Unlocking East Asian Markets to Sub-Saharan Africa 175
Table 4.8 Sub-Saharan African Exporting Firms’ Characteristics, by Dominant Destination Market
Variable
China
India
Middle East & SubSouth European United North Saharan Other Other Africa Union States Africa Africa Asia destinations
Labor productivity
9.28 (9.40)
8.95 (9.06)
9.01 (9.13)
9.20 (9.36)
9.03 (9.17)
8.79 (8.87)
9.50 (9.66)
9.00 (9.17)
8.61 (8.77)
Total factor productivity
5.69 (5.78)
5.66 (5.77)
5.97 (6.08)
6.01 (6.13)
6.04 (6.16)
5.70 (5.89)
5.98 (6.09)
5.70 (5.81)
5.72 (5.84)
Average wage
11.85 11.42 11.10 (11.93) (11.44) (11.12)
11.77 (11.78)
11.75 (11.77)
11.45 (11.45)
11.31 (11.31)
11.34 (11.36)
11.67 (11.68)
Sales
13.67 13.69 14.01 (13.78) (13.76) (14.13)
14.07 (14.16)
14.03 (14.17)
13.82 (13.94)
14.10 (14.22)
13.68 (13.79)
13.93 (14.07)
Capital intensity
8.74 (8.85)
9.05 (9.18)
9.01 (9.15)
8.64 (8.73)
8.76 (8.88)
8.71 (8.85)
8.84 (8.95)
8.72 (8.80)
8.75 (8.87)
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
)
ijk
China US = α + β1 Eijk + β 2 EijkIndia + β3 EijkSA + β 4 EijkEU + β5 Eijk + β6 EijkMENA + β7 EijkSSA
Other Asia ODest + β8 Eijk + β9 Eijk + β10 Sizeijk + β11 Ind j + β12Ctryk + ε i , (4.3)
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
