DAILY FLUCTUATIONS IN WEATHER AND ECONOMIC GROWTH AT THE SUBNATIONAL LEVEL : EVIDENCE FROM THAILAND [†]
Sarun Kamolthip [††]
``` sarun.kamolthip@scb.co.th
```
June 30, 2025
† All data in this study are used only for research purposes. None of these were from or generated by the SCB. In the future, with written permission from the data provider, datasets that are not publicly available may be shared with other academic or commercial organizations I collaborate with to carry out further related research (where this is in the public interest or necessary for the purposes of another organization’s legitimate research interests). I will always make sure that any such sharing for further research is done in compliance with data protection laws, and under terms that protect data privacy and the confidentiality of the data. †† This paper was initially developed during my tenure at Khon Kaen University (KKU) before joining Siam Commercial Bank
(SCB).
I gratefully acknowledge all the support and excellent research resources while working at the Faculty of Economics, KKU. ABSTRACT
This paper examines the effects of daily temperature fluctuations on subnational economic growth in Thailand. Using annual gross provincial product (GPP) per capita data from 1982 to 2022
and high-resolution reanalysis weather data, I estimate fixed-effects panel regressions that isolate plausibly exogenous within-province year-to-year variation in temperature. The results indicate a statistically significant inverted-U relationship between temperature and annual growth in GPP per
capita, with adverse effects concentratedin the agricultural sector. Industrial and service outputs appear insensitive to short-term weather variation. Distributed lag models suggest that temperature shocks have persistent effects on growth trajectories, particularlyin lower-income provinces with higher average temperatures. I combine these estimates with climate projections under RCP4.5 and RCP8.5 emission scenarios to evaluate province-level economic impacts through 2090. Without adjustments for biases in climate projections or lagged temperature effects, climate change is projected to reduce per capita output for 63-86% of Thai population, with median GDP per capita impacts ranging from -4% to +56%
for RCP4.5 and from -52% to -15% for RCP8.5. When correcting for projected warming biases–
but omitting lagged dynamics–median losses increase to 57-63% and 80-86%. Accounting for delayed temperature effects further raises the upper-bound estimates to near-total loss. These results highlight the importance of accounting for model uncertainty and temperature dynamics in subnational climate impact assessments.
All projections should be interpreted with appropriate
caution. 1 Introduction
The calendar year 2024 was the first year in which the average global temperature exceeded 1.5 [◦] C relative to pre
industrial levels. Although such a single year above 1.5 [◦] C does not mean that the long-term
temperature has reached the temperature goals, as measured by 20-year averages relative to a pre-industrial baseline,
of the Paris Agreement, as underlined by the Intergovernmental Panel on Climate Change (IPCC),
Bevacqua et al.
show that, without very stringent climate mitigation, this very first single year at 1.5 [◦] C most probably occurs within the 20-year period. An increasing number of empirical studies provide quantitative estimates of the economic and societal damage caused by climate change. Understanding the economic impacts attributable to global warming is important for a comprehensive projection of climate change risks. However, because studies on the consequences of climate change are highly dependent on data, most previous studies have been conducted either at the global level; Burke
et al.; Kotz et al.; Carleton et al., or regional level), or in large countries that have sufficient data variation; Burgess et al.; Hsiang et al. ; Barwick et al.; Deryugina et al..
Nonetheless, people’s vulnerability to climate change may differ substantially within national geographies. As underlined by the IPCC, subnational institutions play a complementary role to national institutions and are important for mitigation efforts. Therefore, assessments at the
subnational level are needed to identify vulnerability at different subnational administrative units for efficient planning and policy-making. This study aims to assess the impact of climate change on Thailand’s aggregate economic output at the subnational
level. The analysis has two main features. First, I examine the historical relationship between changes in a province’s weather and its economic performance. The main identification strategy exploits the plausibly random year-to-year
variation in weather within a province to estimate the effects of local temperatures on economic outcomes. Specifically,
by conditioning on the province fixed effects, I isolate the within-province year-to-year variation in weather exposure. Because weather fluctuations are unpredictable, it seems reasonable to presume that this variation is as good as randomly assigned and therefore orthogonal to unobserved determinants of aggregate economic outputs. The year fixed effects account for any time-varying trends or shocks that are common to all provinces in Thailand. The use of annual variation to estimate the impact of climate change was pioneered by Deschênes and Greenstone and Schlenker and Roberts
, who both used annual county-level US data to estimate the impacts of weather on US agricultural outputs. The analysis is accomplished using highly spatiotemporal granular weather data and advances in statistical theory,
particularly climate econometrics. I find a statistically significant relationship between the annual growth rate of provincial output per capita and 24-hour average temperatures, particularly on extremely cold and hot days. For
example, in the case of the polynomial functional form, the results show that a day at 35 [◦] C, relative to the reference day of 26 [◦] C, leads to a decrease in the provincial growth rate of 0.07 percentage points on average, while a day at
20 [◦] C decrease this rate by 0.04 percentage points. All results indicate an inverted-U shape relationship between the provincial growth rate and 24-hour average temperatures. The finding about the relationship between temperatures and provincial growth rate, as estimated using a polynomial functional form, is robust to alternative model specifications. I
then examine whether there is heterogeneityin the growth-temperature across provinces. In doing so, the temperature is interacted with a dummy for a province having low income, defined as a province having below-median average
inflation-adjusted GPP per capita across the sample period. The results suggest that we cannot reject the hypothesis that
high- and low-income provinces have the same response functions to temperature changes.
I then follow Dell et al. andinvestigate whether temperature affects the growth or level of GPP per capita to better understand the dynamics of these temperature effects. In doing so, I estimate the distributed lag models with up to five lags of temperature. When assuming that high- and low-income provinces respond identically to changes in
temperature, the lagged temperature effects appear to persist statistically and significantly. The results suggest that changes in temperature may affect the rate of economic growth rather than the level of economic output. However,
when assuming that high- and low-income provinces respond differently to changes in temperature, the growth effects are more pronounced onlyin low-income provinces. To understand the potential channels through which warming temperatures may affect aggregate output, I investigate the impacts of temperature on three components of output growth: the agricultural, industrial, and service sectors. When assuming that high- and low-income provinces respond identically to changes in temperature, I find substantially negative and statistically significant temperature effects in the agricultural sector. The pattern of temperature effects is an inverted-U shape (that is, the growth rate of agricultural components increases as temperature rises from cool to moderate and then declines). The results provide no evidence of the impact of temperature on output growth in both the industrial and service sectors. Similarly, the results, as estimated assuming that high- and low-income provinces respond differently to temperature changes, suggest that temperature changes affect growth in the agricultural sector. These findings are robust to alternative functional forms of the model. As for the other two components of GPP, the results provide no evidence of the effects of temperature on the growth of both industrial and service outputs.
Again,
these results are consistent across all alternative functional forms. The second feature is the projection of future damage for each province from 2023 to 2090. I combine the empirical results with the projected changes in the climate from various climate models and economic development scenarios to generate projections of economic output under climate change in Thailand. Without bias correction in the projected
climate, province-level projections under both RCP4.5 and RCP8.5 emission scenarios show that climate change will negatively affect half of the Thai population in 2050. These negative impacts are statistically uncertain, with an average of 51-57% likelihood that climate change will have positive impacts. An increasing share of the Thai population is projected to be affected by a warming climate, while negative impacts are more certain as we approach the end of this
century. In 2090, projections under the \"business as usual\" RCP8.5 emission scenario show that climate change will make 86% of Thai people poorer in per capita terms than they would be in the absence of climate change, while a more aggressive emission reduction RCP4.5, 63% are. These projections come with an average probability of 0.30-0.47 across provinces that climate change will have positive impacts. These results provide suggestive evidence that climate change will most likely affect Thailand’s economy to a certain extent. Additional projections also show that the differences in the projected impact of a warming climate are mainly due to geographic heterogeneityin the baseline temperatures. In
particular, the provinces in the Upper-North region, which are typically colder than other regions of Thailand, benefit more fromincreased average temperatures. The projections that account for the lagged effects of temperature over time,
however, display substantially more negative impacts in all 77 provinces. Next, I examine the impacts, again, without bias-correction of climate change on Thailand’s gross regional product
(GRP) per capita. The point estimates are uniformly more negative in the higher average temperature regions (i.e.,
Central, East, South, and West). The damages are modest in colder regions, such as the Lower North and Northeast
regions, while the Upper North region, which has the lowest average temperature, benefits the most fromincreased average temperatures. Then, I examine whether the region-level projections are sensitive to different combinations of emission scenarios, specifications, and output growth assumptions. The projections are broadly similar in structure for both the RCP4.5 and RCP8.5 emission scenarios. Projected impacts in models that allow the effects of temperature to persist on regional output growth are less uncertain whether high- and low-income provinces are assumed to respond identically or differently to temperature changes. In models that do not account for lagged effects, the projections become more uncertain whether high- and low-income provinces are assumed to respond identically or differently. Then, I project the impacts without bias-correction of climate change on Thailand’s gross domestic product (GDP) per
capita under the RCP8.5 emission scenario between 2023-2090 for four different historical growth-temperature response functions and three output growth assumptions combination. Similar to both provincial and regional projections, output growth assumptions likely have little impact on the projected change in GDP per capita under the RCP8.5 emission
scenario. As also observedin the regional projections, projections are less uncertain and fall off steeplyin earlier future years in models that allow the effects of temperature to persist on output growth because colder provinces also suffer large damages. In models that do not account for lagged effects, the projections are more uncertain whether
high- and low-income provinces are assumed to respond identically or differently because colder provinces benefit somewhat fromincreased average temperatures, while hotter provinces remain worse off under a warming climate. I then examine the sensitivity of the projected impacts on Thailand’s GDP under the emission stabilization scenario
RCP4.5. Projections under both emission scenarios are broadly similar in structure but varyin magnitude. To examine the impacts of potentially upward biases in future climate projections, I then project the impacts on provincial output per capita using a bias-correction strategy.
