Determinants of labor force participation in developing Countries:

The case of women in Kenya

By Njenga Paul

01/26/15 1

Kenya: Vital Statistics

• Capital: Nairobi

• Total popn (2009 est): 39 Million (California: 38.3 million)

• Population growth rate 2.69% (US:0.98%)

• GDP per capita: $1600 (US: $46400)

• Population below poverty line: 50% (US12%)

• Languages: Local languages (>40), Kiswahili and English

• Literacy rate: 85.1% (M: 90.6% F: 79.7%).

Source: World Fact Book, 2009

Background

• Women participation in the labor market is low in

developed and developing countries (ILO, 2009)

• The gap has been narrowing over the last several

decades

• Gender gaps in education and employment reduce

economic growth (Klasen and Lemanna, 2009)

• Recent Increase in female labor force participation has

been explained by women access to formal education,

empowerment, completion of the fertility cycle,

expansion of service industry and globalization.International labor organization, 2009; Scott, L. et al, Nam S., 1991

Literature review

• Participation in rural off-farm activities is a livelihood strategy among women

• Development policies tend not to ignore women role in economic development process

• There is more emphasis in women reproduction rather than productive roles

• Women are disproportionately represented among the poor

• Empowerment of women is an important entry point in their economic development

• (

Mehhra, 1997, Mduma and Wobst, 2005

Literature review

• Education: Changes attitudes towards women and

women liberation including reduced female

household responsibilities.

• Is positively correlated to female labor force

participation.

• Women from low SES participate in the labor

market as a survival strategy

(Cebula and Coombs, 2008, Nam, 1991)

Literature review cont.

• Marital status: Married women are less likely to

participate in the labor market compared to single or

separated women who are head of households

• Gender discrimination: Gender roles adopted during

socialization and in life do not favor women in terms of

education and empowerment.

(Cebula and Coombs 2008, Standing 1976, Wilmoth and Longino, 2006)

Literature review

• Women living in urban areas are more likely to work compared to women living in the rural areas (Naude and Serumaga-Zake, 2001).

• High fertility (and presence of children at home) constrains women from participating in the labor market (Ackah et al, 2009)

• Muslim are significantly less likely to employed than christians (Togunde, 1999)

Knowledge gap

• Debate on whether type of residence is a significant determinant of FLFP

• Argument is that urban areas have more job opportunities than rural areas

• There is also a ranging debate on the role of religion as a determinant of FLFP

• Women empowerment has not been widely studied as a possible determinant of FLFP

• A number of SES indicators common among African women have not been widely studied

Research question

What factors determine the probability of

women participating in the labor market?

Theoretical framework

• Human Capital perspective put forward by Gary Becker

• Productivity of people in market and non-market situations is

enhanced by investments in education, skills, and knowledge

• Assumes that individuals decide on their education, training

and medical care by weighing the benefits and costs.

• Investment in human capital raises expectations

(Jacob Mincer, Gary Becker 1980, Standing1976)

Conceptual Model

•Type of residence•Region of residence

•Type of residence•Region of residence

•Age•Education•Religion•marital status•Relation to HH head

•Age•Education•Religion•marital status•Relation to HH head

•Contraceptive use•Number of children•Sons at home•Daughters at home

•Contraceptive use•Number of children•Sons at home•Daughters at home

•Health decisions•Large purchase decisions•Daily household decisions•WBJ if she argues •WBJ if she neglects children•WBJ if she goes out

•Health decisions•Large purchase decisions•Daily household decisions•WBJ if she argues •WBJ if she neglects children•WBJ if she goes out

•Household own land•Household own structure•Type of fuel used•Type of water source•Frequency of water availability

•Household own land•Household own structure•Type of fuel used•Type of water source•Frequency of water availability

Hypothesis

• Education will be positively associated with the

probability of labor force participation

• Women with more children will have a lower probability

of labor force participation

• Urban women will have a higher probability of labor

force participation

• Married women will have a lower probability of labor

force participation

Data source

• The 2003 Kenya Demographic and Health Survey

(2003 KDHS).

• This is a national representative sample survey of

8,195 women aged 15-49 yrs.

• Survey utilized a 2 stage sampling method based on

1999 population census.

