constructing an economic and social rights fullfillment index for egypt- eman refaat (7) - pdf
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I
CAIRO UNIVERSITY
FACULTY OF ECONOMICS AND POLITICAL SCIENCE
DEPARTMENT OF STATISTICS
Constructing a Rigorous Economic and Social
Rights Fulfillment Index for Egypt
Prepared by
Eman Refaat Mahmoud Ahmed
Supervised by
Dr. Ali S. Hadi Distinguished University Professor
Chair Department of Mathematics and
Actuarial Science
The American University in Cairo
Dr. Dina M. Armanious Associate Professor of Statistics
Department of Statistics
Faculty of Economics and Political Science
Cairo University
A thesis submitted to the Department of Statistics, Faculty of Economics and Political Science in
Partial Fulfillment of the Requirements for the M.Sc. Degree in Statistics
2013
II
To My Dear Family and My Expected Daughter
Farida
III
Acknowledgement
Foremost, I would like to express my sincere gratitude to my supervisor
Prof. Ali S. Hadi for the continuous support, patience, motivation,
enthusiasm, and immense knowledge. His guidance helped me in all the
time of research and writing of this thesis. I learnt a lot from him either
statistical or research wise and really can't thank him enough.
I am also deeply grateful for my supervisor Dr. Dina M. Armanious who
gave me always her continuous advice, valuable comments and
encouragement. She continually and convincingly conveyed a spirit of
motivation to this thesis. Without her guidance and persistent help this
dissertation would not have been possible.
I would like to thank my committee members; Dr. Mohammed Ismail and
Dr. Ibrahim Hassan for their valuable comments and feedback.
I am thankful for Dr. Sahar El-Tawila for her support and motivation during
the whole process of the thesis.
I take this opportunity to express the profound gratitude from my deep heart
to my parents and for their love and continuous support – both spiritually
and materially.
My heartfelt gratitude to my husband Mahmoud who supported me and
motivated me a lot during my research, his support was an essential part for
the completion of this thesis. He really deserves great acknowledgement and
thanks, as well as my eternal love.
Eman Refaat
I
Abstract
This study aims at constructing a new index for Egypt that measures the fulfillment of
Economic and Social Rights (ESRFI), a composite index to measure the fulfillment of
human rights based on socio-economic surveys. The Proposed ESRF index could strengthen
policy formulation that takes into account economic and social rights fulfillment specially
by highlighting the situation in different regions and disaggregation levels. During the
construction of such an index and for the index to be rigorous, the study highlighted some of
the statistical debatable issues about composite indices and focused mainly on 6 of them.
Those issues are indicators selection, handling missing data, identification of and dealing
with outliers, scale of measurement, computing the margin of error, weights assigned for
indicators and domains and aggregation method.
The measurement process relied on the "Egyptian Household Conditions Observatory
Survey" that was conducted by the Information and Decision Support Centre in 2010 as this
is the national household survey that covers different indicators of the index. Another
advantage for the used survey that it is periodically implemented and have panel part. This
will allow in the future for following up the index trend.
The sample size is 10550 households and is representative at the national, governorates and
urban – rural levels.
The main results of the thesis include: In a scale from 0 to 100, the average score of the
ESRFI is 62.7 with minimum score of 31.2 and maximum 94.6. Inequalities between urban
and rural areas in fulfilling the economic and social rights as well as governorates were
exist. The box plots of dimensions over urban and rural areas show that rural is always
worse than urban areas in all levels of dimensions especially for the right to education and
adequate housing. The fulfillment of the right to decent work scored the lowest 42.6, while
the right to food got the highest score of 90.7. While Giza, Alexandria and Cairo got the
highest scores in fulfilling the economic and social rights, Kafr Al-Sheikh, Sohag and Assiut
got the lowest scores in fulfilling those rights.
II
Across different age groups, the economic and social rights fulfillment is significantly the
highest among youth and young adults. The fulfillment is the lowest among children age
group as well as adults.
Key Words: Composite index – Multivariate outliers – Indicators selection – Missing
values – Margin of error – Economic and Social Rights – Weighting – Aggregation – Scale
of measurement – Meta data.
Supervised by
Dr. Ali S. Hadi Distinguished University Professor
Chair Department of Mathematics and
Actuarial Science
The American University in Cairo
Dr. Dina M. Armanious Associate Professor of Statistics
Department of Statistics
Faculty of Economics and Political Science
Cairo University
III
Name: Eman Refaat Mahmoud Ahmed
Nationality: Egyptian
Date and place of birth: 27/02/1986, Kaliubia, Egypt
Degree: Master Grade: Very Good
Specialization: Statistics
Supervisors:
Dr. Ali S. Hadi Distinguished University Professor
Chair Department of Mathematics and
Actuarial Science
The American University in Cairo
Dr. Dina M. Armanious Associate Professor of Statistics
Department of Statistics
Faculty of Economics and Political Science
Cairo University
Thesis Title: "Constructing a Rigorous Economic and Social Rights Fulfillment Index for
Egypt".
Summery:
A composite index combines equities and/or other factors in a standardized way to provide a
useful statistical measure of overall performance of a targeted phenomenon over time. Such
a composite index must be understandable and easy to describe, conform to “common
sense” notions of the phenomena, able to guide policy, technically solid, operationally
viable, and easily replicable. The construction of an index, however, involves several issues
and debates. The main objective of this thesis is to construct and calculate a new “Rigorous
Economic and Social Rights Fulfillment Index for Egypt” using national survey data. In
order to achieve this main objective, the following objectives should be attained:
1. Selecting the domains and indicators that measures the economics and social rights
fulfilment based on a solid theoretical framework.
2. Highlighting debatable issues in constructing and measuring the Economic and
Social Rights Fulfillment Index (how to detect the issues and how to deal with it).
3. Aggregating all dimensions to get the final rigorous index taking into consideration
the margin of error.
IV
The dataset used to measure and handle the issues was "Egyptian Household Conditions
Observatory Survey" that was conducted by the Information and Decision Support Centre in
2010 as this is the household national survey that covers different indicators of the index.
Six debatable issues were highlighted; indicators selection, handling missing data,
identification of and dealing with outliers, scale of measurement, computing the margin of
error and aggregation and weights assigned for indicators and domains.
The study is divided to six chapters as follows:
Chapter One "Introduction": includes a background about the composite index, statement
of the problem, objectives of the study, literature review and organization of the Study.
Chapter Two "Composite Indices and Challenges": this handles the steps for
constructing a composite index as well as different challenges in the construction of the
composite indices and the focus of the study.
Chapter Three "The Economic and Social Rights Fulfillment Index": this chapter
specifies the theoretical framework behind the Economic and Social Rights Fulfillment
index with the list of domains and indicators and the source of data.
Chapter Four "Methodologies to handle the problems of Composite Indices": focuses
on highlighting the measurement issues of the index especially the ones concerned with;
Missing Data, Outliers, Scale of Measurement, Weighting and Aggregation and Computing
the Margin of Error.
Chapter Five "Results of Calculating the ESRF index for Egypt": presents the main
findings of measuring the Economic and Social Rights Fulfillment index.
Chapter Six "Conclusions and Recommendations": summarizes the main findings of the
study in addition to the recommendations.
V
Table of Contents
Chapter One: Introduction ................................................................................................... 1
1.1. Background ........................................................................................................... 1
1.2. Statement of the problem ....................................................................................... 2
1.3. Objectives of the Study ......................................................................................... 2
1.4. Literature Review .................................................................................................. 3
1.5. Organization of the Study ...................................................................................... 8
Chapter Two: Composite Indices and Challenges ................................................................ 9
2.1. Steps for Constructing a Composite Index ................................................................. 9
2.2. Challenges in the Construction of the Composite Index ........................................... 12
Chapter Three: The Economic and Social Rights Fulfillment Index ................................... 15
3.1. Introduction ......................................................................................................... 15
3.2. Definition of Domains ......................................................................................... 19
3.3. Indicators Selection ............................................................................................. 24
3.4. List of Domains and Indicators in the ESRF Index .............................................. 26
3.5. Source of Data..................................................................................................... 31
3.6. Results of using Cronbach's α on the dimensions of the index ............................. 33
Chapter Four: Methodologies to handle the problems of Composite Indices ...................... 35
4.1. Missing Data ..................................................................................................... 35
4.2. Outliers ............................................................................................................... 47
4.2.1 Definition of Outliers ................................................................................... 47
4.2.2 Detection of Outliers .................................................................................... 48
4.2.3 How to deal with outliers ............................................................................. 53
4.3. Scale of Measurement ......................................................................................... 57
4.4. Weighting and Aggregation ................................................................................. 59
4.4.1 Weighting .................................................................................................... 59
4.4.2 Aggregation ................................................................................................. 62
4.5. Computing the Margin of Error ............................................................................... 63
Chapter Five: Results of Calculating the ESRF index for Egypt ......................................... 65
5.1. Results of the overall Economic and Social Rights Fulfillment Index .................. 65
5.2. Results of the ESRFI five dimensions .................................................................. 74
VI
5.2.1 Right to Adequate Housing .......................................................................... 74
5.2.2 Right to Food ............................................................................................... 80
5.2.3 Right to Decent Work .................................................................................. 85
5.2.4 Right to Education ....................................................................................... 92
5.2.5 Right to Health ............................................................................................. 97
Chapter Six: Conclusions and Recommendations ............................................................ 105
References ....................................................................................................................... 109
Annexes .......................................................................................................................... 115
Annex 1: Indicators Meta Data..................................................................................... 115
Annex2: Results of Neural Networks analysis .............................................................. 135
Annex3: Results of final multiple imputations over decent work variables ................... 143
List of Tables
Table 2.1: Steps for constructing a composite index .......................................................... 10
Table 3.1: List of Domains, Indicators and Variables of the ESRF index ........................... 26
Table 3.2: Sample distribution according to governorates in Egypt .................................... 32
Table 3.3: Sample distribution according to Urban and Rural Areas in Egypt .................... 32
Table 3.4: Reliability Statistics for the right to food ........................................................... 33
Table 3.5: Reliability Statistics for the right to health ........................................................ 33
Table 3.6: Reliability Statistics for the right to adequate housing ....................................... 34
Table 3.7: Reliability Statistics for the right to decent work ............................................... 34
Table 4.1: The 20 variables with not applicable cases ........................................................ 37
Table 4.2: Multiple Imputation Specifications for main characteristics .............................. 43
Table 4.3: Multiple Imputation Constraints on variables .................................................... 43
Table 4.4: Multiple Imputation Results .............................................................................. 44
Table 4.5: Imputation Models ............................................................................................ 44
Table 4.6: Comparison between different imputation options applied ............................... 44
Table 4.7: Testing and training partitions of the Neural Networks analysis of the ESRFI ... 46
Table 4.8: Descriptive Statistics for the results of Neural Networks using Multilayer
Perceptron compared to Radial Basis function ................................................................... 46
Table 4.9: Trimmed mean and median results for outliers detection ................................... 55
VII
Table 4.10: Weight sample characteristics ......................................................................... 61
Table 4.11: Weights for the dimensions of the ESRFI ........................................................ 61
Table 5.1: Descriptive Statistics for the overall ESRFI ...................................................... 65
Table 5.2: Tests of Normality ............................................................................................ 66
Table 5.3: Economic and Social Rights Fulfillment Index for Egypt by Urban - Rural ....... 67
Table 5.4: Economic and Social Rights Fulfillment Index for Egypt by Regions ............... 69
Table 5.5: ANOVA Economic and Social Rights Fullfillment Index for Egypt and
governorates ...................................................................................................................... 70
Table 5.6: Economic and Social Rights Fulfillment Index for Egypt by Governorates ....... 70
Table 5.7: Economic and Social Rights Fulfillment Index for Egypt by Current Marital
Status ................................................................................................................................ 71
Table 5.8: Economic and Social Rights Fulfillment Index for Egypt by Gender ................. 71
Table 5.9: Economic and Social Rights Fulfillment Index for Egypt by Age ...................... 72
Table 5.10: Economic and Social Rights Fulfillment Index for Egypt by Household size... 72
Table 5.11: Economic and Social Rights Fulfillment Index for Egypt by Gender of
household head .................................................................................................................. 73
Table 5.12: Economic and Social Rights Fulfillment Index for Egypt by Education of
household head .................................................................................................................. 73
Table 5.13: Descriptive Statistics of the Right to Adequate Housing .................................. 74
Table 5.14: Right to adequate housing by Urban – Rural ................................................... 75
Table 5.15: Right to adequate housing by Regions ............................................................. 76
Table 5.16: Right to adequate housing by Governorates .................................................... 77
Table 5.17: Right to adequate housing by Current Marital Status ....................................... 77
Table 5.18: Right to adequate housing by Gender .............................................................. 78
Table 5.19: Right to adequate housing by Age ................................................................... 78
Table 5.20: Right to adequate housing by Household size .................................................. 79
Table 5.21: Right to adequate housing by Gender of household head ................................ 79
Table 5.22: Right to adequate housing by Education of household head ............................ 79
Table 5.23: Descriptive Statistics for the Right to Food ..................................................... 80
Table 5.24: Right to food by Urban – Rural ...................................................................... 80
Table 5.25: Right to food by Regions ................................................................................ 81
Table 5.26: Right to food by Governorates ........................................................................ 82
VIII
Table 5.27: Right to food by Current Marital Status .......................................................... 83
Table 5.28: Right to food by Gender .................................................................................. 83
Table 5.29: Right to food by Age...................................................................................... 83
Table 5.30: Right to food by Household size ..................................................................... 84
Table 5.31: Right to food by Gender of household head .................................................... 84
Table 5.32: Right to food by Education of household head ................................................ 85
Table 5.33: Descriptive Statistics of Right to Decent Work ............................................... 85
Table 5.34: Right to decent work components .................................................................. 86
Table 5.35: Right to decent work by Urban - Rural ............................................................ 87
Table 5.36: Right to decent work by Regions .................................................................... 87
Table 5.37: Right to decent work by Governorates ............................................................ 88
Table 5.38: Right to decent work by Current Marital Status ............................................... 89
Table 5.39: Right to decent work by Gender ...................................................................... 89
Table 5.40: Right to decent work by Age ........................................................................... 90
Table 5.41: Right to decent work by Household size.......................................................... 90
Table 5.42: Right to decent work by Gender of household head ......................................... 91
Table 5.43: Right to decent work by Education of household head .................................... 91
Table 5.44: Descriptive Statistics of Right to Education .................................................... 92
Table 5.45: Right to education by Urban - Rural ................................................................ 92
Table 5.46: Right to education by Regions ........................................................................ 93
Table 5.47: Right to education by Governorates ................................................................ 94
Table 5.48: Right to education by Current Marital Status ................................................... 95
Table 5.49: Right to education by Gender .......................................................................... 95
Table 5.50: Right to education by Age ............................................................................... 96
Table 5.51: Right to education by Household size ............................................................. 96
Table 5.52: Right to education by Gender of household head............................................. 96
Table 5.53: Right to education by Education of household head ........................................ 97
Table 5.54: Descriptive Statistics of the Right to Health .................................................... 98
Table 5.55: Right to health components ............................................................................ 98
Table 5.56: Right to health by Urban - Rural ..................................................................... 99
Table 5.57: Right to health by Regions .............................................................................. 99
Table 5.58: Right to health by Governorate ..................................................................... 100
IX
Table 5.59: Right to health by Current Marital Status ...................................................... 101
Table 5.60: Right to health by Gender ............................................................................. 101
Table 5.61: Right to health by Age .................................................................................. 102
Table 5.62: Right to health by Household size ................................................................. 102
Table 5.63: Right to health by Gender of household head ................................................ 102
Table 5.64: Right to health by Education of household head ............................................ 103
Table B.1: Case Processing Summary for multilayer perceptron ...................................... 135
Table B.2: Network Information for multilayer perceptron .............................................. 135
Table B.3: Multilayer Perceptron Independent Variable Importance ................................ 138
Table B.4: Case Processing Summary for radial basis function ........................................ 139
Table B.5: Network Information for radial basis function ................................................ 139
Table B.6: Model Summary for radial basis function ....................................................... 141
Table B.7: Independent Variable Importance for radial basis function ............................. 142
Table C.1: Imputation Specifications ............................................................................... 143
Table C.2: Imputation Constraints ................................................................................... 143
Table C.3: Imputation Results ......................................................................................... 144
Table C.4: Imputation Models ......................................................................................... 144
List of Figures
Figure 3.1: General structure of the ESRF index ................................................................ 17
Figure 4.1: Structure of the Neural Networks ..................................................................... 45
Figure 4.2: Graphical detection of outliers in monthly salary of individuals ....................... 54
Figure 4.3: Graphical detection of outliers in crowdedness variable ................................... 54
Figure 5.1: Histogram of ESRFI scores ............................................................................. 66
Figure 5.2: Normal Q-Q Plot of Economic and Social Rights Fullfillment Index for Egypt 67
Figure 5.3: Box plots for the ESRFI dimensions across urban and rural areas .................... 68
Figure 5.4: Economic and Social Rights Fulfillment Index for Egypt by Governorates ...... 69
Figure 5.5: Right to adequate housing disaggregated by its components ............................ 75
Figure 5.6: Right to adequate housing by Governorates ..................................................... 76
Figure 5.7: Right to food by Governorates ......................................................................... 81
X
Figure 5.8: Right to decent work by Governorates ............................................................. 88
Figure 5.9: Right to education by Governorates ................................................................. 93
Figure 5.10: Right to health by Governorate .................................................................... 100
Figure B.1: Multilayer perceptron Network structure ....................................................... 136
Figure B.2: Multilayer perceptron predicted values versus actual values .......................... 137
Figure B.3: Multilayer perceptron residuals versus predicted values ................................ 137
Figure B.4: Multilayer perceptron Independent Variable Importance ............................... 138
Figure B.5: Radial basis function network structure ......................................................... 140
Figure B.6: Radial basis function predicted values versus actual values ........................... 141
Figure B.7: Radial basis function residuals versus predicted values ................................. 141
Figure B.8: Radial basis function Independent Variable Importance ................................ 142
XI
List of Abbreviations
AAD Average of Absolute Deviations about the Median
ANN Artificial Neural Network
APF Achievement Possibility Frontier
BACON Blocked Adaptive Computationally-efficient Outlier Nominators
BAP Budget Allocation Processes
CCI Current Conditions Index
DQI Development Quality Index
ESCR Economic, Social and Cultural Rights
ESRFI Economic and Social Rights Fulfilment Index
EW Equal Weights
FA Factor Analysis
FAO Food and Agriculture Organization of the United Nations
GDP Gross Domestic Product
HDRs Human Development Reports
IIAG Ibrahim Index of African Governance
ILO International Labour Organization
IQI Institutional Quality Index
MAR Missing at Random
MCAR Missing Completely at Random
MDGs Millennium Development Goals
ME Margin of Error
MI – MCMC Multiple Imputation using Marcov Chain Monte Carlo Simulation
MLP Multilayer Perceptron
NMAR Not Missing at Random
NNs Neural Networks
OECD Organization for Economic Cooperation and Development
OHCHR Office of the High Commissioner for Human Rights
OHCHR Office of the High Commissioner for Human Rights
PCA Principal Component Analysis
RBF Radial basis function
TM Trimmed Mean
TSD Trimmed Standard Deviation
UN United Nations
UNDP United Nations Development Programme
UNESCO United Nations Educational, Scientific and Cultural Organization
UN-HABITAT United Nations agency for human settlements
US United States
WHO World Health Organization of the United Nations
1
Chapter One
Introduction
1.1. Background
A composite index combines equities and/or other factors in a standardized way to provide a
useful statistical measure of overall performance of a targeted phenomenon over time. It is
also well-known that a composite index fulfills the need for a synthetic measure of the
achievements of development in a certain sector or issue.
In the past, usage of composite indices were included in many statistical and social work,
but without mentioning it as a composite index or following its current structure. Nowadays,
the composite indices are widely used in many fields and the advocacy for it and how it is
important in measuring certain phenomena – especially the multidimensional phenomena. In
addition to that, there are some changes happening every day and encourage to work more
in that area, for example; political space is opening, statistical offices around the world are
providing many guides that have a lot of the information that allow for the construction of
different indices, and many researchers around the world are believing that a summary
measure can provide a bird’s eye view and generates political and public interest.
Such a composite index must be understandable and easy to describe, conform to “common
sense” notions of the phenomena, able to guide policy, technically solid, operationally
viable, and easily replicable.
Depending on the process and statistical issues in measuring multidimensional phenomena
and the rights based approach in measuring different dimensions, the study constructed an
index that measures the fulfillment of economic and social rights in Egypt. Economic and
social issues were measured by using single dimensional (e.g. GDP), and different studies
(e.g. studies by the Organization for Economic Cooperation and Development OECD) have
shown that the GDP is not enough in measuring economic and social status that is
complicated and have many dimensions beyond GDP as an example. The study not only
covered this, but also is following in the same time rights based approach in measuring
economic and social rights to ensure the importance of the rights included in the index.
2
1.2. Statement of the problem
The composite index presupposes a deliberate conceptual aggregation of separable facts.
The construction of an index, however, involves several issues and debates, which include
(Indicators selection, Handling missing data, Identification of and dealing with outliers,
Scale of measurement (Normalization), Computing the margin of error, Weights and
aggregation). This study focuses on those 6 issues through the process of the index
construction. Identifying and dealing with these issues determine to what extent the index is
rigorous (stability and variance) and efficient in describing the phenomena of interest.
Hence, this study introduces a construction of a new index for Egypt that measures the
fulfillment of Economic and Social Rights (ESRF), a composite index to measure the
fulfillment of human rights based on socio-economic surveys. During the construction of
such an index, the study highlighted some of the statistical debatable issues about composite
indices and focus mainly on 6 of them as mentioned above. The Proposed ESRF index could
strengthen policy formulation that takes into account economic and social rights fulfillment
specially by highlighting the situation in different regions and different disaggregation
levels.
The question addressed in this thesis is how to construct and calculate a rigorous Economic
and Social Rights Fulfillment Index for Egypt using a national survey data?
1.3. Objectives of the Study
The main objective of this thesis is to construct and calculate a new proposed “Rigorous
Economic and Social Rights Fulfillment Index for Egypt” using national survey data.
In order to achieve this main objective, the following objectives should be attained:
1. Selecting the domains and indicators that measures the economics and social rights
fulfilment based on a solid theoretical framework.
2. Highlighting debatable issues in constructing and measuring the Economic and
Social Rights Fulfillment Index (how to detect the issues and how to deal with it).
3. Aggregating all dimensions to get the final rigorous index taking into consideration
the margin of error.
3
1.4. Literature Review
This study has two main issues, one is related to composite indices in general and another is
related to rigorous tools for diagnostics, the literature review may be classified into two
main parts, part one is the studies related to composite indices in general, part two is the
studies related to rigorous diagnostics tools, and the search came with studies that contain
some of part one and some of part two.
A study was done by UNESCO (1974) about Social indicators, which addresses the
problems of definition and selection. The paper presents three main issues: Problems of
Methodology and Selection; a method for the selection of a compact set of variables and a
method of establishing a list of development indicators. The paper made some conclusions
about social and economic indicators selection. First, increasing the number of indicators
also increases the total amount of information about the country’s level of development
importance. Second, sets of indicators of the same size do not, in general, contain the same
quantity of information about the country’s development level. Third, the total amount of
information given by a set of indicators is generally less than the sum of the quantities of
information contained individually in each indicator of that set. Fourth, despite the fact that
two given indicators may be very important from the point of view of the information they
provide, separately, about a country’s development levels, the contribution of one of them is
insignificant if there is a high degree of correlation between the two.
The Organization for Economic Co-Operation and Development (2008), presents the
processes and achievements of the national experiences, undertaken by the Metagora
community, highlighting their policy relevance and methodological implications. These
experiences illustrate how quantitative methods, properly combined with qualitative
approaches, can be applied for assessing key national issues and enhancing evidence based
reporting and monitoring mechanisms. The study also provides decision and policy makers,
analysts and civil society actors with significant examples of how sensitive data on human
rights and governance issues can be collected and analyzed. It highlights how qualitative
and quantitative data can be interrelated to provide reliable information. It shows how, on
the basis of this information, it is possible to produce national indicators which are relevant
4
and useful for political decisions and actions. It also illustrates that statistical analysis and
quantitative indicators bring significant value-added to the work of national human rights
institutions, as well as to the research and advocacy of civil society organizations.
The Organization for Economic Co-Operation and Development (2008) classifies and lists a
set of indicators related to the right to education identified into three types of indicators:
structural indicators, process indicators, and outcome indicators. Structural indicators
address whether or not the requisite infrastructure is in place that is considered necessary
for, or conducive to, the realization of a specific right. Specifically, structural indicators
evaluate whether a country has established the institutions, constitutional provisions, laws,
and policies that are required. Most structural indicators are qualitative in nature and are not
based on statistical data and many can be answered by a simple yes or no. Process
indicators, along with outcome indicators, monitor the variable dimension of the right to
health that arises from the concept of progressive realization. Their key feature is that they
can be used to assess change over time. Specifically, process indicators assess the degree to
which activities that are necessary to attain specific rights-related objectives are being
implemented and the progress of these activities over time. They monitor effort and not
outcome. The types and amounts of governmental inputs are an important kind of process
indicator. Unlike structural indicators, process indicators require statistical data. Outcome
indicators assess the status of the population’s enjoyment of a right.
The Mo Ibrahim Foundation (2010) publishes a report about the Ibrahim Index of African
Governance (IIAG), which measures the extent of delivery to the citizen of a large number
of economic, social and political goods and services by governments and non-state actors.
