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Article

A Job of One’s Own. Does Women’s Labor Market Participation Influence the Economic Insecurity of Households?

Institut de Govern i Polítiques Públiques (IGOP), Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
Societies 2018, 8(1), 7; https://doi.org/10.3390/soc8010007
Submission received: 31 October 2017 / Revised: 15 December 2017 / Accepted: 28 December 2017 / Published: 19 January 2018
(This article belongs to the Special Issue Towards a Sustainable Community, Work and Family Interface)

Abstract

:
Background: The article investigates the phenomenon of economic insecurity from a feminist perspective, assessing the role of women’s labour market participation in predicting the phenomenon. It draws on the work of Trifiletti (1999) to analyse women’s role in providing welfare for the entire family. Methods: Stemming from a cross-sectional analysis of European Union statistics on Income and Living Conditions (EU-SILC) 2013, logistic regression models (for women in a couple and for single women) are provided for six countries. Results: The descriptive analysis shows that economic insecurity affects single women more than single men, while couples fare better in all countries considered. Transversal factors that explain the phenomenon in logistic regressions are household type and wealth of the family, while the role of women’s labour market participation and economic dependency from partners or from a welfare system varies across countries. Conclusions: Empirical results show that countries only partially comply with the theoretical model proposed by Trifiletti (1999), which proceeded from the welfare regime debate. Italy and Spain show more difference than similarity. The results for Italy and the United Kingdom confirm those of previous investigations that indicate their similarity, while France and Spain are closer to the Mediterranean archetype. The results for the Czech Republic confirm its proximity to the breadwinner model, as Denmark epitomises the heuristic capacity of the Universalist model in Northern European countries.

1. Introduction

Economic insecurity is an increasingly prominent topic of sociological debate [1]. In a period still characterised by the consequences of the financial crisis in Europe, unpredictable life events are more likely to result in economic loss for individuals that have a weak integration into the labour market, which can only be partially mitigated by the welfare provision, in light of the move to austerity. However, the decisive role of gender has been underplayed in this debate [2,3,4,5,6], although the increased likelihood of women to be poorer than men has long been recognised [7]. As the economic contribution of women has proven to be decisive in determining household welfare [8], it might be argued that women’s labour market participation could be an effective protection from economic insecurity for the household in which they live.
The hypothesis guiding this article is that women’s labour market participation can mitigate the risk of economic insecurity that occurs across households, as one-income families should be more likely to suffer economic losses [9] and women have a weaker integration into labour market compared to men [10]. As men tend to be active full-time in the labour market, variation in households’ total labour market participation arises from the different preferences of women regarding work (ranging from inactivity, to full-time participation) [11], and the type and structure of opportunities offered to them by local productive systems [10]. However, the concurrent welfare pillars (family and welfare) might mitigate the risk of economic insecurity, and also change women’s preferences within different institutional contexts. To support this argument, this paper will rely on logistic regressions that have an economic insecurity index as a dependent variable and women’s labour market participation as an independent variable; women’s economic dependency from their partner and from welfare systems will be taken in account as intervenient variables.
The study will consider six different countries (Italy, Spain, France, the United Kingdom, Denmark, and the Czech Republic), which belong to different welfare regimes [12,13,14]. The magnitude of women’s labour market participation depends on the model of solidarity that characterises the social protection (occupational versus universal); the orientation of the national welfare regime (measured by decommodification, defamilisation, and destratification); and, the type of occupational structure (part-time versus full-time; level of EPL; labour market segregation). It is thus reasonable to hypothesise that different welfare regimes assume a different role for women, as highlighted by Trifiletti [15], privileging women either as workers or as mothers and wives. Institutional frameworks could thus magnify the effect of a lack of female participation in the labour market relative to the risk of exposing the household to economic insecurity. Running models for each of the countries involved will help to isolate the effect of the institutional context. Using EU-SILC 2013 (European Union Statistics on Income and Living Conditions) as the primary source of data, two different models will be run for each country (one for single women and one for women in a couple) to understand if the participation of women in the labour market is a determinant in each context, given the different household compositions and the extent to which the role played by economic dependency from their partner or from a welfare regime is critical in increasing exposure to economic insecurity.
The results showed that economic insecurity is a phenomenon that occurs more frequently among single women than women in a couple, although the magnitude of the phenomenon varied by country. Denmark is the most favourable context for all women, while the United Kingdom is the worst for single women. Italy and Spain showed the highest exposure to economic insecurity among women in a couple. The logistic regressions for single women and women in a couple showed that there are two transversal factors influencing the phenomenon: the type of household in which women live, and the wealth possessed by the family. Women’s labour market participation, and economic dependency from welfare and from a partner, have different impacts in different countries. The clusters of countries differed from those predicted by Trifiletti [15], whose research stemmed from the debate concerning welfare regimes: Italy and Spain showed more difference than similarity, with the former being closer to the United Kingdom, as previous empirical studies have indicated [16]. In addition, France was closer to the Mediterranean model, while the Czech Republic showed similarity to the breadwinner model. Finally, the association between Denmark and the universalistic model was confirmed.
The original contribution of this article to the current debate about economic insecurity can be summarised in two points: first, it assumes gender as the main analytical dimension, something thus far neglected by scholars of economic insecurity [1,2,6]; and, second, it shifts the focus from the unequal distribution of money [17] to the unequal distribution of paid work within families, as the main independent variable is labour market participation of women. However, the unequal distribution of money within the household will be taken into account, and the calculation of extent to which the family offers a buffer is based on the proportion of household income provided by the woman’s partner.