Analytical strategy

• Generated simple descriptive statistics to provide

main characteristics of the sample

• Explored Bivariate relationships between labor

force participation and all predictor variables

• Constructed logistic regression model and

interpreted the odds ratio and probability

Descriptive analysis

• Mean age of respondents was 28.07 years (range 15-49yrs)

• About 15% of respondents had no education while 53.1%

had primary education

• About 60% of respondents were married

• About 50% participated in decisions related to their health

• 49.71% reported ever using contraceptives

• 25.82 were breastfeeding at the time of interview

• The mean age of children ever born to a woman was 2.69%

Descriptive results

Variable N Miss Mean Median Mode Std. dev

Skew Min/Max

Age 8195 - 28.07 26.00 20.00 9.31 0.48 15/49

Total No. of children

8195 - 2.69 2.00 0.00 2.74 1.06 0-16

Independent Variables: Demographics Frequency Percentage

A. Education Level (N=8195, Missing=0)

1. No education 1291 15.75

2. Primary 4348 53.06

3. Secondary 1975 24.10

4. Higher 581 7.09

B. Marital Status (N=8195, Missing=0)

1. Never married 2466 30.09

2. Married 4876 59.50

3. Separated 853 10.41

C. Religion (N=7989, Missing=206)

1. Roman Catholic 1919 23.45

2. Protestant Catholic 5045 61.64

3. Muslim 1025 12.52

D. Birth order (N=8195, Missing=0)

1. First 1558 19.33

2. Second 1368 16.97

3. Later born 5136 63.70

E. Household relationships (N=8193, missing=2

1. Head 1569 19.15

2. Wife 3632 44.33

3. Daughter 1944 23.73

4. Other 1048 12.79

Independent Variables: Empowerment Frequency Percentage

A. Health care decision (8150, Missing=45)

0. No 4063 49.85

1. Yes 4087 50.15

B. Large hhold purchase decisions (8181, Miss=14)

0. No 5363 65.44

1. Yes 2718 33.63

c. Decision on daily hh purchase (8088, miss=107)

0. No 4178 50.98

1. Yes 3910 47.71

D. She argues with partner (7946, Missing=249)

0. No 4195 54.33

1. Yes 3751 45.77

E. Neglects children (8182, Missing=193)

0. No 3594 43.86

1. Yes 4408 53.79

F. Goes out without informing partner (8195, Miss=0)

0. No 4063 49.58

1. Yes 4132 50.42

Independent Variables: Reproductive Frequency Percentage

A. Ever used contraceptives (8195, Missing=0)

0. No 4121 50.29

1. Yes 4074 49.71

B. Currently breastfeeding (8195, Missing=0)

0. No 6079 74.18

1. Yes 2116 25.82

C. Sons living at home (8195, Missing=0)

0. No 4063 49.58

1. Yes 4132 50.42

D. Daughters living at home (8195, Missing=0)

0. No 4139 50.51

1. Yes 4056 49.49

Independent Variables: Social Economic Factors

A. HH Own land where structure sits(7907, Miss=288)

0. No 2911 36.82

1. Yes 4996 63.18

B. HH own structure where they live(7903, Miss=292)

0. No 2355 28.74

1. Yes 5548 67.70

Distribution table Cont.

Independent Variables: Social Economic Factors cont. Frequency Percentage

C. Type of fuel used (7901, Missing=294)

0. Electricity, LPG gas, Kerosene 2935 37.15

1. Firewood/straw/cow dung 4966 62.85

D. Main source of water (7521, Missing=674)

0. piped/well/tap 4529 60.22

1. River/ lake/ spring 2992 39.78

E. How frequent water is available (7874, Miss. 321)

0. Infrequent 1725 21.91

1. Always available 6149 78.09

Independent Variables: Environmental factors

A. Region of residence (N=8195, Missing=0)

1. Central 1314 16.03

2. Coast 938 11.45

3. Eastern 993 12.12

4. Nairobi 1169 14.26

5. North Eastern 437 5.33

6. Nyanza 1025 12.51

7. Rift valley 1328 16.21

8. Western 991 12.09

B. Type of residence (N=8195, Missing=0)

Urban 2751 33.57

2. Rural 5444 66.43

Bivariate analysis

• The relationship between labor force participation and each of independent variables was examined

• There was significant relationship between labor force participation and all the independent variables

Multivariate Analysis: Diagnosis

• No problem of multicollinearity (Mean VIF= 2.59)

• Model passed the specification test (linktest=_hat, 0.000,

_hatsq, 0.549)

• Model passed the Hosmer Lemeshow goodness of fit test (χ²=

(6727, N=6778) =6877.85, p=0.097)

• Overall model was statistically significant (χ²= (38, N=6778)

=1901.46, p<.001)

Multivariate analysis: Significant results

• Demographic factors (Age, education, marital status, religion)

• Empowerment factors (participation in decision on own health care and household purchases)

• Reproductive factors (Use of contraceptives, breastfeeding, having children living at home)

• Social economic factors (Use of firewood, water availability)

• Environmental factors (Region of residence)

Significant results cont.

