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Heat shocks, maize yields, and child height in Tanzania


This paper advances previous literature that has posited a climate-nutrition link without identifying a specific pathway via agriculture. We measure the specific effects of exposure to extreme heat on maize yields in Tanzania, and then test whether prenatal heat-induced yield losses predict subsequent child growth outcomes. In the first stage we find that substituting one full day (24 h) exposure to 39 °C for a day at 29 degrees reduces predicted yield for the entire growing season by 6–11%. In the second stage we find that in utero exposure to growing degree days greater than 29 °C predicts lower postnatal HAZ scores for Tanzanian boys 0–5 years of age, but not girls. Consistent with a maternal malnutrition mechanism, we also find a negative association between maize yields and women’s body mass. Insofar as climate change is likely to increase the incidence of heat shocks in much of sub-Saharan Africa, our results suggest a significant risk of adverse nutritional impacts.

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Fig. 1

Source: NASA/Goddard Institute for Space Studies surface temperature data

Fig. 2
Fig. 3

Data availability

All data and replication files will be available on request from the authors.


  1. Growing degree days (as detailed above) measure the cumulative heat exposure of crops during the growing season, defined as the number of days in the growing season in which mean daily temperature is within the useful range for maize growth.

  2. Using a threshold of 30C (instead of 29C) in a multi-country study of maize yields in Africa, Lobell et al. (2011) also estimate a reduction of 1% of yield per GDD above the kink.

  3. All regressions for HAZ exclude children resident in the major urban hub of Dar es Salaam, where domestic yield shocks will be less relevant given the availability of imported cereals.

  4. Applying Eq. (6), we multiply the point estimate for the effect of log maize yield on HAZ times the effect of a single GDD > 29C on yield to obtain the effect of a single GDD > 29C on HAZ, and then scale that product by the mean number of such days: 0.14 ×  (− 0.6) × 7.6 = − 0.64 for boys.

  5. Our data do not distinguish mothers from other women. To at least partially address this measurement issue, we limit the sample of women here to those between the ages of 16 and 45.

  6. In addition, these studies raise the possibility that the data used here reflect a degree of selection bias, as children must have survived in utero and infancy to appear in our data, although Alderman et al. (2011) generally expect such bias to be small.


  • Abu-Saad, K., & Fraser, D. (2010). Maternal nutrition and birth outcomes. Epidemiologic Reviews, 32(1), 5–25.

    PubMed  Google Scholar 

  • Admassu, B., et al. (2017). Body composition at birth and height at 2 yrs: A prospective cohort study among children in Jimma, Ethiopia. Pediatric Research, 82, 209–214.

    PubMed  Google Scholar 

  • Alderman, H., Lokshin, M., & Radyakin, S. (2011). Tall claims: Mortality selection and the height of children in India. Economics & Human Biology, 9(4), 393–406.

    Google Scholar 

  • Amosu, A. M., & Degun, A. M. (2014). Impact of maternal nutrition on birth weight of babies. Biomedical Research, 25(1), 75–78.

    Google Scholar 

  • Banerjee, R., & Maharaj, R. (2020). Heat, infant mortality, and adaptation: Evidence from India. Journal of Development Economics, 143, 102378.

    Google Scholar 

  • Basu, R., Malig, B., & Ostro, B. (2010). High ambient temperature and the risk of preterm delivery. American Journal of Epidemiology, 172(10), 1108–1117.

    Article  PubMed  Google Scholar 

  • Belesova, K., Gasparrini, A., Sié, A., Sauerborn, R., & Wilkinson, P. (2017). Household cereal crop harvest and children’s nutritional status in rural Burkina Faso. Environmental Health, 16, 65.

    PubMed  PubMed Central  Google Scholar 

  • Belesova, K., Gornott, C., Milner, J., Sié, A., Sauerborn, R., & Wilkinson, P. (2019). Mortality impact of low annual crop yields in a subsistence farming population of Burkina Faso under the current and a 1.5 °C warmer climate in 2100. Science of the Total Environment, 691, 538–548.

