Fertility and socioeconomic development: the

role of education, life expectancy and GDP per

capita

Mareen Bastiaans (414723) & Vittorio Romaniello (405961)

Final Paper Bachelor Honours Class 2015 - 2016

Abstract

This paper investigates the role of factors of socioeconomic development namely, education, life

expectancy and GDP per capita, on fertility. Specifically, the role of these factors in relation to a re-

increase in fertility in countries with high Human Development Index (HDI) levels is considered. Panel

data on 141 countries over the period 1975-2005 is analysed using fixed effects regressions. A U-shaped

relationship between fertility and HDI is found. GDP per capita appears to be the main driver in this

relationship. Furthermore, infant mortality and secondary and tertiary education show significant effects

on fertility. Life expectancy and primary education are found to only limitedly influence fertility.

I. Introduction

Low fertility rates are of concern to governments in the developed world. OECD countries have been

experiencing declines in their fertility rates. Women postpone childbirth to a later stage of their life,

and have fewer children. Many developed countries are expected to experience fertility rates below

replacement levels (D’Addio & D’Ercole, 2005).

The OECD warns for the risks of the postponement to the mother and child’s health (D’Addio &

D’Ercole, 2005). Moreover, low fertility levels create challenges on social, economic and political

level; by ageing of the population existing welfare systems are threatened as relatively more elderly

depend on these. Furthermore, population ageing will tend to lower both labour-force and savings

rates, thereby raising concerns about a future slowing of economic growth (Bloom, Canning, & Fink,

2010). Thus, fertility rates below two children per women are not favoured by many developed

countries.

Socioeconomic development and fertility were long considered to be negatively correlated (Willis,

1973). Therefore, low fertility rates in highly developed countries were expected to not re-increase.

However, Myrskylä, Kohler, and Billari (2009) observed a re-increase of fertility in countries with a

high Human Development Index (HDI) level. An increase in HDI is associated with a decrease in

fertility of 1.59 children per woman in low to middle HDI level countries, whereas in countries with

a high HDI (above 0.86) an increase in HDI is associated with an increase in fertility of 4.07 children

per woman. It remains unclear where the reversal stems from and recent studies have disputed the

existence of such a re-increase in fertility (Furuoka, 2009; Harrtgen & Vollmer, 2014).

This paper aims to bridge this gap by studying the relation between fertility and socioeconomic

development. More precisely, we study separately to role of income (growth), education, life

expectancy and infant mortality on fertility rate. By studying the effect of each individual component

of HDI on fertility, we hope to get a better understanding on what caused the reversing of the

relationship between fertility and socioeconomic development. Thus the following research question

will be addressed in this paper:

How do different indicators of socioeconomic development affect fertility?

In this paper, we will consider socioeconomic development as opposed to economic development

(GDP per capita). It seems fitting to consider a broader definition of development, as the literature

indicates that fertility is not only affected by income.

This paper will use panel data of 141 countries over the period 1975-2005 to perform an empirical

analysis using panel data estimation techniques to analyse the relationship between fertility and

socioeconomic development. Our main focus will be on the role of different aspects of economic and

social development.

This paper will enhance the existing knowledge of the relationship between fertility and

socioeconomic development. Specifically, by considering the components of socioeconomic

development, the role of the separate components in the reversal in this relationship will be clarified.

More understanding of the root of the re-increase in fertility in highly developed countries will be

gained.

Moreover, these insights can be used by policymakers to incorporate in models predicting fertility

trends and its effects. Furthermore, this knowledge is highly valuable when designing policy to

enhance fertility rates as to prevent issues caused by low fertility rates and population ageing, such

as vulnerability of welfare systems and economic stagnation.

Our results confirm a quadratic relationship between fertility and human development which is

negative for low levels of HDI and positive for higher levels of HDI. This reversal is caused by GDP

per capita. Infant mortality is also found to be an important factor in the relationship.

This paper is structured as follows. Section 2 gives an overview of the existing theoretical and

empirical literature. Section 3 presents the data, section 4 the methodology. Results will be discussed

in section 5. Lastly, section 6 concludes.

II. Theoretical framework

First our main concepts, fertility and the Human Development Index, will be briefly defined.

Afterwards, literature related to fertility and income will be discussed. Furthermore, some empirical

literature related to fertility will be presented.

