however, are somewhat fragile. In columns I and II, we use both measures of performance for the electricity sector, as in table 3. In this specification, we find no significant results for the power sector. In columns III and IV, we find a significant negative relationship between the share of firms in the region that chose to purchase their own power generator and the productivity of manufacturing firms, but we cannot confirm this result for the number of days with power outages in a given region. The latter measure turns out to be highly correlated with the telecommunications and banking measures (with a correlation coefficient of >0.50 in both cases), making the simultaneous inclusion of these measures problematic. Hence, when entering all three services sectors simultaneously into the equation, we can confirm a significant relationship between productivity and the performance of telecommunications and financial services, but find inconclusive results for the electricity sector.

Table 6: Total Factor Productivity and Services Sector Performance: All

|TFP estimation procedure:|I<br>OLS|II<br>Olley/Pakes|III<br>OLS|IV<br>Olley/Pakes|V<br>OLS|VI<br>Olley/Pakes|
|---|---|---|---|---|---|---|
|Days required to get a phone line|-0.003<sup>**</sup><br>[0.001]|-0.005<sup>**</sup><br>[0.002]|-0.006<sup>***</sup><br>[0.001]|-0.007<sup>***</sup><br>[0.001]|-0.004<sup>***</sup><br>[0.001]|-0.004<sup>***</sup><br>[0.002]|
|Days to clear a domestic currency<br>wire|-0.082<sup>**</sup><br>[0.035]|-0.120<sup>***</sup><br>[0.044]|-0.203<sup>***</sup><br>[0.034]|-0.264<sup>***</sup><br>[0.041]|-0.088<sup>***</sup><br>[0.032]|-0.116<sup>***</sup><br>[0.039]|
|Days with power outages|-0.002<br>[0.004]|-0.006<br>[0.005]|||-0.002<br>[0.003]|-0.006<br>[0.004]|
|Share of firms that own a generator|0.101<br>[0.292]|-0.072<br>[0.400]|-0.960<sup>***</sup><br>[0.272]|-1.260<sup>***</sup><br>[0.322]|||
|Exporter|-0.028<br>[0.048]|0.061<br>[0.057]|-0.011<br>[0.048]|0.127<sup>**</sup><br>[0.059]|-0.027<br>[0.047]|0.060<br>[0.056]|
|Age of the firm|0.014<br>[0.021]|0.061<sup>*</sup><br>[0.026]|0.014<br>[0.021]|0.053<sup>**</sup><br>[0.025]|0.015<br>[0.021]|0.060<sup>**</sup><br>[0.026]|
|Size of the firm|-0.012<br>[0.047]|-0.001<br>[0.061]|0.011<br>[0.053]|-0.014<br>[0.062]|0.013<br>[0.047]|-0.001<br>[0.061]|
|Foreign owned|0.205<sup>***</sup><br>[0.056]|0.196<sup>**</sup><br>[0.065]|0.196<sup>***</sup><br>[0.054]|0.172<sup>***</sup><br>[0.064]|0.204<sup>***</sup><br>[0.055]|0.197<sup>**</sup><br>[0.064]|
|Location in Capital Region|0.282<sup>***</sup><br>[0.074]|0.332<br>[0.095]|0.326<sup>**</sup><br>[0.061]|-0.172<sup>***</sup><br>[0.064]|0.291<sup>***</sup><br>[0.071]|0.325<sup>***</sup><br>[0.088]|
|Country dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Industrydummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|722|636|920|830|722|636|
|Adjusted R-squared|0.38|0.53|0.32|0.46|0.38|0.53|
|<br>Robust standard errors in parentheses.<br>services related variables are used in reg|*, **, *** indic<br>ional averages.|ate statistical si<br>|gnificance at|the 10%, 5% and|1% levels, r|espectively. The|

# **5. Conclusion**

Difficulty in accessing essential producer services is likely to be one of the reasons behind the sluggish performance of firms in Sub-Saharan Africa. Producer services are a vital input for the manufacturing sector, and their availability, quality and cost are likely to be relevant to the productivity of firm operations. This paper uses a representative sample of manufacturing firms from 10 Sub-Saharan African countries, comprising more than 1000 firms, to investigate the 12