The Index groups indicators into four main categories: Safety and the Rule of Law,
Participation and Human Rights, Sustainable Economic Opportunity, and Human
Development. The report contains all details related to the index from the first step of
construction till the values of the index for African countries in a very presentable way,
regarding the method and the methodology. Statistically, there are several challenges in
compiling and constructing the IIAG. These include choosing the most appropriate
statistical method to aggregate the data into one composite index, and at a more basic level,
finding the most suitable set of indicators that appropriately reflect governance as defined
5
by the Board of the Foundation, its Founder, and its Advisory Council and Technical
Committee members. The index uses the same method as in the past, namely, the min-max
method for the normalization of variables, and a statistical technique was used to address
(filter) the outliers, given the high degree of sensitivity of the min-max method to outliers.
The sub-category scores were calculated by averaging the scores of all the component
indicators. Category scores were calculated by averaging the scores of the sub-categories,
and finally, the overall index scores were obtained by simply averaging the scores of the
four categories.
Savitri Abeyasekera (2004) discusses situations where the data determine the form of the
index by use of a multivariate procedure. This still retains the common interpretation of an
index as being a single value that captures the information from several variables into one
composite measure, typically taking the form:
,2211 pp XaXaXaIndex
where the ai's are weights to be determined from the data and the Xi‘s are an appropriate
subset of p variables measured in the survey. It illustrates two ways in which the weights ai
can be determined from the data. One of them is based on a regression modeling approach
and the other on an application of principal component analysis (PCA). The paper
concluded that the application of these methods however requires careful thought, with due
attention to their meaning and their limitations. The success of principal component analysis
for variable reduction for example, depends on being able to summarize a substantial
proportion of the variation in the data by just a few component indices, and being able to
give a meaningful interpretation to each of these. It is also important to think carefully about
the effectiveness of the PCA procedure if only a small part of the variation in the complete
set of variables is accounted for by the first principal component. Sufficient attention should
also be given to the appropriateness of the variables included in the calculation of the index
in relation to the objectives of the analysis.
Sudip, R. B. (2008), introduced a study on a new way to link development to institutions,
policies and geography. To that end, the study attempts to construct a Development Quality
Index (DQI) and an Institutional Quality Index (IQI) using multivariate statistical method of
6
principal components. It shows that (i) higher level of IQI along with economic policy and
geography factors lead to a positive improvement in the level of DQI; and that (ii) results
remain rigorous for IQI and relatively rigorous for economic policy and geography even
when it is compared across cross-section and panel data estimation for a set of 102 countries
over 1980 to 2004. The results strongly indicate that institutions matter in the context of
specific economic policy mixes and geography-related factors illustrated by disease burden,
etc. For normalization, the maximum and minimum values of these indicators are taken
from the world sample. In the case of regional level analysis, the maximum and minimum
values are taken from countries own sample during the period under study. At the end they
succeeded to set the two indices with list of dimension and indicators included using the
methods mentioned, where the higher values of both indices indicate a higher level of
development and institutional quality, respectively, and the indices are comparable over
time and respective weights are obtained from the analysis of principal components.
The UNDP (2007) primer report on measuring human development is intended as a
reference tool that provides guidance on statistical principles for producing evidence-based
policy recommendations and quality human development reports (HDRs). It is aimed at
HDR teams, as well as other practitioners working together to achieve the Millennium
Development Goals (MDGs), human rights and broader human development objectives.
Chapters include: Statistical principles in human development analysis, Select dimensions
of measuring human development, Advocating for change with human development data.
Regarding the composite indices, Chapter one gives a check list for ensuring the quality of
constructing a composite index as follows: For constructing new composite indices, has a
theoretical model been set up? Is the objective of the composite index clear? Are the
constituent indicators well defined, relevant and accessible? Have the inter-relationships
between constituent indicators been analyzed? Has the weighting and aggregation scheme
been adequately explained? Have sensitivity and uncertainty analyses been conducted?
Have the components of the composite indicator been discussed and analyzed?.
Susan R., Sakiko, F. P., and Terra, L. R. (2008) propose a methodology for an index of
economic and social rights fulfillment that captures progressive realization of human rights
subject to maximum available resources. Two calculation methods are proposed: the ratio
7
approach and the achievement possibilities frontier approach. Index Version 1 measures
ESR fulfillment as a ratio between the extent of rights enjoyment (x), and State resource
capacity (y). A country’s raw index score is determined by z = x/y. xi = enjoyment indicator
(e.g., primary school completion rate; 100 - malnutrition rate), y = ln (GDP per capita), zi =
index score. Achievement Possibility Frontier (APF) approach to measure ESR fulfillment.
The study first estimate an achievement possibility frontier for each ESR. This frontier
determines the maximum level of achievement possible in each ESR dimension (xmax) at a
given per capita income level, based on the highest level of the indicator historically
achieved by any country at that per capita GDP level. A country’s rights fulfillment score
(x*) in each ESR dimension is then determined as xji* = xji/xjimax (where j = L or H for Low
& Middle Income countries and High Income countries, respectively, and i refers to the
specific indicator of concern as defined in Version 1 of the index). This can be interpreted as
the proportion of the feasible level achieved. The paper identifies key conceptual and data
constraints. Recognizing the complex methodological challenges, the aim of this paper is
not to resolve all the difficulties, but rather to contribute to the process of building rigorous
approaches to human rights measurement. The proposed index thus has recognized
limitations, yet it is an important first step based on available data. The index updated on
2009 with values and rankings for a large number of countries.
Robert H. McGuckin, Ataman Ozyildirim, and Victor Zarnowitz (2002), A More Timely
and Useful Index of Leading Indicators. The U.S. leading index has long been used to
analyze and predict economic fluctuations; this study describes and tests a new procedure
for making the index more timely. The index significantly outperforms its less timely
counterpart and offers substantial gains in real-time out-of-sample forecasts of changes in
aggregate economic activity and industrial production. The procedure for calculating the
U.S. Leading Index combines seven current financial and non-financial indicators with
simple forecasts of three other indicators that are only available with lags. The two basic
findings of the study are: (1) the leading indicators, properly selected and collected in an
index, convey significant predictive information about the economy’s change in the next
several months, beyond what can be learned from the economy’s recent past. (2) The index
is dramatically more accurate than the old index in forecasting growth of current conditions
8
index (CCI) in the same impending target months. In addition, our results inspire confidence
because they make sense in the light of what is known from many past studies about some
tendencies common in short-term economic forecasts.
According to the literature review, different studies were mainly focusing on the theoretical
items than measuring issues. Also it is noticeable that the majority of national indices are
aggregated from a macro value not at micro level (Individuals or household level). When it
with regard to weights of different indicators or dimensions they are usually set to be equal
either for simplicity or for having no reason to set it unequal. Also when considering
measuring challenges, they are not considered adequately as a group to handle. The ESRFI
is avoiding all these limitations from literature and is introducing a comprehensive
composite indices process.
1.5. Organization of the Study
After the introduction, this study is divided into 5 Chapters where:
Chapter Two "Composite Indices and Challenges": this handles the steps for
constructing a composite index as well as different challenges in the construction of the
composite indices and the focus of the study.
Chapter Three "The Economic and Social Rights Fulfillment Index": specifies the
theoretical framework behind the Economic and Social Rights Fulfillment index with the list
of domains and indicators and the source of data.
Chapter Four "Methodologies to handle the problems of Composite Indices": focuses
on highlighting the measurement issues of the index especially the ones concerned with;
Missing Data, Outliers, Scale of Measurement, Weighting and Aggregation and Computing
the Margin of Error.
Chapter Five "Results of Calculating the ESRF index for Egypt": presents the main
findings of measuring the Economic and Social Rights Fulfillment index.
Chapter Six "Conclusions and Recommendations": summarizes the main findings of the
study in addition to the recommendations.
9
Chapter Two
Composite Indices and Challenges
In the recent days, many studies depend on constructing composite indices to measure
different phenomena and give a direct message about the situation with one aggregated
value. This value can be tracked in different times to check for the trend.
Additionally, the comparisons across different disaggregation levels can be made using the
value of the index to indicate inequalities or gaps. Researchers working on different studies
are not necessarily statisticians and sometimes they do not realize the statistical techniques
they are using and the characteristics of it that may affect the aggregated value at the end
and lead to misleading decisions.
This study is trying to give a model for the way of constructing a composite index and the
main problems that may face researchers especially the ones using households’ survey data.
Although the importance of constructing composite indices, there are many problems and
challenges that need to decide on for each step of the process of constructing the composite
index. In each step of the construction process there are uncertainty item(s) and accordingly
all the steps together makes the process includes large items of uncertainty that should be
taken carefully and the decisions made for a certain problem should be tested and justified.
2.1. Steps for Constructing a Composite Index
The literature shows that there are some steps for constructing a composite index. Table 2.1
shows these steps and the importance of each step (See, for more details, Organization for
Economic Co-Operation and Development (2008), “Handbook on Constructing Composite
Indicators: Methodology and User Guide”).
But as the issue of composite indices is wide and has many tools and applications, the space
is open to add to these steps or even do not use one of them if not applicable to the
phenomenon that is measured. In general those steps shown in Table 2.1 are important to be
followed and applied for a composite index to be more rigorous and scientific.
10
Table 2.1: Steps for constructing a composite index
Step Why it is needed?
1. Theoretical framework: Provides the
basis for the selection and combination of
variables into a meaningful composite
indicator under a fitness-for-purpose
principle (involvement of experts and
stakeholders is envisaged at this step).
To get a clear understanding and definition of
the multidimensional phenomenon to be
measured.
To structure the various sub-groups of the
phenomenon (if needed).
To compile a list of selection criteria for the
underlying variables, e.g., input, output,
process.
2. Data selection: Should be based on the
analytical soundness, measurability,
country coverage, and relevance of the
indicators to the phenomenon being
measured and relationship to each other.
The use of proxy variables should be
considered when data are scarce.
To check the quality of the available
indicators.
To discuss the strengths and weaknesses of
each selected indicator.
To create a summary table on data
characteristics, e.g., availability (across
country, time), source, type.
3. Imputation of missing data: Is needed
in order to provide a complete dataset.
To give a measure for each case in the
analysis at the final aggregated index.
To provide a measure of the reliability of each
imputed value, so as to assess the impact of
the imputation on the composite indicator
results.
4. Multivariate analysis: Should be used
in studying the overall structure of the
dataset, assess its suitability, and guide
subsequent methodological choices (e.g.,
checking for reliability of the tool (index)
that is constructed theoretically).
To check the underlying structure of the data
and its reliability to the constructed index.
To compare the statistically determined
structure of the data set to the theoretical
framework and discuss possible differences.
11
Step Why it is needed?
5. Normalization: Should be carried out
to render the variables comparable.
To select suitable normalization procedure(s)
that respects both the theoretical framework
and the data properties.
To discuss the presence of outliers in the
dataset as they may become unintended
benchmarks.
To make scale adjustments, if necessary.
To transform highly skewed indicators, if
necessary.
6. Weighting and aggregation: Should be
done along the lines of the underlying
theoretical framework.
To select appropriate weighting and
aggregation procedure(s) that respects both the
theoretical framework and the data properties.
7. Uncertainty and sensitivity analysis:
Should be undertaken to assess the
robustness of the composite indicator in
terms of e.g., the mechanism for including
or excluding an indicator, the
normalization scheme, the imputation of
missing data, the choice of weights, and
the aggregation method.
To consider a multi-modeling approach to
build the composite indicator, and if available,
alternative conceptual scenarios for the
selection of the underlying indicators.
To identify all possible sources of uncertainty
in the development of the composite indicator
and accompany the composite scores and
ranks with uncertainty bounds.
8. Back to the data: Is needed to reveal
the main drivers for an overall good or bad
performance. Transparency is primordial
to good analysis and policymaking.
To identify if the composite indicator results
are overly dominated by few indicators and to
explain the relative importance of the sub-
components of the composite indicator.
Source: Handbook On Constructing Composite Indicators: Methodology And User Guide – ISBN 978-92-64-04345-9 - ©
Organization for Economic Co-Operation and Development 2008.
12
Those steps are important to be followed in order to get a rigorous index where the effects of
uncertainty are mitigated. The steps mentioned in Table 2.1 are not rigid and the door is open
for adding more steps if required, but those steps are in general relevant to the majority of
composite indices. Statistical tools in each step vary and open also for adding new tools or
methodologies as the usage of the statistical tools will appear in the analysis step when facing
a certain problem and start searching for the relevant statistical technique.
2.2. Challenges in the Construction of the Composite Index
The construction and calculations of composite indices have many different statistical
challenges. Some of these challenges are related to the measured phenomenon (which is the
fulfillment of economic and social rights in this study), for example:
The majority of phenomena measured by composite indices are complicated in their
nature.
Indices have many different dimensions.
Data limitations as sometimes researchers will need to have all the indicators and
dimensions available in the same dataset while the national surveys may have no data
on some of the dimensions or indicators.
These challenges are crosscutting and reflected mainly in the measurement steps and
challenges. This study focuses on making a diagnostic for a set of limitations – linked mainly
to the measurement steps - that faces the construction and computations of such a composite
index in addition to dealing with them by suitable tools, these limitations or challenges are:
1) Indicators selection, indicators should be selected based on basis like:
a) Soundness,
b) Measurability,
c) Coverage,
d) Relevance to the phenomenon being measured
e) The relationship to each other.
13
This will highly depend on the theoretical framework and the definition of dimensions
followed by data availability and country relevance.
2) Imputation of missing data and dealing with not applicable cases in survey data,
imputation of missing data for certain indicator in general or within certain area and
how to deal with this is a debatable issue. Another issue is that social household
surveys include skips that take us into non applicable questions for a group of cases,
those non applicable needs a relevant codes to be used for them in order to be able to
have a value for them in the dimension and accordingly on the index at the end. A
direct example on missing data from households surveys is when the household
refuses to answer questions about income or expenditures. If the researchers need to
have a complete variable about this, they will have to impute these missing values.
Example for non-applicable cases: consider a set of three questions about a certain good's
consumption by the household as follows:
i. If the household consume this good or not?
ii. If the household sees that the price of this good is increased or not?
iii. If the price increase affected the amount of consumption of this good?
And the researcher is concerned with the third question. All households who do not
consume that good or do not see that the prices increased will be skipped in the third
question and considered not applicable cases. To deal with this, the researcher may for
example give the skipped households a code of zero considering them not deprived from the
good (they are not interested in that good or they are not affected by the prices increase).
Subjective assumptions sometimes lie behind the selected codes.
3) The existence of univariate and multivariate outliers in the data can seriously
affect the values of such an index. Univariate outliers are when outliers are most
frequently sought for each single variable in a given data set. Multivariate outliers
are sought for and based on location and spread of the data. In the multivariate case
not only the distance of an observation from the centroid of the data but also the
shape of the data has to be considered (cases with an unusual combination of scores
14
on different variables). The higher (lower) the analytical result of a sample, the
greater is the distance of the observation from the central location of all
observations; outliers thus, typically, have large distance.
Univariate and multivariate outliers should be detected and if they exist, then there are
methods to deal with for having more rigorous results.
4) Scale of measurement, components (sub-indices) and indicators of a composite index
are often measured in different units and so straightforward summation would not be
valid in all cases. The problem of scale of measurement is a challenge for composite
indices and needs to be justified and relevant when using a specific tool.
5) Weighting and aggregation, very often, the components (sub-indices) and indicators
are assigned equal weights to compute an average. Sometimes unequal weights are
assigned on the basis of prior knowledge or expert views. The weights and
aggregation should be based on a certain relevant methodology or concept depending
on what we are measuring (Linear, Geometric, and other types of aggregation).
6) Computing the margin of error of a composite index is also an issue of concern that
needs to be addressed because of uncertainty and to give accuracy to the estimated
values. This too is a challenging problem especially when it comes to ranking regions
according their sub-indices.
These six limitations/challenges are the most debatable ones in constructing composite
indices (how to detect and deal with them). Some of these challenges exist by nature of
constructing a composite index and some others depend on the data used in the analysis or
construction process. For example the weighting problem exists in all composite indices
because weights must be assigned for domains and indicators in the composite index and
even if equal weights are used a reason for selecting such equal weights should be
mentioned. During the process of constructing the Economic and Social Rights Fulfillment
index, the study will handle and focus on the above six limitations in the sense of how to
detect the problem and how to deal with.
15
Chapter Three
The Economic and Social Rights Fulfillment Index
3.1 Introduction
As proposed by the Economic and Social rights empowerment initiative1; countries are
bound under international law to respect, protect, and fulfill economic and social rights for
citizens. The dimensions of the ESRFI are the rights that are very well known and stated in
many human rights declarations and United Nations resources not only the constitution.
These are five main rights; right to education, right to health, right to adequate housing,
right to food and right to decent work. Chapter Three of the new Egyptian constitution
approved by referendum in 2012 is about Economic and social rights and state in Part two
that “Rights and Freedoms a list of articles that set obligations of the state to fulfil the
economic and social rights to all citizens” as in the following articles of the constitution:
Article 58 High-quality education is a right guaranteed by the State for every citizen. It is
free throughout its stages in all government institutions, obligatory in the primary stage, and
the State shall work to extend obligation to other stages,
Article 62 Healthcare is a right of every citizen, and the State shall allocate a sufficient
percentage of the national revenue. The State shall provide healthcare services and health
insurance in accordance with just and high standards, to be free of charge for those who are
unable to pay.
Article 63 Work is a right, duty and honor for every citizen, guaranteed by the State on the
basis of the principles of equality, justice and equal opportunities. There shall be no forced
labour except in accordance with law. Public sector employees shall work in the service of
the people. The State shall employ citizens on the basis of merit, without nepotism or
mediation. Any violation is a crime punishable by law.
1 The Economic and Social Rights Empowerment Initiative was initiated by Sakiko Fukuda-Parr and Terra
Lawson-Remer at the New School and Susan Randolph at the University of Connecticut at New York is being
undertaken collaboratively with the Social Science Research Council, and is supported in part by National
Science Foundation to all countries with special focus on developing countries.
16
The State guarantees for every worker the right to fair pay, vacation, retirement and social
security, healthcare, protection against occupational hazards, and the application of
occupational safety conditions in the workplace, as prescribed by law.
Article 67 Adequate housing, clean water and healthy food are given rights. The state
adopts a national housing plan, its basis in social justice, the promotion of independent
initiatives and housing cooperatives, and the regulation of the use of national territory for
the purposes of construction, in accordance with public interest and with the rights of future
generations.
The ESRF Index and the human rights indicators are considered tools for assessing progress
in protecting human rights and for formulating human rights-based public policies and
programmes.
In this connection, a report was prepared by the United Nations Office of the High
Commissioner for Human Rights (OHCHR) (2008) on Indicators for Promoting and
Monitoring the Implementation of Human Rights. The Annex to the report provides a list of
illustrative indicators on different rights such as the right to education, the right to adequate
food, the right to participate in public affairs, the right to work, ……. etc.
In addition, there are several tools and guides that are done by UN agencies to introduce
several human rights indicators that can be used as a guide for different human rights tools.
Accordingly, the Economic and Social Rights Fulfillment Index is structured and
constructed.
Figure 3.1 shows the structure of the index where it reflects that the index in general is
divided into five dimensions (Food, Health, Education, Adequate housing and Decent
work), each dimension measured by a set of indicators which contain variable(s) to measure
from the raw survey data.
17
Figure 3.1: General structure of the ESRF index
Index Dimensions Indicators
Eco
no
mic
an
d S
oci
al R
igh
ts F
ulf
illm
en
t In
dex
Right to Food
Individuals live in households decreased or stopped using main goods because of the increase in food prices
Availability of bread by type that were needed by households during the all days of the week
People living in poverty
Expenditure on food
Individuals live in households that are not using X good of the main food goods
Right to Education
Enrollment rate in primary education
Education completion
Drop out from basic education
Education Achievements
Right to Health
Access to water with good quality
Individuals who have problems in health service in the place of residence
Individuals who can found the essential Pharmaceuticals when needed at a place near to their residency
Individuals who can found the essential Pharmaceuticals when needed in adequate price
Individuals who have governmental health insurance
Individuals with disability
Right to Adequate Housing
Access to improved water source
Access to improved sanitation facility
Individuals live in a housing unit with adequate floor material
Individuals who have separate place for cooking (kitchen)
Individuals with sufficient living space
Ownership of main assets for adequate place
Access to safe fuel for cooking
Right to Decent Work
Individuals who are exposed to dangerous work
Work Stability
Time spent to travel from home to work
Weekly hours worked
Monthly earnings
Individuals who are employed and have legal contract with their organization
Individuals employed in organizations that avail legal vacations by type
Individuals who have trade union membership
Individuals who are satisfied by their work
Individuals who have social insurance through work
Individuals who have health insurance through work
Individuals working more than 50 hours per week and this affect their health
Individuals working in organizational that avail insurance against work related danger
18
The five rights/dimensions of the index are defined in separate reports by international
institutions. These reports define these rights and how to measure where for a one right there
are number of reports published by the specific institution in that field to define it. The list
of reports is:
1. The Right to Adequate Housing published by UN HABITAT in 2009.
2. Decent Work Indicators for Asia and the Pacific: A Guidebook for Policy-makers
and Researchers published by International Labour Organization and Asian Decent
Work Decade in 2008.
3. Decent work: Concepts, models and indicators published by International Institute
for Labour Studies in 2002.
4. ILO Declaration on Social Justice for a Fair Globalization adopted by the
International Labour Conference at its Ninety-seventh Session, 2008.
5. ILO Manual First version, Decent Work Indicators Concepts and definitions,
published by ILO in 2012.
6. Facts on Decent Work published by ILO in 2006.
7. World Education report, the right to education: towards education for all throughout
life published by United Nations Educational, Scientific and Cultural Organization
UNESCO in 2000.
8. The Right to Adequate Food published by the Food and Agriculture Organization of
the United Nations FAO in 2010.
9. The Right to Health published by the World Health Organization (WHO) of the
United Nations WHO in 2008.
These reports guided the theoretical framework to identify the exact definition of each right
and what indicators that measures it.
19
3.2 Definition of Domains
1. The right to food: the right to food is a human right recognized by international human
rights law. The Universal Declaration of Human Rights recognizes, in the context of an
adequate standard of living, that: “Everyone has the right to a standard of living adequate
for the health and well-being of himself and of his Household, including food” (article 25).
The food and agriculture organization (FAO) in its fact sheet number 34 about the right to
food stated that the right to food is recognized in the 1948 Universal Declaration of Human
Rights as part of the right to an adequate standard of living, and is enshrined in the 1966
International Covenant on Economic, Social and Cultural Rights. It is also protected by
regional treaties and national constitutions.
As authoritatively defined by the Committee on Economic, Social and Cultural Rights
ESCR (Committee on ESCR) in its General Comment 12: “the right to adequate food is
realized when every man, woman and child, alone and in community with others, has
physical and economic access at all times to adequate food or means for its procurement”
(General Comment 12, 1999, para 6).
Inspired by the above definition, the right to food entails: “the right to have regular,
permanent and unrestricted access, either directly or by means of financial purchases, to
quantitatively and qualitatively adequate and sufficient food corresponding to the cultural
traditions of the people to which the consumer belongs, and which ensures a physical and
mental, individual and collective, fulfilling and dignified life free of fear" as stated by the
Committee on Economic, Social and Cultural Rights
It is important to emphasize certain elements of the right to food that is food must be
available, accessible and adequate.
2. The right to education: the world education report of 2000 that is published by
UNESCO entitled "The right to education -Towards education for all throughout life"
defined the right to education and different tools to measure the fulfilment of it
especially as the number of years of school attendance as an important measure of
20
education fulfilment and quality. The Right to Education in Article 26 of the Universal
Declaration of Human Rights stated that2:
Everyone has the right to education. Education shall be free, at least in the
elementary and fundamental stages. Elementary education shall be compulsory.
Technical and professional education shall be made generally available and higher
education shall be equally accessible to all on the basis of merit.
Education shall be directed to the full development of the human personality and to
the strengthening of respect for human rights and fundamental freedoms. It shall
promote understanding, tolerance and friendship among all nations, racial or
religious groups, and shall further the activities of the United Nations for the
maintenance of peace.
Parents have a prior right to choose the kind of education that shall be given to their
children.
Education creates the “voice” through which rights can be claimed and protected’, and
without education people lack the capacity to ‘achieve valuable functioning as part of the
living. If people have access to education they can develop the skills, capacity and
confidence to secure other rights. Education gives people the ability to access information
detailing the range of rights that they hold, and government’s obligations. It supports people
to develop the communication skills to demand these rights, the confidence to speak in a
variety of forums, and the ability to negotiate with a wide range of government officials and
power holders.
Accordingly the right to education includes; basic education, secondary levels, and higher
levels of education as basic education does not accord an individual with the minimum level
of capacity and knowledge necessary to participate meaningfully in contemporary society.
Moreover, the quality of education is as important as the number of years of school
attendance. The right to education international project gives over 200 indicators to measure
the fulfilment of the right to education in details.
2 Source: Universal Declaration of Human Rights Adopted and Proclaimed by the General Assembly of the United Nations
on the Tenth Day of December 1948, Final Authorized Text. New York, United Nations, 1950.
