the relationship between education and fertility and child mortality
TRANSCRIPT
The relationship between education and fertility and child mortality:
Evidence in China
Zibo Zhao1
537654
1 Zibo Zhao. School of Economics and Management, Tsinghua University. (Email:
[email protected]). I thank Professor Barry Eichengreen, Dawn Powers and Gillian Brunet for
guiding me in research.
Abstract: This paper investigates the relationship between maternal and paternal
education and fertility and child mortality in China. And I also want to find out
advanced education or just education which one has stronger relationship with fertility.
It is based on data published in the 4th and 5th China Census from China Data Online
and China City Statistic Yearbook 2000 from National Bureau of Statistics of China.
Regression results turn out that both maternal and paternal advanced education have
negative relationship with fertility and entire educational level have negative
relationship with child mortality.
1. Introduction
Over the past 20 years, many developing countries have sought to find some
policies designed to reduce rapid population growth, such as China’s One Child
Policy2. Given employment opportunities, wages and the child quantity- quality
trade-off have been studied as factors underlying historical fertility limitation (Crafts
1989; Galloway et al. 1994, 1998; Brown and Guinnane 2002; Dribe 2009; Becker et
al. 2010, 2012; Wanamaker 2012), the role of maternal and paternal education have
been receiving more and more attention.
While some papers focus on primary school construction programs in Taiwan
(Chou, Liu, Grossman and Joyce 2003) and Indonesia (Breierova and Duflo 2004),
and on college openings in the United States (Currie and Moretti 2003), China’s
situation receives little attention. In my paper, I take advantage of China’s Census
data to study the relationship between education and fertility and child mortality.
There are totally 6 demographic census conducted in China and 4th Census in 1990,
5th Census in 2000 and 6th Census in 2010 have county data. The 6th Census, however,
hasn’t been accessible now, so I can only use two period data 1990 and 2000. Given
the fact that there is no shock to the education demand and supply during 1990 to
2000 (There are indeed some shock after 2000, such as dismantling teaching points
and combining schools, nine - year free compulsory education), I can’t prove
2 The one-‐child policy, officially the family planning policy, is the population control policy of the People's Republic of China. a misnomer, as the policy allows many exceptions: rural families can have a second child if the first child is a girl or is disabled, and ethnic minorities are exempt. Families in which neither parent has siblings are also allowed to have two children. Residents of the Special Administrative Regions of Hong Kong and Macau, and foreigners living in China are also exempt from the policy.
education has causal effect on the child mortality and fertility, but from the regression
we can know which variable has significant relationship with mortality and fertility.
After the release of 6th Census data, we can use difference-in-difference estimate to
eliminate the effect of omitted variables.
Since numerous researches have been done about the effect of female education
on fertility and seldom about male education, I also exam the relationship between
paternal education and fertility and child mortality. If the result turns out that male
education level don’t have significant relationship with fertility or child mortality, it
can be used as an argument in favor of targeting educational expenditures towards
girls.
And I also want to know which level of education has stronger relationship with
fertility and mortality. Now China applies most of educational expenditures to
primary education, but if we find advanced education (College, University) has more
significant relationship with fertility, we can change our target and become more
effective.
The rest of the paper is structured as follows. In section 2, I review the previous
study on parental education, fertility and child mortality. In section 3, I describe the
data and my method. In section 4, I present the result of regression.
2. Literature Review
The literature generally points to a negative relationship between education and
fertility (Holsinger and Kasarda, 1976; Easterlin, 1989; Cochrane et al., 1990). Citing
this pattern, policymakers have advocated educating girls and young women as a
means to reduce population growth and foster sustained economic and social welfare
in developing countries.
There are some studies about the effect of female education on the fertility
using data from Europe demographic transition3 (Becker and Cinnirella, 2013;
Knodel and Walle, 1979). In Becker and Cinnirella’s paper, they combine Prussian
county data from three censuses—1816, 1849, and 1867—to estimate the relationship
between women’s education and their fertility before the demographic transition.
Controlling for several demand and supply factors, they find a negative residual effect
of women’s education on fertility.
