nach oben

Population and Environment

Erschienen in:

01.09.2023 | Original Paper

Rainfall, mothers’ time use, and child nutrition: evidence from rural Uganda

verfasst von: Chris M. Boyd

Erschienen in: Population and Environment | Ausgabe 3/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Care provision is a key component of women’s time use with implications for the health and wellbeing of children. Shifting labor demands resulting from weather shocks may imply that women in developing countries have less time for care provision, potentially affecting their children’s nutrition. Nonetheless, a broad literature focusing on the indirect impacts of climate change on child nutrition has yet to explore the mechanisms whereby this occurs, and whether mothers’ time use is one of these mechanisms. Using the Uganda National Panel Survey, a unique data set that gathers data on farming activities, time use, and anthropometric measures, I analyze how rainfall variability affects mother’s time use and whether time use is a mechanism whereby rainfall variability affects child nutrition in the short run (measured as weight-for-age and weight-for-height Z-scores). My results show that increased rainfall variability in the last month decreases mothers’ time share in other household-related activities (e.g., fetching water), while it increases the probability of child wasting. Moreover, using mediation analysis, I find that none of the mothers’ time-use variables appears to be a mediating factor between rainfall variability and child nutrition. These results suggest that mothers adjust their time use due to rainfall variability without jeopardizing their children’s nutritional levels.

Vorheriger Artikel Demographic and socioeconomic effects of environmental policies: the 1927 special goat tax and mountain depopulation in Italy

Nächster Artikel Rainfall shocks, soil health, and child health outcomes

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The disease mechanism suggests that excess rainfall increases the risk of diarrhea incidence and thus decreases child nutrition. The food production mechanism suggests that higher production or crop yields increase food availability and thus child nutrition.

Randell et al. (2021) is the most recent study relating work in agriculture to nutrition and climate. They focus on breastfeeding and measure time in agriculture as days spent planting or harvesting, but they do not measure other types of rural work and do not test whether mothers’ time use is a mechanism explaining the relationship between rainfall variability and child nutrition. Similarly, Dimitrova and Muttarak (2020) examine the impact of floods in India and test some potential mother-related mechanisms, but not mothers’ time use.

Compared to other climate datasets, CHIRPS does not only provide higher resolution but by incorporating satellite imagery and in-situ stations it becomes a more reliable rainfall dataset with a very small resolution (Funk et al., 2015).

The designation of households belonging to the panel changed in 2013–2014, so very few observations are interviewed in the three waves (2011–2012, 2013–2014, and 2015–2016). Thus, I restrict the analysis to the households interviewed in the last two rounds, i.e., in 2013–2014 and 2015–2016, to be able to implement panel regressions including fixed effects with the most possible observations.

Even the surveys supported by the LSMS-ISA project for other sub-Saharan countries gather information in slightly different ways than the UNPS, which avoids performing the following analysis.

Surveys with exhaustive time-use modules for other developing countries had only cross-section data available or did not have rural or agricultural families as part of their target.

Note that children closer to 59 months old in the first wave would not be included in the second wave, although their household will still be part of the sample if there is another child 6 to 59 months old in the second wave. Children that have one observation in each wave are those who were between 6 and 59 months old at the time of both surveys.

Appendix 1 shows a detailed description of how these shares were constructed.

Note that parishes can have different sizes, but the smallest parishes were contained in one 5 × 5 km grid.

I do not use the new difference-in-difference two-period model proposed by Sant’Anna and Zhao (2020) because the droughts occur randomly along the area under study, and there is not a potential control area or comparison group. Moreover, the new difference-in-differences literature has not yet agreed on how to assess continuous treatment variables where no untreated groups are available (Callaway et al., 2021), which is the case of the data I use here.

Sample weights correspond to the average of cross-section sample weights in both waves for each household. For regressions with parish-level fixed effects, no sample weights are used.

Note that the LSMS-ISA (UNPS) for Uganda does not collect data on leisure activities or sleeping.

Appendix 5 shows graphically the relationship between rainfall variability and child nutrition, and the mediation of mothers’ time use.

Note that housework (doing domestic activities) includes caregiving. Although I cannot break into these two categories, I assume the effect of time spent in care and other domestic activities (e.g., food preparation) impact child nutrition in the same direction.

In Appendix 5, I formalize this by proposing a theoretical framework where mothers are the maximizers of their children’s health.

In Appendix 8, I analyze the relationship between mothers’ time shares and child nutrition using Oster bounds, which allow analyzing coefficient stability in the presence of omitted variable bias. The results suggest that only allocating a larger time share at the own farm is negatively related to being underweight. Thus, supporting the argument that mothers allocate their time aiming to maximize their children’s nutritional status.