2. Women Facing Economic Insecurity: The Role of the Labor Market and Welfare Regimes

Until two decades ago, the so-called European Social Model, based on high employment stability, broad and generous welfare programmes, and the persistence of strong family ties and a gendered division of roles [2], was able to protect a large majority of European citizens from negative outcomes of life events. However, following the post-industrial transformation [10], welfare pillars (work, family and welfare) have progressively lost their capacity to ensure the well-being and security of several social groups [2,18,19,20], exposing an increasing proportion of middle class individuals to a feeling of insecurity, which the recent financial crisis has only magnified [21]. ‘Economic insecurity’ [1] is the term scholars have used to refer to the outcomes of this phenomenon, which is a material condition that involves more than simply having a low income. It indicates a situation in which households experience difficulty in autonomously attaining adequate living standards. It thus pertains to how labour market participation, education and household structure impact on the material and living conditions of individuals within a family.
Robert Castel [22,23] was the first to pay scholarly attention to this phenomenon, using the term ‘vulnerability’ to refer to an individual or a household’s scarcity of resources to overcome hazards in the course of life. The concept of vulnerability was first defined by Chambers [24] as a condition of insecurity and exposition to risk and shock; Castel [22] later described it as a dynamic state marked by bi-directional transition from inclusion to exclusion, and characterised by uncertainty. More recently, scholars have used the concept of vulnerability to describe those individuals and households that have difficulties safeguarding their welfare and attaining adequate living conditions, only partially inferable solely by means of monetary measures [3,25]. Vulnerable people are not necessarily affected by permanent poverty or deprivation, but are rather exposed to the risk of recurrent difficulties distributed throughout the course of their life [18], which results in them experiencing almost constant insecurity. Living in such a condition increases the probability of poverty arising in the case of negative events, though not in the same proportion for each of the individuals involved: it is the multiple different configurations of the situations in which the person is included that might increase, in a multiplicative way, their exposure to a negative consequence [5,26]. That is, being vulnerable is not just an outcome of weak labour market integration; vulnerability is located at the intersections between family structure changes and welfare provisions. The result of these entanglements is a condition of economic insecurity that affects a large group of European citizens, preventing them from attaining an ideal balance, temporarily or sporadically; this insecurity even includes some middle-class individuals [21]1.
Within this framework, the employment situation of women plays a decisive role in determining their risk of exposure to economic insecurity, both in terms of their personal risk and that of their household [8]. Men, as the ‘traditional’ primary earners, have a stronger motivation to become firmly integrated into the labour market. Women in advance capitalist societies are culturally allowed to choose between different alternatives, based on their own preferences [11], ranging from assuming a fully shared breadwinner role, signalled by a strong commitment to work and full-time employment and, therefore, limited family engagement, to exclusively concentrating on care [30]. Conversely, employers and states justify lower wages, reduced working hours and casual employment for women on the basis that women are not the main breadwinners, treating their salary as an additional income to that of their partner [31]. In reality, there is a common cultural norm that suggests that the ideal worker is someone who works long hours and demonstrates total commitment to the job—and is usually a man. The unbalanced provision of unpaid work is fundamental in such a context: women typically take responsibility for domestic and care activities, thus allowing men to devote themselves solely to labour, and to increase their personal investment in the labour market and their own career at the expense of their partner’s career and labour market participation.
In such a situation, women are more likely to be exposed to and suffer from difficulties in attaining adequate living standards [32]. When they live alone, the risk of slipping into poverty and social exclusion has been widely documented [7,33,34,35]. When they are in a couple, weaker labour market integration exposes women to the risk of being economically dependent on their partner [36], which in the situation of a family breakup determines their likely exposure to economic insecurity. It can be assumed that it is the weaker labour market participation of women that is the main cause of this. As already evidenced in literature, women are usually the secondary earners in their households, both because unpaid work prevents their equal participation in the labour market, but also because they suffer from a more marginal position in the labour market [37].
There are two pieces of empirical evidence for this assumption: the higher proportion of inactive women compared to men, and the greater exposure of women to non-standard contracts, especially part-time contracts [10]. Although it is usually considered a good compromise between family and work commitments, part-time contracts carry several risks for women. First, they are associated with a lower hourly wage (even if the magnitude of this penalty varies across countries), and they offer limited opportunities for advancement; they lack in job security and they are less likely to be unionised [38]. Part-time contracts are also usually concentrated in the least skilled, lowest grade, and lowest paid jobs [39]. This type of labour market integration brings with it lower welfare contributions and lower individual income in occupational welfare systems [40], as workers work shorter hours and with lower pay. Part-time work also has a long-term impact, as it usually implies a lower pension income in old age, and reduced access to social insurance benefits [41,42], even being entirely excluded if their weekly working hours do not reach a pre-defined amount [39]. Women who are only partially active in the labour market are more likely to be dependent on contributions from welfare and services provided within a national and local welfare framework, but they are also more exposed to the risk of being economically dependent on their partners [36].
In this regard, the role of welfare systems is not neutral [43], as the relationship between paid and unpaid work is central to sustainable social models, discussing the terms of production and reproduction [44]. Welfare systems can promote and sustain different roles for women, as workers, wives and mothers, protecting from the market those who prefer to be inactive in terms of participation in labour for family reasons, and allowing those who prefer to work to be active by supporting them with services in kind [15]. The configuration of relationships between the market, the state, and the family might reinforce some gender roles and not others [31,45], thus affecting the redistribution of resources between mothers, workers and wives, the dependency of women on their partners, and gender inequality in general [30,46]. In all cases—except for socio-democratic countries—the welfare system does not question women’s ownership with regard to the provision of unpaid work in the family, but they differ in terms of how much they support and value women’s contribution in the form of unpaid work.
Scholarly interest in welfare state differences began between 1970 and 1980. In 1990, Esping-Andersen provided one of the first comprehensive comparative studies of what was coined welfare regimes. In Esping-Anderson’s [12] classification, three main types of welfare state were identified, based on different levels of ‘de-commodification’, a Marxist concept that can be translated as the dependence of individuals from the market for the satisfaction of their needs: the liberal regime (where the welfare state is a residual net that is accessible only by means-testing, to those who cannot acquire protection through the market); the social-democratic regime (where a universalistic system, financed through general taxation, gives access to the same benefits to all citizens regardless of their role in the labour market); and, the conservative regime (where the receipt of welfare benefits depends on the level of integration into the labour market, which promotes a status differential between ‘insiders’ and ‘outsiders’, and a social security system that has only partially redistributive effects). A second dimension of classifying welfare regime is destratification, which focuses on the type of provisions given to the middle class. In liberal regimes, the middle class is largely excluded from social benefits, and welfare is targeted toward socially-excluded individuals; in conservative regimes, specific groups of the middle-class have access to differentiated benefits depending on their position in the labour market; in social democratic regimes, the high degree of decommodification allows the middle class to access the social security system, as a universalistic scheme [40].
The approach proposed by Esping-Andersen [12] has been widely discussed and criticised; however, this paper will focus on two main criticisms, both of which concern the role of families as welfare pillars. The first criticism is that, in his tripartite model, Esping-Andersen did not initially distinguish Mediterranean and eastern European countries from conservative countries. For instance, Ferrera [47] distinguished countries like Spain and Italy from the cluster of conservative countries, based on a principle of unsubsidised subsidiarity that the family acts as a welfare pillar, and proposing the southern European model as a separate cluster. For eastern European countries, since their transformation from Soviet societies to market economies is ongoing, it is difficult to identify their main features, which only partially overlap with previously identified clusters.
Second, feminist scholars strongly criticise the implicit gendered assumption of welfare regime theory. Mainstream scholars have underestimated the effects of social provisions in advantaging and disadvantaging the sexes, as women can access welfare benefits through their many possible roles (as single mothers, as wives and mothers, and as workers) when favouring a different balance between paid and unpaid work; systems are not neutral in terms of women’s entitlements to benefits [48]. From the feminist perspective, gender is concurrent with labour market position (decommodification) and class (destratification), and thus shapes access to the welfare state and conditions its social effects [49]. Previous analyses of welfare regimes have not taken into account the fact that these regimes are also based on a gendered division between the public and private spheres, represented by the market and the family, respectively. The unequal division of paid and unpaid work informs the system of social protection, especially in regard to the extent to which the state assigns unpaid work to women and thereby sustains their contribution to society. In a 1999 revalidation of his theory [13], Esping-Andersen, in order to address the feminist critics of his typology of welfare states, introduced the concept of defamilisation, which refers to the degree to which a welfare state allows an individual to attain an acceptable living standard independently from their family. This allowed Esping-Anderson to include new differentiation in the model, separating conservative and Mediterranean countries on the basis of different strategies toward family policies. However, feminist scholars have gone even further.
Building on the work of Lewis [50], Trifiletti [15] proposed an amendment to the Esping-Andersen model that focuses on how the state supports the normal functioning of a family, and to what extent social risks are considered a family responsibility, where public protection is reserved only for those ‘first order’ social risks against which families cannot adequately protect themselves. In this sense, welfare regimes can be classified according to the extent by which they cover individual-level social risks. The two dimensions by which Trifiletti [15] classified countries were whether the state privileges the role of women as mothers and wives, or as workers; and, the capacity of the system to protect individuals from the market (the decommodification criterion proposed by Esping Andersen in 1990). The result is a four-category typology that distinguishes between: universalist regimes, in which the welfare effort supports a two-worker model, but also enables women to be protected from the market in case they prefer not to be active; breadwinner regimes, in which the state views women primarily as wives and mothers, but supports their unpaid work through provisions; Mediterranean regimes, in which the state views women primarily as wives and mothers, but where families, in the first instance, should support their unpaid work by relying on substituting public provision; and, liberal regimes, in which the state does consider women primarily as workers, but does not support them through provisions for their unpaid work.
The advantage offered by the model proposed by Trifiletti [15] is the preeminent role assigned to the individual labour market participation of women, compared to other theories. The subsequent analysis will assume an individual point of view, to explore how the economic dependency of women from their partner and/or from welfare provisions might expose their household to economic insecurity, and will compare the empirical evidence with the theoretical clusters identified by Trifiletti [15].