These corrected projections are substantially different from those without bias correction. Projections under both RCP4.5 and RCP8.5 emission scenarios show that climate change will substantially affect 89-94% of the Thai population in 2050. These projections come with an average probability of 0.10-0.20 that climate change will have positive impacts on any province. The climate-affected share of the Thai population is substantially high throughout the projected future period. Projections show that almost all Thai people will be negatively affected by climate change in 2090 under either the RCP4.5 or RCP8.5 emission scenarios, with an average probability of 0.06-0.07 that climate change will have positive impacts. In models that do not consider the delayed effects of temperature, whether high- and low-income provinces are assumed to respond identically or differently, median projections in all three output growth scenarios show that climate change reduces Thailand’s output per capita by 57-63% under RCP4.5 and 80-86% under RCP8.5, relative to its GDP per capita in the absence of climate change. All models with delayed impacts project that Thailand will lose 94-100% of its output in the absence of climate change. All projections are certain with only 1-6% likelihood of positive impacts. Taken together, these results show that the projected impacts of climate change are sensitive to potential biases in future climate projections, particularlyin models that do not account for lagged effects, in which the colder provinces somewhat benefit fromincreased average temperatures, while hotter provinces remain worse off under a warming climate. However, there are some important caveats to these projections. First, the projected impacts are likely overestimated because adaptability to permanent changes in climate is not considered. Second, the projections also reveal a certain degree of uncertainty caused by two distinct sources of uncertainty: climate projection uncertainty and economic pathway
uncertainty. Future climate projections involve considerable uncertainty arising from an incomplete understanding of the Earth’s physical systems. Similarly, predicting accurate economic growth in any economy is challenging because of a variety of factors that introduce complexity and uncertainty, particularly as financial systems become increasingly
integrated. Third, these projected future damages rely on a number of strong assumptions, including that the climate projections are correct, the future growth of province-level output will remain constant in the baseline scenario or follow the growth paths that are projected by SSP3 and SSP5 pathways, and the demographics of the Thai population and their geographic distribution will remain unchanged. The remainder of this paper is organized as follows: Section 2 describes the data usedin the estimation of the economic growth-temperature relationship andin the impact projections of climate change. Section 3 describes the empirical framework and econometric specifications, presents the results of the econometric analysis, and examines the channels through which the warming temperature may affect aggregate output. Section 4 describes the projection results at different aggregate levels as well as the limitations. Finally, Section 5 concludes the paper. 2 Data sources and descriptive statistics
2.1 Data sources
Economic data . To study the effects on economic output, I used two series of Gross Provincial Product per capita
( GPP pc(https://www.nesdc.go.th/nesdben/main.php?filename=nationalaccount)
[National Economic and Social Development Council (NESDC). The](https://www.nesdc.go.th/nesdben/main.php?filename=nationalaccount) old GPP series covers 11 economic sectors andis provided from 1981–1995. The new GPP series covers 16 economic sectors andis available in both current market prices and chain linking real terms.
The national-level consumer price index (CPI) at constant 2019 price was used to
[deflate the GPP per capita at current market prices. The CPI dataset was obtained from The Trade Policy and Strategy](https://tpso.go.th/economic-data)
[Office, Ministry of Commerce.](https://tpso.go.th/economic-data)
Weather data . As is commonly found when dealing with ground station data, Thailand’s weather station coverage is sparse, and a number of missing observations can also be observed due to the birth and death of weather stations. Reanalysis estimates datasets were usedinstead of weather observations from ground stations because of their temporal resolution, spatial resolution, and data continuity. The primary source of the temperature data is the ERA5-Land reanalysis dataset. The
ERA5-Landis a replay of the land component of the ERA5, which uses avast amount of historical satellite andin-situ
observations as input to the advanced modelling and data assimilation systems. ERA5-Land
dataset comes with the high spatial resolution of 0.1 [◦] x 0.1 [◦] andis provided from 1950 onward. I obtained 2-meter temperature and precipitation estimates at hourly frequency between 1981-2022
(https://climate.copernicus.eu/climate-datasets)
Medium-Range Weather Forecasts (ECMWF). Although precipitation data are also provided by the ERA5-Land dataset, I used the precipitation estimates from the
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as the primary source because precipitation is far less smooth in space than temperature. The CHIRPS incorporates the in-house climatology, satellite imagery, and
in-situ station data to create gridded rainfall time series andis provided from 1981 onward at daily frequency at 0.05 [◦] x0.05 [◦] resolution. The gridded climate data was aggregated to the province-year level, which corresponds to the first administrative level as
[indicatedin the GADM database of Global Administrative Areas. All the main specifications used population-weighted](https://gadm.org/)
temperature and precipitation. The weights were constructed from the polygonized population dataset, which was developed specifically for this study using the annual land use map and annual population count datasets. The land use map has a scale of 1:25000 and provides granular details of land use classification on three levels. The first level consists of five major types of land use: urban and built-up,
agricultural, forest, water bodies, and miscellaneous. The second and third levels provide finer and finest details of land use classification, respectively. The annual land use map dataset in shapefile format between 2006-2019 was obtained from the Land Development Department (LDD). To calculate a spatially disaggregated population that is compatible with the human elements of the economy, I used only the polygons classified as urban and built-up land types.
The land use map of 2006 was used as a proxy for the land use pattern before 2006, whereas that of 2019 was used as a proxy for the land use pattern of 2019 onward. The annual population counts dataset at the third administrative level (or, Tambon
(https://stat.bora.dopa.go.th/newstat/webPage/statByAge.php)
Department of Provincial Administration (DOPA). The population count for each polygon was distributed based on the share of each polygon’s area to the total area of all polygons located within the administrative boundary of a given
1 [Conceptually, this is similar to using the gridded population data, for example, the LandScan dataset produced by the Oak Ridge](https://landscan.ornl.gov/) [National Laboratory and the Gridded Population of the World dataset produced by Columbia University’s Center for International](https://landscan.ornl.gov/) [Earth Science Information Network (CIESIN), which is derived with geostatistical methods using population counts and spatial](https://earthdata.nasa.gov/centers/sedac-daac) datasets. While the gridded population dataset derives the distribution of human population counts on continuous raster grids, I instead derived the distribution of population counts onto the polygons. Appendix Figure A1 illustrates differences between LandScan’s gridded population and this paper’s polygonized population datasets. 2 This administrative level corresponds to, for example, countyin the United States. Tambon. Since population counts are available from 1997 onward, the population weights for the periods between
1982-1996 were calculated using 1997 population data. The resulting polygonized population dataset was used to calculate the weather experienced by the population in each polygon. The grid-level hourly temperature T cj, for example, was first aggregated to the polygon level. The
1 24 24-hour average temperature at grid cell c covered at least partially by polygon j, 24 � h =1 [T] [cj] [, was multiplied by the]
corresponding cell weight w cj and summed over all C j grid cells covered at least partially by polygon j . Note that the cell weight w cj is an approximate fraction of the grid cell c that is covered at least partially by the polygon j and normalized such that they add up to one for each polygon. The polygon-level average temperature was then aggregated to the provincial level using a population weighting scheme. The population weight w jpis equal to the population of polygon j divided by the total population of province p . Given the population weightings w jp for all J p polygons located within the administrative boundary of province p, the 24-hour population-weighted average temperature on day dis expressed as:
24
T pd =
J p �
j =1
C j �
� w jp w cj · 24
c =1
24 � T cj
h =1
A similar procedure was applied for precipitation, but the 24-hour average term was omitted because the CHIRPS
dataset was already provided at a daily frequency. Weather and economic data were then matched at the province-year
level for 1982–2022. All nominal economic output measures were inflation-adjusted to the 2019 Thai bath. The resulting combination of economic and climate data yielded 3,086 province-year observations. The boxplots in Figure 1
summarize the historical population-weighted temperature and precipitation exposure. Summary statistics for the key variables are presentedin Table 1 . Climate and socioeconomic projection data . The main source of future climatic projections is the NASA Earth
Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The NEX-GDDP dataset was downloaded
[from The NASA Center for Climate Simulation. The downscaled projections for the Representative Concentration](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp)
Pathways (RCP) 4.5 and 8.5 from the 21 models and scenarios are produced and distributed. The NEX-GDDP dataset provides maximum and minimum temperatures, and mean precipitation at daily frequency of 0.25 [◦] x 0.25 [◦]. Owing to the limitation in computational resources, only seven of the 21 models between
2023-2090 were used to project the future impacts on economic output. Both temperature and precipitation estimates were processed using the same procedure as the historical weather data, as described previously. To project future impacts on economic output, I used the 2022 population distribution together with the 2019 land use pattern to construct a population-weighted average of the climate projection. Appendix Figure A2 summarizes the projections of the distribution of average annual exposure across 77 provinces in
Thailandin three future periods: 2031-2050, 2051-2070, and 2071-2090. The shift in the temperature distribution was clearly observed under the RPC4.5 and RCP8.5 emission scenarios. In long-term, population-weighted
average temperatures across all provinces in Thailand are projected to rise by 2.86 [◦] under RCP 4.5 emission scenario,
and by 4.06 [◦] C under RCP8.5, relative to population-weighted average temperature observed between 2003-2022. Nonetheless, a comparison of the projected and observed averages and variability between 2018-2022 shows a tendency for biases in all seven climate models. Appendix Figure A3 presents the full distributions of daily mean temperature bins across Thailand, again, between 2018-2022. All projections are consistently upward-biased relative to the observed
temperature. Section 4 discusses the strategy used to examine how the impact projections are sensitive to these potential
biases. To account for plausible alternative trends in economic development in the far future, I considered country-level
projections of socioeconomic drivers for the Shared Socioeconomic Pathways (SSP). The SSP
[data were downloaded from International Institute for Applied Systems Analysis (IIASA). I focus on the projections](https://data.ece.iiasa.ac.at/ssp/#/downloads)
from the OECD ENV-Growth 2023 model, as it is the only model that projects per capita growth rates at the country
level. The available data provide projected GDP per capita in constant 2017 dollar purchasing power parity (PPP) at
five-year intervals at the country level.
