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The Impact of Job Quality on Wellbeing: Evidence from Kyrgyzstan

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Income and hours worked are insufficient to measure job quality yet these domains dominate literature aimed at understanding its relationship with wellbeing. More so, literature considering job quality in any manner has an overwhelming tendency to look at advanced economies, despite “decent work” being a key policy aim of many agencies and organisations working in emerging countries. This article tests the validity of the concept of job quality as a determinant of welfare in the developing world by generating four six-component indices using bespoke and unique data collected in Kyrgyzstan. Cross-sectional analysis of the performance of these indices against ones comprising only income and hours worked show no relationship between job quality and wellbeing in the latter case but a strong and positive relationship in the former. Jointly, this shows both the importance of more suitably measuring job quality in all contexts and the importance of policy objectives that aim to stimulate better, as well as more, jobs in the developing world.
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Vol.:(0123456789)
Social Indicators Research (2019) 144:337–378
https://doi.org/10.1007/s11205-018-1998-9
1 3
The Impact ofJob Quality onWellbeing: Evidence
fromKyrgyzstan
DamirEsenaliev1 · NeilT.N.Ferguson2
Accepted: 10 September 2018 / Published online: 9 November 2018
© The Author(s) 2018
Abstract
Income and hours worked are insufficient to measure job quality yet these domains domi-
nate literature aimed at understanding its relationship with wellbeing. More so, literature
considering job quality in any manner has an overwhelming tendency to look at advanced
economies, despite “decent work” being a key policy aim of many agencies and organisa-
tions working in emerging countries. This article tests the validity of the concept of job
quality as a determinant of welfare in the developing world by generating four six-compo-
nent indices using bespoke and unique data collected in Kyrgyzstan. Cross-sectional analy-
sis of the performance of these indices against ones comprising only income and hours
worked show no relationship between job quality and wellbeing in the latter case but a
strong and positive relationship in the former. Jointly, this shows both the importance of
more suitably measuring job quality in all contexts and the importance of policy objectives
that aim to stimulate better, as well as more, jobs in the developing world.
Keywords Job quality· Decent jobs· Kyrgyzstan· Multidimensional indices· Weighting·
Subjective wellbeing· Development economics· Labour economics
JEL Classication I31· J01· J81· O1
1 Introduction
The typical model of labour market supply defines utility as a trade-off between consump-
tion and leisure time, such that
Ui
=f
(
C
i
,L
i)
. The impact of work on well-being in these
models, therefore, boils down to income (which drives consumption) and leisure time
(which is enjoyable but comes at the price of foregone consumption). A growing body
of work (Clark 2005, 2010; Davoine and Erhel 2006) argues that these two domains,
alone, are insufficient to measure job quality. It follows that they may also be insufficient
* Neil T. N. Ferguson
ferguson@isdc.org
Damir Esenaliev
esenaliev@sipri.org
1 Stockholm International Peace Research Institute, Signalistgatan 9, 16970Solna, Sweden
2 International Security andDevelopment Center, Auguststraße 89, 10117Berlin, Germany
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
338
D.Esenaliev, N.T.N.Ferguson
1 3
to measure the relationship between work and wellbeing. Despite this observation, schol-
arship has tended to focus as much on measuring job quality as on its implications for
welfare (Boccuzzo and Gianecchini 2015; Dahl etal. 2009; Leschke etal. 2008; Muñoz
de Bustillo etal. 2011; Schokkaert etal. 2009). What research that does focus on this rela-
tionship tends to focus on the developed world (Clark 2005, 2015; Drobnič etal. 2010;
Gallie 2009; García-Pérez etal. 2017; Gómez-Salcedo etal. 2017; Green 2007; Wallace
etal. 2007), meaning that developing and transition countries are seldom studied in any
type of job quality analysis and are entirely missing from those using broader definitions
of the concept (Goos and Manning 2007; Houseman 1995; Yogo 2011). In addition to the
academic knowledge gap implicit in this, the creation of “decent jobs” (as well as “more
jobs”) is a key aim of many development agencies and international organisations (Ritter
and Anker 2002; World Bank 2012) suggesting a subsequent policy gap.
In this article, therefore, we ask three key questions. First of all, we seek to understand
the relationship between work and (subjective) wellbeing in a post-transition develop-
ing country. Subsequently, we ask whether or not an index of job quality comprised only
of income and hours worked is sufficient to measure the relationship between work and
welfare in developing/transition contexts. Finally, we test whether or not a broader index,
comprised of a range of domains, performs better in determining this relationship. These
questions develop from a range of theories that link work and welfare. On the one hand,
the effort-reward-imbalance model (Siegrist 1996; Theorell and Karasek 1996) focuses on
the (im)balance between the demands of the job and corresponding monetary and status
rewards. As rewards (i.e. income), at the cost of effort (i.e. foregone leisure time), drive the
relationship, this model may imply that narrower measures are sufficient. By contrast, the
job-demands-control model stipulates that giving workers more control at work reduces
psychological stress and increases job satisfaction through an opportunity to learn and
develop (de Jonge etal. 2000). Such outcomes can, easily, be extrapolated to more gen-
eral measures of welfare, implying that broader measures capture more than narrow ones.
Finally, the person-environment-fit model (Caplan and Harrison 1993) looks at the nega-
tive impacts of misfit between job and person. What determines “misfit”, and whether it is
captured by observable indicators of job quality, however, may imply that neither narrow
nor broader measures of observable job quality are sufficient.
To test these hypotheses, we develop a range of differently weighted narrow and broad
indices of job quality using bespoke and unique data collected in Kyrgyzstan. In the first
round of analyses, we test the relationship between “sub-indices” composed only of wages
and hours worked and self-reported life satisfaction. We follow this by repeating this pro-
cess using broader indices based on the work of Clark (2005). Using cross-sectional OLS
and ordered probits we show no significant relationship between the sub-indices and self-
reported wellbeing. For the full indices, we find a positive and significant relationship such
that higher job quality is associated with greater subjective wellbeing. These results fit with
the job-demands-control model, whilst suggesting that reward based models are insuffi-
cient to describe the relationship between work and welfare in Kyrgyzstan.
These results are of general interest as they show the limitations that can arise when
analyses of job quality are based only on simple indicators of income and hours worked.
These findings are relevant to studies focussing on the developed, as well as the developing
world, lending support to the notion that suitable definition and measurement of job quality
is required. They also make an important contribution to the development and transition
literatures as they show that job quality is just as important a component of welfare in
these economies as in advanced ones. Finally, we also make a contribution to the so-called
“happiness literature”. In this literature, work has focussed on understanding the impact of
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339
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
job quality through a range of domains (Cummins 2000; Diener and Biswas-Diener 2002;
Diener and Oishi 2000; Ferrer-i-Carbonell 2005; McBride 2001; Meier and Stutzer 2008;
Schoon etal. 2005; Wooden etal. 2009), but has tended to focus exclusively on the devel-
oped world. The findings presented here show the relevance of these key concepts in a
developing/transition context.
The rest of this article is structured as follows: in Sect.2, we discuss our data and meth-
ods; in Sect.3 our results; and in Sect.4, our conclusions.
2 Data andMethods
All data used in this study comes from the fourth wave of the Life in Kyrgyzstan Study
(LiK) (Brück etal. 2014), which includes a significantly enhanced jobs module, which was
inserted on request from the authors.1 From the survey we generate a sample of n = 2469
individuals who are engaged in work for monetary remuneration, either self-employed or
as wageworkers. We show summary statistics for these individuals in Table1, splitting by
wageworkers and the self-employed. In our sample, wageworkers are younger than the self-
employed, are more likely to be women (although the workforce as a whole is mostly men)
and are more likely to be of Kyrgyz ethnicity. Wageworkers are more likely to live in urban
areas and display a higher level of risk aversion (Cramer etal. 2002; Ekelund etal. 2005).
Finally, wageworkers exhibit higher job satisfaction but lower self-reported wellbeing.
For each individual, we generate four indices of job quality that build on Clark (2005,
2010). In addition to the five domains suggested by Clark (income; hours worked; job secu-
rity; interestingness of work; and autonomy) we add job formality2 in order to account for one
of the major contextual differences in the labour markets between developed and developing
Table 1 Means of the working population, wageworkers and non-wageworkers
Age is calculated as of the end 2013; Kyrgyz is main ethnic group; Urban criteria follows Kyrgyz admin-
istrative definition and relates mostly to cities; risk attitudes measured in a scale from 1 (low) to 10 (high);
life and job satisfaction are measured in a scale from 0 (low) to 10 (high). More information is in Appendix
Table9
Significant differences are marked by if ***p < 0.01; **p < 0.05; *p < 0.1
Variables (1) (2) (3) (4)
Employed Selfemployed Wageworkers Difference
Age, years 38.15 39.44 37.20 2.24***
Male (dummy) 0.62 0.72 0.54 0.18***
Kyrgyz (dummy) 0.73 0.76 0.70 0.06***
Urban (dummy) 0.40 0.24 0.52 − 0.28***
Risk attitudes 5.19 5.50 4.96 0.54***
Life satisfaction 7.07 7.20 6.97 0.23***
Job satisfaction 6.95 6.71 7.11 0.40***
1 At present, this expanded jobs section is only available in a single wave of the LiK Study, precluding
panel data analysis.
2 We include job formality as a key domain of interest following the work of (for example) Chen (2007). In
this work, a number of key features of informal job contracts, and the adverse outcomes linked to them, are
considered. These include a lack of labour law protection for workers; disguised employment relationships;
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340
D.Esenaliev, N.T.N.Ferguson
1 3
economies (Yamada 1996).3 We proxy job formality for wageworkers by presence of a writ-
ten contract or “workbook”4 and for the self-employed by whether or not their business is
registeredwith the Kyrgyz government. Following the literature (Addison and Grosso 1996;
Baum-Snow and Neal 2009; Edmonds and Pavcnik 2005; Farber 1998; Leete and Schor
1994; Presser 1999), hours worked is derived as standard from time spent working in the pre-
vious week and job security from the time a person has held his or her current position. Ques-
tions on income, interestingness of work and job autonomy are asked directly in the survey.
In addition to these questions, the survey also asks individuals how important they believe
each feature is in determining a “good job”.5 Following Decancq and Lugo (2013), we index
these six domains as follows:
where
wi
is the weight of each domain;
ii
is the normalisation identifier;
j
identifies the
individual; and Y, H, S, F, I, and A respectively refer to: income, hours worked, security,
formality, interestingness and autonomy. Thus, the quality of a person’s job is a function of
the weights and normalisations of our six domains of interest and his or her reported status
for each domain. It follows that any arbitrary change in the weights could have significant
impacts on the measure of job quality and its associated impact on wellbeing. This matches
longstanding critiques of the impact of weighting (Boccuzzo and Gianecchini 2015; Schok-
kaert etal. 2009). We explore this possibility by producing four alternative versions of our
indices, each using a different weighting mechanism, which we normalise to a hypothetical
maximum of one to ensure comparability across the indices.
First, we take the most common and easiest mechanism used in the literature (Decancq
and Lugo 2013) and assume that each domain is equally important. As such, the weight
of each domain is set equal to that of the others. As we have six domains and normalise
to one, each domain is then weighted with the value of 0.167. We denote this Index 1. For
Indices 2, 3 and 4, we make use of the questions asking how important different features
of a job are in determining whether or not it is “good”, which are all measured on a Likert
scale running from 1 (least important) to 5 (most important). In Index 2, we generate the
(1)
JQj
=w
1(
i
1
Y
j)
+w
2(
i
2
H
j)
+w
3(
i
3
S
j)
+w
4(
i
4
F
j)
+w
5(
i
5
I
j)
+w
6(
i
6
A
j)
5 These questions ask: “Thinking about a good job for yourself, how important would … be for that job?”,
where the ellipses are a list of options covering 17 different (potentially) relevant domains, of which we use
the six most directly linked to our indicators. Questions are answered on a Likert scale going from 1 (“not
at all important”) to 5 (“absolutely essential”).
Footnote 2 (continued)
and poorly defined hierarchies and responsibilities. In this regard, we view the inclusion of job formality
as necessary in the discussion of job quality in the developing world, given the extent of informality in
such economies. As per Chen (2007), we note that a number of our other domains might interact posi-
tively with informality. For example, it is possible that informal jobs exhibit higher levels of autonomy and
income than comparable formal jobs. In turn, many informal jobs in our data exhibit a higher "quality" than
many formal ones. Our guiding principle in suggesting that informality interacts negatively with job qual-
ity, therefore, is not designed to imply that all informal jobs are "bad" and that all formal jobs are "good".
Rather, that for two jobs that exhibit the same levels of all five other domains, the formal job should be of
higher quality than the informal job, due to the protections it offers.
3 We note that another domain of interest, particularly in the developed world, relates to work-life balance
(Gallie 2013). Due to the structure of the Kyrgyz economy and, in particular, the lack of a service sector,
priors suggest very little variation in this regard across the sample. Consequently, this information was not
included in the survey.
