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Fertility, Welfare, and the Third World 1Abstract

After 1950 there was rapid population growth across the globe. The surge in population

has brought about many debates and theories as to the impact of this rapid growth, especially in

third world countries. This paper aims to study the implications of this population growth, in the

developing world, in the more recent history. Although reverse causation is difficult to avoid the

study plans to provide evidence of the negative effect high fertility rates have had on the welfare

of developing countries since the turn of the century. This study will measure the welfare

through components of economic growth, health and education. These three aspects can allow

one to see the detrimental impact high fertility rates have on a third world country. Moreover, it

is recognized that this study is not all encompassing, but nonetheless attempts to look at a

number of economic and demographic data to support the aforementioned claim. Having insight

into the significance of high fertility rates on the developing world can allow for better allocation

of resources when aiding countries in need.

Introduction

The World population has increased from 2.5 billion in 1950 to, currently, over 6 billion.

In the period after 1945, rapid population growth resulted from the gap between lower mortality

and high fertility in many of Asias countries. In the 60s other countries, particularly in the

Middle East and South America experienced increasing rates of population growth. These rates

would cause their population size to double in less than 25 years (Bloom, 2). Incredibly the vast

majority of this increase has been experienced in developing countries, as shown in Figure 1.


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Fertility, Welfare, and the Third World 2Figure 1

In the late 1940s it was believed that vast population growth threatened supplies of food

and other natural resources. These concerns have existed since Thomas Malthus wrote his Essay

on Population (1798). Malthus questioned if societies could improve with high population

growth rates. He reasoned that population grows geometrically, whereas food supplies only

arithmetically. Malthus hypothesized that food production would be outrun by the population

increase due to a world with fixed resources and slow technical progress. Although the

Malthusian theory did not exactly pan out the debate about rapid population growth has persisted

since his Essay on Population.

Since 1950 the United Nations (Unite Nations, 1) has periodically reported on population

and its effects. A very convincing argument was made in the 1973 report, which was heavily

influenced by the work of Nobel Laureate Simon Kuznets. Kuznets writes, Country data show

no consistent association between the rate of growth of population and the rate of growth of total


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Fertility, Welfare, and the Third World 3product during the 1950s and 1960s...rapid population growth does not preclude economic

improvement (Birdsall et al., 30). Although much respected and highly regarded, Kuznets

report has not impeded the ongoing debate about population and its implications.

Moreover, the intention is not to prove or disprove theories proposed by either Kuznets or

Malthus. Instead of looking specifically at population growth, mortality, or equilibrium,

historically, this study aims to investigate the implications of fertility rates on the welfare of third

world countries (list of countries can be seen in Appendix) since the turn of the century.

This essay will discuss the analysis of fertility and its negative effects on economic

growth, health and education in developing countries. Although these measures are not all

encompassing the authors feel that with these components one will be able to gauge the

implications of high fertility rates in more recent history.

Intuition

A common and practical method of measuring economic welfare is Gross Domestic

Product (GDP) per capita and it is a convention that will be adopted throughout this paper. GDP

is GDP per capita is gross domestic product divided by midyear population. Figure 2 shows the

relation between GDP per capita and total fertility rates (TFR) for 2008, for all countries. Where

TFR is births per 1000 women aged 15-45 (Barclay, 52). TFR is lagged fifteen years in order to

examine the effects it has had on GDP during the time frame being studied. Although the graph

is only shown for 2008 similar results can be shown for the period between 2000 and 2008. It is

very evident from the figure that there is a strong negative relationship between the fertility rate

and income. It is also important to look at the slope of the curve in Figure 2. The slope decreases

along the curve, which means that countries with very high fertility rates do not see much of a


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Fertility, Welfare, and the Third World 4decrease in income by having an additional child. This only serves to perpetuate the problem

because having an extra child will decrease welfare by less

Figure 2

There are a myriad of plausible explanations for this negative relation but one that stands

out is the burden of age dependency. Age dependency is defined as the ratio of dependent young

and old to the population of working age. The older age groups and the young require intensive

investment in health and education (Bloom et al., 21). Therefore, as fertility rises so does the

burden of providing for the young and the old, which in turn lowers economic welfare.