    CAS  Google Scholar 

  • Belesova, K., Gasparrini, A., Sié, A., Sauerborn, R., & Wilkinson, P. (2018). Annual crop-yield variation, child survival, and nutrition among subsistence farmers in Burkina Faso. American Journal of Epidemiology, 187(2), 242–250.

    PubMed  Google Scholar 

  • Block, S., Kiess, L., Webb, P., Soewarta, K., Moench-Pfanner, R., Bloem, M., & Timmer, C. P. (2004). Macro shocks and micro outcomes: Child nutrition during Indonesia’s crisis. Economics and Human Biology, 2(2), 21–44.

    PubMed  Google Scholar 

  • Bratti, M., Frimpong, P. B., & Russo, S. (2021) “Prenatal Exposure to Heat Waves and Child Health in Sub-saharan Africa”. IZA Institute of Labor Economics Discussion Paper No. 14424

  • Burgess, R., Deschenes, O., Donaldson, D., & Greenstone, M. (2014). The unequal effects of weather and climate change: Evidence from mortality in India. London School of Economics.

    Google Scholar 

  • Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture. American Economic Journal: Economic Policy, 8(3), 106–140.

    Google Scholar 

  • Butler, E. E., & Huybers, P. (2015). Variations in the sensitivity of US maize yield to extreme temperatures by region and growth phase. Environmental Research Letters, 10(3), 034009.

    Google Scholar 

  • Butler, E., Mueller, N. D., & Huybers, P. (2018). Peculiarly pleasant weather for US maize. Proceedings of the National Academy of Sciences USA, 115(47), 11935–11940.

    CAS  Google Scholar 

  • Butler, E., & Huybers, P. (2013). Adaptation of US maize to temperature variations. Nature Climate Change, 3, 68–72.

    Google Scholar 

  • Christian, P., Lee, S. E., Angel, M. D., Adair, L. S., Arifeen, S. E., Ashorn, P., Barros, F. C., Fall, C. H. D., Fawzi, W. W., Hao, W., Hu, G., Humphrey, J. H., Huybregts, L., Joglekar, C. V., Kariuki, S.K., Kolsteren, P., Krishnaveni, G.V., Liu, E., Martorell, R., Osrin, D., Persson, L-A., Ramakrishnan, U., Richter, L., Roberfroid, D., Sania, A., Ter Kuile, F. O., Tielsch, J., Victora, C. G., Yajnik, C. S., Yan, H., Zeng, L., & Black, R. E.  (2013). Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. International Journal of Epidemiology, 42(5), 1340–55.

  • Cil, G., & Cameron, T. A. (2017). Potential climate change health risks from increases in heat waves: Abnormal birth outcomes and adverse maternal health conditions. Risk Analysis, 37(11), 2066–2079.

    PubMed  Google Scholar 

  • Conley, T. G. (1999). GMM estimation with cross sectional dependence. Journal of Econometrics, 92(1), 1–45.

    Google Scholar 

  • Danaei, G., Andrews, K. G., Sudfeld, C. R., Fink, G., McCoy, D. C., Peet, E., Sania, A., Smith Fawzi, M. C., Ezzati, M., & Fawzi, W. W. (2016). Risk factors in childhood stunting in 137 developing countries: A comparative risk assessment at global, regional, and city levels. PLOS Medicine, 13(11), e1002164.

    PubMed  PubMed Central  Google Scholar 

  • Deschênes, O., Greenstone, M., & Guryan, J. (2009). Climate and birth weight. American Economic Review: Papers & Proceedings, 99(2), 211–217.

    Google Scholar 

  • da Silva Lopes, K., Ota, E., Shakya, P., Dagvadorj, A., Balogun, O. O., Peña-Rosas, J. P., De-Regil, L. M., & Mori, R. (2017). Effects of nutrition interventions during pregnancy on low birth weight: An overview of systematic reviews. BMJ Global Health, 2(3), e000389.

    PubMed  PubMed Central  Google Scholar 

  • DiPietro, J. A., & Vogeltine, K. M. (2017). The gestational foundation of sex differences in development and vulnerability. Neuroscience, 342(7), 4–20.

