The effect of climate fluctuation on output in developing countries

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM- NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

THE EFFECT OF CLIMATE
FLUCTUATION ON OUTPUT IN
DEVELOPING COUNTRIES
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

NGO KHANH LIEN HUONG

Academic supervisor

Dr. PHAM KHANH NAM

Ho Chi Minh City, July 2012

ACKNOWLEDGEMENTS
This paper has could not be started and completed without the help of several
individuals who supported me directly and indirectly. First of all, I appreciate my
supervisor Dr. Pham Khanh Nam so much for his enthusiastic assistance. He has not
only given me genuine intellectual guidance in academy but also encouraged me a
lot through the analysis process. It is so hard for me to complete this research
without his profound advice. Thus, I am very grateful to him. I am also thankful to
Dr. Nguyen Trong Hoai for sharing his knowledge and practice experiences in
researching which are very useful for this study. I also thank all my classmates in
class MDE17 for sharing their advantage discussions on econometric techniques,
especially Nguyen Thi Thuy Thanh. Last but not least, I would like to thank my
parents, and all my other family members for their concern and invaluable moral
support.


ABSTRACT
Climate change is one of the critical problems in recent years on over the world.
It impacts on many aspects of human life, that has a lot of researches and evident.

This paper has analysis the effect of climate change on the aggregate output
particularly with two typical elements: temperature and precipitation in developing
countries. Besides, control variables proxy unobserved effects at country level and
country fixed effects are also counted in the relationship of temperature and
precipitation with the country's output. The finding shows that both temperature
and precipitation have significant influence on the GDP of developing countries. It
is specially that temperature affects the GDP in U-shaped, while precipitation
affects the GDP in hump-shaped. Moreover, this paper also conducts the
relationship of temperature and precipitation with the output by two characteristics:
"hot country" and "agricultural country". The results are also significant; however,

the relationship share cannot be identified. Further, this research suggests some
adaptive recommendations for production to changing of climate.
Key word: climate change, output, panel data, developing countries.

ii


TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION ............................................................................ I

1.1

Problem statement ......................................................................................... 1

1.2

Research Objective ........................................................................................ 3

1.3

Chapter summary ........................................................................................... 4

CHAPTER 2: LITERATURE REVIEW ............................................................... 5
2.1. The theory ........................................................................................................ 5
2.2. Empirical studies ............................................................................................. 6
2.3. Conceptual framework ................................................................................. 14
2.4. Chapter summary ......................................................................................... 15
CHAPTER 3: METHODOLOGY ........................................................................ 16
3.1. Econometric model ........................................................................................ 16
3.2. Data ................................................................................................................ 20

3.3. Chapter summary ......................................................................................... 22
CHAPTER 4: RESULT .......................................................................................... 24

4.1. Descriptive statistics ..................................................................................... 24
4.2. Non parametric analysis ................................................................................ 26
4.3. Econometric analysis ..................................................................................... 29

4.4. Chapter summary .......................................................................................... 39

iii


CHAPTER 5: CONCLUSION .............................................................................. 41
5.1. Conclusion ...................................................................................................... 41
5.2. Recommendation ........................................................................................... 42
REFERENCES ........................................................................................................ 44
APPEND IX ............................................................................................................. 46

iv


List of Tables
Table 3.1: Variable descriptions ............................................................................... 22
Table 4.1: Descriptions Statistic ............................................................................... 25
Table 4.2: Correlation matrix .................................................................................... 26
Table 4.3: Temperature and Precipitation - Estimation Result.. ............................... 33
Table 4.4: Estimation result with hot countries dummy ........................................... 36
Table 4.5: Estimation result with agriculture countries dummy ............................... 38

List of Figures

Figure 2.1: The way climate fluctuation affects the country's output.. .................... 15
Figure 4.1: Histogram of temperature -precipitation - GDP per capita ................. 25
Figure 4.2: Average temperature trend ..................................................................... 27
Figure 4.3: Average precipitation trend .................................................................... 27
Figure 4.4: Average temperature vs Average GDP per capita ................................. 28
Figure 4.5: Average precipitation vs Average GDP per capita ................................ 29

v


CHAPTER 1: INTRODUCTION
1.1.

