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Convergence of firm-level productivity, globalisation and information technology: Evidence from France
Economics Letters 116 (2012) 244–246
Contents lists available at SciVerse ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Convergence of firm-level productivity, globalisation and information technology: Evidence from France Paul-Antoine Chevalier a , Rémy Lecat a,∗ , Nicholas Oulton b a
Banque de France, 39 rue Croix-des-Petits-Champs, 75001, Paris, France
b
Centre for Economic Performance at the London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom
article
info
Article history: Received 2 November 2011 Received in revised form 6 February 2012 Accepted 11 February 2012 Available online 22 February 2012
abstract This article studies the firm-level productivity convergence process in the 1990s and the 2000s in France. The speed of convergence has slowed during the course of the 1990s, a fact which is explained principally by the acceleration of the productivity of firms on the technological frontier. Evidence is presented that information technology and globalisation may have had a bigger impact on the most productive firms. © 2012 Elsevier B.V. All rights reserved.
JEL classification: D2 F1 J2 O3 Keywords: Convergence TFP ICT Globalisation
1. Introduction There is substantial dispersion of productivity across firms, even within narrowly defined industries (for example Baily et al., 1992, Oulton, 1998 and Oulton, 2000), which gives rise to a convergence process amongst firms (Oulton, 1998; Griffith et al., 2002). This article describes and explains this convergence process using French firm-level data. We find that the speed of convergence has fallen significantly since 1992. Evidence is presented that globalisation (via the acceleration of exports), and the spread of information and communication technologies (ICT) can explain this slowdown. 2. Data and estimation strategy We mainly use the FiBEn database, a firm level database maintained by the Banque de France which includes fiscal returns of some 282,000 French firms between 1991 and 2004. 45,000 of those firms are observed during the whole period. Although we
∗
Corresponding author. Tel.: +33 1 42 92 95 50; fax: +33 1 42 92 48 08. E-mail address: [email protected] (R. Lecat).
0165-1765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2012.02.022
present our results on the whole set of firms, they hold on the subsample of surviving firms. FiBEn covers the whole of the market sector although industry is covered better than services. This database is used to compute total factor productivity, computed in a constant return to scale Cobb–Douglas production function with the output elasticity of factors set at their shares in value added. We model convergence amongst firms using equation 1.
1qit = β qi,t −1 + Xit ε + uit
(1)
where qit is the log of productivity of the ith firm in year t , Xit is a vector of exogenous control variables, ε is a vector of parameters, and uit is an error term. We include dummy variables for sector (114 sectors according to INSEE classifications), year and region as control variables. For each sector, convergence can therefore be towards different productivity levels since some sectors are more intensive in physical or human capital than others. We also include regional dummies in order to allow for the effects of varying distance from raw materials and to export markets, and also for geographical clustering effects. In equation 1, there is convergence if β < 0: within a given sector, region and in a given year, firms converge towards a common productivity level (β convergence). The more negative is β , the faster the speed of convergence. This does not necessarily imply that productivity dispersion declines (σ convergence) due
P.-A. Chevalier et al. / Economics Letters 116 (2012) 244–246
to the presence of the error term uit . We cannot distinguish here the statistical phenomenon of mean reversion from a catching-up process. Lagged productivity is an endogenous variable since it is correlated with the unobserved firm-level heterogeneity. Hence, we estimate the equation with the generalised method of moments using twice lagged first differences of productivity as instruments for the lagged productivity level qi,t −1 , following the strategy suggested by Arellano and Bover (1995). Equation 1 implies that convergence is symmetric: the productivity of firms which are above the long run level1 falls at the same rate as the productivity of firms which are below it grows. But it might be thought that the process is asymmetric. To test this hypothesis of non-linearity, we introduce dummy variables for the productivity deciles of firms as indicators of lagged productivity levels.
1qit =
9
δ
k k Di,t −1
+ Xit ε + uit .
(2)
k=1
Here Dki,t −1 is a dummy variable equal to one if the ith firm belongs to the kth decile in year t − 1 and zero otherwise. In this equation the δk coefficients should be interpreted as the average growth rate of firms in the kth decile relative to that of firms in the 10th (highest) decile which is chosen as the reference. Finally, we test the impact of some variable zit on the speed of convergence by adding an interaction term into equation 1:
1qit = β0 qi,t −1 + β1 qi,t −1 zi,t −1 + β2 zi,t −1 + Xit ε + uit .
