Who supports whom? Do adult children living at home share their incomes with their parents?

Advances in Life Course Research 40 (2019) 14–29

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Who supports whom? Do adult children living at home share their incomes with their parents?

T

Maria Iacovoua, , Maria A. Daviab ⁎

a b

Department of Sociology, University of Cambridge, United Kingdom Department of Applied Economics, Universidad de Castilla-La Mancha, Spain

ARTICLE INFO

ABSTRACT

Keywords: Europe EU-SILC Family Income sharing Intergenerational relationships Youth

Across the developed world, young adults are now more likely to live with their parents than they were two or three decades ago. This is typically viewed, both in the media and in scholarly research, as an economic burden on parents. This article investigates, for the first time, the extent to which financial support is also given in the opposite direction, with young people contributing to their households’ living costs. We use data on 19 European countries from the 2010 European Union Statistics on Income and Living Conditions (N = 553 in Austria to N = 2777 in Italy). Many young adults do share their incomes with their families, with the degree of sharing being the highest among the poorest households. In a substantial minority of households, particularly in lowerincome countries, the contributions of young adult household members keep households out of poverty.

1. Introduction This paper investigates income-sharing in households where young adults live with their parents. We describe, for the first time, the extent to which these young adults share their incomes with the rest of their households; we analyze the factors which determine the level of sharing; and we assess the importance of young adults’ contributions to overall household budgets. The volume and importance of intergenerational transfers of money and time has been documented in an expanding literature. Transfers down the generations (from parents to their offspring) play a crucial role in helping young people establish their own households, families and careers, particularly through periods of difficulty or uncertainty (Da Vanzo & Goldscheider, 1990; Swartz, Kim, Uno, Mortimer, & O’Brien, 2011). Transfers up the generations (from adult children to elderly parents) may be profoundly important for the well-being of the older generation (Mutran & Reitzes, 1984; Silverstein, Cong, & Li, 2006). Many studies note that transfers in the two directions may be linked by mechanisms of reciprocity (Albertini, Kohli, & Vogel, 2007; Silverstein, Conroy, Wang, Giarrusso, & Bengtson, 2002). Despite the growing interest in this area of research, and its demonstrable importance, there is an almost total absence of research on the contributions made to their families by young adult children who still live in the parental home: all the existing research on upward financial transfers deals with transfers from middle-aged adults to their

⁎

elderly parents, and/or transfers between, rather than within, households. We may point to several reasons for this gap in the research. The first is that for the majority of the second half of the 20th century, it was relatively rare in the United States and Western Europe for young adults to remain living with their parents for extended periods. This is now changing, with an increasing tendency across most of the developed world towards later home-leaving (Bell, Burtless, Gornick, & Smeeding, 2007; Eurofound, 2014; Goldscheider, 1997; ONS, 2012; Settersten & Ray, 2010; and many others). A second reason for the lack of research into income-sharing by young adults is the assumption that individuals at this stage of the lifecourse are recipients, rather than donors, of intra-family transfers. There is ample evidence that in aggregate terms this is entirely true (Mudrazija, 2014 finds that the younger generation remain net recipients of assistance until their parents are aged between 65 and 80). However, the fact that on average young adults are net recipients of transfers should not be allowed obscure the fact that some young adults, even at a relatively early age, may make significant contributions to their families’ finances. A final reason for the dearth of research in this area is a historic lack of suitable micro-level data. Most population-based household surveys collect data on private financial transfers into and out of the household, since this is necessary for the purpose of computing total household income; however, they do not ask about transfers between family members living in the same household (the conventional assumption

Corresponding author. E-mail address: [email protected] (M. Iacovou).

https://doi.org/10.1016/j.alcr.2019.03.005 Received 11 September 2017; Received in revised form 4 February 2019; Accepted 4 March 2019 Available online 12 March 2019 1040-2608/ © 2019 Elsevier Ltd. All rights reserved.

Advances in Life Course Research 40 (2019) 14–29

M. Iacovou and M.A. Davia

being that income is fully pooled between household members). We use data on 19 countries from the European Union Statistics on Income and Living Conditions (EU-SILC), a large-scale household survey covering all countries of the EU. In common with other household surveys, the EU-SILC collects data on transfers between, but not within, households. In 2010, however, it carried a module on incomesharing within households; this represents the first opportunity of which we are aware to address the question at hand using large-scale microdata. We show that substantial numbers of young people do share a significant proportion of their incomes with their households; that the degree of sharing is largest in the most impoverished households; and that in many of these households, the income shared by young adults is of a magnitude likely to make a considerable difference to the household's standard of living.

contemporaneous (for example, a parent may give money to an adult child in exchange for companionship or help around the house). However, it is more commonly conceptualized as a sequential process, with parents providing transfers of cash or in-kind assistance to young adult children, in the expectation that the adult children will support them in their old age – either as an “investment”, in which the return is unconditional, or as “insurance”, in which the return is contingent upon later need (Silverstein et al., 2002). The challenge in these models is to explain why members of the younger generation honour their part in the exchange, rather than defaulting on the agreement, as they could and would do if they were purely self-interested. Andreoni (1990) suggests that even when there is no prospect of future reward, the “warm glow” arising from helping family members may encourage the giving of assistance. Becker (1991) proposes that in the absence of formal sanctions for members of the younger generation who default, the existence of norm-driven “social sanctions” may be enough to ensure that defaulting is rare. Cox and Stark (2005) suggest that the younger generation may provide support to elderly parents in order to demonstrate to their own children the importance of this type of family care, while Silverstein, Conroy, and Gans (2012) invoke a notion of “moral capital” which is passed down the generations, and which may serve to ensure that children look after their elderly parents, even when relations between the generations have been strained. Other authors point out that intergenerational transfers of money, time or other forms of assistance may take place for according to personal preferences (Wall, Aboim, Cunha, & Vasconcelos, 2001), or for emotional reasons (Daatland & Herlofson, 2003), because family members feel attached to one another, invested in one another, or feel love or affection for one another (Katz, Lowenstein, Prilutzky, & Mehlhausen-Hassoen, 2003). A Swedish study (Björnberg & Ekbrand, 2008) found that an overwhelming majority of exchanges were motivated by such emotional motives, and a tiny minority by reciprocity. The different motivations described above are not necessarily mutually exclusive. More than one motivation may be in play at the same time, for example affection and altruism. The “insurance” motive may be thought of as a hybrid between altruism (in the event that the giver does not later need to call in the debt) and reciprocity (in the event that she does). Cox, Hansen, and Jimenez (2004) point out that motivations may differ according to the income of the recipient: a transfer made to a recipient in a precarious economic situation, motivated by altruism, might still be made if the recipient were not in such a difficult situation, but might in this case be differently motivated, for example by exchange. The literature discussed above focuses almost exclusively on young adults as recipients of transfers from the parental generation, or on middle-aged adults as donors of assistance to very elderly parents. The current paper charts new territory, in that we consider, as donors of transfers towards the parental generation, young adults still living with their parents. These young adults are not yet at the stage of life where they would be conceptualized by models of reciprocity as starting to pay back transfers received in their youth. In fact there is likely to be an element of contemporaneous reciprocity at play in families where young people share income with their parents, because all the young people in our sample are receiving in-kind support from their families in the form of accommodation, and many will be benefiting from parental services such as meals, laundry and the use of a car. This type of reciprocity may operate as a (semi)-formal agreement imposed by parents, or it may arise out of feelings of what is “fair” or “right”. However, the notion of a fair financial contribution is likely to be made on the basis of the family's need and the young person's ability to pay; thus, the theory of altruism/contingent assistance may also capture arrangements with an element of reciprocity.

