IS THERE AN HETEROGENEOUS POST-ENTRY EFFECT OF

EXPORTING ON FIRM PRODUCTIVITY?

EVIDENCE FROM A PANEL OF ITALIAN MANUFACTURING FIRMS

Maria Rosaria Ferrante* Marzia Freo* Alessandro Viviani§

*Department of Statistics - University of Bologna, maria.ferrante@unibo.it, marzia.freo@unibo.it

§ Department of Statistics - University of Florence viviani@unibo.it

L’ANALISI DEI DATI DI IMPRESA PER LA CONOSCENZA DEL SISTEMA PRODUTTIVO ITALIANO:

IL RUOLO DELLA STATISTICA UFFICIALE

November 21-22, 2011 ISTAT, Rome, Italy

AIM OF THE PAPER and OUTLINE

RESEARCH QUESTION: firms that move from the status of non exporter to the status of exporters - export starters - experience an increase of their productivity in the period following the entry in the export market?

OUTLINE OF THE PRESENTATION:• Motivations and background• Statistical challenges and proposed solutions• The Quantile Decomposition approach• Research design• Data description• Some results• Conclusions and limitations

MOTIVATIONS and BACKGROUND

• Economic theories and empirical literature on international trade predict a significant productivity premium for internationally involved firms.

• Different interpretation to explain the productivity premium:

Self-selection (pre-entry differences in productivity)

Learning-by-exporting LBE

firms participating in the international market are exposed to more intense international competition and this stimulates firms’ to improve productivity (post-entry differences)

• few contributions • evidence somewhat controversial

MOTIVATIONS and BACKGROUND

Italian exports as per cent of GDP (in red the year covered by micro data)

STATISTICAL CHALLENGES and SOLUTIONS

Starters are not a random sample of the firms’ population pre-entry heterogeneity between groups to be compared (a.e. starters and domestics firms) has to be removed.

Reformulation of the research question: we can observe post-entry TFP of starters but how productivity of export starters would have changed if they had not started exporting? It is necessary to create a counterfactual

Solutions proposed in literature to achieve this goal :a) OLS regression models with a lagged export participationb) Methods developed in the context of the evaluation literature (mainly the

Propensity Score Matching approach)

Main limitation: in the impact evaluation language, both focus on the estimation of the Average Treatment Effect.

THE APPROACH WE PROPOSE

Focus on the whole distribution of the TFP premium as the effect of starting export on the TFP may be different for the different points of the firm’s TFP distribution (at different quantiles)

In the perspective of the impact evaluation, the groups of starters, domestics and incumbents are singled out.

Evaluation of the post-entry “raw” TFP distribution: observed (unconditional) difference between the post entry TFP distribution of the groups.

Estimation of the Quantile Treatment Effect through the Quantile Decomposition approach by decomposing the raw premium in:

• component of pre-entry heterogeneity among groups (selection bias)

• component due to the entry in international markets, that is distribution of the “net” TFP post-entry premium - the LBE effect referred to quantiles(Chernozukov, Fernández-Val and Melly, 2009; Melly, 2005),

THE QUANTILE DECOMPOSITION (1)

A. From the quantile regressions

where

th conditional quantile of the distribution of given covariates

B. by integrating the estimated conditional quantile function over the distribution of x and , we may obtain the unconditional density of y as a function of the distribution of covariates and quantile coefficients :

THE QUANTILE DECOMPOSITION (2)

In particular we may estimate the COUNTERFACTUAL unconditional density of the outcome y as a function of the distribution of covariates of group 2 and quantile coefficients of the group 1

and decompose the differences in distribution at unconditional quantile of groups g=1 and g=2

Selection BIAS

Quantile Treatment Effect on Treated

DATA - The Micro.3 data base

Innovative longitudinal information on micro-data at business firm level ( ISTAT - Grazzi et al., 2009) covering the 1998-2007 period.

Integrated data system arising from three different sources: i) SCI (Sistema dei Conti delle Imprese) data baseii) PMI (Piccole e Medie Imprese) surveyiii) annual reports of incorporated firms, collected by the Central

Balance-Sheet Data Office

Catch-up panel, where a cross-sectional data set is chosen at some time in the past and then the units of analysis are located in the present by subsequent observation.

The validity of the database and its representativeness has been analysed and confirmed in Biffignandi and Zeli (2010).

DESCRIPTIVE RESULTS

The TFP is measured at firm level by estimating eight Cobb-Douglas production functions by industry (Levinhson and Petrin estimator)

Exporter firms:

• are found to be, at the median, from 9 to 13 per cent more productive than non-exporters (raw TFP premium)

• employ 40 to 63 per cent more workers• produce beyond sixty per cent more output • are strongly more capitalised

All the differences reported are significant so that exporters are largely different from non-exporters, both in pre-entry and post-entry TFP and covariates.

BUILDING A QUASI-EXPERIMENTAL DESIGN

Three groups of firms by considering a ten-year sequence of export dummies.

