https://orcid.org/0000-0002-5992-1408
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ABSTRACT
This paper analyses the impact of degree of urbanization on child material deprivation in Spain. Using the EU-SILC 2009 and 2014 special modules on material deprivation, we find that living in a city or town increases child material deprivation to a larger extent than household material deprivation and income. Differentiating by needs, the provision of children’s basic needs does not respond to household material deprivation, income or degree of urbanization, whereas the provision of educational/leisure needs does. Our findings might be of help in designing more effective policies intended to alleviate the incidence of child material deprivation beyond income-related programmes.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
2. In line with Daly et al. (Citation2008), the geographical location of a region is relevant in determining the level of child material deprivation.
3. All the factors described at neighbourhood and public good level are called ‘locally related deprivation’ in Burke and Jones (Citation2019). The last one concerning population size is called the ‘spatial scale’ problem by these authors.
4. In their theoretical framework, Burke and Jones (Citation2019) called this channel ‘household deprivation’.
5. Herrero-Alcalde and Tránchez-Martín (Citation2017) pointed out that territorial differences in access to essential public services are very marked in Spain.
6. See Figure A1 in Appendix A in the supplemental data online.
7. See also Figure A3 in Appendix A in the supplemental data online for specific data.
8. See Figure A2 in Appendix A in the supplemental data online.
9. Murphy and Scott (Citation2014) and Ayala et al. (Citation2021) highlighted the lower effect of economic crises in rural areas, but the literature is not conclusive. Our finding would suggest that even if this were the case, the recovery from the crisis may be smaller as well.
10. We adjust a polynomial of degree 2 given the evidence found in Liddle (Citation2017).
11. Bryan and Jenkins (Citation2016) reviewed this and other modelling approaches using multilevel country data. Given our limited number of regions, the use of multilevel analysis could bias our results.
12. This method avoids problems of forbidden regression, as pointed out in Wooldridge (Citation2010).
13. In Appendix B in the supplemental data online we have included a brief description of both methodologies.
14. The special modules are only available for waves 2009 and 2014.
15. Following Figari (Citation2011), we normalize the index by the sum of all weights to permit comparisons across Spanish regions.
16. The counting approach (Atkinson, Citation2003) is just a special case of this index if one assumes equal weights for all items.
17. See Table A1 in Appendix A in the supplemental data online for specific items defined in EUROSTAT.
18. Eurostat groups together all LAU-2s (local administrative units – level 2/municipalities) using a criterion of geographical contiguity in combination with a minimum population threshold based on population grid square cells of 1 km2.
19. See Figure A4 in Appendix A in the supplemental data online.
20. For specific details about how variables that capture these socio-economic characteristics are built and the main descriptive statistics are relegated, see Tables A2 and A3, respectively, in Appendix A in the supplemental data online.
21. The regional values for these variables are relegated to Table A4 in Appendix A in the supplemental data online. The relationship between child material deprivation at regional level with GDP per capita, inequality and variables concerning public policies to reduce poverty are plotted in Figure A5 in Appendix A in the supplemental data online.
22. This implies 0.005 (0.002) units more (less) of child material deprivation. In other words, 1 SD increase in household material deprivation (household income) accounts for 56.6% (16.4%) of a 1 SD increase in child material deprivation.
23. For specific results, see Table A5 in Appendix A in the supplemental data online.
24. The results for CMP are similar. In particular, the effects become 23.2% and 10.9%, respectively. We only quantify the indirect effect, as the direct effect is no longer statistically different from zero. The total effect would imply an average decrease of 12%.
25. The results for CMP are slightly lower and account for 67.1% in cities and 39.5% in towns.
26. The rest of the regional characteristics are relegated to Table A5 in Appendix A in the supplemental data online.
27. In we also find an inverted ‘U’-shaped relationship, even though we plot their unconditional average values.
28. We have also performed the analysis including other regional variables related to expenditure on public services and the results of our variables of interest do not change (see Table A6 in Appendix A in the supplemental data online). We observe that the significance and sign of our variables of interest remain the same regardless of the disaggregation of the policy variables.
29. The results with the CMP approach are similar.
30. With the CFA methodology, the effect of household material deprivation takes into account the effect of the latent factors, and for HDi (Q2) and HDi (Q3) the combination of both implies an effect which is not significantly different from zero.
31. We relegate all details regarding the regional structure by countries to Appendix B in the supplemental data online.
32. In Appendix C in the supplemental data online, we have also included a discussion regarding the selection of comparisons and estimation results for some countries for which we have enough information about regions. In the EU-SILC dataset, the most similar case in terms of the number of regions available is France. However, as in the case of Europe as a country there is no indirect effect of the degree of deprivation on child-specific material deprivation through household material deprivation.