4 The Kyrgyz workbook stems from the country’s time as a Soviet Republic and is a record of employment,
holding information on the current employment status and place of employment of an individual, which in
effect acts as a written contract.
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341
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
weights based on the proportion of people who indicated that a given indicator was “some-
what important” (4 on the Likert scale) or “absolutely essential” (5 on the Likert scale). In
a second step, we normalise the sum of the proportion to 1 by dividing the proportionwho
regard each individual domain as “somewhat important” or “absolutely essential” by the
sum of the proportion for all six domains. Thus, the relatively greater the number of indi-
viduals who think a domain is important, the heavier the weighting it is given. Index 3
works on a similar principle but is slightly more nuanced by accounting for the full distri-
bution of responses. Here, we first sum the responses of each individual on the importance
of each domain, then perform the same normalisation as before. This imposes that in Indi-
ces 2 and 3, each domain has the same weight for all individuals in society.
Noting that Indices 1–3 impose preference dominance6 we develop a fourth that makes use
of the variation in individual perceptions about which features are important for a good job
to generate a “subjectively weighted index”. This approach allows two individuals with the
same observable job attributes to have a different level of job quality due to the configuration
of his or her perceptions on what constitutes a good job. Normalising these heterogeneous
weights, is more complicated as it is not logically consistent for every individual’s weights to
sum to 1.7 We therefore define a hypothetical maximum of 1 that all weights could add up to,
with each domain having a potential weight of 0.167. As perceptions are garnered on a Likert
scale of 1–5, each marginal decrease in the reported importance of a domain corresponds
with a reduction in this potential weight of 0.033.8 We calculate this index as follows:
where the subscript i denotes a given individual and the subscript j the domain of interest.
Imp
is the importance ranking attached to domain j by individual i. N denotes the number
of domains; and M the extent of the Likert scale from which individual i can choose the
importance of domain j.
Although overcoming preference dominance, such an index is not uncontroversial. Cog-
nitive dissonance literature, for example, suggests that individuals who are dissatisfied with
some aspect of their job are less likely to report those aspects as important. In such a case,
those with higher degrees of cognitive dissonance may appear to have higher quality jobs
without the actually quality of their job being, in any way, higher. In particular, individuals
might rank a particular domain as unimportant in order to psychologically protect them-
selves from an adverse feature of their job.9 Our four indices are designed to straddle these
(2)
w
ij =
1
N[
Impij.
1
M]
6 Preference dominance occurs when weights are equal for all individuals in a society, thus implying that
the preferences of some (hypothetical) individual whose real preferences match these weights dominates the
preferences of everyone in society who does not share those preferences.
7 Ceteris paribus, this implies that someone who thinks all six domains are “not at all important” would
have the same job quality as someone who thinks all six domains are “absolutely essential”.
8 Another approach used in the literature is to regress each domain of interest on self-reported job satis-
faction and to generate weights based on the relative explanatory power of each (Kalleberg etal. 2000).
Although we generate this index, none of our domains of interest are shown to be a significant determinant
of job satisfaction. Therefore, although the results from this analysis do not deviate from those presented in
this article, concerns arise about the usefulness and accuracy of the approach in this context. As such, we
do not present this approach in this text. Results are available from the authors upon request.
9 In addition, this could also affect how individuals answer questions in the survey, with particular differen-
tiations across “objective” and “subjective” indicators. To consider this concern, we run a robustness check
that splits the index into objective indicators (income, hours worked, formality and security) and subjective
ones (interestingness and autonomy). Results in both analyses match those from the full index, suggest-
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342
D.Esenaliev, N.T.N.Ferguson
1 3
differing concerns and to ensure that findings are robust to the strengths and weaknesses
of each. Beginning with Index 1, therefore, we create a weighting regime that does not
vary across the population and that is not at all linked to individuals’ subjective percep-
tions; in Indices 2 and 3, we create an index that does not vary across the population but is
grounded in perceptions that have been averaged across the entire sample. Finally, in Index
4, we create a weighting regime that varies across individuals but that is built, entirely,
from subjective perceptions.10
We normalise each of our six indicators onto the interval
i[1, 1]
, as they are other-
wise incomparable in scale and units. We choose the interval
i[1, 1]
as it is the only
style of interval that remains logically consistent with the weighting mechanism of Index
4. Index 4 requires that an individual who believes that income is an essential component
of a good job but who has an incredibly low income is worse off than an individual with
the same income but who does not think income is important at all. At the other end of this
scale, an individual with a very high income and who thinks income is essential should be
better off than one with a high income who thinks it is unimportant. Although the latter of
these restrictions holds in other identification methods, such as on an interval
, it
does not for the bottom end. We discuss how we implement this normalisation for each
domain below.
2.1 Income
Our distribution of income runs from 0 to 80,000 Soms/month, with a mean of 8669 Soms/
month. While we can safely assume, ceteris paribus, that higher income should be ‘bet-
ter’, it is unclear whether or not an individual with an income twice the mean is doubly
better off as one with a mean income. Indeed, basic economic theory might suggest that
additional income would suffer diminishing marginal returns. To avoid strong statements
on marginal effects, we take deciles of income from the distribution and map them, at even
spaces, onto the interval with the top income decile having a value of 1 and the lowest a
value of −1.
2.2 Hours Worked
Underemployment is likely to be just as indicative of a “bad job” as overemployment in
developing contexts (Behrman 1999; Blattman etal. 2014; World Bank 2012). Thus, in
our main indicator we look at the deviation of hours worked from some optimal, which we
take to be the monthly mean of hours worked across the sample.11 Thus, an individual who
works the monthly mean number of hours takes an outcome of 1. All individuals who work
10 As an additional robustness check, we construct a fifth index that is generated using weightings derived
from a factor analysis. These weights are generated using Stata’s “predict” command and normalised onto
the same scale as the weights of the other indices, such that they sum to one. This index (denoted Index 5)
exhibits the same positive and significant relationship with life satisfaction as the indices described here.
Results from this analysis are shown in Table20. We use Stata’s default factor analysis settings.
11 To account for potential seasonality, particularly for agricultural workers, we use the mean from the
month in which the data was collected, rather than across the whole sample, as a base.
Footnote 9 (continued)
ing our results are not driven only by either subjective or objective indicators. In turn, this implies that
cognitive dissonance plays no major role in our analysis. Results of this robustness check are presented in
Table22.
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343
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
two standard deviations more or less than this mean take a value of − 1. The remainder are
distributed across the interval, based on their exact hours worked.12
2.3 Job Security
Similar to our income domain, we assume that a longer tenure is associated with higher job
quality but wish to avoid making strong statements at the margin. Accordingly, we split the
duration of employment into deciles, with each decile spaced evenly across the interval.
2.4 Job Formality
Both questions that constitute our indicator of job formality are binary variables, taking the
value of 1 if a person’s position is formal and 0 if not. We simply transpose these outcomes
onto the extremes of the interval, taking a value of − 1 if a job is informal and 1 if it is formal.
2.5 Interestingness ofaJob
Interestingness of a job is reported directly on a Likert scale going from 1 (“uninteresting”)
to 3 (“very interesting”), which we space at equal intervals across the interval.13
2.6 Job Autonomy
Autonomy is, again, asked directly but is reported on a Likert scale running from 1 (“no
autonomy”) to 4 (“high autonomy”). These responses are spaced at even intervals across
the interval.
In a final stage, to ensure manageability of the coefficients, each index is then mapped
onto the interval
JQj[0, 100].
We repeat each step using corresponding weighting mech-
anisms for indices comprised only of hours worked and income.
Summary statistics and group comparisons for each of the full indices are shown in
Table 2. Across all indices, wageworkers have significantly better jobs than the self-
employed but in general, job quality is in the medium range. We show the distribution of
Indices 1 and 4 in Fig.1.14
12 We note that this may contrast with the basic model of labour supply, in that all hours worked should
interact negatively with welfare. Therefore, as a robustness check, we transform hours worked into deciles,
which we then transform onto an interval
i[1, 1]
, such that those who work the greatest number of
hours receive a transformed indicator of −1, those who work the least (or not at all) receiving a trans-
formed indicator of 1, with the other deciles equally spaced at intervals in between. Results using the indi-
ces created in this way are shown in Tables18 and 19 and show no material differences in terms of the sign,
scale or significance of the coefficients presented in the main analyses.
13 This implies that an individual reporting “somewhat interesting” (2 on the Likert scale) has a normalised
value of 0, meaning that a person who thinks interestingness is essential and has a somewhat interesting job
will have the same weighted value as one who has a somewhat interesting job but who thinks interesting-
ness is unimportant. We argue that, since it is unclear here who should be better off in this scenario that
this, although potentially undesirable, is not problematic. This still allows all people with “very interesting”
jobs to be better off than those with “somewhat interesting” jobs, who in turn are better off than those with
“uninteresting” jobs.
14 Noting that some of these indicators may inherently be “worse” for young people (e.g. it is almost guar-
anteed that an 18 year old will have low job security by our measure, as he or she has a much lower “maxi-
mum” tenure than older indivduals), we generate an age-weighted version of each index. These indices mul-
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344
D.Esenaliev, N.T.N.Ferguson
1 3
We note a number of potential and interesting trade-offs that can arise from this
approach. For example, while an increase in income acts to improve job quality, this
may come at the cost of increasing the number of hours one works. The number of hours
worked, at least beyond a certain point, acts to deflate an individual’s job quality, however.
In turn, the net impact on job quality might not be immediately obvious. Similarly, a move
from a formal job to a similar informal job may result in increases in income or improve-
ments in the other domains, but reductions in other domains as per Chen (2007). In turn,
the net effect on job quality is, again, ambiguous as declines in one domain (formality) are
Table 2 Summary statistics of
job quality indices
***p < 0.01; **p < 0.05; *p < 0.1
Variables (1) (2) (3) (4)
Employed Selfemployed Wageworkers Difference
Index 1 55.54 52.41 57.88 − 5.47***
Index 2 56.54 53.67 58.67 − 5.01***
Index 3 56.24 53.09 58.58 − 5.49***
Index 4 48.28 45.82 50.12 − 4.30***
Observations 2585 1044 1425
0
.01
.02
.03
.04
Density
20 40 60 80 100
Equal Weighting Index
0
.01
.02
.03
.04
Density
20 40 60 80 100
Subjective Index
0
.01
.02
.03
.04
Density
20 40 60 80 100
Equal Weighting Index
0
.01
.02
.03
.04
Density
20 40 60 80 100
Subjective Index
0
.01
.02
.03
.04
Density
20 40 60 80 100
Equal Weighting Index
0
.01
.02
.03
.04
Density
20 40 60 80 100
Subjective Index
Fig. 1 Histograms showing the distribution of Index 1 (left hand side) and Index 4 (right hand side) for all
workers (top row), the self-employed (middle row) and wageworkers (bottom row)
Footnote 14 (continued)
tiply the final index by the inverse of age. Thus, ceteris paribus, the younger of two people with the same
preferences and job features are better off in this index. We show results from these analysis in Tables14
17
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345
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
counteracted with increases in others. In turn, although we suggest that, ceteris paribus,
formal jobs should be preferred to informal ones, the outcome in the real world is not so
clear as it is unlikely that only the formality of work changes during such a switch. Given
the interaction of (in)formality with the other domains it does not immediately follow that
a move to an informal job results in a decrease in job quality. This mutual reliance amongst
the domains and the kinds of trade-offs implied are suggestive of the essential essence and
richness of the multiple domain index approach.
Informal employment in Kyrgyzstan is defined as an employment without state regis-
tration which is largely in line with our criteria of defining informality. Jobs in informal
settings constitute about two-thirds of total employment in the country (NSC 2017). Most
informal workers are short-term wage employees (39% of all informally employed), farm-
ers (26%) and self-employed (25%). Across sectors, informal jobs are concentrated in agri-
culture (39% of total informal employment), trade (20%), and construction (15%). Due to
the land and small-and medium enterprise privatization in mid-1990s, informal activities
tend to be organised horizontally as there are few large and vertically organised private
enterprises.
We conduct a factor analysis on the sub-domains in order to test the uniqueness of
each, and thus, to consider their contribution to our indices. As can be seen in Table3,
most indicators exhibit a middle to high level of uniqueness, suggesting that they
explain something that the other domains, either alone or in combination, do not. By a
similar token, as shown in Table4, the unconditional correlation between each domain
is generally in a relatively low range. We therefore conclude that the outcomes pre-
sented in this article are driven by the combination of all six domainsand the trade-offs
inherent therein, rather than individual components of the indices.