To calculate the annual projected growth rates, I assumed that Thailand grows at the same growth rate over these five years. 3 Relationship between temperature fluctuations and aggregate economic output
This section presents an empirical framework for the analysis of how temperature can affect aggregate economic output. I first discuss the empirical framework and specifications used to estimate the impact of temperature fluctuations on economic growth. The third subsection reports and discusses the results from estimating models that assume a common response across provinces to temperature changes. To uncover differences in the growth-temperature response functions across population groups, this subsection also reports and discusses the results of estimating the models in which the temperatures are interacted with a low-income province dummy, defined as a province having below median average
inflation-adjusted GPP per capita across the sample period. The last subsection further investigates the impacts of temperature on the components of GPP to shed light on the potential channels through which temperature may affect the aggregate output. 3.1 Empirical framework
Following the empirical framework derivedin Hsiang and Deryugina and Hsiang, I focus on an economyin which agents reallocate capital and labor to respond to temperature changes in away that maximizes profits. On a given dayindexed by d, an economyin province pis conceptualized as one where producers observe the weather,
adjust capital and labor resources in response, and then produce output.
If the same sequence occurs over the course of a year, under the assumption of temporal and spatial separability, i.e. that the economy conditional on temperature is additively separable across locations and moments of time, the annual aggregate output Y py can then be written as the sum of daily outputs observed across all locations l in the province p from each day din the year y
Y py = �
d∈y
9
� Y sd
Although the output data are usually measured at a higher level of aggregation, it is possible to recover nonlinear relationships at the grid cell level at which climatic data are recorded.
Suppose h ( . ) is the local andinstantaneous nonlinear economic-temperature response function that best describes the daily output observed at the end of the day and can be approximated as a linear combination of M simple nonlinear functions. For a given climate on day d and location l,
h ( c ld ) ≈
M � β m h m ( c ld )
m =1
Substituting the approximation from Equation 3 into Equation 2 andinterchanging the order of the summation yields:
Y py = �
d∈y
≈ �
d∈y
� Y sd
l∈p
�
l∈p
M � β m h m ( c ld )
m =1
�� d∈y
� f m ( c ld )
l∈p
�
=
=
M � β mm =1
� h ¯ mpy ~~��~~ ~~�~~
M � β m h [¯] mpy
m =1
�
The regressors h [¯] mpy are the nonlinear transformation of weather (e.g., temperature and precipitation) and, in practice,
are computed at the grid cell level before aggregating the values to the same spatial level as that of the aggregate output data using population weights and summing over days within a year. As suggested by Deryugina and Hsiang, the functional form analogous to Equation 4 was first introduced by
Deschênes and Greenstone. In the context of aggregate economic outcomes, β m has been successfully modeled as an M th order polynomial or restricted cubic spline, or an M -stepwise constant or “binned”
function. Under similar assumptions, an analogous approach has also been usedin the agronomic literature, in the context of health, and to study the impacts of higher temperatures on electricity consumption. 3.2 Econometric specifications
Although the empirical framework Equation 4 focuses on the effect of temperature on the level of economic output,
researchers practically use economic growth as a dependent variable, in part because aggregate economic output measures usually exhibit high serial correlation. Moreover, as a given effect size on the growth path of output will ultimately exceed the same effect size on the level of economic output for the projected long-run effects of climate
3 See Burke et al. for further discussion. change, it is important for policymakers to understand whether the effects of temperature generate a permanent or temporary loss of output relative to the trend (i.e., growth versus level effects). Using economic growth as the dependent variable, Dell et al. demonstrate that a higher temperature can reduce economic growth,
not just the level of output. Following previous climate-econometric literature,
I took the first difference of the logarithm of annual inflation-adjusted GPP per capita. Specifically, I estimated the following fixed-effects panel regression of the form g py = �
ρ n h n ( R py ) + α p + α y + ϵ py
n
β m h m ( T py ) + �
m n where g py is the annual growth rate of output per capita of province p for year y, T py is the daily average temperature
vector, and R py is the daily total precipitation vector. Both T py and R py were summed over the year. The effects of changes in precipitation were accounted for because changes in local annual temperatures and precipitation were historically correlated. Unless otherwise specified, all the main specifications include both province fixed effects and year fixed effects. A set of province fixed effects α p accounts for unobserved time-invariant
differences that influence provinces’ average growth rates, such as their history, culture, or geography. α y is a set of year fixed effects that accounts for time-varying differences that are common across provinces (e.g., technological innovations or changes in national economic policies) as well as year-specific shocks (e.g., a sudden rise in energy prices or a global economic crisis). Following Dell et al., the standard error terms ϵ py were clustered at the province level to account for temporal autocorrelation within each province. To uncover possible nonlinear relationships between temperatures and the annual growth rate of output per capita, I
estimated three different functional forms for h ( T py ) : M th order polynomial, binned regression, and heating/cooling
degree days functional forms. The binned regression is an important functional formin previous climate-impacts
research; Aroonruengsawat and Auffhammer; Deschênes and Greenstone
; Deryugina and Hsiang; Deryugina et al.. However, Deschênes and Greenstone suggested that binned regression likely overestimates the impacts of climate change because it failed to account for adaptations undertaken in response to permanent changes in climate. Similarly, Dell et al. also indicated that the temperature effects, as estimated using a fixed effects panel regression with a linear temperature, may be affected by not accounting for adaptability to permanent changes in climate. Burke et al. argued that an estimation using a fixed effects model with higher-order temperature terms to identify the effects of temperature could bridge this gap somewhat because economic agents with different average temperatures were allowed for historical adaptation to longer-run
temperature changes. The heating/cooling degree days functional form was additionally used to present possible variations in the estimated temperature effects. This functional form is similar to the concept of growing degree days in the agronomic literature.
4 Dell et al. investigate further using Monte Carlo analysis and show that running a growth regression is an effective approach to produce unbiased estimates of both growth and level effects given the properties of their simulated data. The first functional form assumed that h ( T py ) was an M th order polynomial of the form h ( T py ) = [�] [M] m =1 [β] [m] [T] [ m] py [.]
Increasingly flexible specifications up to seventh-order polynomials were estimated to search for the most parsimonious specification that provides sufficient flexibility to capture nonlinearity patterns in the data. Quadratic polynomials of precipitation were used to account for the effects of changes in precipitation in all examined specifications. Figure A4
shows the resulting response functions for the alternative specifications. The preferred specification used a 24-hour
average quadratic polynomial in daily temperature (i.e., h ( T ) = β 1 T py + β 2 T py [). Substituting the function] [ h] [(] [T] [py] [)] [ and]
h ( R py ) in Equation 5, the model becomes g py = β 1 T py + β 2 T py [+] [ ρ] [R] [py] [+] [ ρ] [R] py [+] [ α] [p] [+] [ α] [y] [+] [ ϵ] [py]
The second functional form estimated h ( T py ) using binned regression. Following the notation of Deryugina and
Hsiang, h ( T py ) was a stepwise function of the form h ( T py ) = [�] [M] m =1 [β] [m] [ ˜] [T] [m,py] [, where] [ ˜] [T] [m,py] [ was calculated]
by summing the fraction of days in which the population in province p was exposed to the m th temperature bin across year y . This stepwise function can be considered an important benchmark, as it regresses the outcome on
year-total time within each temperature bin; therefore, it is closest to being fully non-parametric. I explored various combinations of temperature intervals and omitted bins. All combinations used six bins of daily total precipitation to account for the effects of different precipitation levels. The specification selection procedure is further detailedin Appendix A.3. The preferred specification used 5 [◦] C interval and seven bins of 24-hour average of hourly
temperature. 6 � β n R [˜] m,py + α p + α y + ϵ py
n =1
g py =
7 � β m T [˜] m,py +
m =1
The bottom bin lumps the fraction of days in which province pis exposed to temperatures below 13 [◦] C into one bin, summed across the year. T [˜] m =2 sums the fraction of days when T ∈ C across the year, T [˜] m =3
5 Specifically, the gridded hourly temperatures T cj in Eq.1 were first transformed to the desired polynomial order, averaged out over day d, and then summed across the year y to create the annual temperature vector of province pas follows:
24
24
24
C j �
C j �
C j �
J p �
j =1
J p �
j =1
�
d∈y
d∈y
T py =
�� d∈y
� w jp w cj · 24
c =1
24 �
� T cj , �
h =1 d∈
24 �
� T cj [, ...,] �
h =1 d∈
24 � T cj
h =1
� w jp w cj · 24
c =1
J p �
j =1
� w jp w cj · 24
c =1
A similar procedure was applied for precipitation but omitted 241
frequency. 24 � term as the CHIRPS dataset was already provided at a daily h =1
6 To calculate the number of days the population in province pin year y were exposed to m th temperature bin, ˜ Tm,py, with lower bound m [◦] C and upper bound ~~m~~ ~~[◦]~~ C in the binned regression, I summed the fraction of dayin which the population in province p was exposed to temperature bin min day d] across the year as follows:
24
J p �
j =1
C j �
�
T ˜ m,py =
�� d∈y
� w jp w cj · 24
c =1
24 �
� 1 · [ T cj ∈ [ m, ~~m~~ ~~)~~ ]
h =1
The similar procedure applied for precipitation but omitted 241
frequency. 24 � term as the CHIRPS dataset was already provided at daily h =1
sums the fraction of days when T ∈ C across the year, and so on. The top bin lump the fraction of dayin which the temperatures are above 38 [◦] C, summed across the year. The omitted bin, which is captured by the model’s
intercept, is T [˜] m =4 when T ∈ C, summed across the year. The β m estimates represent the marginal effects on the annual growth rate of output per capita resulting from one additional dayin the m th temperature bin compared to a dayin the temperatures in the omitted bin. The daily total precipitation bins R [˜] m,py were similarly constructed across six bins. The bottom bin corresponds to the number of days with no precipitation, and the top bin corresponds to the number of days when the daily total precipitation is greater than 40 mm.