21
3. The right to health is a broad concept that can be broken down into more specific
entitlements such as the rights to: maternal, child and reproductive health; healthy
workplace and natural environments; the prevention, treatment and control of diseases,
including access to essential medicines; access to safe and potable water (quality). It is
known also as the economic, social and cultural right to the highest attainable standard
of health. It is recognized in the Universal Declaration of Human Rights and
International Covenant on Economic, Social and Cultural Rights. The 1948 Universal
Declaration of Human Rights also mentioned health as part of the right to an adequate
standard of living (Article 25). The right to health was again recognized as a human
right in the 1966 International Covenant on Economic, Social and Cultural Rights.
The right to health is an inclusive right. It includes a wide range of factors that can help us
lead a healthy life. The Committee on Economic, Social and Cultural Rights, the body
responsible for monitoring the International Covenant on Economic, Social and Cultural
Rights, calls these the “underlying determinants of health”. They include:
Safe drinking water and adequate sanitation;
Safe food;
Adequate nutrition and housing;
Healthy working and environmental conditions;
Health-related education and information;
Gender equality.
The right to health contains entitlements. These entitlements include:
The right to a system of health protection providing equality of opportunity for
everyone to enjoy the highest attainable level of health;
The right to prevention, treatment and control of diseases;
Access to essential medicines;
Maternal, child and reproductive health;
Equal and timely access to basic health services;
22
The provision of health-related education and information;
Participation of the population in health-related decision making at the national and
community levels.
4. The right to Adequate Housing refers to adequate access, quality in the form of
provision of water and sanitation, and security of housing units (UN-Habitat and
OHCHR 2003). According to the Human Rights Resource Center and the United
Nations Agency for Human Settlements UNHABITAT the adequacy of housing
includes:
Availability of services, materials, facilities and infrastructure. An adequate
house must contain certain facilities essential for health, security, comfort and
nutrition. All beneficiaries of the right to adequate housing should have
sustainable access to natural and common resources, safe drinking water, energy
for cooking, heating and lighting, sanitation and washing facilities, means of
food storage, refuse disposal, site drainage and emergency services;
Affordability. Personal or household financial costs associated with housing
should be at such a level that the attainment and satisfaction of other basic needs
are not threatened or compromised. Steps should be taken by States parties to
ensure that the percentage of housing-related costs is, in general, commensurate
with income levels.
Habitability. Adequate housing must be habitable, in terms of providing the
inhabitants with adequate space and protecting them from cold, damp, heat, rain,
wind or other threats to health, structural hazards, and diseases. The physical
safety of occupants must be guaranteed as well.
Accessibility. Adequate housing must be accessible to those entitled to it.
Disadvantaged groups must be accorded full and sustainable access to adequate
housing resources. Both housing law and policy should take fully into account
the special housing needs of these groups. Within many States parties increasing
23
access to land by landless or impoverished segments of the society should
constitute a central policy goal. Discernible governmental obligations need to be
developed aiming to substantiate the right of all to a secure place to live in peace
and dignity, including access to land as an entitlement.
A number of indicators were defined by different organizations to enable measuring the
extent of fulfilment of the right to adequate housing.
5. The right to decent work: refers to both access and conditions of work. The Decent
Work concept was formulated by the International Labour Organization ILO’s
constituents – governments and employers and workers – as a means to identify the
Organization’s major priorities.
It is based on the understanding that work is a source of personal dignity, Household
stability, peace in the community, democracies that deliver for people, and economic
growth that expands opportunities for productive jobs and enterprise development.
Decent Work reflects priorities on the social, economic and political agenda of countries
and the international system. In a relatively short time this concept gave an international
consensus among governments, employers, workers and civil society that productive
employment and Decent Work are key elements to achieving a fair globalization, reducing
poverty and achieving equitable, inclusive, and sustainable development.
Juan Somavia, ILO Director-General stated in the ILO Declaration on Social Justice for a
Fair Globalization that "The primary goal of the ILO today is to promote opportunities for
women and men to obtain decent and productive work, in conditions of freedom, equity,
security and human dignity." Monitoring progress towards decent work is a long-standing
concern for the ILO’s constituents.
The ILO Framework Work Indicators covers ten substantive elements corresponding to the
four strategic pillars of the Decent Work Agenda (full and productive employment, rights at
work, social protection and the promotion of social dialogue). These include the following:
1. Employment opportunities
2. Adequate earnings and productive work
24
3. Decent working time
4. Combining work, Household and personal life
5. Work that should be abolished
6. Stability and security of work
7. Equal opportunity and treatment in employment
8. Safe work environment
9. Social security
10. Social dialogue, employers’ and workers’ representation
The decent work indicators are formulated in light of these areas.
3.3 Indicators Selection
Indicators selection is a very critical step, and includes uncertainty about why selecting this
specific list rather than another one in addition to that the domains specification prior to the
indicators selection is also critical if not justified. In the case of ESRF the domains were
selected according to the definition of United Nations Human Rights institutions that
defined the economic and social rights in the five main rights as well as the International
Covenant on Economic, Social and Cultural Rights. Within each dimension the indicators
list defined and selected according to:
1. Dimension definitions.
2. Data availability and reliability.
3. Being available at the individual level because the unit of analysis in the ESRF is
individual.
4. Policy responsiveness, in the sense that the indicators are related to the policy tools,
legislations and obligations.
5. Relevance to Egyptian environment.
25
Few number of the selected indicators are removed during the analysis because of various
reasons, these reasons include:
1. Zero variance that will affect the multivariate analysis and will not differentiate
among individuals. For example, in the right to adequate housing the indicator of
having electricity was removed as in Egypt there are around 99% or more having
electricity.
2. Very few applicable cases in a specific indicator that will make the sample size for a
specific question in the data very small. For example, in the right to food there were
question about the individuals attitudes towards increasing in the subsidized bread
prices, the valid cases to answer the question were very few (0.8%) of the total
sample who are experienced prices increasing in the subsidized bread .
The final list of indicators has 35 indicators (71 variables) measuring the ESRFI in the 5
main dimensions.
During the analysis, it was very important to do reliability analysis to these indicators per
dimension to check for the internal consistency, reliability and importance of each set of
indicators to constitute a certain dimension and the overall ESRF index,
Cronbach's (alpha) coefficient has been used where Cronbach's is defined as:
(
∑
)
Where K is the number of components (K-items), is the variance of the observed total
test scores, and
the variance of component i for the current sample of persons.
The standardized α is also used which is based on the assumption that all of the items have
equal variances.
26
3.4 List of Domains and Indicators in the ESRF Index
The list of domains and indicators in the ESRF index are shown in Table 3.1. A list of
indicators is developed to cover the illustrated domains (Rights) with the resource reference of each
indicator as well as the variables measures these indicators in the household survey.
Table 3.1: List of Domains, Indicators and Variables of the ESRF index
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
Right to Food (5
Indicators)
Individuals live in
households decreased or stopped using main
goods because of the
increase in food prices (
14 Variables)
What did you do when the price
of rice has increased?
Proxy for UN
concept on usage of
main goods What did you do when the price
of wheat / flour increased?
What did you do when the price
of pasta increased?
What did you do when the price of meat (beef - mutton)
increased?
What did you do when the price
of poultry (chicken - duck - ..) increased?
What did you do when the price
of fish increased?
What did you do when the price
of milk and cheese increased?
What did you do when the price
of eggs has increased?
What did you do when the price of oil increased food?
What did you do when the price
of margarine and butter
increased?
What did you do when the price
of fruit (orange - Banana -
Guava ..) increased?
What did you do when the price of vegetables (spinach - tomato -
..) increased?
What did you do when the price
of legumes (beans - Lentils - Beans - ..) increased?
3 See for more details about indicators, Annex 1 about indicators Meta Data.
27
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
What did you do when the price
of sugar has increased?
Availability of bread by type that were needed by
households during the all
days of the week (1 variable)
Did you find the bread when needed?
Proxy for UN concept on
usage of
main goods
People living in poverty
(1 variable)
Expenditure quintiles variable is
used as proxy variable to
differentiate between different economic levels.
UN/ FAO/
MDGs
Expenditure on food (1
variable)
Percentage share of the
expenditure on food from the
total expenditure.
UN concept
Individuals live in households that are not
using X good of the main
food goods (14 variables)
Household consumption on rice in the last 3 months : decreased,
as it is, increased, not used
UN concept
Household consumption on wheat / flour in the last 3
months : decreased, as it is,
increased, not used
Household consumption on pasta in the last 3 months :
decreased, as it is, increased, not
used
Household consumption on meat (beef - mutton) in the last
3 months : decreased, as it is,
increased, not used
Household consumption on birds (chicken - duck - ..) in the
last 3 months : decreased, as it
is, increased, not used
Household consumption on fish in the last 3 months : decreased,
as it is, increased, not used
Household consumption on milk
and cheese in the last 3 months : decreased, as it is, increased, not
used
Household consumption on eggs
in the last 3 months : decreased, as it is, increased, not used
Household consumption on food
oil in the last 3 months :
decreased, as it is, increased, not
28
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
used
Household consumption on
margarine and butter in the last
3 months : decreased, as it is, increased, not used
Household consumption on fruit
(orange - Banana - Guava ..) in
the last 3 months : decreased, as it is, increased, not used
Household consumption on
vegetables (spinach - tomato -
choice.) in the last 3 months : decreased, as it is, increased, not
used
Household consumption on
legumes (beans - Lentils - Beans - .) in the last 3 months :
decreased, as it is, increased, not
used
Household consumption on
sugar in the last 3 months :
decreased, as it is, increased, not
used
Right to Education
(4 Indicators)
Enrollment rate in
primary education
There was a challenge in
evaluating the education
variables because of non-
applicability and limitations on some questions. Away to
overcome this is by creating a
variable in the data reflects the individual actual years of
schooling compared to the
optimal years of schooling
according to his/ her age.
UN/ MDGs/
UNESCO
Education completion UN/ MDGs/
UNESCO
Drop out from basic
education
UN/ MDGs/
UNESCO
Education Achievements UN/
UNESCO/
Right to Health (6
Indicators)
Access to water with
good quality (2 variables)
What are the problems related to
drinking water? (Low quality)
UN/ WHO
What are the problems related to
drinking water? (Water pollution)
Individuals who have
problems in health
service in the place of residence (1 variable)
The problems in your area?
Problems of health services
UN
29
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
Individuals who can
found the essential Pharmaceuticals when
needed at a place near to
their residency
(Pharmacy, health unit,….etc). (1 variable)
Are the medicines you usually
need always available in a nearby pharmacies?
UN/ WHO
Individuals who can
found the essential Pharmaceuticals when
needed in adequate price
(1 variable)
Did you get Medicines that is
necessary needed by your Household members?
UN/ WHO
Individuals who have governmental health
insurance (1 variable)
Do you have a government health insurance?
UN/ WHO
Individuals with
disability (1 variable)
Is the individual having any
disability?
UN
Right to Adequate
Housing (7
Indicators)
Access to improved water source (1 variable)
the main source of drinking water
OHCHR/ UN HABITAT
Access to improved
sanitation facility (1 variable)
Sanitation type OHCHR/ UN
HABITAT
Individuals live in a
housing unit with
adequate floor material (1 variable)
Basic material for the floor OHCHR/ UN
HABITAT
Individuals who have
separate place for
cooking (kitchen) (1 variable)
Do you have a kitchen or
cooking in a separate room
place?
OHCHR/ UN
HABITAT
Individuals with
sufficient living space
(Average number of persons per room/
adequate space) (2
variables)
Number of rooms OHCHR/ UN
HABITAT
Number of household members
Ownership of main assets for adequate place (living
conditions) (8 variables)
Color TV OHCHR/ UN HABITAT An air conditioner
Electric Heater
Stove
Refrigerator
Water Heater Bath
Washing Machine
Vacuum cleaner
30
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
Access to safe fuel for
cooking (1 variable)
Type of fuel your Household
use in cooking? If it is it separate or joint?
OHCHR/ UN
HABITAT
Right to Decent
Work (13
Indicators)
Individuals who are
exposed to dangerous
work (1 variable)
Do you work related with using
sharp instruments or materials,
flammable or has dangerous on you?
ILO
Work Stability (1
variable)
What is the type of your work? ILO/ MDGs
Time spent to travel from
home to work (1 variable)
The average time you take from
your home to reach your job (the trip in one direction)?
ILO
Weekly hours worked (1
variable)
How many hours of your work
per week on average?
ILO
Monthly earnings (1
variable)
What is your monthly salary? ILO
Individuals who are employed and have legal
contract with their
organization (1 variable)
Do you have a written legal contract or formal appointment
with your employer?
ILO
Individuals employed in
organizations that avail
legal vacations by type (5
variables)
Is the organization you are
working in avail sick leaves?
ILO
Is the organization you are
working in avail unusual
holidays?
Is the organization you are
working in avail casual leaves?
Is the organization you are
working in avail maternity leave
(for females)?
Is the organization you are working in avail care of a child
leaves (female)?
Individuals who have
trade union membership
(1 variable)
Are you a member of syndicate? ILO
Individuals who are satisfied by their work (1
variable)
Are you satisfied with the nature of work in organization you are
working in?
ILO
Individuals who have
social insurance through work (1 variable)
Is your job made a social
insurance (pension) for you?
ILO
31
Domain/ Right (Number of
indicators between
parentheses)
Indicators3
(Number of variables
between parentheses)
Variables Reference(s)
Individuals who have
health insurance through work (1 variable)
Is your job made a health
insurance for you?
ILO
Individuals working
more than 50 hours4 per
week and this affect their health (1 variable)
Does this have negative impact
on your health?
ILO
Individuals working in
organizational that avail
insurance against work related danger (1
variable)
Do your organization an
insurance against work related
danger?
ILO
3.5 Source of Data
The data used for constructing the ESRF index is the "Egyptian Household Conditions
Observatory Survey" that was conducted by the Information and Decision Support Centre in
2010 as this is the household national survey that has different data on the desired indicators
and will enable for calculating the index for all individuals. Egyptian Families Conditions
Observatory aims at availing continuous measurement of the status of the Egyptian
Household by discussing issues of interest either for the decision maker or citizens such as
identifying citizens reactions towards increasing prices of goods and services and the effects
of that on the consumption patterns of the Egyptian Household, identifying the
characteristics of employed and unemployed people,…etc.
This survey is implemented regularly every three months, and the latest cycle has been
published the by Information and Decision Support Center in September 2010. The sample
was a random sample of households in Egypt and consists of 10550 households, distributed
at all governorates except for frontier governorates (Al-Wadi Al-Gadid , Marsa Matrouh,
Red Sea, North Sinai and South Sinai Governorate) according to number of households in
each governorate and sample weights is assigned to the datasets to handle the distribution
and the non-response rate. Households in the sample within each governorate are
4 Despite the legal hours per week is 40, but in the Egyptian context they asked only if increased
than 50 not 40.
32
represented at the rural and urban "proportional representation to size", where the sample
frame used is from the census of 2006 as validated in 2008, the sample was distributed to
451 cadastral area distributed over 236 primary sampling unit and the average number of
households in each primary sampling unit were about 25 families to minimize the sampling
error. After selecting the sample a one third of households are randomly selected in each
cadastral area to be a sub-sample for the Working module of the survey. This round of the
survey has 4 main questionnaires, the general questionnaire, a module on work conditions, a
module on maternal health and a module on watching TV programmes in Ramadan.
Table 3.2: Sample distribution according to governorates in Egypt
Governorates Frequency Percent
Cairo 1103 10.5
Alexandria 660 6.3
Port Said 87 .8
Suez 76 .7
Helwan 254 2.4
6 October 383 3.6
Dametta 177 1.7
Al Dakahlia 782 7.4
Al Sharkia 783 7.4
Al Kaliubia 647 6.1
Kafr Al Sheikh 381 3.6
Al Gharbia 615 5.8
Al Menofia 477 4.5
Al Behera 679 6.4
Al Ismailia 142 1.3
Giza 496 4.7
Bani Suef 308 2.9
Al Fayoum 351 3.3
Menia 566 5.4
Assiut 453 4.3
Sohag 508 4.8
Qena 325 3.1
Aswan 167 1.6
Luxor 129 1.2
Total 10550 100.0
Table 3.3: Sample distribution according to Urban and Rural Areas in Egypt
Frequency Percent
Urban 4752 45.0
Rural 5798 55.0
Total 10550 100.0
33
3.6 Results of using Cronbach's α5 on the dimensions of the index
Right to food
Table 3.4: Reliability Statistics for the right to food
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.703 .641 31
The α index is considered high (high internal consistency) and it is a good indication for the
right to food dimension. The Cronbach's Alpha is 0.703 and the Cronbach's Alpha Based on
Standardized Items is 0.641 which is also high level of reliability and internal consistency
that support the measurement tool for this right.
Right to health
The α in right to health is not as higher as other dimensions; this may be because the
right to health is a difficult dimension to capture all what measure it from one data set
(like variables related to diseases and specific questions on pharmaceutical access).
Table 3.5: Reliability Statistics for the right to health
Cronbach's Alpha Cronbach's Alpha Based
on Standardized Items
N of Items
.233 .269 7
Even for the α if item deleted to check if there are number of variables that lower the
scale, there were not ones. The list used as the data availability (these 7 items are all
items in that survey as well as majority of other national surveys that measures health
conditions).
Right to adequate housing
The α index for the right to adequate housing dimension is considered high (high
internal consistency) and it is a good indication.
5 The software used for calculating Cronbach's Alpha coefficient is SPSS package.
34
Table 3.6: Reliability Statistics for the right to adequate housing
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.616 .716 14
Right to Decent Work
The alpha for the right to decent work is the highest among other dimensions and it is
very good (showing excellent internal consistency).
Table 3.7: Reliability Statistics for the right to decent work
Cronbach's Alpha Cronbach's Alpha Based on
Standardized Items
N of Items
.908 .882 17
The right to education is a special case as the variables of it were summarized to constitute
one variable that captures the meaning in all education variables and gives valid score for all
individuals and that is why there is no reliability coefficient measured for it. The list of
variables about education in the survey is 6 variables. Those variables are not applicable or
valid for all individuals; they are valid for different groups of individuals depending on their
age. To solve this and to have one education variable that is valid for all individuals in the
index, the study applied the following steps:
A. Created new variable that tells what is the years of education that the individual
should achieve depending on his/ her age.
B. Created another variable about the actual years of schooling that individuals on the
data already achieved.
C. By subtracting variable in point 1 from the variable in point 2 a new variable is
created. This new variable reflects the education achievements taking into
consideration age, dropout and all other conditions that might affect the education
fulfillment.
In general the indicators show a good internal consistency and give indication that the
identification and selection of the list working very well in measuring the ESRFI.
35
Chapter Four
Methodologies to handle the problems of Composite Indices
As mentioned in previous Chapters, there are many steps in constructing composite indices
and each step includes many statistical procedures and an uncertainty about the selection
between certain methodologies within each step. This chapter addresses the methodology in
five main debatable issues in the construction and measuring of composite indices in general
with application on the ESRF index (missing and not applicable data, outliers, scale of
measurement, weighting and aggregation and computing the margin of error).
4.1. Missing Data
Missing data are data desired to be collect but for different reasons are not available and
missed from the survey or data. Missing data often hinder the development of rigorous
composite indicators. Data can be missing in a random or non-random fashion. Missing
completely at random (MCAR) means that the missing values do not depend on the variable
of interest or on any other observed variable in the data set. Missing at random (MAR)
means that the missing values do not depend on the variable of interest, but are conditional
on other variables in the data set. Not missing at random (NMAR) indicates that missing
values depend on the values themselves.
It is important to know why the data are missing; this can help with finding a solution to the
problem. If the values are missing at random there is still information about each variable in
each unit but if the values are missing systematically the problem is more severe because the
sample cannot be a good representation of the population. NMAR are very rare, most of the
methods that impute missing values require a missing at random mechanism, i.e. MCAR or
MAR. Depending upon the situation, missing data may be dealt with in a variety of ways. There are
three general methods for dealing with missing data:
1. Case deletion
2. Single imputation
3. Multiple imputations.
36
Case deletion simply omits the missing records from the analysis. However, this approach
ignores possible systematic differences between complete and incomplete samples and
produces unbiased estimates only if deleted records are a random sub-sample of the original
sample (MCAR assumption). Furthermore, standard errors will generally be larger in a
reduced sample, given that less information is used. As a rule of thumb, if a variable has
more than 5% missing values, cases should not be deleted.
The other two approaches consider the missing data as part of the analysis and try to impute
values through either single imputation, e.g. mean/median/mode substitution, regression
imputation, hot-and cold-deck imputation, expectation-maximization imputation, or multiple
imputations, e.g. Markov Chain Monte Carlo algorithm which is the most familiar method.
The overall approach for imputation is to decide on what is the preferable approach for
different data scenarios prior to analyzing any data. Then, reviewing collected data and,
based on that, choosing the preferable approach. The decision is made depending on the size
of missing values, the type of variable, the existence of outliers and purpose of imputation.
Another type for non-completeness of data which exists widely in household surveys is the
not applicable cases which are a result of questions skips in different questions.
The data used in the ESRF index includes two types of problems:
A. Not applicable
B. Missing values
The method of dealing with each type differs as the not applicable needs to be coded with a
relevant code per question. The study dealt with these two types separately as follows:
A. Not applicable cases
The analysis shows that among the 71 variables used in constructing the ESRF index, there
are 36 variables have cases that are either not applicable or missing. The variables that
include not applicable are 20 variables. For each variable the most important item used to
decide how to recode the not applicable into valid values was the reason for that specific
non response.
37
The main code for solution was to replace the not applicable with zero, and the reason for
this was that the individual is considered not deprived from the specific item.
As in Table 4.1, the question about "What did you do when the price of rice has increased?"
includes 16,929 not applicable cases and the reason for the non-response was that "all non-
response is not applicable cases either who never consume the good or did not mention that
the price of this good is increased. Those not applicable cases are not considered subject to
the phenomena of decreasing consumption on goods, they are not vulnerable in that sense
and accordingly the solution is to replace all non-response with zero.
Table 4.1: The 20 variables with not applicable cases
Variable Number of
respondents
to the
question out
of the total
sample
Number
of not
applicable
Reasons for non-response Solutions – For
Non response
What did you do
when the price
of rice has
increased?
27,110 16,929 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero.
What did you do
when the price
of wheat / flour
increased?
19,546 24,493 All non-response is not applicable cases either who
never consume the good or
did not mention that the price is increased.
Replace the non-applicable with
zero.
What did you do
when the price
of pasta
increased?
21,933 22,106 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero.
What did you do
when the price
of meat (beef -
mutton)
increased?
41,603 2,436 All non-response is not applicable cases either who
never consume the good or
did not mention that the price
is increased.
Replace the non-applicable with
zero.
What did you do
when the price
of poultry
(chicken - duck -
..) increased?
30,412 13,627 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero.
38
What did you do
when the price
of fish
increased?
19,220 24,819 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero
What did you do
when the price
of milk and
cheese
increased?
13,656 30,383 All non-response is not applicable cases either who
never consume the good or
did not mention that the price
is increased.
Replace the non-applicable with
zero.
What did you do
when the price
of eggs has
increased?
15,596 28,443 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero.
What did you do
when the price
of oil increased
food?
16,747 27,292 All non-response is not applicable cases either who
never consume the good or
did not mention that the price
is increased.
Replace the non-applicable with
zero.
What did you do
when the price
of margarine
and butter
increased?
17,526 26,513 All non-response is not
applicable cases either who
never consume the good or did not mention that the price
is increased.
Replace the non-
applicable with
zero.
What did you do
when the price
of fruit (orange -
Banana - Guava
..) increased?
32,816 11,223 All non-response is not
applicable cases either who never consume the good or
did not mention that the price
is increased.
Replace the non-
applicable with zero.
What did you do
when the price
of vegetables
(spinach -
tomato - choice
..) increased?
30,940 13,099 All non-response is not
applicable cases either who never consume the good or
did not mention that the price
is increased.
Replace the non-
applicable with zero.
What did you do
when the price
of legumes
(beans - Lentils -
Beans - ..)
increased?
12,092 31,947 All non-response is not applicable cases either who
never consume the good or
did not mention that the price is increased.
Replace the non-applicable with
zero.
What did you do
when the price
of sugar has
increased?
21,884 22,155 All non-response is not
applicable cases either who never consume the good or
did not mention that the price
is increased.
Replace the non-
applicable with zero.
39
The amounts
you need to
purchase from
(subsidized
bread/ not
subsidized) are
available or not
available seven
days a week?
28,721 15,318 All non-response is not
applicable cases and are those
who not using subsidized bread and who are baking at
home.
Replace the non-
applicable with
zero
What are the
problems related
to drinking
water? (Low
quality)
21,931 22,108 All non-response is not
applicable cases and are those
who have no problems with water.
Replace the non-
applicable with
zero
What are the
problems related
to drinking
water? (Water
pollution)
21,931 22,108 All non-response is not
applicable cases and are those who have no problems with
water.
Replace the non-
applicable with
zero.
The problems in
your area?
problems of
health services
30,692 13,347 All non-response is not
applicable cases and are those who have no problems with
Health services.
Replace the non-
applicable with zero.
Sanitation type? 43,936 103 All non-response are not
applicable cases and are those who do not have sanitation.
Replace the non-
applicable with
zero.
Work type? 12,566 31,473 All non-response are not
applicable cases and are those who not working according to
the definition.
Take the code of
non-deprived as defined in the
variable codes.