Problem statement

Increasing output is one of the most important targets of most of the countries,
especially developing countries. The output of a country has been identified in the
function of elements such as human capital, physical capital, and technology.
However, the changing of climate belongs to environment elements in recent years,
which is considered that it has a critical effect on country's output. Further, the
climate change has economically effected on human health, life and production in
agricultural countries, which makes the studies more attractive and interesting.
Human activities have already changed the atmospheric characteristics such as
rainfall, global warming, sea level rise, increasing global carbon dioxide (C0 2)
emissions and the thinning of ozone layer. Those climate changes happening
constantly as a big problem all over the world have impacted upon many aspects of
human life such as natural ecosystem, biodiversity, human health, landscape, socioeconomic, etc. There are obvious evidences that climate change affects human life
(IPCC 2007). For example, expanding water stress, increasing temperature but
reducing the rainfall has caused a decline in agricultural production in Asian
developing countries while demands for production are increasing more and more.

Climate change has affected the supply, demand, and quality of water resources. For
instance, Northern Pakistan has been facing water shortages because of expanding
winter period over the Himalayas during last 40 years; China is facing up to the
drying up of rivers and lake in the dry weather while water need is increasing.
Likewise, In India, Bangladesh, and Nepal, water shortage is aggravated by climate
change; consequently it has leaded to rising urbanization, and using water
inefficiently. Contrary to the water shortage, glaciers are melting rapidly in Central
Asia, which has threatened human settlement, cities, forest ecosystem, and the
rising of water level caused the disasters of landslides. The 1997/98 El Nino event
in Philippines and Indonesia fired around 2 million ha of peatlands, 9. 7 million

1


hectares of forest (IPCC 2007). That is the extreme climatic event caused
substantial fatalities and economic losses. Climate change puts over 50%
biodiversity of the Asia at risk, so many species could be fragmented or extirpated
such as pine forest in Korea or beech tree in Japan (IPCC 2007). Other critical
sector relating to climatic influence is human health. Siberia ·has been reported a big
number of deaths associated with heatwaves, while there is a spreading disease like
diarrhoeal, cholera, malaria, etc. due to poor drinking water and sewerage system in
flood season. Malnutrition and Diarrhoeal are posed as substantial risk of human
health in year 2000 at Bangladesh, India, Nepal, Maldives, and Myanmar under
climate change (IPCC 2007). Specially, the effects of climate change on the
countries having high temperature climate and agriculture share has been
considered profoundly. Those heavy effects are explained that hot climate countries
are sensitive to any small change of temperature, while agricultural countries are
sensitive to the changing of precipitation. Development activities of human are
continuously creating greenhouse gas, one of the causes of climate chance, although
they also contribute to enhancing adaptation greatly and decreasing vulnerability of

vulnerable sectors to the changing of climate. In general, climate change affects
human life through different channels such as societal (e.g. migration, civilization,
culture), psychological (e.g. mental illness, cognition), physiological (e.g. nutrition,
starvation), and economic (e.g. energy, manufacturing) aspect.
In recent years, there are a lot of researches working on influences of climate
change. The most interesting sector is the climate change's economic consequences.
Climate change is the result of greenhouse gas emission from human activities.
Hence, finding out the relationship between climate change and output will help
suggest the policies or adaptation strategies in socio-economic sectors to control the
effects of climate change within each country. Furthermore, we can adjust the
changes of climate at our will toward increasing output. For instance, the Kyoto
Protocol's main target is to balance greenhouse gas emission (C0 2 , CH4 , N 2 0,
SF 6 ... ) in our environment to prevent dangerous effects from harming the existence

2


and improvement of the human beings. This required developed countries to reduce
greenhouse gas, and submit annual activity reports; meanwhile, developing
countries are encouraged to joint this protocol.
Existing studies about the relationship between climate change and the
aggregate output have been quite rare until now. Most of them were conducted for
the

countries

of OECD

(Organization

for

Economic

Cooperation

and

Development), which are strongly impacted by temperature because they belong to
the relatively low temperature areas. For some other countries, studies mainly
focused on the suffering of separate sectors under climate change such as food
production, fish catching, forest, or agriculture. Moreover, other published
researches estimate the effect of climate change on economic growth based on C02
level. My thesis mainly examines the effects caused by the fluctuation of climate,
temperature and precipitation on aggregate GDP of the developing countries with
the data from 1990 to 2006.