Within economics fertility is considered as the amount of children a women has or has had over her

lifetime. It thus does not refer to the biological capability of having children, but actual children born

and only includes live births. Often fertility is measured by the Total Fertility Rate (TFR), which will

also be used in this paper and is discussed in more detail in the data section.

The Human Development Index is a measure for human development proposed by the United

Nations Development Program (UNDP). It was introduced as an alternative measure for the wealth

or wellbeing of a country as it captures more than GDP per capita, which is often used for this

purpose. The motivation behind the HDI is that human development at its core is the expansion of

people’s choices. The most important components of the HDI are a measure for life expectancy and

health, education and standard of living (UNDP, 1990). HDI will be discussed in more detail in the

data section.

Next, two main streams of literature related to fertility are considered. These theories mainly focus

on the role of income, therefore only two hypotheses on fertility and income and HDI respectively

will be formulated. Household/labour-supply models view children as goods that are produced and

consumed by the household are discussed first. Here fertility can be analysed in a normal consumer

demand theory framework. Afterwards the role of fertility in growth theories will be discussed.

Becker (1960) analyses fertility in an economic framework where children are considered as

consumer durables that provide parents with utility. Parents are faced with a trade-off between

quantity and quality of children. The resources (time, money) spent on children determine their

quality.

In this framework, tastes, income, cost of children, contraceptive knowledge and uncertainty

determine fertility. Income is predicted to have a positive effect on fertility, as children are

considered to not be an inferior good. The income elasticity of quantity is expected to be small and

positive, the income elasticity of quality positive but larger.

However, in practise a negative relationship between income and fertility is observed. Becker (1960)

tries to explain this by considering contraceptive knowledge and access. Higher income families have

better contraceptive knowledge and access, and thus are better able to control their fertility. Thus

the relationship between desired fertility and income is positive, but the relationship between actual

or observed fertility and income is negative.

Mincer (1962) introduced the concept of allocation of time within households. Becker (1965)

formalizes this theory in a model wherein families combine time supplied by family members with

goods and services bought on the market to produce the commodities.

Willis (1973) combines time allocation and the quantity-quality trade-off in a framework of fertility

decisions. Children are considered a good that are both consumed and produced by parents. Parents

need to decide how to spend their limited resources on the number and quality of children and other

goods and services. The production of children is limited by consumption technology, men and

women’s earnings potential and the endowment of the woman’s time and her non-labour income.

Willis (1973) agrees with Becker (1960) that the wealth elasticity of the number of children is small,

however, contrary to the latter the possibility of a negative value is also considered. The effect of the

opportunity costs of child services are considered to have a negative effect on fertility. Willis (1973)

emphasizes the role of the cost of female time. Childbearing and childcare is very time intensive,

women’s wage or value in other non-market activities should thus be considered as opportunity cost

of having children. As higher income is associated with higher value of female’s time, a negative

relationship between income and fertility is expected.

The framework has been further extended as to allow for a dynamic setting in which decisions

regarding fertility are made at multiple points in time (Heckman & Willis, 1975).

Easterlin (1975) has criticized the neoclassical fertility frameworks as presented earlier. These

consumer demand settings are not appropriate to fertility as fertility is not always controlled. Actual

fertility may be above or below desired fertility. A framework that analyses fertility should keep this

in mind.

Another approach to fertility, are those theories that discuss the relationships between fertility,

population growth and income.

In Solow’s model of economic growth (1956), an increase in population growth causes the capital per

worker to decline and thus negatively affects capital accumulation and output per worker. High

fertility causes high population growth and thus results in lower income. Opposed to the models

presented earlier, income does not affect fertility, but fertility negatively affects income.

Galor and Weil (1996) combines growth theories and household/labour-supply theories to analyse

the relationship between economic growth and fertility. A positive loop is discussed. An increase in

capital per worker increases women’s wage, as their productivity is more linked to capital. An

increase in women’s relative wage decreases fertility, as it increases the cost of children through the

time women must spend on them. Low fertility leads to a further increase in capital by worker,

strengthening the process. Low fertility and income growth thus strengthen each other.

On the basis of the literature discussed above, we expect that income and fertility are negatively

linked as an increase in income increases the cost of having children through the opportunity costs

of female time.

Hypothesis 1: Higher levels of GDP per capita in a country lead to lower fertility.

Furthermore, we expect the same to hold for the Human Development Index, as it is positively

correlated with income, which is one of its main determinants.

