Evidence from Firm-level Data in Vietnam

are vectors of parameters; andvitis the error term. The exporter premium is defined as[( Z* exporter* non−exporter* non−exporterit− Zit) / Zit]*100 . After all the parameters are estimated, the simple exporterpremium is calculated as ( α 1 −1) *1e 100 and conditional exporter premium as ( eβ −1) * 100 . These valueswill be reported with the standard errors and t-values of the two parameters α 1and β1to describe thedifference between exporters and non-exporters.Next, the significance of determinants of the decision to export or not to export will be tested withthe closer look on the role of past export status, representing the sunk entry costs. The equation for estimationisYit= λ Y 1 it-1+ λ2Zit− 1+ λ3T+ λ4D+ εi+ ηit(14)where λ1,λ2,λ3and λ4are vectors of parameters; ε ithe time-invariant firm-specific unobservablecharacteristics and η itidiosyncratic error. This equation includes one-year lagged export status. Allobservable firm-specific time-variant characteristics are also lagged one year period to control for anypossible reverse causation. We prefer to use dynamic probit model with random effects in this paper. Onereason for this choice is that the data used in this analysis is a quite short panel, rendering the ease inemploying models with both lags and fixed effects. As we have mentioned, fixed effects models producedbiased and inconsistent parameter estimates. One way to avoid these problems is to fit in first-differencesspecifications with suitable estimators such as that of Arellano-Bond’ (1991). However, first-differencesspecification with lagged explanatory variables makes the sample size shrink considerably, rendering thedynamics of the model. Furthermore, fixed effects models with lagged dependent variableusually makefirm-specific observable effects less important because these effects are possibly indistinguishable from fixedeffects. The dynamic random effects model will not only allow us to deal with unobserved firm-specificeffects but also help model the dynamics properly with the control of initial condition. We test equation (14)in three specifications. First, we fit the following short version of equation (14):Yit= λ Y 1 it-1+ λ2Zit− 1+ λ3T+ λ4D+ uit(15)by using probit model in the pooled data set, ignoring any unobserved effects, i.e. assume thatuit= ε + ηiitis normally distributed and uncorrelated to other explanatory variables. As stated before, this estimation ismore likely to give biased and inconsistent estimates. However, we can yield the upper bound of the effect ofpast export status via this test. Next, we use the Heckman’s (1981a) random effectsdynamic probitframework that is also used by Roberts and Tybout (1997) to fit equation (14) in full. Because this modelcontrols for unobserved effects, dynamic process as well as initial conditions, it is expected to give the bestestimates under the availability and structure of data used in this analysis. It is therefore the most preferredmodel in this paper. In this regression, for the fitting to be eligible in a dynamic random effects format, the13