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Multilevel Modelling of Individual Fertility Decisions in Tunisia: Household and Regional Contextual Effects

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Abstract

In this study we examine the household and the contextual effects on the individual fertility decisions in Tunisia. We use the fourth round of the Multiple Indicator Cluster Surveys (MICS4 Tunisia) conducted in 2011–2012 by the Ministry of Development and International Cooperation in collaboration with the National Institute of Statistics. We exploit the hierarchical structures of the MICS4 data to study the regional and contextual perspectives of fertility in Tunisia and to show that the environment and the place where households live influence their family size. Results are obtained from estimated fixed and random effects multilevel Poisson models. Both household and regional characteristics explain a significant portion of the variation in individual fertility decisions in Tunisia. More specifically, household’s economic situation and women education affect negatively fertility decisions suggesting that higher income households choose to invest more in quality than in quantity of children. In addition, the results suggest that contextual effects, such as the regional poverty rate, positively affect the number of children, while regional unemployment rate and the availability of women’s health centers have a negative impact.

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Notes

  1. The MICS4 Tunisian survey can be downloaded from http://data.unicef.org.

  2. For more information on the MICS surveys, please visit www.childinfo.org.

  3. The eight factors defined by Bongaarts (1978) are: 1. Proportion married, 2. Contraception, 3. Induced abortion, 4. Lactational infecundability, 5. Frequency of intercourse, 6. Sterility, 7. Spontaneous intrauterine mortality, and 8. Duration of the fertile period (see Bongaarts 1978; Bongaarts 1980 and Bongaarts and Potter 1983 for more details).

  4. We have chosen to use the same variables defined by the MICS4 survey: the age of the woman grouped in seven age groups and the age at marriage as a continuous variable.

  5. INS, «Mesure de la pauvreté et des inégalités en Tunisie 2000–2010» (www.ins.tn).

  6. Overmars and Verburg (2006) argued that it is possible to estimate additional random effects (the intercept and the slope parameters) at higher level but they are not estimated with great precision. Given these raisons, only random intercept effects at level 2 (cluster) and level 3 (region) are estimated.

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  1. Higher Institute of Management, University of Sousse, Sousse, Tunisia

    Mohamed Amara

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  1. Mohamed Amara
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Correspondence to Mohamed Amara.

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Amara, M. Multilevel Modelling of Individual Fertility Decisions in Tunisia: Household and Regional Contextual Effects. Soc Indic Res 124, 477–499 (2015). https://doi.org/10.1007/s11205-014-0793-5

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