Ref. code: 25595804040102OXH

THE EFFECTS OF DEMOGRAPHIC CHANGES ON

THE MONETARY POLICY IN THAILAND

BY

MISS TANCHANOK NANTAKIT

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF ECONOMICS

(INTERNATIONAL PROGRAM)

FACULTY OF ECONOMICS

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2016

COPYRIGHT OF THAMMASAT UNIVERSITY

Ref. code: 25595804040102OXH

THE EFFECTS OF DEMOGRAPHIC CHANGES ON

THE MONETARY POLICY IN THAILAND

BY

MISS TANCHANOK NANTAKIT

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF

MASTER OF ECONOMICS

(INTERNATIONAL PROGRAM)

FACULTY OF ECONOMICS

THAMMASAT UNIVERSITY

ACADEMIC YEAR 2016

COPYRIGHT OF THAMMASAT UNIVERSITY

Ref. code: 25595804040102OXH

(1)

Thesis Title THE EFFECTS OF DEMOGRAPHIC

CHANGES ON THE MONETARY POLICY

IN THAILAND

Author Miss Tanchanok Nantakit

Degree Master of Economics (International Program)

Major Field/Faculty/University Economics Thammasat University

Thesis Advisor Asst. Prof. Dr. Teerawut Sripinit Academic Years 2016

ABSTRACT

This study examines how demographic changes alter the effectiveness of

monetary policy in Thailand by developing a dynamic stochastic general equilibrium

model incorporated with an overlapping generation framework. Households are

characterized by up to six age generations with varying life expectancy and labor

productivity. In particular, the older expects to live shorter and has lower

productivity. Therefore, their consumption and effective labor supply are less

sensitive to changes in an interest rate than those of the younger. With these

demographic structures, demographic changes are separated to two effects, namely,

ageing effects and productivity effects. Ageing process weakens the effectiveness of

monetary policy, while the productivity effect amplifies it. The results show that the

effectiveness of monetary policy is boosted from 2001 to 2008 because the

productivity effect is stronger than the ageing effect. On the contrary, from 2008 to

2015, ageing process develops faster while productivity grows slower. As a result,

monetary policy during this period becomes less effective.

Keywords: Demographic Changes, Ageing Economy, Monetary Policy, Thailand, OLG, DSGE

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ACKNOWLEDGEMENTS

This thesis is submitted in partial fulfillment of the requirements for the

degree of Master of Economics (International Program). My thesis would not have

been possibly successful without the assistance and support of very kind people around

me. I therefore would like to express my appreciation and gratitude to all of them.

First of all, I would like to express my immense gratitude towards my

advisor, Asst. Prof. Dr. Teerawut Sripinit, for his uncountable effort and many

insightful suggestions. The most important thing he has given me is his

encouragement, which mentally supported me throughout my struggles. It is very

thoughtful of him to suggest me not only research issues or economic knowledge but

also English pronunciation, writing, presentation, computer skills, programming

technique, or even being nice to other people, and respecting to oneself. Every time,

after he dedicated his valuable time to provide me counsel, it seems metaphorically like the light at the end of the tunnel is brightened. They all therefore have improved

me proficiently, initiated and further refined this thesis completely.

Moreover, I thank Dr. Kittichai Saelee and Dr. Surach Tanboon, who

served as the chairman and a member of my thesis committee, respectively. They

gave me many beneficial suggestions and meaningful comments on my work. Dr.

Kittichai’s comments helped me to improve the model more logically and the results

more reasonably. Dr. Surach proposed and pointed out the practical ideas that made

my thesis realistic.

I could not start my impressive life in this programme at Thaprachan

campus without Mr. Loylom Prasertsri who truly supported me to join this program

and continue to study economics. Then, I had a chance to work as a research assistant

for Asst. Prof. Dr. Pisut Kulthanavit, from whom have learned lots of priceless

lessons. He has taught me to be a better person, and I will always remember his

advice to be generous and empathetic with other people.

I owe a debt of gratitude to all the lectures I attended at the Faculty of

Economics, Thammasat University, where I studied since undergraduate level. They

have enlightened me economic background, econometrics, micro and macro theories,

and applications to the real world. I also thank to every academic and computer staffs

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from the graduate school of economics, the librarians of Puey Ungbhakorn library,

and other officers. They facilitated me everything along my student life and thesis

process. Additionally, I deeply appreciated the Bank of Thailand for the two-year

financial support of my study.

Studying here made me grow strongly with precious memories and

experiences. Here, I have learned not only about academic knowledge but also how to

lead better life. I fortunately met a lot of kind people here, especially my friends and

seniors. I deeply extend my thankfulness for all cozy and genuine friendship,

considerate assistance, meaningful comments, and warm supports you gave me. To

my classmates, Kie, Tak, P’Top, P’Bow, Wat, Jet, Arin, and P’Niwan helped me to

completely pass all classes through collaborative learning. Without all of you, I

cannot graduate within two years. I felt pressured, but the result of having finished the

degree altogether is terrific. To the seniors, P’Parn gave me useful guidance on model

formation, theoretical intuition, and DYNARE codes. P’Joy, P’Pond, P’Tong,

P’Earth, P’Bogy, P’Air, P’Pitt, P’Lookhin, and P’Hui gave me beneficial comments,

theoretical background discussion, and mathematical knowledge to improve my

thesis. I specially thank to my two intimate friends, Vee and Nurse, who always stand

by my side and keep me relaxed whenever I feel tried.

From the bottom of my heart, I am very grateful for the unconditional

love of my family. I would like to express my profound thanks to them for

understanding the way I am, encouraging me whatever endeavor I turn to, and giving a

warm cushion whenever I fell. I thank my parents for raising me up in this freedom but

full of responsibility. My siblings, Games and Keng are really exceptional and splendid.

They have always kept me smiling, rigorously discussed some joyful things and even

cracked stupid jokes on me. I am also grateful for all the decisions I have made that has

brought me to my current stand and led me to complete this thesis potentially.

Finally, I wish to express my cordial thanks to the Buddhist teachings that

I followed; the mindfulness, meditation, and Dhamma quotes sharpen me to be nice

and helped me overcome any difficulty in my life.

Tanchanok Nantakit

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TABLE OF CONTENTS

Page

ABSTRACT (1)

ACKNOWLEDGEMENTS (2)

TABLE OF CONTENTS (4)

LIST OF TABLES (7)

LIST OF FIGURES (8)

CHAPTER 1 INTRODUCTION 1

1.1 Statement of Problem 1

1.2 Objectives 3

1.3 Scope of the Study 4

1.4 Organization of the Study 4

CHAPTER 2 REVIEW OF LITERATURE 5

2.1 Empirical Literature 5

2.2 Theoretical Literature 8

2.2.1 Fundamental Two-Period OLG 8

2.2.2 Large-Scale OLG 9

2.2.3 OLG Model by Gertler (1999) 11

2.2.4 The Other Frameworks 12

2.2.5 Transmission Mechanism 13

2.3 Concluding Remark 14

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CHAPTER 3 RESEARCH METHODOLOGY 16

