Non-take-up of in-work benefits: Determinants, benefit erosion and indexing

Muñoz-Higueras, Diego; Köppe, Stephan; Granell, Rafael; Fuenmayor Fernández, Amadeo Article Non-take-up of in-work benefits: Determinants, benefit erosion and indexing Journal for Labour Market Research Provided in Cooperation with: Institute for Employment Research (IAB) Suggested Citation: Muñoz-Higueras, Diego; Köppe, Stephan; Granell, Rafael; Fuenmayor Fernández, Amadeo (2024) : Non-take-up of in-work benefits: Determinants, benefit erosion and indexing, Journal for Labour Market Research, ISSN 2510-5027, Springer, Heidelberg, Vol. 58, Iss. 1, pp. 1-19, https://doi.org/10.1186/s12651-024-00385-8 This Version is available at: https://hdl.handle.net/10419/308514 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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. https://creativecommons.org/licenses/by/4.0/ ORIGINAL ARTICLE Open Access © The Author(s) 2024. 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. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http: //creativecommo ns. org/lice ns e s/by/4.0/. Muñoz-Higueras et al. Journal for Labour Market Research (2024) 58:22 https://doi.org/10.1186/s12651-024-00385-8 structure has changed significantly since these studies were published. Therefore, and thanks to new data being available, it is more than timely to reassess the take-up of the WFP. With the liberalisation of labour markets in the 1990s and 2000s, many European welfare states have introduced in-work benefits with the aim to mitigate poverty. While the main aim of these reforms was to increase employment; low pay, temporary jobs and decreasing labour market regulations can contribute to in-work poverty. Moreover, one major risk factor for in-work poverty are children in the household (Eurofound 2017; Lohmann and Marx 2018). Policy-makers have aimed to address in-work poverty through a combination of direct (e.g. in-work benefits, tax credits, minimum wage) and indirect measures (e.g. affordable childcare). Nevertheless, means-tested inwork benefits are known to be affected by non-take-up. For instance, several studies indicate non-take-up for 1 Introduction Ireland pioneered in-work benefits before these became more common across the EU. Introduced in 1984 the then called Family Income Supplement was implemented to mitigate the risk of in-work poverty and child poverty. In a political drive to promote the key aims of the scheme it was renamed to Working Family Payment (WFP) in 2018. While in-work benefit schemes in the rest of Europe would also include households without children, the Irish scheme is specifically targeted at working households with children. Since the inception of the scheme low take-up had been a known issue (Callan et al. 1995; Savage et al. 2015) but the labour market and social Journal for Labour Market Research *Correspondence: Diego Muñoz-Higueras [email protected] 1Department of Applied Economics, University of Valencia, Valencia, Spain 2School of Social Policy Social Work and Social Justice, University College Dublin, Dublin, Ireland Abstract Non-take-up of welfare schemes is a key concern of policy effectiveness. Building on studies that have shown the low take-up of minimum income schemes, our case study of Ireland’s Working Family Payment is the first to analyse non-take-up of an in-work benefit and its determinants with a special focus on labour market factors. Based on EU-SILC (2014–2019) we estimate a non-take-up rate between 63 and 76%, which poses a major obstacle for effective poverty prevention. Moreover, we stress that non-take-up of in-work benefits differs to minimum income schemes. We provide new evidence on how labour market characteristics play an important role in explaining non-take-up, especially self-employment and the interaction with unemployment benefits. Benefit erosion is a key factor in declining eligibility, which should be addressed by indexing wages and prices. Furthermore, we propose policy reforms around automatic enrolment or tax credits to mitigate non-take-up and alleviate in-work poverty. Keywords Non-take-up, Administrative design, In-work benefits, In-work poverty, Labour market characteristics, Working family payment JEL Classification C15, D04, H31, H53, I38, J38 Non-take-up of in-work benefits: determinants, benefit erosion and indexing DiegoMuñoz-Higueras1* , StephanKöppe2, RafaelGranell1 and AmadeoFuenmayor1 Page 2 of 19D. Muñoz-Higueras et al. (2024) 58:22 means-tested social assistance schemes is higher than 50% within Europe (Fuchs et al. 2020). If a person or household is eligible to receive an in-work benefit, but finally does not claim their social right, it undermines the effectiveness of the scheme. The problem of non-takeup is further extrapolated if certain strata in society are more prone to non-take-up than others and deteriorates their social inclusion. Non-take-up is a multidimensional phenomenon and is affected by client, administrative, policy design and broader social and legal contexts (Oorschot 1996; Janssens and Van Mechelen 2022). So far, the literature has focussed on non-take-up of general social assistance schemes, with less attention on non-take-up rates and determinants of in-work benefits. In this article, we contribute to this literature with one of the first indepth analysis of a wage supplement. We focus on the individual and policy design characteristics as well as labour market attachment of eligible individuals. As the Irish WFP differs from traditional non-take-up studies on minimum income schemes and non-contributory benefits, we pay more attention to the policy differences between minimum income and in-work benefit schemes. Hence, labour market characteristics as a core eligibility feature of in-work benefits such as occupational class and intensity of employment are a particular focus of our empirical analysis. The remainder of this article is organised as follows. First, we review the relevant literature about non-take-up and in section three we contextualise in-work benefits in Ireland. In section four we present the methods, followed by results. The final section discusses policy recommendations and draws wider conclusions for the relationship of in-work benefits, non-take-up and policy design. 