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1

School of Economics

University College Cork

Working Paper Series

Controlling for Endogeneity in the Relationship between Life Satisfaction

and Employment Status

Working Paper 13-03

Edel Walsh & Rosemary Murphy

Abstract: This paper aims to examine if a simultaneous relationship exists between life

satisfaction and employment status. A sample of 2,576 Irish adults obtained from the

European Social Survey 5 is used to test this hypothesis. A two-stage probit least squares

technique estimates life satisfaction and employment status simultaneously and accounts for

the endogeneity present in the equations in the system. The findings suggest a bi-directional

relationship between life satisfaction and employment status that is, employment affects life

satisfaction but also being satisfied with life increases the predicted probability of being

employed. Furthermore, this paper finds that the effect of employment status on life

satisfaction (as well as the reverse direction effect) is underestimated if simultaneous

endogeneity is not accounted for.

Keywords: Life satisfaction, employment status, endogeneity, two-stage probit least squares,

European Social Survey

JEL classifications: I31, J21

Corresponding author contact details: Dr Edel Walsh, School of Economics, University

College Cork. [email protected]

*These Discussion Papers often represent preliminary or incomplete work, circulated to

encourage discussion and comments. Citation and use of such a paper should take account

of its provisional character. A revised version may be available directly from the author(s).

Further working papers are available at http://www.ucc.ie/en/economics

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1. INTRODUCTION

The direction of the relationship between well-being and employment status has been

questioned in existing literature which suggests that the relationship may be hampered by

endogeneity, making employment status both a cause and a consequence of well-being

(Hamilton et al., 1997; Duarte et al., 2007; Cole et al., 2009). Studies agree that the

endogeneity may be caused by a simultaneous relationship between well-being and

employment status, that is, employment affects life satisfaction1 but also being satisfied with

life is a determinant for securing employment (Hamilton et al., 1997; Cole et al., 2009). If the

causal relationship between life satisfaction and employment status runs in both directions

any estimation without controlling for such simultaneous endogeneity would yield biased and

inconsistent estimates of the structural parameters. The aim of this paper is to estimate life

satisfaction and employment status simultaneously to account for the potential endogeneity of

these two variables.

Existing research is primarily focused on the effect of employment status on well-

being with most studies supporting the finding that unemployment has a large, negative effect

on well-being2 (Di Tella et al., 2001; Frey and Stutzer, 2000, 2002; Helliwell, 2003; Stutzer,

2004). The observed association between unemployment and well-being does, however, not

necessarily mean that unemployment causes poor well-being. The causality may also go in

the other direction, that is, well-being may be a factor in determining employment status.

Individuals who experience low levels of well-being may be more likely to become

unemployed, if for example, they are less productive at work or have high levels of

absenteeism due to poorer health (Paul and Moser, 2009). This simultaneous (or bi-

directional) relationship between well-being and employment status has received much less

attention in the existing literature.

In this paper the relationship between life satisfaction and employment status is

estimated using a sample of 2,576 Irish adults obtained from the European Social Survey 5

(ESS5). The main contribution of this paper is the estimation of life satisfaction and

1 Life satisfaction is a component of subjective well-being (Diener et al., 1999).

2 There are some exceptions to the finding of strong negative effect of unemployment (Graham and Pettinato,

2001; Smith, 2003), however this may be due to small numbers of unemployed in their data.

3

employment status simultaneously to control for the endogeneity of these two variables. A

unique aspect of this paper is that two additional measures of well-being (namely ‘happiness’

and ‘wellbeing’) are used to test the robustness of the results. In addition, the models are

estimated without controlling for endogeneity to allow for a comparison to be made with

models in which simultaneity is not accounted for. A number of other factors associated with

life satisfaction and employment status are also identified. A Two-Stage Probit Least Squares

(2SPLS) empirical methodology is used to control for the endogeneity in the life satisfaction-

employment status relationship.

The remainder of the paper is organised as follows. Section 2 presents a review of the

existing literature in the area. Section 3 outlines the data used and defines the main variables.

The econometric model is explained in Section 4. This is followed by a discussion of the

results obtained and a summary of the main conclusions in Section 5. Section 6 concludes.

