Tsukuba Economics Working Papers
No. 2019-001

Does Education Reduce Fertility in Developing Countries? Evidence
Based on Fuzzy Regression Discontinuity Design in Tanzania
bySalome Maseki and Hisahiro Naito
January 2019

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

Does Education Reduce Fertility in a Low Income
Country ? Evidence based on Fuzzy Regression
Discontinuity Design in Tanzania
Salome Maseki∗
Presidential Office, The Government of Tanzania
Graduate School of Humanities and Social Sciences
University of Tsukuba, Japan
Hisahiro Naito†‡
Department of Economics
Graduate School of Humanities and Social Sciences
University of Tsukuba
January 21, 2019∗

Email: sallymase@gmail.com Address: Graduate School of Humanities and Social Sciences, 1-1-1,
Tennodai, Tsukuba City,Ibaraki Prefecture, Japan Postal Code: 305-8573.
†
Email: naito@dpipe.tsukuba.ac.jp Address: Graduate School of Humanities and Social Sciences,1-1-1, Tennodai, Tsukuba City,Ibaraki Prefecture Japan Postal Code: 305-8573.
‡
The authors appreciate comment from Yuko Nakano, Zeng Fei and Mari Minowa.

Abstract
Using adoption of no-expulsion policy in primary schools in Tanzania and a
fuzzy regression discontinuity design, we examine the causal effect of education
on fertility and child mortality using Tanzania census data sets. A no-expulsion
policy in Tanzania generates a discontinuous change of years of schooling of
females by about 2 years. Using this change of years of schooling, we showthat the effect of education on fertility is non-uniform in the sense that one
year increase of female schooling increases the probability of having at least one
birth by 1.55 percentage point but it decreases the probability of having a large
number of births such as at least 8 births or 10 births by about 3 percentage
points. This suggest that it is not sufficient to focus on the average numberof births to examine the effect of education on fertility. We also find that one
year increase of schooling decreases the number of experienced child death by
0.2 frequency and decrease the child mortality rate by 2 percentage point. Dueto those several offsetting effects, the effect of additional year of schooling on thenumber of surviving children is very close to zero or marginally positive.

1

Introduction

It is well-known that women’s education and fertility has a strong negative associationin the data.(Strauss and Thomas 1995). Some policy makers and researchers suggestthat educating women is one of the key important policy tools to reduce the fertility
in developing countries (Schultz, 2002; Cohen, 2008; Lutz and Samir, 2011). If there isa causal relationship from education to fertility, it would implies that there would be aself-enforcing relation from higher education to lower fertility because a lower fertility
would bring a higher GDP per capita and higher GDP per capita would bring a higher
education. (Caldwell, 1980; Cohen, 2008; Portes, 2006). Some theoretical model of
economic growth assume this negative relationship between education and fertility.
However, whether such an association between education and fertility represents a
causal relationship needs careful examination. The results of the previous studies vary
substantially depending on the estimation method and target countries. An initial
set of studies that uses the simple cross-sectional observation shows that there is anegative relationship between female education and fertility. A study that uses sisterdata shows subtle results implicating there is omitted variable effect. Studies that uses
the exposure of universal education, school construction program or college opening
as an instrumental variable show that there is a causal negative relationship between
female education and fertility (Osili and Long, 2008; Chou et al., 2010; Breierova andDuflo, 2004; Currie and Moretti, 2003; Cygan-Rehm and Maeder, 2013).
Recently, several studies have shown that there is no effect from education to fertility
and some papers show that there is even a positive effect from education to fertility. For
example, using the regression discontinuity design, McCrary and Royer (2011) showthat there is no causal effect of female education on total fertility. Using municipality
level variation of school entry policy in Norway, Monstad et al. (2008) show that there
is no causal effect from schooling to total fertility.
Note that when policy makers advocate education as a policy tool to reduce thefertility, they have developing countries as target countries in their mind. On the other
hand, quasi-experimental research that examine the effect of education on completed
fertility is very limited and, to the best of our knowledge, no paper use the regression discontinuity design, which is thought to be most credible in the literature as a1statistical tool to analyze the causality, to examine the effect of female education oncompleted fertility in developing countries.
The purpose of this paper is to fill this gap. More specifically, in this paper, we use
the change of education policy in Tanzania as a natural experiment and apply the fuzzy
regression discontinuity design to examine the effect of education on fertility. In 1971,
the government of Tanzania issued an order to end the expulsion of primary pupils for
non-payment of school fees. This policy change drastically increased the literacy rate
and years of schooling of women. According to the Census data, literacy rate increased
20 percentage point and the years of schooling increase by two years.
This paper contributes to the previous literature in three aspects. First, as wehave just mentioned, there is a few studies that analyze the causal effect of education
on fertility in developing countries. Given the high population growth rate and lower
years of schooling, the benefit of learning the causal effect of education in developing
countries is substantial. Applying the fuzzy regression discontinuity design, we obtainthe causal effect of education on fertility.
Second, this paper analyzes two waves of census data separately (Tanzania census
2012 and 1988). In the 2012 census data, the threshold age is 48 while the threshold
age in 1988 census is 24. This implies that analyzing based on 2012 census data islikely to measure the effect of eduction on completed fertility and the analysis basedon 1988 census is likely to measure the effect of education on early period fertility.
When we analyze 1988 census, we find that one additional year of education decrease
the fertility by 0.2 which is comparable to the studies by the instrumental variablewhile the analysis based on 2012 fertility shows that the effect of education on thenumber of total birth is much smaller. This suggests that the effect of education on
the number of births depends on the age at which policy change is evaluated.
Third, this paper shows that the effect of education is non-uniform. We find that
education decreases the probability of having a large number of births while it increase
the probability of having at least one birth. Also we find that education decrease
the child death experienced. In other words, educatio transform the fertlity pattern
into more efficient one from large number of birth and large number of child deathto smaller number of births and smaller number of child death. This transformation
will make the numver of surving children unchange or slighly increases. In this sense,2

education does not decrease the population growth rate contray to the assumption ofmany models of economic growth.
The organization of this paper is as follows. In the next section, we review the
related literature and summarize the contribution of this study. In the section 3, we
describe the situation of education policy during 1954 and discuss the environment inthose periods. In section 4, we discuss our identification strategy. In section 5, we
present the results. In section 5, we provide discussion regarding our estimated result
and conclusion.
2

