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The Implications for Health,Depression, and Life Satisfactionfrom a Permanent Increase inIncome for the DisadvantagedElderly: Evidence from TaiwanFu-Min Tsenga & Dennis James Petrieb

a Institution for International Health and Development,Queen Margaret University, Edinburgh EH21 6UU, UKb Centre for Health Policy, Programs and Economics,University of Melbourne, Melbourne 3010, AustraliaPublished online: 19 Jun 2014.

To cite this article: Fu-Min Tseng & Dennis James Petrie (2014) The Implications forHealth, Depression, and Life Satisfaction from a Permanent Increase in Income for theDisadvantaged Elderly: Evidence from Taiwan, Review of Social Economy, 72:3, 311-336,DOI: 10.1080/00346764.2014.927725

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The Implications for Health, Depression, andLife Satisfaction from a Permanent Increase in

Income for the Disadvantaged Elderly:Evidence from Taiwan

Fu-Min Tseng1 and Dennis James Petrie21Institution for International Health and Development, QueenMargaret University, Edinburgh EH21 6UU, UK; 2Centre for

Health Policy, Programs and Economics, University of Melbourne,Melbourne, 3010 Australia

Abstract This paper uses an exogenous increase in income for a specific subgroup

to explore the extent to which higher income leads to higher levels of health and

well-being. In 1995, the Taiwanese government implemented the Senior Farmer

Welfare Benefit Interim Regulation (SFWBIR) that was a pure cash injection to

senior farmers. A difference-in-differences (DiD) approach is used on survey data

from the Taiwanese Health and Living Status of Elderly in 1989 and 1996 to evaluate

the short-term effect of the SFWBIR on self-assessed health, depression, and life

satisfaction (LS). Senior manufacturing workers are employed as a comparison

group for senior farmers in the natural experiment. This paper provides evidence that

the increase in income caused by this pension reform significantly improved the

mental health of senior farmers by reducing 1.697 points of the depression scale in

DiD and 2.178 points in the robust estimation; however, it had no significant short-

term impact on self-assessed health or LS.

Keywords: social welfare benefit, natural experiment, mental health, difference-in-

differences, life satisfaction

1. INTRODUCTION

This paper seeks to identify the effect of a permanent increase in income on health

and life satisfaction (LS) for older disadvantaged individuals. Income can be

crucial for health, whether viewed in physical or psychological terms. From the

q 2014 The Association for Social Economics

Review of Social Economy, 2014

Vol. 72, No. 3, 311–336, http://dx.doi.org/10.1080/00346764.2014.927725

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http://dx.doi.org/10.1080/00346764.2014.927725

physical perspective, a higher income increases investments in health-enhancing

goods, such as nutritious food and better quality medical treatments and from a

psychological perspective higher income reduces the stress associated with a

relatively low income leading to disease (Cohen et al. 1991, 1997; Gerdtham and

Johannesson, 2004). However, income increases for some in society may also

have negative impacts on those others who do not benefit through a sense of

relative deprivation (Wilkinson 1996).

This study investigates to what extent a permanent cash injection to senior

farmers in Taiwan through the Senior Farmer Welfare Benefit Interim Regulation

(SFWBIR) improved their health status and LS. In June 1995, the Taiwanese

government implemented SFWBIR as a compensation for the absence of a

retirement pension in the Farmer Health Insurance (FHI). Before 1995, the FHI

was the only occupational insurance that did not contain a retirement pension.

Senior farmers were unable to receive any pension to secure their later life

income, whereas other workers claimed their retirement pension from their

particular occupational insurance scheme. In 1996, with SFWBIR, those senior

citizens who were 65 years old or older and had been members of FHI for at least 6

months were eligible to claim the benefit until death (NT$ 3,000 which

approximated to US$ 110 or £70 per month). Though SFWBIR is a social welfare

benefit, it can also be regarded as a type of retirement pension for senior farmers.

The attention on senior farmers raises the issue of how the pension system

works for disadvantaged groups in an aging society. In general, Taiwanese

farmers are a relatively disadvantaged group in terms of socio-economic status

with lower income and education. This senior population is also more vulnerable

compared to younger populations in terms of their health status. In 2003, health

expenditure on senior population accounted approximately 28.73% of annual

health expenditure although the senior population only accounted 9.56% of whole

population.1 However, pension systems are also expensive and therefore it is

important to quantify their benefits. This study provides an insight into the effect

of a pension for senior farmers group, who accounted for one-third of senior

citizens in Taiwan. The SWBIR policy is expected to have a positive effect on the

well-being of this group by either preventing financial stress or reducing their

sense of relative deprivation.

A difference-in-differences (DiD) approach is used to estimate the casual

impact of income on health outcomes. Senior agricultural labourers (farmers) are

compared with senior skilled (excluding high skilled), semi-skilled and unskilled

labourers who mostly comprise of manufacturing workers. The manufacturing

workers are considered the closest possible control group for the farmers

1 Annual Health Statistic Report 2003. National Health Insurance Administration, Ministry of Health and Welfare.

REVIEW OF SOCIAL ECONOMY

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according to the Taiwanese socio-economic class categories (Hsiao 2008; Wu

1997). These two groups have low income with the majority of their consumption

being on necessary goods. In the DiD estimations, the health status and happiness

of the senior groups (65- to75-year-olds) from these two occupational groups are

compared before and after the policy implementation. In addition, difference-in-

differences-in-difference (DiDiD) estimations are used to explore the robustness

of the results. In particular, two more comparison groups, non-senior farmers (60–

64 years old) and non-senior manufacturing workers (60–64 years old), are

considered to explore whether there were different trends in health and happiness

across workers from these different sectors.

This study is organized as follows. Section 2 provides a brief review of the

literature of the impact of income on health and LS. Section 3 introduces the social

insurance schemes and welfare benefits in Taiwan and how these changed in the

1990s. Section 4 explains the data used from the Survey of Health and Living

Status of the Elderly in 1989 and 1996. Section 5 explains the empirical strategies

employed to estimate the impact of SFWBIR on health and happiness outcomes.

Section 6 presents the empirical results and Section 7 discusses the conclusions.

2. LITERATURE REVIEW

The existing literature has inconsistent conclusions in terms of how income

influences health. Wilkinson (1996) claims that “income per se does not affect

health” but it affects health through several pathways, for instance, income

comparison, the sense of relative deprivation and income inequality (Deaton and

Lubotsky 2003; Jones andWildman 2008; Subramanian and Kawachi 2004) while

others (Ettner 1996; Gardner and Oswald 2007; Leon-Gonzalez and Tseng 2011;

Pritchett and Summers 1997) argue that the direct impact of income on health, in

terms of what income can buy, is the major mechanism through which income

impacts on health. The latter arguments maintain that money has a positive effect

on population well-being, whereas the former arguments claim the effect on well-

being at the population level can be mixed because while the effect of income may

be positive for those who gain income, it may have a negative impact on those

who do not gain because it increases their sense of relative deprivation.