The remaining bins cover a
10 mm span of the daily total precipitation. The third functional form applied the concept of heating/cooling degree days, which is commonly foundin the agronomic literature. I estimated h ( T py ) using a piecewise linear function in which the impacts of beneficial temperatures and output-decreasing temperatures were separated around the critical temperature
threshold. As discussed previously, binned regression is closest to being non-parametric andis therefore an important
benchmark. I directly used the lower and upper edges of the omitted bin T [˜] m =4 of the preferred specification in Equation
7 as the thresholds for the heating and cooling degree days, respectively, to allow the results to be comparable with those of the binned regression. g py = β HDD HDD py + β CDD CDD py + ρ 1 R py + α p + α y + ϵ py
where the heating degree hours below 23 [◦] C and the cooling degree hours above 28 [◦] C were each summed over day across the year. Linear precipitation was used to account for the effects of changes in precipitation. Figure A4 shows the resulting response functions of the alternative specifications. All alternative specifications exhibit an inverted-U
pattern in the data. While the threshold temperatures of HDD and CDD are simply chosen because they are analogous to the omitted bin of the preferred binned functional form, the preferred specification is also more
conservative, as the slope of the cooling degree days above 28 [◦] Cis less steep than other CDD thresholds, and the slope of the heating degree days below 23 [◦] Cis indifferent to those of the other HDD thresholds. Appendix A.3 provides full details of the specification selection of each alternative functional form, using the same set of fixed effects and controls as describedin Equations 6, 7, and 8, respectively. Note that the identification strategy of all three functional forms relies on the presumably random year-to-year local variation in temperature, so the omitted variable bias is likely less vulnerable. Specifically, by conditioning on the province fixed effects α p, this ensures that the systematic patterns of weather in each province are isolated from the
7 Specifically, the heating degree days ( HDD py ) below 23 ◦ C and the cooling degree days above 28 ◦ C ( CDD py ) were calculated as follows:
24
C j �
, ∀T cj < 23 [◦] C
HDD py =
CDD py =
�� d∈y
�� d∈y
�� d∈y
�� d∈y
� w jp w cj · 24
c =1
� w jp w cj · 24
c =1
24 �
�
h =1
24
J p �
j =1
J p �
j =1
C j �
�
�
, ∀T cj > 28 [◦] C
24 �
�
h =1
within-location year-to-year variation in temperature exposure. Because weather fluctuations are unpredictable and potentially difficult for economic agents to anticipate, it seems reasonable to presume that the variation is as good as randomly assigned. The year fixed effects α y account for any time-varying trends or shocks that are common across all provinces in Thailand. Following Deschênes and Greenstone; Schlenker and Roberts, I assumed that the
detrended year-to-year variations within each province were uncorrelated with year-to-year variations in potentially important factors that might affect economic growth. 3.3 Empirical results
This subsection presents the impact of temperature on the growth of provincial aggregate output, as observedin the historical data. First, I discuss a common growth-temperature response function, as estimated using Equations 6, 7,
and 8. The next results uncover differences in the growth-temperature response functions across population groups byinteracting the temperature with a dummy for a province having low income, defined as a province having below median average inflation-adjusted GPP per capita across the sample period. To better understand the dynamics of the temperature effects in both low-income and no dummy models, I further discuss whether temperature affects growth through level effects or growth effects, using more flexible models with up to five lags of temperature. The results of various robustness checks on alternative specifications are also reported. 3.3.1 A common growth-temperature response function
I begin by estimating Equations 6, 7, and 8.
These simple models examine the null hypothesis that temperature does not affect the growth. The results demonstrate an average growth-temperature response function across 77 provinces in
Thailand. Specifically, the estimation uncovers the average growth-temperature response function h ( T ) in the general panel specification in Equation 5. Each panel in Figure 2 presents the main estimation results of each alternative functional form assumption. The graphs visualize a common temperature response function across 77 provinces in Thailand, evaluated at each 24-hour
average temperature. Interestingly, all three functional forms exhibit analogous patterns in the data, suggesting the
inverted-U shape of the response function: the growth rate of output per capita increases as temperature rises from cool to moderate (specifically, the reference temperature), and then declines. The point estimates can be interpreted as the effect of a single day at each 24-hour average temperature on the growth rate of output per capita relative to a day with an average reference temperature. For example, the results indicate that a day at 35 [◦] C leads to a decrease in the
GPP growth rate of approximately 0.07 percentage points, relative to the reference day of 26 [◦] C for the polynomial functional form (Panel A). Similarly, the results for the degree days functional form (Panel C) indicate that a day at
35 [◦] C, which is equivalent to 7 degree days relative to a temperature threshold of 28 [◦] C, leads to a decrease in the GPP
growth rate of approximately 0.07 percentage points. As for the binned regression (Panel B), a day at 35 [◦] C, of which
8 As described by Burke et al., testing temperature effects at different temperatures is different than typical testing for significant parameter estimates because the models are nonlinear, so the temperature effects vary at different temperature and thus must be evaluated at specific temperatures in order to have meaning. the effect is captured by the coefficient on the 33-38 [◦] C temperature bin, leads to a decrease in the GPP growth rate of approximately 0.10 percentage points, relative to the omitted C temperature bin. These effects are statistically significant at the 95% confidence level. Column 1 in Table 2 additionally details the estimated growth-temperature responses at specific temperatures, relative to a day with the respective reference temperature in each functional form, to facilitate comparison of the estimation
results. The point estimates in the polynomial (Panel A) and degree days (Panel C) functional forms are statistically significant, while those in the binned regression (Panel B) are broadly comparable in magnitude but only statistically significant at some temperatures. Regardless of statistical significance, the estimated impacts of temperature in all functional forms decrease as the temperature rises from cool to moderate and then increase. These results make it difficult to reject the hypothesis that the growth-temperature response is nonlinear.
For a quick understanding of these temperature effects, here, I adapt the procedure described by Deryugina and Hsiang
to calculate a marginal change in the annual growth rate of the economic output of 1 [◦] C warming. Suppose the
GPP growth was uniform across 365 days in a year, then a decrease of 0.014 percentage points of an annual growth from a day at 30 [◦] C, relative to the reference day of 26 [◦] C for the polynomial functional form, indicates the growth of that day is roughly 0 . 014% · 365 = 4 .
990% less than the growth of an average day of 26 [◦] C. Linearizing the effect of temperature relative to the approximate zero effect at 26 [◦] C, a marginal change in an annual GPP growth rate of 1 [◦] C rise in temperature is [−] 4 [.] [◦] C = − 1 . 248% . Analogously, for the binned regression, a decrease of 0.041 percentage points of an annual GPP growth from a day at 30 [◦] C indicates the growth of that day is roughly 14.801% less than the growth of an average dayin the omitted temperature bin. The effects of a 1 [◦] C rise in temperature on an annual GPP growth rate is -3.700%, relative to the approximate zero effect at 26 [◦] C. As for the degree days functional form, the effects of a 1 [◦] C rise in temperature on an annual GPP growth rate is -3.799%, relative to the approximate zero effect at the
CDD threshold of 28 [◦] C. Taken together with the estimated growth-temperature responses at various temperatures, these results suggest that estimates using the second-order polynomial functional form are, if anything, more conservative in estimating the impacts of warming temperatures on economic output. In addition to robustness to alternative functional forms, I considered robustness to alternative model specifications and samples. Appendix Table B2 reports the results of estimating a variety of robustness checks for the average
growth-temperature response function h ( T ) in which the average effect across 77 provinces was recovered. To be comparable with the main estimation results, the regression estimates of the models in Figure 2 are repeatedin the first column of Appendix Table B2 .
Still, three functional form assumptions were estimated for each robustness check. I first dropped precipitation to investigate whether changes in precipitation affect temperature estimates. Columns
3-6 demonstrate models that used an alternative set of controls relative to the baseline model. In column 3, I added
9 Since the population-weighted average temperatures that are projected by seven climate models under RCP4.5 and RCP8.5 between 2071-2090 are 29.37 [◦] C and 30.57 [◦] C, respectively, I therefore take the estimates at 30 [◦] C to showcase this. 10 To be comparable with the calculation of the polynomial functional form, I use 26 ◦ C, which is also the mid-point of the omitted bin, as the reference. If we use 23 [◦] Cor 28 [◦] C as the references, the effects of 1 [◦] C rise in temperature on an annual GPP growth rate are -2.114% and -7.401%, respectively. region-by-year FE; in column 4, I adapted the specification of Burke et al. by adding quadratic province-specific
time trends; in column 5, I adapted the specification of Dell et al. by replacing year FE with region-by-year FE
and poor-year FE; andin column 6, I replaced year FE with quadratic time trends. Column 7 estimated the baseline model using a balanced sample in which all provinces were present in the sample for the entire period.
