Material and social deprivation among one-person households: the role of gender

Fabrizi, Enrico; Mussida, Chiara; Parisi, Maria Laura Article — Published Version Material and social deprivation among one-person households: the role of gender Journal of Population Economics Provided in Cooperation with: Springer Nature Suggested Citation: Fabrizi, Enrico; Mussida, Chiara; Parisi, Maria Laura (2025) : Material and social deprivation among one-person households: the role of gender, Journal of Population Economics, ISSN 1432-1475, Springer, Berlin, Heidelberg, Vol. 38, Iss. 1, https://doi.org/10.1007/s00148-025-01084-5 This Version is available at: https://hdl.handle.net/10419/318552 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Journal of Population Economics (2025) 38:10 https://doi.org/10.1007/s00148-025-01084-5 ORIGINAL PAPER Material and social deprivation among one-person households: the role of gender Enrico Fabrizi1·Chiara Mussida1,2 ·Maria Laura Parisi3 Received: 17 March 2024 / Accepted: 10 January 2025 © The Author(s) 2025 Abstract We explore whether gender has a statistically significant impact on material and social deprivation of single adults after accounting for other characteristics. We use data from the 2022 European Union Statistics on Income and Living Conditions survey for six European countries. By assuming deprivation as an individual latent trait and by treating different deprivation levels as ranked categories, we estimate a proportional odds model separately by country. Our findings suggest a clear role for gender, i.e., the risk of being materially and socially deprived is relatively higher for women everywhere. The novelty is that facing “unexpected expenses” is the worst trouble for women, clearly coming from relative financial and economic fragility. Individual characteristics play a more important role than more aggregate indicators at explaining the risk of material and social deprivation. Finally, the estimated gender gap is robust to a large set of changes in model specification and assumptions. Keywords Gender gap ·Material and social deprivation ·Proportional odds regression model ·Financial fragility Responsible editor: Klaus F. Zimmermann BMaria Laura Parisi [email protected] Enrico Fabrizi [email protected] Chiara Mussida [email protected] 1Department of Economic and Social Sciences, Universitá Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy 2GLO, Essen, Germany 3Department of Economics and Management, University of Brescia, Via San Faustino 74/b, 25122 Brescia, Italy 0123456789().: V,-vol 123 10 Page 2 of 25 E. Fabrizi et al. 1 Introduction Unlike analysis of the labor market (Olivetti and Petrongolo 2008; Castellano and Rocca 2020), gender differentials in poverty and social exclusion are difficult to assess. Most, if not all, measures are household-based, thus implicitly assuming equity of resource sharing within the household, under a Beckerian, or unitary, conception of the household (Becker 1991). Despite the limited information usually available from large social surveys, a stream of literature has tried to shed light on within-household inequalities (Bennett 2013; Corsi et al. 2016; Guio and Van den Bosch 2020; Karagiannaki and Burchardt 2020). We investigate gender differentials in material and social deprivation by focusing on single adult households, with the reference adult aged between 18 and 64 (workingage, non-retiree). The reason for considering this household type is twofold: first, it circumvents the problem of assessing intra-couple or within-household inequality; second, single-person households are on the rise (Karagiannaki and Burchardt 2020) in European societies and are particularly exposed to the risk of poverty (Chzhen and Bradshaw 2012; Treanor 2018). We restrict our attention to working-age, non-retired individuals as poverty and social exclusion of the elderly deserve to be treated as separate problems. Among the numerous measures of poverty, we focus on material and social deprivation, which is routinely monitored in the EU (and other European) countries via the EU-SILC surveys. Material and social deprivation focuses on the extent to which the resources available to a household match the actual needs of its members (Notten and Guio 2019); this ability not only reflects the adequacy of income but also additional assets that can or cannot be available to the household, such as savings, gifts, interhousehold transfers, or services useful to finance the living standard (Israel 2016). The measurement is based on a set of thirteen items, covering several domains both at the household and at the individual level (Guio et al. 2016,2017) and lies on the idea of enforced lack: a living condition is labeled as in deprivation if it is enforced by lack of resources and not by choice. Published rates of material and social deprivation are based on a threshold of 5 lacking items out of 13 (deprivation is severe when an individual lacks 7 items). Our analysis goes beyond that. By assuming material and social deprivation as an individual latent trait and by treating different deprivation levels as ranked categories, we estimate a proportional odds model. Our research objective is to assess whether single women are more exposed than single men to the risk of material and social deprivation. If this gap is observed, can we explain it in terms of observable heterogeneous characteristics (individual characteristics, household characteristics, and macro indicators)? What is the (data-driven) main explanation for which the risk is not gender-neutral? As the answer to these questions may depend on the macroeconomic and social environment, we conduct our analysis separately for six EU countries: the five most populated countries in the EU (Germany, Spain, France, Italy, and Poland) plus Sweden, included as a representative of Northern countries whose societies stand out for their balance between gender roles and welfare regimes. The countries of choice correspond to different welfare regimes, labor markets, and institutions. A secondary aim 123 Material and social deprivation among one-person households… Page 3 of 25 10 of our research is to assess whether there is a difference in the estimated gender gap among these countries. The paper is organized as follows. After reviewing the relevant literature in Section 2, we introduce the model in Section 3and the data and methodology in Section 4; Section 5shows the table of results with a discussion; Section 6reports several analyses of robustness; and Section 7draws concluding remarks and policy implications. 