1. ECON 3039 Labor Economics 2015-16 By Elliott Fan Economics, NTU Elliott Fan: Labor 2015 Fall Lecture 101

2. IV Example: Fan et al. (2015) Elliott Fan: Labor 2015 Fall Lecture 102 Mothers shift the timing of birth for various reasons: – To obtain higher financial support – To better fit doctor’s/her own working schedule – To time childbirth after the school entry cutoff date. Increased C-section procedures are internationally prevalent: – In the US, elective C-section has increased from around 25% around 1995 to 33 % in 2013. – A fast increase is observed Europe and other countries – Health concerns escalated, but estimating health outcomes is potentially difficult

3. IV Example: Fan et al. (2015) Elliott Fan: Labor 2015 Fall Lecture 103 In Taiwan, many parents avoid births in ghost month, the 7 th month of the lunar calendar, which is believed to be the most inauspicious month of the year. Doctors cater to parents’ preference.

4. IV Example: Fan et al. (2015) Elliott Fan: Labor 2015 Fall Lecture 104 We document the shifting pattern around the beginning of ghost month. We estimate the effects of hospital competition (supply side) and parental characteristics (demand side) on shifts of birth timing. Based on the timing of conceive (thus, the due date), we employ an instrumental variable approach to estimate health consequences of expediting births. Our multiple-IVs approach helps address (1) self-selection, and (2) nonlinearity of heath effects of gestational age.

5. IV Example: Fan et al. (2015) Elliott Fan: Labor 2015 Fall Lecture 95 Key variables: Outcome variable (Y): health outcomes at birth Treatment variable (D): gestational age Instrumental variable (Z): due date (due week)

6. Number of births 6

7. 7 Number of births by weeks of gestation

8. Sample: We focus on CS sample, with maternal age between 20 and 40. We drop births from emergency C- section, or mothers with risk factors such as hyper- tension, diabetes, and mal-position. Comparing those born in the last 3 days of June and those born in the first two weeks of June Note that those born in the first week of July are not a proper control group due to selection Regress a health outcome on a dummy indicating births in the last week of June, and other covariates (year of birth, day of the week at birth, maternal age, and maternal insurance type) 8 Simple difference approach

9. The estimates from the simple difference approach are potential biased, why? 9 Simple difference approach

10. 1.Birth weight 2.Respiratory complications at birth (RDS/TTN) a)RDS: Respiratory Depression Syndrome, is a syndrome in premature infants caused by developmental insufficiency of surfactant production and structural immaturity in the lungs. b)TTN: Transient Tachypnea of the newborn, is a respiratory problem that can be seen in the newborn shortly after delivery. It consists of a period of rapid breathing, and is a sign for RDS 3.Other health outcomes – incident of low birth weight, infections, heart complications, and mortality in 365 days after birth. 10 Outcome variables

11. 11 Simple difference regressions Simple Difference Regression Results for Males birthweightRDS+TTN (1)(2)(3)(4)(5)(6)(7)(8) last 3 days of June -28.259**-28.011**-26.271**-26.721**0.010* (12.978)(13.013)(13.053)(13.051)(0.005) birth year fixed effects XXXXXX maternal age XXXX maternal ins. type & prem. XX Observations 6,085

12. 12 Simple difference regressions Simple Difference Regression Results for Females birthweightRDS+TTN (1)(2)(3)(4)(5)(6)(7)(8) last 3 days of June -22.513*-24.584*-26.363*-25.192*0.000 -0.000 (13.681)(13.753)(13.698)(13.687)(0.004) birth year fixed effects XXXXXX maternal age XXXX maternal ins. type & prem. XX Observations 5,140

13. Instrumental variables Theoretically, given that most of CSs are scheduled in week 39 or earlier, those who would reach week 39 before July should have no incentive to alter the birth timing. However, those who would reach w39 in the first week of July would have incentives to shifts the birth delivery from w39 to w38. Specifically, we use three IVs 1.being due in the 2nd week of July 2.being due in the 3rd week of July 3.being due in the 4th week of July 13

14. 14 Gestational age at birth

15. 15 IV strategy – validation (births)

16. 16 IV strategy – validation (maternal age)

17. 17 IV strategy – validation (maternal income)

18. IV Example: Fan et al. (2015) Elliott Fan: Labor 2015 Fall Lecture 918 The 3 requirements: First stage: very strong Independence assumption: Are due weeks randomly determined? Exclusion restriction: Do due weeks affect birth outcomes only through shortened gestation weeks?