2. Intergenerational sharing Despite the shortage of research on intra-household sharing by young adults, a large literature does exist on intergenerational sharing, which is relevant in the current context. We begin this section by discussing the theory and empirical evidence relating to the determinants of sharing at the individual level, and follow with a discussion of the factors which may lead to cross-national differences in observed levels of income-sharing. 2.1. Individual-level factors Silverstein et al. (2002) note an important asymmetry between transfers of money and time up and down the generations. Transfers down the generations may be conceptualized as arising from bioevolutionary processes which optimize the survival of the family's offspring, but this is not true in the case of transfers up the generations, for which we must look to social mechanisms of equity and reciprocity for an explanation. Two broad classes of motive for intergenerational transfers have been proposed in the literature (Eggebeen & Davey, 1998; Mudrazija, 2013). The first is altruism: people care about their close family members, and because of this, reap benefits themselves from the time or money that they give to other family members. In its purest form, the altruism hypothesis (Becker, 1974) assumes that individuals value the utility of other members of their families as much as they value their own, and will pool resources within their households so as to optimize the joint utility of all household members. This assumption of full intrahousehold income pooling (which underpins the majority of contemporary research on income and poverty) is consistently rejected by empirical analysis (Jenkins, 1991; Lundberg, Pollak, & Wales, 1997); it is also rejected in studies which deal specifically with households where young adults live with their parents (Breunig & McKibbin, 2012; Pezzin & Schone, 1997). Although full income pooling within households does not occur in actuality, there is ample evidence in support of “contingency” theory (Fingerman, Miller, Birditt, & Zarit, 2009; Mudrazija, 2013), which proposes that transfers between family members (both within and between households) will be made on the basis of the recipient's need. Many studies (Bonsang, 2007; Ikkink, Van Tilburg, & Knipscheer, 1999; Lowenstein & Daatland, 2006; Mutran & Reitzes, 1984; Silverstein, Gans, & Yang, 2006) find that adult children give more financial assistance to elderly parents whose needs are higher, either because they are in a worse economic situation, or are older, or in poorer health. These effects are common across cultures: in China (Logan & Bian, 2003) and Taiwan (Lee, Parish, & Willis, 1994) the degree of support for ageing parents is far higher than in the United States and Europe, but the determinants of the level of support are similar. A third class of motives for intergenerational transfers is reciprocity: people make transfers to other family members in the expectation that they will receive something in return. Reciprocity may be

2.2. Cross-national variation Several studies have considered intergenerational transfers in a 15

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M. Iacovou and M.A. Davia

Table 1 Country groupings, with regional ranges of key social and economic indicators. Sources: [1] Iacovou and Skew (2011); [2] Gasic and Kurkowiak (2012); [3], [5] and [6] authors’ own calculations using EU-SILC 2010; [4] Eurostat (2017). Countries

[1] Median age at leaving home (women and men) [2] Per capita GDP, 2010 (percentage of EU27 average) [3] Youth wages, % of adult wages [4] Social expenditure 2010, % of GDP [5] Unemployment rate among 18–34s [6] 18–34 unemployment rate as multiple of 35–45 unemployment rate

North/Western Germany Austria Belgium Luxembourg

Southern Greece Spain Italy Portugal Cyprus Malta

Baltic Latvia Lithuania Estonia

Eastern Hungary Czech Rep Slovakia Poland Romania Bulgaria

Early (W) 22.2–24.5 (M) 24.2–26.7 High (118–255 K) Low (50–68) Higher (22.7–29.8) Low (Mean 9.0%) Low (Mean 1.21)

Late (W) 25.5–28.3 (M) 28.3–31.5 Medium (80–104 K) Medium (60–81) Medium (19.3–28.9) Highest (Mean 35.4%) High (Mean 2.17)

Early (W) 23.1–24.6 (M) 24.5–27.5 Low (54–63 K) Higher (70–85) Lower (17.6–19.0%) Medium (Mean 20.6%) Low (Mean 1.29)

Late (W) 24.7–28.1 (M) 28.1–35.8 Low (44–83) Highest (76–90) Lower (17.0–22.6) Medium (Mean 17.0% Highest (Mean 2.46)

by the United States (and represented in Europe by the UK and Ireland), characterized by flexible labour markets and means-tested welfare benefits; and (3) a “corporatist” cluster typified by the continental European countries including Germany, France and the Benelux countries, and characterized by a welfare system based on rights accrued via employment, in a male-breadwinner context. Ferrera (1996) suggested the addition of a fourth type: a distinct Southern European cluster, characterized by a residual welfare state with an emphasis on pension benefits (and an almost complete absence of benefits to young adults); the case for a distinct Southern cluster has been verified empirically by many scholars (Bonoli, 1997; Minas, Jacobson, Antoniou, & McMullan, 2014). Very little attention has hitherto been paid to the post-communist states of Central and Eastern Europe (CEE); indeed, many works on welfare regimes overlook these countries altogether. Several scholars (Castles & Obinger, 2008; Kangas, 1999) note that CEE countries exhibit important differences from other welfare states; Aidukaite (2009) notes specifically that they are typified by very weak institutions of civil society, and by highly developed bureaucratic structures. However, few scholars have advanced either theoretical or empirical arguments as to whether the CEE countries should be thought of as comprising a single welfare-regime cluster, or multiple clusters. Two exceptions are the work of Fenger (2007) and Castles and Obinger (2008), whose analyses suggest that the Baltic states (Latvia, Lithuania and Estonia) comprise a distinct cluster. Casey (2004) suggests that the Baltic states have followed a distinct trajectory from other CEE countries, in having embraced privatization more enthusiastically. Cook (2010) is less enthusiastic about a distinction between the Baltic states and other CEE countries, although her work demonstrates distinct differences between the two groups of countries in terms of social expenditures and higher poverty rates. We treat the Baltic states as a distinct group, partly on the basis of Fenger's work, but also because of an important empirical difference between the Baltic and other CEE countries. Despite certain commonalities in household structures between the two groups of countries, there are important differences in home-leaving behaviour (Iacovou & Skew, 2011): home-leaving typically occurs early (as early as in Western Europe) in the Baltic states, and late (as late as in the Southern European countries) in the other CEE countries. This difference potentially has important implications for the income-sharing behaviour which forms the locus of interest in this paper. Table 1 lists the country groups we identify, and the membership of those groups. As will be explained shortly, our data do not allow us to

cross-national context. Albertini et al. (2007) noted that intergenerational transfers of time and money fall into a broad welfare-regime typology (see Esping-Andersen, 1990, 1999), with more frequent but less intense transfers in the Nordic and Continental European countries than in the Southern countries. Mudrazija (2014) considered a joint model of transfers of time and money. Following Esping-Andersen's welfare-regime typology, he found that welfare states with the lowest levels of spending are characterized by (a) higher levels of private transfers, and by (b) the younger generation becoming net supporters of the parental generation at an earlier age (Mudrazija, 2014). Attias-Donfut, Ogg, and Wolff (2005) also observed a general concordance with welfare regime typologies, with some exceptions (for example, the level of cash transfers was higher in Greece than in other Southern European countries). Deindl and Brandt (2011) found that Northern and Western Europe are characterized by highs levels of financial and practical transfers from parents to their adult children, while Southern and Eastern Europe are characterized by higher levels of support from adult children to their parents. From the perspective of comparative studies, two hypotheses are of interest: the “crowding-out” hypothesis (which predicts that higher welfare spending will reduce the need for familial support), and the “crowding-in” hypothesis (which predicts that if welfare state support to one generation is particularly generous, an increase in private transfers may be observed from that generation). The evidence is mixed: Albertini et al. (2007) conclude that their data provide tentative support for both crowding-in and crowding-out, and the findings of Deindl and Brandt (2011) also point in this direction, but Daatland and Lowenstein (2005) and Motel-Klingebiel, Tesch-Roemer, and Von Kondratowitz (2005) find support for neither hypothesis. This may arise because transfers of time and money are differently affected by welfare state provision: the first two studies consider them separately, while the latter two studies consider them together. 2.3. Country groups: welfare-regime typologies In this paper, we analyze data from a large number of countries. We present some findings broken down by country, but for much of the analysis we divide countries into groups. In defining these groups we take as a starting point the welfare regime typology of Esping-Andersen (1990, 1999) and developed by Arts and Gelissen (2002) and others. This typology consists of (1) a “social-democratic” cluster typified by the Scandinavian welfare states, with an emphasis on universal benefits and a high level of decommodification; (2) a “liberal” cluster typified 16

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M. Iacovou and M.A. Davia

consider the social-democratic or liberal countries. Our “North/Western” group corresponds to Esping-Andersen's “corporatist” cluster; our “Baltic” and “Eastern” groups correspond to Fenger's (2007) “former USSR” and “post-communist” clusters. Table 1 also lists a selection of characteristics of each country group relevant to the current analysis; it demonstrates that as well as representing theoretical commonalities in the institutions and functioning of the welfare state, the groupings also represent divisions in several factors relevant to the current research question. The North/Western countries have the highest levels of GDP and the highest levels of government social expenditures; incomes and social expenditures are lower in the Southern group of countries and lower still in the Baltic and Eastern groups. The situation of young adults varies considerably by country group. Unemployment rates are lowest in the North/Western countries, higher in the Eastern and Baltic regions, and extremely high in the Southern countries. In 2010, unemployment rates were generally high right across Europe as a result of the financial crisis of 2008 and the ensuing recession; in the North/Western and Baltic countries, young adults were only slightly more likely to be unemployed than older adults, whereas in the Southern and (particularly) the Eastern countries, young adults were substantially more likely to be unemployed. For young adults who did have a job, their wages relative to those of older adults were lowest relative to in the North/Western countries, and highest in the Baltic and Eastern countries.

