Public Programs to Promote Firms’ Exports in
Developing Countries:
Are There Heterogeneous Effects by Size Categories?*
Christian Volpe Martincus
p
Bank
Inter-American Development
Jerónimo Carballo
Inter-American Development Bank
Pablo García
Inter-American Development Bank
Exporter Dynamics and Productivity Workshop
Ottawa March 27,
Ottawa,
27 2009
* The views and opinions expressed in the document and the presentation are those of the authors and should not
be attributed to the Inter-American Development Bank, its Executive Directors or its member countries.
OUTLINE
¾ Introduction
¾ Empirical
E i i l Methodology
M th d l
¾ Data
¾ Estimation
E ti ti Results
R
lt
¾ Conclusions
INTRODUCTION
Export Promotion Programs and Firm Size
Effects of export
p
promotion p
p
programs
g
are likely
y to be heterogeneous
g
across firm size
categories.
Smaller firms face greater limitations than larger firms in trading across borders (e.g.,
R b t and
Roberts
d Tybout,
T b t 1997;
1997 and
d Bernard
B
d and
d Jensen,
J
1999 Wagner,
1999;
W
2007) These
2007).
Th
differences across firm-sizes may be related to heterogeneity in access to and ability to
use information.
More concretely, information gathering and communication with foreign markets seem
to be greater obstacles for smaller than for larger firms (e.g., Katsikeas and Morgan,
1994).
INTRODUCTION
Export Promotion Programs and Firm Size
Thus,, for instance,, collecting
g information requires
q
performing
p
g market studies which
entail fixed costs. Larger firms are in a better position to absorb these costs because they
can distribute them over a greater number of units and can accordingly elicit by
themselves the information needed to formulate an effective export market strategy
from such studies ((Wagner
g
1995,, 2001).
)
Furthermore, others’ information on the companies which are critical inputs for
business decisions such as that concerning reliability as a provider and the quality of
th i products
their
d t is
i likely
lik l to
t be
b poorer for
f smaller
ll firms.
fi
INTRODUCTION
Export Promotion Programs and Firm Size
Given that information-related impediments
p
are likely
y to have differential deterring
g
effects for firms with different sizes, we can conceivably think that given trade
supporting actions may potentially have heterogeneous impacts on firms’ export
performance over size categories.
However, so far there is no empirical evidence on whether this is indeed the case. More
precisely, the existing empirical literature includes both studies that have examined the
effects of public policies on firm export behavior without discriminating among firms
with
ith different
diff
t sizes
i
(
(e.g.,
Gi
Girma
ett al.,
l 2007;
2007 Görg
Gö ett al.,
l 2008;
2008 and
d Volpe
V l Martincus
M ti
and
d
Carballo, 2008) and analyses of these effects that exclusively focus on small and
medium-sized companies generally based on small samples (e.g., Denis and Depelteau,
1985; Howard and Borgia, 1990; Moini, 1998, Gencturk and Kotabe, 2001; Álvarez, 2004;
F
Francis
i and
d Collins-Dodd,
C lli D dd 2004;
2004 Wilkinson
Wilki
and
d Brouthers,
B
th
2006) but…
2006),
b t
no systematic examination of the potential existence of different effects for firms in
different size segments as conventionally defined in public policy, i.e., in terms of
employment levels.
INTRODUCTION
Export Promotion Programs and Firm Size
Moreover,, p
policymakers
y
tend to evaluate differently
y two p
programs
g
with the same
average positive effect but whose benefits are mostly accruing to smaller firms in the
first case and to larger firms in the second case.
It is
i well
ll known
k
th t supporting
that
ti
small
ll and
d medium-sized
di
i d companies
i (SMEs)
(SME ) in
i their
th i
incursion in international markets is a common goal of export promotion agencies as
declared by their lead officials and even in their legal statements of purposes.
Hence, information on these impacts is extremely valuable from an economic policy
point of view since it is a key input to assess whether these kinds of public interventions
are well targeted in the sense that that companies which are primarily intended to be
helped benefit most from them.
INTRODUCTION
What Do We Do?
In this p
paper
p we then aim at filling
g the aforementioned g
gap
p in the literature thereby
y
providing insights that are likely to be helpful in ascertaining current export promotion
policies.