1.2.

Research Objective

The overall research objective of my study is to investigate the impact of climate
fluctuation on country's aggregate output with individual national characteristics
within developing countries. I am going to consider the economic impact of climate
change in the short run on output under two typical elements: temperature and
precipitation. Besides, my study also discusses some national characteristics, called
control effects such as Inflation, GDP per capita growth, population density, urban
population, and unemployment. Moreover, according to Del (Dell et al, 2008),
developing countries move toward to be more agricultural and hotter. The reason is
pointed out that developing countries focus on agriculture which requires low

capital, technology, labor skill level (Tol, 2002). Furthermore, high temperature is
advantage condition for increasing pests, pathogens, etc which depreciates
agricultural (Masters and McMillan, 2001; and John, 2009). Therefore, my

3


objective also conducts the effect of climate fluctuation on developing countries
with two special characteristics "hot" and "agriculture".
My research questions are "Is the relationship between temperature,
precipitation and the output of developing countries significant? - Do the impacts of
temperature and precipitation vary across "hot country" and "agriculture country"?
This thesis considers the relationship between temperature, precipitation and
output, which is divided into five chapters. Chapter 1 introduces the problem
statement and research objective. The Reviewing of empirical studies about the
influence of climate change on the output is presented in Chapter 2 - Literature
review. Chapter 3 shows how I collect data for this paper and set up the model.
Chapter 4 reports the economic result of regression, and the discussion about
comparing with other literature. The last chapter, Chapter 5 is used for conclusion
and recommendation.

1.3.

Chapter summary

This chapter has introduced the problem statement that the effect of climate
change on the world's economy has been happening constantly. Thus, the changing
of climate is essential concerned topic, especially in the aspect of the output. The
aim of this paper is conducting the research on the impact of climate fluctuation on
the aggregate output of the developing countries, then answering two questions: "Is

the relationship between temperature and precipitation with the output of
developing countries significant? Do the impacts of temperature and precipitation
vary across "hot country" and "more agriculture country"?

4


CHAPTER 2: LITERATURE REVIEW
2.1. The theory
To demonstrate the relationship between climate and output, I follow the
research of John (2009) and Mankiw et al (1992), which is based on the SolowSwan model. According to this model, the climate is considered as "natural
resource" input in the Cobb-Douglas production function:

- atkat h~t T"Yt
Ytwith Yt is output per capita, kt is physical capital per capita, ht is human capital per
capita, and Tt represents the climate in average temperature at year t. at is all
exogenous country-specific variables, which may be composed of temperature in
history T 0• Thus we specify at= AtTo-0 with At is considered as an technology input
of exogenous elements. The exponents a,

~,

y, and 0 are assumed positive and

identical across countries. The y and 0 capture the impact of historical and
contemporaneous temperature. Let's suppose that the saving rates for physical and
human capital per capita: sk and sh are constant; there are constant depreciation: 8
and constant population growth: n. When Tt = T0 = T and if steady-state output per
capita exists, y* will be formed:
ln y* = \jllnT- (a +~)B ln(n+g+ 8) + aB ln(sk) + ~e ln (sh) + B lnAt

with \jl =- (y + 0)6 and
percent cause

\jl

e=

11(1

-a-~).

(1)

Thus, average temperature changes one

percentage change of steady-state output. It is expected \jl < 0.

The equation (1) will be reformed by:
ln y*
Bt is

country-specific

=

Bt + \jlln T

(2)

elements include

depreciation, and technology changes.

