1

1
Fertility and Sex Selection Analysis and Policy
in Asia

• Avi Ebenstein (UC-Berkeley)
• Harris School of Public Policy
• January 17th, 2006

2
Overview

• Motivation The Missing Girls of Asia
• Preview of Results
• Detailed Results
• Section 1 Evidence of Sex Selection in Census
  Data
• Section 2 Background on Fertility Policy in
  China
• Section 3 Theoretical Model of Sex Selection
  Decision
• Section 4 Empirical Results from Estimation of
  Model
• Section 5 Policy Simulations
• Summary and Conclusions

3
Motivation Asias Missing Girls

• Sen (1990) - Missing Girls
• Coale and Bannister (1994)
• Zeng et al. (1993), Junhong (2001)
• Jha (2006)
• Norberg (2004)
• Oster (2006)

Identified Problem
Fertility surveys indicate some sex-selective
abortion
Biology? Hepatitis?
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Preview of Results

• Sex ratios in Asia are historically high due to
  sex selection following daughters
• Sex ratio in Asia is rising due to
• Declining allowed/desired fertility
• Persistent preference for at least 1 son
• Using a matched sample of Chinese census data and
  fines in China for extra births, I present and
  estimate a simple model of the sex selection
  decision. I find that a son is worth roughly 2.9
  years of peasant income.
• Proposed subsidy to mothers who fail to ever have
  a son can reduce sex selection and out of plan
  fertility in rural areas.

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Section 1Evidence of Sex Selection
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Chinese Births following Girls
One Child Policy
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Declining Fertility, Rising Sex Ratio
Key Fact in China Number of Sons falls by 6
million. Number of Daughters falls by 16
million!
US ? No Change
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In China mothers without a son more likely to
have a son In US, mothers without a daughter more
likely to have a son
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In China mothers without a son MUCH more likely
to have a child In US, mothers without 1 of each
slightly more likely to have a child
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Boys arrive Late
Fewer Female Births ? More Abortions/Infanticide
? Later Arriving Boys
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Section 2Background on Chinas Fertility Policy
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Fertility Policy in China

• Pronatalist Policy 1949-1969
• Two is Enough 1970-1979
• Strict One Child Policy 1979-1983
• Opening Small Holes, Close Large Ones 1984
• Today Federalism

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Fertility Policy in China

• One Child Policy (35)
• Urban residents, population near cities
• 1.5 Child Policy (54)
• Rural areas in inner provinces
• Two Child Policy (10)
• Rural areas in outer provinces, minority groups
• Three Child Policy (1)
• Residents in very remote areas

83 of Missing Girls
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(No Transcript)
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Low Fertility, High Sex Ratios
High Fertility, Low Sex Ratios
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More Education ? Lower Fertility ? Higher Sex
Ratios
First Births Similar, Undistorted
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Fertility Policy in China
Source Fujian Province Regulations (1.5 Child
Policy Region)
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Enforcement of Policy
First, we employ reasoning and education. Then,
we order a pregnancy fine and forced abortion.
For persons with above-quota births, we mete out
fines for those with many births, we confiscate
land and revoke household registration.
Chinese 1995 Survey on birth control practices
Scharping 2003, p. 147
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Do Fines Influence Fertility?
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Higher Fines
Lower Fertility Higher Sex Ratios
(Reduced Form Relationship)
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Note province fixed effects and other
demographic controls included.
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Section 3Sequential Modelof a Mothers Decision
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Features of the Model

• Parents place a value of ? on a first-born son.
• Parents want a son but face a fertility limit
• that is enforced by dollar fines F1 and F2
• for 1st and 2nd extra children. (2nd and 3rd
  Births)
• 3. Parents have access to a sex selection
  technology that is 100 effective and costs A.
• 4. Parents with a son never have another child.

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Decision 1 Having a 2nd Child
Stop
G
G
Boy
Girl
Decision 2 Sex Selection
GB
G(G)
Abort
Decision 3 Having a 3rd Child
Stop
GG
GG
Boy
Girl
GGB
GG(G)
Decision 4 Sex Selection
Abort
GGG
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Decision 1 Having a 2nd Child
Stop
G
G
Boy
Girl
Decision 2 Sex Selection
GB
G(G)
Abort
Decision 3 Having a 3rd Child
Stop
GG
GG
Boy
Girl
GGB
GG(G)
Decision 4 Sex Selection
Abort
GGG
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4th Decision Abort or Stop

• Abortion Payoff
• Dont Abort Payoff

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Intuition of 4th Decision

• Abortion when value of a son is large relative
  to cost of abortion

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Decision 1 Having a 2nd Child
Stop
G
G
Boy
Girl
Decision 2 Sex Selection
GB
G(G)
Abort
Decision 3 Having a 3rd Child
Stop
GG
GG
Boy
Girl
GGB
GG(G)
Decision 4 Sex Selection
Abort
GGG
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3rd Decision Kid or Stop

