A Multilevel Simultaneous Equations
Modelling Approach to Investigate the
Relationship between Poverty and Labour-Force Participation among the Elderly in Egypt
Hend Gabr1, Fiona Carmichael2 and Hui Li3*
1Faculty of Commerce, Menufia University, Egypt
2Birmingham Business School, University of Birmingham, United Kingdom
3School of Mathematics, University of Birmingham, United Kingdom
Submission: September 09, 2019; Published: October 15, 2019
*Corresponding author: Hui Li, School of Mathematics, University of Birmingham, the United Kingdom
How to cite this article: Hend Gabr, Fiona Carmichael and Hui Li. A Multilevel Simultaneous Equations Modelling Approach to Investigate theRelationship between Poverty and Labour-Force Participation among the Elderly in Egypt. Int J Environ Sci Nat Res. 2019; 22(1): 556076. DOI: 10.19080/IJESNR.2019.22.556076. DOI: 10.19080/IJESNR.2019.22.556076
In this study, we explore the effect of participation in the labour force on poverty among older people while addressing the issues of endogeneity and the hierarchical structure in the Egyptian household survey data. The households are nested within governorates, which results in dependency among those within the same governorate. We also find that the endogeneity is caused by a simultaneous relationship between poverty and participation in the labour force. A multilevel simultaneous equations model is implemented, and the results support the hypothesis that participating in the labour force has a significant effect on reducing older people’s poverty.
Keywords: Older people Poverty Labour force Endogeneity Hierarchical analysis Multilevel analysis
Egypt is going through a significant demographic change since the last few decades, in which the population ageing is one of its main characteristics. As the most populous Arab nation, Egypt’s population reaches almost 95 million people. In 1996, there were 5.7% older persons aged 60 and more. By 2006, the ratio increased to 6.1% of the total population . The proportion of persons aged 60+ in the population is expected to reach 11% by 2025 and will continue to grow to 18% by 2050 . With ageing, people often lack the capacity to work and earn. According to 2006 census data, 23.7% of older persons aged 60-64 were engaged in the labour market. This percentage declines to about 15% for the category 65-69 and only 6.6% for the category 75+ .
Older people who can no longer participate in the labour market are more likely to seek other sources of income. Traditionally, the most common formal source of income in old age is the social support system in addition to an informal extended families source of support. However, the formal support system has put older people in a vulnerable position due to limited resources and coverage . Since the societies’ structure shifts away from the tradition multigenerational families to more nuclear families, the extended families also seem to be imperfect . Even where the traditional structure still exists, older people cannot rely on their
relatives as they would have done in the past, because the decline in the fertility rate has reduced the number of family members who can become potential supporters. The United Nations  projects a decline in the potential support ratio in Egypt from around thirteen supporters per one older person in 2007 to only five supporters per older person in 2050.
Considering the significant changes in the structure of the population, this paper explores the main factors that determine older people’s poverty status focusing on the effect of participating in the labour force. We take account of two main modelling. The first issue is that is the hierarchical structure of the data as the households are nested within governorates, which affects the independence of the observations. Both poverty and participation in the labour force show dependency within each governorate. Our modelling results support that 26.45% of the variability in poverty and 6.74% of the variability in labour-force participation can be attributed to differences between governorates. Ignoring the hierarchical structure of the data could result in low estimates of the standard error of the coefficients associated with the variables measured at higher levels, and therefore produce an apparently significant effect of these variables which is false . Treating highly heterogenous governorates as one group also distortsthe results in our study. To overcome this problem, we implement
a standard multilevel model that allows for a generalised covariance
structure so that the households from the same governorate
can be correlated.
The second issue is the endogeneity between poverty and participation
in the labour force. On the one hand, being poor may
force older people into work as a strategy to reduce their poverty,
which indicates a positive relationship between these two variables.
On the other hand, participating in the labour force may
reduce poverty, which suggests a negative relationship between
the two variables. Dealing with this complex interdependency
appropriately enables us to achieve a consistent estimate of the
parameters by using simultaneous equations .