2 A review of the literature The issue of analyzing gender differences in poverty and social exclusion, as explained in the Introduction to this paper, is complex. For instance, Corsi et al. (2016) recognize that official measures of at-risk of poverty based on the assumption of equal sharing of households’ resources incur a serious risk of underestimating the true gender gap in poverty. The existing household data and country heterogeneity, however, are not able to explain the availability and sharing of resources within the household, thus identifying the true difference in the risk of poverty or material deprivation between men and women. Nonetheless, a stream of literature has started shedding light on within-household inequalities (Bennett 2013; Corsi et al. 2016; Guio and Van den Bosch 2020; Karagiannaki and Burchardt 2020). One way to solve for the identification issue is working on sample selection, e.g., selecting single heads of household of both sexes—i.e., not in a cohabiting couple,1 and if necessary single parents (Christopher et al. 2002; Wiepking and Maas 2005).2 This type of households is spreading out in Europe (see, for instance, Karagiannaki and Burchardt 2020), changing the average family structure, and it is particularly exposed to the risk of social exclusion (Chzhen and Bradshaw 2012; Treanor 2018). We learn another way in Guio and Van den Bosch (2020), one of the few attempts to estimate the gender gap in material deprivation for married and cohabiting couples (who may live with other adults) in 23 European countries. The possibility to identify deprivation between female and male individuals in the couple is given by the interview mode.3In almost every country, women turn out to experience an enforced lack of pocket money and leisure, with some heterogeneity across items and countries. A third loose attempt is found in Aisa et al. (2019)’s contribution on a sample of working men and working women in 25 EU countries; in general, the literature recognizes that female-headed households, especially if they are single parents, face an above-average risk of poverty and material deprivation (see, e.g., Chant 2003; 2004). Bárcena-Martín et al. (2014) suggest that individuals who live in households whose reference person is a woman, or single parent, have higher intensity of depriva1Lone individuals live on one’s own without other adults in the household; single individuals may live with other cohabiting non-partner adults (e.g., their own parents) with whom they may have (partially) common financial resources (Provencher and Carlton 2018). 2We follow part of Wiepking and Maas (2005) in the spirit, by using a sample of single adult households. 3In this study, run in 2015, the items subject to material deprivation are 6: clothes, a pair of new shoes, get together with friends, leisure time, pocket money, internet connection. See Section 4and Table 1for details about deprivation items. 123 10 Page 4 of 25 E. Fabrizi et al. tion. Papadopoulos and Tsakloglou (2016) analyze the long-term relationship between material deprivation and poverty in Europe before the crisis period: women formed a “medium-risk” group in chronic material deprivation in all countries, being 1 to 1.5 times at higher risk to accumulate material disadvantage than the population average. Mussida et al. (2023) use EU-SILC data for the period 2014–2018 to find that the risk of falling into (low and high intensity of) severe material deprivation for female heads of household is about 2.8 times higher than their male equivalent, across the Spanish communities. Finally, in an even more general setting, we reviewed articles which estimate the determinants of the probability of falling into material deprivation across countries; among the explanatory variables we find gender (Notten and Guio 2020); age (see Bárcena-Martín et al. 2014; Guio and Van den Bosch 2020; Dudek and Szczesny 2021); age and number of children (Provencher and Carlton 2018); family’s health status (Bedük 2018 for both women and men); the disability of any family member (e.g., Guio and Van den Bosch 2020; Dudek and Szczesny 2021); education (see all abovecited papers, including Christopher et al. 2002); household’s structure (Papadopoulos and Tsakloglou 2016; Dudek and Szczesny 2021); the presence and number of children, especially for single-headed households, is a converting factor (Tsakloglou and Papadopoulos 2002; Boarini and d’Ercole 2006; Dewilde 2008); home tenure status (Bárcena-Martín et al. 2014); country of birth (Busetta et al. 2016); labor market features, such as work intensity (Layte et al. 2001; Halleröd et al. 2006; de Graaf-Zijl and Nolan 2011;Figari2012) or employment type (see, e.g., Bárcena-Martín et al. 2014); and macroeconomic factors, such as welfare regimes and labor market institutions (e.g., Nolan and Whelan 2010;Nelson2012; Alper et al. 2020). 3 Model specification In our modelling exercise, we target the deprivation score, defined as the number of items in deprivation in a multi-item scale. As anticipated, our target population is given by singles aged between 18 and 64 years and not yet retired. Our aim is to investigate whether gender has a statistically significant and relevant impact once other related observable characteristics are accounted for. In previous literature, the deprivation score has been analyzed either using count (Notten 2016; Bedük 2018) or proportional odds (Guio et al. 2012; Notten and Guio 2020) regression methods. In the first case, the score is treated as an additive counting variable, while in the second, deprivation is read as an individual latent trait and different deprivation levels are treated as ranked categories instead of equidistant categories. In line with Notten and Guio (2020), we assume a proportional odds model. Count regression models will also be considered, and their ability to fit the data is compared in terms of popular model selection criteria such as the Akaike information criterion; their results are compared to those we obtain with the