Nkechi S. Owoo and Wim Naudé
Are Informal Household Enterprises also subject to
Agglomeration Economies? Evidence from Rural Africa
AG RICULTURE
IN AFRICA
T E L L I N G FA C T S
FROM MYTHS
Global Development Network (GDN) Conference
June 18-20, 2014
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
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
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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
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
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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 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
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Exploratory Spatial
Data Analysis (ESDA)
Distribution of RNFE
Performance in Ethiopia
and Nigeria
10
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-efficient
WY is the spatially lagged dependent variable
W 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 Relationship
o De Janvry, 2005; Lanjouw and Lanjouw, 2001
– Positive Relationship
o Haggblade et al. 2002; Deichmann et al. 2009
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Bivariate Relationship between RNFE Performance and
Agricultural Activity
Global Moran’s I
Ethiopia
Nigeria
-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 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
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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 enterprises
o 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!
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