To address both of these issues, we developed a multilevel
simultaneous equations model that overcomes the endogeneity
problem and the hierarchical structure of the data. In this study,
we first explore the issue of poverty among older people in Egypt
by constructing a poverty index using data represented at the national
level. We then use the poverty index in the multilevel simultaneous
equations model to investigate the simultaneous relationship
between poverty and participation in the labour force
among people aged 60 and over.
The rest of this paper is organised as follows. In the next section,
we briefly review the literature on multilevel modelling and
endogeneity before addressing its contributions and limitations.
We then present and describe the dataset in section 3. In section 4,
we construct and illustrate the multidimensional measure of poverty
using factor analysis. In section 5, we outline the econometric
model. We present the hypothesis tests in section 6 and discuss
the empirical results in section 7. Further discussions and concluding
remarks on policy relevance are presented in section 8.
The topic of endogeneity has received more attention from researchers
in the context of fixed-effects models than hierarchical
models. Empirical results show that the presence of even a modest
correlation between the regressor and the random disturbance
affects the bias and consistency of the regression parameter .
A widely used approach to overcome the problem of endogeneity
in fixed-effects models is to construct an instrumental variable,
which is correlated with the endogenous regressor but not with
the random term. This instrumental variable is then regressed on
the dependent variable instead of the original endogenous variable.
Spencer  suggested applying the same approach to overcome
endogeneity in hierarchical models. Similarly, Spencer &
Fielding  constructed instrument variables to study how the individual
and contextual characteristics affect their test scores. To
obtain a consistent estimate of the endogenous variable, Rice et al.
 recommended using instrumental variables or conditioning
on the group-level effect. Dee  adopted an assumption-free
approach to account for within-group dependencies in the error
structure rather than using the multilevel model technique
Steele et al.  developed a two-level simultaneous equations
model to examine the endogeneity results from the selection
bias of school resources on pupils’ attainment. They used
the reweighted iterative generalised least squares method. More
recently, Steele et al.  identified other two-level simultaneous
equations to model the relationship between children’s educational
transition and family disruption. They again demonstrated
the importance of tackling endogeneity, since ignoring it
led to over-estimating the effects of disruption in the family on
children’s outcomes. In their most recent study, Steele et al. 
considered a multilevel framework to examine the simultaneous
influence of individuals within the same social group, based on
longitudinal data. They focused on reciprocal parent-child causal
effects and sibling effects by setting two multilevel simultaneous
equations autoregressive cross-lagged model for the parents’ and
A review of the relevant literature shows that most of the existing
studies on older people’s participation in the labour force
have focused on variables measured at the individual level, such
as age, gender, marital status, education and health, as the main
determinants [15,16]. Other studies on participation in the labour
force have also looked at the effects of individual characteristics,
including being a married woman , adult education , losing
a job , and receiving a social security pension [20,21].
Several studies have captured variables that were measured at
the national level. However, some of these attempts encountered
methodological difficulties. Yamada  used Japanese aggregated
census data to identify the determinants of the supply of older
people in the labour market. Their study identified that social security
retirement benefits and the rate of unemployment were the
main determinants of older men participating in the labour force.
A study that heavily depends on aggregated data can result in the
problem of ecological fallacy  as the correlation between the
dependent variable and an independent variable can be distinctively
different for the whole observations than within each group.
Making implications regarding only the overall participation in
the labour force, for example, could justify the use of aggregated
data and reduce aggregation bias. Some studies have combined
variables at an individual and a national level. Munnell et al. 
used a probit regression model to examine how important the following
factors were in determining older people’s participation in
the labour force: the rate of unemployment, the age structure of
the population, the nature of employment and specific individual
characteristics. However, when the individual- and national-level
variables are combined into a single fixed-effects model only, this
violates the assumption of the independence of error, yielding a
highly significant effect of the predictors due to the low estimated
standard error [5,25].
