Taking Stock of Africa’s Manufacturing Sector Economic Performance, Business Constraints and some Policy Considerations

1. Taking Stock of Africa’s Manufacturing Sector Economic Performance, Business Constraints and some Policy Considerations Prepared for Sida Academy, Stockholm, September 17th 2009 Måns Söderbom University of Gothenburg www.soderbom.net Mans.Soderbom@economics.gu.se

2. Today’s talk revolvesaround: • The performance of the manufacturing sector in Sub-Saharan Africa (=“Africa”) • The leading constraints for growth and modernization in the sector • The role of manufacturing in generating jobs and reducing poverty • Manufacturing in rural areas (Ethiopia)

3. Basis for the research: Data • Empirical research. Survey data. Largenumber of enterprises & workers in Africa. • Typically 100s, sometimes 1,000s, of firms; typically 1,000+ workers. Firmscan be followed over time. • Primarily: Ethiopia, Kenya, Tanzania, Ghana; also: Nigeria, Uganda, Morocco, Zambia, Cameroon…. • Main overall lesson: Enormousdiversityin choices & outcomesacrossfirms • Compare & contrast: Somefirmsperformverywell, othersratherbadly. Why?

4. The underlying research • I willfocusmostly on research that I am (havebeen) involved in: • A non-technical general overview (Arne Bigsten & Måns Söderbom, 2006, World Bank Research Observer) • A recent policy paper on Kenya’s MFG sector, forthcoming in a bookedited by Paul Collier and collaborators and published by Oxford University Press and the Central Bank of Kenya. • Recent work on Ethiopia’s rural non-farm sector, commissioned by the World Bank. • Ongoing work on Ethiopia’sindustrializationstrategy, in collaboration with researchers in Oxford and London School of Economics, and funded by DFID.

5. Is this research useful…? • General policy: Research can’t offer complete policy recipes. • Specificprojects: Research won’ttell you how to best design (say) technicalassistance programs. • My take: Good empirical research shapesideas • Sheds light on whethercertainexistingideasabout ’howthings work’ are truein the real world. Example: ”Lack of credit is a severeconstraint on enterprisegrowth”. True? • Sometimesshapesnew ideas. Example - Finding: Improved credit access in Kenya in the 1990s hadverylittleimpact on investment. Not surprising, perhaps, as firmsdidn’twant to growanyway – no demand. Why no demand? New idea: ”Because of high economic and politicaluncertainty”. True?

6. Whymanufacturing? MFG typicallyrelatively small sector in Africa:

7. Whypay so muchattention to MFG? • Twomain arguments • Manufacturing is ”special” • Leading edge of modernization • Creates skilledjobs • Generatestechnologicalspillovereffects • Manufacturinggrowth not constrained by land (scarce). With high population growth & pressure on land, diversificationbeyondagriculture is necessary.

8. 1. General Overview of Findings • Reference: “What Have We Learned from a Decade of Manufacturing Enterprise Surveys in Africa?” 2006, World Bank Research Observer 21:2, pp. 241-265 (Arne Bigsten & Måns Söderbom). • First, the ‘doom and gloom’: • Except for a few countries (e.g. Mauritius), African MFG is underdeveloped: • MFG value-added per capita: • 1972: $98 • 2002: $85 • Africa’sshare in world… • population 11% • income 1.1% • manufacturingvalue-added 0.8%

9. 1. Overview (cont’d) • Foreigninvestorsdo not see Africa as a promisinglocation for investment (e.g. extensive outsourcing to Asia - verylittle to Africa). • Africans (!) keep a largeshare of theirwealthoutside Africa (40% according to Collier, Hoeffler and Pattillo, 2001). • So that’s the bird’seyeview – wehave not yetseen a manufacturingtake-off in Africa, on average. • Once you look at a largenumber of individualfirms: bigdifferences in outcomesacrossfirms – somefirmsremain small and stagnant, others are veryprofitable and grow fast. • Compare & contrastsuchcases. What drives success? • Firm-level data are veryuseful for this type of analysis.

10. 1. Overview (cont’d) • Four mainfindings (primarilybased on data from 1990s and early 2000s): • Investment in new equipment has remained low. Lack of credit appears not to be the main reason; high risk and low demand are more important factors. Caveat: Credit more important for the very smallest firms. • Being exposed to international competition – either through exporting or by competing with imports– raises productivity. • High transaction costs is an important reason as to why manufactured exports have remained low. • Firm performance is hampered by poorly integrated domestic markets for labor, capital and MFG output.