B. Cases with missing values
The problem of missing values that appeared in 16 variables is of a special type, all the 16
variables are in the decent work dimension which is from Section 5 in the survey. This
section includes 5182 eligible individual that randomly selected to be surveyed on work and
employment and only 4000 individuals who respond to the questions of decent work as they
are currently working. The variables are very important and there are no alternatives for
them in the main questionnaire that contains the complete sample. The advantage was that
40
those 5182 cases are selected randomly from the overall sample, which means that the
representation for the whole population still not violated. As the index is per individual, the
solution was to impute or predict the rest non sampled observations (for work module) using
the sampled ones. Those variables are:
1. How many hours do you work per week on average?
2. Does your organization have insurance against work related danger?
3. Does working more than 50 hours per week have negative impact on your health?
4. Is your work related with using sharp instruments or materials, flammable or has
dangerous on you?
5. The average time you take from your home to reach your job (the trip in one
direction)?
6. The average monthly salary?
7. Do you have a written legal contract or formal appointment with your employer?
8. Is the organization you are working in avail unusual holidays?
9. Is the organization you are working in avail casual leaves?
10. Is the organization you are working in avail maternity leave (for females)?
11. Is the organization you are working in avail sick leaves?
12. Is the organization you are working in avail care of a child leaves (female)?
13. Are you a member of syndicate?
14. Are you satisfied with the nature of work in the organization you are working in?
15. Does your job provide a social insurance (pension) for you?
16. Does your job provide a health insurance for you?
Through literature the most two accurate methods for predicting/ imputing value and shown
very good performance and robust results are:
A. Multiple imputation using Marcov Chain Monte Carlo simulation (MI –
MCMC)
B. Neural Networks (NNs)
41
In this study, the two methods are applied to the overall decent work dimension (after
aggregating the 16 variables) using the complete dimensions and some relevant individual
characteristics (gender, urban-rural, marital status). Then after using a training and testing
sample, the two methods are compared to select the one that predicts with better
performance.
A. Multiple imputation using Marcov Chain Monte Carlo simulation
(MI–MCMC)
A multiple imputation procedure (Rubin 1987) replaces each missing value with a set of
plausible values that represent the uncertainty about the right value to impute. The multiply
imputed data sets are then analyzed by using standard procedures for complete data and
combining the results from these analyses.
In general, multiple imputation is a Monte Carlo technique in which the missing values are
replaced by m > 1 simulated versions, where m is typically small (e.g. 3 - 10). In Rubin's
method for repeated imputation' inference, each of the simulated complete datasets is
analyzed by standard methods, and the results are combined to produce estimates and
confidence intervals that incorporate missing-data uncertainty. Rubin (1987) shows that the
efficiency of an estimate based on m imputations increases as it increase.
Rubin (1987) presented this method for combining results from a data analysis
performed m times, once for each of m imputed data sets, to obtain a single set of results.
From each analysis, one must first calculate and save the estimates.
The core of using this method is to get the estimates of decent work dimension and use these
estimates in the aggregation step. The following equations show how to combine these
multiple imputed values and how it is applied:
Suppose that is an estimate of a scalar quantity of interest (e.g. a regression coefficient)
obtained from data set j (j = 1 ,2, ..., m) and is the variance associated with . The
overall estimate is the average of the individual estimates,
42
For the overall standard error, first calculate the within-imputation variance,
The between-imputation variance,
The total variance is:
The overall standard error is the square root of T.
The most commonly used method in multiple imputations is Markov chain Monte Carlo
(MCMC) which is a collection of methods for simulating random draws from nonstandard
distributions via Markov chains. MCMC is one of the primary methods for generating MI's
in nontrivial problems. MCMC is an iterative method that can be used when the pattern of
missing data is arbitrary (monotone or non-monotone). For each iteration and for each
variable in the order specified in the variable list, the fully conditional specification (FCS)
method fits a univariate (single dependent variable) model using all other available variables
in the model as predictors, then imputes missing values for the variable being fit. The
method continues until the maximum number of iterations is reached, and the imputed
values at the maximum iteration are saved to the imputed dataset.
The following steps show the methodology of applying the multiple imputation using
Marcov Chain Monte Carlo simulation6:
1. A 25% of the 4000 complete cases of decent work were selected randomly and
replaced by missing to be used in testing the method and select the best option to
apply at the real step of imputing the overall variable.
6 The software used for calculating different imputations is SPSS package, version 20.
………….……………..(1)
……………………….…..(2)
…………….….…..(3)
…………………….…..(4)
43
2. The following variables are the complete list that used to impute decent work
dimension:
a. Gender.
b. Urban-rural.
c. Marital status.
d. Right to food.
e. Right to health.
f. Right to education.
g. Right to adequate housing.
3. The study tried 4 values for the number of imputations (m = 10, m = 20, m = 50 and
m = 100).
4. The study used two values for the number of iterations (10 and 100).
5. The imputed values were combined together as explained to give a single value for
each case.
6. The different imputations were compared to select the best option.
The following are list of tables that show the results when m = 100
Table 4.2: Multiple Imputation Specifications for main characteristics
Imputation Method Fully Conditional Specification
Number of Imputations 100
Model for Scale Variables Linear Regression
Interactions Included in Models (none)
Maximum Percentage of Missing Values 100.0%
Maximum Number of Parameters in Imputation Model 100
Table 4.3: Multiple Imputation Constraints on variables
Role in Imputation Imputed Values
Dependent Predictor Minimum Maximum
Urban – Rural No Yes
Gender No Yes
Current marital status No Yes
FOOD No Yes
HEALTH No Yes
HOUSING No Yes
EDUCATION No Yes
WORK Yes No (none) (none)
44
Table 4.4: Multiple Imputation Results
Imputation Method Fully Conditional Specification
Fully Conditional Specification Method Iterations 100
Dependent
Variables
Imputed WORK
Not Imputed Urban – Rural, Gender, Current marital
status, FOOD, HEALTH, HOUSING,
EDUCATION.
Not Imputed
Imputation Sequence Urban – Rural, Gender, Current marital
status, FOOD, HEALTH, HOUSING,
EDUCATION, WORK.
Table 4.5: Imputation Models
Model Missing Values Imputed
Values Type Effects
WORK2 Linear Regression Urban – Rural, Gender, Current
marital status, FOOD, HEALTH,
HOUSING, EDUCATION.
1,007 100,700
Table 4.6: Comparison between different imputation options applied
Minimum Maximum Mean Std.
Deviation
Percent of
almost correct
estimation7
Original Decent work .00 .95 .4263 .25958 -
MI-MCMC (m = 10) .00 .99 .4312 .24141 82.95
MI-MCMC (m = 20) .00 .94 .4307 .23994 82.70
MI-MCMC (m = 50) .00 .94 .4303 .23987 82.58
MI-MCMC (m =
100)
.00 .94 .4304 .23992 82.70
Results show that; Different imputations gave close results, but when m=100 this was the
closest (best) option.
7 Using the difference between real values and the imputed one and a difference until 0.09 is
accepted.
45
1. Neural Networks (NNs)
An artificial neural network (ANN), often just called a "neural network" (NN), is a
mathematical model or computational model based on biological neural networks, in other
words, is an emulation of biological neural system.
The structure of neural networks is a function of predictors (also called inputs or
independent variables) that minimize the prediction error of target variables (also called
outputs) as in Figure 4.1.
Figure 4.1: Structure of the Neural Networks
The input layer contains the predictors.
The hidden layer contains unobservable nodes, or units. The value of each hidden
unit is some function of the predictors; the exact form of the function depends in part
upon the network type and in part upon user-controllable specifications.
The output layer contains the complete predicted responses. The neural networks
application module is available for recent versions of SPSS software and some other
statistical packages with two types of predictive applications:
1. Multilayer perceptron (MLP).
2. Radial basis function (RBF).
46
The basic difference between the MLP and the RBF is the way the "black box" process the
input data. The MLP is more common than the RBF. What makes a difference is the number
of hidden layers. Both types have been applied to the decent work dimension to compare the
results with those of multiple imputations. The main characteristics in application are8:
1. Inputs are the same variables used in multiple imputation (gender, urban-rural,
marital status, right to food, right to health, right to education, and right to adequate
housing).
2. The dependent variable was the decent work values.
3. The data were randomly divided (partitioned) into 5 subsets (known as k-fold
methods) as in Table 4.7.
Table 4.7: Testing and training partitions of the Neural Networks analysis of the
ESRFI
N Percent
Sample Training 3,200 80.0%
Testing 800 20.0%
Valid 4,000 100.0%
Excluded 0
Total 4,000
4. The number of hidden layers was either one or two and gave similar results.
Table 4.8: Descriptive Statistics for the results of Neural Networks using Multilayer
Perceptron compared to Radial Basis function
Minimum Maximum Mean Std.
Deviation
Percent of
almost correct
estimation
Original Decent
work
.00 .95 .4263 .25958 -
NNs (MLP) .00 .65 .1528 .11655 37.6
NNs (RBF) .00 .65 .1992 .12210 23.9
8 The software used for the analysis of NNs is SPSS package, version 20.
47
Results9 of both MLP and RBF were compared as in Table 4.8, the MLP shows better
performance than RBF for this kind of data according to the percent of almost correct
estimation.
This concludes also that the technique that is better for predicting the decent work values for
the whole data is the Multiple imputation using Marcov Chain Monte Carlo simulation with
m = 100 as it shows the better performance over Neural Networks.
The usage of such tools in dealing with missing values is allowing for having a more
rigorous imputations and results for the overall index.
4.2 Outliers
4.2.1 Definition of Outliers
Although definitions vary, an outlier is generally considered to be a data point that is far
outside the norm for a variable or population. Outlier also defined as an observation that
“deviates so much from other observations as to arouse suspicions that it was generated by a
different mechanism”. (Dixon, 1950) defined Outliers as values that are “dubious in the eyes
of the researcher”
Outliers can often interact in such a way that they mask each other and can occur by chance
in any distribution, but they are often indicative either of measurement error or that the
population has a heavy-tailed distribution (extreme values).
In dealing with different data types and especially survey data it is very important to address
the problem of outliers. The presence of it is potentially have strong influence on the
different estimates and could lead to mistaken conclusions and inaccurate predictions.
The study is distinguishing between univariate and multivariate outliers. It is not enough to
address only the univariate outliers per variable as the problem of outliers might exist as
well in multivariate dimension for getting more rigorous values of the ESRF index.
9 See annex 2 for the full results of both MLP and RBF.
48
4.2.2 Detection of Outliers
Outlier detection methods can be divided between univariate methods and multivariate
methods.
Univariate outliers detection
Outliers can sometimes be accommodated in the data through the use of trimmed means,
other scale estimators apart from standard deviation (McBean and Rovers, 1998). In
calculations of a trimmed mean, a fixed percentage of data is dropped from each end of an
ordered data, thus eliminating the outliers. The mean is then calculated using the remaining
data. Windsorization method for imputing missing values involves accommodating an
outlier by replacing it with the next highest or next smallest value as appropriate (Rustum &
Adeloye, 2007). The box plot is a useful graphical display for describing the behavior of the
data in the middle as well as at the ends of the distributions as well as Scatter plots also can
be used here. Other effective mathematical tools are the α% trimmed mean (TM) and
trimmed standard deviation (TSD) and the median and the average of absolute deviations
about the median (AAD) are also used to detect univariate outliers.
Multivariate Outlier Detection
In many cases multivariable observations cannot be detected as outliers when each variable
is considered independently.
Statistical methods for multivariate outlier detection often indicate those observations that
are located relatively far from the center of the data distribution. Several distance measures
can be implemented for such a task. The Mahalanobis distance is a well-known criterion
which depends on estimated parameters of the multivariate distribution. The Mahalanobis of
a multivariate vector with N items (T stands for transpose of the vector):
…………………. (1)
from a group of values with distance mean:
…………………. (2)
49
and covariance matrix S then the Mahalanobis distance is defined as:
√ ………………. (3)
Mahalanobis distance (or "generalized squared interpoint distance" for its squared value)
can also be defined as a dissimilarity measure between two random vectors and of the
same distribution with the covariance matrix S:
√ …………………. (4)
As in the one-dimensional procedures, the distribution mean (measuring the location) and
the variance-covariance (measuring the shape) are the two most commonly used statistics
for data analysis in the presence of outliers (Rousseeuw and Leory, 1987). The use of robust
estimates of the multidimensional distribution parameters can often improve the
performance of the detection procedures in presence of outliers. Ali S. Hadi (1994),
addresses this problem and proposes to replace the mean vector by a vector of variable
medians and to compute the covariance matrix for the subset of those observations with the
smallest Mahalanobis distance.
The BACON (Blocked Adaptive Computationally-efficient Outlier Nominators) algorithms
proposed by Billor, Hadi, and Velleman (2000), reliably detect multiple outliers suitable for
even very large data sets. BACON Algorithm for multivariate Outliers Detection shows a
better performance in detecting outliers in data than other methods that might not show
outliers in all dimensions of the data.
BACON Algorithm for Outliers Detection
This study used BACON algorithm in detecting multivariate outliers as it have many
advantages that could be explained in the following section.
In general outliers detection methods especially in multidimensional data with a large
number of variables have suffered in the past from a lack of generality and computational
costs that escalated rapidly with the sample size. The BACON algorithm for the
identification of outliers in multivariate data is described as follows:
50
Given a matrix X of n rows (observations) and of p columns (variables), Step 1 of
Algorithm 1 – described below – requires finding an initial basic subset of size m > p. This
subset can either be specified by the data analyst or obtained by an algorithm. The analyst
may have reasons to believe that a certain subset of observations is “clean". In this case, the
number m and/or the observations themselves can be chosen by the analyst. There is some
tension between the assurance that a small initial basic subset will be outlier-free and the
need for a sufficiently large basic subset to make stable estimates of the model. If the
desired basic subset size is m = cp, where c is a small integer chosen by the data analyst,
then the estimation of parameters is based on at least c observations per parameter. The
simulation results show that c = 4 or 5 perform quite well. The initial basic subset can also
be found algorithmically in one of two ways as given in the algorithm below.
Algorithm 1: the general BACON algorithm
Step 1: Identify an initial basic subset of m>p observations that can safely be assumed free
of outliers, where p is the dimension of the data and m is an integer chosen by the data
analyst.
Step 2: Fit an appropriate model to the basic subset, and from that model compute
discrepancies for each of the observations.
Step 3: Find a larger basic subset consisting of observations known (by their discrepancies)
to be homogeneous with the basic subset. Generally, these are the observations with
smallest discrepancies. This new basic subset may omit some of the previous basic subset
observations, but it must be as large as the previous basic subset.
Step 4: Iterate Steps 2 and 3 to refine the basic subset, using a stopping rule that determines
when the basic subset can no longer grow safely.
Step 5: Nominate the observations excluded by the initial basic subset as outliers.
51
The discrepancies can be displayed to check for gaps and to identify points that just barely
were nominated as outliers or just barely failed to be so nominated. Hadi (1992; 1994) and
Hadi and Simonoff (1993,1997) give methods for identifying initial basic subsets for
multivariate and regression situations, respectively. We use these methods here for Step 1
(after some modifications that make them even more computationally efficient), in part
because extensive experience has shown that they work well.
The iterations in Steps 2 to 4 increase the basic subset size, but restrict membership to
observations consistent with the current basic subset, and thus reliably not outliers. The
larger subset size yields more reliable estimates of the model and the corresponding
discrepancies, refining the definition of the basic subset as it grows.
Algorithm 2: Initial basic subset in multivariate data: Input: An n by p matrix X of
multivariate data and a number, m, of observations to include in the initial basic subset.
Output: An initial basic subset of at least m observations. Version 1 (V1): Initial subset
selected based on Mahalanobis distances:
,,,2,1,)xx()xx(),x( 1 nid iT
ii SS …………………. (5)
Where and S are the mean and covariance matrix of the observations. Identify the cp
observations with the smallest values of di( ; S). Nominate these as potential basic subset.
Version 2 (V2): Initial subset selected based on distances from the medians. For i =1, …, n,
compute ||xi – m||, where m is vector containing the coordinate wise median, xi is the ith row
of X and ||·|| is the vector norm. Identify the observations with the smallest values of ||xi –
m||. Nominate these as potential basic subset.
Algorithm 3: the BACON Algorithm for in multivariate data:
Input: An n by p matrix X of multivariate data. Output: set of observations nominated as
outliers and the discrepancies for all observations based on discrepancies in step 2 relative to
the final basic subset.
Step 1: Select an initial basic subset of size m using either V1 or V2 of previous Algorithm.
Step 2: Compute the discrepancies
52
,,,2,1,)xx()xx(),x( b1
bb nid ibT
ibi SS …………………. (6)
Where and S are the mean and covariance matrix of the observations in the basic subset.
Step 3: Set the new basic subset to all points with discrepancy less than where
is the (1-α) percentile of the chi square distribution with degrees of p; freedom, cnpr =
cnp + chr is correction factor, chr =max{0; (h - r)/(h + r)}; h = [(n + p + 1)/2]; r is the size of
the current basic subset, and
. …………………. (7)
When the size of the basic subset r is much smaller than h, the elements of the covariance
matrix tends to be smaller than they should be.
Thus, one can think of chr as variance inflation factor that is used to inflate the variance
when r is much smaller than h. Note also that when r = h, cnpr reduces to cnp.
Step 4: The stopping rule: Iterate Steps 2 and 3 until the size of the basic subset no longer
changes.
Step 5: Nominate the observations excluded by the final basic subset as outliers.
This is just a brief introduction about BACON technique, other details about this technique
are found in Computational Statistics & Data Analysis (2000), Billor, Hadi and Velleman
(2000).
According to the existence of outliers in the data or not different methods in addition to
BACON method will be used to detect the outliers.
53
4.2.3 How to deal with outliers
There is a great debate about the decision of how to deal with outliers. The decision is
mainly depending on why an outlier exists in the data. Where outliers are illegitimately
included in the data, it is only common sense that those data points should be removed.
The decision of keeping legitimate outliers and still not violating the assumptions is used in
many situations.
Another decision is the use of transformations in accommodating outliers. By using
transformations, extreme scores can be kept in the data set, and the relative ranking of scores
remains, yet the skew and error variance present in the variable(s) can be reduced.
One alternative to transformation is truncation, wherein extreme scores are recoded to the
highest (or lowest) reasonable score. Through truncation the relative ordering of the data is
maintained, and the highest or lowest scores remain the highest or lowest scores, yet the
distributional problems are reduced.
Rigorous methods, Instead of transformations or truncation, researchers sometimes use
various “rigorous” procedures to protect their data from being distorted by the presence of
outliers. These techniques “accommodate the outliers at no serious inconvenience - or are
rigorous against the presence of outliers” (Barnett & Lewis, 1994).
A common rigorous estimation method for univariate distributions involves the use of a
trimmed mean, which is calculated by temporarily eliminating extreme observations at both
ends of the sample and replace the outliers by a value that is a function on the trimmed
mean. Alternatively, researchers may choose to compute a Windsorized mean, for which
the highest and lowest observations are temporarily censored, and replaced with adjacent
values from the remaining data (Barnett & Lewis, 1994).
As shown for so many reasons, dealing with outliers depends on the data analysis step
where the variables will guide the decision on how to deal with outliers.
54
First: Univariate outliers (in two variables as there are only two scale variables in the
list of index variables):
Graphical tools10
(Box plot Scatter of individuals Histogram and Quintiles graph)
1. The Monthly salary variable:
Figure 4.2: Graphical detection of outliers in monthly salary of individuals
For the variable about monthly salary the graphical tools show the existence of multivariate
outliers after 4000 EGP.
2. The Crowdedness variable:
Figure 4.3: Graphical detection of outliers in crowdedness variable
10 The software used for the univariate outliers is Stata 11.2 package.
0 2,000 4,000 6,000 8,000 10,000Monthly salary
0
5.0e
-04
.001
.001
5
Den
sity
0 2000 4000 6000 8000 10000Monthly salary
0
2000
4000
6000
8000
1000
0
Qua
ntile
s of
Mon
thly
sal
ary
0 .25 .5 .75 1Fraction of the data
0
2000
4000
6000
8000
1000
0
Mon
thly
sal
ary
0 5.000e+08 1.000e+09ID
0 1 2 3 4 5 6 7 8CROWDEDNESS
0.5
11
.5
De
nsi
ty
0 2 4 6 8CROWDEDNESS
01
23
45
67
8
Qua
ntil
es
of C
RO
WD
ED
NE
SS
0 .25 .5 .75 1Fraction of the data
01
23
45
67
8
CR
OW
DE
DN
ES
S
0 5.000e+08 1.000e+09ID
55
For the variable about Crowdedness (average number of individuals per room in the
household) the graphical tools show also the existence of multivariate outliers after 5
individuals per room.
Mathematical tools:
With respect to Univariate outliers, as the data are heavily skewed to the right main two
mathematical methods are used to detect the outliers and their cut offs:
The alpha% trimmed mean (TM) and trimmed standard deviation (TSD). Where
any value larger than TM + c*TSD is considered to be an outlier, where alpha is
usually taken to be 10 and c = 3. The outlier is then replaced by TM + c*TSD.
The median and the average of absolute deviations about the median (AAD).
Then any value larger than median + c*AAD is considered to be an outlier.
The outlier is then replaced by median + c*AAD.
Table 4.9 shows the results of calculating trimmed mean and median with detailed values.
Results show that for the Monthly salary any value above 1471 or 1300 is considered an
outlier and for the crowdedness variables any value above 2.7 or 2.3 is considered an outlier.
Table 4.9: Trimmed mean and median results for outliers detection
Crowdedness Salary
TM 1.475 763.2
TSD 0.4197967 235.950
C 3 3
M 1.333333333 700
AAD 0.33333337 200
Cutoff TRIMM 2.7343901 1471.0512
Cutoff MEDIAN 2.333333443 1300
Cases with outliers using trimmed mean 6% 10%
Cases with outliers using median 10% 11%
The detection tools either graphical or mathematical tools show the existence of univariate
outliers. For the values that are outliers, these values are usual in Egypt and differentiate
between different individuals in the data. Accordingly the decision made is keeping these
56
values as it is because this is the nature of the current situation in Egypt that might be a
result of inequity and poverty (Keeping legitimate outliers).
Second: Multivariate outliers (in two groups of variables):
In this section BACON algorithm for multivariate outliers is applied in the two versions of
BACON.
The application11
made in two groups as that there are data that is valid only for 4000 cases
in the decent work module and the rest of the data that are valid for the whole data set where
the majority of variables exist (this is explained in details in the missing values section).
1. Complete cases group for the majority of the variables (54 Variable)
Version (1) of BACON
bacon Q334_01REC Q334_02REC Q334_03REC Q334_06REC Q334_07REC Q334_08REC Q334_09REC
Q334_10REC Q334_11REC Q334_12REC Q334_13REC Q334_15REC Q334_16REC Q334_17REC
Foodexpcomp Q330_01REC Q330_02REC Q330_03REC Q330_06REC Q330_07REC Q330_08REC
Q330_09REC Q330_10REC Q330_11REC Q330_12REC Q330_13REC Q330_15REC Q330_16REC
Q330_17REC Q118REC Q217REC Q221_3REC Q221_4REC Q229_DRED Q319REC Q320REC Q122REC
Q224REC Q205REC Q206REC Q228_1REC Q228_3REC Q228_5REC Q228_6REC Stove Washingmachine
Q228_9REC Q228_14REC Q207REC WORKSTATUSREC YEARSSCHOOLREC Expenditure2
BREADAVAILREC CROWDEDNESS2, gen(out) version(1) c(4)
Result:
Total number of observations: 44039
BACON outliers (p = 0.15): 0
Non-outliers remaining: 44039
Version (2) of BACON
bacon Q334_01REC Q334_02REC Q334_03REC Q334_06REC Q334_07REC Q334_08REC Q334_09REC
Q334_10REC Q334_11REC Q334_12REC Q334_13REC Q334_15REC Q334_16REC Q334_17REC
Foodexpcomp Q330_01REC Q330_02REC Q330_03REC Q330_06REC Q330_07REC Q330_08REC
Q330_09REC Q330_10REC Q330_11REC Q330_12REC Q330_13REC Q330_15REC Q330_16REC
Q330_17REC Q118REC Q217REC Q221_3REC Q221_4REC Q229_DRED Q319REC Q320REC Q122REC
Q224REC Q205REC Q206REC Q228_1REC Q228_3REC Q228_5REC Q228_6REC Stove Washingmachine
Q228_9REC Q228_14REC Q207REC WORKSTATUSREC YEARSSCHOOLREC Expenditure2
BREADAVAILREC CROWDEDNESS2, gen(out2) version(2) c(4)
Result:
Total number of observations: 44039
BACON outliers (p = 0.15): 0
Non-outliers remaining: 44039
11 The software used for BACON analysis is Stata 11.2 package.
57
2. Group 2 of Decent work 4000 cases (16 Variable)
Version (1) of BACON
bacon Q512REC Q516_2REC Q525_3REC Q542REC Q540REC Q518REC Q517REC Q525_1REC
Q525_2REC Q525_4REC Q525_5REC Q526REC Q531REC Q548REC Q549REC MonthSalary, gen(out)
version(1) c(4)
Result:
Total number of observations: 4000
BACON outliers (p = 0.15): 0
Non-outliers remaining: 4000
Version (2) of BACON
bacon Q512REC Q516_2REC Q525_3REC Q542REC Q540REC Q518REC Q517REC Q525_1REC
Q525_2REC Q525_4REC Q525_5REC Q526REC Q531REC Q548REC Q549REC MonthSalary, gen(out2)
version(2) c(4)
Result:
Total number of observations: 4000
BACON outliers (p = 0.15): 0
Non-outliers remaining: 4000
Results for both groups show that the multivariate outliers problem doesn’t exist in the
ESRF data.