proportional odds as a robustness check. Results related to partial proportional odds models (see, for instance, Peterson and Harrell Jr. 1990; Williams 2016) are not reported, since the evidence of their superior fit is not supported by Brant test results (Brant 1990) in all countries we 123 Material and social deprivation among one-person households… Page 5 of 25 10 consider, and would not compensate the increased model complexity. In any case, results obtained relaxing the odds proportionality for the gender indicator and possibly a few other selected variables are discussed below. The regression results cannot consider sampling weights and other survey design aspects, because published sampling weights account for the unequal inclusion probabilities plus corrections aimed at attenuating the effect of non-response. However, we use these weights when computing estimates of population descriptive quantities in Section 4. In that context, accounting for unequal inclusion probabilities is essential to obtain consistent results. In the regression problem, the opportunity of using weights depends on whether they are informative, given the covariates. We tested this hypothesis according to a procedure illustrated in Pfeffermann and Sverchkov (1999) and Long (2022), which led to the non-rejection of the null hypothesis in all cases. As a further check, we also estimate survey-weighted ordinal regression models using the methodology illustrated in Lumley (2011) (chapter 6), obtaining point estimates largely consistent with those obtained without weights. A full consideration of the sampling design (stratification, clustering, varying inclusion probability, and poststratification adjustment) can have an impact on the estimation of the standard errors. Unfortunately, we do not have access to all relevant sampling design information (stratum and cluster identifiers) because of disclosure constraints. Let ηibe the latent unobserved deprivation of individual iin the sample. We assume that the material and social deprivation scale divides this continuous variable into K+1=14 intervals by means of (latent) increasing constants c1≤···≤ck≤···≤ CK, so that the observed deprivation score Yiwith support {0, ..., K=13}is such that Yi=⎧ ⎪ ⎨ ⎪ ⎩ 0η≤c1 kc k≤ηi<ck+1(k=1,...,K−1) Kηi≥cK The proportional odds regression model is based on assuming the cumulative logit to be a linear function of the regressors. In this case, log P(Yi≥k) P(Yi<k)=αk+xT iβ(1) Moreover, it is specific to this model the assumption that, while we have a deprivation level-specific intercept αk, the slopes are assumed common. This parsimonious specification entails that regressors impact the odds of moving from deprivation level kto k+1inthesameway,regardlessofk. This somewhat restrictive assumption is implied by assuming that regressors have an impact on the latent variable ηionly through its location (Fullerton and Anderson 2021). 4 Data and samples We use data from the EU-SILC survey, that is based on a methodology and definitions that are standardized across most members of the European Union (Eurostat 2010). The 123 10 Page 6 of 25 E. Fabrizi et al. Table 1 Items in the material and social deprivation indicator (EU-SILC) # Items Variable code 1 Face unexpected expenses HS060 2 Afford one week annual holiday away from home HS040 3 Avoid arrears (in mortgage rent, utility bills and/or hire purchase instalments) HS011 HS031 4 Afford a meal with meat, chicken, fish or vegetarian equivalent every second day HS050 5 Afford keeping their home adequately warm HH050 6 Have access to a car/van for personal use HS110 7 Replace worn-out furniture HD080 8 Replace worn-out clothes with some new ones PD020 9 Have two pairs of properly fitting shoes PD030 10 Spend a small amount of money each week on him/herself (“pocket money”) PD070 11 Have regular leisure activities PD060 12 Get together with friends/family for a drink/meal at least once a month PD050 13 Have an internet connection PD080 topics covered by the survey are living conditions, income, social exclusion, housing, work, demographics, and education of individuals. We select cross-sectional data for six European countries in 2022, corresponding to the income year 2021.4In detail, we settle on Germany, Spain, France, Italy, and Poland, which are the most heavily populated countries in the EU, with the addition of Sweden. We decided to explore the phenomenon in these countries as they are representative of different welfare regimes, labor markets, and institutions. Nelson (2012), for instance, shows that a meaningful part of the cross-country variation in the levels of material deprivation comes from different social assistance and benefits provided by governments. According to the 2030 Agenda of the United Nations, as well as to the previous Europe 2020 strategy of the European Commission, measuring non-financial poverty, and deprivation in particular, is very important for monitoring social exclusion (Guio et al. 2012). Our variable of interest is therefore the material and social deprivation score (Guio et al. 2016). This score is based on 13 selected deprivations (items) defined either at the individual or the household level. Individual items come from a questionnaire filled out by each adult in the household; questions on household items are included in the household questionnaire (filled out by a reference person for each household). Each item is based on the idea of an enforced lack, so an individual/household is deprived with respect to an item if she/he cannot afford the specific good (and not for other reasons based on preferences, see Guio and Van den Bosch 2020) or is not capable of the specific social activity/interaction (Guio et al. 2016,2017). According to the Eurostat guidelines, an individual is defined as materially and socially deprived if she/he suffers from a lack of at least 5 out of 13 items. The full list of items is provided in Table 1. 4COVID-19 is not an issue for our analyses for at least two reasons. First, given that we explore deprivation and not income, our main reference year is the survey year, i.e., 2022. Second, the negligible impact of the pandemic on the deprivation rate was already recovered in 2022 (see Eurostat statistics). 