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Social Indicators Research

Erschienen in:

01.01.2019

Explaining Differences Within and Between Countries in the Risk of Income Poverty and Severe Material Deprivation: Comparing Single and Multilevel Analyses

verfasst von: Pim Verbunt, Anne-Catherine Guio

Erschienen in: Social Indicators Research | Ausgabe 2/2019

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Abstract

This study investigates the differences between the risk factors of income poverty and severe material deprivation by assessing to what extent both indicators are subject to the same underlying household-level and country-level determinants. Given that these two indicators encompass the majority of the Europe 2020 social inclusion target group, it is crucial for policy makers to better know which determinants are effective in explaining the differences within and between countries in the risk of these two target components. We employ a series of single-level and multilevel logistic multinomial models to explain differences between those who suffer from income poverty ‘only’, from severe material deprivation ‘only’, from both problems and none. The comparative use of single-level and multilevel methods allow for the first time to confront the respective within and between-country explanatory power of both types of models. In addition to the usual econometric approach of identifying significant relationships, we employed the Shapley decomposition method to compare the relative contribution of independent variables at the household-level (i.e. work intensity, household education, household costs, socio-demographic variables) and country-level (i.e. size of social spending (total, in-cash, in-kind), pro-poorness of social benefits, median income levels and the unemployment rate) to within and between-country explained variance measures. We apply our method to the cross-sectional European Union Statistics on Income and Living Conditions for the year 2012.

Vorheriger Artikel Enhancing the Measurement of Composite Indicators of Corporate Social Performance

Nächster Artikel The Effect of Benefit Underreporting on Estimates of Poverty in the United States

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See Whelan et al. (2003), Whelan et al. (2004), Guio et al. (2010), Fusco et al. (2011).

See Moller et al. (2003), Brady (2005), Brady and Kall (2008), Diris et al. (2017) for child poverty rates.

For the risk of income poverty see Callens and Croux (2004), Wiepking and Maas (2005), Tai and Treas (2008), Brady et al. (2009), Lohmann (2009), Reinstadler and Ray (2010), Bäckman and Ferrarini (2010), Chzhen and Bradshaw (2012), Chzhen (2014), Saltkjel and Malmberg-Heimonen (2016). For the risk of material deprivation, see Kim et al. (2010), Nelson (2012), Whelan and Maître (2012), Whelan and Maître (2013), Bárcena-Martín et al. (2014), Visser et al. (2014), Saltkjel and Malmberg-Heimonen (2016), Bárcena-Martín et al. (2017).

See Bárcena-Martín et al. (2016) for a first multilevel study investigating the relationship between benefit targeting and child poverty.

Note that all individuals in the dataset (i.e. including the individuals that are missing in the regression analysis) were used for the computation of the at-risk-of poverty indicator. This is in line with the standard EU approach.

All individuals younger than 18 are considered children.

The household reference person or household head is defined as the individual responsible for the accommodation. If more than one person bears this responsibility, the oldest person is chosen.

For the risk of income poverty see Moller et al. (2003), Callens and Croux (2004), Brady (2005), Brady and Kall (2008), Tai and Treas (2008), Brady et al. (2009), Lohmann (2009), Kim et al. (2010), Diris et al. (2017), Saltkjel and Malmberg-Heimonen (2016). For the risk of material deprivation see Dewilde (2008), Nelson (2012), Bárcena-Martín et al. (2014), Visser et al. (2014), Saltkjel and Malmberg-Heimonen (2016).

In more detail, the cash benefits include cash benefits for sickness/health care (paid sick leave), disability (pension, early retirement, care allowance and economic integration of the handicapped), family/children (income maintenance benefit in the event of childbirth, birth grant, parental leave benefit, family or child allowance) and unemployment (full and partial unemployment benefit, early retirement, vocational training allowance, redundancy compensation).

In more detail, spending on in-kind benefits includes healthcare services (direct provision or reimbursement), disability (accommodation, assistance in carrying out daily tasks, rehabilitation), family/children (child care, accommodation, home help), unemployment (mobility and resettlement, vocational training, placement services and job-search assistance) and housing benefits (rent benefit). It is also important to note that we do not include public expenditure on education in our analysis. Although it is a very important in-kind benefit, aggregate data on education was unavailable for some countries in our dataset.

