A Multidimensional Approach to Measuring Decent Work in Five
Countries using Count Panel Data Models
SAMEH M. ELMETWALLY
Department of Public Administration, Faculty of Economics and Political Science
Cairo University, Giza
EGYPT
Abstract: - This paper proposes a methodology for measuring Decent Work (DW) from a multidimensional
perspective using Alkire and Fosters methodology. According to Decent Work Country Programmes (DWCPs),
we created a multidimensional index of DW, and takes into account five dimensions, the dimensions include
indicators on the availability of employment opportunities, availability of adequate earnings and productive
work, availability of stability and security of work, availability of equal opportunity and treatment in
employment and availability of social security. Despite the fact that the variables included in this index are not
exhaustive due to the aforementioned data constraints, they serve to illustrate to what extent countries are
working to provide the greatest number of Decent Work opportunities (DWO) using a data set specifically
designed to measure the Decent Work indicator (DWI).Following recommendations made by the existing
literature on work quality and the number of DWO provided by countries. In our numerical application, we use
count panel data (CPD) models to investigate the impact of some dimensions on the number of DWO for five
countries (Bahrain, China, Egypt, Jordan, and Nigeria) that have implemented DW country projects and
programmes to construct a synthetic indicator of DW at a country level from 1999 to 2019.The results
generated by this indicator show that the methodology used can allow policymakers to identify and focus on the
most vulnerable workers in a labour market. The results of this index are then analyzed to highlight the
contribution that the indicator can make to the discussion of labour markets in countries , and arranges
countries according to the level of DW, through which these countries can measure their level of progress
towards DW, The findings degrees different levels of DW among the five countries studied, with Nigeria and
Jordan presenting very poor results in terms of the index; Egypt falling into the middle range of achievement;
and Bahrain and China achieving better results.
Key-Words: - Conditional Maximum Likelihood Estimation; Count Panel Data Models; Decent Work; Fixed
and Random Effects Model; Hausman test; ILO; Labor Regulations; Multidimensional Index.
Received: April 27, 2021. Revised: January 19, 2022. Accepted: January 30, 2022. Published: February 16, 2022.
1 Introduction
Work dominates most people’s lives because it
takes so much of their time, effort, and energy. It is
one of the most common kinds of social integration
and an important factor in the development of self-
esteem and identity. Work is also the place in
people’s lives where economic and social goals
interact, and it is the primary source of income and
livelihood for the vast majority of them, so the
nature of the work they do is a critical determinant
of their quality of life, as well as the driving force
for society’s growth and development, see Poschen
[1]. In the context of the continuous development of
work standards in various parts of the world and in
light of the efforts being made to improve working
circumstances and upgrade the conditions of
workers, the concept of Decent Work (DW)
emerged to summarize the entirety of the principles
and standards of work that must be provided to all
workers; as it is every person’s right to be able to
get a job that enables him to live in dignity. To
achieve this goal, a set of basic standards for decent
occupations and jobs must be developed. It is
suggested that DW is a prerequisite for poverty
reduction and fair and inclusive globalization, see
ILO [2]. The term DW was first introduced by
former ILO Director-General Juan Somavia in his
report to the Eighty-Seventh Session of the
International Labor Conference in June 1999. Since
2005, the United Nations (UN) has adopted DW as
one of the Millennium Development Goals. UN
Economic and Social Council resolutions -
Resolutions 2007, 2008 - emphasized the
importance of adopting a multi-tiered and
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multidimensional approach that focuses on
productive employment and DW [3]. DW has been
included in the sustainable development agenda,
where the eighth goal is: enhance sustained and
comprehensive economic growth, as well as
employment that is both full and productive, and
DW for all, UN [4].
The essence of DW is an emphasis on
employment quality, in addition to generating the
greatest number of jobs possible. Therefore, the DW
agenda is particularly pertinent in the developing
world, where there is a high rate of
underemployment, joblessness, and informal
employment is common and often the quality of
jobs (such as minimum wage and a healthy and safe
work environment) is foregone in order to create
jobs for as many people as possible. One of the
ILO’s goals is to enhance opportunities for people to
have DW. DW is a universal aspiration for people
everywhere, embodying their aspirations to obtain
productive work in conditions of equality, freedom,
security, and human dignity. It involves a fair
income, freedom for people to express their
concerns, participating in the decisions that affect
their lives, social integration and better prospects for
personal development, social protection for families
and security in the workplace, and equal opportunity
and treatment in employment.
DW is work that respects basic human rights as
well as worker’s rights in terms of safety conditions
for work and remuneration [5]. It is a multifaceted
concept that helps unravel the interconnectedness of
the policy measures necessary to ensure the dignity
of the human being through his career path [6].
Under DW, employees feel safe and satisfied, as it
enhances their dignity through humanizing work
and providing them with meaningful job
opportunities, as well as ensuring job security,
adequate wages, providing safe and healthy working
conditions, and giving opportunities to develop
human capabilities, and these factors are extremely
important to increasing individual productivity [7].
Thus, DW is necessary not only because all human
beings deserve the opportunity to live a decent life,
but also to ensure that there is sustainable economic
growth [8]. DW changes the way the global
economy operates so that its benefits reach more
and more people. Productive employment and DW
are essential for achieving equitable globalization
and poverty reduction. The ILO has established an
agenda for the community of work that emphasizes
job creation, rights at work, social dialogue, and
social protection, with gender equality as a cross-
cutting goal. Following the 2008 global financial
and economic crisis, there has been a greater urge
among international policymakers to create high-
quality jobs, as well as social protection and respect
for worker’s rights, in order to enhance sustainable,
comprehensive economic development and
eradicate poverty. Recent research has focused on
the establishment of a Decent Work Indicator
(DWI), see Rodgers [9].
ILO constituents have long been concerned
about monitoring progress towards DW. However,
the DW Agenda’s multifaceted nature, which
combines social protection with full and productive
employment, as well as the promotion of social
dialogue and rights at work, means that
measurement is a complex task. ILO constituents
have debated the complexities of finding a
measurement framework that fully accounts for the
multidimensional nature of DW on numerous
occasions and have provided guidance on the
various possible ways and methods for measuring
the dimensions of DW to prepare inclusive
recommendations for consideration by the ILO’s
Governing Body. In an effort to reduce the global
deficit in DW, the ILO provides support to countries
through DW country projects and programmes that
are developed in coordination with the
organization’s tripartite constituencies governments,
employers, and worker’s organization’s and whose
priorities and goals are defined within national
development frameworks. These projects and
programmes provide resources and advice to
countries and aim to integrate DW into national
policies.
On the other hand, in the econometrics
literature, panel data or longitudinal data sets relate
to the pooling of observations on a cross-section of
families, countries, enterprises, and so on, spanning
various time periods. The use of panel data to
estimate dynamic econometric models is becoming
commonplace. When compared to solely cross-
sectional or strictly time-series data, panel data has
various advantages, including the ability to
compensate for individual heterogeneity, provide
more meaningful data, and better investigate
adjustment processes. However, when a panel data
models response variable is a non-negative integer
number, the model is referred to as a count panel
data (CPD) model. Additionally, count data analysis
has witnessed explosive growth in recent decades in
econometrics and in many applied fields. In fact,
CPD models are now widely used in a variety of
economic applications, including health economics,
company productivity, transportation, and
education.
The current paper is unique in that, after a
comprehensive revision of research on labour
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regulations, we were unable to find any Decent
Work Composite Index that arranges countries and
ranks them according to the level of DW, through
which these countries can measure and monitor their
level of progress towards DW, as this is a long-
standing concern for the ILO’s constituents.
