FERTILITY RISK IN THE LIFE CYCLE

• Author: Sekyu Choi
• Published date: 01 February 2017
• Date: 01 February 2017
• DOI: http://doi.org/10.1111/iere.12215

INTERNATIONAL ECONOMIC REVIEW

Vol. 58, No. 1, February 2017

FERTILITY RISK IN THE LIFE CYCLE∗

BYSEKYU CHOI1

University of Bristol, U.K.

In this article, I study fertility decisions with special emphasis on the timing of births and abortions over the life

cycle. Given the policy debate regarding abortion availability and recent evidence of its positive impact on women’s

outcomes, understanding the fertility process should help guide the discussion. Here, I present a life-cycle model of

consumption–savings and fertility decisions in an environment with uninsurable income shocks and imperfect fertility

control. My model presents a framework in which both opportunity costs of child rearing and technological restrictions

(in the form of contraception effectiveness) have roles shaping lifetime fertility choices.

1. INTRODUCTION

The policy debate regarding abortion legality and availability resurfaces frequently in the

United States. This is fueled in part by the fact that one in four pregnancies ends in an abortion,

a statistic stable over the last decade.2In terms of access, there are still signs of restrictions to

access: Upadhyay et al. (2014) estimate that a significant number of women get turned down at

abortion clinics or are unable to procure an abortion due to strict term limits or lack of clinics in

certain regions. As for its impact, there is empirical evidence showing that abortion availability

is related to better labor market and health outcomes for women, as noted by Angrist and

Evans (2000) and Coleman (2011), respectively, as well as to improvements in eventual child

investment decisions by mothers, a result found in Gruber et al. (1999).

Given the significance of failed contraception and the effects of abortion on women’s out-

comes and welfare, in this article I introduce a model of stochastic fertility in order to understand

fertility decisions in a broad sense. The model is characterized by an incomplete markets frame-

work with uncertain income and exogenous marital status transitions (similar to Hong and

R´

ıos-Rull, 2012), where females cannot control perfectly the timing of births, thus creating in-

centives to abort some pregnancies. The model is rich enough to predict heterogeneity of births

and abortions across educational groups and is useful to quantify the sources of such hetero-

geneity. Also, given that the model is embedded in a standard incomplete markets framework

(where agents have access to savings), the model is able to produce predictions on the interplay

between household assets and fertility decisions over the life cycle.

The model I present builds on Mincer (1963) and Becker (1960, 1965), who put forward

an optimal “allocation of time” theory to rationalize the negative income–fertility correlation

observed in the data: Given that child rearing requires time away from the market, individuals

with higher skills (thus, higher value of their market time) choose optimally to have fewer

∗Manuscript received November 2012; revised September 2015.

1This is part of my Ph.D. dissertation at the University of Pennsylvania and previously circulated under the title “Life-

Cycle Fertility: Means vs. Motives vs. Opportunities.” I would like to acknowledge the support and encouragement

of Jos´

e-V´

ıctor R´

ıos-Rull throughout this project. This article benefited from discussions with Jeremy Greenwood,

Dirk Krueger, Jes´

us Fern´

andez-Villaverde, Virginia S´

anchez-Marcos, Iourii Manovski, Ken Wolpin, Hal Cole, John

Knowles, Nezih Guner, Juan Carlos Conesa, two anonymous referees, and seminar participants at several institutions.

I gratefully acknowledge financial support from the Spanish Ministry of Economy and Competitiveness through grant

ECO2012-32392 and through the Severo Ochoa Programme for Centres of Excellence in R and D (SEV-2011-0075).

All errors are mine. Please address correspondence to: Sekyu Choi, Department of Economics, University of Bristol,

Bristol, Avon, U.K. E-mail: sekyu.choi@bristol.ac.uk.

2See, for example, Henshaw (1998), Finer and Henshaw (2006), and Kocharkov (2012).

237

C

(2017) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social

and Economic Research Association

238 CHOI

20 25 30 35 40

0

0.05

0.1

0.15

0.2

age

Births per # of women

Age-specific fertility rate

20 25 30 35 40

0

0.1

0.2

0.3

0.4

0.5

0.6

age

% of pregnancies

Abortions

High School

High School (smooth)

College

College (smooth)

SOURCE: 1995 wave of the National Survey of Family Growth. “High school” is the group of all those with a high school

diploma or less education; “college” is all the rest. “Smooth” refers to a fourth-order polynomial on age.

FIGURE 1

AGE-SPECIFIC FERTILITY RATES AND ABORTIONS PERCENTAGES,BY EDUCATION ATTAINMENT OF THE RESPONDENT

children.3In this article, I expand this theory to allow for the possibility of fertility risk, in the

sense of agents not being able to fulfill their fertility plans perfectly.

This negative income–fertility correlation can be observed from the left panel of

Figure 1, where I tabulate information on births from the 1995 National Survey of Family

Growth (NSFG).4Compared to their college counterparts, individuals in the high school group

have higher and earlier birth rates. Since they also have lower earnings (see Figure A.3 in the

Appendix) this represents a negative correlation between fertility and labor income.

The second panel of Figure 1, on the other hand, hints at the need to expand the “allocation

of time” story. It documents the prevalence and timing of abortions across educational groups.

The figure shows that the rate between number of abortions and pregnancies is higher for the

college group earlier in the life cycle and that the age profile for both groups is decreasing in age,

converging at around age 25. Although the allocation of time theory might explain the fact that

the college group exhibits a higher abortion rate, the declining trend for both educational groups

and its fast convergence hints at some complementary mechanism at play: If the high school

group has less opportunity costs from childbearing, why do they chose to abort (relatively)

earlier in their life cycle as their college counterparts do?

Here, I use the findings in Rosenzweig and Schultz (1989), who show that more educated

individuals are more efficient using birth control methods. Thus, my model combines both

“allocation of time” and “differential fertility risk” mechanisms to account jointly for the facts.

Assuming the same preferences for children across individuals, I calibrate the model using data

from the 1995 NSFG, the Panel Study of Income Dynamics (PSID), and the Current Population

Survey (CPS) using a simulated method-of-moments approach.

The model predicts that the “differential fertility risk” mechanism is key to simultaneously

account for the differences in births along with similarities in abortion trends across educational

groups. Thus, this result suggests that the most direct way to affect abortions and fertility

decisions is through policies aimed at awareness efforts (e.g., sex education at early ages)

instead of at income subsidies (e.g., child-care subsidies or paid maternal leave). The model

also implies an important role of asset accumulation in determining the rates of births and

abortions and correctly predicts the relationship between assets and births, given longitudinal

evidence from the PSID: The level of assets has a positive and significative effect in predicting

births (after controlling for education and marital status of the mother).

3See Galor and Weil (1996) and Greenwood et al. (2005) for an updated view. See also Jones et al. (2010) for a

discussion in dynastic models and Erosa et al. (2010) for one in a life-cycle context.

4For a detailed description of the data, see the Appendix.