The effects of weather conditions on economic growth: Evidence from global subnational economic output and income

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The effects of weather conditions on economic growth: Evidence from global subnational economic output and income

https://doi.org/10.21203/rs.3.rs-3334030/v1

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The effects of weather on economic growth continue to be debated. Previous studies focused on economic output, but income better reflects living standards; income and output are the same at the national level, but differ at a finer spatial scale. This study assembles a unique database comprising global subnational GDP and GDI per capita data from over 1600 regions across more than 180 countries and analyzes the effects of weather conditions on economic growth. The results reveal the following: 1) There is a significant negative effect of annual mean temperature on GDI per capita growth, while weather conditions do not significantly affect GDP per capita growth. 2) We find significant interaction effects between weather and weather variability, as well as different adaptations between rich and poor regions. 3) The omission of data from a large number of poor and hot countries in previous subnational research leads to an underestimation of the economic impact of weather shocks. Focusing on output rather than income and omitting data, previous studies appear to have underestimated the impact of climate change.

Scientific community and society/Social sciences/Economics

Earth and environmental sciences/Environmental social sciences/Climate-change impacts/Governance

climate change

climate damages

gross domestic product

gross domestic income

panel regression

Introduction

Estimates of the effects of weather conditions on economic growth are important for the social cost of carbon (1–3), the inequitable impact of climate change (4–6), and the physical risks to financial institutions (7–9). Early estimates focused on national impacts and thus had difficulty disentangling the effect of climate from the effects on institutions (10–12). Later studies used subnational records (states, provinces, etc.) to partially overcome this and, as nationally averaged rainfall is a relatively meaningless concept, to document the impact of precipitation on economic growth (13, 14). One limitation of such studies is that they focus on economic output to measure economic activity, rather than income.

At the national level, ignoring statistical differences, economic output, Gross Domestic Product (GDP), and economic income, Gross Domestic Income (GDI), are the same as the value of goods and services produced equals the income earned from producing those goods and services. However, this is not true at the subnational levels. The greatest discrepancies are in business districts in inner cities (high output, low income), in retirement enclaves (high income, low output) and in mines, and oil and gas fields (high output, low income). Output is recorded at the location of the economic activity, while income is recorded at the place of residence. The difference between output and income is correlated with climate. For instance, the exploitation of oil and gas in the harsh climates of Siberia and the Arabian desert yields high output, but hardly anyone lives there and certainly not the owners (SI appendix, Fig. S3c). Growth rates also differ (Fig. 1).

However, because output data is easier to collect for subnational regions, previous studies use output as the measure of economic activity (15–17), resulting in a potentially biased estimation of the impact of weather on people’s living standards (18). In addition, previous subnational studies (13–14) have a large number of missing observations in Africa, which may bias the results. To address these issues, this study established a unique database comprising global subnational GDP per capita and GDI per capita data from over 1600 regions across more than 180 countries combined with temperature and precipitation data. We estimate the effects of weather conditions on both output and income growth. Weather is measured by the annual mean temperature, total precipitation, the change in temperature and precipitation, and the month-to-month variability of temperature and precipitation. We also study the interactions between weather conditions and heterogeneity between different regions.

Main findings

Weather, weather change, and weather variability have no significant effect on output per capita growth (SI Appendix, Table S4, column 6), consistent with some prior research (12, 13). However, we find significant effects of temperature and precipitation on income per capita growth, with optimum temperatures and precipitation levels of 10℃ and 250 cm, respectively (Fig. 2, SI Appendix, Table S4, column 3). The current average regional mean temperature (19℃) exceeds the optimum temperature. If the annual mean temperature further increases by 1°C, income per capita growth is expected to decrease by 1.4%. In contrast, the current average regional total precipitation (120 cm) is well below the optimum precipitation, with a 1 cm increase in precipitation resulting in an increase of 1.9% in GDI per capita growth. However, the projected change in precipitation is highly heterogeneous across the world. While some regions may experience a substantial increase in precipitation, others may experience a decrease. Therefore, the results of our analysis regarding the averaged regional total precipitation should be interpreted with caution.