2 Determinants of non-take-up in the literature Non-take-up of public social protection schemes is an increasingly recognised problem for welfare states. For instance, means-tested social assistance benefits in Europe report non-take-up rates between 40% and 70% (Eurofound 2015; Fuchs et al. 2020). These estimates vary by the type of benefit, the data and the methodology used, which is further complicated by data constraints (Goedemé and Janssens 2020). Understanding the drivers of non-take-up is relevant as it affects both, the effectiveness (i.e. poverty reduction in this case) and efficiency (in terms of cost-benefit) of welfare schemes and creates inequalities between eligible populations (Hernanz and Malherbet 2004). In addition, despite the fact that governments generally focus more on the overpayment of benefits to reduce public expenditure (Matsaganis and Levy, 2010), the short-term budgetary savings of non-take-up may create significant long-term costs. For instance, prolonged child poverty may impact a child’s health, employability and criminality in the long-term with much higher costs for society (Dubois and Ludwinek 2014). The literature differentiates between two main nontake-up types (van Oorschot 1996): Primary non-take-up refers to an eligible individual or household who is not claiming the benefit. Secondary non-take-up describes a person or household who starts the application procedure, and despite being eligible, finally does not receive it. This is also referred to as administrative error. In our empirical analysis, we will focus on primary non-take-up. Four main determinants of non-take-up are identified in the literature: client, administration, policy design (van Oorschot 1996) – and more recently – the broader social and legal context (Janssens and van Mechelen 2022). Each of these determinants have been investigated extensively (Bargain et al. 2012a; Bruckmeier and Wiemers 2012; Hernanz and Malherbet 2004). In the most general understanding the take-up of benefits is the result of a trade-off between the costs (e.g. information costs, administrative burden, social stigma) and benefits (amount and duration). Therefore, eligible households will only submit an application if the anticipated benefits outweigh the perceived costs (Kerr 1982; Matsaganis and Levy, 2010). 2.1 Client At the client level, several determinants have been identified. First, a large body of evidence suggests that insufficient benefit generosity increases non-take-up (Hernanz and Malherbet 2004). Estimations for various policy contexts suggest that a 10% increase of the benefit level reduces non-take-up by 0.5-2% (Bargain et al. 2012a; Riphahn 2001). However, in terms of benefit duration, the evidence is less clear and seems to depend more on the degree of future dependency of claimants (Bruckmeier and Wiemers 2012). Higher probabilities of claiming a benefit are observed among jobseekers, families with children and pensioners (Hernanz and Malherbet 2004). The costs associated with claiming could be grouped by information costs and process costs, both of them contributing to higher non-take-up. Information costs are the predicted, observed, and experienced costs that a person must spend to understand the application rules of a benefit, and process costs are the costs of making the application (Janssens and van Mechelen 2022). For instance, the ease of available information, the lesser documentation required and proximity of welfare offices, reduce non-take-up (Janssens et al. 2021; MuñozHigueras et al. 2023). Lack of knowledge contributes to non-take-up, and is greater among immigrants and those with a higher degree of social exclusion (Aizer and Currie 2004). Page 3 of 19Non-take-up of in-work benefits: determinants, benefit erosion and indexing (2024) 58:22 Social and physiological costs, in particular the stigma associated with means-tested benefits contribute to non-take-up. For example, the more unconditional and universal the scheme is, the lower the stigma associated with it. Evidence from the UK suggests that in-work tax credits have the lowest stigma out of five means-tested schemes (Baumberg 2016), but we know little about stigma of other in-work schemes. Moreover, metropolitan areas guarantee a higher level of anonymity to avoid stigma and, hence, report higher take-up (Riphahn 2001). Social networks might reduce administrative and information costs of an application (Bouckaert and Schokkaert 2011), but the role of non-take-up behaviour is uncertain. For example, individuals who mainly interact with people in employment find it more difficult to obtain information about social provision (Bertrand et al. 2000). Trigger events are defined as sudden disruptive circumstances that may can lead people to claim benefits (van Oorschot 1991). Such events (e.g. health problems, loss of job) change the cost-benefit balance and increase the likelihood to submit a claim. 2.2 Administration The literature has highlighted multiple administrative barriers for take-up (Peeters 2020), but with our empirical data this dimension can be less explored directly. Improving government information on eligibility conditions and application procedures, as well as targeted information such as large-scale mailing campaigns, sending social service workers to the homes of potential claimants, and avoiding stigmatizing communication play an important role in increasing the take-up of a social benefit scheme (Finn and Goodship 2014; van Gestel et al. 2022). Effective collaboration between agencies offering similar social programs is also an important factor in reducing non-take-up (Raeymaeckers and Dierckx 2012; Muñoz-Higueras et al. 2023). While online applications reduce the transaction costs for most people, these are less effective for groups such as older people or households in extreme poverty (Kopczuk and Pop-Eleches 2007). Furthermore, the availability of linked administrative records and the quality of data play an important role in the possibility for reaching out to potential beneficiaries and developing automatic enrolment schemes (Janssens and van Mechelen 2022). 