Literature Review and Contribution of This Study

For empirical sides, there are numerous papers that examine the relationship between
female education and fertility (Schultz, 1997; Cochrane, 1979). However, researchers
have been cautious about interpreting the observed association between education andfertility as causal.
Researchers used different identification strategy to examine the effect of female
education on fertility. Currie and Moretti (2003) used the opening of two an four year
collage in 1940-1990 as the instrument to examine the effect of female education on
fertility. Breierova and Duflo (2004) use the large-scaled school construction program inIndonesia as an instrument. They did not find statistically significant effect of female
education on fertility. Osili and Long (2008) examine uses the regional variation of
the exposure to the introduction of universal education in Nigeria as an instrument.
He finds that one additional year of education reduces 0.25 birth. Monstad et al.
(2008) use cross-municipality variation of school reforms in Norway to estimate theeffect of education on fertility. They did not find an evidence that education decrease
the fertility. Fort et al. (2016) compare the effect of education on fertility in UK andcontinental European countries. While they find a negative effect of education in UK,
they find such a positive effect in continental Europe.
Recently, several studies have shown that there is no effect from education to fertility
and some papers show that there is even a positive effect from education to fertility. For
example, using the regression discontinuity design (RDD), McCrary and Royer (2011);
Kan and Lee (2018); Geruso and Royer (2018) show that there is no causal effect of3

female education on total fertility. On the other hand, Silles (2011) applies the RDD
to UK and Northern Ireland and find that education reduces teenage childbearing.
3

Background of Education Policy in Tanzania

Since becoming independent in 1961 the government of Tanzania has made educationa national priority, formulating education policies and carrying out various education
reforms in order to increase access to education at all levels. However, expanding theexisting primary education set-up was not of primary importance to the Government.
The first two development Plans (1961-1964 and 1964-1969) did not prioritize theexpansion of primary education as it was believed there was no direct economic benefits
from primary school growth (Bogonko, 1992). However, after ten years from the firstdevelopment plan, almost as many children in 1971 as in 1961 were unable to getplaces in primary schools. The President Nyerere stated that this was unacceptable toa country which claims to be building socialism (Bogonko, 1992). The presidential alert
caused an immediate action and in 1971 the government ordered to end the expulsionof primary pupils for non-payment of school fees. The effect of this government order(no-expulsion policy) can be seen in the current census data. Figure 1(a) and (b) show
the literacy rate and years of schooling of female individuals for each age group in2012 Population and Housing Census (PHC) for United Republic of Tanzania(PHCT).
Those who were aged 47 or younger is the treatment cohorts and those whose age ismore than or equal to 49 are control cohorts. Figure 1(a) and (b) show that there are
discontinuous jumps in literacy rate and year of schooling between those aged 49 and
47. The literacy rate increases from 49 percent to 70 percent and the year of schoolingincreases about 3.5 years to 5.6 years. 1
Figure 2 (a) and (b) show the literacy rate and years of schooling over age in 1988
Population and Housing Census of Tanzania. Those aged 23 or younger is the treatmentgroup and cohort aged 25 or older is the control group. The cohort age 24 can be either
treatment group or control group. Thus, the cohort whose age is equal to 24 is dropped
from the Figure 2. Figure 2 (a) and (b) show, there is a clear discontinuous jump at
1

For the discussion on the classification of treatment and control cohorts, see the discussion in thenext sub-section.

4

Figure 1: Literacy Rate and Years of Schooling of Different Cohorts in 2012 Census
Years of Schooling over age (Census 2012)

0.2

average literacy rate
.4
.6

average years of schooliing
2
4
6

.8

8

Literacy over age (Census 2012)2040

age=4860age

80

Sample average within bin

100

20

Polynomial fit of order 340

age=4860age

80

Sample average within bin

100

Polynomial fit of order 3(a) Literacy rate over age(b) Years of schooling over age
Notes: Literacy rate and years of schooling of female individuals of different cohorts in 2012
Population and Housing Census for United Republic of Tanzania (PHCT). Age shows theage in August 2012. Individuals aged 48 are dropped because those individuals cannot be
uniquely classified as the treatment or the control group. See the text for the discussion.
The vertical red line is drawn at age=48.

Figure 2: Literacy Rate and Years of Schooling of Different Cohorts in 1988 Census
Years of Schooling over age (1988 census)

00average literacy rate
.2
.4
.6

average years of schooliing
2
4

.8

6

Literacy over age (1988 census)

20

age=2440

60

80

20age=24

100

40

age
Sample average within bin

60

80

100

age
Polynomial fit of order 3

Sample average within bin(a) Literacy rate over age

Polynomial fit of order 3(b) Years of schooling over age

Notes: Sample is female individuals of different age group in 1988 PHCT. Age shows the age
in August 1988. Female individuals aged 24 are dropped because those individuals cannot
be classified uniquely as the treatment or the control group. The vertical red line is drawn
at age=24.

age 24 in both literacy rate and years of schooling, which is consistent with Figure 1.
In this paper, we use this discontinuous jump as identification strategy.

