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Unemployment and self-rated Unemploymentand self-rated
health in Ghana: are there gender health in
differences? Ghana
Iddisah Sulemana 1155
Department of Economics, University of Ghana, Accra, Ghana, and
Ebenezer Bugri Anarfo and Louis Doabil Received 5 April 2018Revised 20 June 2019
Business School, Ghana Institute of Management and Public Administration, Accepted 7 August 2019
Achimota, Ghana
Abstract
Purpose – A large extant literature examines the association between unemployment and self-rated health. Most
of these studies reveal that unemployment diminishes self-rated health. Another strand of this literature, albeit
sparse, suggests that the relationship between unemployment and self-rated health is gendered. The purpose of
this paper is twofold: first, to examine whether unemployment is correlated with self-rated health in Ghana; and
second, to explore whether and to what extent men differ from women on the basis of this relationship.
Design/methodology/approach – The authors used data from the Wave 6 of World Values Survey in
Ghana (n¼ 1552) and probit and instrumental variable probit regressions to empirically examine the
association between unemployment and self-rated health in Ghana.
Findings – The results confirm that unemployment is negatively correlated with self-rated health among
Ghanaians. Specifically, the unemployed are about 6.84–7.20 percent less likely to report good health status in
a pooled sample. Further, after correcting for endogeneity, unemployed men are about 26.68 percent less
likely to report good health. However, the association is not statistically significant for unemployed women.
Originality/value – The study contributes to the literature by providing empirical evidence from Ghana.
Keywords Ghana, Unemployment, Self-rated health, Gender differences, Self-reported health
Paper type Research paper
1. Introduction
Ghana has been bedeviled by unemployment for a long time (e.g. Sackey and Osei, 2006;
Overå, 2007), a phenomenon that is more pervasive in urban towns than in rural areas
(Sackey and Osei, 2006). Although data on unemployment statistics for the country are
scanty, estimates from the micro-level survey data suggest that the unemployment rate in
Ghana is high. For instance, the unemployment rate based on Afrobarometer Survey in 2008
was 28.8 and 27.4 percent in both 2012 and 2014. Unemployment has many deleterious
consequences. Among these, research in epidemiology has shown that unemployment is
strongly correlated with diverse adverse health outcomes (e.g. Forbes and McGregor, 1984;
Gerdtham and Johannesson, 2003). A longitudinal study of Swedish adults followed
between 10 and 17 years revealed that “Unemployment significantly increases the risk of
being dead at the end of follow-up by nearly 50%” (Gerdtham and Johannesson, 2003,
p. 505). In the USA, a unit increase in the unemployment rate is associated with a 6 percent
increase in the probability of dying among unemployed people (Halliday, 2014).
The majority of studies that have examined the relationship between unemployment
and health have focused on how unemployment influences the objective measures of
health such as mortality (e.g. Iversen et al., 1987; Moser et al., 1987; Martikainen, 1990;
Stefansson, 1991; Martikainen et al., 2007; Lundin et al., 2010; Garcy and Vågerö, 2012),
suicide (e.g. Smith, 1985a, b; Preti, 2003; Blakely et al., 2003; Voss et al., 2004; Andres, 2005; International Journal of SocialEconomics
Kuroki, 2010), alcoholism (e.g. Crawford et al., 1987; Montgomery et al., 1998) and Vol. 46 No. 9, 2019pp. 1155-1170
© Emerald Publishing Limited
0306-8293
JEL Classification — E24, I18, J16, J64 DOI 10.1108/IJSE-03-2018-0166
IJSE depression (e.g. Dooley et al., 1994; Stankunas et al., 2006). For instance, von der Lippe and
46,9 Rattay (2016) found that unemployment had the strongest negative effect on self-rated
health among Germans. Iversen et al. (1987) examined the effect of unemployment on
mortality in Denmark and found that after controlling for other covariates, mortality rates
were higher among the unemployed relative to their employed counterparts. Other studies
have corroborated the finding that unemployment increases the risk of mortality in
1156 Sweden (e.g. Stefansson, 1991; Lundin et al., 2010; Garcy and Vågerö, 2012), Finland
(e.g. Martikainen, 1990; Martikainen et al., 2007), UK (Moser et al., 1984, 1987; Blakely et al.,
2003) and the USA (e.g. McLeod et al., 2012; Kposowa, 2001; Halliday, 2014).
There are several explanations for the negative correlation between unemployment and
health. One explanation is that the relationship may be due to a selection bias (e.g. Schmitz,
2011). It may be that workers with poor health become unemployed, and evidence on this is
provided by several studies (e.g. Arrow, 1996; Riphahn, 1999). Conversely, being unwell
might lead to long-term or protracted unemployment (Stewart, 2001). Beyond income,
gainful employment affords people social relationships that define the meaning of life
( Junankar, 1991). Such social relationships may bestow on the individual higher stocks of
social capital. Because social capital significantly influences health (e.g. Kawachi et al., 1999;
Yip et al., 2007), the loss of social relationships emanating from unemployment could
diminish the individual’s health. Furthermore, both the unemployed and their immediate
family may suffer from unemployment (Smith, 1985a, b) because unemployment may cause
stress and anxiety for them ( Junankar, 1991). Unemployment may also lead to depression,
social exclusion and increased risk of suicide (e.g. Kieselbach, 2003; Milner et al., 2013).
