nasadiuk I. 2015 Determinants of Export Performance of Ukrainian Firms Equilibrium Quarterly Journal of Economics and Economic Policy vol. 10(3) pp. 91–103.

  • [12]

    Cieślik A. Gauger I. Michałek J.J. 2017a Firm Heterogeneity Location and Export Performance Empirical Evidence from Ukrainian Firm-Level Data Journal Transition Studies Review vol. 24(2) pp. 65–81.

  • [13]

    Cieślik A. Gauger I. Michałek J.J. 2017b Determinants of Productivity of Ukrainian Firms Comparative Economic Research 20(1) 5–19.

    • Crossref
    • Export Citation
  • [14]

    Cieślik A. Michałek J.J. Nasadiuk I. 2017c The regional heterogeneity of productivity determinants: Evidence from Ukrainian firms Miscellanea Geographica – Regional Studies on Development vol. 21 (1) pp. 44–50.

    • Crossref
    • Export Citation
  • [15]

    Collard-Wexler A. (2013) Demand Fluctuations in the Ready-Mix Concrete Industry Econmetrica Vol. 81 (3) pp. 1003-1037.

  • [16]

    Derzhkomstat 2015. Derzhavniy Komitet Statystyky Ukrainy. Available from www.ukrstat.gov.ua

  • [17]

    Earle J & Gehlbach S. (2014) Productivity Consequences of Political Turnover: Firm-Level Evidence from Ukraine’s Orange Revolution IZA Discussion Paper No. 8510. Available from ftp.iza.org/dp8510.pdf [September 2014]

  • [18]

    Eaton J. and Kortum S. (2002) Technology Geography and Trade Econometrica vol. 70(5) pp. 1741–79.

    • Crossref
    • Export Citation
  • [19]

    Ericson R. and Pakes A. (1995) Markov-Perfect Industry Dynamics: A Framework for Empirical Work Review of Economic Studies vol. 62(1) pp. 53–82.

    • Crossref
    • Export Citation
  • [20]

    Foster L. Haltiwanger J. and Syverson Ch. (2008) Reallocation Firm Turnover and Efficiency: Selection on Productivity or Profitability? American Economic Review vol. 98(1) pp. 394–425.

    • Crossref
    • Export Citation
  • [21]

    Fox J.T. and Smeets V. (2011) Does Input Quality Drive Measured Differences in Firm Productivity? National Bureau of Economic Research Working Paper 16853.

  • [22]

    Garicano L. and Heaton P. (2010). Information Technology Organization and Productivity in the Public Sector: Evidence from Police Departments Journal of Labor Economics vol. 28(1) pp.167-201.

    • Crossref
    • Export Citation
  • [23]

    Griliches Z. and Mairesse J. (1998) Production Functions: The Search for Identification In Econometrics and Economic Theory in the Twentieth Century: The Ragnar Frisch Centennial Symposium ed. Steinar Strřm pp. 169–203. Cambridge; New York and Melbourne: Cambridge University Press.

  • [24]

    Haltiwanger J. Scarpetta S. and Schweiger H. (2008) Assessing Job Flows across Countries: The Role of Industry Firm Size and Regulations National Bureau of Economic Research Working Paper 13920.

  • [25]

    Hopenhayn H.A. (1992) Entry Exit and Firm Dynamics in Long Run Equilibrium Econometrica vol. 60(5) pp. 1127–50.

    • Crossref
    • Export Citation
  • [26]

    Hortaçsu A. and Syverson Ch. (2007) Cementing Relationships: Vertical Integration Foreclosure Productivity and Prices Journal of Political Economy vol. 115(2) pp. 250–301.

    • Crossref
    • Export Citation
  • [27]

    Huynh K P Jacho-Chavez D T Kryvtsov O Shepotylo O Vakhitov V 2015 ‘The Evolution of Firm-Level Distributions for Ukrainian Manufacturing Firms’ Journal of Comparative Economics Available from: doi:10.1016/j.jce.2015.10.004. Forthcoming.