(3)
The speed of convergence of the ith firm in year t is then β0 + β1 zi,t −1 . If β1 > 0 then the speed of convergence is decreasing in zit and if β1 < 0 then it is increasing in zit . 3. Results 3.1. Stylised facts about convergence We find a significant degree of β -convergence in TFP (Table 1). For a firm at the lowest productivity decile, half the gap with its ‘‘target’’ level of productivity is eliminated in less than two years. Estimating β -convergence year by year (Eq. (1) in GMM and GLS and Spearman rank correlation) shows that the speed of convergence falls during the 1990s, i.e. beta becomes less negative in Chart 1. Dispersion is also significantly increasing throughout the period (standard deviation in TFP rose from 0.6 in 1991 to 0.7 in 2004). By analysing the convergence process by year and by decile, we can see that the reduction in the speed of convergence derives from an increase in the growth rate of productivity in the most productive firms (those in the 10th decile), relative to that of other firms. From Chart 2, we see that:
• the δk , representing the average growth rate relative to the highest productivity decile, are all positive and the curves are parallel, demonstrating overall convergence (growth is relatively higher, the lower the decile); • the δk generally all fall over time, demonstrating that growth in the 10th (reference) decile is rising relative to growth in the other deciles. Thus the fall in the speed of convergence is due to a relative increase in the productivity growth rate of the most productive firms, which are a rather stable population (71% of the firms in the most productive decile remain there the next year and 16% are in the 9th decile). We therefore investigate the impact of
1 In the long run, from Eq. (1), the productivity level converges to −X ε/β . it
245
Table 1 Explaining the fall in the speed of convergence. Dependant variable Lagged TFP level
1 log TFP
−0.374*** (0.0304)
−0.274*** (0.0116)
Lagged ICT share
0.969*** (0.0563)
Lagged ICT share × TFP Level
0.607*** (0.0380)
Lagged export share (NES36)
0.0533** (0.0173)
Lagged export share × TFP level
0.143*** (0.0290)
N p-value Sargan
583,759 0.725
583,759 0.612
Note: estimation of Eq. (1) in column 1 and Eq. (3) for column 2. GMM estimates with controls for sector, year and region. Instruments are twice lagged differenced TFP, interaction terms and firm age. Constants included but not reported. Standard errors in parentheses. ** p < 0.01. *** p < 0.001.
information and communications technology (ICT) and openness to trade, which may have benefited most the firms which were already highly productive, owing to their greater capacity to profit from waves of innovation and globalisation. The ICT and export shares and interaction terms are included simultaneously in the regression. To measure the diffusion of ICT, we employ the share of ICT in total profit at the industry level, which is available in EUKLEMS (NACE 52). We find a positive relationship between ICT and productivity growth (Table 1): firms belonging to industries which have a one-point higher share of ICT have a productivity growth rate which is one-point higher on average than the other firms in the same sector. The interaction term is positive and significant: a firm which has higher productivity than the average for its sector grows more rapidly if it belongs to an ICT-intensive industry within that sector. Holding the ICT share constant at its mean level (12%), the interaction term reduces the speed of convergence by about a quarter. The increase in ICT intensity can explain an important part of the recent slowdown in the speed of convergence: the mechanism is productivity acceleration in highly-productive firms belonging to ICT-intensive industries. The second hypothesis is that globalisation stimulated productivity growth, the mechanism being the rapid and prolonged growth of exports on the part of firms focused on markets abroad. Previous works have documented that exporting firms also have the highest productivity (Clerides et al., 1998), since there are large fixed costs incurred in entering foreign markets. The issue is whether, in addition to this selection effect, there is also a positive effect of globalisation on productivity growth (the ‘‘learning effect’’, see Lileeva and Trefler, 2007). If these two effects (selfselection and learning by exporting) combine, this would help to explain the stylised fact that the fall in the speed of convergence is accompanied by a relative increase in the productivity growth rate of leading firms. We therefore estimate Eq. (3) with the share of exports in value added at the sector level (36 sectors according to the NES INSEE classification) as the zit variable. The results show that there is a positive relationship between measures of exporting and productivity growth (Table 1). Given that exporting is largely confined to the most productive firms, this positive impact supports the intuition that globalisation is slowing down the convergence process.
246
P.-A. Chevalier et al. / Economics Letters 116 (2012) 244–246
Year 0
1993
1995
1997
1999
2001
2003
Convergence coefficient
-0.05
-0.1
-0.15
-0.2
-0.25
-0.3
GMM
GLS
Spearman correlation
Chart 1. Changes in speed of convergence, 1993–2004 (year-by-year estimation of Eq. (1)). Source: FiBEn, Banque de France.
