Does forced solidarity hamper entrepreneurial activity? Evidence from seven West-African countries

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1 Does forced solidarity hamper entrepreneurial activity? Evidence from seven West-African countries Michael Grimm International ti Institute t of Social Studies, Erasmus University it Rotterdam, The Hague, The Netherlands Flore Gubert DIAL-IRD, Paris, France Ousman Koriko AFRISTAT, Bamako, Mali Jann Lay German Institute of Global and Areas Studies (GIGA), Hamburg and University it of Göttingen, Germany Christophe J. Nordman DIAL-IRD, Paris, France Funding by the Austrian German Norwegian and Korean Government through the Multi Donor Funding by the Austrian, German, Norwegian, and Korean Government through the Multi Donor Trust Fund Project: Labor Markets, Job Creation, and Economic Growth, Scaling up Research, Capacity Building, and Action on the Ground is gratefully acknowledged.

2 Motivation Usually emphasis of benefits of social networks (see e.g. Fafchamps, 1996, 2001, 2002 and Minten and Fafchamps, 1999) such as the reduction of transaction costs, the reinforcement of collective action, the generation of learning spin-offs, or risk sharing. Yet, social networks can also be seen as an important constraint faced by the informal entrepreneur (see e.g. Portes and Sensenbrenner, 1993; Platteau, 2000; Hoff and Sen, 2006; Luke and Munshi, 2006). For instance if family ties imply redistributive pressure, then such ties may put brakes on entrepreneurial activity. 2

3 Motivation The economic success of an individual breeds parasitic behaviour, which [ ] does not stop until the rich individual is ruined and brought back to the fold The negative effects of traditional norms of generosity and redistribution in terms of incentives to savings and innovations are not necessarily confined to the countryside but may also affect modern cities where many proprietors are unable to resist kinship demands d to any great extent, especially so in Sub-Saharan Africa Platteau (2000). 3

4 Motivation If such behaviour is widespread it may partly explain the failure of many African micro and small enterprises to grow. It might also explain why minority entrepreneurs like the Indians in East Africa and the Lebanese and Syrians in West Africa are often more successful. However, so far little empirical backup for the existence of negative effects of social networks on entrepreneurial activity. 4

5 Motivation Duflo et al. (2009) shows that impatient Kenyan farmers forgo highly profitable investments in fertilizer Impatience would be partly rooted in the difficulty of protecting savings from consumption demands. Di Falco and Bulte (2009) find some evidence that kinship size is associated with higher budget shares for non-sharable goods. They also find evidence that compulsory sharing leads to free riding and attenuates incentives for self-protection against shocks (Di Falco and Bulte, 2010). Baland, Guirkinger and Mali (2007) find that some people take up credits even without liquidity constraint just to signal to their kin that they are unable to provide financial assistance. 5

6 This paper Whether social networks, more specifically those related to the family and kin, act as a constraint to potentially successful entrepreneurs or whether the positive social network effects dominate. 6

7 Outline Theoretical framework Data and Context Empirical Analysis Transfers and social networks Social networks and factor use Social networks and enterprise performance Conclusion 7

8 Theoretical framework Model inspired by agricultural household models. Urban households combine consumer and firm. Utility function: Max U = U(X, l) Cash income constraint: p (X - Q)+ R + wl h + rk h + p v V p F(L, K, V ) + w L m + rk m + S Production technology Q = F(L, K, V, Z h ) 8

9 Theoretical framework Transfers R to other households are assumed to be a share of the firm s value added, VA, i.e. transfers are treated like a tax on VA R = s VA, with 0 s 1 with VA = p F(L,K,V,Z h ) - p v V These transfers are imposed by social norms, failing to deliver them, implies prohibitive social sanctions (exclusion, deny of being buried in village etc.). 9

10 Theoretical framework In the most general case, the share s (the tax rate or solidarity tax ) depends on egalitarian i norms prevailing in the entrepreneur s kin, N, on the size of the kin, T, and on the costs to observe the entrepreneur s profits, C, Thus: s = f (N, T, C)

11 Theoretical framework FOC imply that factor use in household business declines with tax : (1 s) Q L w p (1 s) Q K r p 11

12 Data Set of surveys (Enquêtes 1-2-3) gathered in seven economic WAEMU capitals in the early 2000s. Cross-sections surveyisamultilayer multi-layer survey organised in three phases and specially designed to study the informal sector Phase 1 is a representative labour force survey Phase 2 is a survey which interviews a sub-sample of informal production units identified in Phase 1 Phase 3 is a household expenditure survey (again sub-sample) Two samples: Sample A Several firms per HH; Sample B Several firms within the same HH aggregated to one firm. 12

13 Descriptive statistics Owner characteristics Sample A Sample B Mean S.D. Mean S.D. Male (=1) Age owner Speaks French (=1) No diploma Primary completed Some secondary Other post primary Ethnic group Ethnic group Ethnic group Migrant (=1) N

14 Descriptive statistics Household characteristics Sample A Sample B Mean SD S.D. Mean SD S.D. Household size Only informal firm Public wage earner Private formal wage earner Other combination N