We assess whether
W
h th the
th effects
ff t off trade
t d supporting
ti
activities
ti iti by
b Argentina’s
A
ti ’ national
ti
l
agency Fundación ExportAR on firms’ export performance varies with their size and,
specifically, whether these effects are larger for smaller companies.
INTRODUCTION
What Do We Do?
We specifically
p
y address three main q
questions: Are trade p
promotion p
programs
g
effective
in improving firms’ export performance? Are impacts of these programs heterogeneous
across firm size categories? Are these impacts larger for smaller firms?
IIn answering
i
th
these
questions,
ti
we use a rich
i h dataset
d t t including
i l di
hi hl disaggregated
highly
di
t d
export as well as export assistance and employment data for (almost) the whole
population of Argentinean exporters over the period 2002-2006.
In order to uncover the effects of trade assistance over the firm size segments, we
estimate group-average treatment effects by difference-in-differences and, as robustness
checks, by double robust estimation, matching difference-in-differences, and
semiparametric difference
difference-in-differences
in differences à la Abadie (2005).
INTRODUCTION
What Do We Find?
We find that export
p
promotion p
p
programs
g
administered by
y Fundación ExportAR
p
have
been effective in favoring the growth of Argentinean firms’ exports, primarily along the
country-extensive margin, i.e., the number of destination markets.
IImportantly,
t tl these
th
programs do
d nott seem to
t have
h
affected
ff t d all
ll firms
fi
t the
to
th same extent.
t t
More specifically, as expected, smaller companies derive larger benefits from these
public initiatives than larger firms in terms of improved export performance. Thus,
trade supporting actions are associated with increased rate of growth of total exports
and
d number
b off countries
t i in
i the
th case off small
ll and
d medium-sized
di
i d companies,
i but
b t they
th do
d
not seem to have any distinguishable impact on the export outcomes of large firms.
These results are robust across alternative specifications of the estimating equations and
to using different econometric methods.
OUTLINE
¾ Introduction
¾ Empirical
E i i l Methodology
M th d l
¾ Data
¾ Estimation
E ti ti Results
R
lt
¾ Conclusions
EMPIRICAL METHODOLOGY
The Statistical Problem
In order to identify
y the effects of export
p
promotion,, one would need to compare
p
p
a firm’s
export behavior when receiving export support with that when not receiving such a
support. Since export outcomes under both states cannot be simultaneously observed
for the same firm, the individual treatment effect can never be observed. This is the socalled fundamental p
problem of causal inference ((Holland,, 1986).
)
Formally, firm i’s observed export outcome can be expressed as follows:
Yit = DitYit1 + (1 − Dit )Yit0
The impact of trade support is given by: ΔYit = Yit1 − Yit0 . Since it is impossible to observe
both Yit1 and Yit0 for the same unit, the population of firms is generally used to learn about
properties
p
of the p
potential outcomes and compute
p
an average
g treatment effect.
ff
the p
EMPIRICAL METHODOLOGY
The Statistical Problem
In p
particular,, when p
participation
p
in the p
programs
g
under consideration is voluntary,
y, it
seems more relevant to determine their effects on those who participated and
accordingly an average treatment effect on the treated is estimated:
γ = E (Yit1 | Dit = 1) − E (Yit0 | Dit = 1) = E (ΔYit | Dit = 1)
This parameter measures the average rate of change in exports between the actual
exports of those firms that have been assisted by Fundación ExportAR and the exports
of these had they
y not been assisted by
y Fundación ExportAR.
p
Clearly,
y when this
parameter is positive, export promotion services stimulate (does not have any impact
on) firms’ exports.
In our empirical
p
exercise we use the firms that do not receive a service from Fundación
ExportAR as the control group to derive the counterfactual and accordingly estimate the
treatment effect. To avoid selection bias in the estimated impacts firm heterogeneous
characteristics need to be controlled.
EMPIRICAL METHODOLOGY
Difference-in-Differences
Many
y of these characteristics ((e.g.,
g , sector of activity,
y, location of headquarters,
q
, etc)) are
likely to be fixed over time, especially over relatively short horizons.
