Factory Employment and Fertility Decisions: Field Experimental Evidence from Ethiopia Sandra K. Halvorsen Norwegian School of Economics Chr. Michelsen Institute UNU-WIDER Seminar Series 27 March 2019 1/27
Motivation Industrialisation has potentially large impacts on several developmental goals: ◮ Economic growth and trade ◮ Job creation ◮ Poverty reduction ◮ Increased female labor force participation ◮ Income (?) ◮ Fertility decisions (?) ◮ Women’s empowerment (?) 2/27
Literature Impacts of increased female labor force opportunities in manufacturing industries in developing countries ◮ Amin et al. (1998); Atkin (2009, 2016); Blattman and Dercon (2018); Heath (2014); Heath and Mobarak (2015); Kabeer (2002); Kagy (2017); Majlesi (2016); Sivasankaran (2014). Impact of female employment on fertility and empowerment (household decision-making) ◮ Anderson and Eswaran (2009); Dharmalingam and Morgan (1996); Getahun and Villanger (2017); Jensen (2012); Van den Broeck and Maertens (2015). 3/27
Experiments Studies using experimental design to investigate impacts of female employment 4/27
Experiments Studies using experimental design to investigate impacts of female employment ◮ Jensen (2012) ◮ Randomizes recruitment services for women in the BPO industry by villages in India. ◮ He finds higher female labor supply and postschool training, higher age of marriage and first childbearing, increased aspirations for careers, and increased investment in younger girls. 4/27
Experiments Studies using experimental design to investigate impacts of female employment ◮ Jensen (2012) ◮ Randomizes recruitment services for women in the BPO industry by villages in India. ◮ He finds higher female labor supply and postschool training, higher age of marriage and first childbearing, increased aspirations for careers, and increased investment in younger girls. ◮ Blattman and Dercon (2018) ◮ Randomize entry-level applicants in the manufacturing industry into industrial job, entrepreneurship program, or control group in Ethiopia. ◮ They find little impact of industrial jobs on employment and wages, and increases in serious health problems. The entrepreneurial program provided better outcomes by raising earnings and providing steady working hours. 4/27
Our Contribution ◮ The first paper to study employment effects on fertility by randomization on individual level. ◮ Experimental design to circumvent the problems of endogeniety in the female employment - fertility relationship. ◮ Survey all women in the study on an individual level including a large set of questions to investigate mechanisms. ◮ A different and larger geographical area than many of the earlier studies. ◮ A different sample, only including already married, but still young, women, which is an important group with regards to family planning policy. 5/27
The Female Labor Supply and Fertility Relationship Female labor supply is expected to affect fertility through three channels: ◮ Income effect ◮ Becker 1960, Becker and Lewis 1973, Willis 1973. ◮ Substitution effect ◮ Mincer 1963, Becker 1965, Willis 1973. ◮ Empowerment effect ◮ Chiappori and others on collective models. 6/27
The Female Labor Supply and Fertility Relationship In a developing country context these channels may be weaker or stronger than in industrialized countries: ◮ Jobs may be more compatible with childcare. ◮ Closer networks allowing for more responsibility sharing of childcare. ◮ Preference for many children. ◮ Access to contraceptives may be limited. 7/27
The Context Women’s labor force participation and fertility Source: Ethiopia DHS (2016) 8/27
The Context The manufacturing industry in Ethiopia ◮ The development of the manufacturing sector plays a considerable role for the implementation of Ethiopia’s vision to become middle income country and top light manufacturing hub by 2025. 9/27
The Context The manufacturing industry in Ethiopia ◮ The development of the manufacturing sector plays a considerable role for the implementation of Ethiopia’s vision to become middle income country and top light manufacturing hub by 2025. ◮ Since 2004, Ethiopia has experienced high economic growth, averaging 10.6% GDP growth annually. The manufacturing sector’s value added as share of GDP has remained relatively stable at 3.5 - 5.5%. (Source: World Bank national accounts data) 9/27