Table 3 Factor analysis showing
uniqueness of each domain Variables (1) (2) (3)
Factor 1 Factor 2 Uniqueness
Income 0.4116 0.4204 0.6538
Hours 0.3689 0.1380 0.8449
Formality 0.7070 − 0.3471 0.3796
Interesting 0.7631 0.0863 0.4102
Autonomy 0.1660 0.5552 0.6642
Security 0.2092 − 0.6528 0.5302
Observations 2585 2585 2585
Table 4 Unconditional correlations of each domain
Income Hours Formality Interesting Autonomy Security
Income 1.000
Hours 0.0621 1.000
Formality 0.0610 0.0697 1.000
Interesting 0.1478 0.1205 0.3271 1.000
Autonomy 0.0522 0.0035 − 0.0076 0.0773 1.000
Security 0.0072 0.0095 0.1216 0.0048 − 0.0008 1.000
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346
D.Esenaliev, N.T.N.Ferguson
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We match each index to self-reported wellbeing, derived from a question that asks,
“How satisfied are you with your life, all things considered?” Responses are given on a
11-point Likert scale running from 0 (“completely dissatisfied”) to 10 (“completed sat-
isfied”). We show the distribution of this variable in Fig.2. A previous study shows the
suitability of the LiK data for the research of subjective wellbeing (Bertram-Hümmer and
Baliki 2015). We control for robust determinants of subjective wellbeing (see Dolan etal.
2008, for a review), including: age, gender and ethnicity; educational background; partici-
pation in religious, social or community groups; health; regional controls; and personality
and attitudes to risk and other circumstances. We define all included controls in Table9.
The use of self-reported wellbeing is not uncontroversial (Andrews and McKennell
1980; Pavot and Diener 1993) as a number of features of an individual’s psyche may influ-
ence the response. Should these traits also correlate with those that affect labour market
performance (Borghans etal. 2008; Brunello and Schlotter 2011; Groves 2005; Heckman
etal. 2006) biases may arise in OLS models. To overcome this, we include two sets of
personality controls. The first are “attitudinal” indicators, comprising risk profiles and
response to circumstances. The second uses data reduction techniques on a 21-question
personality test.15 In combination, these variables overcome typical sources of bias, par-
ticularly because subjective wellbeing should not influence job quality directly. In turn, this
suggests that OLS and probit modelling are sufficient. We thus estimate:
where
SWBj
is subjective wellbeing for individual
j
;
JQij
is job quality for individual
j
measured by index
i
;
Xj
is an
(h×1)
vector of
h
. control variables;
PERSj
is an
(l×1)
vec-
tor of
l
personality controls;
Regionk
is a regional fixed effect for location
k
;
uj
is an idi-
osyncratic error term; and
𝛽i
,
𝛾
,
𝜌
and
𝛿k
are vectors of regression coefficients.
As
SWBj
is implicitly ordinal, we repeat the analysis using ordered probits. We thus
implement:
where
SWB
j
is a latent variable measuring individual
j
’s self-reported welfare; and the
other components of Eq.(4) are as previously described. For any given individual, it is
likely that a high level of job quality will translate into a high level of welfare and that low
(3)
SWBj=𝛼+𝛽iJQij +𝛾Xj+𝜌PERSj+𝛿Regionk+uj
(4)
SWB
j
=𝛽iJobQualityij +𝛾hXhj +𝜌hpersonalityhj +𝛿koblastk+u
j
Fig. 2 Histogram showing the
distribution of subjective wellbe-
ing
0
.05
.1
.15
.2
.25
Density
0 2 4 6 8 10
Subjective Well Being
15 To generate these variables, we conduct a factor analysis on the full set of 21 questions, and focus on the
factors that explain most of the variation. In this particular case, we include each factor that explains more
than 10% of the variation. In the second step, we include in the regressions the question that is most highly
correlated with each of these factors.
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347
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
job quality will translate into low welfare. Therefore, the observed and coded discrete sub-
jective wellbeing,
SWB
j
is determined from the model as follows:
3 Results
We present results in Tables5, 6, 7 and 8. In Tables5 and 6, we display the outcomes from
the OLS analyses on Indices 1–4. Tables7 and 8 show the results from ordered probit anal-
yses. Results from the truncated indices composed of hours worked and income are shown
in Tables5 and 7, with those from the full indices in Tables6 and 8. Each table comprises
four columns, with each corresponding, respectively, to the four weighting mechanisms
discussed in Sect.2.
As can be seen in Tables5 and 7, these analyses show no statistically significant rela-
tionship between the truncated job quality indices, composed only of hours worked and
income, and life satisfaction. By contrast the full indices in Tables6 and 8 show a signifi-
cant and robust relationship with all four versions of the index. As can be seen in Table20,
these results are also robust to the use of a fifth index, based on weights generated from
a factor analysis on the included domains. Thus, while we present evidence that higher
(5)
SWB
j=
0if −∞SWB
j𝜇1(completely dissatisfied
)
m if 𝜇n+1SWB
j𝜇m
if 𝜇10 SWB
j
(completely satisfied)
Table 5 OLS analysis of income and hours worked sub-indices on self-reported wellbeing
Standard errors in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1. Demographic, regional, health, participa-
tion, personality and attitudes refer to broad groupings of control variables
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
Wage-hours1 0.00191
(0.00146)
Wage-hours2 0.00177
(0.00142)
Wage-hours3 0.00177
(0.00147)
Wage-hours4 0.00164
(0.00163)
Demographic Yes Yes Yes Yes
Regional Yes Yes Yes Ye s
Health Yes Yes Yes Yes
Participation Yes Yes Yes Yes
Personality Yes Yes Yes Ye s
Attitudes Yes Yes Yes Ye s
Observations 2460 2460 2460 2460
R-squared 0.356 0.356 0.456 0.356
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348
D.Esenaliev, N.T.N.Ferguson
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Table 6 OLS analysis of full job
quality indices on self-reported
wellbeing
Standard errors in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1.
Demographic, regional, health, participation, personality and attitudes
refer to broad groupings of control variables
Variables (1) (2) (3) (4)
index1 Index2 index3 index4
index1 0.00768***
(0.00246)
index2 0.00744***
(0.00252)
index3 0.00683***
(0.00256)
index4 0.00760***
(0.00294)
Demographic Yes Yes Yes Yes
Regional Yes Yes Yes Ye s
Health Yes Yes Yes Yes
Participation Yes Yes Yes Yes
Personality Yes Yes Yes Ye s
Attitudes Yes Yes Yes Ye s
Observations 2460 2460 2460 2460
R-squared 0.358 0.358 0.358 0.358
Table 7 Ordered probit analysis of income and hours worked sub-indices on self-reported wellbeing
Standard errors in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1. Demographic, regional, health, participa-
tion, personality and attitudes refer to broad groupings of control variables
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
Wage-hours1 0.00142
(0.00108)
Wage-hours1 0.00132
(0.00105)
Wage-hours3 0.00133
(0.00108)
Wage-hours4 0.00125
(0.00120)
Demographic Yes Yes Yes Yes
Regional Yes Yes Yes Ye s
Health Yes Yes Yes Yes
Participation Yes Yes Yes Yes
Personality Yes Yes Yes Ye s
Attitudes Yes Yes Yes Ye s
Observations 2460 2460 2460 2460
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349
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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job quality results in increased subjective well-being in Kyrgyzstan, we note that truncated
measures based on hours worked and income are insufficient to capture or explain this rela-
tionship. At the headline level these results show the relevance of job quality concept to
developing world and transition contexts, whilst also implying the need for more sophis-
ticated measures of job quality in these contexts. In the first instance, this confirms the
need for “decent jobs” as well as employment more generally in developing countries; and
second, there is need to consider job quality more broadly than just income and the time
spent working, as per the classic model of labour supply, and in line with the job-demands-
control model.
More specifically Table6 shows that an increase in job quality of 1 point on a 100-point
scale is associated with an improvement in subjective wellbeing of between 0.0068 and
0.0077 points. Although this effect is, superficially, economically small, it implies that at
the mean, a 10-point increase in job quality leads to an increase in self-reported wellbeing
of between 0.07 and 0.08 points. In Index 3, where the coefficient is smallest, this implies
that subjective wellbeing increases from 7.06 at the mean to 7.14; in Index 1, where the
coefficient is largest, subjective wellbeing increases to 7.15. As shown in Fig.2, the dis-
tribution of subjective well-being is strongly clustered at five or higher. In proportional
terms, therefore, such a change is not an insignificant improvement. While the effect is
not very pronounced, a large number of individual, household, community, country and
regional factors are also captured by a measure as wide as subjective life satisfaction. In
turn, we posit that nuanced measures of job quality remain important and significant driv-
ers of wellbeing.
Full results from these analyses can be seen in Tables10, 11, 12 and 13 and robustness
checks using slight variations in the construction of the indices or weighting regimes in
Tables18, 19 and 20. In all regressions, we find robustly that age is negatively correlated
with subjective wellbeing, meaning that, in Kyrgyzstan, younger people tend to be more
Table 8 Ordered probit analysis
of full job quality indices on self-
reported wellbeing
Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
Demographic, regional, health, participation, personality and attitudes
refer to broad groupings of control variables
Variables (1) (2) (3) (4)
Index1 Index2 Index3 Index4
Index1 0.00584***
(0.00182)
Index2 0.00565***
(0.00187)
Index3 0.00521***
(0.00190)
Index4 0.00575***
(0.00218)
Demographic Yes Yes Yes Yes
Regional Yes Yes Yes Ye s
Health Yes Yes Yes Yes
Participation Yes Yes Yes Yes
Personality Yes Yes Yes Ye s
Attitudes Yes Yes Yes Ye s
Observations 2460 2460 2460 2460
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350
D.Esenaliev, N.T.N.Ferguson
1 3
satisfied than older people. That age squared is positive, however, implies that although life
satisfaction declines in age, it does so at a decelerating rate. Those with higher education
are typically those with higher wellbeing, whilst wageworkers display lower wellbeing than
the self-employed. We find that personality features are correlated with wellbeing, with
self-reported ingenuity and strong and positive driver of life satisfaction. Those with low
risk aversion exhibit higher wellbeing as do those who report that they adapt well to chang-
ing circumstances. Finally, we find evidence that wellbeing is linked to the oblast (region)
in which an individual is resident.
4 Conclusion
In this article, we explore job quality—measured both broadly, in the spirit of Clark (2005),
and narrowly in terms of hours worked and income, as per the classical model of labour
market supply. We analyse the relationship between job quality and (subjective) wellbe-
ing in the developing and transition context of Kyrgyzstan. Despite a long line of litera-
ture focussing on how to measure job quality, analyses of the relationship between wider
measures of job quality and welfare has been dominated by work focussing on advanced
economies (see Gallie 2009; Green 2007). Consequently, general questions remain. These
include whether or not the concept of job quality is equally valid in developing contexts
as developed ones, and about the extent to which wider measures are more satisfactory
in those contexts than narrower ones. In this article, we overcome these issues by setting
our analysis in Kyrgyzstan, a post-Soviet lower middle-income country and by developing
two competing conceptualisations of job quality: a broad index built around six domains
(income, hours worked, autonomy, interestingness, security and formality) and a narrow
one, built only from income and hours worked.
We show that the indices comprising only hours worked and income are insufficient to
derive a relationship between job quality and welfare but that the broader indices exhibit a
positive and significant relationship, showing that life satisfaction increases as job quality
improves. These results are robust across a range of weighting regimes and two empirical
specifications. In addition, they are also robust to the inclusion or exclusion of groups of
control variables and to alternative indices that allow job quality to vary by age.
In general, these results are grounded in a wider literature from the developed world
that supports this general relationship. More so, the findings fit with the job-demands-con-
trol theory of the relationship between work and welfare, whilst contradicting the effort-
reward-imbalance model. The latter model suggests that the link between welfare and work
is a function of the balance between the demands of the job and the corresponding rewards.
In other words, the major drivers of job quality are analogous to those in the basic model
of labour-supply. By contrast, the job-demands-control model focuses on “higher-order”
concepts, such as workers’ level of control. Our results support the notion that these higher-
order aspects of work, such as the interestingness of the job, the complexity of the tasks,
and the autonomy one has to complete them are important factors in how work links to
welfare. Effort and reward (viewed in terms of hours worked and income) are insufficient
to explain the relationship between work and welfare.
At the same time wider literature has previously suggested that narrower measures of
job quality should also be sufficient to capture the relationship with subjective wellbeing.
McBride (2001), Diener and Oishi (2000), Cummins (2000), Ferrer-i-Carbonnel (2005)
and Diener and Biswas-Diener (2002) all show a positive relationship between income and
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351
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
wellbeing, whilst Wooden etal. (2009), Meier and Stutzer (2008), and Schoon etal. (2005)
show the same relationship with hours worked. We show that hours worked and income,
alone, are insufficient to determine this relationship in Kyrgyzstan. This may imply that
general working conditions are more important in Kyrgyzstan than anticipated in this pre-
vious body of work. In many ways, perhaps this is understandable. More than a third of our
sample work informally, implying certain layers of exclusion from wider society that are
significantly less relevant in the developed world, where significantly fewer people work
informally. Similarly, more than half of our sample have jobs that, by duration of tenure,
we do not consider “secure”, whilst over 45% are self-employed, which is a significantly
larger proportion than one expects in the developed world. In combination, these features
of the Kyrgyz labour market may act to underpin the results presented hereand to rein-
force the requirement to more deeply understand job quality in developing and transition
economies.