While studies on the effect of unemployment on health are replete in the literature, nearly
all of them have been done within developed country contexts. In particular, studies on this
topic in Africa are lacking. The objectives of this paper are twofold. First, we examine whether
there is an association between unemployment and self-reported health in Ghana[1]. Second,
we explore whether there are gender differences in the association between unemployment
and self-rated health. To empirically address these issues, we utilize survey data from the
Wave 6 of the World Values Surveys. The results from probit and instrument variable (IV)
probit regressions confirm that unemployment is negatively correlated with self-rated health
among Ghanaians. Specifically, the unemployed are about 6.84–7.20 percent less likely to
report good health status in a pooled sample. Further, after correcting for endogeneity,
unemployed men are about 26.68 percent less likely to report good health. However, the
association is not statistically significant for unemployed women.
2. Background literature
2.1 Unemployment and self-rated health
As a result of the difficulty of observing the actual health condition of people in developing
countries, scholars embrace self-reports of health status because they are generally easier, less
costly and less time-consuming to generate (Freidoony et al., 2015). From a labor economics
perspective, unemployment typically refers to physically and psychologically fit individuals of
legal working age who are actively seeking work but do not have a paid job within a reference
period (International Labour Organization, 1990). There is ample evidence from diverse fields
such as life sciences (e.g. medicine) and social sciences (e.g. psychology) that unemployment
has adverse effects that go beyond those associated with income losses (Frey, 2008;
McKee-Ryan et al., 2005). Unemployment negatively impacts physical health including
cardiovascular diseases (Griep et al., 2015; McKee-Ryan et al., 2005), psychosomatic disorders
(Paul and Moser, 2006; Griep et al., 2015) and subjective physical health (Griep et al., 2015;
McKee-Ryan et al., 2005). Unemployment is also associated with measures of psychological
ill-being such as depression, anxiety, reduced hope and increased dissatisfaction with life
(Frese and Mohr, 1987; Paul and Moser, 2006; McKee-Ryan et al., 2005; Mohr and Otto, 2011).
Prior studies have shown that unemployment is a salient predictor of self-rated health Unemployment
(e.g. Forbes and McGregor, 1984; Gerdtham and Johannesson, 2003; Giatti et al., 2010; Blakely and self-rated
et al., 2003; Voss et al., 2004; Andres, 2005; Kuroki, 2010). In a study to examine whether health in
unemployment and socio-economic and neighborhood characteristics of people are correlated
with self-rated health in Brazilian cites, Giatti et al. (2010) found a strong association between Ghana
unemployment and poor self-rated health, irrespective of the personal or contextual
characteristics of the individual. Similarly, Tøge and Blekesaune (2015) used longitudinal data 1157
for 28 European countries from 2008 to 2011 to examine the effect of unemployment –
purportedly exacerbated by the Great Recession – on health. They observed that declines in
health status across the respondents were partly attributable to unemployment as people
tended to report poorer health status as they became unemployed.
Marcus (2013) employed the German Socio-Economic Panel Study to analyze the effect of
unemployment on mental health of Germans and found that in addition to the unemployed
being directly affected, their spouses were also as much strongly affected by the
unemployment as the unemployed with respect to mental health deterioration. This led the
author to conclude that previous empirical studies that examined the nexus between
unemployment and health had underestimated the public health costs of unemployment as
they usually failed to account for the potential damages experienced by spouses of
unemployed people. Other studies have shown that the effect of unemployment is even more
detrimental during periods of high and prolonged unemployment (e.g. Kieselbach, 2003;
Kostrzewski and Worach-Kardas, 2014; Drydakis, 2015). Long-term unemployment may even
lead to social exclusion, especially among the youth (Kieselbach, 2003), and increased risk of
suicide (Milner et al., 2013). Nonetheless, a limited number of empirical studies have found no
significant association between unemployment and health (e.g. Merriam, 1987; Schmitz, 2011).