3.1 Different Features between Tanboon (2008) and This Model 17

3.2 Model 19

3.1.1 Demographic Structure 19

3.1.2 Household 21

3.1.2.1 Decision problem of each age generation g 21

3.1.2.2 Optimal condition of each age generation g 24

3.1.2.3 Aggregation 26

3.1.3 Final Goods Firms 26

3.1.4 Capital Goods Firms 28

3.1.5 Central Bank 30

3.1.6 Market Clearing Conditions 31

3.1.7 General Equilibrium 31

3.3 Research Analysis Framework 32

3.4 Parameterization 35

3.4.1 Demographic Parameters 35

3.4.1.1 Ageing Parameters 35

3.4.1.2 Labor Productivity Parameters 36

3.4.2 Available Parameters 38

CHAPTER 4 RESULTS 39

4.1 Representative Household Model and Heterogeneous Household Model 39

4.2 Heterogeneous Household Model across Demographic Structures 42

4.2.1 Ageing Effects 42

4.2.2 Productivity Effects 45

4.2.3 Demographic Effects (Both Ageing and Productivity Effects) 48

CHAPTER 5 POLICY IMPLICATIONS 52

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CHAPTER 6 CONCLUSION 55

6.1 Conclusion 55

6.2 Limitation 57

REFERENCES 59

APPENDICES 63

APPENDIX A DECISION PROBLEM OF HOUSEHOLDS 64

APPENDIX B CALIBRATION OF DEMOGRAPHIC PARAMETERS 69

BIOGRAPHY 72

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LIST OF TABLES

Tables Page

3.1 Different Features between Tanboon (2008) and This Model 18

3.2 All Scenarios (S) Used to Analyze the Effects of Demographic Changes 34

3.3 Demographic Parameter Values 37

3.4 Available Parameter Values 38

5.1 Macroeconomic Stability under each Demographic Scenario 52

5.2 Macroeconomic Stability under Alternative Policy Regimes 53

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LIST OF FIGURES

Figures Page

3.1 Steady State of Population Share in each Scenario 33

3.2 Labor Productivity Parameter Values 33

4.1 IRF to a 25 bps Increase in a Policy Rate in the Representative and

Heterogeneous Household (HH) Model 40

4.2 IRF to a 25 bps Increase in a Policy Rate in the Heterogeneous HH Model:

Comparison between each Age Generation (g) 41

4.3 IRF to a 25 bps Increase in a Policy Rate: Ageing Effects as the

Probability of Surviving Becomes Higher at Given Productivity At 2008

(Aggregate Level) 43

4.4 IRF to a 25 bps Increase in a Policy Rate: Ageing Effects as the

Probability of Surviving Becomes Higher at Given Productivity At 2008

(Generation Level) 44

4.5 IRF to a 25 bps Increase in a Policy Rate: Productivity Effects as

Productivity Becomes Higher at Given Probability of Surviving At 2015

(Aggregate Level) 46

4.6 IRF to a 25 bps Increase in a Policy Rate: Productivity Effects as

Productivity Becomes Higher at Given Probability of Surviving At 2015

(Generation Level) 47

4.7 IRF to a 25 bps Increase in a Policy Rate: Demographic Effects as both

Probability of Surviving and Productivity Vary Over Time

(Aggregate Level) 49

4.8 IRF to a 25 bps Increase in a Policy Rate: Demographic Effects as both

Probability of Surviving and Labor Productivity Vary Over Time

(Generation Level) 50

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CHAPTER 1

INTRODUCTION

1.1 Statement of Problem

There has been a significant declining trend in the world population

growth rate. Low fertility rate and high life expectancy are two primary determinants

influencing this global trend and have shifted the age structure toward a greater share

of the elderly. This trend has caused demographic changes in many countries. In

Thailand, the number of older persons aged over 60 years in 2015 accounted for 15.81

percent of the total population. With this number, Thailand has already considered the

ageing society as defined by Coulmas (2007) and the UN2 and this phenomenon has

occurred faster compared to some developed countries in the West. The large number

of older people in Thailand is mainly due to the rapid decline in fertility rate

influenced by The National Family Planning Programme to promote the use of

contraception since 1970. Meanwhile, improved life expectancy has also served as an

important factor to drive population ageing in Thailand.

Demographic changes have caused widespread impacts on both micro and

macroeconomy and posed policy makers many challenges of the appropriate

preparation and successful measures. Several literature on the fiscal side agreed upon,

for example, higher taxes, lower pension benefits, and longer working lives. However,

there has been a few literature on the relationship between demographic changes and

monetary policy. The monetary authorities should not ignore these changes since they

1 According to the World Population Prospects: The 2015 Revision, UN 2 Under this definition, a society can be classified into three types based

on the proportion of the elderly population. Ageing society is defined when there are

more than 10% of who aged 60 and over or 7-14% of who aged 65 and over. Aged

society is defined when there are more than 20% of who aged 60 and over or 14-21%

of who aged 65 and over. Hyper-aged or super-aged society is defined when there are

more than 21% of who aged 65 and over.

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are specified as the structural variables, which can alter the economic structure and

the steady-state level in the country.

According to the monetary policy aspect, demographic changes interact

with a household’s behavior and expectation, as described by the life-cycle hypothesis

initially proposed by Modigliani and Brumberg in the early 1950s. The origin of this

theory states that people tend to save to smooth out their life-time consumption and

also points out the distinct behavior between the young and the old. At the beginning

of life, the young who holds no assets relies on wage income and borrowing. They

can anticipate to age to be retirees, so they increasingly adjust their savings for

retirement. The old who holds many accumulated assets can anticipate to live shorter,

so they have less incentive to save and increasingly adjusts their consumption. As a

result, the aggregate behavior of heterogeneous households can affect the

transmission mechanism of monetary policies. The saving pattern of the young and

the dissaving one of the old can influence the wealth channel. In the meantime, net-

borrowered behavior of the young and net-lendered behavior of the old can influence

the credit channel. Eventually, changes in the transmission mechanism of monetary

policy crucially affect the effectiveness of monetary policy to stabilize the whole

economy. Hence, monetary authorities should consider the important role of the

demographic structure since transmission mechanism, and effectiveness have changed

across the population structure.

Given that the population ageing in Thailand has become more significant

and has caused major structural changes, this study aims to examine how

demographic shifts into the population ageing affect the monetary policy in Thailand,

considering transmission mechanism and effectiveness. All objectives are considered

under a structural model rather than a statistical one, because demographic changes

are recognized as the structural shock that has a dynamic effect in the long run. The

statistical model is based on the assumption of the static economic structure, so

adopting the empirical data available under the current economic structure is

inappropriate to forecast any impacts of these changes under the distinct economic

structures. Additionally, even if the statistical model can reflect the current situation

in the economy, it is unable to trace any mechanism that proceeds the result. The

structural model can fulfill this drawback. It enables to investigate the process toward

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the end result and also express the role of expectation which is not practically

demonstrated by a historical data.

The structural model used in this study is the dynamic stochastic general

equilibrium (DSGE) model incorporated with the overlapping generation (OLG)

framework, as well as the set of calibrated parameters which represents the

characteristics of Thailand. The DSGE model is employed because it has an immense

contribution towards the field of monetary economics. One of the advantages of

employing this theoretical methodology is the clarification on the role of expectation,

because the statistical model based on historical data lacks this issue. In particular, the

DSGE model can take Lucus critique into account with the assumption of rational

expectation, by which the economic agents optimize their decision conditional on

changes in policies. DSGE model combines micro-foundation into the analysis, expedites the interaction among economic agents in the system, investigates the

transmission mechanism of monetary policy inside the economy, creates some

stochastic shocks to determine the adjustment of each interested variable, and

incorporates the empirical data by adopting the calibrated parameters. Moreover, the

augmented OLG framework allows the investigation of the aggregate implications of

the life-cycle theory, departs from the assumption of the infinite-lived household

agent, introduces the heterogeneous household to examine the intra- and

intergeneration effects, and enables the study of the distributional implications of

economic policies. Thus, the DSGE model incorporated with the OLG framework is

comprehensive enough to analyze the effects of demographic changes on the

monetary policy in Thailand.

1.2 Objectives

1. To investigate how demographic changes influence the transmission

mechanism of the monetary policy

2. To analyze the effect of demographic changes on the monetary policy

effectiveness

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1.3 Scope of the Study

This study employs the DSGE model augmented with the OLG

framework to achieve all objectives. Most features of this model are relevant to that

of Tanboon (2008). The heterogeneous household is based on the OLG framework

called the probabilistic ageing (PA) model, in the spirit of Grafenhofer et al. (2006).

A household agent is separated into six age generations across the length of age, that

represents the population and labor force structure in Thailand. The supply side is

assumed by price rigidities. The set of calibrated parameters used in the model

satisfies the characteristics of Thailand.

1.4 Organization of the Study

This paper is organized as follows. Next to the introduction in Chapter 1,

Chapter 2 reviews the related literature on both empirical and theoretical studies.

Chapter 3 states the explanation and the setting of the model. Chapter 4 reports the

results and the analysis of the model, and provide support to the policy implication in

the Chapter 5. Finally, Chapter 6 presents the conclusion, and limitation of the study.

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CHAPTER 2

REVIEW OF LITERATURE

This chapter provides a review of related literature on the effects of

demographic changes on the economy, highly focused on monetary policy impacts.

Generally, literature is initially based on the life-cycle hypothesis, firstly proposed by

Modigliani and Brumberg in the early 1950s. The origin of this theory stated that

people tend to save to smooth out their life-time consumption. In details, at the

beginning of life, the young whose income relies on wage and borrowing can

anticipate to get older, so they tend to save for retirement. The old whose income

relies on accumulated assets can anticipate to live shorter, so they have no incentive

to save. As a result, the life-cycle hypothesis pointed out the distinct behavior

between the young and old; the young has a saving pattern and tends to be net

borrowers but the old has a dissaving pattern and tends to be net lenders. This theory

has justified only individual level. Then, the OLG framework has fruitfully been

applied because it enables the aggregate implication of life-cycle hypothesis and

introduces the heterogeneous household to examine the intra and inter-generation

effects. Thus, to study demographic changes, literature either carried out the OLG

framework or incorporated it with the suitable model to achieve their objective.

