Intergenerational Transmission of Poverty in Slovakia: Evidence from EU-SILC Data

While high socioeconomic status (SES) indicators generally protect against mental health issues such as mood disorders in overall populations, evidence suggests that these protective effects might be attenuated for marginalized groups such as immigrants, in comparison to groups who are more socially privileged. This phenomenon is referred to as Marginalization-related Diminished Returns (MDRs). Existing knowledge on diminished returns of SES indicators primarily stems from cross-sectional comparisons of racial groups of adults in the US. Therefore, there is a crucial need to investigate these dynamics using longitudinal data of youth in European countries that are host countries for migrants from many different countries. This study aims to compare the impact of family SES indicators (parental education, parental employment, family income), and family composition on subsequent incidence of mood disorders in youth from immigrant and non-immigrant families. Utilizing a retrospective cohort design, we analyzed data from nationwide registers in Sweden from Jan 1, 2001, to Dec 31, 2020, and included immigrant and non-immigrant children 12-18 years. Independent variables included parental education, parental employment, family income, and family composition. The outcome variable was a mood disorder diagnosis based on the national patient register. Sex and study year served as covariates, while immigration background acted as a moderator. Cox regression models were employed for statistical analysis. All SES indicators (higher parental education, parental employment, higher family income), and two-parent family composition were associated with a lower risk of mood disorders. However, the protective effects of parental education and employment were weaker for immigrant youth compared to native-born youth, as documented by statistically significant interaction tests. Our findings reveal weaker protective effects of parental education and employment for youth from immigrant families compared to youth from native-born families in Sweden. Prevention of mood disorders among immigrant families in Sweden may require two sets of policy interventions: one that supports education and employment in general and one that supports employed and educated families to harness their available human capital, with particular attention to immigrants from Africa and the Middle East.

This data brief describes the European Union Statistics on Income and Living Conditions (EU-SILC). Detailed data on income and taxes are collected, as well as information on material deprivation, labour, housing, childcare, health, access to and use of services, and education. Although primarily a social policy instrument that addresses the information needs of policymakers and is used for social monitoring at the European level, EU-SILC is also closely geared to the needs of researchers and provides an excellent database for evidence-based research on a wide variety of aspects of income, income poverty, material poverty, health, and well-being in Europe. EU-SILC is composed of national probability sample surveys and is conducted annually. The target population comprises private households. Observation units are households and all current household members. EU-SILC provides cross-sectional and longitudinal data. The data are composed of a fixed core module, and annually changing ad-hoc modules. Launched in 2003 and revised with effect from 2021, EU-SILC is currently implemented in all EU Member States and in 11 non-EU countries. During the revision process, many suggestions from the research community were incorporated.

Background: The European Union Statistics on Income and Living Conditions (EU-SILC) are increasingly used in demographic analysis, due to their large country coverage, the availability of harmonized socioeconomic measures, and the possibility to merge partners. However, so far there exists no comprehensive analysis of the representativeness of the fertility behavior reported by EU-SILC. Objective: This paper quantifies the quality of periodic fertility measures in EU-SILC. Methods: We compare periodic fertility measures obtained with EU-SILC to unbiased measures from the Human Fertility Database (HFD) for several European countries, by applying a cross-sectional perspective. Results: We show that EU-SILC measures of periodic fertility are biased downward, mainly due to attrition, while births of order one for ages 20‒29 are particularly underreported. However, we find no evidence of socioeconomic differentials in attrition. Conclusions: Our results suggest that for the majority of European countries, EU-SILC can be used for the analysis of childbearing behavior when respecting the measures of precaution mentioned in this article. Contribution: These precautions contain, for example, applying a retrospective approach and differentiating by rotation groups when calculating aggregate measures of periodic fertility.

Background:Electronic cigarette (e-cigarette) use among adolescents is a growing public health concern, particularly in low-income and Black communities. However, little is known about how social determinants of health shape e-cigarette perceptions in this population.Aims:This study examined social determinants associated with perceptions of e-cigarette safety among Baltimore high school students.Methods:A cross-sectional survey (CEASE Youth: School Survey) was conducted with 604 Baltimore high school students aged 14–20. Participants completed a questionnaire assessing perceptions of e-cigarette safety, as well as parental education, race/ethnicity, parental employment, household composition, and community tobacco use.Results:Higher parental education was associated with lower perceived e-cigarette safety among students. Students in higher grades also reported lower perceived e-cigarette safety. In contrast, male students—particularly those in upper grades—were more likely to perceive e-cigarettes as safe. Race/ethnicity, household composition, parental employment, and community tobacco exposure were not associated with perceived e-cigarette safety.Conclusion:Higher parental education, female gender, and being in higher grades were associated with perceiving e-cigarettes as unsafe. These findings highlight the need for targeted interventions to address vaping perceptions among youth in urban settings.

If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance. Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys on income and living conditions which lack accurate sample design variables.