3. Materials and Methods

The main goal of this study is to assess to what extent the labour market participation of women is a determinant of, and thus able to predict, the risk of economic insecurity for the household in which they live. To fulfil this objective, this paper will first present a descriptive analysis of economic insecurity across different countries to show how the percentage of individuals and households involved in the phenomenon of economic insecurity changes depending on individual gender and household composition. Second, a logistic regression will analyse the risk of economic insecurity at the household level by focusing on the labour market participation of women and introducing the two other welfare pillars (family and welfare) as possible buffers of its effect (as it is shown in Figure 1). This second step will focus solely on women: this strategy fulfils the need to carry out analysis at individual level, as is recommended in the feminist approach, although the dependent variable in this study is calculated at the household level.
Economic dependency from welfare and from partners will be used as proxy to test the defamilisation and decommodification hypothesis that underlies the welfare regime theory, using the theoretical frame proposed by Trifiletti [15]. Drawing on the theoretical approach of Trifiletti and her four proposed models [15], this study hypothesises that the results for different countries will reveal different risk profiles for women:
  • Universalist Welfare Regimes: it is expected that in countries like Demark, economic dependency from welfare will not represent a source of economic insecurity, as decommodification is at its highest here. Being excluded from the labour market (i.e., unemployment) should constitute a source of insecurity here, as the country aspires to be a dual worker model.
  • Breadwinner Welfare Regimes: it is expected that in countries like France, the reduced participation of women in the labour market will not be a source of economic insecurity, in the case that women are supported by a partner.
  • Mediterranean Welfare Regimes: it is expected that only families with sufficient wealth might support inactivity, for family reasons, in Italy and Spain, while low-skilled women will support the family balance with at least a partial income to avoid descending into economic insecurity. States do not support families.
  • Liberal Welfare Regimes: it is expected that in countries like United Kingdom economic dependency from a partner or from welfare will be a strong determinant of economic insecurity at the household level, and women’s participation in the labour market will be a predictor of less risk of economic insecurity.
Hypothesis are resumed in Table 1.
The null hypothesis (labour market participation of women is not a predictor of household economic insecurity) is true only in the breadwinner model and, to a limited extent, in the Mediterranean one. As set out by Trifiletti [15], the Universalist and liberal models require two incomes to ensure the welfare of the household. The Czech Republic has an unclear collocation in the welfare regime theory, but it is expected that it will be closer to Breadwinner models [51].
The EU Statistics on Income and Living Conditions survey (EU-SILC) provides access to information on income, social exclusion, housing conditions, labour, education and health, which is comparable across European countries [52]. The survey is representative of all persons living in private households within the national territory of the country concerned, and it is representative at the national level. In this paper, however, the population is further refined to answer the research question; thus, the subsequent analysis will refer only to the head of the household (defined as the person who is responsible for accommodation), and, if present, for his/her partner. All of the other cohabitant members of households (children, parents, relatives) were excluded from direct investigation (although their presence will be controlled via variables characterising the household, determined prior to sample selection, including: household type, presence of children or dependent adults in the household). Second, all of those households whose head was aged over 64 and/or was a person receiving a pension (disregarding their age), were excluded from the sample, based on the assumption that the economic insecurity of those households is not related to the current participation of their members in the labour market. For the same reason, all individuals under 20 were excluded from the analysis, as they are mostly inactive and likely to still be economically dependent on their parents. From the logistic analysis, same-sex households were also excluded.
These assumptions and exclusions resulted in a select sample of the population under investigation: observations included in the analysis will be presented in the following table, where sampled individuals collocate in the working-age range from 20 to 64 years old. Table 2 also shows the main characteristics of the sample by country.
Following an approach previously employed in the literature [16,53], the present study measured economic insecurity via an additive index of three variables. The three variables were dichotomised, where 1 indicates the presence of an insecurity factor, and 0 indicates absence of the same. The three variables—all measured at household level—are: (1) capacity to meet unexpected financial expenses; (2) exposure to bills arrears; (3) ability to make ends meet. The first two variables returned an objective measurement of economic stress (the household is able/not able to meet a payment), while the last measured a more subjective state of insecurity as experienced by the respondent. The last variable was further dichotomised following an already tested procedure [53]: only if the person answered “with great difficulty” or “with difficulty” was the relative household considered vulnerable to this dimension of economic insecurity. All the variables were measured at the household level. The index of economic insecurity varied from a value of 0, for “well-being” (no factor of risk), to 3, “extreme risk” (three out of three factors of economic insecurity). In the logistical model that will be discussed in the next paragraph, the dependent variable was determined using the union approach, as theorised by Akire and Foster [27], whereby a person is included in the risk category if there is at least one dimension according to which he/she can be defined economically insecure. This means that, in the present study, an individual is considered at risk of economic insecurity if the person has answered ‘yes’ to at least one of the three questions included in the index. Although the study is based on a cross-sectional perspective, the index allows for measuring the dynamic situation of economic stress and risk, not a static situation as determined by a fixed threshold. The index has been tested in previous studies focusing on insecurity and material deprivation [1,16,21,53]. The economic insecurity index was then regressed based on the labour market participation of women, measured as a variable defining a class, divided into the following categories: full-time; part-time (less than 30 h/week); careerist (over 40 h/week); short-hours contracts (less than 8 h/week); unemployed; inactive. The inclusion of women who work over 40 h/week enabled the researcher to test for the relevance of the ideal worker model, while those on short-hours contracts tested the most vulnerable position of part-time employment, which results in working poor.
The regression analysis was separated into single women and women in a couple, as it was hypothesised that the presence of a partner would be one possible buffer that might protect a household from economic insecurity, even if the woman was not directly participating in the labour market. In particular, this variable measures the partner’s contribution to household income in relation to the total income of the household (it equates to one when all the household income is provided by woman’s partner); as it measures the economic dependency of women from their partner, it can be considered a proxy for the defamilisation hypothesis. It was also hypothesised that different family compositions would also show a different likelihood of exposure to economic insecurity, but also have an influence on women’s preferences with regard to labour market participation and the type of entitlements they receive from the welfare system. In this sense, family is simultaneously an antecedent and an intervenient variable in this model. In addition, control for net household income deciles and the ownership of accommodation were included in the models as a proxy of family wealth. The second intervenient variable is the welfare buffer, which was measured on the basis of amount of monetary transfers to the household in relation to total household income (it equates to one when all the household income is provided by welfare transfers). This variable estimates the economic dependency of the household income on state support, and could be used as a proxy for capacity in the decommodification of the welfare system.
The other variables in the model can be classified under the following groups: individual-level controls (age, migrant background, a factor of interaction between age and migrant background, educational level, instability in labour market participation); and household-level controls (household type, presence of children and dependent adults, divorce/separation, homogamy). In more detail, the control variables were the following: age, in classes (20 s, 30 s, 40 s and over 50 s2); household type, also divided into classes (couples without children, couples with children, one-person households, single parents and those living with other adults, which are households where the head of the household lives with other adults without a partner being present, not considering children); and, migrant background. Regarding the final factor, it was not possible to distinguish between foreigners from affluent and non-affluent countries. This may affect the capacity of the variable to correctly predict exposure to economic insecurity of newcomers from economically disadvantaged countries. The interaction factor between age (in years) and migrant background (dummy) controls for the possible different age structure between the native and migrant populations. The presence of children under three years old and dependent adults (such as those who are permanently disabled or/and unfit to work) was controlled via a dummy variable. The two variables (children and dependent adult) control for the possible presence of care needs in the family, which might require that women do not participate in the labour market in order to take care of dependent members of her family. To control for instability in the labour market, there is a variable to measure the number of months in the last year that the person had been active in the labour market, using an index ranging from 0 to 12:0 means the person is employed for all 12 months in the year and 12 means the person is unemployed/inactive all year long. Education was measured using a three-category ordinal variable, based on ISCED classification: the first class considered all those individuals that have an ISCED level below 2, the second class includes ISCED 3, while the final category includes all those in post-secondary non tertiary and tertiary education (ISCED 4-8).
Some of the variables were present only in the models for singles or women in a couple. The dummy for divorce/separation checks for single women, to establish if they are as such after a family breakup, which is considered one of the main factors causing individuals to be exposed to insecurity. For women in a couple, homogamy among partners was controlled for using a dummy; this refers to couples who have the same educational level (ranked according to five classes). Comparative analysis was carried out by running parallel logistical models for each of the six countries, one for individuals who are cohabitating with a partner, and one for single-headed households. The main analytical aim was to assess the similarities and differences between countries in terms of factors influencing the risk of a woman living in an economically insecure household.
Finally, the main methodological issue that this article highlights concerns the level of analysis. Studies on economic insecurity and material deprivation have mostly measured risk at the household level, since resources are considered to be pooled in a couple [1,8], whereas the feminist perspective recommends focusing on the individual in order to reveal inequalities between men and women [54,55]. Using data aggregated at the household level implies the assumption that resources are equally shared across household members [56]. In a feminist approach, this is not assumed to always be the case: it is thus important to distinguish the situation of each member of the family as an individual, questioning the assumption that resources are distributed altruistically within households [30,36,56]. In addition, ‘goods’ that are important for well-being, such as educational and employment opportunities, the effects of attributes such as migrant background and age of labour market activation, and unequal care demands, cannot be evaluated at the household level [57]. The implication for the understanding of the dynamics of economic insecurity is believed to be relevant, if one questions the assumption that well-being might not be experienced by the individuals in a couple to the same level [54]. It is for this reason that the present study assumes a fully individual analytical perspective, but is inadequate for studying gender gaps within couples as the dependent variable is measured at the household level. The issue has been addressed by carrying out relevant analysis focusing mostly on women, and running separated models for different family compositions. In the opinion of the author, the dependent variable fulfils very well the aims of the analysis carried out for this study: it measures effectively the condition of economic insecurity, while the discrepancy in the level of analysis can be overcome via separate models and by being extremely clear that estimations of economic insecurity refer to the household in which the woman live, and no to her personal condition of economic insecurity. Of course, this brings with it the inability to fully assess the gender gap in economic insecurity, a question that in any case is beyond the scope of this article. In fact, it discusses the role of women’s individual labour market participation in predicting the risk of exposure to economic insecurity for the household in which they live.