5

saving rates,

population growth,


2.2. Empirical Studies
The researches about the relationship between climate shock and economic
activities are expanding in many approaches. Generally, empirical studies can be
divided into three groups as summary of Tol (2009). First group is the studies which
measure damaging cost or influence cost function of climate change by statistical
method. Another category of studies concentrate on effect of climate on separate
sectors, such as agriculture, fish catching, forest. Final group analyses the effect of
climate on aggregate income with many different approaches. In this section, I will
summarize some empirical literatures in the order of time.
Masters and McMillan (2001) is one of the studies that consider temperature's
effect on particular sectors, not on aggregate income. They used frost- free days as
proxy for temperature to test a role of frost frequency in overall economic
performance. Their regression includes dependent variable - growth of average
income of a year, and independent variables such as the scale of economy, seasonal
frost, and control variables. The scale of economy represents three different
dimensions involving the size of domestic population, direct of aggregate trade as
one part of GDP, and linguistic barriers in people's communication. The frost
variable was measured by average frost-day number of each month in winter.
Finally, other variables capture investment rate of the countries (investment over
GDP), human capital accumulation (school enrollment), trade policy, and the

quality of domestic institutions. Masters and McMillan (200 1) have made
conclusions that the income level of temperate countries is higher than tropical
countries, which related to the expanding the market and economic scale. Their
reasonable explanation is that maybe climate assisted temperate regions in growth
since they transferred from agriculture into productivity sectors across countries;
but it is contrast with tropical countries. They also found out that frost can reduce
pathogens and pest, which is missed by considering under average temperature.
Moreover, they identified that the number of frost-free days significantly affects

6


productivity and population density. However, they went on to look at the effect of
temperature, or frost on incomes.
Like Masters and McMillan (200 1), Maddison (2003) also considers the impact
of climate on single sector, not on overall GDP; however, his topic is "the amenity
value of climate change" with approaching the household production function. He
applied the Household Production Function (HPF) theory which combines all
marketed goods by a fixed production technology. The HPF is commented that it
can be the best motivation in implication determining· varieties of surveyed
expenditures by climatic variables. Moreover, the advantages of the procedure
incorporating environmental variables and demand equations are the nature of
environmental amenities' role in defining consumption patterns clearly, and
limitations of applying initiated utility functions is already understood. The
approach used two assumptions. First, based on the levels of the environmental
amenities, the fixed costs are not only translated but also added to or subjected from
the household operation; second, the effective prices of goods are increased follow
demographic scaling. Although these assumptions are required, they are not
necessary to be linear because it is a certainly offensive in the condition of climate
variables. The cross-sectional data of 60 countries is used to examine the climate

role in determining consumption then calculate price for a climate variables range
of each country. Madison (2003) concluded that climate change benefits on cold
countries, while in hot countries any temperature rising decreases welfare and
changes in the cost of living in those countries enormously. For further exercise,
this author suggests more discussions on this paper's limitations. For example,
under environmental amenities, what is the different effect of demand system and
commodity aggregations on prices; applying other function forms which are more
flexible to answer the climate is normal, luxury, or inferior good; or using other
variables relating to climate such as ratio of heating days, precipitation.

7


There was one other type of studies about the impact of climate change on
economic measures and the range of climate effects on social welfare in a given
period known as Fankhauser and Tol (2004). They analysed the climate change
dynamic effects and social welfare with particular capital and saving formation,
then pointed out how to maximize future welfare. To set up their model, they based
on DICE model which is considered as the typical model of the world economic.
DICE does not separate four channels: non market, market, health, capital- through
which growth is affected by climate, but it excludes the emission reduction and fix
temperature scenario. However, they run their model by adjusting and combining
four different models to make their regression be available for their purposes. First,
the Solow-Swan model is used to compare the climate change impact directly on
GDP with saving and technology are both exogenous. Second, the Ramsey-CassKoopmans model which is the primary model structure of DICE is used to compare
the important of saving effect with the Solow-Swan model. This specification has
get saving rate is endogenous, but technical progress is exogenous. Third, in the
Mankiw-Romer-Weil growth model, savings are fixed and technology is
exogenous. Comparing this model with the Solow-Swan indicates unlike dynamic
effects since climate change influence human capital formation, but the saving rate

is the same in the Solow-Swan model. Finally, the Romer model is similar to the
Mankiw-Romer-Weil specification without human capital: savings are exogenous,
technological progress is endogenous. Fankhauser and Tol (2004) assumed that all
saving rate and parameter in the Romer model is equal in the Solow-Swan model.
Generally, the first two models highlighted physical capital formation, but climate
change has less been vulnerable; meanwhile it contrasts with the last two models.
They have paid attention that with a given savings rate, when climate change
reduces output, it will also reduce investment, which will lead to decrease capital
accumulation or future production. Although this paper is more representative for
growth model, it still has limitations. such as not suggest policy for climate change,
used closed economy. Therefore, they draw open directs for further researches such

