Could Weather Fluctuations Affect Local - Specialized Research AI at MIT

Could Weather Fluctuations Affect Local
Economic Growth? Evidence from Counties
in the People’s Republic of China
Chengzheng Li, Jiajia Cong, and Haiying Gu∗

This paper uses historical ﬂuctuations of weather variables within counties in
the People’s Republic of China to identify their effects on economic growth
from 1996 to 2012. We ﬁnd three primary results. First, higher temperatures
signiﬁcantly reduce the growth rate of county-level gross domestic product
per capita: an increase in the annual average temperature of 1°C lowers the
growth rate by 1.05%–1.25%. The effect of higher temperatures is nonlinear.
Second, ﬂuctuations in temperature and precipitation not only have a level
effect, they also have a substantial cumulative effect. Third, weather ﬂuctuations
have wide-ranging effects. Beyond their substantial effects on the growth rate
of agricultural output, they also affect nonagriculture sectors, labor productivity,
and investment. Our ﬁndings provide new evidence for the impact of weather
changes on economic development and have major implications for adaptation
policies.

Keywords: climate change, economic growth, precipitation,
weather shocks
JEL codes: O13, O44, Q54, Q56

temperature,

I. Introduction

It is a controversial question whether climate conditions are central to
economic development. Cross-country analysis shows that hot countries tend to
be poor. The average growth rate of tropical countries was 0.9% lower than that of
nontropical countries from 1965 to 1990 (Gallup, Sachs, and Mellinger 1999). The
income per capita of Africa in 1992 was equivalent to the income level of Western
Europe in 1820 (Maddison 1995). The prevalence of tropical climate diseases and

∗Chengzheng Li: Institute for Economics and Social Research, Jinan University, Guangzhou, People’s Republic of
China (PRC). E-mail: lichengzheng0614@gmail.com; Jiajia Cong (corresponding author): School of Management,
Fudan University, Shanghai, PRC. E-mail: jjcong@fudan.edu.cn; Haiying Gu: Antai College of Economics and
Management, Shanghai Jiao Tong University, Shanghai, PRC. E-mail: ghy@sjtu.edu.cn. This research is supported
by the National Natural Science Foundation of China (71333010), National Social Science Foundation of China
(16ZDA019), Young Scientists Fund of the National Natural Science Foundation of China (71903074), Young
Scientists Fund of Natural Science Foundation of Guangdong Province (2018A030310658), and Shanghai Pujiang
Program (2019PJC007). We thank Benjamin Jones, Baohua Zhu, Xi Zhu, Mingwang Cheng, Qinghua Shi, and Deyu
Zhao, as well as the managing editor and the anonymous referees for helpful comments and suggestions. The Asian
Development Bank recognizes “China” as the People’s Republic of China. The usual ADB disclaimer applies.

Asian Development Review, vol. 37, no. 2, pp. 201–224
https://doi.org/10.1162/adev_a_00154

© 2020 Asian Development Bank and
Asian Development Bank Institute.
Published under a Creative Commons
Attribution 3.0 International (CC BY 3.0) license.

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202 Asian Development Review

the lack of suitable weather conditions for agriculture are the major reasons for the
underperformance of African countries (Sachs 2001, Masters and McMillan 2001,
Sachs 2003). However, many other studies cast doubt on these results. They ﬁnd
that the impacts of geography and climate on economic development are negligible
once national characteristics (e.g., institutions and trade policy) are controlled for in
cross-sectional regressions (Acemoglu, Johnson, and Robinson 2002; Sachs 2003;
Rodrik, Subramanian, and Trebbi 2004). Recent studies using a global database
with resolution of 1° latitude by 1° longitude (Nordhaus 2006) and subnational data
at the municipal level (Dell, Jones, and Olken 2009) ﬁnd that the negative effect of
temperature on income remains, but that its magnitude is attenuated.1

In addition to possible omitted variable bias, the cross-sectional analysis
utilized in the above studies cannot reﬂect the contemporaneous effect of weather
since cross-sectional regression describes the long-run equilibrium relationship
between climate variables and the economy. Recently, a growing number of
empirical studies exploit country-level panel data to estimate the effect of weather
ﬂuctuations on national income (e.g., Dell, Jones, and Olken 2012; Heal and Park
2013; Burke, Hsiang, and Miguel 2015). The panel approach identiﬁes the effects
of weather variables by exploiting their variations within an economy over time.
Since variations in weather variables are strictly exogenous and stochastic, this
approach can easily yield causative identiﬁcation (Deschenes and Greenstone 2007,
Deschenes 2014, Barreca et al. 2016) and can clearly isolate the effects of weather
from time-invariant country characteristics (Dell, Jones, and Olken 2012).

This paper uses county-level panel data on temperature and precipitation in
the People’s Republic of China (PRC) from 1996 to 2012 to examine their effects on
the growth rate of county-level gross domestic product (GDP) per capita.2 Our panel
estimation shows that the growth rate of the county-level economy is negatively
related to the average temperature: increasing the annual average temperature by
1°C reduces the growth rate of county-level GDP per capita by 1.05%–1.25%.
Precipitation ﬂuctuations have a negative effect on the growth rate, especially in
agricultural counties and poor counties. Panel-distributed lag models show that the
impacts of weather variations are persistent over time since the cumulative effects
of weather variations are larger than their instantaneous effects. Increasing annual
average temperature by 1°C reduces the cumulative growth rate of county-level
GDP per capita by 2.03%–3.84%. When 5 or 10 lags of weather variables are
incorporated into the regression, the cumulative effect of precipitation becomes

1For example, when temperature rises by 1°C, the average income per capita of 12 countries in the Americas
is reduced by 1.2%–1.9% at the municipal level (Dell, Jones, and Olken 2009). The results from cross-sectional data
show that a 1°C increase in temperature is associated with an 8.5% decrease in national income per capita (Horowitz
2009; Dell, Jones, and Olken 2009).

