Market access, supplier access, and Africa's manufactured exports: A firm level analysis

Abstract

In a large cross-country sample of manufacturing establishments drawn from 188 cities, average exports per establishments are smaller for African firms than for businesses in other regions. Based on the estimation of firm level exporting equations, we show that this is mainly because, on average, African firms face more adverse economic geography and operate in poorer institutional settings. One part of the effect of geography operates through Africa's lower ‘foreign market access’: African firms are located further away from wealthier or denser potential export markets. A second occurs through the region's lower ‘supplier access’: African firms face steeper input prices, partly because of their physical distance from cheaper foreign suppliers, and partly because domestic substitutes for importable inputs are more expensive. Africa's poorer institutions reduce its manufactured exports directly, as well as indirectly, by lowering foreign market access and supplier access. Both geography and institutions influence average firm level exports significantly more through their effect on the number of exporters than through their impact on how much each exporter sells onto foreign markets.

Key words:

• Economic geography
• institutions
• international trade
• economic development
• manufacturing
• Africa

Acknowledgements

The authors would like to thank Pierre Richard Agenor and Jim Tybout for comments on an earlier version of the paper. We are also grateful to an anonymous referee for extensive and very useful comments made beyond the call of duty. The views expressed here are those of the authors and do not necessarily reflect the official views of the World Bank.

Notes

¹ See especially Wood (1997), Wood and Mayer (1998), and Wood and Berge (1997).

² Examples are Bloom and Sachs (1998), Gallup and Sachs (1998), and Sachs and Warner (1997).

³ See, for example, Elbadawi (1999) and Elbadawi and Soludo (1999).

⁴ This is because cross-country samples would have one more level of variation in geography and institutions.

⁵ Ours is not the only micro-econometric analysis of the determinants of manufactured exports in Sub-Saharan Africa based on World Bank business survey data. Clarke (2005) analyzes similar data- from eight African countries to find that firm level exports are hampered by restrictive trade and customs regulation, but without going into the role of economic geography. See also Eifert et al. (2005) for a firm level econometric investigation of the role of institutions and infrastructure in the international competitiveness of African manufacturers.

⁶ See also Redding and Venables (2000) for an earlier version of the paper on which our generation of the empirical analogue of the concepts of supplier access and foreign market access draws.

⁷ The following expression dispenses with firm or variety indices by using the fact that, in the model, all varieties produced in each country i are demanded by country j in the same quantity, so that aggregate quantity demanded in country j of varieties produced in i is obtained as the product of the number of varieties (or of firms) in i and the value of i's exports to j per variety.

⁸ Equation (2) is arrived from an expression in which weighted prices are summed over countries by using the fact that, in equilibrium, the price of an individual variety produced in country i and exported to country j (=p _ij ) is equal to the price of the same variety produced in country j and exported to country i (=p _ij ).

⁹ Melitz (2003) sets out another new trade theoretic model generating a firm level exporting equation in which geography and institutions figure as explanatory variables. From the point of the issue addressed in this paper, the Melitz model has the advantage that it allows for firm heterogeneity right from the outset. The reason that we have chosen the Redding and Venables model as our conceptual framework is that it helps identify separately the supply side effects of economic geography from demand side effects, which are lumped together into a single trade-costs effect in the Melitz model.

¹⁰ Other studies of firm performance and international trade based wholly or partially on the dataset include, Bigsten et al. (1999), Dollar et al. (2004), Mengistae and Pattillo (2004), Eifert et al. (2005), and Clarke (2005).

¹¹ The ratio of c.i.f prices to f.o.b. prices of imports is a direct measure of iceberg transport costs which bilateral physical distances can, at best, proxy. Iceberg transport cost measures the cost of transport in terms of the amount that needs to be transported in order for a unit of the good in question to arrive at its point of consumption. Let T be the amount of the good that needs to be transported for a unit of the good to reach the point of consumption. Then the (iceberg) transport cost is given by T−1.

¹² We thank Steve Redding for kindly providing the estimates. The equation estimated in Redding and Venables (2000) is

where, x _ij is aggregate exports from country i to partner j; u is a stochastic disturbance term, and other variables are as described above in equations (13) and (14).

¹³ Because trade statistics were incomplete for Africa, we had to use the ‘mirrored’ (reported by partners) data for African countries. We limited this exercise to the top ten trading partners of each country since these accounted for more than 90% of annual trade volumes in each case.

¹⁴ In anticipation of a result we report later in the paper, the rule of law index also influences firm level exports, but almost entirely through its influence on access variables and the size, age and ownership distribution firms. We therefore have excluded it from the specification estimated in Table 3.

¹⁵ They are therefore reported here mainly as baseline regression with the help of which we can get a feel of the magnitude of the bias. While the coefficients reported in column 1 of are not strictly comparable with corresponding coefficients in column 4, there is a strong indication of OLS bias in that the coefficient of foreign market access in column 1 is negative and statistically significant but positive and statistically significant in column 4.

¹⁶ Recognizing the endogeneity of access variables in this sense and using the appropriate estimation technique is one way of addressing the problem of the fact that the standard errors that we would otherwise estimate would be invalid, as suggested in Gawande (1997). As already noted, we have addressed the problem of incorrect standard errors in Table 3 by other means. That, however, leaves unaddressed the problem of inconsistency of estimates due to the endogeneity of the regressors arising from their measurement with error.

¹⁷ That is to say, due to a combination of simultaneity and measurement error (arising from the fact that some of the regressors are estimated).

¹⁸ A second consequence is that conventional standard errors of many IV estimators including 2SLS, and inferences based thereof, become unreliable.

¹⁹ That is to say the minimum eigenvalue of the matrix analog of the F-statistic (of the joint significance of the instruments) from the first-stage regression of two-stage least squares. The Stock – Yogo test should not be confused with the Cragg – Donald test, which is a test of the null that the model is under-identified.

²⁰ As shown in Newey (1987), the estimator is asymptotically equivalent to the application of Amemiya's generalized least squares (AGLS) as set out in Amemiya (1978). The AGLS is a two-step procedure whereby, in this case, one first estimates the reduced form tobit and then uses generalized least squares to solve for the structural parameters. The resulting estimates of the structural parameters are more efficient than those generated by the two-step conditional maximum likelihood estimator of Smith and Blundell (1986), on which the exogeneity tests reported in the table are based.

²¹ This is consistent with results of Wood and Jordan (2000) and of Limao and Venables (2001). The former found Uganda's poor transport infrastructure to be one of the main reasons why its manufactured exports were not as high as Zimbabwe's. It contrasts, though, with Clarke (2005), who found no evidence that domestic transport infrastructure mattered.

²² The decomposition is discussed in Wooldridge (2002), from which we have adopted the notation used in Tables 6.

²³ We have ignored the partial effects of country area and openness to trade in this calculation. Otherwise the relative share of institutional variables would have been greater.