4.3 Scale of Measurement
Normalization is required prior to any data aggregation to solve the problem of scale of
measurement as the indicators in a data set often have different measurement units. There
are a number of normalization methods including:
1. Ranking is the simplest normalization technique. This method is not affected by
outliers.
2. Standardization (or z-scores) converts indicators to a common scale with a mean of
zero and standard deviation of one.
3. Min-Max normalizes indicators to have an identical range [0,1] by subtracting the
minimum value and dividing by the range of the indicator values.
4. Distance to a reference measures the relative position of a given indicator vis-à-vis a
reference point.
58
5. Categorical scale assigns a score for each indicator.
6. Indicators above or below the mean are transformed such that values around the
mean receive 0, whereas those above/below a certain threshold receive 1 and -1
respectively.
The objective is to identify the most suitable normalization procedures to apply to the
problem at hand, taking into account their properties with respect to the measurement units
in which the indicators are expressed, and their robustness against possible outliers in the
data. The majority of composite indicators contains variety of variables that are of different
measurements scales and units, this require having a suitable tool - depending on objective
and data nature - that help in dealing with different scales.
For the ESRF index, it is required to reach to all levels of aggregation by having same
minimum and maximum for all the variables in the analysis and the index itself at the end.
This bring the Min-Max method to be the selected one to use in the ESRF variables and
indicators rescaling as the advantage of the Min-Max scaling is that all variables will have
the same minimum (0) and maximum (100) values. That is for all variables, two actions
were taken:
1. Recoding all variables in the analysis where the lowest value is the worst and the
highest is the best.
2. Rescaling all variables using the Min-Max scaling according to the following
equation:
(
)
Where Yi is the new scaled values of the variable Xi and Min(Xi) and Max(Xi) are the
minimum and the maximum of the variable Xi.
This normalization has been applied to all the 71 variables used in constructing the ESRF
index.
59
4.4 Weighting and Aggregation
4.4.1 Weighting
There exist a number of weighting techniques in the literature, some are derived from
statistical or from participatory methods like budget allocation processes (BAP) (see:
Organization for Economic Co-Operation and Development (2008), “Handbook on
Constructing Composite Indicators: Methodology and User Guide”), Regardless of which
method is used, weights are essentially value judgments. While some analysts might choose
weights based only on statistical methods, others might reward (or punish) components that
are deemed more (or less) influential, depending on expert opinion, to better reflect policy
priorities or theoretical factors.
Some researchers are dealing with composite indices by giving equal weights (EW), i.e. all
variables are given the same weight either for simplicity, cost, or other reasons. This
essentially implies that all variables are “worth” the same in the composite, but it could also
disguise the absence of a statistical or an empirical basis. Moreover, if variables are grouped
into dimensions and those are further aggregated into the composite, then applying equal
weighting to the variables may imply an unequal weighting of the dimension (the
dimensions grouping the larger number of variables will have higher weight). This could
result in an unbalanced structure in the composite index.
When using equal weights, it may happen that – by combining variables with a high degree
of correlation – an element of double counting may be introduced into the index: if two
collinear indicators are included in the composite index with a weight of w1 and w
2 , the
unique dimension that the two indicators measure would have weight ( w1
+ w2
) in the
composite. The existing literature offers a quite rich menu of alternative weighting methods
all having pros and cons. Statistical models such as principal components analysis (PCA) or
factor analysis (FA) could be used to group individual indicators according to their degree
of correlation. Weights, however, cannot be estimated with these methods if no correlation
exists between indicators. Alternatively, participatory methods that incorporate various
stakeholders – experts, citizens and politicians – can be used to assign weights. Public
opinion polls have been extensively used over the years.
60
The selection of the most relevant method for weights setting depends on the phenomenon
under analysis and a method that is relevant for a certain study might not be relevant for
another.
In this study the five dimensions of the ESRF index are all basic rights as the index uses
rights based approach, accordingly, two options are most relevant:
1. Using equal weights as these rights have the same importance for the Egyptian
citizens and the humans in general.
2. Using the people’s opinion, where people are the ones who should weigh their rights
by giving them the relative importance according to their point of view.
The second option of using public opinion polls have been applied in this study to set the
weights for the five dimensions of the ESRF index.
Accordingly, a one page questionnaire has been designed to assess the relative importance
of the five economic and social rights. A representative sample was selected from all Egypt
and on urban rural areas. The question was:
Please arrange/ order the following rights according to their relative importance for you (1
the least important and 5 the most important.
Right to education □
Right to health □
Right to food □
Right to decent work □
Right to adequate housing □
To apply this on the field, the public opinion poll center of the Egyptian Cabinet inserted
this question in a periodic relevant telephone survey that they are applying nationally.
The overall sample size is 1002 individuals with the following characteristics in table 4.10.
61
Table 4.10: Weight sample characteristics
Percentage
Urban-Rural Urban 46.7%
Rural 53.3%
Sex Male 48.6%
Female 51.4%
Education level Less than secondary 41.4%
Secondary and equivalent 40.8%
University or Higher 17.8%
Age 18 to less than 30 39.1%
30 to less than 50 37.5%
50 or more 23.4%
Economic Status Low 33.0%
Medium 18.7%
High 48.3%
For the region, 53% are living in rural areas while 47% are in urban areas. Males represent
around 49%.
To obtain the weights values an average score for each dimension is computed and then
normalized to give the weights with summation equals to 1 by dividing the average score by
summation of scores for different dimensions. Table 4.11 shows that the right to adequate
housing came at the first importance followed by right to food, right to decent work, right to
education and the right to health as the last important one.
Table 4.11: Weights for the dimensions of the ESRFI
Right
Education Health Food Decent work Adequate
housing
Score 2.53 2.41 3.85 3.71 4.08
Weight 15.2% 14.5% 23.2% 22.4% 24.6%
These weights in Table 4.11 were used to aggregate the overall index.
62
4.4.2 Aggregation
Aggregation methods also vary and the decision on which method to use depends on the
data and the phenomenon of interest. While the linear aggregation method is useful when all
individual indicators have the same measurement unit, provided that some mathematical
properties are respected. Geometric aggregations are better suited if the analyst wants some
degree of non-compensability between individual dimensions.
In both linear and geometric aggregations, weights express trade-offs between indicators. A
deficit in one dimension can thus be offset (compensated) by a surplus in another. This
implies an inconsistency between how weights are conceived and the actual meaning when
geometric or linear aggregations are used. In a linear aggregation, the compensability is
constant, while with geometric aggregations compensability is lower for the composite
indicators with low values. In terms of policy, if compensability is admitted, a country with
low scores on one indicator will need a much higher score on the others to improve its
situation when geometric aggregation is used.
For the economic and social rights fulfillment index there are some points to consider:
1. After rescaling using the Min-Max method, all variables that are included in the
index have the same measurement scale.
2. Compensability between dimensions is hardly possible as the weights varied among
dimensions.
3. The index is constructed for the first time and assuming linearity will be simpler in
further application or replication.
Accordingly, linear aggregation is more appropriate for the ESRF index and is the method
used in aggregation the dimensions of ESRF index. The following formula is used:
∑
With ∑ and , for all i = 1,2,3,4,5.
is the weight for dimension i and is the dimension number i.
63
4.5. Computing the Margin of Error
The Margin of Error ME = Critical value (obtained from standard normal distribution for
example) x Standard error.
The margin of error is a statistic expressing the amount of random sampling error in
a survey's results. The larger the margin of error, the less confidence level is in the results.
The margins of error should be taken into consideration as that the index will be computed
is a point estimate and the quality of this point estimate can be judged by its standard error.
For example if there are two regions A and B with indices scored 60 for region A and 65 for
region B, does this mean that the two regions are statistically different? To answer this
question knowing the standard errors of the two estimates and of their difference is a must.
So, computing the standard errors of the index is an issue of debate and importance.
The most rigorous, familiar and widely used approach is by bootstrapping all estimated
confidence intervals or other estimates.
The bootstrap approach is widely used because of its simplicity, it is straightforward to
derive estimates of standard errors and confidence intervals for complex estimators of
complex parameters of the distribution, such as percentile points, proportions, odds ratio,
and correlation coefficients.
Moreover, it is an appropriate way to control and check the stability of the results. In
general, Bootstrapping is a method for deriving robust estimates of standard errors and
confidence intervals for estimates such as the mean, median, proportion, odds ratio,
correlation coefficient or regression coefficient. It may also be used for constructing
hypothesis tests.
Bootstrapping is most useful as an alternative to parametric estimates when the assumptions
of those methods are in doubt, or where parametric inference is impossible or requires very
complicated formulas for the calculation of standard errors (as in the case of computing
confidence intervals for the median, quartiles, and other percentiles).
64
That is for the ESRFI all estimates will be associated with a confidence interval that is
calculated after bootstrapping12
to give a robust and rigorous comparisons across all
disaggregation levels of the index.
12 The software used in Bootstrapping is SPSS package, version 20.
65
Chapter Five
Results of Calculating the ESRF index for Egypt
Based on the methodology presented in the previous chapters, the calculations of the
Economic and Social Rights Fulfillment Index have been made. In this Chapter the results
of index scores as well as the dimensions are presented with bootstrapped 95% confidence
intervals. Results are presented with disaggregation levels to present the gaps – if any –
between different groups. Results for the overall ESRF index is presented, then different
results on each dimension are presented. For all the 5 dimensions the overall score and the
score by different characteristics are presented. All scores are measured on a scale from 0 to
100, where 0 is the worst value and 100 is the best value.
For all the results, unless otherwise noted, bootstrap results are based on 1000 bootstrap samples.
5.1 Results of the overall Economic and Social Rights Fulfillment Index
A. Overall Score
Table 5.1 shows the results of the overall ESRFI, where the average score is 62.7 with
minimum score of 31.2 and maximum 94.6. According to the 95% Confidence Interval after
bootstrapping the average score is considered very accurate and representative in measuring
the ESRF in Egypt as the interval is very narrow (62.6 , 62.8).
Table 5.1: Descriptive Statistics for the overall ESRFI
Statistic Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Economic and
Social Rights
Fulfillment Index
for Egypt
N 44039 0 0 44039 44039
Minimum 31.16
Maximum 94.57
Mean 62.6864 -.0008 .0438 62.5996 62.7717
Std. Deviation 9.34435 -.00012 .03003 9.28744 9.40404
Variance 87.317 -.001 .561 86.257 88.436
Valid N (listwise) N 44039 0 0 44039 44039
66
Figure 5.1 shows the distribution of the overall index, the figure shows that it is very close
to the normal distribution.
Figure 5.1: Histogram of ESRFI scores
Table 5.2 is for testing normality using Kolmogorov-Smirnov test. Results show that the
test is significant which means than the data is not normally distributed despite what looks
like normal at the histogram.
Table 5.2: Tests of Normality
Kolmogorov-Smirnov
Statistic df Sig.
Economic and Social Rights
Fullfillment Index for Egypt .044 44039 .000
The Quantile-Quantile plot as in Figure 5.2 shows the values on the tail of the distribution
that violates normality. Working on those values by transformations or replacing them may
achieve normality.
40.00 50.00 60.00 70.00 80.00 90.00
Economic and Social Rights Fullfillment Index for Egypt
1%
2%
3%
4%
Perc
en
t
67
Figure 5.2: Normal Q-Q Plot of Economic and Social Rights Fullfillment Index for
Egypt
B. Score by different characteristics
a. Urban – Rural
Urban-Rural comparisons show a significant difference between the urban areas and the
rural ones in Egypt. The urban areas have significantly a higher score of 66.5 in fulfilling
the economic and social rights than the rural areas that scored around 59.8.
Table 5.3: Economic and Social Rights Fulfillment Index for Egypt by Urban - Rural
Urban - Rural
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Urban 66.5060 -.0006 .0668 66.3709 66.6377
Rural 59.8434 -.0014 .0531 59.7412 59.9407
Total 62.6864 -.0008 .0438 62.5996 62.7717
Figure 5.3 shows the box plots of dimensions over urban and rural areas, it shows that rural
is always worse than urban areas in all levels of dimensions especially for the right to
education and adequate housing.
47.7
373761.8
5372
79.2
3461
20
40
60
80
100
Econom
ic a
nd S
ocia
l R
ights
Fullf
illm
ent
Index f
or
Egypt
62.68639 78.0564747.31631
20 40 60 80 100Inverse Normal
Grid lines are 5, 10, 25, 50, 75, 90, and 95 percentiles
68
Figure 5.3: Box plots for the ESRFI dimensions across urban and rural areas
For the ESRFI, the box plot of rural areas is located lower than the box of urban areas with
lower values (minimum, mean and maximum).
The more inequality appears clearer for the right to education where the gap between
individuals in rural areas is very large when compared to urban areas.
Also for the right to education, the rural have minimum values that are very low than the
urban.
Additionally, the right to adequate housing is clearly appears with inequalities between
urban and rural, where rural still the worst.
b. Regions
Table 5.4 shows the ESRFI by regions, the table shows that Rural Upper Egypt have
significantly the lowest score (58.7) in fulfilling the economic and social right than other
regions. Metropolitan have significantly the highest score of 67.1.
69
Table 5.4: Economic and Social Rights Fulfillment Index for Egypt by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Metropolitan 67.1097 -.0018 .0981 66.9269 67.3107
Urban Lower Egypt 66.2976 -.0043 .1229 66.0345 66.5418
Rural Lower Egypt 60.9423 -.0006 .0765 60.7909 61.0865
Urban Upper Egypt 65.4634 .0042 .1240 65.2223 65.7218
Rural Upper Egypt 58.6920 -.0019 .0728 58.5557 58.8425
Total 62.6864 -.0008 .0438 62.5996 62.7717
c. Governorates
The fulfillment of economic and social rights varied across governorates. While Giza,
Alexandria and Cairo got the highest scores in fulfilling the economic and social rights, Kafr
Al-Sheikh, Sohag and Assiut got the lowest scores in fulfilling those rights.
Figure 5.4: Economic and Social Rights Fulfillment Index for Egypt by Governorates
Table 5.5 represent the ANOVA test, it shows that differences in general either between or
within groups of governorates are significance. But some of the governorates overlap
together in confidence intervals where the differences between those specific groups are
insignificant.
57.7 58.6 58.9 59.1 59.4
60.2 60.6 60.7 61.2 61.5
62.3 62.4 62.4 63.2 63.4
64.1 64.8 65.1
66.0 66.5
67.0 67.9 68.0 68.1
52
54
56
58
60
62
64
66
68
70
70
Table 5.5: ANOVA Economic and Social Rights Fullfillment Index for Egypt and
governorates
Sum of Squares df Mean Square F Sig.
Between Groups 453205.575 23 19704.590 255.685 .000
Within Groups 3392052.154 44015 77.066
Total 3845257.729 44038
Table 5.6: Economic and Social Rights Fulfillment Index for Egypt by Governorates
Governorate
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Cairo 67.8641 -.0055 .1697 67.5214 68.1778
Alexandria 68.0300 -.0036 .1913 67.6436 68.4319
Port Said 67.0179 .0134 .3103 66.3935 67.6295
Suez 65.0512 -.0031 .2684 64.5556 65.5780
Helwan 65.9614 -.0043 .2433 65.4628 66.4446
6 October 61.2073 -.0036 .2176 60.7593 61.6190
Dametta 66.5187 -.0103 .2644 65.9831 67.0295
Al Dakahlia 63.3775 .0037 .1782 63.0376 63.7287
Al Sharkia 61.4570 -.0019 .1730 61.1243 61.7990
Al Kaliubia 62.4110 -.0001 .1874 62.0384 62.7707
Kafr Al Sheikh 58.8932 -.0039 .2138 58.4700 59.3190
Al Gharbia 64.7584 -.0131 .1930 64.3608 65.1180
Al Menofia 62.3062 -.0036 .1792 61.9434 62.6462
Al Behera 60.7359 -.0018 .1781 60.3885 61.0942
Al Ismailia 64.0549 .0042 .2680 63.5507 64.5846
Giza 68.1094 .0007 .2165 67.6864 68.5341
Bani Suef 59.4152 .0054 .1971 59.0348 59.8291
Al Fayoum 59.0875 .0063 .1822 58.7264 59.4582
Menia 60.2254 .0047 .1870 59.8781 60.6020
Assiut 57.7106 -.0037 .1706 57.3720 58.0507
Sohag 58.5508 -.0060 .1870 58.1915 58.9024
Qena 63.2039 -.0046 .2238 62.7472 63.6523
Aswan 62.3729 .0052 .2483 61.9117 62.8828
Luxor 60.5572 .0021 .2676 60.0055 61.0711
Total 62.6864 -.0008 .0438 62.5996 62.7717
71
d. Current Marital Status
According to the marital status, those who have the highest score of fulfilling their
economic and social rights are the ones who have never married or the ones who are in the
step of having marriage contract. Widowed individuals have the lowest score in fulfilling
their economic and social rights with a score 55.3.
Table 5.7: Economic and Social Rights Fulfillment Index for Egypt by Current Marital
Status
Current Marital Status
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Never married 66.5229 -.0012 .0952 66.3375 66.7048
Engaged 65.6576 .0004 .3035 65.0360 66.2553
Contracted 67.0594 .0024 1.2172 64.7561 69.4252
Married 63.2743 -.0008 .0785 63.1085 63.4332
Widowed 55.3457 .0023 .2779 54.7916 55.9108
Divorced 61.8615 .0291 .7269 60.4011 63.2806
Separated 62.4134 .0377 1.5241 59.5791 65.6241
Total 62.6864 -.0008 .0438 62.5996 62.7717
e. Gender
Table 5.8 shows that females have significantly higher levels in fulfilling economic and
social rights than males.
Table 5.8: Economic and Social Rights Fulfillment Index for Egypt by Gender
Gender
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Male 62.3598 -.0014 .0582 62.2377 62.4735
Female 63.0342 -.0003 .0670 62.9000 63.1629
Total 62.6864 -.0008 .0438 62.5996 62.7717
72
f. Age
Table 5.9 shows that across different age groups, the economic and social rights fulfillment
is significantly the highest among youth and young adults. The fulfillment is the lowest
among children age group as well as adults.
Table 5.9: Economic and Social Rights Fulfillment Index for Egypt by Age
Age
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
0-18 61.8237 .0005 .0474 61.7299 61.9173
18-29 65.0543 -.0004 .0958 64.8640 65.2428
29-35 64.7025 -.0092 .1758 64.3434 65.0394
35-45 63.6268 .0010 .1503 63.3309 63.9321
45 and above 60.4645 -.0011 .1308 60.2165 60.7367
Total 62.6864 -.0008 .0438 62.5996 62.7717
g. Household size
Table 5.10 shows the ESRFI by household size, the table shows that the fulfillment of the
ESRF is significantly the lowest among individuals living in households where the size of
household is more than 6 individuals (60.4).
Table 5.10: Economic and Social Rights Fulfillment Index for Egypt by Household size
Household size
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
3 Individuals or less 62.3535 .0026 .1200 62.1249 62.5931
4 to 6 individuals 63.3293 -.0008 .0517 63.2238 63.4288
More than 6 individuals 60.3594 -.0063 .1015 60.1643 60.5624
Total 62.6864 -.0008 .0438 62.5996 62.7717
73
h. Gender of household head
Table 5.11 shows that individuals living in female headed households are significantly
lower in fulfilling the economic and social right than those living in male headed
households.
Table 5.11: Economic and Social Rights Fulfillment Index for Egypt by Gender of
household head
Gender of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Male 62.8531 -.0008 .0440 62.7642 62.9367
Female 60.6935 -.0015 .1783 60.3465 61.0445
Total 62.6864 -.0008 .0438 62.5996 62.7717
i. Education of household head
Table 5.12 shows the ESRFI by education of household head, the fulfillment is significantly
the highest when the household head has a university degree or higher and the lowest when
the household head is illiterate.
Table 5.12: Economic and Social Rights Fulfillment Index for Egypt by Education of
household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Illiterate 56.4781 -.0001 .0701 56.3466 56.6153
Read and Write 60.0730 -.0007 .0946 59.8911 60.2763
Primary 62.5423 .0008 .1033 62.3328 62.7464
Preparatory 63.7629 .0356 .6694 62.5183 65.1159
Secondary or average 65.6755 -.0015 .0648 65.5491 65.7961
University or Higher 72.1698 -.0055 .1120 71.9307 72.3843
Total 62.6864 -.0008 .0438 62.5996 62.7717
74
5.2 Results of the ESRFI five dimensions
5.2.1 Right to Adequate Housing
A. Overall Score
In Table 5.13 the descriptive statistics of the right to adequate housing is presented. The
table shows that the average score of fulfilling the right to adequate housing is 61.4.
This reflects the extent of accessing improved water and sanitation, quality of floor
materials and cooking fuel, sufficiency of living space, having separate kitchen and the
ownership of living conditions main assets.
The minimum value of right to adequate housing is 8.82 and the maximum is 99.2, this
reflects a large inequality among individuals.
According to the 95% confidence interval, the estimate is very accurate.
Table 5.13: Descriptive Statistics of the Right to Adequate Housing
Statistic Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Right to adequate
housing
N 44039 0 0 44039 44039
Minimum 8.82
Maximum 99.21
Mean 61.4133 .0010 .0559 61.3072 61.5296
Std. Deviation 11.79130 -.00223 .04703 11.69080 11.88467
Variance 139.035 -.050 1.109 136.675 141.245
Valid N (listwise) N 44039 0 0 44039 44039
Figure 5.5 shows the score of Right to Adequate Housing disaggregated by its components.
It shows that, the lowest scores are for the items of: electric heater, air conditioner, vacuum
cleaner, water heater and crowdedness. Other elements of the right to adequate housing that
got score above 90 are: having a separate place for cooking, stove, washing machine, color
TV and refrigerator.
75
Figure 5.5: Right to adequate housing disaggregated by its components
B. Score by different characteristics
a. Urban – Rural
Table 5.14 shows the fulfillment of the right to adequate housing by urban and rural areas.
The table shows a significant inequality between urban and rural areas, the Urban scored
68.6 while the Rural areas scored 56.1.
Table 5.14: Right to adequate housing by Urban – Rural
Urban - Rural
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Urban 68.6065 .0000 .0770 68.4517 68.7570
Rural 56.0593 .0030 .0597 55.9400 56.1819
Total 61.4133 .0010 .0559 61.3072 61.5296
b. Regions
Table 5.15 shows that Metropolitan (70.1) region has significantly the highest score for the
right to adequate housing while Rural Upper Egypt (53.5) has the lowest one. Inequality
between urban and rural regions also assured from the table.
4.8 6.9 13.7 15.3
41.3
60.6 72.2
85.3 86.3 92.8 93.1 94.2 95.8 97.6
76
Table 5.15: Right to adequate housing by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Metropolitan 70.1129 -.0001 .1155 69.8772 70.3419
Urban Lower Egypt 68.7999 .0022 .1242 68.5600 69.0569
Rural Lower Egypt 58.6278 .0033 .0780 58.4805 58.7994
Urban Upper Egypt 65.4610 -.0029 .1402 65.1759 65.7268
Rural Upper Egypt 53.4583 .0021 .0864 53.2844 53.6296
Total 61.4133 .0010 .0559 61.3072 61.5296
c. Governorates
Table 5.6 shows that, while Cairo, Alexandria and Giza governorates got significantly the
highest score in fulfilling the right to adequate housing compared to other governorates.
Governorates of Assiut, Al Fayoum and Bani suif got the lowest scores.
Some of the governorates are close in the score of the right to adequate housing with
insignificant difference reflected in overlapping of their confidence intervals.
Figure 5.6: Right to adequate housing by Governorates
52.3 53.8 54.0 54.4 54.7 56.9 58.5 58.6 58.6 58.9 59.1 59.7 60.8 62.0
63.8 64.7 65.7 66.3 69.7 69.7 69.8 70.2 71.4 71.5
0
10
20
30
40
50
60
70
80
77
Table 5.16: Right to adequate housing by Governorates
Governorate
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Cairo 71.4767 .0039 .1963 71.0875 71.8518
Alexandria 71.3687 .0020 .2136 70.9613 71.8182
Port Said 69.6861 -.0037 .3890 68.9319 70.4948
Suez 69.6693 .0034 .2682 69.1188 70.1812
Helwan 66.2819 -.0120 .3110 65.6824 66.8809
6 October 60.7524 -.0009 .2869 60.2012 61.3628
Dametta 69.8347 .0013 .2738 69.2888 70.3803
Al Dakahlia 61.9912 -.0018 .1910 61.6208 62.3576
Al Sharkia 58.4732 -.0026 .1761 58.1209 58.7993
Al Kaliubia 64.6870 -.0142 .2014 64.3049 65.0845
Kafr Al Sheikh 56.8764 .0084 .2336 56.4285 57.3394
Al Gharbia 63.8193 .0078 .2194 63.4071 64.2675
Al Menofia 59.7139 .0064 .2103 59.3007 60.1386
Al Behera 58.6495 -.0033 .2049 58.2522 59.0282
Al Ismailia 65.7124 .0091 .2738 65.1839 66.2831
Giza 70.1714 .0038 .2436 69.6827 70.6675
Bani Suef 53.9583 .0147 .2169 53.5594 54.4054
Al Fayoum 53.7846 -.0082 .2320 53.3058 54.2391
Menia 54.7405 .0034 .2184 54.2976 55.1625
Assiut 52.2907 -.0003 .1994 51.8863 52.6826
Sohag 54.3523 -.0044 .1987 53.9381 54.7302
Qena 58.9121 .0047 .2907 58.3286 59.4652
Aswan 58.5986 -.0038 .2554 58.1132 59.0849
Luxor 59.1330 .0017 .3137 58.5100 59.7663
Total 61.4133 .0010 .0559 61.3072 61.5296
d. Current Marital Status
Table 5.17 shows the scores of the right to adequate housing by the current marital status.