2A longer version of the weather dataset from 1980 to 2012 is employed when lags of weather variables are

needed.

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Could Weather Fluctuations Affect Local Economic Growth? 203

signiﬁcantly negative. In other words, the cumulative effect of precipitation only
shows up in the medium run.

By examining the relationship between weather and the growth rate of three
sectors, we ﬁnd that high temperatures have a substantial negative effect on the
growth rate of the value added of primary industry and tertiary industry, while
the negative effect on the growth rate of secondary industry is insigniﬁcant. With
seasonal average temperature, we ﬁnd that only high temperatures in spring and
summer have signiﬁcantly negative effects on the growth rate of GDP per capita.
The negative effect of seasonal temperature is mainly caused by the effect on
primary industry. We construct temperature bins to detect the nonlinear effect of
daily average temperature on growth. Compared with the reference bin [15, 20)°C,
temperatures above 20°C have signiﬁcantly negative effects on the growth rate of
GDP per capita, while extremely low temperatures and temperatures within [0,
15)°C have no signiﬁcant effect. The negative effect of temperatures above 20°C
is mainly levied on primary industry. Consistent with Colmer (2018) and Emerick
(2018), we ﬁnd that high temperatures have signiﬁcantly positive effects on the
growth rate of secondary industry, which indicates evidence of resource reallocation
among economic sectors. Most temperature bins do not show a signiﬁcant effect on
the growth rate of tertiary industry. Regarding other possible effects of weather on
county-level economies, we ﬁnd that high temperatures have a signiﬁcantly negative
effect on the growth rate of labor productivity and ﬁxed asset investment.

The effects identiﬁed through short-run weather ﬂuctuations may differ from
the long-run effects. For example, counties may adapt to climate change in the long
run and mitigate the short-run effects of weather variations. However, our data span
only 17 years, and the long-run effects cannot be fully investigated. Following Dell,
Jones, and Olken (2012), we make an initial attempt in this direction to explore
the effects of changes in weather in the medium run. The medium-run estimates
show that temperature change and precipitation change each have a signiﬁcantly
negative effect on the growth rate, and the coefﬁcients are larger in magnitude than
their counterparts in the panel analysis. This result implies that counties in the PRC
adapt poorly in our sample period and the negative impacts of changes in weather
accumulate over time.

Most existing studies on weather ﬂuctuations and growth use cross-country
analysis (Dell, Jones, and Olken 2012; Burke, Hsiang, and Miguel 2015; Heal
and Park 2013). A notable exception is Burke and Tanutama (2019) who use
district-level data from 37 countries. But their study does not control for any weather
variable other than temperature, which could cause omitted variable bias. In contrast
to the obvious shortcomings in cross-country analysis (Burke and Tanutama 2019),
our study enjoys several advantages. First, our cross-county analysis substantially
increases the number of cross-sectional observations. Although there are more
than 200 countries (regions) in the world, fewer than 140 of them are applicable
in cross-country analysis. In contrast, our data contain 1,800 counties. Second,

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204 Asian Development Review

our cross-county analysis reduces the risk of omitted variable bias. Possible
omitted variables—such as institutions, industrial policy, trade policy, and other
unobserved time-invariant factors—are similar for different counties within a
country. Moreover, our empirical analysis contains all major climatic variables,
which further reduces the omitted variable bias. Third, our county-level panel
data have rich statistics, and we can explore many possible channels through
which weather ﬂuctuations affect economic outcomes. In contrast, studies using
country-level data can only test very limited channels due to data deﬁciencies. To
the best of our knowledge, this paper is the ﬁrst to study the effect of weather
ﬂuctuations on the economic growth of counties in the PRC and provide novel
evidence on potential channels for weather–economy relationships.3

The remainder of this paper is organized as follows. Section II introduces
the data and provides descriptive statistics. In section III, we establish a theoretical
framework and describe our estimation strategy. Section IV presents the main
results and various robustness checks. Section V examines potential channels
through which weather affects the growth of aggregate economic outcomes. In
section VI, we estimate the impacts of weather changes in the medium run. The
discussion and conclusion are presented in section VII.

II. Data and Summary Statistics

Data

Our weather data come from the China Meteorological Data Sharing Service
System, which is directed by the National Meteorological Information Center.
This grid dataset provides nationwide terrestrial daily average temperature and
daily total precipitation data at 0.5° × 0.5° degree resolution, spanning from 1
January 1980 to 31 December 2012. Zhang, Zhang, and Chen (2017) highlight the
importance of weather variables other than temperature and precipitation. Thus,
other weather variables—including atmospheric pressure, wind speed, sunshine
hours, and relative humidity—are introduced into our empirical analysis. Other
weather variables are drawn from the United States’ National Oceanic and
Atmospheric Administration (NOAA). Relative humidity is not reported directly in
the data, but is constructed based on the standard meteorological formula provided
by NOAA using temperature and dew point temperature.

We use geospatial software ArcGIS to aggregate the grid weather data to
the county-day level and eliminate counties with observations that are omitted or

3Deryugina and Hsiang (2014) estimate the effect of temperature on the income per capita of counties in
the United States, but they do not examine its effect on GDP growth, which has attracted more attention in the
development literature.

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Could Weather Fluctuations Affect Local Economic Growth? 205

have fatal errors.4 Then, we calculate the annual average temperature, days in each
temperature bin, annual average precipitation, annual average relative humidity,
annual average atmospheric pressure, and total sunshine hours for counties. The
ﬁnal balanced panel of weather data contains weather information for 2,376
counties from 1980 to 2012.