Column 8
estimated the baseline model plus one lag of the per capita growth rate to account for potential time-varying omitted
variables. Role of precipitation. When not controlling for changes in precipitation, the point estimates in column 2 show that the temperature effects on growth are similar in magnitude to those in the baseline results. These results suggest that whether or not accounting for precipitation does not substantially affect temperature estimates. Notably, the effects of precipitation are typically statisticallyinsignificant and broadly consistent across a range of alternative model specifications for both the polynomial (Panel A) and temperature bins (Panel B) functional forms. Nonetheless, the effects of precipitation in quadratic province-specific time trends, balanced sample, and lagged dependent variable specifications are statistically significant and almost identical to the baseline results for the degree days functional form (Panel C). Alternative model specifications and samples. The results in columns 3-6 of the polynomial functional form (Panel
A) are broadly consistent in magnitude and statistical significance as I change the set of controls. In contrast, the temperature estimates and statistical significance in the binned functional form appear to be sensitive to alternative sets of controls, suggesting that they should be interpreted with caution. These results are not unexpected since the binned model is the method that is demanding of the data and columns 3-6 all have more control variables, compared to the baseline specification. The results in columns 3-6 of the degree days functional form (Panel C) are somewhat mixed. The HDD estimates in columns 3, 5, and 6 are comparable in magnitude to the baseline specification. However, for the CDD estimates, only the results in columns 3 and 5 are similar in magnitude to the baseline specification. The results in column 4, which is demanding of the data as the model incorporates
province-specific time trends as well as province FE and year FE, appear to be statisticallyinsignificant and smaller in magnitude than the baseline results for both HDD and CDD estimates. The results in column 8 of all three functional forms are close in both magnitude and standard errors to those in the baseline results, suggesting that the results are not affected by the lack of control for time-varying omitted variables. Column 7 shows how the temperature estimates change for alternative samples. Both the point estimates and standard errors of all three functional forms are close to those in the baseline specification. I further investigated the alternative formulation of the growth-temperature response function h ( T ) to understand the nonlinear response observedin Figure 2 . Following Burke et al., I substituted temperature interacted with average temperature and temperature interacted with average GPP per capita for the quadratic temperature termin
Equation 6. Appendix Table B3 presents these estimation results. In the absence of interaction with the province’s average income, the results show strong evidence of a nonlinear and concave temperature response. However, when the
11 The effects of annual precipitation on growth are not shown to conserve space. Estimates are available upon request. temperature-income interaction was included, the results indicate that the growth-temperature responses are driven by differences in average temperature and are affected by differences in income. See Appendix B.2 for further details. 3.3.2 Heterogeneityin the growth-temperature response function
The above results, as estimated from the pooled model where response functions were assumed to be the same across provinces, reject the null hypothesis that temperature has no effect on growth. Earlier studies at the global scale
suggested that hot countries tended to be poor and cold countries were rich. Dell et al.
found that hot countries exhibited the larger (negative) temperature effect than that of the cold countries, but being poor was a critical factor that determined this relationship. However, Burke et al. found limited evidence of heterogeneityin temperature response between rich and poor countries. This section further investigates by allowing high- and low-income provinces to respond differently to temperature changes. To uncover the differential growth-temperature response functions across provinces, the temperature was interacted with a dummy for a province having low income, defined as a province having below-median average inflation-adjusted GPP
per capita across the sample period. Each column in Figure 3 presents the estimation results for each alternative functional form assumption. The graphs display temperature response functions for both low-income (Panels D, E, and F) and high-income (Panels A, B, and
C) provinces at each 24-hour average temperature. The point estimates can be interpreted as the effect of a single day at each 24-hour average temperature on the growth rate of output per capita, relative to a day with an average reference temperature. Both high- and low-income provinces broadly exhibit an inverted-U pattern in the data, except for the flatter response function in the high-income subsample in the binned regression (Panel B). The point estimates between the high- and low-income subsamples in the polynomial and degree days functional forms are broadly similar in magnitude, especially at high temperatures. However, these estimates between the high- and low-income subsamples
are substantially different in the binned regression. Because the data are broken into subsamples and binned regression is a method that is demanding of the data, these results are not unexpected. In no case are the response functions in both high- and low-income provinces statistically different from the point estimates of the benchmark model, as estimated assuming a common response across high- and low-income provinces to temperature changes. These results suggest that we cannot reject the hypothesis that high- and low-income provinces have the same response functions to temperature changes. To facilitate the comparison of the estimation results, columns 1-2 in Table 3 detail the estimates of the temperature response functions at specific temperatures, relative to a day with the respective reference temperature in each functional form for both high- and low-income provinces. The results in the polynomial (Panel A) and degree days (Panel
C) functional forms show that the temperature responses of both high- and low-income provinces are negative and statistically significant at high temperatures. The estimates in the binned regression (Panel B) are consistent in sign but not always statistically significant. These results suggest that we cannot reject the hypothesis that the growth-temperature responses of either low- or high-income provinces are zero at all points in the temperature distribution. Robustness checks. Appendix Table B4 reports the results of estimating the baseline specification and a variety of robustness checks for differential growth-temperature response functions. All alternative specification checks are analogous to those in Appendix Table B2. Regardless of statistical
significance, the estimated parameters in the baseline specification of all three alternative functional forms exhibit a nonlinear and concave structure of the growth-temperature response function h ( T ) in both low- and high income provinces. No interaction terms in the baseline specification of the polynomial (Panel A) and the degree days
(Panel C) functional forms are statistically significant, but the interaction terms of the binned functional form (Panel
B) are only statistically significant in the two highest temperature bins [◦] C and > 38 [◦] C bins).
These results suggest, at least for the polynomial and the degree days functional forms, that we cannot reject the hypothesis that high and low-income provinces respond identically to changes in temperature. 3.3.3 Growth effects versus level effects
The above sections discuss the results of estimating simple models with no lags. This section investigates further by considering more flexible models with up to five lags of temperature to test whether temperature affects the growth or level of GPP per capita to better understand the dynamics of these temperature effects. I
followed Dell et al. and estimated the generalized forms of Equations 6, 7, and 8 discussed previously by adding lags of temperature and precipitation. The growth versus level effects are identified by adding both the immediate and lagged effects of temperature fluctuations across years. If the cumulative effect of temperature and its lags shrinks to
zero, it indicates that temperature affects the level of aggregate economic output.
However, if the summed effects are similar to or even larger in absolute magnitude than the immediate effect, these results suggest growth effects. Table 2 reports the estimated temperature responses at various temperatures, assuming that high- and low-income
provinces respond identically to changes in temperature. Column 1 shows the immediate effects of temperature, while columns 2-4 shows the cumulative effect of temperature and its lags (up to five lags). Again, these estimates can be interpreted as the effect of a single day at each 24-hour average temperature on the growth rate of output per capita
relative to a day with an average reference temperature. Virtually every point estimate in the polynomial (Panel A) and degree days (Panel C) functional forms is statistically significant and appears to persist (that is, the cumulated effects of the contemporaneous temperature and all lags do not sum to zero) as more lags are included. The only exception is the cumulative effects with five lags at the cold end of the temperature distribution, which are still negative but no longer statistically significant. Because binned regression is a method that is already demanding of the data and the statistical uncertainty also increases as more lags are included, the estimated effects in the binned model are rarely statistically significant.
Notably, the cumulative effects of a day at 35 [◦] C in the binned regression (Panel B) are all negative but no longer statistically significant after 5 lags are accounted for. I further tested the growth effects versus the level effects when the temperature response was assumed to differ between
high- and low-income provinces. Table 3 presents the temperature responses at various temperatures. In low-income
provinces, most of the point estimates at the hot end of the temperature distribution are statistically significant when estimated using the polynomial (Panel A) and degree days (Panel C) functional forms. These estimates remain substantially negative as more lags are included, indicating growth effects at the hot end of the temperature distribution. However, most of the estimates in the binned regression (Panel B) are not statistically significant. Both the immediate effect and the cumulative effects in the binned regression are, however, negative and statistically significant at 35 [◦] C in low-income provinces. For the high-income provinces, most of the temperature responses, as estimated using the polynomial (Panel A) and degree days (Panel C) functional forms, are substantially negative, particularly at the hot end of the temperature distribution. Nonetheless, only a few of these estimates are statistically significant. In summary, the lagged temperature effects appear to statistically and significantly persist when assuming that high- and
low-income provinces identically respond to changes in temperature. The results suggest that changes in temperature may affect the rate of economic growth, rather than the level of economic output. When assuming that high- and
low-income provinces respond differently to changes in temperature, the estimates of low-income provinces using the polynomial and degree days functional forms show suggestive evidence of the growth effects on the output, particularly at the hot end of the temperature distribution. While most of the estimates using the binned regression show no evidence of the growth effects, the results at 35 [◦] C provide somewhat substantial evidence of the growth effects on the output growth of low-income provinces. Adding increasing numbers of lags tends to make the cumulative effects for high-income provinces more negative, particularly at the hot end of the temperature distribution. However, these estimates are also more uncertain when more lags are included. 3.4 Impacts of temperature at the sector level
This subsection further investigates the impact of temperature on the components of output growth to understand the potential channels through which warming temperatures may affect aggregate output. Specifically, I tested the null hypotheses of no effects of temperature on the growth in real value added of the agricultural, industrial, and service
sectors. First, I discuss the relationship between temperature changes and growth in each economic sector, as estimated by assuming that both high- and low-income provinces respond identically to temperature changes. Then I discuss the results as estimated, assuming that high- and low-income provinces respond differently to temperature changes.