123 Material and social deprivation among one-person households… Page 7 of 25 10 Our sample includes single adult households, i.e., one-person households (single with no children), in which the adult is 18–64 years old and not retired. We use the same age limit for retirement in each country, that is 64 years, i.e., the threshold considered by Eurostat in its publications. In any case, we exclude retired workers even if aged less than 64. This solves the identification issue discussed in Section 2, because we are able to distinguish between female and male heads of household, their resources, and their enforced lacks. The selection leaves us with 19,886 observations for one-person households. Table 2shows the (weighted) material and social deprivation rates estimated by country and gender, as well as the gender gap, for our target population computed on the 2022 EU-SILC sample, and for the general population aged between 18 and 64 (Eurostat). As for the gender gap, we calculate t-tests for its statistical difference, and when statistically significant at least at 5% level, it is in italics. In Table 2, it is evident that the gender differences are statistically significant in the general population, with the only exception of Italy and Sweden. We find a disadvantage for women, i.e., a negative gender gap in Spain, France, and Poland and a positive one in Germany. With respect to the target population, however, the gap is significant, and negative, only in Spain (−8.2 p.p.). We selected a sample (by country) of one-person households because this type of household is the only one that enables to really understand if there is a “genuine” gender gap in material and social deprivation. This indicator, in fact, is usually calculated by considering items both at the household and at the individual level (for the list, see Table 1). In the selected household type, consisting of one person only, there is no problem of sharing the inability to afford items with other household members. Starting from this consideration, we now explore the raw gender gap (and its statistical significance) in the deprivation items by country. The calculations are reported in Table 3. On the one hand, we see that the items with a (significant) gender gap to the disadvantage of women are (i) facing unexpected expenses, (ii) affording to keep their home adequately warm, (iii) replacing worn-out furniture, (iv) pocket money, and (v) leisure. On the other hand, “arrears” is the item with a relatively higher disadvantage for men. By looking at the country columns, Spain has the highest number of negative and Table 2 Weighted material and social deprivation prevalence (in percentage) by gender in 2022 Target households General population MF Gap MF Gap Germany 17.8 17.1 0.7 16.9 14.1 2.8 Spain 15.8 24 −8.2 14.9 17.8 −2.9 France 17 19.2 −2.2 15.2 17.3 −2.1 Italy 11.9 13.6 −1.7 11.1 11.5 −0.4 Poland 12.3 11.7 0.6 11.7 13.8 −2.1 Sweden 7.8 7.7 0.1 6.8 6.6 0.2 Note: Gender gaps among target sample and general population. Italics indicate a statistical significant difference in (male–female) means at least at 5% level. Target households are one-person households, age 18–64. Source: 2022 EU-SILC and Eurostat data 123 10 Page 8 of 25 E. Fabrizi et al. Table 3 Gender gap in mean deprivation of the items by country, target sample DE ES FR IT PL SE 1. Unexpected expenses (gap) −2.20 −6.11 −5.93 −2.25 −6.74 −3.34 %male 42.9 37.8 34.3 37.3 30.7 24.1 %female 45.1 43.9 40.2 39.5 37.5 27.5 2. One week holidays away 3.33 −5.34 −0.73 −2.30 5.28 −1.99 %male 29.0 32.1 25.5 33.9 25.8 11.0 %female 25.7 37.5 26.3 36.2 20.5 13.0 3. Arrears 1.80 −0.94 2.94 1.17 3.79 0.75 %male 8.0 13.6 12.4 6.3 13.7 9.7 %female 6.2 14.6 9.5 5.2 9.9 9.0 4. Meal with meat/fish/vegetables −1.19 −3.46 −4.10 0.87 3.05 −1.85 %male 15.2 6.1 12.9 10.9 9.8 2.8 %female 16.4 9.6 17.0 10.0 6.8 4.7 5. Warm home −0.39 −4.78 −6.14 −1.00 3.13 −2.94 %male 8.1 20.0 12.0 14.1 9.1 2.3 %female 8.5 24.8 18.1 15.1 6.0 5.2 6. Access to a car 1.35 −5.26 −0.09 −0.05 −1.51 −2.04 %male 15.5 7.1 7.8 4. 6 8.6 8.5 %female 14.2 12.4 7.8 4.6 10.2 10.6 7. Worn-out furniture −0.34 −7.74 −4.78 −4.20 2.32 −0.10 %male 21.6 27.1 29.1 19.7 17.9 9.7 %female 21.9 34.8 33.8 23.8 15.5 9.8 8. Worn-out clothes 1.62 −4.85 −2.75 −2.19 2.30 −0.53 %male 11.9 9.6 11.7 7.2 9.6 5.3 %female 10.3 14.5 14.5 9.4 7.3 5.8 9. Two pairs of shoes 2.35 −0.41 1.36 0.52 2.12 0.37 %male 5.5 3.6 5.3 4.6 3.3 0.9 %female 3.2 4.0 4.0 4.1 1.1 0.5 10. Pocket money 0.94 −9.41 −5.87 −3.27 −2.28 −2.29 %male 13.6 13.2 10.8 5.9 9.9 8.8 %female 12.7 22.6 16.7 9.2 12.2 11.0 11. Leisure 0.59 −5.10 −4.45 −3.03 −3.89 −3.28 %male 17.2 13.9 16.8 10.8 10.2 7.0 %female 16.6 19.0 21.3 13.9 14.1 10.3 12. Get with friends/family 0.28 −2.74 −0.33 −1.15 1.50 −1.58 %male 10.0 7.4 6.9 5.4 8.8 1.3 %female 9.7 10.1 7.2 6.6 7.4 2.9 123 Material and social deprivation among one-person households… Page 15 of 25 10 Table 6 continued DE ES FR IT PL SE 8|9 2.538∗∗∗ 2.640∗∗∗ 2.270∗∗∗ 3.664∗∗∗ 9.969∗∗∗ 3.588∗∗ (0.196) (0.264) (0.364) (0.221) (0.309) (1.410) 9|10 3.244∗∗∗ 3.248∗∗∗ 2.866∗∗∗ 4.175∗∗∗ 10.538∗∗∗ 4.917∗∗∗ (0.204) (0.273) (0.373) (0.230) (0.322) (1.493) 10|11 3.981∗∗∗ 3.931∗∗∗ 3.668∗∗∗ 4.598∗∗∗ 11.169∗∗∗ 17.341∗∗∗ (0.219) (0.292) (0.396) (0.241) (0.346) (1.493) 11|12 5.111∗∗∗ 5.281∗∗∗ 5.282∗∗∗ 5.869∗∗∗ 11.763∗∗∗ 17.834∗∗∗ (0.269) (0.383) (0.538) (0.309) (0.382) (1.493) 12|13 6.810∗∗∗ 6.681∗∗∗ 32.764∗∗∗ 8.136∗∗∗ 13.891∗∗∗ 18.607∗∗∗ (0.485) (0.630) (0.538) (0.737) (0.764) (1.493) Observations 7104 3230 2634 4270 1600 1048 Note: Standard errors in parentheses. ∗p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01. Source: authors’ estimations based on EU-SILC 2022 data into deprivation from zero, while the gender difference at higher levels of deprivation is smaller. In fact, when we use logistic regressions for either being materially and socially deprived or severely deprived (i.e., the official indicators of the probability of being deprived if lacking 5 or 7 items, respectively), we observe a decrease in magnitude and significance of the gender effect (controlling for explanatory variables and a constant term). Results are reported in Table 3 in the Appendix and discussed in Section 6.6This conclusion is confirmed by the application of the partial proportional odds model to our data, relaxing the proportionality assumption only for the gender variable. For all countries, the signs