Interaction effects

Interactions between temperature and temperature variability, as well as precipitation and precipitation variability, have a statistically significant effect on income per capita growth, but cross-variabilities do not display such effects (SI Appendix, Table S5). Specifically, the effect of temperature on income per capita growth remains unchanged with changes in temperature variability (Fig. 3b), but the temperature variability is expected to reduce its negative effect on income per capita growth with increasing temperature (Fig. 3c). Particularly in hot regions (30℃), the rise of temperature variability leads to an increase in income per capita growth rather than a reduction. Precipitation and precipitation variability show a positive synergistic effect on income per capita growth, a tendency that intensifies with an increase in precipitation and precipitation variability (Fig. 3e-f). These findings suggest that the effective adaptation that large weather variability in harsh climates does not exacerbate the negative effects on the economy. The interaction effects of weather conditions on output are almost the same as those on income but with wider confidence intervals (SI Appendix, Figure S4).

Heterogeneity analysis

Economic income in poor regions displays higher sensitive to the annual mean temperature compared to rich regions (Fig. 4a; SI Appendix, Table S6, columns 1 and 2). Specifically, a 1℃ increase in temperature leads to an additional reduction of 0.09% in income per capita growth in poor regions compared to 0.07% in rich regions. However, the income per capita growth in the poor regions shows lower sensitivity to temperature change and variability, as well as all precipitation conditions (Fig. 4a-b). These results suggest the different adaptations of rich and poor regions to different weather conditions. In particular, the effect of temperature variability in poor regions reverses from negative to positive with the temperature increase, while it remains negative in rich regions (Fig. 4c-b). The effect of precipitation variability shows a similar trend that the positive effect of precipitation variability in poor regions is more pronounced with the increase of precipitation compared to the rich regions (Fig. 4e-f).

In terms of economic output, we find that poor regions are more sensitive to all effects of temperature conditions and the effect of precipitation (SI Appendix, Figure S5a-b). In addition, although the increase in temperature mitigates the negative effect of temperature variability in both rich and poor regions, this effect is more pronounced in rich regions than in poor regions (SI Appendix, Figure S5c-f). These results suggest that the economic output in poor regions is more sensitive to weather conditions than their economic income. Studies based on economic output advocate that poor regions are expected to have higher economic damage with the increase of climate change. However, when considering the economic income, the effects of weather conditions on poor regions may not be as bad as people expected.

Robustness Checks

We compared our main findings with those derived from two widely used output databases: the World Bank database (11) and the Kalkuhl database (13). Since the World Bank data is at the country-level, we aggregated the subnational data to the country-level and focused on shared countries with the same time series to ensure the comparability of results. We find that our results are similar to those based on the World Bank database (SI Appendix, Table S15, columns 1 and 2), suggesting the reliability of our GDP per capita data. However, when we use the Kalkuhl database, the statistical significance of annual mean temperature and annual total precipitation is substantially lower than the results based on the other two databases (SI Appendix, Table S15, columns 3). In addition, when we subset our database with the same countries as the Kalkuhl database (subnational level), the coefficients of annual mean temperature and annual total precipitation are also lower than those based on whole observations (SI Appendix, Table S16, columns 1 and 3). This may be because the Kalkuhl database only covers 77 countries and omits vast majority of countries in Africa, Southeast Asia and Central America, which are regions that experience higher temperature, precipitation and poverty. Our research shows that these regions’ economic output is more sensitive to the weather. Omitting these regions, therefore, may underestimate the effects of temperature and precipitation on economic output growth. We also replaced our weather database from CRU to ERA5 and used the weighted anomaly standardized precipitation. All of these results are consistent with our main results.

The effect of weather conditions on the economy provides valuable insight into the damage caused by climate change. However, empirical findings in the literature are often contradictory. In addition, previous literature uses economic output as the measure of economic activity, even though income better reflects living standards.

Using a global subnational economic database comprising over 1600 regions across more than 180 countries, our study finds that weather conditions have no growth effects on economic output per capita. However, we find strong evidence that the changes in annual mean temperature and annual total precipitation affect income per capita growth. This finding suggests that using the output to measure economic activity will underestimate the damage of weather conditions.