Lastly,
I reports the results as estimated using the distributed lag form of Equation 6 and discuss whether temperature affects the growth or level of the components of the output. 3.4.1 A common growth-temperature response function on the components of GPP
Table 4 reports the temperature response functions for the components of GPP at various temperatures, assuming that
high- and low-income provinces respond identically to changes in temperature. The growth-temperature responses in the agricultural (Panel A), industrial (Panel B), and service (Panel C) real value added were calculated using regression estimates from Equation 6 with up to five lags. Column 1 shows the immediate effects of temperature, while columns
2-4 shows the cumulative effect of temperature and its lags. The contemporaneous temperature effects, as estimated from the regression with no lags in the agricultural sector, are substantially negative and statistically significant. The pattern of the temperature effects is an inverted-U shape. The growth rate of the agricultural component increases (that is, the estimated impacts of temperature become less pronounced) as the temperature rises from cool to moderate and then declines. I also find the inverted-U shape in the pattern of the contemporaneous temperature effects on the growth in service real value added, although these estimates are not statistically significant. Column 1 of Panel B shows the impact of mixed temperature on the growth of the industrial component. The temperature effects are negative at the cold end of the temperature distribution and become positive at higher
temperatures, and none of these effects are statistically significant. Overall, the results suggest that we can reject the null hypothesis that temperature changes do not affect output growth in the agricultural sector. However, the results provide no evidence of the impact of temperature on output growth in the industrial sector. This finding is different from Hsiang which reports substantial impacts of temperature shocks in industrial production compared to those in agricultural production. Note that, as suggested by Dell et al.,
the results of this study onlyindicate the net effects of temperature on aggregate industrial output, without providing specific insights into how different industries might be affected. I also find no evidence that temperature changes affect output growth in service real value added. 3.4.2 Heterogeneityin the growth-temperature response function on the components of GPP
The results in Panel A show substantially negative effects of contemporaneous temperature in both low-income and high-income provinces on the growth in agricultural real value added. The point estimates show an inverted-U shape in the pattern of temperature effects on the growth in agricultural output in both high- and
low-income provinces. These effects are all statistically significant, suggesting that we can reject the null hypothesis of no temperature effects on the growth of agricultural output. Notably, the immediate effects of temperature in both subsamples are close in magnitude and statistically significant, suggesting that the growth-temperature response functions in high- and low-income provinces are more likely to be the same. Panel B shows the impact of temperature on the growth of industrial output. The results for both low-income and high-income provinces are not statistically significant, suggesting that we cannot reject the null hypothesis that temperature changes do not affect growth in industrial output. While the contemporaneous temperature effects on the growth in service real value addedin high-income provinces are all negative and exhibit an inverted-U shape pattern, none of these effects are statistically significant at the conventional confidence level. Column 1 of Panel C presents the point estimates of the impact of temperature on the growth of industrial output in low-income provinces. The results show that low-income provinces have a slightly flatter response function, with none of these effects being statistically significant. These results suggest that we cannot reject the null hypothesis that temperature changes do not affect growth in service output in both high- and low-income provinces. Appendix Figure B5 displays the robustness of the growth-temperature response functions on the components of GPP,
as estimated using the polynomial (left column), temperature bins (middle column), and degree days (right column)
functional forms, allowing high- and low-income provinces to respond differently to changes in temperature. The results in Panels A, B, and C consistently show an inverted-U shape in the pattern of temperature effects on the growth of agricultural output in both high- and low-income provinces.
The temperature effects in all three functional forms are statistically significant at the hot end temperature distribution in both high- and low-income provinces, suggesting that for both low- and high-income provinces, we can reject the hypothesis that the growth-temperature responses in agricultural real value added are zero at all points in the temperature distribution. Panels D, E, and F show the temperature effects on the growth of industrial output. The pattern of temperature effects on the growth in industrial real value addedin both high- and low-income provinces is somewhat convex in all three functional forms, although the estimates are not statistically significant. The results suggest that we cannot reject the hypothesis of no temperature effects on the growth of industrial output in both high- and low-income provinces. The results for the service sector are shown in Panels G, H, and I. The temperature effects on the growth in service output are inverted-U shape in
high-income provinces and flatter in low-income provinces. The results are consistent across all three functional forms. These effects are not statistically significant at the conventional confidence level, suggesting that we cannot reject the hypothesis of no temperature effects on the growth in service output in both high- and low-income provinces. Taken together, the results, as estimated assuming that high- and low-income provinces respond differently to temperature
changes, suggest that we can reject the null hypothesis that temperature changes affect the growth in agricultural real value added. The findings are consistent whether the regression estimates used second-order polynomial, temperature bins, or degree days functional forms. As for the other components of GPP, the results provide no evidence of the impact of temperature on the growth of both industrial and service outputs. Again, these results are consistent across all three functional forms. I further tested the growth effects versus the level effects on the growth of the components of GPP. Since the above results suggest that we cannot reject the null hypothesis of no temperature effects on the growth in both industrial output and service output, I then focused only on the growth in agricultural real value added and rule out both level effects and growth effects on the growth in industrial output and service output. Again, the growth versus level effects are identified by adding both the immediate and lagged effects of temperature fluctuations across years. This section discusses the results of estimating the second-order polynomial Equation 6 with 1, 3, and 5 lags. Panel A
in Table 4 reports the contemporaneous and cumulated lag temperature effects on the growth in agricultural output,
as estimated by assuming that high- and low-income provinces respond identically to temperature changes. Almost all point estimates are substantially negative and statistically significant. The estimates are fairly stable as more lags are included, except for the cumulative effects with five lags at the cold end of the temperature
distribution. The results provide suggestive evidence of the growth effects at the hot end of the temperature distribution. Table 5 reports the cumulative temperature effects on the components of GPP at various temperatures, as estimated by assuming different growth-temperature response functions between high- and low-income provinces. Similar to the
12 Following Dell et al., a failure to reject the null hypothesis that temperature does not affect growth wouldindicate an absence of both level and growth effects.
case of the pooled growth-temperature response function, the results for agricultural real value added (Panel A) provide suggestive evidence of the growth effects, especially at the hot end of the temperature distribution in both high- and
low-income provinces. 4 Projections of future damages on economic output
The above sections discuss the relationship between historical temperature changes and aggregate economic output. This section combines the empirical results from Section 3 with the projected changes in climate to generate projections of economic output under climate change. The first two subsections outline how I used the estimation in Equation 6 to project these responses into the future to calculate the economic risk of climate change and how these projections were
aggregated. Subsequently, the projection results without bias correction are discussed. The next subsection presents the
bias-corrected projections and discusses the impacts of potentially upward biases in future climate projections.
The last subsection discusses the limitations of these projections. 4.1 Methods: Impact projections of climate change
I followed Burke et al. and used historical response functions to predict the annual GPP per capita growth rate,
relative to the growth rate absent climate change, in which temperatures are assumed to be fixed at their 2003-2022
average. Following the notation of Burke et al., the projections of the future growth rate of economic output per
capita (denoted g py [+] [) under different climate scenarios consist of two parts: the growth rate absent climate change and]
the differential effect of temperature changes. As a baseline scenario, I first assumed that each province would grow in the future at its observed average growth rate over 2003-2022.
The projected output per capita growth in province pin year y after 2022 is then given by g py [+] [= ¯] [g] [p] [+] [ δ] [py]
where ¯ g pis the observed average growth rate of output per capita during 2003-2022 and δ py denotes the differential effect of temperature changes on output growth of province pin year y and was estimated by
δ py = h ( T py [+] [)] [ −] [h] [( ¯] [T] [p] [)]
where T py [+] [ ¯] [T] [p] [is the average temperature in province]
p across the baseline period of 2003-2022. In addition to the above baseline scenario, I also considered the economic development projected by the SSP3 and SSP5
scenarios. The available data provide projected GDP per capita in constant 2017 dollars purchasing power parity (PPP)
at five years interval at the country level.
I assumed that Thailand grows at the same growth rates between these five years interval to calculate the annual projected growth rates. To disaggregate national output per capita into provincial output per capita and leverage the projected annual growth rates data derived from the SSPs, I assumed that the output
13 As such, the annual projected growth rates of Thailand’s GDP in the future year y is given by 1 / 5 − 1 . growth of each province pin year y was influenced by country’s growth in the previous year, and estimated panel regression using the sample between 2003-2022. I then used these derived coefficients to project the annual growth rate of each province in the absence of climate change ( η ). For a given SSP scenario, the projected output per capita growth in province pin each future year y under climate change is then given by g py [+] [=] [ η] [py] [+] [ δ] [py]
where η py is the projected annual growth rate absent climate change of province pin each future year yin a given SSP
scenario. I follow Burke et al.
and focus on only two SSPs scenarios: SSP3 and SSP5. There are two other important sources of uncertaintyin the projected impacts of climate change: physical uncertaintyin climate projections and uncertaintyin the estimate of h ( T ) . To account for uncertaintyin the climate projections, I
estimated the differential effects using seven climate projections under both the RCP4.5 and
RCP8.5 emission scenarios. This distribution of climate projections is expected to capture some uncertainties in the climate system through 2090. As for the uncertaintyin the projected impacts arising from the econometric estimation of response functions h ( T ), I executed a block bootstrapping simulation by adapting the procedure in Burke et al.. First, I randomly drew a set of sampling provinces with replacement to account for autocorrelation. Second, for each
bootstrap, I fitted the second-order polynomial model in Equation 6 and stored the resulting parameters. Third, using these parameters in combination with province-specific values of output growth provided by the baseline scenario and the projected climate provided by a given emission scenario from each of the seven climate projections, I predicted a response function for each of the 77 provinces. Finally, this process was repeated 1,000 times for each province, climate projection model, emission scenario, and output growth assumption combination. These 1,000 response functions were then used as the resulting distribution of estimates to characterize projection uncertainty for each year between
2023-2090. To understand the sensitivity of these impact projections to alternative specifications, I explored how projections change when accounting for lagged effects of temperature over time and when allowing high- and low-income provinces to respond differently to temperature changes. In the former setting, the growth of output per capita in a given year was assumed to be affected by temperature in that year and the previous five years to account for the dynamic effects of temperature over time. In the latter setting, provinces in the future year y could move to the high-income response function if, in the previous period y − 1, their output per capita rose above the median output per capita across all provinces (and vice versa if it falls). Hence, four separate estimates were generated for each province, climate projection model, emission
scenario, and output growth assumption combination. The same block bootstrapping simulation procedure, as described
previously, was also executed to project the impacts arising from econometric estimation. In all projections, parameters from estimating the second-order polynomial Equation 6 were usedin combination with province- and year-specific
values of output growth provided by each output growth scenario. Finally, I examined the impacts of potentially upward biases in future climate projections, as describedin Section 2. Because it was very challenging to directly correct the projected temperature and it was also not a focus of this study,
I instead assumed that these biases found between the observed and projected temperatures during 2018-2022 were systematic and remain unchanged throughout the future impact projections. To correct for these assumed systematic biases, the differential effect of temperature changes on output growth, δ py, was subtracted by the presumably fixed impact correction observed during 2018-2022 (denote δ [¯] p ). Equation 10 therefore becomes
′ δ py = δ py − δ [¯] p
= [ h ( T py [+] [)] [ −] [h] [( ¯] [T] [p] [)]] [ −] [[] [h] [] [ −] [h] []]
where δ py ′ [denotes the bias-corrected differential effect of temperature changes on output growth, and] [ ¯] [δ] [p] [denotes the]
impact correction term used to offset the upward biases in the climate projections. For a given climate model, the impact correction of each province pis the differential effect of the average projected temperature between 2018-2022
and the average observed temperatures between 2018-2022 on output]
growth. Again, I assumed that these impact correction terms remain fixed throughout the future impact projections. 4.2 Aggregation: Impact projections of climate change at higher aggregate level
To enable the calculation of the higher level of aggregate economic output, like Gross Regional Product and Gross
Domestic Product, I additionally assumed that the population share of each province, relative to either the entire country or its respective region, was fixed at its observed median population share over 2003-2022. Gross Regional Product
(GRP) of region R in each future year y were then constructed by
GRPcap Ry = � ω i · GPPcap iy
i∈R
where ω i is province i ’s population share in region R . Note that there are six regions based on the NESDC designation:
North, Northeast, Central, East, West, and South. To demonstrate different impacts of climate change, however, the North region is additionally dividedinto Upper North (containing Chiangmai, Chiangrai, Lampang, Lamphun, Maehongson,
Man, Phayao, Phrae, and Uttaradit) and Lower North (containing Kamphaengphet, Nakhonsawan, Phetchabun, Phichit,
Phitsanulok, Sukhothai, Tak, and Uthaithani) regions following the National Geographical Committee designations. Similarly, the Gross Domestic Product in each future year y is given by
GDPcap y = � ω p · GPPcap py
p
where ω pis province p ’s population share relative to the entire country. 14 Notably, the population share of each province have barely changed since 2003. Average standard deviation of population share during 2003-2022 is 0.00089. 4.3 Results: projected impacts of climate change at various aggregate levels without bias-correction
Figure 4 A shows the projected impacts \" without bias-correction \" of climate change on output per capita in the \"baseline\"
output growth scenario of each province, relative to its output per capita in the absence of climate change, between
2023-2090. These projections were estimated using Equations 9 and 10 together with regression estimates from
second-order polynomial Equation 6 with no lags, and assuming that both high- and low-income provinces respond identically to changes in temperature. The red and blue lines represent the median impacts under RCP8.5 and RCP4.5
scenarios, respectively.
