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Paper We Study the Association Between Development and Fertility with Focus on Contemporary Developed World

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Paper We Study the Association Between Development and Fertility with Focus on Contemporary Developed World Human Development and Low Fertility Preliminary draft, March 7 2008 Do not cite without permission Mikko Myrskylä1, Hans-Peter Kohler2, Francesco Billari3 Abstract We study the relationship between development indicators and fertility with focus on low fertility countries. The most developed countries led both the first and second demographic transitions, and during these transitions, development indicators were inversely correlated with fertility levels. Results from data on 100 countries for the period 1975-2005 suggest that countries with highest development are again leading the transition by showing signs of fertility recovery from low and lowest-low levels closer to replacement levels, and among the most developed countries, the association between fertility and development has changed signs from negative to positive. 1 Population Studies Center, University of Pennsylvania, 250 McNeil Bldg, 3718 Locust Walk, Philadelphia, PA 19104, USA. Email [email protected]. 2 Professor, Department of Sociology, University of Pennsylvania, 272 McNeil Bldg 3718, Locust Walk Philadelphia, PA 19104 3 Professor of Demography, IMQ - Università Bocconi, viale Isonzo 25, I-20135 Milano, Italy Acknowledgements: Kristen Harknett, Janice Madden, and participants of the demographic research seminar at the University of Pennsylvania have provided helpful comments to previous versions of this manuscript. 1 Introduction In the western world both fertility and mortality started declining in the 19th century, and by now almost all other countries are following suit. In the last quarter of the 20th century this first demographic transition was followed by a second transition, where the role of family started to change rapidly, and in conjunction with this change fertility rates fell below replacement levels. The countries with highest development were the first to experience the first transition, and less developed countries lagged behind. The same holds for the second transition. Moreover, in many countries with high development, fertility continued to decline in the 1980s and 1990s. This raises the question whether continuing development – rising standard of living, improving education, and increasing longevity – always means declining fertility. In this paper we study the association between development and fertility with focus on contemporary developed world. There is little new in studying the co-variation between development and fertility, but most of the work has focused on developing countries (e.g. Bongaarts and Watkins 1996; Bryant 2007). This research has confirmed a strong negative association between development and fertility. Our results, obtained using yearly data for 100 countries from 1975 to 2005, suggest that among the most developed countries the picture is changing: the countries with highest level of development are again leading the transition by entering a new of regime where development and fertility are positively correlated. The paper is organized as follows. Section 2 sets the background for the study by reviewing what is known about development and low fertility, Section 3 introduces the data. Empirical analyses are conducted in Section 4, and results are discussed in Section 5. 3 2 Low fertility and development In most developed countries fertility is below replacement level (defined as total fertility rate TFR<2.1), and low fertility has been spreading fast to developing countries (Kohler et al. 2006). There is no evidence that once fertility starts to decline, it would converge to replacement. In fact, when fertility rates plummeted below replacement, there was little if any resistance at TFR=2.1. In 2002 there were already 17 European countries with TFR below 1.3, and over half of the European population lived in countries with TFR below 1.5 Countries with very high development, measured by any conventional development index, were the first to experience below replacement fertility. In Europe, Denmark, Netherlands, and United Kingdom entered below replacement regime already in late 1960s or early 1970s. Most other developed countries followed suit, and now the lowest fertility levels among developed countries are in the Eastern Europe and Mediterranean countries (Kohler et al. 2006). In both Eastern and Western Europe the fall of fertility has been accompanied by a progressive postponement of childbearing (Andersson and Neyer 2004). Therefore part of the low contemporary fertility may be attributable to postponement of fertility, in contrast to decreasing quantity of fertility. However, in many countries, for example Scandinavian and Mediterranean countries, low fertility has persisted so long that there is not doubt about the fact that also the level of fertility is below replacement. The historical fertility decline, which occurred during the first demographic transition, was associated with increasing levels of development. The first demographic transition first started in Western