nkechi s. owoo and wim naudé are informal household enterprises also subject to agglomeration...
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Nkechi S. Owoo and Wim Naudé
Are Informal Household Enterprises also subject to Agglomeration Economies? Evidence from Rural Africa
Global Development Network (GDN) ConferenceJune 18-20, 2014
A G R I C U L T U R E
I N A F R I C AT E L L I N G F A C T SF R O M M Y T H S
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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
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LITERATURE REVIEW
• Most literature on enterprise productivity deals with advanced
economies
– Productivity levels widely dispersed across firms
oManagerial 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
oMarket access, poor infrastructure, weak governance, financial services, etc
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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
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LITERATURE REVIEW
• Fewer spatial studies of rural nonfarm enterprises in developing countries– spatial proximity important for firm performance oMcCormick (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
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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
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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
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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)
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EMPIRICAL METHODOLOGY
• Exploratory Spatial Data Analysis (ESDA)– Series of tests that account for spatial nature of dataoQuantile MapsoGlobal Moran’s I Statisticso Local Indicators of Spatial Autocorrelation
• Econometric Specification– Multivariate regression of RNFE performance on set of control variablesoOLSo Spatial Lago Spatial error
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Exploratory Spatial Data Analysis (ESDA)
Distribution of RNFE Performance in Ethiopia and Nigeria
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GLOBAL MORAN’S I STATISTICS
ETHIOPIA NIGERIA
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LOCAL SPATIAL AUTOCORRELATION
ETHIOPIA NIGERIA
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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
𝜖 is the vector of errors p is the spatial lag co-efficientWY is the spatially lagged dependent variableW is the weight matrix
𝝺 is the spatial error co-efficient 𝝻 is the vector of errors
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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
ETHIOPIA NIGERIA
EA Individual EA Individual
Rho (p) 0.527*(1.85)
0.572***(21.77)
0.240(0.74)
0.133***(4.32)
Lambda (𝝺) 0.310(0.73)
0.582***(21.91)
0.0263(0.07)
0.136***(4.39)
Control Vars YES YES YES YES
# Obs 259 1, 230 379 2, 001
t statistics in parentheses : * p < 0.10, ** p < 0.05, *** p < 0.01
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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
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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 RelationshipoDe Janvry, 2005; Lanjouw and Lanjouw, 2001
– Positive RelationshipoHaggblade et al. 2002; Deichmann et al. 2009
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Bivariate Relationship between RNFE Performance and Agricultural Activity
Ethiopia Nigeria
Global Moran’s I -0.0225082**(0.049)
-0.0400949***(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
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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?
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RESEARCH LIMITATIONS
• Small sample size– only 259/ 379 observations
• Scale of spatial analysis
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Myth OR Fact?
Summary
FACT - Evidence of spatial effects in developing countriesMYTHISH- There are positive linkages between farming and non-farm
enterprises in developing countriesRNFE performance highest in areas with lower farming activityAdditional research required
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POLICY IMPLICATIONS
• Spatially differentiated approach to RNFE support
• Encourage asset and knowledge accumulation of existing firms– Improve skills and technology of leading enterpriseso Spillover to proximate enterprises
• Encouragement of entrepreneurial and management education for enterprise performance
• Need for investments in local infrastructure
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THANK YOU FOR YOUR ATTENTION!