8


as there are other channels through which changing of climate impact growth, for
example: structure economies, not yet saving and capital accumulation; climate
would affect the capital supply as well as investment ratio.
The study of Nordhaus (2006) directly estimates the impact of welfare, called
the statistical approach by using observed variations in prices and spending within
single country. He estimated the aggregate impact of climate on income for the
world with assuming that under respect of climate and geography, the economies
are equilibrium in long run. It is called "climate-economies equilibrium". However,
the climate-economies equilibrium of those areas in which climate shock change
very slowly is not reasonable. His objective is examining that the farther from
equator, the higher output per capital. Other studies which used the observation unit
are countries, meanwhile Nordhaus (2006) tested the relationship between
economic and geography by grid cells of each country. It means that economic
activity of the world is measured at one degree latitude by one degree longitude
scale. The reasons he explained for selecting are plentiful global environment data,

and statistical independent economic data. Although he developed "gridded output"
data, or called GCP whose concept is the same as gross domestic production, he still
had to use various methodologies to collect data for various group of countries such
as the United State, the high-income countries, the middle-income countries, and
the lowest-income countries. He run a multivariate equation including dependent
variable that is logarithm output per kilometer square, and independent variables
that are precipitation, temperature, geography variables such as soil categories,
coastline famess. One more difference between the studies of Nordhaus (2006) and
other economic growth theory is omitting endogenous variables: coastal density,
capital formation, technology, health status, education, etc; but focus on exogenous
variables: population, geography, etc. He compared the existing economic
productivity with two scenarios of climate change to measure the climate change
impact. The first scenario is assumed that there is only temperature change 3°C,
precipitation is unchanged; and the result is that global warming reduce world

9


output 0.93% GDP. Second scenario is which gets both assumptions: temperature
change as in the first, and precipitation decrease 15% in mid-latitude regions, but
precipitation raise 7% in others. The consequence of the second indicates that
climate change decline world output about 1.05% GDP, however, assumptions of
the second scenario are hard to establish because of lack of reality. He also proved
that the far from equator, the much rising of income per capital. Although Nordhaus
(2006) conducted the globe into a gross cell product which relies on one degree
latitude and one degree longitude by cross-sectional regression include temperature,
country-specific variable dummies, the result is need to be considered with caution.
First, approach weakens linkage between economic data and geographic data.
Second, follow his consequence, Africa's geography actually has enormous
economic disadvantage with respect to temperature, but it is true in comparing with

low-latitude countries. Third, the negative impact of global warming and
greenhouse gas was estimate significantly larger than existing studies base on the
G-Econ data. Fourth, the result pointed out that there was positive relationship
between economic activities and geographic condition, especially on elements:
temperature, coastal proximity, and precipitation.
In the same data collection manner with Nordhaus (2006), Choiniere and
Horowitz (2006) also used cross-section from 97 countries to conduct the incometemperature correlation. Their specific objectives are discussing proposition that
temperature will low accumulate capital level in hot countries, and what will happen
as global becomes warmer in the future. In this paper, they added one more input temperature- in the Cobb-Douglas function; and according to Fankhauser and Tol
(2004) they also adopt the Mankiw-Romer-Weil and the Solow-Swan model to set
up their regression. The scale, human capital, function of labor, and physical capital
are assumed to be constant. Besides, they fixed country - specific such as saving
rate, depreciation, and population growth. Moreover, in this function, technology is
an exogenous input. The author estimated the effect of temperature on output per
capital, human and physical capita both singly and jointly. To measure physical