Acharya, A., Blackwell, M., & Sen, M. (2016). Explaining causal findings without bias: Detecting and assessing direct effects. American Political Science Review, 110(3), 512–529.CrossRef

Asfaw, S., & Maggio, G. (2018). Gender, weather shocks and welfare: Evidence from Malawi. Journal of Development Studies, 54(2), 271–291. https://doi.org/10.1080/00220388.2017.1283016CrossRef

Bakhtsiyarava, M., Grace, K., & Nawrotzki, R. J. (2018). Climate, birth weight, and agricultural livelihoods in Kenya and Mali. American Journal of Public Health, 108(S2), S144–S150.CrossRef

Balagamwala, M., Gazdar, H., & Mallah, H. B. (2015). Synergy or trade-off between agricultural growth and nutrition women’s work and care. The Pakistan Development Review 54,(4, Papers and Proceedings : PARTS I and II The 30th Annual General Meeting and Conference of the Pakistan Society of Development Economists, Islamabad), 897–913.

Bevis, L. E. M., & Villa, K. (2020). Intergenerational transmission of maternal health: Evidence from Cebu, the Philippines. Journal of Human Resources, 57(5), 1425–1465. https://doi.org/10.3368/jhr.58.1.0819-10372R2CrossRef

Branco, D., & Féres, J. (2021). Weather shocks and labor allocation: Evidence from rural Brazil. American Journal of Agricultural Economics, 103(4), 1359–1377. https://doi.org/10.1111/ajae.12171CrossRef

Brown, M. E., et al. (2014). Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Population and Environment, 36(1), 48–72.CrossRef

Bryan, E., Theis, S., Choufani, J., De Pinto, A., Meinzen-Dick, R. S., & Ringler, C. (2017). Gender-sensitive, climate-smart agriculture for improved nutrition in Africa south of the Sahara. In A. De Pinto, & J. M. Ulimwengu (Eds.), A thriving agricultural sector in a changing climate: Meeting Malabo Declaration goals through climate-smart agriculture (Chapter 9, pp. 114–135). Washington, D.C.: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/9780896292949_09

Callaway, B., Goodman-Bacon, A., & Sant’Anna, P. H. (2021). Difference-in-Differences with a Continuous Treatment. http://arxiv.org/abs/2107.02637

Cooper, M., Brown, M. E., Azzarri, C., & Meinzen-Dick, R. (2019). Hunger, nutrition, and precipitation: Evidence from Ghana and Bangladesh. Population and Environment, 41(2), 151–208.CrossRef

Debela, B. L., Gehrke, E., & Qaim, M. (2021). Links between maternal employment and child nutrition in rural Tanzania. American Journal of Agricultural Economics, 103(3), 812–830. https://doi.org/10.1111/ajae.12113CrossRef

Deuchert, E., & Felfe, C. (2015). The tempest: Short- and long-term consequences of a natural disaster for children’s development. European Economic Review, 80, 280–294. https://doi.org/10.1016/j.euroecorev.2015.09.004CrossRef

Dimitrova, A., & Muttarak, R. (2020). After the floods: Differential impacts of rainfall anomalies on child stunting in India. Global Environmental Change, 64(102130), 1–19. https://doi.org/10.1016/j.gloenvcha.2020.102130CrossRef

Funk, C., et al. (2015). The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes. Scientific Data, 2, 1–21.CrossRef

Grace, K., Davenport, F., Funk, C., & Lerner, A. M. (2012). Child malnutrition and climate in Sub-Saharan Africa: An analysis of recent trends in Kenya. Applied Geography, 35(1–2), 405–413. https://doi.org/10.1016/j.apgeog.2012.06.017CrossRef

Johnston, D., et al. (2018). Review: Time use as an explanation for the agri-nutrition disconnect: Evidence from rural areas in low and middle-income countries. Food Policy, 76, 8–18. https://doi.org/10.1016/j.foodpol.2017.12.011CrossRef

Kadiyala, S., et al. (2014). Agriculture and nutrition in India: Mapping evidence to pathways. Annals of the New York Academy of Sciences, 1331(1, Paths of Convergence for Agriculture, Health, and Wealth), 43–56. https://doi.org/10.1111/nyas.12477

Komatsu, H., Malapit, H. J. L., & Theis, S. (2018). Does women’s time in domestic work and agriculture affect women’s and children’s dietary diversity? Evidence from Bangladesh, Nepal, Cambodia, Ghana, and Mozambique. Food Policy, 79(February), 256–270. https://doi.org/10.1016/j.foodpol.2018.07.002CrossRef

Lee, Y., Haile, B., Seymour, G., & Azzarri, C. (2021). The heat never bothered me anyway: Gender-specific response of agricultural labor to climatic shocks in Tanzania. Applied Economic Perspectives and Policy, (January): 1–18.