Some cross-national surveys, such as the European Social Survey (ESS), specify a common design for sampling frames, questionnaires, etc., to maximize consistency between countries (“input harmonization”). The EU-SILC follows an “output harmonization” protocol which specifies which variables must be collected, but which allows countries a good deal of freedom in design and implementation; this means there are some substantial variations between national surveys. The most important variation is between the “register” and the “survey” countries. In the “survey” countries, all individuals aged 16 or over in sample households were eligible for interview. In the “register” countries (Denmark, Finland, Iceland, Netherlands, Norway, Sweden, and Slovenia), data on income and several other variables was collected via national registers, and other information was collected via interview with a single, randomly-selected individual aged 16 or over in each household. Features of the survey in each country are described in annual quality reports (see Eurostat, 2013b for the report relating to the data used in this paper). The EU-SILC is based on a common core of questions asked each year, plus rotating modules which change from year to year. In 2010, a module on the intra-household sharing of income was included (Eurostat, 2012; Nagy, Medgyesi, & Lelkes, 2012; Ponthieux, 2013). It is this module which provides the variable of interest for this study; other variables come from the 2010 cross-sectional file.1 Our sample consists of households in which one or more individuals aged 16–34 live with one or both of their parents. We selected 16 (rather than, for example, 18) as a lower age limit because although a large majority of 16- and 17-year-olds in the sample are still in school, 14% of them report having some income of their own, and of these, 20% report sharing the entire amount with their households.2 We selected the upper age limit of 34 because our sample contains several countries in which young adults (particularly young men) routinely live with their parents until well into their thirties.3 We exclude a small number of young people who live with their parents and also with a spouse, partner or child(ren), since in these cases it is not clear whether the income which the young person reports sharing, is shared with the family of origin, as opposed to the young person's partner or children. This yields sample sizes ranging from 716 in Austria to 6439 in Italy; for the majority of the analysis, we exclude cases in which the young adult has no income, which yields smaller sample sizes ranging from 553 in Austria to 2777 in Italy (see Table 2).

2.4. Hypotheses The discussion in the foregoing section gives rise to the following hypotheses. The first two arise from altruism/contingency theory, and are predictions which should hold across all welfare-regime types: H1. In all welfare regime types, young adults will be more likely to share their incomes, the higher their own incomes. H2. In all welfare regime types, young adults will be more likely to share their incomes, the lower the incomes of the rest of their households. A third hypothesis arises from an aggregate form of altruism/contingency theory. In the Baltic and Eastern country groups where GDP is low, there will be more households where the parental generation is in need of assistance. In addition, the Baltic and Eastern countries are the countries where young adults’ incomes are higher relative to the incomes of the parental generation.

3.1. Income sharing In the module on income sharing, respondents living with at least one other person aged 16 or over were asked the following question:

H3. Levels of income-sharing by young adults will be higher in the Baltic and Eastern country groups, than in the North-Western and Southern groups.

“What proportion of your personal income do you keep separate from the common household budget?”

3. Data and methods The analysis in this paper is based on data from the 2010 European Union Statistics on Income and Living Conditions. The EU-SILC is an annual household-level survey administered by Eurostat, the statistical agency of the European Union; it covers all 27 countries of the European Union, as well as a handful of other non-EU countries which have elected to conduct the same survey. The EU-SILC launched in 2003 with six participating countries; by 2005, all 25 EU countries were participating, with Bulgaria joining in 2006 and Romania in 2007 (Eurostat, 2013a). In the majority of countries, the survey is a four-year rolling panel: households stay in the sample for four years, with one quarter of households being replaced each year. In all countries, the survey is based on a nationally representative probability sample of households; unit non-response is adjusted for by weighting, and item non-response by imputation, with weighting and imputation being carried out by national statistical institutes.

1 Data from the rotating modules are released only as part of a cross-sectional file, and cannot be merged with other waves of data. Thus, even though the EUSILC has a longitudinal component, we are unable to analyze income sharing in a longitudinal context. 2 It is unusual for those aged 16 and 17 to live away from their parents; in our sample, 97.5% 16- and 17-year olds still live at home. However, in most EU countries it is legal (sometimes only with parental consent) to leave home before 18, and young people may do this due to family breakdown, or to live with a partner, for education, or to take up a job or apprenticeship. 3 This paper is based on the concept of intergenerational co-residence rather than young people remaining in the parental home. In fact, responses to a question on “responsibility for the accommodation”, the means via which the EU-SILC defines household headship, reveals that 98% of our sample members may be categorized as living in their parents’ home, rather than being independent householders whose parents live with them. We do not exclude from our analysis the 2% of young adults who fall into this latter category.

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Table 2 Percentages of young people sharing their incomes, by country. (1) No income to share

(2) None

(3) Some (1–49%)

(4) Most (50–99%)

(5) All

(6) % sharing any income, of those who have any

(7) N (all)

(8) N (any income)

North/Western

Austria Belgium Germany Luxembourg Group

21.1 57.9 37.3 62.1 38.9

40.2 28.9 43.2 30.4 41.5

30.6 5.6 11.0 4.6 11.3

6.7 3.8 4.8 1.5 4.7

1.4 3.8 3.8 1.4 3.7

49.0 31.3 31.2 19.8 32.2

716 1410 2163 1575 6961

553 569 1345 580 3771

Southern

Cyprus Malta Greece Spain Portugal Italy Group

61.7 24.6 50.4 48.6 47.5 55.3 51.9

31.3 64.6 25.3 31.8 32.7 23.8 27.6

3.4 3.8 9.0 6.5 7.6 6.3 6.7

2.2 5.0 11.0 5.9 6.6 10.3 8.4

1.4 2.0 4.5 7.1 5.7 4.4 5.4

18.2 14.3 49.1 38.0 37.7 46.9 42.7

1937 1725 2137 5025 1696 6439 18,959

727 1250 1028 2309 804 2777 8895

Baltic

Estonia Lithuania Latvia Group

60.4 60.3 56.7 59.2

19.2 9.0 13.1 12.4

6.5 11.5 8.5 9.5

9.6 12.3 17.4 13.4

4.3 6.9 4.3 5.6

51.5 77.3 69.8 69.8

2015 1721 1906 5642

780 653 791 2224

Eastern

Bulgaria Czech Rep Hungary Poland Romania Slovakia Group

53.9 51.6 51.5 52.2 50.6 45.7 51.2

8.0 16.4 10.8 11.7 5.0 17.4 10.4

11.8 19.9 9.7 13.7 7.4 21.0 12.6

17.6 8.6 15.8 15.0 28.1 13.5 18.1

8.8 3.5 12.2 7.5 8.9 2.5 7.7

82.7 66.1 77.8 75.5 89.9 68.0 78.6

2128 2663 3585 2789 2131 3718 17,014

949 1271 1685 1257 1016 1967 8145

Total

51.2

21.2

9.6

12.0

6.0

56.7

48,576

23,035

Note: As well as excluding those with no income, column (8) also excludes individuals with any of the variables included in regressions; thus, it gives an indication of the sample sizes used in the later analysis. Figures in columns 1–6 are weighted. Mean personal incomes, and mean equivalised rest-of-household incomes, are given in Appendix 1.

Respondents were asked to select from the following answers:

in this release of the data. The UK is therefore the only remaining representative of the liberal regime type; the UK sample is small and was also excluded.

1 = all my personal income is kept separate from the household budget; 2 = more than half of my personal income is kept separate; 3 = about half is kept separate; 4 = less than half is kept separate; 5 = none is kept separate; 6 = the respondent has no personal income.