2.2 Genderedness of the relationship between unemployment and health
As noted above, the overwhelming majority of studies on the health consequences of
unemployment have revealed that unemployment is associated with deteriorating health
status. A pertinent interest that naturally arises therefore is whether there are gender
dimensions of the association between unemployment and health. In this regard, some scholars
have examined the relationship through gender lenses (e.g. Artazcoz et al., 2004; Gordo, 2006;
Drydakis, 2015; Reine et al., 2013). The results are mixed. On the one hand, some studies have
argued that the effect of unemployment on health for men exceeds the effect for women
(e.g. Artazcoz et al., 2004; Drydakis, 2015; Reine et al., 2013). For instance, Drydakis (2015)
reported that in Greece, the effect of unemployment on health or mental health was worse for
women than for men during the global financial crisis. Reine et al. (2013) have discovered that
although unemployment leads to high alcohol consumption, the effect of unemployment on
health is greater for women in Northern Sweden. In addition, life expectancy tends to be higher
among females than for males, yet women tend to report a relatively poorer health status than
their male counterparts ( Jylhä et al., 1998; Hanibuchi et al., 2012; Phaswana-Mafuya et al., 2013).
A plausible explanation for this observation is that males tend to suffer from life-threatening
diseases relatively much more than women, while women are usually beset with relatively
more nonfatal chronic conditions ( Jylhä et al., 1998).
On the other hand, very few studies have advanced the contrary argument, which is that
women are more affected by unemployment than by men (Gordo, 2006; Kaleta et al., 2008;
Bambra and Eikemo, 2008). In their study of the association between unemployment and
risk of morbidity and mortality in 23 European countries and focusing on the differences in
state social protection programs, Bambra and Eikemo (2008) found a relatively stronger
effect of unemployment for men than for women. Their explanations are that first, women
tend to receive lower social insurance unemployment benefits because they tend to have a
checkered employment history. Second, it may be that the selection effect may be stronger
IJSE for women than for men because unhealthy women may be more likely to become
46,9 unemployed than their male counterparts (Bambra and Eikemo, 2008, p. 97). Kaleta et al.
(2008) also found that economic circumstances are associated with health for only men in
that men with lower levels of income are at a higher risk of reporting poor health.
The evidence on the genderedness of the association between unemployment and
health may also take a temporal dimension. For instance, the results from a German study
1158 have shown that while unemployment does influence health status among men in both the
short and long run, it only affects women’s health in the long run (Gordo, 2006). However,
other scholars do not find any gender differences with respect to this relationship
(e.g. Hultman et al., 2006; Hammarström et al., 2011). For instance, Hammarström et al.
(2011) found that although unemployment was associated with various health outcomes
(e.g. smoking and alcohol consumption) for the pooled sample, no significant gender
differences were found for self-rated health in Northern Sweden, leading them to conclude
that “Men are not hit more by the health consequences of unemployment in a Swedish
context with a high participation rate for women in the labor market” (p. 192). Thus, the
extant literature remains inconclusive on whether or not there are gender differences in
the association between unemployment and health. Our study contributes to this literature
by providing empirical evidence from Ghana.
3. Data and methods
We used data from the Wave 6 of the World Values Survey conducted in Ghana in 2012
for our empirical analyses. The World Values Survey is a global research network that
conducts surveys in many countries around the world to gauge public attitudes and
perceptions about varied social, economic and political phenomena (World Values Survey
Association, 2016). In Ghana, a nationally representative sample of 1,552 respondents was
interviewed on a face-to-face basis in eight of ten regions by using random sampling but
stratified by gender[2]. Some researchers contend that self-rated health, albeit a subjective
assessment of one’s own health status (e.g. Darviri et al., 2012; Damián et al., 2008;
Sun et al., 2007), can be construed as a comprehensive perception of health, encompassing
the biological, psychological and social aspects of one’s health (Guimarães et al., 2012) that
may be subjected to the physical, psychological and environmental factors affecting
them (Damián et al., 2008). Extensively used as a reliable indicator of health in public
health research, self-rated health is often used to predict actual future health, disability,
functional ability and mortality (Arnadottir et al., 2011; Alves and Rodrigues, 2005),
especially among elderly people (Sun et al., 2007; Damián et al., 2008; Darviri et al., 2012).
Consistent with the standard operationalization of self-rated health in the literature
where a single question is often used (Mansyur et al., 2008; Flavin et al., 2011; Gandelman
and Hernández-Murillo, 2013; Olafsdottir et al., 2014), we operationalized our dependent
variable (good health¼ 1 if the respondent indicated “good” or “very good” and 0 otherwise)
based on responses to the World Values Survey question:
All in all, how would you describe your state of health these days?
The responses were 1¼ very good, 2¼ good, 3¼ fair and 4¼ poor. To the extent that
perceptions about one’s health are closely correlated with their clinical outcomes (Idler and
Benyamini, 1997) and strongly connected to mortality and morbidity among others
(Sun et al. 2007; Phaswana-Mafuya et al., 2013; Molarius and Janson 2002), one could argue
that self-rated health is a useful and reliable measure of actual health (Franks et al., 2003;
Hennessy et al., 1994). The respondents were also asked about their employment status.
We coded “Unemployed” as equal to 1 if the respondent reported being unemployed at the
time of the survey, and 0 otherwise. We control for the sociodemographic characteristics and
other variables identified as covariates of self-rated health in the empirical literature.