The economic data come from the Support System for China Statistics
Application. The county-level dataset includes various annual statistics of GDP,
population, employment, wage, investment, banking, public ﬁnance, trade, and
social welfare, among others. The economic dataset includes data on 1,800 counties
from 1996 to 2012.

Summary Statistics

We merge a county’s weather data and economic data based on the name
and administrative code. The ﬁnal combined panel covers 1,657 counties and
spans from 1996 to 2012, containing each county’s weather variables and annual
economic statistics. All monetary values are expressed in constant 2013 Chinese
yuan. Summary statistics of key variables are presented in Table A.1 of the online
Appendix.5

Figure 1 depicts the evolution of the average temperature of sample counties
from 1980 to 2012. The average temperature has risen gradually over the past 3
decades. The peak annual average temperature of 13.97°C appears in 2012; the
lowest annual average temperature of 11.23°C appears in 1984. Figure 2 describes
the evolution of the average precipitation of sample counties from 1980 to 2012.
The year of maximum precipitation is 2012, with daily average precipitation of up to
2.8 millimeters (annual precipitation is 1,024.8 millimeters). The year of minimum
precipitation is 2011, with an average daily precipitation of 2.1 millimeters (annual
precipitation is 766.5 millimeters).

Figure 3 depicts the relationship between counties’ average temperature and
the growth rate of GDP per capita from 1996 to 2012. Figure 4 presents the
relationship between counties’ average precipitation and the growth rate of GDP
per capita from 1996 to 2012. The growth rate of county-level GDP per capita is
negatively correlated with average temperature and average precipitation.

4We use a geographic-weighted approach to calculate the average temperature and average precipitation
at the county-day level, where the weights are proportions of a county’s area within a speciﬁc grid. Counties that
consistently have 0-value observations for temperature and precipitation are dropped. Weather variables other than
temperature and precipitation are constructed following Zhang et al. (2018).

5The online Appendix can be found at the corresponding author’s homepage: https://sites.google.com/site/

jiajiacong/research.

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206 Asian Development Review

Figure 1. Annual Average Temperature, 1980–2012

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Note: Temperature is measured in degrees Celsius.
Source: Authors’ calculation based on weather data from the China Meteorological Data Sharing Service System.

III. Theoretical Framework

In this section, we develop a theoretical framework for how weather variables
affect economic growth and present the estimation strategy used for the empirical
analysis.

Theoretical Framework

Our theoretical framework is based on Bond, Leblebicio˘glu, and Schiantarelli
(2010) and Dell, Jones, and Olken (2012). Consider the production function of
county i in year t:

Y (Cit ) = eβCit AitK(Cit )αL(Cit )1−α

˙Ait
Ait

= gi + γ Cit

(1)

(2)

Y is aggregate output; K measures the capital stock; L measures population; A
represents total factor productivity; and C measures the weather conditions of this
county. Equation (1) captures the level effect of weather on economic production

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Could Weather Fluctuations Affect Local Economic Growth? 207

Figure 2. Annual Average Precipitation, 1980–2012

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Note: Precipitation is measured in millimeters.
Source: Authors’ calculation based on weather data from the China Meteorological Data Sharing Service System.

(e.g., the effect of current weather on aggregate output). Equation (2) captures the
growth effect of weather (e.g., the effect of weather variables that affect the growth
of total factor productivity).

Dividing both sides of equation (1) by population L, we have

y(Cit ) = eβCit Aitk(Cit )α

(3)

where y is output per capita and k measures capital stock per capita. Taking logs
of equation (3) and differentiating with respect to time, we have a dynamic growth
equation as follows:

gy(Cit ) = gi + αgk (Cit ) + (β + γ )Cit − βCit−1

(4)

where gy and gk represent the growth rates of output per capita and capital stock
per capita, respectively. Equation (4) indicates two features of weather shocks on
economic growth. First, there is a lagged effect of weather on growth. Weather
conditions in the previous year affect the growth rate of the current year. Second,
weather conditions affect the growth rate through the level effect β, which comes
from equation (1) and the growth effect γ from equation (2). Equation (4) clearly
identiﬁes these two effects: (i) the level effect β is the coefﬁcient of Cit−1, and

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208 Asian Development Review

Figure 3. Growth of County-Level Gross Domestic Product per Capita and Average
Temperature, 1996–2012

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GDP = gross domestic product.
Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and
the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China
Statistics Application.

(ii) the growth effect γ can be derived by summing the coefﬁcients of Cit and
Cit−1. The level and growth effects can still be clearly identiﬁed for more general
model structures, such as dynamic models including lagged dependent variables
and lagged weather variables, as we demonstrate in the online Appendix.

B. Model Speciﬁcation

To estimate weather effects, we adopt the following regression speciﬁcation:

git =

P(cid:2)

p=0

λpCit−p + X (cid:3)φ + μi + δt + (cid:8)it

(5)

C is a vector of annual average temperature and average precipitation with up to P
lags included; X is a vector of control variables containing other weather factors; μi
are county ﬁxed effects; δt are year ﬁxed effects; and (cid:8)it are error terms.6 The error

6In the robustness check, we use alternative ﬁxed effects and other speciﬁcations to cluster error terms. The
growth rate of the capital stock per capita gk is not controlled for in the regression because, based on our theory, gk

Could Weather Fluctuations Affect Local Economic Growth? 209

Figure 4. Growth of County-Level Gross Domestic Product per Capita and Average
Precipitation, 1996–2012

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GDP = gross domestic product, mm = millimeter.
Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and
the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China
Statistics Application.

terms are simultaneously clustered by county and province-year to allow arbitrary
serial correlation within counties and arbitrary spatial correlation within provinces
in a year.