0.14
0.12
δ k coefficients
0.10 0.08 0.06 0.04 0.02 0.00 1993
1995
1997
1999
2001
2003
decile1
decile 2
decile 3
decile 4
decile 5
decile 6
decile 7
decile 8
decile 9
Chart 2. Changes in speed of convergence over time and across deciles (year-by-year estimation of Eq. (2)).
The interaction term reduces the speed of convergence by 20% for firms in sectors with the mean rate of exporting (38%). Hence, the effect on the speed of convergence is significant but weaker than that of ICT. But in summary the fact that the shares of both ICT and of exporting have been rising over time helps to explain the falling speed of convergence. Acknowledgements The views expressed here are those of the authors and not those of their respective institutions. This paper has been published in an extended and preliminary version in Economie and Statistique (in French). The authors thank Philippe Askenazy, Gilbert Cette, Francesco Daveri, Sébastien Roux and Patrick Sevestre and an anonymous referee for their valuable comments on an earlier version of this paper and Nicolas Berman and Laurent Eymard for excellent research assistance. This research benefited from a stay
by Nicholas Oulton at the Banque de France which was financed by the Fondation Banque de France. References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-component models. Journal of Econometrics 68 (1), 29–51. Baily, M., Hulten, C., Campbell, D., 1992. Productivity dynamics in manufacturing plants. Brookings Papers on Economic Activity: Microeconomics 2, 187–249. Clerides, S., Lach, S., Tybout, J.R., 1998. Is learning by exporting important? microdynamic evidence from Colombia, Mexico, and Morocco. Quarterly Journal of Economics 113 (3), 903–947. Griffith, R., Redding, S., Simpson, H., 2002. Productivity convergence and foreign ownership at the establishment level. Working Paper No. 02/22. Institute for Fiscal Sudies. Lileeva, A., Trefler, D., 2007. Improved access to foreign markets raises plant-level productivity. . . for some plants. NBER Working Paper 13297. Oulton, N., 1998. Competition and the dispersion of labour productivity amongst UK companies. Oxford Economic Papers 50, 23–38. (January). Oulton, N., 2000. A tale of two cycles: closure, downsizing and productivity growth in UK manufacturing, 1973–89. National Institute Economic Review 173, 66–80. July.
Contents lists available at SciVerse ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Convergence of firm-level productivity, globalisation and information technology: Evidence from France Paul-Antoine Chevalier a , Rémy Lecat a,∗ , Nicholas Oulton b a
Banque de France, 39 rue Croix-des-Petits-Champs, 75001, Paris, France
b
Centre for Economic Performance at the London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom
article
info
Article history: Received 2 November 2011 Received in revised form 6 February 2012 Accepted 11 February 2012 Available online 22 February 2012
abstract This article studies the firm-level productivity convergence process in the 1990s and the 2000s in France. The speed of convergence has slowed during the course of the 1990s, a fact which is explained principally by the acceleration of the productivity of firms on the technological frontier. Evidence is presented that information technology and globalisation may have had a bigger impact on the most productive firms. © 2012 Elsevier B.V. All rights reserved.
JEL classification: D2 F1 J2 O3 Keywords: Convergence TFP ICT Globalisation
1. Introduction There is substantial dispersion of productivity across firms, even within narrowly defined industries (for example Baily et al., 1992, Oulton, 1998 and Oulton, 2000), which gives rise to a convergence process amongst firms (Oulton, 1998; Griffith et al., 2002). This article describes and explains this convergence process using French firm-level data. We find that the speed of convergence has fallen significantly since 1992. Evidence is presented that globalisation (via the acceleration of exports), and the spread of information and communication technologies (ICT) can explain this slowdown. 2. Data and estimation strategy We mainly use the FiBEn database, a firm level database maintained by the Banque de France which includes fiscal returns of some 282,000 French firms between 1991 and 2004. 45,000 of those firms are observed during the whole period. Although we
∗
Corresponding author. Tel.: +33 1 42 92 95 50; fax: +33 1 42 92 48 08. E-mail address: [email protected] (R. Lecat).
0165-1765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2012.02.022
present our results on the whole set of firms, they hold on the subsample of surviving firms. FiBEn covers the whole of the market sector although industry is covered better than services. This database is used to compute total factor productivity, computed in a constant return to scale Cobb–Douglas production function with the output elasticity of factors set at their shares in value added. We model convergence amongst firms using equation 1.