3.2. Explanatory variables In multivariate analysis, we draw on the literature on intergenerational exchanges (Bucx, van Wel, & Knijn, 2012; Eggebeen & Hogan, 1990; and others) to identify a set of controls based on the characteristics of the young person, his or her parent(s), and the household in general. Two key explanatory variables are the young person's own income, and the incomes of other household members (that is, total household income less the income of the young person). These are measured as continuous variables in the EU-SILC. We adjust the household-level income variable by a factor reflecting the economic needs of the household, based on the numbers and ages of household members (the modified OECD equivalence scale, as proposed by Hagenaars, De Vos, & Zaidi, 1996), and used widely in the analysis of income and poverty5). In making this adjustment, we omit the young adults themselves from the equivalence scale, thus obtaining a measure of the adequacy of the income of the rest of the household, for the purposes of meeting the

Since few respondents answered “about half”, we combined categories (3) and (4), and generated a new reverse-coded variable indicating the proportion of personal income which is shared with the household (none, less than half, half or more, all, and no income to share).4 We used this re-coded variable to generate three new binary variables indicating whether the individual shared any of their income; half or more of their income, or all of their income. These variables were set to missing for individuals with no income to share. We excluded the “register” countries, because the variable on income sharing is missing for around 70% of the sample (in these countries, only one member of each household was eligible for personal interview, and this was often not the young adult). We also excluded France, because of very high levels of non-response to the question on income sharing, and Ireland, because of problems in coding responses

5 The modified OECD equivalence scale assigns to the first adult in the household a weight of 1, to additional adults in the household the weight of 0.5, and gives children a weight of 0.3. Thus, a household consisting of three adults and two children would be calculated as needing 2.6 times more income as a single person would need, to maintain the same standard of living. Dividing household income by this equivalence factor thus gives an indication of the sufficiency of the household income to meet the needs of the household members.

4 It should be borne in mind while reading that paper that our analysis is based on a question asking about the proportion of income kept separate from the common budget, rather than the proportion shared. It is by no means unreasonable to assume that the proportion of income not kept separate is in some sense shared with other household members; however, it is also possible that responses may differ if the survey asked directly about shared income.

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needs of the other household members. We transform young adults’ incomes to a percentile score reflecting their position in the income distribution of young people living with their parents in their own country – thus, those whose incomes fall into the lowest 1% have a score of 1 in the transformed variable; those at the median have a score of 50, and those in the highest 1% have a score of 100. We follow a similar procedure for our measure of rest-of-household income. Converting to percentiles has two advantages. First, it means that complex net-to gross calculations are not needed (in the EUSILC, incomes are recorded net of tax and benefits in some countries, and as gross amounts in others). Second, it provides a means of adjusting for the (relatively small) differences in incomes between countries in the same group. We also test for nonlinearities in the effects of income and interactions between young adults’ and household incomes (see Section 4). In addition to income, we control for the following characteristics of the young person: age, gender, educational qualifications (degree/ upper secondary school qualifications/sub-secondary qualifications), and labour market status (in education; in work; and not in employment, education or training – “NEET”). We also control for the following characteristics of the young person's parents: whether the young person is living with both parents, with a lone parent, or in a formal (married) or informal stepfamily; parents’ age (the age of the older parent, in the case of two parents); educational attainment (the higher, in the case of two parents); whether one or both parents were foreignborn; whether either parent has poor health or a limiting health problem; and parental labour market status (parent/s fully employed, one parent employed and one non-employed, or no parental employment). We also experimented with further variables relating to the composition of the household (additional adults and children); a more sophisticated specification of parental health problems, distinguishing between poor health and disability; and more complex specifications of parental employment. These yielded no additional insights and were not included in the final models.

probabilities of sharing vary with the young adult's own income and the income of the rest of the household, holding other factors constant.7 Because the sample contains some households with more than one young person, we have adjusted the standard errors in all models to take this clustering into account, via Stata's, cluster option. 4. Results Table 2 shows the distribution of the dependent variable across all countries. Column (1) shows the percentages of young people in the sample who report having no income of their own. Columns (2)–(5) show the percentages of young people who share none, some (1–49%), most (50–99%) and all of their incomes with other household members; Columns (1)–(5) sum to 100%. Column (6) shows the percentage of young people who share any of their incomes with other household members, as a percentage of those who report having any income to share. Columns (7) and (8) show sample sizes by country, for the whole sample, and for those with any income. There are clear differences between countries and country groupings. The percentage of the sample reporting no income ranges from 39% in the North/Western group, to 59% in the Baltic group. This reflects differences between countries in both youth employment rates (see Table 3) and the availability of state support to those without jobs. Differences in sharing behaviour are most easily described with reference to Column (6). Of young people with any income, the percentage sharing any of their incomes with other household members ranges from only 32% in the North/Western group, to 42% in the Southern group, 70% in the Baltic group, and 79% in the Eastern group. There are some overlaps between groups (for example, Cyprus and Malta fall into the range of the North/Western countries). Nevertheless, these overlaps are few, and interestingly, the figures in Column (6) distinguish completely between the North/Western and Southern groups on the one hand, and the Baltic and Eastern groups on the other. These results provide preliminary confirmation of Hypothesis (3), which predicted that more sharing would take place in the Baltic and Eastern countries, where household incomes are lowest and where young adults’ wages are highest relative to the wages of older adults (see Table 1). Table 3 presents means of explanatory variables for the sample of young adults who have any income. There are more men than women in the sample, owing to the later age at leaving home among men. The mean age ranges from 21.9 in the North/Western countries to 25.7 in the Southern countries; the proportion not in employment, education or training (NEET) ranges from 9% in the North/Western countries to 14% in the Baltic countries. The figures at the top of the table are mean income percentiles, for the young person's own income and the income of the rest of the household. These are reported separately for households where the young person does, and does not, share any income (across the full sample, the means would by definition be equal to 50). In all but the Southern group of countries, the incomes of young people who share are higher than the incomes of those who don’t share; in all country groups, average rest-of-household incomes are lower where the young adult does share, than when he or she does not share.

3.3. Weights Data are supplied with weights correcting for non-response. For the analysis based on groups of countries, weights have been adjusted to reflect the population of each country and normalized within groups to sum to the total sample size in that group. Data from large countries thus influence the results more heavily than data from small countries (for example, in the North/Western group, Germany, with a population of almost 70 million, influences the results far more than Luxembourg, with a population of under 350,000).6 3.4. Analytic methods We estimate logistic models over three thresholds (any sharing versus no sharing; sharing 50% or more versus sharing none or a lesser percentage; and sharing all versus sharing a lesser amount. A number of authors (Allison, 1999; Mood, 2010; Winship & Mare, 1984) have shown that coefficients from logistic regressions cannot straightforwardly be compared across different models or different samples, because (unlike coefficients in linear regression) they are affected by unobserved heterogeneity, which is likely to vary between nested models estimated on the same sample, and across the same model estimated on different samples. This issue is clearly relevant in a context where we compare coefficients between groups of countries. Following Mood (2010) we present our results in the form of average marginal effects (AME); we also present graphs showing how the predicted

4.1. Multivariate analysis Table 4 presents estimates from a model in which both the young adult's income and rest-of-household income are expressed as linear 7 As a robustness check we also compared our estimates with estimates from the linear probability (LP) model (which is robust to problems of unobserved heterogeneity, although it does have other potential defects with discrete dependent variables; see Long & Freese, 2014; Mood, 2010). We have also estimated MFX at several points over the income distribution. These results are available from the authors.

6 We have also estimated results with a sample weighted by the square root of population size as suggested by Penrose (1946); this procedure gives very similar results.

