1. ECON 4009 Labor Economics 2017 Fall By Elliott Fan Economics, NTU
Elliott Fan: Labor 2017 Fall Lecture 10

2. Elliott Fan: Labor 2017 Fall Lecture 10
IV Example: Fan et al. (2015)
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
Elliott Fan: Labor 2017 Fall Lecture 10

3. Elliott Fan: Labor 2017 Fall Lecture 10
IV Example: Fan et al. (2015)
In Taiwan, many parents avoid births in ghost month, the 7th month of the lunar calendar, which is believed to be the most inauspicious month of the year.
Doctors cater to parents’ preference.
Elliott Fan: Labor 2017 Fall Lecture 10

4. Elliott Fan: Labor 2017 Fall Lecture 10
IV Example: Fan et al. (2015)
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.
Elliott Fan: Labor 2017 Fall Lecture 10

5. Elliott Fan: Labor 2017 Fall Lecture 10
IV Example: Fan et al. (2015)
Key variables:
Outcome variable (Y): health outcomes at birth
Treatment variable (D): gestational age
Instrumental variable (Z): due date (due week)
Elliott Fan: Labor 2017 Fall Lecture 10

6. Elliott Fan: Labor 2017 Fall Lecture 10
Number of births
We find that the total number of C-section births in the last week of June increased by 26%, if compared with the first 3 weeks of June, followed by a dip in the first week of July.
A minor jump can be observed for natural births too.
Not many natural births were changed to cs to avoid july delivery
Elliott Fan: Labor 2017 Fall Lecture 10

7. Elliott Fan: Labor 2017 Fall Lecture 10
Number of births by weeks of gestation
Shifting happens to birth at different weeks, most significantly for births at 37 weeks.
What is surprising is the jump for preterm births
Elliott Fan: Labor 2017 Fall Lecture 10

8. Elliott Fan: Labor 2017 Fall Lecture 10
Simple difference approach
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)
The medical records of newborns in the NHI data does not provide mother’s information and the birth certificates data only has mothers’ ID but not newborns’ ID, therefore we could not match mothers and newborns in the NHI data in a direct way. We link the medical records of a newborn to her mother using the relation between insurer and the insurant. For example, we are able to match the newborn’s mother when the newborn is insured by its mother, or when both of the mother and newborn are insured by the father.
Elliott Fan: Labor 2017 Fall Lecture 10

9. Elliott Fan: Labor 2017 Fall Lecture 10
Simple difference approach
The estimates from the simple difference approach are potential biased, why?
The medical records of newborns in the NHI data does not provide mother’s information and the birth certificates data only has mothers’ ID but not newborns’ ID, therefore we could not match mothers and newborns in the NHI data in a direct way. We link the medical records of a newborn to her mother using the relation between insurer and the insurant. For example, we are able to match the newborn’s mother when the newborn is insured by its mother, or when both of the mother and newborn are insured by the father.
Elliott Fan: Labor 2017 Fall Lecture 10

10. Elliott Fan: Labor 2017 Fall Lecture 10
Outcome variables
Birth weight
Respiratory complications at birth (RDS/TTN)
RDS: Respiratory Depression Syndrome, is a syndrome in premature infants caused by developmental insufficiency of surfactant production and structural immaturity in the lungs.
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
Other health outcomes – incident of low birth weight, infections, heart complications, and mortality in 365 days after birth.
Elliott Fan: Labor 2017 Fall Lecture 10

11. Simple Difference Regression Results for Males
Simple difference regressions
Simple Difference Regression Results for Males
birthweight
RDS+TTN
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
last 3 days of June ** ** ** **
0.010*
(12.978)
(13.013)
(13.053)
(13.051)
(0.005)
birth year fixed effects X
maternal age
maternal ins. type & prem.
Observations
6,085
Elliott Fan: Labor 2017 Fall Lecture 10

12. Simple Difference Regression Results for Females
Simple difference regressions
Simple Difference Regression Results for Females
birthweight
RDS+TTN
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
last 3 days of June * * * *
0.000
-0.000
(13.681)
(13.753)
(13.698)
(13.687)
(0.004)
birth year fixed effects X
maternal age
maternal ins. type & prem.
Observations
5,140
Elliott Fan: Labor 2017 Fall Lecture 10

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
being due in the 2nd week of July
being due in the 3rd week of July
being due in the 4th week of July
Elliott Fan: Labor 2017 Fall Lecture 10

14. Elliott Fan: Labor 2017 Fall Lecture 10
Gestational age at birth
However, for those who are expected to reach week 40 in the second week of July would need to schedule a C-section at week 38 to avoid a July delivery. Similarly, the incentive to avoid July birth would drive mothers who are expected to reach week 40 in the third week of July to expedite the birth to week 37, and those who are expected to reach week 40 in the fourth week of July will have to deliver at week 36.
Our IVs can be verified using Figure 3, where we demonstrate the density distribution of gestational weeks for different groups categorized by the timing of due date. The grey-dashed curve describes the distribution of gestational weeks (from 35 to 40 weeks) for births that are due in the first three weeks of June. This group of mothers are used as the benchmark as they do not have any incentive to have their births expedited to avoid July deliveries. (We need to know the exact numbers)
We start with the distribution for those due in the first week of July (red curve), which appears to be close to the benchmark curve. The only indication of expedited births for this group is that the proportion of births at week 40 is somewhat lower than that of the benchmark group, while the proportion of births at week 39 is slightly higher. The indication of expedited births is much more obvious for those due in the second week of July (green curve), as the distribution curve exhibits a significantly higher proportion of births at 38 weeks (43.5% vs 39%), and a significantly lower proportion of 39-week births (15%), in comparison with the benchmark group (39% and 22%, respectively). At the same time, the proportions of births at all other weeks (35, 36, 37, and 40 weeks) all coincide with the corresponding proportions for the benchmark group.
Elliott Fan: Labor 2017 Fall Lecture 10