1qit = β qi,t −1 + Xit ε + uit
(1)
where qit is the log of productivity of the ith firm in year t , Xit is a vector of exogenous control variables, ε is a vector of parameters, and uit is an error term. We include dummy variables for sector (114 sectors according to INSEE classifications), year and region as control variables. For each sector, convergence can therefore be towards different productivity levels since some sectors are more intensive in physical or human capital than others. We also include regional dummies in order to allow for the effects of varying distance from raw materials and to export markets, and also for geographical clustering effects. In equation 1, there is convergence if β < 0: within a given sector, region and in a given year, firms converge towards a common productivity level (β convergence). The more negative is β , the faster the speed of convergence. This does not necessarily imply that productivity dispersion declines (σ convergence) due
P.-A. Chevalier et al. / Economics Letters 116 (2012) 244–246
to the presence of the error term uit . We cannot distinguish here the statistical phenomenon of mean reversion from a catching-up process. Lagged productivity is an endogenous variable since it is correlated with the unobserved firm-level heterogeneity. Hence, we estimate the equation with the generalised method of moments using twice lagged first differences of productivity as instruments for the lagged productivity level qi,t −1 , following the strategy suggested by Arellano and Bover (1995). Equation 1 implies that convergence is symmetric: the productivity of firms which are above the long run level1 falls at the same rate as the productivity of firms which are below it grows. But it might be thought that the process is asymmetric. To test this hypothesis of non-linearity, we introduce dummy variables for the productivity deciles of firms as indicators of lagged productivity levels.
1qit =
9
δ
k k Di,t −1
+ Xit ε + uit .
(2)
k=1
Here Dki,t −1 is a dummy variable equal to one if the ith firm belongs to the kth decile in year t − 1 and zero otherwise. In this equation the δk coefficients should be interpreted as the average growth rate of firms in the kth decile relative to that of firms in the 10th (highest) decile which is chosen as the reference. Finally, we test the impact of some variable zit on the speed of convergence by adding an interaction term into equation 1:
1qit = β0 qi,t −1 + β1 qi,t −1 zi,t −1 + β2 zi,t −1 + Xit ε + uit .
(3)
The speed of convergence of the ith firm in year t is then β0 + β1 zi,t −1 . If β1 > 0 then the speed of convergence is decreasing in zit and if β1 < 0 then it is increasing in zit . 3. Results 3.1. Stylised facts about convergence We find a significant degree of β -convergence in TFP (Table 1). For a firm at the lowest productivity decile, half the gap with its ‘‘target’’ level of productivity is eliminated in less than two years. Estimating β -convergence year by year (Eq. (1) in GMM and GLS and Spearman rank correlation) shows that the speed of convergence falls during the 1990s, i.e. beta becomes less negative in Chart 1. Dispersion is also significantly increasing throughout the period (standard deviation in TFP rose from 0.6 in 1991 to 0.7 in 2004). By analysing the convergence process by year and by decile, we can see that the reduction in the speed of convergence derives from an increase in the growth rate of productivity in the most productive firms (those in the 10th decile), relative to that of other firms. From Chart 2, we see that:
• the δk , representing the average growth rate relative to the highest productivity decile, are all positive and the curves are parallel, demonstrating overall convergence (growth is relatively higher, the lower the decile); • the δk generally all fall over time, demonstrating that growth in the 10th (reference) decile is rising relative to growth in the other deciles. Thus the fall in the speed of convergence is due to a relative increase in the productivity growth rate of the most productive firms, which are a rather stable population (71% of the firms in the most productive decile remain there the next year and 16% are in the 9th decile). We therefore investigate the impact of
1 In the long run, from Eq. (1), the productivity level converges to −X ε/β . it
245
Table 1 Explaining the fall in the speed of convergence. Dependant variable Lagged TFP level
1 log TFP
−0.374*** (0.0304)
−0.274*** (0.0116)
Lagged ICT share
0.969*** (0.0563)
Lagged ICT share × TFP Level
0.607*** (0.0380)
Lagged export share (NES36)
0.0533** (0.0173)
Lagged export share × TFP level
0.143*** (0.0290)
N p-value Sargan
583,759 0.725
583,759 0.612
Note: estimation of Eq. (1) in column 1 and Eq. (3) for column 2. GMM estimates with controls for sector, year and region. Instruments are twice lagged differenced TFP, interaction terms and firm age. Constants included but not reported. Standard errors in parentheses. ** p < 0.01. *** p < 0.001.