2.3 Policy design While eligibility conditions are ultimately a political decision, such design choices not only limit the eligible population but also indirectly affect take-up through the interplay of political communication, administrative priorities (Muñoz-Higueras et al. 2023) and client relationships (Janssens and van Mechelen 2022). The higher the degree of targeting, the higher the nontake-up (van Oorschot, 2002). Targeted programmes are associated with more stigma (Bruckmeier and Wiemers 2012) and increase the information costs for claimants. For instance, complex targeting criteria to assess claimants’ means increase the time and effort required to understand the benefit. In addition, longer waiting periods increase nontake-up (Muñoz-Higueras et al. 2023) and cash transfers generate higher take-up rates than in-kind benefits (Schanzenbac, 2009). Finally, while discretion offers flexibility and more personal targeting for administrators, it increases the probability of administrative errors (type II non-take-up) and claimants’ uncertainty (Higueras and Pérez 2020; Peeters 2020). 2.4 Social and legal context The client, administrative and policy design levels are influenced by the broader social and institutional context (Eurofound 2015; Janssens and van Mechelen 2022). For instance, Reijnders et al. (2018) demonstrate that social conventions about when it is acceptable to ask for help are a barrier to people seeking social support. Another example are how public perceptions of deservingness influence the policy design. The majority of non-take-up studies focus on social assistance schemes, with a clear lack of in-work benefits. As a result, the literature has highlighted key client characteristics, but has not paid attention to labour market factors. Hybrid systems, like in Germany, which combine minimum income support with in-work benefits, report similar low take-up rates and determinants, but studies have not considered labour market characteristics nor could isolate those only eligible to in-work benefits (Bruckmeier et al. 2021; Wilke 2023). With our empirical analysis we will make novel contributions to this literature, while focussing on the client and policy design determinants. Due to the case study design we cannot control for the social context and the data contains less variables to test for administrative barriers. Based on the existing literature on minimum income schemes we expect similar socio-economic determinants to be associated with non-take-up for pure in-work benefits like the WFP. In addition, we explore factors associated with labour market precarity and how these interact with the usual suspects. Considering the lack of previous studies and theorising, we use an empirical driven approach without a priori hypotheses and consider our findings in relation to existing theories in the discussion. Page 4 of 19D. Muñoz-Higueras et al. (2024) 58:22 2.5 Working family payment in the Irish labour market Ireland’s economy, labour market and welfare state have some peculiar features in the context of the international literature that are relevant to gain a deeper understanding of the take-up of a wage supplement scheme like the Working Family Payment. When the old Family Income Supplement was introduced in 1984, Ireland had one of the lowest GDPs per capita in the Organization for Economic Cooperation and Development (OECD), the economy was in recession and unemployment was soaring (O’Hagan and O’Toole 2017). Since then, Ireland became one of the richest countries in the OECD per capita. Still, the labour market is characterised by low wage labour, low female labour force participation, medium employment levels and the highest gross income inequality within the European Union. In this context, minimum wage legislation and wage supplements remain essential policies to mitigate in-work poverty risks. In addition, the liberal welfare system contributes to poverty traps (NESC, 2020). Although the income tax system is highly progressive and contributes largely to relative low net income poverty rates (Roantree et al. 2021), the reliance on means-tested and flat rate benefits contributes to stigmatisation of benefit claimants. Ireland has a relative high rate of joblessness among households with children and the strong means-testing is a key contributing factor (Härkönen 2011). Due to the means-tested system not only benefits taper off quickly, also access to services such as childcare and primary medical care is affected, which creates disincentives to work (Regan et al. 2018) and stigma (Keane et al. 2021). Furthermore, insufficiently guaranteed working hours in zero hour or no contract work arrangements are relatively common in Ireland, which is masked in the high rates of standard measure of permanent contracts (LMAC, 2022). Irish family policies used to favour strongly the male breadwinner model (Fahey and Nixon 2014), but recent policy advances in childcare provision, leave entitlements (Köppe 2023) and activation measures have increased work incentives for families and weakened the male-breadwinner model. During the observation years 2014–2019, Ireland experienced a period of recovery from a deep economic financial crisis (Roche et al. 2017), with high economic growth rates and rising employment opportunities. Most of the austerity-imposed cuts were reinstated to pre-crisis levels, employees experienced real income growth and activation measures brought almost full employment (Köppe and MacCarthaigh 2019). Despite these labour market inequalities in-work poverty in Ireland has been significantly below the EU average (Nolan 2008). Nolan (2008) stresses that the relative low in-work poverty rate, which is calculated for employees, is related to higher levels of self-employment (esp. agriculture), higher share of households with three or more adults and joblessness. In particular, lone parent households with children are more likely to be out of work than being counted as in-work poor. For households with dependent children, i.e. those eligible to the WFP, the in-work poverty rate has been at 8.5% in 