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Cited by 573Climate Change Policy and EconomicsClimate change impacts on agricultureClimate Change and Health Impacts

Climate econometrics

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Hsiang, Solomon·2016·Annual Review of Resource Economics
DOI 10.1146/annurev-resource-100815-095343
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Abstract

Identifying the effect of climate on societies is central to understanding historical economic development, designing modern policies that react to climatic events, and managing future global climate change. Here, I review, synthesize, and interpret recent advances in methods used to measure effects of climate on social and economic outcomes. Because weather variation plays a large role in recent progress, I formalize the relationship between climate and weather from an econometric perspective and discuss the use of these two factors as identifying variation, highlighting trade-offs between key assumptions in different research designs and deriving conditions when weather variation exactly identifies the effects of climate. I then describe recent advances, such as the parameterization of climate variables from a social perspective, use of nonlinear models with spatial and temporal displacement, characterization of uncertainty, measurement of adaptation, cross-study comparison, and use of empirical estimates to project the impact of future climate change. I conclude by discussing remaining methodological challenges.

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Crossref · 10.1146/annurev-resource-100815-095343

Cites

93 of 93 references verified0 citation errors · 0 not checkable

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  • After conditioning on the fixed effects, the coefficients on maximum temperature and precipitation days can be interpreted as causal effects on the 30-day occurrence of mental health issues.

    “The estimated coefficients from f ( AVG . TMAXijkst ) and g ( PRECIP . DAYSijkts ) from this estimation can thus be interpreted as the causal effects of maximum temperature and precipitation days on the 30-d occurrence of mental health issues (45, 62).”

    From Empirical evidence of mental health risks posed by climate change (2018) · cites this paper as [45]

    In §2.1.2 (page unavailable), the paper says unit fixed effects absorb time-invariant differences and describes weather variation as usually assumed exogenous. It also warns that time-varying confounders and omitted, correlated weather variables can bias the coefficients. It therefore supports the general identification approach, but not the claim that conditioning on fixed effects alone makes these particular temperature and precipitation coefficients causal for 30-day mental-health outcomes. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Not controlling for average diurnal temperature range, cloud cover, relative humidity, and wind speed could bias the primary estimates.

    “We control for average diurnal temperature range, percentage of cloud cover, relative humidity, and wind speed, which are represented via Z η , as failure to do so may bias our primary estimates (45).”

    From Empirical evidence of mental health risks posed by climate change (2018) · cites this paper as [45]

    Section 2.1.2 (page unavailable) says estimates may be biased when important, correlated climatic variables are omitted. It does not identify diurnal temperature range, cloud cover, relative humidity, or wind speed as necessary controls, so it supports the general rationale but not the specific list. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Demographic variables may be affected by Katrina exposure, so specifications excluding demographic controls are preferred.

    “However, as these demographic variables may be affected by Katrina exposure, we prefer the specifications that exclude demographic controls (45).”

    From Empirical evidence of mental health risks posed by climate change (2018) · cites this paper as [45]

    Section 2.1.2, “Identification in time series” (page not provided), warns that controlling for nonclimatic factors affected by climatic events can introduce bad-control bias. It does not specifically address whether Katrina exposure affected demographic variables. The preference for specifications without those controls is the citing authors’ choice, not a finding attributed to this source. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Demographic variables may themselves be driven by longer-term meteorological changes and thus act as bad controls, so specifications excluding demographic controls are preferred.

    “However, as these demographic variables may themselves be driven by longer-term changes in meteorological variables and thus, serve as 'bad controls,' we prefer the specifications that exclude demographic controls (45).”

    From Empirical evidence of mental health risks posed by climate change (2018) · cites this paper as [45]

    Section 2.1.2, “Identification in time series” (page not provided), warns that time-varying nonclimatic factors can be bad controls if climatic events affect them. It gives crop prices and avoidance behavior as examples, but does not specifically address demographic variables or longer-term meteorological changes. The preference for specifications without demographic controls is the citing authors’ own choice. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Nonclimatic control variables are omitted from the main equation to avoid posttreatment bias in the parameters of interest.

    “We omit nonclimatic control variables from Eq. 1 because of their potential to generate posttreatment bias in our parameters of interest (18, 45).”

    From Empirical evidence of mental health risks posed by climate change (2018) · cites this paper as [45]

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Cites

Flagged first, then by citations

Research works printed but not linked: 6

  1. [24]24. Camargo SJ, Hsiang SM. 2016. Tropical cyclones: from the influence of climate to their socioeconomic impacts. In Extreme Events: Observations, Modeling, and Economics, ed. M Chavez, M Ghil, J Urrutia- Fucugauchi, pp. 303-42. Washington, DC: AGU/Wiley
  2. [28]28. Costinot A, Donaldson D, Smith C. 2016. Evolving comparative advantage and the impact of climate change in agricultural markets: evidence from a 9 million-field partition of the earth. J. Polit. Econ. 124(1):205-48
  3. [33]33. Deryugina T. 2015. Disaster insurance versus social insurance in the aftermath of hurricanes. Work. Pap., Cent. Energy Environ. Policy, MIT, Cambridge, MA
  4. [68]68. Kurukulasuriya P, Mendelsohn RO. 2008. A Ricardian analysis of the impact of climate change on African cropland. Afr. J. Agric. Resour. Econ. 2(1):105-26
  5. [76]76. Mas-Colell A, Whinston MD, Green JR. 1995. Microeconomic Theory. New York: Oxford Univ. Press. 1st ed.
  6. [105]105. Wooldridge J. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press

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Authors
Hsiang, S.
Affiliations
National Bureau of Economic Research (US)
Corresponding author
Hsiang, Solomon
ORCID
Hsiang, Solomon
Venue
Annual Review of Resource Economics
Publisher
Annual Reviews
Published
2016-10-05
Volume
8
Issue
1
Pages
43-75
ISSN
  • 1941-1340 (ISSN-L)
  • 1941-1359
DOI
10.1146/annurev-resource-100815-095343
OpenAlex
W4234502055
Cited by
573
References
93
Citations by year
2026: 73 · 2025: 86 · 2024: 98 · 2023: 67 · 2022: 49 · 2021: 65 · 2020: 37 · 2019: 41 · 2018: 33 · 2017: 18 · 2016: 3 · 2015: 1 · 2013: 1
FWCI
9.71
Type
journal article
Language
en
Open access
Locations
  • Annual Review of Resource Economics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Climate change impacts on agriculture
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
Keywords
weather variability, climate shocks, uncertainty quantification, history of economic development, climate policy

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