The review of literature is categorized into three main groups: (1)

empirical literature, (2) theoretical literature, and (3) concluding remark.

2.1 Empirical Literature

Most of empirical research pays heed to fiscal side’s effects such as

healthcare system, pension fund, and government budget while it has limitedly

established the effects of demographic changes on the monetary side.

One of the main effects of demographic changes is on macroeconomic

variables. Yoon, Kim, and Lee (2014) examined those effects together with fiscal

ones by using panel models in 30 OECD economies. The former are for example

economic growth, current account balance, savings, and investment, while the later

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are government budget balance, revenue, and expenditure. Their results showed

various impacts relying on the particular stage in the demographic transition. To be

relevant with monetary targets, they found the positive effect of population growth on

inflation, or in other words, the population ageing reduces inflation. The net inflation

impact depends on the magnitude changes of both supply and demand side response

to population growth. A population decline can decrease either labor productivity

which in turn forces higher inflation, or total consumption which in turn forces lower

inflation. Thus, the positive effect of population growth on inflation is caused by slow

aggregate supply adjustment relative to aggregate demand adjustment.

Focusing on monetary policy effectiveness, Imam (2015) interpreted it

from time-varying VAR as (i) the cumulative impact of an interest rate shock on

inflation and unemployment over five years and (ii) the maximum corresponding

response. Under this model, the monetary policy effectiveness is less sensitive over

the time that a share of elderly had become greater. After the monetary policy

effectiveness was estimated from time-varying VAR, they found a long run

relationship between it and old-age dependency ratio under Co-integration. The result

significantly clarifies weakening monetary policy effectiveness in ageing economy,

which can be explained by an aggregate response through a credit channel and a

wealth channel. In ageing economy, a credit channel becomes less powerful because

young population tends to be borrowers than the old. On the contrary, a wealth

channel seems more powerful when the population structure consists of a higher share

of elderly who holds a lot of assets. When a credit channel dominates a wealth

channel, monetary policy effectiveness in ageing economy declines.

In regard to monetary policy conduct, Juselius and Takáts (2015) found a

U-shaped pattern between age structure and inflation by using a panel of 22 countries

over the 1955–2010 period. This pattern is that the young and old relate to higher

inflation, while the middle-aged relates to lower inflation. It suggests that population

ageing eventually leads to higher inflation. Then, they interpreted the relationship

between the age structure and monetary policy conduct which is represented by real

interest rate setting and a deviation from Taylor rule. Their result shows that the

monetary policy reinforces the effect of a high share of workers on low inflation but

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mitigates the effect of population ageing on high inflation. Thus, the central bank is

prone to keep real interest rates relatively low throughout the whole sample.

According to monetary policy effectiveness but adopting quite different

interpretation from the other studies, Wong (2014) and Wong (2015) took the

expenditure of young and old cohorts response to a monetary policy shock into

account. Wong (2014) applied VAR model and found that ageing may dampen the

aggregate expenditure response to a monetary shock. It is due to the two reasons after

an increase in policy rate that young cohorts face tighter financial constraint because

they tend to be borrowers, and they also face higher unemployment risk because they

are workers. Both a tighter financial constraint and a higher unemployment risk lead

to a massive drop in aggregate expenditure if there are many young cohorts in the

economy. Thus, a lower number of young cohorts in ageing economy can reduce a

decline of aggregate expenditure response to interest rate shock, which indirectly

represents weakening monetary policy effectiveness. Wong (2015) applied a simple

regression and found that the young has higher consumption sensitivity and a higher

propensity to refinance in response to a policy rate shock than the old does. Hence,

the more elderly in the economy, the less aggregate consumption response to a policy

rate shock, which also indirectly represents weakening monetary policy effectiveness.

Wong (2015) study is particularly separated into an empirical and a theoretical part.

The later part takes advantage of explaining these empirical results which will be

described in the following part in the theoretical literature.

To summarize, all of the empirical literature above has remarked the

effects of demographic changes on the economy. Corresponding to this study’

objectives, Juselius and Takáts (2015) showed the significant relationship between

population structure and monetary policy conduct but did not conclude the

effectiveness. It is agreed to be weaker in the ageing economy by Imam (2015), Wong

(2014), and Wong (2015) but only Wong (2015) clearly have an economic

explanation of the result proposed in his theoretical part.

There are drawbacks of empirical literature. Even if they reflect the actual situation in the economy, they cannot represent the mechanism that proceeds the end

results. It lacks investigating the transmission mechanism of monetary policy inside

the economy, explaining the interacting behavior between each heterogeneous

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household, and incorporating the economic agents’ expectation. Consequently,

theoretical literature that can enhance these drawbacks is taken in highly focus.

2.2 Theoretical Literature

The consequences of population ageing have theoretically been analyzed

fruitfully under the OLG framework. It allows to investigate the aggregate

implications of life-cycle hypothesis, departs from the assumption of infinite-lived

household agent, incorporate the heterogeneous household to examine the intra and

inter-generation effects, strengthens the micro-foundation of the analysis, and enables

the study of the distributional implications of economic policies. In a monetary policy

perspective, since the DSGE model has emerged as the dominant analytical tool,

much theoretical literature has incorporated heterogeneous households from the OLG

framework into this model. It can increase the range of economic question about the

distributive effects of monetary policies that can be addressed with such model.

2.2.1 Fundamental Two-Period OLG

The basic OLG model first proposed by Samuelson (1958) and Diamond

(1965) considers the period as the whole length of life states; young and old. This

framework is simple and feasible to solve for numerical solution. However, a long

time in only one period is unrealistic and seems inappropriate to analyze temporary

shocks which affect the economy in short run; such as a policy rate shock.

Bullard, Garriga, and Waller (2012) employed this framework with

capital to study the redistribution policies between young and old cohorts. In their

model, social planners can access only an inflationary policy. If they increase the

inflation rate which reflects the opportunity cost of money, individuals will shift

portfolio decision from money toward the capital. An increase in this capital

accumulation raises the marginal product of labor and the wage rate but reduces the

capital rate of return. Hence, the young whose source of income relies on the wage

rate will prefer high wages, high inflation rates, and low interest rates while the old

who does not work prefers low wages, low inflation rates and high interest rates of

return from their savings. They highlighted that the political power of either the

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young or old whose population dominates the economy would be able to influence the

direction of the inflationary policy that benefits for them. Thus, in the ageing

economy, a central bank is apt to implement relatively low inflation targeting.

Kantur (2013) also employed the DSGE model incorporated with the two-

period OLG. His result showed that there is a chance of a rise in output response to a

tightening monetary policy shock. In response to this shock, the young decreases their

consumption but increases their saving to smooth out their life-time consumption

while the old can increase their consumption as their wealth increases. Moreover,

when the young with high saving switches to be the old in the next period, they

eventually can increase their consumption. Thus, the aggregate output response to a

positive interest rate shock depends on the old age dependency ratio; the higher this

ratio is, the less a fall in output becomes. He concluded that in ageing economy, the

monetary policy is less effective and the policy trade-off between inflation and

unemployment decreases since the aggregate output sensitivity to the interest rate

declines.

2.2.2 Large-Scale OLG

Starting with the work by Auerbach and Kotlikoff (1987), the large-scale

OLG model improves the period under the two-period OLG because the whole

population can be separated into any age groups along the year since birth.

Additionally, it departs the finite household from the assumption of infinite life in a

representative household by assuming that the last group is restricted to die. Although

this framework is more realistic than the two-period OLG, the yearly period provides

so little frequency that details of analyzing temporary shocks such as a policy rate

shock is not represented under this framework.

Miles (2002) employed this framework to examine the interest rate effects

in the ageing economy. Additionally, the different pension schemes were considered

to investigate these effects because they influence the individuals’ saving pattern. The

result showed that ageing economy causes a decline in the interest rate under all

pension schemes and the more generosity of pension schemes is, the more incentive

to reduce saving becomes. In other words, less generosity of pension schemes may

increase saving incentive, thereby amplifying a decline in the interest rate in the

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ageing economy. Furthermore, he applied the partial equilibrium OLG model with

portfolio allocation to examine the effects of the interest rate and the transmission

mechanism of monetary policy on the ageing economy. In this second model,

individuals face portfolio choices between a safe asset with known return and a risky

asset with volatile return. According to the interest rate effect, the result is as same as

that of the first model. According to the transmission mechanism of monetary policy

effect, a wealth channel is more powerful because there are more older people who

hold many financial assets while a credit channel is less powerful because there are

few young cohorts who are more prevalent to credit constraint.