2008, but has since declined to 5% of the employed population (author’s analysis of Eurostat, 2022). This represents about 13% of all people in poverty (Daly 2019). Research that considers the policy factors to reduce in-work poverty has shown that the relative generous minimum wage and the Irish social protection system as a whole contribute significantly to a reduction of in-work poverty and identified Ireland as a successful outlier in this regard (Cantillon et al. 2013). However, Cantillon et al. (2013) cannot single out the WFP in their analysis of meanstested transfer schemes. Recent simulations by Roantree and Doorley (2023) also show that increasing WFP thresholds is a very effective way to reduce child poverty rates. Although multiple and complex policy schemes aim to alleviate in-work poverty, the WFP is the key wage supplement scheme to top-up earnings for low-income households with children. Furthermore, in comparative studies, the WFP is used to model take-up and poverty reduction effects of in-work benefits in Ireland (Lohmann and Marx 2018). The eligibility criteria are as follows (Citizens Information, 2023), which did not change during the observation period: 1. Work 38 or more hours per fortnight. These can be combined with working hours of the spouse, civil partner or cohabitant. Self-employed work and job creation schemes (e.g. Community Employment, JobBridge) are not considered in computing the 38h per fortnight. 2. At least one co-resident child. The children must be under 18 (or between 18 and 22 if they are in fulltime education). 3. The household falls below the income threshold by number of children (see Table1). 4. The job is likely to last at least 3 months. The benefit covers the 60% of the gap between the average weekly family income and the WFP income limit for this family size. It is a tax-free benefit. The first main feature of the scheme is the work requirement of 19 h per week. This equals part-time work for a single parent household, while couple households can combine either two casual jobs or one parttime employment, mostly associated with the male breadwinner model. The second key criterion is exclusion Page 5 of 19Non-take-up of in-work benefits: determinants, benefit erosion and indexing (2024) 58:22 of households without children or adult children in the households. Finally, the exclusion of self-employed work is a design feature we investigate further in the empirical analysis. The low take-up of WFP has been noted early on in the literature. Callan et al. (1995) estimated 25% takeup among the eligible population in 1987 based on The Survey of Income Distribution, Poverty and Usage of State Services, a precursor survey to EU-SILC (European Union Statistics on Income and Living Conditions). Subsequent studies estimated a wider range but never higher than a third of the eligible population would claim WFP, 33% in 2010 based on EU-SILC (Savage et al. 2015); 17% in 2001 based on Living in Ireland (LLI), Bargain and Doorley, 2011). The most recent simulations based on EU-SILC show that estimates vary between 13 and 53%, depending on the data source and microsimulation model used. The lower take-up is based on EUROMOD, while the higher rate is based on SWITCH. While EUROMOD is an off the shelf micro-simulation tool across the EU, SWITCH is more tailored for the Irish policy context and based on administrative data. Hence, Doorley and Kakoulidou (2023) claim that the 47% nontake-up rate is more accurate. Furthermore, this study suggests that EUROMOD overestimates non-take-up of the WFP. Administrative data suggests an increase of total claimants between 2004 and 2014, although the number of children benefiting decreased (Millar et al. 2018). Yet, none if this aggregate analysis controls for population increases nor eligibility, nor does the Department of Social Protection publishes any official take-up rate (DEASP, 2018). Besides these discrepancies and inaccuracies in measuring overall take-up, there is a clear lack of quantitative studies on the determinants of WFP non-take-up. Yet, qualitative studies highlight the inflexibility and bureaucratic burden associated with WFP. The strict two-week assessment for the benefit inadequately accounts for seasonal work and precarious gig economy, excluding certain employees from eligibility. Moreover, the multiple forms and employer signatures increase the hurdle for applications (Millar et al. 2018; Pembroke 2018). Further descriptive studies also show a higher share of single parents among claimants (Gray and Rooney 2018), but cannot control for other socio-demographic characteristics. Although reporting descriptive findings of nontake-up, other studies have focussed more on welfare outcomes of the WFP such as poverty reduction and employment incentives (Doorley et al. 2022; Bargain and Doorley 2011b; DEASP, 2018). Relevant for non-take-up is that WFP has a positive effect on income adequacy and a lower stigma than other means-tested benefits (Millar et al. 2018). Bargain and Doorley (2011b) report lower working hours for men and low work intensity for partnered women. In a similar vein, the means-tested unemployment benefit (JSA – Jobseekers Allowance) is more generous than the WFP on minimum wage and part-time work, because JSA claimants can work three days a week without a benefit reduction (DEASP, 2018). This means JSA claimants who comply with the 3-day-rule are better off than WFP claimants who spread the same working hours across the week. In contrast to these earlier studies, we can control for the policy interaction of WFP and JSA with the available data (see Data and methods’ section). Finally, policy studies simulate different reform options (Doorley et al. 2022). The exclusion of self-employed has been problematized (Gray and Rooney 2018). Moreover, in their conclusion Keane et al. (2021) argue that a refundable tax credit would eliminate non-take-up. In brief, non-take-up of WFP is a known issue, but more recent studies that take advantage of the much more accurate EU-SILC data since 2014 are missing. Moreover, none of the studies has explored the determinants of non-take-up quantitatively, especially