The 95% confidence intervals shows in general overlapping between different groups that
declares the insignificance of differences between different groups.
Table 5.17: Right to adequate housing by Current Marital Status
Current marital
status Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Never married 61.6122 .0046 .1221 61.3828 61.8566
Engaged 60.2746 -.0019 .3712 59.5218 60.9947
Contracted 64.4073 -.0498 1.3663 61.5708 67.0139
Married 62.0897 -.0004 .0884 61.9218 62.2738
Widowed 61.0040 .0033 .3290 60.3782 61.6234
Divorced 60.1158 -.0128 .8022 58.5454 61.7084
Separated 61.6469 .0179 1.9102 57.5751 65.2236
Total 61.4133 .0010 .0559 61.3072 61.5296
78
e. Gender
Table 5.18 shows that there is no significant difference between males and females in
fulfilling the right to adequate housing as the overlap in the confidence interval.
Table 5.18: Right to adequate housing by Gender
Gender
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 61.3737 -.0016 .0779 61.2257 61.5252
Female 61.4555 .0038 .0800 61.2974 61.6180
Total 61.4133 .0010 .0559 61.3072 61.5296
f. Age
Age groups do not significantly differ in fulfilling the right to adequate housing as there is
an overlapping in the 95% confidence intervals except for the children age group (0-18
years) that have significantly the lowest score.
Table 5.19: Right to adequate housing by Age
Age
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
0-18 60.4482 .0006 .0918 60.2677 60.6367
18-29 61.3595 .0043 .1221 61.1443 61.6076
29-35 62.1405 .0085 .2106 61.7523 62.5951
35-45 62.1975 -.0017 .1596 61.8616 62.4938
45 and above 62.6061 -.0034 .1338 62.3273 62.8477
Total 61.4133 .0010 .0559 61.3072 61.5296
g. Household size
Table 5.20 shows the scores of right to adequate housing by household size. The table
shows that individuals living in households with size more than 6 individuals have
significantly the lowest score in fulfilling the right to adequate housing.
79
Table 5.20: Right to adequate housing by Household size
Household size
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
3 Individuals or less 62.7806 -.0019 .1378 62.5123 63.0472
4 to 6 individuals 62.1694 .0022 .0689 62.0333 62.3142
More than 6 individuals 56.4381 -.0004 .1235 56.1918 56.6780
Total 61.4133 .0010 .0559 61.3072 61.5296
h. Gender of household head
Gender of household head is not significantly differ in the score of fulfilling the right to
adequate housing. The 95% confidence interval shows an overlapping.
Table 5.21: Right to adequate housing by Gender of household head
Gender of household
head Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 61.4475 .0007 .0583 61.3361 61.5690
Female 61.0051 .0048 .2104 60.5854 61.4244
Total 61.4133 .0010 .0559 61.3072 61.5296
i. Education of household head
Table 5.22 shows that individuals living in households where the household head have a
university or higher degree are significantly higher than other individuals in fulfilling the
right to adequate housing (73.3).
Table 5.22: Right to adequate housing by Education of household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Illiterate 54.3752 -.0038 .0813 54.2200 54.5398
Read and Write 59.7010 .0039 .1221 59.4711 59.9422
Primary 61.2442 .0004 .1442 60.9674 61.5384
Preparatory 60.4202 .0195 .8049 58.9183 62.1017
Secondary or average 63.6965 .0047 .0901 63.5215 63.8756
University or Higher 73.2812 -.0027 .1426 73.0107 73.5492
Total 61.4133 .0010 .0559 61.3072 61.5296
80
5.2.2 Right to Food
A. Overall Score
Table 5.23 shows the descriptive statistics for the dimension of the Right to Food. The table
declares that the Right to Food dimension got an average score of 90.7, which is a high
score. The Right to Food got a minimum score of 58.3 and a maximum score of 98.7 which
shows a gap between different individuals in such a very basic right.
The confidence interval shows that the estimate is very accurate.
Table 5.23: Descriptive Statistics for the Right to Food
Statistic Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Right to food
N 44039 0 0 44039 44039
Minimum 58.33
Maximum 98.68
Mean 90.7402 .0002 .0225 90.6976 90.7870
Std. Deviation 4.64522 .00070 .01759 4.61219 4.67983
Variance 21.578 .007 .163 21.272 21.901
Valid N
(listwise) N 44039 0 0 44039 44039
B. Score by different characteristics
a. Urban – Rural
Table 5.24 shows the Urban and Rural levels of the right to food. Even if the score of the
urban areas is slightly higher than the rural areas, the confidence interval shows non
overlapping that declares that the difference between urban and rural is significant.
Table 5.24: Right to food by Urban – Rural
Urban – Rural
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Urban 91.0456 .0002 .0352 90.9746 91.1194
Rural 90.5129 .0002 .0285 90.4605 90.5696
Total 90.7402 .0002 .0225 90.6976 90.7870
81
b. Regions
The Urban Upper Egypt region shows the highest significant score in the right to food
compared to other regions. But in general the differences among different regions in the
right to food dimension are significant except for rural upper Egypt that overlap with
metropolitan.
Table 5.25: Right to food by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Metropolitan 90.7133 .0015 .0533 90.6135 90.8233
Urban Lower Egypt 90.9876 -.0004 .0674 90.8565 91.1095
Rural Lower Egypt 90.3198 .0011 .0411 90.2416 90.4034
Urban Upper Egypt 91.6046 -.0004 .0647 91.4806 91.7293
Rural Upper Egypt 90.6774 -.0011 .0389 90.5995 90.7492
Total 90.7402 .0002 .0225 90.6976 90.7870
c. Governorates
Table 5.26 shows the right to food scores by different governorates. The table shows that,
while Kafr Al Sheikh and Ismailia show the lowest score in the right to food, Al Gharbia,
Helwan and Qena governorates got the highest score among all governorates.
Figure 5.7: Right to food by Governorates
85
86
87
88
89
90
91
92
93
88.2 88.7
89.1 89.6
89.9 90.0
90.6 90.7 90.7 90.8 90.8 90.9 90.9 90.9 91.0 91.1 91.1 91.3 91.5 91.6 91.6 91.8 91.9
92.8
82
Table 5.26: Right to food by Governorates
Governorate
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Cairo 89.5580 -.0019 .1019 89.3513 89.7520
Alexandria 91.1066 -.0024 .0986 90.9085 91.3050
Port Said 90.8950 .0038 .1112 90.6914 91.1074
Suez 91.0283 .0017 .1209 90.8026 91.2733
Helwan 91.9362 .0122 .1277 91.7057 92.2035
6 October 91.6318 -.0041 .1047 91.4228 91.8390
Dametta 90.8050 -.0019 .1160 90.5650 91.0405
Al Dakahlia 90.6183 .0000 .0893 90.4328 90.7906
Al Sharkia 91.3100 -.0001 .0910 91.1238 91.4857
Al Kaliubia 91.1243 .0055 .0978 90.9417 91.3097
Kafr Al Sheikh 88.2165 .0018 .1308 87.9638 88.4651
Al Gharbia 91.7662 .0021 .1188 91.5449 92.0045
Al Menofia 89.9079 .0021 .1050 89.7152 90.1188
Al Behera 90.6774 -.0005 .0751 90.5242 90.8265
Al Ismailia 88.7201 -.0004 .1347 88.4384 88.9743
Giza 91.6318 -.0011 .1385 91.3441 91.9098
Bani Suef 91.4687 .0010 .0904 91.2893 91.6384
Al Fayoum 90.7154 -.0021 .1099 90.4820 90.9253
Menia 89.1474 .0016 .0874 88.9902 89.3220
Assiut 90.8571 -.0027 .1100 90.6377 91.0694
Sohag 90.8478 .0003 .0784 90.6922 90.9969
Qena 92.8073 -.0038 .0643 92.6821 92.9331
Aswan 90.9338 .0010 .1300 90.6650 91.1836
Luxor 89.9510 .0003 .1069 89.7431 90.1660
Total 90.7402 .0002 .0225 90.6976 90.7870
Results shows some overlapping between some of the governorates that declares clustering
in fulfilling the right to food that assured by regions comparisons.
d. Current Marital Status
Table 5.27 shows the right to food scores by current marital status of individuals. The table
shows slight differences among scores, but these differences tend to be insignificant
according to overlapping in different confidence intervals.
83
Table 5.27: Right to food by Current Marital Status
Current marital status
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Never married 91.0733 -.0013 .0495 90.9789 91.1716
Engaged 90.8623 .0081 .1571 90.5764 91.1935
Contracted 91.0612 -.0075 .6524 89.7090 92.2989
Married 90.8091 .0003 .0336 90.7482 90.8753
Widowed 89.6787 .0031 .1334 89.4296 89.9530
Divorced 89.7028 -.0093 .3175 89.0947 90.3438
Separated 89.5031 .0088 .6248 88.2963 90.7296
Total 90.7402 .0002 .0225 90.6976 90.7870
e. Gender
Results in table 5.28 shows that there is no significant difference between males and
females in the right to food scores.
Table 5.28: Right to food by Gender
Gender
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 90.7994 .0002 .0299 90.7436 90.8613
Female 90.6772 .0001 .0338 90.6123 90.7469
Total 90.7402 .0002 .0225 90.6976 90.7870
f. Age
Table 5.29 shows that there is no significant difference of the right to food scores among
different age categories.
Table 5.29: Right to food by Age
Age
Mean
Bootstrapa
Bias Std. Error 95% Confidence Interval
Lower Upper
0-18 90.6501 .0002 .0347 90.5822 90.7175
18-29 90.8610 -.0014 .0476 90.7674 90.9574
29-35 90.6267 .0013 .0805 90.4701 90.7930
35-45 90.7299 .0005 .0645 90.6018 90.8607
45 and above 90.8357 .0013 .0526 90.7325 90.9446
Total 90.7402 .0002 .0225 90.6976 90.7870
84
g. Household size
Table 5.30 shows the right to food scores by the size of household where the individuals live
in. The table shows that small households have slightly smaller score than big households.
There is no significance difference in fulfilling the right to food between individuals living
in households with 4 to 6 individuals and those living in households with more than 6
individuals
Table 5.30: Right to food by Household size
Household size
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
3 Individuals or less 90.3939 -.0023 .0540 90.2864 90.4977
4 to 6 individuals 90.8195 .0003 .0272 90.7669 90.8730
More than 6 individuals 90.8417 .0029 .0555 90.7303 90.9509
Total 90.7402 .0002 .0225 90.6976 90.7870
h. Gender of household head
Table 5.31 shows that individuals that are living in female headed households have a score
of 89.7 in the right to food while the ones living in male headed households got significantly
higher score of 90.8.
Table 5.31: Right to food by Gender of household head
Gender of household head
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 90.8276 .0002 .0227 90.7863 90.8737
Female 89.6951 -.0006 .0908 89.5183 89.8681
Total 90.7402 .0002 .0225 90.6976 90.7870
i. Education of household head
Table 5.32 shows that individuals living in households where the household head has a
university degree or higher are significantly having the highest score of the right to food. In
addition, individuals living in households where the head is illiterate or read and write have
the lowest score among other individuals.
85
Table 5.32: Right to food by Education of household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Illiterate 89.8121 .0005 .0402 89.7347 89.8934
Read and Write 90.0677 -.0030 .0578 89.9474 90.1742
Primary 90.4524 .0008 .0665 90.3218 90.5886
Preparatory 91.0551 -.0191 .3691 90.2909 91.7505
Secondary or average 91.0779 .0002 .0382 91.0042 91.1514
University or Higher 92.8823 .0018 .0563 92.7728 92.9947
Total 90.7402 .0002 .0225 90.6976 90.7870
5.2.3 Right to Decent Work
A. Overall Score
Table 5.33 shows the Right to Decent Work score, the average score is 42.6 which is
considered the lowest score among other economic and social rights. The minimum value
for the right to decent work is 3.4 which means that there are individuals working with
almost no decency at all. The maximum is 94.7 showing a large inequality between
individuals in fulfilling the right to decent work items that needed to be considered.
According to the 95% confidence interval, the estimated value is very accurate (41.8, 43.4).
Table 5.33: Descriptive Statistics of Right to Decent Work
Statistic Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Right to decent
work
N 4000 0 0 4000 4000
Minimum 3.37
Maximum 94.74
Mean 42.6340 .0075 .4214 41.8099 43.4530
Std. Deviation 25.95764 -.00519 .16068 25.63889 26.25572
Variance 673.799 -.244 8.340 657.353 689.363
Valid N
(listwise) N 4000 0 0 4000 4000
86
Table 5.34 shows the right to decent work disaggregated by its components. Results show
that the items of having an insurance against work related danger, monthly salary and being
a member of a syndicate got a very low score. Items that got a high score are Satisfaction
with the nature of work in the organization, Working additional hours and not having
negative impact on health, Average time from home to reach job, Work related with using
sharp instruments or materials, flammable or has dangerous and Work Stability.
Table 5.34: Right to decent work components
Decent work Items Average
Score
If organization avail an insurance against work related danger 6.5
Monthly Salary 9.4
If member of syndicate 13.3
Hours of your work per week on average 29.2
If organization avail care of a child leaves (females) 33.5
If organization avail maternity leave (females) 34.2
If organization avail health insurance 34.6
If organization avail a social insurance (pension) 36.0
If organization avail casual leaves 36.8
If organization avail incidental holidays 37.1
If organization avail sick leaves 37.9
Having a written legal contract with employer 38.4
Satisfaction with the nature of work in the organization 71.9
Working additional hours and not having negative impact on health 77.2
Average time from home to reach job 82.1
Work related with using sharp instruments or materials, flammable or has
dangerous
82.8
Work Stability 87.2
B. Score by different characteristics
a. Urban – Rural
Table 5.35 shows the right to decent work by Urban and Rural areas. The table shows that
Rural areas significantly lower than the Urban areas in fulfilling the decent work.
87
Table 5.35: Right to decent work by Urban - Rural
Urban – Rural
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Urban 47.9517 -.0052 .6295 46.7278 49.1698
Rural 38.0721 .0201 .5355 37.0308 39.1430
Total 42.6340 .0075 .4214 41.8099 43.4530
a. Regions
Scores by region shows that the Rural Upper Egypt has significantly the lowest score in
fulfilling the right to decent work dimension with a score of 35.7. Other regions are not
significantly differ in fulfilling the right to decent work as they are overlapping in the 95%
confidence interval.
Table 5.36: Right to decent work by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Metropolitan 47.4721 .0004 .8924 45.7704 49.1768
Urban Lower Egypt 49.9969 -.0001 1.2227 47.6492 52.3279
Rural Lower Egypt 39.6627 .0155 .6902 38.3277 41.1556
Urban Upper Egypt 46.7471 -.0050 1.1191 44.5690 48.9600
Rural Upper Egypt 35.6779 .0185 .7839 34.2008 37.2416
Total 42.6340 .0075 .4214 41.8099 43.4530
b. Governorates
Table 5.37 shows that Cairo have the highest score of 51.6 in fulfilling the right to decent
work, while Al Fayoum have the lowest score of 31.4. Majority of governorates do not have
significant differences as the confidence intervals are overlapping.
88
Figure 5.8: Right to decent work by Governorates
Table 5.37: Right to decent work by Governorates
Governorate
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Cairo 51.5587 -.0182 1.5220 48.5138 54.7125
Alexandria 45.0576 .0585 1.7868 41.6697 48.5410
Port Said 46.5268 .0354 2.2862 41.8927 51.1717
Suez 47.0234 -.1461 2.6693 41.0738 52.3720
Helwan 44.3789 .0234 2.5755 39.2428 49.5021
6 October 38.0865 -.0275 1.9559 34.2934 41.8167
Dametta 42.3114 .0566 2.6327 37.3556 47.7327
Al Dakahlia 43.2846 .0211 1.6134 40.0755 46.4389
Al Sharkia 42.8398 -.1189 1.4244 40.0096 45.5763
Al Kaliubia 43.0087 .0427 1.8737 39.2932 46.9934
Kafr Al Sheikh 35.9434 .0260 1.8841 32.3773 39.8548
Al Gharbia 46.3696 .0698 1.9102 42.4548 49.9359
Al Menofia 44.5900 .0045 2.0078 40.8416 48.9027
Al Behera 38.2310 .0203 1.7547 34.8909 41.7793
Al Ismailia 48.7236 -.0081 2.0853 44.6322 52.8635
Giza 48.1232 .0141 1.7871 44.5483 51.6637
Bani Suef 33.9545 -.0147 1.8925 30.3375 37.9053
Al Fayoum 31.7401 .0652 1.6892 28.6205 35.3754
Menia 40.2722 .0145 2.2731 35.9101 44.8789
Assiut 34.6740 -.0047 1.6997 31.3165 38.1546
Sohag 37.7819 .0097 1.9797 33.9790 41.6436
Qena 50.7274 -.0055 2.2738 46.0935 55.3617
Aswan 42.7802 .0993 2.8643 37.3882 48.5155
Luxor 42.7505 -.0271 2.5542 37.6585 47.7788
Total 42.6340 .0075 .4214 41.8099 43.4530
31.7 34.0 34.7 35.9
37.8 38.1 38.2 40.3
42.3 42.8 42.8 42.8 43.0 43.3 44.4 44.6 45.1 46.4 46.5 47.0 48.1 48.7 50.7 51.6
0
10
20
30
40
50
60
89
c. Current Marital Status
The overlapping in the 95% confidence intervals of different groups of marital status shows
insignificant differences among the marital status groups.
Table 5.38: Right to decent work by Current Marital Status
Current Marital Status
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Never married 33.0157 .0309 .7052 31.6339 34.4208
Engaged 35.3758 .0905 2.3274 30.7176 40.2043
Contracted 42.6125 -.1785b 11.5957
b 19.4054
b 66.8777
Married 45.6777 -.0048 .5081 44.7144 46.6645
Widowed 46.5689 .0595 2.7836 41.1853 52.2534
Divorced 49.4109 .0335 5.2603 39.4249 59.3421
Separated 53.8235 -.0520 10.0709 34.8250 73.0598
Total 42.6340 .0075 .4214 41.8099 43.4530
d. Gender
Table 5.39 shows the right to decent work by gender. The table shows that Females are
significantly higher than males in the right to decent work with a score of 61.0 compared to
39.4.
Table 5.39: Right to decent work by Gender
Gender
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 39.3629 .0065 .4300 38.5578 40.2098
Female 61.0258 .0060 1.0313 58.8881 63.0051
Total 42.6340 .0075 .4214 41.8099 43.4530
e. Age
Table 5.40 shows that individuals in the age of 15 to less than 18 that are considered
children have significantly the lowest score in the right to decent work compared to other
age groups.
90
Table 5.40: Right to decent work by Age
Age
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
15-18 22.5639 .0270 .7112 21.2004 24.0521
18-29 33.5629 .0199 .6605 32.2896 34.8192
29-35 39.2029 .0379 .9854 37.3876 41.1387
35-45 44.3308 .0010 .8422 42.7613 46.0563
45 and above 52.0904 -.0154 .7802 50.5771 53.6298
Total 42.6340 .0075 .4214 41.8099 43.4530
Individuals with age 45 and above have significantly the highest score in fulfilling the right
to decent work.
f. Household size
Individuals living in households with size of more than 6 individuals have the lowest score
of the right to decent work compared to other groups. Additionally, there is no significant
difference between individuals living in households with 3 individuals or less and the ones
living in households with 4 to 6 individuals.
Table 5.41: Right to decent work by Household size
Household size
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
3 Individuals or less 41.8316 .0065 .8027 40.2614 43.4139
4 to 6 individuals 44.0335 .0084 .5066 43.0856 44.9873
More than 6 individuals 35.8302 -.0010 1.1502 33.6902 38.0662
Total 42.6340 .0075 .4214 41.8099 43.4530
g. Gender of household head
Results in table 5.42 show that the gender of the household head do not significantly differ
in fulfilling the right to decent work.
91
Table 5.42: Right to decent work by Gender of household head
Gender of household
head Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 42.9157 .0017 .4363 42.0346 43.7366
Female 38.8914 .0709 1.5507 36.0092 42.2099
Total 42.6340 .0075 .4214 41.8099 43.4530
h. Education of household head
Table 5.43 shows the fulfillment of the write to decent work by education of household
head. The table shows that individuals there is no significant difference between individuals
in fulfilling the right to decent work by household head education level except for the levels
of illiterate, read and write and primary.
Table 5.43: Right to decent work by Education of household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Illiterate 27.8814 -.0030 .4789 26.9574 28.8245
Read and Write 32.2007 .0325 .8525 30.5256 33.8822
Primary 37.1216 -.0184 1.0820 34.9585 39.1430
Preparatory 55.8249 .1641 7.0742 41.3233 70.1921
Secondary or average 49.7602 -.0115 .7608 48.2224 51.1846
University or Higher 66.4462 .0129 .8503 64.7759 68.1194
Total 42.6340 .0075 .4214 41.8099 43.4530
95% confidence intervals for preparatory, secondary or average and university or higher are
overlapping showing an insignificant differences. This conclude that in general individuals
with household head education level primary or less are significantly lower that individuals
with preparatory or higher.
92
5.2.4 Right to Education
A. Overall Score
The dimension of right to education scored 56.8 on average with 95% confidence interval of
(56.5, 57.1). Table 5.44 shows that the minimum value for the right to education is 0.00
where individuals did not get any education and 100 for the individuals who already
achieved their required years of education.
Table 5.44: Descriptive Statistics of Right to Education
Statistic Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Right to
Education
N 44039 0 0 44039 44039
Minimum .00
Maximum 100.00
Mean 56.7989 .0106 .1355 56.5549 57.0764
Std. Deviation 29.61195 .00079 .08516 29.44578 29.77271
Variance 876.868 .054 5.043 867.054 886.414
Valid N (listwise) N 44039 0 0 44039 44039
B. Score by different characteristics
a. Urban – Rural
Table 5.45 shows significant inequalities between Urban and Rural areas in fulfilling the
right to education. The rural areas got a score of 53.8 compared to a score of 60.9 in the
urban areas.
Table 5.45: Right to education by Urban - Rural
Urban - Rural
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Urban 60.8501 .0065 .1971 60.4810 61.2177
Rural 53.7834 .0136 .1912 53.4152 54.1577
Total 56.7989 .0106 .1355 56.5549 57.0764
93
b. Regions
Rural Upper Egypt shows significantly the lowest score in the right to education compared
to other regions with a score of 52.5. Urban Lower Egypt region has the highest score of
62.5.
Table 5.46: Right to education by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Metropolitan 61.1860 -.0068 .2845 60.6354 61.7499
Urban Lower Egypt 62.5493 .0237 .3673 61.8380 63.2863
Rural Lower Egypt 55.1317 .0129 .2664 54.5945 55.6475
Urban Upper Egypt 58.3039 .0080 .3755 57.6013 59.0408
Rural Upper Egypt 52.4884 .0131 .2823 51.9819 53.0833
Total 56.7989 .0106 .1355 56.5549 57.0764
c. Governorates
Table 5.47 shows the scores of the right to education by governorates. The table shows that
Port Said, Al Ismailia and Cairo are significantly the highest governorates while Al Fayoum,
Assiut and Kafr El Sheikh are the lowest.
Figure 5.9: Right to education by Governorates
50.1 52.0 52.0 52.4 52.5 53.2 53.2 54.9 55.8 56.6 56.7 57.2 57.8 58.0 58.3 59.6 59.8 59.8 60.2 60.9 61.6 62.1 62.4 62.9
0
10
20
30
40
50
60
70
94
Table 5.47: Right to education by Governorates
Governorate
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Cairo 62.1378 -.0139 .5156 61.0677 63.1106
Alexandria 61.6385 -.0082 .5635 60.5394 62.7737
Port Said 62.9353 .0032 .7120 61.5022 64.3476
Suez 59.8370 -.0241 .7818 58.1482 61.3370
Helwan 58.3047 .0107 .7728 56.8233 59.8425
6 October 54.8699 .0045 .7679 53.3744 56.3812
Dametta 59.7702 .0464 .8137 58.2177 61.4171
Al Dakahlia 55.8330 .0287 .6152 54.6755 57.0391
Al Sharkia 56.6228 .0120 .6001 55.5114 57.8447
Al Kaliubia 57.7987 .0086 .6274 56.5707 58.9901
Kafr Al Sheikh 51.9963 .0012 .8031 50.3809 53.5842
Al Gharbia 60.8906 .0080 .5973 59.7719 62.0948
Al Menofia 60.2278 .0295 .6288 59.0127 61.5066
Al Behera 53.2185 .0045 .6287 52.0059 54.4238
Al Ismailia 62.3707 .0288 .7421 60.9471 63.8027
Giza 59.5800 .0208 .6543 58.3909 60.9227
Bani Suef 52.4398 .0077 .7590 50.9260 53.9026
Al Fayoum 50.0573 .0266 .7925 48.5741 51.6236
Menia 52.4721 -.0023 .6775 51.1699 53.8742
Assiut 51.9862 -.0040 .6698 50.6824 53.3116
Sohag 53.1516 .0247 .6426 51.9038 54.3964
Qena 58.0354 .0006 .7037 56.5690 59.4519
Aswan 56.7159 -.0280 .8194 55.0708 58.3318
Luxor 57.1900 .0779 .7958 55.7664 58.8205
Total 56.7989 .0106 .1355 56.5549 57.0764
Some governorates especially the ones from the same region are overlapping in the 95%
confidence intervals declaring an insignificant differences.
d. Current Marital Status
According to table 5.48, Widowed individuals have significantly the lowest score in
fulfilling the right to education. Other marital status categories are overlapping in the 95%
confidences intervals declaring and insignificant differences.