See Snijders and Bosker (2012) for the multilevel equivalent of McKelvey and Zavoina's pseudo R²-measure for the binary logistic multilevel model.

The independence assumption implies that the covariance between the error terms and the independent variables is equal to zero.

See Browne (2015, p. 28 and p. 177) for a formal definition and discussion of the Deviance and DIC criteria in a multinomial logistic regression context.

Note that the effective number of parameters (\(p_{D}\)) is an estimate and might differ from the actual (nominal) number of parameters (which is always an integer number) due to dependencies between the random effects and the actual number of parameters (Browne 2015).

See Deutsch and Silber (2006) for a Shapley decomposition of the likelihood ratio in a binary logistic model estimating the odds on poverty.

For example, in the case of three variables (A, B, C) there are seven possible submodels (ABC, AB, AC, BC, A, B, C). Submodels S with 3 variables (|S| = 3; ABC) obtain a weight of 2/6, submodels S with two variables (|S| = 2; AB, AC, BC) obtain a weight of 1/6 and submodels S with one variable (|S| = 1; A, B, C) obtain a weight of 2/6. The average contribution of variable A to the R²-value (\(C_{A}\)) equals \(\frac{1}{3}*(R_{ABC}^{2} - R_{BC}^{2} ) + \frac{1}{6}*\left( {R_{AB}^{2} - R_{B}^{2} } \right) + \frac{1}{6}*\left( {R_{AC}^{2} - R_{C}^{2} } \right) + \frac{1}{3}*\left( {R_{A}^{2} - 0} \right)\). The average contribution of variable B to the R²-value (\(C_{B}\)) then equals \(\frac{1}{3}*(R_{ABC}^{2} - R_{AC}^{2} ) + \frac{1}{6}*\left( {R_{AB}^{2} - R_{A}^{2} } \right) + \frac{1}{6}*\left( {R_{BC}^{2} - R_{C}^{2} } \right) + \frac{2}{6}*\left( {R_{B}^{2} - 0} \right)\). The average contribution of variable C to the R²-value (\(C_{C}\)) equals \(\frac{1}{3}*(R_{ABC}^{2} - R_{AB}^{2} ) + \frac{1}{6}*\left( {R_{AC}^{2} - R_{A}^{2} } \right) + \frac{1}{6}*\left( {R_{BC}^{2} - R_{B}^{2} } \right) + \frac{2}{6}*\left( {R_{C}^{2} - 0} \right)\). The R²-value then neatly equals the sum of the individual contributions of the variables (\(R_{{}}^{2} = C_{A} + C_{B} + C_{C} )\).

Young (1985) showed that the Shapley value is the only solution that simultaneously satisfies three attractive properties: efficiency, symmetry and monotonicity. Firstly, the efficiency axiom stipulates that the R² of the full model is decomposed among the independent variables). Secondly, the symmetry axiom ensures that substitutes with the same explanatory power obtain equal valuation (equal treatment). Finally, the monotonicity axiom ensures that an increase in the R²-value does not reduce the explanatory value attributed to any variable.

When median income levels and the unemployment rate are not co-regressed, in-cash social benefit levels have a strong statistically significant negative relationship with all the categories of the dependent variable.

We also used an alternative specification of the pro-poorness measure to verify the robustness of our results (Pro-poorness bottom20). The alternative pro-poorness variable is defined as the share of transfers that is distributed to the lowest two deciles in the pre-transfer household income distribution, instead of the share that is distributed to the broader bottom five deciles. The results we obtained using Pro-poorness bottom20 were similar to the results obtained using Pro-poorness bottom50.

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Titel: Explaining Differences Within and Between Countries in the Risk of Income Poverty and Severe Material Deprivation: Comparing Single and Multilevel Analyses
verfasst von: Pim Verbunt
Anne-Catherine Guio
Publikationsdatum: 01.01.2019
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 2/2019
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-018-2021-1

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