Simultaneity among variables is another important issue that
should be taken into consideration whenever it exists; otherwise,
it leads to inconsistent results for the effects of key variables. Parsons’
 study stresses the importance of the endogeneity of
self-reported health by using different health indicators to compare the determinants of the supply of older people in the labour
market. Chang & Yen  and Bound et al.  found that self-reported
health is an endogenous variable, and that those who are
in good health are less likely to retire unless they have plentiful
economic resources. Although Mitchell and Dwyer  found
no support for the evidence of the endogeneity of self-reported
health, they believe that poverty has a significant influence on retirement
Broadly relating to poverty, researchers have identified several
endogenous variables. Cameron & Cobb-Clark  considered
whether receiving financial transfers from children is endogenous
to the supply of older people in the labour market. They found
that the only significant effect of receiving transfers was to reduce
the supply of co-resident mothers (mothers who live with their
children) in the labour force. More recently, they explored the effects
of living with children as another endogenous variable that
should, along with transfers from children, determine the supply
of older people in the labour market . Life satisfaction is
another variable that has been found to be endogenous to older
people’s participation in the labour force . Amuedo-Dorantes
 investigated the endogeneity of poverty on the involvement
of household heads in the informal sector and found that there is
a positive relationship between poverty and working in the informal
This study uses data from the Egyptian Household Observatory
Survey – Round 7, Egypt 2010 database . In 2010, Egypt
was divided for administrative purposes into twenty-nine governorates
representing three main regions: Upper Egypt, which has
ten governorates, Lower Egypt, which has fourteen governorates,
and the frontier regions, which have five governorates. The data
includes households in twenty-four governorates from Lower
Egypt and Upper Egypt; the governorates in the frontier regions
were excluded due to their low population density. The Household
Observatory Survey was conducted to collect information on
a broad range of topics related to households’ health, socio-economic
circumstances and perception of a wide range of services.
All the households headed by a person aged 60 or over were used
in this study, resulting in data from a total of 2,102 households.
The definitions of the variables used, along with their summary
statistics, are presented in Table 1.
Meanwhile, three variables measured at the governorate level
were merged with the dataset. For each governorate in the same
year (2010), these variables describe the unemployment rate, the
percentage of the population in the labour force, and the level of
income inequality, which was measured using the Gini coefficient.
The data was obtained from the Egypt Human Development Report
 and is presented in Table 2. For example, 163 households
were selected from the Cairo governorate, where 29.21% of
the population is in the labour force and the unemployment rate
Poverty is not restricted to a lack of income: it also includes
factors that reduce the quality of life. The most widely used measure
of poverty is based on a money metric dimension using poverty
lines. Another dimension is wealth, which can be measured
using various indicators of household welfare, such as ownership
of durable goods and housing conditions .
One of the major concerns of older people is insecurity, which
can be measured by access to health insurance and pension
schemes. Access to health insurance is more important for older
people than for other age groups. In this study, we used information
about an individual’s inability to afford the necessary medical
treatment as a proxy for lack of access to health insurance.
A pension is another important source of security. In Egypt, the
government is facing serious difficulty with providing income
security for older people because of the low level of coverage of
formal pension schemes. More than two-thirds of older Egyptians
who have reached state pension age do not receive a pension .When older people do not have a secure source of income, they
can become trapped in a cycle of poverty. Thus, we consider lack
of access to a pension scheme as an indicator of poverty.
We also considered subjective poverty, which relies on an individual’s
perception of his or her economic status and provides
information about poverty from those who are directly experiencing
it. To bring the above-mentioned multidimensional perspectives
together, we then used factor analysis to assign a weight to
each indicator of poverty. The model can be expressed as:
where L is a matrix of factor loadings, ij l is the loading of the ith
variable on the jth factor, F1, F2, …, Fm are the common factors, which
are assumed to have a mean of 0 and an identity variance-covariance
matrix, and 1 2 , ,...... p ε ε ε are the specific factors, which are
assumed to follow the normal distribution with a mean of 0 and a
The factor score is estimated as a linear combination of the
original variables. Thus, for the individual K, the factor score is
calculated according to the following formula:
where Ik is the index score of the individual K, ˆ
i f is the factor
score coefficient of the ith variable, zik is the standardised value of
the ith variable for the individual K, and is the number of variables.
