Table 6: Working Capital and Distance to Export Destination

|Lagged LogWorkingCapital|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log weighed distance to export|0.382|0.464*|0.668**|0.739**|
|destination|(0.237)|(0.241)|(0.338)|(0.349)|
|Controls for Cost, Productivity,||Yes||Yes|
|short-term Liabilities, Years|||||
|Exporting, Export Price to|||||
|Domestic Price,|||||
|Interact sectors*Log distance|No|No|Yes|Yes|
|Observations|848|780|848|780|
|R-squared|0.007|0.091|0.017|0.099|
|Number of frms|459|438|459|438|
|Standard errors inparentheses,**|*p<0.01,**|p<0.05,|*p<0.1||

Notes: 2002 ICA World Bank (with recall data from 2000, 2001.). Regressed using fixed effects for the sample exporting firms only. Distance data from Centre d’Etudes Prospectives et d’Informations Internationales (CEPII), at http://www.cepii.fr/anglaisgraph/bdd/distances.htm using distance measured from the capital city. Up to five countries were reported in the survey (only for 2001 and 2002) and the weighted average distance was calculated by the proportion of export revenue of each country. Productivity calculated from structural estimation of the production function (see **?** ). None of the coefficients for the interactive terms for sector and log distance were significant and are not reported. Log Yrs Exporting is averaged across products. Full regression in Appendix 14 in Table 14 makes it’s export decision based on current working capital levels.<sup>27</sup> The destination for exports would systematically differ according to industry, and the interaction between sectors and export distance were also included to improve fit.

The specification was estimated using fixed effects and the results are shown in (the full set of estimates are shown in Appendix 14 in Table 14). Column (1) shows the estimation with no other control variables and there is no significant correlation between working capital and the distance to export destination. Column (2) includes controls for cost, productivity and other variables, and columns (3) and (4) include interaction between sectors and export distance.

The estimated results shows that the correlation between working capital and export distance is positive and significant when relevant controls are included. Furthermore, the coefficients for the control variables: Lagged cost, current cost and productivity are correctly signed and significant (see in full estimate results in Appendix 14 in Table 14). The results show that the size of the estimated coefficient for distance is much larger than the estimated coefficients for cost and productivity. The _distance_ variable may also be proxying for additional transport costs associated with shipping longer distances. However, the significant and positive coefficient suggests that the distance to the export destination could be the largest factor in determining the liquidity needs of the firm, and this could be due to the delays in the receipt of revenues associated with longer shipping times.

# **5.3 Export Status of the Firm**

The model in the Section 4 provides a set of theoretical predictions that could be put to the data: i) when firms are financially constrained, export status is driven by the level of working capital and less by productivity

> 27This specification is an approximation given different industries will have different turnover time. The Bangladesh data has a majority of garment and textile firms reflecting the Bangladesh economy and the specification on the timing of working capital and export distance one year ahead fits this particular data set. Specification using the same time period, i.e. _Xt_ and _distancet_ yields a negative and insignificant coefficient - that supports the specification for distance as a forward variable.

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