Socioeconomic Influences on Fertility Rate Fluctuations in Developed and Developing Economies

Socioeconomic Influences on Fertility Rate Fluctuations in Developed and Developing Economies

Kayla M. Good (Indiana University of Pennsylvania, USA) and Anthony M. Maticic Jr. (Indiana University of Pennsylvania, USA)
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-1093-3.ch007
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Abstract

This study investigates what socioeconomic factors determine the varying fertility rates among developed and developing nations and the implications of this information. Social and economic variables are analyzed using a panel of 20 nations with annual data from 1991-2015 to determine the most sizable and significant variables that impact fertility rates. A one-way fixed effects model is utilized. This study includes an aggregate model as well as two models isolating the fertility rates of developed nations and of developing nations, in accordance with Chow-Test results. The results find that there is a divergence between the determinants of fertility rates, based upon the development level. It is clear from these results that fertility and population control issues are specific to the state of a nation's development; thus, blanket policies will not fully address the issue of excessive population growth.
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Introduction

In the past century, the world’s population has expanded greatly even though the number of births per woman has generally been stagnant or on the decline. This global population growth has led to overcrowding, depletion of resources, and social unrest. On the surface, the causes of such a large increase in population seem obvious: greater access to healthcare, longer lifespans, and higher birth rates. However, the population has been growing more rapidly in developing economies, such as those on the continents of Africa and Asia, in comparison to industrialized western nations. Population growth has become such an acute concern that the United Nations and World Bank devote much of their efforts to providing family planning services to the developing world that is struggling to support their growing populations (“World Population Plan of Action”, 2018). At the same time, fertility rates have been declining or stagnating worldwide, with the greatest decrease occurring in developed economies, compared to a smaller decline in the developing world. The factors contributing to high fertility rates in underdeveloped economies is of great importance to the global community as it grapples with population managment.

In 1961, the worldwide average fertility rate was 5.01; in other words, each woman will give birth to about five children in her lifetime. In 2015, this rate was cut in half to approximately 2.45 children per woman. However, this aggregate measure does not adequately describe the story of reproductive behavior for developed economies in the same way that it does for developing economies. For example, data from 2015 show that in the Democratic Republic of the Congo, the fertility rate is still more than five births per woman, whereas in South Africa, the fertility rate is nearing only one birth per woman. The hypothesized cause of this disparity is socioeconomic differences between these two nations.

Understanding the factors that lead to lower fertility rates across diverse economies has the potential to encourage a sustainable level of population growth around the world without implementing severe one-child policies that have been used in nations like China. Therefore, this study will use a similar approach to Gries and Grundmann (2012), Lutz and Qiang (2002), and Gauthier and Hatzius (1997), which utilized fertility rates or childhood health levels as the dependent variables and designed panels using socioeconomic data from a sample of countries with several possible explanatory variables. The dataset also includes the most recently available data from 1991-2015, which sets it apart from previous research.

This chapter is examined from the perspective of researchers in an economically developed nation with low fertility rates in comparison to many countries in the models. The motivation for this research stemmed from interests in reproductive policies, the advertised necessity of immigration, and the apparent decline in the populations of developed countries compared to developing countries. The objectives of the chapter are to determine which socioeconomic variables exert the most influence on the change in fertility rate in economies at differing stages of development and economic output.

This paper attempts to determine which socioeconomic variables, health determinants, and country-specific population control policies contribute to the fertility rates in developed and developing nations. A literature review that explores previous studies regarding fertility rates in relation to historical data from the World Bank can be found in Section 2. Section 3 describes the panel dataset analyzed in this paper and includes justifications for the inclusion of each explanatory variable, as well as their respective expected signs according to the literature. Section 4 details the use of a one-way fixed effects model ordinary least squares regression estimation followed by an exploration of the econometric issues that were encountered and subsequently resolved. To conclude, an interpretation of the regression results can be found in Section 5 with conclusions, policy implications, and future avenues of research in Section 6.

Key Terms in this Chapter

Cross-Sectional: Analyzes data at one point-in-time across multiple samples.

Panel Data: Dataset that includes both a time series and cross-sectional element for variables.

Structural Break: A point in a dataset where there is a divergence or change in the behavior of the data in question.

Diversified Economy: An economy which has various means of producing output and whose economy would not be severely hampered by a decline in one specific industry.

Developing Economies: Any nation with real gross domestic product per capita of 12,500 USD or less.

Chow-Test: An econometric test used to determine if there is a structural break in a dataset.

Developed Economies: Any nation with a real gross domestic product per capita greater than 12,000 USD.

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