Our estimation proceeds as follows. First, we estimate equation (5) with no

lags, focusing on the null hypothesis that weather does not affect growth:

H0(P = 0): λ0 = 0

(6)

Failing to reject this null hypothesis indicates the absence of both the level effect
and growth effect. Second, we estimate equation (5) with lags and test the null
hypothesis that the weather variables have no instantaneous effect on the growth
rate:

H ∗
0 (P > 0): λ0 = 0

(7)

is a function of Cit . Including both gk and Cit in the regression would generate the “over-control problem,” which
results in an underestimation of the effects of weather variables (Dell, Jones, and Olken 2014).

210 Asian Development Review

and the null hypothesis that the weather variables have no cumulative effect on the
growth rate:

H ∗∗
0 (P > 0):

P(cid:2)

λp = 0

(8)

p=0
(cid:3)
P
p=0

λp corresponds to the growth effect γ in equation (4) as well as
The value of
the more general concept of growth effects in models with longer lag structures, as
demonstrated in the online Appendix.

IV. Results

Level Effect

We estimate equation (5) without including any lagged weather variables;
that is, we test whether ﬂuctuations in the weather variables have a level effect
on growth. The null hypothesis is presented in equation (6). Column 1 of Table
1 shows that there is a signiﬁcantly negative relationship between the growth rate of
county-level GDP per capita and average temperature: the growth rate decreases
by 1.05% when annual average temperature increases by 1°C. Column 2 shows
a negative and signiﬁcant relationship between the growth rate and average
precipitation. These results are robust to controlling for other weather variables
including atmospheric pressure, wind speed, and sunshine hours, as shown in
column 3. Other weather variables are controlled for in all the following regressions.
Next, we investigate the heterogeneous effects of weather ﬂuctuations on
different counties. We deﬁne a county’s agriculture ratio as its sum of gross output
value of agriculture from 1996 to 2012 divided by the sum of its GDP from
1996 to 2012. A county is deﬁned as an agricultural county if its agriculture
ratio exceeds the median agriculture ratio of the sample counties. As shown in
column 4, the coefﬁcient of the interaction between average temperature and
the agricultural county dummy is not statistically signiﬁcant,
indicating that
the effects of temperature on agricultural and nonagricultural counties do not
differ substantially. However, agricultural counties are more adversely affected
by average precipitation than are nonagricultural counties. We deﬁne a county as
a hot county if its average temperature from 1996 to 2012 exceeds the median
of the sample counties. Column 5 shows that the coefﬁcient of the interaction
between average temperature and the hot county dummy is insigniﬁcant, indicating
that the level effects of temperature on hot counties and cold counties are not
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average precipitation. We deﬁne a county as a poor county if its average wage and
average net income per capita of rural residents from 1996 to 2012 are smaller
than the corresponding medians of the sample counties. Column 6 shows that

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Could Weather Fluctuations Affect Local Economic Growth? 211