A possible concern for the interpretation of these results arises from potential
endogeneity of parental education with respect to fertility. For example,
time-persistent differences in fertility patterns could be a source of reverse causation
from reduced fertility allowing more education (in my paper, this issue is addressed
by my inclusion of lagged fertility measures, I use data in 1990 to measure the
education level and data in 2000 to measure the fertility). More generally, any
unobserved variable that is correlated with both women’s education and fertility could
bias the estimates. For example, areas with more “liberal” cultures may have a
3 Demographic transition (DT) refers to the transition from high birth and death rates to low birth and death rates as a country develops from a pre-‐industrial to an industrialized economic system.
Figure 1 The female enrollment rate in1816. Note: Ratio of girls enrolled in
primary and middle schools over the number of girls aged 6-14.
Figure 2. The child-women ratio in 1867. Note: Number of children 10-19 over
women aged 40-69.
tendency to accept women to be both educated and have fewer children. They
implement two strategies to address such endogeneity concerns. The first approach
directly models a plausibly exogenous source of variation in mothers’ education. In an
instrumental-variable (IV) approach, they use landownership concentration in 1816 as
an instrument for mothers’ education (Galor et al. 2009; Becker et al. 2010). This
specification exploits exogenous variation in primary school enrollment rates driven
by the opposition to education of the landed nobility that had no interest in having an
educated labor force. The IV estimates suggest that the negative effect of mothers’
education on fertility is causal. To rule out that the cross-sectional estimates just pick
up unobserved county characteristics, their second approach builds a two-generation
panel ( To build that panel, I need at least three period Census data), where the first
phase spans 1816–1849 and the second phase 1849–1875. Panel estimation results
with county fixed effects corroborate the significant negative effect of women’s
education on their fertility. This result rules out that the findings are driven by some
unobserved characteristic that is fundamentally different about locations that have
high parental education in the cross-sectional analysis.
A few studies also have tried to address the omitted variable bias due to the
woman’s unobserved abilities by using instrumental variables. In McCrary and
Royer’s paper (2006), they present new evidence on the effect of female education on
fertility and infant health in the United States using school entry policies as an
instrument for education. In particular, they exploit the fact that the year in which a
child starts school is a discontinuous function of exact date of birth. For example, in
California and Texas, their two study states, children must be 5 years old on
December 1st (California) or September 1st (Texas) in the year in which they begin
kindergarten. As a consequence of these policies, children born within one or two
days of one another enter school at different ages and have different levels of
education throughout school enrollment. Because individuals born near in time are
likely similar along non-education related dimensions, differences in education at
motherhood for women born near these entry dates are arguably exogenous. Using
large samples of birth records, they reach conclusions: Education does not
significantly impact fertility, which is different from Becker and Cinnirella’s. So there
is still argument about the causal effect of female education on fertility.
There are also numerous studies report strong associations between parental
education and child mortality or other measure of children’s human capital (Strauss
and Thomas, 1995). Significant effects of maternal schooling have also been reported
for a variety of inputs into child health (e.g., number and timeliness of prenatal visits,
likelihood of obtaining immunizations, etc.). Several of these studies report that
female education is more strongly associated with these outcomes than male
education (Breierova and Duflo, 2004). This evidence has been used as an argument
in favor of targeting educational expenditures towards girls. Breierova and Duflo’s
paper takes advantage of a massive school construction program that took place in
Indonesia between 1973 and 1978 to estimate the effect of education on fertility and
child mortality. Time and region varying exposure to the school construction program
generates instrumental variables for the average education in the household, and the
difference in education between husband and wife. They show that female education
is a stronger determinant of age at marriage and early fertility than male education.
However, female and male educations are equally important factors in reducing child
mortality.
3 data & methodology
Because different counties in China have different education levels, so I use
cross-sectional data. And the data I use are China Census data 1990 and China Census
data 2000 for provinces: Anhui, Gansu, Shanxi, Jiangsu and Qinghai. There are 25
provinces have county level data in the Census 1990 and 2000. I chose randomly 5
provinces from them and get the information of all counties in the provinces. I also
find out the GDP of counties from China City Statistic Yearbook 2005 downloaded
from the website of National Bureau of Statistics of China.