2. LITERATURE REVIEW

The relationship between well-being and employment status has undergone extensive

investigation in existing literature, focusing mainly on the effect of employment status on

well-being (Blanchflower, 1996; Brereton et al., 2008; Clark and Oswald, 1994; Frey and

Stutzer, 1999; Winkelmann and Winkelmann, 1998). These and other studies find that the

relationship tends to run in the direction from employment status to well-being, specifically

that unemployment leads to lower levels of well-being and employment leads to higher levels

of well-being, up to a certain point (Korpi, 1997; Di Tella et al., 2002; Frey and Stutzer,

2000; Helliwell, 2003; Stutzer, 2004). However, more recently the direction of the

relationship between employment status and well-being has been questioned and research

suggests that the relationship goes in both directions (simultaneous endogeneity), that is,

employment status affects well-being but also having high levels of well-being determines

the probability of being employed (Hamilton et al., 1997; Cole et al., 2009). Research

suggests that people with low levels of well-being may have a higher probability of losing

their jobs and furthermore, when unemployed, exhibit negative traits which hinder their

chances of finding new employment (Toppen, 1971; Winefield, 1995; Mastekaasa, 1996). In

contrast high levels of well-being, and in particular self-esteem, can improve an individual’s

4

chances of employment. Winefield and Tiggemann (1985), Caplan et al., (1989) and Waters

and Moore (2002) find that high levels of self-esteem are associated with finding

employment.

Paul and Moser (2009) propose that an individual’s unemployment is a consequence

and not a cause of poor well-being. They find that the effect could be the result of several

processes. Firstly, poor well-being might reduce an employee’s performance at work or might

increase absenteeism due to illness. Issues such as this might in turn increase the likelihood of

dismissal (Mastekaasa, 1996; Paul and Moser, 2009). Hesselius (2007) finds that an increase

in the number of sick days in addition to an increase in the duration of sick spells are

associated with a higher risk of unemployment. Secondly, distress may play a role with

regard to the job search process. An employer’s hiring decision is likely to be influenced by

the applicant’s impression on management, a variable that may be influenced by well-being

(Paul and Moser, 2009). Thirdly, poor well-being may be expected to reduce the effort and

the efficiency of an individual’s job search which in turn reduces the probability of re-

employment (Paul and Moser, 2009; Cole et al., 2009). Similarly, Crossley and Stanton

(2005) suggest that the distress resulting from unemployment has a negative relationship with

both interview and job-search success. These studies support the idea that well-being is a

significant factor in determining one’s employment status. However, controlling for

simultaneous endogeneity is a major empirical problem when estimating the effect of

employment on well-being (Gerdtham and Johannesson, 2003).

Few existing studies however, have estimated the relationship between well-being and

employment status while controlling for simultaneous endogeneity and, therefore, risk

reporting biased results. In Ireland, existing research (Whelan, 1992a, 1992b, 1994; Hannan

et al., 1997; Brereton et al., 2008) has estimated the effect of employment status on well-

being without controlling for endogeneity. Brereton et al., (2008) examine the relationship

between employment status and well-being in Ireland using data from 2001. The study

examines the welfare impacts of a number of employment status categories on life

satisfaction, including part-time employment, disconnection from the labour force and being

disabled, unable to work in Ireland. They find that being long-term unemployed, disabled and

unable to work or in part-time employment, have significant negative effects on life

5

satisfaction, particularly for Irish males. However, Brereton et al., (2008), while

acknowledging that it may be an issue, fail to control for the potential simultaneous

endogeneity in the relationship between life satisfaction and employment status. Furthermore,

their study may be less relevant in the context of the current economic climate in Ireland3.

Hannan et al., (1997) assume that the direction of causality is from unemployment to

psychological distress. Similarly, Whelan (1992a; 1992b; 1994) analysed the effect of

unemployment on psychological distress. These studies take psychological distress rather

than life satisfaction as a measure of well-being which makes it difficult to compare their

findings with much of the existing well-being literature. Neither Whelan (1992) nor Hannan

et al., (1997) controlled for simultaneous endogeneity in their research and assumed the

direction of causality was from unemployment to well-being (psychological distress).

Simultaneous endogeneity has been recognised as a significant problem in the

existing literature. It occurs when at least one of the explanatory variables is determined

along with the dependent variable (Wooldridge, 2002). The presence of endogeneity in a

model is a problem as it can seriously bias the independent effect of the endogenous

variables. The methods of estimating such a relationship have been widely discussed in the

statistical literature (Gujarati, 1995; Pindyck and Rubinfeld, 1991; Davidson and MacKinnon,

1993; Greene, 2000; Judge et al., 1985). The issue is that standard estimation methods, such

as Ordinary Least Squares (OLS), used to estimate models in the presence of simultaneous

endogeneity will result in biased and inconsistent estimates (Keshk, 2003). Endogeneity can

be controlled for by using empirical methodologies such as Indirect Least Squares (ILS) or

Two-Stage Least Squares (2SLS). For studies involving systems of equations where one

endogenous variable is continuous and the other endogenous variable is dichotomous, Two-

Stage Probit Least Squares (2SPLS) methodology should be used.

Some previous research attempted to control for endogeneity in the well-being-

employment status relationship using indirect methods or through two stage procedures.