World, and less developed countries lagged behind. Recent research has also established a strong inverse link between fertility and development in contemporary developing countries (Bryant 2007). The inverse link between development and fertility raises the question what will happen to fertility in the long run. If inverse link holds everywhere, the answer is obvious. But prior research has shown that that the association between fertility and many societal variables has changed signs during the last 10-15 years. These include the correlations between fertility and female labor force participation (Ahn and Mira 2002), mean age at first birth, mean age at first marriage, and total divorce rate (Prskawetz et al. 2006). Few studies, however, have considered the association between development and fertility in the developed world. This study attempts to fill this gap in knowledge by analyzing the link between development and fertility with focus on low fertility and developed world. 4 3 Data We use data on total fertility rate TFR, absolute human development index aHDI, and components of aHDI. The data source is World Bank World Development Indicators Online Database (World Bank 2008). The data is yearly, covering period 1975-2005 for 100 countries. Both TFR and standard human development index HDI are readily available in the Development Indicators Database. The period total fertility rate TFR, which is the sum of age-specific fertility rates in a given period, suits well for the purposes of this paper: it is a simple, easy-to-interpret measure of the quantum of fertility, describing in a single figure the average number of children women would have if they lived through the reproductive years. Because TFR ignores mortality, a slightly higher TFR value than 2 is needed for reproduction. We use TFR=2.1 as an approximation to replacement level fertility. TFR is subject to tempo effects, that is changes in the timing of childbearing (e.g. Bongaarts and Feeney 1998). Thus in some situations changes in TFR may reflect more changes in timing than changes in level. Therefore we adjust the fertility rates for changes in mean age at childbearing. This adjustment is possible for only a handful of countries for which good data on mean age at childbearing is available. Our focus, however, is in the most developed countries for which the data is available. This it is unlikely that the results would be biased because adjustment is not possible for all the countries. To measure development, we use a modified measure of the standard human development index HDI called absolute HDI (aHDI). This is because the standard HDI is not comparable over time, whereas aHDI is. The standard HDI measures the average achievements in a country in three basic dimensions of human development: i) mortality conditions, as measured by life expectancy at birth, ii) knowledge, as measured by adult literacy rate (with two-thirds weight) and the combined primary, secondary, and tertiary gross enrolment ratio (with one-third weight), and iii) standard of living, as measured by gross domestic product (GDP) per capita at purchasing power parity (PPP) in USD. For every year all three indicators are scaled, or standardized, between 0 and 1. In the standardization, component x of HDI is standardized to s(x) using the formula sx()=− [ x min()]/[max()min()] x x− x , where max(x) and min(x) are the maximum and minimum of x in all countries in that year. HDI is then calculated as the arithmetic mean of the three standardized components. Because of the way HDI is calculated, individual country’s HDI level and HDI ranking depend both on the country itself and on the minimum and maximum values of all countries. Therefore country’s development index and development ranking may change even if there are no changes in any of the HDI components. Moreover, individual country’s HDI may increase (decrease) even if all the components of HDI decrease (increase). To overcome this problem, we calculate an absolute human development index 5 aHDI. In aHDI, the scaling values (minimum and maximum of HDI components) change from year to year, but in aHDI they are fixed. We use the minimum and maximum values of year 2000 in the sample of 100 countries as the scaling values. To calculate aHDI, individual components of HDI, or proxies of the components, are required. For many countries, data on all components is not available in the World Development Indicators database on an annual basis. Therefore we use linear interpolation to impute the missing data. The data was almost complete for TFR, life expectancy and GDP per capita at purchasing power parity. We decided not to impute GDP or TFR values because these are subject to unpredictable fluctuation. Life expectancy, on the other hand, evolves relatively slowly, so we used linear interpolation to impute missing values. For enrollment ratios and literacy rates the proportion of missing data was much larger. However it is unlikely that the imputation has any large effect on the results, since both enrollment ratio and literacy rate evolve very slowly and are not subject to large variation, unlike GDP per capita. Therefore linear interpolation is a reasonable imputation method.
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