We construct a composite poverty index using factor analysis.
First, we construct an index of durable goods owned by each
household. Then we set up an index of households’ housing conditions (these results are available upon request). Finally, we create
the poverty index from the two indices of durable goods and housing
conditions, along with other variables that represent different
dimensions of poverty. These variables include poverty status by
the Egyptian objective poverty line (OP) and subjective poverty
(SP). More specifically, the OP indicates whether or not per capita
spending is below or above the official Egyptian poverty line;
and the SP is measured by the individual’s responses to questions
about their perception of their poverty status. We also incorporate
two variables to reflect the security dimensions: access to
health insurance and coverage by a pension scheme. The definitions
of the variables used to construct this composite poverty
index, along with the summary statistics, are presented in Table 3.
A factor analysis of these six variables yields a two-factor solution,
as reported in Table 4. The communalities of the variables
used to construct the poverty index ranged from 0.274 to 0.743.
The amount of variance accounts for by the principal component
is about 57%. The Kaiser-Meyer-Olkin (KMO) value is 0.7, which
shows that the model is an acceptable fit. Moreover, Bartlett’s test
of sphericity is significant at the level of 0.01, so we are confident
the validity in the analysis. To integrate the two factors into the
poverty index, we weight each factor’s score by the percentage
of variance it explained as a proportion of the total percentage of
variance explained by the two factors together. The factor is rescaled
to range between 0 and 1 and then multiplied by 100. Therefore,
each individual is assigned a score in the poverty index that
lay between 0 and 100, where 0 represents the poorest and 100
represents the richest.
We need to address two issues in the modelling. The first is the
hierarchical structure of the data, and the second is endogeneity.
As the households are nested within governorates, this results in a
certain degree of dependency among households within the same
governorate; for example, they may share similar geographical
features, social norms and regulations. Consequently, this violates
the ordinary least squares (OLS) assumption of independence of
observations and biases the variances of the estimates. For example,
it would produce a lower estimate of the standard errors that
yield unrealistically significant results (Hox, 2010) . Moreover,
we believe that older people are likely to be poor when they do
not receive income from employment. However, being poor might
force those people to join the labour force as a strategy to reduce
their poverty; thus, poverty may be diminishing in response to
participation in the labour force. This endogeneity from the simultaneous
relationship between poverty and participation in the
labour force should be addressed appropriately in the modelling.
To do so, our model considers the hierarchical structure of the
data while correcting for the endogeneity of participation in the
labour force. The standard approach to modelling the hierarchical
structure of the data with clustering at the governorate level is to
fit a two-level random-effects model and then allow the interceptterm and other parameters to vary randomly across governorates
(Snijders and Brsker, 1999; Steele et al., 2007) . We propose
the following models:
where qij X stands for the poverty index, which is derived in the
previous section, qij X represents the qth predictor for an individual
i nested in a governorate j, ij ε is the qth parameter associated
with qij X , 0, j β is the intercept term of governorate j, uj and ij ε are
the random error terms for the governorates and the households,
respectively, which are assumed to be normally distributed:
~ (0, 2 ) j u u N σ and ~ (0, 2 ) ij e e N σ .
The individual-level variables in equation (3) include both
demographic and socio-economic variables, such as AG1, AG2,
MALE, MRR, ILLTER, UNI, HHSUP, INCS, RU, and INLAB and the
interaction term ij ij INCS × RU .
The difference caused by random effects can be modelled
by letting 0, j β , 1, j β and 2, j β consist of a fixed component which
represents the population average and the random governorate
difference from the population average. Specifically, we construct
the following three sets of equations to capture this variation by
introducing random terms to 0, j β , 1, j β and 2, j β :
Other slopes parameters are assumed to be fixed across governorates,
i.e., q, j q,0 β =γ for q = 3, 4, …, 11. We let j = 0 because
there is no variation between governorates in the effect of the predictors
on the poverty index.