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< p * * * d n a , 5 0 . 0 < p * * , 1 . 0 < p * . s e i t n u o c d n a s r a e y - y b - e c n i v o r p n i d e r e t s u l c e r a s r o r r e d r a d n a t S e g a r e v a a y t n u o c l a r u t l u c i r g A × e r u t a r e p m e t e g a r e v A e g a r e v a y t n u o c l a r u t l u c i r g a × n o i t a t i p i c e r P e r u t a r e p m e t e g a r e v A e l b a i r a V n o i t a t i p i c e r p e g a r e v A e g a r e v a b y r t n u o c t o h × e r u t a r e p m e T e g a r e v a y r t n u o c t o h × n o i t a t i p i c e r P e g a r e v a c y t n u o c r o o p × e r u t a r e p m e T y r t n u o c r o o p × n o i t a t i p i c e r P e r u s s e r p c i r e h p s o m t a e g a r e v A d e e p s d n i w e g a r e v A s r u o h e n i h s n u s l a t o T s n o i t a v r e s b O d e r a u q s - R h t w o r g e h t s i s n m u l o c l l a r o f e l b a i r a v t n e d n e p e d e h T : s e t o N 212 Asian Development Review an increase in average temperature has a signiﬁcantly negative effect on growth and that the effect is mitigated in poor counties. However, average precipitation has a signiﬁcantly negative effect only on poor counties. In column 7, we add all interaction terms into the regression. The results show that average temperature still has a signiﬁcantly negative effect and average precipitation has more adverse effects on agricultural counties and poor counties. Therefore, we can clearly reject the null hypothesis that weather ﬂuctuations have no level (instantaneous) effect on the growth rate. Increasing the annual average temperature by 1°C would lower the growth rate of county-level GDP per capita by 1.05%–1.25%; average precipitation has a signiﬁcant and negative effect on the growth rate of agricultural counties and poor counties. B. Robustness of the Level Effect In this section, we check whether the results of the level effect are robust to alternative regression speciﬁcations and other measures of temperature. 1. Alternative Regression Speciﬁcations Table 2 reports the results under different regression speciﬁcations. Many studies have found that temperature may have a joint impact with humidity (Zhang et al. 2018). In column 1 of Table 2, we use the heat index to measure the joint inﬂuence of temperature and humidity as a robustness check. The construction of the heat index follows the standard formula provided by NOAA.7 The heat index and average precipitation still have signiﬁcantly negative effects on growth. In column 2, we cluster error terms by counties instead of province-by-years and counties in the main speciﬁcation. Column 2 shows that the effects of average temperature and average precipitation are signiﬁcantly negative, consistent with the baseline result. In column 3, we replace the year ﬁxed effect in the baseline regression with a region × year ﬁxed effect because the eastern, middle, and western regions of the PRC have remarkable development gaps and may have different growth patterns.8 The result shows that the signiﬁcant negative effect of average temperature remains. In column 4, we replace the year ﬁxed effect with 1–4 order time trends because the development of county economies may exhibit nonlinear trends. Column 4 shows that the negative level effect of temperature is still signiﬁcant. Therefore, our main results, especially the negative level effect of temperature, are robust to different regression speciﬁcations. 7The formula is available at https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml; alternatively, please refer to Zhang et al. (2018). 8Following Deryugina and Hsiang (2014), we do not control for province-by-year ﬁxed effects since it may remove too much identiﬁcation variation, which may produce attenuation bias that would overwhelm the results (Fisher et al. 2012). l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Could Weather Fluctuations Affect Local Economic Growth? 213 Table 2. Robustness Checks with Alternative Regression Speciﬁcations (2) Alternative Cluster (3) Region by Year (4) (5) 1–4 Order Hot Counties Hottest Year Excludedb Excludeda Time Trends (6) (1) Heat Index −0.0045** (0.0020) −0.0122*** −0.0146*** −0.0070*** (0.0040) (0.0040) −0.0047* −0.0054** −0.0017 (0.0027) (0.0026) (0.0024) Yes Yes Yes (0.0025) −0.0014 (0.0026) Yes −0.0117*** (0.0042) −0.0079** (0.0031) Yes −0.0139*** (0.0047) −0.0042 (0.0028) Yes Yes Yes No No Yes Yes No No Yes No Yes No Yes No No Yes Yes Yes No No Yes Yes No No 25,318 0.0799 25,318 0.0801 25,318 0.0909 25,318 0.0720 23,800 0.0786 23,665 0.0846 Variable Heat index Average temperature Average precipitation Other weather variables Country ﬁxed effect Year ﬁxed effect Region × year ﬁxed effect 1–4 order time trends Observations R-squared Notes: The dependent variable for all columns is the growth rate of county-level gross domestic product per capita. Standard errors are clustered in province-by-years and counties. Temperature is measured in degrees Celsius and precipitation is measured in millimeters. *p < 0.1, **p < 0.05, and ***p < 0.01. aIn column 5, counties with an average temperature above 20°C are excluded. bIn column 6, the hottest year 2012 is excluded. Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from Support System for China Statistics Application. In column 5 of Table 2, we exclude counties whose average temperatures from 1996 to 2012 are higher than 20°C to test whether the negative effect of temperature is solely driven by hot counties. The result shows that temperature also has a signiﬁcantly negative effect on cool counties. In column 6 of Table 2, we exclude the year 2012, which has the highest annual average temperature, from the sample, and test whether the main results are driven by this particularly hot year. The negative effect of temperature on the growth rate remains. 2. Different Measures of Temperature We introduce different measures of temperature by constructing county-level seasonal average temperature and temperature bins. A cross-country analysis of 28 Caribbean countries indicates that high-temperature shocks have a negative effect on income only when they occur during the hottest season (Hsiang 2010). Table A.2 in the online Appendix reports how seasonal average temperature affects the growth rates of county-level GDP per capita and the value added of different sectors. Column 1 shows that only high temperatures in spring and summer have l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 214 Asian Development Review signiﬁcantly negative effects on the growth rate of GDP per capita. The negative effect of seasonal temperature is mainly caused by the effect on primary industry. Many empirical studies have found that temperatures have a nonlinear effect on economic activities (Burke, Hsiang, and Miguel 2015; Zhang et al. 2018; Burke and Tanutama 2019). To check for a possible nonlinear effect of weather variables on growth, we construct temperature bins to measure county-level temperature conditions. These new results are reported in Table A.3 of the online Appendix. Consistent with the literature, our study ﬁnds that temperatures have a nonlinear effect on the growth rate of county-level economies. Column 1 shows