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Cited by 221Agricultural Innovations and PracticesEntrepreneurship Studies and InfluencesAgricultural Development and Management

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Non-farm entrepreneurship in rural sub-Saharan Africa: New empirical evidence

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Nagler, Paula, Naudé, Wim·2017·Food Policy
DOI 10.1016/j.foodpol.2016.09.019PMC PMC5384454
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Abstract

We report on the prevalence and patterns of non-farm enterprises in six sub-Saharan African countries, and study their performance in terms of labor productivity, survival and exit, using the World Bank's Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). Rural households operate enterprises due to both push and pull factors and tend to do so predominantly in easy-to-enter activities, such as sales and trade, rather than in activities that require higher starting costs, such as transport services, or educational investment, such as professional services. Labor productivity differs widely: rural and female-headed enterprises, those located further away from population centers, and businesses that operate intermittently have lower levels of labor productivity compared to urban and male-owned enterprises, or enterprises that operate throughout the year. Finally, rural enterprises exit the market primarily due to a lack of profitability or finance, and due to idiosyncratic shocks.

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Fig. 7 Productivity dispersal – by gender. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises with a female enterprise owner and the dotted lines the productivity of enterprises with a male enterprise owner. Authors’ own calculations based on LSMS-ISA data.
Fig. 7 Productivity dispersal – by gender. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises with a female enterprise owner and the dotted lines the productivity of enterprises with a male enterprise owner. Authors’ own calculations based on LSMS-ISA data.CC BY · PubMed Central
Fig. 8 Productivity dispersal – by ability to read & write. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises where the enterprise owner has the ability to read & write and the dotted lines the productivity of enterprises with an illiterate enterprise owner. Authors’ own calculations based on LSMS-ISA data.
Fig. 8 Productivity dispersal – by ability to read & write. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises where the enterprise owner has the ability to read & write and the dotted lines the productivity of enterprises with an illiterate enterprise owner. Authors’ own calculations based on LSMS-ISA data.CC BY · PubMed Central
Fig. 9 Productivity dispersal – by shock experience. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises where the corresponding household experienced a shock during the 12 previous months and the dotted lines the productivity of enterprises where the corresponding household did not experience a shock. Authors’ own calculations based on LSMS-ISA data.
Fig. 9 Productivity dispersal – by shock experience. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises where the corresponding household experienced a shock during the 12 previous months and the dotted lines the productivity of enterprises where the corresponding household did not experience a shock. Authors’ own calculations based on LSMS-ISA data.CC BY · PubMed Central
Fig. 10 Productivity dispersal – by location. Note(s): In (a) and (b) the continuous lines represent the productivity of rural enterprises and the dotted lines the productivity of urban enterprises. Malawi (1) shows the productivity measure using profit in the numerator, and Malawi (2) additionally makes use of time-use information of workers in the denominator.
Fig. 10 Productivity dispersal – by location. Note(s): In (a) and (b) the continuous lines represent the productivity of rural enterprises and the dotted lines the productivity of urban enterprises. Malawi (1) shows the productivity measure using profit in the numerator, and Malawi (2) additionally makes use of time-use information of workers in the denominator.CC BY · PubMed Central
Fig. 11 Productivity dispersal – by gender. Note(s): In (a) and (b) the continuous lines represent the productivity of enterprises with a female enterprise owner and the dotted lines the productivity of enterprises with a male enterprise owner. Malawi (1) shows the productivity measure using profit in the numerator, and Malawi (2) additionally makes use of time-use information of workers in the denominator.
Fig. 11 Productivity dispersal – by gender. Note(s): In (a) and (b) the continuous lines represent the productivity of enterprises with a female enterprise owner and the dotted lines the productivity of enterprises with a male enterprise owner. Malawi (1) shows the productivity measure using profit in the numerator, and Malawi (2) additionally makes use of time-use information of workers in the denominator.CC BY · PubMed Central
Fig. 1 Contribution of activity to total household income. Note(s): Weighted shares. Rural areas only.
Fig. 1 Contribution of activity to total household income. Note(s): Weighted shares. Rural areas only.CC BY · PubMed Central
Fig. 2 Productivity dispersal – by location. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises that are located in rural areas and the dotted lines the productivity of enterprises that are located in urban areas.
Fig. 2 Productivity dispersal – by location. Note(s): In (a)–(d) the continuous lines represent the productivity of enterprises that are located in rural areas and the dotted lines the productivity of enterprises that are located in urban areas.CC BY · PubMed Central
Fig. 3 Productivity dispersal – by region and distance.
Fig. 3 Productivity dispersal – by region and distance.CC BY · PubMed Central
Fig. 4 Months in operation. Note(s): Malawi not reported due to a lack of data. Enterprises that are less than one year in operation are excluded. Rural areas only.
Fig. 4 Months in operation. Note(s): Malawi not reported due to a lack of data. Enterprises that are less than one year in operation are excluded. Rural areas only.CC BY · PubMed Central
Fig. 5 Months in operation – by location. Note(s): Survey weights included.
Fig. 5 Months in operation – by location. Note(s): Survey weights included.CC BY · PubMed Central
Fig. 6 Entries and exits of non-farm enterprises. Note(s): PP = Post-Planting, PH = Post-Harvest. Rural areas only.
Fig. 6 Entries and exits of non-farm enterprises. Note(s): PP = Post-Planting, PH = Post-Harvest. Rural areas only.CC BY · PubMed Central

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Metadata

Authors
Nagler, P., & Naudé, W.
Affiliations
  • United Nations University – Maastricht Economic and Social Research Institute on Innovation and Technology (NL)
  • IZA - Institute of Labor Economics (DE)
Corresponding author
Nagler, Paula
ORCID
Venue
Food Policy
Publisher
Elsevier BV
Published
2017-02
Volume
67
Pages
175-191
ISSN
0306-9192 (ISSN-L)
DOI
10.1016/j.foodpol.2016.09.019
PMC
PMC5384454
OpenAlex
W2534141963
Cited by
221
References
0
Citations by year
2026: 17 · 2025: 23 · 2024: 23 · 2023: 32 · 2022: 24 · 2021: 24 · 2020: 23 · 2019: 24 · 2018: 20 · 2017: 8
FWCI
59.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Food Policy (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion, other-oa)
Topics
  • Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Entrepreneurship Studies and Influences
  • Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Development and Management
Keywords
non-farm enterprises, rural entrepreneurship, sub-Saharan Africa, labor productivity, business survival, firm exit, urban-rural differences, push and pull factors, idiosyncratic shocks, household livelihoods

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Nagler, P., & Naudé, W. (2017). Non-farm entrepreneurship in rural sub-Saharan Africa: New empirical evidence. Food Policy. https://doi.org/10.1016/j.foodpol.2016.09.019