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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,

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Presentation on theme: "Women’s Work and Economic development Author(s): Kristin Mammen and Christina Paxson Source: The Journal of Economic Perspectives, Vol. 14, No. 4, (Autumn,"— Presentation transcript:

1 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 Published by: American Economic Association Stable URL: Presented by Moshe Shaul (S.ID ) 16/07/20008

2 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. 2Moshe Shaul Student ID

3 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. 3Moshe Shaul Student ID

4 Women’s Work and Economic Development in Theory Moshe Shaul Student ID Women’s labor supply decision EarnedUnearned Cost of her work = Education level, Experience and Skill level Earning of her husband and transfer income of her family Positive affect Negative affect

5 How should Economic Development Affect the Labor Force of Women? Moshe Shaul Student ID Man’s opportunities improve relative to women’s, It plays an important role how women’s labor force participation changes with development.

6 Competitive Labor Market Moshe Shaul Student ID 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.

7 Agriculture to Urbanization (industrialization) Process Moshe Shaul Student ID 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 )

8 Women’s Labor Force in Developed Countries Moshe Shaul Student ID What explain the increase of women’s labor force in developed countries? a) Growth of white-collar jobs b) Gains of women education

9 U-Shaped Relationship Moshe Shaul Student ID Economic Development Women's labor force participation Poor Counties Women works in farm Urbanization and social barriers against women Women education level raise and start to work in white-collar jobs

10 Social Norms and White/Blue Collar Jobs Moshe Shaul Student ID Why should social norms dictate that white-collar jobs are “acceptable” for married women whereas blue-collar jobs are not? a) Women dislike factory work and marriage can be used as escape opportunity. b) In some countries it is typical for manufacturing firms to terminate women’s jobs upon marriage. c) 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) d) Factory does not pay wives enough to compensate for the fixed costs of working away from home.

11 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. 11Moshe Shaul Student ID

12 Database Description and Objectives Moshe Shaul Student ID 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

13 Research's Evidence-Education Analysis Moshe Shaul Student ID Top curve description: 1) 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). 2) Women's education levels have risen over time especially for lower and middle income countries.

14 Research's Evidence-Education Analysis, cont Moshe Shaul Student ID 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.

15 Research's Evidence- Women’s labor force participation vs. Income level relation Analysis Moshe Shaul Student ID African  South Asian Country South America  Central AmericaSouth European  East Asian  North European  North American

16 Research's Evidence- Women’s labor force participation vs. Income level relation Analysis Moshe Shaul Student ID 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.)

17 Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis Moshe Shaul Student ID Income,Family enterprise  Paid jobs, Fertility

18 Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis. Cont’ Moshe Shaul Student ID 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

19 Research's Evidence- Women’s wages VS. Fertility Rate relation Analysis. Cont’ Moshe Shaul Student ID Other factor may drive fertility decline : a) 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. b) Development of public or private pension systems decries the expectation from children to provide income for their parents especially in their old age.

20 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. 20Moshe Shaul Student ID

21 Two examples: Thailand and India These countries were not chosen to be representative of the entire developing world, indeed, no two counties can be. 21Moshe Shaul Student ID CharacteristicThailandIndia Recent growth (1980,1995 GDP per capita) +125%+69% Women’s Labor forceRelative highRelative low Women’s ownership and control of lands AcceptableAgainst social norms Distribution of rural household lands Fairly evenly distributed 72% own the land 11% rent the land 16% farm laborer Less equal distribution 36% farm laborer

22 Thailand Across Cohort Patterns 22Moshe Shaul Student ID 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: a)Below data are not applicable to India because of lack of available data but we expect to have the same behavior. b)The relatively flat cohort lines indicate that this fraction varies little over time within cohorts.

23 Male-Female Gap in Education VS. Expenditure 23Moshe Shaul Student ID a)Educational achievement is lower for women then man at both graphs. b)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.

24 Women Labor force VS. Expenditure 24Moshe Shaul Student ID a)Labor force definition: applicable to women as employee, employer or self employment ( e.g. free family labor ) b)Women labor force is lower in both countries for urban areas ( due to fewer work opportunities ). c)In urban areas women labor force follows the “U shape”

25 Women Working for Wages VS. Expenditure 25Moshe Shaul Student ID a)The probability of working as an employee increases steadily with living standard. b)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. Thai Free family labor India’s women must work due to lack of lands

26 Education affect (regression results) 26Moshe Shaul Student ID a)Secondary school- I.In India women with secondary school has minor affect on the probability to be part of labor force. II.In Thai secondary school has no affect on the probability to be part of labor force. b)Post-secondary school- In both countries Post-secondary school has large affect on the probability to be part of labor force. c)Rural women- In both countries are more likely to be part of labor force d)Spousal education- Has negative affect on participation in labor force in India but not in Thai. e)Special tribe- In India being a member of a specific tribe may increase the affect on participation in labor force. f)Following are more affects of education: I.More likely to work in non-manual jobs (e.g. white-collar) II.More likely to work as employee rather than self-employed

27 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. 27Moshe Shaul Student ID

28 Gender Gaps in developed Countries 28Moshe Shaul Student ID a)Girls in poor countries receive less education on average than boys, that gaps decline as living standards raise. b)Women’s life expectancy rise with income and have improved over time given per capita GDP (i.e. development). c)Women’s life expectancy improves relatively to male as income raise.

29 Gender Gaps in developed Countries. cont’ 29Moshe Shaul Student ID d)Women and girls in developed countries receive fewer resources within the household. e)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. f)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. g)One possible reason why parents invest less in girl is the higher future returns to parents.

30 Conclusion 30Moshe Shaul Student ID Mortality rate and education levels indicates that women’s well being improves on average with development, both in absolute term and relative to men.


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