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The estimated AME on the young person's own income is positive in all of the four country groups, and statistically significant in all except the Southern group; the estimated AME on income of other household members is significantly negative in all country groups. These results suggest, in line with our Hypotheses (1) and (2), that young people are more likely to share their incomes (a) the more money they have themselves, and (b) the less money the other members of their households have. We will return later to a fuller analysis of the relationship between incomes and sharing. Other coefficients indicate that young adults are more likely to share their incomes with lone parents and with parents who do not have a job; they are not more likely to share with older parents, or parents in poor health, probably because the parents in this sample are relatively young themselves, with a mean age of around 53. In all country groups, the probability of sharing income rises with the age of the young person8 ; in none of the regions does it vary by gender. In three of the four country groups young people who have a job are significantly more likely to share their incomes than students9 ; those who are not in employment or education (“NEETs”) are also more likely than students to share, though to a lesser extent. Further investigation reveals that the unemployed, who constitute about 60% of NEETs, are only slightly more likely than students to share their incomes, and the effect is mainly driven by the long-term sick or disabled, who form about 20% of NEETs, and whose incomes are largely made up by social benefits. One apparent divergence between our results and the findings of previous research is the sign of the coefficients on education. Mutran and Reitzes (1984) and Albertini et al. (2007) found that families with higher levels of education (in either generation) were more likely to make intergenerational gifts, in both directions, of cash and in-kind help. In contrast, we find a generally negative relationship between education and the probability of sharing. The difference between our findings and those of earlier studies may relate to different stages in the life course: our sample consists of young people living at home, with relatively young parents, while the other studies consider much older parent-child dyads. Taken together, the two sets of findings suggest that (controlling for other factors) young adults with more education are less likely to give money to their parents while they still live at home, but will be more likely to make contributions to their parents, in cash and in kind, once they have left home and their parents are older. Only one coefficient in Table 4 differs significantly between country groups, namely the indicator of whether one or both parents were foreign born. This coefficient is significantly positive in the North/ Western and Southern countries, insignificant in the Baltic countries, and significantly negative in the Eastern countries. This reflects differences in the composition of the foreign-born population across the country groups; it may also reflect the fact that the foreign-born population forms only a very small proportion of the sample in the Eastern countries. These differences aside, the determinants of income sharing are similar between country groups; this is all the more striking, given the huge cross-European differences in income levels and living arrangements, as well as very substantial differences in the variable of interest itself. In Table 5, we present income coefficients from three binary logistic models, estimated over three different cut-off points of the dependent variable: sharing any income (as in Table 4); sharing 50% or more; and sharing all income. The estimated effects of household income are

Table 3 Means of explanatory variables, by country group: young adults with any income, by country groups.

Income variables (percentiles)

North/ Western

Southern

Baltic

Eastern

57.75

50.37

55.61

52.95

46.96

51.09

39.31

42.70

39.66

44.89

45.66

46.80

52.85

55.72

52.15

56.45

Age Range SD Male Female Education: below secondary Education: secondary Education: degree Employment: student Employment: in work Employment: NEET

21.890 16–34 (3.787) 0.578 0.422 0.347

25.713 16–34 (4.482) 0.593 0.407 0.283

23.637 16–34 (4.313) 0.610 0.390 0.283

24.899 16–34 (4.293) 0.642 0.358 0.157

0.461 0.192 0.307 0.606 0.087

0.417 0.300 0.124 0.752 0.125

0.462 0.255 0.244 0.619 0.136

0.609 0.234 0.151 0.755 0.094

Age (of elder parent) Range SD Both parents present in HH Lone parent Stepfamily One or both poor health One or both foreign born Education: below secondary Education: secondary Education: tertiary Employment: both employed Employment: one parent in work Employment: no parent in work

52.486 34–80 (6.655) 0.781

56.568 31–80 (7.491) 0.794

52.296 35–80 (7.400) 0.657

53.376 33–80 (6.840) 0.728

0.213 0.006 0.148

0.196 0.010 0.145

0.318 0.024 0.187

0.264 0.009 0.191

0.097

0.062

0.186

0.012

0.067

0.540

0.062

0.155

0.418 0.515 0.665

0.283 0.177 0.364

0.390 0.547 0.566

0.655 0.190 0.552

0.213

0.338

0.237

0.231

0.122

0.297

0.197

0.217

3047

8895

2224

8145

YA's own income

Rest of household income

Characteristics of young person

Characteristics of parent/s

N

YA shares income YA doesn’t share YA shares income YA doesn’t share

Notes: This table presents means for all explanatory variables used in multivariate regressions; the sample is restricted to young adults with any income, which is the sample used in regressions. Most variables are categorical variables, in which case the mean represents the percentage of observations which fall into that category. The range of both income variables, for all groups defined by sharing and by country group, is 1–100. Ranges and standard deviations for continuous variables are given in parentheses. The age variable is topcoded at 80. “NEET” means not in employment, education or training.

terms. Estimates are from logistic regressions in which the dependent variable is whether the young adult shares any income, transformed into average marginal effects. AMEs denote the average predicted change in the dependent variable (here, the probability that the young person will share their income) associated with a one-unit change in each explanatory variable. Thus, an AME of −0.022 for rest-of-household income (Southern countries) in Table 4 means that on average, we expect the probability that a young person shares any of their income would be 2.2% lower if they lived in a family whose income was on the 30th rather than the 20th percentile, an additional 2.2% lower in a family whose income was on the 40th percentile, and so on.

8 We also checked whether estimated income effects were affected by age. We found no evidence that the coefficient on rest-of-household income varies with age. For two country groups, we found some evidence, under certain specifications, that the age effects were stronger for younger than for older sample members. However, these effects disappear in the interacted model estimated for Fig. 1. 9 In the Southern countries the probability of having a job is highly correlated with the young person's age.

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Table 4 Average marginal effects (AME) from logistic regressions, by country groups. Dependent variable: the probability that a young person shares any personal income. North/Western Income variables

Young adult's own income (effect of 10 percentile points) Rest of household income (effect of 10 percentile points)

Characteristics of young person

Age

***

Gender (ref: male) Female Education (ref: below secondary) Secondary Degree Employment status (ref: student) In work NEET (not in employment, education or training) Characteristics of parent/s

Age (of elder parent) Family type (ref: two biological parents) Lone parent Stepfamily Health status (ref: no parent in poor health) One or both poor health Country of birth (ref: no foreign-born parent) One or both foreign-born Education (ref: below secondary) Secondary Degree Employment (ref: two employed parents) One parent in work No parent in work N Pseudo R-squared

Southern

Baltic **

Eastern

0.016 (0.005) −0.017*** (0.005)

0.002 (0.003) −0.022*** (0.003)

0.009 (0.004) −0.007* (0.004)

0.004* (0.002) −0.008*** (0.002)

0.013*** (0.004)

0.004** (0.002)

0.006* (0.004)

0.007*** (0.002)

0.008 (0.021)

−0.002 (0.012)

−0.004 (0.020)

−0.011 (0.010)

−0.007 (0.029) −0.028 (0.038)

−0.021 (0.015) −0.112*** (0.018)

−0.048** (0.024) −0.023 (0.034)

−0.071*** (0.017) −0.122*** (0.020)

0.058* (0.031) 0.057 (0.042)

−0.010 (0.022) 0.027 (0.026)

0.285*** (0.026) 0.182*** (0.033)

0.189*** (0.017) 0.142*** (0.021)

−0.004* (0.002)

−0.000 (0.001)

−0.001 (0.002)

0.000 (0.001)

0.135*** (0.029) 0.055 (0.070)

0.181*** (0.017) 0.061 (0.060)

0.111*** (0.025) −0.058 (0.055)

0.051*** (0.014) −0.004 (0.050)

0.013 (0.031)

−0.021 (0.018)

0.017 (0.028)

−0.015 (0.015)

0.055 (0.037)

0.152*** (0.026)

0.026 (0.026)

−0.130*** (0.038)

−0.075* (0.042) −0.063 (0.044)

−0.007 (0.015) −0.043** (0.020)

−0.003 (0.053) 0.016 (0.054)

−0.043** (0.022) −0.080*** (0.024)

0.059** (0.029) 0.090** (0.040)

0.046*** (0.017) 0.064*** (0.019)

0.071*** (0.025) 0.107*** (0.033)

0.051*** (0.015) 0.050** (0.020)

3771 0.0842

8895 0.0542

2224 0.1540

8145 0.1068

Notes: Based on the sample of young adults who have any income. Standard errors in parentheses. * p < .05. ** p < .01. *** p < .001.

negative across all four country groups, for all three thresholds. In the Baltic and Eastern countries, the effects are reasonably stable over all thresholds, while in the North/Western and Southern groups, the effects are reduced at the higher thresholds; nevertheless, this provides convincing evidence that young adults are responding to the level of need in their households. By contrast, the estimated effects of the young person's own income change markedly across the three thresholds. Young people with higher incomes are more likely to share any of their incomes with their families; however, they are progressively less likely to share larger proportions of their incomes. This is entirely plausible, since a contribution of (say) 50 Euros per week to the household budget may constitute the entire income of a young adult towards the lower end of the distribution, but well under half of the income of a young adult in one of the higher centiles. It would seem reasonable to predict that the cash amounts shared would increase with the young adult's income, even though the proportion contributed falls with income, although we cannot test this with the available data.