Several theories have attempted to explain how income affects subject well-

being (Clark and Oswald 1996; Dixon 1997; Easterlin 2001; Frank 1997; Ng

1997; Oswald 1997). Easterlin (2001) finds a paradoxical relationship between

subject well-being and income over the life cycle, which may be explained by

changing material aspirations. Ferrer-i-Carbonell (2005) and Clark and Senik

(2010) claim that the income of an individual’s comparison reference group is one

determinant of subjective well-being and find that comparisons are mostly

THE IMPLICATIONS FOR HEALTH, DEPRESSION, AND LIFE SATISFACTION

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upwards and comparison intensity decreases with income with low income

individuals attaching more weight to income comparison. Knight et al. (2009)

found that two-thirds of survey respondents of Chinese households reported that

they mainly compare themselves with individuals in their own village.

Many studies have investigated the empirical relationship between health and

income based on the assumption of a single causal direction, from income to

health (Blakely et al. 2001; Deaton and Lubotsky 2003; Gravelle et al. 2002).

However, while a strong social gradient in health may exist, from this alone one

cannot disentangle the extent to which lower income causes poor health or poor

health causes lower income. To investigate the causal relationships between

income (or wealth) and health, both cross-sectional (Case 2004; Ettner 1996;

Thomas and Strauss 1997;) and longitudinal data (Adams et al. 2003; Adda et al.

2003; Contoyannis et al. 2004; Frijters et al. 2004; Frijters et al. 2005; Gardner

and Oswald 2007; Lindahl 2005; Meer et al. 2003;) have been used.

Using cross-sectional data, Ettner (1996) investigates the effect of income on

self-assessed health and chronic health limitations using a two-stage least squares

(2SLS) method in which the respondent’s wage rate and non-earnings income

instrument for family income, as these are less likely to be impacted by health

shocks. Thomas and Strauss (1997) and Chin (2010) also employ 2SLS using

commodity price indices and non-labour income as instruments in urban Brazil

and rural Malawi, respectively.

With longitudinal data, some have used the sequencing of changes in income

and health to identify the causal impact. For example, Adams et al. (2003) use

innovations in health conditions and wealth to implement tests for the direct

causal links from socio-economic status to health and from health conditions to

wealth in the elderly American population. Adda et al. (2003) replicate this

approach for the UK and Sweden while Contoyannis et al. (2004) employ a

dynamic panel ordered probit approach to investigate the determinants of self-

assessed health, in particular the effect of income and educational attainment.

Other longitudinal studies use instruments for income and wealth that are less

likely to be associated with health, such as Meer et al. (2003) who use inheritance,

Lindahl (2005) and Gardner and Oswald (2007) who use lottery wins and Frijters

et al. (2004) who use a change in the political regime. Most of these empirical

studies rely on exogenous income shocks to identify the impact of income on

health, but the impact of a permanent increase in income may have a very different

impact on health and LS than an income shock of a similar size.

The above studies investigate the effects of a variety of factors such as income,

wealth, income inequality, and socio-economic status on alternative measures of

outcomes, for example, mortality, nutritional status, self-reported health status,

psychological health, and LS. Some studies find the income increases improve


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physical health, mental health, mortality, and LS (Case 2004; Ettner 1996; Frijters

et al. 2004; Gardner and Oswald 2007; Lindahl 2005; Leon-Gonzalez and Tseng

2011). Case (2004) also finds that the increasing income in the form of an old age

pension improves the health status of the beneficiaries and their householdmembers

as long as the household pools income. Some studies find evidence that income

inequality or relative deprivation harms population health (Deaton and Lubotsky

2003; Jones and Wildman 2008; Subramanian and Kawachi 2004). These findings

imply that people not only care about the amount of income they have but also care

about their position in the income distribution in a society. However, some studies

find limited evidence of the relationship between income inequality and health

(Adams et al. 2003; Gravelle et al. 2002; Meer et al. 2003). Meer et al. (2003) find

that the connection between wealth and health is not observed in the short run when

considering inheritances. Adams et al. (2003) do not find a causal link from socio-

economic status to some health outcomes (mortality plus acute and sudden-onset of

diseases), whereas this link is found for the incidence of mental problems. For

chronic and degenerative diseases, the evidence is mixed.

In this study, we explore the impact of an increase in permanent income, caused

by a pension reform on the health and LS of a lower socio-economic group, senior

farmers. Peoplewith lower socio-economic status are likely to benefit themost from

pension systems. Pensions provide a type of insurance policy that protects

individuals against loss of future income potential from ill-health and from living

longer than expected (Sinn 2004). Jensen and Richter (2003) find that among the

elderly with a great dependency on the pension system, the loss of the pension

causes reduced intake of calories and protein, reduced use of health services and

medications, with those affected pensioners being 5% more likely to die in

following two years. Apart from physical health, pensions may also influence

mental health through the pathway of improving one’s ability to cope with stressful

life events and relieving financial strain (Adler et al. 1994; Angel et al. 2003). Kahn

and Pearlin (2006) find that long-term financial hardship is reflected in a range of

health outcomes at late life, and Duflo (2003) and Case (2004) show that not only

pensioners but also their household members benefit from the pensions.

3. SOCIAL INSURANCE SCHEMES AND BENEFITS IN TAIWAN

Social insurance schemes are a crucial component of the social security system

in Taiwan. Such schemes can secure the economic well-being of senior citizens and

lighten the burden on the younger generation in the family (Gratton 1996). Before

1990, the occupational social schemes implemented in Taiwan included the Labour

Insurance (LI), the Government Employee Insurance, the Private School Employee

Insurance, the FHI and the Military Insurance (MI). These occupational social


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schemeswere compulsory and specific to particular occupations and, apart from the

FHI and MI, they comprised three main components—pension, coverage of health

expenses and payment for vital events such as births, disability and funerals. FHI

managed the coverage of health expenses and payment for vital events but did not

include a pension, and MI included the payment of a pension and payment for vital

events but did not cover health expenses.