In addition, Ghana has ten administrative regions that differ with respect to culture, Unemployment
institutions and economic circumstances[3]. Hence, we control for region-level and self-rated
heterogeneities by including region-level fixed effects. health in
We note that unemployed people are more likely than their employed counterparts to
report poor health. However, unhealthy people are more likely to be unemployed. Therefore, Ghana
a potential endogeneity problem arises. To address this problem, we implement IV probit
estimations. Let HS be the individual’s health status. The formal model for the association 1159
between health and unemployment is given by the following equations:
HSi ¼ UibþXigþei; (1)
Ui ¼ XiaþZ iyþA i; (2)
where U is unemployment, X is a vector of exogenous control variables and Z is an
instrumental variable for unemployment. β, γ, α, and θ are the parameters to be estimated, ε
and ϵ are the idiosyncratic error terms, and i indexes the individual.
A suitable instrument for unemployment should be correlated with unemployment but
uncorrelated with the error term in Equation (1). We used two of such instruments: sector of
employment and whether the town of residence of the respondent is a city. The public sector
in Ghana provides more job stability and security than the private sector. Hence,
employment in the private sector is more associated with the probability of a layoff. This is
consistent with Farré et al. (2018) who used employment in the construction sector as an
instrument for unemployment in Spain where the collapse of the sector resulted in a lot of
job losses. In addition, Kuhn et al. (2009) used plant closure as an instrument for
unemployment. Prior studies have also shown that unemployment is more pervasive in
urban towns in Ghana than in the rural areas (Otoo et al., 2009; Sackey and Osei, 2006).
Hence, we also used “cities” to instrument for unemployment[4].
4. Results and discussion
Table I reports the summary statistics along with the descriptions of the variables used in
the study. About 88.72 percent of the respondents reported being in good health. About
14.95 percent of the respondents reported being unemployed[5]. The respondents’ ages
ranged from 18 to 82 with an average age of 31 years. About 49.74 percent of them were
female. Regarding educational attainments, about 45.43 percent of the respondents had
attained a maximum of primary school education, 43.11 percent had attained secondary
school education, while only 11.47 percent had attained a university education. The majority
of them were Protestants compared with 13.66 and 12.18 percent Roman Catholics and
Muslims, respectively. Nearly 60 percent of them also reported being Akan, 13.40 percent
were Ewe and 9.79 percent were Ga-Adangbe. In addition, 44.78 percent of the respondents
reported being married, while only 3.87 percent of them were divorced or separated. The
majority of the respondents (51.35 percent) were either single or widowed. The average
respondent had less than two children.
The distribution of the responses to the health question by gender is reported in Figure 1.
About 2.10 percent of men and 2.60 percent of women reported being in poor health status,
while 6.90 percent of men and 10.90 percent of women reported their health status as fair.
Regarding good and very good health status, 35.60 percent and 55.30 percent of men
reported their health status as good and very good, respectively, with the corresponding
figures for their female counterparts being 36.70 percent and 49.90 percent. The observation
from these reports is that women reported relatively worse health status than men.
Altogether, only about 11.30 percent of the respondents reported being in either poor health
IJSE or fair health compared with 88.72 percent who indicated being in good or very good health
46,9 as reported above.
Table II reports the probit and IV probit regression results when good health is regressed on
unemployment status while controlling for other variables. We started by running probit
regression with andwithout the region-level fixed effects for the pooled sample (Models 1 and 2).
We then corrected for endogeneity and reran the models using IV probit (Models 3 and 4).
1160 Our results revealed that unemployment is negatively and significantly correlated with good
health at the 1 percent level for Models 1 and 2 and at the 10 percent level for Models 3 and 4.
