by Degol Hailu and Admasu Shiferaw
We published a blog on 26 July 2018 titled the Manufacturing Challenges in Resource-Rich Countries. In it, we argued that the GDP share of manufacturing in resource-rich countries is lower than the share in resource-poor countries. Our econometric results showed that there is a negative and statistically significant association between manufacturing value added relative to GDP and the intensity of mineral and hydrocarbon extraction. We therefore concluded, “There seems to be an important Dutch-Disease type mechanism that works through weak manufacturing activities.”
But, our conclusion then was based on analysis at the aggregate level. In other words, we examined total manufacturing value added at a country level. While this type of investigation is consistent with the resource-curse hypothesis, it doesn’t tell us much about the underlying microeconomic processes that precipitate lower manufacturing activities in resource-rich countries.
As a complementary analysis, we have looked at the performance of manufacturing firms. We used firm-level data from African countries collected through the World Bank’s Enterprise Surveys conducted over the period between 2006 and 2017.
The question we asked was: Is the performance of manufacturing firms in resource-rich countries different from those in resource-poor countries?
We tested productivity performances at the firm level in three ways. First, we compared the Total Factor Productivity (TFP) between manufacturing firms of resource-rich countries with their resource-poor counterparts. Second, we used labor productivity as an alternative index of firm performance. Third, we looked at the cost share of factors of production, namely the cost of labor, capital and intermediate inputs. Finally, we used firm size as an additional indicator of firm performance and estimated its relationship with resource dependence.
The econometric results show significantly lower TFP among manufacturing firms in resource-rich countries compared to the TFP in resource-poor countries. We also find that labor productivity is lower among manufacturing firms in resource-rich countries. Manufacturers in these countries pay significantly more in intermediate inputs per unit of sales as compared to their counterparts in resource-poor countries. The cost share of labor is particularly higher among manufacturers in resource-rich countries. No cost differences are observed in the purchase of capital goods. In terms of size, the results show that firms in resource-rich countries are larger. However, the model reveals substantially less sales and employment levels among manufacturing firms in resource-rich countries.
The four indicators of productivity above suggest relatively inferior performance of manufacturing firms in resource-rich countries relative to firms in resource-poor countries. These results are consistent with the findings in our previous blog of a negative association between resource extraction and the GDP share of manufacturing value added in Africa.
Policy makers in mineral and hydrocarbon economies face the task of improving manufacturing performance to meet SDG target 9.2—achieving inclusive and sustainable industrialization. Securing a fair share of resource rents remain the most feasible channel. Promoting local content, beneficiation and valued addition have the potential to stimulate manufacturing activities. Utilizing extractive related infrastructure (roads, rails, utilities, communication etc.) can also facilitate the export of manufactured goods. So does the implementation of the Continental Free Trade Area. Wish them luck!
 Details on the estimation of total factor productivity and other measures of firm performance are provided in the World Bank’s Enterprise Surveys.
 Our econometric model is FPijct = α0 + β1RRc + β2 MFGj + β3 RRc * MFGj + δ1 + γ1 + t1 + ε1. FP stands for alternative indicators of firm performance. RR measures resource rents at the country level and takes the value one for countries in which resource rents account for at least 3% of GDP and otherwise takes the value of zero. We distinguish manufacturing firms using the dummy variable MFG. RR*MFG identifies manufacturing firms in resource-rich countries. δ captures differences in institutions and initial conditions. δ captures industry characteristics that affect firm performance such as technology. Time specific shocks are captured through t.