These results, therefore, provide important information on measuring job quality in
labour markets in general and for those in the developing world, in particular. On the one
hand, that a strong and significant relationship endures between job quality and wellbe-
ing in Kyrgyzstan suggests that the job quality concept is just as important in developing
contexts as they are in advanced economies, adding credence to interventions focussed on
“decent jobs”. As such outcomes are dependent on the mechanism used to define job qual-
ity, however, the role of poor conceptualisation or measurement of job quality should not
be underestimated in these scenarios. It is, thus, important to consider job quality in devel-
oping contexts as much in terms of security, formality, interestingness and the autonomy
they afford as it is to focus on work in terms of only income or the time devoted to generat-
ing this income.
Acknowledgements We are grateful to participants of the LEADS Conference in Berlin 2016, IZA con-
ferences in Rome and London 2016, the Life in Kyrgyzstan Conference in Bishkek in 2015, First World
Congress of Comparative Economics in Rome 2015 and to the attendees at economics seminars at Ruhr-
Universität Bochum in 2014 and Universität Potsdam in 2015 for valuable comments. We additionally thank
Anastaisa Aladysheva, Kathryn Anderson, Armando Barrientos, Charles Becker, Tilman Brück, Hartmut
Lehmann and Susan Steiner for additional reviews, critiques and suggestions. All remaining errors are our
own.
Funding This document is an output from a project funded by the UK Department for International Devel-
opment (DFID) and the Institute for the Study of Labor (IZA) for the benefit of developing countries. The
views expressed are not necessarily those of DFID or IZA.
Compliance with Ethical Standards
Conict of interest The authors declare that they have no conflict of interest.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna-
tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
Appendix
See Tables9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22.
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352
D.Esenaliev, N.T.N.Ferguson
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Table 9 Description of control variables
Control Variable name Description
Age Age*
Age2* Respondent’s age reported in the survey as of end 2013, reported as a continuous number between 18 and
62 for men and between 18 and 58 for women. 62 and 58 are the retirement ages in Kyrgyzstan for men
and women, respectively
Respondent’s age squared
Male Gender* Dummy variable indicating respondent’s gender (male or female). Value taken is 1 for men and 0 for
women
Kyrgyz Ethnicity* Dummy variable indicating respondent’s ethnicity, split into Kyrgyz and other ethnic groups(including
Russian and Uzbek). Value taken is 1 for Kyrgyz and 0 for other
Urban Urban* Variable accounting for whether the respondent lives in an urban or rural oblast (administrative area). Takes
value of 1 if area is urban and 0 if rural
Education Education* Ordinal variable listing individual’s highest level of educational attainment. Takes a value between 1 (no
formal education) and 8 (advanced tertiary education)
Illness Illness1
Illness2* Variables indicating whether or not an individual has suffered an acute serious illness in the year before the
survey was taken. “illness1” counts the number of illnesses an individual experienced and “illness2” a
binary variable taking the value of one if one or more serious illnesses were suffered and 0 otherwise
Health condition Condition1
Condition2* Variables indicating whether or not an individual is suffering from a chronic health condition. As above,
condition1 is a count variable of the number of conditions and condition2 a binary variable
Community Community1*
Community2 Variables indicating individual’s involvement in community, social and religious groups. community1 is
a binary variable of involvement, taking the value of 1 if a respondent has been involved in one or more
groups and 0 otherwise. community2 is a count variable of the number of groups in which an individual
participates
Religion Religion* Binary variable indicating whether or not an individual belongs to a religious group, taking value of 1 if an
individual is involved in a religious group and 0 otherwise
Personality Personality1*
Personality2*
Personality3*
Personality2*
Set of variables based on a factor analysis of a 21-question personality test. All factors that explained at
least 10% of the variation in personality are included and the individual questionthat is most highly
(positively) correlated with the factor included. Only the first four factors satisfied this criterion. The
individual variables correlated with these factors reflect, respectively: ingenuity; sociability; depressed-
ness; and nervousness. Each indicator is measured on a 1–5 Likert Scale, with 5 indicating that a person
strongly associated him-/herself with that indicator and 1 if he/she does not associate themselves with that
trait at all
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353
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 9 (continued)
Control Variable name Description
Attitudes Risk*
Circumstances* Set of indicators based directly on questions asked in the survey, with individuals reporting their attitudes
on Likert scales. Risk reflects risk averseness and circumstances how adaptable individuals believe they
are to a chance in circumstances. Both are both measured on 11-point Likert scales, with 0 indicating
a person is entirely risk averse/responds poorly; and 10 indicating that a person is highly risk loving/
responds well
An asterisk in this table shows that the specified variable is included in the preferred specification of the analysis
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354
D.Esenaliev, N.T.N.Ferguson
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Table 10 Full results from OLS analysis of hours worked and wage sub-indices
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
Wage-hours 0.00191 0.00177 0.00177 0.00164
(0.00146) (0.00142) (0.00147) (0.00163)
Age − 0.0414** − 0.0414** − 0.0414** − 0.0413**
(0.0189) (0.0189) (0.0189) (0.0189)
Age2 0.0440* 0.0441* 0.0441* 0.0439*
(0.0239) (0.0239) (0.0239) (0.0239)
Gender − 0.0491 − 0.0488 − 0.0486 − 0.0459
(0.0645) (0.0645) (0.0646) (0.0645)
Ethnicity 0.0559 0.0557 0.0555 0.0550
(0.0692) (0.0692) (0.0692) (0.0692)
Urban − 0.0759 − 0.0758 − 0.0757 − 0.0736
(0.0887) (0.0887) (0.0888) (0.0887)
Education 0.0525** 0.0527** 0.0528** 0.0533**
(0.0237) (0.0237) (0.0237) (0.0238)
Employer 0.0148 0.0155 0.0159 0.0216
(0.296) (0.296) (0.296) (0.296)
Wageworker − 0.157** − 0.156** − 0.156** − 0.157**
(0.0700) (0.0700) (0.0700) (0.0700)
Family 0.0436 0.0429 0.0425 0.0378
(0.118) (0.118) (0.118) (0.118)
Illness2 − 0.0240 − 0.0239 − 0.0238 − 0.0234
(0.0648) (0.0648) (0.0648) (0.0648)
Condition2 − 0.121 − 0.121 − 0.121 − 0.122
(0.0802) (0.0802) (0.0802) (0.0802)
Community1 0.0594 0.0596 0.0598 0.0598
(0.0575) (0.0575) (0.0575) (0.0575)
Religion − 0.262 − 0.263 − 0.263 − 0.265
(0.256) (0.256) (0.256) (0.256)
Personality1 0.0994*** 0.0995*** 0.0996*** 0.100***
(0.0312) (0.0312) (0.0312) (0.0312)
Personality2 − 0.0370* − 0.0370* − 0.0370* − 0.0369*
(0.0220) (0.0220) (0.0220) (0.0220)
Personality3 − 0.0472* − 0.0471* − 0.0470* − 0.0468
(0.0285) (0.0285) (0.0285) (0.0285)
Personality4 0.0308 0.0309 0.0310 0.0312
(0.0286) (0.0286) (0.0286) (0.0286)
Risk 0.0632*** 0.0632*** 0.0633*** 0.0635***
(0.0112) (0.0112) (0.0112) (0.0112)
Circumstances 0.489*** 0.489*** 0.489*** 0.490***
(0.0177) (0.0177) (0.0177) (0.0177)
Oblast1 0.823*** 0.824*** 0.824*** 0.823***
(0.171) (0.171) (0.171) (0.171)
Oblast2 0.397** 0.397** 0.397** 0.396**
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The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 10 (continued)
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
(0.180) (0.180) (0.180) (0.180)
Oblast3 − 0.418** − 0.417** − 0.417** − 0.414**
(0.200) (0.200) (0.200) (0.200)
Oblast4 1.119*** 1.120*** 1.121*** 1.122***
(0.177) (0.177) (0.177) (0.177)
Oblast5 0.299* 0.300* 0.300* 0.302*
(0.180) (0.180) (0.180) (0.180)
Oblast6 0.861*** 0.863*** 0.863*** 0.866***
(0.191) (0.191) (0.191) (0.191)
Oblast7 0.709*** 0.710*** 0.711*** 0.714***
(0.169) (0.169) (0.169) (0.169)
Oblast8 0.447*** 0.449*** 0.449*** 0.452***
(0.153) (0.153) (0.153) (0.153)
Constant 3.540*** 3.545*** 3.544*** 3.538***
(0.447) (0.447) (0.447) (0.447)
Observations 2460 2460 2460 2460
R-squared 0.356 0.356 0.356 0.356
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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356
D.Esenaliev, N.T.N.Ferguson
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Table 11 Full results from OLS
analysis of full indices Variables (1) (2) (3) (4)
Index1 Index2 Index3 Index4
Index 0.00768*** 0.00744*** 0.00683*** 0.00760***
(0.00246) (0.00252) (0.00256) (0.00294)
Age − 0.0443** − 0.0447** − 0.0431** − 0.0438**
(0.0189) (0.0189) (0.0189) (0.0189)
Age2 0.0460* 0.0464* 0.0449* 0.0456*
(0.0239) (0.0239) (0.0239) (0.0239)
Gender − 0.0424 − 0.0457 − 0.0435 − 0.0432
(0.0635) (0.0636) (0.0636) (0.0636)
Ethnicity 0.0563 0.0563 0.0557 0.0574
(0.0691) (0.0691) (0.0691) (0.0691)
Urban − 0.0797 − 0.0793 − 0.0804 − 0.0759
(0.0884) (0.0884) (0.0885) (0.0884)
Education 0.0348 0.0365 0.0382 0.0391
(0.0245) (0.0245) (0.0245) (0.0245)
Employer − 0.0252 − 0.0216 − 0.0198 − 0.00986
(0.295) (0.295) (0.296) (0.295)
Wageworker − 0.166** − 0.163** − 0.164** − 0.163**
(0.0699) (0.0699) (0.0699) (0.0699)
Family 0.0765 0.0756 0.0717 0.0651
(0.118) (0.118) (0.118) (0.118)
Illness2 − 0.0234 − 0.0228 − 0.0241 − 0.0228
(0.0647) (0.0647) (0.0647) (0.0648)