In March 1995, the National Health Insurance (NHI) was implemented. NHI is

a pure health insurance scheme which merged the coverage of health expenses

from the above social insurances and expanded the coverage of health insurance to

low income families, government employee’s families, private school employee’s

families, and retired government employees and their families. A few studies have

investigated the impact of the increased coverage due to the NHI on precautionary

savings and health outcomes (Chen et al. 2007; Chou et al. 2003; Keng and Sheu

2012). These studies demonstrate that for those who were insured as a result of

NHI reforms compared to those who were already covered by other social

insurance schemes pre-NHI, it reduced their savings, increased their utilization of

health services and decreased their mortality.

In the same year, the government implemented SFWBIR to care for senior

farmers because they were a relatively disadvantaged group in Taiwan. The

amount in 1996 was NT$3,000 approximate US$110 or £70 per month. The

reported minimum required living expense per month in Taiwan province, Taipei

city, and Kaohsiung city at the time was considered to be NT$5,400, NT$6,640,

and NT$5,400, respectively.2 Thus, the SFWBIR benefit approximated to half of

the minimum required living expenses.

Though NHI and SFWBIR were implemented in the same year, the impact of

NHI is likely to be negligible in our analyses. This is because for farmers and

manufacturing workers, the NHI implementation simply integrated the authorities

managing their health expense payments rather than changing their insurance or

access. Studies evaluating the impact of NHI grouped farmers and manufacturing

workers together because both were covered by social insurances pre-NHI (Chen

et al. 2007; Chou et al. 2003; Keng and Sheu 2012).

4. SURVEY OF HEALTH AND LIVING STATUS OF THE ELDERLY

The analysis uses data from the Survey of Health and Living Status of the Elderly

(SHLSE). This longitudinal survey measures the changes in health and living

status of the elderly in Taiwan and includes six waves conducted in 1989, 1993,

2 Source: Directorate-General of Budget, Accounting and Statistics, Executive Yuan National Statistics, Taiwan

(1996).


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1996, 1999, 2003 and 2007. The first wave, cohort I, comprises 4,049 respondents

(one respondent per household) who were aged 60 years and older. Given the age

group, there are high attrition levels, thus a new cohort of 50- to 66-year-olds was

added both in 1996, cohort II, and 2003, cohort III.3 Therefore, some individuals

have multiple data points over different years while others only have one.

SHLSE comprises questions on demographics, health, occupation, residence,

and economic/financial well-being. It contains the current and historical

information with respect to marital status, employment and retirement, and

living arrangement/residence. Health information includes self-assessed general

health status and a measure of depression, Centre for Epidemiologic Studies

Depression Scale (CES-D). The CES-D measures how often in the past week a

respondent experienced symptoms associated with depression, such as restless

sleep, poor appetite and feeling lonely, which is on cognitive and affective

components of depression rather than the physical manifestations of depression

(Hann et al. 1999; Radloff 1977). Anxiety and stress may be perceived as part of

the symptoms of depression. The questions for LS are also included in the survey

but were absent in 1993. A section devoted to the financial well-being of the

respondents comprises of the sources of income, asset structure, and management

of finances. However, accurate income information is scant. Income is a

categorical variable and available in all waves but the categories in the first two

waves are inconsistent with the following four waves.4

Information on the actual SFWBIR benefit claimed is unavailable, so we

presume that all eligible individuals claimed the SFWBIR benefit. The criterion

for claiming the SFWBIR benefit in 1996 is senior citizens who have participated

for at least 6 months in the FHI and do not claim any pension from other social

schemes or the government. Non-farmer retired citizens can participate in FHI as

long as they meet the criteria of FHI whether they could claim a pension from their

previous job social schemes or not.5 However, if they claim the SFWBIR benefit,

they need to give up their other pension, which is unlikely to happen because the

pension from other schemes was greater than the SFWBIR benefit. Even if the

existent of SFWBIR, in the short run this would only impact younger farmers in

terms of changing occupation from farmers to manufacturing workers because

3 The sample size of the new cohort in 1996 was 2,462 and the sample size of the new cohort in 2003 was 1,599.

4 In the first wave, the income is respondent’s monthly income and the categories are NT$0–3,000, NT$3,000–

4,999, NT$5,000–9,999, NT$10,000–14,999, NT$15,000–19,999, NT$20,000–49,999, and more than NT

$49,999. However, in the third wave, the income is respondent and spouses’ annual income and the categories

are NT$0–100,000, NT$100,000–299,999, NT$300,000–599,999, NT$600,000–999,999, NT$1,000,000–

1,999,999, NT$2,000,000–4,999,999, and more than NT$4,999,999.

5 We use “retired” citizens instead of “senior” citizens here because the people covered by Labour Insurance (LI)

can retire and claim a retired pension before 65 years old as long as they match one of three conditions. Those

conditions are described in the same paragraph. The age of 65 years is the forced retirement age.


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older farmers close to retirement would have had little to gain from swapping to

become manufacturing workers prior to the pension. This is because

manufacturing workers are forced to retire at 65 years old.

5. METHODS

In a natural experiment, a treatment or shock occurs to some individuals but not to

others. In this study, the implementation of SFWBIR is considered a treatment to

senior farmers, and manufacturing workers are selected as a comparison group

because they have the most comparable socio-economic backgrounds. Using

SFWBIR as an instrument for income changes is advantageous because it is easy

to use the SFWBIR criteria to partition the sample into the treatment or control

group and the SFWBIR covered approximately one-fifth of senior citizens in 1995

and 1996.6

The sample inclusion criteria are farmers or manufacturing workers aged

between 60 and 75 years old. The number from cohort I in 1989 is 1,095, however,

only 208 remained in 1996 due to being either out of the age range (60–75), lost to

attrition or dead and the youngest individuals were 67 years old. Thus, cohort II

was added to replenish the sample in 1996. The sample size of cohort II in 1996

fitting our selection criteria is 725 (of these, 63 are between 65 and 67 years old

and 662 are between 60 and 65 years old). Finally, the sample sizes in 1989 and

1996 are 1,095 and 933, respectively. Although the survey is longitudinal, we

implement the estimations as repeated cross-sections due to the attrition of cohort

I in 1996.7 DiD implemented with repeated cross-sectional data compares the

average outcomes of treatment and control groups where each group may have

different samples at different time points. This assumes that the same group, even

with different samples, should have the same average outcomes at difference time

points after excluding changes in macro-effects over time and the treatment effect.

Repeated cross-sectional data have been widely used in DiD design (Chou et al.

2003; Stewart 2004).

The farmers and manufacturing workers are each partitioned into subgroups by

age (under 65s are defined as non-senior and 65s and older are defined as senior).

The senior farmer subgroup is the treatment group and the other three groups,

senior manufacturing workers, non-senior farmers and non-senior manufacturing

workers, are considered as possible control groups.