Hamilton et al., (1997) apply a maximum likelihood, simultaneous equation generalized

3 Figures from the CSO (2013) signal the current unemployment rate in Ireland is 14 per cent. Brereton et al.,

(2008) utilise Irish data from 2001 when the unemployment rate was 4 per cent.

6

probit model to jointly estimate the determinants of an individual’s employment status and

their mental health in Canada. Duarte et al., (2007) use a simultaneous equation generalized

probit model but do not find support for a simultaneous relationship and conclude that the

relationship runs from well-being to employment. Using a two-stage least squares regression

technique on Australian data, Cole et al., (2009) tests the hypothesis of a simultaneous

relationship between employment status and well-being and find support for such a

relationship.

This paper attempts to control for the simultaneous endogeneity in the life

satisfaction-employment status relationship by estimating the system of equations via two-

stage probit least squares (2SPLS). The null hypothesis is that no simultaneous relationship

exists between life satisfaction and employment status. Using 2SPLS it is possible to

ascertain if life satisfaction is affecting employment status and employment status affecting

life satisfaction simultaneously.

3. DATA

The European Social Survey (ESS) is a biennial, cross-sectional survey of about 30

countries in Europe including Ireland. The initial sample, ESS1, was collected in 2002. The

ESS draws a different sample of individuals each time it is conducted. Respondents are

interviewed on a range of topics including their age, gender, educational status, employment

status and life satisfaction. The analysis in this paper is based on Irish data from the European

Social Survey 5 (ESS5) which was conducted in 2010. This dataset provides a random

sample of 2,576 Irish respondents. Within the ESS5 the following question on life

satisfaction is asked; ‘All things considered, how satisfied are you with your life as a whole

nowadays?’ Respondents are asked to rank their answer on an eleven-point scale ranging

from 0 “extremely dissatisfied” to 10 “extremely satisfied”. This elicits a measure for life

satisfaction and this measure is frequently used in economic studies on well-being

(Winkelmann and Winkelmann, 1998; Helliwell, 2003; Frey and Stutzer, 2005).

7

As well as life satisfaction, the ESS5 contains detailed information on a sample of

Irish respondents’ employment status. Life satisfaction and employment status are the

dependent (endogenous) variables in the system of equations estimated in Section 4. A

number of other personal and socio-economic characteristics namely age, gender, education,

disability status, area of residence (rural/urban) and marital status are also contained in the

ESS5. These are used as the independent (exogenous) variables in the model. Table 3.1

includes a detailed description and summary statistics of the endogenous and exogenous

variables used in this study. Due to the qualitative nature of most of the variables it was

necessary to create dummy variables, where a value of 1 or 0 indicates the presence or

absence of an attribute.

<Insert Table 3.1 about here>

<Insert Figure 3.1 about here>

Table 3.1 displays the means and standard deviations for the dependent and

independent variables used in this paper. Life satisfaction, which is measured on a 0-10 scale,

has a mean value of 6.455. The modal score is 8 with over 21% of respondents indicating this

as their life satisfaction score (Figure 3.1). Approximately 7% of respondents are ‘extremely

satisfied with life’ as indicated by a maximum score of 10 and 1% are ‘extremely dissatisfied

with life’ indicated by the lowest score of 0. Of those in the employment status category, 945

respondents reported being in paid employment and 393 reported being unemployed (either

actively seeking work or not actively seeking work). In the sample, the distribution of

respondents by gender was equitable and their ages ranged from 15 to 101 years, with the

mean age being 46 years approximately.


Table 3.2 displays information pertaining to life satisfaction and employment status of

individuals in the sample. Almost 60% of unemployed respondents indicated a score of 5 or

lower on the 11-point life satisfaction scale. By contrast the proportion of employed

respondents with scores at the bottom of the life satisfaction scale was much lower. 27.7% of

the employed in the sample reported scores of 5 or lower on the life satisfaction scale. The

percentage of employed reporting a life satisfaction score of 7, 8, 9 or 10 is twice that of the

unemployed in each of these categories. Moreover, over 72% of those who have jobs reported

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their life satisfaction to be 6 or higher whereas, only 40% of unemployed individuals rated

their life satisfaction as 6 or above.

4. Econometric Model

This paper uses a two-stage probit least squares (2SPLS) estimation method described

in Maddala (1983) for simultaneous equation models in which one of the endogenous

variables is continuous and the other endogenous variable is dichotomous. The method

estimates life satisfaction and employment status simultaneously and accounts for any

endogeneity in the equations in the system4.