Combine equations (3) to (6), a single equation for the poverty
model is given by:
Similarly, the structural equation for participation in the labour
force was set up by using a multilevel logistic model. The
log odds of being in the labour force at the individual level can be
expressed using the following logit link function via the logistic
where ηij is the predicted log odds of being in the labour force
as opposed to being in the base category (not in the labour force),
qij X is the qth predictor for the individual i (nested) in governorate
q, j β is the qth parameter associated with the qth predictor,
0, j β
is the intercept of governorate j, and is the poverty index. The
slope parameters, *
q, j β are assumed to have fixed effects across
governorates. We denote this by letting j = 0, since the effect of
the predictors on participation in the labour force does not vary
across governorates. Thus, * *
q, j q,0 β =γ for q = 1, …, 13. Here, the
individual-level variables are demographic and socio-economic,
such as AG1, AG2, MALE, MRR, ILLTER, UNI, DISB, CHR, DISBCHR,
HHSUP, INCS, RU and PI.
With a further assumption of the random effects in the intercept
0, j β is written as:
where is the intercept term, hj Z
is the hth governorate-level
predictor of governorate j,
0,h γ is the hth parameter associated
with, and 0 j V is the white noise error term. In our study, the predictors
at the governorate level included the rate of unemployment
within the governorate (UNEMP), the percentage of the population
in the labour force (PRIN) and income inequality within
the governorate (INEQUAL).
Putting (8) and (9) together, we now have a single equation
version of the multilevel logistic model as follows:
Thus, the full set of structural equations consists of equations
(7) and (10). To identify these equations, the set of explanatory
variables included in equation (7) should contain at least one variable
that is not included in the explanatory variables in equation
(10). The selected variables must have a direct effect on participation
in the labour force, but they should not have a direct influence
on poverty. We consider using health condition because several
studies have found out that health is one of the main determinants
of participation in the labour force Barnay, 2009 [15,16]. Bound
et al.  argue that older people who are in good health are less
likely to retire unless they have plentiful economic resources.
Health is measured by three indicators: having a disability, having
a chronic disease, or both. These three indicators are excluded
from equation (7) but are used in equation (10). The results of
the multilevel reduced-form model of participation in the labour
force show that having these health problems has a highly significant
effect on the log odds of older people participating in the
labour force. Thus, an argument can be made that in the context
of the older population, healthy people tend to continue working
beyond their 60s. This indicates a negative relationship between
health problems and participation in the labour force. The effect
of health on poverty among older people passes through the channel
of the labour force participation.
In addition, to account for endogeneity, we develop a multilevel
model for the endogenous variable (INLAB) using regressors
assumed to be exogenous and independent of the random part of
model 7. The exogenous variables at the individual level included
AG1, AG2, MALE, MRR, ILLTER, UNI, DISB, CHR, DISBCHR, HHSUP,
INCS, RU, while we includedUNEMP, PRIN and INEQUAL as
the exogenous variables at the governorate level. Hence, the reduced-
form equation for the log odds of participating in the labour
force is expressed as follows:
This reduced-form equation is fitted using the logistic model,
and its estimates are used to obtain the predicted values of participation
in the labour force, . These predicted values, , do not
correlate with the error term in the poverty model in equation
(7) as the original variable, which has a correlation with the errorterm. The predicted values were used in the multilevel equation
of poverty, and the two-stage least-squares (2SLS) procedure was
applied to estimate the multilevel model of poverty as follows:
Among the terms in equation (12), 11
0,0 1,0 2,0 3,0 ,0
ij ij ij q qij
γ γ ILLTER γ INCS γ INLAB γ X
+ + + + Σ
represents the fixed effects at the individual level. This is followed
by a governorate-level effect of 3
, h hj
and a cross-level interaction
term of 3 3
h hj ij h hj ij
γ Z ILLTER γ Z INCS
Σ +Σ . The last component in (12) captures
the random part of the model, which includes an individual-
level random component,, and the governorate-level random
component, 0 j 1 j ij 2 j ij U +U ILLTER +U INCS .