information and communications technology (ICT) and openness to trade, which may have benefited most the firms which were already highly productive, owing to their greater capacity to profit from waves of innovation and globalisation. The ICT and export shares and interaction terms are included simultaneously in the regression. To measure the diffusion of ICT, we employ the share of ICT in total profit at the industry level, which is available in EUKLEMS (NACE 52). We find a positive relationship between ICT and productivity growth (Table 1): firms belonging to industries which have a one-point higher share of ICT have a productivity growth rate which is one-point higher on average than the other firms in the same sector. The interaction term is positive and significant: a firm which has higher productivity than the average for its sector grows more rapidly if it belongs to an ICT-intensive industry within that sector. Holding the ICT share constant at its mean level (12%), the interaction term reduces the speed of convergence by about a quarter. The increase in ICT intensity can explain an important part of the recent slowdown in the speed of convergence: the mechanism is productivity acceleration in highly-productive firms belonging to ICT-intensive industries. The second hypothesis is that globalisation stimulated productivity growth, the mechanism being the rapid and prolonged growth of exports on the part of firms focused on markets abroad. Previous works have documented that exporting firms also have the highest productivity (Clerides et al., 1998), since there are large fixed costs incurred in entering foreign markets. The issue is whether, in addition to this selection effect, there is also a positive effect of globalisation on productivity growth (the ‘‘learning effect’’, see Lileeva and Trefler, 2007). If these two effects (selfselection and learning by exporting) combine, this would help to explain the stylised fact that the fall in the speed of convergence is accompanied by a relative increase in the productivity growth rate of leading firms. We therefore estimate Eq. (3) with the share of exports in value added at the sector level (36 sectors according to the NES INSEE classification) as the zit variable. The results show that there is a positive relationship between measures of exporting and productivity growth (Table 1). Given that exporting is largely confined to the most productive firms, this positive impact supports the intuition that globalisation is slowing down the convergence process.
246
P.-A. Chevalier et al. / Economics Letters 116 (2012) 244–246
Year 0
1993
1995
1997
1999
2001
2003
Convergence coefficient
-0.05
-0.1
-0.15
-0.2
-0.25
-0.3
GMM
GLS
Spearman correlation
Chart 1. Changes in speed of convergence, 1993–2004 (year-by-year estimation of Eq. (1)). Source: FiBEn, Banque de France.
0.14
0.12
δ k coefficients
0.10 0.08 0.06 0.04 0.02 0.00 1993
1995
1997
1999
2001
2003
decile1
decile 2
decile 3
decile 4
decile 5
decile 6
decile 7
decile 8
decile 9
Chart 2. Changes in speed of convergence over time and across deciles (year-by-year estimation of Eq. (2)).
The interaction term reduces the speed of convergence by 20% for firms in sectors with the mean rate of exporting (38%). Hence, the effect on the speed of convergence is significant but weaker than that of ICT. But in summary the fact that the shares of both ICT and of exporting have been rising over time helps to explain the falling speed of convergence. Acknowledgements The views expressed here are those of the authors and not those of their respective institutions. This paper has been published in an extended and preliminary version in Economie and Statistique (in French). The authors thank Philippe Askenazy, Gilbert Cette, Francesco Daveri, Sébastien Roux and Patrick Sevestre and an anonymous referee for their valuable comments on an earlier version of this paper and Nicolas Berman and Laurent Eymard for excellent research assistance. This research benefited from a stay
by Nicholas Oulton at the Banque de France which was financed by the Fondation Banque de France. References Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-component models. Journal of Econometrics 68 (1), 29–51. Baily, M., Hulten, C., Campbell, D., 1992. Productivity dynamics in manufacturing plants. Brookings Papers on Economic Activity: Microeconomics 2, 187–249. Clerides, S., Lach, S., Tybout, J.R., 1998. Is learning by exporting important? microdynamic evidence from Colombia, Mexico, and Morocco. Quarterly Journal of Economics 113 (3), 903–947. Griffith, R., Redding, S., Simpson, H., 2002. Productivity convergence and foreign ownership at the establishment level. Working Paper No. 02/22. Institute for Fiscal Sudies. Lileeva, A., Trefler, D., 2007. Improved access to foreign markets raises plant-level productivity. . . for some plants. NBER Working Paper 13297. Oulton, N., 1998. Competition and the dispersion of labour productivity amongst UK companies. Oxford Economic Papers 50, 23–38. (January). Oulton, N., 2000. A tale of two cycles: closure, downsizing and productivity growth in UK manufacturing, 1973–89. National Institute Economic Review 173, 66–80. July.