Condition2 − 0.119 − 0.119 − 0.120 − 0.121
(0.0801) (0.0801) (0.0801) (0.0801)
Community1 0.0410 0.0431 0.0443 0.0457
(0.0577) (0.0577) (0.0578) (0.0577)
Religion − 0.256 − 0.257 − 0.257 − 0.261
(0.256) (0.256) (0.256) (0.256)
Personality1 0.0970*** 0.0969*** 0.0978*** 0.0990***
(0.0311) (0.0311) (0.0311) (0.0311)
Personality2 − 0.0370* − 0.0370* − 0.0370* − 0.0370*
(0.0220) (0.0220) (0.0220) (0.0220)
Personality3 − 0.0499* − 0.0495* − 0.0497* − 0.0494*
(0.0285) (0.0285) (0.0285) (0.0285)
Personality4 0.0285 0.0284 0.0293 0.0290
(0.0285) (0.0285) (0.0285) (0.0285)
Risk 0.0631*** 0.0630*** 0.0631*** 0.0631***
(0.0112) (0.0112) (0.0112) (0.0112)
Circumstances 0.486*** 0.486*** 0.487*** 0.487***
(0.0176) (0.0177) (0.0177) (0.0176)
Oblast1 0.824*** 0.827*** 0.827*** 0.836***
(0.171) (0.171) (0.171) (0.171)
Oblast2 0.363** 0.367** 0.371** 0.377**
(0.180) (0.180) (0.180) (0.180)
Oblast3 − 0.424** − 0.424** − 0.425** − 0.422**
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Table 11 (continued) Variables (1) (2) (3) (4)
Index1 Index2 Index3 Index4
(0.200) (0.200) (0.200) (0.200)
Oblast4 1.108*** 1.112*** 1.114*** 1.122***
(0.177) (0.177) (0.177) (0.177)
Oblast5 0.290 0.294 0.291 0.305*
(0.180) (0.180) (0.180) (0.180)
Oblast6 0.819*** 0.823*** 0.832*** 0.845***
(0.191) (0.191) (0.191) (0.191)
Oblast7 0.706*** 0.708*** 0.708*** 0.719***
(0.169) (0.169) (0.169) (0.169)
Oblast8 0.427*** 0.429*** 0.434*** 0.441***
(0.153) (0.153) (0.153) (0.153)
Constant 3.441*** 3.440*** 3.427*** 3.433***
(0.447) (0.448) (0.449) (0.449)
Observations 2460 2460 2460 2460
R-squared 0.358 0.358 0.358 0.358
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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358
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Table 12 Full results from ordered probit analysis of wage and hours-worked sub-indices
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
Wage-hours 0.00142 0.00132 0.00133 0.00125
(0.00108) (0.00105) (0.00108) (0.00120)
Age − 0.0303** − 0.0304** − 0.0304** − 0.0303**
(0.0140) (0.0140) (0.0140) (0.0140)
Age2 0.0323* 0.0324* 0.0324* 0.0323*
(0.0177) (0.0177) (0.0177) (0.0177)
Gender − 0.0376 − 0.0374 − 0.0373 − 0.0354
(0.0476) (0.0476) (0.0476) (0.0476)
Ethnicity 0.0441 0.0439 0.0438 0.0434
(0.0510) (0.0510) (0.0510) (0.0510)
Urban − 0.0650 − 0.0649 − 0.0648 − 0.0633
(0.0656) (0.0656) (0.0656) (0.0656)
Education 0.0394** 0.0395** 0.0396** 0.0400**
(0.0175) (0.0175) (0.0175) (0.0175)
Employer 0.0353 0.0358 0.0361 0.0403
(0.222) (0.222) (0.222) (0.222)
Wageworker − 0.113** − 0.113** − 0.113** − 0.114**
(0.0516) (0.0516) (0.0516) (0.0516)
Family 0.0223 0.0218 0.0215 0.0183
(0.0874) (0.0874) (0.0874) (0.0874)
Illness2 − 0.0277 − 0.0276 − 0.0276 − 0.0272
(0.0479) (0.0479) (0.0479) (0.0479)
Condition2 − 0.0854 − 0.0855 − 0.0855 − 0.0864
(0.0591) (0.0591) (0.0591) (0.0591)
Community1 0.0433 0.0435 0.0436 0.0435
(0.0426) (0.0426) (0.0426) (0.0426)
Religion − 0.180 − 0.180 − 0.180 − 0.182
(0.190) (0.190) (0.190) (0.190)
Personality1 0.0764*** 0.0765*** 0.0765*** 0.0768***
(0.0230) (0.0230) (0.0230) (0.0230)
Personality2 − 0.0292* − 0.0292* − 0.0292* − 0.0292*
(0.0163) (0.0163) (0.0163) (0.0163)
Personality3 − 0.0332 − 0.0331 − 0.0331 − 0.0330
(0.0210) (0.0210) (0.0210) (0.0210)
Personality4 0.0248 0.0249 0.0250 0.0251
(0.0211) (0.0211) (0.0211) (0.0211)
Risk 0.0484*** 0.0484*** 0.0484*** 0.0485***
(0.00840) (0.00840) (0.00840) (0.00840)
Circumstances 0.361*** 0.361*** 0.361*** 0.361***
(0.0141) (0.0141) (0.0141) (0.0141)
Oblast1 0.606*** 0.606*** 0.607*** 0.606***
(0.126) (0.126) (0.126) (0.126)
Oblast2 0.292** 0.292** 0.292** 0.291**
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Table 12 (continued)
Variables (1) (2) (3) (4)
Wage-hours1 Wage-hours2 Wage-hours3 Wage-hours4
(0.132) (0.132) (0.132) (0.132)
Oblast3 − 0.308** − 0.307** − 0.307** − 0.305**
(0.147) (0.147) (0.147) (0.147)
Oblast4 0.875*** 0.876*** 0.876*** 0.877***
(0.131) (0.131) (0.131) (0.131)
Oblast5 0.205 0.205 0.206 0.207
(0.132) (0.132) (0.132) (0.132)
Oblast6 0.622*** 0.623*** 0.623*** 0.625***
(0.140) (0.140) (0.140) (0.140)
Oblast7 0.519*** 0.520*** 0.520*** 0.523***
(0.124) (0.124) (0.124) (0.124)
Oblast8 0.343*** 0.344*** 0.345*** 0.346***
(0.112) (0.112) (0.112) (0.112)
Constant cut1 − 1.626*** − 1.629*** − 1.628*** − 1.624***
(0.461) (0.461) (0.461) (0.461)
Constant cut2 − 0.906** − 0.910** − 0.909** − 0.905**
(0.356) (0.356) (0.356) (0.356)
Constant cut3 − 0.674* − 0.678** − 0.677** − 0.673*
(0.345) (0.345) (0.345) (0.346)
Constant cut4 0.102 0.0975 0.0984 0.103
(0.332) (0.332) (0.332) (0.333)
Constant cut5 0.675** 0.671** 0.672** 0.676**
(0.331) (0.331) (0.331) (0.331)
Constant cut6 1.518*** 1.513*** 1.514*** 1.518***
(0.331) (0.331) (0.331) (0.331)
Constant cut7 2.210*** 2.206*** 2.206*** 2.210***
(0.332) (0.331) (0.331) (0.332)
Constant cut8 2.932*** 2.928*** 2.929*** 2.933***
(0.333) (0.333) (0.333) (0.333)
Constant cut9 3.692*** 3.688*** 3.689*** 3.693***
(0.334) (0.334) (0.334) (0.334)
Constant cut10 4.185*** 4.181*** 4.182*** 4.186***
(0.336) (0.336) (0.336) (0.336)
Observations 2460 2460 2460 2460
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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360
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Table 13 Full results from
ordered probit analyses of full
indices
Variables (1) (2) (3) (4)
Index1 Index2 Index3 Index4
Index 0.00584*** 0.00565*** 0.00521*** 0.00575***
(0.00182) (0.00187) (0.00190) (0.00218)
Age − 0.0326** − 0.0329** − 0.0317** − 0.0322**
(0.0140) (0.0140) (0.0140) (0.0140)
Age2 0.0339* 0.0342* 0.0331* 0.0336*
(0.0177) (0.0177) (0.0177) (0.0177)
Gender − 0.0324 − 0.0350 − 0.0333 − 0.0329
(0.0469) (0.0470) (0.0469) (0.0469)
Ethnicity 0.0443 0.0443 0.0438 0.0451
(0.0510) (0.0510) (0.0510) (0.0510)
Urban − 0.0682 − 0.0679 − 0.0687 − 0.0651
(0.0655) (0.0655) (0.0655) (0.0655)
Education 0.0260 0.0273 0.0285 0.0293
(0.0181) (0.0181) (0.0181) (0.0181)
Employer 0.00545 0.00803 0.00947 0.0172
(0.223) (0.223) (0.223) (0.223)
Wageworker − 0.120** − 0.118** − 0.119** − 0.117**
(0.0517) (0.0516) (0.0517) (0.0516)
Family 0.0476 0.0469 0.0441 0.0391
(0.0872) (0.0873) (0.0874) (0.0872)
Illness2 − 0.0269 − 0.0265 − 0.0275 − 0.0264
(0.0479) (0.0479) (0.0479) (0.0479)
Condition2 − 0.0841 − 0.0841 − 0.0846 − 0.0854
(0.0591) (0.0591) (0.0591) (0.0591)
Community1 0.0297 0.0313 0.0322 0.0333
(0.0428) (0.0428) (0.0428) (0.0428)
Religion − 0.176 − 0.176 − 0.176 − 0.179
(0.190) (0.190) (0.190) (0.190)
Personality1 0.0746*** 0.0745*** 0.0751*** 0.0761***
(0.0230) (0.0230) (0.0230) (0.0230)
Personality2 − 0.0293* − 0.0293* − 0.0293* − 0.0293*
(0.0163) (0.0163) (0.0163) (0.0163)
Personality3 − 0.0353* − 0.0351* − 0.0352* − 0.0350*
(0.0210) (0.0210) (0.0210) (0.0210)
Personality4 0.0232 0.0231 0.0238 0.0236
(0.0211) (0.0211) (0.0211) (0.0211)
Risk 0.0484*** 0.0484*** 0.0484*** 0.0483***
(0.00839) (0.00839) (0.00839) (0.00839)
Circumstances 0.360*** 0.360*** 0.359*** 0.360***
(0.0141) (0.0141) (0.0141) (0.0141)
Oblast1 0.608*** 0.610*** 0.610*** 0.616***
(0.126) (0.126) (0.126) (0.127)
Oblast2 0.267** 0.270** 0.273** 0.278**
(0.133) (0.133) (0.132) (0.132)
Oblast3 − 0.314** − 0.313** − 0.314** − 0.311**
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The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 13 (continued) Variables (1) (2) (3) (4)
Index1 Index2 Index3 Index4
(0.147) (0.147) (0.147) (0.147)
Oblast4 0.869*** 0.871*** 0.872*** 0.878***
(0.131) (0.131) (0.131) (0.131)
Oblast5 0.198 0.201 0.199 0.209
(0.132) (0.132) (0.132) (0.132)
Oblast6 0.591*** 0.594*** 0.600*** 0.610***
(0.141) (0.141) (0.140) (0.140)
Oblast7 0.519*** 0.520*** 0.519*** 0.528***
(0.124) (0.124) (0.124) (0.124)
Oblast8 0.328*** 0.330*** 0.333*** 0.339***
(0.112) (0.112) (0.112) (0.112)
Constant cut1 − 1.545*** − 1.545*** − 1.534*** − 1.543***
(0.461) (0.461) (0.462) (0.462)
Constant cut2 − 0.829** − 0.829** − 0.818** − 0.826**
(0.357) (0.357) (0.358) (0.358)
Constant cut3 − 0.597* − 0.597* − 0.586* − 0.593*
(0.347) (0.347) (0.347) (0.347)
Constant cut4 0.179 0.180 0.190 0.184
(0.334) (0.334) (0.334) (0.334)
Constant cut5 0.753** 0.753** 0.763** 0.757**
(0.332) (0.332) (0.333) (0.333)
Constant cut6 1.598*** 1.598*** 1.607*** 1.601***
(0.332) (0.332) (0.333) (0.333)
Constant cut7 2.292*** 2.291*** 2.300*** 2.294***
(0.333) (0.333) (0.334) (0.333)
Constant cut8 3.016*** 3.015*** 3.024*** 3.018***
(0.334) (0.334) (0.335) (0.335)
Constant cut9 3.777*** 3.776*** 3.785*** 3.778***
(0.336) (0.336) (0.336) (0.336)
Constant cut10 4.270*** 4.270*** 4.278*** 4.272***
(0.337) (0.337) (0.338) (0.338)
Observations 2460 2460 2460 2460
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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362
D.Esenaliev, N.T.N.Ferguson
1 3
Table 14 Full results from OLS analyses of age-weighted wage and hours worked sub-indices
Variables (1) (2) (3) (4)
Awage-hours1 Awage-hours2 Awage-hours3 Awage-hours4
Age-wage-hours 0.000881 0.000770 0.000744 0.000639
(0.00130) (0.00126) (0.00130) (0.00146)
Age − 0.0369* − 0.0374* − 0.0375* − 0.0379*
(0.0197) (0.0197) (0.0197) (0.0199)
Age2 0.0400 0.0405* 0.0406* 0.0409*
(0.0244) (0.0243) (0.0243) (0.0244)
Gender − 0.0414 − 0.0410 − 0.0407 − 0.0392
(0.0643) (0.0643) (0.0643) (0.0643)
Ethnicity 0.0538 0.0536 0.0535 0.0533
(0.0692) (0.0692) (0.0692) (0.0692)
Urban − 0.0718 − 0.0715 − 0.0713 − 0.0700
(0.0888) (0.0888) (0.0888) (0.0887)
Education 0.0550** 0.0552** 0.0553** 0.0556**
(0.0236) (0.0237) (0.0237) (0.0237)
Employer 0.0297 0.0304 0.0308 0.0329
(0.296) (0.296) (0.296) (0.296)
Wageworker − 0.157** − 0.156** − 0.156** − 0.157**
(0.0700) (0.0700) (0.0700) (0.0700)
Family 0.0315 0.0304 0.0297 0.0269
(0.118) (0.119) (0.119) (0.119)
Illness2 − 0.0246 − 0.0245 − 0.0245 − 0.0243