with a focus on client and policy design determinants. Simulation studies have pointed to certain policy solutions to address poverty reduction (e.g. increasing thresholds), but none had an empirical focus on non-take-up. More broadly, the Irish WFP highlights specific policy characteristics of in-work benefits (households with children). Therefore, this study contributes to the wider literature on take-up of means-tested benefits, with a specific focus on in-work benefits in a liberal labour market regime. Table 1 WFP income limits for period analysed (€ per week) Children in household 2014 2015 2016 2017 2018 2019 1 506 506 511 511 521 521 2 602 602 612 612 622 622 3 703 703 713 713 723 723 4 824 824 834 834 834 834 5 950 950 960 960 960 960 6 1,066 1,066 1,076 1,076 1,076 1,076 7 1,202 1,202 1,212 1,212 1,212 1,212 8 or more 1,298 1,298 1,308 1,308 1,308 1,308 Note: own elaboration following WFP citizen information Page 6 of 19D. Muñoz-Higueras et al. (2024) 58:22 3 Data and methods Due to the limitations of the off-the-shelf microsimulations tools such as EUROMOD regarding WFP (see Doorley and Kakoulidou 2023), we estimate take-up directly with original EU-SILC data in Stata. For the analysis we use the cross-sectional data of the EU-SILC for Ireland from 2014 to 2019. Prior to 2014 the WFP was aggregated in the survey item with other ‘Family/Children related allowances’ and we can only identify WFP claimants correctly since then through the HY051G variable, which is provided by the Irish government through administrative data. Moreover, we exclude the Covid19 pandemic years as this exceptional period had complex and unique effects on the labour market and distort take-up estimates. EU-SILC provides comparable, crosssectional and longitudinal multidimensional data on income, poverty, social exclusion and living conditions in the EU, which allows us to simulate benefit take-up and control for a number of socio-demographic determinants of take-up. 3.1 Eligibility and non-take-up simulations Based on the benefit eligibility criteria explained previously we assess if households meet the conditions in a four-step process (see appendix for more details), using original EU-SILC data. First, we identify the number of hours worked per fortnight as an employee and aggregate all hours by household. If someone is self-employed these hours are not taken into account. Second, despite that a generalpurpose survey like EU-SILC could not contain all the information that we need to identify the eligible household, we assume that people who are working at the time of responding to the survey have been working for at least three months. Third, we use the information about the number and age of children living in the household. In addition, we can identify whether a child aged over 18 and under 22 years old is in full-time education. In this case, these dependent children qualify for the WFP and are included. Fourth, we can identify all assessable income. We disregard the benefits/allowances that are exempt from the WFP means-test (e.g. child benefit). Although not all exemptions can be uniquely identified, these are overall negligible to get accurate take-up measurements (see appendix table A.2). After adding up all assessable income, in a second step, we calculate the theoretical amount a household is eligible to. We draw this information from the WFP thresholds (DSP, 2019). Based on Table1 we adjust for annual changes and calculate the eligibility threshold for each household by the number of children living in the household. If the household meets the first three conditions and its adjusted disposable income is below the eligibility threshold, it is considered eligible for the WFP. In the model we assign a binary variable for eligibility (0 is not eligible, 1 is eligible). Since we can identify now clearly the eligible population and those claiming WFP, we can use the standard formula to calculate non-take-up: No n − take − up rate = Eligible people,but notreceiving thebenefit Eligible population Based on the literature, non-take-up refers to one person that is eligible to receive a social transfer, but finally does not claim it. Divided by the eligible population, the nontake-up rate provides a relative estimate of non-take-up over time. One of the strengths of our estimates is the fact that this policy does not have a wealth test. Wealth is often one of the most difficult issues to assess and traditional estimates need to simulate different scenarios to perform validity and sensitivity checks (Fuchs et al. 2020). Furthermore, compared to minimum income benefits, this policy does not include eligibility conditions such as the citizenship of the claimant, the residence status, or the time when the family unit was formed. Therefore, the eligibility determination is much simpler and most likely more accurate than in other studies. In order to check the robustness of our simulated results, we conducted a sensitivity check, increasing and decreasing assessable income by 5%, which also accounts for the period effects in the EU-SILC. This also means, based on very precise estimation of eligibility criteria, we could achieve an accurate match of 73%. This is in line with other research using general purpose surveys (Frick and Groh-Samberg 2007). Once the eligible population for the WFP has been estimated, we provide simple summary descriptive statistics by subpopulations: beneficiaries, eligible and non-takeup. These are explained as follows (Table A.3): ‘Beneficiaries’ are households who are currently receiving the WFP, as stated by variable HY051G in the database; ‘Eligible’ includes households who are eligible to receive the WFP, based on our simulations; ‘Non-take-up’ refers to households who are eligible to receive the WFP, but are not receiving it. 3.2 Estimating the determinants of non-take-up: a probit model To estimate the determinants of non-take-up, we consider using a probit model. This kind of model is used when the dependent variable is binary. In addition, it can control for unobservable factors, which is rather common in the analysis of non-take-up (Goedemé and Janssens 2020). Furthermore, the