Growing and Learning When Consumption Is Seasonal: Long-Term Evidence From Tanzania

Abstract

This article shows that the seasonality of food consumption during childhood, conditional on average consumption, affects long-run human capital development. We develop a model that distinguishes differences in average consumption levels, seasonal fluctuations, and idiosyncratic shocks, and estimate the model using panel data from early 1990s Tanzania. We then test whether the mean and seasonality of a child’s consumption profile affect height and educational attainment in 2010. Results show that the negative effects of greater seasonality are 30 % to 60 % of the magnitudes of the positive effects of greater average consumption. Put differently, children expected to have identical human capital based on annualized consumption measures will have substantially different outcomes if one child’s consumption is more seasonal. We discuss implications for measurement and policy.

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Notes

  1. 1.

    Wealthy country consumers also eat seasonal diets, although the fluctuations seem to be less pronounced and occur around a greater average level than in low-income countries (de Castro 1991; Ma et al. 2006).

  2. 2.

    It is unclear whether something about a variable diet itself, at any level of consumption, impedes growth. We found little discussion of this in the literature. The mechanism discussed here suggests that seasonal fluctuations around high levels of consumption will not impede growth if lean-season consumption never falls below the threshold required for a child to reach his or her potential. In section Modeling Consumption: Estimation Results and in section D of Online Resource 1, we discuss heterogeneity in seasonality across the consumption distribution.

  3. 3.

    In the first round, 840 households were surveyed; 81 of these did not appear in the last round, the majority of which had moved outside the study area. Seventy-five households were selected as replacements for the 81 that dropped out, leading to 915 total unique households included across rounds.

  4. 4.

    For clarity of presentation, we exclude Nuts/Pulses and Beverages from Fig. 2. These represent a very small share of consumption in this population.

  5. 5.

    Of the 758 households interviewed in all four rounds of KHDS 1, 23 did not complete the full food consumption and expenditure modules in all four rounds. The 736 with complete consumption data constitute the main analysis sample.

  6. 6.

    This model was informed by our reading of Ligon and Schechter (2003) and Arndt et al. (2006).

  7. 7.

    This decomposition does not rule out the possibility that the drivers of seasonality could be stochastic. The key distinction is between those stochastic factors whose distributions vary systematically across the days of the year and those whose distributions do not. For example, the rain cycle is an annual event that introduces some seasonality, but the timing and duration of the rainy season in any given year is partly stochastic. The seasonality term in our model captures the average contribution of all seasonally varying factors, on any day of the year, to the consumption cycle.

  8. 8.

    For this reason, we use data from only those households surveyed in all four rounds, so as not to extrapolate to dates more than a year before or after the most recent interview.

  9. 9.

    Because we allow Z ydh to evolve over time, \( {\widehat{s}}_h^2 \) is not exactly analogous to \( {\upsigma}_{\gamma h}^2 \) from Eq. (4). We could fully align the two if we took the additional steps of modeling the evolution of the variables in Z ydh and added the appropriate adjustment to Eq. (4). We elect not to do this because it would introduce complexity with little value. The important point is that \( {\upsigma}_{\gamma h}^2 \) need not be constant over time because it is a function of some time-varying household characteristics. Hence, \( {\widehat{s}}_h^2 \) measures the average seasonal component for the household over the study period.

  10. 10.

    We also estimated specifications using the observed values of m h and s h , calculated directly from the four observations in KHDS 1. The pattern of results from those specifications is similar to what we report in the Results section. However, this alternative approach is clearly misspecified because the raw statistics m h and s h contain both seasonal and idiosyncratic variation. Results are available upon request.

  11. 11.

    Average differences in outcomes across cohorts are not problematic because they are controlled for by age group effects. The question is whether confounding trends in the seasonality of consumption allowed the different age groups to consume at significantly different levels of seasonality during the unobserved years. We know of no ancillary evidence suggesting that consumption in Kagera became markedly more or less seasonal during the decade before or after 1991–1994.

  12. 12.

    The specific reasons for the persistence of seasonal fluctuations in food prices and consumption across much of sub-Saharan Africa, even to this day, remain a puzzle. See Basu and Wong (2015), Burke (2014), Dillon (2017), Fink et al. (2014), Kaminski et al. (2014, 2016), and Stephens and Barrett (2011).

  13. 13.

    Parameter estimates are provided in Online Resource 1, Table S1.

  14. 14.

    This is an approximation because some allowance must be made for suppressed variation in the projected variable \( {\widehat{s}}_h \)—hence, the rationale for bootstrapping standard errors.

  15. 15.

    In Online Resource 1, section D, we show that there is no statistically significant heterogeneity across \( {\widehat{m}}_h \) in the effects of seasonality on human capital.

  16. 16.

    For height, we can reject the hypotheses that the Age Group 2 coefficient is less than the Age Group 1 coefficient (p value = .067) and that the Age Group 3 coefficient is less than the Age Group 1 coefficient (p value = .056).

  17. 17.

    Unfortunately, we cannot determine in the KHDS 1 data whether attendance drops during the lean season, which would have aided in the interpretation of this result. During every KHDS 1 survey month, a majority of respondents answered “Yes” when asked whether a child was attending school “now,” even though school was not in session during some of those months. Hence, respondents clearly interpreted the question about current attendance to be more about recent enrollment or general attachment to school.

  18. 18.

    For Age Groups 1 and 3, p values for the one-sided comparisons between boys (column 8) and girls (column 7) are below .10.

  19. 19.

    For more details on the Guatemala study, see Stein et al. (2008).

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Acknowledgments

We are grateful to the African Development Bank, World Bank, and Cornell University for funding through the STAARS program. We thank the Editors, three anonymous referees, Abraham Abebe Asfaw, Chris Barrett, Anirban Basu, Jere Behrman, Leah Bevis, Joshua Blumenstock, Jennifer Burney, Michael Carter, Arun Chandrasekhar, Norma Coe, Alison Cullen, Joachim De Weerdt, Andrew Foster, Rachel Heath, Heather Hill, Kalle Hirvonen, Martijn Huysmans, Nathan Jensen, Arianna Legovini, Mark Long, Linden McBride, Tyler McCormick, Ellen McCullough, Craig McIntosh, Robert Plotnick, Jenny Romich, Jacob Vigdor, and seminar participants at Cornell, KU Leuven, the World Bank, the University of Washington, the Second Annual Global Food Security Conference, the STAARS conference in Addis Ababa, the 2017 Pacific Development Conference, and the 2017 Annual Bank Conference on Africa for helpful discussions and comments. We are joint first authors on the article. Any errors are our responsibility.

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Christian, P., Dillon, B. Growing and Learning When Consumption Is Seasonal: Long-Term Evidence From Tanzania. Demography 55, 1091–1118 (2018). https://doi.org/10.1007/s13524-018-0669-4

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Keywords

  • Seasonality
  • Nutrition
  • Human capital
  • Height
  • Education