Variable Description Mean SD Range
Good health ¼ 1 if the respondent indicated good or very good to the 0.8872 0.3164 0–1
question: “All in all, how would you describe your state of
health these days?”, 0 otherwise
Unemployed ¼ 1 if the respondent was unemployed, 0 otherwise 0.1495 0.3567 0–1
Age Age of the respondent 30.9246 12.7027 18–82
Female ¼ 1 if female, 0 otherwise 0.4974 0.5002 0–1
Income scale Income decline within which the respondent believes that 4.8524 2.0605 1–10
their household income belongs to Ghana
Generalized trust 0.0496 0.2172 0–1
Education
Primary or less ¼ 1 if the respondent’s highest educational attainment was 0.4543 0.4981 0–1
primary school education, or had no formal education,
0 otherwise
Secondary ¼ 1 if the respondent’s highest educational attainment was 0.4311 0.4954 0–1
secondary school education, 0 otherwise
University ¼ 1 if the respondent’s highest educational attainment was 0.1147 0.3188 0–1
university education (including diploma, bachelor’s degree,
or more), 0 otherwise
Roman Catholic ¼ 1 if the respondent’s religious denomination was Roman 0.1366 0.3435 0–1
Catholic, 0 otherwise
Protestant ¼ 1 if the respondent’s religious denomination was 0.5631 0.4962 0–1
Protestant (e.g. Anglican, Methodist, Presbyterian, etc.),
0 otherwise
Muslim ¼ 1 if the respondent’s religious denomination was Muslim, 0.1218 0.3271 0–1
0 otherwise
Other religious ¼ 1 if the respondent’s religious denomination was other 0.1785 0.3830 0–1
denomination denomination other than Roman Catholic, Protestant, or
Muslim, 0 otherwise
Akan ¼ 1 if the respondent indicated their ethnic group as Akan, 0.5954 0.4910 0–1
0 otherwise
Ewe ¼ 1 if the respondent indicated their ethnic group as Ewe, 0.1340 0.3408 0–1
0 otherwise
Ga-Adangbe ¼ 1 if the respondent indicated their ethnic group as 0.0979 0.2973 0–1
Ga-Adangbe, 0 otherwise
Other ethnic group ¼ 1 if the respondent indicated other ethnic group other 0.1720 0.3775 0–1
than Akan, Ewe, or Ga-Adangbe, 0 otherwise
Married ¼ 1 if the respondent was married or living together as a 0.4478 0.4974 0–1
couple, 0 otherwise
Divorced/Separated ¼ 1 if the respondent was divorced or separated, 0 otherwise 0.0387 0.1928 0–1
Other marital status ¼ 1 if the respondent was single or widowed, 0 otherwise 0.5135 0.5000 0–1
Children Respondent’s number of children 1.5296 2.0056 0–8a
Table I. City ¼ 1 if the town has at least 50,000 people, 0 otherwise 0.0238 0.1526 0–1
Variable descriptions Private sector ¼ 1 if the respondent was employed or previously employed 0.6347 0.4817 0–1
and summary in the private sector, 0 otherwise
statistics Note: a8¼ 8 or more children
60.0 Unemployment
and self-rated
health in
50.0 Ghana
40.0
Men 1161
30.0 Women
Pooled
20.0
10.0 Figure 1.
Self-rated health
among men and
0.0 women in Ghana
Poor Fair Good Very good
Thus, people who were unemployed were significantly less likely to report good health
status. Specifically, for the pooled sample, the unemployed were about 6.84 percent less likely to
report good health status (Table AI). When we controlled for regional heterogeneities
(e.g. culture, geography, etc.) by including the region-level fixed effects, this association
increased to about 7.2 percent. Thus, our results confirm the findings of a large extant empirical
literature that has shown that unemployment hurts human health (e.g. Forbes and McGregor,
1984; Gerdtham and Johannesson, 2003; Giatti et al., 2010; Blakely et al., 2003; Voss et al., 2004;
Andres, 2005; Kuroki, 2010; Artazcoz et al., 2004; Gordo, 2006; Kaleta et al., 2008; Bambra and
Eikemo, 2008; Drydakis, 2015; Reine et al., 2013). The F-statistics in the first stage regressions
for Models 3 and 4 were 15.49 and 11.74, respectively, which were both higher than 10 (Stock
and Yogo, 2004). In addition, the weak instrument-robust test by Finlay and Magnusson (2009)
was rejected at the 10 percent level. Hence, we conclude that the instruments are valid.
A plausible and rather obvious explanation is lost income from being unemployed ( Jin et al.,
1995; Ehlert, 2012). In developed countries, social unemployment insurance attenuates the
effect of unemployment on health ( Jin et al., 1995). In Ghana, there is no scheme for
unemployment benefits. Hence, being unemployed really leads to significant income losses.
Lost income also means that the unemployed may utilize health care less frequently, which
could then adversely affect their health (Kraut et al., 2002; Åhs and Westerling, 2006).
In addition to income losses and increased indebtedness (Klasen and Woolard, 2009), perhaps,
unemployment bereaves the unemployed of their social relationships that may be very
important in their life ( Junankar, 1991). By reducing their social capital and therefore leading to
social exclusion (e.g. Kieselbach, 2003; Milner et al., 2013), it could be that unemployment
negatively influences the health status of unemployed people (e.g. Kawachi et al., 1999;
Yip et al., 2007). Moreover, unemployment may lead to stress, anxiety and depression for the
unemployed (Smith, 1985a, b; Junankar, 1991; Kieselbach, 2003; Milner et al., 2013).
If unemployment is negatively correlated with good health in Ghana, are there gender
differences in this relationship? To answer this question, we split the sample into female and
male subsamples. We then reran separate probit and IV probit regressions for them. The
results are reported in Models 5–8. We found that being unemployed is significantly and
negatively correlated with reporting being in good health for only men. As shown in Models 5
and 7, the coefficient of unemployment for the female subsample was statistically
insignificant. It was, however, statistically significant at the 1 and 5 percent levels for males in
Percent of respondents
IJSE
46,9
1162
Table II.
