00:00

Econometric Modeling of Household Poverty in Egypt: Policy Recommendations and Simulations

Examining the determinants of poverty in Egypt using data from 2011, 2013, and 2015, this study focuses on household demographics, education, employment, and regional factors. By employing econometric models and policy simulations, the objective is to identify effective strategies to alleviate poverty and improve welfare in Egypt.

manivesa
Download Presentation

Econometric Modeling of Household Poverty in Egypt: Policy Recommendations and Simulations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Household Poverty In Egypt Econometric Modeling and Policy Simulations Dr. Shereen Nosier Mohamed Mahrous, R.A Reham Salah, R.A SIMAR, Bibliotheca Alexandrina 1

  2. Objective Recognizing the roots of poverty, its elements and determinants in Egypt to come up with policy recommendations that help eradicate poverty and ameliorate welfare using the most recent HIECS for the years 2011, 2013 and 2015. 2 2

  3. Methodology Family size Poverty Determinants HH Gender Data Policy Recommendations Household’s demographics 2011 2013 2015 HH Age HH Marital status ERF HIECS Fixed Effects Regression Poverty Profile HH Education educational level Poverty Simulation Logistic Regression CAPMAS Poverty Lines Health Medical expenditures Number of Earners HH L.E 3,076 L.E 3,920 L.E 5,787 2011 2013 2015 Employment Employment Sector HH Household Head (HH) Employment Industry 3 3

  4. Poverty Measures 4 4

  5. Headcount Rate P0 % 40 35 30 25 20 15 10 5 0 National Rural Urban 2011 2013 2015 Poverty Gap Index P1 Severity of Poverty P2 % % 6 1.8 1.6 5 1.4 4 1.2 1.0 3 0.8 2 0.6 0.4 1 0.2 0 0.0 National Rural Urban National Rural 2013 Urban 2011 2015 2011 2013 2015 5 5

  6. Regional Poverty Measure % Headcount Rate P0 50 45 40 35 30 25 20 15 10 5 0 Urban Lower Urban Lower Rural Upper Urban Upper Rural Border Urban Border Rural 2011 2013 2015 6 6

  7. Governorates Poverty: 2015 Headcount Rate P0 % 55 50 45 40 35 30 25 20 15 10 5 0 7 7

  8. Characteristics of Poor Households o Demographic Factors • Household Size • Age of household head • Gender of household head • Marital Status of household head o Education Status • Educational level of the head of household o Employment Status • Industry classification for the main job of the head • Sector of employment of household head 8 8

  9. Demographic Factors P0 % P0% 80 9.0 Urban 70 14.8 60 50 19.4 Rural 30.2 40 30 15.2 National 20 23.6 10 0 5 10 15 20 25 30 35 0 1 2 3 4 5 6 7 8 >8 Family Size Female Male HH Gender National Rural Urban Po% 35 30 25 20 15 10 5 0 Married Mono. Divorced Widow Never Married HH Marital Status National Rural Urban 9 9

  10. Demographic Factors Percentage of poor households hikes as age of HH increases up to the age of 50 and then decreases. In 2015, almost 31.7% and 29.1% from households headed by individuals in the two age groups (30–40 and 40–50) are found to be poor. Which is the highest poverty incidence among other households. 10 10

  11. Employment Status:2015 Industry and Sector of Employment of HH P0% Headcount Rate 55 50 45 40 35 Industry of Employment of HH 30 25 20 15 10 5 0 Agriculture Commerce Construction Manufacture Public Admin. Transportation Services Others Rural Urban  All poverty measures exhibit low levels for households headed by individuals who work in the public sector than other sectors, followed by government sector. 11 11

  12. Education Status:2015 Headcount Ratio of Households Based on Head’s Educational Level P0% 35 30 25 20 15 10 5 0 None Primary Secondary Post Secondary University Post Graduate National Rural Urban 12 12

  13. Models’ Specification 13 13

  14. Fixed Effects Regression ?? ??= ?1??????+ ?2????????+ ?3????+ ?4??????+ ?5????+ ?6??????? + ?7??????+ ?8????????+ ?9???????????+ ?10???????+ ?11??????? + ?12?????+ ?13?????????????+ ?14?????????+ ??+ ?? 14 14

  15. Logistic Regression ??= ?0+ ?1??????+ ?2????????+ ?3????+ ?4??????+ ?5????+ ?6??????? + ?7??????+ ?8????????+ ?9???????????+ ?10???????+ ?11??????? + ?12?????+ ?13?????????????+ ?14?????????+ ?? 15 15

  16. Regression Models’ Variables Variable Name Type Specification Dependent Variables Consumption expenditures Numerical Natural log of household’s total consumption expenditures in EGP Poverty status Dummy Takes the value of 1 if the household is poor and 0 if the household is non poor Explanatory Variables Indicates the region of residency of household:0Rural region, 1 Urban region Residency Dummy Household size Numerical Number of persons\ household Number of Earners Numerical Number of earners\ household Age of HH Numerical Age of HH Gender of HH Dummy 0Female, 1Male Marital Status of HH Categorical 0 Never married, 1Married, 2Divorced, 3Widowed Employment status of HH Categorical 0Unemployed, 1Employed, 2Pensioner Employment status of first spouse of HH Categorical 0Unemployed, 1Employed, 2Pensioner Sector of employment of HH Categorical 0Other Sectors, 1Public Sector, 2Private Sector 0Others, 1Agriculture, 2 Commerce, 3Construction and Electricity, 4Manufacturing, 5Public Administration, 6Transportation, 7Services Industry of employment of HH Categorical 0Illiterate, 1Primary, 2Preparatory, 3Secondary and Post-secondary, 4Professional 5University and above Educational level of HH Categorical Source of income of HH Categorical 0Other, 1Salaries and wages, 2Remittances, 3 Household business Health expenditures Medical expenditures\ person in EGP per year. Continuous The ratio of number of persons under the age of 15 and over the age of 65 to the number of persons between, ages 15 – 65. Dependency ratio Continuous 16 16