Actually, the starting point towards eliminating the
global deficit of DW is the process of measuring the
level of DW within the countries, in order to know
the shortcomings that exist in those countries and
accurately identify them in order to put the
dimensions of DW that need improvement in those
countries at the top of the priorities of the DW
country programs. To date, all literature has used
national averages or microeconomic indicators to
measure DW, such as the percentage of young
employed [10]. The ability to target populations in
vulnerability and observe their behavior based on
factors such as industry, age, gender, wage type, and
geographical zone is one of the advantages of
having a microeconomic indicator. All of this
translates into the ability to adopt and implement
targeted public policies while saving financial
resources. This paper suggests a methodology for
measuring DW from a multidimensional perspective
in five countries (Bahrain, China, Egypt, Jordan,
and Nigeria). Using a dataset designed specifically
to assess employment conditions. Building on
previous work on multidimensional poverty and
employment indicators, the paper used five
dimensions and eleven indicators to create a
synthetic indicator of the DW for all countries by
applying CPD models.
The rest of the paper is organized as follows:
After this introduction, Section 2 presents a brief
overview of DW in theory and practice, definition
and measurement of DW, reducing the DW deficit:
a global challenge, Decent Work country
programmes (DWCPs), and a literature review to
analyze the current DWI. While Section 3 provides
panel data modeling, fixed and random effects
models. Section 4 discusses the proposed estimators
for Poisson and negative binomial models in both
fixed and random effects instances. In Section 5, the
numerical application of DWI is presented. Section
6 presents the theory behind the multidimensional
index and explains the method used for aggregation
and estimating the index. The results of the
numerical application have been presented in
Section 7. Finally, Section 8 offers the concluding
remarks.
2 A Brief Overview of Decent Work
in Theory and Practice
Holistically, the DW definition extends beyond the
ILO’s four fundamental labour standards enshrined
in the DW agenda; social security, worker’s rights,
social dialogue, and employment. It is imperative
that the concept of DW must include all types of
jobs, as well as all individuals and families. To
accomplish so, it must acknowledge the multi-
dimensional nature of people’s lives since these
aspects are inextricably linked and indivisible and
so must be dealt with in a holistic human rights
framework. The mix of dimensions regarding
worker’s social relations and strictly work-related
dimensions under a single framework makes the
DW conceptually perfect or ideal for all types of
employment and comprehensive of the greatest
number of the working population. However, in
practice, converting a broad concept like DW into
policy instruments that are comparable and
quantifiable for a varied world has been a tiresome
and never-ending process. That is why the 2008 ILO
Declaration on Social Justice for a Fair
Globalization recommended the development of
appropriate indicators to monitor and evaluate
progress in implementing the Decent Work Agenda.
The ILO has supported member states through
technical assistance and capacity building at the
national, sub-regional, and regional levels in this
regard.
From a practical point of view, fundamental
principles and rights are the prerequisites for DW,
while the quality and security of work are its
content, and social dialogue is the process by which
it can be achieved. The DW Program, through its
four main pillars, contributes to promoting human
development. By creating job opportunities and
developing projects, it is possible to secure income
and livelihood resources for individuals, achieve
equity, facilitate participation, and deepen the sense
of pride and dignity.
2.1 The Concept and Measurement of
Decent Work
The ILO established the DW approach to give
globalization a social dimension and to begin an
intensive and comprehensive human-oriented
approach for dealing with the issues and challenges
provided by globalization in the workplace. The
ILO defines DW as productive work for men and
women in conditions of security, freedom, equity,
and human dignity. Furthermore, Somavia [11]
defines DW as productive labour in which human
rights are respected, insurance coverage is available,
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and the opportunity to participate in collective
decisions is possible. According to Ermida [12], the
concept of DW includes the following aspects and
characteristics: productive and secure labor; wages
are adequate; there is social protection; labour rights
are respected; there is social dialog; collective
bargaining and participation; and union freedom. In
order to achieve the goal of DW, the International
Labor Organization created the DW Agenda. The
ILO Declaration on Social Equality for an Equitable
Globalization is an expression of the DW Agenda’s
global characteristics; the agenda provides a
framework for equitable and sustainable
development and advocates for global progress.
There are four main pillars of the DW Agenda as
follows [13]:
Creating and providing job opportunities with
decent wages.
Social protection.
Promote social dialogue between workers and
employers and enable workers to have the
right to negotiate with employers in order to
defend their rights and improve their working
conditions.
The standards, principles, and basic rights at
work contained in the declaration of the ILO,
which include the elimination of all forms of
forced labor, the effective elimination of child
labor, and the elimination of discrimination in
employment and occupation.
In one of the first studies to attempt to quantify the
concept of DW, Bescond et al. [14] conducted an
international comparison of 40 nations using a
single-valued index based on seven decent-work
macro indicators. Bonnet et al. [15] provide a more
comprehensive and detailed analysis of how to
measure DW at meso (firm or enterprise), macro
(population aggregate), and micro (individual)
levels. They created a composite index at each level
using seven work-related securities and their
accompanying indicators. The ILO’s People’s
Security Survey (PSS), which was initiated in mid-
2000, was based on the seven work-based security
theoretical and analytical frameworks; see Kantor et
al. [16]. This framework is adaptable to
regional/local needs and suitable for obtaining an
overview as well as horizontal (dimension-wise)
disaggregation of DW at various measurement
levels. This theoretical framework for a macro-level
analysis is adapted in our study, which horizontally
explores and analyses the DW conditions and
circumstances for the countries under study.
2.2 Reducing the Decent Work Deficit: A
Global Challenge
Despite the importance of DW, the shortcomings of
difficulties we see all around us demonstrate how
tough it is to make it a reality for all workers in the
world. Former ILO Director-General Juan Somavia
has expressed deep concern about the massive
global DW deficit Somavia [17]; these are evident
in the absence of adequate work opportunities,
denial of rights at work, insufficient social
protection, and deficiencies in social dialogue, see
ILO [18]. The organizations current director-
general, Guy Ryder, also noted that the DW deficit
remains widespread and that additional efforts are
needed to improve the job quality for workers and to
ensure that growth gains are equitably shared [19].
Some indicators show deficits in providing DW, as
follows:
The number of unemployed people worldwide
is estimated at 172 million, and this number is
expected to increase by one million people
every year [20]. The total under-use of labour is
more than twice the size of unemployment,
which affects more than 470 million people
worldwide. This reflects the mismatch between
labour supply and demand. There are also more
than 630 million workers around the world who
still live in extreme or moderate poverty [21], of
whom about 126 million are young people, or
30% of the working youth, see ILO [22].
Informality is increasing over time in many
countries, where the informal economy employs
more than 60% of the world’s workforce and
two billion people live deprived of DW
conditions in light of high poverty rates in the
informal economy [23].
Every year, 78 million people die as a work-
related illnesses accidents or diseases, and there
are approximately 374 million non-fatal work-
related injuries each year. The economic burden
of poor occupational safety and health practices
is estimated at 3.94% of global gross domestic
product (GDP) each year [24].
There are 152 million children globally in child
labour [25], of whom 73 million are involved in
hazardous work that endangers their health,
safety, or growth. The ILO estimates that about
22,000 children die at work every year, and it is
not known how many are injured or sick
because of their work. Also, there are 25 million
adults and children in forced labour [26].
Contemporary labor markets are still marked by
gender discrimination. In 2019, the female labor
force participation rate was only 47%, 27
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percentage points lower than the male rate of
74%. Women continue to earn 77 percent of
what males earn [27], and the gender wage gap
is 20% on average worldwide. Moreover,
Women make up just 27.1 percent of managers
and leaders globally, a figure that has been
relatively constant over the last 27 years, see
ILO [28].