Regression results for
the association
between
unemployment and
self-rated health in
Ghana
Probit IV probit Probit IV probit
Pooled sample Pooled sample Female Male Female Male
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Unemployed −0.3857*** (0.1118) −0.4123*** (0.1138) −0.6981* (0.3949) −0.6730* (0.3993) −0.1275 (0.1503) −0.9438*** (0.1888) −0.2037 (0.4153) −1.8985** (0.9367)
Age −0.0226*** (0.0044) −0.0236*** (0.0045) −0.0232*** (0.0045) −0.0241*** (0.0045) −0.0251*** (0.0061) −0.0159** (0.0073) −0.0254*** (0.0063) −0.0149** (0.0075)
Gender (ref.¼male)
Female −0.1838** (0.0917) −0.1884** (0.0929) −0.1630* (0.0953) −0.1718* (0.0962)
Income scale 0.0592*** (0.0225) 0.0741*** (0.0230) 0.0526** (0.0239) 0.0686*** (0.0245) 0.0944*** (0.0329) 0.0667 (0.0348) 0.0920*** (0.0350) 0.0557 (0.0369)
Generalized trust 0.0194 (0.2013) 0.0444 (0.2067) 0.0153 (0.2013) 0.0393 (0.2066) 0.2799 (0.2959) −0.2842 (0.3107) 0.2781 (0.2960) −0.2970 (0.3129)
Education (ref.¼ primary or less)
Secondary 0.1065 (0.1009) 0.1210 (0.1019) 0.0933 (0.1024) 0.1099 (0.1033) 0.1175 (0.1422) 0.1264 (0.1563) 0.1142 (0.1432) 0.0982 (0.1606)
University 0.0531 (0.1678) 0.0116 (0.1709) 0.0252 (0.1715) −0.0151 (0.1755) 0.1813 (0.2888) −0.1264 (0.2255) 0.1694 (0.2951) −0.1989 (0.2388)
Religious denomination (ref.¼ other)
Roman Catholic −0.1049 (0.1582) −0.1557 (0.1619) −0.0860 (0.1603) −0.1400 (0.1638) −0.1846 (0.2170) −0.1521 (0.2640) −0.1806 (0.2180) −0.0800 (0.2763)
Protestant −0.0066 (0.1235) −0.0382 (0.1289) −0.0042 (0.1238) −0.0341 (0.1292) 0.0710 (0.1702) −0.1609 (0.2131) 0.0710 (0.1703) −0.1410 (0.2172)
Muslim 0.0059 (0.1933) 0.0361 (0.2099) 0.0342 (0.1970) 0.0501 (0.2114) 0.3041 (0.3057) −0.2185 (0.3075) 0.3112 (0.3082) −0.2192 (0.3127)
Ethnicity (ref.¼ other)
Akan 0.3177** (0.1479) 0.2823* (0.1670) 0.3231** (0.1486) 0.2944* (0.1682) 0.2526 (0.2233) 0.2524 (0.2576) 0.2603 (0.2266) 0.2396 (0.2623)
Ewe 0.2225 (0.1815) 0.0471 (0.2306) 0.2273 (0.1823) 0.0574 (0.2315) −0.0863 (0.3056) 0.1812 (0.3790) −0.0793 (0.3077) 0.1516 (0.3857)
Ga-Adangbe 0.2682 (0.1908) 0.1537 (0.2208) 0.2748 (0.1917) 0.1632 (0.2216) 0.1207 (0.3075) −0.0455 (0.3288) 0.1324 (0.3133) −0.1296 (0.3441)
Marital status (ref.¼ single/widowed)
Married 0.0956 (0.1065) 0.0987 (0.1078) 0.1031 (0.1072) 0.1038 (0.1083) 0.1445 (0.1400) −0.0511 (0.1929) 0.1481 (0.1413) −0.0784 (0.1974)
Divorced/Separated −0.1547 (0.2109) −0.1611 (0.2108) −0.1447 (0.2122) −0.1504 (0.2121) −0.0439 (0.2718) −0.5782 (0.3622) −0.0392 (0.2732) −0.5971 (0.3689)
Number of children 0.0257 (0.0313) 0.0318 (0.0316) 0.0234 (0.0315) 0.0303 (0.0318) 0.0406 (0.0427) 0.0059 (0.0500) 0.0411 (0.0428) −0.0111 (0.0536)
Intercept 1.4964*** (0.2380) 1.6025*** (0.2768) 1.5784*** (0.2589) 1.6712*** (0.2952) 1.1945** (0.3631) 1.7948*** 0.4199) 1.2217*** (0.3888) 1.9934*** (0.4679)
Region’s fixed effects No Yes No Yes Yes Yes Yes Yes
Pseudo R2 0.0736 0.0893 0.0885 0.1379
LR χ2/Wald χ2 80.52*** 97.65*** 69.84*** 83.06*** 54.03*** 65.59*** 48.99*** 41.05***
Log likelihood −506.42066 −497.8529 −278.1197 −205.0322
n 1,552 1,552 1,552 1,552 772 780 772 780
Notes: Standard errors are in parentheses. *po0.10; **po0.05; ***po0.01
Models 6 and 8, respectively. As shown in Model 6a (Table AI), unemployed men were Unemployment
13.38 percent less likely to report being in good health. This association increases to and self-rated
26.68 percent when endogeneity is corrected for (Model 8a). Thus, while our results revealed a health in
strong negative association between unemployment and self-rated health among Ghanaian
men, we found no evidence for a negative association for Ghanaian women. Ghana
One possible explanation for the null relationship between unemployment and self-rated
health for women may due to compensatory factors (e.g. Stefansson, 1991). In addition, 1163
Ghana is a patriarchal society where men are preponderantly responsible for the provision
of food, clothing and shelter for their families. Their inability to provide these due to
unemployment and its concomitant income decline may result in “bitterness, shame,
emptiness, waste” and a feeling of worthlessness or loss of dignity (Smith, 1985a, p. 1024).
