Evidence from Firm-level Data in Vietnam

lagged dependent variables. Probit with fixed effects is difficult to compute and may render estimatedcoefficients and statistics inconsistent, especially in the case when large panel is not available. Probit withrandom effects fits better to specification with lagged dependent variables, if the problems caused by theassumption of uncorrelated relation between error term and independent variables are acceptable [Helmersand Trofimenko (2007)]. Roberts and Tybout (1997) use Heckman’s (1981a) dynamic random-effects probitestimator with binary-choice model. Although this approach has a drawback due to the assumption usuallyseen in random effects models, it is plausible in models of short panel with lagged dependent variables andthe proper treatment of dynamic decision process with initial conditions controlled. In many other papers, inorder to avoid the treatment of lagged dependent variables, researchers try to alleviate sunk cost variable outof their model, by simply ignoring it or using no-status-switcher subsample. Arnold and Hussinger (2004) usea probit model in a subsample of firms with persistent export behavior, arguing that it helps exclude thelagged dependent variable from the set of explanatory variables, enable them to abstract from the effect ofentry costs to check for the robustness of the effects of the remaining explanatory variables.Concerning the potential simultaneity problems caused by the existence of two-way relationshipbetween exporting behaviors and characteristics of firms, econometric theories suggest the use ofsimultaneous equation models. However, the difficulty in facing with the identification condition hinders theuse of this approach. Actually, it is not easy to find sufficient instrumental variables to estimate thesimultaneous equation system in a firm-level data. One approach frequently used by almost all the research inthis literature is to lag all firm characteristics by one period. This eliminates the possible effect of exportstatus to firm characteristics, helping to analyze the other direction of effects, i.e., the determinants ofexporting behavior.Taking the above-mentioned discussion, our purpose and data characteristics into consideration, wechoose estimation specifications as follows. In order to illustrate differences between exporters andnon-exporters, we derive exporter premium across a range of characteristics: revenue, productivity, size, inputintensity, labor skill and age. First, we do that by running simple regression of each of these characteristics onexport status of firms to find and test simple exporter premium at the mean in the pooled data set as follows:ln Z + u*it = α 1Yitit(12)*where i indexes firms and t indexes time; Z it is value level of the characteristic in consideration; Yitisthe export status; α1is the parameter; anduitis the error term. After doing that, we condition this premiumon other characteristics that may affect the characteristic in consideration and may bias the result derived bythe simple regression. Specifically, we will estimate the following multivariate regression in the pooled dataset:ln Z β Z + β T + β D + v(13)*it =1Yit+ β2it3 4 itwhereZitis the vector of firm characteristics, including productivity, size, input intensity, labor skill and age;T is the vector of time dummies; D is the vector of industry and location dummies; β1; β2;β3and β412