• One year increase in age was associated with 5.5% increase

in likelihood of working, controlling for other predictors

• Having primary level of education was associated with 54.8%

more likelihood of working, controlling for other predictors

• Married women were 25.6% less likely to work compared to

women who have never been married

• Compared to Muslim women, women who profess the catholic

faith are 37.6% more likely to work while those who identify as

protestants are 60.9% more likely to work.

Significant results cont.

• Participation in decisions related to own health care was associated with 66.2% more likely to work.

• Use of contraceptive was associated with 62.3% more likelihood of working.

• Use of firewood was significantly associated with

40.7% more likelihood of working

• Regular water availability was associated with 29.7% more likelihood of working

Labor force participation and predictors

Significant results: Demographics Odds ratio (SE) Z

• Age 1.055*** (0.007) 8.62

• Education Primary 1.548*** (.171) 3.97

Secondary 1.309* (.165) 2.14

Higher 2.521*** (.437) 5.33

• Marital Status Married 0.744* (.095) -2.33

• Religion Protestant 1.609*** (.210) 3.65

Catholic 1.376* (.191) 2.29

• Household relationships Daughter 0.589*** (.073) -4.26

Other 1.461** (.206) 2.69

Significant findings: Empowerment Odds ratio (SE) Z

• participate-Health care decision 1. Yes 1.662*** (.117) 7.22

• participate-large hh purchase decision 1. Yes 1.208* (.106) 2.15

• Participate- daily hh purchase decision 1. Yes 2.184*** (.179) 9.55

• Justify wife beat if she argues 1. Yes 1.282** (.095) 3.35

Levels of significance p<.001*** , p<.01**, p<.05*, Pseudo R2=.2065

Significant results: Reproductive Odds ratio (SE) Z

A. Ever used contraceptives Yes=1 1.623*** (.113) 6.98

B. Currently breastfeeding Yes=1 0.822* (.063) -2.57

C. Sons living at home Yes=1 1.204* (.097) 2.30

D. Daughters living at home Yes=1 1.186* (.094) 2.14

Significant results: Social Economic Factors

Type of fuel 1. Firewood/straw 1.407*** (.138) 3.49

Water availability 1. Always available 1.297*** (.095) 3.56

Significant results: Environmental factors

Region of residence 1. Eastern 0.591*** (.082) -3.78

2. North Eastern 0.397*** (.087) -4.22

3. Nyanza 2.069*** (.285) 5.28

4 Western 1.666*** (.223) 3.81

Levels of significance p<.001*** , p<.01**, p<.05*, Pseudo R2=.2065

Labor force participation and predictors

Discussion

• Results on education, marital status and fertility are consistent with existing literature and confirm the hypothesis

• Type of residence (urban/rural) was not a significant determinant of determinant of FLFP.

• Availability of water, type of fuel used and participation in household decision making are significant determinants of FLFP.

• Empowerment and SE factors included in the model have not been widely studied

Limitations

• The results may not be generalized because of unique characteristics of individual nations

• Missing values in a number of predictor variables

• High Collinearity between time it takes to get to water source and region of residence.

• Husband or partner variables not included

Future research

• Examine the changes if husband partner variables are included

• Replicate the findings with the 2008 KDHS data to see if the findings have changed

• Use longitudinal data (which is available) to examine changes over time

Implications

• This study contributes to the existing debate on factors that determine the probability of FLFP.

• There is need to include women in poverty eradication strategies

• These is need to expand education opportunities for women

• There is need for policy makers to tailor Interventions that would address low rates of LFP in Northern part of the country

• In particular, there is need to address the low rates of labor force participation among Muslim women

Acknowledgement

My appreciation and gratitude to;

• My dear wife, Dinah

• Dr. Shanta Pandey, Research Professor

• Nora Wikoff, Research TA

• Marsela Dauti, Statistics TA

• My wonderful research specialization colleagues

Thank you!I can now take questions, clarifications, AND compliments.