10


capital stock, they collected data on total GDP, rates of capital depreciation,
investment, and population; while measuring human capita, they took average
school attainment of population aged older 25 from each country. This setting up
human capital data is different with the Mankiw-Romer-Weil model. About the
climate data, they based· on capital city's average annual temperature data of each
country for 30 years by calculating minimum and maximum monthly temperature
data then sum them up to take average. They have concluded that the relationship
between income and temperature is explicit and powerful since temperature affects
more than 45% country's income stock; and the answer for their second objective is
that 1% raising temperature will lead to reduce 2-3.5% GDP per capita. However,
this model is too simple to show a strong general conclusion about the impact of

global warming on income or growth, because temperature has just affected capital
and labor of production function, but it might influence many other aspects such as
savings, population growth, or technological progress. Thereby, we need further
research developed more sophisticated models about this problem to provide
enough exact evidences.
While Nordhaus (2006) used cross-sectional data, Dell et al (2008) used panel
data to study climate change affects growth. They examined the variation of
precipitation and annual temperature of 136 countries over 50 years through world
economic activities with adding country-specific dummy in equation. Their
dynamic approach was effective in capturing influence of climate in long run. They
began estimating simply without lag and based on dynamic growth form which
includes country fixed influence, time fixed influence, and climate variables:
temperature and precipitation; then they were continuous with 10 temperature lags.
Furthermore, Dell et al (2008) also conducted political instability effects on the
relationship of temperature and growth. They divided the research into two parts: in
the short run, to measure climate fluctuation effects reduce approximately 1%
income per capita since temperature raised 1°C, and in long run to consider
historical climate change effects. They found three primary conclusions. First,

11


higher 1°C of temperatures significantly reduce total GDP around 11% in poor
countries, but slightly in rich countries. Therefore, the income gap between poor
and rich countries will be increase by clime change in future. Second, the negative
effect of temperatures on growth rate is more than output. Final, higher
temperatures reduce agriculture output, industrial output, aggregate investment, and
expanding political instability in poor countries. However, Dell et al (2008) run
regression by measuring effect of climate less than weather, so they pointed out
issue that weather effect is not the same with climate effect on economic. Moreover,

although world income mainly contributed by rich countries, their estimate showed
that temperature has just increased income of rich countries slightly. In the other
hand, there are some precise distinctions between this paper and other traditional
studies. They used aggregate data which can catch and reflect interactions between
important channels; then they considered climate effects on rich and poor countries
differently. Finally, they examined the possibility of temperature affects on both
growth rate and level of output. The authors also suggest further analysis about
particular causal the climate-economy mechanisms.
Both previous studies had been relevant to the research of John (2009). He also
conducted the relationship between income and temperature by top-down approach
with mortality data. The reason is that he thinks that mortality data is representative
for temperature's historical role, and has strong correlation with average
temperature of countries. He used cross-section data for income per capita,
changing temperature in long run, and some explanatory variables of 100 countries
which stand for 94.7% world population and 95.4% world GDP. The dependent
variable is the average GDP per capita from 2002 to 2004, which accounts for both
inside and outside economic activities. There are three reasons for John (2009) took
averaged monthly temperature of stations in cities to construct the year average
temperature data, which is same as Choiniere and Horowitz (2006): first, he
considers that all growth of area, infrastructure will increase temperature in cities
more than other places of countries; second, it seems to be the most exogenous;

12


finally, it is easy to calculate. This is the first difference between John (2009),
Nordhaus (2006), and Dell et al (2008). While Nordhaus (2006) took geographic
average temperature, John (2009) said that unit was one-degree cell of
latitude/longitude than countries; consequently, economic data of some cells were
excluded. On the other hand, John (2009) considers that using the populationweighted average temperature as Dell et al (2008) has less endogenous, although it

is unknown and slight implications. Besides, John (2009) also used other
explanatory variables which have same exogenous with temperature on country
level, such as saving rate, percentage of population living around 1OOkm at a coast,
population growth, energy resource, and form of government. He demonstrated the
effect of temperature on GDP by depending on the Slow-Swan model and the CobbDouglas production function. To estimate across regression, he used the log-log
form, and the log-cubic form to capture the hump_. shaped relationship of very cold
climate and economic activities. Briefly, John (2009) analyzed directly the effect of
temperature changing in long period on income with combine all sectors. The
consequence that he has found out is that increasing 1°C will decrease around 12%
world income without mortality data. In the contrast, temperature flattens world
income explicitly with mortality since 1°C average temperature raising leads to 3.84.2% GDP reducing. This figure is considered be bigger than result found by
bottom-up approach. He also analyzed separately 38 African countries and 14
Soviet republic countries because of their size in the effect of temperature on
income, however, his report had just demonstrated the pervasiveness of the incometemperature correlation due to less observation. Moreover, John (2009) put dummy
variable for wealthy resources countries such as the OPEC, he has come to the
conclusion that there are not difference between these countries and others in the
log-log regression. Although all his estimations are significant, there are some
limitations in this paper. Measuring the income-temperature relationship when
temperature gets over 2°C may be impossible since it requires expanding sample.
The prediction does not include non-market losses or economic growth because it