  • [28]

    Ichniowski C. Shaw K. and Prennushi G. (1997) The Effects of Human Resource Management Practices on Productivity: A Study of Steel Finishing Lines American Economic Review vol. 87(3) pp. 291–313.

  • [29]

    Jovanovic B. (1982) Selection and the Evolution of Industry Econometrica vol. 50(3) pp. 649–70.

    • Crossref
    • Export Citation
  • [30]

    KVED-2005 classification 2005. Available from http://www.dkrp.gov.ua/info/842

  • [31]

    KVED-2010 classification 2010. Available from http://minfin.com.ua/taxes/-/normdoc/kved2010.html [February 2015]

  • [32]

    Lazear E.P. (2000) Performance Pay and Productivity American Economic Review vol. 90(5) pp. 1346–61.

    • Crossref
    • Export Citation
  • [33]

    Levinsohn J. and Petrin A. (2003) Estimating Production Functions Using Inputs to Control for Unobservables Review of Economic Studies vol. 70(2) pp. 317–41.

    • Crossref
    • Export Citation
  • [34]

    Levinsohn J Petrin A 2003 ‘Estimating Production Functions Using Inputs to Control for Unobservables’ Review of Economic Studies vol. 70(2) pp. 317-341. Available from: doi: 10.1111/1467-937X.00246

    • Crossref
    • Export Citation
  • [35]

    Maksimovic V. and Phillips G. (2002) Do Conglomerate Firms Allocate Resources Inefficiently across Industries? Theory and Evidence Journal of Finance vol. 57(2) pp. 721–67.

    • Crossref
    • Export Citation
  • [36]

    Melitz M.J. 2003. “The Impact of Trade on Intraindustry Reallocations and Aggregate Industry Productivity Econometrica vol. 71(6) pp.1695–1725.

    • Crossref
    • Export Citation
  • [37]

    Olley G. Steven and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry.” Econometrica 64(6): 1263–97.

    • Crossref
    • Export Citation
  • [38]

    Pavcnik N. (2002) Trade Liberalization Exit and Productivity Improvement: Evidence from Chilean Plants Review of Economic Studies vol. 69(1) pp. 245–76.

    • Crossref
    • Export Citation
  • [39]

    Pivovarsky A 2003 ‘Ownership concentration and performance in Ukraine’s enterprises’ IMF Staff Papers vol. 50 (1). Available from https://www.imf.org/external/pubs/.../Pivov.pdf [February 2015]

  • [40]

    Schmitz J.A. 2005. “What Determines Productivity? Lessons from the Dramatic Recovery of the U.S. and Canadian Iron Ore Industries following Their Early 1980s Crisis.” Journal of Political Economy vol. 113(3) pp. 582–625.

    • Crossref
    • Export Citation
  • [41]

    Schoar A. (2002) Effects of Corporate Diversification on Productivity Journal of Finance vol. 57(6) pp. 2379–2403.

    • Crossref
    • Export Citation
  • [42]

    Shepotylo O Vakhitov V 2015 ‘Services liberalization and productivity of manufacturing firms’ Economics of Transition vol. 23 (1) pp. 1–44.

    • Crossref
    • Export Citation
  • [43]

    Syverson Ch. (2011) What Determines Productivity? Journal of Economic Literature vol. 49:2 pp. 326–365

  • [44]

    Syverson Ch. (2004) Market Structure and Productivity: A Concrete Example. Journal of Political Economy vol. 112(6) pp. 1181–1222.

    • Crossref
    • Export Citation
  • [45]

    Verhoogen E.A. (2008) Trade Quality Upgrading and Wage Inequality in the Mexican Manufacturing Sector Quarterly Journal of Economics vol. 123(2) pp. 489–530.

    • Crossref
    • Export Citation
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