EPAR Technical Report #335
Tue, 11/21/2017
C. Leigh Anderson
Travis Reynolds
Pierre Biscaye
Didier Alia
David Coomes
Terry Fletcher
Jack Knauer
Josh Merfeld
Isabella Sun
Chelsea Sweeney
Emma Weaver
Ayala Wineman
EPAR has developed Stata do.files for the construction of a set of agricultural development indicators using data from the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA). We are sharing our code and documenting our construction decisions both to facilitate analyses of these rich datasets and to make estimates of relevant indicators available to a broader audience of potential users. 

Code is currently available on our website for seven survey instruments:

  • Ethiopia Socioeconomic Survey (ESS) Waves 1-3: 2011-12, 2013-14, 2015-16 (LSMS-ISA)
  • Nigeria General Household Survey-Panel (GHSP) Waves 1-3: 2010-11, 2012-13, 2015-16 (LSMS-ISA)
  • Tanzania National Panel Survey (TNPS) Wave 1-4: 2008-09, 2010-11, 2012- 13 2014-15 (LSMS-ISA)
  • Ethiopia Agricultural Commercialization Cluster Survey (ACC) 2016
  • India Rice Monitoring Survey (RMS) 2016
  • Tanzania Baseline Household Survey (TBS) 2016
  • Nigeria Baseline and Varietal Monitoring Survey (NIBAS) 2016

Our output includes .do files for each wave and country.

The .do files process the data and create three final data sets at the household, individual, and plot levels with labeled variables, which can be used to estimate summary statistics for the indicators and for a variety of intermediate variables. These .dta files are also available for download from our website. The raw survey data files for LSMS are available from the World Bank LSMS-ISA website.

We have compiled a set of summary statistics for the final indicators restricted to rural households only in an excel spreadsheet.This spreadsheet includes estimates for all countries and waves. The LSMS instruments are nationally-representative samples and the estimates are reported for each country as a whole. The other instruments are representative at regional or state levels. The Ethiopia ACC includes representative samples for the four main regions of Ethiopia; the India RMS includes representative samples for four states: Bihar, Odisha, Uttar Pradesh, and West Bengal; the Tanzania TBS includes representative samples from three zones: the Lake Zone, the Northern Zone, and the Southern Highlands; and the Nigeria NIBAS includes representative samples from six states: Benue, Kaduna, Kano, Katsina, Nassarawa, and Niger. Estimates for each region or state are reported separately.

The spreadsheet includes estimates of the following indicators created in our code files:

  • Income:
    • Total income (annual, in $ and PPP$)
    • Per capita income (PPP$/person/year)
    • Total farm income ($, annual)
    • Total crop income ($, annual)
    • Total livestock income ($, annual)
    • Total non-farm income ($, annual)
    • Share of non-farm and farm income in total income (proportion)
    • Rural poverty headcount using 2011 PPP (number of individuals)
  • Agricultural production
    • Average household farm size (ha)
    • Crop yield (kg/ha, main growing season) on all plots and on purestand plots separately
    • Crop production cost per hectare ($/ha, annual), separate estimates include explicit costs only and both implicit and explicit costs
    • Inorganic fertilizer application rate (kg/ha)
    • Share of crop production value sold (proportion)
    • Crop labor productivity ($/labor day, annual)
    • Crop land productivity ($/ha, annual)
    • Milk productivity (liters/milk-producing animal, annual)
    • Egg productivity (eggs/egg-producing animal, annual)
    • Average daily wage rate in agriculture ($/day)
    • Distance to agricultural dealer outlets (km or minutes)
    • Gender
    • Prevalence of women of reproductive age consuming a diet of minimum diversity (MDDW) (proportion)
    • Average number of food groups consumed by women of reproductive age in last day (MDDW Score, 0-10)
    • % of women who make decisions about use of household income (WEIA) (proportion)
    • % of women who are sole or joint owners of productive assets (including land and livestock) (WEIA) (proportion)
    • % of women with formalized land rights (proportion)
    • Gender crop land productivity gap (%)
  • Adoption
    • Households adopting improved seed varieties (proportion)
    • Households using inorganic fertilizer (proportion)
    • Households using livestock vaccines (proportion)
    • Households using improved breeds (proportion)
    • Households reached by extension (public or private) (proportion)
    • Households using financial services (proportion)

Estimates are disaggregated by gender as appropriate depending on the indicator (by gender of the individual, the plot manager, or head of household).Crop related estimates (yield, value sold, etc.) are disaggregated by crop type as appropriate. We also include population total estimates (regional or national) for selected indicators, for both the total population and for the rural population when possible. Farm-related estimates are disaggregated by farm size where appropriate. Farm size categories include: 0 ha, 0-1 ha, 1-2 ha, 2-4 ha, and > 4 ha.

In addition to the documentation of indicator construction decisions in the Stata .do files, we also have outlined our construction decisions for each indicator across survey instruments. We attempted to follow the same construction approach across instruments, and note any situations where differences in the instruments made this impossible. This information is available as part of our indicator spreadsheet. A final web page outlines general principles and considerations for constructing these indicators.  

All output related to this project can be found on our project resource page, including .do files, .dta files, estimate spreadsheet, and construction decisions.

Type of Research: 
Data Analysis
Research Topic Category: 
Sustainable Agriculture & Rural Livelihoods
Agricultural Productivity, Yield, & Constraints
Agricultural Inputs & Farm Management
Market & Value Chain Analysis
Household Well-Being & Equity
Food Security & Nutrition
Technology Adoption
Rural Populations
Smallholder Farmers
Geographic focus: 
West Africa Region and Selected Countries
South Asia Region and Selected Countries
East Africa Region and Selected Countries
Other Datasets