Research has also shown that fertility has an impact on education. It is the case that the

amount of education invested in each child is a function of the number children the household

has to educate(Birdsall et al., 261). As the number of children in a household increases the

fewer resources there are to promote adequate human capital gains. Negative effects on

education have an adverse effect on long run economic growth.

0

20000

40000

60000

80000

100000

120000

1 2 3 4 5 6 7 8 9

GDP

PerCapitain

2008

TFR in 1993


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Fertility, Welfare, and the Third World 5Moreover, health is also imperative to the wellbeing of a society. Children in large

families tend to have poorer health and lower survival probabilitylarge family size also

appears to inhibit physical development, possibly through lower quality maternity care and

poorer nutrition (Birdsall et al., 203). Due to the fact that these countries are already poor,

consistently high fertility rates only exacerbate the problem.

Analysis: Simple Models

Note Throughout the paper there will be recurring themes that would be best addressed at thistime. All of the regressions will be done using Ordinary Least Squares (OLS) and as a form of

convention the symbol will be used to denote the error term, and twill represent time.

Moreover, although some models with the termb1

TFR(t15)2

may not seem to deserve an intercept

term the OLS regression presents one nonetheless, which for convention will be denoted as a0.

When referring to terms that are not linear, in variables, such asb1

TFR(t15)2

the results obtained

from OLS may have an initial positive coefficient but will be referred to as a negatively related.

For example, in the termb1

TFR(t15)2

, b1

could well be positive, but this relation is negative

because as TFR increases the fraction becomes smaller. Towards the end of this paper there willbe another interpretation presented using basic calculus.

As you can recall from Figure 2there is a distinguished relationship between GDP per

capita and fertility. In order to confirm the significant correlation between the two a regression

was performed. The results from the regression are located in Table 1. To better suit the data the

squared reciprocal of TFR was taken fort-15 years, where tis the years that range from 2000-

2008. (This same manipulation of TFR will also take place in the regressions that follow). With

those specifications the resulting model is displayed by Equation 1 (Eq.1):

Eq. 1: GDP per capitat ! a0 b1

TFR(t15)2

It

Table 1: Simple Relationship Between GDP per capita and TFR


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Fertility, Welfare, and the Third World 6Dependent Variable: GDP per Capita. Based on 147 observations

TFR-2 R2

2000t-statistic

26,777.27.03

0.25

2008 36,169.2

7.05

0.25

Note that the t-statistic is very significant and means that TFR has a negative influence on GDP.

A very telling sign of the negative effect fertility has on GDP is the age structure of a

country. The effect is greater when the working population must care for a greater number of the

dependent population. This will be measured with the Age Dependency Ratio (ADR). ADR of a

population at a given point in time is the ratio of the population in the ages below 15 (P15) and

over 65 (P65) to the population between ages 15 and 65 (P15-65). Put another way

As we can see in Figure 3 there is a very close link between age dependency and fertility.

The graph is very telling because it shows that as fertility increases so does this burden of age

dependency. This is especially true for countries with rapid fertility growth because a smaller

working population must now provide for a larger dependent population. On the other hand,

Slowing population growth through lower fertility produces a demographic dividend, whereby

the proportion of persons of working age increases with respect to that of children and the

elderly(United Nations, 2).

Figure 3

ADR !(P15 P65 )

P1565


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Fertility, Welfare, and the Third World 7

GDPPer Capitat

! a0 b1ADR It

In order to solidify the claim of age dependency as a burden an OLS regression was

performed. The analysis is depicted in Table 2.GDP per capita is the dependent variable and age

dependency is used as the independent variable. The regression was performed linear in variables

like so: Eq2:

Table 2 reveals a significant negative impact of the age dependency ratio on GDP per capita.