Wong (2015) employed this large-scale OLG framework under his

theoretical part of his study but limited it to be the partial equilibrium model. He

focused on fixed-rate mortgages structure and fixed cost to refinance as the main key

of heterogeneity in the transmission of monetary policy to consumption. In his model,

the young with larger loan size holding have a higher propensity to both adjust their

loan and consume so that the consumption response generally decreases along the

year of age. After an expansionary policy rate shock, the young adjusts their

consumption more than the old does because they are likely to refinance their loan by

switching from the renter to be the homeowner which boosts the consumption of

housing services. This can imply that the aggregate consumption would less respond

to a policy rate shock in the ageing economy since there are few young cohorts who

have a high propensity to consume. This result and explanation can support his work

in the empirical part in the previous section.

Heer, Rohrbacher, and Scharrer (2017) employed this large-scale OLG

framework to investigate the driving forces behind the Great Moderation in 1985 to

2005. The two main factors are in concerned; the pure demographic effect when many

older workers who supply low volatility of labor increased, and the shift effect when

the volatility of labor supply of all age reduces. They found that low volatility of

aggregate output at that time was mainly driven by the shift effect but pure

demographic effect plays a marginal role.

Chateau (2003) employed this large-scale OLG framework to answer

whether economic uncertainty stemmed from either a productivity shock or a birth

rate shock. His result showed that more than 90% of the variance of output can be

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explained by Solow residual whereas a births rate shock can less explain it. However,

a births rate shock plays an important role to explain a fluctuation in hours worked

and the reverse relationship between the dependency ratio and hours worked. Firstly,

this ratio increases when many children were born, so the hours worked falls. Then it

decreases when those children become workers, so the hours worked rises. Lastly, it

increases again after those workers enter in retirement age, so the hours worked falls.

This pattern shows the reverse relationship between the dependency ratio and hours

worked, which causes this ratio negatively related to output and consumption either.

2.2.3 OLG Model by Gertler (1999)

Both two-period and large-scale OLG is not concerned about more

realistic demography such as risks of death and losing income in retirement age. Then, the Perpetual Youth model by Blanchard and Fischer (1989) took the former

risk into account by simply assuming that economic agents face the constant mortality

rate. It implies that the propensity to consume is the same for all heterogeneous agent,

thereby lacking life-cycle aspect in the details of distinct consumption and saving.

Gertler (1999) can recognize the strengths and weaknesses of each framework, hence

extended Blanchard’s view to OLG model so as to capture both risks of death and

income. He distinguished between working and retirement states of life. Workers face

a risk of losing income when they randomly move to be retirees while retirees face a

risk of death. In particular, the period under this framework can be relaxed to capture

any frequency, so temporary shocks can be suitably analyzed in the case of specifying

high frequency of the period such as quarter and month.

Fujiwara and Teranishi (2008) employed the DSGE model incorporated

with Gertler (1999)’s view to examine the demographic changes in three objectives.

First, they found that the degree of the impulse response to technology and policy rate

shocks varies under different demographic structures. Specifically, there is more

volatility of macroeconomic variables response to shocks in the ageing economy.

Second, they were interested in the interest rate effect and its interaction with the

presence of pension system. They found a decline in the interest rate in the ageing

economy because of the net of a rise in capital-output ratio. A rise in it dominates a

fall in it because people prefer to save for retirement rather than to raise the supply of

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low productive labor in the retirement age to maintain an optimized level of

consumption. Third, their result showed the asymmetric effects of heterogeneous

agents. The worker with higher productivity gains more as a result of a structural

shock, but the retiree with greater wealth holding becomes better off in response to a

tightening monetary shock. This can highlight the political power that either if a

central bank cares more about the retiree or if the retiree has more bargaining power,

the central bank may be biased to a high interest rate.

Kara and von Thadden (2016) employed the DSGE model incorporated

with Gertler (1999)’s view to examine the interest rate effect on the demographic

changes under different scenarios of PAYG pension system. They found that ageing

economy causes a decline in the interest rate under all pension schemes. Specifically,

less generosity of on reform scenario leads to an increase in private saving which

directly rises the capital-labor ratio, so that the interest rate largely reduces.

2.2.4 The Other Frameworks

Prettner (2013) introduced OLG in the spirit of Blanchard and Fischer

(1989) to the growth models, therefore ignored different behaviors in consumption

and saving of heterogeneous agents under the life-cycle theory. Otherwise, he was

interested in the consequences of population ageing for long-run economic growth

instead. The result from endogenous growth model with strong technological

spillovers showed that the population ageing fosters the economic growth because the

positive longevity effect dominates the negative fertility effect. In particular, an

increase in longevity positively affects economic growth whereas a decrease in

fertility negatively affects economic growth. It was due to the assumption of strong

technological spillovers that (i) a longer-lived population can extend their working

life as scientists in R&D sector, which in turn raises an economic growth and (ii) a

smaller number of population in the economy reduce the scientist workforce in the

R&D sector, which in turn drop an economic growth. Moreover, the result from semi-

endogenous growth model with weak technological spillovers showed that the

economic growth depends on the relative changes between mortality and fertility. It

was due to the assumption of weak technological spillovers that population growth

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can increase the flow of scientists into the R&D sector but also reduce a wage per

scientists, which in turn may cause a decline in an economic growth.

Ikeda and Saito (2014) employed the DSGE model with two distinct

households; workers and retirees, which is not as same as any OLG framework. Their

model paid attention to the role of collateral constraint to describe a fall in the real

interest rate in the ageing economy. This result is mainly explained by three channels

through which demographic changes reduce the real interest rate. First, an increase in

a loanable supply by the household can cause a fall in the real interest rate since

workers follow a life-cycle hypothesis to consume less and save more to smooth out

their life-time consumption. Second, a decrease in the loanable demand by firms can

cause this effect since a lower working-age ratio reduces the marginal product of

capital and land which drive the firm demands less for inputs and loans. The third

channel is a fall in land prices in the presence of collateral constraint. When a fall in

demand for land pressures its prices down, the amount of money firms can borrow

would decline as land serves a collateral. However, the demographic changes are not

the dominant sources of fluctuations in the real interest rate, but total-factor-

productivity growth is.

2.2.5 Transmission Mechanism

According to all theoretical literature above, there are three main channels

through which the monetary policy and demographic structures can be transmitted to

the economy. The first channel operates through the wealth channel since a greater

share of elderly can accumulate wealth from their working-age period. Fujiwara and

Teranishi (2008), and Kantur (2013) and Prettner (2013) introduced the cost of child

rearing into the working age which postpones working age’s saving for retirement.

Moreover, Fujiwara and Teranishi (2008), and Heer et al. (2017) also highlighted on

the production side when demographic structure affects labor force participation. The

first study stated that ageing population can either increase or decrease the capital-

labor ratio, thereby influences real interest rates. They found an increase in capital-

labor ratio dominates a decreasing one because people are more likely to save for

retirement than to raise the supply of low productive labor in the retirement age to

maintain an optimized level of consumption. The second paper claimed that the old

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would supply lower volatility of labor, even if marginally pressures a fall in output

volatility.

The second channel operates through the credit channel. Ikeda and Saito

(2014), Wong (2015), and Miles (2002) introduced credit constraints in their model.

The first study emphasized the importance of land as a collateral limiting the amount

of money households can borrow. The second study underlined household refinancing

behavior from high cost of borrowing to low cost which benefits for more amount of

money left they can use to consume. The third study implied the uncertainty in risky

asset and the financial market efficiency which affects saving pattern and the real

interest rate.

The third and last channel operates through the political power channel.

Fujiwara and Teranishi (2008) and Bullard et al. (2012) similarly highlighted on this

channel that a larger share of age-specific population can influence the policy that

benefits for them. According to the first study, when the ageing economy is

dominated by a lot of the old, the central bank tends to implement a high interest rate

because the old gains more saving returns. According to the second study, when the

ageing economy is dominated by a lot of the old, the social planner whom their model

restricted to access only the inflationary policy tends to implement low inflation rates.

This policy affects the portfolio decision from saving toward money, which drives an

incline in the interest rate. Thus, the old whose source of income relies on saving is

better off under this kind of policies.

2.3 Concluding Remark

All of literature above discusses the consequences of population ageing.

They started with the life-cycle hypothesis which points out the distinct behavior

between the young and the old but explains only individual level. Then, theoretical

literature analyzed their results under the OLG framework, which enables the

aggregate implication of the life-cycle aspect. Some of them have augmented the

OLG framework to the appropriate model such as the growth model, the partial

equilibrium model, and the DSGE model for their objectives. To be relevant to the

objectives of this study, the transmission mechanism of monetary policy is concluded

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in the previous section because only theoretical literature using a structural model can

investigate these impacts. The other objective, the effectiveness of monetary policy

can be concluded as follow.