There are significant interactions between temperature and temperature variability, and between precipitation and precipitation variability. The increase of temperature and precipitation is expected to have a positive effect on the relationship between weather variability and economic growth. The negative effects of adverse weather conditions on poor regions may not be as high as in some previous studies. Although the change in annual mean temperature has more negative effects on poor regions, they are less sensitive to the annual change and monthly variability of temperature. In addition, the annual changes and monthly variability of precipitation are expected to have positive effects on poor regions.

Overall, there are three contributions of this study. Despite this progress, further analysis is required to reveal the mechanisms behind these effects. For instance, more research is needed to explain why annual changes and monthly variability in precipitation have positive effects on poor regions. As a first contribution, this study fills the research gap on the effects of weather conditions on income growth, highlighting the negative effects of weather conditions on people’s living standards. It also provides a basis for updating the damage function in the social cost of carbon estimation. Second, using global economic subnational data, this study provides a complete picture of the effects of weather conditions on economic growth. Studies that omitted tropical counties may underestimate the effects of weather conditions on economic growth. Third, this study provides more evidence of different adaptations of regions to different weather conditions.

Data Source

The temperature and precipitation data used in this study were derived from the CRU database, which provides a high-resolution (0.5°×0.5° resolution), monthly grid of land-based observations going back to 1901. The CRU database has implemented a degree of homogenization and revealed no substantial discrepancies with other climate databases. It has been widely used throughout the literature, allowing us to compare our results with other research findings (13, 19, 20). We also conducted a robustness check by using the reanalysis data from the ERA5 dataset.

The gross domestic income per capita (2011 PPP) used in this study is obtained from the Global Data Lab (https://globaldatalab.org/shdi/table/lgnic/). For high-income countries and some middle-income countries, subnational GDI per capita was obtained based on data derived from national statistical offices and Eurostat. In the case of most low- and middle-income countries, where official statistical data are lacking or unreliable, subnational GDI per capita data were obtained based on the International Wealth Index (IWI). It is an asset-based wealth index constructed on data from 165 surveys over 2.1 million households in 97 low- and middle-income countries. It measures household wealth on the basis of information from asset ownership, housing quality, and access to public services. Compared to statistical data from various countries and other surveys, the most significant feature of the IWI is its comparability across countries and over time, as it relies on a common set of assets(21). Based on the IWI score, the subnational GDI per capita data were estimated using a regression model between national GDI per capita and national IWI score. It is further scaled to make their population weighted mean for a given year equals the national GNI values derived from the UNDP-HDI database (SI Appendix, Table S1). Up to now, the subnational GDI per capita data has been used in sub-national HDI assessments and relevant studies within developing countries (22, 23, 24).

The gross domestic product per capita (2011 PPP) is obtained from Kummu, Taka and Guillaume (25). The database was initially collected by Gennaioli, La Porta, Lopez-de-Silanes and Shleifer (15) based on various government statistical agencies. It includes GDP data from 1569 subnational regions across 110 countries between 1990 and 2010 and covers most countries in Central and South Africa, which is generally omitted by other GDP databases. Kummu, Taka and Guillaume (25) extended the time series of this database from 2010 to 2015 and filled in the missing countries based on national GDP data. Overall, the database extended by Kummu, Taka and Guillaume (25) covers the global subnational GDP data with no missing data areas and converts the data to constant international US dollars from 2005 to 2011, which is consistent with the gross domestic income data used in this study (SI Appendix, Table S1).

Two other socioeconomic variables, mean years of schooling, and population, are included in our regression model as control variables since they are also regarded to significantly impact economic growth. These data are also collected from the Global Data Lab (Access data: 22 November 2022).