In addition, Figure 5 displays median projections of the impacts \" without bias-correction \"
on province-level output per capita, as also estimated using Equations 9 and 10 together with regression estimates from second-order polynomial Equation 6, under the RCP8.5 emission scenario in 2090 for each combination of four historical growth-temperature response functions and three output growth assumptions. Province-level projections under both RCP4.5 and RCP8.5 emission scenarios show that climate change will negatively affect half of the Thai population in 2050. These negative impacts are statistically uncertain, with an average 51-57% likelihood that climate change will have positive impacts. Figure 4 Band C show that an increasing share of the Thai population is projected to be affected by a warming climate, while negative impacts are more certain as we approach the end of this century. In 2090, projections under the \"business as usual\" RCP8.5
emission scenario show that climate change will make 86% of the Thai population poorer in per capita terms than they would be in the absence of climate change, while with more aggressive emission reductions RCP4.5, 63% are. However, the projections are statistically uncertain, with an average probability of 0.30-0.47 across provinces that climate change will have positive impacts. These projections provide suggestive evidence that climate change will most likely affect Thailand’s economy to certain
extent. Projections in column 1 of Figure 5 further show that the differences in the projected impact of a warming climate are mainly due to geographic heterogeneityin the baseline temperatures. In particular, the provinces in the
Upper-North region, which are typically colder than the other regions of Thailand, benefit more fromincreased average temperatures. These province-level projections are broadly consistent in magnitude, whether high- and low-income
provinces are assumed to respond identically or differently to temperature changes. The projections in columns 2 and 4 of Figure 5 ), which account for the lagged effects of temperature over time, display substantially more negative impacts in all 77 provinces.
As shown in Tables 2, as lagged effects of temperature are factoredin, the estimated growth-temperature response functions are substantially more negative in models that allow growth effects to persist over subsequent years. Next, I examined the impacts, again, \" without bias-correction \" of climate change on Thailand’s gross regional product
(GRP) per capita. To illustrate the distribution of projections in each future year, projections were visually weighted
to present the probability that an economic output trajectory is observedin the 1,000 bootstrapped response functions. The darker areas represent a higher likelihood that the projected impact will pass through a given value in a given year. Figure 7 presents the projected impacts of climate change on regional output per capita
under the RCP8.5 emission scenario between 2023-2090 for baseline (left column), SSP3 (middle column), and SSP5
(right column) output growth assumptions. To derive GRP per capita projections, the province-level output per capita
projections were aggregated following Equation 13. Again, these province-level outputs per capita were estimated using
Equations 9 and 10 together with regression estimates from the second-order polynomial Equation 6 with no lags and assuming that both high- and low-income provinces respond identically to changes in temperature. Similar to provincial
projections, the output growth assumptions likely have little impact on the structure of the projected change in GRP
per capita under the RCP8.5 emission scenario. Point estimates are uniformly more negative in the higher average temperature regions (that is, Central, East, South, and West regions). The damages are modest in colder regions like
Lower-North and Northeast regions, while the Upper-North region, which has the lowest average temperature, benefits the most fromincreased average temperatures. To examine whether the region-level projections are sensitive to different combinations of emission scenarios, specifica
tions, and output growth assumptions, Appendix Figure C6 presents regional projected impacts under both RCP4.5 and
RCP8.5 emission scenarios between 2023-2090 using regression estimates from second-order polynomial Equation
6 for each of the four historical growth-temperature response functions with a baseline output growth scenario. The projections are broadly similar in structure for both emission scenarios. Projected impacts in models that allow the effects of temperature to persist on regional output growth are less uncertain whether high- and low-income provinces respond identically or differently to temperature changes. The estimates in Tables 2 and 3
provide the reason for this: as the effects of temperature are assumed to be cumulative and affect growth in a given year,
cold provinces couldinitially benefit on net, and hotter provinces remain worse off with further warming. However,
as a given province’s average temperature becomes warmer, the impacts of future warming climate worsen in each
future, which makes net economic impacts negative for provinces that are initially cold. In models that do not account for lagged effects, the projections become more uncertain whether high- and low-income provinces are assumed to respond identically or differently. This is because the estimated growth-temperature response functions are substantially flatter than the response function with lagged effects. Cold provinces increasingly benefit fromincreased average temperatures, whereas hotter provinces remain worse off under a warming climate. This explanation can also be applied to the different median estimates observedin the lower north and northeast regions
.
Notably, differences in the response function drastically benefit provinces in the upper northern region of Thailand. Lastly, I projected the impacts \" without bias-correction \" of climate change on Thailand’s gross domestic product
(GDP) per capita under RCP8.5 emission scenario between 2023-2090 for four different historical growth-temperature response functions and three output growth assumptions combination. The province-level output per capita
projections were aggregated following Equation 14 to derive the GDP per capita projections. These province-level
outputs per capita were first estimated using Equations 9 and 10 together with regression estimates from the second order polynomial Equation 6, under the RCP8.5 emission scenario in 2090 for each combination of four historical
growth-temperature response functions and three output growth assumptions. Projections were visually weighted
to illustrate the distribution of projections and to present the density of projected impacts observedin the bootstrapped response functions. Median estimates are more uniformly negative in models with delayed impacts,
whether high- and low-income provinces are assumed to respond identically or differently. Similar to both provincial and regional projections, the output growth assumptions likely have little impacts on projected change in GDP per capita under RCP8.5 scenario. As observedin the regional projections, projections are less uncertain and fall off steeply at earlier future years in models that allow the effects of temperature to persist on output growth because colder provinces also suffer large damages. In models that do not account for lagged effects, the projections are more uncertain whether high- and low-income provinces are assumed to respond identically or differently because colder provinces somewhat benefit fromincreased average temperature, while hotter provinces remain worse off under warming climate. Appendix Figure C7
presents the sensitivity of the projected impacts on Thailand’s GDP under the emission stabilization scenario RCP4.5. Projections under both emission scenarios are broadly similar in structure, but varyin magnitude.
Notable results are in the \"Common, 5 lags\" models under RCP4.5, in which projections are more uncertain than those under RCP8.5
for all three output growth assumptions. This is because emission stabilization scenario RCP
4.5, which results in a lower temperature rise than that of RCP8.5, makes net economic impacts less negative for cold provinces, especially provinces in the higher latitude regions (i.e., Upper-North, Lower-North, and Northeast regions). The same explanation can also be applied to the more positive median impacts observedin models that do not account for lagged effects, whether high- and low-income provinces are assumed to respond identically or differently to temperature changes under the RCP4.5 emission scenario. Note that the plots in both rows 1 and 3 also display the impacts of using different output growth assumptions in projections. In a given RCP
emission scenario, a given model with the baseline scenario, in which each province is assumed to grow in the future at its observed average growth rate over 2003-2022, provide more negative projections than those predicted by the same model with either the SSP3 or SSP5 pathways. When considered alongside the impacts of the projected climate under the RCP4.5 emission scenario described above, these results are not unexpected because the output growth of provinces derived from either SSP3 or SSP5 is mostly larger than their corresponding \"fixed\" ones in the baseline scenario, particularly those colder provinces in the Upper-North region. To summarize the projections numerically, Table 6 reports some important percentiles in the bootstrapped distribution of climate-impact projections on GDP per capita in 2090 for each combination of model specifications and output growth assumptions under the RCP4.5 (Panel A) and RCP8.5 (Panel B) emission scenarios. With the \"business as
usual\" RCP8.5 emission scenario (Panel A), models that do not take delayed effects of temperature into account (that
is, \"No lags\" models) with baseline output growth assumption project that climate change reduces Thailand’s output per capita by a half, relative to its GDP per capita in the absence of climate change, whether high- and low-income
provinces are assumed to respond identically (\"Common, No lags\" model in Panel B) or differently (\"High/Low, No
lags\" model in Panel B). Nonetheless, the inner 90% credible interval, determined as the 5th and 95th percentile values,
are substantially wide, with a probability of 0.32-0.37 for positive impacts. Using the \"No lags\" specification with SSP3
and SSP5 output growth assumptions, the models project smaller damages of 15% for identical response functions across provinces and 22% for different response functions between high- and low-income provinces. Projections are more uncertain, with a 46% likelihood of positive impacts. Taken together, in models with no delayed effects of temperature, the estimates varyin magnitude depending on the output growth assumptions and model specification being used, although the structure are similar. With the emission stabilization scenario RCP 4.5, all models provide projections similar to those under RCP8.5. Models that do not consider the delayed effects of temperature project small losses in the baseline scenario and substantial gain in SSP3 and SSP5 output growth assumptions, with a probability of 0.49-0.63 for positive impacts. As described previously, this is because the climate under the RCP4.5 emission scenario projects a lower temperature rise than that of RCP8.5, and cold provinces benefit on net, whereas hotter provinces remain worse off. Models allowing for delayed effects of temperature under both RCP4.5 and RCP8.5 emission scenarios project substantially large damages to economic output.