The monetary policy effectiveness has been found to weaken in the

ageing population but can be explained by distinct mechanisms. Fujiwara and

Teranishi (2008), and Miles (2002) did not conclude the aggregate effects on

effectiveness, although both studies concerned the response to a policy rate shock;

asymmetric consumption of the young and old in the former, and the transmission

mechanism in the latter. Fujiwara and Teranishi (2008), and Kantur (2013) found less

economic sensitivity to the interest rate through the wealth channel. Wong (2014),

and Wong (2015) also focused on credit channel since they introduced the credit

constraint in their model. The explanation of Imam (2015)’s empirical result based on

Miles (2002) that the negative credit channel dominates the positive wealth channel.

To conclude this chapter, although demographic changes are structural

changes influencing steady state levels of an economy, there have been limited

studies focusing on them. The empirical literature came up with inconclusive results

while the theoretical literature was based on unrealistic issue that maybe not represent

the actual economy. This study tries to fulfill these research gaps and enlightens the

relationship between demographic structure and monetary policy by adopting the

structural model. The quantitative model in use is the DSGE model augmented with

the probabilistic ageing (PA) model in the spirit of Grafenhofer et al. (2006). The PA

model is the generalized aspect of all OLG framework and can represent more

realistic issues of the monetary policy analysis. The period under this framework is

relaxed to indicate any frequency. The household can be separated into any age

generations and can be practically classified along any kinds of characteristics besides

years of age. This framework takes both risks of death and losing income into account

since an individual in each age generation stochastically switches to the next age

generation and also possibly dies in each period. In this study, six age generations

would be introduced into the household agent so that the demographic structure can

capture both population and labor structure in Thailand. The model in this study takes

tremendous advantages of the DSGE and the PA model together with the characteristics

of Thailand to achieve all objectives, and it will be described in the following chapter.

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CHAPTER 3

RESEARCH METHODOLOGY

This chapter describes the quantitative model used in this study. To

capture the characteristics of the Thai economy, the study uses the dynamic stochastic

general equilibrium (DSGE) model based on Tanboon (2008), and is augmented with

the overlapping generation (OLG) framework in the spirit of Grafenhofer et al.

(2006), called the probabilistic ageing (PA) model, in order to support the study of

demographic shifts. Key features of the model are the uncertainty of the time of death

and retirement, price rigidities, and costs of adjusting investment. the Figure 3.1

below illustrates the interaction among economic agents in the model.

Figure 3.1

The Interaction among Economic Agents in the Model

Source: Author’s illustration

As shown in the Figure 3.1, there are four economic agents in the

economy; a household, final goods firms, capital goods firms, and the central bank.

Firstly, the household is separated into six age generations, all of which consume final

goods, and supply of effective labor. Moreover, the demographic structure describes

the transition probability of one age generation into the following one. Second, final

goods firms produce final goods, using effective labor and physical capital as inputs.

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They sell their products in a perfectly competitive market under price rigidities. Third,

the capital goods firms rent out the physical capital to the final goods firms. They

combine existing capital stock and new investment to produce the physical capital

subject to investment adjustment cost. Finally, the central bank is assumed to follow

the Taylor rule.

3.1 Different Features between Tanboon (2008) and This Model

The model in this study differs from that of Tanboon (2008) in some

important ways, as described in Table 3.1 below. According to the consumption side,

the heterogeneous household across the years of age is introduced into the DSGE

model of Tanboon (2008), albeit with different assumptions in the household's

preferences at the time of solution. Tanboon (2008) followed the von Neumann-

Morgenstern (VNM) preferences (i.e., the household is indifferent at the time of

solution), whereas this study follows the PA model, which employs Kreps-Porteus

(KP) preferences (i.e., the household is not indifferent at the time of solution). It is

due to the two key features that the uncertainty of both income and death varies

across age generations. In this model, the heterogeneous household consumes without

habit persistence as in that of Tanboon (2008), which is ignored to reduce the verities

in degree of persistence across each age generation and to reduce the complication

that consumption in this period should be related to consumption in the same or in the

previous generation. In addition, the labor market in this model assumes a perfect

competition and excludes wage rigidities so that the model can vividly describe the

effects of demographic changes on the labor market, and equilibrium wage represents

actual labor productivity which is one of demographic parameters. Then, the capital

accumulation is optimized by the capital goods firms instead of the household, which

is relevant to the production side. However, the mechanism of capital accumulation by the capital goods firm is still as same as that by the household. The reason of this

agent in addition is to simplify the derivation of household's problem under KP type

of lifetime utility function. In this study, while focusing on the heterogeneous household, some economic agents in the model of Tanboon (2008) are exclude; i.e.,

banks, government, export firm, and external sector. After all, the main mechanism in

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this model remains in the household agent which is heterogeneous in age.

Particularly, an individual decides how much to consume by considering his whole

life span when he gets older, expects to live shorter, and loses his productivity to

work.

Table 3.1

Different Features between Tanboon (2008) and This Model

Consumption Side

1. Household

o - consume with habit persistence

- deposit funds with banks; trade off with foreign debt

In this model: The heterogeneous household consume without habit persistence

and save without banks.

- invest and accumulate physical capital

In this model: This process is under the capital goods firm's decision instead.

- supply of labor and set wage with wage rigidities

In this model: The labor market is perfectly competitive and excludes wage

rigidities.

Production Side

2. Domestic Firm

o - produce domestic products by hiring labor, capital, and imported intermediate

goods as inputs

In this model: Final goods firms use effective labor and capital as inputs, and

sell their products within the domestic market.

3. Export Firm

x - hire labor and capital inputs, and sell their products aboard

4. Capital Firm

o - None

In this model: The capital goods firm invest and accumulate the physical capital

instead of the household.

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Table 3.1 (Continued)

Financial Side

5. Bank

x - take deposits from households and lend to domestic firms

In this model: No bank agent exists.

Regulator Side

6. Government

x - spend according to some rule

In this model: No government exists.

7. Central Bank

ü - set interest rate according to the Taylor rule

x External Sector

x 8. Foreign Exchange Agent

- accumulate foreign debt from households

x 9. The Rest of the World

- buy final goods produced by export firms

In this model: No external sector exists.

Note: 1. o represents adjusted features from Tanboon (2008) 2. ü represents straightforward features from Tanboon (2008) 3. x represents excluded features from Tanboon (2008)

Source: Tanboon (2008) and Author’s interpretation

3.2 Model

3.1.1 Demographic Structure

By following the PA model of Grafenhofer et al. (2006) and (2007), the

total population in this study consists of six age generations, g={1,2,3,4,5,6}, which

characterizes the population and labor force structure in Thailand.

This age generation’s classification is in line with both international

standards of the UN and the International Labour Organization (ILO) and the Thai

official document of National Statistical Office of Thailand (NSO). Particularly, the

total population is determined by a specific age range, that is, youth population aged

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less than 15 years, working-age population aged 15 to 60 years, and older population

aged 60 years or over. Additionally, this classification is empirically based on the

Labour Force Survey (LFS) data, which is consistent with several empirical studies.

Lathapipat and Poggi (2016) considered those who aged between 15 to 24 years as

low-skilled labor, so the first age generation starts at the age of 15 to 24 years. The

second generation continues at the age generation of 25 to 44 years, because many

university graduates initially participate in the labor force. Then, as pointed out by

Soonthornchawakan and Kulthanavit (2015), middle-aged workers in Thailand are

those who aged around 45 to 50 years, and their interview shows that workers in the

private sector tend to retire at 50 years old. Thus, the third generation collapses under

the age generation of 45 to 59 years. In the public sector, many government officers

observe the Civil Service Act, B.E. 2551 (2008) to stipulates mandatory retirement

after 60 years old. Older persons are then defined as those aged 60 years or over. The

older groups are therefore subdivided into three generations relying on their labor

productivity to work. The fourth, fifth, and sixth generations are those aged between

60 to 69, 70 to 79, and 80 to 99, respectively.

Each generation differs in four attributes; namely, labor productivity θg,

probability of surviving to the next period γg, the probability of not ageing ωg, and

the life-cycle history a=(a1,a2,…, a6).In particular, the life-cycle history is a vector

that records the past dates of ageing events, and affirms that the histories of cohorts in

the generation g have g elements because he has aged g times, i.e., a = (15, 25, 45, 60,

70, 80), corresponding to age generations, where a1= 15 is the date of birth or the

actual age when an agent starts an economic life. For example, a cohort with 35 years

old belongs to the second age generation, g = 2, and his life-cycle history is a2 = (15,

25) since he aged twice. Hence, using this approach makes it easier to represent the

initial age in each age generation.