6 Source: Department of Statistics, Ministry of Interior.

7 We also only use cohort I samples in 1989 and 1996 to estimate the policy impact and the results are similar to the

results shown in Table 2.


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Given that the SFWBIR policy occurred in 1995, the data in 1989 and 1993

represent pre-policy data and the data in 1996 represent post-policy data.

However, the data in 1993 are excluded for two reasons: (1) some questions on LS

were not asked in 1993 and (2) the sample under 65 years old in 1993 was too

small to use for comparison purposes.8 The data after 1996 are also excluded

because the qualification criteria and amount of SFWBIR benefit increased in the

following years.

The impact of SFWBIR is estimated for three outcomes: (1) self-assessed

health status (SAH), (2) depression (CES-D) and (3) LS. SAH has five categorical

responses: very good, good, fair, poor and very poor. We follow Jones and

Wildman (2008) and convert SAH to a dichotomous variable (Hit), where 1

represents very good or good health and 0 any of the other categories. This allows

the implementation of a Probit model where the binary observed variable, Hit, in

Equation (1) is determined by a continuous latent variable, H*it , which can be

regarded as the health stock of the individuals. Individuals only report their health

status as being very good or good when their health stock is above 0. Thus, a

binary variable indicating the sign of H*it is observed:

Hit ¼1; if H*

it . 0;

0; if H*it # 0:

8<: ð1Þ

Depression is measured using the scale of the CES-D which ranges from 0 (no

depression) to 30 (extreme depression). LS, which is regarded as an indicator of

happiness (Layard et al. 2008), is measured on a scale between 0 and 10, where a

higher score represents greater LS. Ordinary least squares estimation is used in

both CES-D and LS analyses.

The explanatory variables controlled for include age, sex, number of children,

income, and dummy variables for education, marital status, household size,

region, and job. Age, sex, marital status, and education are primary demographic

variables. The association between marital status and well-being has been

recognized and so has the association between education and health (Manzoli

et al. 2007; Ross and van Willigen 1997; Stack and Eshleman 1998). The

number of children and household size are regarded as the proxies of social or

emotional support that may have a positive association with health (Uchino

2006). There are many reasons why income and job type, which reflect socio-

economic status, may influence health and LS (Frijters et al. 2004; Iverson and

8 Depression (CES-D) contains 17 questions and LS comprises 10 questions in the first wave (1989) and both

contain 10 questions, respectively, in the third wave (1996). However, in the second wave (1993), the CES-D has

11 questions but LS only has four questions.


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Maguire, 2000; Strully 2009; Tsutsumi et al. 2001) however, the empirical

evidence is mixed. Regional dummy variables control for differences in

treatment access and health hazards that vary across geographical locations. To

control for income differences, the initial income, defined as the income in 1989,

is used so as to avoid the possible endogeneity problem where final income may

be influenced by the SFWBIR payment. The initial income predicted by an

interval regression is employed as the income variable in the estimations. For

cohort I, the predicted initial income is obtained by implementing the interval

regression using the categorical income in 1989 as the dependent variable and

1989s individual characteristics, such as sex, age, education, marital status

(married, divorce, and widow), region (north, middle, and south), working status,

number of children in the family and income source (from children, from

pension, and from invested gain) as independent variables. However, the income

information in 1989 is unavailable for cohort II. Thus, we use the coefficients

obtained from the interval regression of cohort I with income categories in 1989

and individual characteristics in 1996 and the individual characteristics of cohort

II to predict cohort II’s initial income.

5.1 Difference-in-Differences

In the DiD design, the effect of SFWBIR is estimated by comparing the changes in

outcomes over time for senior farmers with that for senior manufacturing workers,

after controlling for changes in other explanatory factors. The underlying

assumption is that without the implementation of SFWBIR, senior farmers would

have experienced an identical change in outcomes as the senior manufacturing

workers.

The test of Equation (2) . 0 implies that SFWBIR improves health and LS.

DSFWBIRSenior group ¼ HAfter SFWBIR

Senior Farmer 2 HBefore SFWBIRSenior Farmer

� �

2 HAfter SFWBIRSeniorManuf:worker 2 HBefore SFWBIR

SeniorManuf:worker

� �: ð2Þ

The first parenthesis on the right hand side in Equation (2) presents the changes

in outcomes of senior farmers from before to after the SFWBIR implementation.

The changes are assumed to be caused by SFWBIR (policy impact) plus other

factors related to senior farmers (OthSF). Likewise, the second parenthesis is equal

to OthSM (the impact of other factors related to senior manufacturing workers)

because the policy is assumed to have no effect on senior manufacturing workers.

The key assumption in DiD is that the policy impact is the only difference between

two groups which implies OthSF ¼ OthSM, and the impact of other factors on the


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changes of senior farmers’ outcomes is identical to that on the changes in senior

manufacturing workers’ outcomes. Thus, DSFWBIRSenior group ¼ (P þ OthSF) 2

(OthSM) ¼ P.

The pooled sample observed in 1989 and 1996 is used to estimate the effect of

SFWBIR. The equation is given as Equation (3).

Hit ¼ a0 þ d1Post1995it þ d2Farmerit þ d3Post1995*itFarmerit þ a1Xit þ nit;

ð3Þwhere Hit is self-assessed health status, the score of depression or LS for

individual i in period t, Post1995 is a dummy variable for the period after

implementation of SFWBIR, Farmer is a dummy variable for farmers, X is a

vector of observable individual characteristics which controls for the impact of

changes in these variables over time and thus reduces the potential bias caused by

omitted variables, and n is a random error term. The effect of SFWBIR in

Equation (3) can be expressed as: DSFWBIRSenior group ¼ ½ðd1 þ d2 þ d3Þ2 d2�2 ½d1 2

0� ¼ d3 the coefficient d3 measures the DiD defined in Equation (2).

5.2 Difference-in-Difference-in-Differences

We employ DiDiD estimation as a robustness check. Two more groups, the non-

senior farmer group and the non-senior manufacturing worker group, are included

in the model. The framework of DiDiD is given as Equation (4) to Equation (6).

DSFWBIRSenior group ¼ HAfter SFWBIR

SF 2 HBefore SFWBIRSF

� �

2 HAfter SFWBIRSM 2 HBefore SFWBIR

SM

� �; ð4Þ

DSFWBIRNon–senior group ¼ HAfter SFWBIR

NSF 2 HBefore SFWBIRNSF

� �

2 HAfter SFWBIRNSM 2 HBefore SFWBIR

NSM

� �; ð5Þ

DSFWBIR ¼ DSFWBIRSenior group 2 DSFWBIR

Non–senior group; ð6Þ

where DSFWBIR and H have been defined above. SF and SM indicate the senior

farmers and senior manufacturing workers. NSF and NSM indicate the non-senior

farmers and non-senior manufacturing workers.