Consistent with this argument, Artazcoz et al. (2004) concluded that gender differences in
the relationship between unemployment and health are due to family responsibilities and
social class. Thus, it could be that men’s inability to provide for their family when
unemployed negatively affects their health. Another explanation may be that men’s values
tend to be more tied to their work, while women tend to prioritize their family and other
aspects of their life (Virtanen et al., 2003). Unemployment also leads to alcoholism among
Ghanaian men (Akyeampong, 1995), which has deleterious consequences for their health.
While the debate on whether unemployment diminishes health more for men than for
women remains unsettled, as Norström et al. (2014) noted, a larger number of the studies
exploring this issue seem to conclude that the relationship is stronger for men than for
women (e.g. Gordo, 2006; Kaleta et al., 2008; Bambra and Eikemo, 2008). Thus, our findings
are consistent with the results of a previous and growing strand of the literature
(e.g. Norström et al., 2014; Artazcoz et al., 2004; Drydakis, 2015; Reine et al., 2013).
Among the control variables, age, gender, income and ethnicity were significantly
correlated with self-rated health. A year increase in age was negatively correlated with the
likelihood of reporting good health by between 0.21 and 0.50 percent. This is consistent with
the results of previous studies and is attributable to the idea that older people are usually at
a higher risk of morbidity and mortality (e.g. Sung et al., 2006; Christensen et al., 2009).
Consistent with Figure 1, our results also show that women were about 3.3 percent less
likely than men to report being in good health. Among the ethnic group categories, Akans
were between 4.85 and 5.64 percent less likely to report being in good health relative to
people in the “other ethnic group” category, although there were no significant differences
between this latter group on the one hand and Ga-Adangbe and Ewes on the other hand
(see e.g. Addai and Adjei, 2014).
5. Summary and conclusion
A large extant literature examines the association between unemployment and self-rated
health. The majority of these studies reveal that unemployment diminishes self-rated health.
Another strand of this literature suggests that the relationship between unemployment and
self-rated health is gendered. In this study, we examined two main issues. First, we
examined whether unemployment is associated with self-rated health in Ghana. We found
that unemployment is negatively correlated with self-rated health among Ghanaians as the
unemployed are about 6.84–7.20 percent less likely to report good health status in a pooled
sample. Second, we examined the genderdness of this relationship. After correcting for
endogeneity, we found that unemployed men are about 26.68 percent less likely to report
good health. However, we did not find evidence of a significant association between
unemployment and self-rated health for unemployed women.
Our study contributes to a large empirical literature on the relationship between
unemployment and health. While there are numerous studies on this topic for developed
countries, studies on it are lacking for Africa. Therefore, we present empirical evidence on
IJSE the topic from a Ghanaian perspective. A policy implication is that since unemployment
46,9 hurts health in Ghana, policymakers ought to recognize that the non-pecuniary cost of
unemployment to both the individual and society may be higher than that which is usually
assumed (e.g. Schmitz, 2011). Therefore, every effort should be made to provide employment
opportunities to as many citizens who are willing and able to work as possible. This would
support the achievement of Goal 3 of the Sustainable Development Goals which seeks to
1164 “ensure healthy lives and promoting well-being for all at all ages” (United Nations
Environment Programme, 2015). Nevertheless, our results should be interpreted with
caution. The reason is that we have not established causation between unemployment and
self-rated health. Rather, our results have only shown conditional correlations.
Notes
1. Self-rated health, self-reported health and self-appraised health are used interchangeably in the
literature.
2. Six new regions have been created. However, our data set was based on eight of the ten regions at
the time of the surveys.
3. The surveys were not conducted in the Upper West and Upper East Regions. In addition, six new
regions have recently been created by the government.
