Nkechi S. Owoo and Wim Naudé Are Informal Household Enterprises also subject to Agglomeration Economies? Evidence from Rural Africa Global Development Network (GDN) Conference June 18-20, 2014 AGRICULTURE IN AFRICA TELLING FACTS FROM MYTHS

Page 2 INTRODUCTION Non-farm enterprises ubiquitous in rural Africa o Brewing, retail, running a restaurant or coffee-shop, running a taxi, etc – 42% of rural households operate non-farm enterprises (Nagler and Naudé, 2014) – 40-50% of rural household income in Africa from rural non-farm enterprises (Rijkers and Costa, 2012; Haggblade et al., 2010) Non-farm economies increasingly vital for job creation and livelihoods (De Brauw et al, 2013; Javry and Sadoulet, 2010) – Growth in rural populations – Declines in agricultural employment

Page 3 LITERATURE REVIEW Most literature on enterprise productivity deals with advanced economies – Productivity levels widely dispersed across firms o Managerial competence- Mano et al. (2012); Bloom and Van Reenen (2010) o Innovation and absorption of technology- Bernard (2010) o External shocks- Rijkers and Soderbom (2013) Fewer studies on developing countries – Aspects of African business environment hindering firm growth o Market access, poor infrastructure, weak governance, financial services, etc

Page 4 LITERATURE REVIEW Productivity of firms depends on productivity of other firms in close proximity – Enterprises clustering together is advantageous for individual productivity Again, most studies of spatial clustering of firms examined in advanced economies.... – Wennberg and Lindqvist (2010)- Sweden – Rupasingha and Contreras (2010)- rural USA – Baumgartner et al (2012)- rural Switzerland – Martin et al. (2011)- France Spatial Effects DO matter! - Deller, 2010

Page 5 LITERATURE REVIEW Fewer spatial studies of rural nonfarm enterprises in developing countries – spatial proximity important for firm performance o McCormick (1999); Siba et al (2012) Ali and Peerlings (2011) & Ayele et al. (2009) – Clustering helps enterprises in handloom industry in Ethiopia to improve productivity No explicit spatial techniques applied – Spatial nature of data biased estimates

Page 6 RESEARCH QUESTION (MYTH OR FACT??) Spatial effects matter for RNFEs in developing country settings There are positive linkages between farming and non-farm enterprises in developing countries

Page 7 DATA 2011 Ethiopian Rural Socioeconomic Survey (ERSS) – 259 EA observations 2010/11 Nigeria General Household Survey (NGHS) – 379 EA observations – Information on primarily rural areas Basic demographic information – Education, health, labour, non-farm economic activities GIS information – Analysis at EA level

Page 8 STUDY VARIABLES: (BASED ON THEORETICAL LITERATURE) Dependent Variable – Sales of RNFEs Household-head Characteristics – Age – Sex – Marital status – Education – Religion – Household size Location and Infrastructure Characteristics – Co-operative – Phone – Microfinance Institution – Distance to asphalt road – Distance to market (see paper Table 1A & 1B for summary statistics)

Page 9 EMPIRICAL METHODOLOGY Exploratory Spatial Data Analysis (ESDA) – Series of tests that account for spatial nature of data o Quantile Maps o Global Moran’s I Statistics o Local Indicators of Spatial Autocorrelation Econometric Specification – Multivariate regression of RNFE performance on set of control variables o OLS o Spatial Lag o Spatial error

Exploratory Spatial Data Analysis (ESDA) Distribution of RNFE Performance in Ethiopia and Nigeria 10

Page 11 GLOBAL MORAN’S I STATISTICS ETHIOPIANIGERIA

Page 12 LOCAL SPATIAL AUTOCORRELATION ETHIOPIA NIGERIA

Page 13 ECONOMETRIC SPECIFICATION OLS (BASE MODEL)- SPATIAL LAG MODEL- SPATIAL ERROR MODEL- ; where; Y is the dependent variable, X is the vector of household and community independent variables, β is the vector of regression co-efficients is the vector of errors p is the spatial lag co-efficient WY is the spatially lagged dependent variable W is the weight matrix is the spatial error co-efficient is the vector of errors

Page 14 Empirical Results: EA/ Individual Level Regression of RNFE performance on household and community variables – Other control variables omitted; have expected signs (see paper Tables 3A and 3B) SPATIAL PARAMETERS ETHIOPIANIGERIA EAIndividualEAIndividual Rho (p)0.527* (1.85) 0.572*** (21.77) (0.74) 0.133*** (4.32) Lambda ()0.310 (0.73) 0.582*** (21.91) (0.07) 0.136*** (4.39) Control VarsYES # Obs2591, , 001 t statistics in parentheses : * p < 0.10, ** p < 0.05, *** p < 0.01

Page 15 Conclusions from Empirical Estimations Evidence of spatial correlation – EA vs. Individual level analyses Education, religious affiliation and marital status of household head are important determinants of RNFE performance in Ethiopia Age and sex of head, education and presence of microfinance institutions are important determinants of RNFE performance in Nigeria

Page 16 Bivariate Relationship between RNFE Performance and Agricultural Activity Spatial interactions between concentration of agricultural activities and RNFE performance – Are high performance RNFEs clustered, not to be near one another, but to be near high prevalence farming areas? Strong linkages between farm and non-farm activity – Negative Relationship o De Janvry, 2005; Lanjouw and Lanjouw, 2001 – Positive Relationship o Haggblade et al. 2002; Deichmann et al. 2009

Page 17 Bivariate Relationship between RNFE Performance and Agricultural Activity EthiopiaNigeria Global Moran’s I ** (0.049) *** (0.002) Negative spatial relationship between farm activity and RNFE performance – High (low)-performing non-farm enterprises are surrounded by other communities with low (high) engagement in farming activities

Page 18 Bivariate Relationship between RNFE Performance and Agricultural Activity Increases in farm activity not necessarily associated with increases in non-farm enterprise productivity in the same region Contrary to ‘most prominent view amongst development practitioners’ (Deichmann et al., 2008: 1) Requires more research – Type of rural non-farm enterprise? – Some other unexplained characteristics?

Page 19 RESEARCH LIMITATIONS Small sample size – only 259/ 379 observations Scale of spatial analysis

Page 20 Myth OR Fact? Summary FACT - Evidence of spatial effects in developing countries MYTHISH- There are positive linkages between farming and non-farm enterprises in developing countries RNFE performance highest in areas with lower farming activity Additional research required

Page 21 POLICY IMPLICATIONS Spatially differentiated approach to RNFE support Encourage asset and knowledge accumulation of existing firms – Improve skills and technology of leading enterprises o Spillover to proximate enterprises Encouragement of entrepreneurial and management education for enterprise performance Need for investments in local infrastructure

Page 22 THANK YOU FOR YOUR ATTENTION!