4.2. A model including nonlinearities and interactions In the previous model, income variables were included as linear terms. We now consider a model which allows for (a) nonlinearities in the effects of income; and (b) interactions between the young adult's income and the rest-of-household income. We tested several specifications for the income variables (linear, quadratic and logarithmic functions, and categorical variables), finding strong nonlinearities in the effect of household income, which was best captured by a set of five binary variables (highest 20% of the income distribution, next 20%, and so on, down to the lowest 20%). The degree of nonlinearity was much smaller in the case of young adults’ own incomes; a quadratic term improved the adjusted model fit somewhat. Interaction effects between the two sets of income variables were insignificant in most cases, regardless of the specification of the income variables, but they were significant for the Eastern countries and were retained in the model for all country groups. Thus, our final model produces four coefficients for household income, two coefficients for 21

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Table 5 Average marginal effects (AME), for income variables from logistic regressions estimating the probability of sharing over three cut-off points, by country groups. North/Western

Southern

Baltic

Eastern

Regression estimating the probability that a young person shares ANY income Young person's own income 0.016*** (effect of 10 percentile points) (0.005) Income of the rest of the household −0.017*** (effect of 10 percentile points) (0.005)

0.002 (0.003) −0.022*** (0.003)

0.009** (0.004) −0.007* (0.004)

0.004* (0.002) −0.008*** (0.002)

Regression estimating the probability that a young person shares ≥50% of income Young person's own income −0.005 (effect of 10 percentile points) (0.004) Income of the rest of the household −0.006* (effect of 10 percentile points) (0.004)

−0.008*** (0.002) −0.019*** (0.002)

−0.004 (0.005) −0.018*** (0.004)

−0.005 (0.003) −0.008*** (0.003)

Regression estimating the probability that a young person shares ALL income Young person's own income −0.008*** (effect of 10 percentile points) (0.003) Income of the rest of the household −0.001 (effect of 10 percentile points) (0.003)

−0.010*** (0.002) −0.008*** (0.002)

−0.009** (0.004) −0.011*** (0.003)

−0.013*** (0.002) −0.005** (0.002)

Notes: Based on the sample of young adults who have any income. Standard errors in parentheses. Each of the three panels presents estimates from a separate binary logistic regression with the dependent variable indicated by the header to that panel. Each regression controls also for all the variables in Table 4. * p < .05. ** p < .01. *** p < .001.

the young adult's income, and eight interaction terms.10 This more complex model generates more nuanced predictions of the effects of income than the simpler model presented in Tables 4 and 5; however, all of the other coefficients are for practical purposes identical between the two models. In Fig. 1, we present predicted probabilities from the logistic models described above, computed using the SPost13 ado-file in Stata (Long & Freese, 2014) estimated across all three cut-off points in the sharing variable (any/most/all). The top panel of Fig. 1 presents predicted probabilities for young adults sharing any of their incomes; the middle and lower panels present predicted probabilities of young adults sharing most, and all, of their incomes respectively. These predicted probabilities are calculated on the basis of an individual with the mean characteristics of the sample (in the case of continuous variables) or the modal characteristics (in the case of discrete variables). This hypothetical individual is a 25-year-old male, with secondary education, employed, and living with both parents (who are both aged 55, educated to secondary level, native-born, and in employment). For comparative purposes, predicted probabilities have also been plotted for two other hypothetical individuals (see Appendices 2a and 2b): a 19-year-old student, who our results suggest would be less likely to share income at the “share any” margin; and a 32-year old man with a job, living with an unemployed lone parent, all characteristics which would make him more likely to share income. All three plots tell a similar story. We observe striking nonlinearities in the relationship between sharing and household income. In virtually all cases, young adults are progressively more likely to share their incomes, the poorer the rest of their households are; but they are much more likely to share when their households are in the bottom fifth of their country's income distribution. The difference between the poorest fifth and all the others is particularly pronounced in the “share any income” panel for the North/ Western and Southern countries; it is arguably the major driver of the relationship between household income and the probability of a young person sharing, across all regions. Confidence intervals (CI) are not

shown on Fig. 1; however, analysis confirms that while the 95% confidence intervals overlap for the four better-off household quintiles, the confidence interval for the poorest quintile is well separated from the CI for the richest quintile (and in some cases the other lower quintiles) in the Southern and Eastern countries (bottom and middle panels) and the North/Western countries (bottom panel only). The relative unimportance of interaction effects may be noted from the fact that in each group of five lines on the graph, the lines are more or less parallel. A slight “fanning out” may be seen in cases where the interaction terms were significant, most noticeably for the Eastern countries in the “share any income” regression. In this case, the uninteracted model yielded a significant positive coefficient on the young adult's own income; when interactions are included, the estimated effect of own income is negative. This result is rather counter-intuitive; it may arise from the fact that the question on the proportion of income kept separate contains only a few broad categories, and there may be differences between population groups in the propensity to report sharing no income, as opposed to a very small percentage. However, without more differentiated data, we cannot investigate this further. Considering the hypotheses we proposed earlier, our interacted model now lends only partial support to Hypothesis 1 (young adults will be more likely to share their incomes, the higher are their own incomes). In the case of sharing any income, we observe only small effects in the North/Western and Baltic countries, and no effect in the other two country groups. In the case of sharing higher proportions of income, the estimated effects are consistently negative, though we should not infer too much from these estimates, as they refer to proportions and not to cash amounts. Hypothesis 2 (young adults will be more likely to share, the lower are the incomes of other household members) is unequivocally confirmed, with the additional observation that the relationship between household income and sharing is in many cases driven primarily by the poorest households. Hypothesis 3 (levels of sharing will be higher in the Baltic and Eastern countries) is also confirmed, as evidenced by the generally higher predicted probabilities of sharing in these countries.

10 In more detail, this set of coefficients comprises one for each of the five categories of household income, minus one for the reference group; two coefficients for the young adult's income (one for the linear term and one for the squared term) and eight interaction terms (one for each household income category other than the reference category interacted with the linear term in the young adult's income, and another four for the interactions between household income and the squared term in young adult income).

4.3. The magnitude of young adults’ contributions We have shown that in many countries, a majority of young adults who live with their parents share some of their incomes with their families, and a sizeable proportion share half or more of their incomes. This sharing is most likely to occur in the very poorest households, so it 22

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M. Iacovou and M.A. Davia

Fig. 1. Predicted probabilities of a young person sharing any, half or more, or all their income, by the young person's own income and the income of other household members, by country groups.

is possible that it may form a substantial proportion of the incomes of those households. If we had data on the cash amounts which young people were sharing with their families, it would be easy to assess the importance of these transfers to the family, because we have reasonably reliable measures of the incomes of the young adults themselves, and of other household members. Given the available categorical variable, we need to make some (rather strong, though not entirely implausible) assumptions about the amounts being shared. We proceed as follows. Where the young person reports that none, half or all their income is shared, we know exactly, or almost exactly, the amount of income which is shared. We assume that those reporting sharing “some” income share 25% (the midrange of the band), and those reporting sharing “most” share 75% (also the midrange). This gives an estimate of the amount contributed by the young adult. In households which contain more than one young adult, we add up these contributions. We then divide this total by a sum representing the “common pot” of the household – that is, the contributions of young adult members, plus the total incomes of all other household members. This fraction is an estimate of the proportion of the “common pot” contributed by the young adults in the household. Note that it is likely to be a very conservative estimate, as we have assumed that all other members of their household contribute their entire incomes to the household and keep back nothing for themselves. The first column of Table 6 shows the mean percentage of the “common pot” contributed by young adults, calculated over all sample households, even those where the young adults make no contribution. This percentage ranges from 4% in the North/Western countries to 10% in the Baltic countries. Column (2) of Table 6 shows the same percentages for households where the young adults share at least part of their incomes; here, the mean percentages are much higher, ranging from 21% in the North/Western countries to 34% in the Baltic group. Column (3) shows the percentages of sample households in which young adults’ contributions account for over one third of the common