Hypothesis 2: The relationship between fertility and HDI is negative.

We will now move on to a discussion of empirical literature regarding fertility. Specifically, the

empirical paper that inspired this research.

Myrskyla et al (2009) discovered a reversal in the relationship between HDI and TFR. Among

countries of low and middle HDI levels a negative association was found, as predicted by the

household/labour-supply models discussed above. However, among highly developed countries this

pattern had reversed and the countries with higher development levels experienced higher fertility

rates. The relationship between HDI and TFR is suggested to be characterized as a J-shape, with a

turning point at a HDI of 0.86.

Luci-Greulich and Thévenon (2014) find a similar reversal in the relationship between economic

development and fertility that is robust for birth postponement. By decomposing GDP per capita,

female employment is found to be associated with the decline in fertility. Furthermore, they point

out that fertility rates can only partly be explained by economic development, highlighting the

importance of institutional factors.

Not all evidence supports the J-shape found by Myrskyla. Employing threshold analysis, Furuoka

(2009) does not find a reversal in the relationship between TFR and HDI. The reversal is also not

found using the revised HDI from the UNDP nor when development is decomposed in the sub-indices

health, education and life expectancy (Harttgen & Vollmer, 2014).

Thus from the empirical literature it is unclear whether a re-increase in fertility is likely to be found.

III. Data

Data come from the World Bank World Development Indicators Online Database. Our dataset covers

a period of 31 years, from 1975 to 2005, and contains data for 141 countries.1 The panel dataset is

unbalanced (3436 observations for 141 countries), due to missing observations. The time span and

the countries are chosen in line with the analysis of Myrskylä et al. (2009). 2

Our dependent variable, fertility, is measured as the total fertility rate (TFR). TFR is the total amount

of children a woman is predicted to have over her lifetime, based on age-specific fertility factors

(United Nations Sustainable Development, n.d.). Our key variable of interest is the Human

Development Index (HDI), a development indicator produced by the United Nations Development

Programme. The index summarizes human development in a country based on three sub-indices:

1 A list of countries included can be found in Appendix. 2 With respect to Myrskylä et al. (2009), we had to drop South Korea and Brazil because of too many missing

observations.

health (life expectancy), education (enrolment in primary education and adult literacy) and standard

of living (GNI per capita) (UNDP, n.d.). Data for TFR and HDI are directly retrieved from Myrskylä et

al (2009) for its completeness.

In our analysis we use the different components of HDI separately. GNI per capita is included in the

HDI as a measure for standard of living, we use GDP per capita for this. The sub-index for education

consists of enrolment in primary education and adult literacy. Data on adult literacy was very limited,

therefore we only consider enrolment in primary education. Lastly, life expectancy as a measure for

health is included.

Traditionally, development is represented by growth in GDP per capita. Development is accounted in

this perspective by including a measurement for GDP per capita. We select GDP per capita as a proxy

for economic development, because it can easily be compared across countries. It would have been

preferred to also account for inflation by including GDP per capita PPP, however data on this was not

available for our time frame. GDP per capita is transformed in growth rates using a logarithmic

transformation.

As a proxy of education, gross enrolment in primary education is chosen. Measured as percentage of

the population with primary school age that is actually enrolled. Gross enrolment in secondary or

tertiary education may be preferred because primary education has become standard in many

countries. However, very few data is available for these measures for the beginning of our time span

as compared to gross enrolment in primary education. Therefore the latter is chosen. Robustness

checks on gross enrolment in secondary and tertiary education will be included.

Life expectancy at birth is included.

Further we add several control variables which are considered to have an effect on fertility. Two

measures for child mortality are included as control variable: mortality under five and infant

mortality. Infant mortality is used, child mortality will be used in a robustness check.

Furthermore, a dummy variable for OECD countries is created using information from the World

Bank. This will be used to conduct analyses on a subsample of developed countries.

