The effect of climate fluctuation on output in developing countries

This paper has analysis the effect of climate change on the aggregate output particularly with two typical elements: temperature and precipitation in developing countries.. Those heavy e

VIET NAM – THE NETHERLANDS PROGRAMME FOR MASTER IN

DEVELOPMENT ECONOMICS -

THE EFFECT OF CLIMATE FLUCTUATION ON OUTPUT IN DEVELOPING COUNTRIES

By

NGO KHANH LIEN HUONG

(VIETNAM)

Academic supervisor

Dr PHAM KHANH NAM

This paper was submitted in partial fulfillment of the requirements

for Master’s degree in Development Economics

Ho Chi Minh City, July 2012

VIET NAM – THE NETHERLANDS PROGRAMME FOR MASTER IN

DEVELOPMENT ECONOMICS -

THE EFFECT OF CLIMATE FLUCTUATION ON OUTPUT IN DEVELOPING COUNTRIES

By

NGO KHANH LIEN HUONG

(VIETNAM)

Academic supervisor

Dr PHAM KHANH NAM

This paper was submitted in partial fulfillment of the requirements

for Master’s degree in Development Economics

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

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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

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TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION……….1

1.1 Problem statement……… 1

1.2 Research Objective……….4

1.3 Chapter summary……… 5

CHAPTER 2: LITERATURE REVIEW………6

2.1 The theory………6

2.2 Empirical studies……… …7

2.3 Conceptual framework……… …17

2.4 Chapter summary……… …19

CHAPTER 3: METHODOLOGY……….20

3.1 Econometric model………… ………20

3.2 Data……… …24

3.3 Chapter summary……… …27

CHAPTER 4: RESULT……… …28

4.1 Descriptive statistics………28

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4.2 Non parametric analysis…… ……… ………30

4.3 Econometric analysis……… ………33

4.4 Chapter summary……… ……….44

CHAPTER 5: CONCLUSION……….…… 45

5.1 Conclusion………45

5.2 Recommendation……… ……47

REFERENCES……….……49

APPENDIX……… …52

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List of Tables

Table 3.1: Variable descriptions………26

Table 4.1: Descriptions Statistic………29

Table 4.2: Correlation matrix……….30

Table 4.3: Temperature and Precipitation - Estimation Result……… ……37

Table 4.4: Estimation result with hot countries dummy……… …….40

Table 4.5: Estimation result with agriculture countries dummy……… 42

List of Figures Figure 2.1: The way climate fluctuation affects the country’s output………… …… 18

Figure 4.1: Histogram of temperature – precipitation – GDP per capita………….……28

Figure 4.2: Average temperature trend……… …31

Figure 4.3: Average precipitation trend……….…31

Figure 4.4: Average temperature vs Average GDP per capita……… 32

Figure 4.5: Average precipitation vs Average GDP per capita………33

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CHAPTER 1: INTRODUCTION

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 (CO2) 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, socio-economic, 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 Nĩno event in Philippines and Indonesia fired around 2 million ha of peatlands, 9.7 million 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 (CO2, CH4, N2O, SF6…) in our environment to prevent dangerous effects from harming the existence 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 CO2 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

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 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

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”?

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 Solow-Swan model

According to this model, the climate is considered as “natural resource” input in the Cobb-Douglas production function:

yt = atkαt hβt T-γt

with 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 T0 Thus we specify

at = AtT0-Ø with At is considered as an technology input of exogenous elements The

exponents α, β, γ, and Ø are assumed positive and identical across countries The γ and Ø 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: δ and constant population growth: n When Tt = T0 = T and if steady-state output per capita exists, y* will be formed:

ln y* = ψlnT – (α +β)ϴ ln(n+g+ δ) + αϴ ln(sk) + βϴ ln (sh) + ϴ lnAt (1)

with ψ = - (γ + Ø)ϴ and ϴ = 1/(1 - α - β) Thus, average temperature changes one percent

cause ψ percentage change of steady-state output It is expected ψ < 0

The equation (1) will be reformed by:

ln y* = Bt + ψ ln T (2)

Bt is country-specific elements include saving rates, population growth, depreciation, and

technology changes

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 (2001) 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 productivity and population density

However, they went on to look at the effect of temperature, or frost on incomes

Like Masters and McMillan (2001), 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

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-Cass-Koopmans 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 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 farness 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 30C, precipitation is unchanged; and the

result is that global warming reduce world 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 income-temperature

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 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 10C, and in long run to

consider historical climate change effects They found three primary conclusions First,

higher 10C 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; 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 using the population-weighted 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

100km at a coast, population growth, energy resource, and form of government He

demonstrate the effect of temperature on GDP by depending on the Slow-Swan model

and the Cobb-Douglas 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 10C will decrease around 12% world

income without mortality data In the contrast, temperature flattens world income

explicitly with mortality since 10C average temperature raising leads to 3.8-4.2% 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 income-temperature 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 20C may be impossible since it requires

expanding sample The prediction does not include non-market losses or economic

growth because it 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

Figure 2.1: The way climate fluctuation affects the country’s output

 Urban population

 Unemployment

 GDP per capital growth

Production

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

CHAPTER 3: 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

The general function form is:

Ln(GDP)it = β0+ β1 x Ln(Temperature)it + β2 x Ln(Precipitation)it

+ β3 x Ln(Temperature)2

it + β4 x Ln(Precipitation)2

it + β5 x Zit + αi + εi,t (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, α is a fixed effect for the country, and represented for unobserved elements that impact GDP per capita within country level such as saving, population, εi,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

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:

+ β3 x Ln(Temperature)2it + β4 x Ln(Precipitation)2it + β5 x Zit (inflation,

population density, urban population, GDP per capita growth, unemployment) + εi,t