(0.0648) (0.0648) (0.0648) (0.0648)
Condition2 − 0.122 − 0.122 − 0.122 − 0.123
(0.0803) (0.0803) (0.0803) (0.0802)
Community1 0.0611 0.0613 0.0614 0.0615
(0.0575) (0.0575) (0.0575) (0.0575)
Religion − 0.267 − 0.268 − 0.268 − 0.269
(0.257) (0.257) (0.257) (0.257)
Personality1 0.101*** 0.101*** 0.101*** 0.101***
(0.0312) (0.0312) (0.0312) (0.0312)
Personality2 − 0.0364* − 0.0364* − 0.0364* − 0.0364*
(0.0220) (0.0220) (0.0220) (0.0220)
Personality3 − 0.0464 − 0.0463 − 0.0463 − 0.0461
(0.0285) (0.0285) (0.0285) (0.0285)
Personality4 0.0321 0.0323 0.0324 0.0325
(0.0286) (0.0286) (0.0286) (0.0286)
Risk 0.0636*** 0.0636*** 0.0636*** 0.0637***
(0.0112) (0.0112) (0.0112) (0.0112)
Circumstances 0.490*** 0.490*** 0.490*** 0.490***
(0.0177) (0.0177) (0.0177) (0.0177)
Oblast1 0.820*** 0.821*** 0.821*** 0.820***
(0.171) (0.171) (0.171) (0.171)
Oblast2 0.397** 0.397** 0.397** 0.397**
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The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 14 (continued)
Variables (1) (2) (3) (4)
Awage-hours1 Awage-hours2 Awage-hours3 Awage-hours4
(0.180) (0.180) (0.180) (0.180)
Oblast3 − 0.413** − 0.413** − 0.413** − 0.411**
(0.200) (0.200) (0.200) (0.200)
Oblast4 1.127*** 1.128*** 1.128*** 1.129***
(0.177) (0.177) (0.177) (0.177)
Oblast5 0.303* 0.303* 0.304* 0.305*
(0.180) (0.180) (0.180) (0.180)
Oblast6 0.872*** 0.873*** 0.874*** 0.876***
(0.191) (0.191) (0.191) (0.191)
Oblast7 0.717*** 0.718*** 0.719*** 0.721***
(0.170) (0.170) (0.170) (0.169)
Oblast8 0.458*** 0.459*** 0.460*** 0.462***
(0.153) (0.153) (0.153) (0.153)
Constant 3.441*** 3.458*** 3.461*** 3.471***
(0.477) (0.474) (0.475) (0.485)
Observations 2460 2460 2460 2460
R-squared 0.356 0.356 0.356 0.356
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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364
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1 3
Table 15 Full result from OLS
analyses of age-weighted full
indices
Variables (1) (2) (3) (4)
Age-index1 Age-index2 Age-index3 Age-index4
Age-index 0.00612*** 0.00579** 0.00515** 0.00590**
(0.00222) (0.00228) (0.00230) (0.00266)
Age − 0.0173 − 0.0184 − 0.0195 − 0.0210
(0.0207) (0.0208) (0.0211) (0.0209)
Age2 0.0243 0.0253 0.0257 0.0271
(0.0248) (0.0249) (0.0251) (0.0250)
Gender − 0.0378 − 0.0407 − 0.0390 − 0.0390
(0.0635) (0.0635) (0.0636) (0.0636)
Ethnicity 0.0513 0.0517 0.0512 0.0525
(0.0691) (0.0691) (0.0691) (0.0691)
Urban − 0.0785 − 0.0781 − 0.0783 − 0.0749
(0.0885) (0.0885) (0.0886) (0.0885)
Education 0.0397 0.0415* 0.0433* 0.0436*
(0.0243) (0.0242) (0.0243) (0.0242)
Employer − 0.00420 − 0.000612 0.00154 0.00713
(0.295) (0.295) (0.295) (0.295)
Wageworker − 0.164** − 0.162** − 0.163** − 0.161**
(0.0699) (0.0699) (0.0700) (0.0699)
Family 0.0716 0.0693 0.0650 0.0611
(0.118) (0.118) (0.118) (0.118)
Illness2 − 0.0237 − 0.0232 − 0.0242 − 0.0228
(0.0647) (0.0648) (0.0648) (0.0648)
Condition2 − 0.121 − 0.121 − 0.121 − 0.123
(0.0801) (0.0801) (0.0802) (0.0802)
Community1 0.0475 0.0492 0.0507 0.0511
(0.0576) (0.0576) (0.0576) (0.0576)
Religion − 0.254 − 0.255 − 0.257 − 0.258
(0.256) (0.256) (0.256) (0.256)
Personality1 0.0974*** 0.0974*** 0.0982*** 0.0990***
(0.0311) (0.0312) (0.0312) (0.0312)
Personality2 − 0.0369* − 0.0368* − 0.0369* − 0.0369*
(0.0220) (0.0220) (0.0220) (0.0220)
Personality3 − 0.0506* − 0.0500* − 0.0501* − 0.0499*
(0.0285) (0.0285) (0.0285) (0.0285)
Personality4 0.0295 0.0295 0.0303 0.0301
(0.0285) (0.0285) (0.0285) (0.0285)
Risk 0.0632*** 0.0632*** 0.0633*** 0.0632***
(0.0112) (0.0112) (0.0112) (0.0112)
Circumstances 0.487*** 0.487*** 0.487*** 0.488***
(0.0177) (0.0177) (0.0177) (0.0177)
Oblast1 0.824*** 0.826*** 0.826*** 0.834***
(0.171) (0.171) (0.171) (0.171)
Oblast2 0.371** 0.374** 0.378** 0.383**
(0.180) (0.180) (0.181) (0.180)
Oblast3 − 0.419** − 0.419** − 0.420** − 0.417**
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Table 15 (continued) Variables (1) (2) (3) (4)
Age-index1 Age-index2 Age-index3 Age-index4
(0.200) (0.200) (0.200) (0.200)
Oblast4 1.117*** 1.119*** 1.122*** 1.128***
(0.177) (0.177) (0.177) (0.177)
Oblast5 0.289 0.292 0.291 0.302*
(0.180) (0.180) (0.180) (0.180)
Oblast6 0.834*** 0.838*** 0.846*** 0.855***
(0.191) (0.191) (0.191) (0.190)
Oblast7 0.710*** 0.712*** 0.712*** 0.720***
(0.169) (0.169) (0.169) (0.169)
Oblast8 0.434*** 0.437*** 0.441*** 0.446***
(0.153) (0.153) (0.153) (0.153)
Constant 2.777*** 2.803*** 2.861*** 2.881***
(0.527) (0.535) (0.543) (0.540)
Observations 2460 2460 2460 2460
R-squared 0.358 0.357 0.357 0.357
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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366
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Table 16 Full results from ordered probit analyses of age-weighted wages and hours worked sub-indices
Variables (1) (2) (3) (4)
Awage-hours1 Awage-hours2 Awage-hours3 Awage-hours4
Age-wage-hours 0.000673 0.000592 0.000574 0.000521
(0.000959) (0.000933) (0.000961) (0.00108)
Age − 0.0269* − 0.0273* − 0.0274* − 0.0276*
(0.0146) (0.0145) (0.0145) (0.0147)
Age2 0.0293 0.0297* 0.0297* 0.0299*
(0.0180) (0.0179) (0.0179) (0.0180)
Gender − 0.0320 − 0.0317 − 0.0315 − 0.0305
(0.0474) (0.0474) (0.0474) (0.0474)
Ethnicity 0.0425 0.0424 0.0423 0.0422
(0.0510) (0.0510) (0.0510) (0.0510)
Urban − 0.0619 − 0.0616 − 0.0615 − 0.0606
(0.0656) (0.0656) (0.0657) (0.0656)
Education 0.0412** 0.0414** 0.0414** 0.0416**
(0.0175) (0.0175) (0.0175) (0.0175)
Employer 0.0465 0.0471 0.0474 0.0488
(0.222) (0.222) (0.222) (0.222)
Wageworker − 0.113** − 0.113** − 0.113** − 0.113**
(0.0516) (0.0516) (0.0516) (0.0516)
Family 0.0135 0.0128 0.0123 0.0105
(0.0874) (0.0875) (0.0875) (0.0874)
Illness2 − 0.0281 − 0.0280 − 0.0280 − 0.0278
(0.0479) (0.0479) (0.0479) (0.0479)
Condition2 − 0.0863 − 0.0863 − 0.0864 − 0.0868
(0.0591) (0.0591) (0.0591) (0.0591)
Community1 0.0444 0.0446 0.0447 0.0446
(0.0426) (0.0426) (0.0426) (0.0426)
Religion − 0.183 − 0.183 − 0.183 − 0.184
(0.190) (0.190) (0.190) (0.190)
Personality1 0.0774*** 0.0775*** 0.0776*** 0.0777***
(0.0230) (0.0230) (0.0230) (0.0230)
Personality2 − 0.0288* − 0.0288* − 0.0288* − 0.0288*
(0.0163) (0.0163) (0.0163) (0.0163)
Personality3 − 0.0326 − 0.0326 − 0.0325 − 0.0325
(0.0210) (0.0210) (0.0210) (0.0210)
Personality4 0.0259 0.0260 0.0260 0.0261
(0.0211) (0.0211) (0.0211) (0.0211)
Risk 0.0486*** 0.0486*** 0.0487*** 0.0487***
(0.00840) (0.00840) (0.00840) (0.00839)
Circumstances 0.362*** 0.362*** 0.362*** 0.362***
(0.0141) (0.0141) (0.0141) (0.0141)
Oblast1 0.603*** 0.604*** 0.604*** 0.603***
(0.126) (0.126) (0.126) (0.126)
Oblast2 0.292** 0.292** 0.292** 0.292**
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Table 16 (continued)
Variables (1) (2) (3) (4)
Awage-hours1 Awage-hours2 Awage-hours3 Awage-hours4
(0.132) (0.132) (0.132) (0.132)
Oblast3 − 0.304** − 0.304** − 0.304** − 0.302**
(0.147) (0.147) (0.147) (0.147)
Oblast4 0.880*** 0.880*** 0.881*** 0.881***
(0.131) (0.131) (0.131) (0.131)
Oblast5 0.207 0.208 0.208 0.209
(0.132) (0.132) (0.132) (0.132)
Oblast6 0.630*** 0.631*** 0.631*** 0.632***
(0.140) (0.140) (0.140) (0.140)
Oblast7 0.525*** 0.526*** 0.526*** 0.528***
(0.124) (0.124) (0.124) (0.124)
Oblast8 0.351*** 0.352*** 0.352*** 0.353***
(0.112) (0.112) (0.112) (0.112)
Constant cut1 − 1.549*** − 1.561*** − 1.563*** − 1.568***
(0.477) (0.476) (0.477) (0.482)
Constant cut2 − 0.830** − 0.842** − 0.844** − 0.848**
(0.376) (0.374) (0.375) (0.382)
Constant cut3 − 0.598 − 0.610* − 0.612* − 0.617*
(0.366) (0.364) (0.365) (0.372)
Constant cut4 0.177 0.164 0.162 0.158
(0.354) (0.352) (0.353) (0.360)
Constant cut5 0.750** 0.737** 0.735** 0.731**
(0.353) (0.351) (0.352) (0.359)
Constant cut6 1.592*** 1.580*** 1.578*** 1.573***
(0.353) (0.351) (0.352) (0.359)
Constant cut7 2.285*** 2.272*** 2.270*** 2.266***
(0.354) (0.351) (0.353) (0.360)
Constant cut8 3.007*** 2.995*** 2.993*** 2.988***
(0.355) (0.352) (0.354) (0.361)
Constant cut9 3.767*** 3.754*** 3.752*** 3.748***
(0.356) (0.354) (0.355) (0.362)
Constant cut10 4.259*** 4.247*** 4.245*** 4.240***
(0.358) (0.355) (0.357) (0.364)
Observations 2460 2460 2460 2460
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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368
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Table 17 Full results from ordered probit analyses of age-weighted full indices
Variables (1) (2) (3) (4)
Age-index1 Age-index2 Age-index3 Age-index4
Age − index 0.00462*** 0.00437*** 0.00390** 0.00441**
(0.00164) (0.00169) (0.00170) (0.00197)
Age − 0.0122 − 0.0130 − 0.0138 − 0.0151
(0.0153) (0.0154) (0.0156) (0.0154)
Age2 0.0174 0.0182 0.0185 0.0197
(0.0184) (0.0184) (0.0186) (0.0184)
Gender − 0.0290 − 0.0312 − 0.0299 − 0.0298
(0.0469) (0.0469) (0.0469) (0.0469)
Ethnicity 0.0404 0.0408 0.0404 0.0414
(0.0510) (0.0510) (0.0510) (0.0509)
Urban − 0.0672 − 0.0669 − 0.0670 − 0.0642
(0.0655) (0.0655) (0.0655) (0.0655)
Education 0.0298* 0.0311* 0.0324* 0.0328*
(0.0180) (0.0179) (0.0179) (0.0179)
Employer 0.0210 0.0236 0.0254 0.0298
(0.222) (0.222) (0.222) (0.222)
Wageworker − 0.119** − 0.117** − 0.118** − 0.116**
(0.0517) (0.0516) (0.0516) (0.0516)
Family 0.0434 0.0417 0.0385 0.0355
(0.0873) (0.0874) (0.0875) (0.0873)
Illness2 − 0.0272 − 0.0269 − 0.0276 − 0.0265
(0.0479) (0.0479) (0.0479) (0.0479)
Condition2 − 0.0852 − 0.0853 − 0.0856 − 0.0866
(0.0591) (0.0591) (0.0591) (0.0591)
Community1 0.0345 0.0358 0.0368 0.0373
(0.0427) (0.0427) (0.0427) (0.0427)
Religion − 0.174 − 0.175 − 0.176 − 0.177
(0.190) (0.190) (0.190) (0.190)
Personality1 0.0749*** 0.0749*** 0.0755*** 0.0761***
(0.0230) (0.0230) (0.0230) (0.0230)
Personality2 − 0.0292* − 0.0291* − 0.0292* − 0.0292*
(0.0163) (0.0163) (0.0163) (0.0163)
Personality3 − 0.0359* − 0.0354* − 0.0355* − 0.0353*
(0.0210) (0.0210) (0.0210) (0.0210)
Personality4 0.0239 0.0239 0.0246 0.0244
(0.0211) (0.0211) (0.0211) (0.0211)