4. Results

4.1. The Phenomenon of Economic Insecurity in the Six Countries

Confirming the results from previous investigations [16,32], economic insecurity is a phenomenon that is widespread across Europe, and that affects more women than men, as shown in Table 3. As already discussed, provided the measure of economic vulnerability occurs at the household level it is not possible to clearly identify a gender effect among individuals who live in a couple. A gender gap in well-being in this case refers mostly to men and women not cohabitating with a partner, as shown in Table 3.
Almost a half of the population in the six countries studied is affected by one dimension of economic vulnerability, though the magnitude of the phenomenon varies by country. The highest rate of well-being, i.e., none of the dimensions of economic insecurity are present, was found in Denmark and France (67.1%), while the lowest was identified in Spain, Italy and the Czech Republic. On the other side of the distribution, the area of extreme risk (when all the factors of economic insecurity are present) was limited, but still affected 6.5% of the sample. This varied from country to country: in the Czech Republic and Denmark it affected just a residual percentage of the population, while in Italy it occurred in approximately one individual in every 10. Focusing on the intermediate states of insecurity, it is interesting to note that approximately 20% of the population in all countries is affected by at least one dimension of it, while the medium grade risk of economic insecurity (two factors) is far more dependent on the country considered.
The wide diffusion of economic insecurity that can be seen in Table 3 might support the hypothesis that the middle classes are also at least partially affected by the phenomena [21].
The results show that, as far as gender and household composition are concerned, women who were not currently living with their partners were always more affected by economic vulnerability than their male counterparts. Couples also seemed better protected from economic vulnerability compared to the average scores for the population. In any case, the degree of protection offered by couples, and the magnitude of the disadvantage suffered by single women, varied from country to country. Previous studies [9] have shown that sharing resources at the household level might constitute one of the stronger protections against economic insecurity. Living alone affects the ability to share the costs of collective goods, or to combine incomes. Conversely, couples, even those with children, can more easily maintain an adequate standard of living; in particular, families with two income earners are relatively less vulnerable [9]. Nevertheless, the birth of a child heightens the household’s poverty risk, in two ways: the addition of another household member increases the financial burden on the household; and, the need to provide parental care impairs the parents’ earning capacity, in particular during the child’s pre-school years (under three years old). Parents, though usually the mother, may reduce their time in paid work or avoid it altogether, with a loss of market income being the consequence, which affects the household’s exposure to economic insecurity. It can thus be assumed that women’s risk of exposure to economic insecurity is dependent on several factors, including the structure of the family, the presence of children, and the way in which paid work is divided amongst the household.
Descriptive analysis is not sufficient to test the relevant hypothesis formulated by Trifiletti [15]; the following paragraph will examine this matter further based on logistic regression analysis.

4.2. Determinants of Economic Insecurity Amongst Women

The analysis of the interactions between country, household type and labour market participation confirm the previous descriptive results (see Table A3 and Figure 2). Women in a couple were found to be less likely to experience economic insecurity, but the magnitude of the difference between single women and women in a couple varied by country and according to the women’s labour market participation. It is interesting to note how having a different status in the labour market influences this difference: for those women who are employed more than 40 h per week (the so-called careerists), the risk of living in economic insecure household is almost equal to those in a couple. However, typically, when the woman is participating in the labour market the risk of living in an economically insecure household is reduced, while unemployment is the labour market condition that exposes women most (especially if they are not cohabitating with their partners). Having a job of one’s owns seems to be best strategy for women wishing to reduce the economic insecurity of their households.
But what are the determinants that inform these trends? The next step was to consider the factors that influence the different incidences of the phenomena observed across countries and across family compositions.

4.3. The Logistic Regressions for Single Women and Women in a Couple3

Taking single women first, the determinants across countries varied slightly, but certain factors, such as household type and wealth, were quite stable in their ability to predict the risk of economic insecurity in the countries considered. The results of logistic regression in Table 4 showed that women who lived with other adults in all countries were more exposed to the risk of living in economic insecure households (although the risk varied depending on context, while being a single parent had a significant effect in all countries, even if for Spain it was significant at the 0.1 level (+6.1% compared to women who live alone). Being a single parent exposed women to greater risk of economic insecurity than living alone in all the countries considered; in Italy and Spain only, women who lived with other adults had a significantly lower risk of economic insecurity compared to lone parents (−11.5% and −6.3% respectively), confirming the protection offered by families. Surprisingly, the presence of children under three years old in the family was not found to be significant as a risk factor in itself in all countries: in Denmark, it even protects against economic insecurity (−25.1%).
The second transversal factor was the wealth of the household, which was controlled by the household income deciles and the dummy for rented accommodation. When households do not own the house in which they live, the risk of economic insecurity is always significant, and increases the risk of economic insecurity (ranging from 15.5% of Denmark to 9.2% of Spain) At the same time, a loss of one income decile increases the exposure to economic insecurity by approximately 5% in each of the countries studied, with the notable exception of Denmark, where the risk increases by 8.4%.
For analytical purposes, it is interesting to observe how determinants varied across the countries considered in the study. Focusing on the main independent variable, unemployment represents a condition that exposes single women to economic insecurity in Denmark, France and Italy: unemployment was a disadvantageous condition in comparison to full-time employment (+25.9%, +20.3% and +20.9%) and part-time employment (in France +16.6% and in Italy +16.8%). In the case of France and Italy, unemployed women were always more exposed to economic insecurity compared to all other labour market conditions, but especially relative to careerists (+28.3% in FR, and +29.8% in IT); the same occurs in Czech Republic (+19.1%). In Italy and France, being employed for more than 40 h a week protected against economic insecurity, even compared to full-time employment (−8.9% in IT and −8% in France). This can be considered a strong indication that the labour market plays a role in protecting against the exposure of women to economic insecurity, if they do not cohabitate with their partner. A low work intensity (working less than 8 h per week) was significant in predicting the risk of economic insecurity in France, Italy, and the Czech Republic, with approximately 16% greater risk of exposure to economic insecurity compared to those who were employed full-time. Spain and the United Kingdom were the only countries where the employment condition was not significant in predicting exposure to economic insecurity.
Turning now to individual characteristics, the most important factor was found to be education. With the notable exception of Denmark, in all the other countries educational level played a role in predicting the risk of economic insecurity, but in Czech Republic and France no significant difference emerged between those who have a lower secondary and an upper secondary educational level. Having been through a divorce was found to be a significant factor in almost all of the countries (ranging from +5% in CZ to +13.3% in ES), with the notable exception of the United Kingdom, where the factor is not significant. In regard to economic dependency as linked to welfare, all countries showed an increased risk of economic insecurity when women lived in households that were dependent on welfare transfers, with the relevant exception of Denmark (where the variable was not significant). Nevertheless, in all the countries, women who lived alone fared far better than those who had to share accommodation with other adults, or when they were single, with their children.
As already shown, women in a couple were less affected by economic insecurity, but they showed a similar risk structure when analysing the transversal determinants of this phenomenon, as shown in the logistic regression of Table 5. First of all, in all countries, couples with children were more frequently exposed to economic insecurity compared to childless couples, despite the magnitude of the risk varying slightly across contexts (+13.1% and +12.5% for FR and the UK respectively, but an increase of +4.9% in Spain). Having children younger than three years old did not constitute an additional risk source in any of the countries. In three countries (the Czech Republic, Italy, and the United Kingdom) the presence of a dependent adult exposed the family to economic insecurity, although the magnitude of the risk varied (+10.6% for CZ, +14.5% for IT, and +21.4% for the UK). Again, household income deciles and rented accommodation were sources of risk in all contexts: in United Kingdom the increase in risk was +17.5%, and in the Czech Republic only +9.5%.
Focusing on the main independent variable, being unemployed constituted a risk factor for women in a couple, compared to those in full-time employment, only in the Czech Republic, Denmark, and Spain (+22.2% in CZ, +14.6% in DK, and +9.3% in ES). Being employed with a short-hours contract increased the economic insecurity of families in Spain, in Denmark, in France, and with limited significance in Italy. Whereas, in the United Kingdom, it reduced the likelihood of living in economically insecure households (compared to full-time −11.3% and to 40 h/week, −11%). Being in part-time employment was also a source of insecurity for Spanish women (+4.6% compared to full-time), while in Italy and the United Kingdom being part-time reduced the risk of being exposed to a condition of insecurity compared to being in full-time employment (−3.3% in IT, −5.6% in the UK). The case of the UK is particularly interesting: for women there, being inactive was the most favourable labour market condition protecting against economic insecurity (−19.5% even compared to full-time).
As evidenced in the case of single women, education was the most important individual factor explaining the risk of economic insecurity, with the exception of Denmark, where only lower secondary educated women were 5.3% more likely to live in economically insecure households. Women with low educational achievement were always more exposed to the risk of living in economic insecure families, but, focusing on homogamy, it was in Italy that homogamous families were less likely to be exposed to the phenomenon (−3.2%) and with a limited significance in Spain (–2.1%).
Turning now to the economic dependency factors, it was only in the Czech Republic that economic dependency on welfare did not represent a source of economic insecurity for women in a couple, while being economically dependent from their partner was only a source of economic insecurity in France (+6.8%). In the Czech Republic, economic dependency from a partner even had a protective effect against exposure to economic insecurity (–6.9%).