5

4

Identification Strategies and Data set

To estimate the effect of education on fertility and child mortality, we use the Fuzzy
Regression Discontinuity Design, which can be implemented by two stage least squares(2SLS) with continuous control functions. Consider the following 2SLS equations:
Yci = β0 + β1 Schoolci + f (c) + ci(1)Schoolci = α0 + α1 treatc + h(c) + ηci(2)where i is the index of each individual and c is a index of cohort. Yi is the outcome
variable such as the number of births and the number of child death that a respondent
experienced. Schoolci is the years of schooling. treatc is the treatment dummy. If
no-expulsion policy is already implemented at age of 7 for cohort c, which is an entry
age of Tanzanian educational system, treatc is equal to one and otherwise it is equal
to zero.
More specifically, we construct treatc and the data set as follows. The no-expulsion
policy was implemented from January of 1972 and that the academic calender of Tanzania starts from January. Thus, cohort whose age is 7 or younger in January 1stof 1972 is the treatment group and older cohort is the control group. Also note that
individuals who were age 7 in January 1st in 1972 was born in January 2 to December
31 in 1964. On the other hand, all Tanzania censuses were conducted at the end of
August and the censuses did not ask the birth month and birth year but the censuses
asked only each individual’s age at the end of August. This implies that in each census,
there is one age group in which some of them belongs to treatment group and othermember of this age group belong to the control group. For example, in 2012 Tanzania
Census, those who were born from September to December of 1963 and from January
to mid August of 1964 are aged 48. However, among those aged 48, those who wereborn from January to mid-August of 1964 belong to treatment group and those who
were born from September to December 1963 belong to the control group. In order tomake our identification strategy clear, we drop those aged 48 from the 2012 census forthe analysis. For the same reason, we drop cohorts aged 24 from 1988 census.
For the selection of the bandwidth size and functional form of f(c) and g(c), note6

that in our data set, age is only measured by years since Tanzania census did notcollect the information on birth month. This implies that running variable is discrete
and we do not have so much data point near the threshold. This prevents us to use
local linear regression discontinuity. Instead, we use wider window size and 3rd or 4thorder polynomial to control the time trend. This global approach is quite standard in
the regression discontinuity design literature and is used by researchers who examines
the effect of years of schooling using a change of compulsory schooling (Oreopoulos,
2006; Devereux and Hart, 2010).2 We use 30 years for the window size and use cubic
function for f and g. We also conduct robustness check for by applying quantic function.
Because the running variable is discrete, we apply the clustering robust standard error
assuming that error term who has the same age are correlated. As we mentioned above,
we drop individuals whose age is equal to 48.
f (c) and h(c) in equation (1) and (2) are continuous function of c. For the shape off (c) and h(c), we use cubic functions fully interacted with treatment dummy for the
census data in 2012. When we use the census data set in 1988, the threshold year is age
24. This implies that the treatment group might include those aged from 14-16. The
year of schooling continuously increases as age progresses (Figure 2(b)). To capture
this effect, we use quintic function for f (c) and h(c).
Regarding the data set, we use the IPUMS ten percent 2012 census of Tanzania. To
analyze how the education affect fertility behaviors of young individual females, we alsouse 10 percent IPUMS sample of 1988 census of Tanzania where the threshold age is 24.
Regarding other census data set, we do not use the Census data 2002 for our analysis
since there is 370 thousands refugees from the neighbouring countries during the period
1989-2002. The census bureau of Tanzania itself admits that population estimate in2002 census is biased due to the inflow of refugees from neighboring countries (National
Bureau of Statistics Ministry of Planning, Economy and Empowerment Volume X,
2006).3
2

For example, Oreopoulos (2006); Devereux and Hart (2010) analyze the effect of a change ofcompulsory schooling law in the UK when the running variable is year. They use 30 years as window
size and apply quantic function to control the time trend.
3
When we compare age-education distribution of 2002 census with 1988 and 2012 census, we also
confirmed that age-education distribution in 2002 census is different from 2012 and 1988 census.

7

5
5.1

Results
Results based on 2012 Census Data Set

Summary Statistics
Table 1 shows the summary statistics of the data set that we use in our analysis. The
Panel A is the summary statistics of the main variable in our control group where theage of female individuals are greater than or equal to 49 and less than or equal to 53.
The range of the age of treatment group is from 42 to 47. In the control group, theaverage years of schooling is about 3.5 years and literacy rate is about 50 percent point.
Those female individuals in the control group have about 6 births in their lives and theaverage number of surviving children is 5.9. 94 percent point women in this group has
an experience of having at leat one birth and 35 percent point of those women have anexperience of having 8 births in their lives.
For calculating mortality variable, we restrict the sample to female individuals who
experienced at least one birth.
The Effect on Intensive and Extensive Margin of Births
Table 2 shows the first stage regression result of our 2SLS estimation which implement Fuzzy regression discontinuity design. Our instrumental variable is the dummy
variable indicating the age of the respondent is lower than or equal to 47. In the firstand second column, we include the cubic function interacted with treatment. In thethird and fourth column, we include quadratic functions interacted with treatment
dummy. In column 2 and column 4, we include the birthplace dummy and current
residence dummy as additional control variables. In all cases, the Kleibergen-Pappstatistics, the robust version of the first F-statistics, is great than 10, which showsthat our instrumental variable is quite strong. Table 2 shows that at the threshold,individuals who are in the treatment group have about 2 years longer schooling thatthe individuals at the threshold in the control group. This is consistent with graphshown in Figure 1(b).
Table 3 shows the results of the second stage estimation results on birth outcomes.
Panel A and Panel B show the estimated coefficient of the second stage regression when
the dependent variable of the second stage is the number of births and the dummy
8Table 1. Summary Statistics
Control
Variables