Only 45% of the world’s population is
effectively covered by at least one social
protection benefit, while the remaining 55% (up
to 4 billion people) are unprotected. The ILO
also estimates that only 29% of the world’s
population is covered by comprehensive social
protection systems [29].Recent economic trends
in recent decades have increased working hours,
and have also led to concerns about workers
capacity to manage work and personal life, and
family responsibilities [30].
This widespread deficit in DW not only causes
economic inefficiencies but also threatens social
cohesion within states. In 2019, seven of the world’s
11 sub-regions witnessed an increase in protests,
indicating that dissatisfaction with the social,
economic, or political situation is increasing [31].
While global average incomes are increasing and
the global economy has immense potential for
innovation and productivity, these gains are
accompanied with persisting inequality, expanding
exclusion, insecurity induced by economic swings,
and a sense that the rules are unjust. Reducing the
DW deficit is the road to poverty reduction and to
greater legitimacy of the global economy. DW is a
goal in its own right but there is also an economic
dividend - economic and social efficiency can go
together. An integrated approach is essential - each
element of DW reinforces the others and all play a
part in achieving broad goals such as poverty
eradication.
In an effort to reduce the global deficit in DW,
the ILO provides support to countries through DW
country projects and programmes that are developed
in coordination with the organization’s tripartite
constituencies governments, employers, and
worker’s organizations and whose priorities and
goals are defined within national development
frameworks. These projects and programmes
provide resources and advice to countries and aim to
integrate DW into national policies.
2.3 Decent Work Country Programmes
In 2004, time-bound and resourced country
programmes were introduced by the ILO,
known as DWCPs. They are based on ILO
standards and ethics as well as the priorities and
interests of the ILO’s constituents
(governments, employer’s organizations, and
labour unions) and national development goals.
DWCPs depict the ILO support and help
required to achieve measurable progress at the
national level in the pursuit of the DW goal for
all men and women and reflect the constituent’s
commitment to achieve this goal and to promote
it both individually and in collaboration with
one another, especially through development
partnerships. All stages of the DWCP are
overseen by ILO Country Offices. Policy advice
and technical support are provided by Decent
Work Technical Support Teams (DWTs) in the
various regions, in conjunction with
headquarters technical specialists, in DWCPs
design and implementation in response to the
needs and interests of constituents. Within the
teams, technical specialists and experts from the
Bureau of Worker’s Activities (ACTRAV) and
the Bureau of Employer’s Activities
(ACTEMP) take the lead role in incorporating
the perspectives of workers and employer’s
organizations into DWCPs.
The DWCPs were designed to be harmonized
at the country level with other programmes on the
advancement of work-life run by the UN and the
ILO, to make the most efficient use of limited
resources. The DWCPs were designed to highlight
the ILO’s unique and distinct contribution to United
Nations country programmes (UNCP) and form one
main tool for better integrating regular budget and
extra-budgetary technical cooperation. The
outcomes of the ILO biennial programme were
created and designed to align well with the goals of
sustainable development, enabling the field
structures, Centenary Initiatives, flagship
programmes, ILO Global Technical Teams, and
DWCPs to work together and collaborate within the
UN system to help and support the Member States,
see ILO [11]. DW aimed to create a more inclusive
and sustainable future and to place people at the
center of development by advocating for equality,
dignity, quality jobs, healthy and safe working
conditions and environments, and a fair income, see
ILO [32]. With the approval of the 2030 Agenda for
Sustainable Development in September 2015,
DWCPs preparation and implementation enter a
new stage in which action by ILO will have to be a
visible part of the inclusive UN efforts.
In the drafting and evolution of the DWCPs,
there has been a logical, long-term consistency, as
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well as the implementation, coordination, and
independent evaluation of the programmes
accomplishments. The ILO developed a guideline
for national-level actor’s support in 2015 to
facilitate the DWCPs preparation. The goal of this
guideline is to provide a comprehensive, well-
informed, but short diagnostic narrative of the DW
situation and trends, productive employment, and
growth of every country. It also provides the ILO’s
constituents and other national stakeholders with
coherent data on and analysis of the situation and
progress associated with DW in every country. At
the same time, it acknowledges the major DW
challenges that face the country. The country’s
analytical report provides data for the national
development discourse. The country’s situation
analysis can also be utilized as a base for national
training, as well as capacity building and planning
for ILO’s constituents and other main stakeholders
as indicated by Figure 1, see [33].
The process of creating a country programme
document has shared features across institutions. As
illustrated in Figure 1, the creation of all country
programme documents by the UN, including the
documents of agency-specific programme, and by
multilateral institutions often begin with preliminary
discussions with the government and other
stakeholders.
The second phase is a country diagnostic
process that aids in the establishment of priorities
and often entails extensive data collection and
analysis. The third phase is the preparation of the
main country programme document, which includes
a context overview, a declaration of priorities, the
identification of the key results and their
measurement, and budgetary information. Finally,
implementation, monitoring, and evaluation
procedures build in the main document, forming an
essential component of country programme
implementation and the learning of lessons for
future programmes.
Moreover, the country diagnostics offer a
comprehensive framework for national
development, including demographics and statistics
related to health, human development and
education, the economy’s structure and efficiency,
aspects related to vulnerability, inequality, and
poverty in the country, the labour force, the labour
market and employment, fundamental principles and
rights at work, the international labour standards
implementation, occupational safety and health
(OSH) and DW conditions, questions related to
social dialogue and social protection, and equal
opportunities and treatment in employment, to help
recognize and identify the main DW challenges
ahead. Figure 2, illustrates the logic of the ILO
DWCP process, see ILO [34]
The following are some examples of the
DWCP’s main contents: Child employment
reduction and elimination of its hazard forms;
increased and improved employment opportunities
for vulnerable groups; and the creation of Decent
Work Opportunities (DWO) that help in poverty
reduction with a special focus on young women and
men. The selected priorities are based on the
deficiencies identified in the country’s DW
diagnosis, see ILO [35].
Fig. 1: Process of Country Programming, see [34].
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Fig. 2: Cycle of ILO Results-based Programming, see [34].
2.4 Literature Review
The ILO’s 2008 Declaration on Social Equality for
Fair Globalization recommended the development
of suitable indicators to track and assess progress in
implementing the DW agenda The use of DW
indicators enables national-level analyses to monitor
changes and trends in the labour market over time,
as well as cross-national comparisons, and thus the
discussion of indicators is closely related to the
goals and meanings of DW in various institutional
and structural contexts, see Ghai [36].There is no
doubt that indicators for DW are needed. In their
absence, governments, workers, and employers are
not in a position to know their conditions compared
to other countries, and their absence significantly
reduces the ILO’s ability to communicate its
messages and influence public discussions on labour
and social issues. For an assessment of DW
indicators for countries can assist in reviewing these
indicators, and it can also encourage statistical
agencies to include additional variables that may be
required to study DW more precisely, see Bescond
et al. [14].
The DW assessment was initiated with the aim
of developing a wide range of employment
indicators that enable cross-country comparisons
along with an assessment of individual labor
markets, see Sehnbruch et al. [37]. The study of
Standing [38] identified several dimensions of DW:
income security, security for skill reproduction, job
security, work security, employment security, and
security for representation and expression. The
study of Bescond et al. [14] focused on six
dimensions of DW: job opportunities, work in an
atmosphere of
freedom, productive work, justice at work, job
security, and dignity at work. The Council of the
European Union adopted its policies on measuring
DW, which focused on five main dimensions of
DW: quality of work and employment; ensuring job
security; maintaining the health and safety of
workers; developing skills and competencies; and
balancing work and personal life [39].