When repeated observations on firms are available, this time-invariant heterogeneity
properly
p y accounted for using
g the difference-in-differences
ff
ff
estimator. This estimator
can be p
is a measure of the difference between the before and after change in exports for assisted
firms and the corresponding change for non assisted firms (Smith, 2000; Jaffe, 2002).
p
function is
If we allow for covariates X and assume that the conditional expectation
linear and that unobserved characteristics can be decomposed into a firm-specific fixedeffect; a year, common macroeconomic effect; and a temporary firm specific effect:
Yit = X it θ + γDit + λi + ρ t + ε it
In this case, identification is based on the assumption that selection into the treatment is
independent of the temporary firm-specific effect.
Estimation of the previous equation can be potentially affected by severe serial
correlation problems. We therefore allow for an unrestricted covariance structure over
time within firms, which may differ across them (Bertrand et al., 2004).
EMPIRICAL METHODOLOGY
Difference-in-Differences
So far we have assumed a common treatment effect,, i.e.,, γ = γ i ∀i .
However, effects can be anticipated to systematically vary with firm size. More
formally, they are likely to be heterogeneous by observed covariates.
We therefore test whether this is the case using the non-parametric test proposed by
Crump et al. (2008). This test is based on a sieve approach to non-parametric estimation
for average treatment effects (e.g., Hahn, 1998; Imbens et al., 2006; Chen et al., 2008).
Given the p
particular choice of the sieve, the null hypothesis
yp
of interest can be
formulated as equality restrictions on subsets of the parameters. Specifically, in our case,
the null hypothesis is that the average treatment effect conditional on the covariates is
identical for all subpopulations.
If heterogeneity were to be detected, then the correct specification of the estimating
equation would be (Djebbari and Smith, 2008):
Yit = X itθ + (γ + γ X X it )Dit + λi + ρt + ε it
EMPIRICAL METHODOLOGY
Other Approaches
¾ Double Robust Estimation: This method combines regression
g
with weighting
g
g by
y the
propensity score (i.e., the probability to participate in trade promotion activities
conditional on observed covariates). This estimator may eliminate remaining biases
leading to a consistent estimate of the treatment effect as long as the parametric model
for the p
propensity
p
y score or the regression
g
function is specified
p
correctly
y ((Robins and
Ritov, 1997). Further, precision can be improved when covariates are incorporated to the
regression function (Imbens, 2004).
¾ Semiparametric
p
Difference-in-Differences Estimation and Matching
g Difference-inDifferences Estimation: These methods re-weight the before and after differences for
assisted and non-assisted firms to account for their differences in the distribution of
observed characteristics using the propensity scores (Abadie, 2005; and Blundell and
) Thus, the second estimator compares
p
the change
g in before and after
Costa Dias, 2002).
exports of assisted firms with that of matched non-assisted ones, so that imbalances in
the distribution of covariates between both groups are accounted for and time-invariant
effects are eliminated. These estimators impose less parametric restrictions.
OUTLINE
¾ Introduction
¾ Empirical
E i i l Methodology
M th d l
¾ Data
¾ Estimation
E ti ti Results
R
lt
¾ Conclusions
DATA
Dataset
Our dataset combines three main databases.
The first database has annual firm-level export data disaggregated by product (at the 10digit HS level) and destination country over the period 2002-2006 from Argentinean
customs.
Second, Fundación ExportAR kindly provided us with a list of the firms assisted by the
agency in each year of the period 2002-2006. It is worth mentioning that this list
primarily
p
y includes firms that have interacted closely
y with the agency.
g
y
Finally, we have data on employment and location from the National Administration of
Public Revenues, AFIP.
