costs. In most African countries, indirect costs average as high as 20-30 percent of total costs, often dwarfing labor costs.

# **3. Data and Empirical Strategy**

Our establishment-level data come from the World Bank Enterprise Surveys. Data from these surveys are available for a cross-section of firms from 10 Sub-Saharan African countries which were surveyed following the same methodology. The countries are Ethiopia, Kenya, Madagascar, Mali, Mauritius, Senegal, South Africa, Tanzania, Uganda and Zambia, and the surveys were undertaken between 2001 and 2005.

The Enterprise Survey Data contain detailed information on output and production inputs, which we transform into real values using the respective GDP deflator and then convert into US dollars using the yearly average exchange rate. We estimate total factor productivity (TFP) as the residual of the Cobb-Douglas production function in equation 1, with real output as a function of capital, labor and intermediate inputs. Particular attention was given to the capital variable. In firm-level data sets, this is usually the variable which creates the strongest concerns regarding correct measurement, because firms have an incentive to overstate depreciation and understate the book value of their physical capital for tax reasons. To avoid this problem, the Enterprise Surveys contain a question about the current resale value of the machinery and equipment if the firm had to sell it the next day, and we use this information to estimate the value of the capital stock. We estimate the production function as where yi represents real firm output, li is labor, ki capital and mi intermediate inputs. The estimated coefficients vary at the level of each manufacturing industry s, which include food and beverages, textiles and garments, chemicals and pharmaceuticals, metals and metal products, non-metallic mineral products as well as wood products and furniture.<sup>2</sup> The parameters of the production function (1) are identified using two approaches: the first is an ordinary least square estimator, and the second is the semi-parametric estimator suggested by Olley and Pakes (1996). The latter uses the firm’s investment behavior to control for a possible estimation bias that may arise if a firm makes its input choices contingent on an unobserved productivity shock affecting μi.<sup>3</sup> The second building block of our analysis is information about the performance of services sectors, which is also available from the Enterprise Surveys. The surveys contain both subjective and objective measures of local services performance. The subjective measures are firms’ valuation on a scale from 1 to 5 as to how much of a constraint they consider electricity, telecommunications and access to finance for their businesses. The objective measures come

> 2 Unfortunately, we were not able to obtain reasonable production function estimates for the chemicals and pharmaceuticals industry using the Olley and Pakes approach, and had to drop this sector from the regressions when we use the TFP estimates originating from that procedure.

> 3 Other applications of the Olley/Pakes procedure which contain detailed descriptions of the methodology can be found in Pavcnik (2002) or Arnold and Hussinger (2006).

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