The study by Banerjee and Kundu [40] sought
to recognize the achievements of DW for informal
workers in rural and urban areas in the Hooghly
region in India. The study used the theoretical
framework for seven dimensions of DW: labour
market security, employment security, job security,
work security, skill reproduction security, income
security, and representation security. These are the
same dimensions that were used in the study of
Standing [38]. The study then constructed seven
sub-indicators and one composite indicator for DW
at the individual level, using the primary survey
data. The study by Mackett [41] attempted to
provide a systematic starting point for measuring
DW using a national labour force survey and the
available variables for such a scale. The study
understood the scarcity of variables by merging
some indicators and saw that some indicators could
be swapped against some of them, while the
directional nature of other variables (for example,
the gender wage gap) posed a challenge to the
composite indicator’s design.
From the above, it is noted that despite the use
of many scales in an attempt to assess DW based on
a set of indicators, there is no consensus among
researchers on a single scale. In addition, each of
these attempts failed to provide a comprehensive
scale that fully reflects the basic pillars of DW and
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its various dimensions; where there was a focus on
certain dimensions of DW and ignoring the rest of
the dimensions. There were also restrictions due to
lack of data; as there were some indicators for
which data were not available for some countries,
which made it difficult for international comparison,
and this prompted some researchers to report the
difficulty of measuring DW such as Burchell et al.
[42] and Sehnbruch et al. [37], or to conduct a study
of DW at the micro-level in a particular industry so
that there is an ease in collecting data, or evaluating
DW at the national level without making an
international comparison.
However, understanding the dynamics of DW
requires measuring it across several local, national,
regional, and global levels, and this requires
indicators that evaluate several levels of analysis,
and this will make the pursuit of DW more effective
and sustainable according to the study of Ferraro et
al. [43, 44]. Realizing this, this study attempted to
find a set of indicators for DW that can capture all
its dimensions to give an integrated understanding
and paint a comprehensive picture of it. These
indicators were evaluated for the five countries
under study, and the extent to which the population
of the countries under study feels about the
improvement in DW levels within their countries
achieved by DW country projects and programs.
Moreover, this study not relied on the questionnaire,
which is the method that was used in most of the
previous literature to measure and evaluate DW, but
this study relied on the method of panel data
analysis.
3 Panel Data Modeling
The connection between variables and the error term
in linear regression causes inconsistencies in the
estimated parameters. In the case of longitudinal
data, Hsiao [45] presents a review. The count data
regression suffers from the same issue, resulting in
skewed parameter estimates. As Winkelmann [46]
points out, if standard estimating processes are
corrected, consistent estimates can be found.
However, fixed effects (FE) model and random
effects (RE) model are perhaps the most widely
estimated models in panel data modelling. Boucher
and Denuit [47] compared FE model and RE model.
They demonstrated that on a joint distribution with
RE model, typical estimate methods such as
classical maximum likelihood can still be applied.
Indeed, the resulting parameter estimates, while
biassed, indicate the apparent effect on claim
frequency, which is exactly what is of interest when
linked omitted variables cannot be employed in
classification, for more details of panel data models;
see e.g. [48, 49].
Even though all slopes are the same, the FE
model allows for a separate intercept term for each
cross-sectional () unit. In its most basic form, the
FE model can be written as;
(1)
where is the dependent variable for individual
at time , is the intercept, is the
observation on dependent variables, is the
regression coefficients vector, and is the model’s
error term.
The error term now has new assumptions, whereas
the RE model shoulders that there is a single
constant term (α) for all across units and that the
changes in the intercept term may be reproduced in
the error term. The RE model is justified by the
supposition that, different the FE model, variation
across entities is random and that the unit’s error
term is uncorrelated with the forecasters. The RE
model is defined as follows:
(2)
where ; this signifies that the models
error term is made up of two components, while
denotes the unobservable impacts that are unique to
each individual, and represents the variations in
disturbances as a function of units and time.
Both the fixed and random effects estimators would
be in agreement if the RE model was accurately
defined. A Hausman test [50] can be based on the
difference between the two estimators. Cameron and
Trivedi [51] propose the following representation of
the test:
, (3)
where is the Hausman test statistic,
are the
estimated parameters obtained from the FE model
and
are the estimated parameters obtained from
the RE model. To estimate the variance
term
, we can use a panel bootstrap
method as;
,
where
are the estimates obtained
from the bootstrap replication.
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4 Count Panel Data Distributions
In panel data models, if the response (dependent)
variable is not normally distributed; especially, if
the response variable has non-negative integer
values (count data). For example, the number of
occupations, accidents in various places, days off
for many people over time, protests in various
countries over time, medical visits, and the number
of occurrences of a certain health event for each of
many patients over time. In the econometrics
literature, however, Poisson and negative binomial
models are frequently used to fit this data.
4.1 Poisson Distribution
The Poisson model shoulders that the dependent
variable () requires a Poisson distribution by a
probability density function (PDF);
(4)
where is the mean predicted or expected of .
The mean and variance of it must be equal, i.e.
, in this model.
The individuals heterogeneity term in the fixed
effects Poisson (FEP) model captures all non-time-
varying traits. The regressors are missing an
intercept since the intercept is incorporated into.
The conditional probability function (CPF) for the
FEP model is as follows:
(5)
where
. The conditional maximum
likelihood estimation (CMLE) proposed by
Hausman et al. [52] can be used to estimate the
parameters of this model. Since and
are
shadow the Poisson distribution, then the
conditional joint PDF for the observation be
situated;
,
The conditional log-likelihood is calculated by
captivating the logarithm of conditional joint PDF
and summing ended all individuals as follow;
, (6)
it can get the FEP model estimated parameters by
solving;
.
On the other hand, to estimate the parameters of the
random effects Poisson (REP) model, the
individual-specific impact necessity must a given
distribution. In this model, we expected that the
individual-specific influence requires a gamma
distribution by parameters. The parameters of
this model were estimated using the maximum
likelihood estimation (MLE) approach. For the
observation, the MLE function is:
This model includes the intercept, which has been
incorporated into. The log-maximum likelihood
function is defined as follows:
(7)
It is possible to acquire of this model estimated
parameters by solving;
.
4.2 Negative Binomial Distribution
Classically, when the data set has an excessive
dispersion problem, the negative binomial (NB)
distribution is a useful another to the Poisson model;
this problem arises once.
Because the NB model needs a dispersion
parameter, it permits the variance to be bigger
than the nasty because the dispersion parameter
gives the count distribution a wider shape than the
Poisson distribution model.
In the fixed effects negative binomial (FENB)
model, Hausman et al [52] proved that the
conditional joint PDF for the observation be
situated;
, (8)
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whe
, and is the gamma function.
The CMLE of the FENB model can be got by
maximizing the next log-conditional maximum
likelihood function:
}, (9)
In the random effects negative binomial (RENB)
model, Hausman et al [52] expected to be
independent and identically distributed NB, and
where
, is distributed as
beta with parameters . The expected and the
variance of are and,
respectively. Then for the observation in the
RENB model the conditional joint PDF is;
The next log-maximum likelihood function is
maximized, yields the MLE of the RENB model;
}
(10)
5 Numerical Application
In order to measure the DW, the author relied on
descriptive and analytical statistics to evaluate DW
indicators for the countries under study using CPD
analysis. The sample of the study was selected
based on available data on the number of Decent
Work Opportunities (DWO) in the countries under
study and the data set has been obtained through the
ILO and the World Bank (WB) website; the study
population consists of countries that have
implemented DW country projects and programmes
from 1999 to 2019. The study sample consists of
five countries (Bahrain, China, Egypt, Jordan, and
Nigeria). These five countries represent various
regions, cultures, and economic and social
development levels. This enriches the study and
gives an opportunity to evaluate DW indicators in
various environments. Moreover, the reasons for
choosing these countries are:
Bahrain: was among the first eight
countries selected in the world to implement
the DW Pilot Program in October 2000, and
Bahrain is one of the countries with the
most advanced labor laws in the Gulf
region. The unemployment insurance
program was also recently introduced,
which is setting an example in the region.