11. Investment

12. Exports & productivity • Exports and productivity positively correlated • In the literature there are two competing hypotheses with regard to the relation between exporting and productivity: • self-selection: more efficient firms choose to export (causality from efficiency to exporting) • learning-by-exporting: firms become more efficient as a result of exporting (causality from exporting to efficiency) • The two hypotheses are not mutually exclusive. • Influential papers by Bernard and Jensen (1999) and Clerides, Lach, and Tybout, (1998). • Most studies have found evidence supporting the self-selection hypothesis but not learning-by exporting.

13. Summary of the evidence • But now some evidence that learning-by-exporting holds for Africa - including Bigsten et al. (2004) and Van Biesebroeck (2005) for a range of SSA countries. • This suggests that active policies encouraging exports may help African firms to become more competitive. • But breaking into exports market is costly – when we estimate entry costs the results indicate these are high. • Reducing entry costs will give these firms access to a larger market.

14. Case Study I: Industry & Policy in Kenya Reference: Arne Bigsten, Peter Kimuyu & Måns Söderbom (2010). “Chapter 10: The Manufacturing Sector,” in (ed.) C. Adam, P. Collier and N. Ndung’u, Kenya: Policies for Prosperity. Oxford University Press and Central Bank of Kenya. • Draws on >10 years of research on Kenya’s MFG sector. • Discusses the Kenyan context and main challenges for manufacturing. • Provides a forward-looking discussion of policy issues for the future, cast in terms of policy options and choices rather than recommendations.

15. Kenya: Context • A resource poor country, with rapid population growth and a large agricultural sector. • With limited access to fertile land, the agricultural sector cannot be relied upon to deliver sustained growth in per capita income. Need growth in the non-farm sector. • The government: manufacturing has an important role to play for long-term economic development. • The growth targets for manufacturing stated by the government in its Vision 2030 document are ambitious and require rapidly increasing investment levels, eventually reaching levels above 30% of GDP.

16. Kenya: Overview • 2003/4-2007, strong phase: high growth, rising investment, high MFG growth. Investment at 20% of GDP is high, but a long way away from the long-term target of investments of 30% of GDP. • Post-election unrest in 2008 a major setback. • Manufacturing’s share in GDP constant at 10% - no major take-off for manufacturing production in Kenya.

17. Kenya: Findings from FirmSurveys • Largeproductivitydifferencesbetween small & largefirms.* • These are mirrored by differences in the averageamount of physicalcapitalused by workers in small & largefirms. *Wecareaboutproductivity for manyreasons: one is that higherproductivityenablesfirms to payhigherwages.

18. Kenya: Findings from FirmSurveys • Investment and technology upgrading is doneprimarily by largefirms that are outwardoriented(e.g. exporters).

19. Kenya: Findings from FirmSurveys • Physicalcapital and technology determinehowmucheachworker is able to produce. • Weneed to understandwhy investment is low. • Weturn to the investment climate, especially: • Demand • Uncertainty and instability (political, economic) • Imperfections in credit markets • Variation in demand matters but less than you mightthink. That is, investment is not veryresponsive to demandfluctuations. • Uncertaintyappears to play an importantrole. • Lack of credit – mixed evidence

20. Kenya: Findings from FirmSurveys Illustration: the relationshipbetween investment & perceivedmacroinstability (Kenya, 2007): • Note: • Each point in the graph is an average across firms in a location, size category and sector. • The vertical axis shows average investment rates. • The horizontal axis shows the proportion of firms in a particular location-size-sector cell that rate macroeconomic instability as a "very severe obstacle". Source: Bigsten, Kimuyu and Söderbom, 2010.

21. Kenya: Findings from FirmSurveys • For Kenya, lack of stability remains a problem. The turmoil and clashes in January 2008 and beyond, following the Presidential elections, likely caused significant damage to investor confidence. • On top of this, Kenya’s exports are adversely affected by the demand effects of the financial crises. Disincentivizes investment further. • Theoretical investment models, emphasizing real options, predict that the response of investment to demand growth is weak if uncertainty is high (see e.g. Bond, Bloom and Van Reenen, 2006). This suggests that when the global business cycle recovers, firms in Kenya will be slower to respond than firms in more politically stable countries.