Treatment

All

mean

sd

mean

sd

mean

sd

Years of Schooling

3.046

3.620

5.509

3.547

4.662

3.759

Dummy: Literatre

0.474

0.499

0.744

0.437

0.651

0.477

Number of births

6.192

3.477

5.153

2.878

5.510

3.136

Number of surviving children

4.852

2.921

4.464

2.527

4.597

2.675

Dummy: At least 1 birth

0.942

0.234

0.961

0.194

0.954

0.209

Dummy: At least 5 births

0.671

0.470

0.557

0.497

0.596

0.491

A. Years of Schooling and Fertility

Dummy: At least 8 births

0.363

0.481

0.202

0.402

0.258

0.437

Dummy: At least 10 births

0.182

0.386

0.0777

0.268

0.114

0.317

N

77,294

147,510

224,804

B. Mortality Related Variables
Number of child death

1.357

1.545

0.705

1.109

0.926

1.310

Child Mortality Rate

0.193

0.223

0.117

0.187

0.143

0.203

Dummy: At least one child death

0.579

0.494

0.389

0.488

0.454

0.498

Dummy: At least 5 child death

0.06650.249

0.01480.121

0.03240.177

N72,787

141,728

214,515

Notes: Sample is female individuals in 2012 PHCT. Individuals aged from 33 to 47 are treatmentgroup and those individuals who were subject to no-expulsion policy. Individuals aged from 49 to
63 are control group and those individuals are not subject to no-expulsion policy. Individuals aged
48 is dropped due to ambiguity of classification between treatment group and control group. In
Panel B, the sample is restricted to female individuals who experienced at least one birth among
the sample used in Panel A.

variable indicating that the individual has at least one birth, respectively. Panel A
shows that in all specification, additional year of schooling decrease the number ofbirths. It shows that one year increase of schooling decrease the number of births
about by 0.1 frequency. The estimated coefficient is significant at 5 percent. Since theaverage of the number of birth is about 5, the effect is relatively small.
Panel B shows that in all specification having additional year of schooling increase
the probability of having at least one birth in all specification It shows that one additional year of schooling reduce the probability of having at least one birth by about
1.5 percentage point and it is statistically significant at 1 percent.
Figure 3(a) displays the average of number of births over each age group in 20129

Table 2. The Effect of Years of Schooling on Fertlity (2SLS)First Stage Regression Results
Years of Schooling

Dependent Variable
Explanatory Variables(1)
1.944***(2)
1.908***(3)
2.141***(4)
2.188***(0.411)(0.354)(0.560)(0.487)

Bandwidth

15

15

15

15

Order of Polynominal

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

Kleibergen‐Paap Rank Wald

22.38

28.99

14.63

20.15

R‐squared

0.105

0.174

0.106

0.174

N

224,804

224,804

224,804

224,804

Dummy(Age<48)Notes: Sample is female individuals in 2012 PHCT. Bandwidth k means that all female
individuals aged [48-k,48+k] are used for the analysis.

Table 3. The Effect of Years of Schooling on Number of Births
and Extensive Margin: Second Stage Estimates in 2SLS
Dependent Variable
(1)

(2)

‐0.160***‐0.128***N‐0.113**‐0.0828**(0.0291)(0.0262)(0.0494)(0.0416)

224,804

224,804

224,804

224,804

Panel B.
Years of Schooling(4)

Number of Births

Panel A.
Years of Schooling(3)

Dummy(at least having 1 births)
0.0107***0.0103***0.0167***0.0154***(0.00362)(0.00251)(0.00512)(0.00345)

224,804

224,804

224,804

224,804

Bandwith

15

15

15

15

Order of Polynominal

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

N
Specifications

Notes: The table displays the estimated coefficients of years of schooling and theirstandard errors in the second stage of 2SLS estimation when different dependent variables are used. Clustering robust standard errors are in parentheses. The sample isfemale individuals in 2012 PHCT whose age is between [48-k,48+k] except 48 where kis the bandwidth. For all specifications, N=224,804.

10

census. It demonstrates that the number of birth decreases slightly at the thresholdin the treatment cohort compared with the control cohort. Figure 3(b) displays theprobability of having at least one birth over each age group. Figure 3 (b) showsthat, contrary to Figure 3(a), the probability of having at least one birth increases at
the threshold in the treatment cohort. Thus, Figure 3(a) and Figure 3(b) also show
contradictory effects of education on fertility outcomes, which is shown in Table 3.
Figure 3: Number of Births and Probability of Having at least One Birth over Age

.9

4.5

5

.92

probability.94

number of birth
5.5
6

.96

.98

Probabiity of having at least one birth (Census 2012)

6.5

number of births of different cohorts (Census 2012)30

40
Sample average within bin

50
age

60

70

30

Polynomial fit of order 240
Sample average within bin(a) Number of births

50
age

60

70

Polynomial fit of order 2(b) Probability of having at least 1 birth

Notes: Sample is female individuals of 2012 PHCT. Age shows the age in August 2012.
Individuals aged 48 are dropped because this age group cannot be classified uniquely as
treatment or control group. The vertical red line is drawn at age=48.

To examine the contradictory effect of years of schooling on the intensive marginand extensive margin of birth outcome, we look at probabilities of having different
number of births in Table 4 and Table 5. Panel A, Panel B and Panel C of Table 4shows the effect of years of schooling on probability of having at least two births, threebirths and four births in 2SLS estimation. Each Panel show the estimated coefficientof years of schooling and its standard error in 2SLS.
Panel A of Table 4 shows that additional year of schooling increases the probabilityof having at least two birth by about 1.7 percentage point and statistically significant
at one percent in all specification. Panel B that the effect of years of schooling on
probability of having at least 2 birth is not robust and it is statistically significant onlyin column (4). Panel C shows that the effect of years of schooling have at least fourbirths become negative in some cases and the estimated coefficients become unstable.
Table 5 displays the effect of years of schooling on large number of births such as at11

Table 4. Effect of Years of Schooling on Probability of Having at least Few Births :Second Stage Estimates of 2SLS
Dependent Variable
(1)

(4)

0.00910**0.00871***

0.0186**0.0171***(0.00401)(0.00261)(0.00747)(0.00518)

Dummy (having at least 3 births)

Panel B.
Years of schooling(3)

Dummy (having at least 2 births)

Panel A.
Years of schooling(2)

0.002330.001840.01690.0148**(0.00492)(0.00342)(0.0104)(0.00732)

Dummy (having at least 4 births)

Panel C.