Let me introduce the variables I used. There are several control variables and
several variables of interest. They all respond to things people care about when they
are deciding how many children they want, in other words, fertility rate. First, the
opportunity cost of childbearing and rearing, educated people are more likely to find
jobs and earn more, so their opportunity cost are higher (Becker, 1981; Schultz, 1981).
So I want to use women and men’s unemployment rate to measure how easy they find
a job in different counties. Second, education may lower fertility through
improvements in child health and reduced rates of child mortality as women need to
have fewer births to yield the same desired family size (Lam and Duryea, 1999;
Schultz, 1994). So I design a variable to measure the mortality rate of children:
child_mortality: the ratio of deaths aged 0 to 14 in a county to the population of that
area; expressed per 1000 per year. And households’ wealth also can affect desired
number of children. I use people’s GDP in a county in 2000 to representative the
wealth. And if people spend more time to get education, they will have less time to
give birth to children, so we also need to control people’s age of marriage. I use the
average age of first marriage in a county in 2005 to represent the effect of education
on marriage age.
The crux of the paper is how to measure the education. There are two situations:
one is that advanced education has more significant relationship with fertility or child
mortality and another one is that just education has more significant relationship with
fertility or child mortality. And the 2000 China census contains information on the
number of women by different education level and by county. So I have three main
variables of interest to be the proxies of those two situations. First one named
femalehigh is constructed as the number of women got senior middle school degree or
above over the number of total women. Second one named femaleedu is constructed
as the number of educated women over the number of total female. Third one is
femalehigh_low constructed as the number of women got senior middle school degree
or above over the rest of educated women. (There are also malehigh, maleedu and
malehigh_low constructed similar to the above three variables, but it uses male’s data.)
The first variable measures the effect of advanced education. Different district has
different culture. For example, areas with more “liberal” cultures may have a
tendency to accept women to be both educated and have fewer children. So we
introduce 4 dummy variables to index different province. For example, if county
belongs to Anhui, duman equals to one; county belongs to Gansu, dumgan equals to
one.
As our measure of fertility, we would ideally like to have the different number of
children of the parents who got different education level. Unfortunately, we cannot
observe fertility for exactly these cohorts. But we have detailed population censuses
that allow us to link the number of children in 2005 with female education levels in
2005. Thus, as fertility measure we use the child–woman ratio constructed as the
number of children aged 0-4 over the number of women aged 20-29 in 2005.
In sum, in our main specification variation across counties of the child–woman
ratio in 2005 is expected to capture variation in fertility of mothers (1) with an
educational level presented by the female enrollment rates in primary school in 2000,
and (2) whose demand and supply factors are captured by our set of control variables
for 2000. From figure 3-6, we know most observations have similar values and slope
of the fitted line is flat.
Figure 3
Figure 4
0.5
11.
52
Ferti
lity
0 .2 .4 .6Female's high level education
children to women ratio Fitted values
Scatter Plot of Fertility on Female's high level educationwith Best-Fit Line
0.5
11.
52
Ferti
lity
.2 .4 .6 .8 1Female's education
children to women ratio Fitted values
Scatter Plot of Fertility on Female's educationwith Best-Fit Line
Figure 5
Figure 6
0.5
11.
52
Ferti
lity
0 .5 1Male's high level education
children to women ratio Fitted values
Scatter Plot of Fertility on Male's high level educationwith Best-Fit Line
0.5
11.
52
Ferti
lity
.4 .6 .8 1Male's education
children to women ratio Fitted values
Scatter Plot of Fertility on Male's educationwith Best-Fit Line
4. Result
Without control variables, both female and male’s high level education and entire
level education have significant relationship with fertility.
When I compare the relationship of advanced education and just education, I find the
relationship between high level education and fertility is more significant.
After adding control variables, none of them have significant relationship with
fertility and to my astonishment; male high level education has a little bit significant
relationship with fertility.
Without control variables, both female and male’s high level education and entire
level education have significant relationship with child mortality.
When I compare the relationship of advanced education and just education, I find the
relationship between entire education level and fertility is more significant.
After adding control variables, both female and male’s educational level have
significant relationship with child mortality.
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