5. Discussion

The analysis of the case of single women provided evidence for different factors that determine the exposure of single women to economic insecurity across the six countries, apart from those that determine the phenomenon transversally across contexts (e.g., wealth and family breakup). The three factors on which the countries varied in terms of their influence on exposure to economic insecurity were: labour market participation of women; education level; and, the effect of economic dependency on welfare. Four main clusters could be identified from the results regarding single women:
  • Denmark: was the only country in which education was not significant in predicting exposure to economic insecurity, which was mainly predicted by being unemployed. Women who lived in one-person households were less likely to be exposed to the phenomenon (−21.6% compared to living with other adults, −18.1% compared to single parents). Economic dependency on welfare was not a source of economic insecurity.
  • The Czech Republic: had the most similar profile to Denmark, although it differed in that the role of education is particularly strong (passing from lower secondary to tertiary education reduces the risk of being economically insecure by about 25.3%). Being active in the labour market to a limited extent (i.e., less than 8 h/week) was a source of economic insecurity, while economic dependency from welfare was only slightly significant as an explanation for economic insecurity.
  • Italy and France: in these two countries, reduced participation in the labour market (unemployment or short-hours contracts) increased exposure to economic insecurity, while working more than 40 h/week reduce the risk of living in economically insecure households. Educational level and economic dependency from welfare effect the risk of economic insecurity.
  • Spain and the United Kingdom: the labour market participation of women did not predict the risk of economic insecurity. Economic insecurity in single women’s households can be predicted by educational level, economic dependency from welfare, and the structure of those households.
The analysis of women in a couple confirmed the transversal risk factors found in the analysis of single women: families with children and wealth were the two factors that played a role in increasing the likelihood of exposure to economic insecurity in all countries. Countries differed in terms of education, women’s labour market participation, and economic dependency. The clusters that emerged regarding women in a couple were as follows:
  • The Czech Republic: was the only country in which economic dependency did not increase the likelihood of economic insecurity for women in a couple, nor for welfare or partners. By contrast, being economically dependent on their partner’s income reduced women’s risk of economic insecurity. Labour market participation by women was relevant for explaining economic insecurity only when they were unemployed or inactive.
  • Denmark: the labour market played a prevalent role (especially in the case of unemployment and low work intensity), but education was only relevant to the low education profiles, and to a reduced extent (+5.3% compared to those with a tertiary degree), confirming the country’s egalitarian profile. Economic dependency on welfare was a source of economic insecurity (+13.8%).
  • Spain and France: lack of participation in the labour market increased economic insecurity under conditions such as part-time employment and unemployment (ES), and this was exacerbated when individuals had short-hours contracts (ES and FR), and when a woman was economically dependent on her partner (FR). Being dependent on welfare was a strong predictor of economic insecurity (+22.6% for Spain, +25.9% for France). Women with tertiary degrees were less likely to be economically insecure.
  • Italy and the United Kingdom: being part-time appears to be a good strategy for women, as it was shown to reduce their economic insecurity, even compared to full-time workers. It can be argued that this is related to unpaid work and care loads, as economic insecurity was strongly associated with the presence of a dependent adult in the household (but not with the presence of children under three years old). Being dependent on welfare was a source of economic insecurity, but with a reduced magnitude compared to Spain and France (+19.9% for Italy and 15.1% for UK). Education can predict the risk that one will become economically insecure.
Comparing the findings of this study with Trifiletti’s [15] theory (Table 6), it is clear that the previous hypotheses (Table 1) were only partially confirmed. First, the Mediterranean cluster was very ambiguous: Spain and Italy showed more differences than similarities, and Italy was actually more similar to the United Kingdom in the case of women in a couple, confirming previous empirical results [16]. The results of the present study also revealed that two countries showed strong segmentation by education level, and the role of labour market participation in these instances was clear. When women are in a couple, part-time contracts are a good strategy in Italy and in the United Kingdom to address the imbalance arising from unpaid work in the context of a strong segmentation by education (in line with Mediterranean W.R.). By contrast, in Spain part-time work is not a good strategy for women in a couple, as it increases their risk of living in economically insecure households; although labour market participation was not a source of economic insecurity when the partner was absent as is the case in the United Kingdom. France is closer to the Mediterranean model than the Breadwinner model: being economically dependent from a partner is a source of economic insecurity, while the other welfare pillar (labour market and economic dependency from welfare) shows trends that lie mid-way between Italy and Spain. The Czech Republic has the most hybrid profile, as it is the closest country to Denmark, separated only by the preeminent role of education in the post-soviet country. Despite this, the effect of economic dependency on reducing the risk of economic security arguably makes the Czech Republic closer to the Breadwinner W.R. Finally, the relatively lower diffusion of economic insecurity amongst Danish single women confirmed that Denmark is a country where women can better cope with economic insecurity without a partner, confirming the relatively lower importance of economic dependence on a partner. It is also important to highlight that it is the country in which educational level counts less, demonstrating that economic insecurity is also a matter of how different women are relative to one another in the other national contexts.

5.1 Conclusions

This article has addressed the issue of economic insecurity by assuming a feminist approach, which required an individual-level analysis [30,54,55,56,57]. The analysis mostly focused on women in an attempt to first, isolate the main determinants of risk of being part of an economic insecure household, and, second, to identify clusters of countries based on the different factors that expose women to the phenomenon of economic insecurity. The role of economic dependency both on a partner and on welfare has also been investigated.
Beginning with the descriptive level analysis, women in a couple were found to fare better than women living alone: Figure 2 shows that the gap between women in a couple and single women also depends on the type of labour market participation in which they are engaged. Two factors were identified as transversal in predicting economic insecurity both for women in a couple and single women (Table 4 and Table 5): household composition, and the wealth of the household in which they live, signified by living in rented accommodation and measured by household income deciles. However, countries varied significantly in terms of what type of labour market participation increases or decreases exposure to economic insecurity, as well in the role played by economic dependency on welfare and on a partner.
Trifiletti [15] identified four ideal types of welfare regime that try to explain the different roles of particular ‘pillars’ (state, family and market) in ensuring the welfare of women across Europe: Universalistic, Breadwinner, Mediterranean and Liberal Welfare regimes. However, the theory only partially explains the results of this study: Spain and Italy showed more differences than expected, while the United Kingdom appeared closer to the one-and-a-half model that Trifiletti [15] associates with Mediterranean countries. This might be explained by the costs of private care services in the UK, which might make it more convenient for women to reduce or even stop their participation in the labour market if they are needed to provide unpaid childcare within their families. France also appeared consistent with the Mediterranean model, as economic dependency on a partner was a source of economic insecurity, as well as low labour market participation. Despite its hybridity, the Czech Republic can be interpreted as exemplifying the Breadwinner model: for women in a couple, economic dependency on welfare or on a partner was not a source of economic insecurity. Finally, Denmark represented the closest empirical representation of the Universalistic regime, as a strong role was played by women’s labour market participation, while education level was less influential, reducing the role of stratification in predicting exposure to social insecurity (in contrast with the other countries). Finally, the empirical results showed the lower heuristic capacity of the liberal model when used for interpreting the six countries.

5.2. Strength and Limitations

As already observed, the main strength of the article lies in its attempt to study the phenomenon of economic insecurity using a feminist perspective, which examines how well individuals fare, and not households. Despite the methodological limitations, which mean the data is derived from a dependent variable measured at household level, the study afforded some insights into what factors can expose individual women to the risk of living in insecure families, given the types of households in which they live.
However, the present study has an important limitation, as it was not possible to obtain information about the distribution of unpaid work within the household, as EU-SILC does not offer a satisfactory measure for this purpose. This lack of information does not allow for measuring the total amount of work that is completed in the household (comprised of both paid and unpaid work), and the share of it that is delegated to childcare, housekeeping services, or to the extended family instead of being provided by members of the household. This limitation may affect the relevance of the analysis. From a feminist perspective, only a complete assessment of the amount of work provided in the family (paid and unpaid) is able to inform an understanding of the dynamics of intra-household inequality. The understanding of the dynamics of inequality is thus only partial if unpaid work is not taken into account, as individual and household well-being depends heavily on the non-market sphere [59]. However, it was not possible within the scope of this study to overcome this limitation, as the relevant information was not provided in the original dataset.