Employment status, among other covariates, is assumed to be a function of life

satisfaction. Endogeneity is an issue because life satisfaction is also a function of

employment status. This paper addresses the endogeneity issue and estimates the following

simultaneous structural equations for life satisfaction (LS) and employment status (ES);

11

'

1211 εβγ +Χ+= ∗ESLS (1)

22

'

2122 εβγ +Χ+=∗ LSES (2)

where:

LS1 = continuous endogenous variable Life Satisfaction5

∗2ES = dichotomous endogenous variable, Employment Status

which is observed as a 1 if ∗2ES > 0, and a 0 otherwise

X1 = matrix of exogenous variables in (1)


'

1β = vector of parameters in (1)

'


1γ = parameters of the endogenous variables in (1)

4 Using the cdsimeq command the statistical software package Stata ensures that all the necessary procedures for

obtaining consistent estimates for the coefficients, as well as their corrected standard errors are obtained. 5 Helliwell (2003) and Ferrer-i-Carbonell and Frijters, (2004) claim that it makes little difference if the life

satisfaction variable is treated as ordinal or cardinal.

9


1ε = error term of (1)

2ε = error term of (2)

The estimation follows the two-stage estimation process used in simultaneous equation

modelling as prescribed by Keshk (2003). In the first stage the following two equations (3

and 4) are fitted using all of the exogenous variables in equations (1) and (2) (Maddala,

1983),

1

'

11 ν+ΧΠ=LS (3)

2

'

22 ν+ΧΠ=∗∗ES (4)

where:

LS1 = life satisfaction

∗∗2ES = employment status

1Π , 2Π = vectors of parameters to be estimated

X = matrix of all the exogenous variables

v1, v2 = error terms

In estimating the first stage regressions equation (3) is estimated via OLS and equation (4) is

estimated via probit. A condition for simultaneous equation models is that there is at least one

independent variable that does not appear in the other equation (Cai and Kalb, 2006). This

paper includes a dummy variable for having a disability or health problem that hampers an

individual’s daily life6 in the life satisfaction equation and a dummy variable for marital

status in the employment status equation. The decision to use these variables is based on

existing literature (Hamilton et al., 1997; Duarte et al., 2007). From these reduced form

estimates the predicted values from each model are obtained for use in the second stage

(Maddala, 1983):

6 This is a physical rather than a psychological measure of the health status of the respondent.

10

111ˆ Χ∏=

∧

LS (5)

222ˆ Χ∏=

∗∗∧

ES (6)

where;

1

∧

LS = predicted value of Life Satisfaction

1∏̂ = vector of parameters

X1 = vector of exogenous variables used to estimate 1

∧

LS

∗∗∧

2ES = predicted value of Employment Status

1∏̂ = vector of parameters

X2 = vector of all exogenous variables used to estimate∗∗∧

2ES

In the second stage the original endogenous variables in equation (1) and equation (2) are

replaced by their respective predicted values in equations (5) and (6). In the second stage, the

following models are fitted:

111211 εβγ +Χ+=∗∗∧

ESLS (7)

222122 εβγ +Χ+=∧

∗∗ LSES (8)

where;

LS1 = life satisfaction

∗∗2ES = employment status



∗∗∧

2ES = predicted value of employment status in (7)

11

1

∧

LS = predicted value of life satisfaction in (8)





1ε = error term in (7)

2ε = error term in (8)

As before, equation (7) is estimated via OLS and equation (8) is estimated via probit.

The estimated standard errors for each model in the second stage (equations 7 and 8)

are based on ∗∗∧

2ES and 1

∧

LS and not on the appropriate ∗∗2ES and LS1 and so are incorrect

(Keshk, 2003). The correction needs to be implemented on the variance-covariance matrices

of equation (7) and (8) which are α1 and α2 respectively (Keshk, 2003). The asymptotic

covariance matrix can be derived by using a procedure similar to the one used by Amemiya

(1979). Given the estimable parameters in the model the following is defined according to

Maddala (1983):

),( '

121

'

1 βσγα = (9)

=

2

'

2

2

2'

2 ,σ

β

σ

γα

(10)

c = 121

2

1 2 σγσ − (11)

−

=

2

12

2

22

1

2

2 2σ

σ

σ

γσ

σ

γd

(12)

H = (П2, J1) (13)

G = (П1, J2) (14)

12

V0 = Var )ˆ( 2∏ (15)

In probit models 2σ is normalised to 1 and therefore the corrected variances of α1 and α2 are

obtained as follows:

V ( )1α̂ = c(H` X` XH)-1

+ (γ1 σ2)2(H` X` XH)

-1 H`X`V0X`XH(H`X`XH)

-1 (16)

V ( )2α̂ = (G` V0-1

G)-1

+ d(G` V0-1

G)-1

G` V0-1

(X`X)-1

V0-1

G(G` V0-1

G)-1 (17)

where;

2

1σ = variance of the residuals from (3)

V0 = variance-covariance matrix of (4)

J1 and J2 = matrices with ones and zeros such that XJ1 = X1 and

XJ2 = X2

According to Achen (1986) these corrected standard errors are superior to the uncorrected

standard errors. The procedure described above is carried out using the data analysis and

statistical software package Stata. The results from the equations (7) and (8) with corrected

standard errors, are discussed in Section 5.