‐0.0102**‐0.00934*

0.01220.0116*(0.00485)(0.00529)(0.00904)(0.00610)

Bandwith

15

15

15

15

Order of Polynominal

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

Years of schoolingSpecifications

Notes: The table displays the estimated coefficients of years of schooling and their standard
errors in the second stage of 2SLS estimation when different dependent variables are used.
Clustering robust standard errors are in parentheses. The sample is female individuals in2012 PHCT whose age is between [48-k,48+k] except 48 where k is the bandwidth. For all
specifications, N=224,804.

least having 8 births in 2SLS estimation. Note that as seen summary statistics, in our
sample, 26 percent of our sample have at least 8 births. Thus, having a large numberof births for this sample is not uncommon. Panel A of Table 5 displays the estimatedcoefficients of years of schooling in 2SLS estimation when the dependent variable isa dummy variable indicating a respondent has at least 8 births. Panel A shows that
increasing one year of schooling decreases the probability of having at least eight births
by about 3 percentage point and the estimated coefficients are statistically significant at
one percent in all specifications. Panel B and Panel C of Table 5 display the estimatedcoefficients of years of schooling when the dependent variable is a dummy variable indicating whether a responding has nine birth or ten births, respectively. Panel B andPanel C shows that one additional year of schooling will decrease the probability of
having at least nine births and ten births by 2.7 percentage point and 2.2 percentage
point, respectively. In all specification, the estimated coefficient are statistically signif-

12

Table 5. Effect of Years of Schooling on Probability of
Having a Large Number of Birtths : Second Stage Estimates of 2SLS
Dependent Variable
(1)

(4)

‐0.0295***‐0.0237***‐0.0365***‐0.0297***(0.00527)(0.00473)(0.00637)(0.00583)

Dummy (having at least 9 births)

Panel B.
Years of schooling(3)

Dummy (having at least 8 births)

Panel A.
Years of schooling(2)‐0.0216***‐0.0165***‐0.0334***‐0.0274***(0.00554)(0.00508)(0.00641)(0.00527)

Dummy (having at least 10 births)

Panel C.

‐0.0172***‐0.0135***‐0.0272***‐0.0227***(0.00401)(0.00361)(0.00460)(0.00344)

Bandwith

15

15

15

15

Order of Polynominal Controls

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

Years of schoolingSpecifications

Notes: The table displays the estimated coefficients of years of schooling and their standard errors in the second stage of 2SLS estimation when different dependent variables
are used. Clustering robust standard errors are in parentheses. The sample is female
individuals in 2012 PHCT whose age is between [48-k,48+k] except 48 where k is the
bandwidth. For all specifications, N=224,804.

icant at one percent. Those results explain why we have conflicting results regarding
the effect of years of schooling on the intensive margin and extensive margin. Those
results demonstrates that additional years of schooling reduce the probability of having
a large number of births such as 8 births but it increase the probability of having atleast one and 2 births. Those skewed effect of years of schooling on birth outcomes
results in conflicting results in intensive and extensive margin of the birth outcomes.
Figure 4 shows that graphical relationship of the probability of having different
number of birth for each cohorts. Figure 4(c) and Figure 4(d) shows that a there issubstantial discontinuous drop for younger cohort at the threshold regarding having atleast 8 birth or 10 births, respectively.

13

Figure 4: Probability of Having Multiple Births in 2012 census
Probabiity of having at least 5 births (Census 2012)

.5

.86

.55

.88

probability

probability
.6

.9

.65

.7

.92

Probabiity of having at least 2 births (Census 2012)

30

40

50
age

Sample average within bin

60

70

30

Polynomial fit of order 2

40

50
age

Sample average within bin(a) At least 2 births

60

70

Polynomial fit of order 2(b) At least 5 births

Probabiity of having at least 10 births (Census 2012)

.05

.15

.2

.1

probability.15

probability
.25
.3

.2

.35

.4

.25

Probabiity of having at least 8 births (Census 2012)30

40
Sample average within bin

50
age

60

70

30

Polynomial fit of order 2

40

50
age

Sample average within bin(c) At least 8 births

60

70

Polynomial fit of order 2(d) At least 10 births

Notes: Sample is female individuals of the Tanzania Population and Housing census 2012.
Age show the age at August 1988. Individuals aged 48 are dropped because this age groupcannot be classified as treatment or control group. The vertical red line is drawn at age=48.

The Effect on Child Mortality and Number of Surviving Children
In Table 3, we have shown that additional year of schooling will reduce the numberof births. On the other hand, the number of surviving children, which eventually
determine the population growth rate, is affected not only by the total number ofbirths but also by child mortality. Table 6 examines the effect of years of schooling on
several child mortality related variables. To estimate the child mortality, we restrict
the sample to female individuals who experience at least one birth. Panel A of Table
6 examines the effect of years of schooling on the number of child death a respondent
experienced. Panel A shows that one year increase of schooling reduces the number of14

child death about 0.18 frequency. The estimated coefficients are stable and they are
significant at 1 percent in all specifications. Panel B of Table 6 examines the probabilityof having at least one child death. Panel B of Table 6 shows that one additional yearsof schooling reduces the probability of experiencing at least one child death by about
3.4 percentage point. Panel C, D, E and F of Table 6 display the effect of additional
year of schooling on the probability of experiencing at least two, three, four or fivechildren death, respectively. In our sample, five child death is the maximum number.
In all specifications, the those panel show that one additional year of schooling reduce
the probability of two, three, four or five children’s death about 2-3 percentage points.
Figure 4 show the graphical relationship between child death of different cohort.
Figure 4 shows that for a the number of child death, probability of experiencing at least
one child death, experiencing two child death, four child death, there is a discontinuous
jump at the threshold cohort.
Table 7 examines the effect of years of schooling on the number of surviving children
and the probability of having at least certain number of surviving children (child).
Panel A of Table 7 show that additional year of schooling on the number of surviving
children is unstable. It is worth noticing that the coefficient is positive when we control
the birthplace, current residence and apply cubic polynomial function to control thetime trend. Thus, we do not find a compelling evidence that additional years of female
education decrease the number of surviving children. Panel B show the effect of yearsof schooling on the probability of having at least one surviving child. As expectedfrom the results of Table 5 and Table 6, the estimated coefficient is positive in all
specification and it is statically significant. Panel B show that one additional yearof schooling increase the probability of having at least one surviving child and two
surviving children by 1.8 percentage point and 2.4 percentage points, respectively.
Panel D and Panel E show that effect of years of schooling on the probability of
having a very large number of surviving children. Panel D shows that additional yearsof schooling will decrease the probability of having at least 8 surviving children by
1.8 percent point. Panel E shows that additional year of schooling does not affect theprobability of having at least 10 surviving children.
Over all, Table 7 shows that we do not find a strong evidence that additional yearof schooling reduces the number of surviving children in a compelling way. This is15