  17. Models’ Results 17 17

  18. Fixed Effects Regression Results: 2015 In the demographic characteristics category, consumption of a household in rural areas was lower than that in urban areas by 8%. An increase in age of HH by one year causes an increase in consumption by 0.4%. Speaking of the gender of the head, households headed by females had a 3.5% higher consumption\ person than those headed by males. Concerning the marital status of the HH, married heads had higher consumption levels by 17.6%, 16.6% and 13.4% over single, divorced and widowed heads respectively. 18 18

  19. Fixed Effects Regression Results: 2015  For the industry of employment , agriculture workers-headed households tended to have 4.7% lower consumption than service workers-headed households do.  An increase in dependency ratio by 1% caused a decrease in consumption expenditure by 5%.  Regarding the source of income, families who had heads with wages and salaries had a 3.1% higher consumption compared to families with heads who depended on remittances.  Moreover, when medical expenditures increased by 1,000 EGPs, consumption\ person tended to increase by 12%.  Consumption of a household with a head who had a university degree or above was 54%, 43%, 34%, 17%, and 27% higher than that of a household with a head with no education, or with primary, preparatory, secondary and professional levels respectively. 19 19

  20. Logistic Regression Results: 2015 In the demographic characteristics category, rural area residents are more than 2 times likely to fall in poverty than their urban counterparts. Additionally, an extra family member increased the likelihood of being poor by 87%. Furthermore, with each additional year of HH age, the probability that the household fall in poverty decreases by 2%. Speaking of the gender of the head, male-headed households are 70% more likely to suffer from poverty.  Concerning the marital status of the HH, eventuality of being poor for households headed by divorced persons was almost twice higher than that of households headed by married individuals. 20 20

  21. Logistic Regression Results: 2015  For the industry of employment, the likelihood of being poor for households headed by individuals working in agriculture is 48% higher than for those headed by persons working in the services industry.  If the dependency ratio were increased by 1%, the odds ratio would be increased by 52%.  As far as medical expenditures are concerned, a one-pound increase in the medical expenses\ person in a household decreases the probability of being poor by about 0.05%.  Speaking of educational attainment of the head, the estimated coefficient for illiterate heads (2.51), suggests a higher chance of being poor for households with illiterate heads than those headed by university degree holders.  Additionally, the odds for households with primary educated heads are 55% lower than the odds for their illiterate counterparts. 21 21

  22. Results’ Conclusion Households Less likely to be poor Households More likely to be poor - - - - - - - - - Urban residents Small families Headed by females Headed by married heads Headed by employees Service sector workers Low dependency ratio High number of earners University and above educated heads - - - - - - - - - Rural residents Large families Headed by males Headed by divorced heads Headed by pensioners Agriculture sector workers High dependency ratio Low number of earners Illiterate heads 22 22

  23. Poverty Simulation 23 23

  24. Simulation Goals Figure out the influence of changes in the levels of determinants of poverty on probability of being poor Show the possible consequences and effects of potential poverty lessening policies and plans 24 24

  25. Poverty Simulation Results The results illustrate how the probability of falling in poverty differs according to: Family size Family income source HH’s gender HH’s working status HH’s marital status HH’s working industry 25 25

  26. 2015  An extra family member increased the probability of being poor at all levels of family size, however, an extra person had a higher influence in larger households.  Reducing family size by half results in an approximately 45% decrease in the probability of being poor on average.  Females at rural areas are more probable to have poor families compared to urban areas.  Households headed by divorced individuals suffer the most from poverty incidence.  Households headed by agriculture sector workers were the highest facing the risk of being poor, with 31% and 22% probability in rural and urban areas respectively.  On average, a head working in any industry in a rural area has a 7.8% more probability of being poor than his or her urban counterpart.  Illiterate heads and heads with primary and preparatory educational levels are the most vulnerable to poverty.  The enhancement of educational attainment of household head from being an illiterate to having primary degree will reduce the likelihood of falling into poverty by 9.7% on average. 26 26

  27. Policy Recommendations 27 27

  28. Birth control and family planning in urban and rural areas are of a high importance to reducing and slowing population growth rate. This necessitates the need for some policies to encourage lessening number of births, and to promote family planning programs. Women-empowerment policies as well as cash transfers directed to women who care for children in rural areas is essential. Households headed by divorced individuals shall be better off with policies that provide their heads with employment opportunities and access to labor force. Serious policies targeting pensioners are needed as they are the most susceptible group to poverty. 28 28

  29. Development actions in agriculture sector are of paramount importance to help pull many families out of poverty. This can be done through promoting investment towards agro-business. Targeting rural areas’ industries for economic development is fundamental. It is recommended to activate the rule making early stages of education mandatory. Public education improvement and reduction in the disparities in access and quality of basic education among different regions is substantial government reform. Agricultural education should also be reformed to better supply qualified labor helping redress agriculture sector 29 29

  30. Thank You 30 30

More Related