Weather variability

Extreme weather events have been found to have a negative impact on human psychology, water supply, and agricultural production that further systematically increase the risk of conflict, violence, or political instability (26–31). However, most recent macroeconomic studies have focused on the level effects of weather conditions on economic growth while neglecting to identify the effects of extreme weather events, which have been found to have pronounced effects on economic activity as well (14, 32). This study therefore uses the Anomaly Standardized Precipitation (ASP) and Anomaly Standardized Temperature (AST) index as measures to identify the effects of weather variability. The ASP and AST are defined by an annual sum of monthly precipitation or temperature anomalies from their climatological means:

$${S}_{r,y}=\sum _{m=1}^{12}\frac{{vec{T}}_{r,m,y}-{\stackrel{-}{vec{T}}}_{r,m}}{{\sigma }_{r,m}}$$

Where ${S}_{r,y}$ is the anomaly standardized precipitation/temperature in region $r$ and year $y$. ${vec{T}}_{r,m,y}$ is the monthly total precipitation or monthly mean temperature. ${\sigma }_{r,m}$ is the historical standard deviation, for 25 years, of monthly total precipitation or monthly mean temperature in that region. The annual ${S}_{r,y}$ is further standardized by its standard deviation over 25 years to obtain a measure of the relative severity of annual precipitation/temperature variability (see SI Appendix for the origins of the method):

$${ASvec{T}}_{r,y}={S}_{r,y}/{\sigma }_{{S}_{r}}$$

Empirical model

We use the fixed-effects regression model to identify the effects of weather conditions on economic growth since this model can strengthen the causal effects identification by controlling for both unobserved time-invariant and time-varying influences, such as the influences from geographical location and institutional differences (10–14). Apart from the effects of weather variability, we also consider the effects of weather and weather change in our regression model. In particular, the effect of weather is measured by $\beta T+\beta {T}^{2}$. It captures the nonlinear effect of the prevailing weather conditions on transitory and long-run economic growth. The difference between transitory and long-run growth effects is that the transitory effects imply economic growth can eventually reverse itself as the weather conditions return to their prior state. However, the long-run growth effects are not reversed, and a failure to innovate in one period leaves the country permanently further behind (10). (We additionally conducted a long-difference regression to clarify the specific effects revealed by the quadratic function of temperature and precipitation. See SI Appendix, Table S8). The effect of weather change is measured by $\beta Delta T+\beta Delta T\bullet T$ and captures a sudden change in weather conditions on contemporaneous growth rates. If there is no change between the two years, this effect disappears. The interaction term $\beta Delta T\bullet T$ is to capture the moderating effects of the prevailing weather conditions $T$ on ∆T. The regression model is thus defined as follows:

$${g}_{i,t}={\beta }_{1}{Delta vec{T}}_{vec{i},vec{t}}+{\beta }_{2}{Delta vec{T}}_{vec{i},vec{t}}\bullet {vec{T}}_{vec{i},vec{t}}+{\beta }_{3}{vec{T}}_{vec{i},vec{t}}+{\beta }_{4}{vec{T}}_{vec{i},vec{t}}^{2}+{\beta }_{5}{vec{A}vec{S}vec{T}}_{vec{i},vec{t}}+{\beta }_{6}{vec{A}vec{S}vec{T}}_{vec{i},vec{t}}^{2}+$$

$${X}_{i,t}+{\alpha }_{i}+{\delta }_{t}+{\epsilon }_{i,t}$$

where ${\text{g}}_{i,t}$ is the GDP per capita or GDP per capita growth rate within year $t$ in region $i$. ${vec{T}}_{vec{i},vec{t}}$ is a vector of annual mean temperature levels (T, in ℃) and annual mean precipitation values (P, in m). Coefficients ${\beta }_{1}$ and ${\beta }_{2}$ capture the effect of weather change on economic growth. Coefficients ${\beta }_{3}$ and ${\beta }_{4}$ represent the effect of weather on economic growth. ${\beta }_{5}$ and ${\beta }_{6}$ capture the effect of weather variability on economic growth. ${\alpha }_{i}$ and ${\delta }_{t}$ are region and year dummies to consider region and year fixed effects, respectively. ${X}_{i,t}$ are control variables, including the mean year of schooling and population. ${\epsilon }_{i,t}$ is the error term.