    CAS  PubMed  Google Scholar 

  • Geruso, M., & Spears, D. (2018). Heat, humidity, and infant mortality in the developing world. National Bureau of Economic Research, Working Paper 24870. July. Cambridge, MA

  • Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. International Journal of Climatology, 34, 623–642.

    Article  Google Scholar 

  • Headey, D., & Masters, W. A. (2021). Agriculture and nutrition. In K. Otsuka & S. Fan (Eds.), Agricultural development: New perspectives in a changing world. IFPRI.

    Google Scholar 

  • Headey, D., Stifel, D., You, L., & Guo, Z. (2018). Remoteness, urbanization, and child nutrition in sub-Saharan Africa. Agricultural Economics, 49(6), 765–775.

    Google Scholar 

  • Hoddinott, J., Alderman, H., Behrman, J., Haddad, L., & Horton, S. (2013). The economic rationale for investing in stunting reduction. Maternal and Child Nutrition, 9(Suppl 2), 69–82.

    PubMed  PubMed Central  Google Scholar 

  • Hoddinott, J., & Kinsey, B. (2001). Child growth in the time of drought. Oxford Bulletin of Economics and Statistics, 63(4), 409–436.

    Google Scholar 

  • Hoddinott, J., Maluccio, J., Behrman, J., Flores, R., & Martorell, R. (2008). Effect of a nutrition intervention during early childhood on economic productivity in Guatemalan adults. The Lancet, 371, 411–416.

    Google Scholar 

  • Hoogenboom, G., Porter, C. H., Boote, K. J., Shelia, V., Wilkens, P. W., Singh, U., White, J. W., Asseng, S., Lizaso, J. I., Moreno, L. P., Pavan, W., Ogoshi, R., Hunt, L. A., Tsuji, G. Y., & Jones, J. W. (2019). The DSSAT crop modeling ecosystem. In K. J. Boote (Ed.), Advances in crop modeling for a sustainable agriculture (pp. 173–216). Burleigh Dodds Science Publishing.

    Chapter  Google Scholar 

  • Intergovernmental Panel on Climate Change (IPCC). (2014). 5th assessment report. United Nations Environment Program

  • Isen, A., Rossin-Slater, M., & Walker, R. (2017). Relationship between season of birth, temperature exposure, and later life wellbeing. Proceedings of the National Academy of Science (PNAS) 114(51), 13447–13452.

  • Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security—a review. Progress in Natural Science, 19(12), 1665–1674.

    Article  Google Scholar 

  • Kraemer, S. (2000). The fragile male. British Medical Journal, 321(7276), 1609–1612.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kudamatsu, M., Persson, T., & Strömberg, D. (2012). Weather and infant mortality in Africa, CEPR Discussion Papers 9222, C.E.P.R. Discussion Papers

  • Levy, K., Woster, A. P., Goldstein, R. S., & Carlton, E. J. (2016). Untangling the impacts of climate change on waterborne diseases: A systematic review of relationships between diarrheal diseases and temperature, rainfall, flooding, and drought. Environmental Science and Technology, 50(10), 4905–4922.

    CAS  PubMed  Google Scholar 

  • Lobell, D. B., & Field, C. B. (2007). Global scale climate–crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2(1), 014002.

    Google Scholar 

  • Lobell, D. B., Bänziger, M., Magorokosho, C., & Vivek, B. (2011). Nonlinear hear effects on African maize as evidenced by historical yield trials. Nature Climate Change, 1, 42–45.

    Google Scholar 

  • Lohmann, S., & Lechtenfeld, T. (2015). The effect of drought on health outcomes and health expenditures in rural Vietnam. World Development, 72, 432–448.

    Google Scholar 

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

    Google Scholar 

  • Maluccio, J., Hoddinott, J., Behrman, J., Martorell, R., Quisumbing, A., & Stein, A. (2009). The impact of nutrition during early childhood on education among Guatemalan adults. Economic Journal, 119, 734–763.

    Google Scholar 

  • Miller, R. (2017). Childhood health and prenatal exposure to seasonal food scarcity in Ethiopia. World Development, 99, 350–376.