The Kingdom of Bahrain also won the
original membership of the ILO’s board of
directors in the elections that took place
during the 106th session of the International
Labor Conference in June 2017
.
China: is the second-largest economy in the
world and a major global trading partner.
China has the largest population in the
world and nearly a quarter of the global
workforce, and has the largest volume of
employment of 650,207,177 workers, see
WB [53].It has been able to benefit from
human resources and translate it into an
amazing annual GDP growth of 9.5% on
average, and China has also developed a
large-scale social safety net that has lifted
800 million Chinese out of poverty over the
past 40 years and has emerged in it a strong
middle class, ILO [54].
In Egypt: a set of DW projects were
implemented as part of a roadmap for
recovery after the events of the 25 January
2011 revolution [55]. Egypt adopted
reforms and major structural economic
programs aimed at accelerating its path
towards comprehensive growth. The
minimum wage was raised periodically, and
the Egyptian government launched the
National Action Plan to combat the worst
forms of child labor in Egypt and support
the family [56], and Egypt also launched the
Takaful and Karama Cash Transfer
Program, which included 2.4 million
families or 10 million people in 2019.
Jordan: is at the forefront of the Arab
countries that signed the DW Country
Program in 2006 and was chosen from
among the nine countries globally and the
only one from the Arab States region to test
the Global Jobs Pact that was adopted
during the International Labor Conference
in June 2009 [57]. Jordan provides an
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international public benefit by hosting the
second largest percentage of refugees in the
world, and Jordan was also the first Middle
Eastern country to sign the Social Security
(Minimum Standards) Convention (No. 102
of 1952).
Nigeria: is the largest economy in Africa,
with a GDP of 448.12 billion USD [58], and
constituting 17% of the continents GDP.
Nigeria was one of the three African
countries selected to receive ILO assistance
to implement the Global Jobs Pact in
response to the global financial crisis the
Government of the Federal Republic of
Nigeria [59]. Nigeria has half the population
of West Africa with nearly 202 million
people, has one of the largest proportions of
youth in the world, and is abundant in
natural resources, and is the largest oil
exporter in Africa, see WB [60].
6 Dimensions and Indicators
As discussed above, the DWI presented here uses
available information from the framework presented
by labour statistics experts at the tripartite
international meeting held by the ILO in September
2008, which included ten axes for DW: employment
opportunities, adequate earnings and productive
work, decent working time and combining work
with family and personal life, work that should be
abolished, stability and security of work, equal
opportunity and treatment in employment, safe work
environment, social security, social dialogue, and
worker’s and employer’s representation, see ILO
[61]. Resulting in a composite index for DW
composed of five dimensions and eleven indicators.
By following the recommendations offered by the
available literature on quality of work and the
number of DWO provided by countries, the
dimensions of DWI include indicators on the
availability of employment opportunities, the
availability of adequate earnings and productive
work, the availability of stability and security at
work, the availability of equal opportunity and
treatment in employment and the availability of
social security. Ferraro et al [43]. The variables
included in this index serve to illustrate to what
extent countries are working to provide the largest
possible number of DWO. The dimensions and
indicators together are summarized in Table 1
below. The DW indicators adopted by the study to
measure and evaluate DW in the countries under
study can be reviewed as follows:
6.1 Availability of Employment
Opportunities
Employment refers to all forms of paid and unpaid
work, self-employment, formal and informal work,
full-time work, and part-time work. The priority of
job creation is not exaggerated, as getting to work is
the surest way out of poverty, just as getting people
into productive activities is the way to create wealth
that enables the achievement of social policy goals.
There should be enough work for everyone to have
complete access to opportunities for generating
income, see Tipple [62].The creation of employment
opportunities is the political mandate of the ILO, the
one that comes from the streets, the one that comes
from the individuals, the mandate to the foundation
of more and better jobs. With full employment as a
goal, there is a specific focus on three critical
determinants of employment: macroeconomic
policies, transformations of production systems and
enterprise strategy, and equal access to employment
and labor markets. In all cases, the goal will be to
integrate employment objectives into national
policies. The availability of employment
opportunities in the countries under study will be
measured based on the following two indicators:
6.1.1 Employment to the working-age population
ratio
The employment-to-working-age population ratio
(EPR) is a fundamental measure for determining the
economy’s total demand for employment and
provides information about the economy’s ability to
create jobs. The EPR is defined as the percentage of
people in the working-age population who are
employed. When the EPR rises over time, it usually
means that there is an increased demand for workers
within the economy [3]. It is noted that the
relationship between the EPR indicator and the DW
is a positive relationship, meaning that the greater
the value of this indicator, the better the level of
DW within the country, which means that the
impact of this indicator will be positive on the DW
in the country. The EPR indicator can be measured
by:
6.1.2 Unemployment Rate
The unemployment rate (UR) shows the economy’s
inability to provide employment opportunities for
individuals who want to work, are available for
work, and are actively seeking work. As a result, it’s
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seen as a barometer of the economy’s efficiency and
efficacy in absorbing its workforce, as well as the
labour market’s performance. The UR is defined as
the proportion of unemployed individuals in the
labour force. The labour force is composed of the
number of individuals who are employed and those
who are unemployed, see ILO [63]. It is noticed that
the relationship between the UR indicator and the
DW is an inverse relationship, meaning that the
greater the value of this indicator, the worse the
level of DW within the country, which means that
the impact of this indicator will be negative on DW
in the country. The following formula is used to
calculate this indicator:
6.2 Availability of Adequate Earnings and
Productive Work
Adequate earnings and productive work indicate
that there will be remuneration for the work
performed by the individual that helps him lead a
decent life, and this income must be sufficient and
commensurate with the high prices of goods and
services that the individual needs. The work should
result in a wage (cash or in-kind) that meets the
basic needs of the worker and his family members.
One study confirmed that the minimum wage can
achieve positive results in alleviating poverty by
improving the living conditions of workers and their
families, and it also helps boost productivity, see
Saget [64]. Productive work is crucial for workers to
have decent living conditions for themselves and
their families, as well as to achieve long-term
growth. The availability of adequate earnings and
productive work in the countries under study will be
measured based on the following three indicators:
6.2.1 Labour Productivity Growth Rate
Labour productivity growth rate (LPR) is a key
indicator closely related to economic development,
competitiveness, and living standards. Labor
productivity indicates the total volume of output
expressed in terms of GDP output per unit of
labour-measured by the number of employed
individuals-during a given reference period. It is
noticed that the relationship between the LPR
indicator and the DW is a positive one, meaning that
the higher the value of this indicator, the better the
level of DW within the country, meaning that the
effect here will be positive on the DW in the
country. The LPR indicator is calculated as follows,
see ILO [65]:
6.2.2 Labour Income Rate
The labour income rate (LIR) is the amount that
workers earn by working. This concept is used to
distinguish it from capital income, as asset owners
obtain capital income due to their property. The LIR
includes employee wages and a portion of the self-
employed income, as self-employed workers earn
from their work and capital ownership. LIR’s share
of GDP is total employee compensation given as a
percentage of GDP. It is noted that the relationship
between the LIR indicator and the DW is a positive
one, meaning that the higher the value of this
indicator, the better the level of DW within the
country, meaning that the effect here will be
positive on the DW in the country. This indicator
can be calculated as follows:
6.2.3 Inflation and Consumer Prices Indices
Inflation measured by the consumer price index
(CPI) can be defined as the change in the prices
levels of a basket of goods and services that are
frequently bought by certain categories of
households. The CPI is one of the most commonly
used indicators for detecting periods of inflation or
deflation. It is noticed that the relationship between
the CPI indicator and the DW is an inverse
relationship, meaning that the greater the value of
this indicator, the worse the level of DW within the
country, which means that the impact of this
indicator will be negative on DW in the country.