13


does not capture exogenous factors such as technological change. Finally, it cannot
reflex exactly in measuring temperature change by the cross-sectional model.
2.3. Conceptual Framework

According to existing empirical literature, it can ·be expected that there is a
climate fluctuation - output relationship which is summarized as Figure 2.1.

The existing studies have indicated that climate fluctuation is caused by global
warming as the result of accumulating greenhouse gas from human activities in
industry, agriculture, etc. They also reach the conclusion that climate shock affects
many sectors of country such as economy, ecosystem, costs of live with typical
elements are temperature and precipitation. In this paper I will address this problem
in aspect the output of country in short run. The aggregate economic effects of
climate fluctuation on country in term of monetary value, is measured by country's
GDP per capita which is representative for the country's total economic benefit (or
cost). Hence, it is necessary to conduct the correlation between climate fluctuation
and production then suggest policies which is not only essential for adapting
changing of climate, but also relying on those changing to develop the world
economy. Moreover, there is different characteristic between each country, so
keeping some country-specific variables about social-economy prospective is
important. The control variables differ from each one in each country as inflation,
population density, urban population, unemployment, the growth rate of GDP per
capita. However, human activities through production, especially such an industry,
also impact environment; then create greenhouse gas in long run. Thus, in this
paper's framework I pass the influence of production on greenhouse gas (which is
showed by the grey dotted-line) for further research.

14


Control variables
~----------------------------------------------------,

I
I
I
I

I
I
I
I
I
I
I

Climate fluctuation

..:,

Greenhouse
gas

•
•

Temperature
Precipitation

•
•

I
I
I
I
I
I

I
I
I
I
I

.

Production

1

Inflation
Population
density

•

Urban
population

•
•

Unemployment
GDP per
capital growth

I

GDP per
capita

Figure 2.1: The way climate fluctuation affects the country's output
2.4. Chapter summary

This chapter has described briefly studies about the relationship between climate
change and the country output both in short time and long time by many different
approaches. The effect of climate change with temperature and precipitation - two
typical elements on the output is significant. However, poor countries have to be
vulnerable changing of climate more than rich countries while rich countries get
benefit on GDP from rising temperature, it contradicts poor countries. Besides,
unobserved effects at country level also must be considered in this relationship to
make robustness the result.

15


CHAPTER3:METHODOLOGY
3.1. Econometric model

To analysis by econometric, I am going to conduct a panel analysis for testing
the relationship between climate fluctuation and the output. The reason for choosing
panel data is that it is useful in spreading the number of observations to avoid bias
in analysis progress due to data of developing countries is very spare to collect,
especially far past years. In the econometric model, GDP per capita is a function of
temperature, precipitation and control variables such as inflation, population
density, urban population, GDP per capita growth rate, and unemployment. I take
log of the climate variables and GDP per capita to demonstrate the elasticity of
variables. Economic growth theories have mentioned the role of both endogenous

factors such as policy, technology; and exogenous factors as population. Although
those factors are important, in this study I have just focused on exogenous factors
because developing countries are considered that there are not much differences
about endogenous factors.
According to previous empirical studies, for instance, Mendelsohn et al (2000),
Nordhaus (2006), and John (2009) the relationship of GDP with temperature and
precipitation is not linear, but in a quadratic equation. It implies that an increasing
temperature and precipitation originally benefits the output of country when GDP
gets upward trend, however there is a peak. When rising of temperature and
precipitation reaches certain point at peak, then it makes GDP decrease. In this
study, I apply a polynomial regression base on three previous models to test the
hump-shape relationship between dependent variable- GDP per capita and two
independent variables- temperature and precipitation of developing countries.