4. Within the Ghanaian context, towns with at least 50,000 people are considered cities.
5. By gender, 10.7 percent of men were unemployed compared with 20.8 percent for women.
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Corresponding author
Iddisah Sulemana can be contacted at: idsulemana@ug.edu.gh
IJSE Appendix
46,9
1170
Table AI.
Marginal effects for
the association
between
unemployment and
self-rated health in
Ghana
Probit IV probit Probit IV probit
Pooled sample Pooled sample Female Male Female Male
Model 1a Model 2a Model 3a Model 4a Model 5a Model 6a Model 7a Model 8a
Unemployed −0.0684*** (0.0198) −0.0720*** (0.0198) −0.0620 (0.0695) −0.0667 (0.0695) −0.0252 (0.0297) −0.1338*** (0.0267) −0.2035 (0.4140) −0.2668** (0.1187)
Age −0.0040*** (0.0008) −0.0041*** (0.0008) −0.0040*** (0.0008) −0.0041*** (0.0008) −0.0050*** (0.0012) −0.0023* (0.0010) −0.0254*** (0.0063) −0.0021** (0.0011)
Gender (ref.¼male)
Female −0.0326** (0.0163) −0.0329** (0.0162) −0.0320* (0.0168) −0.0325* (0.0167)
Income scale 0.0105*** (0.0040) 0.0129*** (0.0040) 0.0106** (0.0042) 0.0129*** (0.0042) 0.0187*** (0.0065) 0.0095 (0.0049) 0.0920** (0.0352) 0.0078 (0.0053)
Generalized 0.0034 (0.0357) 0.0077 (0.0361) 0.0018 (0.0356) 0.0063 (0.0360) 0.0554 (0.0585) −0.0403 (0.0441) 0.2780 (0.2958) −0.0417 (0.439)
trust
Education (ref.¼ primary or less)
Secondary 0.0189 (0.0179) 0.0211 (0.0178) 0.0218 (0.0181) 0.0236 (0.0180) 0.0232 (0.0281) 0.0179 (0.0222) 0.1141 (0.1432) 0.0138 (0.0227)
University 0.0094 (0.0298) 0.0020 (0.0298) 0.0177 (0.0303) 0.0089 (0.0306) 0.0359 (0.0571) −0.0179 (0.0320) 0.1693 (0.2950) −0.0279 (0.0331)
Religious denomination (ref.¼ other)
Roman −0.0186 (0.0281) −0.0272 (0.0283) −0.0177 (0.0283) −0.0264 (0.0285) −0.0365 (0.0429) −0.0216 (0.0374) −0.1806 (0.2180) −0.0113 (0.0388)
Catholic
Protestant −0.0012 (0.0219) −0.0067 (0.0225) −0.0014 (0.0219) −0.0070 (0.0225) 0.0141 (0.0337) −0.0228 (0.0302) 0.0709 (0.1702) −0.0198 (0.0305)
Muslim 0.0010 (0.0343) 0.0063 (0.0367) 0.0028 (0.0349) 0.0067 (0.0369) 0.0602 (0.0605) −0.0310 (0.0436) 0.3110 (0.3077) −0.0308 (0.0438)
Ethnicity (ref.¼ other)
Akan 0.0564** (0.0262) 0.0493* (0.0291) 0.0538** (0.0263) 0.0485* (0.0293) 0.0500 (0.0441) 0.0358 (0.0365) 0.2602 (0.2263) 0.0337 (0.0369)
Ewe 0.0395 (0.0322) 0.0082 (0.0403) 0.0381 (0.0322) 0.0070 (0.0404) −0.0171 (0.0605) 0.0257 (0.0537) −0.0793 (0.3075) 0.0213 (0.0542)
Ga-Adangbe 0.0476 (0.0339) 0.0268 (0.0386) 0.0459 (0.0339) 0.0264 (0.0387) 0.0239 (0.0608) −0.0064 (0.0466) 0.1323 (0.3129) −0.0182 (0.0481)
Marital status (ref.¼ single/widowed)
Married 0.0170 (0.0189) 0.0172 (0.0188) 0.0150 (0.0190) 0.0156 (0.0189) 0.0286 (0.0277) −0.0072 (0.0274) 0.1480 (0.1411) −0.0110 (0.0277)
Divorced/ −0.0274 (0.0374) −0.0281 (0.0368) −0.0287 (0.0375) −0.0292 (0.0369) −0.0087 (0.0538) −0.0820 (0.0513) −0.0392 (0.2731) −0.0838 (0.0516)
Separated
Number of 0.0046 (0.0055) 0.0056 (0.0055) 0.0044 (0.0056) 0.0054 (0.0055) 0.0080 (0.0084) 0.0008 (0.0071) 0.0411 (0.0427) −0.0016 (0.0075)
children
Notes: Standard errors are in parentheses. *po0.10; **po0.05; ***po0.01