probit methodology captures the possible non-linear relationship between variables. All of these methodological issues can help in the interpretation of robust coefficients. Page 7 of 19Non-take-up of in-work benefits: determinants, benefit erosion and indexing (2024) 58:22 In this case, the targeted population for this model includes all households eligible for receiving the WFP (work at least 38 h as employees per fortnight with dependent children and income below the WFP threshold). All households considered eligible to receive the policy are used for the analysis of non-take-up determinants (Table2). The model will explain why households are claiming the WFP. Households who are eligible but do not receive the benefit (non-take-up) take the value ‘1’, while those receiving the benefit take the value ‘0’ (take-up). In addition, as a robustness test, we have estimated two different models. First, an OLS approach. The results of the model are robust across the variables. While the probit models are the more accurate estimators considering the discrete variable, the OLS findings confirm the observed direction and significance of the determinants. Secondly, in order to control for possible selection bias (Heckman 1979), we applied a Heckprobit model. Again, the results do not differ from our approach (see Appendix). The independent variables from the descriptive findings are introduced in three separate probit models. Model one identifies the stable socio-demographic characteristics. The second model adds labour market features that are specifically relevant for an in-work benefit, compared to social assistance schemes without work requirements. Finally, the third model also considers the economic situation of the household. Our selection of variables is based on the determinants identified in the literature review. First, related to sociodemographic characteristics, we consider the number of adults in the household and the marital status, as selfperception of being a family is considered as an important factor in the decision to take-up the benefit. Other characteristics, such as the educational level attained by the household head, are used as a proxy for both stigma and information cost. In this sense, as we are analysing an in-work benefit, we expect a different result compared to a minimum income scheme. In the WFP, almost 40% of the beneficiaries had attained tertiary education. We expect that the educational level reduces the information cost and not be influenced by the possible negative stigma of being in need (or at least this influence will be very weak). Related to housing tenure, there is not a wealth test in the WFP, so this variable should not have any influence on the non-take-up of the benefit. Since we consider that the stigma will be lower for in-work benefits than for a minimum income scheme, the degree of urbanisation should be a less important determinant in our model. We keep both, housing tenure and degree of urbanisation as robustness checks to avoid misspecification problems and to be able to compare our model with results reported in the literature. The WFP does not have any citizenship requirements, so we do not expect a negative influence of citizenship in the decision of take-up the benefit. Finally, we expect that the number of children reduces non-take-up, since the family responsibilities of an individual increase the likelihood of applying (Schenk, 2018). Then, we consider different labour market characteristics. Compared to minimum income studies, which do not include these, we explore several novel determinants in our models, but expect a higher relevance in general. First, we create ‘Intensity of employment’ to analyse the possible differences between employment structures in a household: it takes different values depending on the working-related characteristics of the household. It takes value ‘1’ if there is a person working as employee in the household; value ‘2’ if there is a person working as employee and another one working as self-employed or family worker in the household; and value ‘3’ if there are two people working as employee in the household. We must remark that the self-employed hours are not considered in the eligibility test, which means households with two people working as self-employed are not eligible for receiving the WFP and are not in included in our target population (see Table2). Second, we consider the occupation of the head of the household, as a proxy of status and social class. We also consider the different reasons for working less than 30h per week, considering the relative low working hours requirement of 19h per week within the eligibility criteria. We expect that people working more than 30h will show higher non-take-up likelihood, as they do not consider themselves in a situation of need. Finally, we analyse independently whether the household head works less than 30h due to housework/care work. It is important to differentiate that because of the particular situation of Ireland. The childcare services cost is extremely high, so we expect that families working part-time to care for others have a higher likelihood of take-up. The last set of variables included are the economic characteristics of the household. Due to the evidence that being within the welfare system (Stuber and Schlesinger 2006) reduces stigma and information cost, we expect that if the household is receiving another means-tested benefit, the non-take-up will be lower. We create the variable ‘Benefit’ and it takes value ‘1’ if the household is receiving: old-age benefits, survivor’s benefits, sickness benefits, disabled benefits and educational grants. In order to control by unemployment benefits separately, we create the variable ‘Unemployment benefits’ and it takes value ‘1’ if the household is receiving jobseeker benefits. As discussed, the 3-day working rule in the Jobseeker Page 8 of 19D. Muñoz-Higueras et al. (2024) 58:22 Probit model Non-take-updeterminants Model 1 Model 2 Model 3 Socio-demographic determinants Number of adults (ref. two-adults) 1 adult − 0.2093(0.0712) *** − 0.1107 (0.0940) − 0.3220(0.0985) *** 3 or more adults 0.3629 (0.1545) ** 0.3797 (0.1671) ** 0.2903 (0.1864) Marital Status (ref. married) Never Married − 0.0548 (0.0760) − 0.0027 (0.0787) − 0.1157 (0.0813) Separated/Divorced 0.1228 (0.0890) 0.1329 (0.0926) 0.1629 (0.0965) + Widowed 0.6149 (0.1807) *** 0.6394 (0.1956) *** 0.8906 (0.2046) *** Educational Level (ref. primary) Secondary − 0.3505 (0.0978) *** − 0.3322 (0.1001) *** − 0.3839 (0.1056) *** Post-secondary non-tertiary − 0.4076 (0.1115) *** − 0.3441 (0.1159)*** − 0.4139 (0.1223) *** Tertiary − 0.4111 (0.1002) *** .