In DiDiD, we relax the assumption in DiD that the impact of other factors

on the changes in farmers’ and manufacturing workers’ outcomes is

identical (OthSF – OthSM). Instead, the assumption in the DiDiD estimation

is that, OthSF 2 OthSM is equal to OthNSF 2 OthNSM which implies that that


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any occupational difference from before to after the policy implementation

is the same across our two age groups. Thus, DSFWBIR is equal to the policy impact.

The DiDiD estimator can be expressed within a regression framework with

the pooled data observed in 1989 and 1996. The regression is given as

Equation (7):

Hit ¼ b0 þ g1Post1995it þ g2SCit þ g3Farmerit þ g4Post1995*itSCit

þ g5SC*itFormerit þ g6Post1995

*itFarmerit

þ g7Post1995*itSC

*it Farmerit þ b1Xit þ 1it;

ð7Þ

where SC is a dummy for senior citizens and 1 is a random error term and other

variables have been defined in Equation (3). The effect of SFWBIR in Equation

(7) can be expressed as DSFWBIR ¼ {[(g1 þ g2 þ g3 þ g4 þ g5 þ g6 þ g7) 2

(g2 þ g3 þ g5)] 2 [(g1 þ g3 þ g6) 2 g3]} 2 {[(g1 þ g2 þ g4) 2 g2] 2 [g1 2

0]} ¼ g7. The coefficient g7 measures DiDiD defined in Equation (6).

6. EMPIRICAL RESULTS

6.1 Sample Descriptive Statistics

Table 1 depicts the sample statistics. The average self-assessed health of the two

farmer groups is lower than their comparison groups whether in 1989 or 1996

(senior farmers vs. senior manufacturing workers and non-senior farmers vs.

non-senior manufacturing workers). For depression, the senior farmers in 1989

reported higher depression scores than the senior manufacturing workers but the

senior manufacturing workers reported higher depression than the senior

farmers in 1996. The non-senior farmers reported higher depression than the

non-senior manufacturing group in both 1989 and 1996. With respect to LS, the

mean of the non-senior farmers was higher than their comparison groups in both

years.

For education, on average, farmers were more likely to report themselves as

illiterate than their manufacturing counterparts. The educational distribution of

the farmer and manufacturing groups are skewed to the right. More than 80%

of farmers and manufacturing workers report their education level as illiterate or

primary school level which is 6 years or less. The regional dummies reveal that the

majority of farmers live in the middle and south while a higher percentage of

manufacturing workers live in the middle and north. For the household scale, the

percentages of large household (over 10 people) for both groups decrease over the


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Table 1: Sample Statistics: (a) 1989 and (b) 1996

Farmer group Manufacturing worker group

Senior group

Non-senior

group Senior group

Non-senior

group

Mean (SD) Mean (SD) Mean (SD) Mean (SD)

(a) 1989

Health indicators

SAH 0.351 (0.478) 0.402 (0.491) 0.370 (0.484) 0.488 (0.501)

CES-D 5.646 (4.192) 5.537 (4.130) 4.856 (4.056) 5.200 (4.193)

LS 6.277 (2.404) 6.543 (2.413) 6.013 (2.243) 5.975 (2.519)

Educational dummy

Illiterate 0.524 (0.500) 0.398 (0.490) 0.380 (0.487) 0.241 (0.429)

Primary S. 0.430 (0.496) 0.560 (0.501) 0.462 (0.500) 0.632 (0.484)

Junior H.S. 0.029 (0.169) 0.032 (0.175) 0.101 (0.303) 0.098 (0.298)

Senior H.S. 0.013 (0.111) 0.007 (0.084) 0.044 (0.206) 0.017 (0.131)

University 0.002 (0.046) 0.004 (0.059) 0.013 (0.112) 0.011 (0.107)

Marital status dummy

Married 0.689 (0.463) 0.820 (0.385) 0.595 (0.492) 0.724 (0.448)

Divorce 0.019 (0.136) 0.000 (0.000) 0.070 (0.255) 0.075 (0.264)

Widow 0.282 (0.450) 0.173 (0.379) 0.272 (0.446) 0.132 (0.340)

Single 0.006 (0.079) 0.007 (0.084) 0.063 (0.244) 0.069 (0.254)

Age 69.57 (3.169) 62.19 (1.294) 68.97 (2.932) 62.02 (1.368)

Male 0.668 (0.471) 0.694 (0.462) 0.677 (0.469) 0.782 (0.414)

Household scale dummy

1–4 people 0.390 (0.488) 0.444 (0.498) 0.481 (0.501) 0.477 (0.501)

5–10 people 0.532 (0.499) 0.475 (0.500) 0.481 (0.501) 0.454 (0.499)

Over 10 people 0.077 (0.267) 0.081 (0.273) 0.038 (0.192) 0.069 (0.254)

Regional dummy

North 0.113 (0.317) 0.092 (0.289) 0.399 (0.491) 0.425 (0.496)

Middle 0.413 (0.493) 0.461 (0.499) 0.316 (0.467) 0.264 (0.442)

South 0.363 (0.481) 0.363 (0.482) 0.241 (0.429) 0.241 (0.429)

East 0.111 (0.314) 0.085 (0.279) 0.044 (0.206) 0.069 (0.254)

(continued)


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Table 1: (Continued)


Senior group

Non-senior

group Senior group

Non-senior

group


Job type dummy

Self-employed 0.313 (0.464) 0.493 (0.501) 0.032 (0.176) 0.057 (0.233)

Family business 0.002 (0.046) 0.011 (0.103) 0.000 (0.000) 0.029 (0.168)

Employee 0.040 (0.195) 0.082 (0.274) 0.196 (0.398) 0.494 (0.501)

Retired 0.008 (0.091) 0.011 (0.103) 0.152 (0.360) 0.138 (0.346)

Others 0.637 (0.481) 0.404 (0.492) 0.620 (0.487) 0.282 (0.451)

No. of children 5.561 (2.190) 5.145 (1.744) 4.550 (2.170) 4.042 (2.124)

Predicted 1989 income

(NT$1,000)

5.298 (12.338) 5.838 (5.814) 6.708 (6.547) 8.460 (6.587)

Sample size 479 284 158 174

(b) 1996

Health indicators

SAH 0.371 (0.484) 0.401 (0.491) 0.423 (0.499) 0.525 (0.500)