Table 6. Effect of Years of Schooling on Number of Child Death and Child Mortality
Second Stage Estimates of 2SLS
Dependent Variable(1)

Panel B
Years of Schooling
Panel C
Years of Schooling
Panel D
Years of Schooling(4)‐0.162***‐0.157***‐0.191***‐0.179***(0.0361)(0.0304)(0.0440)(0.0349)

Dummy (having at least one child death)‐0.0344***‐0.0326***‐0.0370***‐0.0339***(0.00915)(0.00823)(0.0103)(0.00867)

Dummy (having at least 2 child death)‐0.0448***‐0.0433***‐0.0528***‐0.0493***(0.00879)(0.00729)(0.0110)(0.00872)

Dummy (having at least 5 child death)‐0.0173***‐0.0172***‐0.0209***‐0.0200***(0.00418)(0.00350)(0.00520)(0.00405)

Child mortality

Panel E
Years of Schooling(3)

Number of Child Death

Panel A.
Years of Schooling(2)‐0.0181***‐0.0177***‐0.0225***‐0.0210***(0.00519)(0.00413)(0.00619)(0.00463)

Bandwith

15

15

15

15

Order of Polynominal Controls

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

F. Specifications

The above table displays the estimated coefficients of years of schooling and their standard
errors. Clustering robust standard errors are in parentheses assuming that the error term
are correlated within each age cell. The sample is female individuals in 2012 PHCT who
experienced at least one birth and whose age is within [48-k,48+k] except age being 48 where
k is the bandwidth. In all cases, N=214,515

due to several factors. First additional year of schooling will increase the probabilityof having at least one child but decrease a large number of birth. Second, additional
years of schooling reduces the child mortality.

16

Figure 5: Number of Experienced Child Death and Other Probabilities (2012 census)
probability of experiencing child death (Census 2012)

.3

.5

.4

probability.5

number of child death
1
1.5

.6

2

.7

number of child death over age

30

40

50
age

Sample average within bin

60

70

30

Polynomial fit of order 2

40

50
age

Sample average within bin(a) Number of experienced child death

60

70

Polynomial fit of order 2(b) Probability of experiencing at least one
child deathChild mortality rate of different cohorts (Census 2012)

0

.1

.05

probability.1mortality rate
.15
.2

.15

.2

.25

probability of experiencing at least 4 child death30

40
Sample average within bin

50
age

60

70

30

Polynomial fit of order 240
Sample average within bin(c) probability of experiencing at least 4 child
death

50
age

60

70

Polynomial fit of order 2(d) child mortality rate

Notes: The sample is female individuals of 2012 PHCT. Age shows the age at August 2012.
Female individuals aged 48 are dropped because this age group cannot be classified uniquely
as treatment or control group. The vertical red line is drawn at age=48.

17

Table 7. Effect of Years of Schooling on Number of Surviving Children
Second Stage Estimates of 2SLS
Dependent Variable
(1)

(3)

(4)

Number of Surving Children

Panel A.
Years of Schooling(2)‐0.00693

0.02170.07040.0902*(0.0395)(0.0253)(0.0663)(0.0473)

Panel B.

Dummy (having at least one surviving child )Years of Schooling

0.0125**0.0118***0.0196***0.0178***(0.00497)(0.00344)(0.00675)(0.00462)

Panel C.

Dummy (having at least 2 surviving children )Years of Schooling

0.0136**0.0128***0.0265***0.0239***(0.00594)(0.00364)(0.00981)(0.00651)

Panel D.
Years of Schooling

Dummy (having at least 8 surving children)‐0.00853**‐0.00333‐0.0163***‐0.0107***(0.00381)(0.00357)(0.00365)(0.00313)

Panel E.

Dummy (having at least 10 surviving children)

Years of Schooling‐0.00229*

7.62e‐05‐0.00492***‐0.00227(0.00135)(0.00132)(0.00168)(0.00147)

Bandwith

15

15

15

15

Order of Polynominal

2

2

3

3

Current Residene and Birthplace

No

Yes

No

Yes

F. Specifications

Notes: The table displays the estimated coefficients of years of schooling and their standard
errors in the second stage of 2SLS estimation when different dependent variables are used.
Clustering robust standard errors are in parentheses. The sample is female individuals in2012 PHCT whose age is between [48-k,48+k] except 48 where k is the bandwidth. For all
specifications, N=224,804.

18

Figure 6: Number of Surviving Children over Ageprobability of having at least one surviving child3.85

probability.9number of surviving children
3.5
4
4.5

.95

5

number of surviving children over age

30

40

50

60

70

80

30

40

50

age
Sample average within bin

60

70

80

age
Polynomial fit of order 2(a) Number of surviving children

Sample average within bin

Polynomial fit of order 2(b) Probability of having at least one survived
childNotes: Sample is female individuals of 2012 PHCT. Age shows the age at August 2012.
Those aged 48 are dropped because this age group cannot be classified uniquely as thetreatment or as the control group. The vertical red line is drawn at age=48.

