travel-time distance (in number of minutes) connecting any given pair of districts. Origin and destination points are located at the geographic center of each district, and the travel-time estimate is based on the least time-consuming path between the two. Time is computed<sup>8</sup> using Geographic Information Systems (GIS) as the length of the road between two points with assumptions about the speed of travel according to different road categories<sup>9</sup> . The same travel-time measure is also used to compute _Port j_ , the distance of a given district to the closest of the 13 largest trading ports in the country. Access to a major merchandise chipping port should, in theory, positively impact the probability of starting to export.

# **_Infrastructure Variables_**

The next set of variables deals with the general quality of infrastructure within a district, since one would expect that the general business environment would have a positive impact on the probability of a firm to enter export markets. Such variables are also particularly interesting to policy makers since, unlike agglomeration, targeted investments within a location can help to improve infrastructure and make a location more business-friendly. I include the density of roads, i.e. the length of roads per square kilometer within a district, as a proxy for transport infrastructure. I computed these values using ArcGIS data, and the density data is time-invariant<sup>10</sup> . I assess quantitatively the role played by human capital by including the proportion of the population within the district with a high-school education in a given year, captured by the education variable - _Ed jt_ . I define _X jt_ as a measure of ‘natural advantage’ through the embedded quality and availability of infrastructure in the district. I use the availability of power (proxied by the proportion of households with access to electricity) within a location as an indicator of the provision of infrastructure. In addition I also use the proportion of households within a district with a telephone connection as an indicator of communications’ infrastructure.

_W jt_ is an indicator of labor costs in location _j_ , and is given by nominal district-level wage rates (i.e. non-agricultural hourly wages). The expected effect of this variable is hard to pin down theoretically. On the one hand, if wages were a measure of input costs then one would expect export activity to be inversely related to wages. However, it is also important to control for the skill set of the workers since a positive coefficient on wages could be a proxy for more skilled labor. Although I am unable to directly control for the ability of the worker, I include ‘education’ as a proxy for the level of human capital within the district. And thus, the proportion of high-income households ( _WE jt_ ) within a district is an indicator of the general level of wealth, or more specifically, consumer expenditure within a district. The variable is constructed

> 8 The author is grateful to Brian Blankespoor of the World Bank for carrying out the computations and for making the data available for this analysis.

> 9 Some examples of road types are motorways, primary roads, secondary roads, trunk links, tertiary roads, residential roads etc.

> 10 This is a strong assumption, and with the availability of better data the paper will attempt to update these results at a later stage.

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