    Google Scholar 

  • Mulmi, P., Block, S., Shively, G., & Masters, W. (2016). Climatic conditions and child height: Sex-specific vulnerability and the protective effect of sanitation and food markets in Nepal. Economics and Human Biology, 23, 63–75.

    PubMed  PubMed Central  Google Scholar 

  • National Bureau of Statistics (NBS). (2017). Government of Tanzania, Dodoma, Tanzania. National Panel Survey 2008-2009, Wave 1.

  • Niang, I., Ruppel, O. C., Abdrabo, M. A., Essel, A., Lennard, C., Padgham, J., & Urquhart, P.` (2014). Africa. In V. R., Barros, C. B., Field, D. J., Dokken, M. D., Mastrandrea, K. J., Mach, T. E., Bilir, M., Chatterjee, K. L., Ebi, Y. O., Estrada, R. C., Genova, B., Girma, E. S., Kissel, A. N., Levy, S., MacCracken, P. R., Mastrandrea, & L. L., White (Eds.) Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199–1265

  • Ortiz-Bobea, A., & Just, R. (2012). Modeling the structure of adaptation in climate change impact assessment. American Journal of Agricultural Economics, 95, 244–251.

    Google Scholar 

  • Porter, J. R., & Semenov, M. A. (2005). Crop responses to climatic variation. Philosophical Transactions of the Royal Society B, 360, 2021–2035.

    Google Scholar 

  • Roberts, M. J., Schlenker, W., & Eyer, J. (2013). Agronomic weather measures in econometric models of crop yield with implications for climate change. American Journal of Agricultural Economics, 95(2), 236–243.

    Google Scholar 

  • Rocha, R., & Soares, R. R. (2015). Water scarcity and birth outcomes in the Brazilian semiarid. Journal of Development Economics, 112, 72–91.

    Google Scholar 

  • Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., & Toll, D. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381–394.

    Google Scholar 

  • Rojas-Downing, M. M., Nejadhashemi, A. P., Harrigan, T., & Woznicki, S. A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Climate Risk Management, 16, 145–163.

    Google Scholar 

  • Rosenfeld, C. (2015). Sex-specific placental responses in fetal development. Endocrinology, 156(10), 3422–3434.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rowhani, P., Lobell, D. B., Linderman, M., & Ramankutty, N. (2010). Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology, 151, 449–460.

    Google Scholar 

  • Russo, S., Sillmann, J., Sippel, S., Barcikowska, M. J., Ghisetti, C., Smid, M., & O’Neill, B. (2019). Half a degree and rapid socioeconomic development matter for heatwave risk. Nature Communications, 10, 136.

    PubMed  PubMed Central  Google Scholar 

  • Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences USA, 106(37), 15594–15598. and Appendix.

    CAS  Google Scholar 

  • Snyder, R. L. (1985). Hand calculating degree days. Agricultural and Forest Meteorology, 35, 353–358.

    Google Scholar 

  • Steward, P. R., Dougill, A. J., Thierfelder, C., Pittelkow, C. M., Stringer, L. C., Kudzala, M., & Shackelford, G. E. (2018). The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields. Agriculture, Ecosystems & Environment, 251, 194–202.

    Google Scholar 

  • Verma, S., & Shrivastava, R. (2016). Effect of maternal nutritional status on birth weight of babies. International Journal of Contemporary Medical Research, 3(4), 943–945.

    Google Scholar 

  • WHO Multicentre Growth Reference Study Group. (2006). WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. World Health Organization.

    Google Scholar 

  • Wilde, J., Apouey, B. H., & Jung, T. (2017). The effect of ambient temperature shocks during conception and early pregnancy on later life outcomes. University of South Florida.

    Google Scholar 

  • Wineman, A., Anderson, C., Reynolds, T., & Biscaye, P. (2019). Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania. Food Security.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016). Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment International, 86, 14–23.

