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Intertemporal evaluation of household food security and its determinants: evidence from Rwanda

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Abstract

The food security literature has reiterated the importance of having an indicator that simultaneously captures the different dimensions of the food security definition. In this regard, this study builds on previous studies using a harmonized food security indicator within a relatively high frequency panel data from Rwanda to examine the dynamics of household food security. Summary statistics show the transitory nature of food security where households go either from complete state of food insecurity to being partially food insecure or from being partially food insecure to being completely food secure during the harvesting seasons and later fall back to their initial state during the sowing and growing seasons. This underscores the intertemporal dimension and transitory nature of food security over time. We also employed a correlated random effects multinomial logit model to further identify the determinants of households being in any of the identified states of food security. Results suggest that agricultural seasons of major food crops do determine households’ state of food security. Also, household socio-demographic characteristics, household assets, income diversification, and location of the household dwelling are some of the consistent factors that increase or decrease the likelihood of the household being food insecure.

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Notes

  1. Gauss-Hermite quadrature is a form of Gaussian quadrature for approximating values of integrals

  2. Based on the data description, we believe that the first visit in this round was conducted in October 2013 and subsequent questions about consumption were related to the previous visit. An example of such question is: “How much was spent on a particular type of food since my last visit”. Thus, we can imply that the collection of food consumption data did not start until November. It is important to also note that each period varies from household to household because households were randomly assigned cycle numbers between 1 to 10 such that the data of each period was obtained from the participating households in 10 cycles. However, the intervals between cycles are predetermined. Thus, data on each period are obtained within a time interval.

  3. In each district, the EAs are stratified based on urban and rural strata and are ordered first by urban, semi-urban, peri-urban, and rural, and subsequently geographically according to sector, cell, village, and the EA codes.

  4. Temporary workers are workers that have fixed-term contract with their employer.

  5. In the questionnaire used to obtain the data, there is a question on what percentage of the profit from the business belongs to the household.

  6. Dependency ratio is the ratio of children below the age of 15 and the elderly who are above 64 years to the household size. Age 15 to 64 is considered the active or working class

  7. The aggregation of both produced and purchased food in the calculation of food expenditure per capita suggests that our food expenditure indicator has some elements of food availability imbedded in it.

  8. We treated these households as incomplete data because there is no way, no matter how little that a household will not incur expenditure on perishable food items within the space of about a month

  9. We made use of the FAO food group classification specified in the guidelines for measuring household and individual dietary diversity. However, we merge some food classes like meat and fish because they are substitutes.

  10. Some information about the agricultural seasons of major food crops can be found at this FAO website link. http://www.fao.org/giews/countrybrief/country.jsp?code=RWA

    As stated in footnote 5, the data for each period is obtained from the household over 10 cycles and based on the data description, there was an initial visit in October against which subsequent data on consumption and expenditure data were collected. Thus, we assume that data collection on food consumption and expenditure did not start until November. Since the data was collected over a 12-month period and the first month was an initial visit, our best guess was to divide the remaining 335 days of the year into 10 to give us an idea of what each period of data is likely to be. It is based on this calculation that we constructed the agricultural seasonal dummies for major food crops.

  11. Kindly note that the seasonal dummies are not mutually exclusive because they do overlap. For example, the growing season B for Maize and Sorghum overlaps with the harvesting season A of beans. More information can be found at http://www.fao.org/giews/countrybrief/country.jsp?code=RWA

  12. These major food crops include beans, maize and sorghum. More information can be found at http://www.fao.org/giews/countrybrief/country.jsp?code=RWA

  13. Page 16 of Horticulture and floriculture in Rwanda presents a table for the production schedule for tomato. The document can be obtained from http://edepot.wur.nl/370322

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Authors and Affiliations

  1. Department of Food and Resource Economics, University of Florida, Gainesville, FL, USA

    Olufemi Daniel Bolarinwa

  2. Institute for Justice Research and Development, Florida State University, Tallahassee, FL, USA

    Kolawole Ogundari

  3. Department of Agricultural Economics & Extension, Adekunle Ajasin University, Akungba-Akoko, Nigeria

    Adebayo B. Aromolaran

Authors
  1. Olufemi Daniel Bolarinwa
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  2. Kolawole Ogundari
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  3. Adebayo B. Aromolaran
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Correspondence to Olufemi Daniel Bolarinwa.

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Table 6 Seasonality dummies
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Bolarinwa, O.D., Ogundari, K. & Aromolaran, A.B. Intertemporal evaluation of household food security and its determinants: evidence from Rwanda. Food Sec. 12, 179–189 (2020). https://doi.org/10.1007/s12571-019-00986-z

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