5.3. Future Research Directions

The findings also open up new directions for future research. The differences in women’s labour market participation are crucial evidence of discrepancies between countries, more so than the role of welfare and partners. Further evidence could be collected regarding the quality of women’s employment and its effects on economic insecurity, which would help to further differentiate countries; for example, the extent to which choice led to non-standard employment can explain the role played by part-time employment in protecting against economic insecurity, or, the role played by segregation of women’s jobs, which are usually associated with short-hours contracts, few career prospects, and precariousness.
Finally, the results confirmed that the ideal types work well at the theoretical level, but welfare regimes theory must always be adapted to the empirical configurations of different countries, which only partially conform to the theoretical clusters. The differences between Spain and Italy constituted the clearest evidence of this, although other countries, including the United Kingdom, France and the Czech Republic did not clearly fit with the welfare regime theory. Thus, future research should focus on developing clusters based more on empirical analysis than on theoretical reflection alone. Relying on exploratory methods, as suggested by Bambra [60], could be a fruitful line of investigation for scholars seeking to investigate the differences between welfare regimes.

Acknowledgments

The article received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 747433. A preliminary draft of this article was developed during a visit to LISER (Luxembourg Institute of Socio-Economic Research). During this visit in October 2016, it received support under the European Commission’s 7th Framework Programme (FP7/2013-2017) under grant agreement No. 312691, InGRID—Inclusive Growth Research Infrastructure Diffusion. The present research would not have been possible without the help and support of Alessio Fusco and Maike van Damme, senior scholars of LISER (Luxembourg Institute for Socio-Economic Research), the institution that hosted me under the InGRID Grant scheme. I also thank Margarita Leon and Costanzo Ranci for reading this article in draft and for providing invaluable comments. They are excused for subsequent failings.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

