disaggregated data to draw stronger inferences on the similarities between the local economies, as districts trade in specific agricultural products. In the absence of such disaggregated data, we can still infer from the index that overall similarity across districts is relatively high; however Darjeeling is less similar to its Nepalese counterparts than Uttar Dinajpur. And Ilam and Darjeeling, despite being geographically alike, seem to be economies most different among the four district pairs, suggesting greater gains from new trade opportunities.

**Table 9: Cosine index of employment similarity between Indian and Nepalese districts**

||**Ilam**|**Jhapa**|
|---|---|---|
|**Uttar Dinajpur**|0.946|0.960|
|**Darjeeling**|0.812|0.821|

_Source: Authors’ elaboration based on India NSS and Nepal Labor Force Survey_ To some extent the employment data at this level of aggregation suffers from similar problems to the production data. As the agricultural sector dominates tradable sector’s employment across districts, the absence of employment disaggregated data within this sector masks any potential differences in employment structures. Therefore, we complement this index with one which relies on the similarity in households’ production in one district with consumption in the other. For the Indian districts we only have data on consumption, thus we use data on production for the districts in Nepal and Bangladesh and match them with the adjoining Indian districts.

More formally, the cosine index is computed as: where P is total households’ sales of product _i_ in district _j_ , ���!<sup>�</sup> is average sales across products in _j_ ; _C_ is total consumption of product _i_ in district _s_ , ����"<sup>�</sup> is average consumption across products in _s_ ; _N_ is the total number of products for the district pair.

Such an index has two main advantages over the employment structure index. First, it relies on product level information thus capturing much of production (and consumption) structure within the agricultural sector. Second, it provides a closer measure of trade potential as it captures potential supply and potential demand by looking at current production and consumption patterns.

The use of this measure also comes at a cost. The main one is that it disregards non-household production and consumption, including the firm and the public sector, which are important especially in the non-agricultural sectors. Households essentially produce only agricultural and related products, while they consume all sorts of goods. Therefore an index that used all households’ consumed goods would underestimate the true complementarity between economies. Because of that we compute the index only for agricultural and animal products, which are consumed, produced or both produced and consumed by the households across districts. So it is effectively an index of complementarity in agriculture. Given the prominence of the sector in the overall economic structure of these districts, this limitation may not be so severe at least when evaluating the trade potential in the short term. Tea may in fact be the exception to this rule as much of the local production is not consumed by households – as explained below - but rather sold to intermediaries and processors. This may generate a downward bias in the index as production would not match household consumption.

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