also increase the likelihood for denser interactions between exporters, no matter what the proportion of exporters in the overall cluster. Thus, not only does the specification control for the effect of other exporters, by industry and otherwise, within a district, it also includes measures of own-industry and input-output agglomeration and industrial diversity. It should be noted that clustering could also be associated with diseconomies such as congestion or increased competition. Thus, the estimations will capture the net effect of the positive and negative impacts on export participation.

> Localization economies (  _jkt_ ) can be measured by own industry employment in the district, own industry establishments in the district, or an index of concentration, which reflects disproportionately high concentration of the industry in the district in comparison to the country. I measure localization economies as the proportion of industry _k_ ’s firms in district _j_ as a share of all of all industry _k_ firms in the country for a given year _t_ . The variable takes a different value for each industry in a given district, across districts. It identifies spillovers that are associated with within-industry clustering, regardless of the final markets that these firms serve. The higher this value, the higher the expectation of intra-industry concentration benefits in the district.

There are several approaches for defining inter-industry linkages: input-output based, labor skill based and technology flow based. Although these approaches represent different aspects of industry linkages and the structure of a regional economy, the most common approach is to use the national level input-output accounts as templates for identifying strengths and weaknesses in regional buyer-supplier linkages (Feser and Bergman 2000). The strong presence or lack of nationally identified buyersupplier linkages at the local level can be a good indicator of the probability that a firm is located in that region. To evaluate the strength of buyer (supplier) linkages for each industry, a summation of regional (here district) industry firms weighted by the industry’s input (output) coefficient column (row) vector from the national inputoutput account is used: where,  _jk_ is the strength of the buyer (supplier) linkage, _wk_ is industry _k’_ s national input (output) co-efficient column (row) vector and _n jk_ is the total number of firms in industry _k_ in district _j_ in year _t_ . The measure examines local level inter-industry linkages based on national input-output accounts. The national I-O coefficient column vectors describe intermediate goods requirements for each industry, whilst the I-O coefficient row vectors describe final good sales for each industry. Assuming that local industries follow the national average in terms of their purchasing (selling) patterns of intermediate (final) goods, national level linkages can be imposed to the local level industry structure for examining whether district _j_ has a right mix of buyersupplier industries for industry _k_ . By multiplying the national I-O coefficient vector, which is time-invariant, for industry _k_ and the size of each sector in district _j_ , simple 8