household budget. As expected, this percentage is fairly low in the North/Western countries; nevertheless, even in these countries, it accounts for 1 in 30 households. In the Baltic and Eastern countries, the figure is much higher, standing at around 1 in 9 households. Thus, although in every group of countries those households which rely heavily on the contributions of their young adult members are in a minority, that minority is not vanishingly small in any group, and is indeed sizeable in the lower-income nations of Eastern Europe. Column (4) shows households in which young adults’ incomes account for over onethird of the common household budget, as a percentage of households in which young adults share any income at all. In the North/Western group, young adults’ contributions account for over one-third of the common household budget in 26% of households where young adults share any of their incomes; in the Baltic and Eastern country groups, this figure rises to over 40% of households. Column (5) of Table 6 takes a different approach, following standard methods in income and poverty analysis. It defines a “poor” household as one with a total equivalized income under 60% of the national median (European Commission, 2009). We calculate whether each household is poor, under its current composition. We then recalculate the household's poverty status, under the hypothetical assumption that the young adult(s) leave the household, taking with them their personal incomes. Column (5) shows the percentage of non-poor households in each country which would be poor if their young adult members moved out.11 ,12 This percentage shows less variation between country groups than the other indicators, ranging from 6% in the North/Western region to 11% in the Baltic countries. 11 Note that it is also possible for young adults themselves to face changes to their poverty status on leaving home. We do not consider this, since the current analytical framework cannot accurately model welfare benefits that they may receive on leaving home; a microsimulation approach would be called for. 12 The numbers in Column 5 refer to young adults who have any income; the figures across the entire sample of households would be lower.

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Table 6 Indicators of the magnitude of young adults’ contributions to household budgets, by country groups. (1)

(2)

(3)

The incomes shared by young adults, as % of the “common pot” – mean across households

(4)

% of households where young adults contribute over 1/3 of the “common pot”

All households with a YA

Households with a YA who shares any income

All households with a YA

Households with a YA who shares any income

(5) % of non-poor households that would be poor without one young adult's income

North/Western

Austria Belgium Germany Luxembourg Group

3.8 3.3 3.7 2.0 3.7

28.3 25.8 20.5 28.6 21.4

2.5 3.5 3.5 1.7 3.4

34.6 34.8 24.6 27.9 26.0

6.0 3.5 7.0 6.2 6.0

Southern

Cyprus Malta Greece Spain Portugal Italy Group

2.6 4.4 6.9 6.5 5.4 6.2 6.3

34.5 36.3 29.0 33.5 27.5 27.9 29.8

3.6 5.5 7.5 7.4 5.8 6.6 6.9

58.1 47.8 36.9 43.3 33.5 33.7 37.1

8.7 21.6 7.7 8.8 7.9 8.0 8.7

Baltic

Estonia Lithuania Latvia Group

5.2 9.6 12.1 9.6

24.6 33.3 37.7 33.7

5.3 11.0 15.2 11.2

29.6 39.5 49.9 41.9

5.9 13.1 11.7 10.8

Eastern

Bulgaria Czech Republic Hungary Poland Romania Slovakia Group

11.0 6.6 14.4 5.3 13.3 11.4 9.3

30.4 25.6 35.7 27.7 30.6 31.9 30.4

13.0 4.7 19.2 6.0 18.8 12.3 11.5

40.6 28.2 51.2 35.2 45.7 43.5 42.2

6.2 4.4 13.1 8.2 6.9 9.0 7.8

Total

7.6

30.2

8.8

39.6

8.0

literature, but we have shown that it is of great potential relevance – not because the overall volume of transfers is particularly large in comparison with the volume of transfers from parents to adult children, but because these transfers are concentrated in the poorest households and in the poorest regions, and are likely to play a pivotal role in maintaining living standards in many such households. We proposed three hypotheses. Hypothesis 1 – that young adults would be more likely to share their incomes, the higher their own incomes – did not find unequivocal support. Although a simple model (Table 5) showed that young adults with higher incomes were more likely to share any income, we also found that (a) this effect was dwarfed by the effect of household income; (b) it did not hold when we analyzed sharing larger proportions of income [though we noted that this result may be different if we knew what cash amounts were being shared, rather than just the proportion] and (c) in two of the four country groups, this effect disappeared when a more complex interacted model was considered. Hypothesis 2 – young adults would be more likely to share their incomes, the lower the incomes of the rest of their households – was strongly supported. In all groups of countries, and at all thresholds considered, there was strong evidence that young adults respond to economic need in their households when deciding whether to share their incomes. Hypothesis 3 – that levels of income-sharing by young adults would be higher in the Baltic and Eastern country groups, than in the North-Western and Southern groups – was also supported, via descriptive analysis and the plots in Fig. 1. Our findings provide compelling evidence in support of the hypothesis that transfers are made on the basis of need in the household – the “contingency” hypothesis (Fingerman et al., 2009; Mudrazija, 2013). This is the case both at the individual level (young adults are more likely to share in poorer households, and much more likely to share in the very poorest households) and cross-nationally (the level of sharing is much higher in poorer countries). These findings are

Taken together, the indicators in Table 6 suggest that in aggregate, the amount of income shared by young people does not constitute a major part of their households’ budgets. However, for a minority of households, particularly in Eastern Europe and the Baltic countries, the contributions of young adults do form a significant part of household budgets, and for around 10% of families, are instrumental in keeping their households out of poverty. It is worth noting that intra-household transfers by young adults are hugely larger in volume than transfers by young adults living independently. The EU-SILC contains data on transfers made out of the household by young adults living independently; it enables us to separate child support and alimony from other types of payments, but it does not allow us to “match” donor with recipient households, and thus to identify which payments are going to young adults’ families of origin. Even if we assume that all of these payments are going to families of origin, and even if we make extremely conservative assumptions about the volume of young adults’ intra-household transfers (that those sharing “some” are sharing only 5%, while those sharing “most” are sharing only 55%), the volume of within-household transfers dwarfs the volume of between-household transfers. In 10 of the 20 countries in our sample, the volume of between-household transfers is less than 1% of the volume of within-household transfers; in 6 countries it is between1% and 2%; in 3 countries it is between 2% and 4%; and it is over 4% only in Cyprus, where it is 13%. 5. Discussion This study extends our understanding of intergenerational support between family members by analysing (a) the extent to which young adults who are still living with their parents share their incomes with the rest of their households; (b) the determinants of sharing; and (c) the importance of young adults’ contributions to their families’ budgets. This is an area which has been almost completely overlooked in the 24

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consistent with previous research on transfers from middle-aged children to elderly parents (Mutran & Reitzes, 1984; Silverstein, Gans, et al., 2006, and many others), and illustrate a tendency for transfers up the generations to be made on the basis of need, at much earlier ages than have previously been studied. It is worth pointing out, however, that the support which our results provide for contingency theory should not be taken as evidence that any of the other theories used to explain intergenerational transfers are not valid. In particular, our results might also be consistent with a scenario of reciprocity, with young adults paying their parents for the accommodation and other benefits they are receiving; or with some young adults starting to repay their parents for time or money spent on them earlier in the young adults’ youth; but because of data limitations, we cannot draw any conclusions as to whether our analysis provides support to the hypothesis of reciprocity. We have also noted a rich literature on the bonds of affection between families; again, we have not been able to assess the role of these factors, and further research, both quantitative and qualitative, is called for. In addition, the differences we noted between country groups, with much higher levels of sharing in the poorer Baltic and Eastern European countries, are consistent with the contingency-based hypothesis that this sharing is given on the basis of greater need in these countries. However, they may also be consistent with a “crowding-out” explanation: it may be that higher levels of intra-household sharing are necessitated by lower levels of expenditures on welfare benefits in the Baltic and Eastern countries (see Table 1); further investigation would be needed to tease these factors out. Our findings suggest some interesting insights about the effects of social policy. Social policies relating to young people include policies on education and training, active management of youth labour markets; the eligibility of young people for social assistance, and the provision of affordable housing. Previous research (Aassve, Billari, Mazzuco, & Ongaro, 2002 and others) shows clearly that youth social policy is an important determinant of whether young people leave home or remain living with their parents. However, our finding that young adults’ incomes are a far less important determinant of income-sharing than the need of their families, suggests that income-sharing by young adults may be influenced far more by policies which alleviate poverty across the population as a whole, rather than by policies relating specifically to young people. Our findings also have implications for the study of home-leaving. Models of home-leaving (Sironi & Furstenberg, 2012; South & Lei, 2015) typically assume that young adults leave home (sometimes with the assistance of their parents) when they can afford to do so. The finding that many young adults living in the poorest households are making an important contribution to their families’ budgets raises the question of whether some of them remain living with their parents precisely in order that they may be able to provide financial assistance (it is, of course, possible to provide assistance while living away from the parental home, but the additional costs of maintaining an independent home may make this impossible).