The CPI indicator is calculated as follows:
6.3 Availability of Stability and Security of
Work
Stability and security of work include employment-
related concerns such as employment security or
protection from unfair dismissal, and employment
stability consistent with economic dynamics. For
most people, losing a job or work is a dangerous
event, and there is no doubt that job security is
viewed by most individuals as a significant aspect of
DW. The availability of stability and security of
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work in the countries under study will be measured
based on the following two indicators:
6.3.1 Formal Employment Rate
The regular or formal employment rate (FER) is the
total number of salaried workers as a proportion of
the total employees, and they are workers who work
in the jobs specified as "paid employment jobs",
where the incumbents have explicit (written or oral)
or implicit employment contracts that provide them
with a basic salary that is not directly dependent
upon the income of the unit for which they work,
see WB [66]. The relationship between the FER
indicator and the DW is a positive one, meaning that
the higher the value of this indicator, the better the
level of DW within the country, which means that
the impact will be positive on the DW in the
country.
6.3.2 Vulnerable Employment Rate
The vulnerable employment rate (VER) indicates
the percentage of workers whose jobs put them at a
higher risk of losing their jobs than other workers.
The VER is defined as the percentage of the total
number of self-employed workers or contributing
family members. High levels of this indicator may
indicate poor working conditions and a lack of job
creation in the formal sector [3]. Vulnerable
employment is frequently characterized by
insufficient revenues, low productivity, and tough
working conditions that jeopardize worker’s basic
rights. The lifting of restrictions and regulatory
controls, market liberalization, privatization, and the
desire for a flexible labour market has led to the
spread of informal employment [5]. Most of the
people who are poor in the developing world
already have a job, but it is mostly in the informal
economy. Contributing family workers and self-
employed workers are most at risk and therefore the
most vulnerable to falling into poverty, and they are
the least likely to have safety nets to protect against
economic shocks [60]. It is noted that the
relationship between the VER indicator and the DW
is an inverse relationship in the sense that the
greater the value of this indicator, the deterioration
of the level of DW within the country, meaning that
the effect here will be negative on the DW in the
country. This indicator can be calculated as follows:
6.4 Availability of Equal Opportunity and
Treatment in Employment
The availability of equal opportunity and treatment
in employment provides information on the
employment of men and women. The word
"employment" means a group of jobs whose tasks
and duties are broadly similar. This dimension
highlights the extent to which men and women
benefit from different chances and treatment in the
workplace. Equality is at the core of the DW
concept, and ILO Convention No. 111 continues to
provide the basis for positive policies to promote
equality, see Hepple [67]. DW implies work without
any kind of discrimination. Investing in gender
equality and DW for women and empowering them
is vital to achieving economic and social equity and
can translate into tangible and sustainable
improvements in women’s position at work, see
Charlesworth [68]. This dimension will be measured
in the countries under study based on the following
two indicators:
6.4.1 Ratio of Females to Males in Employment
to the Working-age Population Rate
It is noted that the relationship between the ratio of
females to males in employment to the working-age
population (FMER) and the DW is a positive
relationship in the sense that the greater the value of
this indicator, the better the level of DWI within the
country, meaning that the effect here will be
positive on the DW in the country. This indicator
can be calculated as follows:
6.4.2 Share of Women in the Wage Employment
Rate
The Share of women in the wage employment rate
(FPER) indicator displays the share of females in
wage employment as a proportion of total wage
employment. The extent of women’s access to wage
employment could indicate their incorporation into
the monetary economy while providing a far more
consistent and monetary income, and this, in turn, is
likely to have a favorable influence on women’s
independence and their decision-making abilities. It
is noted that the relationship between the FPER
indicator and the DW is a positive one, meaning that
the higher the value of this indicator, the better the
level of DW within the country, meaning that the
effect here will be positive on the DW in the
country. The following formula can be used to
determine this indicator:
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6.5 Availability of Social Security
Social security refers to the general actions taken in
response to levels of vulnerability, risk, and
deprivation that are deemed socially unacceptable
within a particular system or society. These
measures include labour market interventions, social
safety nets, and pensions, along with interventions
to enhance conditions of normality and deal with
regular and often persistent deprivation. Social
protection is important during the privatization
process and as trade liberalization progresses.
National experiences show that coordinated action
of social security policies can contribute to ease the
transition from informal to formal economies. The
availability of social security will be measured in
the countries under study based on the following
two indicators:
6.5.1 Dependency Ratio
A dependency ratio (DR) shows the number of
individuals in the dependent age groups children
under the age of 15 and people over the age of 64 to
the number of individuals in the working-age group
between 15 and 64 years. Thus, it shows the number
of people in dependent age groups for every 100
persons of working age [69]. A higher DR means a
greater demand for social security expenditures for
this vulnerable group, which is unable to work and
secure their living expenses. When the dependency
ratio is high, it makes financing social security plans
difficult, as there are few people of working age, of
whom a few are productively employed, and the
direct financial revenues (through income tax) and
indirect (through consumption tax) will be low, and
this means lower financial revenues, and thus the
governments will not be able to finance social
security plans, see Harasty and Ostermeier [70].
Hence, an increase in the dependency ratio means a
decrease in social security. It is noted that the
relationship between the DR indicator and the DW
is an inverse relationship in the sense that the
greater the value of this indicator, the deterioration
in the level of DW within the country, meaning that
the effect here will be negative on the DW in the
country.
6.5.2 Working Poverty Rate
The concept of "poor employment" aims to measure
the number of workers who live in poverty despite
the fact that they are employed. Consequently, the
working poverty rate (WPR) shows the percentage
of the working population living in families
classified as poor that is, having levels of
consumption or income levels below the national or
international poverty line specified. The WPR is
illustrated by the number of working poor as a
percentage of the employed population, see ILO
[71]. Poverty is a concept that applies to families,
not individuals, and is based on the assumption that
families pool their income to lift the entire family
out of poverty. Social security is one of the most
important tools that reduce poverty risks, see
Cantillon [72]. An increase in the WPR means a
decrease in social security. DW and empowerment
reinforce each other in a powerful cycle of making
economic growth more pro-poor, as they will have
more access to good-quality jobs. Productive work
is the best way out of poverty. Reducing the DW
deficit is also a way to reduce poverty. Full and
productive employment and DW for all are essential
to achieving the Millennium Development Goals
and eradicating poverty [5]. Employment should be
a means of lifting people out of poverty, but this is
only true if the quality of the job is adequate,
including adequate earnings, job security, and safe
work environments. The regions with the highest
working poverty rates are also those with the highest
rates of informal employment, see Gammarano [73].
The relationship between the WPR indicator and the
DW is an inverse relationship in the sense that the
greater the value of this indicator, the worse the
level of DW within the country, meaning that the
effect here will be negative on the DW in the
country. This indicator can be calculated as follows:
7 Results
As a numerical application, this paper is concerned
with studying the significant impact of five
dimensions on the number of DWO using data set
for five countries (Bahrain, China, Egypt, Jordan
and Nigeria) during period from 1999 to 2019. The
data set is limited by the amount of information
available for each state involved. This paper follows
the methodology by Youssef et al. [74] for applying
CPD models.