Maccini, S., & Yang, D. (2009). Under the weather: Health, schooling, and economic consequences of early-life rainfall. American Economic Review, 99(3), 1006–1026.CrossRef

Michler, J. D., Josephson, A., Kilic, T., & Murray, S. (2021). Estimating the impact of weather on agriculture. Retrieved February 25, 2023, from http://hdl.handle.net/10986/36643

Miller, G., & Piedad Urdinola, B. (2010). Cyclicality, mortality, and the value of time: The case of coffee price fluctuations and child survival in Colombia. Journal of Political Economy, 118(1), 113–155.CrossRef

Nicholas, K., et al. (2021). Climate anomalies and childhood growth in Peru. Population and Environment, 43(1), 39–60. https://doi.org/10.1007/s11111-021-00376-8CrossRef

Nsabimana, A., & Mensah, J.T. (2020). Weather shocks and child nutrition: Evidence from Tanzania. WIDER Working Paper 2020/57. Helsinki: UNU-WIDER. https://doi.org/10.35188/UNU-WIDER/2020/814-6

Omiat, G., & Shively, G. (2020). Rainfall and child weight in Uganda. Economics and Human Biology, 38. https://doi.org/10.1016/j.ehb.2020.100877

Quisumbing, A. R., Kumar, N., & Behrman, J. A. (2018). Do shocks affect men’s and women’s assets differently? Evidence from Bangladesh and Uganda. Development Policy Review, 36(1), 3–34.CrossRef

Rabassa, M., Skoufias, E., & Jacoby, H. (2014). Weather and child health in rural Nigeria. Journal of African Economies, 23(4), 464–492.CrossRef

Randell, H., Grace, K., & Bakhtsiyarava, M. (2021). Climatic conditions and infant care: Implications for child nutrition in rural Ethiopia. Population and Environment, 42(4), 524–552. https://doi.org/10.1007/s11111-020-00373-3CrossRef

Randell, H., Gray, C., & Grace, K. (2020). Stunted from the start: Early life weather conditions and child undernutrition in Ethiopia. Social Science and Medicine, 261(July), 113234. https://doi.org/10.1016/j.socscimed.2020.113234CrossRef

Rao, N., Gazdar, H., Chanchani, D., & Ibrahim, M. (2019). Women’s agricultural work and nutrition in South Asia: From pathways to a cross-disciplinary, grounded analytical framework. Food Policy, 82, 50–62. https://doi.org/10.1016/j.foodpol.2018.10.014CrossRef

Sachs, C. E. (1996). Gendered fields: Rural women. Agriculture, and Environment. https://doi.org/10.4324/9780429493805CrossRef

Sant’Anna, P. H. C., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219, 101–122.CrossRef

Stevano, S., et al. (2019). Time-use analytics: An improved way of understanding gendered agriculture-nutrition pathways. Feminist Economics, 25(3), 1–22. https://doi.org/10.1080/13545701.2018.1542155CrossRef

Thiede, B. C., & Strube, J. (2020). Climate variability and child nutrition: Findings from Sub-Saharan Africa. Global Environmental Change, 65(October), 102192. https://doi.org/10.1016/j.gloenvcha.2020.102192CrossRef

Tiwari, S., Jacoby, H. G., & Skoufias, E. (2017). Monsoon Babies: rainfall shocks and child nutrition in Nepal. Economic Development and Cultural Change, 65(2), 167–188.CrossRef

Udry, C. (1996). Gender, agricultural production, and the theory of the household. Journal of Political Economy, 104(5), 1010–1046.CrossRef

WMO. (2012). Standardized precipitation index user guide. Retrieved January 29, 2023, from https://library.wmo.int/doc_num.php?explnum_id=7768

Yosef, S., Jones, A. D., Chakraborty, B., & Gillespie, S. (2015). Agriculture and nutrition in Bangladesh: Mapping evidence to pathways. Food and Nutrition Bulletin, 36(4), 387–404.CrossRef

Zhao, H., & Ma, Y. (2019). Evaluating the drought-monitoring utility of four satellite-based quantitative precipitation estimation products at global scale. Remote Sensing, 11(17), 2010. https://doi.org/10.3390/rs11172010CrossRef

Titel: Rainfall, mothers’ time use, and child nutrition: evidence from rural Uganda
verfasst von: Chris M. Boyd
Publikationsdatum: 01.09.2023
Verlag: Springer Netherlands
Erschienen in: Population and Environment / Ausgabe 3/2023
Print ISSN: 0199-0039
Elektronische ISSN: 1573-7810
DOI: https://doi.org/10.1007/s11111-023-00428-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2023

The effects of environmental stress on global agricultural landownership

Demographic and socioeconomic effects of environmental policies: the 1927 special goat tax and mountain depopulation in Italy

Impacts of rainfall shocks on out-migration are moderated more by per capita income than by agricultural output in Türkiye

Population and food systems: what does the future hold?

The influence of population aging on global climate policy

Rainfall shocks, soil health, and child health outcomes