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Which Companies Benefit from Liberalization? a Study of the Influence of Initial Productivity

Abstract

Theoretical research shows that competition has positive effects on productivity, for companies that are initially efficient, but not for unproductive firms. Our empirical analysis on a panel data of Czech companies, years 1995–2004, confirms this result. In addition, our analysis shows that when economic reforms affect both domestic and foreign competition, controlling for domestic competition is crucial when assessing the impact of trade liberalization. Otherwise, the effect of trade liberalization on firm productivity is upward biased.

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Notes

  1. For instance, see Winters (2004) for the impact of openness on firm performance. Also see Djankov and Murell (2002) for a survey of literature for transition economies.

  2. We are aware of the paper by Topalova and Khandelwal (2011) but the original Topalova (2004) working paper is more directly showing results depending on the initial productivity levels of the firms.

  3. As we go directly for TFP effects in our study, this is an aspect where the Iacovone (2012) paper differs from ours. Papers that primarily base their results on labor productivity usually have to discuss quite thoroughly that their results also reflects overall TFP patterns, see also Lileeva and Trefler (2010).

  4. We have also included a measure of firm age and its square in our regressions. As these variables were not significant we decided to drop them from the equations.

  5. The log transformation does not really capture the non-linearity of a U shape. We favored this specification as the alternative specifications based on quadratic terms induced problems of multicollinearity. In addition, estimations based on quadratic form specifications have shown that in most cases our firms have a position on one leg of the parabola only.

  6. As pointed out in the literature by e.g. Klette and Griliches (1996), Katayama et al. (2003) and Katayama et al. (2009) then estimates of the coefficients of the production function may be biased when prices at the firm level are not available but prices at the industry level are used instead. This is a complicated issue and as we do not have access to prices at the firm level, we use the industry level prices referring to the results of Mairesse and Jaumandreu (2005) that indicates that deflating value added with PPI rather than a firm specific price index leads to very similar estimates of the coefficients in the production function.

  7. We have performed a Hausmann test for firm specific effects and the null of no effects was actually rejected. In this test, however, we did not take the hidden dynamics into account.

  8. It can be argued that our results are valid for the transition period and that this period must be considered more or less over with respect to Tariff changes at the end of our sample. Hence extending the sample to include even more recent periods may not be meaningful.

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Acknowledgments

This paper has benefited from extensive discussions and feedback from Pascalis Raimondos-Møller. We gratefully acknowledge his help. Also we thank Steffen Andersen, Jan. Hanousek, Lubomir Lizal, Stephan Juraida, Anders Sørensen, Moira Daly and Jonathan Temple for valuable comments and insights. We also thank the Danish Social Science Research Council for financial support.

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Correspondence to Lisbeth Funding la Cour.

Appendices

Appendix 1 – Data Description

Firm level data are from Amadeus. Amadeus is a pan-European commercial database, provided by Bureau van Dijk, which contains financial information on public and private companies. We used data from all versions of the Amadeus database since 1996 with information on medium and large firms. Most of the Czech firms included in the database produce goods in several industries at 4 digit NACE level. We have classified firms according to their main activity.

We did the following modifications to the data:

i. we excluded all companies that had less than 10 employees:

ii. we excluded firms with non-positive investment levels when estimating firm productivity.

iii. Since we did not have enough observations in three industries at 2-digit NACE level (16 – manufacture of tobacco, 19 - Leather manufacturing, 22 – Publishing and printing, 23 – manufacture of coke, refined petroleum and nuclear fuel, 26 - Non-metallic mineral products, 30 – manufacture of office machinery and computers, 32 - Radio, television and communication equipment and apparatus 37 - Recycling) to estimate the production function we dropped companies from this sector.

iv. the data was trimmed at the 1 % and 99 % quantiles.

Tables 6, 7, 8, 9

Table 6 Variables
Table 7 Descriptive statistics – observations based on which firm productivity is estimated
Table 8 Descriptive statistics – observations based on which the impact of competition on firm productivity is estimated
Table 9 Number of firms and their average size by year

Appendix 2: Time line for important reforms in Czech Republic 1990–2005

Table 10

Table 10 Main economic reforms

Appendix 3: Results of the Estimation of the Production Function

Table 11, 12, Fig. 1

Table 11 OLS and Wooldridge estimates of production function
Table 12 Statistics of the distributions of the TFP estimates by 2 digit industry (based on the Wooldridge estimates of the production function)
Figure 1.
figure 1

Density of firm productivity (pooled sample)

Appendix 4 – Robustness Checks

Table 13, 14, 15

Table 13 The impact of competition on firm productivity using OLS Productivity
Table 14 The impact of competition on firm productivity using OP productivity
Table 15 The impact of competition on firm productivity if market concentration rather than the Herfindahl index is used to measure domestic competition

Appendix 5 Estimation Methods for Coefficients of the Production Function

Wooldridge (2009) estimation

This appendix section explains the approach, based on Wooldridge (2009), used to estimate the production function parameters and recover the productivity term used in our analysis. We assume a value-added Cobb-Douglas production function:

$$ {\mathrm{y}}_{\mathrm{it}}={\upbeta}_{\mathrm{l}}{\mathrm{l}}_{\mathrm{it}}+{\upbeta}_{\mathrm{k}}{\mathrm{k}}_{\mathrm{it}}+{\upomega}_{\mathrm{it}}+{\upeta}_{\mathrm{it}} $$

where y it is the log of value added, l it is the log of employment, k it is the log of real capital stock, ω it the transmitted component of the firm specific productivity shock, and η it represents firm specific iid productivity shock or measurement errors.