95
Table 5.48: Right to education by Current Marital Status
Current marital status
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Never married 66.1324 -.0002 .2255 65.6880 66.5669
Engaged 62.8200 .0372 .7280 61.4993 64.2575
Contracted 63.4483 .0875 2.5657 58.2500 68.8278
Married 38.8748 .0041 .2213 38.4467 39.3253
Widowed 16.4040 .0115 .6288 15.1936 17.6298
Divorced 37.2600 .0516 1.9530 33.5702 41.0973
Separated 38.1690 -.0703 3.8448 30.3654 45.5775
Total 56.7989 .0106 .1355 56.5549 57.0764
e. Gender
Males are significantly better than Females in fulfilling the right to education. Table 5.49
shows that the score of fulfilling the right to education among males is 59.6 while this score
among females is 53.8.
Table 5.49: Right to education by Gender
Gender
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 59.5914 .0085 .1764 59.2531 59.9573
Female 53.8247 .0132 .2127 53.4339 54.2729
Total 56.7989 .0106 .1355 56.5549 57.0764
f. Age
Table 5.50 shows that younger individuals are fulfilling the right to education more that
older ones.
Children in the age category 0 to less than 18 have a score of 77.5 in fulfilling their right to
education.
96
Table 5.50: Right to education by Age
Age
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
0-18 77.5725 .0012 .0513 77.4691 77.6749
18-29 57.9592 .0088 .2550 57.4496 58.4700
29-35 48.0786 .0097 .4479 47.1823 48.9676
35-45 39.8885 .0088 .4263 39.0657 40.7713
45 and above 28.4290 .0038 .3305 27.7766 29.0885
Total 56.7989 .0106 .1355 56.5549 57.0764
g. Household size
Table 5.51 shows the right to education scores by household size. Individuals living in
households with 3 or less individuals are significantly the lowest in fulfilling the right to
education.
Table 5.51: Right to education by Household size
Household size
Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
3 Individuals or less 46.1448 .0213 .3540 45.5090 46.8165
4 to 6 individuals 59.8521 .0130 .1589 59.5676 60.1822
More than 6
individuals 57.2962 -.0145 .3646 56.5159 58.0098
Total 56.7989 .0106 .1355 56.5549 57.0764
h. Gender of household head
Male headed households are significantly better than female headed households in the right
to education scores.
Table 5.52: Right to education by Gender of household head
Gender of household
head Mean
Bootstrap
Bias Std.
Error
95% Confidence Interval
Lower Upper
Male 57.7009 .0065 .1375 57.4404 57.9844
Female 46.0135 .0602 .5622 44.9369 47.2215
Total 56.7989 .0106 .1355 56.5549 57.0764
97
i. Education of household head
Table 5.53 shows that the Education of household head significantly differentiates
individuals. As the household head get much education, the individuals are achieving higher
scores in fulfilling the right to education reached to 77.1 when the household head have
university degree or higher.
Table 5.53: Right to education by Education of household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Illiterate 40.2344 .0208 .3086 39.6530 40.8752
Read and Write 48.1840 .0153 .3846 47.4476 48.9401
Primary 58.6000 .0194 .3463 57.9290 59.2909
Preparatory 58.0224 .0942 2.1120 53.7431 62.1810
Secondary or
average 67.1192 -.0053 .1578 66.8220 67.4323
University or Higher 77.1454 -.0046 .1485 76.8402 77.4205
Total 56.7989 .0106 .1355 56.5549 57.0764
There is no significant difference between primary and preparatory as the overlap in the
95% confidence interval.
5.2.5 Right to Health
A. Overall Score
Considering the access to water with good quality, having health problems in living area,
finding essential pharmaceuticals when needed, having governmental health insurance and
disability, the right to health scored 78.1 on average.
Table 5.54 shows that the score of the right to health has a minimum of 14.3 and a
maximum of 100. This big gap between minimum and maximum shows inequality between
individuals in fulfilling the right to health.
98
Table 5.54: Descriptive Statistics of the Right to Health
Statistic Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Right to Health
N 44039 0 0 44039 44039
Minimum 14.29
Maximum 100.00
Mean 78.0985 -.0025 .0665 77.9585 78.2188
Std. Deviation 13.77805 -.00037 .05569 13.66581 13.88269
Variance 189.835 -.007 1.534 186.754 192.729
Valid N (listwise) N 44039 0 0 44039 44039
Table shows the average score of right to health components. The scores show that the
item that are lagging are having a governmental health insurance and accessing necessary
pharmaceuticals that is needed by household.
Table 5.55: Right to health components
Item Average Score
Having a government health insurance 46.92
Medicines needed by your family members and necessary 52.44
Problems related to drinking water (Low quality) 77.32
Problems related to drinking water (Water pollution) 89.5
Drugs usually need are always available in nearby pharmacies 90.71
Problems of health services in the area 91.22
Have any disability 98.57
B. Score by different characteristics
a. Urban – Rural
Table 5.56 shows the right to health fulfillment score by urban and rural areas. Urban areas
have significantly higher score of 80.8 in fulfilling the right to health than Rural areas that
scored 76.1.
99
Table 5.56: Right to health by Urban - Rural
Urban - Rural
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Urban 80.8120 -.0007 .0918 80.6188 80.9859
Rural 76.0788 -.0042 .0907 75.8914 76.2490
Total 78.0985 -.0025 .0665 77.9585 78.2188
b. Regions
Scores of the right to health by region shown in table 5.57 declare that Rural Lower Egypt
is significantly the lowest region among other regions in fulfilling the right to health
followed by Rural Upper Egypt.
Table 5.57: Right to health by Regions
Regions
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Metropolitan 81.7001 -.0038 .1264 81.4392 81.9340
Urban Lower Egypt 78.1939 .0001 .1972 77.8240 78.5889
Rural Lower Egypt 75.5935 -.0004 .1313 75.3371 75.8588
Urban Upper Egypt 81.9660 .0021 .1564 81.6675 82.2812
Rural Upper Egypt 76.3146 -.0086 .1272 76.0506 76.5480
Total 78.0985 -.0025 .0665 77.9585 78.2188
Metropolitan region and Urban Upper Egypt have the highest score in the right to health
compared to other regions, but differences between those two regions are insignificant as
their confidence intervals overlapped.
c. Governorates
Table 5.58 shows the Right to Health scores by governorates. The table shows that Al
Kaliubia governorate is the lowest one in fulfilling the right to health among other
governorates with a score of 70.9. Giza (85.0), Helwan (84.6) and Cairo (83.7) got the
highest score in fulfilling the right to health. Some of the governorates especially the ones in
the same region have no significant differences among them.
100
Figure 5.10: Right to health by Governorate
Table 5.58: Right to health by Governorate
Governorate
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Cairo 83.7350 .0042 .1949 83.3670 84.1062
Alexandria 81.8121 -.0039 .2129 81.3660 82.2290
Port Said 79.4276 -.0183 .4097 78.6229 80.2309
Suez 75.1682 -.0185 .4797 74.1901 76.0553
Helwan 84.6379 -.0035 .2227 84.2074 85.0618
6 October 75.8214 -.0007 .3250 75.1703 76.4453
Dametta 81.4338 -.0110 .3099 80.7980 82.0490
Al Dakahlia 80.5588 .0026 .2029 80.1607 80.9665
Al Sharkia 76.1015 -.0005 .2828 75.5398 76.6705
Al Kaliubia 70.8849 .0022 .3401 70.2462 71.5535
Kafr Al Sheikh 74.0428 .0064 .3819 73.2724 74.7890
Al Gharbia 76.7499 -.0002 .3203 76.1465 77.4086
Al Menofia 77.0970 -.0102 .2973 76.5001 77.6416
Al Behera 75.5302 -.0054 .3302 74.8981 76.1765
Al Ismailia 76.0611 .0086 .4551 75.1729 76.9445
Giza 84.9819 .0038 .2246 84.5272 85.4283
Bani Suef 79.3851 .0006 .2939 78.7704 79.9763
Al Fayoum 80.4302 -.0017 .2507 79.9239 80.9317
Menia 82.1950 -.0051 .2160 81.7652 82.6050
Assiut 74.4916 -.0125 .2988 73.8854 75.0614
Sohag 72.9304 -.0240 .3568 72.2110 73.6309
Qena 79.2215 -.0052 .2749 78.6871 79.7495
Aswan 78.8886 .0109 .3820 78.1108 79.6202
Luxor 72.0154 -.0009 .5307 70.9866 73.0201
Total 78.0985 -.0025 .0665 77.9585 78.2188
60
65
70
75
80
85
70.9 72.0
72.9 74.0 74.5 75.2 75.5 75.8 76.1 76.1 76.7 77.1
78.9 79.2 79.4 79.4 80.4 80.6
81.4 81.8 82.2 83.7
84.6 85.0
101
d. Current Marital Status
Table 5.59 shows that there is no significant differences among individuals in fulfilling the
right to health by different marital status groups, this is declared in the overlapping of the
95% confidence intervals.
Table 5.59: Right to health by Current Marital Status
Current marital status
Mean
Bootstrap
Bias Std. Error 95% Confidence
Interval
Lower Upper
Never married 75.8101 -.0072 .1456 75.5281 76.0759
Engaged 72.4244 -.0160 .4729 71.4596 73.3613
Contracted 73.7274 .0311 1.6811 70.4278 76.9419
Married 74.1909 -.0010 .0958 74.0005 74.3709
Widowed 75.3808 .0114 .3109 74.7790 76.0231
Divorced 74.4381 .0770 .8637 72.8404 76.2246
Separated 72.5017 -.0193 1.9523 68.5546 76.2577
Total 78.0985 -.0025 .0665 77.9585 78.2188
e. Gender
The difference between males and females in fulfilling the right to health is significant,
males have a score higher than females in fulfilling the right to health.
Table 5.60: Right to health by Gender
Gender
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 78.4545 -.0050 .0955 78.2596 78.6343
Female 77.7193 .0002 .0927 77.5310 77.9034
Total 78.0985 -.0025 .0665 77.9585 78.2188
f. Age
Table 5.61 shows the right to health fulfillment by age groups. The table shows that children
below age of 18 are the highest in fulfilling the right to health with a score of 84.7. Other
age groups do not significantly differ.
102
Table 5.61: Right to health by Age
Age
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
0-18 84.7270 -.0031 .0922 84.5503 84.9061
18-29 72.3583 .0000 .1298 72.0975 72.5937
29-35 73.0817 -.0056 .2112 72.6555 73.4810
35-45 73.9675 -.0064 .1765 73.6128 74.3151
45 and above 75.7872 .0046 .1440 75.5118 76.0777
Total 78.0985 -.0025 .0665 77.9585 78.2188
g. Household size
Table 5.62 shows that individuals who are living in households with size of 4 to 6
individuals are significantly the highest in fulfilling their right to health.
Table 5.62: Right to health by Household size
Household size
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
3 Individuals or less 75.9868 .0045 .1479 75.6979 76.2789
4 to 6 individuals 78.7747 -.0031 .0828 78.6045 78.9217
More than 6 individuals 77.8935 -.0073 .1704 77.5499 78.2348
Total 78.0985 -.0025 .0665 77.9585 78.2188
h. Gender of household head
Male headed households are significantly higher than female headed households in fulfilling
the right to health.
Table 5.63: Right to health by Gender of household head
Gender of household head
Mean
Bootstrap
Bias Std. Error 95% Confidence Interval
Lower Upper
Male 78.2790 -.0024 .0694 78.1280 78.4066
Female 75.9411 -.0029 .2329 75.4917 76.3969
Total 78.0985 -.0025 .0665 77.9585 78.2188
103
i. Education of household head
Table 5.64 shows the scores of right to health by household head education level. The table
shows that overlapping among different groups existed and reflecting insignificant
differences.
Table 5.64: Right to health by Education of household head
Education of household head
Mean
Bootstrap
Bias Std.
Error
95% Confidence
Interval
Lower Upper
Illiterate 75.1921 -.0032 .1226 74.9343 75.4390
Read and Write 77.0638 .0033 .1733 76.7356 77.4185
Primary 77.7398 -.0185 .1901 77.3503 78.0973
Preparatory 80.3127 -.0013 1.0954 78.2679 82.6043
Secondary or average 79.7732 -.0012 .1199 79.5370 80.0095
University or Higher 81.9496 .0061 .1653 81.6473 82.2912
Total 78.0985 -.0025 .0665 77.9585 78.2188
The dimensions results show scores that explain the overall sores of the ESRF index (62.7).
The right to food score was the highest one of 90.7, despite that the score is very high, but
by looking at the other side, there are around 9.3 left that express non fulfillment of a very
basic initial right which is food. The second right in order of scores is the right to health
with a score of 78.1 followed by the right to adequate housing (61.4), then the right to
education (56.8) and at the lowest score came the right to decent work that scored 42.6.
The right to decent work is the lowest one with a score not even reaches 50; this means that
decent work is not fulfilled in Egypt with even acceptable levels and need more attention by
policy makers and other stakeholders. The attention required is from all entities,
governmental and private sector to apply the decent work principals.
104
Considering the right to food, differences among individuals in the majority of
characteristics is significant but very small. Significant differences between Urban and
Rural are in the rights of health, adequate housing, education and decent work.
Marital Status is not significantly differentiating between individuals in the majority of
economic and social rights.
Rural Upper Egypt has the lowest scores in the fulfillment of rights to adequate housing,
education and decent work.
Cairo governorate has always better scores in the economic and social rights.
When the household head has a university degree or higher, more chances are available for
individuals to fulfill the economic and social rights.
On the other hand, individual living in households where the head is female or illiterate have
lower chances in fulfilling the rights to education and decent work.
105
Chapter Six
Conclusions and Recommendations
This chapter highlights the main finding of the study as well as the recommendations for
policy makers:
A. Conclusions
1. The process of constructing composite indices includes many challenges that should
be taken into consideration and highlighted.
2. Economic and social rights are basic and essential rights to all citizens, those rights
should be fulfilled completely. These rights are very well known and stated in all
human rights declaration and specifically in the Egyptian constitution, the
constitutions set obligation of the state to fulfill those rights.
3. The economic and social rights used in the index are five rights, the right to food,
right to education, right to health, right to adequate housing and right to decent work,
all rights are defined in details through theoretical framework.
4. At its final structure, the ESRFI constitutes of 5 main dimensions, 35 indicators and
71 variables.
5. The Egyptian Family Conditions Observatory dataset that is used in calculating the
ESRFI is available with periodical rounds and panel part. This motivates to
recalculate the index for different periods and follow up the trend of fulfilling the
Economic and Social Rights.
6. Indicators should be selected in cautious taking into consideration different aspects
of availability, reliability, relevance to the phenomena of interest and with solid
theoretical framework. After selecting the list of indicators, some of them might be
eliminated due to data characteristics especially zero variance or very few applicable
cases.
106
7. The problem of missing and not applicable cases existed in the variables that
constitute the ESRFI. The multiple imputation using Marcov Chain Monte Carlo
simulation used in imputing the missing values. Non-applicable cases treated by
setting relevant code, setting the codes for not applicable cases was justified for each
variable and made to be consistent with other codes.
8. The right to food, adequate housing and decent work got a high reliability and
internal consistency values in the Cronbach's Alpha coefficient. The right to health
got the lowest score in Cronbach's Alpha coefficient.
9. Univariate outliers existed in the two variables of income and crowdedness but the
values were legitimate. The decision was to keep those legitimate outliers.
Multivariate outliers checked using BACON Algorithm and weren't exist.
10. Despite that multiple imputation using Marcov Chain Monte Carlo simulation shows
a better performance in imputing the missing values over the Neural Networks, this
is not a general rule, this applies for this dataset in particular after being tested.
11. The weighting technique used in setting dimension weights was selected based on
relevance to the ESRFI by making a survey to the people to prioritize their 5 rights
according to relative importance to them. The scores came from the survey used in
setting the weights. The people ordered the rights as follows:
a. Right to Adequate housing
b. Right to Food
c. Right to Decent Work
d. Right to Education
e. Right to health
This sets a priority list for policy makers.
12. Bootstrapping has been applied with 1000 replications over the sample and shows a
rigorous estimates.
13. In a range from 0 to 100, the overall score of the ESRFI was 62.7 which is
considered not enough for such a very basic and essential rights to the people.
107
Inequalities between Urban and Rural areas over the index and dimensions were
very clear. The Rural is always worse than the Urban.
14. The highest score among dimensions was for the right to food (90.7) while the
lowest fulfillment score was for the right to decent work (42.6).
15. The highest fulfillment score across governorates was for the governorates of Giza,
Alexandria and Cairo respectively. The lowest score was for the governorates of
Kafr El-Sheikh, Sohag and Assiut.
16. Fulfilling the Economic and Social rights is not a gender issue as the differences
between males and females is not so large.
17. Inequalities in fullfilling the Right to health is in a considerable level, the minimum
is 14.3 and the maximum is 100. This is also applied for the Right to adequate
housing as well as Right to Education.
18. Education of household head is a very important factor for fullfilling the economic
and social rights.
B. Recommendations
1. It is very important to consider Meta Data part in the construction of any composite
index to allow for better understanding and future improvements by others. Meta
data about each indicator of the ESRFI were presented in details.
2. It is recommended to use an accurate method when calculating the Margin of Error
or confidence intervals for the different estimations of the composite indices. This
makes the comparisons and conclusions much more accurate and solid.
3. The measuring challenges in composite indices should be considered all together as
a group to give a final rigorous index.
4. Following up the trend in the ESRFI is very important to evaluate the performance
and extent of fullfillment.
108
5. Future interventions should focus more in Rural areas regarding the economic and
social programmes as they are the most marginalized groups.
6. Despite that the right to food has a high score, but there are a group got a minimum
score of 58.3, those need more in depth work to know the reasons for having this
score and identify their needs.
7. Policy makers and different stakeholders should give more focus in fulfilling
people's rights to decent work, education and adequate housing in particular.
8. Education interventions should focus more on households with crowdedness level
more than 4 individuals as well as female headed households.
9. Decency of work needs big effort from policy makers to meet the standards of
decent work as stated by ILO and different labour organizations.
109
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Annexes
Annex 1: Indicators Meta Data
The expression "data about data" is often used to express the meaning of Meta data. Many
times when statisticians and other researchers read some measures, indicators or indices,
they suffer from asking about a number of questions related to the indicator, e.g. What is the
data source of this indicator?, How does the researcher calculated it?, ..., etc.
Metadata is a formalized description of a data set that provides information about the data's
content, quality, condition, and other characteristics. The indicators Meta data documents
information about a statistical dataset's background, purpose, content, collection, processing,
quality, and related information that an analyst needs to find, understand, and manipulate
statistical data.
As such, the metadata for a statistical dataset broadens the number and diversity of people
who can successfully use a data source once it is released.
The main items of Meta data are:
Definition
Domain of indicator
Number of variables
Method of Computation
Data Source
Data Availability
References
Periodicity of measurement
Gender and disaggregation issues
Year
Limitations of the indicator
The following is the application of Meta Data on the indicators of the ESRF index detailing
all its characteristics.
116
1. INDICATOR Individuals live in households decreased or stopped using main
goods because of the increase in food prices
DEFINITION This indicator is defined as the total number of individual who are
living in a households who were have to stop or decrease their usage of the main/ basic goods as a result of the food prices increase that
affected all goods either main or not, the basic goods is defined by
the institutions working on this issues in Egypt.
DOMAIN/ RIGHT Right to Food
NUMBER OF VARIABLES 14
METHOD OF
COMPUTATION
The main goods is defined by IDSC/ CAPMAS as 14 goods, each
good is represented as a variable in the dataset, this variable is rescaled to be ranged from 0 to 100 where 0 reflect the worst case
(stopping usage) and 100 is the best case (not affected by prices
increase).
DATA SOURCES Egyptian Household Conditions Observatory survey - round 8
DATA AVAILABILITY Available
REFERENCE(S) Proxy for UN concept on usage of main goods
PERIODICITY Quarterly-Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS The definition of the 14 goods might be quite subjective
2. INDICATORS Availability of bread by type that were needed by households
during the all days of the week
DEFINITION Levels of bread availability when need at any time by the households
by different types of bread.
DOMAIN/ RIGHT Right to Food
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
according to the level of availability, this indicator is rescaled from 0
to 100 where 0 is that the bread is not available at all and 100 is that the bread is available when needed at any day during the week
DATA SOURCES Egyptian Household Conditions Observatory survey - round 9
DATA AVAILABILITY Available
REFERENCE(S) Proxy for UN concept on usage of main goods
117
PERIODICITY Quarterly-Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS
3. INDICATORS People living in poverty
DEFINITION This is a reflection for the economic status of individuals as the
individuals living in poor households will tend to be food deprived
than others.
DOMAIN/ RIGHT Right to Food
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The quintiles of expenditure have been used as a proxy for economic level and rescaled from 0 to 100.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 10
DATA AVAILABILITY Available
REFERENCE(S) UN/ FAO/ MDGs
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS Poverty definitions and measurements variation.
4. INDICATORS Share of Expenditure on food out of total expenditure
DEFINITION This is the percentage of expenditure on food out of total household
expenditure.
DOMAIN/ RIGHT Right to Food
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
Households are asked directly about the percentage that they allocate
for food expenditure out of their total expenditure.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 11
DATA AVAILABILITY Available
118
REFERENCE(S) UN concept
PERIODICITY Semiannual-Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS
5. INDICATORS Individuals live in households that are not using X good of the
main food goods
DEFINITION This indicator is defined as the total number of individual who are
living in a households who are not using main/ basic goods in
general, the basic goods is defined by the institutions working on this issues in Egypt. The importance of main goods is for all individuals
(children, youth and adults)
DOMAIN/ RIGHT Right to Food
NUMBER OF VARIABLES 14
METHOD OF
COMPUTATION
The main goods are defined by IDSC/ CAPMAS as 14 goods, each
good is represented as a variable in the dataset and the households are
classified according to usage.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 12
DATA AVAILABILITY Available
REFERENCE(S) UN concept
PERIODICITY Semiannual-Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS The definition of the 14 goods might be quite subjective
6. INDICATORS Enrollment rate in primary education
DEFINITION The enrolment ratio in primary education is the ratio of the number
of children of official primary school age who are enrolled in primary
education to the total population of children of official primary
school age.
DOMAIN/ RIGHT Right to Education
NUMBER OF VARIABLES 1
119
METHOD OF
COMPUTATION
To calculate the indicator, it is necessary to first determine the
population of official primary school age, preferably by reference to the theoretical starting age and duration of Level 1 (primary
education). Then, the number of pupils of the official primary school
age who are enrolled in primary education is divided by the
population for the same age-group and the result is multiplied by 100.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 13
DATA AVAILABILITY Available
REFERENCE(S) UN/ MDGs/ UNESCO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural
Regions Governorates
YEAR 2010
LIMITATIONS here there was a challenge in evaluating the education variables
because of non-applicability and limitations on some questions, away
to overcome this done by creating a variable in the data that is reflect the individual actual years of schooling compared to the optimal
years of schooling according to his/ her age.
7. INDICATORS Education completion
DEFINITION The extent of completing a certain level in education.
DOMAIN/ RIGHT Right to Education
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
This indicator is calculated by identifying the last year completed
successfully in a certain grade and if this year is the last or not. This will depend also on the status of individual if he/she finished
education or still enrolled.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 14
DATA AVAILABILITY Available
REFERENCE(S) UN/ MDGs/ UNESCO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural Regions
Governorates
120
YEAR 2010
LIMITATIONS Here there was a challenge in evaluating the education variables
because of non-applicability and limitations on some questions, away
to overcome this done by creating a variable in the data that is reflect the individual actual years of schooling compared to the optimal
years of schooling according to his/ her age.
8. INDICATORS Drop out from basic education
DEFINITION Individuals dropped out from school before completing basic
education
DOMAIN/ RIGHT Right to Education
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The number of individuals who dropped out from education before completing basic education divided by the total number of
individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 15
DATA AVAILABILITY Available
REFERENCE(S) UN/ MDGs/ UNESCO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS Here there was a challenge in evaluating the education variables
because of non-applicability and limitations on some questions, away to overcome this done by creating a variable in the data that is reflect
the individual actual years of schooling compared to the optimal
years of schooling according to his/ her age.
9. INDICATORS Education Achievements
DEFINITION Achievements levels in education by individuals enrolled.
DOMAIN/ RIGHT Right to Education
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
This indicator supposed to be calculated through achievements tests, but these tests are hardly applied. As a result this is calculated by
success in passing education levels.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 16
121
DATA AVAILABILITY Available
REFERENCE(S) UN/ UNESCO/
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural
Regions Governorates
YEAR 2010
LIMITATIONS application is hard in the field
10. INDICATORS Access to water with good quality
DEFINITION Households who have an access to water source without problems in the quality in water or/ and water pollution.
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 2
METHOD OF COMPUTATION
The number of individuals who do not have problems in water quality divided by total number of individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 17
DATA AVAILABILITY Available
REFERENCE(S) UN/ WHO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions Governorates
YEAR 2010
LIMITATIONS
11. INDICATORS Individuals who have problems in health service in the place of
residence
DEFINITION Individuals who have problems in health services in the place of residence when needed
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who have problems in accessing health
services in their area divided by total number of individuals
122
DATA SOURCES Egyptian Household Conditions Observatory survey - round 18
DATA AVAILABILITY Available
REFERENCE(S) UN
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS
12. INDICATORS Individuals who can found the essential Pharmaceuticals when
needed at a place near to their residency (Pharmacy, health
unit,….etc.)