of the estimated gender coefficient are positive, but their magnitude decreases as the threshold increases (i.e., there is a weaker and weaker difference between men and women), and they turn non-significant for high deprivation levels. Detailed results about these models are available upon request. This evidence is in favor of the “zero” threshold proposed by Bedük (2018). A possible explanation for these results resides in the relative economic fragility of women. Table 3in Section 4highlights a strong disadvantage for women in facing unexpected expenses, everywhere, which is the item with the largest prevalence of deprivation for women in our samples across countries. Moreover, if we consider the subset of individuals with exactly one item in deprivation, in more than 50% of the cases, it has to do with the inability to face unexpected expenses. Lack of pocket money and leisure activities play a similar role, that recurs very often among the deprivation of individuals with a few (one or two) items in deprivation. We note that the availability of pocket money and leisure (social deprivations) is related to financial distress, and they appear to go at a strong disadvantage to female heads of household—except in Germany (see also Guio and Van den Bosch 2020). Spanish and French women seem 6We also estimated probit models on the two official indicators. Again, we note a decrease in the magnitude and significance of the gender effects when moving from a threshold of 5 items to 7 items. For the sake of brevity, we do not report the results, which are available upon request. 123 10 Page 16 of 25 E. Fabrizi et al. particularly vulnerable on these dimensions, followed by Italian women (in line with Mussida et al. 2023). Financial fragility arises for several reasons. Individuals may have low financial literacy, they may overestimate or underestimate personal competencies, experience unexpected large income declines, and suffer from wrong financial choices. Aristei and Gallo (2022), for example, show that there is a gender gap in all these dimensions, detrimental to women. They argue that behavioral and psychological traits play a big role in financial decision-making; combined with social norms, they shape the perception of gender roles and contribute to expose women more to financial and poverty risks (see also Sholevar and Harris 2019; Zhou and Gan 2023). Lusardi and Mitchell (2017) show that women have more difficulty coping with financial troubles. Moreover, the fact that women earn less in their life-cycle and have low tolerance towards risk (Dawson 2023) decreases their saving behavior in the short and in the long run; this fact on average reduces their well-being relative to men (Fisher 2010). In our results, people’s condition about past marital status has a positive association with the risk of deprivation in all countries but Italy and Poland. Marital status is a dummy variable equal to 1 if the individual in the past had been married/cohabiting or is widowed; it is zero for never married/cohabiting people. Our findings are in line with England (2002), who explores the issue by gender. Italy and Poland represent an exception in our sample, no matter what “past life” individuals had, and it does not affect the probability of material and social deprivation. The unclear role of marital dissolution on deprivation in Italy might be partly due to the presence of strong family/intergenerational ties, that is proximity and parental support (Dalla Zuanna 2001). As for Poland, it is defined as one of the EU countries with the lowest level of family support (Szikra and Szelewa 2010). Although our findings contrast with Bárcena-Martín et al. (2014) in the fact that, overall, individual characteristics play a more important role with respect to macro indicators at explaining the probability of deprivation, some gender differences across countries may arise because of existing gender norms, attitudes towards gender roles, welfare regimes, and transfer programs—more or less favorable to women, and other institutional differences, in the labor market and active labor market policies. AccordingtoOECD(2023), for example, in 2021, Italy had the highest employment gender gap among the six countries, and Sweden and France had the lowest. Nelson (2012) finds that about 16% variation of material deprivation can be explained by country differences in the levels of social benefits. Social assistance is divided into a system of contributory benefits (e.g., social insurance, parental leaves, minimum wage schemes, house benefits, child benefits, tax credits) and public services (e.g., provision of care for dependents). Social assistance levels are fairly low in Eastern European countries (such as Poland) relatively to other European regions; it is relatively high in Germany, Italy, and Sweden, although in the former two—plus Spain—large regional heterogeneity exists in the implementation of the benefits. Furthermore, the Italian welfare model is getting far from the European social system, because it is based more heavily on family network support (Addabbo et al. 2015). Material and social deprivation has a non-linear relationship with age for Germany, Spain, France, and Sweden. The association we find is in line with the existing literature 123 Material and social deprivation among one-person households… Page 17 of 25 10 (see, for instance, Bárcena-Martín and Moro-Egido 2013, Guio and Van den Bosch 2020; Dudek and Szczesny 2021). It suggests that the youth are particularly exposed to the risk of material and social deprivation (Whelan and Maître 2010;Fabrizietal. 