CES-D 6.756 (4.476) 7.346 (4.757) 7.481 (4.889) 6.319 (3.500)

LS 6.500 (2.488) 6.558 (2.372) 6.180 (2.561) 6.296 (2.375)

Educational dummy

Illiterate 0.384 (0.488) 0.353 (0.479) 0.327 (0.474) 0.172 (0.378)

Primary S. 0.551 (0.499) 0.583 (0.494) 0.491 (0.505) 0.658 (0.475)

Junior H.S. 0.051 (0.220) 0.035 (0.184) 0.164 (0.373) 0.085 (0.279)

Senior H.S. 0.005 (0.068) 0.026 (0.160) 0.000 (0.000) 0.066 (0.248)

University 0.009 (0.096) 0.003 (0.054) 0.018 (0.135) 0.019 (0.136)

Marital status dummy

Married 0.810 (0.393) 0.857 (0.350) 0.709 (0.458) 0.890 (0.313)

Divorce 0.023 (0.151) 0.015 (0.120) 0.018 (0.135) 0.034 (0.183)

Widow 0.162 (0.369) 0.114 (0.318) 0.218 (0.419) 0.047 (0.212)

Single 0.005 (0.068) 0.015 (0.120) 0.055 (0.229) 0.028 (0.166)

Age 69.20 (2.819) 58.13 (3.872) 68.96 (2.236) 55.20 (3.736)

Male 0.75 (0.434) 0.569 (0.496) 0.655 (0.480) 0.734 (0.443)

(continued)


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years. This could be caused by industrialization which forces young adults who

live in the middle, south and east to move north for work.

Comparing the senior subgroups, the retired proportion in senior farmer group

is much lower than that in senior manufacturing group before SFWBIR. This may

be due to no legally forced age of retirement for farmers.9 In 1996, post-SFWBIR,

the retired proportion in the senior farmer group slightly increases, whereas it

reduces rapidly in the senior manufacturing group.

Table 1: (Continued)


Senior group

Non-senior

group Senior group

Non-senior

group


Household scale dummy

1–4 people 0.556 (0.498) 0.402 (0.491) 0.509 (0.505) 0.414 (0.493)

5–10 people 0.407 (0.492) 0.551 (0.498) 0.473 (0.504) 0.558 (0.497)

Over 10 people 0.037 (0.189) 0.047 (0.211) 0.018 (0.135) 0.028 (0.166)

Regional dummy

North 0.102 (0.303) 0.090 (0.287) 0.418 (0.498) 0.226 (0.419)

Middle 0.495 (0.501) 0.446 (0.498) 0.327 (0.474) 0.354 (0.479)

South 0.324 (0.469) 0.405 (0.492) 0.218 (0.417) 0.389 (0.488)

East 0.079 (0.270) 0.058 (0.235) 0.036 (0.189) 0.031 (0.175)

Job type dummy

Self-employed 0.486 (0.501) 0.523 (0.500) 0.073 (0.262) 0.171 (0.377)

Family Business 0.023 (0.151) 0.018 (0.131) 0.018 (0.135) 0.025 (0.158)

Employee 0.056 (0.231) 0.061 (0.240) 0.581 (0.498) 0.778 (0.416)

Retired 0.019 (0.136) 0.009 (0.093) 0.036 (0.189) 0.003 (0.056)

Others 0.416 (0.494) 0.389 (0.488) 0.291 (0.458) 0.022 (0.148)

No. of children 5.139 (1.974) 4.245 (1.609) 3.818 (2.118) 3.536 (1.371)

Predicted 1989 Income

(NT$1,000)

7.187 (4.651) 20.742 (8.531) 10.582 (8.431) 26.445 (9.163)

Sample size 216 343 55 319

9 Manufacturing workers who have claimed their retirement pension and continue to work after the legally forced

age are able to participate in the Occupational Accident Insurance, which is a voluntary insurance. The premium

is paid by employers only.


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The gap of predicted initial income for senior groups and non-senior groups in

1989 was 26.61% and 44.91%, respectively. However, in 1996, the gap of

predicted initial income among senior groups broadened to 47.24% but the gap of

predicted initial income among non-senior groups narrowed down to 27.49%.

This implies that some initial income predictors, such as age, working status and

major income source, in 1996 differ from those in 1989.

Although there are still some differences in characteristics between the senior

farmers and senior manufacturing workers, the difference are small compared

with other occupational groups.10

Figure 1 presents the trends of CES-D and LS for the senior farmer group and

the senior manufacturing worker group before and after the policy intervention.

Figure 1: Trends of depression (CES-D) and life satisfaction (LS) for both groups before

and after 1995. Note: 1. The score of depression ranges from 0 to 30 and it ranges from 0 to

4 in LS. 2. A high score in depression means more depression and the high score in LS is

more satisfaction.

10 Such difference between treatment and control groups also exists in other similar studies, including Chou et al.

(2003).


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The non-senior groups are not included in the figure due to lack of data of the non-

senior farmers and manufacturing workers in 1993. Note that due to the limited

number of LS questions asked in 1993, the scales shown in the figure are only

based on the questions common to all three years. Overall, senior farmers have

higher depression than senior manufacturing workers and follow a consistent

pattern before the policy, whereas this reverses after the pension implementation.

For LS, the changes for both groups follow a similar patter before the policy

change with senior farmers being more satisfied but the difference between the

two groups narrows in 1996.

6.2 DiD Estimation

In Table 2, the column of SAH for the basic specification suggests that SFWBIR

had no statistically significant effect on senior farmers’ self-assessed health when

other factors are left uncontrolled. In the bottom rows, the other control variables

identified in Equation (2) are included. In the full specification model of SAH, the

coefficient of SFWBIR on the senior farmers is negative but not statistically

significant at the 10% level. Education, but only the junior high school and

university levels, contribute to self-assessed health significantly at the 5%

and 10% levels, respectively. People whose education level at junior high school

and university levels have a higher probability of reporting good self-assessed

health compared with illiterate subjects, holding other characteristics constant.

The dummies of marital status do not have significant effects on self-assessed

health. Age has a non-significant positive effect on self-assessed health status.

However, males report better self-assessed health than females at the 5% level.

With respect to regional dummies, the north has positive and significant effects

on self-assessed health compared with the east at the 5% level. Self-employed,

employed and retired people have a significantly higher probability of reporting

good self-assessed health than those who are defined as the category of others with

a significant level of 1% and 5%, respectively.11

The column of CES-D suggests that SFWBIR reduces depression of senior

farmers in the basic specification model but this is only significant at the 10% level.