− 0.4073 (0.1046) *** − 0.3931 (0.1104) *** House Tenure (ref. owner-occupied) Private renting − 0.1031 (0.0771) − 0.1040 (0.0801) − 0.0259 (0.0847) Social housing − 0.0272 (0.0649) − 0.0466 (0.0679) − 0.0732 (0.0717) Degree of Urbanisation (ref. densely-populated area) Otherwise − 0.0682 (0.0555) − 0.0082 (0.0582) − 0.0500 (0.0614) Citizenship (ref. Irish) EU country − 0.5862 (0.0800) *** − 0.6053 (0.0831)*** − 0.5919 (0.0860) *** Other country 0.0198 (0.1277) − 0.0732 (0.1281) − 0.1063 (0.1323) Number of children − 0.1458 (0.0213) *** − 0.1625 (0.0220) *** − 0.2196 (0.0239) *** Labour market determinants Working intensity (ref. one employee) Employee + Self-employed/Family Worker 0.8719 (0.1385) *** 0.9547 (0.1427) *** Employee + Employee − 0.0687 (0.0794) 0.0391 (0.0817) Occupation (ref. Managers + Professionals) Technicians and associate professionals − 0.2338 (0.1141) ** − 0.3309 (0.1186) *** Clerical support -0. 3737 (0.1032) *** − 0.4304 (0.1082) *** Service and sales -.01790 (0.0902) ** -2761 (0.0959) *** Skilled agricultural. Craft, Trades − 0.3236 (0.1068) *** − 0.3209 (0.1111) *** Elementary occupations − 0.2078 (0.1036) ** − 0.3118 (0.1092)*** Reason for working less than 30h (ref. > 30h) Do not want to work more hours − 0.9254 (0.1754) *** − 0.7747 (0.1828) *** Wants to work more hours Other reasons (education, disability…) − 0.3168 (0.1060) *** − 0.5104 (0.1196) *** − 0.2499 (0.1088) ** − 0.3561 (0.1247) *** Housework, Care work (ref. No) − 0.9255 (0.0860) *** − 0.8146 (0.0902) *** Economic determinants Non-means-tested benefits (ref. No) − 0.1558 (0.0628) *** Unemployment benefits (ref. No) 0.4840 (0.0579) *** Income Gap Income Gap squared -0195 (0.0033) *** 0.0003 (0.0000) *** Table 2 Regression output of the probit model Page 15 of 19Non-take-up of in-work benefits: determinants, benefit erosion and indexing (2024) 58:22 The ‘fail’ households receive on average 35% less than the ‘correct’ group. This is consistent with our hypothesis: at some point during the year, they were eligible to receive the benefit, but on an average basis, there were not eligible. If the assessable income is reduced by 10%, our correct estimations increase to 77%. Following Frick and Groh-Samberg (2007), we consider this ‘fail’ as an eligible population. In the assessable income estimation, the following benefits/allowances are deducted from the total disposable household income. They are provided by administrative data. Table A.2 Deducted income for the Working Family payment Family/children related allowances Working Family Payment (WFP) Child Benefit (CB) Respite Care Grant Scheme (RCG) Domiciliary Care Scheme Social exclusion allowances Optical Benefit Dental Benefit Exceptional Needs Payments, DRAS Refund scheme Humanitarian Assistance Scheme Creche Supplement Travel Supplement ‘Other Supplements’ from DEASP admin data Amount received from charitable organisation Direct Provision Allowance Housing allowances Rent Allowance (RA), Rent Supplement (RENT) Mortgage Supplement (MORT) HAP Exceptional Needs Payments Heat Supplement Other rent subsidy ID’d from RTB data Household Benefits (Free TV License + Electricity Allowance or Gas Allowance) Fuel Allowance Telephone Support Allowance Living Alone Increase Income received by people aged under 16 Note: own elaboration following WFP citizen information Nevertheless, we cannot disregard whether income from letting a property qualifies as non-assessed income, so we compute all income from letting of property. In addition, related to student grants, we are unable to identify both the ‘1916 Bursary Fund from the Department of Education’ or ‘University payments made under the Higher Education Scholarships for Adult Learners’, which are not taken into account to the assessable income of up to a maximum of €7000 per year. As we mentioned before, one of the main factor identified in the literature as a problem in the estimation of assessable income is the income period mismatch (Goedemé and Janssens 2020). It has not been possible to solve this problem, but we have tried using the LONG data from EU-SILC. This keeps the same household for 4 years, so a priori it might be a better strategy. However, when using the LONG dataset, we lost information necessary to estimate the eligibility of the household. We don’t have information on the number of months the child is studying (necessary to qualify a child as a dependent child) or the total number of hours usually worked in a second or third job (necessary to meet the hours worked per fortnight condition). It was therefore not possible to use this dataset to estimate the eligibility of a household. Differences between subpopulations Our subpopulations of interest are explained as follows; ‘Beneficiaries’: households who are currently receiving the WFP, as stated by variable HY051G in the database; ‘Nontake-up’: households who are eligible to receive the WFP, but are not receiving it; ‘Eligible’: households who are eligible to receive the WFP, according with our simulations. This group is the sum of the other two. Table A.3 Differences between subpopulations Sub-populations Observations Beneficiaries (1,086) Nontake-up (2,468) Eligible (3,544) Socio-demographic characteristics Age 39.66 41.16 40.61 Sex Male 23.72% 24.83% 24.38% Female 76.28% 75.17% 75.62% Marital Status 1. Never Married 34.95% 35.57% 35.13% 2. Married 47.27% 45.78% 45.57% 3. Separated or divorced 15.56% 14.11% 15.01% 4. Widowed 2.19% 7.93% 4.14% Citizenship Irish 76.51% 84.96% 82.88% EU 19.79% 10.17% 12.45% OTH 3.69% 4.86% 4.66% Highest Educational Level Attained 1. Primary education 6.61% 11.75% 10.32% 2. Secondary Education 39.88% 42.03% 42.36% 