Where the dependent variable represents the productivity index32 for firm i at time t; Xit
is our variable of interest that should be correlated with performance; Y is a set of time
variant firm’s characteristics such as the age of the firm, the ownership status, and size
but also other controls introduced in specific estimations that can explain firm
performance; k are time invariant controls such as industry and location33, and h is the
set of year dummies that controls for macroeconomic shocks common to all firms. Our
main focus will be the magnitude and the sign of the α1 coefficient.
The first step is to check for the relationship between productivity and Import and
Export intensity variables separately. The results from estimating equation (8) using
standard OLS correcting for heteroskedasticity and adjusting standard errors for
clustering at the year-sector level34 are reported in tables 6 a and b.
The first and the second column of both tables show the regressions with the
dichotomous variables35. In particular, columns (1) are premium-type regressions were
the independent variables are all binary controls. The coefficients of the Import dummy
is never significant, while the coefficient on the export dummy is significant only if
other controls such as ownership status and firm age are not introduced. In contrast,
when import and export are introduced as “intensities”, columns (3), then both
coefficients become positive and significant indicating a positive relationship between
the productivity index and the share of inputs imported or the share of output exported
even though the results are not robust to the inclusion of additional controls such as
technology and innovation proxies.
In columns (4), the hypothesis of non linear (quadratic) relationship in import and export
share is tested and rejected.
32 The production function that we have estimated using sales to proxy for quantities produced could
introduce a bias. This is because, the value of output does not depend only on technology but it includes
both prices and quantities. Therefore , this measured productivity it is likely to capture profitability in a
broader sense rather than strict technical efficiency. However, the choice of trade practices is based on
expected profits, which on turn will depend both on technical efficiency and on the demand side. For this,
the use of “efficiency in generating value of output” as measure of performance does not bias the results.
33 To control for the location of firms, instead of dummies indicating Indian States, we use a dummy that
assumes the value 1 if the firm is located in a coastal State, and a variable that quantifies, on a scale from 1
to 4 the investment climate of the State (World Bank and CCI, 2002)
34 introducing any aggregate variables (in this case industry) in micro units OLS regressions leads to an
underestimation of the standard errors (Moulton ,1990). For this reason, we correct the standard errors for
correlation between the observations belonging to the same industry in a given year.
35 Which take value one if the respective firm’s import share or export share are grater than zero,
otherwise takes zero value.
17