Based on the models described in section 5, we have proposed
the following hypotheses tests. We first examine whether or not
the variance of governorate-level residuals in the poverty model
differed significantly among governorates; in other words, whether
there was evidence of the hierarchical structure of the data.
More specifically, for the intercept and each variable in
poverty model 7, we test H1: var (Uqj) > 0 for q = 0,1,2,…11 and
j = 1,2,…24 against the null hypothesis of no variation. We include
a set of demographic and socio-economic variables, such as age,
gender, marital status, education, household or family support, location
and participation in the labour force. We expected to find
evidence of var (U0j) > 0, which would emphasise the importance
of accounting for the hierarchical data structure.
Similarly, for the reduced form of the labour-force participation
model, we test whether or not the variance of governorate-
level residuals differs significantly among governorates.
Thus, for the intercept and each variable, we test H2: var '
qj V > 0
for q = 0,1,2,…11and j = 1,2,…24 against no effects. We expect the
results to support H2.
Meanwhile, among the factors that would influence poverty,
we are particularly interested in testing the age-related variables.
In Egypt, the retirement age is 60. According to data from the
2006 Egypt census, 23.7% of older people aged between 60 and
64 were participating in the labour market. This percentage fell to
about 15% for people in the 65-69 age group and only 6.6% for
people aged 75 or older. Thus, against the null hypothesis of no
effect, we test 3 1 : 0 AG H γ > and 4 2 : 0 AG H γ > . We expected to reject the
null hypothesis and find that people in the older age groups are
more likely to be poor than people in the 60-64 age group; that is,
we expected to find evidence to support the alternative hypotheses
of 3 H and 4 H .
Finally, as far as the effect of participation in the labour force
is concerned, we expected to find that there is a significant relationship
with poverty and that joining the labour force decreases
poverty. The alternative hypothesis is 5: 0 INLAB H γ < against the
null hypothesis of no effect.
The results from the likelihood ratio test (Table 5) suggest
that the effects of being illiterate (ILLTER) and receiving other
sources of income (INCS) differ substantially across governorates
in the poverty index model with the χ 2value of 19.614,which is
highly significant at the level of 1%. We also found that there is a
significant variation across the intercept terms. Of the variability
in labour-force participation, 6.74% lies between governorates,
and this proportion increases to 26.45% of the variability in the
poverty model. We then assign random components to the parameters
ILLTER and INCS and allow them to vary across governorates.
The varying slopes of ILLTER and INCS by governorate are
plotted in Figure 1. Thus, the results support H1 and H2 on the heterogeneous
effects of ILLTER and INCS across governorates.
Meanwhile, the results presented in Table 6 show that when
the multilevel structure is accounted for, the model is more able to
explain the variability in labour-force participation between governorates.
The variance partition coefficient (VPC) is 3.04% when
accounting for the multilevel structure, which is half the VPC
when the multilevel structure is not considered. Furthermore, the
results indicate that a multilevel approach is necessary for both
the poverty and the labour-force participation models.
***denotes that the estimated coefficient is Significant at .01** denotes
that the estimated coefficient is Significant at .05* denotes
that the estimated coefficient is Significant at .1.
In the previous section, we established the endogeneity of
participation in the labour force with respect to poverty. The resultsin Table 6 are used in a reduced form to obtain the predicted
values of participation in the labour force, ij INLAB . ij INLAB was then
included in equation 12. To obtain unbiased estimates, we applied
the 2SLS model. For the purposes of comparison, all the modelling
results for the determinants of poverty using the OLS, 2SLS and
multilevel 2SLS modelling approaches are presented in Table 7.
We focus on the multilevel 2SLS model in this section, because it
addresses the issues of endogeneity and the hierarchical structure
of the data.
Two main sets of variables were expected to affect poverty
among older people. The first set is at the individual level and includes
demographic and socio-economic characteristics, such as
age, gender, marital status, educational attainment, household potential
support ratio, receipt of other sources of income, and place
of residence. The second set of variables includes the governorate
characteristics: income inequality, the rate of unemployment and
the percentage of the population participating in the labour force.