The Context The manufacturing industry in Ethiopia ◮ The development of the manufacturing sector plays a considerable role for the implementation of Ethiopia’s vision to become middle income country and top light manufacturing hub by 2025. ◮ Since 2004, Ethiopia has experienced high economic growth, averaging 10.6% GDP growth annually. The manufacturing sector’s value added as share of GDP has remained relatively stable at 3.5 - 5.5%. (Source: World Bank national accounts data) ◮ During the 2016/2017 fiscal year, 1.7 million jobs were created in the Ethiopian manufacturing industry. (Source: Xinhua, 04/2018) 9/27
Experimental design Job randomization ◮ 30 factories in five regions ◮ Job offer randomization to eligible married women ◮ Baseline + three follow-up surveys ◮ Sample size: 1460 ◮ Follow-up 1: 1228 ◮ Follow-up 2: 800 (not completed) ◮ Balanced sample ◮ Treatment not predictive of attrition 10/27
Experimental Design Timeline Baseline May, 2016 March, 2018 11/27
Experimental Design Timeline Baseline May, 2016 March, 2018 Follow-up 1 Oct, 2016 Dec, 2018 11/27
Experimental Design Timeline Baseline May, 2016 March, 2018 Follow-up 1 Oct, 2016 Dec, 2018 Follow-up 2 June, 2017 March, 2019 11/27
The Factories ◮ Medium and large factories ◮ Textiles, apparel, shoes, cosmetics, and plastics ◮ Starting monthly wage 600-680 ETB (70-80 International $ at 2016 PPP terms) ◮ Women primarily work at the floor or as floor managers 12/27
Sample Descriptives ◮ 24 years old ◮ 9.3 years of education ◮ 93% are married ◮ 67% have ever given birth ◮ 1.2 children on average ◮ Desired lifetime fertility is 4 children ◮ No difference in income by treatment group ◮ Respondent’s income last twelve months: 4 200 ETB (480 International $ at 2016 PPP terms) ◮ Husband/partner’s income last twelve months: 30 500 ETB (3 500 International $ at 2016 PPP terms) ◮ No difference in ever had a job before 13/27
Estimation Strategy Intention-to-treat Y i = β 0 + β 1 T i + γX i + b l + ǫ i (1) Local Average Treatment Effect Z i = β 0 + β 1 T i + γX i + b l + ǫ i (2) Y i = β 0 + β 1 ˆ Z i + γX i + b l + ǫ i (3) Y i = Currently pregnant or had a baby since baseline (0/1); Lifetime wanted number of children. T i = Treatment status by randomization. X i = Set of baseline control variables: Pregnant at baseline, Age, religion, education, number of household members, total household income last six months, dummy indicating whether respondent had any wage job the last six months, lifetime wanted fertility. b l = Block fixed effect based on randomization rounds. Z i = Having had any formal wage job since baseline. 14/27
Employment and Income At first follow-up At second follow-up (1) (2) (3) (4) (5) (6) (7) Total Total Started Currently Currently Currently Currently income income working employed employed employed employed last 6 last 6 in the in the in any in the in any months months factory factory job factory job (ETB) (ETB) Treatment 0.458*** 0.280*** 0.211*** 1,164*** 0.225*** 0.109*** 501* (0.024) (0.023) (0.026) (219.045) (0.026) (0.033) (287.610) Controls Yes Yes Yes Yes Yes Yes Yes Block fixed effects Yes Yes Yes Yes Yes Yes Yes Observations 1,228 1,228 1,228 1,228 800 800 800 Control mean 0.149 0.105 0.224 2798 0.0825 0.268 3872 15/27
Employment and Income By first follow-up Sample 1460 Control Treatment 728 732 (50%) (50%) Factory job Other job No job Attrition Factory job Other job No job Attrition 98 89 444 102 378 47 177 130 (13%) (12%) (61%) (14%) (52%) (6%) (24%) (18%) Quit Retained Quit Retained Quit Retained Quit Retained 28 66 15 74 133 245 7 40 (30%) (70%) (17%) (83%) (35%) (65%) (15%) (85%) 16/27
Treatment Effect on Childbearing At first follow-up At second follow-up Currently pregnant Currently pregnant Have given birth or given birth since or given birth since Currently pregnant since baseline baseline baseline (1) (2) (3) (4) (5) (6) (7) (8) OLS IV OLS IV OLS IV OLS IV Treatment -0.015 0.021 0.051*** -0.025 (0.018) (0.028) (0.020) (0.023) Any formal wage job since baseline -0.039 (0.046) Any formal wage job since baseline 0.074 0.183** -0.088 (0.100) (0.073) (0.079) Controls Yes Yes Yes Yes Yes Yes Yes Yes Block Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,228 1,228 800 800 800 800 800 800 Control mean 0.155 0.191 0.059 0.131 First stage results Treatment 0.377*** 278*** 278*** 278*** (0.023) (0.032) (0.032) (0.032) p-value from F-test 0.000 0.000 0.000 0.000 17/27
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