Success and failure of African Exporters

1. Success and failure of African Exporters Olivier Cadot (World Bank) Leonardo Iacovone (WorldBank) Ferdinand Rauch (LSE) Martha DenissePierola (WorldBank)

2. Presentation overview • Motivation • Research question • Literature review • Stylized facts • Regression results

3. Motivation

4. Why study survival rates of African exports? • “… trade has a quantitatively large and robust, though only moderately statistically significant, positive effect on income.” This is a causal effect. (Frankel and Romer 1999) • Key to export growth are survival rates of goods on export markets (Besedes and Prusa 2007): developing countries tend to have lower survival rates • African countries in particular have a low level of exports and low survival rates

5. Survival rates are especially low in Africa Source: Brenton, Pierola and Uexküll (2008)

6. Conditional on survival very high growth rates (e.g. Tanzania) Firms products

7. Research questions • What determines (first year) survival of African exporters? • What is the role of “spillovers” and “agglomeration”? • What is the role of firm and product characteristics? • Can we identify the mechanisms behind survival?

8. Related literature Importance of discovery - Rodrik and Hausmann (2003) • “Discovery”: Profitable export of a new good to a new destination Uncertainty, diversification, survival - Albornez, Pardo, Corcos and Ornelas (2009) • Ex ante exporters don’t know their own ability • Firms have to learn it in nearby markets, before exporting more • Rauch and Watson (2003) starting small in “uncertain” market Survival on exports markets – various papers • Freund and Pierola (2011) model with heterogenous firms and uncertainty consistent with evidence across various countries • Besedes, Prusa (2007) analyze survival along various dimensions • Brenton, Saborowski, Uexkull (2009): Low survival rates, particularly in developing countries, highlight “learning by doing” • Literature largely relies on customs data (Eaton, Kortum, Kramarz (2008) • Evenett and Venables (2002) show that selling existing products accounted for only about one-third of export growth for 23 developing countries. Developing countries lower export performance due mostly to “lack of sustained export flows” • World Bank (2009) - Breaking into new markets - “The analysis […] suggests that the larger the initial size of a new trade flow, the greater the chance that flow will survive

9. Countries studied

10. GNI per capita 2008 (PPP)

11. Data: Two novel datasets 1. Firm-level customs data • Customs data from Malawi, Mali, Senegal and Tanzania • Collected by World Bank Export Survival Project from local customs authorities • Advantage over existing data (comtrade): Detailed down to product level information: Contains for each exported product of these countries: exporting firm, product classification, destination, quantity 2. Exporters survey • We use results from an original World Bank survey of African exporters • Answers from around 100 randomly selected exporters (some of them successful and other unsuccessful)

12. Qualitative evidence: role of “agglomeration/spillovers” and experience

13. Role of “agglomeration/spillovers” for contacting buyers First time exporters: How was first contact made? Exporters: How did the company approach its buyers

14. …still on the role of “agglomeration/spillovers” and experience to start exporting How did the opportunity to export a new product come about? How did the buyers normally approach the company?

15. Evidence from customs transactions level data

16. Customs transactions data Firmf Productf1 Productf2 Destinationf11 Destinationf12 Destinationf13 t1 t2 t2 t1 t2 t2 t1 t2

17. Preliminary descriptive statistics

18. Exports decomposition: Importance of extensive margin • Decompose number of firm product destinations into four mutually exclusive groups: • New firm (f) • New product (p) • New destination (d) • Continued firm-product-destination • Numerically continued firm-product-destinations are less than 30 percent of all firms • They contribute to over 70 percent of the value of exports • A lot of action on the extensive margin, but on small scale Numbers (Tanzania) Values (Tanzania)

19. Tanzania: Low initial survival… ….but increasing through time See also Brooks (2004)

20. Results also hold for Senegal….

21. …and for Mali and Malawi as well Malawi See also Brooks (2004)

22. Econometric model • Data aggregated to unique origin-firm-product-destination-time units • All following regressions use the subsample of entrants to export markets (new firm-product-destinations) only • Define survival: • 1 if f-p-d present in t and t+1 and not t-1 • 0 if f-p-d present in t and not in t+1 and not t-1 • Estimate: Probit: survivalfpdt = Xfpdt+ µ1t + µ2d + µ3i + εfpdt OLS: log_valuefpdt = Xfpdt+ µ1t + µ2d + µ3i + εfpdt • µ1t: Time fixed effect, µ2d: Destination fixed effect, µ3i: Industry fixed effect • Xfpdt includes measures for firm experience, agglomeration and market attractiveness (they will be introduced one by one shortly) • Robust standard errors are clustered at level of product-destinations

23. Baseline results

24. Baseline results – cont.

25. Placebo test

26. Robustness checks – extended network

27. Robustness checks – extended network

28. What is going on? Focus on mechanisms

29. Focus on mechanisms • The presence of other companies exporting same products to same destination may • Provide information to the new exporter on preferences and other “demand attributes” – through imitation or just because it easier to find buyer, third parties, that can provide valuable information • Provide information to financial institutions about “profitability” of the export ventures • If these “synergy” effects are due to “information spillovers” then they should matter more for those products for which information is more valuable or needed to survive  higher quality heterogeneity • Proxy quality heterogeneity with UV dispersion from COMTRADE (coefficient of variation of UV of all exporters at HS6 level in 2000) • If these “information spillovers” operate through financial institutions (reduce information asymmetry and scope for moral hazard) should matter for sectors more sensitive (dependent) to external finance

30. Mechanisms: information spillovers

31. Mechanisms: information spillovers 2

32. Mechanisms: information spillovers 3

33. Additional robustness checks • Drop cases where there is only 1 exporter per product-country pair – only focus on variation of sectors with more than 1 exporter • Use count instead of log of the count • Include 6-digit product fixed effects instead of just 2-digit product fixed effects • Re-estimate the model using a linear probability model instead than a probit – when introducing interactions terms

34. Summary • We document high rates of first year exit among new exporters • “Agglomeration” helps to foster survival probability • This effect appears to be driven by “information spillovers” • At the same time, exit rates depend significantly on the experience that the exporter has with the product and the • Consistently with multi-product firms models core products show higher probability of survival

35. Conclusions and policy questions • Role of information, experience and networks in determining survival • Importance of firm experience with a market/product and importance of agglomeration effects • What policy interventions possible to provide public goods that generate market knowledge and information? • What markets could be developed for these “goods” and how to solve coordination failures?

36. Thanks for your comments!