• Have a kid
• Dont

Payoff of Abortion
Payoff of Stoppingexp(0)
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Intuition of 3rd Decision

• Have a kid when son preference is large relative
  to fine.
• Have a kid when payoff in round 4 is large, which
  happens when son preference exceeds abortion cost

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Decision 1 Having a 2nd Child
Stop
G
G
Boy
Girl
Decision 2 Sex Selection
GB
G(G)
Abort
Decision 3 Having a 3rd Child
Stop
GG
GG
Boy
Girl
GGB
GG(G)
Decision 4 Sex Selection
Abort
GGG
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2nd Decision Abort or Keep

• Abort
• Keep

Payoff of 3rd Kid
Payoff of Stoppingexp(0)
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Intuition of 2nd Decision

• Abort when ? A E(V3)
• E(V3) .51? F2 .49 E(V4)
• Abort if third fine is large!

Extreme case Mother will never die without son.
Now or Later scenario ? Abort when F2 .49A
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Decision 1 Having a 2nd Child
Stop
G
G
Boy
Girl
Decision 2 Sex Selection
GB
G(G)
Abort
Decision 3 Having a 3rd Child
Stop
GG
GG
Boy
Girl
GGB
GG(G)
Decision 4 Sex Selection
Abort
GGG
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1st Decision Kid or Quit

• Abort
• Keep

Payoff of 3rd Kid
Payoff of Stopping
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Heterogeneity
Maximum Likelihood Estimation Choose optimal
ß1-ß7 given observed data
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Basic Intuition of the Model

• Abort 3rd Girl if Value of Son Exceeds Cost of
  Sex Selection (?A)
• Have 3rd Child if Value of Natural or Augmented
  chance of having a son exceeds the fine.
• Implication Those who wont abort more likely
  to stop.
• Abort 2nd Girl to Avoid a 3rd Child! Fine is
  expensive.
• Simple Case For mothers who know they will
  abort eventually, the decision is Now or
  Later.
• When the (3rd Child Fine 49 of Abortion
  cost),
• abort the second child.

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Section 4Empirical Calibration of the Model
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Sample Means for Calibration
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Question Is the fit good? Yes, reasonably good
in-sample forecasting.
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Question Who really wants a son in
China? Answer Less educated women, farmers.
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Section 5Current Proposed Fertility Policy
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Welfare Implications of Sex Selection

• Marriage market
• Among those who marry, women do relatively better
  and men do relatively worse. Men lose, women win.
• Increase in unmatched men. Crime? Unrest?
• 23 million boys will not find Chinese brides
• The guang gun Bare Branches
• It wont fix itself!
• Parents prefer a potentially unmarried son to a
  married daughter.

Poston and Glover (2000)
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Policy Current Proposed in China

• Care for Girls campaign
• Outlawing sex selection
• Black Market for ultrasound
• 3. Raise fertility limits???

At best, slow process
Price is 6-40 - Cheap
Original Problem!
4. Social Insurance for those fail to have a son
Thought experiment. If sons provide a dollar
value ?, one could tax those with a son ½ of ?
and reward those with daughters with ½ of ?
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Policy Simulations

• Q What if you could reduce ? by subsidizing
  those who fail to have a daughter? How much would
  that cost?
• A A lot. But as I will show, it will accomplish
  the dual objective of lowering fertility and
  reducing the sex ratio.

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Smaller Deficit
Indirect cost
Direct cost
Declining Efficiency
Falling Fertility
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Summary of Findings

• Sex selection responsible for rising Sex Ratio
• Declining fertility in combination with
  persistent son preference yield high Sex Ratio in
  Asia
• Policy suggestion subsidize those who fail to
  have a son. Only way to address dual concern of
  fertility rates and sex ratio.
• Epilogoue Sex Ratio at birth in 2005 is 118!
• Chinas history, Indias future?

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THE END
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Acknowledgements

• Ron Lee, David Card, Bill Lavely, Ken Chay, Raj
  Chetty, Susan Greenhalgh, Michael Greenstone,
  Jonathan Gruber, David Levine, David Romer, Ken
  Wachter, Feng Wang, Gretchen Donehower, Danzhen
  You, Kevin Stange, Kenneth Train, Jerome Adda,
  Damon Jones, Claudia Sitgraves

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Hepatitis?

• Claim Hepatitis B is a partial explanation for
  missing girls
• (Oster, Journal of Political Economy 2006)
• Facts
• Male fraction of first births is about 51.
• Sex ratio at birth rising throughout vaccination
  window.
• Tibetans and other minorities have higher
  hepatitis rates, but lower sex ratios.
• Possible Explanation for Osters findings
• Result driven by correlation between hepatitis
  and son preference (e.g. Guangdong).