To algebraically account for the law of motion of population, the number

of cohorts at period t in the age generation g with the same life-cycle history a is

given by Ntg. Then, in the next period t+1, the age generation is divided into three

subgroups: (i) those who die and the life-cycle history is updated to 𝑎#, (ii) those who

survive and remain in the same age generation g with the life-cycle history a, and (iii)

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those who survive and switch to the sequential age generation g+1 with the life-cycle

history updated to a’=(a1,a2,…, a6):

(i) Nt+1† ≡Nt

g 1-γg

(ii) Nt+1g ≡Nt

gγgωg

(iii) Nt+1 g ≡Nt

gγ g 1-ωg

The law of motion for the age generations, 1<g≤6 is

Nt+1 g =γ gωgNt

g+γ g-1 1-ωg-1 Ntg-1, ωg=1 (1)

The law of motion for the first age generation, g=1 is

Nt+11 =γ 1ω1Nt1+N0 (2)

where N0 denotes newborns.

The aggregate law of motion for total population is

Nt+1=Nt+N0- 1-γg Ntg

6

g=1 , Nt= Nt

g6g=1 (3)

The key demographic parameters are the birth rate, transition rates to the

next age generation, and the mortality rates, which are all exogenous and independent

of economic influences. Hence, a balanced growth path of the demographic structure

becomes

N1=N0

1-γ1ω1 , Ng=

γg-1 1-ωg-1

1-γgωgNg-1

3.1.2 Household

A household agent in this model is separated into six age generations

corresponding to the demographic structure, as described above. Newborns flow into

the age generation 1 which captures a total length of 10 years between the age of 15

to 24 or 40 periods when t is quarter. The rest of sequential age generations contains

different lengths of 20, 15, 10 and 25 years until the age of 99.

3.1.2.1 Decision problem of each age generation g

The representative households in each age generation g, with index j,

optimize their lifetime utility by making decisions on consumption Ctj,g, labor supply

to firms Ltj,g, and saving Dt

j,g, as algebraically described by

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V(Dt-1 j,g)= max

Ct j,g , Lt

j,gUt

j,g (Ct j,g , Lt

j,g)ρ

+γgβ Vt+1 j,g ρ 1 ρ (4)

subject to Pt Ctj,g+Dt

j,g≤1+rt-1γg

Dt-1j,g+Yt

j,g (5)

where Ut j,g Ct

j,g , Ltj,g =logCt

j,g-φLLt

j,g 1+η

1+η (6)

Vt+1j,g≡ωgVt+1

j,g + 1-ωg Vt+1j,g+1 (7)

Ytj,g=Wtθ

gLtj,g+Φt

j+Θtj (8)

According to the aggregator of the value function in the equation (4),

Utj,g(Ct

j,g , Ltj,g) is the instantaneous utility which depends on Ct

j,g and Ltj,g. Taken the

form of (7), the next period value Vt+1j,g is averaged between (i) Vt+1

j,g denotes the next

period value if he remains in the same age generation g which is weighted by the

probability of remaining ωg and (ii) Vt+1 j,g+1 the next period value if he switches into

the next sequential age generation g+1 which is weighted by the probability of ageing

1-ωg. Moreover, the parameter φL, η and β are the scaling for the disutility of labor

supply, the inverse of Frisch elasticity of labor supply, and the subjective discount

factor, respectively. Intuitively, the effective discounted factor of an individual in

each age generation is γgβ not only β due to the presence of the probability of

surviving in the next period γg. In this case, getting older decreases the probability of

surviving, therefore the effective discounted factor reduces. It implies that when

getting older, an individual is likely to be more impatient and feels less important of

the next period value.

Regarding the budget constraint in (5), Pt and rt denote the price of

consumption and the nominal interest rate on saving that is also a policy interest rate.

As shown in (8), Ytj,g denotes the nominal wage-related income which differs across

age generations but is identical within the same age generation. In each age

generation, an individual source of income comes from the nominal wage per

effective labor Wt, the profit from owning the monopolistic firm Φtj, and the profit

from owning the capital goods firm Θtj. Particularly, θg ∈ [0,1] represents the

productivity of a unit of the labor supply in each age generation g, therefore the

nominal wage per a unit of labor is Wtθg.

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In the spirit of the PA model, there are two assumptions to generate two

idiosyncratic risks individuals face throughout their lifetime, which in turn simplify

the derivation and aggregation of individual’s decision rules. Firstly, an individual

faces a risk of losing income or uncertainty of retirement which is represented by the

probability of ageing (1-ωg) and productivity (θg). When he randomly gets older, he

becomes less productive to work as a labor and can earn a smaller amount of wages.

The income an individual receives varies across his lifetime. An individual is thus

assumed to have the preference that separates risk aversion from intertemporal

substitution and feels not indifferent in the time of solution. Constant elasticity of

substitution (CES) non-expected utility as proposed by Kreps and Porteus (1978)1 and

Epstein and Zin (1989) is then applied because it can separate risk aversion from the

intertemporal substitution aspect and allows households not to be indifferent in the

time of solution. Compared with the household problem set-up in Tanboon (2008),

the class of time-additive Von Neumann-Morgenstern (VNM) preferences is

employed. It constrains the coefficient of relative risk aversion to be the reciprocal of

the elasticity of intertemporal substitution and not allows the separation between

them. Hence, the VNM preferences is practical for Tanboon (2008), when the

representative household in his model is homogenous, does not face a risk of losing

income and household’s preference is indifferent in the time of solution. Additionally,

in equation (4), this particular preference, as discussed by Weil (1990) and Farmer

(1990), restrict individuals to be risk neutral with respect to variations in income but

allow for an arbitrary intertemporal elasticity of substitution2, represented by

σ=1/ (1-ρ).

1 Kreps and Porteus (1978) initially proposed that the preference in the

time of solution is not indifferent, so called “KP preference”. Under this preference,

the risk aversion is separated from the intertemporal substitution. It differs from VNM

preference which assumes indifference in the time of solution and non-separation

between both two coefficients. 2 A generalization of the KP preference which drives the equation (4) is

showed in Weil (1990) and Epstein and Zin (1989) which are pioneers in this kind of

set-up.

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The other idiosyncratic risk individuals face is a risk of death or

uncertainty of the time of death which is represented by the probability of surviving

(γ g). Following Yaari (1965) and Blanchard (1985), a perfectly competitive annuities

market is introduced to provide perfect insurance against this kind of risk within the

same age generation. In each period, insurance firms allocate the assets between those

who die and those who survive. Algebraically, they collect assets 1-γ g Dt

j,g from

those who dies, but pay premiums γtgπt

gDt j,g to those who survive, where πt

g is given

as an insurance premium rate of each age generation. Under the perfect insurance

assumption, the break-even premium becomes

πgγg=1-γg or 1+πg=1/γg

Consequently, individual’s assets next period are

Dt+1 j,g = 1+rt-1 1+πg Dt

j,g=1+rtγg

Dtj,g

which indicates in the budget constraint in the equation (5).

3.1.2.2 Optimal condition of each age generation g

Given an individual’s decision problem as specified in the section 3.1.2.1

Decision problem of each age generation g, an individual in each age generation g

maximizes the aggregator value function (4) subject to the budget constraint (5). The

first-order condition with respect to consumption and labor supply are (see Appendix

A.1 Decision Problem of Each Age Generation g);

Utj,g ρ-1 1

PtCtj,g =γ

gβηt+1j,g (9)

φL Ltj,g η=

1Ctj,g

WtPt

θg (10)

where ηtj,g=β 1+rt-1 ηt+1

j,g , which is defined that

ηt

j,g= Vt j,g ρ-1 dVt

j,g

dDt-1j,g

and ηt+1 j,g = Vt+1

j,g ρ-1 dVt+1 j,g

dDt j,g = Vt+1

j,g ρ-1 ωg dVt+1

j,g

dDt j,g + 1-ω

g dVt+1 j,g+1

dDt j,g+1

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After all, the optimal condition for consumption of each age generation

can be simply rewritten to (see Appendix A.2 Simply Rewritten Optimal

Consumption of Each Age Generation g)

Ut j,g ρ-1 1

Ctj,g =γ

gβ 1+rt

Et  ωg

γgVt+1j,g

Vt+1j,g

1-ρ Ut+1 j,g ρ-1

Ct+1j,g + 

1-ωg

γg+1Vt+1j,g+1

Vt+1j,g

1-ρ Ut+1 j,g+1 ρ-1

Ct+1j,g+1

PtPt+1

(11)

where Ut j,g and Vt+1

j,g can be defined as (6) and (7), respectively.

