Abstract
This paper begins a new line in the estimate and classification of New household formation in Spain. It starts with the study of the emancipation of young people dependent of their parents and proposes a micro-econometric analysis to find and measure socioeconomic factors that affect the decisions youngsters make when they leave their parent’s home. In the first place a discrete choice model three level nested multinomial logit based in population characteristics is proposed. In order to improve the results avoiding systematic biases and making use of all the information in the data source, the model is replaced by a sequence of three binary logits. The period of study extends from 2008 to 2011 so it will be useful to find evidence of how the economic crisis has affected the current trends of Spanish growing New household formation levels and increasing emigration of young dependents. The gap between Spain and the rest of European countries concerning Emancipation and New household formation levels is reducing since the last nineties but the high level of unemployment in the current crisis has supposed a brake in that trend.
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Notes
- 1.
Household stands for the whole group of people living together in the same house.
- 2.
Family nucleus stands for a couple living together, a couple and their children living together or just one parent living with some of her/his children.
- 3.
Extended family stands for a family nucleus and other relatives.
- 4.
The elevation factor is the amount of total population represented by each case in the study. The cross- elevation factor is the representation of one case over the total population 1 year. The longitudinal elevation factor is the representation of the same case over the population who is present two consecutive years.
- 5.
In fact, in a decision process as the one described in Fig. 1 these two models are mathematically the same except for the missing data -LOST and RNC cases-.
- 6.
For instance, according to the data in Fig. 1, the next probabilities are considered:
$$\displaystyle\begin{array}{rcl} & P(DE2\,=\,1\vert DE1\,=\,1)\,=\, \frac{Populationrepresentedbythe293caseswhereDE2=1} {Populationrepresentedbythe331caseswhereDE1\,=\,1differentfromLOST} & {}\\ & P(DE3 = 1\vert DE2 = 1) = \frac{Populationrepresentedbythe92caseswhereDE3=1} {Populationrepresentedbythe208caseswhereDE2=1differentfromRNC}& {}\\ \end{array}$$ - 7.
In a binary prediction model, a Receiver Operating Characteristic,ROC curve, is a representation for different threshold settings of the fraction of true positive rate vs. false positive rate. For this analysis three ROC curves are calculated −i = 1, 2, 3-. True positive rate stands for the rate of well-predicted DEi = 1 survey cases and false positive rate stands for the rate of wrong-predicted DEi = 0 -survey cases where DEi = 1 is predicted by the model though real decision has been DEi = 0-.
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Acknowledgements
This is a paper of a complete research presented as a final work of the Master in Economy which conducts to the Ph.d. in Economy at the National University of Distance Education of Spain. I would like to express my most sincere gratitude to Mr. Jose MarĂa Labeaga Azcona, for his direction as my academic tutor. I also deeply thanks Mr. Alberto A. Alvarez and Mr. Alberto A. Pinto for the opportunity to present my work at the MPE 2013 congress.
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Peinado, O.M. (2015). Micro-Econometric Analysis of New Household Formation in Spain. In: Bourguignon, JP., Jeltsch, R., Pinto, A., Viana, M. (eds) Dynamics, Games and Science. CIM Series in Mathematical Sciences, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-16118-1_28
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DOI: https://doi.org/10.1007/978-3-319-16118-1_28
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