DATA
Argentina’s Aggregate Export Performance
Aggregate Export and Assistance Indicators
Year
Total Exports
Number of
Products
Number of
Countries
Number of Firms
Number of Firms
Assisted by
EXPORTAR
2002
25,575
182
12,232
12,909
163
2003
29,355
185
11,631
13,709
336
2004
34,291
196
11,993
14,089
429
2005
39,495
193
12,328
14,752
433
2006
46,225
195
12,393
15,054
547
DATA
Average Export Performance and Trade Assistance by Firm Size Categories
Average Exports and Assistance Indicators by Firm Size Categories
Year
Number of Firms
Average Number Average Number
Average Exports
of Countries
of Products
Number of Firms
Assisted by
EXPORTAR
Small ((<=50 Employees)
p y )
2002
7,868
302.84
2.35
6.89
87
2003
8,169
334.13
2.45
6.45
198
2004
8,494
369.00
2.51
6.28
242
2005
9,004
382.48
2.62
6.38
217
2006
9,256
381.43
2.61
6.40
312
Medium (50<Employees<=200)
2002
1,698
2,507.17
5.07
12.67
43
2003
1,890
2,308.11
5.20
11.96
77
2004
2 104
2,104
2 158 53
2,158.53
5 23
5.23
12 00
12.00
114
2005
2,257
2,413.05
5.40
12.05
128
2006
2,421
2,637.44
5.31
11.78
143
Large (>200 Employees)
2002
650
28,581.85
10.86
32.93
25
2003
738
29,679.76
10.93
28.61
44
2004
810
32,297.90
11.13
29.69
63
2005
912
32,891.40
11.21
30.20
78
2006
972
36,613.02
11.24
31.38
71
DATA
Distribution of Firms
across Product-Market Export Patterns (2006)
Small
Medium
Large
DATA
Distribution of Export Shares
across Firms with Different Product-Market Export Patterns (2006)
Small
Medium
Large
OUTLINE
¾ Introduction
¾ Empirical
E i i l Methodology
M th d l
¾ Data
¾ Estimation
E ti ti Results
R
lt
¾ Conclusions
ESTIMATION RESULTS
Average Assistance Effects on Assisted Firms
Average Effect of Assistance by Fundación ExportAR
Full Sample, 2002-2006
Difference-in-Differences Estimates
Without Covariates
With Covariates
Export Outcome
Controlling for Size Controlling for Size
0.193***
0.132***
Total Exports
(0.030)
(0.037)
0.137***
0.099***
Number of Countries
(0.014)
(0.017)
0.097***
0.093***
Number of Products
(0.0179)
(0.024)
-0.042
-0.006
Average Exports per Country and Product
(0.026)
(0.035)
0.056**
0.034
Average Exports per Country
(0.024)
(0.032)
0 095***
0.095***
0 039
0.039
A
Average
Exports
E
per Product
P d
(0.028)
(0.034)
ESTIMATION RESULTS
Average Assistance Effects on Assisted Firms
Alternative Samples
Average Effect of Assistance by Fundación ExportAR
Difference-in-Differences Estimates, Alternative Samples
Export Outcome
Total Exports
Number of Countries
Number of Products
Average Exports per Country and Product
Average Exports per Country
Average Exports per Product
Firms Not Assisted the Previous Year
2003-2006
Without Covariates
With Covariates
C
Controlling
lli for
f Size
Si
C
Controlling
lli for
f Si
Size
0.228***
(0.054)
0.136***
(0.024)
0 104***
0.104***
(0.032)
-0.013
(0.049)
0.091**
(0 046)
(0.046)
0.123**
(0.050)
0.141***
(0.051)
0.080***
(0.022)
0 060*
0.060*
(0.033)
-0.049
(0.047)
0.011
(0 044)
(0.044)
0.031
(0.047)
Firms Never Assisted Before
2003-2006
Without Covariates
With Covariates
C
Controlling
lli for
f Size
Si
C
Controlling
lli for
f Si
Size
0.202***
(0.050)
0.180***
(0.062)
0 091***
0.091***
(0.033)
-0.004
(0.047)
0.018
(0 044)
(0.044)
0.031
(0.047)
0.177**
(0.081)
0.123**
(0.068)
0 069
0.069
(0.095)
-0.015
(0.147)
0.055
(0 139)
(0.139)
0.208
(0.154)
ESTIMATION RESULTS
Heterogeneous Effects?
