Academic journal article Journal of Community Positive Practices

Perceptions of Child Costs as Proximate Determinant for Romanian Fertility Decline 1

Academic journal article Journal of Community Positive Practices

Perceptions of Child Costs as Proximate Determinant for Romanian Fertility Decline 1

Article excerpt


Like most other Central and Eastern European countries, Romania has experienced a demographic decline since the early 1990s. The latest census (2011) showed the population total set to drop below 20 million. This result arguably increased the salience of the issue. Questions were raised over a range of topics covering possible long-term effects, from the functioning of the labour market and the potential for economic growth to the sustainability of public finances and the social insurance system.

The aim of this paper is to contribute to a better understanding of Romania's demographic decline. Using the theoretical model for explaining fertility transition put forward by Karen Oppenheim Mason, we look at several determinants for fertility decline using both macro level and individual level data. In the first part, we analyze the long term trends in population indicators in the country, infant mortality, and prenatal birth controls. In the second part, by use of survey data, we look at the issue of child costs as perceived reasons for not having children.

Methodology and data

Secondary data analysis is employed to follow key elements in the Oppenheim Mason model of fertility transition in two stages. In the first one, at macro level, we use population data regarding several indicators such as crude birth and death rate, infant mortality, abortion rate, covering the period since World War Two. The source is official data provided by the Romanian National Statistics Institute (INS), collected by population census, statistical surveys, and administrative reports. In the second stage, at individual level, we use data from a nationwide representative survey for the adult population of Romania with a sample size of 1, 212. The survey was based on a probabilistic tri-stadial stratified sample. Data collection was carried through computer assisted telephone interviews by the CCSCC polling company for the Liberal Institute "Bratianu". Phone numbers included both mobile and fixed telephony. The margin of error for the sample is +/- 2.81 at the 95 per cent level of confidence. Available variables in the dataset include social-demographical and economic variables, but not cultural variables. In addition, no variable in the dataset covers religious affiliation or behaviour.

All the variables used in the individual-level analysis are categorical (marital status, age category, level of education, Internet usage, subjective wellbeing, gender, etc.). On the issue of ethnicity, the dataset features a common problem in Romanian surveys, namely the underrepresentation of the Roma/Gipsy population. In the sample, the Roma/Gipsy group totals 0.7 per cent, as compared to 3.3 per cent (620, 000) of the population at the 2011 census and an estimate of 1.5 million put forward by the Research Institute for Quality of Life in 2002 (Zamfir and Preda, 2002: 13). The reason is that some Roma/Gipsy respondents self-report themselves as Romanians.

The questionnaire also included an open ended, indirect question phrased as follows: "In your opinion, what prevents Romanians from having the number of children they desire?" The responses were coded in several categories. We use chi-square test to see if social, economic and other variables, including the desired versus actual number of children, significantly influence the variation of the responses to the abovementioned question.

Explaining fertility transition: the Oppenheim Mason model

In its classic formulation, demographic transition theory dates to the period around the mid 20th century, close to the end of the Second World War. According to Notenstein (1944), all societies experience a transition from an initial stage of high mortality and high fertility to a final stage of low mortality and low fertility. Socio-economic factors drive the transition, most visible through the inter-related processes of industrialisation and urbanisation. In turn, these factors lead to changes in society in terms of way of life, values and norms. …

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