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Women’s Work and Economic development. Author(s): Kristin Mammen and Christina Paxson Source: The Journal of Economic Perspectives, Vol. 14, No. 4, (Autumn, 2000), pp. 141-164 Published by: American Economic Association Stable URL: http://www.jstor.org/stable/2647079

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women s work and economic development

Women’s Work and Economic development

Author(s): Kristin Mammen and Christina Paxson

Source: The Journal of Economic Perspectives, Vol. 14, No. 4, (Autumn, 2000), pp. 141-164

Published by: American Economic Association

Stable URL: http://www.jstor.org/stable/2647079

Presented by Moshe Shaul (S.ID 029429438) 16/07/20008

essay objective
Essay Objective
  • How the role of women in the labor force varies with the level of economic development.

Note: Although there is a diversity across countries at similar income levels in how much women work and the types of jobs they perform, several clear patterns emerge from the data.

Moshe Shaul Student ID-029429438

content
Content
  • First part: presents the theoretical Model.
  • Second part: Presents evidence from cross country data.
  • Third part: verifies the consistency between the cross-country data and patterns within India and Thailand
  • Forth part: discusses the recent literature on resource allocation to women within households in poor counties.

Moshe Shaul Student ID-029429438

women s work and economic development in theory
Women’s Work and Economic Development in Theory

Women’s labor supply decision

Earned

Unearned

Cost of her work = Education level, Experience and Skill level

Earning of her husband and transfer income of her family

Positive affect

Negative affect

Moshe Shaul Student ID-029429438

how should economic development affect the labor force of women
How should Economic Development Affect the Labor Force of Women?

Man’s opportunities improve relative to women’s, It plays an important role how women’s labor force participation changes with development.

Moshe Shaul Student ID-029429438

competitive labor market
Competitive Labor Market
  • Labor markets do not function competitively in developing counties, especially for women. Following are some of the factors restricting women from working outside her home:
      • Laws
      • Custom or social norms (manual jobs)
      • Not compatible with child rearing creating fixed cost of working off-farm.

Moshe Shaul Student ID-029429438

agriculture to urbanization industrialization process
Agriculture to Urbanization (industrialization) Process
  • The process of urbanization and new blue-collar jobs decrease the number of farms and thus decrease the opportunities for women in absolute terms (Goldin 1995).
  • Until women can acquire the required schooling and transferable skills to find a suitable employment in firms of modern economy the opertunities relative to men may decline (Schlutz 1988)

Moshe Shaul Student ID-029429438

women s labor force in developed countries
Women’s Labor Force in Developed Countries
  • What explain the increase of women’s labor force in developed countries?
    • Growth of white-collar jobs
    • Gains of women education

Moshe Shaul Student ID-029429438

u shaped relationship
U-Shaped Relationship

Women's labor force participation

Economic Development

Urbanization and social barriers against women

Women education level raise and start to work in white-collar jobs

Poor Counties

Women works in farm

Moshe Shaul Student ID-029429438

social norms and white blue collar jobs
Social Norms and White/Blue Collar Jobs
  • Why should social norms dictate that white-collar jobs are “acceptable” for married women whereas blue-collar jobs are not?
    • Women dislike factory work and marriage can be used as escape opportunity.
    • In some countries it is typical for manufacturing firms to terminate women’s jobs upon marriage.
    • Societies stigmatize the husbands of women who do blue-collar work (e.g. lazy, avoid his obligation to the family etc), Note: this reason is not relevant to white-collar jobs (since such women typically have more educated husbands which have relative high income)
    • Factory does not pay wives enough to compensate for the fixed costs of working away from home.

Moshe Shaul Student ID-029429438

content1
Content
  • First part: presents the theoretical Model.
  • Second part: Presents evidence from cross country data.
  • Third part: verifies the consistency between the cross-country data and patterns within India and Thailand
  • Forth part: discusses the recent literature on resource allocation to women within households in poor counties.

Moshe Shaul Student ID-029429438

database description and objectives
Database Description and Objectives
  • Geographic location: 90 counties
  • Dates: from the 1970s and 1980s
  • Objectives: trace out the relationships between:
    • Economic development
    • And
    • Several indicators of women’s status:
      • Investment in education
      • Labor force participation (self employment, employer or employee)
      • Participation in wage work (employee)
      • Fertility

Moshe Shaul Student ID-029429438

research s evidence education analysis
Research's Evidence-Education Analysis
  • Top curve description:
  • Women's education levels and economic development
    • Women's education levels increase with economic development both in absolute and relative terms to those of men.
    • Note: Education is a normal consumer good, (more of which is demanded at higher income levels).
  • Women's education levels have risen over time especially for lower and middle income countries.

Moshe Shaul Student ID-029429438

research s evidence education analysis cont
Research's Evidence-Education Analysis, cont
  • Bottom curve description:
  • Show the results for female - male education gap. (Average years of adult female minus Average years of adult male schooling at age of 25).
    • The largest gap (about 2 years) at a per capita GDP of about 1000$, than shrinks as income rises.
  • Since education level for both men and women rise with income, the gap of education narrows throughout the income range.

