We propose an index to measure the degree of ability or desire of the population in a given country to have children, via an analysis of certain factors that may have a positive or negative influence on the fertility rate of that country. Using data for the twenty-eight countries of the European Union, and Principal Components Analysis, we construct the International Multidimensional Fertility Index as a combination of four dimensions: (1) Economy and family, (2) Attitudes and habits, (3) Work–Life Balance, and (4) Policy, along with nineteen distinct variables. We find that Denmark, the Netherlands, and Luxembourg are among the countries with the highest value of the index, and they also have high fertility rates within the EU. At the other end of the spectrum, Latvia, Cyprus, and Greece, are ranked in the last positions according to our index, countries that also present low values in their fertility rates. We also find a positive correlation between the value of our index and country fertility rates, an indication that our index may be capturing country differences in the conditions for bearing children, with higher values of the index indicating better conditions for childbirth and childrearing. To the extent that international data becomes available, our methodology will allow for the construction of international rankings, helpful in identifying cross-country differences in the conditions for fertility.
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If we understand fertility as the production of a live birth (natality), several measures can be used to measure fertility in a country: the “child-woman ratio”, defined as the number of children under age 5 per 1000 women of childbearing age in a given year, the “crude birth rate”, defined as the number of live births per 1000 population in a given year, the “general fertility rate”, defined as the number of live births per 1000 women aged 15–49 in a given year, and the “total fertility rate”, defined as the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Any fertility measure has its advantages and disadvantages. For instance, crude fertility rates are not good for cross-population comparisons, as variations in the age distribution of the populations compared will affect the birth rate. For a complete view of the fertility behavior of women in a country, we refer to “total fertility rate” throughout the paper.
When we refer to EUROSTAT for the year 2012, we have considered the average values of the years 2010, 2011 and 2012 when the information is available for the indicator.
The use of fertility intentions data has been criticized, as respondents tend to give socially desirable answers, many individuals revise their fertility goals over the course of their lives, and there is a high level of uncertainty attached to reproductive plans (Ní Bhrolcháin and Beaujouan 2012). Despite these shortcomings, childbearing preferences play a central role in fertility decision-making and are typically considered to be influential predictors of future childbearing behavior (Philipov 2009).
PCA analysis is usually used in a framework of reflective models, where causality is determined from the concept to the variables chosen to measure the concept [see Diamantopoulos et al. (2008) for a review]. However, in the current context, a "formative" model should be used, as we aim to measure the concept (e.g., fertility rates) with several variables. Thus, we cannot talk about causality, but only about correlations or associations between the fertility rates of the countries, and the index and components created using the PCA technique.
Originally, the IMFI was composed of 27 variables, distributed in 4 dimensions. After the application of the criteria for the selection of variables, and confirmatory factor analysis, we have dropped 8 variables from the index: “Crude divorce rates”, “Ideal number of children for males”, “Importance for job to be family-friendly”, “Women need children to be fulfilled”, “Average holidays”, “% of people working at home”, “Commuting time per working day”, and “Average payment on maternity leave”. More information about data sources for these variables can be obtained on request.
We have also run an OLS regression of fertility rates on the four dimensions of the index. We observe that dimensions 1 and 2 have coefficients that are not statistically significant, while coefficients of dimension 3 and 4 are statistically significant at the 95 % level. Thus, it seems that the Work-life balance and Policy dimensions have a higher explanatory power for fertility rates, in comparison with the Economy and family and Attitudes/habits dimensions. However, the current analysis does not allow us to establish a causal link between fertility rates and the dimensions, as the definition of the dimensions will change over time, as will the relationship across the variables.
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This paper has benefited from funding from the Spanish Ministry of Economics (Project ECO-Project ECO2012-34828).
Conflict of interest
The authors declare that they have no conflict of interest.
Human and Animal Rights
This research does not involve Human Participants, nor Animals.
This paper was partially written while Jose Maria Fernandez-Crehuet was Visiting Fellow at the London School of Economics and Political Science (UK), to which he would like to express his thanks for the hospitality and facilities provided.
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Fernandez-Crehuet, J.M., Gimenez-Nadal, J.I. & Danvila del Valle, I. The International Multidimensional Fertility Index: The European Case. Soc Indic Res 132, 1331–1358 (2017). https://doi.org/10.1007/s11205-016-1341-2
- International Multidimensional Fertility Index
- Fertility rates
- Principal components analysis