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EPAR

Data and Code

Welcome to EPAR’s data access page. Part of EPAR’s mission includes a “commitment to open access tools and broadly accessible dissemination.”  As part of this mission, we release all of the data we create for further research, including links to agricultural survey data, more than 150 indicators constructed from these data disaggregated by gender, crop, country, farm type, etc.; the cleaning and construction decisions; code that will allow you to replicate, alter or tailor the indicators with different cleaning and construction decisions; and visualizations of the indicators.

Data Curation

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) as well as multiple other surveys. These LSMS-ISA data and additional information on each survey can be downloaded from the World Bank. Our analysis focuses on the following:

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. To find out more about this project please visit EPAR’s Technical Report #335 or our code GitHub page.

Data Dissemination

  • Access our online data tool AgQuery
  • Try AgQuery+ for more options for slicing and visualizing data.
  • Access our indicator curation page

The STATA .dta files generated by our data curation project can be downloaded below:

Code Distribution

We created these estimates using the Stata software package. Our code is available below, either as a direct download or from our GitHub page.

AgQuery was created using R Shiny and can easily by modified for your own data projects. The code is available below through direct download or our GitHub repository:

Visualizations

We have several interactive visualizations which utilize these data to investigate questions about smallholder farmers, household categorizations, and the stability of indicators over time. Examples of visualizations created using these data include:

  • Sixteen Categories of African Agricultural Households: This divides households into categories using four binary measures: poverty, asset base, market orientation, and diversification, and demonstrates how they change over time
  • Year Over Year Threshold Variability: This examines the movement of households across different thresholds (e.g. 2 or 4 hectares) using several common indicators (e.g. land) used in definitions of smallholder households.
  • Detailed Household Comparisons: These allow users to input their own smallholder definitions and compare them for particular countries. Currently we have:

For more visualizations, including some using non-LSMS-ISA data, please check our Visualizations Page.

Published Research

In addition to creating the data, these data and the process of creating them have led to published papers written by EPAR staff.

Anderson, L., Reynolds, T. (2019). Measurement choices with consequences: How we measure yield, crop diversity and smallholders can mischaracterize contributions of agrobiodiversity to smallholder livelihoods. Invited contribution to Mainstreaming Agrobiodiversity in Sustainable Food Systems, Bioversity International.

Wineman, A., Njagi, T., Anderson, C. L., Reynolds, T., Wainaina, P., Njue, E., Biscaye, P., Ayieko, M. W (2020). “A case of mistaken identity? Measuring rates of improved seed adoption in Tanzania using DNA fingerprinting”. Journal of Agricultural Economics. http://doi: 10.1111/1477-9552.12368

Wineman, A., Anderson, C. L., Reynolds, T., Biscaye, P. (2019) “Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania,” Food Security. https://doi.org/10.1007/s12571-019-00980-5