15. Elliott Fan: Labor 2017 Fall Lecture 10
IV strategy – validation (births)
Firstly, as shown in Figure 4, number of births due every day appear to be smooth over our sample period. For either natural births or C-section births, the curve does not exhibit any discontinuity at July first, nor does it show a mean difference between the daily number of births due in June and those due in July.
Elliott Fan: Labor 2017 Fall Lecture 10

16. Elliott Fan: Labor 2017 Fall Lecture 10
IV strategy – validation (maternal age)
Firstly, as shown in Figure 4, number of births due every day appear to be smooth over our sample period. For either natural births or C-section births, the curve does not exhibit any discontinuity at July first, nor does it show a mean difference between the daily number of births due in June and those due in July.
Elliott Fan: Labor 2017 Fall Lecture 10

17. Elliott Fan: Labor 2017 Fall Lecture 10
IV strategy – validation (maternal income)
Firstly, as shown in Figure 4, number of births due every day appear to be smooth over our sample period. For either natural births or C-section births, the curve does not exhibit any discontinuity at July first, nor does it show a mean difference between the daily number of births due in June and those due in July.
Elliott Fan: Labor 2017 Fall Lecture 10

18. Elliott Fan: Labor 2017 Fall Lecture 10
IV Example: Fan et al. (2015)
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?
Elliott Fan: Labor 2017 Fall Lecture 10

19. Elliott Fan: Labor 2017 Fall Lecture 10
IV strategy
We use three the IVs for three endogenous variables -- gestational period of 36, 37, and 38 weeks, leaving 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
Elliott Fan: Labor 2017 Fall Lecture 10

20. Respiratory complications
Second stage results
Table 8: 2SLS results
Birth weight
Respiratory complications
(1)
(2)
(3)
(4)
Males
Females
Week36 **
0.504***
0.179
( )
( )
(0.177)
(0.118)
Week37 ** *
0.184**
0.016
( )
( )
(0.083)
(0.044)
Week38
***
0.093*
0.020
( )
( )
(0.051)
(0.036)
Observations
11,663
9,812
The first estimate in column (1) suggests that, compared to the benchmark group (births at weeks), births due in the second week of July are more likely to be born at 38 weeks by 8.78 percentage points, but the first estimates in columns (2) and (3) suggest that these births exhibit similar proportions of births at 37 and 36 weeks. Births due in the third week of July are 8.05 percentage points less likely to be born at 38 weeks, but percentage points more likely to be born at 37 weeks. Finally, Births due in the fourth weeks of July,
Elliott Fan: Labor 2017 Fall Lecture 10

21. Respiratory complications
OLS results
Table 8: 2SLS results
Birth weight
Respiratory complications
(1)
(2)
(3)
(4)
Males
Females
Week36
***
***
0.064***
0.036***
(16.834)
(20.251)
(0.011)
(0.010)
Week37
***
***
0.020***
0.015***
(10.336)
(11.161)
(0.004)
Week38
***
***
0.002
0.001
(9.086)
(9.445)
(0.003)
(0.002)
Observations
11,663
9,812
The first estimate in column (1) suggests that, compared to the benchmark group (births at weeks), births due in the second week of July are more likely to be born at 38 weeks by 8.78 percentage points, but the first estimates in columns (2) and (3) suggest that these births exhibit similar proportions of births at 37 and 36 weeks. Births due in the third week of July are 8.05 percentage points less likely to be born at 38 weeks, but percentage points more likely to be born at 37 weeks. Finally, Births due in the fourth weeks of July,
Elliott Fan: Labor 2017 Fall Lecture 10

22. Elliott Fan: Labor 2017 Fall Lecture 10
Discussion and Conclusions
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
Elliott Fan: Labor 2017 Fall Lecture 10

23. IV and DD Example: Edlund, Liu and Liu (2015)
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.
Elliott Fan: Labor 2017 Fall Lecture 10

24. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Elliott Fan: Labor 2017 Fall Lecture 10

25. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Elliott Fan: Labor 2017 Fall Lecture 10

26. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
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.
Elliott Fan: Labor 2017 Fall Lecture 10

27. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
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.
Elliott Fan: Labor 2017 Fall Lecture 10

28. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Elliott Fan: Labor 2017 Fall Lecture 10

29. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Elliott Fan: Labor 2017 Fall Lecture 10

30. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Elliott Fan: Labor 2017 Fall Lecture 10

31. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Key variables:
Outcome variable (Y): fertility + divorce hazard
Treatment variable (D): supply of ‘brides’
Instrumental variable (Z): local sex ratio + policy change
Elliott Fan: Labor 2017 Fall Lecture 10

32. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
Difference-in-Difference estimation: Treat: is a dummy variable indicating whether it is post-2003 policy or not; SexRatiosQv: are imputed at the township-level for those between age in 2000 and we divide villages into quartiles by sex ratio
Elliott Fan: Labor 2017 Fall Lecture 10

33. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
IV estimation: The first stage is:
Elliott Fan: Labor 2017 Fall Lecture 10

34. Elliott Fan: Labor 2017 Fall Lecture 10
Edlund, Liu and Liu (2015)
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?
Elliott Fan: Labor 2017 Fall Lecture 10