Table 1 Descriptive statistics

Variable Observations Mean Std. Dev Min Max

TFR 3436 3.88 1.99 1.09 8.5

HDI 3436 0.67 0.18 0.25 0.97 log(GDP per capita) 3436 7.48 1.55 4.57 11.28 Life Expectancy 3436 64.73 10.59 29.75 82.03 Mortality Under 5 3436 74.44 73.24 3.1 368.3

Infant Mortality 3436 -49.5 41.24 2.4 184 Primary Enrolment 3436 95.43 21.43 14.12 161.13 Secondary Enrolment 2929 60.8 33.8 0.64 162.61 Tertiary Enrolment 2668 20.04 19.5 0.01 97.1

IV. Methodology

To investigate the existence of a quadratic relationship between TFR and various indicators of

socioeconomic development, regression models are employed. Given the nature of the data being

used, observations for a time span of 31 years for 141 countries, a panel regression with year and

country fixed effects is used. Furthermore, heteroskedasticity robust standard errors, clustered at

the country level are employed.

To control for time varying factors across countries year fixed effects are included. Country fixed

effects are incorporated to control for time invariant country specific factors that have time invariant

effect on fertility, such as cultural norms. The fixed effects model allows us to consider the time

invariant effects of the explanatory variables on TFR. Thereby, we assume that the effects are

constant over time and across countries.

First, we replicate the findings of Myrskyla et. al. (2009) and investigate the existence of a nonlinear

relationship between TFR and HDI. This is done comparing a linear fixed effects model for HDI to our

baseline specification:

𝑇𝐹𝑅𝑖𝑡 = 𝛽0 + 𝛽1𝐻𝐷𝐼𝑖𝑡 + 𝛽2𝐻𝐷𝐼𝑖𝑡2 + 𝛾𝑖 + 𝛿𝑡 + 𝑢𝑖𝑡

𝑇𝐹𝑅𝑖𝑡 is the total fertility rate for country i in year t. 𝛾𝑖 represents the country fixed effects, 𝛿𝑡 the

year fixed effects, 𝑢𝑖𝑡 is the idiosyncratic error term. Here 𝛽1 and 𝛽2 capture the partial effect of HDI

and 𝐻𝐷𝐼2 respectively on TFR, irrespective of country and year.

Second, we account separately for the factors influencing development in a country: GDP per capita,

life expectancy, and gross enrolment in primary education, and control for infant mortality. The

squared terms are included in order to allow for nonlinearity in the effect on TFR. The results will be

compared to a regression without the squared terms.

𝑇𝐹𝑅𝑖𝑡 = 𝛽0 + 𝛽1𝑙𝑜𝑔(𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎)𝑖𝑡 + 𝛽2𝑙𝑜𝑔(𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎)𝑖𝑡2 + 𝛽3𝐿𝑖𝑓𝑒𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦𝑖𝑡

+ 𝛽4𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦𝑖𝑡2 + 𝛽5𝐼𝑛𝑓𝑎𝑛𝑡𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝑖𝑡 + 𝛽6𝐼𝑛𝑓𝑎𝑛𝑡𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦𝑖𝑡

2

+ 𝛽7𝐺𝑟𝑜𝑠𝑠𝐸𝑛𝑟𝑜𝑙𝑚𝑒𝑛𝑡𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡 + 𝛽8𝐺𝑟𝑜𝑠𝑠𝐸𝑛𝑟𝑜𝑙𝑚𝑒𝑛𝑡𝑃𝑟𝑖𝑚𝑎𝑟𝑦𝑖𝑡2 + 𝛾𝑖 + 𝛿𝑡 + 𝑢𝑖𝑡

Again, 𝑇𝐹𝑅𝑖𝑡 represents the total fertility rate in country i in year t. Country fixed effects, 𝛾𝑖 , and year

fixed effects, 𝛿𝑖 , are included. 𝑢𝑖𝑡 represents the idiosyncratic error term. Beta’s can be identified as

the partial effect of the respective variable on fertility, which is constant over time and across

countries.

Finally, we combine to two previous regressions to investigate whether the development indicators

used in equation 2 can capture the effect of HDI on TFR.

The same analysis is conducted for a subsample including only 31 OECD countries and for only 110

non OECD countries (see appendix). This is done to analyse whether the relationship between

fertility and socioeconomic development is similar or different for developed and less developed

countries.

We run various robustness checks on the complete and subsamples. Namely, we include gross

enrolment in secondary and tertiary education, and child mortality in the regression models. This

procedure is employed to verify whether the relationships found for the considered development

indicators are still valid when using different proxies. Throughout the analysis we indicate

significance at the 5% level.

V. Results

Our main results are presented in Table 2 (see Appendix). In column 1 the coefficient of HDI is

included only linearly. The coefficient is not significant. In column 2 we add the squared term, both

coefficients are significant at the 1% significance level. We thus confirm results by Myrskylä et al.