Table A1. Correlation table for the variables occurring in the model studying single women.
Table A1. Correlation table for the variables occurring in the model studying single women.
Economic Insecurity IndexLabor Market ParticipationAge in ClassMigrant Background (Dummy)Age * Migrant (Interaction Factor)Educational Level in ClassInstability in LM (Dummy)Type of HouseholdDivorced/Separated (Dummy)Presence of 3 y. o. Children (Dummy)Presence of Dependent Adult (Dummy)Household Income DecileRented Accommodation (Dummy)Economic Dependency from Welfare Transfer
Economic Insecurity Index1.00
Labor Market Participation0.211.00
Sig.0.00
Age in class0.090.001.00
Sig.0.000.97
Migrant background (dummy)0.120.080.121.00
Sig.0.000.000.00
Age * Migrant (interaction factor)0.120.090.200.901.00
Sig.0.000.000.000.00
Educational level in class0.290.27−0.170.020.021.00
Sig.0.000.000.000.000.00
Instability in LM (dummy)0.240.770.000.080.080.311.00
Sig.0.000.000.790.000.000.00
Type of Household in class0.11−0.010.260.030.04−0.060.001.00
Sig.0.000.030.000.000.000.000.58
Divorced/Separated (dummy)0.13−0.05−0.13−0.01−0.020.02−0.060.081.00
Sig.0.000.000.000.240.000.000.000.00
Presence of 3 y. o. children (dummy)0.040.070.410.070.11−0.090.090.33−0.091.00
Sig.0.000.000.000.000.000.000.000.000.00
Presence of dependent adult (dummy)0.090.23−0.15−0.02−0.020.130.24−0.090.03−0.061.00
Sig.0.000.000.000.000.000.000.000.000.000.00
Household income decile0.420.250.110.110.100.290.30−0.030.20−0.030.101.00
Sig.0.000.000.000.000.000.000.000.000.000.000.00
Rented accommodation (dummy)0.260.090.230.210.220.080.100.010.110.060.050.311.00
Sig.0.000.000.000.000.000.000.000.040.000.000.000.00
Economic dependency from welfare transfer0.260.340.030.020.020.170.400.100.120.100.280.360.281.00
Sig.0.000.000.000.000.000.000.000.000.000.000.000.000.00
Table A2. Correlation table for the variables occurring in the model studying women in a couple.
Table A2. Correlation table for the variables occurring in the model studying women in a couple.
Economic Insecurity IndexLabor Market ParticipationAge in ClassMigrant Background (Dummy)Age * Migrant (Interaction Factor)Educational Level in ClassInstability in LM (Dummy)Type of Household in ClassPresence of 3 y. o. Children (Dummy)Presence of Dependent Adult (Dummy)Homogamous Family (Dummy)Economic Dependency from Welfare TransferHousehold Income decileRented Accommodation (Dummy)Economic Dependency from Partner’s Income
Economic Insecurity Index1.00
Labor Market Participation0.211.00
Sig.0.00
Age in class0.090.001.00
Sig.0.000.97
Migrant background (dummy)0.120.080.121.00
Sig.0.000.000.00
Age * Migrant (interaction factor)0.120.090.200.901.00
Sig.0.000.000.000.00
Educational level in class0.290.27−0.170.020.021.00
Sig.0.000.000.000.000.00
Instability in LM (dummy)0.240.770.000.080.080.311.00
Sig.0.000.000.790.000.000.00
Type of Household in class0.11−0.010.260.030.04−0.060.001.00
Sig.0.000.030.000.000.000.000.58
Presence of 3 y. o. children (dummy)0.040.070.410.070.11−0.090.090.331.00
Sig.0.000.000.000.000.000.000.000.00
Presence of dependent adult (dummy)0.090.23−0.15−0.02−0.020.130.24−0.09−0.061.00
Sig.0.000.000.000.000.000.000.000.000.00
Homogamous family (dummy)0.03−0.05−0.01−0.01−0.02−0.07−0.060.01−0.030.001.00
Sig.0.000.000.100.060.000.000.000.030.000.88
Economic dependency from partner’s income−0.17−0.090.09−0.05−0.04−0.12−0.100.030.10−0.12−0.141.00
Sig.0.000.000.000.000.000.000.000.000.000.000.00
Household income decile0.420.250.110.110.100.290.30−0.03−0.030.100.11−0.311.00
Sig.0.000.000.000.000.000.000.000.000.000.000.000.00
Rented accommodation (dummy)0.260.090.230.210.220.080.100.010.060.050.07−0.160.311.00
Sig.0.000.000.000.000.000.000.000.040.000.000.000.000.00
Economic dependency from welfare transfer0.150.29−0.030.040.040.190.34−0.080.090.30−0.07−0.130.170.061.00
Sig.0.000.000.000.000.000.000.000.000.000.000.000.000.000.00
Table A3. Logistic regression on household economic insecurity—interaction between labor market participation, household type and country (Marginal Effect).
Table A3. Logistic regression on household economic insecurity—interaction between labor market participation, household type and country (Marginal Effect).
MarginStandard ErrorZ Statistic
full-time # In Couple # Czech Republic0.3740.0132.07
full-time # In Couple # Denmark0.1530.0117.35
full-time # In Couple # Spain0.3150.0129.10
full-time # In Couple # France0.2710.0128.33
full-time # In Couple # Italy0.3350.0136.38
full-time # In Couple # United Kingdom0.3200.0123.05
full-time # Single # Czech Republic0.6180.0228.07
full-time # Single # Denmark0.4010.0313.24
full-time # Single # Spain0.4840.0222.36
full-time # Single # France0.5310.0227.12
full-time # Single # Italy0.5370.0234.91
full-time # Single # United Kingdom0.5930.0224.88
part-time # In Couple # Czech Republic0.3540.057.36
part-time # In Couple # Denmark0.1530.026.24
part-time # In Couple # Spain0.4970.0224.25
part-time # In Couple # France0.3520.0217.92
part-time # In Couple # Italy0.4000.0223.59
part-time # In Couple # United Kingdom0.3320.0220.29
part-time # Single # Czech Republic0.7580.0710.15
part-time # Single # Denmark0.4090.103.90
part-time # Single # Spain0.6390.0415.35
part-time # Single # France0.7080.0320.34
part-time # Single # Italy0.7370.0324.44
part-time # Single # United Kingdom0.8010.0233.90
Over 40 h # In Couple # Czech Republic0.3470.0215.06
Over 40 h # In Couple # Denmark0.1330.026.88
Over 40 h # In Couple # Spain0.3550.0214.68
Over 40 h # In Couple # France0.2110.0212.12
Over 40 h # In Couple # Italy0.3370.0214.74
Over 40 h # In Couple # United Kingdom0.2470.0212.99
Over 40 h # Single # Czech Republic0.4920.0413.38
Over 40 h # Single # Denmark0.3930.076.02
Over 40 h # Single # Spain0.4890.0411.62
Over 40 h # Single # France0.3390.049.53
Over 40 h # Single # Italy0.4420.0314.29
Over 40 h # Single # United Kingdom0.3680.0311.53
unemployed # In Couple # Czech Republic0.7460.0419.24
unemployed # In Couple # Denmark0.4250.067.69
unemployed # In Couple # Spain0.6770.0240.06
unemployed # In Couple # France0.5330.0413.80
unemployed # In Couple # Italy0.6740.0228.01
unemployed # In Couple # United Kingdom0.6940.0710.54
unemployed # Single # Czech Republic0.8860.0518.53
unemployed # Single # Denmark0.8000.0810.00
unemployed # Single # Spain0.8240.0330.54
unemployed # Single # France0.9020.0329.13
unemployed # Single # Italy0.8900.0333.12
unemployed # Single # United Kingdom0.9570.0238.96
inactive # In Couple # Czech Republic0.5180.0226.45
inactive # In Couple # Denmark0.3180.0213.32
inactive # In Couple # Spain0.5330.0137.92
inactive # In Couple # France0.5120.0225.41
inactive # In Couple # Italy0.6160.0159.52
inactive # In Couple # United Kingdom0.5450.0227.47
inactive # Single # Czech Republic0.8610.0325.05
inactive # Single # Denmark0.7090.0415.83
inactive # Single # Spain0.7240.0325.84
inactive # Single # France0.7810.0324.55
inactive # Single # Italy0.7010.0229.18
inactive # Single # United Kingdom0.9200.0162.40
Less than 8 h # In Couple # Czech Republic0.4790.0412.23
Less than 8 h # In Couple # Denmark0.4000.076.06
Less than 8 h # In Couple # Spain0.6130.0227.41
Less than 8 h # In Couple # France0.5230.0316.89
Less than 8 h # In Couple # Italy0.5690.0318.84
Less than 8 h # In Couple # United Kingdom0.4000.048.94
Less than 8 h # Single # Czech Republic0.8890.0516.97
Less than 8 h # Single # Denmark0.5930.096.27
Less than 8 h # Single # Spain0.7590.0418.46
Less than 8 h # Single # France0.8240.0419.92
Less than 8 h # Single # Italy0.8450.0421.42
Less than 8 h # Single # United Kingdom0.8410.0615.25
* All coefficients are significant at 95%, p < 0.001.
Table A4. Logistic regression on household economic insecurity—Single Women (Marginal Effect).
Table A4. Logistic regression on household economic insecurity—Single Women (Marginal Effect).
Predicted Probability on Economic Insecurity Index
Labor Market Participation Czech Republicp-ValueDenmarkp-ValueSpainp-ValueFrancep-ValueItalyp-ValueUnited Kingdomp-Value
part-time vs. full-time0.1320.07−0.0190.86−0.0340.410.0360.380.0410.250.0060.83
40 plus vs. full-time−0.0190.610.0860.21−0.0520.16−0.0800.04−0.0890.00−0.0300.27
unemployed vs. full-time0.1720.060.2590.040.0080.910.2030.000.2090.000.0820.43
inactive vs. full-time0.1010.380.1680.15−0.0660.350.0860.30−0.0070.920.0010.99
8 less vs. full-time0.1710.030.0670.520.0550.270.1520.010.1600.000.0260.71
40 plus vs. part-time−0.1510.050.1050.38−0.0190.71−0.1170.02−0.1300.00−0.0360.31
unemployed vs. part-time0.0400.710.2770.070.0420.560.1660.020.1680.010.0760.46
inactive vs. part-time−0.0310.810.1870.20−0.0320.680.0500.56−0.0470.48−0.0050.95
8 less vs. part-time0.0390.700.0860.540.0890.130.1150.070.1190.040.0200.78
unemployed vs. 40 plus0.1910.050.1730.230.0600.410.2830.000.2980.000.1120.30
inactive vs. 40 plus0.1200.320.0820.54−0.0130.870.1670.060.0820.250.0300.75
8 less vs. 40 plus0.1900.03−0.0190.870.1080.070.2320.000.2490.000.0560.45
inactive vs. unemployed−0.0710.48−0.0900.45−0.0740.12−0.1170.10−0.2160.00−0.0810.32
8 less vs. unemployed−0.0010.99−0.1920.160.0470.44−0.0510.49−0.0490.46−0.0560.60
8 less vs. inactive0.0700.54−0.1010.390.1210.060.0650.410.1670.020.0250.78
Age in class
40 s vs. over 500.1410.000.1250.020.1010.000.0790.010.0030.910.0830.00
30 s vs. over 500.0420.360.1540.040.1320.000.0450.240.0430.140.1080.00
20 s vs. over 500.0910.130.0800.290.0650.150.0740.080.0660.070.0680.11
30 s vs. 40s−0.0990.020.0290.710.0310.36−0.0350.330.0400.150.0250.36
20 s vs. 40s−0.0500.40−0.0450.57−0.0350.44−0.0060.880.0630.09−0.0150.70
20 s vs. 30s0.0490.42−0.0740.40−0.0660.150.0290.500.0230.55−0.0400.29
Migrant background (dummy)
1−0.2820.42−0.1770.410.0680.510.0290.810.1400.020.0660.48
+SD−0.0300.45−0.0260.440.0160.530.0060.820.0430.040.0160.50
Marginal−0.2640.44−0.1840.440.0710.530.0290.820.1520.040.0690.50
Age * Migrant (interaction factor)
10.0590.330.0150.850.0090.780.0190.640.0140.57−0.0390.09
+SD0.0360.340.0060.850.0080.780.0140.650.0130.57−0.0410.09
Marginal0.0620.350.0150.850.0090.780.0190.650.0140.57−0.0380.09
Educational level in class
(upper) secondary vs. tertiary0.1810.000.0240.610.1390.000.0920.000.0520.030.1040.00
lower secondary vs. tertiary0.2530.000.0020.980.2300.000.1030.010.1920.000.1710.00
lower secondary vs. (upper)secondary0.0720.17−0.0220.700.0910.010.0110.750.1400.000.0670.04
Type of Household in class
Living with other adults vs. One person household0.2060.000.2160.000.1760.000.1700.000.1620.000.1650.00
Single Parent vs. One person household0.1410.000.1810.000.0610.080.1620.000.0990.000.1520.00
Single Parent vs. Living with other adults−0.0650.09−0.0350.64−0.1150.00−0.0080.83−0.0630.03−0.0140.66
Divorced/Separated (dummy)
10.0500.080.0980.020.1330.000.0560.030.0940.000.0220.29
+SD0.0250.090.0470.030.0670.000.0270.030.0460.000.0110.29
Marginal0.0520.090.0990.030.1440.000.0570.030.0990.000.0220.30
Presence of 3 y. o. children (dummy)
10.0950.22−0.2510.030.0040.940.0710.280.0600.190.0680.15
+SD0.0220.26−0.0420.070.0010.940.0170.300.0130.200.0240.17
Marginal0.1020.26−0.2720.070.0040.940.0740.300.0610.210.0720.17
Presence of dependent adult (dummy)
10.0340.73−0.0510.600.0330.55−0.0870.210.0460.490.0820.09
+SD0.0090.73−0.0160.600.0080.55−0.0190.210.0070.490.0280.11
Marginal0.0340.74−0.0520.600.0340.55−0.0850.210.0470.500.0870.12
Instability in LM (discrete, 12 values)
1−0.0050.620.0040.650.0070.24−0.0010.83−0.0020.71−0.0030.65
+SD−0.0210.620.0210.650.0360.23−0.0060.83−0.0100.71−0.0170.65
Marginal−0.0050.620.0040.650.0070.24−0.0010.83−0.0020.71−0.0030.65
Household income decile
10.0590.000.0840.000.0420.000.0430.000.0500.000.0360.00
+SD0.1290.000.1430.000.1030.000.0870.000.1140.000.0720.00
Marginal0.0610.000.0850.000.0430.000.0440.000.0510.000.0370.00
Rented accommodation (dummy)
10.0990.000.1550.000.0920.000.1540.000.1350.000.1210.00
+SD0.0470.000.0770.000.0390.000.0800.000.0630.000.0630.00
Marginal0.1070.000.1600.000.0970.000.1680.000.1450.000.1340.00
Economic dependency from welfare transfer
10.1410.030.0220.860.1240.000.1460.010.1180.000.1460.00
+SD0.0450.060.0080.860.0440.010.0510.020.0340.010.0670.00
Marginal0.1600.070.0220.860.1330.010.1590.020.1260.010.1670.00
Average Marginal predictionNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt risk
0.3510.650.5050.500.3850.620.3970.600.3910.610.2960.70
Table A5. Logistic regression on household economic insecurity—Women in a couple (Marginal Effect).
Table A5. Logistic regression on household economic insecurity—Women in a couple (Marginal Effect).
Predicted Probability on Economic Insecurity Index
Labor Market Participation Czech Republicp-ValueDenmarkp-ValueSpainp-ValueFrancep-ValueItalyp-ValueUnited Kingdomp-Value
part-time vs. full-time−0.0260.58−0.0210.380.0460.030.0190.32−0.0330.05−0.0560.00
40 plus vs. full-time0.0000.990.0120.640.0050.84−0.0110.58−0.0480.04−0.0030.89
unemployed vs. full-time0.2220.000.1460.010.0930.000.0430.320.0490.18−0.1040.14
inactive vs. full-time0.0980.050.0480.20−0.0010.980.0130.74−0.0020.96−0.1950.00
8 less vs. full-time0.0530.210.1030.060.0730.010.0640.030.0560.07−0.1130.00
40 plus vs. part-time0.0260.600.0330.31−0.0410.15−0.0300.23−0.0160.540.0530.03
unemployed vs. part-time0.2480.000.1670.000.0460.180.0240.590.0820.03−0.0480.50
inactive vs. part-time0.1250.050.0700.09−0.0470.16−0.0060.880.0310.37−0.1390.00
8 less vs. part-time0.0790.180.1240.030.0270.350.0450.160.0880.01−0.0580.12
unemployed vs. 40 plus0.2210.000.1340.030.0870.020.0540.240.0980.02−0.1010.17
inactive vs. 40 plus0.0980.070.0370.40−0.0060.870.0240.560.0470.23−0.1920.00
8 less vs. 40 plus0.0530.260.0910.120.0680.040.0750.030.1040.00−0.1100.01
inactive vs. unemployed−0.1230.01−0.0980.06−0.0930.00−0.0300.41−0.0510.04−0.0910.13
8 less vs. unemployed−0.1680.01−0.0430.55−0.0190.510.0210.640.0060.87−0.0090.89
8 less vs. inactive−0.0450.360.0550.340.0740.010.0510.180.0570.090.0820.04
Age in class
40 s vs. over 500.1310.000.0520.010.0200.210.0490.010.0050.710.0430.04
30 s vs. over 500.1260.000.0290.220.0120.530.0870.000.0010.940.0680.00
20 s vs. over 500.1040.000.0250.370.0350.260.0260.280.0210.470.1150.00
30 s vs. 40 s−0.0050.81−0.0230.32−0.0080.650.0390.03−0.0040.780.0240.25
20 s vs. 40 s−0.0280.42−0.0270.380.0140.64−0.0230.330.0150.580.0720.02
20 s vs. 30 s−0.0220.49−0.0040.890.0220.45−0.0610.000.0190.460.0470.08
Migrant background (dummy)
10.1080.460.0660.530.1200.070.1220.100.1070.07−0.0750.26
+SD0.0140.460.0100.500.0310.080.0230.090.0280.07−0.0210.28
Marginal0.1060.460.0590.490.1200.080.1150.080.1060.07−0.0790.29
Age * Migrant (interaction factor)
1−0.0230.450.0070.760.0040.80−0.0100.570.0180.230.0030.86
+SD−0.0140.450.0040.760.0050.80−0.0080.570.0200.230.0030.86
Marginal−0.0240.460.0070.760.0040.80−0.0100.570.0180.230.0030.86
Educational level in class
(upper) secondary vs. tertiary0.1400.000.0230.140.1040.000.1210.000.0950.000.0820.00
lower secondary vs. tertiary0.3030.000.0530.030.2120.000.1890.000.2100.000.1690.00
lower secondary vs. (upper)secondary0.1630.000.0300.200.1090.000.0670.000.1150.000.0870.00
Instability in LM (discrete, 12 values)
1−0.0060.13−0.0030.220.0000.940.0000.990.0020.540.0160.00
+SD−0.0290.13−0.0150.21−0.0010.940.0000.990.0090.540.0820.00
Marginal−0.0060.13−0.0030.220.0000.940.0000.990.0020.540.0160.00
Type of Household in class
Couple with children vs. Couple without Children0.0860.000.0950.000.0490.000.1310.000.0720.000.1250.00
Presence of 3 y. o. children (dummy)
10.0120.67−0.0300.160.0300.120.0040.81−0.0180.290.0150.50
+SD0.0050.67−0.0100.180.0110.120.0020.81−0.0070.290.0060.50
Marginal0.0120.67−0.0320.190.0300.120.0040.81−0.0180.290.0150.50
Presence of dependent adult (dummy)
10.1060.000.0260.410.0420.110.0330.310.1450.000.2140.00
+SD0.0290.000.0070.400.0100.110.0070.300.0210.000.0490.00
Marginal0.1040.000.0240.390.0420.110.0320.300.1440.000.1970.00
Homogamous family (dummy)
10.0380.090.0120.50−0.0210.09−0.0080.55−0.0320.000.0140.39
+SD0.0160.080.0050.49−0.0100.09−0.0040.55−0.0150.000.0060.39
Marginal0.0370.080.0120.49−0.0210.09−0.0080.55−0.0320.000.0140.38
Economic dependency from partner’s income
1−0.0690.040.0590.150.0100.630.0680.030.0240.21−0.0060.82
+SD−0.0180.040.0120.120.0030.630.0150.020.0070.21−0.0020.82
Marginal−0.0710.050.0540.120.0100.630.0650.020.0240.21−0.0060.83
Household income decile
10.0490.000.0330.000.0420.000.0460.000.0530.000.0440.00
+SD0.1300.000.0940.000.1180.000.1240.000.1440.000.1190.00
Marginal0.0490.000.0310.000.0420.000.0450.000.0530.000.0430.00
Rented accommodation (dummy)
10.0950.000.1260.000.1530.000.1310.000.1450.000.1750.00
+SD0.0320.000.0430.000.0500.000.0570.000.0530.000.0730.00
Marginal0.0930.000.1050.000.1520.000.1220.000.1440.000.1630.00
Economic dependency from welfare transfer
10.0220.730.1380.040.2260.000.2590.000.1990.000.1510.00
+SD0.0040.720.0240.020.0610.000.0460.000.0380.000.0360.00
Marginal0.0220.720.1140.020.2300.000.2360.000.2000.000.1410.00
Average Marginal predictionNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt riskNo riskAt risk
Resumed in Table 3 in text0.5810.420.8130.190.5320.470.6660.330.5360.460.6360.36