Table2: Simple Relationship Between GDP per capita and Age Dependency

Dependent Variable: GDP per Capitat , based on 147 observations

The impact of high fertility along with age dependency can also be felt in the educational

attainment of third world countries. Inadequate allocation of resources is apparent in the case of

education inade1because the inability to control fertility lowers human-capital investments in

children (Birdsall et al., 204). Poor investment in human capital will only perpetuate the

problem of poverty in the third world. Reducing fertility can allow for efficient allocation of

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7 8

DependencyR

atio

TFR (2008)

year Age65 R2

2000t-statistic

-91.73-5.5

0.1764

2008 -179.9-5.75

0.1849


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Fertility, Welfare, and the Third World 8resources and allow countries to spend substantially more in the health and education of each

child than those with higher fertility (United Nations, 1). A prime example can be witness in

Latin American countries where Twenty-one year old children in households with six children

or more have on average two years less of education than children in households with one or

even three children(Birdsall et al., 279). The graph of this example is displayed in Figure 4.

Figure 4

Figure 4

Birdsall, et al., 279

It is also the case that as TFR increases student-to-teacher ratios. This increase in school and

class size has a detrimental impact on instructional quality, thus inhibiting educational progress.

Moreover, the result on education is also exemplified in Table 3 where the negative impact of

the fertility rate on education is evident for the third world countries being studied. The measure

for education (dependent variable) used is tertiary education. Tertiary education normally

requires, as a minimum condition of admission, the successful completion of education at the


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Fertility, Welfare, and the Third World 9secondary level (World Bank). This measure of education was used for two reasons. One, the

data available was more complete than other measure such as literacy rate, for example. Two,

and most importantly, it allows one to gauge long run, economic, implications. By seeing the

negative effect high fertility has on tertiary education it is evident that rapid growth inhibits the

growth of higher education that could lead to prosperity in the future. Furthermore, the

regression model that is used involves squaring the reciprocal of the total fertility rates

(independent variable) like so:

Eq. 3: Tertiary Educationt!a0

b1

TFR(t

15)

2I

t

Table 3: Simple Relationship Between Tertiary Education and TFR

Dependent Variable: Tertiary Educationt

TFR-2 R2

2005t-statistic

94.477.4

0.2704

2008 103.6

11.44

0.4761

Based on 147 observations

Once again the t-statistic is very significant both in 2005 and 2008. Education plays a hug role

and from Table 3 it is evident that TFR has a detrimental effect on education.

The issue of health in the third world is a critical one. The spreading of disease and other

illness can spread with increases in fertility. Further, the combination of a larger household and a

low-income level implies that there will be less care per child. This includes lack of adequate

health care and undernourishment. In fact greater risk of undernourishment appears in larger

households (Birdsall et al., 238). Further, at a macroeconomic level high TFRs lead to a greater

demand of public sector services. These services include allocating different resources for a


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Fertility, Welfare, and the Third World 10countrys overall health. The capacity of the least developed countries to expand public sector

services, such as education and health, is challenged by the rapidly increasing numbers of

children and youth, which have been rising faster than service supply (United Nations, 3). The

regression displayed in Table 4 supports the aforementioned ideals. In this table the health

measure used was Total Health Expenditure Per Capita (dependent variable). Total health

expenditure is the sum of public and private health expenditures as a ratio of total population. It

covers the provision of health services (preventive and curative), family planning activities,

nutrition activities, and emergency aid designated for health but does not include provision of

water and sanitation.(World Bank). The rationale behind this choice was straightforward. The

amount spent on health care is crucial for the quality obtained. Therefore, the authors felt that the

relation between this health measure and TFR would be an indicator of the effect TFR has on the

quality of health. The regression model that is used involves the squaring of the reciprocal of

total fertility rates (independent variable) like so:

Eq. 4: Total HealthExpenditurePer Capita! a0

b1

TFR(t15)2 I t

As was the case with GDP per capita and tertiary education it is clear that an increase in

TFR leads to a decrease in Total Health Expenditure Per Capita. This result is significant not

only quantitatively but qualitatively as well because one can see that not only is the population

growing, and in poverty, but are also allocating less resources to health.

Table 4: Simple Relationship Between Total Health Expenditure and TFR

Dependent Variable: Total Health Expendituret

TFR-2 R2

2003t-statistic

1293.018.11

0.3136

2007 1826.6 0.3721


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Fertility, Welfare, and the Third World 119.32

Based on 147 observations.

Analysis: Extended Model

Although, the argument is that from the years 2000 to 2008 high TFRs have hurt

economic welfare, one may ask if the opposite is what is occurring. What if a low GDP per

capita is what perpetuates significantly high fertility levels? Indeed, this is a very valid question.