Non-Parametric Test for Heterogeneous Effects
Constant Conditional ATE
Test
Variables
Total Exports
Statistics
P-value
Number of Countries
Statistics
P-value
Number of Products
Statistics
P-value
Average Exports per Country and Product
Statistics
P-value
Statistics
Average Exports per Country
P-value
Statistics
Average Exports per Product
P-value
Chi-square
19.751
[[0.003]]
20.597
[0.002]
2.213
[0.899]
13.641
[0.034]
17.145
[0.009]
23.196
[0 001]
[0.001]
Normal
3.970
[[0.000]]
4.214
[0.000]
-1.093
[0.137]
2.206
[0.014]
3.217
[0.001]
4.964
[0 000]
[0.000]
ESTIMATION RESULTS
Assistance Effects and Firm Size Categories
Average Effect of Assistance by Fundación ExportAR by Size Category
Full Sample, 2002-2006
Difference-in-Differences Estimates
Small
Medium
Export Outcomes
0.102*
0.150**
p
Total Exports
(0.053)
(0.069)
0.099***
0.085***
Number of Countries
(0.026)
(0.032)
0.071*
0.103**
Number of Products
((0.036))
((0.044))
-0.068
-0.038
Average Exports per Country and Product
(0.050)
(0.065)
0.003
0.065
Average Exports per Country
(0.046)
(0.061)
0.032
0.047
Average
g Exports
p
p
per Product
(0.048)
(0.065)
Large
0.138
(0.088)
0.061*
(0.028)
0.079
((0.052))
-0.022
(0.090)
0.057
(0.080)
0.059
(0.090)
ESTIMATION RESULTS
Assistance Effects and Firm Size Categories
Alternative Samples
Average Effect of Assistance by Fundación ExportAR
Difference-in-Differences Estimates, Alternative Samples
Export Outcome
Total Exports
Number of Countries
Number of Products
Average Exports per Country and Product
Average Exports per Country
Average Exports per Product
Firms Not Assisted the Previous Year
2003-2006
Small
Medium
Large
0.077**
0
077**
(0.036)
0.099***
(0.034)
0.040
((0.051))
-0.062
(0.071)
-0.022
(0.068)
0.037
(0 072)
(0.072)
0.126**
0
126**
(0.064)
0.050
(0.044)
0.060
((0.065))
0.016
(0.079)
0.076
(0.071)
0.067
(0 076)
(0.076)
0.104
0
104
(0.133)
0.064
(0.046)
0.073
((0.069))
-0.033
(0.138)
0.040
(0.119)
0.031
(0 143)
(0.143)
Firms Never Assisted Before
2003-2006
Small
Medium
Large
0.130**
0
130**
(0.061)
0.170**
(0.080)
0.025
((0.116))
-0.065
(0.163)
-0.040
(0.158)
0.105
(0 179)
(0.179)
0.252**
0
252**
(0.123)
0.233**
(0.100)
0.108
((0.162))
0.027
(0.036)
0.038
(0.040)
0.054
(0 064)
(0.064)
0.389
0
389
(0.300)
0.264
(0.167)
0.513
((0.466))
-0.066
(0.079)
-0.144
(0.493)
-0.124
(0 194)
(0.194)
ESTIMATION RESULTS
Assistance Effects and Firm Size Categories
Robustness Checks
Our results are also robust to:
¾ Using alternative definitions of firm size categories.
¾ Using
g alternative econometric approaches:
pp
Propensity score-weighted difference-in-differences
Semiparametric difference-in-differences
Matching
g difference-in-differences ((nearest neighbor,
g
kernel, and radius))
¾ Accounting for year-specific Fundación ExportAR’s country and sector prioritizations
and for firms’ export composition in terms of goods traded (i.e., share of differentiated
products)) and distribution across destination markets ((i.e., share of OECD countries).
p
)
OUTLINE
¾ Introduction
¾ Empirical
E i i l Methodology
M th d l
¾ Data
¾ Estimation
E ti ti Results
R
lt
¾ Conclusions
CONCLUSIONS
What Did We Learn?
Even though
g the overall effectiveness of trade p
promotion p
programs
g
has been
documented and there is some partial and limited evidence on their specific effects on
small and medium-sized enterprises, the empirical literature is still silent on whether
these effects are heterogeneous over firm size categories. Knowing this is critical to
assess to what extent these p
public activities are well targeted
g
In this paper, we examine whether and how export promotion programs executed by
Argentina’s national agency Fundación ExportAR affect export outcomes of firms
belonging
g g to different size segments.
g
We find that these public programs have non-uniform effects over the size distribution
of firms. They seem to be well targeted in the sense that significant effects are only
registered
g
for small and medium-sized companies.
p
More specifically,
p
y support
pp
from
Fundación ExportAR seem to have resulted in increased exports from these firms
primarily through an expansion of the set of destination countries.
priors since information barriers tend to be more severe
This is consistent with our p
when attempting to enter new export markets than when pursuing to expand exports to
countries that are already among firms’ destination markets and their trade inhibiting
effects are especially strong for smaller business units.