5. RESULTS

The presence of simultaneous endogeneity in the model implies that in the life

satisfaction equation the coefficient on employment status would be significant and in the

employment equation the coefficient on life satisfaction would be significant (Cai and Kalb,

2006). To establish the correlation between life satisfaction and employment status the

equations are first estimated without controlling for endogeneity. This corresponds to

estimating equations (1) and (2) above by OLS and Probit respectively. The estimates

presented in the first column of Table 5.1 show that being employed increases life

satisfaction by 1.16 points on the life satisfaction scale7. Furthermore, the result is statistically

significant at the 1 per cent level in explaining life satisfaction. The second column shows

7 A full set of results for this estimation can be found in Table A1 in Appendix A.

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that life satisfaction is also found to be statistically significant in explaining employment

status. The positive coefficient indicates that higher levels of life satisfaction increase the

predicted probability of being employed.

These results suggest that the relationship between life satisfaction and employment

status is bi-directional. As previously indicated, however, these OLS and probit estimates are

biased and inconsistent as life satisfaction and employment status are jointly determined in

the presence of simultaneous endogeneity. To address this simultaneity the equations are now

estimated by Two-Stage Probit Least Squares which controls for the endogeneity of life

satisfaction and employment status and produces unbiased and consistent estimates.


5.1. Two Stage Probit Least Squares (2SPLS) Estimation Results

The 2SPLS estimation results for the life satisfaction equation and the employment

status equation are presented in Table 5.28. These results are estimated controlling for the

endogeneity of life satisfaction and employment status. The first column of Table 5.2

presents the estimated coefficients and their corrected standard errors for the life satisfaction

equation where life satisfaction (on a scale 0-10) is the dependent variable, while the second

column shows the estimations for employment status, where a binary variable for

employment status (0 unemployed, 1 employed) is the dependent variable.


The results from the life satisfaction equation will be discussed first. The first

independent variable relates to the individual’s employment status. Employment is associated

8The data analysis and statistical software package Stata 11 was used to conduct the econometric analysis.

14

with a 1.5 point increase in life satisfaction and the result is statistically significant at the 1

per cent level. This is consistent with much of the existing literature. For example, Flatau et

al., (2000) find that the employed enjoy greater mental health and well-being than the

unemployed or part-time employed in Australia. In a study similar to this one however,

Duarte et al., (2007) find a positive but insignificant coefficient on the employment variable

in the well-being equation and conclude that the direction of the relationship runs from well-

being to employment status only. Using a simultaneous equation generalized probit model

Hamilton et al., (1997) find that the higher values of the latent index of employability are

associated with better health. The result here indicates that, other things being equal, higher

life satisfaction increases the probability of being employed.

The results show that there is no statistically significant relationship between

education and life satisfaction. There is a positive relationship between life satisfaction and

being 24 years of age or younger. This result is significant at the 1 per cent level. Being aged

between 25 and 34 is also positively correlated with life satisfaction and statistically

significant at the 5 per cent level. Being in the youngest age category (age ≤24) increases an

individual’s life satisfaction by 1.1 points on the 11 point life satisfaction scale, holding other

factors constant. If an individual is aged between 25 and 34 this increases their life

satisfaction by 0.43 points. All of the other age variables are insignificant. Using a

simultaneous equation model similar to the one used in this paper, Duarte et al., (2007) also

report insignificant coefficients on their age variables in the well-being equation.

With respect to gender, males are more satisfied with life than females. This is in

contrast to the literature which generally finds women are happier than men (Alesina et al.,

2004). However, the result here is significant at the 1 per cent level and suggests that being

male increases life satisfaction by about half a point on the scale. Living in a rural area in

Ireland also emerges significant and positive in the regression. The coefficient indicates that

if an individual is living in a rural area their life satisfaction increases by 0.378 points on the

life satisfaction scale compared to those living in an urban area, ceteris paribus. Earlier

research has found a strong correlation between measures of objective health status and life

satisfaction. The results here suggest that having a disability decreases life satisfaction by

0.52 points on the scale and this result is significant at the 10 per cent level. This finding is

15

consistent with the Australian study by Cole et al., (2009) who find that having a health

problem or disability has a significant and negative impact on well-being. Furthermore,

Duarte et al., (1994) find that being physically limited reduces well-being considerably.