Moshe Shaul Student ID-029429438

research s evidence women s labor force participation vs income level relation analysis
Research's Evidence- Women’s labor force participation vs. Income level relation Analysis

Country

AfricanSouth Asian

South America Central America

South European  East Asian  North European  North American

Moshe Shaul Student ID-029429438

research s evidence women s labor force participation vs income level relation analysis1
Research's Evidence- Women’s labor force participation vs. Income level relation Analysis
  • Women’s labor force participation vs. Income level relation has “U-shape” (Goldin 1995)
    • High participation in very low and very high incomes.
  • Labor force participation is measured for women aged 45-59, who are past the child rearing stage. (however, similar results would be obtained if younger women were included.)

Moshe Shaul Student ID-029429438

research s evidence women s wages vs fertility rate relation analysis
Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis
  • Income ,Family enterprise  Paid jobs, Fertility

Moshe Shaul Student ID-029429438

research s evidence women s wages vs fertility rate relation analysis cont
Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis. Cont’

If we assume that children are normal goods then

wage may have the following affect:

Wage  Fertility

But if we consider the opportunity cost of women’s time raises and it has largest effect then wage may have the following affect:

Wage  Fertility

Moshe Shaul Student ID-029429438

research s evidence women s wages vs fertility rate relation analysis cont1
Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis. Cont’
  • Other factor may drive fertility decline :
    • Reduction in child mortality that come with development may allow for lower fertility level if the “demand” for complete family size which increases or remains the same.
    • Development of public or private pension systems decries the expectation from children to provide income for their parents especially in their old age.

Moshe Shaul Student ID-029429438

content2
Content
  • First part: presents the theoretical Model.
  • Second part: Presents evidence from cross country data.
  • Third part: verifies the consistency between the cross-country data and patterns within India and Thailand.
  • Forth part: discusses the recent literature on resource allocation to women within households in poor counties.

Moshe Shaul Student ID-029429438

two examples thailand and india
Two examples: Thailand and India
  • These countries were not chosen to be representative of the entire developing world, indeed, no two counties can be.

Moshe Shaul Student ID-029429438

thailand across cohort patterns
Thailand Across Cohort Patterns

Thai Socioeconomic surveys allow us to track birth cohorts over time, we can examine how women's labor force participation and work activity have changed during a period of rapid economic growth.

Notes:

Below data are not applicable to India because of lack of available data but we expect to have the same behavior.

The relatively flat cohort lines indicate that this fraction varies little over time within cohorts.

Moshe Shaul Student ID-029429438

male female gap in education vs expenditure
Male-Female Gap in Education VS. Expenditure

Educational achievement is lower for women then man at both graphs.

In both countries, the male-female gap in education rises with expenditure (e.g. spending) in rural areas, but remain steady with expenditure in urban areas.

Moshe Shaul Student ID-029429438

women labor force vs expenditure
Women Labor force VS. Expenditure

Labor force definition: applicable to women as employee, employer or self employment (e.g. free family labor)

Women labor force is lower in both countries for urban areas (due to fewer work opportunities).

In urban areas women labor force follows the “U shape”

Moshe Shaul Student ID-029429438

women working for wages vs expenditure

India’s women must work due to lack of lands

Thai Free family labor

Women Working for Wages VS. Expenditure

The probability of working as an employee increases steadily with living standard.

In India there is relatively large number of landless or near landless poor household, the member of which often work as casual on lager farms. Thus work as “free family labor” is not an option for many women in the poorest families.

Moshe Shaul Student ID-029429438

education affect regression results
Education affect (regression results)
  • Secondary school-
    • In India women with secondary school has minor affect on the probability to be part of labor force.
    • In Thai secondary school has no affect on the probability to be part of labor force.
  • Post-secondary school-

In both countries Post-secondary school has large affect on the probability to be part of labor force.

  • Rural women-

In both countries are more likely to be part of labor force

  • Spousal education-

Has negative affect on participation in labor force in India but not in Thai.

  • Special tribe-

In India being a member of a specific tribe may increase the affect on participation in labor force.

  • Following are more affects of education:
    • More likely to work in non-manual jobs (e.g. white-collar)
    • More likely to work as employee rather than self-employed

Moshe Shaul Student ID-029429438

content3
Content
  • First part: presents the theoretical Model.
  • Second part: Presents evidence from cross country data.
  • Third part: verifies the consistency between the cross-country data and patterns within India and Thailand
  • Forth part: discusses the recent literature on resource allocation to women within households in poor counties.

Moshe Shaul Student ID-029429438

gender gaps in developed countries
Gender Gaps in developed Countries
  • Girls in poor countries receive less education on average than boys, that gaps decline as living standards raise.
  • Women’s life expectancy rise with income and have improved over time given per capita GDP (i.e. development).
  • Women’s life expectancy improves relatively to male as income raise.

Moshe Shaul Student ID-029429438

gender gaps in developed countries cont
Gender Gaps in developed Countries. cont’
  • Women and girls in developed countries receive fewer resources within the household.
  • More labor market opportunities, higher wages may increase women’s well being both through direct affect and also by strengthening their bargaining position within households.
  • Difference in men’s and women’s well being and achievement as adult may be rooted in difference in investment they received as boys and girls.
  • One possible reason why parents invest less in girl is the higher future returns to parents.

Moshe Shaul Student ID-029429438

conclusion
Conclusion
  • Mortality rate and education levels indicates that women’s well being improves on average with development, both in absolute term and relative to men.

Moshe Shaul Student ID-029429438