Social and economic policies are inextricably linked with population health outcomes in Europe, yet few datasets are able to fully explore and compare this relationship across European countries. The European Union Statistics on Income and Living Conditions (EU-SILC) survey aims to address this gap using microdata on income, living conditions and health. EU-SILC contains both cross-sectional and longitudinal elements, with nationally representative samples of individuals 16 years and older in 28 European Union member states as well as Iceland, Norway and Switzerland. Data collection began in 2003 in Belgium, Denmark, Ireland, Greece, Luxembourg and Austria, with subsequent expansion across Europe. By 2011, all 28 EU member states, plus three others, were included in the dataset. Although EU-SILC is administered by Eurostat, the data are output-harmonized so that countries are required to collect specified data items but are free to determine sampling strategies for data collection purposes. EU-SILC covers approximately 500,000 European residents for its cross-sectional survey annually. Whereas aggregated data from EU-SILC are publicly available [http://ec.europa.eu/eurostat/web/income-and-living-conditions/data/main-tables], microdata are only available to research organizations subject to approval by Eurostat. Please refer to [http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/eu_silc] for further information regarding microdata access.

Background: Higher socioeconomic status (SES) is generally associated with lower engagement in health-risk behaviors, in part due to increased access to health information, preventive resources, and supportive environments. However, emerging evidence suggests that this protective pattern may not extend uniformly to all forms of substance use, including adolescent e-cigarette use, and may vary by gender. For instance, some studies have found higher rates of e-cigarette use among adolescents from higher SES backgrounds. Aim: This study examined whether the associations between family SES and tobacco use differ between girls and boys. We also explored whether these associations vary by age group. Methods: A cross-sectional survey was conducted among students (age 14–20) attending public high schools in Baltimore City. Family SES was assessed using three indicators: parental education, parental employment, and household income. Tobacco use was measured using self-reported past use of e-cigarettes and conventional cigarettes. Demographic covariates included age, sex, race/ethnicity, and household composition. Separate logistic regression models were estimated for each tobacco use outcome, adjusting for covariates. To examine subgroup differences, analyses were stratified by gender and age. Results: Higher parental education was associated with lower odds of e-cigarette use, but no SES indicators were significantly associated with conventional cigarette use. Subgroup analyses showed that the protective association of parental education against e-cigarette use was evident among girls but not boys and among older but not younger adolescents. Conclusions: These findings differ from previous studies that reported a positive association between SES and adolescent e-cigarette use. In this predominantly low-income, urban sample, higher parental education appeared to be protective for girls but not for boys. These results suggest that SES influences on tobacco use may be context- and subgroup-specific. Further research is needed to better understand how sociodemographic and contextual factors shape adolescent tobacco use behaviors.

Measuring Income in Household Panel Surveys for Germany: A Comparison of EU-SILC and SOEP

BackgroundInternational comparisons of the disability employment gap are an important driver of policy change. However, previous comparisons have used the European Union Statistics on Income and Living Conditions (EU-SILC), despite known comparability issues. We present new results from the higher-quality European Social Survey (ESS), compare these to EU-SILC and the EU Labour Force Survey (EU-LFS), and also examine trends in the disability employment gap in Europe over the financial crisis for the first time.MethodsFor cross-sectional comparisons of 25 countries, we use micro-data for ESS and EU-SILC for 2012 and compare these to published EU-LFS 2011 estimates. For trend analyses, we use seven biannual waves of ESS (2002–2014) with a total sample size of 182,195, and annual waves of EU-SILC (2004–2014) with a total sample size of 2,412,791.Results(i) Cross-sectional: countries that have smaller disability employment gaps in one survey tend to have smaller gaps in the other surveys. Nevertheless, there are some countries that perform badly on the lower-quality surveys but better in the higher-quality ESS. (ii) Trends: the disability employment gap appears to have declined in ESS by 4.9%, while no trend is observed in EU-SILC – but this has come alongside a rise in disability in ESS.ConclusionsThere is a need for investment in disability measures that are more comparable over time/space. Nevertheless, it is clear to policymakers there are some countries that do consistently well across surveys and measures (Switzerland), and others that do badly (Hungary).

