supply (caused by a shift of US farmers from cotton acreage to cereals production for bio fuel). The increase in cashew nuts price was also due to a decline in supply in the world market following unfavorable weather conditions in Vietnam and Brazil<sup>12</sup> .

# **4. Data and Descriptive Statistics**

The Tanzanian dataset used for the empirical analysis was obtained as part of the first ever cross-country exporter-level customs transaction database that is being collected under the Export Growth and Dynamics Project by the Trade and International Integration Unit of the World Bank’s Research Department (Freund and Pierola, 2011). The novel dataset contains daily information on shipments of Tanzanian exporters between 2003 and 2009. The original source of this dataset is the Tanzania Revenue Authority<sup>13</sup> . Each daily observation contains information on arbitrary<sup>14</sup> firm ID, product information (at HS-6 digits), date, destination (ISO3), for shipment value in US dollars<sup>15</sup> and net weight. In light of merging this dataset with other data, it is very important to ensure that the product codes belong to a single classification<sup>16</sup> .

In order to obtain the final dataset used for this analysis, daily transactions are aggregated (summed) by firm-product-destination-year quartets (i.e. exports of coffee by a Tanzanian firm to Germany in 2003). This paper focuses on annual exports for several reasons: first, to avoid dealing with seasonality in both daily and monthly data, and also because most firms do not export a given product to a given market in every day or month (not even in every year); second, the empirical analysis of this paper studies the relationship between firms' export prices and destination characteristics, some of which are available yearly (i.e. GDP). As a consequence, using a daily or monthly dependent variable (price) could generate misleading standard errors. In fact, using price at daily or monthly level would cause an increase in the number of observations (due to the increased frequency of the dependent variable) but would also lead to

> 12 Bank of Tanzania Annual Report 2007/8.

> 13 Special thanks are also due to Josaphat Paul Kweka for his crucial contribution in the negotiation of the access to the data directly with the authorities from the TRA.

> 14 The firm ID is not the official identification number, for confidentiality issues. Also for confidentiality issues, the database did not contain any description or characteristic of the firm and its activities. For this reason it was not possible to identify and/or exclude wholesalers from the results.

> 15 The shipment values are converted from local currency to US dollars using average annual exchange rates from IFS-IMF.

> 16 The HS revision used in the Tanzanian dataset has not been officially confirmed. For this reason, in this paper the codes of the products in the Tanzanian dataset are compared with the standard product codes by year and classification. More specifically, if a code from the Tanzanian dataset is included only in one of the four HS classifications (1992, 1996, 2002, or 2007), it is categorized under that classification. This procedure indicates that the HS96 is used in 2003-2004 and the HS07 is used in 2008-2009, while for 2005-2007 several classifications are used.  As a consequence, the codes related to the years 2005-2007 are classified product by product. Finally, every code is converted to the HS02 classification. Unfortunately, some codes do not belong to any classification. However these are less than 1/50 of all observations, so the associated measurement error related to keeping them can be considered small.

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