Moreover, it is impossible to completely avoid this concern but the authors attempt to address

this question by modifying the previous regressions. To provide additional support to the claim

that negative effects of high TFRs harm third world countries, the authors will add control

variables to the previous regressions in order to solidify the aforementioned results.

To avoid redundancy the control variables and their definitions will be provided first.

1. Foreign Direct Investment (FDI): the net inflows of investment to acquire a lasting

management interest (10 percent or more of voting stock) in an enterprise operating in an

economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings,

other long-term capital, and short-term capital as shown in the balance of payments. (World

Bank)

2. Labor Force Participation Rate (LFP): the proportion of the population ages 15 and older that

is economically active: all people who supply labor for the production of goods and services

during a specified period. (World Bank)

3. Internet Users (IU): Internet users are people with access to the worldwide network. (World

Bank)


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Fertility, Welfare, and the Third World 124. Gross national income (GNI), PPP: GNI per capita based on purchasing power parity (PPP) is

gross national income (GNI) converted to international dollars using purchasing power parity

rates. (World Bank)

5. Percent of Population aged 15-64 (P15-64): The percentage of the total population that is in the

age group 15 to 64. Population is based on the de facto definition of population. (World Bank)

6. Political Structure Dummy Variable (D1): A dummy variable will be introduced that will

represent the political structure in a given country. A value of one will be assigned to countries

that have either a democratic political structure, democratic republics or full/semi presidential

systems and a zero will be assigned to countries that have a monarchy, oligarchy, dictatorship or

other unstable forms of government. The political structure dummy is included in all of the

models because one would expect a democratic system to have a positive effect on GDP per

Capita, Health Expenditure per person and Tertiary Education.

Additionally, in order to help interpret the following control models it would be helpful

to examine the understanding of the slope coefficient a bit further with basic calculus. If the

authors reference a negative relation with a positive coefficient it is because the model that is

being used is not linear in the variables. For example take

Total HealthExpenditurePer Capita! a0 b1

TFR(t15)2

b2P1564,t b3FDIt b4 IUt b5D1,t I t

If b1 yields a positive value this does not signify a positive relation. On the contrary if we were to

take a partial derivative to obtain the actual slope, it is evident that for every TFR the slope is

negative. Here is the example after taking a partial derivative with respect to TFR:


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Fertility, Welfare, and the Third World 14as well. Lastly, FDI is included because this was felt to increase standard of living and thus the

amount each person spends on health care. Table 5shows the regression results.

Table 5: Regression Estimates (with Control Variables) of the Relationship Between Total

Health Expenditure and TFR (2005-2008).

Dependent Variable: (Total Health Expenditure per Capita)t

Year a0 TFR-2

t-15 P(15-64) FDIt IUt (D1)t

R2

2003t-statistic

-523.2-2.6

716.72.8

10.012.8

0.530.35

7.95x107

0.4214.80.5

0.35

2004 -555.8-2.53

894.153.3

10.662.7

-0.98-0.32

4.5x10-70.25

23.10.67

0.36

2005 -530.1-2.23

1102.33.9

9.992.39

1.240.38

6.9x10-70.43

19.80.53

0.40

2006 35.60.41

1502.67.46

0.530.36

-6.4x10-80.5

3.2x10-80.66

20.890.62

0.34

2007 -1025.3-4.5

1181.975.08

18.54.72

2.630.94

-5.6x10-7-0.51

19.10.43

0.47

OLS-estimates, n=147.

It is clear from Table 5 that an increase in fertility negatively impacts Total Health Expenditure.

In order to more clearly see the effect of fertility it is best to illustrate the interpretation with an

example. Using the year 2005 as a sample year and a TFR of 5, for instance, leads to a decrease

of $17.6368 (using Eq.5) in total health expenditure per capita. Now, if one considers a TFR of

6, for the same year, the decrease in total health expenditure per person would be $ 10.206. If

you increase TFR from 5 to 6 in 2005 the average effect on GDP per capita will be -13.92 in

total health expenditure per person.