Risk 0.0484*** 0.0484*** 0.0484*** 0.0484***
(0.00839) (0.00839) (0.00839) (0.00839)
Circumstances 0.360*** 0.360*** 0.360*** 0.360***
(0.0141) (0.0141) (0.0141) (0.0141)
Oblast1 0.608*** 0.609*** 0.609*** 0.614***
(0.126) (0.126) (0.126) (0.127)
Oblast2 0.273** 0.275** 0.278** 0.282**
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Table 17 (continued)
Variables (1) (2) (3) (4)
Age-index1 Age-index2 Age-index3 Age-index4
(0.132) (0.132) (0.132) (0.132)
Oblast3 − 0.309** − 0.309** − 0.310** − 0.308**
(0.147) (0.147) (0.147) (0.147)
Oblast4 0.875*** 0.876*** 0.878*** 0.882***
(0.131) (0.131) (0.131) (0.131)
Oblast5 0.197 0.200 0.199 0.207
(0.132) (0.132) (0.132) (0.132)
Oblast6 0.602*** 0.605*** 0.610*** 0.618***
(0.140) (0.140) (0.140) (0.140)
Oblast7 0.521*** 0.522*** 0.522*** 0.528***
(0.124) (0.124) (0.124) (0.124)
Oblast8 0.334*** 0.336*** 0.339*** 0.343***
(0.112) (0.112) (0.112) (0.112)
Constant cut1 − 1.042** − 1.061** − 1.104** − 1.128**
(0.505) (0.510) (0.515) (0.512)
Constant cut2 − 0.329 − 0.348 − 0.390 − 0.414
(0.413) (0.418) (0.424) (0.421)
Constant cut3 − 0.0971 − 0.116 − 0.159 − 0.182
(0.403) (0.409) (0.415) (0.412)
Constant cut4 0.679* 0.659* 0.617 0.595
(0.393) (0.398) (0.404) (0.401)
Constant cut5 1.252*** 1.233*** 1.190*** 1.168***
(0.392) (0.397) (0.403) (0.400)
Constant cut6 2.096*** 2.077*** 2.033*** 2.011***
(0.392) (0.397) (0.403) (0.401)
Constant cut7 2.790*** 2.770*** 2.727*** 2.704***
(0.393) (0.398) (0.404) (0.401)
Constant cut8 3.514*** 3.494*** 3.450*** 3.428***
(0.394) (0.399) (0.405) (0.402)
Constant cut9 4.275*** 4.254*** 4.210*** 4.188***
(0.395) (0.401) (0.407) (0.404)
Constant cut10 4.768*** 4.747*** 4.703*** 4.681***
(0.397) (0.402) (0.408) (0.405)
Observations 2460 2460 2460 2460
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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370
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Table 18 Main OLS results for
full indices using linear hours
worked
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
Variables (1) (2) (3) (4)
Index 1 Index 2 Index 3 Index 4
Index1b 0.00275**
(0.00134)
Index2b 0.00276**
(0.00134)
Index3b 0.00268**
(0.00135)
Index4b 0.00308*
(0.00164)
Constant 6.700*** 6.702*** 6.694*** 6.676***
(0.511) (0.511) (0.511) (0.513)
Observations 2467 2467 2467 2467
R-squared 0.102 0.102 0.102 0.102
Table 19 Main OLS results for
truncated indices using linear
hours worked
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
Variables (1) (2) (3) (4)
Index 1 Index 2 Index 3 Index 4
Wage-hours1b − 0.000681
(0.000429)
Wage-hours2b − 0.000679
(0.000427)
Wage-hours3b − 0.000710
(0.000446)
Wage-hours4b − 0.000281
(0.000465)
Constant 3.615*** 3.613*** 3.614*** 3.572***
(0.448) (0.448) (0.448) (0.448)
Observations 2460 2460 2460 2460
R-squared 0.356 0.356 0.356 0.356
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Table 20 Results from Index 5
Variables (1) (2) (3) (4) (5)
Index 5 Index 5 Index 5 Index 5 Index 5
Index5 0.00879*** 0.00854*** 0.00791*** 0.00834*** 0.00843***
(0.00229) (0.00229) (0.00230) (0.00227) (0.00229)
Age − 0.0548** − 0.0547** − 0.0552** − 0.0465** − 0.0437*
(0.0230) (0.0230) (0.0229) (0.0226) (0.0223)
Age2 0.0551* 0.0589** 0.0585** 0.0485* 0.0432
(0.0290) (0.0290) (0.0289) (0.0285) (0.0282)
Gender 0.0944 0.0541 0.0623 0.0500 0.0322
(0.0746) (0.0757) (0.0759) (0.0749) (0.0741)
Ethnicity 0.0880 0.110 0.0690 0.0650 0.00167
(0.0784) (0.0786) (0.0792) (0.0783) (0.0812)
Urban − 0.263*** − 0.236*** − 0.194** − 0.193** − 0.324***
(0.0762) (0.0766) (0.0774) (0.0771) (0.104)
Education 0.139*** 0.136*** 0.133*** 0.100*** 0.0965***
(0.0284) (0.0284) (0.0284) (0.0283) (0.0283)
Employer 0.271 0.238 0.204 0.0570 0.182
(0.358) (0.357) (0.357) (0.351) (0.348)
Wageworker − 0.235*** − 0.247*** − 0.258*** − 0.261*** − 0.225***
(0.0822) (0.0823) (0.0822) (0.0809) (0.0822)
Family 0.299** 0.299** 0.306** 0.330** 0.148
(0.140) (0.140) (0.140) (0.138) (0.138)
Illness2 − 0.0665 − 0.0570 − 0.0665 − 0.136*
(0.0759) (0.0759) (0.0749) (0.0753)
Condition2 − 0.329*** − 0.347*** − 0.297*** − 0.235**
(0.0959) (0.0960) (0.0949) (0.0942)
Community1 0.210*** 0.201*** 0.235***
(0.0659) (0.0651) (0.0676)
Religion − 0.0781 − 0.193 − 0.296
(0.309) (0.305) (0.302)
Personality1 0.235*** 0.188***
(0.0359) (0.0365)
Personality2 − 0.0358 − 0.0279
(0.0260) (0.0258)
Personality3 − 0.160*** − 0.131***
(0.0336) (0.0334)
Personality4 0.0217 0.0471
(0.0338) (0.0335)
Region1 0.548***
(0.200)
Region2 − 0.183
(0.209)
Region3 − 0.396*
(0.233)
Region4 0.938***
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372
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Table 20 (continued)
Variables (1) (2) (3) (4) (5)
Index 5 Index 5 Index 5 Index 5 Index 5
(0.205)
Region5 0.0764
(0.210)
Region6 0.222
(0.223)
Region7 0.125
(0.195)
Region8 0.376**
(0.177)
Constant 7.256*** 7.321*** 7.367*** 6.871*** 6.763***
(0.449) (0.450) (0.449) (0.480) (0.508)
Observations 2484 2477 2470 2467 2467
R-squared 0.032 0.039 0.042 0.075 0.106
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 21 OLS analysis on
heterogeneous effects for Index 1 Variables (1) (2) (3) (4)
Old Young Women Men
Index1 0.0109*** 0.0155*** 0.0139*** 0.0116***
(0.00329) (0.00323) (0.00377) (0.00294)
Age − 0.118 − 0.122 − 0.0686 − 0.0171
(0.130) (0.0919) (0.0420) (0.0264)
Age2 0.120 0.189 0.0717 0.0133
(0.132) (0.158) (0.0541) (0.0329)
Gender 0.0272 0.0104
(0.110) (0.101) –
Ethnicity 0.0702 − 0.0464 0.125 − 0.0973
(0.119) (0.111) (0.135) (0.101)
Urban − 0.477*** − 0.0807 − 0.267 − 0.393***
(0.148) (0.146) (0.168) (0.132)
Education 0.0703 0.0532 0.0269 0.0857**
(0.0449) (0.0394) (0.0498) (0.0364)
Employer 0.189 − 0.0166 0.685 − 0.0136
(0.403) (0.743) (0.779) (0.386)
Wageworker − 0.225* − 0.321*** − 0.127 − 0.327***
(0.116) (0.117) (0.159) (0.0957)
Family − 0.0611 0.381** − 0.0959 0.642***
(0.214) (0.182) (0.214) (0.196)
Illness2 − 0.0792 − 0.138 − 0.280** − 0.0901
(0.115) (0.100) (0.127) (0.0940)
Condition2 − 0.136 − 0.407*** − 0.253* − 0.150
(0.123) (0.152) (0.143) (0.126)
Community1 0.174* 0.247** 0.0466 0.371***
(0.0888) (0.106) (0.100) (0.0927)
Religion − 0.679 0.0447 − 0.739 − 0.101
(0.443) (0.412) (0.575) (0.351)
Personality1 0.259*** 0.110** 0.149** 0.202***
(0.0532) (0.0502) (0.0608) (0.0455)
Personality2 − 0.0179 − 0.0406 − 0.0779* 0.00749
(0.0379) (0.0353) (0.0417) (0.0328)
Personality3 − 0.124*** − 0.137*** − 0.220*** − 0.0686
(0.0479) (0.0467) (0.0536) (0.0425)
Personality4 0.0394 0.0555 − 0.0112 0.0744*
(0.0493) (0.0456) (0.0549) (0.0422)
Region1 0.201 0.892*** 0.980*** 0.327
(0.305) (0.267) (0.336) (0.247)
Region2 − 0.629** 0.250 0.0278 − 0.363
(0.319) (0.280) (0.356) (0.258)
Region3 − 0.451 − 0.398 − 0.301 − 0.485*
(0.363) (0.303) (0.405) (0.285)
Region4 0.882*** 0.942*** 0.784** 0.974***
(0.322) (0.265) (0.370) (0.246)
Region5 − 0.217 0.333 0.422 − 0.152
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374
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Table 21 (continued) Variables (1) (2) (3) (4)
Old Young Women Men
(0.330) (0.272) (0.368) (0.255)
Region6 0.0416 0.283 0.577 0.0100
(0.339) (0.294) (0.365) (0.280)
Region7 − 0.460 0.593** 0.411 − 0.0837
(0.309) (0.251) (0.332) (0.240)
Region8 − 0.113 0.648*** 0.538* 0.259
(0.281) (0.226) (0.301) (0.218)
Constant 8.576*** 7.693*** 7.690*** 6.024***
(3.195) (1.338) (0.881) (0.609)
Observations 1202 1265 946 1521
R-squared 0.138 0.113 0.125 0.130
Standard errors in parentheses
***p < 0.01; **p < 0.05; *p < 0.1
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The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
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Table 22 OLS and ordered probit on “objective” and “subjective” components of job quality index (equal
weighting version)
Variables OLS Oprobit
(1) (2) (3) (4)
obj sub obj sub
obj 0.0122*** 0.0191*** 0.00751*** 0.0121***
(0.00290) (0.00429) (0.00180) (0.00268)
Age − 0.0453** − 0.0323 − 0.0288** − 0.0209
(0.0223) (0.0222) (0.0139) (0.0138)
Age2 0.0453 0.0331 0.0291* 0.0217
(0.0282) (0.0281) (0.0175) (0.0175)
Gender 0.0146 0.0811 0.00695 0.0484
(0.0743) (0.0743) (0.0462) (0.0462)
Ethnicity 0.000264 − 0.0106 0.00211 − 0.00487
(0.0811) (0.0811) (0.0503) (0.0503)
Urban − 0.312*** − 0.353*** − 0.205*** − 0.231***
(0.104) (0.104) (0.0645) (0.0647)
Education 0.0878*** 0.0867*** 0.0552*** 0.0540***
(0.0286) (0.0286) (0.0178) (0.0178)
Employer 0.179 0.141 0.126 0.103
(0.348) (0.348) (0.219) (0.219)
Wageworker − 0.239*** − 0.257*** − 0.147*** − 0.158***
(0.0823) (0.0825) (0.0511) (0.0513)
Family 0.139 0.160 0.0832 0.0964
(0.138) (0.138) (0.0858) (0.0860)
Illness2 − 0.134* − 0.142* − 0.0922** − 0.0960**
(0.0753) (0.0752) (0.0469) (0.0469)
Condition2 − 0.232** − 0.237** − 0.143** − 0.146**
(0.0941) (0.0940) (0.0584) (0.0584)
Community1 0.245*** 0.225*** 0.155*** 0.142***
(0.0672) (0.0676) (0.0421) (0.0423)
Religion − 0.260 − 0.290 − 0.156 − 0.172
(0.302) (0.302) (0.188) (0.188)
Personality1 0.185*** 0.192*** 0.118*** 0.122***
(0.0365) (0.0364) (0.0227) (0.0227)
Personality2 − 0.0310 − 0.0316 − 0.0206 − 0.0212
(0.0258) (0.0258) (0.0161) (0.0161)
Personality3 − 0.127*** − 0.131*** − 0.0780*** − 0.0805***
(0.0334) (0.0334) (0.0207) (0.0207)
Personality4 0.0466 0.0533 0.0304 0.0346*
(0.0335) (0.0335) (0.0208) (0.0208)
Region1 0.548*** 0.560*** 0.348*** 0.354***
(0.199) (0.199) (0.124) (0.124)
Region2 − 0.143 − 0.205 − 0.0970 − 0.136
(0.209) (0.209) (0.129) (0.129)
Region3 − 0.401* − 0.435* − 0.252* − 0.273*
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376
D.Esenaliev, N.T.N.Ferguson
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References
Addison, J. T., & Grosso, J.-L. (1996). Job security provisions and employment: Revised estimates. Indus-
trial Relations: A Journal of Economy and Society, 35(4), 585–603.