The second column of results in Table 5.2 presents the results for the estimated

employment status equation (8) above. The coefficient for life satisfaction emerges positive

and significant suggesting that, all other things being equal, higher levels of life satisfaction

increase the predicted probability of being employed. Hamilton et al., (1997), Duarte et al.,

(2007) and Cole et al., (2009) all find that higher levels of well-being (or some other self-

assessed well-being/health variable) lead to an increased probability of participation in the

labour force in Canada and Australia.

Other significant variables in the employment status equation include education, age,

gender and marital status. As one would expect higher levels of education (post-secondary

and tertiary education) increase the probability of being employed. Most of the age categories

are insignificant in the employment status equation which is consistent with Hamilton et al.,

(1997). Exceptions here are that being less than 24 years of age is negatively associated with

the probability of being employed. Males have a lower probability of being employed than

women and this is highly statistically significant at the 1 per cent level. Finally, marriage is

correlated with having a job and this coefficient is significant at the 10 per cent level.

To test the robustness of the results the models were again estimated using 2SPLS

using two alternative measures for well-being. Instead of life satisfaction, alternative

dependent variables ‘happiness’ and ‘wellbeing’ were used to estimate the model. Similar to

the ‘life satisfaction’ variable ‘happiness’ is also measured on a scale from 0 to 10; where 0

indicates extreme unhappiness and 10 indicates extreme happiness. ‘Wellbeing’ is a variable

comprised of the following three questions; ‘I have felt cheerful and in good spirits’ ‘I have

felt calm and relaxed’ ‘I have felt active and vigorous’. Respondents answered; ‘All of the

time’, ‘Most of the time’, ‘More than half of the time’, ‘Less than half of the time’, ‘Some of

the time’, ‘At no time’. Each respondent was given a score ranging from 3 (lowest well-

being) to 18 (highest well-being). The results suggest that a bi-directional relationship exists

16

between well-being and employment status regardless of the measure of well-being used. The

results from these regressions are presented in Tables B1 and B2 in Appendix B.

Based on the results the null hypothesis of no simultaneous relationship between life

satisfaction and employment status is rejected. This paper suggests that a simultaneous

relationship exists in the model, where, employment has a positive and significant effect on

life satisfaction and life satisfaction has a positive and significant effect on employment

status. The finding corroborates existing research by Cole et al., (2009) who find a

significant, simultaneous relationship between labour market status and well-being in

Australia. Similarly, Hamilton et al., (1997) use mental health as a proxy for well-being and

find a simultaneous relationship between employment and mental health in Canada. The

results contribute to the existing body of literature on well-being in Ireland by establishing

the life satisfaction-employment status relationship simultaneously.

6. SUMMARY AND CONCLUSION

This paper has estimated the determinants of life satisfaction and employment status

in Ireland using 2010 data from the ESS5. A simple OLS estimation revealed that there is

indeed a positive and statistically significant correlation between life satisfaction and

employment in Ireland. However, OLS does not account for simultaneous endogeneity

present in a model. The main focus of this paper was to control for this simultaneous

relationship between life satisfaction and employment status. The results find support for a

simultaneous or bi-directional relationship and indicate that employment leads to higher

levels of life satisfaction and higher levels of life satisfaction increase the predicted

probability of being in employment. The results from the 2SPLS model indicate that the

relationship is seriously underestimated if endogeneity is not controlled for. Two alternative

well-being variables were used to test the robustness of the results and it was found that the

bi-directional relationship holds regardless of the measure of well-being used.

Since they do not account for endogeneity many of the existing well-being studies

may underestimate the importance of the relationship between life satisfaction and

17

employment status. This paper expands the analysis on well-being in Ireland by using two-

stage methods to control for endogeneity. In agreement with previous literature employment

is found to affect life satisfaction, and vice versa, but after controlling for endogeneity the

result is found to be even more important. Other results find that men are more satisfied with

their lives than women and that people living in rural areas have higher levels of life

satisfaction than people living in big cities. Age is another factor found to affect life

satisfaction with individuals less than 34 years of age more satisfied with life than middle

aged (35-44) individuals. Moreover, having a disability or serious health problem has a

negative and statistically significant effect on life satisfaction.

With respect to employment this paper finds that apart from life satisfaction

education, gender, age and marital status all affect the probability of being employed. Not

surprisingly, higher levels of education have a positive and significant effect on the predicted

probability of being employed. Furthermore, younger people have a significantly lower

chance of being in employment. Women and those who are married are also found to have a

higher probability of being in employment.

In conclusion, the main results from this paper find support for a simultaneous

relationship between life satisfaction and employment status using 2010 Irish data from the

ESS5. After controlling for simultaneous endogeneity, using a Two Stage Probit Least

Squares estimation technique, this research finds that employment leads to higher levels of

life satisfaction and higher levels of life satisfaction increase the predicted probability of

being employed. Much of the current literature has failed to correctly identify and control for

this simultaneous relationship and therefore report biased estimates. This paper suggests that

relationship between life satisfaction and employment may be underestimated in the current

literature which has failed to control for simultaneous endogeneity. Therefore, the results

from this paper shed new light on the extent of the relationship between these variables and

provide new information on the determinants of life satisfaction and employment status

which may prove useful for the government and other policy makers in Ireland and

elsewhere.