Looking back at the t-statistics from Table 4 one can see the statistical significance is

very high. Although, the significance decreases in the extended health model, due to the


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Tertiary Educationt

! a0 b1

TFR(t15)2

It

inclusion of control variables, the result is the same. Table 5 still displays significant t-statistics

at a 5% significance level. Moreover, in order to see if the population coefficients are different

form zero, in other words checking if R2 is different from zero, an F-test was performed. This

test concludes that the null hypothesis (H0: R2=0) is rejected for every single year studied. The

details of this test can be found in the appendix.

As expected the coefficient of the control variable P15-64 is positive and significant.

Although it is meaningful the impact of TFR remains statistically significant. Interestingly, the

effect of D1 is positive however, not significant. This implies that countries with different

political structures do not affect total health expenditures (for the countries studied).

Education Model

Recall that the education measure used is tertiary education and regression model used

was (Recall Eq.3)

Once again control variables will be added to this model in order to see if TFR still negatively

effects tertiary education. The control variables included to the education model are GNI per

capita, IU, and the political structure dummy variable. After implementing the control variables

the new model is

Eq. 7: Tertiary Educationt ! a0 b1

TFRt152 bxb4D1.t It

where b is a column vector of b2, b3, and x is a row vector of GNIt and IUt.


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Fertility, Welfare, and the Third World 17Looking back at the t-statistics from Table 3 one can see the statistical significance is

very high. Although, the significance decreases in the extended education model, due to the

inclusion of control variables, the result is nonetheless the same. Table 6 still displays significant

t-statistics at a 5% significance level. Moreover, the F-test yields the same result in rejecting that

R2=0 (refer to appendix).

GDP Models

Recall that the GDP/ADR regression model was (recall Eq. 2)

GDPPer Capitat ! a0

b1ADRI

t

As before the model will be revised and control variables will be included to examine the effect

of age dependency on GDP. For this model the control variables added were LFP, FDI, IU, and

D1. The control model looks like so

Eq. 8:

GDP per Capitat ! a0 b1ADRt bxb5D1,t I t

where b is a column vector of b2, b3, b4, and x is a row vector of LFPt, FDIt, IUt.

LFP was included because it is sensible to expect a positive effect on GDP per capita as more

workers enter the labor force. FDI was included because one would expect foreign investment to

increase GDP as well. Further, IU is implemented because the authors correlate internet growth

with affluence and internet use can potentially lead to more jobs which can lead to and increase

in economic welfare. Table 7 contains the results of the modified extended model.

Table 7: Regression Estimates (with Control Variables) of the Relationship Between GDP

and ADR (2000-2008)


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t-statistic

2143.3

0.96

26,645.7

6.26

8.8

0.10

-23.1

-0.7

0.0001

1.13

-572.7

-0.78

0.27

2001 2095.2

0.98

26,653.9

6.66

18.1

0.33

-24.2

-0.75

7.4x10-5

0.83

-488.7

-0.7

0.28

2002 1731.2

0.77

29,433.7

7.14

-0.93

-0.02

-19.6

-0.6

3.4x10-5

0.6

-490.1

-0.7

0.30

2003 2197.1

0.89

33,346.2

7.45

-5.6

-0.14

-26.8

-0.73

3.1x10-5

0.63

-560.1

-0.7

0.32

2004 2498.5

0.87

37,183.8

7.37

-51.5

-0.62

-26.8

-0.63

3.6x10-5

0.78

-776.9

-0.83

0.32

2005 1949.06

0.6

37,052.3

6.64

48.7

0.54

-19.8

-0.41

-3.6x10-5

-0.81

-398.9

-0.4

0.29

2006 3748.7

0.98

36,563.3

6.3

-27

-0.3

-37.3

-0.68

-3.2x10-5

-0.77

-330.7

-0.29

0.27

2007 3598.2

0.83

39,651.5

6.5

3.5

0.04

-34.8

-0.55

-2.8x10-5

-0.89

-345.1

-0.26

0.27

2008 6134.8

1.32

36,800.8

6.4

-72.3

-0.81

-54.12

-0.8

-2.6x10-5

-1.04

-488.23

-0.35

0.27

OLS-estimates, n=147.