19. 19 IV strategy We use three the IVs for three endogenous variables -- gestational period of 36, 37, and 38 weeks, leaving 39-40 weeks as the benchmark group. Our IV sample comprises C-section births who are due in the first four weeks of July and those who are due in the last weeks of June, despite their actual birthdates

20. 20 Second stage results Table 8: 2SLS results Birth weightRespiratory complications (1)(2)(3)(4) MalesFemales MalesFemales Week36-779.078**-495.6720.504***0.179 (390.363)(338.266)(0.177)(0.118) Week37-462.192**-236.760*0.184**0.016 (192.054)(137.471)(0.083)(0.044) Week38-363.204***-126.6720.093*0.020 (122.047)(110.476) (0.051)(0.036) Observations11,6639,812 11,6639,812

21. 21 OLS results Table 8: 2SLS results Birth weightRespiratory complications (1)(2)(3)(4) MalesFemales MalesFemales Week36-472.926***-437.629***0.064***0.036*** (16.834)(20.251)(0.011)(0.010) Week37-282.665***-261.611***0.020***0.015*** (10.336)(11.161)(0.004) Week38-157.913***-177.549***0.0020.001 (9.086)(9.445)(0.003)(0.002) Observations11,6639,812 11,6639,812

22. Our IV results are different from OLS results from medical studies, mostly suggesting more serious health complications caused by shortened gestational period. OLS estimates may be plagued by confounding factors We don’t know much about our compliers so far: – We know that they are more likely to be self-employed Next: finding more demand side factors; long run impacts; IV for competition 22 Discussion and Conclusions

23. IV and DD Example: Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1023 In recent years, one in ve marriages in Taiwan was to a foreign bride, mainly from China and Vietnam, fellow East Asian countries but substantially poorer. Studying the impact on the domestic marriage market.

24. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1024

25. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1025

26. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1026 Background: Taiwan is one of the earliest East Asian countries importing foreign brides -- the share of foreign brides in South Korea today is about at the level that Taiwan reached in 1997. In the late 2003, the Taiwanese government took measures to radically reduce the flow of foreign brides. Between 2003 and 2004, the share of foreign bride (FBS) among new marriages dropped by 25 percent nation-wide.

27. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1027 In recent years, one in ve marriages in Taiwan was to a foreign bride, mainly from China and Vietnam, fellow East Asian countries but substantially poorer. Studying the impact on the domestic marriage market. An influx of foreign brides could have two effects. – Faced with a better alternative, men's divorce threshold could shift, triggering divorce at a higher fertility level. Faced with heightened risk of marriage termination, women might increase effort, resulting in a rightward shift in the fertility distribution. – Whereas the net effect of divorce risk is ambiguous, the prediction for fertility is clear: fertility would increase.

28. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1028

29. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1029

30. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1030

31. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 931 Key variables: Outcome variable (Y): fertility + divorce hazard Treatment variable (D): supply of ‘brides’ Instrumental variable (Z): local sex ratio + policy change

32. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1032 Difference-in-Difference estimation: Treat: is a dummy variable indicating whether it is post-2003 policy or not; SexRatiosQ v: are imputed at the township-level for those between age 25-44 in 2000 and we divide villages into quartiles by sex ratio

33. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 1033 IV estimation: The first stage is:

34. Edlund, Liu and Liu (2015) Elliott Fan: Labor 2015 Fall Lecture 934 The 3 requirements: First stage: very strong Independence assumption: Are policy timing and local sex ratio random? Exclusion restriction: Do policy timing and local sex ratio affect outcomes only through changing foreign bride share?