22. Kenya: Findings from FirmSurveys • Mixed findings on the effects of financialconstraints & imperfectcredit markets on investment. • Data from subjective questions suggest that poor access to credit is a major problem. Across 34 African countries, 50% of the respondents (managers, CEOs etc.) indicate that finance is a “major or very severe” constraint; and in Kenya, more than 70% said so (World Economic Forum, 2007). • However non-subjective data do not back this up. Statistical analysis shows that the response of investment to changes in internal funds is weak – which can be interpreted as evidence that financial constraints are not binding (Bigsten et al., 1999).

23. Kenya: Findings from FirmSurveys Illustration: the relationshipbetween investment & perceived access to finance (Kenya, 2007): • Note: • Each point in the graph is an average across firms in a location, size category and sector. • The vertical axis shows proportion of investors. • The horizontal axis shows the proportion of firms in a particular location-size-sector cell that rate finance access as a "very severe obstacle". Source: Bigsten, Kimuyu and Söderbom, 2010.

24. Kenya: Findings from FirmSurveys • In recent years, the lending regime in Kenya has improved, lowering the direct costs of credit (note: prior to global financial crisis). • We see this in the survey data; the proportion of firms reporting being financially constrained has fallen. • Still, many firms still rate finance as a serious obstacle. Small firms in particular have a hard time accessing credit for start-up and expansion.

25. Kenya: Policy Considerations • Leading general constraints to investment, technology acquisition and sustained long term growth: high transaction costs and significant uncertainty. • Difficult to achieve fast improvements in these areas, as these obstacles are difficult (costly) to tackle. Still, the long-term costs of failing to address these problems are likely substantial. • In the short and medium term, several options are open, for example: • Change the tariff structure so as to promote participation of Kenyan firms in the global production chain • Promote capabilities in the area of outsourcing and root out inefficiencies leading to delays in the supply system (e.g. slow and inefficient procedures at airports and the Mombasa port) • Encourage the formation of modern industrial clusters, economizing on indirect costs and nurturing potential externalities • Promote exporting, e.g. by rejuvenating export processing zones.

26. Kenya: Policy Considerations • In general, policy makers are walking a fine line – intervene but do not interfere. • Focus on removing general obstacles to business, possibly targeting sectors or types of activities where Kenyans have a good chance of competing internationally (a recent example is cut flowers). • Effects are long-term. • Acknowledge that policy makers (and academics) know much less about how to run a business than entrepreneurs; therefore do not attempt to ‘pick winners’ amongst individual enterprises!

27. Case study II: Can ManufacturingGrow in Rural Areas? Evidence from Ethiopia • Starting point: African economies need to become less dependent on agriculture in order for poverty to decrease. In rural areas, small nonfarm enterprises may play an important role in the early stages of diversifying beyond agriculture, however there is dispute in the literature regarding this issue. References: Loening, Josef, Bob Rijkers and Måns Söderbom (2008). “Nonfarm Microenterprise Performance and the Investment Climate: Evidence from Rural Ethiopia,” Policy Research Working Paper 4577. Washington D.C: The World Bank. Loening, Josef, Bob Rijkers and Måns Söderbom (2009). “Mind the Gap? A Rural Urban Comparison of Manufacturing Firms," June 2009, Policy Research Working Paper 4946. Washington D.C: The World Bank.

28. Ethiopia: Manufacturing in Rural Areas? • One view: nonfarm activities provide a dynamic pathway out of poverty • A less optimistic view: nonfarm enterprises are set up by households primarily as a survival strategy, perhaps as a substitute for agriculture for the landless. • Still, promotion of nonfarm enterprise activity is considered to be a promising catalyst for development by the Ethiopian government, as manifested in the Plan for Accelerated and Sustainable Development to end Poverty (PASDEP).

29. Ethiopia: Manufacturing in Rural Areas? • Understanding better the opportunities and constraints in Ethiopia’s rural nonfarm enterprise sector is the goal of this project. • The empirical basis is the Rural Investment Climate Survey (RICS), implemented in the Amhara region by the World Bank in December 2006 and January 2007 in collaboration with Ethiopia’s Central Statistical Agency (CSA). • Detailed data at the household, enterprise and community level.

30. Ethiopia: Manufacturing in Rural Areas? • Main research questions: • How economically important are NFEs? • What are their main constraints? • Do NFEs provide a ‘dynamic pathway out of poverty’? • We focus on: • Participation • Productivity • Growth & Investment • There is an important gender dimension in this context, which I will also touch upon.