2023). Overall, both labor and non-labor income components are negatively associated with the risk of material and social deprivation. However, the effect of the former is relatively stronger (in magnitude and significance, see Table 6) than the latter, which is even non-significant in Italy. Notably, these findings should reflect the relative higher importance of the labor income share of total income in all countries. As far as Italy is concerned, there is a signal of the presence of non-labor income and/or transfers, which should not be effective in reducing the risk of deprivation (Bonanno et al. 2023). Likewise, full work intensity is negatively associated with material and social deprivation in all samples. However, work intensity estimates reveal that when individuals work less than 20% of the workable months in a year, they have very low work intensity and are exposed to a higher risk of material and social deprivation, in every country (after controlling for those who never worked and for income components of the household income). People with a low wage need to work a relevant number of hours, ending up with strong social and financial constraints. Likewise, people working few hours/months face difficulties to cope with expenses to conduct a decent way of life. Our results show that this may be true in every country, independently on the wage level or the gender wage differentials (Layte et al. 2001; Whelan et al. 2004; Halleröd et al. 2006; de Graaf-Zijl and Nolan 2011;Figari2012). Job uncertainty (such as that spurred by the COVID-19 crisis) and the expectations of future employment and income decrease household financial resources and increase the risk of material deprivation (Crettaz 2015; Friedrich and Teichler 2024; Pérez-Corral et al. 2024). As regards social class, we observe that only pertaining to the professional occupations’ category is negatively associated with the risk of deprivation in the investigated countries, with the exception of Italy. This finding is in line with Bedük (2018) who argues and finds a role for social class in the risk of deprivation (when zero items are taken as a threshold). Non-European citizens have a higher risk of deprivation in Germany, Italy, Poland, and slightly in France (as regards the disadvantage of foreigners, see, for instance, Bárcena-Martín et al. 2014; Busetta et al. 2016). Tertiary education is negatively associated with the probability of being deprived in at least one item, with the exception of Sweden (where the estimate is negative and non-significant). This result is in line with the evidence discussed in the literature section. There is a very large consensus among scholars about the protective role of high (secondary and mainly tertiary) education. Disability, that is limitation in daily activities, is positively associated with the risk of material deprivation in all countries. These findings are strictly linked to the indirect (long-term or permanent) impact of disability and caring activities on one’s own or other household members’ labor market participation, as found by, among others, Fabrizi and Mussida (2020). Finally, among the individual characteristics, outright ownership provides a relatively (compared with tenant paying for rent, our base category) lower risk of being materially and socially deprived, though not in Poland. Among the macro-level variables, we do not find a clear role for the degree of urbanization. There is only a significant positive association for both densely and 123 10 Page 18 of 25 E. Fabrizi et al. thinly populated areas (with respect to our base category, i.e., intermediate) in Poland and France. Deprivation is not a monetary indicator, and the presence/absence of items should be less strongly associated with the degree of urbanization, since it is a more complex phenomenon. For instance, social deprivation may arise both in a densely populated area and in a rural area, if people experience a reduction of employment, financial constraints, or a scanty social environment without friends/family support. The local—macro-regional—unemployment rate does not exert a clear role on the risk of material and social deprivation. When significant (in Spain and Italy), it has a positive sign. More checks on the role of the unemployment rate are conducted in the next section. Finally, as shown in the bottom part of Table 6, we see that intercepts are in most cases equally spaced or close to this condition, which corroborates the good fit of our models. Marked variable spacing only seldom happens in our analysis, and when it does, it is only for very high levels of deprivation which are infrequent in the data and for which the ability of the models to discriminate is limited. 6 Robustness checks In this section, we offer some robustness checks for the findings of our benchmark model, in which we change methods, specifications, and/or samples. The additional analyses can be summarized as follows: (1) separation of the material and social deprivation indicator in the “material” and “social” part, to understand their contribution to the overall indicator; (2) separate risk regressions for 13 deprivation items to inspect which deprivation contributes more to the gender difference; (3) logit of the standard material deprivation indicator and the standard severe material indicator, i.e., the lack of 5 or more items and 7 or more items, respectively, set by Eurostat; (4) models with interactions between gender and education (low/primary and high/tertiary educational attainment level); models estimated on separate sub-samples identified by median age; (5) (three) different model specifications on pooled data. All the attempts are reported in Tables 1 to 5 in the Appendix file. For the sake of brevity, in all these tables, we only report the main coefficients of interest. In the first check, we separate the “material and social deprivation” indicator into its “material” and “social” part. The former includes nine items of the European standard indicator for material deprivation (HS040, HS060, HS011, HS031, HS050, HH050, HS110, HS100, HS080, HS070 in the EU-SILC code), while the latter includes the “new” items, i.e., the items added to obtain the “material and social deprivation” indicator after 2013 (PD050, PD060, PD070, HD080, PD020, PD030, PD080 in the EU-SILC code). Table 1 in the Appendix reports the estimated coefficients of gender for the material part, the social part, and the overall material and social indicator (which is our benchmark, see Table 6above). The decomposition of the total indicator in the social and material components suggests interesting reflections. First, where the gender was significant for the overall material and social deprivation indicator, i.e., a relatively higher risk of being deprived for single women than for men, theseparate 123 Material and social deprivation among one-person households… Page 19 of 25 10 social and material parts are both significant. Germany provides a partial exception, because gender is