To simplify coding the model, let define the gross inflation rate and the

gross nominal rate of return that Πt+1=Pt+1/Pt and Rnt= 1+rt . Then, substituting

these definitions and rearranging (11) yields

Ut j,g ρ-1 1

Ctj,g =γ

gβRnt

Et  ωg

γgVt+1

j,g

Vt+1 j,g

1-ρ Ut+1 j,g ρ-1

Ct+1 j,g + 

1-ωg

γg+1Vt+1

j,g+1

Vt+1 j,g

1-ρ Ut+1 j,g+1 ρ-1

Ct+1 j,g+1

1Πt+1

(12)

and defining the real wage Wrt=Wt/Pt into (10) obtains

φL Ltj,g η=

1Ctj,g Wrtθ

g (13)

To explain the intuition of the optimal condition for consumption, the

equation (12) represents the Euler’s equation of consumption’s intertemporal trade-

off which the real marginal loss is equal to the real marginal gain. In practice, the real

marginal loss in the left-hand-side (LHS) is implied by the real marginal utility loss if

reducing one unit consumption in this period. The real marginal gain in the right-

hand-side (RHS) consists of four terms; (i) the real marginal utility gain if increasing

one more unit consumption in the next period multiplying with (ii) the gross return,

which the whole term is discounted back to show the current value by (iii) the

subjective discounted factor, and then all the three terms is divided by (iv) the next

period gross inflation in order to get rid of the differences in prices between the two

periods and to adjust the nominal into the real term. Specifically, the first term (i) in

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the RHS, the real marginal utility gain is from it in the current age generation g and in

the next period g+1.

Additionally, to explain the intuition of the optimal condition for supply

of labor, the equation (13) represents the intratemporal trade-off between supply of

labor and consumption, which the real marginal disutility of labor supply is equal to

the real marginal utility of labor supply. In practice, the real marginal disutility of

labor supply is shown in the LHS. The real marginal utility of labor supply in the

RHS implies that an individual uses wages, which he receives after supplying labor,

to consume goods. It is thus mathematically shown by the multiplication between the

real marginal product of effective labor and the real marginal utility of one unit

consumption. To focus on the elderly people, none of their labor productivity (θg= 0)

leads to no benefit for supply of labor. Their optimal decision for labor supply is

equal to zero (Ltg,j= 0), therefore their source of income from labor supply does not

exist.

3.1.2.3 Aggregation

To drop the index j by using the total number of population in the age

generation g Ntg, the consumption in each age generation level g satisfies

Ctg=Ct

j,gNtg (14)

Then, the aggregate consumption of a household sector can be written as

Ct= Ctg6

g=1 (15)

The labor supply can be considered as two units; the labor supply and the

effective labor supply. The aggregate labor supply of each age generation g and a

whole economy can respectively be represented by

Ltg=Lt

j,gNtg

Lt= Ltg6

g=1

while the aggregate effective labor supply is

LtS,g= θgLt

j,g Ntg (16)

LtS= 6g=1 Lt S,g (17)

3.1.3 Final Goods Firms

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A representative final good firm buys two inputs in the perfectly

competitive market; effective labor LtS at the nominal effective wage Wt, and physical

capital Kt at the nominal rental price RtK. It minimizes total cost function subject to the

Cobb-Douglas production function with labor-augmented productivity At to produce

the amount of output Yt, as given by

minLtS, Kt

WtLtS+RtKKt

subject to Yt= AtLtS1-α

Kt α (18)

where the exogenous process for the technology productivity At is

log At =ρAlog At-1 +εA (19)

A representative domestic firm’s Lagrangian can be written as

ℒ=WtLtS+RtKKt+MCt Yt- AtLtS1-α

Kt α

where MCt denotes the Lagrange multiplier implying the nominal marginal cost for

producing one additional unit of output. Thus, the real marginal cost can be defined as

mct=MCt/Pt.

The first-order condition with respect to two inputs becomes

WtLtS= 1-α MCtYt (20)

RtKKt=αMCtYt (21)

The real term of the equations (20) and (21) are

WrtLtS= 1-α mctYt (22)

rtKKt=αmctYt (23)

where Wrt and rtK denote the real wage per effective labor and real return on capital,

respectively.

In the flexible price setting, the optimal price Pt* is set as a mark-up price

µD over marginal cost MCt;

Pt*=µDMCt=µDmctPt (24)

or Πt*=µDmctΠt (25)

where Πt*=Pt*/Pt-1 denotes the gross optimal inflation.

In the presence of price rigidities, the monopolistic firm wishes to set its

price Pt as close as possible to the optimal price Pt*, but it also faces the quadratic loss

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of setting the price of which sluggishness is in the price level and its inflation.

Specifically, it is costly when (i) the current price deviates from the optimal price, Pt

-Pt*, and (ii) this period’s inflation deviates from previous period’s inflation,

∆Pt-∆Pt-1;

minPt

E0 ∞

t=0

βt Pt -Pt*

2+ξD ΔPt

-ΔPt-1 2

where ξD represents the degree of price rigidity. Note that ∆Pt is defined as the

current price that deviated from the index price (P0), so ∆Pt=Pt-P0.

The first-order condition with respect to Pt becomes

Pt=Pt*+ξ D - ∆Pt-∆Pt-1 +β Et∆Pt+1-∆Pt (26)

or Πt =Πt*+ξ

D βΠt Et Πt+1

-1 - Πt -1 (27)

The presence of price rigidity can be interpreted that in the case of no price rigidity;

ξD= 0, a representative firm sets its price equal to the optimal price, Pt=Pt*=µDMCt.

On the contrary, in the case of the price rigidity; ξD> 0, it will adjust its price under

the optimal condition (26). If the inflation in this period is higher than the previous

period’s; ∆Pt − ∆Pt-1 > 0, it implies that Pt is too high and needs to be adjusted down.

If the inflation in the next period is expected to be higher than this period’s,

Et ∆Pt+1 − ∆Pt > 0, it implies that Pt is too low relative to Pt+1 and needs to be

adjusted up.

3.1.4 Capital Goods Firms

A representative capital goods firm maximizes its profit under a perfectly

competitive market. In each period, it rents out the capital Kt to the final goods firm at

the nominal rental rate RtK and after the production of final goods is completed it also

purchases the investment It at the price Pt. A representative capital firm’s profit in

each period is given by

ΠtK=RtKKt-PtIt

Then, it combines undepreciated capital and investment goods to produce

new capital goods Kt+1, subject to investment adjustment costs as proposed by

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Christiano, Eichenbaum, and Evans (2005). Its production technology of capital is

represented by

Kt+1= 1-δ Kt+F It,It-1 (28)

where, the function F summarizes the technology that transforms the current and the

past investment into installed capital for use in the following period. Specifically,

F(It,It-1) takes the form:

F It,It-1 = 1-ξI

2It

It-1-1

2

It (29)

where ξI is the investment adjustment cost parameter. The presence of investment

adjustment cost can be interpreted that when there is no investment adjustment cost;

ξI = 0, the investment technology turns out to be equal the investment goods;

F(It,It-1) = It. On the contrary, in the case that ξI > 0, the investment adjustment cost

occurs when the investment grows.

A representative capital firm’s problem can be written in the Lagrangian

as

ℒ = maxIt,Kt+1

E0 βtλt RtKKt-Pt It +Qt

K 1-δ Kt+F It,It-1 -Kt+1∞

t=0

where λt denotes the marginal disutility of saving in period t. The multiplication of λt

implies the total utility of income in each period the household can receive from

owning the capital firms.

The first-order condition with respect Kt+1 becomes

QtK=βEt

λt+1λt

Rt+1K + 1-δ Qt+1K

or Qt

K

Pt=βEt

λt+1λt

Pt+1Pt

Rt+1K

Pt+1+ 1-δ

Qt+1K

Pt+1 (30)

The equation (30) implies the decision rule for capital firm to accumulate capital

which the real marginal loss is equal to the real marginal gain. The LHS term shows

losing marginal benefit from one unit capital installed. At the meantime, the RHS

term is (i) the discounted expected gain of the next period value of one unit capital,

which acquires from (ii) the nominal rental rate by renting out the unit of capital in

the next period, and (iii) its remaining rate after depreciation.