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The general function form is:
Ln(GDP)it = ~ 0 +

~~

x Ln(Temperature )it+ ~ 2 x Ln(Precipitation)it

+ ~ 3 x (Temperatureiit + ~ 4 x (Precipitation) it + ~s x Zit+ ai + Ei,t
2

(3)

Consider country i at year t, thus Z is control variables such as inflation, population

density, urban population, GDP per capita growth, unemployment of country i at
year t. While, a is a fixed effect for the country, and represented for unobserved
elements that impact GDP per capita within country level such as saving,
population, Ei,t is an error term.
The control variables are assumed that they are linear in trend. The regression is
also assumed that all countries get same slope, however, the intercept may be
different for each country due to different country's structure about population
growth, saving, etc. (Dijkgraaf et al, 2005, and Aslanidis, 2009)
The analysis will follow "top-down" approach (Mendelsohn et al, 2000) with
three steps. The "top-down" approach is simple to consider changing of the
relationship of climate change and output under different variables, although the
numbers of observations might be reduced because of missing data.
(i) First, I test the pure impact of climate change with output. Thus, a function
which GDP per capita dependents on temperature and precipitation, is
applied.
(ii) Second, I test the relationship of climate change with output under impact of
some other elements which are mentioned in the Cobb-Douglas
production function. So, I conduct function (i) with adding control
variables.
(iii)

Final, as there is a variety of unobserved inherent influences between
countries, those influences have different and important effects on the
country's output. Therefore, I add fixed effects into function (ii) to
examine the overall relationship of climate change and output.

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I am going to apply upper three steps into five models. The first three models are

used to answer the first research question about the relationship between
temperature and precipitation with the output of developing countries. The last two
models are tested to answer the second research question about the vary impacts of
temperature and precipitation across "hot country" and "agriculture country". The
analysis process is summarized as follow:

•

Modell:

Ln(GDP)it =Po+ PIx Ln(Temperatureh + P2 x Ln(Precipitation)it
2
+ P3 x (Temperature) it + P4 x (Precipitation) 2it + Ei,t

•

Model2:

Ln(GDP)it =Po+ P1 x Ln(Temperature)it + P2 x Ln(Precipitation)it
2
+ P3 x (Temperature) it + P4 x (Precipitation) 2it
+

Ps

x Zit (inflation, population density, urban population, GDP

per capita growth, unemployment) + Ei,t

•

Model3:

Ln(GDP)it = Po+ PI x Ln(Temperature)it +P2 x Ln(Precipitation)it
2
+ P3 x (Temperature) it + P4 x (Precipitation) 2it
+

Ps x Zit (inflation, population density,

capita growth, unemployment)
+ ai (country fixed effects)+ Ei,t

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urban population, GDP per


•

Model4:

Ln(GDP)it = ~ 0 + ~ 1 x Hot country dummy+ ~ 2 x Ln(Temperature )it
+~ 3

x

Ln(Precipitation)it +

~4

x

(Temperature )2it

+

~5

x

2

(Precipitation) it
+ ~ 6 x Ln(Temperature)it x Hot country dummy
+ ~ 7 x Ln(Precipitation)it x Hot country dummy
+ ~ 8 x (Temperature) 2it x Hot country dummy
+ ~ 9 x (Precipitation) 2it x Hot country dummy
+ ~10 x Zit (inflation, population density, urban population, GDP per
capita growth, unemployment)
+ ai (country fixed effects)+ si,t

•

ModelS:

Ln(GDP)it = ~o+ ~ 1 x Agriculture dummy+ ~ 2 x Ln(Temperature)it
+~3

x

Ln(Precipitation)h +

~4

x

(Temperature)2it

+

~5

x

(Precipitation) 2it
+ ~ 6 x Ln(Temperature)it x Agriculture dummy
+ ~ 7 x Ln(Precipitation)it x Agriculture dummy
+ ~8 x (Temperature) it x Agriculture dummy
2

2

+ ~ 9 x (Precipitation) it x Agriculture dummy
+ ~1o x Zit (inflation, population density, urban population, GDP per
capita growth, unemployment)
+ ai (country fixed effects)+ Ei,t

19