firms in Cameroon is reduced by the following factors: number of days required to clear customs for imports, average duration of power and of water outages, shipment losses, and time waiting for a phone connection (panel A of figure 6.5). These factors are common to Mauritania, Burkina Faso, and Niger since the countries are pooled together for estimation purposes. Country-specific results show that the largest contributions to average log productivity come from the number of days waiting to clear customs, duration of power outages, and from the time waiting for a phone connection (panel B of 6.5). Infrastructure represents 41.6 percent of average log TFP.

**Mauritania (MRT).** Mauritania ranks 10th in terms of per capita GDP, 13th in term of the total absolute contribution of infrastructure to average log productivity, 17th in terms of firm perceptions, and 15th in terms of demeaned aggregate productivity. These results are common to Burkina Faso, Cameroon, Niger, Ethiopia, and Eritrea since those countries are pooled together for estimation purposes. Delays in customs while importing represent 22 percent of average log productivity and 12 percent of allocative efficiency. In terms of simulation, a 20 percent improvement in this variable could cause a 13.9 percent increase in average productivity and a 7.8 percent one in allocative efficiency. Low-quality provision of electricity and water and its indirect costs also reduce average productivity in Mauritania (see figure 6.13).

**Senegal (SEN).** Senegal reveals a high infrastructure impact on the TFP of manufacturing firms. The percentage contribution of infrastructure to the average log productivity of this country is 58.5 percent; the indirect costs stemming from the low-quality provision of electricity represent 9.3 percent of this (see figure 6.18). The use of own-power infrastructure partially alleviates the negative impact of the low quality of electricity provision. The relative importance of problems in transport services (such as low-quality supplies) is very high; this variable represents 23.4 percent of average log productivity and 14 percent of allocative efficiency. A 20 percent reduction in the percentage of lowquality supplies received may cause a 3.2 percent increase in average productivity. Notice that the simulation of a 20 percent improvement in the percentage of low-quality supplies received causes a decrease in allocative efficiency of -0.4 percent. The reason for this phenomenon is clear: the allocative efficiency is simply the covariance between productivity at the firm level and share of sales. Therefore, a negative rate of change of the allocative efficiency indicates that the firms receiving a larger share of low-quality supplies are those with the largest market shares.

**Benin (BEN)** . Benin is ranked 12th in per capita GDP, 11th in terms of demeaned aggregate productivity, and 13th in terms of DBR (2007). The time waiting for phone connections and to clear customs in order to export are the two factors that most negatively contribute to average log productivity (see panel B of figure 6.2). An independent 20 percent improvement in these two variables could increase average productivity by 3.8 percent and 4.3 percent, respectively (panel D of figure 6.2). The same holds for the allocative efficiency term (panels C and E).

**Mali (MLI).** Mali is ranked 14th in per capita GDP terms, in demeaned aggregate productivity, and in the ACR (2007). The total contribution of infrastructure to average log productivity in Mali is 42.7 percent. The low quality of electricity, water, and phone provision accounts for almost 32 percent

57