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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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Fertility, Welfare, and the Third World 15
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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Fertility, Welfare, and the Third World 202000
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
Fc>F* for all years, therefore we reject the joint null hypothesis, that all partial slope coefficientsare equal to zero.
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
Fc>F* for all years, therefore we reject the joint null hypothesis, that all partial slope coefficientsare equal to zero.
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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8/3/2019 Final Paper Done
26/26
Fertility, Welfare, and the Third World 26