In addition, a gross of the literature shows that the increase in global temperature results in an observable increase in both the intensity and frequency of anomalous events (33–35). In addition, the effect of the global mean temperature on such events is nonlinear, with a minor increase in mean temperature leading to a substantial escalation in the frequency and intensity of anomalous events (36–38). In this case, the increase in temperature and precipitation (weather effect) is expected to intensify the effects of change in anomalous events (variability effect) on economic growth and vice versa. To assess the interaction effects between the level and variability of weather conditions, we further included the interaction term of them in Eq. (3) as follows:

$${\gamma }_{1}{L}_{i,t}\bullet {V}_{i,t}+{\gamma }_{2}{L}_{i,t}^{2}\bullet {V}_{i,t}+{\gamma }_{3}L\bullet {V}_{i,t}^{2}+{\gamma }_{4}{L}_{i,t}^{2}\bullet {V}_{i,t}^{2}+{X}_{i,t}+{\alpha }_{i}+{\delta }_{t}+{\epsilon }_{i,t}$$

Where ${L}_{i,t}$ is the annual mean temperature level or annual mean precipitation value within year $t$ in region $i$. ${V}_{i,t}$ is the AST or ASP.

Given the limited adaptive capacity and a higher share of agriculture in economic activity, poor regions are regarded to be more vulnerable to climate change than rich regions (24). To assess the heterogeneity of the level and variability effects, as well as the interaction effects of climate conditions on economic growth, we reassess our results separately for subnations with above- and below- median subnational GDI per capita or GDP per capita. The regression model for heterogeneity of level and variability effects reads:

$${g}_{i,t}={\beta }_{1}D\bullet {Delta vec{T}}_{vec{i},vec{t}}+{\beta }_{2}D\bullet {Delta vec{T}}_{vec{i},vec{t}}\bullet {vec{T}}_{vec{i},vec{t}}+{\beta }_{3}D\bullet {vec{T}}_{vec{i},vec{t}}+{\beta }_{4}D\bullet {vec{T}}_{vec{i},vec{t}}^{2}+{\beta }_{5}D\bullet {vec{A}vec{S}vec{T}}_{vec{i},vec{t}}+{\beta }_{6}D\bullet {vec{A}vec{S}vec{T}}_{vec{i},vec{t}}^{2}+$$

$${X}_{i,t}+{\alpha }_{i}+{\delta }_{t}+{\epsilon }_{i,t}$$

The regression model for heterogeneity of interaction effects reads:

$${\gamma }_{1}D\bullet {L}_{i,t}\bullet {V}_{i,t}+{\gamma }_{2}D\bullet {L}_{i,t}^{2}\bullet {V}_{i,t}+{\gamma }_{3}D\bullet L\bullet {V}_{i,t}^{2}+{\gamma }_{4}{D\bullet L}_{i,t}^{2}\bullet {V}_{i,t}^{2}+{X}_{i,t}+{\alpha }_{i}+{\delta }_{t}+{\epsilon }_{i,t}$$

Where $D$ is a binary variable that equals 1 when GDI per capita or GDP per capita is below the median and 0 otherwise.

Considering that the regions’ adaptation to climate change may not change rapidly over a short period, we took the five-year average of GDI per capita and GDP per capita for each region and determined their median values separately. The regions with averaged GDI per capita or GDP per capita above the median are considered to be rich, while those below the median are considered to be poor.

Author Contributions: Jinchi Dong collected and analyzed the data and wrote the manuscript; Richard S.J. Tol designed this study and conceptualized the structure of this manuscript; Jinnan Wang provided critical revisions.

Acknowledgments

The authors would like to extend their sincere appreciation to Dr Shqiponja Telhaj for her invaluable comments. This work was supported by the China Scholarship Council (Grant No. 202206190157).

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There is NO Competing Interest.

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The effects of weather conditions on economic growth: Evidence from global subnational economic output and income

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The effects of weather conditions on economic growth: Evidence from global subnational economic output and income

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Abstract

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Main Text

Introduction

Main findings

Interaction effects

Heterogeneity analysis

Robustness Checks

Discussion

Materials and Methods

Data Source

Weather variability

Empirical model

Declarations

References

Additional Declarations

Supplementary Files

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