• Incumbents: firms always observed to export along the ten-year - sequence 1-1-1-1-1-1-1-1-1-1

• Domestics: non exporting during nine out of the ten years of the time window - within the sequence only one 1 may be retrieved

• Starters: observed starting to export in the 2000- 2003 period

In order to increase the sample size of the starters group:

• their sequence nests a sub-sequence of this type 0-0-1-1-1-1-1

• we fix a variable treatment period (t=0) in the year when the firm starts export (2000-2003) and we align all observations with respect to the treatment period (we observe a four-years period after the treatment)

RESULTS – Pre and post entry heterogeneity

NUMBER OF OBSERVATIONS IN TFP Pre treatment Post treatment TFP -2 -1 +1 +2 +3 +4 Incumbents 1,799 1,811 1,783 1,787 1,782 1,779 Starters 197 205 196 195 192 193 Domestics 351 346 262 207 169 154

AVERAGES AND FREQUENCY DISTRIBUTION OF KEY VARIABLES AT TIME -2 BY STATUS Incumbents Starters Domestics TFP (rew premium vs. domestics) 0.279 0.159 -

Producers of intermediates goods 45.0 43.9 48.4 durable goods and instr. 33.8 32.2 20.9 non durable goods 21.2 23.9 30.7 Producers located in North-west 49.5 41.1 23.8 North-est 36.9 33.8 20.4 Center 9.7 15.5 19.7 South 3.9 9.6 36.1 Total output (thousands) 76,244 50,206 18,754 Total employment 324 203 109 Capital stock (thousands) 16,159 12,036 6,643

RESULTS – Tests of pre-entry selection (1)

RAW TFP PREMIUM: PRE-ENTRY LEVELS AND DYNAMICS

LEVELS

Incumbents vs. domestics Starters vs. domestics Starters vs. incumbents

t q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8

-2 0.312 ** 0.264 ** 0.300 ** 0.171 ** 0.159 ** 0.206 ** -0.140 ** -0.106 ** -0.094 °

-1 0.297 ** 0.273 ** 0.329 ** 0.147 ** 0.116 ** 0.196 ** -0.150 ** -0.156 ** -0.132 **

DYNAMICS t-1 vs t-2

Incumbents vs. domestics Starters vs. domestics Starters vs. incumbents

q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8

-1 0.044 ° 0.011 -0.003 0.010 0.000 -0.009 -0.034 -0.011 -0.006 °, *, ** significant at 90, 95 and 99 per cent.

j=-1,…,+4

RESULTS – Tests of pre-entry selection (2)

RAW TFP - Starters and Incumbents vs. Domestic

• cycles homogeneous to the macro-cycle• for the most firms time=+0 is year=2000 and time=+3 is year=2003

RESULTS – Test of post-entry effect

There is a net TFP export premium - NTFP - along the whole TFP distribution?

Focusing on the four post-entry periods - denoted by +1,+2,+3,+4

we test if NTFP significantly differ across groups at 0.2, 0.5, 0.8 quantiles:

j=+1, +2, +3, +4

RESULTS, DECOMPOSITION (1)

Estimation of the t-quantile regression model for each group explaining raw TFP (g =S,D,I):

: the post- entry raw TFP level

: principal industry group dummies (k=1999)

: macro-area dummies (l= 1999)

: the pre-entry raw TFP level (time -2 with respect to treat.)

i = 1,…,n firmsg = S, D, I groups j = +1,+2,+3,+4 time

RESULTS, DECOMPOSITION (2)

ESTIMATES OF COEFFICIENTS OF TFP, IN TIME -2 INTO THE QUANTILE REGRESSIONS Median Interquantile

time Domestics Starters Incumbents Domestics Starters Incumbents

-1 0.872 ** 0.884 ** 0.912 ** 0.146 0.137 * 0.002

0 0.773 ** 0.795 ** 0.829 ** 0.105 0.105 0.052

+1 0.879 ** 0.788 ** 0.759 ** 0.165 -0.026 0.077

+2 0.778 ** 0.723 ** 0.674 ** 0.242 -0.040 0.118 *

+3 0.739 ** 0.667 ** 0.608 ** 0.371 ** 0.022 0.114 *

+4 0.632 ** 0.585 ** 0.632 ** 0.286 0.183 0.114 *

Estimation of the quantile regression model

RESULTS, DECOMPOSITION (3)

DECOMPOSITION OF THE TFP LEVEL WITH RESPECT TO TIME -2 COMPONENT ATTRIBUTABLE TO DIFFERENCES IN COEFFICIENTS.

TFP Incumbents vs. Domestics Starters vs. Domestics Starters vs. Incumbents

time q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8 q=0.2 q=0.5 q=0.8

-1 0.077 ** 0.069 ** 0.071 * 0.037 0.036 0.018 -0.032 -0.030 * -0.052 *

0 0.132 ** 0.124 ** 0.140 ** 0.100 ** 0.073 * 0.081 * -0.020 -0.037 ° -0.054 *

+1 0.123 ** 0.079 * 0.059 0.067 ° 0.029 0.014 -0.075 ** -0.055 ** -0.056 °

+2 0.126 ** 0.062 0.017 0.086 * 0.043 -0.007 -0.070 * -0.037 ° -0.048

+3 0.102 0.039 -0.039 0.051 0.004 -0.053 -0.034 -0.039 ° -0.037

+4 0.086 0.054 0.037 0.078 0.038 -0.011 -0.004 -0.018 -0.058 *

°, *, ** significant at 90, 95 and 99 per cent, standard errors in brackets

Component attributable to difference in coefficients – LBE effect

RESULTS, DECOMPOSITION (4)

Starters vs. Domestic and Incumbents

Starters and Incumbents vs. Domestic

CONCLUSIVE REMARKS AND FUTURE WORK

The LBE effect is not uniformly distributed across firms, we find that it refers to lower and medium performer starter firms.

Net premiums of best performer starter firms are statistically not significant.

For these firms the policy indication indication should address the increase of TFP itself rather than the internationalisation.

Future work• year and cyclical effects should be better disentangled• extensions to further performance features (i.e. profitability)• robusteness checks: sensitivity to allignment • consideration of selection on unobservable