addition, the question did not ask directly about the proportion of income shared, but about the proportion kept separate from the household budget. It is possible that results might differ in some respects if a direct question on income sharing were used; this would be an interesting avenue for further research if such a question were to become available. Finally, the fact that the cross-sectional “bolt-on” module on income sharing could not be combined with the longitudinal component of the EU-SILC, means that we were restricted to examining transfers in a cross-sectional context, and unable to observe how they evolve over time – in response, for example, to changes in income or employment, or in relation to developments in other spheres of life, such as homeleaving. Having noted these limitations of the EU-SILC, it is worth reiterating its value – as far as we are aware, it is to date the only large-scale data set to have included questions on intra-household sharing which could be used to address the questions at hand. A further limitation of this study lies in the fact that our sample is highly selected, being composed of young adults who are (a) co-resident with their parents, and (b) have some income to share. Moreover, the selection process and may (and very likely does) differ between countries or welfare regime types, and in part at least may be the product of policy related to welfare regimes. There is a large literature on the determinants of the home-leaving decision (Avery, Goldscheider, & Speare, 1992; Ermisch & Di Salvo, 1997); this mentions many of the same individual-level factors as we have examined as determinants of income-sharing (the incomes of the young person and his or her family, employment status, education, and so on). In addition, several nationallevel factors are known to have a important impact: Aassve et al. (2002) and Mulder (2006) note that the lack of well-functioning mortgage markets or a stock of affordable rented accommodation is a key factor underlying late home-leaving in Southern Europe, while Mandic (2008) makes a similar argument in relation to Eastern Europe in the aftermath of reforms in the 1990s. As well as influencing home-leaving, housing markets may also influence income-sharing on the part of young adults: if housing is expensive relative to young adults’ incomes, or if mortgage lenders require large deposits on a house, young adults will, as well as living with their parents for longer, need to save more while they are living at home. We do not control for selection into the sample; thus, to the extent that social policy influences within-family transfers, we cannot separate out the direct effects of policy on transfers from indirect effects via effects on living arrangements. Such an analysis is beyond the scope of this study but would be an extremely interesting direction for future research. 5.2. New directions This paper has opened up several avenues for further investigation. It is possible to explore some of these using EU-SILC data; however, for others to become viable, it would be necessary for large-scale longitudinal household surveys to start carrying questions on the intrahousehold sharing of income. The case for this has already been made by authors questioning the assumption of full income pooling within households, particularly in response to concerns that this assumption may lead to underestimates of poverty among women (Meulders & OöDorchai, 2013); without this information, we also cannot approach a fuller understanding of the nuances of intergenerational transfers. We would argue that surveys on intra-household transfers should ask for this information in cash amounts, and should collect data in terms of broad percentage-based bands only when respondents are unable to provide more detailed information. An interesting feature of income-sharing by young adults is that, unlike most other forms of intergenerational sharing which are entirely voluntary, or at least policed only via social norms, young adults’ contributions to household budgets may be both solicited and enforced by parents, who have the outside option of requiring the young adult to

5.1. Limitations The chief limitations of this study relate to features of the EU-SILC data. Differences in survey design between countries meant that we were unable to include in our analysis countries with “social-democratic” welfare regimes; these countries are a special case of early home-leaving, high incomes and a comprehensive cradle-to-grave welfare state, and would have provided an excellent comparator to the other welfare regimes. Another constraint on our research was the question on income sharing. The categorical responses, recorded as percentages, mean that we were not able to calculate exact amounts of income shared; information on cash amounts would have enabled us to estimate several aspects of income-sharing more accurately than we were able to do. In 25

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live elsewhere unless they pay their way. It would therefore be interesting to collect information about the bargaining processes (or other considerations of fairness, reciprocity, norms, etc.) which lead to the relevant decisions on this type of sharing. The greatest challenge will be to conceptualize income-sharing by young adults as part of a life trajectory. We have already mentioned the challenge of incorporating the home-leaving decision and features of the housing market into a fuller analysis; in the same way, it would be interesting to consider the role of young adults’ expectations of future bequests. It is well known that the expectation of a substantial bequest leads to (a) lower levels of saving (Weil, 1994), and (b) closer relationships with parents (Caputo, 2002); what we do not know, in the European context, is how this may play out in terms of the residence patterns of the younger generation, or intra-household transfers of those who live with their parents. If income-sharing were embedded in household longitudinal surveys, we could investigate not only the determinants of young adults’ contributions, but how they evolve over time – are they a short-term stop-gap in times of need, or a longer-term arrangement? We should also explore the relationship between income-sharing when an adult child lives at home, and what happens during and after the transition to independent living. Do children who have made substantial contributions while living at home continue to support their families of origin once they have left home, or are they (temporarily at least) relieved of their obligations? Do parents whose children have contributed substantially to household living expenses reciprocate when their children leave home, by helping them with the costs of setting up home? Anecdotal evidence from parenting discussion boards suggests that many parents use their adult children's contributions as a mechanism to enforce saving, returning their children's contributions to them when they leave home. On the other hand, it may be that the households that have received the most assistance from their adult children are simply too poor to give their children much help in setting up their own homes. Note that in order for these questions to be addressed, surveys would need to collect not only the total volume of transfers into and out of the household, but also details of which individual was at the other end of the transfer. This could in its simplest form consist of information on

the relationship between the donor and recipient, but in the case of longitudinal surveys where people are followed when they leave a sample household, a much more interesting prospect is possible, namely that the transfer could be linked to the full records of the donor or recipient, providing a valuable resource for research into all aspects of intergenerational support, including, but by no means limited to, support between adult children and their parents. Conflicts of interest The authors declare that they have no relevant conflicts of interest. They were paid only their regular salaries while conducting this research, and have no links with individuals or organizations, and no assets, which may stand to gain materially from the publication of this research. Ethics and informed consent Our analysis is based on data from human subjects. We conducted only secondary analysis of a data set which had been collected under a protocol compliant with accepted standards of ethics, informed consent, and the protection of human subjects, and we agreed to the relevant conditions of confidentiality and other terms of use. Further ethical approval from our own institution was not required. Acknowledgements The idea for this paper came about in the course of the project Poverty among Youth: International Lessons for the UK, funded by the UK's Joseph Rowntree Foundation. Further work was supported by grants from the UK's Economic and Social Research Council (RES-062-231455: Life Chances and Living Standards across Europe) and Spain's Fundación Ramón Areces (XII Ayudas a la Investigación en Economía). Data were provided by Eurostat. We are grateful to colleagues at our own universities, and at the University of Essex (particularly Arnstein Aassve, Letizia Mencarini and Richard Berthoud) for their encouragement and constructive comments.

Appendix 1. Mean annual incomes, in Euros, in households containing a young adult sample member aged 16–34 living with one or both parents

Austria Belgium Germany Luxembourg Cyprus Malta Greece Spain Portugal Italy Estonia Lithuania Latvia Bulgaria Czech Republic Hungary Poland Romania Slovakia

Personal income (young adult)

Rest of household income (equivalized)

Incomes measured

12,326.4 11,015.6 8678.8 19,150.3 10,746.0 9851.6 8478.2 9454.6 6784.1 11,974.1 3049.4 3812.7 4211.3 2530.8 4811.4 3759.7 2854.6 1738.1 5420.3

26,162.3 23,669.2 23,221.0 35,476.7 21,732.1 10,977.5 14,706.0 14,723.1 10,539.4 20,791.1 6417.6 5051.5 4885.2 3973.3 8968.6 4256.5 4662.7 2432.2 7335.9

net gross gross net gross gross net net net net net gross net net gross gross net net gross

Notes: Based on authors’ own analysis of EU-SILC 2010. All incomes are measured in Euros.

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Appendix 2a Predicted probabilities of a young person sharing any, half or more, or all their income, by the young person's own income and the income of other household members. The reference person is a 19-year-old male student living with two parents, both employed; other characteristics are set to the sample mean or mode

Appendix 2b Predicted probabilities of a young person sharing any, half or more, or all their income, by the young person's own income and the income of other household members. The reference person is a 32-year-old male with a job, living with one parent who is unemployed; other characteristics are set to the sample mean or mode

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