We add to the second set of variables some cross-level interaction
variables which result from the significant variation between
governorates in the effect of the predictors (ILLTER and INCS) on
Being male (MALE), holding a university degree or above
(UNI), having a high household potential support ratio (HHSUP)
and participating in the labour force () ij INLAB all reduce the likelihood
of being in poverty. The interaction effect of ij INCS ij × RU is
significant and positive, which indicates that older people living
in rural areas and receiving income from fewer sources other than
work are more likely to be living in poverty. Our data show that
approximately 30% of older people living in rural areas receive
government assistance and more than 23% receive support from
relatives. For households in urban areas, the other main source of
income is pension schemes. In urban areas, 72.3% of residents receive
a pension; in rural areas this figure is only 38%. This broadly
supports the finding that far fewer households living in rural areas
receive income support.
As far as the governorate-level variables are concerned, the
higher the income inequality rate, the more people in the labour
force and higher unemployment rate have a significant effect on
reducing poverty. Due to the complex function of income-distribution
channels during a country’s economic growth, there is no
consensus on the exact impact of income inequality on growth,
and, therefore, on poverty . In our study, we found that there
is a significant, negative relationship between income inequality
and poverty status.
As noted in H1 and H2, we have assigned two random slopes
for ILLTER and INCS to account for the heterogeneity of effects.
To do so, we consider the cross-level interaction between the
governorate-level variables and these two variables. The results
show that the interactions between these variables and some of
the governorate-level variables have a significant effect on poverty.
The results show that the interaction between illiteracy and
the percentage of the population participating in the labour force
(ILTERij ) j × PRIN exerts a significant, positive influence on the poverty
index. This further indicates that for older people who are
illiterate, participating in the labour force has a smaller effect on
reducing poverty. The results also suggest that in governorates
with a high level of income inequality, receiving other sources of
income significantly reduces poverty.
As far as the random factor is concerned, the results suggest
that variance at the governorate level ( 00 τ ) decreases substantially
from 117.9 (Table 3) to 0 (Table 7). Accordingly, a multilevel
simultaneous equations model reduces the variance partition coefficient
(VPC) from 26.45% (Table 5) to only 10.34% (Table 7).
It is also interesting to compare the results of the multilevel simultaneous
equations model with those of the OLS and 2SLS models
(Table 7). The first estimation is the OLS model, which ignores
the endogeneity and the hierarchical structure of the data. The
second is the 2SLS model, which accounts for endogeneity but ignores
the hierarchical structure of the data. The results show that,
in general, the multilevel 2SLS model yields larger standard error
terms than the other two models for the variables whose effects
differ significantly among governorates (i.e. ILLTER and INCS) and
for the cross-level interaction variables. This is because applying
single-level models of OLS and 2SLS on our hierarchical data violate
the assumption of independence of observations. They disaggregate
the variables measured at the governorate level to the
individual level, which yields lower standard errors for the estimated
parameters and, therefore, exaggerates their significance.
Take, for example, the effect of being in the labour force (INLAB),
which is the variable we have focused on in this study. When
using the OLS model without correcting the endogeneity, this
variable has a significant effect on increasing poverty; after fixing
the endogeneity problem, INLAB has a significant effect on reducing
poverty. The single-level 2SLS model and the multilevel 2SLS
model both suggest that the effects of participating in the labour
force have a signisssficant effect on reducing poverty.
We have investigated the main determinants of poverty
among people aged 60 and over, with a particular focus on the relationship
between poverty among older people and their participation
in the labour force. Our study has used a multidimensional
measure of poverty in later life to capture a range of dimensions
of poverty rather than depending solely on financial deprivation.
We then used this poverty index as a dependent variable to model
the determinants of poverty among older people.
The study contributes to developing a multilevel 2SLS model
that simultaneously accounts for the two key issues of endogeneity
and the hierarchical structure of the data. We have constructed
a variance framework by allowing two of the regression parameters
to vary across governorate-level units. We have also compared
the multilevel 2SLS model with the traditional OLS model
and the basic 2SLS model. It is interesting to observe the change in
the coefficient of INLAB from a positive to a negative relationship
with a reduction in poverty.
