important advantage of our data since entry and exit from the data implies real entry and exit, unlike data from other countries which usually require a certain employment or revenue threshold to be included in the data.

Table 1 displays information about the composition of the sample. The full sample includes 7010 firms there are missing values and potential outliers. For the production function estimations, we exclude the top and bottom one percent of the distribution of output and inputs.<sup>22</sup> As a result, we are left with 4161 unique firms or 11810 firm-year observations. Table 2 shows the distribution of firms by panel length. In general there are a larger number of missing values in the earlier years, but all possible panel length (1-6) are well represented in our data. As mentioned, we estimate separate production functions for 2-digit industries when possible. However, since the sample size is limited for some industries, we also need to pool several industries and estimate the same production function for these industries.<sup>23</sup> Table B.1 in Appendix B reports the resulting number of firms by industry.

We augment the INS data with population estimates from Cˆote d’Ivoire’s 1998 Census, GIS data on administrative boundaries and conflict data from the Armed Conflict Location and Event Data (ACLED, Raleigh et al 2012). The conflict rate variable is calculated as the number of armed conflicts<sup>24</sup> as reported per year by department, per 100,000 inhabitants. Population data is taken from the 1998 Census. The conflict rate ranges between 0 to 8.04 and the mean conflict rate is 1.28 for all firms in the sample. The average rate of conflicts targeting foreingers increase from 0.63 to 2.38 after 2000. Figure 3 maps the distribution of firms in the sample in two periods.<sup>25</sup> It shows that firms’ concentration has somewhat changed over time but the majority of firms still concentrated in Abidjan both before and during the crisis.

# **5.1 The**

Summary statistics of the variables of interest are reported in Table 3, including summary statistics for the sub-sample used to estimate the production functions, which excludes observations whose monetary values are in the first and ninety-ninth percentiles of the full sample. All monetary variables, including sales, value added and material costs are deflated using the corresponding industry deflators taken from the INS, where the industry classification is similar to the US 2-digit SIC classification. The use of industry-specific deflators helps limit the problem of product/input quality

> 22The results are very similar if we instead drop observations with abnormal (one-percent tails) year-to-year growth in output and inputs.

> 23We base our pooling on the similarity of the industries i.e. if they have similar capital labor ratios. Nevertheless, we need to drop some industries with a very small number of firms and no obvious similar grouping. These are electric and water utilities, ores and minerals, and petroleum industries. We further exclude financial services and government, education and health services since they are potentially very different from the rest of the industries.

> 24We include five types of events reported by ACLED: Battle-Government regains territory, Battle-No change of territory, Battle-Rebels overtake territory, Riots/Protests and Violence against civilians.

> 25Since the coded location data was collected upon in-corporation, the data might not be accurate if the firm has relocated. When the registered location does not match a firm’s physical address, we use the city and department information from the address to recode the location.

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