(2009) on the nonlinear relationship between HDI and TFR. Furthermore, the sign of the coefficients

indicates a U-shaped relationship, where TFR and HDI are negatively associated for low to middle

levels of HDI and positively associated for high HDI levels (see Figure 1).

Figure 1 Fixed effects regression of HDI on TFR

In column 3, HDI is replaced by its components and infant mortality, as a control variable. All the

variables are included linearly. The coefficients indicate that both GDP per capita and infant mortality

have a positive effect on TFR. Life expectancy and enrolment in primary education are not significant

when included linearly.

Squared terms of the variables in column 3 are added in column 4 to allow for nonlinearity in the

relationship with TFR. GDP per capita, infant mortality and the respective squared terms are

significant at the 5% significance level. Life expectancy and enrolment in primary education remain

insignificant also after including the squared terms.

Furthermore, the sign of the coefficients for GDP per capita (-1.37) and the squared term (0.12)

indicate a U-shaped relationship between GDP per capita and TFR, similar to the relationship

between TFR and HDI. Therefore the results indicate that GDP per capita is an important driving

factor in the U-shaped relationship between TFR and HDI.

A possible explanation is that increases in income cause higher opportunity costs of childbearing,

negatively affecting fertility. Yet, in countries with very high income, labour market conditions may

allow for a better combination of childbearing, childcare and work; decreasing the opportunity costs

of childbearing, thus positively affecting fertility.

For infant mortality an opposite relationship with TFR is observed. As infant mortality decreases

fertility also decreases, however, for very high levels of infant mortality, a decrease is associated with

an increase in fertility. The positive relationship between fertility and infant mortality was expected;

lower infant mortality causes fewer births to be needed to obtain the desired number of children. On

the contrary, the negative association among high levels of infant mortality is unexpected. However,

such cases are only limitedly found and might be due to cultural factors.

Robustness checks including higher levels of education and a different proxy for child mortality are

conducted in column 5, 6 and 7.

In column 5 we include enrolment in secondary education and its squared term. Due to several

missing observations we drop 507 observations. GDP per capita and infant mortality remain

significant. Enrolment in secondary education exhibits a significant effect on fertility via a U-shaped

relationship, similar to that of GDP per capita. After including enrolment in secondary education, we

observe that the effect of GDP per capita on TFR becomes less pronounced. Enrolment in secondary

education and GDP per capita are positively correlated (0.803), thus the effect of secondary education

was previously captured by GDP per capita. The effect of education on fertility is likely to be through

income, thus affecting the opportunity cost of childbearing. Enrolment in primary education did not

display this effect, probably because, nowadays, it has become more standard and accessible across

countries and thereby affecting income less.

In column 6, we include enrolment in tertiary education to the specification in column 4. Here, we

maintain enrolment in primary education as we believe that the spread between primary and tertiary

education is too large for the variables to be replaceable. In this specification 768 observations are

dropped. GDP per capita loses strength in the effect on fertility to similar levels as the specification

in column 5. This can be explained by the same mechanisms as mentioned above, as GDP per capita

and tertiary education are also positively correlated (0.696).

Only the squared terms of primary and tertiary education are significant. Tertiary education has a

positive effect on fertility, that is stronger for higher levels of enrolment. Primary education has the

opposite effect, which is unexpected. However, both effects are very small.

Infant mortality is replaced, in column 7, by a different proxy for child mortality: mortality under 5.

The same results as in column 4 are found, which is not surprising considering the very strong

correlation between the two variables (0.980).

In column 8 we add HDI and HDI^2 to our specification in column 4, to see to what extent infant

mortality and the components of HDI capture the effect of HDI on fertility that was observed in

column 2. All variables are significant at the 5% significance level. The significance of the terms for

life expectancy and enrolment in primary education might be explained by confounding effects that

were present in specification 4. In fact, the variables are strongly correlated with HDI (0.945 and

0.590, respectively), but turn out to have an opposite effect on fertility compared to HDI. These effects

are in contrast with our expectations and economic theory, we are therefore unable to explain these

findings.