All models with delayed impacts project that Thailand will lose
95-99% of its output in the absence of climate change, with very unlikely positive impacts. 4.4 Results: projected impacts of climate change at various aggregate levels with bias-correction
To examine the impacts of potentially upward biases in future climate projections, this subsection presents the projected impacts on provincial output per capita using the bias-correction strategy discussed previously. Analogous to Figure
4, Figure 6 A displays the median projected impacts of climate change on provincial output per capita with baseline output growth assumption under RCP4.5 (blue lines) and RCP8.5 (red lines) emission scenarios between 2023-2090. All projections were based on the \" bias-corrected \" differential effect of temperature changes on output growth δ py ′ [, as]
estimated using Equation 12 and regression estimates from the second-order polynomial Equation 6 with no lags and assuming that both high- and low-income provinces respond identically to changes in temperature. Figure 6 A shows the projected impacts of climate change \" with bias-correction \" on output per capita in the \"baseline\"
output growth scenario of each province, relative to its output per capita in the absence of climate change, between
2023-2090, with red lines representing median impacts under RCP8.5 and blue line under RCP4.5.
These corrected projections are substantially different from those displayedin Figure 4 A, particularlyin cold provinces that benefit fromincreased average temperatures. With bias correction, projections under both the RCP4.5 and RCP8.5 emission scenarios show that climate change will substantially affect 89-94% of the Thai population in 2050. These projections come with average probability 0.10-0.20 that climate change will have positive impacts on any provinces. The climate-affected share of Thai population are substantially high through out the projected future period. Projections show that almost all Thai people will be negatively affected by climate change in 2090 under either the RCP4.5 or
RCP8.5 emission scenarios. Figure 6 C also show more certaintyin projections. In 2090, the likelihood that climate change will have positive impacts is substantially low, with an average probability of 0.06-0.07. These corrected projections provide suggestive evidence that climate change will affect Thailand’s economy to a certain
extent. Compared with Figure 4 A, the impacts of potentially upward biases in the climate projections are removed, and cold provinces (for instance, Mae Hong Son, Chiangmai, and Chiangrai in the Upper-North region) benefit much less on net, while hotter provinces remain worse off. A larger share of the Thai population is also expected to be affected by the warming climate in 2050 under both RCP4.5 and RCP8.5 emission scenarios, with a probability 0.10-0.20
that climate change will have positive impacts on any province. The projections are even more certain for 2090. The likelihood that climate change will have positive impacts is substantially low, with an average probability of 0.06-0.07. Finally, to show how the projected impacts of climate change are sensitive to potential biases in future climate projections, I reproduced Table 6 by replacing δ py with δ py ′
effect of temperature changes on output growth. Analogous to Table 6, Table 7 reports important percentiles in the bootstrapped distribution of climate-impact projections on GDP per capita in 2090 for each combination of model specifications and output growth assumptions under the RCP4.5 (Panel A) and RCP8.5 (Panel B) emission scenarios. In models that do not consider the delayed effects of temperature, whether high- and low-income provinces are assumed to respond identically (\"Common, No lags\" models) or differently (\"High/Low, No lags\" models), median projections in all three output growth scenarios show that climate change reduces Thailand’s output per capita by 57-63% under
RCP4.5 and 80-86% under RCP8.5, relative to its GDP per capita in the absence of climate change. With bias correction in the projections, these models project substantially larger damages than those without bias correction. Moreover, the inner 90% credible interval, determined as the 5th and 95th percentile values, are also substantially narrower. Again,
the reason for this is that as the impacts of potentially upward biases in the climate projections are removed, cold provinces benefit much less on net, while hotter provinces are affected less. These differences in the projected impacts can be easily observedin Figure 4 A and Appendix Figure 6 A. The cold provinces that benefit fromincreased average temperature (that is, negative damages) in Figure 4 A become worse off when the projected impacts are bias-corrected
in Appendix Figure 6 A. However, the median projections of climate impacts with bias correction show little change in models that allow for delayed effects of temperature under both the RCP4.5 and RCP8.5 emission scenarios. These models still project substantially large damages to economic output. Projections show that Thailand will lose 94-100%
of its output in the absence of climate change. Nonetheless, the inner 90% credible intervals are substantially narrower for the models with delayed temperature effects. All projections are very certain with only 1-6% likelihood of positive
impacts. Overall, the results in this subsection show that the projected impacts of climate change are sensitive to potential biases in future climate projections, particularlyin models that do not account for lagged effects. Colder provinces benefit fromincreased average temperatures, whereas hotter provinces remain worse off under a warming climate. Hence, all projections should be interpreted with caution. 4.5 Limitation
As described throughout this section, these projected future damages rely on a number of strong assumptions, including that the climate projections are correct, the future growth of province-level output will remain constant in the baseline scenario or follow the growth paths that are projected by SSP3 and SSP5 pathways, and the demographics of the Thai population and their geographic distribution will remain unchanged. The above impact projections with bias correction present the sensitivity of the projected impacts of climate change to the climate projections. Nonetheless, as suggested by Deschênes and Greenstone, although these assumptions are strong, they allow for a transparent projection based on the available data. Apart from statistical uncertainty, the above projections also reveal a certain degree of uncertainty caused by two distinct sources: climate projection uncertainty and economic pathway uncertainty. Future climate projections involve considerable uncertainty arising from an incomplete understanding of the Earth’s physical systems. This paper are however unable to account for this uncertainty.
I follow previous research on the impacts of climate change; Burke et al.; Carleton et al.) and use seven climate projections under two RCPs to provide some variation in the possible future climate. Similarly, predicting accurate economic growth in any economy is already challenging due to a variety of factors that introduce complexity and uncertainty, particularly as financial systems become increasinglyintegrated. Different output growths adoptedin the projections are expected to provide a sense of the potential consequences of different future scenarios. Finally, as indicated by Deschênes and Greenstone that their estimates using binned regression failed to account for permanent changes in climate and likely overestimated the impacts of climate change. Using a fixed effects panel regression with a linear temperature, Dell et al. also suggested that their estimated temperature effects may be affected by not accounting for adaptability to permanent changes in climate. To tackle such possible overestimation,
this study estimated a fixed effects model with higher-order temperature terms to identify the effects of temperature. This identification strategy uses both within-province and cross-province variation and allows for historical adaptation to longer-run temperature changes because provinces with different average temperatures are permitted to respond differently to within-province temperature changes. 5 Conclusion
This studyinvestigates the historical relationship between temperature fluctuations and aggregate economic output in the context of a tropical country such as Thailand. An identification strategy exploits the random year-to-year variation in temperature within a province, after controlling for shocks common to all provinces in Thailand, to estimate the effects of temperature changes on the annual growth rate of output per capita. Specifically, I used province fixed effects
15 See Tebaldi and Knutti for further discussion on the challenges in interpreting multi-model ensembles for climate projections and year fixed effects to isolate within-location year-to-year variation in temperature exposure from the systematic patterns of weather in each province. The results show inverted-U shape temperature effects on the annual growth rate of provincial output per capita in all estimated functional forms. The estimated marginal change in the annual growth rate of provincial output per
capita of 1 [◦] C temperature increase are -1.248, -3.700, and -3.799 percentage points for a second-order polynomial,
temperature bins, and degree days functional forms, respectively. In all estimated functional forms, the estimates assuming different growth-temperature response functions between high- and low-income provinces are found to be statisticallyinsignificant different from those estimated assuming a common response function across high- and
low-income provinces. The estimates using the polynomial and degree days functional forms also suggest that warming temperatures may affect the growth of economic output rather than its level, while little evidence is found when estimated using binned regression. However, these growth effects are more pronouncedin low-income provinces with higher average temperatures. I also investigate the impact of warming temperature on the three components of aggregate
output. The results provide suggestive evidence of the impact of temperature on output growth onlyin the agricultural
sector, but not in the industrial and service sectors.
Finally, I combined the empirical results with the projected changes in the climate to generate projections of economic output under climate change. Province-level projections under both RCP4.5 and RCP8.5 emission scenarios show that climate change will affect half of the Thai population by 2050. Projections under the \"business as usual\" RCP8.5
emission scenario show that climate change will make 86% of Thai people poorer in per capita terms than they would be in the absence of climate change in 2090, while a more aggressive emission reduction RCP4.5, 62% are. In both
cases, the likelihood of non-negative climate impacts is substantially low with a probability of 0.01-0.03. These projections provide suggestive evidence that climate change will most likely affect Thailand’s economy to certain
extent. Nonetheless, I find that the projected impacts of climate change are sensitive to potential biases in future climate projections, particularlyin models that do not account for lagged effects. Hence, all projections should be interpreted
cautiously.