5.2

Analysis based on 1988 Census

In the previous sub-section, we have shown that additional years of schooling reduces
the probability of having a large number of births and the number of child death andchild mortality rate. One natural question is the generalizability of our result. Since
the threshold age of our 2012 census data is 48, it is possible that our results are driven
by the change of fertility behavior of women after the age of 35. For example, literatureon health research shows that birth after 35 have risks such as low birth weight of newborn babies (Jacobsson et al., 2004). It is possible that a woman who have longer yearsof schooling knows the risk of having birth after 35 and decide to birth before 35 and as
a result, such women experience lower child mortality. If this is so, then it implies thatthe effect of longer years of schooling can be replaced with much cheaper intervention
such as giving female individual knowledge on the risk of childbirth after 35.
To examine whether such a case occurred, we use the census data in 1988 in which
the threshold age is 24. The following table shows our regression results. As Figure2(b) shows, that the years of schooling increases with very steep slope between age 14
to age 18. To apply the regression discontinuity design, I restrict the sample from age
16 to 34 except age 24. As in the previous analysis that use the 2012 census, cohort

19whose age equal to 24 cannot be classified into one group. To make our identification
clear, we drop the cohort whose age is equal to 24. Since the increase of years ofschooling during age 16-23 is very steep, we use cubic or quintic function to control
the time trend.
Table A1 is the summary statistics of the census data in 1988. Table 8 is theregression results. Panel A of Table 8 shows that additional year of schooling reduces
the number of birth by about 0.4 units. This number is larger than the estimatethat uses 2012 census data set. This is likely to the fact that the estimate that use
2013 cuneus measure the effect f years of schooling on completed fertility while theestimates that use the 2012 census estimate the effect of years of schooling on thenumber of birth during young periods. Panel B show the effect of years of schooling on
the probability of having at least one birth. The estimates are unstable and depends
on the order of control function and controlling covariates. This is perhaps due to thefact that education increases the extensive margin of fertility but reduces the numberof births. Panel C, D, E show the effect of years of schooling on the probability of
having at least two births, three births and four births. It shows that additional yearsof schooling reduces those probabilities. Panel E and F show the effect of years ofschooling on the number of child death experienced and the child mortality. Theyshow that additional years of schooling reduce the number of child death and childmortality by 0.18 frequency and 3 percentage points. Table 8 show that effect of yearsof schooling on the number of birth, the number of child death and child mortality is
consistent with estimates obtain using 2012 census data sets.

20

Table 8. Effect of Years of Schooling on Birth Outcome Using 1988 Census Data
Second Stage Estimates of 2SLS
Dependent Variable
(1)

(2)

N

‐0.416***‐0.428***‐0.372***‐0.377***(0.0504)(0.0512)(0.0675)(0.0647)

161,279

161,279

161,279

161,279

Dummy (having at least one birth)

Panel B
Years of SchoolingN‐0.0731***‐0.0754***0.01630.0178(0.0178)(0.0189)(0.0174)(0.0174)

161,279

161,279

161,279

161,279

Panel C.
Years of SchoolingN

Dummy (having at least 2 births)‐0.0520***‐0.0529***‐0.0319**‐0.0313*(0.0108)(0.0113)(0.0161)(0.0165)

110,777

110,777

110,777

110,777

Dummy (having at least 3 births)

Panel D.
Years of SchoolingN‐0.0500***‐0.0512***‐0.0354***‐0.0358***(0.00879)(0.00907)(0.0129)(0.0130)

161,279

161,279

161,279

161,279

Number of Child Death

Panel E.
Years of SchoolingN(4)

Number of births

Panel A.
Years of Schooling(3)‐0.106***‐0.106***‐0.119***‐0.117***(0.0198)(0.0191)(0.0305)(0.0286)

110,777

110,777

110,777

110,777

Child morality rate

Panel F.
‐0.0107***‐0.0106***‐0.00804**‐0.00762*(0.00266)(0.00296)(0.00369)(0.00406)

110,777

110,777

110,777

110,777[8, 15][8, 15][8, 15][8, 15]

Order of Polynominal

3

3

4

4

Current Residene and Birthplace

No

Yes

No

Yes

Years of SchoolingN
G. Specifications
Bandwith

Notes: The table displays the estimated coefficients of years of schooling and their standard errors
in the second stage of 2SLS estimation when different dependent variables are used. Clusteringrobust standard errors are in parentheses Bandwidth [a,b] implies that for the left side of thethreshold, bandwidth a is used and for the right side of the threshold bandwidth b is used.
The sample is female individuals whose age is within [24-a, 24+b] except 24 where a and b areparameters of bandwidth. For Panel D and E, the sample is restricted further to those who
experienced at least one birth.

21

6Discussion and Conclusion

This study examines the effect of years of schooling on fertility and child mortality
using no-expulsion policy in Tanzania as a quasi-natural experiment in the fuzzy regression discontinuity design framework. We obtain several important findings fromthe analysis. First, the effect of years of schooling is non-uniform. Additional years ofschooling increases the probability of having at least one birth by 1.5 percentage point
but decreases the probability of having a large number of births such as at least 8 births
or 10 birth about 2-3 percentage points. With two effects combined, additional year of
schooling reduces the number of births by about 0.08 frequency which is equivalent toabout 2 percent reduction of the number of births. Second, additional year of schooling
reduces the number of child death by about 0.2-0.3 frequency and child mortality rateby 3-4 percent point. Third, as a result, the effect of years of schooling on the numberof surviving children become zero or marginally positive. Fourth, we find a similar
pattern when we use the 1988 census data where the threshold age of the regression
discontinuity is 24. This implies that the reduction of the total number of births, thenumber of child death and child mortality are observed when the sample is restricted
to young female individuals.
The finding of this paper has several implications. First, as a policy tool to reducea higher population growth rate of developing country, encouraging education is not
the solution. Our analysis shows that education is not likely to reduce the population
growth rate since the education does not decrease the number of surviving children.
On the other hand, if the objective of a policy maker is to find a tool transform fertility
pattern from many births and many child death into less birth and less child death,
our analysis shows that encouraging education is a very effective tool. We find that
additional increase of one year will reduce the probability of having at least 8 birth by 2
percentage point and reduce the child mortality by 3 percentage point. This pattern isobserved even when we restrict our sample to relatively young female individuals. Thistransformation of fertility pattern seems more beneficial to mothers since it will giveless physical burden with the similar number of surviving children. Thus education
can transform fertility pattern into more efficient pattern.
Second, our finding could suggest one reason why different studies regarding the22effect of education on fertility have different results. In the previous literature, regarding the effect of education on fertility, some studies find zero effect and other studies
find negative effect. Our study shows that the effect of education on fertility is nonuniform. Education increase the probability of having at least one birth but it decreases
the probability of having a large number of births. Since the previous studies focus
on the average number of births, they might have had different results. This suggestthat for studying the effect of education on fertility, it is not sufficient to focus on the
average number of births. A new study need to examine how the distribution of births
are affected.