In order to perform of the CPD models for this
application, we used softwares in our research are
"STATA version 15" and "R version 4.1.1" with
(pglm package). Table 2 summarizes the descriptive
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statistics for the variables (dependent and
independent variables). Since the p-value of Jarque
and Bera [75] test is greater than 0.05 for all
variables, we may conclude that the data are not
having large variation and are distributed normally.
Table 1. Dimensions and Indicators for Decent Work
Measuring
Unit
Indicators
symbols
Indicators
Dimensions
Count
DWO
Decent Work opportunities
Dependent variable
%
EPR
Employment to the working-age
population ratio
Availability of employment
opportunities
%
UR
Unemployment rate
%
LPR
Labour productivity growth rate
Availability of adequate
earnings and productive work
%
LIR
Labour income rate
%
CPI
Inflation and consumer prices Indices
%
FER
Formal employment rate
Availability of stability and
security of work
%
VER
Vulnerable employment rate
%
FMER
Ratio of females to males in
employment to the working-age
population
Availability of equal
opportunity and treatment in
employment
%
FPER
Share of women in the wage
employment rate
%
DR
Dependency ratio
Availability of social security
%
WPR
Working poverty rate
Table 2. Descriptive Statistics of the Variables
Variables
Mean
Max.
Min.
Std. Dev.
JB. Test
P-value
DWO
18600,749
95800
11268
28800,872
5.073
0.094
EPR
54.085
75.200
32.830
14.138
1.757
0.462
UR
7.003
16.850
0.950
4.813
0.611
0.089
LPR
2.705
13.632
-5.842
4.284
3.313
0.191
LIR
42.816
67.000
25.800
13.115
0.595
0.069
CPI
5.728
29.507
-1.401
5.599
0.703
0.392
FER
61.101
97.350
10.480
29.056
1.879
0.495
VER
34.243
89.220
8.183
30.545
1.475
0.367
FMER
51.161
86.072
15.305
28.541
3.416
0.079
FPER
26.497
48.124
14.157
11.069
2.640
0.259
DR
59.051
88.592
26.964
20.181
4.266
0.108
WPR
59.051
88.592
26.964
20.181
2.387
0.523
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Table 3. Correlation Matrix and VIF values
Variables
DWO
EPR
UR
LPR
LIR
CPI
FER
VER
FMER
FPER
DR
WPR
DWO
1
EPR
0.623
(0.000)
1
UR
0.299
(0.201)
0.451
(0.046)
1
LPR
0.493
(0.027)
0.650
(0.024)
0.321
0.167)
1
LIR
0.714
(0.001)
0.574
(0.008)
0.268
0.254)
0.798
(0.004)
1
CPI
0.495
(0.027)
0.446
(0.049)
0.053
(0.826)
0.439
(0.053)
0.491
(0.051)
1
FER
-0.025
(0.916)
0.216
(0.360)
0.598
(0.005)
-0.097
(0.684)
-0.255
(0.277)
-0.037
(0.877)
1
VER
-0.282
(0.228)
0.685
(0.061)
-0.723
(0.030)
0.574
(0.028)
0.495
(0.038)
-0.392
(0.002)
-0.679
(0.001)
1
FMER
0.870
(0.060)
-0.037
(0.877)
0.439
(0.053)
-0.679
(0.005)
0.525
(0.017)
-0.016
(0.969)
0.484
(0.017)
0.629
(0.036)
1
FPER
-0.494
(0.019)
-0.457
(0.025)
-05.229
(0.330)
0.657
(0.003)
-0.502
(0.061)
0.671
(0.093)
0.639
(0.007)
0.574
(0.006)
0.439
(0.053)
1
DR
0.590
(0.013)
0.650
(0.001)
-0.271
(0.310)
0.345
(0.247)
-0.781
(0.017)
0.692
(0.012)
-0.532
(0.019)
0.704
(0.028)
0.198
(0.094)
0.195
(0.190)
1
WPR
0.423
(0.025)
0.419
(0.036)
0.064
(0.805)
0.490
(0.061)
0.693
(0.057)
0.601
(0.069)
-0.505
(0.039)
0.492
(0.008)
-0.086
(0.074)
0.434
(0.059)
-0.348
(0.010)
1
VIF
------
2.482
5.182
3.169
6.392
2.906
3.092
1.582
6.173
4.182
8.091
3.396
Fig. 3: Boxplots of the Number of DWO for Each Country studied
7.1 Testing the Multicollinearity and Outliers
for Dataset
The first stage in data processing is to make sure
that two or more explanatory variables do not have a
high linear association. When there is
multicollinearity, statistical inferences are unreliable
because it causes estimates of regression
coefficients to be erroneous, inflates their standard
errors, deflates partial t-tests for them, generates
false non-significant p-values, and decreases the
model predictability. To find multicollinearity, we
employ the most widely used methods: (i) the
Pearson correlation matrix between each pair of
predictor variables, and (ii) the Variance Inflation
Factor (VIF), see [76,77]. Table 3 presents the
pairwise correlation coefficients between all
variables associated with two-tailed significant t-test
in parentheses. It is worth noting that the correlation
between DWO and EPR is larger, whilst the
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correlation between CPI and FMER is the least.
Also, because all of the VIF values are less than 10,
Table 3 shows that the data set does not have an
issue with multicollinearity.
On the other hand, Figure 3 displays the boxplots of
the number of DWO for the countries studied. The
disparity in the number of DWO between countries
is depicted in this graph, however the distribution of
DWO within each country is essentially
symmetrical and without outlier values.
Moreover, the classical (non-robust) estimator is
ineffective when the data contains outlier values,
and the regression parameters must be estimated
using a robust estimator. Many publications in
several regression models discuss a variety of robust
estimators; see e.g. [78 79, 80, 81].
7.2 Selecting the Appropriate CPD Model
In order to choose the appropriate CPD model for
this data, we will during in analysis do the following
steps:
1. The four CPD (FEP, REP, FENB and RENB)
models will be estimating.
2. Testing the Hypothesis;
: The random effects model is appropriate.
: The fixed effects model is appropriate.
3. Conducting the Hausman [50] test to compare
the fixed and random effects models.
4. In the final step, the selection criteria
(goodness-of-fit measures) will be used to
select the appropriate CPD model.
The findings of the FEP and REP models are
presented in Table 4. The CMLE approach was used
to estimate the parameters in fixed effects models,
whereas the MLE method was used to estimate the
parameters in random effects models. Because the
Wald test P-value is less than 0.05, the two models
(FEP and REP) are statistically significant. Based on
the results of Hausman test, the P-value of chi-
squared is less than 0.05, then we can reject the null
hypothesis, this means that FEP model is more
appropriate.
However, the findings of CML estimates of the
FENB model and MLE estimates of the RENB
model are presented in Table 5. Because the Wald
test P-value is less than 0.05, the two models (FENB
and RENB) are statistically significant. The FENB
model is more appropriate because the P-value of
the Hausman test is less than 0.05.
Moreover, the values of the Hausman test statistic,
values, are shown in Figure 4 based on the
number of bootstrap replications (for the bootstrap
variance matrix estimated in the Hausman test). As
the P-value of the Hausman test is less than 0.05 or
any lower level of significance after 1000
replications, then we reject the null hypothesis,
implying that the fixed effects model is more
appropriate.
Based on the results in Tables 4 and 5, we
concluded that FEP and FENB models are
preferable to REP and RENB models. The Akaikes
information criterion (AIC) and the Bayesian
information criterion (BIC) should then be used to
select the appropriate model (FEP or FENB). On the
other hand, the good model, correlates to reduce
AIC and BIC criteria and largest values of P-value.
The formulae that are utilized to determine these
approaches are as follows.