Then, the first-order condition with respect It becomes

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λtPt=λtQtKF1 It,It-1 +βEtλt+1Qt+1

K F2 It+1,It

or Qt

K

Pt=

1F1 It,It-1

1-βEtλt+1λt

Qt+1K

Pt+1Pt+1Pt

F2 It+1,It (31)

where F1 It,It-1 =1-ξI It

It-1It

It-1-1 -

ξI

2It

It-1-1

2

F2 It+1,It =ξI It+1

It

2 It+1It

-1

In the LHS, QtK/Pt represents Tobin’s q marginal which is the ratio of the real value of

one unit capital installed QtK to the replacement cost of an additional investment Pt. In

the case that F(It , It-1) = It, F1(It , It-1) = 1 , and F2(It , It-1) = 0, so the equilibrium

condition is then given by QtK/Pt = 1. On the contrary, if Tobin’s q marginal is greater

(less) than unity, the investment needs to be adjusted up (down).

Defining real return on capital RtK/Pt=rtK, real Tobin’s q QtK/Pt=qt and the

stochastic discounted factor β λt+1λt

Pt+1Pt

=SDFt+1,t and rewriting the equations (30) and

(31) obtain

qt=βEtSDFt+1,t rtK+ 1-δ qt+1 (32)

qt=1

F1 I1,It-1 1-βEtSDFt+1,tqt+1F2 It+1,It (33)

where SDFt+1,t takes the form; (See the form of SDFt+1,tj,g in the Appendix A.3 The

Form of The Stochastic Discounted Factor (SDFt+1,t))

SDFt+1,t= SDFt+1,tj,g6

g=1 Ntg (34)

3.1.5 Central Bank

The central bank is assumed to follow a simple monetary rule:

rt=ρRrt-1+ 1-ρR r SS+κ π Etπt+1-π +ε R

This is known as a simple Taylor rule with interest rate smoothing; i.e., this period

nominal interest rate rt is the weighted average of the previous period’s rate and the

targeted policy rate in this period with the weights given by ρR and 1−ρR,

respectively. In particular, the targeted policy rate depends on the steady-state

nominal interest rate rSS and the deviation of the expected inflation rate Etπt+1 to the

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targeted inflation rate π. The parameter κπ>0 represents the responsiveness of the

central bank’s reaction to the inflation deviation. Particularly, the form of Taylor rule

used in coding is

Rnt=RntρR RnSS

EtΠtΠ

κΠ 1-ρR

exp εR (35)

which is equal to the Taylor form above, after taking log in the equation (35).

3.1.6 Market Clearing Conditions

The final goods, effective labor, and physical capital are at the market

clearing conditions at any time t. In particular, the market clearing in the final goods

is given by

Yt=Ct+It (36)

3.1.7 General Equilibrium

An equilibrium in this model consists for all period t of the sequences of

55 endogenous and 8 exogenous variables that satisfy the system of equations (1), (2),

(4), (6), (7), (12)-(19), (22), (23), (25), (27)-(29), and (32)-(36) 3. The endogenous

ones are (a) household’s decision {Ct j,g, Lt

j,g, Ut j,g, Vt

j,g, Vt j,g

, Ct g, Lt

S,g, Ct ,

LtS, SDFt+1,t}, (b) production side {Kt , It , Ft , qt , mct, Yt}, and (c) price process

{Wrt, rtK, Πt, Πt*}. The exogenous ones are (a) demographic processes {Ntg}, (b)

technology productivity {At}, and (c) policy-related variables {Rnt}.

According to the steady-state condition, the model is determined along

the balanced growth path at ongoing inflation growth. Specifically, the system of

equations above is in the detrended form.

3 The variables with the superscript g are supposed to be numbered 6

times since each of them refers to a variable of each age generation as g =

{1,2,3,4,5,6}. As a result, the equation (1) is five because in this case, g only includes

2 to 6 and the equation (2) is already shown the case that g=1. The other equations

(4), (6), (7), (12)-(14), and (16) are six in general.

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3.3 Research Analysis Framework

There are two sets of demographic parameters in the model; namely,

ageing parameter, and labor productivity parameter. Both of them represent

demographic structure in the economy. To analyze the effects of demographic

changes in Thailand, four main scenarios of distinct demographic structures should be

considered, with each scenario capturing the demographic structures in 2001, 2008,

2015, and forecasted 2035. The first selected year is 2008 to correspond to the most

updated mortality table in also 2008. Then, 2001 and 2015 are chosen because of the

available data for these years in the quarterly LFS.4 The last scenario in 2035 is the

forecasted of Thai population by the UN, as stated in the report “World Population

Prospects: The 2015 Revision”. In particular, in 2035, the proportion of the elderly

population who aged 65 years and above is anticipated to account for 22.84%, so the

Thai economy will by then be defined as hyper-aged or super-aged society.5

The Figure 3.1 shows that the older group’s shares in g=4, 5, 6 are larger,

whereas those of the workers in g=1, 2, 3 are smaller from 2001, 2008, 2015, to 2035.

This transition implies the occurrence of the ageing process when the population

shifts into the larger share of elderly. Specifically, an increase in γ g alters ω g and N0,

which in turn, drive the new steady-state value of the population share in the model.

In addition, the Figure 3.2 shows that the set of limited labor productivity { θ g } has

increased across time. It implies the productivity effect across time, which also

influences the new steady-state value in the model.

4 The LFS data can be traced back to 2000, but in this year, the

questionnaire differed from the others. Thus, 2001 is selected instead. The most

updated data from Q1 to Q3 are provided in 2015. Otherwise, the calibration is still

on the average of three rather than four quarters. 5 According to Coulmas (2007) and the UN, see footnote 2 in the Chapter 1.

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Figure 3.1

Steady State of Population Share in each Scenario 2001 2008 2015 2035

Source: Author’s illustration

Figure 3.2

Labor Productivity Parameter Values

(a) The Labor Productivity Parameter from 2001, 2008, to 2015

(a.1) 2001 (a.2) 2008 (a.3) 2015

(b) The Comparison of Labor Productivity across time

(b.1) Calibrated Labor Productivity (b.2) Limited with TFP growth

g=1

g=2

g=3

g=4

g=5

g=6

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Source: LFS data, and author’s calculation When both of γ g and θ g increase, each effect is accurately measured

separately before including them into the demographic effects. The Table 3.2 shows

the scenarios used to compare and analyze each effect on the monetary policy. The

row and the column represent the values of probability of surviving and productivity,

respectively, in each year. They also related to the code of each scenario. Particularly,

the first number corresponds to γ g in each year, while the second number is θ g. For

example, S1.1 is under γ g in 2001 and θ g in also 2001; then S1.2 is still under γ g in

2001 but θ g in 2008.

After that, firstly to measure only ageing effects, the set of the probability

of surviving becomes higher across time at the given set of productivity in 2008. S1.2,

S2.2, S3.2, and S4.2 are analyzed, and shown by the dotted blue box. Secondly, to

measure only productivity effects, the set of productivity becomes higher across time

at the given set of the probability of surviving in 2015. S3.1, S3.2, and S3.3 are used

for comparison and are denoted by the thick red box. Finally, when both probability of

surviving and productivity vary to capture the actual economy, the demographic effects

are measured. S1.1, S2.2, and S3.3 are interpreted and shown by the shaded grey box.

Table 3.2

All Scenarios (S) Used to Analyze the Effects of Demographic Changes

θ g

1 2 3

2001 2008 2015

γ g

1 2001 S1.1 S1.2

2 2008 S2.2

3 2015 S3.1 S3.2 S3.3

4 2035 S4.2

Note: 1. The dotted blue box includes only the ageing effect (at given the set of θ g in 2008)

2. The thick red box includes only the productivity effect (at given the set of γ g and ω g in 2015) 3. The shaded grey box includes both the ageing and productivity effects into the demographic effects

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Source: Author’s study 3.4 Parameterization

The parameter value used in this model is qualified to refer to the

characteristics of Thai economy as pioneered by Tanboon (2008) and for the

unavailable parameters, they are newly calibrated based on the empirical data. The

parameters calibration can be grouped into (1) demographic parameters which are

unavailable, and (2) available parameters.

3.4.1 Demographic Parameters

The demographic parameter can be classified into two sets; ageing

parameters and labor productivity parameters.

3.4.1.1 Ageing Parameters

The set of ageing parameters γ g, ω g, N0 are empirically calibrated by

using Thai Labour Force Survey (LFS). There are 4 ageing parameter sets

corresponding to demographic structures in 2001, 2008, 2015, and 2035 as described

in the previous section in 3.3 Research Analysis Framework.

The calibration of γ g is based on the mortality table in 2008 from Office

of Insurance Commission (OIC). ω g is straightforward from PA model. Specifically, both γ g and the expected duration (m) an individual lives in each age generation g

determine the value of ω g. Besides, in the last age generation, ω6 restricts to be one

which implies