DEFINITION Availability of essential Pharmaceuticals when needed at a place near
to residency (Pharmacy, health unit,….etc.)
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who have access to essential
Pharmaceuticals when needed at a place near to their residency
(Pharmacy, health unit,….etc.) divided by total number of individuals who needs essential Pharmaceuticals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 19
DATA AVAILABILITY Available
REFERENCE(S) UN/ WHO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural
Regions Governorates
YEAR 2010
LIMITATIONS
13. INDICATORS Individuals who can found the essential Pharmaceuticals when
needed in adequate price
DEFINITION availability of essential Pharmaceuticals when needed at a place near to residency (Pharmacy, health unit,….etc) with adequate price
123
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The number of individuals who have access to essential Pharmaceuticals when needed at adequate price divided by total
number of individuals who needs essential Pharmaceuticals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 20
DATA AVAILABILITY Available
REFERENCE(S) UN/ WHO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS
14. INDICATORS Individuals who have governmental health insurance
DEFINITION the coverage of governmental health insurance according to the programme of insurance for all (universal coverage)
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who have governmental health insurance
divided by total number of individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 21
DATA AVAILABILITY Available
REFERENCE(S) UN/ WHO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS Other types of insurance that is available.
15. INDICATORS Individuals with disability
124
DEFINITION The number of individuals who have any type of disability.
DOMAIN/ RIGHT Right to Health
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who have disability divided by total
number of individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 22
DATA AVAILABILITY Available
REFERENCE(S) UN
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Gender Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS Sensitivity of question.
16. INDICATORS Access to improved water source
DEFINITION The individuals who have access to improved water source in their
shelter (Water pipes into dwelling
Water pipes outside dwelling - Mineral water).
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The number of individuals who have access to improved water source divided by total number of individuals, the differentiation
made between different types when dealing with the index (Water
pipes into dwelling - Water pipes outside dwelling - Public tap - Well
with water pipe - Covered Well - From Trucks - Local car with a small tank - Mineral water - From neighbors - Public Tap from a
nearby village).
DATA SOURCES Egyptian Household Conditions Observatory survey - round 23
DATA AVAILABILITY Available
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions Governorates
125
YEAR 2010
LIMITATIONS
17. INDICATORS Access to improved sanitation facility
DEFINITION The individuals who have access to improved sanitation facility in
their shelter (Public Sanitation).
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who have access to improved sanitation
facility divided by total number of individuals, the differentiation made between different types when dealing with the index (Public
Sanitation - Septic / Tranch - Bayara - Pipe connected to bank
(Community sanitation) Pipe Connected to underground water (Eason) - No Sanitation).
DATA SOURCES Egyptian Household Conditions Observatory survey - round 24
DATA AVAILABILITY Available
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS
18. INDICATORS Individuals live in a housing unit with adequate floor material
DEFINITION The individuals who are living in adequate in their shelter (Parquet /
Wood colored - Ceramic / Marble -Cement Tiles
Carpeting -Guenalteix / Vinyl).
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The number of individuals who have access to adequate floor material divided by total number of individuals, the differentiation
made between different types when dealing with the index (Soil/
Sand - Non finished wood - Parquet / Wood colored - Ceramic /
Marble - Cement Tiles - Cement - carpeting - Guenalteix / Vinyl).
DATA SOURCES Egyptian Household Conditions Observatory survey - round 25
DATA AVAILABILITY Available
126
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS
19. INDICATORS Individuals who have separate place for cooking (kitchen)
DEFINITION Individuals who are living in a housing unit that includes a separate place for cooking.
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who are living in a housing unit that
includes a separate place for cooking divided by total number of
individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 26
DATA AVAILABILITY Available
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS
20. INDICATORS Individuals with sufficient living space
DEFINITION Average number of persons per room.
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 2
METHOD OF COMPUTATION
The number of individuals in a household divided by number of rooms in the housing unit.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 27
DATA AVAILABILITY Available
127
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Urban-Rural Regions
Governorates
YEAR 2010
LIMITATIONS
21. INDICATORS Ownership of main assets for adequate place (living conditions)
DEFINITION Individuals living in a household where the main assets for adequate place are available.
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 9
METHOD OF
COMPUTATION
The number of individuals who are living in a housing unit that
includes basic assets divided by total number of individuals,
differentiation made between different types when dealing with the
index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 28
DATA AVAILABILITY Available
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Urban-Rural
Regions
Governorates
YEAR 2010
LIMITATIONS definition of main assets
22. INDICATORS Access to safe fuel for cooking
DEFINITION Individuals living in a household where there is an access to safe fuel
to be used in cooking.
DOMAIN/ RIGHT Right to Adequate Housing
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who are living in a housing unit where
there is an access to safe fuel to be used in cooking divided by total
number of individuals, differentiation made between different types when dealing with the index
128
DATA SOURCES Egyptian Household Conditions Observatory survey - round 29
DATA AVAILABILITY Available
REFERENCE(S) OHCHR/ UN HABITAT
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
23. INDICATORS Individuals who are exposed to dangerous work
DEFINITION Individuals which their work is related with using sharp instruments or materials, flammable or has dangerous on them.
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals which their work is related with using
sharp instruments or materials, flammable or has dangerous on them
divided by total number of individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 30
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
24. INDICATORS Work Stability
DEFINITION individuals who are working in a stable job (Permanent)
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who are working in a stable job divided
by total number of individuals, differentiation made between different types when dealing with the index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 31
129
DATA AVAILABILITY Available
REFERENCE(S) ILO/ MDGs
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
25. INDICATORS Time spent to travel from home to work
DEFINITION Individuals who spend a long time to reach to their job.
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
The number of individuals who spend a long time to reach to their
job divided by total number of individuals, differentiation made
between different types when dealing with the index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 32
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
26. INDICATORS Weekly hours worked
DEFINITION individuals who are working more than legal hours per week
according to ILO definition
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
The number of individuals who are working more than legal hours per week according to ILO definition divided by total number of
individuals, differentiation made between different types when
dealing with the index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 33
130
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
27. INDICATORS Monthly earnings
DEFINITION Individuals' income per month.
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
differentiation made between different income levels using a scale
variable when dealing with the index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 34
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS Sensitivity of income to work type and capabilities
28. INDICATORS Individuals who are employed and have legal contract with their
organization
DEFINITION Individuals who are employed and have legal contract with their
organization that avail for them their rights to the institution.
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
Individuals who are employed and have legal contract with their organization that avail for them their rights to the institution divided
by total number of individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 35
DATA AVAILABILITY Available
131
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
29. INDICATORS Individuals employed in organizations that avail legal vacations
by type
DEFINITION Individuals employed in organizations that avail legal vacations according to ILO definition (sick leaves - unusual holidays - casual
leaves - maternity leave (for females) - care of a child leaves
(female)).
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 5
METHOD OF
COMPUTATION
Individuals employed in organizations that avail legal vacations
according to ILO definition (sick leaves - unusual holidays - casual leaves - maternity leave (for females) - care of a child leaves
(female)) divided by total number of individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 36
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
30. INDICATORS Individuals who have trade union membership
DEFINITION Individuals who have trade union membership that secure their rights
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
Individuals who have trade union membership that secure their rights
divided by total number of individuals
DATA SOURCES Egyptian Household Conditions Observatory survey - round 37
132
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
31. INDICATORS Individuals who are satisfied by their work
DEFINITION Individuals who are satisfied by their work in a certain organization.
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
differentiation made between different satisfaction levels using a
scale variable when dealing with the index
DATA SOURCES Egyptian Household Conditions Observatory survey - round 38
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
32. INDICATORS Individuals who have social insurance through work
DEFINITION individuals who are working in organizations that make social
insurance for employee
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
Individuals who are working in organizations that make social
insurance for employee divided by total number of individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 39
DATA AVAILABILITY Available
REFERENCE(S) ILO
133
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
33. INDICATORS Individuals who have health insurance through work
DEFINITION individuals who are working in organizations that make health insurance for employee
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF
COMPUTATION
Individuals who are working in organizations that make health
insurance for employee divided by total number of individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 40
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
34. INDICATORS Individuals working more than 50 hours per week and this affect
their health
DEFINITION Individuals working more than 50 hours per week and this affect their
health according to ILO definitions
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
Individuals working more than 50 hours per week and this affect their health according to ILO definitions divided by total number of
individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 41
DATA AVAILABILITY Available
REFERENCE(S) ILO
134
PERIODICITY Annual
GENDER AND
DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
35. INDICATORS Individuals working in organization that avail insurance against
work related danger
DEFINITION Individuals which their work is related with using sharp instruments
or materials, flammable or has dangerous on them but their
organization avail insurance against work related danger
DOMAIN/ RIGHT Right to Decent Work
NUMBER OF VARIABLES 1
METHOD OF COMPUTATION
Individuals which their work is related with using sharp instruments or materials, flammable or has dangerous on them but their
organization avail insurance against work related danger divided by
total number of individuals.
DATA SOURCES Egyptian Household Conditions Observatory survey - round 42
DATA AVAILABILITY Available
REFERENCE(S) ILO
PERIODICITY Annual
GENDER AND DISAGGREGATION ISSUES
Gender
YEAR 2010
LIMITATIONS
135
Annex2: Results of Neural Networks analysis
Multilayer Perceptron
Table B.1: Case Processing Summary for multilayer perceptron
N Percent
Sample Training 3,200 80.0% Testing 800 20.0%
Valid 4,000 100.0% Excluded 0 Total 4,000
Table B.2: Network Information for multilayer perceptron
Input Layer Factors 1 Urban - Rural
2 Gender
3 current marital status
Covariates 1 FOOD1
2 HEALTH1
3 HOUSING1
4 EDUCATION1
Number of Unitsa 15
Rescaling Method for Covariates None
Hidden Layer(s) Number of Hidden Layers 2
Number of Units in Hidden Layer 1a 8
Number of Units in Hidden Layer 2a 6
Activation Function Hyperbolic tangent
Output Layer Dependent Variables 1 WORK1
Number of Units 1
Rescaling Method for Scale Dependents None
Activation Function Identity
Error Function Sum of Squares
a. Excluding the bias unit
Model Summary
Training Sum of Squares Error 60.024
Relative Error .558
Stopping Rule Used 1 consecutive step(s) with no decrease in errora
Training Time 0:00:00.92
Testing Sum of Squares Error 13.839
Relative Error .511
Dependent Variable: WORK1
a. Error computations are based on the testing sample.
136
Figure B.1: Multilayer perceptron Network structure
137
Figure B.2: Multilayer perceptron predicted values versus actual values
For scale-dependent variables, the predicted-by-observed chart displays a scatterplot of
predicted values on the y axis by observed values on the x axis for the combined training
and testing samples. Ideally, values should lie roughly along a 45-degree line starting at the
origin. The points in Figure B.2 form vertical lines at the right side as a group rather than the
points grouped at the left of decent work.
Figure B.3: Multilayer perceptron residuals versus predicted values
Figure B.3 displays a residual-by-predicted-value chart for the scale-dependent variable.
There should be no visible patterns between residuals and predicted values. This chart
shows a clear pattern between the residuals and predicted values with two major clusters.
138
Table B.3: Multilayer Perceptron Independent Variable Importance
Importance Normalized Importance
Urban - Rural .026 8.4%
Gender .080 26.2%
current marital status .226 74.4%
FOOD .011 3.7%
HEALTH .304 100.0%
HOUSING .106 34.9%
EDUCATION .247 81.3%
Figure B.4: Multilayer perceptron Independent Variable Importance
139
Radial Basis Function
Table B.4: Case Processing Summary for radial basis function
N Percent
Sample Training 3,187
79.7%
Testing 813
20.3%
Valid 4,000
100.0%
Excluded 0
Total 4,000
Table B.5: Network Information for radial basis function
Input Layer Factors 1 URBAN_COUNTRY
Urban - Rural
2 Q108 Gender
3 Q110 current marital
status Covariates 1 FOOD1
2 HEALTH1
3 HOUSING1 4 EDUCATION1
Number of Units 15
Rescaling Method for Covariates None Hidden Layer Number of Units 10
a
Activation Function Softmax
Output Layer Dependent Variables 1 WORK1
Number of Units 1
Rescaling Method for Scale Dependents None
Activation Function Identity Error Function Sum of Squares
a. Determined by the testing data criterion: The "best" number of hidden units is the one that yields
the smallest error in the testing data.
140
Figure B.5: Radial basis function network structure
141
Table B.6: Model Summary for radial basis function
Training Sum of Squares Error 87.561
Relative Error .808 Training Time 0:00:01.49
Testing Sum of Squares Error 21.637a Relative Error .819
Dependent Variable: WORK1
a. The number of hidden units is determined by the testing data criterion: The "best" number of hidden units is the
one that yields the smallest error in the testing data.
Figure B.6: Radial basis function predicted values versus actual values
Figure B.7: Radial basis function residuals versus predicted values
142
Table B.7: Independent Variable Importance for radial basis function
Importance Normalized Importance URBAN_COUNTRY Urban - Rural .117 42.1% Q108 Gender .279 100.0% Q110 current marital status .239 85.8% FOOD1 .020 7.1% HEALTH1 .114 40.9% HOUSING1 .103 37.1% EDUCATION1 .128 46.1%
Figure B.8: Radial basis function Independent Variable Importance
143
Annex3: Results of final multiple imputations over decent work variables
Table C.1: Imputation Specifications
Imputation Specifications
Imputation Method Fully Conditional Specification
Number of Imputations 100
Model for Scale Variables Linear Regression
Interactions Included in Models (none)
Maximum Percentage of Missing Values 100.0%
Maximum Number of Parameters in
Imputation Model
100
Analysis Weight Variable Weight
Table C.2: Imputation Constraints
Imputation Constraints
Role in Imputation Imputed Values
Dependent Predictor Minimum Maximum
Urban - Rural No Yes
Gender No Yes
current marital status No Yes
Right to food No Yes
Right to health No Yes
Right to adequate housing No Yes
Right to education No Yes
Right to decent work Yes Yes (none) (none)
144
Table C.3: Imputation Results
Imputation Method Fully Conditional Specification
Fully Conditional Specification Method Iterations 1000
Dependent Variables Imputed WORK1
Not Imputed(Too Many
Missing Values)
Not Imputed(No Missing Values)
URBAN_COUNTRY,Q108,FOOD1,HEALTH1,HOUSING1,EDUCATI
ON1
Imputation Sequence URBAN_Rural, Gender, Marital status , FOOD1, HEALTH1,
HOUSING1, EDUCATION1,
WORK1
Table C.4: Imputation Models
Model Missing
Values
Imputed
Values Type Effects
Right to
decent
work
Linear
Regression
URBAN_Rural, Gender, Marital
status ,FOOD1,HEALTH1,
HOUSING1,
EDUCATION1,WORK1
40039 4003900
a. This variable with role as predictor only has missing values which were imputed for
internal purposes.
جامعة القاهرة كلية االقتصاد والعلوم السياسية
قسم اإلحصاء
االقتصادية واالجتماعية في مصربناء مؤشر قوي لمدى وفاء الحقوق
إعداد إيمان رفعت محمود أحمد
إشراف د/ دينا مجدي أرمانيوس
األستاذ المساعد بقسم اإلحصاء كلية االقتصاد والعلوم السياسية
جامعة القاهرة
/ علي هاديأ.د أستاذ الجامعة المميز
رئيس قسم الرياضيات والعلوم اإلكتوارية الجامعة األمريكية بالقاهرة
اإلحصاء في الماجستير درجة علي للحصول متمم كمتطلب السياسية والعلوم االقتصاد بكلية اإلحصاء قسم إلي مقدمة الرسالة
( 3102القاهرة )
اإلجازة
ابتقاا حججالماجيستتريف تتح اإل تتا أجاات لجنج اامجانش تهذاامجراالةجانحصااتنمجندرجاا ج داا ج حجاامج ججيتتج جتتج
ج،جبع جاصت فتءججش عجانطدبتل.ج71/71/3772بتتح خج
ج
اللجنة
الروقيع الجفجة العلمية االسم
أ.د. علي هادي - 1
أصتتلجانجتشعمجانشش ج حئ سجهصمج
انجتشعمجج- انح تض تلج انعد مجاإلكت اح م
جاألشح ك مجبتنقترحة
ج.........................................
أ.د. محمد علي إسماعيل - 2كد مجاالهتجت ججج-جأصتتلجبقصمجاإلرجتء
ججتشعمجانقترحةج- انعد مجانص تص مجج.........................................
أ.د. إبراهيم حسن إبراهيم - 3كد مجانتجتحةجج-اإلرجتءجانتطب ق جأصتتلج
ججتشعمجرد انج- إ احةجاأل شت جج.........................................
دينا مجدي أرمانيوس .د - 4كد مجج-جأصتتلجشصت جبقصمجاإلرجتء
جتشعمجج-جاالهتجت ج انعد مجانص تص مج
جانقترحة
ج.........................................
ج
المستخلصتهدف هذه الدراسة الي بناء مؤشر جديد لمصر يقيس مدي استيفاء الحقوق االقتصادية واالجتماعية، وهو مؤشر يقيس
استيفاء حقوق انسانية باالعتماد علي مسح قومي يتضمن بيانات عن الحالة االقتصادية واالجتماعية لالفراد. مديا الي مساعدة متخذي القرار في صياغة السياسات االقتصادية واالجتماعية من خالل تسليط ويهدف هذا المؤشر ايض
المناطق الجغرافية المختلفة )علي مستوي المحافظات، الضوء علي مدي استيفاء الحقوق االقتصادية واالجتماعية في ية للسكان كالنوع والحالة االجتماعية والسن.فالحضر والريف( وبعض الخصائص الديموجرا
تتعرض الدراسة خالل عملية انشاء المؤشر المركب الي القاء الضوء علي بعض القضايا االحصائية محل الخالف حول في النهاية الي مؤشر يتسم بالقوة والثبات. ويتم تسليط الضوء علي ستة قضايا رئيسية هي المؤشرات المركبة حتي تصل
عملية اختيار المؤشرات المكونة للمؤشر المركب، والتعامل مع القيم المفقودة، واكتشاف القيم الشاذة وطرق التعامل ا طرق ، وكيفية جمع المؤشرات، واخير معها، ومشكلة توحيد وحدات القياس، وتحديد االوزان الترجيحية المختلفة
حساب هامش الخطأ في التقديرات المختلفة.
ته مركز المعلومات ودعم ا" والذي يقوم بجمع بيان0202تم استخدام بيانات مسح "مرصد احوال االسرة المصرية عام بإجمالي حجم والريف اتخاذ القرار، وهو مسح قومي ممثل علي المستوي القومي وعلي مستوي المحافظات والحضر
ومن الجدير بالذكر ان هذا المسح يتم جمعه بصفة دورية وهي ميزة تتيح امكانية حساب المؤشر أسرة. 02002عينة في المستقبل وتتبع تطوره.
الل الدراسة: علي مقياس من صفر الي مائة كانت متوسط قيمة المؤشر خمن اهم النتائج التي تم التوصل اليها من واعلي قيمة 3070واقل قيمة وصل اليها المؤشر .607ي استيفاء الحقوق االقتصادية واالجتماعية ديقيس مالذي . وكانت هناك مستويات ملحوظة لعدم المساواة بين المحافظات المختلفة وعلي مستوي الحضر والريف 9476
وخاصة فيما يتعلق بالحق في التعليم والحق في المسكن المالئم.
اص خوهو االقل بين االبعاد االخري في حين بلغت قيمة البعد ال 4076قيمة البعد الخاص بالحق في العمل بلغت . وبينما حصلت محافظات الجيزة واالسكندرية والقاهرة .927بالحق في الغذاء اكبر قيمة بين االبعاد االخري بدرجة
ة، كانت كال من محافظات كفر الشيخ وسوهاج علي اعلي مستويات في استيفاء الحقوق االقتصادية واالجتماعي واسيوط هي االقل.
كان مدي استيفاء الحقوق االقتصادية واالجتماعية االعلي بين الشباب وصغار البالغين بالنسبة للفئات العمرية المختلفة، في حين كانت االقل بين االطفال والبالغين.
الحقوق –هامش الخطأ –القيم الشاذة -القيم المفقودة -شرات اختيار المؤ -الكلمات الدالة: المؤشرات المركبة تعريف المؤشرات. –وحدات القيس –التجميع –الترجيح –االقتصادية واالجتماعية
إشراف د/ دينا مجدي أرمانيوس
األستاذ المساعد بقسم اإلحصاء كلية االقتصاد والعلوم السياسية
جامعة القاهرة
/ علي هاديأ.د أستاذ الجامعة المميز
رئيس قسم الرياضيات والعلوم اإلكتوارية الجامعة األمريكية بالقاهرة
إيمان رفعت محمود أحمد اإلسم: مصرية الجنسية:
، القليوبية، مصر. 20/0996/.0: محل الميالدو تاريخ ا التقدير: ماجيستير الدرجة: جيد جد
إحصاء التخصص: المشرفين:
د/ دينا مجدي أرمانيوس األستاذ المساعد بقسم اإلحصاء كلية االقتصاد والعلوم السياسية
جامعة القاهرة
/ علي هاديأ.د أستاذ الجامعة المميز
رئيس قسم الرياضيات والعلوم اإلكتوارية الجامعة األمريكية بالقاهرة
الحقوق االقتصادية واالجتماعية في مصر.بناء مؤشر قوي لمدى وفاء عنوان الرسالة:
ملخص الرسالة:
تعرف المؤشرات المركبة بانها طريقة لجمع عدد من العوامل او المؤشرات بطريقة قياسية علمية لإلمداد بمقياس احصائي يقيس اداء ظاهرة معينة ويمكن تتبعه خالل الزمن، ومن الضروري في هذا المقياس ان يكون واضحا وسهل
ا.ف ويعطي معني دقيق حول الظاهرة وله القدرة علي توجيه السياسات وان يتسم بالدقة احصائي الوص
تهدف هذه الدراسة الي بناء و تؤخذ في االعتبار. نعملية بناء المؤشر المركب تتضمن العديد من القضايا التي يجب اوهو مؤشر يقيس مدي استيفاء حقوق انسانية مؤشر جديد لمصر يقيس مدي استيفاء الحقوق االقتصادية واالجتماعية،
باالعتماد علي مسح قومي يتضمن بيانات عن الحالة االقتصادية واالجتماعية لالفراد، ومن اجل تحقيق هذا الهدف العام فانه يجب تحقيق االهداف الفرعية التالية:
جتماعية باالعتماد علي إطار إختيار األبعاد والمؤشرات التي تقيس مدي استيفاء الحقوق االقتصادية واال .0 .نظري دقيق
ل الخالف خالل عملية انشاء المؤشر.حتسليط الضوء علي بعض القضايا االحصائية م .0 تحديد درجة دقة وثبات المؤشر من خالل التحقق من كل خطوة تم اتباعها. .3
ته مركز المعلومات ودعم ان" والذي يقوم بجمع بيا0202تم استخدام بيانات مسح "مرصد احوال االسرة المصرية عام اتخاذ القرار، وهو مسح قومي ممثل علي المستوي القومي وعلي مستوي المحافظات والحضر والريف وتم توزيع العينة
وحدة معاينة اولية. 036قطعه مساحية موزعة في 400علي عدد
ؤشر المركب، والتعامل مع القيم تم تسليط الضوء علي ستة قضايا رئيسية هي عملية اختيار المؤشرات المكونة للمالمفقودة، واكتشاف القيم الشاذة وطرق التعامل معها، ومشكلة توحيد وحدات القياس، وتحديد االوزان الترجيحية
ا طرق حساب هامش الخطأ في التقديرات المختلفة.المختلفة، وكيفية جمع المؤشرات، واخير
وتنقسم الرسالة الي سبعة فصول كالتالي:
ض خلفية عامة للمؤشرات المركبة، مشكلة الدراسة، هدف الدراسة ومراجعة ر ويع ل االول "مقدمة الدراسة":الفص ا تنظيم الدراسة.االدبيات واخير
ويتناول هذا الفصل خطوات انشاء المؤشرات المركبة الفصل الثاني "التعريف بالمؤشرات المركبة والتحديات": علي التحديات التي تهتم بها الدراسة. زختلفة مع التركيميات الدوالتح
ويعرض االطار النظري للمؤشر مع قائمة باالبعاد الفصل الثالث "مؤشر وفاء الحقوق االقتصادية واالجتماعية": والمؤشرات المستخدمة في تكوين المؤشر المركب ومصدر البيانات.
ويسلط هذا الفصل الضوء علي مشكالت مشكالت المؤشرات المركبة": عالفصل الرابع "منهجية التعامل مالتعامل مع القيم المفقودة، واكتشاف القيم الشاذة وطرق التعامل معها، قياس الخاصة بحساب المؤشر وخاصة ال
ومشكلة توحيد وحدات القياس، وتحديد االوزان الترجيحية المختلفة، وكيفية جمع المؤشرات، واخيرا طرق حساب المختلفة. هامش الخطأ في التقديرات
ا ذويعرض هالفصل الخامس "نتائج قياس مؤشر مدي وفاء الحقوق االقتصادية واالجتماعية في مصر": الفصل نتائج حساب مؤشر مدي وفاء الحقوق االقتصادية واالجتماعية في مصر.
راسة.ويلخص هذا الفصل النتائج والتوصيات التي توصلت اليها الد"الخاتمة والتوصيات": دسالفصل السا