31. Ethiopia: Manufacturing in Rural Areas? Summary of findings: • The nonfarm enterprise sector is sizeable, particularly important for women, and plays and important role during the low season for agriculture, when alternative job opportunities are limited. • Returns to nonfarm enterprise employment are low on average. That is, these enterprises are not particularly profitable (relative to, say, the going rate for a casual worker in agriculture) • Profits are particularly low amongst female-headed enterprises.

32. Ethiopia: Manufacturing in Rural Areas? Summary of findings: 4. At the same time, women have much higher participation rates than men, which attest to their marginalized position in the labor market. 5. Most enterprises are very small and rely almost exclusively on household members to provide the required labor inputs. • Few firms grow after startup – little investment or new employment. • Enterprise performance is affected by the localized nature of sales and limited market integration for nonfarm enterprises.

33. Ethiopia: Manufacturing in Rural Areas? Summary of findings: • Comparing the performance of small rural enterprises to that of small urban enterprises we find some interesting results: • Urban firms operate in better integrated and more competitive markets, where they have much better access to inputs. Urban firms are larger, more capital-intensive and produce more output per worker. Not very surprising, you might say. • Distinguishing between enterprises in rural towns and enterprises in remote rural areas, we find that the underlying productivity does not differ if you compare firms in rural towns to those in large urban areas. But the productivity of rural/remote firms is much lower.

34. Ethiopia: Manufacturing in Rural Areas? • The problem in rural towns is not that firms are (relatively) unproductive, but that there is very little growth. • An optimistic way of interpreting this is as saying that rural towns is a promising location for small-scale manufacturers, but efforts need to be made to provide incentives for growth. • We are back to the question as to what determines investment again. • Uncertainty, again? Interestingly, yes! Significant negative correlation between the standard deviation in rainfall – a useful measure of risk here - and investment of nonfarm enterprises. • Better market integration might help to reduce at least this type of risk (as access to a larger market reduces the adverse effect of local rainfall shocks). The good news coming out of the productivity analysis, is that firms in rural towns appear able to compete with firms in large urban areas.

35. Case study III: IndustrializationStrategy in Ethiopia • The International Growth Centre: A new research centredirected and organized from hubs at the LSE and Oxford, initiated by and funded by DFID. Comprises country offices across the developing world and a global network of partners. See www.internationalgrowthcentre.org. • The mission: provide demand-led policy advice based on frontier research. • A team from the IGC has been set up this summer to provide analysis and advice to the Ethiopiangovernmentregardingindustrialstrategy. • The initiative is coming from the Ethiopiangovernment. The PM and his economicadviser are veryinterested in the work we are initiating. • I’d like to give you an outline of this work, as I think this is an interesting (new) model for how researchers, donors and policy makerscan work together.

36. Ethiopia: IndustrializationStrategy • Context: • The economy has grown at about 10% annually since 2000. Growth has been driven mainly by expansion of domestic agriculture and services. Due to the impact of the global recession, growth is projected to slow to 6.5% in 2008/09. • Public investment a major factor behind this. The share of private investment in GDP is low (even by African standards); the share of private investment has fallen recently. • Ethiopia’s imports are nearly 4 to 5 times the value of its exports, and – despite rapid growth of some non-traditional exports – imports are growing faster than exports.

37. Ethiopia: IndustrializationStrategy • Context: • Agriculture remains the dominant sector - 51% per cent of GDP in 2007/08 and dominating exports • More than 80% of the population depends on various forms agricultural production for their livelihoods. Vulnerability to climatic shocks and high food insecurity. • With low wages, favorable demography and improved investment climate in recent years, one would expect Ethiopia to be competitive in labor-intensive, low-skill manufacturing products.

38. Ethiopia: IndustrializationStrategy • Instead, Ethiopia has one of the smallest manufacturing sectors in the world, and the sector’s share in GDP has been falling in recent years—from 5.7 percent in 2003/2003 to 4.7 percent in 2006/2007 – notwithstanding recent successes in footwear and garments. • The proportion of Ethiopian businesses that are owned by foreign investors is low (only 7% in 2006) relative to low income countries (12%) or African averages (18%). • During the last five years the government has assigned a high priority to industrial development, especially for exports. The investment climate has been improved over the last 5 years, and there is now considerable excitement about investment opportunities (e.g. in agribusiness). Incentives: free land, duty-free imports, exemption from profit taxes.

39. Ethiopia: IndustrializationStrategy • Purpose of the IGC research: • Despite these recent successes, however, questions remain both within the government and in the business community concerning the government’s strategy for industrial development. • Most new consumer and light manufacturing is likely to focus on the domestic market. • Policy challenge: encourage entry and investment in manufacturing without sheltering inefficient industries behind protection and subsidies in the long run.