The coefficients of GDP per capita and its squared terms decrease relative to column 4. Therefore the

effect of GDP per capita on fertility observed in column 4 was partly due to HDI. Thus we reject

Hypothesis 1 as a nonlinear relation between GDP per capita and TFR is found, instead of a negative

relationship. The coefficients of HDI also decrease, indicating that part of their effect observed in

column 2, is now captured by its separate components. However, the effect of HDI remains significant

implying that the separate factors cannot fully capture the effect HDI has on fertility. Thereby we

reject Hypothesis 2 stating a negative relationship between TFR and HDI, as a nonlinear association

is observed.

Table 3 and 4 present the results of the main specifications for OECD and non-OECD countries. For

OECD countries we find a significant U-shaped relationship between TFR and HDI (Table 3, column

1). On the contrary, non-OECD countries do not exhibit this relationship (Table 4, column 1), not

surprisingly as the reversal is found at higher levels of HDI. Considering the components of HDI

separately, we find a significant U-shaped relationship between GDP and TFR for both subsamples

(Table 3, column 2 & Table 4, column 2). Life expectancy and infant mortality display the same effects

as found for the complete sample, however, life expectancy is only significant in the subsamples.

Finally, in column 3, we add HDI and HDI^2 to the specifications. For OECD countries we observe that

the separate components are able to capture the effect of HDI, as the coefficients of HDI are not

significant. Contrary, in non-OECD countries HDI still displays a significant effect but GDP per capita

is no longer significant. These results indicate that for higher developed countries GDP per capita

plays an important role in fertility rates, whereas in lower developed countries this is not the case.

VI. Conclusion

In this paper we aim to understand the effect of different indicators of socioeconomic development

on fertility. A U-shaped relationship between fertility and HDI is found, thereby confirming the

results of Myrskylä et al. (2009). HDI and fertility are negatively correlated for low levels of HDI, and

positively correlated for high levels of HDI. A similar relationship was found between GDP per capita

and TFR. Infant mortality is positively associated with fertility, but for higher levels of infant

mortality the association is negative. Furthermore, primary education and life expectancy are only

significant when HDI is including, displaying a reversed U-shaped relationship with fertility. We find

secondary education to affect fertility similar to GDP per capita, and it is likely that the effect runs

through the same mechanism. Tertiary education seems to positively affect fertility and the effect

depends on the level of enrolment. However, interpretation is difficult as only the squared term is

found to be significant.

GDP per capita and infant mortality seem to be the main factors underlying this quadratic

relationship between HDI and TFR. Although, the components do not fully capture this relationship.

Thus the effect HDI has on fertility is beyond the effect of its separate components combined.

Overall, similar results are found for the subsample of OECD and non-OECD countries. Among OECD

countries, the effect of HDI on TFR is fully captured by GDP per capita, infant mortality and life

expectancy. Whereas among non-OECD countries a nonlinear relationship between TFR and HDI is

not found, and GDP per capita does not significantly affect fertility when considering HDI.

Thus a U-shaped relationship between fertility and socioeconomic development is found. The main

factors of socioeconomic development affecting this relationship are GDP per capita and infant

mortality. Income negatively affects fertility as it increases the opportunity costs of childbearing, but

for high levels of income opportunity costs are likely to decrease due to labour market conditions

that improve the ability to combine children and work. The reversal in fertility rates is likely to occur

through this mechanism. A decline in infant mortality reduces the amount of births needed to obtain

the desired number of children, thereby reducing fertility.

Our results indicate that fertility rates re-increase in highly developed countries and that the effect

is mainly due to higher levels of GDP per capita. This is likely to be caused by labour market

conditions that increase the opportunities for females to combine children and work. Policymakers

could therefore improve labour market conditions to stimulate fertility rates preventing the issues

arising from population ageing and low fertility, such as stagnation of economic growth and threats

to existing welfare systems.

This paper has a number of limitations. First, we did not use proxies for institutional differences and

labour market conditions among countries. Therefore we are unable to observe to which extent these

factors influence fertility and it may lead to biased estimates of coefficients. Furthermore, lacking

data on inflation for several of the years considered in the analysis, we could not correct the measure

of GDP per capita and base it on the real purchasing power. Moreover, our dataset is unbalanced this

could lead to biased estimates in case the missing the observation are not random.

Further research may address the limitations presented above. Specifically, to consider the role of

labour market conditions in the re-increase in fertility rates. In addition, the effect of life expectancy

and enrolment in primary education could be further investigated to uncover their role as our results

were surprising.