References
Bogonko, S. N. (1992). Reflections on education in East Africa. Oxford University
Press.
Breierova, L. and Duflo, E. (2004). The impact of education on fertility and childmortality: Do fathers really matter less than mothers? Technical report, National
bureau of economic research.
Caldwell, J. C. (1980). Mass education as a determinant of the timing of fertility
decline. Population and development review, pages 225–255.
Chou, S.-Y., Liu, J.-T., Grossman, M., and Joyce, T. (2010). Parental education andchild health: evidence from a natural experiment in taiwan. American Economic
Journal: Applied Economics, 2(1):33–61.
Cochrane, S. H. (1979). Fertility and education: What do we really know? Washington.
DC Johns Hopkins University Press 1979.
Cohen, J. E. (2008). Make secondary education universal. Nature, 456(7222):572.
Currie, J. and Moretti, E. (2003). Mother’s education and the intergenerational transmission of human capital: Evidence from college openings. The Quarterly Journalof Economics, 118(4):1495–1532.
23

Cygan-Rehm, K. and Maeder, M. (2013). The effect of education on fertility: Evidence
from a compulsory schooling reform. Labour Economics, 25:35–48.
Devereux, P. J. and Hart, R. A. (2010). Forced to be rich? returns to compulsory
schooling in britain. The Economic Journal, 120(549):1345–1364.
Fort, M., Schneeweis, N., and Winter-Ebmer, R. (2016). Is education always reducing fertility? evidence from compulsory schooling reforms. The Economic Journal,
126(595):1823–1855.
Geruso, M. and Royer, H. (2018). The impact of education on family formation: quasiexperimental evidence from the uk. Technical report, National Bureau of Economic
Research.
Jacobsson, B., Ladfors, L., and Milsom, I. (2004). Advanced maternal age and adverse
perinatal outcome. Obstetrics & Gynecology, 104(4):727–733.
Kan, K. and Lee, M.-J. (2018). The effects of education on fertility: Evidence fromtaiwan. Economic Inquiry, 56(1):343–357.
Lutz, W. and Samir, K. (2011). Global human capital: integrating education andpopulation. Science, 333(6042):587–592.
McCrary, J. and Royer, H. (2011). The effect of female education on fertility and infant
health: Evidence from school entry policies using exact date of birth. American
Economic Review, 101(1):158–95.
Monstad, K., Propper, C., and Salvanes, K. G. (2008). Education and fertility: Evidence from a natural experiment. Scandinavian Journal of Economics, 110(4):827–
852.
National Bureau of Statistics Ministry of Planning, Economy and Empowerment Volume X (2006). Tanzania census 2002 analyticalreport. Technical report, National
Bureau of Statistics Ministry of Planning, Economy and Empowerment.

24

Oreopoulos, P. (2006). Estimating average and local average treatment effects of education when compulsory schooling laws really matter. American Economic Review,
96(1):152–175.
Osili, U. O. and Long, B. T. (2008). Does female schooling reduce fertility? evidence
from nigeria. Journal of development Economics, 87(1):57–75.
Portes, A. (2006). Institutions and development: A conceptual reanalysis. Population
and Development Review, 32(2):233–262.
Schultz, T. P. (1997). Demand for children in low income countries. Handbook of
population and family economics, 1:349–430.
Schultz, T. P. (2002). Why governments should invest more to educate girls. Worlddevelopment, 30(2):207–225.
Silles, M. A. (2011). The effect of schooling on teenage childbearing: evidence usingchanges in compulsory education laws. Journal of Population Economics, 24(2):761–
777.

25

Appendix

Table 1. Summary Statistics of 1988 Census
Variables
A. Years of Schooling and Fertility
Years of Schooling
Dummy: Literatre
Number of births
Number of surviving children
Dummy: At least 1 birth
Dummy: At least 2 births
Dummy: At least 3 births
Dummy: At least 4 births
N

All

Controlmean
sdTreatment
mean
sd

mean

sd

3.484
0.559
3.805
3.163
0.914
0.816
0.679
0.516

5.585
0.809
0.893
0.759
0.487
0.229
0.09520.0406

4.602
0.692
2.256
1.884
0.687
0.504
0.368
0.263

3.320
0.462
2.429
2.037
0.464
0.500
0.482
0.440

3.467
0.496
2.498
2.103
0.281
0.388
0.467
0.500

75,506

2.841
0.393
1.278
1.095
0.500
0.420
0.293
0.197

85,773

161,279

B. Mortality Related Variables
Number of child deathChild Mortality Rate
Dummy: At least one child death
Dummy: At least 2 child death
Dummy: At least 3 child death
Dummy: At least 4 child death
Dummy: At least 5 child death
N

0.670
0.128
0.384
0.168
0.06680.02730.0114

1.120
0.189
0.486
0.374
0.250
0.163
0.106

68,9880.202
0.06430.149
0.03530.009400.003210.00148

0.590
0.163
0.356
0.184
0.09650.05650.038541,787

0.493
0.982
0.104
0.182
0.295
0.456
0.118
0.322
0.0452 0.208
0.0182 0.134
0.00767 0.0873
110,775

Notes: Sample is female individuals in 1988 PHCT. Control cohorts is female individuals aged from25 to 33 and treatment cohorts is female individuals aged from 15 to 23. In Panel B, the sample isrestricted to female individuals who experienced at least one birth among the sample used in PanelA.

26