As in Olley and Pakes (1997), Levinsohn and Petrin (2003), or Wooldridge (2009) we express productivity as a function of the state variable (k it ) and a proxy variable. Following e.g. Petrin and Sivadasan (2013) we use the Wooldrige version of Levinsohn and Petrin and use intermediate inputs (materials) m it as the proxy variable:

$$ {\upomega}_{\mathrm{it}}=\mathrm{g}\left({\mathrm{k}}_{\mathrm{it}};{\mathrm{m}}_{\mathrm{it}}\right) $$

We restrict the dynamics of productivity shocks in the following way:

$$ \mathrm{E}\left[{\upomega}_{\mathrm{i}\mathrm{t}}|{\mathrm{k}}_{\mathrm{i}\mathrm{t}};{\mathrm{l}}_{\mathrm{i}\mathrm{t}-1};{\mathrm{k}}_{\mathrm{i}\mathrm{t}-1};{\mathrm{m}}_{\mathrm{i}\mathrm{t}-1};\dots; {\mathrm{k}}_{\mathrm{i}1};{\mathrm{l}}_{\mathrm{i}1};{\mathrm{m}}_{\mathrm{i}1}\right]=\mathrm{E}\left[{\upomega}_{\mathrm{i}\mathrm{t}}|{\upomega}_{\mathrm{i}\mathrm{t}-1}\right]=\mathrm{f}\left(\mathrm{g}\left({\mathrm{k}}_{\mathrm{i}\mathrm{t}-1};{\mathrm{m}}_{\mathrm{i}\mathrm{t}-1}\right)\right) $$

And assume that variable inputs (l it andm it ) are correlated with productivity innovations (a it ) but k it and all past values of l , k and m are uncorrelated with a it , i.e. E[a it | k it  ; l it - 1 ; k it - 1 ; m it - 1 ;  …  ; k 1 ; l 1 ; m 1] = 0.

Then we can rewrite the production function as:

$$ {\mathrm{y}}_{\mathrm{it}}={\upbeta}_{\mathrm{l}}{\mathrm{l}}_{\mathrm{it}}+{\upbeta}_{\mathrm{k}}{\mathrm{k}}_{\mathrm{it}}+\mathrm{f}\left(\mathrm{g}\left({\mathrm{k}}_{\mathrm{it}-1};{\mathrm{m}}_{\mathrm{it}-1}\right)\right)+{\mathrm{u}}_{\mathrm{it}} $$

Where u it  ≡ a it +η it

The moment conditions to identify the parameters are

$$ \mathrm{E}\left[{\mathrm{u}}_{\mathrm{i}\mathrm{t}}|{\mathrm{k}}_{\mathrm{i}\mathrm{t}};{\mathrm{l}}_{\mathrm{i}\mathrm{t}-1};{\mathrm{k}}_{\mathrm{i}\mathrm{t}-1};{\mathrm{m}}_{\mathrm{i}\mathrm{t}-1};\dots; {\mathrm{k}}_{\mathrm{i}1};{\mathrm{l}}_{\mathrm{i}1};{\mathrm{m}}_{\mathrm{i}1}\right]=0 $$

We follow the literature and approximate f(g(k it - 1; m it - 1)) using a third order polynomial. In addition to the exogenous state variable (k it ) we use first lags of materials and labor as instruments. The estimation is undertaken separately for each 2-digit industry. Table A3.1 summarizes the coefficient estimates and we compute productivity as \( {\omega}_{it}={y}_{it}-{\widehat{\beta}}_l{l}_{it}-{\widehat{\beta}}_k{k}_{it} \)

Olley and Pakes (1996) Estimation

The OP method is extensively used in the literature and therefore we do not describe it in detail here. This method could be also used to control for the exit bias (i.e. firms with more capital may sustain higher adverse shocks without exiting and therefore, if these shocks are not taken into account, the coefficient of capital are biased downwards). We do not control for it because in our sample there are too few firms that exit the market.

We first estimated a Cobb-Douglas production function, y i , t  = β l l i , t  + (β 0 + β k k i , t  + h(i i , t , k i , t )) + υ i , t , where y is value added, l is log of labor(employment), k is log of capital, h() accounts for firm productivity which according to the OP assumptions can be written as a function of capital and investment, i, (h() was approximated by a 3rd degree polynomial function), and υ is white noise. Subscripts i and t are firm and time indices, respectively. From this step we retained the coefficient of labor, β l . Further, to determine β k , we estimated \( {y}_{i,t}-{\beta}_l{l}_{i,t}={\beta}_k{k}_{i,t}+\theta \left({\widehat{y}}_{i,t-1}-{\widehat{\beta}}_l{l}_{i,t-1}-{\beta}_k{k}_{i,t-1}\right)+{\xi}_{i,t} \) where, given the OP assumption that productivity follows a first order Markov process, firm productivity is a function, θ(), of its t-1 value. ξ i , t is the error term. We used non-linear estimations where θ() was approximated by a 3rd degree polynomial function. Time varying firm total factor productivity is then calculated as \( p{r}_{i,t}={y}_{i,t}-{\widehat{\beta}}_l{l}_{i,t}-{\widehat{\beta}}_k{k}_{i,t} \).

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Baghdasaryan, D., la Cour, L.F. & Schneider, C. Which Companies Benefit from Liberalization? a Study of the Influence of Initial Productivity. J Ind Compet Trade 16, 101–125 (2016). https://doi.org/10.1007/s10842-015-0203-y

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Keywords

  • Firm productivity
  • Trade liberalization
  • Competition
  • Initial productivity

JEL

  • D24
  • F10