significantly associated only with material deprivation. In Italy, for which the coefficient associated with gender is not different from zero overall, significance arises for the social component. The responsible items of explaining the disadvantage of single women in those components are “unexpected expenses” for Germany and “leisure” and “pocket money” for Italy (as stylized in the descriptive statistics, Table 3). The second check deals with the relevance of each item. The gender impact is relatively higher when individuals are exposed to few deprivation items, i.e., women are at maximum of their disadvantage when they move from zero to one deprivation item. This observation requires to run separate regressions—one for each deprivation item. The estimated gender coefficients for each item and country are reported in Table 2 of the Appendix. On the one hand, we see that women are highly penalized when it comes to “unexpected expenses” (positive and significant estimate for all countries, with the partial exception of Italy), “pocket money,” and “leisure.” On the other hand, being female reduces the risk of having “arrears” and, though to a lesser extent, “two pairs of fitting shoes.” In the former case, i.e., a significant disadvantage for females in “unexpected expenses,” “pocket money,” and “leisure,” we find a confirmation of the hypothesis of relative financial fragility of women; in the latter case, we find a confirmation of the relative lower tolerance of women towards risk, as discussed in Section 5for the main results (Table 6). This exercise, therefore, verifies that our main findings in the benchmark model are robust. In fact, the heterogeneity across countries is confirmed (Italian and Swedish women appear not to be different from their male counterparts in terms of “unexpected expenses”). To conduct our analysis within an official framework of material deprivation measures, in the third check, we estimate the probability of material deprivation and severe material deprivation (that is the lack of 5 items and 7 items, respectively) by using simple logit models (as mentioned in Section 5). Results for the estimated gender coefficients are reported in Table 3 in the Appendix. There is evidence that the coefficient associated with gender in material deprivation logit is strongly positively significant only for Spain and moderately significant for Poland. As far as severe material deprivation, the coefficient is positive and statistically significant for Spain and negative and significant for Germany. The rest of the estimates are non-significant. Two conclusions from this evidence emerge: (i) the vast majority of individuals in all countries report up to one or two items of deprivation, with only a few deprived with respect to many items. Females are significantly more at risk when we consider no threshold, while this effect disappears when dichotomizing the deprivation status based on high thresholds; (ii) the probability of severe material deprivation even reverses at the expense of men in Germany, i.e., the probability to be deprived of seven or more items becomes higher for men than women (with a significance at 5% level). Detailed results about these models are available upon request. This provides evidence in favor of the “zero” threshold proposed by Bedük (2018), and to the fact that what we propose with a proportional odds model brings to light otherwise hidden/insignificant relationships. As an additional check, we also run probit models and the findings are in linewith 123 10 Page 20 of 25 E. Fabrizi et al. those just discussed. For the sake of brevity, we do not report probit estimates, which are available from the authors upon request. The fourth check explores whether education and age interacted with gender lead to different findings or interpretations. Specification (i) includes interactions between gender and education classes (low/primary and high/tertiary educational attainment level), and specification (ii) separates samples below and above the country’s median age, to estimate the impact of gender and education. Table 4 in the Appendix reports the estimated coefficients for the main variables of interest. In model specification (i), we observe that low-educated women are not different from highly educated women, in general. In Germany and Poland, low-educated women are at a bit less risk of deprivation than low-educated men. In model specification (ii), on age-split samples, old women appear to be at a significantly higher risk of deprivation than old men, except in Poland (against the common intuition that older women, especially when educated, face equal risks as men), while young women appear to be at a significantly higher risk than young men in France, Spain, and Poland, even if tertiary education protects them. We also calculated t-tests for statistically significant differences between old and young: only Italy, Poland, and Sweden have slightly different gender estimates by age. These analyses, therefore, support the results of our benchmark model.7 Finally, the last robustness analysis concerns pooling the data. We estimate three model specifications by pooling countries together to elaborate more on country differences: (1) pooled data with country dummies; (2) pooled data with the interaction between gender and country dummies; (3) pooled data with the interaction between gender and the local unemployment rate. Table 5 in the Appendix reports the results for the three specifications. Gender disparities remain in place, after controlling for country dummies or the interactions. In particular, the coefficient for the main gender variable is positive and significant, i.e., there exists a positive association between being female and the risk of material and social deprivation. However, we cannot speculate on countries’ differences for gender here. It appears that country dummies are statistically significant (i.e., different from the reference category, Germany, the country with only one deprivation item against women, “unexpected expenses,” see Table 3). Only Poland shows a relatively higher risk of deprivation (all other countries show negative signs, that is a relatively lower risk than Germany). This should be due, as we explain in Section 5, to the fact that the social assistance level is fairly low in Eastern European countries relatively to other European regions. It is relatively high in Germany, Italy, and Sweden, although for the former two—plus Spain—large heterogeneity exists in the implementation of the benefits among regions within countries (Nelson 2012). Column 2 shows that the gender gap per se becomes non-significant when interactions are included, because of country heterogeneity. A female disadvantage with respect to German females can be detected in Spain, France, and Sweden (not in Italy and Poland). Country dummies—Germany as the reference category—give very 7As an additional exercise, we estimated models separately by median age including the interactions between gender and education. As for models (i) and (ii) above, we do not find important differences across population sub-groups. For the sake of brevity, we do not report these estimates here. Results are available upon request. 