AIC, (11)
BIC, (12)
where is the log-likelihood function value for the
estimated model, is the number of parameters, and
is the sample size. Table 6 indicates that the
FENB model has the lowest AIC and a BIC value,
as well as higher R-Squared values, implying that it
is the best model for fitting the data set.
The national DWI is obtained by adding up the
indexes for the five dimensions and normalizing the
result used the Alkire/Foster (AF) method [82]. This
can be done for five countries, i.e. those for which
all five of the dimensions indexes could be
estimated with the data available. As can be seen in
Figure 5, the highest scores on the DWI are Bahrain,
followed by China, Egypt, Jordan, finally Nigeria.
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Table 4. Poisson Panel Models Estimates
Variables
FEP Model
REP Model
Estimate
T-value
P-value
Estimate
T-value
P-value
Intercept
--------
--------
--------
-5.523
-3.980
0.002
EPR
0.452
4.664
0.003
0.312
0.664
0.713
UR
-0.153
-1.096
0.276
-0.782
-6.096
0.001<
LPR
0.676
16.541
0.001<
0.983
8.541
0.001<
LIR
0.835
0.563
0.527
-0.529
-0.903
0.492
CPI
-0.947
-2.547
0.013
0.498
2.547
0.039
FER
0.765
10.209
0.001<
0.929
13.209
0.001<
VER
-0.896
-7.547
0.001<
-0.107
-0.147
0.195
FMER
-0.418
-3.696
0.004
-0.582
-3.953
0.003
FPER
0.906
1.680
0.056
0.790
18.691
0.001<
DR
-0.649
-1.507
0.113
0.369-
64.20-
0.001<
WPR
-0.952
-11.462
0.001<
0.112-
-0.256
0.809
Wald Test
Hausman Test
Table 5. Negative Binomial Panel Models Estimates
Variables
FENB Model
RENB Model
Estimate
T-value
P-value
Estimate
T-value
P-value
Intercept
-12.586
-21.638
0.001<
-19.169
-30.892
0.001<
EPR
0.937
7.835
0.001<
0.019
492.3
0.023
UR
-0.492
-1.284
0.316
-0.685
-4.096
0.001<
LPR
1.890
0.495
0.090
3.693
2.541
0.001<
LIR
0.720
0.836
0.005
0.284
0.539
0.574
CPI
-0.793
-2.904
0.017
-0.962
-1.947
0.019
FER
3.848
14.209
0.001<
0.091
2.609
0.007
VER
-0.569
-2.170
0.005
2.837
0.547
0.041
FMER
2.721
16.696
0.001<
0.284-
-0.562
0.569
FPER
0.839
18.778
0.001<
8.128
17.061
0.001<
DR
0.419-
26.57-
0.001<
0.197-
-1.521
0.209
WPR
-0.0915
-3.694
0.002
6.830-
-3.092
0.002
Wald Test
Hausman Test
Table 6. Measures of Goodness-of-fit for FE Models
Measure
FEP Model
FENB Model
AIC
7182.962
4746.501
BIC
7190.846
4751.385
Log likelihood
-5629.841
-3162.012
R-Squared
0.7187
0.8490
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Fig. 4: The Hausman test values are determined by the number of bootstrap replications
Fig. 5: Decent Work index for Each Country
Based on previous research results, we
recommend the following:
Strong political will is required among countries
to integrate DW goals into national economic
and social policy priorities, as well as to provide
the economic, administrative, and infrastructure
requirements for proper implementation of
DWCPs.
Governments should take a set of measures and
procedures to improve compliance with labour
standards. Chief among them is strengthening
the capacity of labour inspectors, providing
additional supervisory personnel, and applying
deterrent penalties until employers comply with
occupational safety and health requirements. A
greater focus should also be placed on workers
in the informal economy, and a preventive
national culture of safety and health must be
instilled within society.
The need for coordination between education
outputs and the labour market to maximize the
benefit from human resources so that it does not
turn into an unemployment problem that
disturbs countries, see [83]. Information systems
and labour market analysis must be
strengthened, and employment-related training
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opportunities should also be expanded with an
emphasis on enabling people with disabilities to
acquire the skills necessary to secure
employment. Convergence and consistency
between economic, social, and environmental
policies must also be ensured to create more
green jobs. It is important to increase the
effectiveness of growth by being more inclusive
so that disadvantaged groups can benefit on a
large scale from comprehensive improvements
in living standards and working conditions.
Growth must be labor-intensive enough to
create more jobs for a larger workforce.
It is necessary to hasten the transition from the
informal to the formal economy, see [84]. While
deterrence measures such as the imposition of
sanctions and denial of privileges are necessary
and important, the components of compliance
strategies are most effective when combined
with awareness-raising and providing
information and guidance. It is noted that
websites, apps, social media, and the media can
be used to raise awareness and improve
compliance.
Preparing special programmes to provide
women with the skills required in their work
according to market needs, while striving to
provide job opportunities for women in various
sectors of the national economy, removing
barriers that limit their active participation in the
labour market, and eliminating social and
cultural prejudices, as women are an important
partner in development.
Donors should provide more funding for DW
country projects and programs, as a lack of
funding can be an obstacle that leads to stalled
implementation and the failure of the project or
program. Donors must also ensure that these
grants are managed in an efficient and effective
manner in the recipient countries.
Achieving a perfect balance among both work
and family life is essential through establishing
family-oriented policies in the work
environment, such as the use of a flexible work
time policy and a telework policy so that
employees can work from their homes.
For social dialogue to succeed the state is
responsible for creating a stable political and
civil climate in which employers and workers
organizations can operate freely and without
fear, and to include the voice of the various
segments of workers who are not currently
represented.
The minimum wage must be reviewed regularly,
based on accurate and up-to-date information,
and accompanied by tax measures and other
benefits to effectively combat poverty. Equity
and social cohesion must also be strengthened
by expanding social security systems. Social
security should be seen as an investment in
human capital, not a cost, because it helps
contain inequality, and has an important impact
on ensuring sustainable and inclusive growth
and moving out of poverty, which will also help
eliminate child labor.
8 Concluding Remarks
This paper shows that the Alkire Foster (AF)
method for calculating multi-dimensional indices
can be usefully applied to the measurement of DW.
In this paper, we examined the effect of five
dimensions and eleven indicators on the number of
DWO in five countries over the period from 1999 to
2019 by applying four CPD models, For more
details about CPD models, see e.g. [85] The
Hausman test has been conducted to compare fixed
and random effects models; the results of the
Hausman test indicate that FE models are better than
RE models. Using selection criteria (AIC, BIC and
R-Squared), we find that the FENB model is the
appropriate for this data, because it has the lowest
AIC, BIC values and higher values of R-Squared.
We found that the FENB model results indicated
that the EPR,FER, FMER and FPER indicators have
a positive significant result on the number of DWO,
implying that the greater the value of this indicator,
the better the level of DW within the country.
However, the UR, VER, DR, WPR indicators have a
negative significant effect on the number of DWO.
While the LPR, LIR, CPI indicators have little
bearing on the number of DWO. The results show
that the five countries analyzed have varying levels
of DW, with Nigeria and Jordan scoring very low on
the index, Egypt obtaining a median level of
achievement, and Bahrain and China scoring higher.
It is worth noting that these findings are reliable.
The aggregated measures of the DWI allow for the
construction of a countries ranking and produces
internationally comparable results across a range of
countries with differing levels of development.
Acknowledgement:
The author would like to appreciate and thank Prof.
Salwa Shaarawy Gomaa, the Professor of Public
Policy, Faculty of Economics and Political Science,
Cairo University, Giza, Egypt, for her support and
help during my study.
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