Andrews, F. M., & McKennell, A. C. (1980). Measures of self-reported well-being: Their affective, cogni-
tive, and other components. Social Indicators Research, 8(2), 127–155.
Table 22 (continued)
Variables OLS Oprobit
(1) (2) (3) (4)
obj sub obj sub
(0.233) (0.233) (0.144) (0.144)
Region4 0.948*** 0.919*** 0.626*** 0.609***
(0.205) (0.205) (0.128) (0.128)
Region5 0.0942 0.0156 0.0394 − 0.0102
(0.210) (0.210) (0.130) (0.130)
Region6 0.254 0.260 0.140 0.142
(0.221) (0.221) (0.137) (0.137)
Region7 0.123 0.102 0.0692 0.0563
(0.195) (0.195) (0.121) (0.121)
Region8 0.376** 0.371** 0.241** 0.238**
(0.177) (0.177) (0.109) (0.109)
6.602*** 6.078*** –
(0.510) (0.523) –
Constant cut1 − 3.215*** − 2.856***
(0.414) (0.423)
Constant cut2 − 2.648*** − 2.298***
(0.338) (0.351)
Constant cut3 − 2.463*** − 2.118***
(0.330) (0.343)
Constant cut4 − 1.840*** − 1.503***
(0.320) (0.333)
Constant cut5 − 1.365*** − 1.032***
(0.318) (0.331)
Constant cut6 − 0.656** − 0.325
(0.317) (0.331)
Constant cut7 − 0.0757 0.258
(0.317) (0.330)
Constant cut8 0.527* 0.862***
(0.317) (0.331)
Constant cut9 1.166*** 1.501***
(0.317) (0.331)
Constant cut10 1.591*** 1.927***
(0.318) (0.332)
Observations 2467 2467 2467 2467
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
377
The Impact ofJob Quality onWellbeing: Evidence fromKyrgyzstan
1 3
Baum-Snow, N., & Neal, D. (2009). Mismeasurement of usual hours worked in the census and ACS. Eco-
nomics Letters, 102(1), 39–41.
Behrman, J. (1999). Labor markets in developing countries. Handbook of Labor Economics, 3, 2859–2939.
Bertram-Hümmer, V., & Baliki, G. (2015). The role of visible wealth for deprivation. Social Indicators
Research, 124(3), 765–783.
Blattman, C., Fiala, N., & Martinez, S. (2014). Generating skilled self-employment in developing coun-
tries: Experimental evidence from Uganda. The Quarterly Journal of Economics, 129(2), 697–752.
Boccuzzo, G., & Gianecchini, M. (2015). Measuring young graduates’ job quality through a composite
indicator. Social Indicators Research, 122(2), 453–478.
Borghans, L., Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008). The economics and psychology
of personality traits. Journal of Human Resources, 43(4), 972–1059.
Brück, T., Esenaliev, D., Kroeger, A., Kudebayeva, A., Mirkasimov, B., & Steiner, S. (2014). Household
survey data for research on well-being and behavior in Central Asia. Journal of Comparative Eco-
nomics, 42(3), 819–835.
Brunello, G., & Schlotter, M. (2011). Non cognitive skills and personality traits: Labour market rel-
evance and their development in education & training systems (No. IZA Discussion Paper 5743).
Bonn.
Caplan, R. D., & Harrison, R. (1993). Person-environment fit theory: Some history, recent developments,
and future directions. Journal of Social Issues, 49(4), 253–275.
Chen, M. A. (2007). Rethinking the informal economy: Linkages with the formal economy and the formal
regulatory environment (No. ST/ESA/2007/DWP/46).
Clark, A. (2005). Your money or your life: Changing job quality in OECD countries. British Journal of
Industrial Relations, 43(3), 377–400.
Clark, A. (2010). Work, jobs, and well-being across the millennium. In E. Diener, J. F. Helliwell, & D. Kah-
neman (Eds.), International differences in well-being (p. 489). Oxford: Oxford University Press.
Clark, A. (2015). What makes a good job? Job quality and job satisfaction. IZA World of Labor (215).
Cramer, J., Hartog, J., Jonker, N., & Van Praag, C. (2002). Low risk aversion encourages the choice for
entrepreneurship: An empirical test of a truism. Journal of Economic Behavior & Organization, 48(1),
29–36.
Cummins, R. (2000). Personal income and subjective well-being: A review. Journal of Happiness Studies,
1(2), 133–158.
Dahl, S.-Å., Nesheim, T., & Olsen, K. M. (2009). Quality of work: Concept and measurement (No. REC-
WP 05/2009). SSRN Electronic Journal.
Davoine, L., & Erhel, C. (2006). Monitoring employment quality in Europe: European employment
strategy indicators and beyond (No. CES Document 66).
de Jonge, J., Bosma, H., Peter, R., & Siegrist, J. (2000). Job strain, effort-reward imbalance and
employee well-being: A large-scale cross-sectional study. Social Science & Medicine (1982), 50(9),
1317–1327.
Decancq, K., & Lugo, M. A. (2013). Weights in multidimensional indices of wellbeing: An overview.
Econometric Reviews, 32(1), 7–34.
Diener, E., & Biswas-Diener, R. (2002). Will money increase subjective well-being? Social Indicators
Research, 57(2), 119–169.
Diener, E., & Oishi, S. (2000). Money and happiness: Income and subjective well-being across nations.
In E. Diener & E. M. Suh (Eds.), Culture and subjective well-being (pp. 185–218). Cambridge: MIT
Press. https ://doi.org/10.1186/2193-9039-3-13.
Dolan, P., Peasgood, T., & White, M. (2008). Do we really know what makes us happy? A review of the
economic literature on the factors associated with subjective well-being. Journal of Economic Psychol-
ogy, 29(1), 94–122. https ://doi.org/10.1016/j.joep.2007.09.001.
Drobnič, S., Beham, B., & Präg, P. (2010). Good job, good life? Working conditions and quality of life in
Europe. Social Indicators Research, 99(2), 205–225.
Edmonds, E., & Pavcnik, N. (2005). Child labor in the global economy. Journal of Economic Perspectives,
19(1), 199–220.
Ekelund, J., Johansson, E., Järvelin, M.-R., & Lichtermann, D. (2005). Self-employment and risk aver-
sion—Evidence from psychological test data. Labour Economics, 12(5), 649–659.
Farber, H. S. (1998). Are lifetime jobs disappearing? In J. C. Haltiwanger, M. Manser, & R. H. Topel (Eds.),
Labor statistics measurement issues (p. 478). Chicago: University of Chicago Press.
Ferrer-i-Carbonell, A. (2005). Income and well-being: An empirical analysis of the comparison income
effect. Journal of Public Economics, 89(5), 997–1019.
Gallie, D. (2009). Employment regimes and the quality of work. Oxford: Oxford University Press.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
378
D.Esenaliev, N.T.N.Ferguson
1 3
Gallie, D. (2013). Economic crisis, quality of work and social integration: The European experience.
Oxford: Oxford University Press.
García-Pérez, C., Prieto-Alaiz, M., & Simón, H. (2017). A new multidimensional approach to measuring
precarious employment. Social Indicators Research, 134(2), 437–454.
Gómez-Salcedo, M. S., Galvis-Aponte, L. A., & Royuela, V. (2017). Quality of work life in Colombia: A
multidimensional fuzzy indicator. Social Indicators Research, 130(3), 911–936.
Goos, M., & Manning, A. (2007). Lousy and lovely jobs: The rising polarization of work in Britain. Review
of Economics and Statistics, 89(1), 118–133.
Green, F. (2007). Demanding work: The paradox of job quality in the affluent economy. Princeton: Prince-
ton University Press.
Groves, M. O. (2005). How important is your personality? Labor market returns to personality for
women in the US and UK. Journal of Economic Psychology, 26(6), 827–841.
Heckman, J. J. J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on
labor market outcomes and social behavior. Journal of Labor Economics, 24(3), 411–482.
Houseman, S. (1995). Job growth and the quality of jobs in the U.S. economy. Labour (Special Issue),
93, S124.
Kalleberg, A. L., Reskin, B. F., & Hudson, K. (2000). Bad jobs in America: Standard and nonstandard
employment relations and job quality in the United States. American Sociological Review, 65(2),
256–278.
Leete, L., & Schor, J. B. (1994). Assessing the time-squeeze hypothesis: Hours worked in the United
States, 1969–89. Industrial Relations, 33(1), 25–43.
Leschke, J., Watt, A., & Finn, M. (2008). Putting a number on job quality: Constructing a European Job
Quality Index (No. ETUI-REHS Working Paper No. 2008.03).
McBride, M. (2001). Relative-income effects on subjective well-being in the cross-section. Journal of
Economic Behavior & Organization, 45(3), 251–278.
Meier, S., & Stutzer, A. (2008). Is volunteering rewarding in itself? Economica, 75(297), 39–59.
Muñoz de Bustillo, R., Fernández-Macías, E., Antón, J.-I., & Esteve, F. (2011). Measuring more than
money: The social economics of job quality. Cheltehham: Edward Elgar.
NSC. (2017). Kyrgyz Republic: Employment and Unemployment in 2016. Bishkek: National Statistical
Committee of the Kyrgyz Republic.
Pavot, W., & Diener, E. (1993). The affective and cognitive context of self-reported measures of subjec-
tive well-being. Social Indicators Research, 28(1), 1–20.
Presser, H. B. (1999). Toward a 24-hour economy. Science, 284(5421), 1778–1779.
Ritter, J. A., & Anker, R. (2002). Good jobs, bad jobs: Workers’ evaluations in five countries. Interna-
tional Labour Review, 141(4), 331–358.
Schokkaert, E., Verhofstadt, E., & Van Ootegem, L. (2009). Measuring job quality and job satisfaction.
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium.
Ghent University.
Schoon, I., Hansson, L., & Salmela-Aro, K. (2005). Combining work and family life. European Psy-
chologist, 10(4), 309–319.
Siegrist, J. (1996). Adverse health effects of high-effort/low-reward conditions. Journal of Occupational
Health Psychology, 1(1), 27–41.
Theorell, T., & Karasek, R. A. (1996). Current issues relating to psychosocial job strain and cardiovas-
cular disease research. Journal of Occupational Health Psychology, 1(1), 9–26.
Wallace, C., Pichler, F., & Hayes, B. (2007). First European Quality of Life Survey: Quality of work and
life satisfaction.
Wooden, M., Warren, D., & Drago, R. (2009). Working time mismatch and subjective well-being. Brit-
ish Journal of Industrial Relations, 47(1), 147–179.
World Bank. (2012). World Development Report 2013: Jobs. The World Bank Group.
Yamada, G. (1996). Urban informal employment and self-employment in developing countries: Theory and
evidence. Economic Development and Cultural Change, 44(2), 289–314.
Yogo, U. T. (2011). Social network and wage: Evidence from Cameroon. Labour, 25(4), 528–543.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
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... This phenomenon has gained attention in the empirical literature because of the effects underemployment has on workers' health and well-being (Bunting 2011;Hilbrecht et al. 2017;Esenaliev and Ferguson 2019;Lepinteur 2019;Mousteri et al. 2020). There has been an increasing number of articles in the empirical literature that study the relationship between underemployment and subjective well-being of workers (Friedland and Price 2003;Wilkins 2007;Angrave and Charlwood 2015;Bell and Blanchflower 2019). ...
... Wunder and Heineck (2013) use life satisfaction measures to approximate workers' utility and explain how working time mismatches affect welfare. Nevertheless, there is still a gap to fill with regards to the economic valuation of underemployment in developing and recently developed countries with more informal economies (Esenaliev and Ferguson 2019), therefore, we propose to study the case of Chile and its particular labor market. ...
... In the literature, underemployment has gained attention because its effects on health and well-being have been extensively documented (Friedland and Price 2003;Bunting 2011;Angrave and Charlwood 2015;Esenaliev and Ferguson 2019;Lepinteur 2019;Mousteri et al. 2020). It is well known that individuals' subjective well-being depends on many factors, but job and labor market status are often the main determinants of life satisfaction, happiness and health for workers (Radcliff 2005;Taht et al. 2019). ...
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