123 Material and social deprivation among one-person households… Page 21 of 25 10 similar results as in column 1, again with Poland showing a slightly higher risk of deprivation. Column 3 includes the interaction between gender and the local unemployment rate. While the estimated gender coefficient loses significance, the interaction with the unemployment rate is positive and significant. Country dummies give similar evidence as in column 1. Therefore, country differences remain even if we adjust for the difficulty of obtaining jobs, i.e., the local unemployment rates. There is a gender gap, and, of course, this is particularly true for unemployed single women. Overall, these additional analyses corroborate the results of our benchmark model, which seems to be robust to alternative assumptions. 7 Conclusions We investigate gender differentials in material and social deprivation by focusing on single adult heads of household, aged between 18 and 64, in six European countries. The sample selection circumvents the problem of assessing intra-couple or within-household inequality to better identify individual command over resources. Moreover, single-person households are on the rise (Karagiannaki and Burchardt 2020) in European societies and are particularly exposed to the risk of poverty (Chzhen and Bradshaw 2012; Treanor 2018). We estimate proportional odds models separately by country, to capture the intensity of deprivation and heterogeneity in the gender gap. Our findings suggest a clear role for gender, i.e., the risk of (cumulative) material and social deprivation for singles is relatively higher for women than for men in all the explored countries. The impact of gender is evident especially at low levels of deprivation, as women are more likely to experience deprivation in the items of financial distress, such as facing unexpected expenses or lack of pocket money, when the deprivation level is low. The effect tends to be less evident if we work with thresholds like 5 or 7, that identify more severely but clearly a much smaller set of deprived individuals. It is highest when women step into deprivation from zero. In other words, it is riskier to fall into deprivation of at least one item for women with respect to men (here, we do not intend that men are not at risk themselves). At higher levels of deprivation, the gap is more difficult to identify from a statistical point of view, because of the small number of individuals with those levels of deprivation. Moreover, while it is plausible that the COVID-19 pandemic has spread financial concerns among households, especially if their main earners lost part of her/his income, the difficulty to face “unexpected expenses” and financial constraints are not exclusively related to the pandemic period. Unfortunately, we do not have information about the extent of unexpected expenses for individuals and households (whether large or small). The other individual characteristics play a role in line with expectations. We find a relatively less important role for macro indicators than individual features. Nonetheless, the answer to our research question is that the gender gap in material and social deprivation remains in place everywhere, after controlling for individual, macro variable and a set of robustness analyses. If we look at single items of deprivation, the most important one for women is “facing unexpected expenses,” i.e., the item showing 123 10 Page 22 of 25 E. Fabrizi et al. the relatively higher gap at the disadvantage of women. The main explanation for this finding stands in the relative financial and economic fragility of women (even if controlling for protecting factors such as tertiary education, outright home ownership, full work intensity, and labor/non-labor income). Overall, our findings offer important policy implications to reduce the fragility of women. With respect to financial fragility, interventions aimed at increasing financial literacy would be desirable. As for economic fragility, more general interventions should aim at improving the labor market conditions of women, especially reducing the pay gap which is strongly and positively associated with the risk of deprivation. Supplementary Information The online version contains supplementary material available at https://doi. org/10.1007/s00148-025-01084-5. Acknowledgements We thank editor Klaus F. Zimmermann and two anonymous referees for their constructive comments. We also thank the participants at the GLO Berlin 2024 conference and the AISSEC conference in Vicenza (Italy) for their discussion of the main issues. Author contribution All authors contributed to the study conception and design. Material preparation was performed by Enrico Fabrizi, Chiara Mussida, and Maria Laura Parisi, data collection was performed by Chiara Mussida, and data analysis was performed by Enrico Fabrizi. The first draft of the manuscript was written by Chiara Mussida and Maria Laura Parisi, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding Open access funding provided by Università degli Studi di Brescia within the CRUI-CARE Agreement. Data availability The data that support the findings of this study are available from Eurostat, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of Eurostat. Declarations Conflict of interest The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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