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Appendix Table 2Fixed effects regression results, complete sample, 141 countries, period 1975-2005

Table 3 Fixed effect regression results, OECD countries, 31 countries, period 1975-2005

(1) (2) (3) VARIABLES TFR TFR TFR

HDI -60.56** -19.96

(23.08) (25.49) HDI^2 32.92** 10.68

(13.55) (14.28) log(GDP per capita) -2.930*** -2.626***

(0.657) (0.868) log(GDP per capita)^2 0.143*** 0.129***

(0.0296) (0.0418) Life Expectancy 1.457*** 1.707***

(0.455) (0.498) Life Expectancy^2 -0.00963*** -0.0112***

(0.00297) (0.00322) Infant Mortality 0.0769*** 0.0758***

(0.0262) (0.0266) Infant Mortality^2 -0.000868** -0.000843**

(0.000357) (0.000351) Primary Enrolment 0.0405 0.0465

(0.0704) (0.0682) Primary Enrolment^2 -0.000187 -0.000218

(0.000341) (0.000330)

Observations 807 807 807

Table 4 Fixed effects regression, non-OECD countries, 110 countries, period 1975-2005

(1) (2) (3)

VARIABLES TFR TFR TFR

HDI 1.124 -17.37***

(4.779) (5.318)

HDI^2 -3.548 13.05***

(4.064) (4.862)

log(GDP per capita) -1.095*** -0.530

(0.400) (0.402) log(GDP per capita)^2 0.0975*** 0.0575*

(0.0291) (0.0313)

Life Expectancy 0.242*** 0.334***

(0.0893) (0.0842)

Life Expectancy^2 -0.00198** -0.00269***

(0.000842) (0.000797)

Infant Mortality 0.0369*** 0.0370***

(0.00821) (0.00798)

Infant Mortality^2 -0.000137*** -

0.000147***

(3.46e-05) (3.32e-05)

Primary Enrolment 0.0143* 0.0200***

(0.00822) (0.00753) Primary Enrolment^2 -8.46e-05* -0.000103**

(4.44e-05) (4.22e-05)

Observations 2,629 2,629 2,629

R-squared 0.759 0.832 0.839

Number of country 110 110 110

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5 Countries included in dataset

Country

Albania

Algeria

Angola

Argentina

Argentina

Armenia

Australia

R-squared 0.428 0.664 0.666 Number of country 31 31 31 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Austria

Azerbaijan

Bahrain

Bangladesh

Belarus

Belgium

Belize

Benin

Bolivia

Botswana

Bulgaria

BurkinaFaso

Burundi

CaboVerde

Cambodia

Cameroon

Canada

CentralAfricanRepublic

Chad

Chile

China

Colombia

Congo,Dem.Rep.

Congo,Dem.Rep.

Congo,Rep.

CostaRica

Coted'Ivoire

Croatia

Cyprus

CzechRepublic

Denmark

DominicanRepublic

Ecuador

Egypt,ArabRep.

ElSalvador

EquatorialGuinea

Estonia

Ethiopia

Finland

France

Gabon

Georgia

Germany

Ghana

Greece

Guatemala

Guinea

Honduras

Hungary

Iceland

India

Indonesia

Iran,IslamicRep.

Ireland

Israel

Italy

Jamaica

Japan

Jordan

Kazakhstan

Kenya

Kuwait

KyrgyzRepublic

LaoPDR

Latvia

Lesotho

Lithuania

Luxembourg

Macedonia,FYR

Madagascar

Malawi

Malaysia

Mali

Malta

Mauritania

Mauritius

Mexico

Moldova

Mongolia

Morocco

Mozambique

Namibia

Nepal

Netherlands

NewZealand

Nicaragua

Niger

Nigeria

Norway

Oman

Pakistan

Panama

Paraguay

Peru

Philippines

Poland

Portugal

Romania

RussianFederation

Rwanda

Samoa

SaudiArabia

Senegal

SierraLeone

SlovakRepublic

Slovenia

SouthAfrica

Spain

SriLanka

Sudan

Suriname

Swaziland

Sweden

Switzerland

SyrianArabRepublic

Tajikistan

Tanzania

Thailand

Togo

Tonga

TrinidadandTobago

Tunisia

Turkey

Uganda

Ukraine

UnitedArabEmirates

UnitedKingdom

UnitedStates

Uruguay

Vanuatu

Venezuela,RB

Vietnam

Yemen,Rep.

Zambia

Zimbabwe