    Article  PubMed  Google Scholar 

  • Xu, H., Twine, T. E., & Girvetz, E. (2016). Climate change and maize yield in Iowa. PLoS ONE, 11(5), e0156083.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Authors are grateful to Kyle Emerick (Tufts University) and Avery Cohn (ex-Tufts University) as well as Kalle Hirvonen (IFPRI) for their guidance on the relevant literature and techniques on the measurement of weather variability. We thank Wahid Quabili (IFPRI) for his support during the weather data processing. This research was supported by a grant from the Bill and Melinda Gates Foundation to IFPRI in support of the Advancing Research on Nutrition and Agriculture (ARENA) project.


The work presented here was supported by a project led at IFPRI on Advancing Research in Nutrition and Agriculture (ARENA) funded by the Bill & Melinda Gates Foundation as OPP1177007 through the International Food Policy Research Institute (project number 301052.001.001.515.01.01).

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LY constructed the datasets. SB lead the statistical analysis with support from BH and DH. All authors participated in writing.

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Correspondence to S. Block.

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The authors have no relevant financial or non-financial interests to disclose.


Appendix A

(see Table

Table 7 Distribution of Tanzania National Panel Survey sample by survey round, area of residence, and share of maize growers

7; Figs. 

Fig. 4
figure 4

Spatial distribution of Tanzania’s National Panel Survey GPS data


Fig. 5
figure 5

The distribution of long-season maize yields in Tanzania across all rounds


Additional details about the weather data

The Climate Research Unit Time Series Grid Version 3.23 (CRU TS v. 3.23) at the University of East Anglia provides a monthly 0.5 degree spatial resolution gridded weather product from 1901 to 2014. Among the available climate variables, are maximum (max) and minimum (min) temperature, which reflects average daily max and min temperature for the month in °C. In addition, total monthly rainfall is reported in millimeters. Data from over 4000 weather stations are used to assign the temperature and rainfall grid values [1].

The CRU temperature and rainfall data from 1981 to 2014 were downloaded and processed as.netcdf files [2]. Once data were downloaded, a point shapefile of the household clusters for Tanzania were used to generate the value of each point for each monthly temperature and rainfall pixel it intersects with. Points that fall into a pixel with missing data were moved to the nearest pixel with data.

The output is a.csv table of every date and the temperature and rainfall values of the points for each household coordinate point for every month.

The final data was converted to a Stata file. Five columns were added as the first five columns of the original survey data table: (1) FID- the ID of the point shapefile, which can be linked back to the shapefile created to map. (2) cru_temp_min -The daily average minimum temperature for the month in °C. (3) cru_temp_max -The daily average maximum temperature for the month in °C. (4) cru_rain_mm -The total monthly rainfall in millimeters. 5) date- time is in the following format: YYYY_M or YYYY_MM.

Daily rainfall data

CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) was downloaded from as daily 0.05 degree resolution grids for all of Africa. The years 2000-present were selected.

A point shapefile of households were used to generate the value of each point for each daily rainfall pixel it intersects with. The output is a.csv table of every date and the rainfall values of the points for each household coordinate point.

Some of the coordinates in Tanzania do not intersect with the rainfall data for any day. These coordinates are near water and the rainfall pixels do not have data when the majority of pixel contains water. To compensate, the points were moved to the nearest pixel and given the value of the nearest pixel to which they were moved.

The final data was converted to a Stata file with three columns added to the first three columns of the original survey data table: (1) fid—the id of the point shapefile, which can be linked back to the shapefile created to map. (2) CHIRPS_daily_mm—The daily rainfall values in milimeters. A value of 0 simply means no rainfall for that day. (3) date—time is in the following format: YYYY.MM.DD.

If interested python script for data generation, minus the movement of the coordinates to the nearest pixel, can be found at: Please contact for any questions.

Daily temperature data

The Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS) has several simulated land surface parameters. The data are in 0.25 degree resolution and range from February 24, 2000 to present. The temporal resolution is 3-h. The simulation was created by: “combination of NOAA/GDAS atmospheric analysis fields, spatially and temporally disaggregated NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP) fields, and observation based downward shortwave and longwave radiation fields derived using the method of the Air Force Weather Agency’s AGRicultural METeorological modeling system (AGRMET)”(39).