1. Introduction In this article we check for unobserved heteroskedasticity due to gender in adult learning in Europe. It is largely accepted by the economic literature that human capital is a continuous process starting at school and keeping being diffused in the labour market through adult learning. Indeed, skills can be accumulated not only before getting a job, through pre-occupational education but also during working life by fostering continuous learning and/or training. Although the European Jobs Strategy's emphasizes adult education during working life, empirical literature, by focusing on the growth effects of the initial education, does not seem to take sufficiently into account the contribution of workforce adult learning as an additional source of human capital and growth. As for the determinants of adult learning, some empirical regularities have been found: young and better educated workers, involved in highly-skilled occupations and in large firms enjoy greater learning opportunity (2). These findings can be easily defined as stylized facts. However, there is no accepted evidence of which gender is more likely to receive any adult learning. When the training definition is considered, some papers (i.e. Bassanini et al. 2007) show that being female is associated with a higher probability of being involved in training. Arulampalam et al. (2004) find these results in 4 countries; conversely, in the other 6 countries there is not a significant difference between males and females. Oppositely, Pischke (2001) estimates that men in Germany are more likely to access to training. When considering a broader learning activity, Drewes (2008) finds that female are more likely to participate in educational programs, but less likely to take training courses. Thalassinos et al. (2009) have analysed gender inequalities in shipping. For the UK, in Jenkins et al. (2002) females are six percentages points more likely to undertake lifelong learning, while Sargant et al. (1997) show that men are more likely to be involved in training and education. Also Simonsen and Skipper (2008) find that men and women have different enrollment patterns: women are more likely to attend basic or postsecondary training courses, whereas men are more likely to get enrolled in vocational ones. Our results shows that in Europe there exists a significant unobserved hetetoskedasticity due to gender in the adult learning. In the empirical model we used the European Union Statistics on Income and Living Conditions (EU-SILC), the new homogenized European panel survey. Since 2005 (3) EU-SILC has succeeded the European Community Household Panel (ECHP): there, the number of countries is increased, indicators are updated, and common guidelines, definitions, and procedures are used. This empirical paper is organized as follows. The next section describes the data. Section 3 reports the results of the empirical model. In the last section we present our main conclusions. 2. The Data Our data are from the 2005 first wave, of EU-SILC, the new homogenized panel survey that has replaced ECHP. Similarly to the ECHP, EU-SILC is an attractive source of information because it adopts the same community questionnaire used by the national data collection units in each included country, which obviously makes comparisons across nations easier. EU-SILC has three main advantages with respect to other similar datasets. Firstly, the set of economies is fully comparable. This desirable feature is obtained through the use of common guidelines, definitions and procedures. Secondly, all the old and the new European member states are surveyed, while the ECHP covered only 14 economies. The new dataset, thus gives information on many of the so-called new entrants. Finally it updates the whole of the indicators. Indeed, our comparison involves 21 European member states whose labor market institutions and adult learning systems are known to have different characteristics. …

Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).

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Although monitoring and evaluating child poverty has been recognized as important, there is little statistical information focused on children. Because the annual EU-Statistics on Income and Living Conditions (EU-SILC) survey does not include child-specific information on an annual basis, this study proposes a measure of child exposure to household material deprivation based on this dataset. The study considers four domains of deprivation that have a direct impact on child development: housing conditions, household financial capacity, household durable goods, and environmental living conditions. Although developing a child-centered measurement of child deprivation is important, the EU-SILC considers the household as the unit of measurement. Therefore, our proposal is household-based, allowing annual monitoring of children’s exposure to deprivation—an important insight for social policy purposes to tackle the problem of child poverty. Using the 2017 Portuguese sample, we applied graded response models to assess the psychometric properties of the EU-SILC items and fit separate indexes per domain and the composite index. Item selection was based on their characteristic curves and information functions. The results allow for the selection of more informative items for every domain to obtain the composite index. In general, the empirical analysis confirmed the theoretical approach for item selection. The methodology may be directly applied to the full EU dataset or to each country individually.

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due to complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC) poverty risk estimates are published with a 2–3 years delay. This paper presents a method that can be used to estimate (“nowcast”) the current at-risk-of-poverty rate for the European Union (EU) countries based on EU-SILC microdata from a previous period. The EU tax-benefit microsimulation model EUROMOD is used for this purpose in combination with up to date macro-level statistics. The method is validated by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008–2012 and to compare the predictions with actual EU-SILC and other external statistics. The method is tested on eight EU countries which are among those experiencing the most volatile economic conditions within the period: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal and Romania.

ObjectivesTo assess the sensitivity of prevalence and inequality estimates of Global Activity Limitation Indicator (GALI) to the choice of survey in European countries.MethodsWe use logistic regression to estimate adjusted risk ratios, quantifying differences in prevalence and educational inequalities, the impact of survey characteristics and Kendall’s tau to assess similarity in country rankings between surveys. We include the European Health Interview Survey (EHIS), European Social Survey (ESS) and European Union Statistics on Income and Living Conditions (EU-SILC).ResultsEHIS estimates higher prevalence than EU-SILC 17% (men) and 23% (women), and ESS 24% (men) and 29% (women). Prevalence does not differ significantly between EU-SILC and ESS. EU-SILC estimates 52.5% (men) and 28.1% (women) higher inequalities than EHIS and 63.2% (men) and 32.7% (women) higher inequalities than ESS. Survey characteristics do not account for differences in prevalence or inequalities. Country rankings do not agree for prevalence or inequalities.ConclusionsSurvey choice strongly impacts estimates of GALI prevalence and educational inequalities. Further study is necessary to understand these discrepancies. Caution is required when using these surveys for cross-country comparisons of (educational inequalities in) GALI disability.