4 minute read
by Dominant Destination Market
Table 4.8 Sub-Saharan African Exporting Firms’ Characteristics, by Dominant Destination Market
Variable China India South Africa European Union United States Middle East & North Africa
SubSaharan Africa Other Asia
Other destinations
Labor productivity Total factor productivity 9.28 (9.40) 5.69 (5.78)
Average wage
11.85 (11.93) Sales 13.67 (13.78)
Capital intensity 8.74 (8.85)
8.95 (9.06) 5.66 (5.77) 11.42 (11.44) 13.69 (13.76) 8.72 (8.80)
9.01 (9.13) 5.97 (6.08) 11.10 (11.12) 14.01 (14.13) 8.75 (8.87)
9.20 (9.36) 6.01 (6.13) 11.77 (11.78) 14.07 (14.16) 9.05 (9.18)
9.03 (9.17) 6.04 (6.16) 11.75 (11.77) 14.03 (14.17) 9.01 (9.15)
8.79 (8.87) 5.70 (5.89) 11.45 (11.45) 13.82 (13.94) 8.64 (8.73)
9.50 (9.66) 5.98 (6.09) 11.31 (11.31) 14.10 (14.22) 8.76 (8.88)
9.00 (9.17) 5.70 (5.81) 11.34 (11.36) 13.68 (13.79) 8.71 (8.85)
8.61 (8.77) 5.72 (5.84) 11.67 (11.68) 13.93 (14.07) 8.84 (8.95)
Source: World Bank calculations. Note: The table reports the mean values of the performance indicators for firms having a dominant position in one of the markets shown. The destination is considered “dominant” if it receives the highest share of the firm’s exports. Median values are reported in parentheses.
The results suggest that firms selling goods to China tend to have a higher labor productivity and average wage than firms exporting to the EU or the US. However, firms with a dominant market in the EU or the US are more capital intensive and productive than the other destination subgroups.
So far, we have suggested that the country of destination matters as a source of heterogeneity among traders. To determine whether these results hold in a regression framework, where other parameters are controlled for, we estimate the following equation:
ln X E ijk ijk China 1 E E ijk India 2 3 ijk SA E E ijk EU 4 5 ijk US E E ijk MENA 6 7 ijk SSA
E E Size Ind Ctryijk OtherAsia ijk ODest ijk j k i9 10 11 12 ,8
where E denotes the dummy for exporters having as their dominant market one of the nine identified destinations. The results from OLS regressions are reported in table 4.9, along with quantile and robust fixed-effects regression results. Column (1) presents the results with no specific controls, column (2) includes market destinations as well as industry fixed effects, and column (3) reports the results with innovation parameters. The results from the quantile regressions are summarized in columns (4) to (8), and the fixedeffects regression results are in column (9). The OLS results reveal positive
and statistically significant effects of employment, capital intensity, and foreign ownership on firms’ labor productivity, as predicted by the literature.
On the market destination parameters in table 4.9, column (2), exporters are approximatively 5 percent more productive than nonexporters. Firms that export to destinations in China, other Asian countries, and Sub-Saharan Africa as dominant markets also present a positive, large, and statistically significant effect on productivity. Firms that export mostly to the EU display additional productivity gains; however, the gains are lower than those of their counterparts exporting to the United States.
Columns (4) to (8) report the results from estimating a similar model using the fixed-effects quantile regression and the fixed-effects robust regression of Verardi and Wagner (2012). The quantile regression results are reported for the 10th, 30th, 50th (median), 70th, and 90th percentiles of the conditional productivity distribution. The results on the control variables are largely similar to those from using OLS regression in sign and significance. We also find that the size of the coefficients is generally smaller, at the median and other percentiles, when compared with the OLS results. The observations from the robust fixed-effects regression are largely similar to the OLS results for the control variables, for the size and significance of the coefficients. The coefficients on the trade dummies are positive and significant and display a similar pattern to the OLS results.
Overall, the results suggest that export market destination matters, because the productivity premiums are larger for Sub–Saharan African firms selling most of their products to less-developed regions, and this is consistent with South–South exports having increased much faster than exports from high-income countries to the South.
Productivity Gains of Innovating Sub-Saharan African Firms
To investigate the relative importance of innovation and export strategies, we next regress the dependent variable on different innovation and export variables. As expected, innovative activities are significantly correlated with firm productivity. This result is clearly in accordance with the literature on the R&D-productivity link. The quantile regression results (table 4.9) show statistically significant coefficients for the product and process innovation dummies. In addition, the results from the robust fixed-effects regression are consistent with the OLS results and indicate positive and significant coefficients for exporters and innovators.
However, the results point to the dominant importance of product innovation relative to process innovation. First, this could be explained by endogenous growth models, which endogenize the rate of innovation and predict the dynamic effects of international trade on innovative activity. The competition in international markets forces exporting firms to improve their products to remain competitive, thus increasing their probability of innovation. Second, trading with countries that have a comparable level of development smooths the diffusion of technology through imitation.