40. Ethiopia: IndustrializationStrategy • For the IGC, the first step in supporting the development of a second generation industrial development strategy is to invest in the information base needed to inform public decisions. • Ethiopia is fortunate to be among the handful of African countries that have conducted repeated industrial censuses over the past ten years, but these data have only been partially analyzed. • Answers to such key questions as: what capabilities do Ethiopian forms possess, where did they acquire these capabilities, how do they source their inputs and access their markets, are lacking. Specialist studies of key areas such as micro- and small-scale enterprises are also needed.

41. Ethiopia: IndustrializationStrategy • As an example of the type of research that will feed into the work on industrialization strategy, I’d like to talk briefly about ongoing research on the links between trade liberalization and firm performance in Ethiopia’s industrial sector. • Reference: Bigsten, Arne, Mulu Gebreeyesus and Måns Söderbom (in progress) “Firm-Level Productivity and Trade Liberalization: Evidence from Ethiopia”.

42. Our Question • How has trade liberalization (lower tariffs on imported goods) affected the performance of domestic manufacturing firms in Ethiopia? • Firm-level panel data on Ethiopian manufacturing firms, 1996 -- 2005. • Trade reforms in Ethiopia gathered pace in 1993, so we observe a period over which adjustment to new policies will have taken place.

43. Trade reform in Ethiopia • 1991: New government. Undertook extensive policy reforms to transform the economy into a market oriented one. SAP in 1992/1993. • The trade reform program aimed at first dismantling quantitative restrictions and then gradually reducing the level and dispersion of tariff rates. • Gradual trade liberalization • Six successive custom tariff reforms between 1993 and 2003. • In the first round (August 1993) the maximum tariff was reduced from 230 percent to 80 percent. • It was then gradually reduced and reached 35 percent in the sixth reform round in 2003. [Show Table 1]

44. Gradual Trade Liberalization Tariff reform steps in Ethiopia (1993-2003)

45. Data: Firms • Two sources of data: firms & trade policy. • Firms: annual manufacturing firms' census data collected by the Ethiopian Central Statistics Agency (CSA) between 1996 and 2005. Panel. • Covers all firms with employment>9. The original data consists of 7870 firm/year observations. • Each census has information on output, inputs (local and imported), sales (local and export), employment, location, ownership type, and a variety of costs. • From current to real values: Sector-level deflators.

46. SummaryStatistics: Firms Number of plants, employment and output

47. Data: Tariffs and Imports • Data on tariffs were collected from the Ethiopian Customs Authority (ECA) from 1997 to 2006. Raw data on values of imported goods and tariffs paid for commodities transformed from 6-digit level to 4-digit ISIC product codes. Enables us to match the trade data with the firm-level data. • Weighted average tariff rate in the sector - calculated from import duties collected and the CIF value of imports, and the average is weighted by the value of imports of each commodity. • About 40 sectors are represented in the firm data, and tariffs vary quite a bit across sectors, and over time. Hence, a lot of variation in key explanatory variable. • Import penetration ratio in the sector - defined as the share of imports in the total domestic market for that sector.

48. Tariffs and Firm-LevelOutcomes • (1): Import penetration ratio rises as tariffs are cut • (2): Value-added per worker rises as tariffs are cut • (3)-(6): No statistically significant correlation with entry rates, exit rates, investment or hiring. • (7): Evidence of a nonlinear relationship between tariffs and value-added per worker – see graph on the next slide.

49. Tariffs and Value-Added per Worker • At moderate tariff levels (10-20%) there is no evidence that (small) changes to the tariff rate would affect firm-level productivity. • However, at high tariff levels we document statistically highly significant, and economically important, positive effects of lowering tariff rates on productivity • Suggests that it is primarily high tariffs that have adverse effects on productivity. To the extent that moderate tariffs provide an important source of revenue, and abstracting from consumer welfare implications (which with our data we cannot say anything about), our results thus suggest that tariffs set just above zero may be justifiable from an economic point of view.

50. Summing Up • Performance in the private non-farm sector in Africa must improve – agriculture and aid will not solve the continent’s development problem. • By analyzing firm-level data, we can learn about certain relationships and mechanisms in Africa’s industrial sector that would be masked in aggregate data. • I would argue such research can be useful for policy makers. • I’ve tried to give you an overview of some of the key findings, plus I have reviewed new policy-oriented work that I am involved in.