The data are located on the OPENDAP NASA web server as GRIB and netcdf files. 22 land surface parameters are available, our interest for now was just the “near surface air temperature” parameter in Kelvins.

Once data were downloaded. A point shapefile for Tanzania were used to generate the value of each point for each 3-hourly temperature pixel it intersects with. The output is a.csv table of every date and the temperature values of the points for each household coordinate point for every 3-h.

Several of the household coordinates do not intersect with the temperature data for any day. These coordinates are near water and the temperature pixels do not have data when the majority of pixel contains water. To compensate, the points were moved to the nearest pixel and given the value of the nearest pixel to which they were moved.

The final data was converted to a Stata file. There are 8 temperature points of the 3-hourly data for each day. We take the minimum and the maximum among the eight data points as daily minimum and maximum temperatures, the average of the eight data points is the daily temperature. Five columns were added as the first three columns of the original survey data table: (1) fid- the id of the point shapefile, which can be linked back to the shapefile created to map. (2) dailyMin- The daily minimum temperature values in Kelvins. (3) dailyMax- The daily maximum temperature values in Kelvins. (4) dailyTemp- The daily average temperature values in Kelvins. (5) day- time is in the following format: YYYYDDD. The last column is “Data”, where 0 is when the household coordinate was moved to nearest pixel.

Appendix B

Heterogeneity of heat shock effects on maize yields

The yield impacts of temperature shocks may be more severe for lower productivity farmers because of lower levels of inputs, lower quality inputs (e.g. soil) or poorer management practices. To explore this possibility we employ quantile regressions, which allows us to explore the full distribution of yield data. Table 3 presents simultaneous quantile regressions at the 25th, 50th, and 75th percentiles (q25, q50, and q75, respectively), and tests the difference between q75 and q25. We find a statistically significant yield discontinuity at all three points along the yield distribution, controlling for rainfall and region in columns (1–3). Households at the 25th percentile of the yield distribution appear to suffer greater effect of high heat than more productive households (e.g., those at the 75th percentile), though the difference (column 4) is not statistically significant. Adding controls for household characteristics (columns 5–7) somewhat increases the difference in point estimates between the 25th percentile and 75th percentile households, though the difference remains statistically insignificant. The point estimates suggest, however, that households at the 25th percentile of yield lose 0.7% of maize yield for each GDD > 29C, as compared with a loss of 0.4% for households at the 75th percentile. Figure 4 illustrates these differences as a function of additional days at given temperatures over the relevant range.

(See Table

Table 8 Quantile regressions of log maize yield at the 25th, 50th, 75th percentiles of the yield distribution with interquartile differences


(See Fig. 

Fig. 6
figure 6

Effect of an additional degree day of exposure to temperatures on predicted maize yield at the 25th, 50th, and 75th Percentiles of the Maize Yield Distribution


These differences across quantiles relate directly to the role of cross-sectional geographic effects as the source of identifying variation to the extent that lower-yielding versus higher-yielding households may live in different places that vary by exposure to high heat. Figure 5 explores this by comparing the cumulative distribution functions of degree days > 29 °C across quartiles of the yield distribution for data pooled across three survey rounds. The differences across quartiles with respect to heat exposure are striking. As reflected in both the cumulative density functions and the kernel densities, the lower-yielding households (q25) have much greater exposure to extreme heat than the higher-yielding households (q75). Thus lower-yielding households both face greater exposure to high heat and suffer greater losses in yield for each hot day. Our reliance on geographic variation, however, fails to preclude the existence of potential unobserved confounding factors and thus limits a causal interpretation of these results.

(See Fig. 

Fig. 7
figure 7

Cumulative distribution functions of exposure to degree days > 29 °C, by quartile of the yield distribution (q25 (q75) indicates the 25th (75th) percentile of the yield distribution, median is q50


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Block, S., Haile, B., You, L. et al. Heat shocks, maize yields, and child height in Tanzania. Food Sec. 14, 93–109 (2022).

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  • Yield
  • Heat
  • Maize
  • Nutrition
  • Tanzania