15 Descriptive statistics Firm characteristics Sample A Sample B Mean S.D. Mean S.D. Age of firm Ann. VA in intl.$ PPP Monthly hours owner Total monthly hours Total staff incl. owner Hired paid staff No physical capital (=1) Physical. cap. in intl. $ PPP Physical cap. (lowest 33%) Physical cap. (middle 33%) Physical cap. (highest 33%) No. of firms N

16 Measures of social network effects and family ties The share of the population p from the same ethnic group in the neighbourhood. The share of the population from the same area of origin. The geographical distance to the area of origin. Population density at origin. Total fertility rate at origin. 16

17 Measures of social network effects and family ties Total sample Sample A Sample B Mean S.D. Mean S.D. Share same ethnic group Share same origin Distance to origin N Migrants only sample Share same ethnic group Share same origin Distance to origin Pop. density at origin Total fertility rate at origin N

18 Empirical strategy 1. Show that the intensity of social networks is indeed related to households h transfers given and received. 2. Examine effect of social network intensity on factor use physical capital, total hours of labour employed, owner s total hours of labour employed controlling for other factors that may determine social network intensity and factor use. 3. Examine effect of social network intensity on value added and check whether factor use is indeed a relevant transmission channel between social networks and value added. 18

19 Social networks and transfers (1) (2) (3) (4) Transfers made OLS Tobit Sample A Sample B Sample A Sample B Share same ethnic group * 0.984* 1.022* (0.448) (0.405) (0.514) (0.566) Share same origin i (2.822) (2.682) (4.413) (4.594) Ln distance to origin *** *** ** ** (0.075) (0.058) (0.119) (0.102) Ln value added 0.156*** 0.113*** 0.156*** (0.041) (0.041) (0.060) (0.061) Migrant (=1) 0.868** 0.610** 1.136* (0.361) (0.273) (0.585) (0.500) R-squared N Gender and age of owner/manager, Household size, Ethnicity, Activity portfolio of household, Country effects 19

20 Social networks and transfers (5) (6) (7) (8) Transfers received OLS Tobit Sample A Sample B Sample A Sample B Share same ethnic group 0.952** 0.770** 1.949** (0.410) (0.381) (0.887) (0.967) Share same origin i (2.977) (2.844) (7.670) (7.949) Ln distance to origin 0.129* 0.132** (0.066) (0.059) (0.212) (0.174) Ln value added (0.039) (0.039) (0.104) (0.103) Migrant (=1) ** ** (0.334) (0.318) (1.041) (0.853) R-squared N

21 Social networks and use of capital (1) (2) (3) (4) OLS Tobit Sample A Sample B Sample A Sample B Share same ethnic group *** ** (0.123) (0.122) (0.143) (0.156) Share same origin 1.367* 3.291*** ** (0.744) (0.869) (1.100) (1.192) Ln distance to origin *** *** (0.022) 022) (0.020) 020) (0.032) 032) (0.029) 029) Migrant (=1) *** *** R-squared (0.108) (0.101) (0.160) (0.144) N Same controls as before plus additional human capital variables and sector effects.

22 Social capital and use of labour hours Effects on total hours labour employed *** ** *** *** Share same ethnic group Effects on owner's total hours provided 1 *** *** Share same Ln distance Migrant Share same origin to origin ethnic Migrant group 0 Share same origin *** Ln distance to origin *** Migrant Migrant An increase by 10 perc. points in the share of same ethnic group in neighbourhood increases the owner s labour hours by about t13%(32h/ 1.3% (3.2 h/month at sample mean). A decrease in the distance to origin by 50%, decreases labour hours provided by the owner by about 2.7% (6.5 h at sample mean). Migrants provide 25-30% less h to their firms. 22

23 Social capital and value added (1) (2) (3) (4) Sample A Sample B Sample A Sample B Share same ethnic group (0.092) 092) (0.097) 097) (0.085) 085) (0.088) 088) Share same origin 1.418*** 1.778*** 1.189** (0.524) (0.609) (0.483) (0.542) Ln distance to origin *** *** (0.018) (0.015) (0.016) (0.014) Ln physical capital 0.159*** 0.149*** (0.011) (0.012) No capital 0.438*** 0.414*** (0.071) (0.081) Ln total labour hours 0.426*** 0.519*** (0.021) (0.023) Migrant (=1) *** *** *** *** (0.089) (0.079) (0.078) (0.070) R-squared N Social network effects partly work through factor use! 23

24 Summary Local social networks within the city have positive effects on factor use Physical capital Total hours of labour Owner s hours of labour and value added. (Local) networks are characterized by reciprocity (transfers). Robust negative effects associated with social networks tied to the village of origin. Effects get diluted with geographical distance. Households seem to split up their enterprises to reduce the transfers that t have to be paid to the kin. 24

25 Conclusion An expansion of formal support mechanisms that can help in case local social networks are lacking. implementation or expansion of formal insurance mechanism, may reduce the necessity for inter-household transfers. Next steps Investigate in more detail the nature of these networks. Investigate interaction of network effects with investment and capital returns. 25