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See detailMeasuring Poverty Persistence
Fusco, Alessio UL; van Kerm, Philippe UL

Report (2022)

This chapter reviews the literature on the measurement of poverty persistence. The review has two parts. We first cover the literature on poverty persistence indicators which develops ` principled ... [more ▼]

This chapter reviews the literature on the measurement of poverty persistence. The review has two parts. We first cover the literature on poverty persistence indicators which develops ` principled', descriptive summary measures. We then review the econometric literature which teases out the determinants of poverty persistence. Finally, we describe the challenges and limitations the literature on poverty persistence face. [less ▲]

Detailed reference viewed: 103 (1 UL)
Peer Reviewed
See detailRotation group bias in European Union social indicators
Fusco, Alessio UL; Gallo, Giovanni; van Kerm, Philippe UL

in Guio, Anne-Catherine; Marlier, Eric; Nolan, Brian (Eds.) Improving the understanding of poverty and social exclusion in Europe (2021)

EU-SILC relies on a 4-wave rotating panel design. A new population sample is drawn every year and selected respondents are interviewed annually for up to four years. A complete EU-SILC cross-section ... [more ▼]

EU-SILC relies on a 4-wave rotating panel design. A new population sample is drawn every year and selected respondents are interviewed annually for up to four years. A complete EU-SILC cross-section dataset therefore contains data from samples drawn independently in four different years. This paper applies influence function regressions methods to examine to what extent the rotating panel design of EU-SILC influences the estimates of four social indicators such as income poverty rates or income inequality measures, in other words whether a “rotation group bias” is observed. Our analysis of the 2014 EU-SILC cross-sectional data highlights that estimates of income inequality and poverty rates for newer rotation groups are often higher than for older ones. Individuals interviewed at least twice (and especially those who have been three or more years in the sample) tend to drag the selected social indicators downwards, compared to individuals interviewed for the first time. These impacts remain significant even when accounting for different socio-demographic characteristics of households. Not all countries are affected by the bias however. [less ▲]

Detailed reference viewed: 31 (1 UL)
Peer Reviewed
See detailForeign-born households in the income distribution and their contribution to social indicators in European countries
Fusco, Alessio UL; Sohst, Rhea Ravenna UL; van Kerm, Philippe UL

in Guio, Anne-Catherine; Marlier, Eric; Nolan, Brian (Eds.) Improving the understanding of poverty and social exclusion in Europe (2021)

This chapter provides new evidence about the relative differences in the incomes and living conditions of native- and foreign-born households exploiting EU-SILC data for 2007 and 2018. For the 28 ... [more ▼]

This chapter provides new evidence about the relative differences in the incomes and living conditions of native- and foreign-born households exploiting EU-SILC data for 2007 and 2018. For the 28 countries with a satisfying coverage of immigrant populations, we document where the foreign-born stand along the distribution of incomes and living conditions and then derive their contribution to seven social indicators. We find that individuals living in foreign-born households have lower incomes and higher levels of poverty and deprivation in all countries examined. No clear improvement in the relative position of foreign-born households is observed between 2007 and 2018. Although there is much heterogeneity in the incomes of foreign-born households, their generally disadvantaged situation implies that, on the whole, they tend to push inequality, poverty and deprivation indicators upwards. This effect persists in many countries, albeit mitigated in magnitude, when we account for the different characteristics of foreign-born compared to natives. [less ▲]

Detailed reference viewed: 21 (1 UL)
See detailBIPOLAR: Stata module to calculate four measures of income bi-polarization
Fusco, Alessio UL; van Kerm, Philippe UL

Software (2020)

-bipolar- calculates four measures of income bi-polarization.

Detailed reference viewed: 81 (1 UL)