3. Post-secondary non-tertiary education 14.29% 13.41% 13.84% 4. Tertiary education 39.20% 32.53% 33.46% Page 16 of 19D. Muñoz-Higueras et al. (2024) 58:22 Table A.3 Differences between subpopulations Sub-populations Observations Beneficiaries (1,086) Nontake-up (2,468) Eligible (3,544) House Tenure 1. Outright owner 39.58% 40.03% 39.12% 2. Tenant or subtenant paying rent at prevailing or market rate 25.28% 22.97% 23.,96% 3. Accommodation is rented at a reduced rate or is provided free 35.13% 36.99% 37.29% Degree of urbanisation 1. Densely-populated area 30.24% 27.59% 28.27% 2. Intermediate area and Thinlypopulated area 69.75% 72.40% 71.72% Number of children 2.38 2.29 2.28 Labour Market characteristics Status in employment Head Self-employed 3.13% 5.21% 4.50% Employee 96.86% 94.78% 95.49% Partner Self-employed 3.74% 12.38% 10.27% Employee 96.25% 87.61% 89.72% Occupation (ISCO-08) 1. Managers and Professionals 6.87% 10.81% 9.74% 2. Technicians and Associate Professionals 10.51% 9.51% 9.58% 3. Clerical Support Workers 15.22% 12.58% 13.61% 4. Service and Sales Workers 36.65% 36.11% 36.39% 5. Skilled Agricultural, Forestry and Fish + Craft and Related Trades Workers + Plant and Machine Operators 13.75% 14.31% 14.01% 6. Elementary Occupations 17.97% 16.65% 16.63% Table A.3 Differences between subpopulations Sub-populations Observations Beneficiaries (1,086) Nontake-up (2,468) Eligible (3,544) Reason for working less than 30h 1. Working more than 30h per week 66.57% 83.99% 79.41% 2. Do not want to work more hours 3.69% 1.33% 1.94% 3. Wants to work more hours but cannot find a job(s) or work(s) of more hours Personal illness or disability 7.57% 5.34% 5.74% 4. Housework, looking after children or other persons 16.38% 5.51% 8.49% 5. Undergoing education or training + Number of hours in all job(s) are considered as a full-time job + Other reasons 5.77% 3.80% 4.38% Type of contract Head of household Permanent job/ work contract 93.80% 84.64% 87.12% Temporary job/ work contract 6.19% 15.35% 12.87% Partner Permanent job/ work contract 89.31% 83.18% 85.17% Temporary job/ work contract 10.68% 16.81% 14.82% Number of hours usually worked per fortnight 98.99 74.41 76.94 Income characteristics WFP assessable income 28,502.59 18,670.25 19,644.58 Is receiving non means-tested benefits? Yes No 32.51% 67.48% 25.44% 77.30% 26.45% 73.54% Is receiving unemployment benefits Yes No 40.64% 59.35% 49.10% 50.89% 46.47% 53.53% Income Gap 26.29% 43.46% 41.23% Information cost Internet and computer Internet or computer 84.78% 15.21% 78.24% 21.75% 79.91% 20.08% Note: own elaboration There are important differences between the sub-populations of interest. These relate to socio-demographic characteristics. In terms of labour market characteris- Page 17 of 19Non-take-up of in-work benefits: determinants, benefit erosion and indexing (2024) 58:22 tics. Self-employed are more than 10% in the non-takeup group, but virtually non-existent in the other groups. There are also differences by occupation. Non-take-up is higher in skilled agriculture, forestry and fishing, which overlaps with self-employed farmers. Two thirds of the non-take-up group want to work more hours but cannot find another job or because they are looking after children. The relationship with the labour market is more unstable in the non-take-up group. The non-take-up group is comparatively poorer than the others. One fifth of the non-take-up group receive another means-tested benefit, compared to one third of the beneficiaries. Heckman selection model As a robustness check, we considered the Heckman approach to account for potential selection bias (Heckman 1979). In this sense, non-take-up refers to a highly selective demographic group of the target population. We rather could assume that the eligible population has specific social characteristics and behaviours compared to the non-eligible population. Standard logit/probit models maybe cannot control for such a bias and, therefore, we tried the Heckprobit as a robustness control, which is specifically designed to handle this problem. These models have been widely used in the economic literature (Ayala and Paniagua 2019; Oliver and Spadaro 2017; Fuenmayor et al. 2024) and also in the specific context of analysing non-take-up (Frick and Groh-Samberg 2007; Fuchs et al. 2020) to control for the potential selection bias of the sample. The results demonstrate the presence of selection bias in the sample. However, this does not affect the outcomes of the models, as the direction and significance of the coefficients remain consistent. Consequently, we have decided to retain a straightforward probit model, as it is easily interpretable. Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . 1 1 8 6 / s 1 2 6 5 1 - 0 2 4 - 0 0 3 8 5 - 8 . Supplementary Material 1 Acknowledgements We are also grateful to the UCD Geary Institute for Public Policy to facilitate a guest researcher exchange. This article is based on data from Eurostat, EU-SILC, 2014–2019, under the research proposal RPP 201/2020-EU-SILC-HBS. The responsibility for all conclusions drawn from the data lies entirely with the authors. Authors contribution CRediT matrix by author. DM: Diego Muñoz-Higueras; SK: Stephan Köppe; RG: Rafael Granell Pérez; AF: Amadeo Fuenmayor Fernández. Funding This work was supported by project HIECPU/2019/2 of Regional Ministry of Finance and Economic Model of the Generalitat Valenciana. 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. Declarations Ethics approval and consent to participate The research has been approved by the Eurostat Ethics review process (RPP 201/2020-EU-SILC-HBS). Consent for publication All authors consent the publication of this research. Competing interests The authors report there are no competing interests to declare. Received: 24 November 2023 / Accepted: 13 October 2024 References Aizer, A., Currie, J.: Networks or neighborhoods? Correlations in the use of publiclyfunded maternity care in California. J. Public. Econ., 88(12) (2004) Ayala, L., Paniagua, M.: The impact of tax benefits on female labor supply and income distribution in Spain. Rev. Econ. 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