Concluding Remarks

The topic of fertility, population and economic growth has been a source of debate for

over two centuries. In this study the issue of fertility and its effect on third world countries is

examined. With, already, limited resources high fertility rates can be another obstacle for


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Fertility, Welfare, and the Third World 21developing countries to overcome and this is exactly what is observed in this study. This paper

begins by examining the simple correlation of fertility on GDP, health, and education. What is

seen is that simple correlations show that fertility has a negative impact on all three. The reason

Health and education are measured aside from GDP is for the simple fact that GDP is not the

only measure of welfare in a country. The authors felt that if the results for all three measures

were negative then the impact of high fertility on a country will be solidified. In addition, age

structure also seems to play a vital role in a countrys wellbeing. ADR regression is closely

related to fertility and also has a negative impact on GDP per capita. Having a working

population care for an even larger dependent population is damaging to a developing country and

high fertility rates exacerbate this issue. Moreover, in order to minimize the doubts regarding

reverse causality the authors attempt to show strong a correlation by implementing control

variables. The authors attempted to choose variables that would contribute positively to GDP per

capita, health expenditures, and tertiary education in order to test the significance of TFR. After

analyzing these models the conclusion is the same. TFR still significantly impacts GDP, health

expenditure, and tertiary education.

Although this study is only a small look into the issue of ramped fertility it does hope to

offer some insight. Many times there is this notion of lending and spending money in third world

countries in order to reach prosperity. Through this study the authors hope to show that this is not

the only manner in which this can be handled. Effective contraceptive methods as well as

integrating family planning with other health services, especially those related to maternal and

child health, can also lead to development and eventual long run success.


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Fertility, Welfare, and the Third World 24Appendix

F-test with Corresponding F-Table

1.

Year F-Stat (F*)

2000 10.2

2001 11.2

2002 6.8

2003 13.3

2004 13.12

2005 11.6

2006 10.32007 13.57

2008 10.36

E=0.05

Degrees of Freedom (d.f.) numerator = 5, d.f. Denominator= 141, Fc=2.21 (critical value)

Joint-Hyphothesis that B1,B2,B3,B4,B5 are simultaneously equal to zero. H0:B1=B2=B3=B4=B5=0,or H0: R

2=0

Fc>F* for all years, therefore we reject the joint null hypothesis, that all partial slope coefficientsare equal to zero.

2.

Year F-Stat (F*)

2000 6.4

2001 6.8

2002 6.76

2003 7.35

2004 7.32

2005 5.96

2006 6.26

2007 6.32

2008 6.86

E=0.05, d.f numerator = 5, d.f denominator =141, Fc=2.21

Joint-Hyphothesis that B1,B2,B3,B4,B5 are simultaneously equal to zero. H0:B1=B2=B3=B4=B5=0,or H0=R

2

GDPPer Capitat

! a0 b1

TFR(t15)2

b2LFPt b3FDIt b4 IUt b5D1,t It

GDPPer Capitat

! a0 b1ADRt b2LFPt b3FDIt b4 IUt b5D1,t I t


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Fertility, Welfare, and the Third World 25Fc>F* for all years, therefore we reject the joint null hypothesis, that all partial slope coefficientsare equal to zero.

3.

Year F-Stat (F*)

2005 16.1

2006 33.5

2007 39.9

2008 35.7

E=0.05, d.f numerator = 4, d.f denominator =142, Fc

=2.37

Joint-Hyphothesis that B1,B2,B3,B4 are simultaneously equal to zero. H0: B1=B2=B3=B4=0,H0=R

2


4.

Year F-Stat (F*)

2003 15.3

2004 16.16

2005 18.7

2006 14.4

2007 24.7

E=0.05, d.f numerator = 5, d.f denominator = 141, Fc=2.21

Joint-Hyphothesis that B1,B2,B3,B4,B5 are simultaneously equal to zero.H0: B1=B2=B3=B4=B5=0,H0=R

2


TertiaryEducationt

! a0 b1

TFR( t15)2

b2GNIt b3IUt b4D1,t I t

Total HealthExpenditurePer Capita! a0 b1

TFR( t15)2

b2P1564,t b3FDIt b4 IUt b5D1,t I t


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