Table 7: Export Status Estimation Results

|Probit|E|xport Status||
|---|---|---|---|
||(1)|(2)|(3)|
|Financially Constrained|-0.4867***|-0.420***|-0.314|
||(0.0917)|(0.137)|(0.202)|
|FC*Log Working Capital|0.1070***|0.101***|0.0682**|
||(0.0317)|(0.0281)|(0.0298)|
|FC*Productivity|-0.1284|-0.169*|-0.126|
||(0.0937)|(0.0966)|(0.107)|
|Log Working Capital|0.0162|0.0288**|0.0298**|
||(0.0112)|(0.0123)|(0.0124)|
|Productivity|0.3353***|0.355***|0.0621|
||(0.0357)|(0.0477)|(0.0419)|
|Controls|No|Yes|Yes|
|Sector dummies|No|No|Yes|
|Observations|959|936|936|
|Pseudo Rsqr|0.149|0.261|0.391|
|Log pseudolikelihood|-557.6|-472.7|-389.6|

_Source:_ Bangladesh 2002 Investment Climate Assessment Survey, World Bank Notes: Only 2002 data used for estimation, Bootstrap standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Reported marginal effects, Base line for Equation (3) is Garments.

the data set are: export price to domestic price ratio, log labor cost, log weighted input price, age and sector dummies are also included (base sector as Garments).<sup>31</sup> The results of export status is shown in Table 7 (full results in Appendix A.4 in Table 13). Results show that under financial constraints, working capital is significant and positive in determining export status of the firm, and this result remains robust even with additional controls across all three specifications. The effects of working capital, without the interaction with financial constraints, is small or insignificant in determining export status. This suggests that working capital increases the likelihood of a firm exporting mainly when the firm is financially constrained. A 10 percent increase in working capital raises the probability of exporting by over 10 percent for a financially constrained firm but by only about 3 percent for a firm that is not constrained. All estimates pass joint significance tests of _β_ 1 and _β_ 3. A possible explanation as to why working capital for non financially constrained firms remains significant under additional controls, even though the size is small (in regressions (2) and (3)), could be due to the financial constraint measure not capturing all possible firms that are constrained.

The estimates for productivity for all regressions are signed according to the model’s prediction, that is, _β_ 2 is negative and _β_ 4 is positive. However the size and significance of the coefficients vary across the 3 regressions. Joint significance test of _β_ 2 and _β_ 4 can reject the null for specifications (1) and (2). In the specification (3), both coefficients for productivity are jointly insignificant when sector specific dummies are included. This could be due to the small sample size within each sector in the data set. However, in other empirical studies, such as **?** where empirical specifications always include controls for sectors, they too find that the estimated coefficient for productivity is not significant and their sample contains 23,641 observations. This could be due to the fact that there is more variation in productivity between sectors than between firms within a sector, and this may be in part due to how productivity is estimated. Specification (3) show that

> coefficients. See Appendix for productivity measures.

> 31See Appendix A.3 for table of variables used in estimation.

22