that compared with the reference bin [15, 20)°C, high temperatures above 20°C have signiﬁcantly negative effects on the growth rate of GDP per capita. Temperatures within [–10, 15)°C and extremely low temperatures have no signiﬁcant effect. Column 2 shows that temperatures within [–20, –10)°C and high temperatures above 20°C have negative effects on primary industry. In contrast, high temperatures above 20°C have signiﬁcantly positive effects on the growth rate of secondary industry. This ﬁnding is consistent with micro evidence from Colmer (2018) and Emerick (2018), which indicates that the nonagriculture sector (mainly manufacturing) could beneﬁt from weather shocks through labor reallocation between the agriculture sector and nonagriculture sector.9 For the growth rate of tertiary industry, most temperature bins show no signiﬁcant effect. C. Cumulative Effect This section uses panel-distributed lag models with up to 10 lags of the weather variables to explore the dynamics of weather effects.10 The distributed lag models nest both the level effect (instantaneous effect) and the growth effect (cumulative effect). Table 3 reports the results of estimating equation (5) with 0 lags, 1 lag, 3 lags, 5 lags, and 10 lags of the weather variables. The ﬁrst and second rows present the level effect of the weather variables, and the bottom two rows report the sum of all weather lags (i.e., the cumulative effect). Columns 2–5 of Table 3 show that a 1°C increase in temperature cumulatively reduces the growth rate of county-level GDP per capita by 2.03%–3.84%. The magnitude of temperature’s negative cumulative effect increases as more lagged weather variables are included. The cumulative effect of precipitation is signiﬁcant 9Chen and Yang (2019) and Zhang et al. (2018) use micro-level data from manufacturing industries and ﬁnd a signiﬁcant negative effect of high temperatures on output and total factor productivity. Our results are different but not necessarily inconsistent with their ﬁndings. First, our focus is the growth rate of output rather than output or productivity per se. Second, the secondary industry here includes many industries other than manufacturing. Third, we study the growth rate of all ﬁrms, including small and medium-sized ﬁrms, while Chen and Yang (2019) and Zhang et al. (2018) focus on large ﬁrms with annual sales above 5 million Chinese yuan. 10Our panel of economic variables spans from 1996 to 2012, and the panel of weather variables spans from 1980 to 2012. Even with 10 lags of the weather variables, the balanced panel of weather and economies still has 17 years of observations. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Could Weather Fluctuations Affect Local Economic Growth? 215 Table 3. Regression with Lags Variables Average temperature Average precipitation Other weather variables Observations R-squared Cumulative effect of average temperature Cumulative effect of average precipitation (2) 1 Lag (1) 0 Lags (4) 5 Lags (3) (5) 3 Lags 10 Lags −0.0122*** −0.0116*** −0.0104** −0.0112** −0.0084* (0.0045) (0.0043) (0.0039) (0.0046) −0.0054** −0.0052** −0.0067*** −0.0047* (0.0026) (0.0025) (0.0024) Yes Yes Yes 25,318 25,318 25,318 0.0816 0.0814 0.0801 −0.0203*** −0.0244** −0.0201 (0.0123) (0.0111) (0.0062) −0.0135** −0.0541*** −0.0067 −0.0024 (0.0066) (0.0052) (0.0032) (0.0026) Yes 25,318 0.0833 −0.0384*** (0.0148) (0.0039) −0.0051** (0.0024) Yes 25,318 0.0805 (0.0109) Notes: The dependent variable of all columns is the growth rate of county-level gross domestic product per capita. All columns include county ﬁxed effects and year ﬁxed effects. Standard errors are clustered in province-by-years and counties. Given space constraints, the table only reports the sum of coefﬁcients of all lagged weather variables (cumulative effect). Temperature is measured in degrees Celsius and precipitation is measured in millimeters. *p < 0.1, **p < 0.05, and ***p < 0.01. Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China Statistics Application. only when 5 lags or 10 lags are included. In other words, the cumulative effect of precipitation only shows up in the medium run. Increasing average precipitation by 1 millimeter (i.e., increasing annual precipitation by 365 millimeters) cumulatively reduces the growth rate of county-level GDP per capita by 1.35%–5.41%. Table 3 demonstrates that the cumulative effect of average temperature is larger in magnitude than its level effect; average precipitation also generates a larger cumulative effect than the level effect when 5 lags and 10 lags are introduced. These results imply that the level effect of weather ﬂuctuations in each period accumulates, rather than reverses, and that counties do not fully adapt to weather ﬂuctuations. V. Channels In this section, we explore the channels through which the weather variables exert their effects on growth. Many macroeconomic studies of weather effects focus on limited channels, especially agriculture and income levels, due to data limitations (Dell, Jones, and Olken 2012). Our data contain various economic statistics, so we can explore whether there are other channels for weather to inﬂuence economic activities. A. Primary, Secondary, and Tertiary Industries We investigate the level effect and cumulative effect of average temperature and average precipitation on the growth rates of primary, secondary, and tertiary l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 216 Asian Development Review Table 4. Channels: Primary, Secondary, and Tertiary Industry Growth Rate of A. Models with no lags Average temperature Average precipitation Observations R-squared B. Models with 5 lagsa Average temperature Average precipitation Cumulative effect of average temperature Cumulative effect of average precipitation Observations R-squared C. Models with 10 lags Average temperature Average precipitation Cumulative effect of average temperature Cumulative effect of average precipitation Observations R-squared (1) Value Added of Primary Industry (2) Value Added of Secondary Industry (3) Value Added of Tertiary Industry −0.0253*** (0.0051) −0.0091*** (0.0027) 25,760 0.0673 −0.0313*** (0.0058) −0.0105*** (0.0028) −0.0338** (0.0158) −0.0111 (0.0073) 25,760 0.0703 −0.0276*** (0.0058) −0.0117*** (0.0028) −0.0316* (0.0187) −0.0389*** (0.0118) 25,760 0.0729 −0.0110* (0.0061) −0.0027 (0.0038) 25,711 0.0679 −0.0118* (0.0067) −0.0022 (0.0040) 0.0005 (0.0199) −0.0103 (0.0105) 25,711 0.0683 −0.0103 (0.0068) −0.0030 (0.0041) −0.0292 (0.0230) −0.0465*** (0.0179) 25,711 0.0695 −0.0034 (0.0047) −0.0049 (0.0033) 24,796 0.0505 0.0010 (0.0051) −0.0037 (0.0034) −0.0328** (0.0154) −0.0077 (0.0088) 24,796 0.0520 0.0031 (0.0052) −0.0048 (0.0035) −0.0534*** (0.0183) −0.0310** (0.0141) 24,796 0.0539 Notes: All columns include other weather variables, county ﬁxed effects, and year ﬁxed effects. Standard errors are clustered in province-by-years and counties. Given space constraints, part B and part C only report the sum of coefﬁcients of all lagged weather variables (cumulative effect). Temperature is measured in degrees Celsius and precipitation is measured in millimeters. *p < 0.1, **p < 0.05, and ***p < 0.01. Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China Statistics Application. industries. Part A of Table 4 begins with the model with no lagged weather variables and shows the level effect. Column 1 of part A shows that average temperature and precipitation have signiﬁcantly negative effects on the growth rate of primary industry. Columns 2–3 of part A show that the negative effects of average l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Could Weather Fluctuations Affect Local Economic Growth? 217 temperature and precipitation on secondary and tertiary industry are negative but insigniﬁcant. Parts B and C introduce 5 lags and 10 lags of the weather variables, respectively, to examine the cumulative effects of average temperature and average precipitation. In part B, temperature has signiﬁcantly negative cumulative effects on the growth of primary industry and tertiary industry. Precipitation has negative but insigniﬁcant cumulative effects on all industries. When 10 lags are introduced, as shown in part C, temperature still has signiﬁcantly negative cumulative effects on the growth of primary industry and tertiary industry. The cumulative effects of precipitation on all industries become signiﬁcantly negative. This is consistent with our previous ﬁnding that the cumulative effect of precipitation emerges only in the medium run. B. Average Wage, Investment, and Output of Agriculture and Large Firms We investigate how average temperature and precipitation affect the growth rate of average wage, ﬁxed asset investment, the gross output value of agriculture, and the gross output value of enterprises above a designated size. Part A of Table A.4 in the online Appendix begins with models without lags and reports the level effect. Column 1 shows that an increase in temperature and precipitation reduces the growth rate of the average wage. Since the average wage represents the productivity of labor, this result implies that the productivity of labor is affected by weather ﬂuctuations. Column 2 shows that average temperature does not have a signiﬁcant effect on the growth rate of ﬁxed asset investment, while precipitation has a signiﬁcantly positive effect on it. Column 3 shows that an increase in temperature and precipitation lowers the growth rate of the gross output value of agriculture, which is consistent with our ﬁnding that the agriculture sector is substantially inﬂuenced by weather ﬂuctuations. Column 4 shows that both average temperature and precipitation have negative effects on the growth of large ﬁrms, but only the effect of precipitation is signiﬁcant. Parts B and C introduce 5 lags and 10 lags of the weather variables, to examine the cumulative effects of average temperature and respectively, precipitation. The ﬁrst column of parts B and C show that only temperature has a signiﬁcantly negative cumulative effect on average wages. The second column shows that the cumulative effect of temperature on ﬁxed asset investment is signiﬁcantly negative and that the cumulative effect of precipitation is signiﬁcantly positive.11 As shown by the third column of part C, temperature has a signiﬁcant cumulative effect on agriculture. The fourth column of parts B and C show that 11One possible reason for this positive cumulative effect is that increasing precipitation accelerates the depreciation of ﬁxed assets. Thus, the growth rate of ﬁxed asset investment has to be increased to compensate for the depreciated ﬁxed assets. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 218 Asian Development Review average temperature does not have a signiﬁcant cumulative effect on the gross output of large ﬁrms. In summary, in addition to the well-studied channel of agriculture, we ﬁnd that average temperature has signiﬁcantly negative cumulative effects on the growth rates of average wages and ﬁxed asset investment, and that average precipitation has a signiﬁcantly positive cumulative effect on the growth rate of ﬁxed asset investment. VI. Medium-Run Estimates The comparison of the level effect and cumulative effect implies that the level effect of weather ﬂuctuations in each period accumulates and that counties do not fully adapt to weather ﬂuctuations. To further verify whether counties adapt to weather ﬂuctuations in the medium run, we use a long difference approach to explore the medium-run relationship between the growth rate and weather variables. Our speciﬁcation is similar to Dell, Jones, and Olken (2012) and Burke and Emerick (2016). Speciﬁcally, given two periods, a and b, each period contains n years. We deﬁne the average growth rate of county i in period a as gia = 1 t∈a git. We deﬁne n the vector of average weather variables as Cia. It contains the average temperature, average precipitation, and other weather variables in period a. The relationship between the average growth rate and average weather variables in period a can be described as (cid:3) gia = ψ + κCia + μi + (cid:8)ia This relationship is derived by taking averages on both sides of equation (5) with 0 lags. The relationship between the average growth rate and average weather variables in period b can be derived similarly. Then, we can have the following regression speciﬁcation: gib − gia = c + κ (Cib − Cia) + ((cid:8)ib − (cid:8)ia) (9) The unobservable county ﬁxed effects μi are eliminated. Compared with the cross- sectional models, the long difference approach is free of omitted variable problems caused by heterogeneity μi. Compared with panel-data models that investigate the short-run effects, the long difference model investigates the effects of temperature and precipitation on the growth rate in the medium run. If the estimated κ is smaller than the estimated level effect λ0 in magnitude, the county’s economy shows adaptation in the medium run; if not, the effects of weather ﬂuctuations accumulate over time. In the following analysis, we set each period at 4 years: period a is 1996–1999, and period b is 2009–2012. Figure 5 shows the changes in the average temperature and growth rate across periods a and b. This demonstrates a clear l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Could Weather Fluctuations Affect Local Economic Growth? 219 Figure 5. Changes in Growth Rate and Average Temperature, 1996–1999 and 2009–2012 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / Notes: Temperature is measured in degrees Celsius. Sources: Authors’ calculation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China Statistics Application. negative relationship between the change in growth rate and average temperature change. Figure 6 shows the changes in average precipitation and the growth rate across these two periods. To facilitate comparison, part A of Table 5 reports the panel results. Column 1 of part B shows that a 1°C increase in temperature would lower the growth rate of county-level GDP per capita by 6.17%; the average precipitation change also has a signiﬁcantly negative effect on the growth rate. Columns 2–4 of part B show that temperature has a signiﬁcantly cumulative negative effect on all industries, while precipitation has a signiﬁcantly negative effect on secondary industry and tertiary industry. Table A.5 in the online Appendix reports the effects of temperature and precipitation on the growth rates of the