In the full specification, the effect of SFWBIRon senior farmers continues to reduce

depression but it becomes significant at the 5% level. It is estimated that the

SFWBIR decreases the depression scale of senior farmers by 1.697 points. As for

other control variables, people in the median household (5–10 people) and large

household (over 10 people) report significantly (0.565 points) lower depression at

the 5% level compared with those in a small household (1–4 people).

11 The result estimated with five levels of SHA is consistent with Table 2.


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Table 2: DiD Empirical Results

SAH CES-D LS

Coeff. (SE) Coeff. (SE) Coeff. (SE)

Basic specification

Post 1995 0.137 (0.203) 2.625*** (0.687) 0.167 (0.393)

Farmer 20.052 (0.123) 0.791** (0.399) 0.277 (0.227)

Farmer*Post1995 2 0.082 (0.229) 2 1.515* (0.775) 0.057 (0.445)

Full specification

Post1995 20.048 (0.232) 3.074*** (0.735) 0.007 (0.413)

Farmer 0.065 (0.148) 0.727 (0.465) 20.013 (0.261)

Farmer*post1995 2 0.055 (0.255) 2 1.697** (0.810) 0.102 (0.458)

Primary school 0.020 (0.106) 20.267 (0.340) 0.214 (0.192)

Junior high school 0.494** (0.217) 20.646 (0.704) 0.558 (0.399)

Senior high school 0.096 (0.357) 0.195 (1.181) 0.511 (0.680)

University 1.077* (0.614) 21.754 (1.805) 20.358 (1.002)

Marital status: married 20.048 (0.665) 2.707 (1.991) 0.113 (1.107)

Marital status: divorce 0.054 (0.708) 2.563 (2.142) 21.611 (1.190)

Marital status: widow 20.086 (0.669) 3.419* (2.004) 20.608 (1.114)

Age 0.529 (0.747) 1.119 (2.373) 0.534 (1.332)

Square of age 20.004 (0.005) 20.008 (0.017) 20.004 (0.010)

Male 0.298** (0.118) 20.334 (0.370) 0.091 (0.211)

Household scale

5–10 people 0.020 (0.097) 20.770** (0.309) 0.116 (0.175)

Over 10 people 20.038 (0.208) 21.425** (0.659) 0.717 (0.367)

Region-north 0.471** (0.201) 20.564 (0.626) 0.069 (0.357)

Region-middle 0.246 (0.177) 20.297 (0.541) 0.327 (0.308)

Region-south 0.286 (0.182) 0.221 (0.554) 0.375 (0.316)

Job type

Self-employed 0.536*** (0.117) 20.693* (0.369) 0.572*** (0.210)

Family business 0.435 (0.485) 20.672 (1.623) 20.309 (0.971)

Employed 0.366** (0.172) 20.783 (0.553) 0.113 (0.309)

Retired 0.534** (0.252) 20.361 (0.817) 0.941** (0.460)

No. of children 0.019 (0.022) 20.060 (0.071) 0.044 (0.040)

Predicted 1989 Income 0.0003 (0.004) 20.026* (0.015) 0.012 (0.008)

Sample size 850 844 811†

R 2 0.068 (pseudo) 0.078 0.070

***, **, and * present statistically significant at the 1%, 5% and 10% levels, respectively.

† The sample size in the estimates of LS is smaller than that in the estimates of SAH and CES-D

because more missing values exist in the questions of LS.


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The column of LS presents the effect of SFWBIR on senior farmers as positive

but not significant at the 10% level in the basic specification. In the full

specification, it remains positive and not significant. With respect to other

covariates, self-employed and retired people report significantly higher LS at the

1% level and 5% level, respectively. The R 2s presented in the three regressions

are low, which means that there are still many unexplained or unknown factors

explaining the variation in health and happiness.12

6.3 ROBUSTNESS TESTS

6.3.1 DiDiD estimation. The columns of SAH and LS in Table 3 show that the

effects of SFWBIR on self-assessed health and LS of senior farmers are positive

but not significant at the 10% level. The column of CES-D again show that

SFWBIR reduces the depression of senior farmers by 2.178 points but it is only

significant at the 10% level.

6.3.2 Robustness test of retirement effect. The age for senior farmers to

claim the SFWBIR benefit is at 65 years that is the legal age of retirement in other

industries. Though this legal age is not applicable to farmers because farmers can

retire at any age, we exam the retirement effect in the farmer group. We compare

senior farmers with non-senior farmers only using the 1996 data and the model is

shown in Equation (8)

DRetiredFarmer group ¼ HRetired

SC 2 HNon-retiredSC

� �2 HRetired

Non-SC 2 HNon-retiredNon–SC :

� � ð8Þ

The senior farmer group in 1996 was the group that claimed the SFWBIR benefit.

This comparison identifies the retired effect in farmer group. Table 4 shows that

the retirement does not have a significant impact on depression in farmers. This

result provides a robust evidence of the SFWBIR effect on depression.

7. CONCLUSION

This paper uses a natural experiment to identify the effect of the SFWBIR on self-

assessed health, depression, and LS for senior farmers in Taiwan. SFWBIR

appeared to significantly reduce the depression of senior farmers and this result

was robust to different specifications. In summary, the pure permanent cash

injection had no significant short-term effect on self-assessed health and LS but

had a significant short-term effect on relief of depression. This provides evidence

12 The R 2s in other studies are also low, for example, Li and Zhu (2006) and Gardner and Oswald (2007).


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that a permanent increase in income can improve mental health. It also shows that

there is a lack of evidence that in the short term a permanent increase in income is

a significant determinant of happiness or LS. The permanent increase in income

Table 3: DiDiD Empirical Results

SAH CES-D LS

Coeff. (SE) Coeff. (SE) Coeff. (SE)

Full specification†

Post 1995 0.004 (0.213) 1.477** (0.702) 20.093 (0.395)

SC (senior citizen) 20.279 (0.203) 21.220* (0.661) 0.377 (0.373)

Farmer 20.105 (0.148) 0.259 (0.487) 0.362 (0.274)

Farmer*SC 0.147 (0.184) 0.677 (0.597) 20.389 (0.336)

Farmer*Post1995 20.044 (0.253) 0.704 (0.829) 20.186 (0.469)

Post1995*SC 20.115 (0.304) 1.417 (0.985) 0.064 (0.555)

Farmer*SC*Post1995 0.059 (0.351) 2 2.178* (1.138) 0.319 (0.645)

Sample size 1,470 1,457 1,400††

R 2 0.071 (Pseudo) 0.078 0.084

***, **, and * present statistically significant at the 1%, 5%, and 10% level, respectively.

† The covariates shown in Table 2 are controlled but not shown here.