18

Table 3.1: Descriptive Statistics

Variable Definition N Mean Std

Dev

Life

Satisfaction

Individual’s satisfaction with life on a scale between 0-10, where, 0

indicates extreme dissatisfaction with one’s life and 10 indicates

extreme satisfaction with one’s life.

2572 6.455 2.281

Employed =1 if the individual is employed and 0 if the individual is

unemployed

1338 0.706 0.456

Primary

Education

=1 if the highest level of education achieved is primary education, 0

otherwise

2576 0.168 0.374

Secondary*

Education

=1 if the highest level of education achieved is secondary education,

0 otherwise

2576 0.465 0.499

Post-Secondary

Education

=1 if the highest level of education achieved is post-secondary (non-

tertiary) education, 0 otherwise

2576 0.118 0.322

Tertiary

Education

=1 if the highest level of education achieved is third level education,

0 otherwise

2576 0.234 0.424

Age < 24 years =1 if the individual is 24 years or younger, 0 otherwise 2576 0.158 0.365

25 – 34 years =1 if the individual is between 25 and 34 years of age, 0 otherwise 2576 0.178 0.383

35 – 44 years* =1 if the individual is between 35 and 44 years of age, 0 otherwise 2576 0.196 0.397



Age >65 years =1 if the individual is 65 years or older, 0 otherwise 2576 0.189 0.391

Male =1 if the individual is male, 0 if female 2576 0.462 0.499

Rural = 1 if the individual lives in a country village or a farm or home in

the countryside, 0 otherwise

2575 0.415 0.493

Disability =1 if the individual is hampered in their daily activities by

illness/disability/infirmary/mental problem, 0 otherwise

2574 0.159 0.366

Married =1 if the individual is married, 0 otherwise 2576 0.423 0.494

(Source: European Social Survey 5, 2010. * Indicates base category)

Table 3.2: Life Satisfaction and Employment Status

Life

Satisfaction

Unemployed

%

Employed

%

0 1.79 0.53

1 2.04 0.85

2 8.16 2.44

3 15.31 6.15

4 15.31 6.26

5 16.84 11.45

6 9.44 8.7

7 11.73 23.44

8 12.24 23.86

9 3.32 10.18

10 3.83 6.15

(Source: European Social Survey 5, 2010. N = 1335)

19

Figure 3.1: The Distribution of Life Satisfaction in Ireland from 0

‘extremely dissatisfied to 10 ‘extremely satisfied’ ______________________________________________________

_______________________________________________________

(Source: European Social Survey 5, 2010)

Table 5.1: Regressions (without controlling for endogeneity)

Life Satisfaction

Equation (OLS)

Employment Status

Equation (Probit)

Employed 1.161***

(0.133)

-

Life Satisfaction -

0.145***

(0.018)

N 1333 1334 (Notes: * ** denotes significance at the 1% level. Coefficients reported with standard errors in parenthesis. The

Life Satisfaction equation was estimated using education, age, gender, rural/urban and disability status as

covariates. The Employment Status equation was estimated using education, age, gender and marital status as

covariates. The reference categories were secondary education, age 35-449. Source: European Social Survey 5,

2010.)

9 The estimated coefficients for the covariates are included in Table A1 in Appendix A.

05

10

15

20

Perc

enta

ge o

f R

espondents

0 2 4 6 8 10Life Satisfaction Score

Distribution of Life Satisfaction

20

Table 5.2: Two-Stage Probit Least Squares Results (controlling for endogeneity)

Life Satisfaction

Equation

Employment Status

Equation

Employed 1.500***

(0.366)

-

Life Satisfaction -

0.370***

(0.115)

Primary Educ -0.256

(0.307)

-0.075

(0.174)

Post-Secondary Educ -0.313

(0.267)

0.281**

(0.130)

Tertiary Educ 0.019

(0.300)

0.256*

(0.150)

Age ≤ 24 1.114***

(0.368)

-0.614***

(0.165)

Age 2534 0.430**

(0.208)

-0.093

(0.143)

Age 4554 -0.155

(0.226)

0.102

(0.122)

Age 5564 0.285

(0.282)

0.014

(0.161)

Age ≥ 65 0.221

(0.635)

0.379

(0.350)

Male 0.486***

(0.189)

-0.323***

(0.084)

Rural

0.378**

(0.174)

-0.071

(0.113)

Disability -0.520*

(0.309)

Married - 0.205*

(0.128)

Constant 4.757***

(0.274)

-1.627**

(0.587)

N 1333 N 1333

R2 0.151 LR χ

2 206.04

Adj R2 0.143 Prob > χ

2 0.000

Root MSE 2.100 Pseudo R2 0.128

(Notes: * denotes significance at the 10% level; ** at the 5% level;*** at the 1% level. Variable definitions are

given in Table 3.1. Reference categories: secondary education, age 35-44. Source: European Social Survey 5,

2010.)