In addition to overcoming these two methodological issues,
we have identified several main determinants of poverty among
older people. All the models tested showed that being in the 70+
age group significantly increases poverty. It is widely believed
that female-headed households are poorer than their male counterparts
because women have fewer opportunities to obtain a
university degree and, as a consequence, less access to equal employment
opportunities. Our study also supports the assertion
that gender has a significant influence on poverty, with older men
being less likely to be in poverty than older women. Our resultsshowed that holding a university degree or higher also decreases
poverty significantly, compared with other education groups. The
ratio of potential supporters in the household, where the burden
is placed on younger members of the household to support their
older relatives, showed that as the number of potential supporters
increases, poverty among older people decreases. Consistent
with previous studies, rural residents are poorer than their urban
counterparts. Moreover, rural residents who receive income
from sources other than work are poorer than those who do not
receive other sources of income. The results showed that the most
common source of income other than pay among urban residents
is a retirement pension; while in rural areas, receiving assistance
from either relative or the governorate is almost as high compared
to urban residents.
At the governorate level, the rate of unemployment showed
a negative association with poverty. As high unemployment can
happen when the economy either grows rapidly or is in recession,
it is not necessarily an indicator of reduced poverty. Because participation
in the labour force measures the overall strength of the
labour market, including people who are looking for part-time
jobs, increasing the percentage of the population in the labour
force will decrease poverty. A large volume of literature discusses
the theoretical link between income inequality and economic
growth, and the influence of these factors on poverty . There
is no consensus in theory or the empirical evidence. From our
cross-sectional data, we have found that income inequality decreases
poverty status. This could be an effect of Egypt’s economic
growth policy, which was put in place to reduce poverty but has
also widened the dispersion of income.
Within the broader debate on how best to improve the welfare
of older people in Egypt, there are calls to improve accuracies in
modelling. Our study has revealed that being in the labour force
has a significant effect on decreasing poverty. To encourage older
people to stay in the labour market will require a strong focus
from policymakers, especially in light of the fact that the percentage
of older people in the labour force is expected to decrease tojust 7.5% in 2020 from 31.9% in 1980 . This can be done in
various ways; for example, by informing older people who are
nearing retirement of the advantages of continuing to work. Older
people can be encouraged to work beyond their 60s by providing
more opportunities for them to extend their working life. Our
study provides evidence to support Egypt’s new retirement policy,
which calls for extending the retirement age from 60 to 65.
Our study calls for policymakers to take an innovative approach
to reforming Egypt’s social support systems. Our results
have also revealed that people who are older, female, have fewer
potential supporters and are not working are more likely to be
poor. To strengthen the support systems in place for these vulnerable
groups, and for older people in general, requires collaboration
between governmental organisations, non-governmental
organisations and the private sector. Social security systems and
safety nets must be improved to protect older people’s well-being
and ensure they receive an adequate income. Furthermore, health
insurance and access to a pension scheme should be available to
all older people [37-52].
Also, to ensure that information is represented accurately in
poverty studies, researchers should consider specific indicators
of poverty for older people. Further research can be carried out in
several areas. For instance, this study stresses that poverty levels
and participation in the labour force differ among governorates.
However, within each governorate there are still heterogeneous
groups. In addition, if data is made available on the characteristics
of the neighbourhoods within each governorate, this will allow
another level of data hierarchy to be considered. Another important
issue that can be explored further is that of gender differences.
In Egypt, the percentage of older men participating in the labour
force is expected to decrease dramatically. However, the participation
rate for older women is expected to increase. Thus, it may be
useful to model the determinants of poverty separately for older
men and older women. Furthermore, it is also important to consider
poverty throughout a person’s life to differentiate between
those who were poor before retirement and those who became
trapped in the cycle of poverty after leaving work. Therefore,
longitudinal studies that examine the factors associated with the
transition from and into the poverty cycle will be valuable in the
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