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Adaptation mitigates the negative effect of temperature shocks on household consumption

Nature Human Behaviour volume 6, pages 837–846 (2022)Cite this article

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Abstract

Consumption plays an important role in economic growth, but little is known about its response to weather extremes. This paper examines the effect of temperature shocks on consumption using high-frequency and fine-scale data from the world’s largest payment network. Our analysis shows that excessive heat and cold have a direct and immediate negative effect on various consumption activities in the short run, leading to an inverted U-shaped relationship between temperature and consumption. Consumption sensitivity varies by climate region, with cold regions being more sensitive to excessive heat. The long-run projections show that without adaptation, climate change would reduce aggregate consumption under both moderate and aggressive scenarios by the end of the century. However, no evidence of consumption reduction arises once adaptation is accounted for. The findings highlight the importance of incorporating the moderating role of adaptation in understanding consumption responses to climate change.

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Fig. 1: Consumption responses to temperature shocks.
Fig. 2: Ten-day effect on consumption categories (varying y-axis scale).
Fig. 3: Consumption responses in hot, mild and cold regions.
Fig. 4: Consumption–temperature relationship by city.
Fig. 5: End-of-century consumption projection by climate condition (equation (2)).
Fig. 6: End-of-century consumption projections by city (equation (2)).

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Data availability

The credit and debit card transaction data that support the findings of this study are from UnionPay and are confidential. We cannot disclose the data to the public under the nondisclosure agreement. Interested researchers can contact UnionPay Advisors at 86-21-61005911 or yinlianzhice@unionpayadvisors.com. The air pollution and weather data for this analysis are from public sources (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim and https://air.cnemc.cn:18014/). The data are also uploaded to Zenodo (https://zenodo.org/record/5830776#.YdzLOWhBxPY). Source data are provided with this paper.

Code availability

All computer codes and a readme file for this analysis are provided on Zenodo (https://zenodo.org/record/5830776#.YdzLOWhBxPY).

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Acknowledgements

We thank D. Rao at Cornell University for excellent research assistance, and A. Ortiz-Bobea and C. Kling, both at Cornell University, for helpful comments. The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. School of Economics, Shanghai University of Finance and Economics, Shanghai, China

    Wangyang Lai

  2. Dyson School of Applied Economics and Management, Cornell University, NBER and RFF, Ithaca, NY, USA

    Shanjun Li

  3. International Food Policy Research Institute, Washington, DC, USA

    Yanyan Liu

  4. Department of Economics, Cornell University and NBER, Ithaca, NY, USA

    Panle Jia Barwick

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  1. Wangyang Lai
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Contributions

W.L., S.L., Y.L. and P.J.B. designed and performed the research and wrote the paper. W.L. and S.L. analysed the data.

Corresponding authors

Correspondence to Wangyang Lai, Shanjun Li, Yanyan Liu or Panle Jia Barwick.

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Nature Human Behaviour thanks James Rising, Ashwin Rode and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Consumption Trends: UnionPay vs. National Accounts.

This figure plots annual GDP (blue) and total retail consumption (red) which are sourced from the National Bureau of Statistics of China (NBS), and total bank card spending (green), sourced from UnionPay.

Source data

Extended Data Fig. 2 The Number of Active Bank Cards per Capita, 2015.

Bank cards include debit and credit cards. Active bank cards are defined as cards that have been used at least once in a given year. Each card is assigned to one primary city based on the location of its most frequent usage. Population measure is year-end registered population of each city. The shapefile is from the Digital Map Database of China from Harvard Dataverse64 (https://doi.org/10.7910/DVN/DBJ3BX).

Source data

Extended Data Fig. 3 Fraction of Days by temperature bins.

This figure plots the fraction of days by temperature bin in hot, mild and cold regions. The three regions are classified based on the 30-year average temperature from 1981 to 2010. The hot, mild, and cold regions include cities with the average temperature in the highest 30%, middle 30% and lowest 40% of the distribution, respectively. The number of observations is 594,706.

Source data

Extended Data Fig. 4 Robustness Checks of Eq. (1).

The figure presents robustness checks on the inverted U-shaped relationship between temperature and consumption from Equation (1). Panel (a) excludes air pollution and other weather variables as controls. Panel (b) uses log consumption as the dependent variable. Panel (c) replaces city by year-quarter fixed effects with city-specific linear time trends. Panel (d) uses the number of days in each temperature bin as the key regressors. The coefficients are interpreted as the impact from exchanging one day from a given temperature bin with one day from the reference bin. All panels include city fixed effects, city by holiday fixed effects, and day-of-the-sample fixed effects. Shaded areas show the 95% confidence intervals and the centers measure the change in spending. Standard errors are clustered at the city level in all panels. The number of observations is 594,706.

Source data

Extended Data Fig. 5 Residuals.

The figure reports residuals from our baseline model Equation (1). Panel (a) shows the histogram of residuals, where the blue line indicates a normal density function. Panel (b) shows the Q-Q plot, where the blue line indicates the 45 degree line. Panel (c) reports the residuals over time from our baseline model Equation (1). The residuals do not exhibit any seasonable patterns, suggesting that the baseline model performs well in capturing seasonality. Panel (d) accesses the robustness to extreme values by trimming 1%, 5% and 10% on both ends of the sample distribution (that is, trimming 2%, 10% and 20% in total). Shaded areas show the 95% confidence intervals and the centers measure the change in spending. First, the qualitative results remain after trimming 2%, 10%, and even 20% of data, indicating that the inverted U-shaped relationship between temperature and spending is not driven by the tails of the distribution. Second, on cold days, the size of the estimated effects remains quite stable. Third, on hot days, the magnitudes reduce (gradually) from -7.03 to -4.15 yuan (-5.85% to -3.45%) after trimming from 2% to 10% of data, using the bin of >=85∘F as an example, suggesting relatively larger heterogeneity in effect size across spending levels in the right tail of the distribution. Nevertheless, the magnitude of impact is more stable across specifications with trimming from 10% to 20% of the data. Note that the distribution of expenditure data tends to have positive skewness intrinsically. If trimming off a large proportion of the sample, the representativeness of our sample as well as inference could be compromised. Therefore, the specification with 2% trimming (1% on each side) is kept as the baseline specification.

Source data

Extended Data Fig. 6 Robustness Checks of Eq. (2).

The figure assesses the robustness of consumption-temperature relationships by climate zone according to Equation (2). Panel (a) excludes air pollution and other weather variables as controls. Panel (b) uses log consumption as the dependent variable. Panel (c) replaces city by year-quarter fixed effects with city specific time trends. Panel (d) uses a more demanding splines with knots at 10-degree increments from 40∘F to 90∘F (see Supplementary Table 5 for coefficient estimates). All panels include city fixed effects, city by holiday fixed effects and day-of-the-sample fixed effects. Shaded areas show the 95% confidence intervals and the centers measure the change in spending. Standard errors are clustered at the city level in all panels. The number of observations is 594,706.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Tables 1–7, summary statistics, regression results and sections ‘Intertemporal substitution’, ‘The role of income’, ‘Robustness to payment methods’, ‘Seasonality’ and ‘Alternative modeling choice’.

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Lai, W., Li, S., Liu, Y. et al. Adaptation mitigates the negative effect of temperature shocks on household consumption. Nat Hum Behav 6, 837–846 (2022). https://doi.org/10.1038/s41562-022-01315-9

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