21

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25

Appendix A

Table A1: Regressions (without controlling for endogeneity)

Life Satisfaction

Equation (OLS)

Employment Status

Equation (Probit)

Employed 1.161***

(0.133)

-

Life Satisfaction -

0.143***

(0.018)

Primary Educ -0.700***

(0.213)

-0.273*

(0.139)

Post-Secondary Educ 0.110

(0.175)

0.363***

(0.120)

Tertiary Educ 0.777***

(0.138)

0.484***

(0.099)

Age ≤ 24 0.391*

(0.219)

-0.530***

(0.146)

Age 2534 0.450***

(0.156)

0.068

(0.111)

Age 4554 -0.034

(0.167)

0.092

(0.116)

Age 5564 0.632***

(0.194)

0.150

(0.136)

Age ≥ 65 1.474***

(0.347)

0.764***

(0.289)

Male 0.126

(0.115)

-0.331***

(0.080)

Rural

0.600***

(0.120)

0.080

(0.084)

Disability -1.089***

(0.194)

Married - 0.396***

(0.089)

Constant 4.733***

(0.180)

-0.471***

(0.139)

N 1333 N 1334

R2 0.180 LR χ

2 259.08


2 0.000


(Notes: * denotes significance at the 10% level; ** at the 5% level;*** at the 1% level. Variable definitions are

given in Table 3.1. Reference categories: secondary education, age 35-44. Source: European Social Survey 5,

2010.)

26

Appendix B

Table B1: Two-Stage Probit Least Squares Results (controlling for endogeneity) –

Alternative Dependent Variable ‘Happy’

Happiness

Equation

Employment Status

Equation

Employed 1.570***

(0.367)

-

Happy -

0.349***

(0.110)

Primary Educ -0.491

(0.305)

-0.001*

(0.187)


(0.267)

0.213

(0.136)

Tertiary Educ 0.080

(0.300)

0.241

(0.154)

Age ≤ 24 1.036**

(0.368)

-0.572***

(0.157)

Age 2534 0.308

(0.208)

-0.039

(0.133)

Age 4554 -0.210

(0.225)

0.118

(0.120)

Age 5564 0.215

(0.282)

0.046

(0.155)

Age ≥ 65 -0.141

(0.637)

0.515

(0.327)

Male 0.498**

(0.188)

-0.318***

(0.083)

Rural

0.189

(0.174)

-0.005*

(0.099)

Disability -0.548*

(0.307)

Married - 0.208*

(0.127)

Constant 5.231***

(0.272)

-1.692**

(0.612)

N 1334 N 1334

R2 0.183 LR χ

2 206.23


2 0.000


(Notes: * denotes significance at the 10% level; ** at the 5% level;*** at the 1% level. Dependent variable

‘happy’. Reference categories: secondary education, age 35-44. Source: European Social Survey 5, 2010.)

27

Table B2: Two-Stage Probit Least Squares Results (controlling for endogeneity) -

Alternative Dependent Variable ‘Wellbeing’

Wellbeing

Equation

Employment Status

Equation

Employed 0.842**

(0.381)

-

Wellbeing -

0.240***

(0.073)

Primary Educ -0.764**

(0.327)

-0.125

(0.164)


(0.279)

0.397***

(0.124)

Tertiary Educ -0.172

(0.310)

0.511***

(0.105)

Age ≤ 24 1.145**

(0.396)

-0.657***

(0.165)

Age 2534 0.767***

(0.216)

-0.068

(0.136)

Age 4554 -0.058

(0.234)

0.090

(0.120)

Age 5564 0.279

(0.291)

0.145

(0.144)

Age ≥ 65 0.188

(0.631)

0.778**

(0.299)

Male 0.618**

(0.196)

-0.400***

(0.087)

Rural

0.122

(0.180)

0.092

(0.089)

Disability -1.453***

(0.328)

Married - 0.364***

(0.098)

Constant 12.557***

(0.290)

-2.762**

(0.916)

N 1333 N 1333

R2 0.083 LR χ

2 206.19


2 0.000


(Notes: * denotes significance at the 10% level; ** at the 5% level;*** at the 1% level. Dependent variable

‘wellbeing’. Reference categories: secondary education, age 35-44. Source: European Social Survey 5, 2010.)

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