average wage, ﬁxed asset investment, the gross output value of agriculture, and the gross output value of enterprises above a designated size across period a and period b. To facilitate comparison, part A repeats the panel results. Part B shows that temperature change has a signiﬁcantly negative effect on most variables except the average wage, which is positively affected; precipitation change has a signiﬁcantly negative effect on the growth rate of gross output of enterprises above a designated size. / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 220 Asian Development Review Figure 6. Changes in Growth Rate and Average Precipitation, 1996–1999 and 2009–2012 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / Notes: Precipitation is measured in millimeters. Sources: Authors’ calculation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China Statistics Application. We introduce region ﬁxed effects and alternative time periods for a and b in equation (9) to check the robustness of our results under a long difference approach. The results are reported in Tables A.6–A.9 of the online Appendix; most results persist. We also use the long difference method in Burke and Tanutama (2019) and ﬁnd that the negative effect of temperature is larger in magnitude than the level effect, consistent with our results in column 1 of Table 5. In summary, comparing the coefﬁcients of average temperature in part A and part B of Table 5, the medium-run effect of temperature is larger than its level effect in magnitude, which indicates evidence of intensiﬁcation. Therefore, we can conclude that counties in the PRC adapt poorly to temperature changes during our sample period. The effects of temperature accumulate over time and lead to a larger loss in the medium run. VII. Conclusion This paper exploits weather data and economic data from counties in the PRC during the period 1996–2012 to examine the relationship between weather variables and economic growth. We ﬁnd a signiﬁcantly negative relationship between the / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Could Weather Fluctuations Affect Local Economic Growth? 221 Table 5. Long Difference Regression (I) A. Panel results Growth rate of Average temperature Average precipitation Observations R-squared B. Long difference regression Change in growth rate of Average temperature change Average precipitation change Early period Late period Observations R-squared (1) (2) County-Level Value Added of Primary Industry −0.0253*** (0.0051) −0.0091*** (0.0027) 25,760 0.0673 GDP per Capita −0.0122*** (0.0039) −0.0054** (0.0024) 25,318 0.0801 (1) (2) County-Level Value Added of Primary Industry −0.0629*** (0.0184) 0.0092 (0.0111) 1996–1999 2009–2012 1,600 0.0331 GDP per Capita −0.0617*** (0.0165) −0.0388*** (0.0103) 1996–1999 2009–2012 1,652 0.0314 (3) Value Added of Secondary Industry −0.0110* (0.0061) −0.0027 (0.0038) 25,711 0.0679 (3) Value Added of Secondary Industry −0.1102** (0.0506) −0.0693*** (0.0232) 1996–1999 2009–2012 1,574 0.0262 (4) Value Added of Tertiary Industry −0.0034 (0.0047) −0.0049 (0.0033) 24,796 0.0505 (4) Value Added of Tertiary Industry −0.0642** (0.0321) −0.0347** (0.0161) 1996–1999 2009–2012 1,573 0.0226 GDP = gross domestic product. Notes: Other weather variables are controlled for in all columns. The robust standard errors are reported in parentheses. *p < 0.1, **p < 0.05, and ***p < 0.01. Sources: Authors’ estimation based on weather data from the China Meteorological Data Sharing Service System and the National Oceanic and Atmospheric Administration, and on economic data from the Support System for China Statistics Application. growth rate and average temperature. Increasing average temperature by 1°C lowers the growth rate of county-level GDP per capita by 1.05%–1.25%. The negative effect of precipitation mainly occurs in agricultural counties and poor counties. Using models with lags, we ﬁnd that the cumulative effects of temperature are far greater than its level effects. Models with 1–10 lags indicate that a 1°C increase in average temperature cumulatively lowers the growth rate of county-level GDP per capita by 2.03%–3.84%. When 5 lags and 10 lags are introduced, the cumulative effects of precipitation become signiﬁcantly negative, implying that the cumulative effect of precipitation emerges only in the medium run. In addition to the well- studied agriculture channel, we ﬁnd that temperature has considerable impacts on tertiary industry, labor productivity, and ﬁxed asset investment. The long difference approach ﬁnds that counties in the PRC adapt poorly to weather changes in our sample period. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 222 Asian Development Review Our study has signiﬁcant policy implications. First, we verify that weather ﬂuctuations have a considerable negative impact on economic growth. The unignorable threat of weather changes demands appropriate government responses such as introducing crop diversity to help farmers insulate yields and income against weather extremes (Auffhammer and Carleton 2018). Second, we ﬁnd that in addition to the agriculture sector, weather ﬂuctuations can inﬂuence economic growth by inﬂuencing the productivity of labor, ﬁxed asset investment, and the production of nonagriculture sectors. These nonagricultural channels deserve more attention because the share of agriculture in the PRC’s GDP continues to decline, and nonagricultural channels will become key channels in the future. Third, we ﬁnd that counties in the PRC adapt poorly to weather changes. This is the main reason that the cumulative effects of weather ﬂuctuations outweigh their level effects. Adaptability to weather changes requires additional investment and technological innovation. References Acemoglu, Daron, Simon Johnson, and James A. 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Zhang, Peng, Junjie Zhang, and Minpeng Chen. 2017. “Economic Impacts of Climate Change on Agriculture: The Importance of Additional Climatic Variables Other Than Temperature and Precipitation.” Journal of Environmental Economics and Management 83: 8– 31. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 224 Asian Development Review Online Appendix The online Appendix is posted at https://sites.google.com/site/jiajiacong/ research. It contains the following contents: Table A.1. Summary Statistics of Key Variables Table A.2. Seasonal Temperature Effects Table A.3. Nonlinear Effects with Temperature Bins Table A.4. Channels: Average Wage, Investment, and Output Values of Agriculture and Large Firms Table A.5. Long Difference Regression (II) Table A.6. Robustness of Long Difference Regression: Including Region Fixed Effect (I) Table A.7. Robustness of Long Difference Regression: Including Region Fixed Effect (II) Table A.8. Robustness of Long Difference Regression: Alternative Time Interval (I) Table A.9. Robustness of Long Difference Regression: Alternative Time Interval (II) Model of the Growth Effect of Weather Fluctuations in a Distributed Lag Model l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 7 2 2 0 1 1 8 4 6 8 3 7 a d e v _ a _ 0 0 1 5 4 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3
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