†† The sample size in the estimates of LS is smaller than that in the estimates of SAH and CES-D

because more missing values exist in the questions of LS.

Table 4: Robustness check of the retired effect†

CES-D Coeff. (SE)

SC (senior citizen) 20.112 (0.447)

Retired 0.562 (1.811)

SC*Retired 0.060 (2.408)

Sample size 1,250

R 2 0.037

***, **, and * present statistically significant at the 1%, 5% and 10% levels, respectively.

† This regression controls for martial status, age, sex, household scale, region, number of children and

predicted 1989 income.


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might improve mental health through direct mechanisms, for example, achieving

minimum standards of living and alleviating financial pressure but may also work

through indirect mechanisms such as changes in relative incomes across peer

groups. In particular, when senior farmers’ income increase and manufacturing

workers’ income retains the same, senior farmers may perceive themselves better

off when comparing themselves with manufacturing workers (and manufacturing

workers may see themselves as worse off now in comparison to senior farmers).

The policy effect on depression appears quicker than what is observed in the

work of Gardner and Oswald (2007) where they observe the impact of a lottery

win on mental health after two years of the lottery win. Two reasons might explain

this difference. First, the instruments of income innovation are different. The

policy intervention here is permanent and anticipated, whereas lottery wins are a

short-term and unanticipated shocks. Second, the population concerned in this

paper is senior farmers whose socio-economic status is relatively low. For

farmers, financial embarrassment may cause depression. The anticipated effect

after policy announcement might start to reduce stress and the income injection

can immediately relieve stress after the policy is implemented.

The income injection shows the impact on mental health but not on self-

assessed health and LS. The following reasons may explain this. First, income

might have lagged effects on both indicators. Income may have a lagged effect on

self-assessed health as health investments may take time to translate into health.

With respect to LS, for the population of low social strata, reducing financial

embarrassment will relieve living stress first and potentially raise LS afterwards.

However, this paper cannot reveal any information about long-term effects of two

years or longer. Only the first year after policy implement can be detected in the

current study due to changes in the magnitude of the SFWBIR payment post 1996.

Second, the questions of CES-D focus on current mental status, whereas the

questions of LS focus on current status, the comparison of current status with the

past, the evaluation of the whole life and the expectation of future. It is an

evaluation of a longer time span; thus, the policy effect on LS might be ambiguous

or be observed to move more slowly than that on depression.

Finally, the NHI was also implemented in 1995. Due to this reason, the policy

effect identified above might be treated with scepticism. However, this scepticism

can be excluded because of two reasons. First, NHI was unlikely to significantly

influence the individuals who had been previously covered by the occupational

social schemes (both farmers and manufacturing workers). Second, NHI covers

major populations including farmers and manufacturing workers and therefore it

may be expected that any impact would be experienced equally by both groups

and therefore would not show up in our estimated effect. Another scepticism is

due to the retired effect in the farmer group. However, the retired effect in the


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farmer group does not show a significant impact on depression, which provides

robust evidence for the SFWBIR effect on depression.

Hauck and Rice (2004) show that the more disadvantaged groups suffer poorer

mental health and experience more periods of ill health and the appreciate

prevention policies targeting mental illness within different risk groups are needed.

Our result provides evidence that long-term social benefits (pensions) somewhat

relieve the depression experienced by senior farmers in the short-term which is

consistent with the results reported by Case (2004). However, we are unable to

reveal the long-term impact of this social benefit in this study and it is possible that

the impact in long term may slowly disappear due to hedonic adaptation, whereby

individuals grow accustomed to their new income level (Frederick and

Loewenstein 1999). Further research is needed to examine the long-term effect

of SFWBIR; however, the benefit of the SFWBIR increases over time, which

makes any such analysis more complicated. These answers can provide a direction

for policymakers to make relevant pension policies for the future.

While this study examines the impact of a pension reform implemented over

15 years ago, the results from this study are still relevant to current policy debates.

First other countries are currently deciding on whether and when to implement

universal pensions systems which cover the poorest in system and Taiwan itself is

still progressing towards a universal pension coverage system. Much of the current

debate in Taiwan is focused on how to fill the coverage gap to achieve universal

coverage. International organizations such as theWorldBank, International Labour

Organisation (ILO) and United Nations Development Programme and advocacy

groups suggest using non-contributory or social pensions as a way to address the

coverage gap (Palacios and Sluchynsky 2006). In Taiwan, there are some non-

contributory social pension schemes for the elderly population and disadvantaged

groups apart from public and private occupational and contributory pension

schemes. In 2008, the Taiwanese government implemented the National Pension

Scheme, which is a contributory pension scheme. It aims to provide a more

comprehensive safety net and integrate the fragmented old-age allowance systems

step by step. This study provides information about the possible benefit filling the

coverage gap with social pension schemes.

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NOTES ON CONTRIBUTORS

Fu-Min Tseng was appointed as a Research Fellow in Health Economics at the

IIHD in June 2013. He has previously worked as a Research Fellow at the

University of Dundee and a Research Assistant at the China Medical University,

Taiwan. Fu-Min’s research interests are Health Economics, Happiness Economics

and Policy Evaluation. He worked on exploring the socio-economic cost of

bereavement in Scotland and used NHS administrative data to explore the cost-

effectiveness of drug and alcohol treatment programmes when he was in Dundee.

Currently, he is working on exploring the impact of health system on household

financing in the countries that ever had conflict in the past decades. He is a

member of the ReBuild project.

Dennis James Petrie was appointed as a Senior Research Fellow in the Centre for

Health Policy, Programs and Economics (CHPPE) at the University of Melbourne

in 2012. For the 5 previous years, he was a Lecturer in Health Economics at the

University of Dundee, Scotland. He is also an Honorary Research Fellow at the

Health Economics Research Unit (HERU), University of Aberdeen. He has a dual

degree, B.Econ (Hons) majoring in Econometrics and a B.Sc. majoring in

Mathematics plus a Ph.D. in Health Economics from the University of Queensland

(UQ), Australia. He has worked on the economics of illicit drugs and alcohol, the

longitudinal measurement and evaluation of health inequalities, the economics of

bereavement, the impact of adherence and has been involved in a number of

economic evaluations of healthcare interventions. He has extensive experience in

analysing large and complex data-sets and is currently working on developing a

Type I diabetes model and modelling the impact of health insurance on those with

Type II diabetes.


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the implications for health, depression, and life satisfaction from a permanent increase in income...

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