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1
The phenomenon of vulnerability is inherently multidimensional [5,21,27,28,29] and relates to several factors, such as housing, health, care burden, geographical positioning and individual characteristics, which cannot be analysed by focusing solely on income and participation in employment. In this paper, some of the controls in the models also try to assess different domains—e.g., the presence of dependent adults and children in the family, within the care domain, or the lack of own housing. However, a wider study comprising a multidimensional approach to the study of insecurity is the best strategy for ensuring a wider and deeper understanding of the phenomenon [27,28], but this goes beyond the scope of the present article.
2
The age is divided in class in order to put in evidence the eventual presence of generational cleavages among women.
3
The results of the models will be presented in tables presenting odds ratios (Table 4 and Table 5), while marginal effects will be used to present the results (they are also reported in Appendix A, Table A3 and Table A4, for completeness). In the paper, Bernardi et al. [58] (2017)’s perspective on the significance threshold is adopted, in order to focus on the substantive significance of the factors under analysis. In certain cases, results that are significant at a 0.10 level will also be commented on, because of their substantive value in analytical terms.
Figure 1. The figure shows the relations between the independent variable (labour market participation of women) and the dependent variable (economic insecurity), indicating the possible influence of the family (antecedent and intervenient variable) and welfare (intervenient variable). The figure also shows the level of analysis at which the variables are measured.
Figure 1. The figure shows the relations between the independent variable (labour market participation of women) and the dependent variable (economic insecurity), indicating the possible influence of the family (antecedent and intervenient variable) and welfare (intervenient variable). The figure also shows the level of analysis at which the variables are measured.
Societies 08 00007 g001
Figure 2. The figure shows the relationships between the independent variable (labour market participation of women) and the dependent variable (economic insecurity), as distinguished by country, and showing the different probabilities for living in an economic insecure household. Source: Author’s own elaboration on EU SILC (2013).
Figure 2. The figure shows the relationships between the independent variable (labour market participation of women) and the dependent variable (economic insecurity), as distinguished by country, and showing the different probabilities for living in an economic insecure household. Source: Author’s own elaboration on EU SILC (2013).
Societies 08 00007 g002
Table 1. The structure of the hypothesis derived from Trifiletti [15]’s theory 1.
Table 1. The structure of the hypothesis derived from Trifiletti [15]’s theory 1.
Source of Economic InsecurityUniversalist W.R.Breadwinner W.R.Mediterranean W.R.Liberal W.R.
Role of the state (Trifiletti, 1999)State protectsState protectsFamily first (state does not protect)Market first (state does not protect)
Welfare culture (Trifiletti, 1999)State views women as workersState views women as wives and mothersState views women as wives and mothersState views women as workers
Family model (Trifiletti, 1999)Two incomes are betterOne income is sufficientSecond partial income necessaryTwo incomes are necessary
Welfare effort (Trifiletti, 1999) 2+++−−+−−−
Women’s LM participationYesNoOnly for someYes
Economic dependency from partnerYesNoOnly for someYes
Economic dependency from welfareNoNoYesYes
DenmarkFrance, Czech RepublicItaly, SpainUnited Kingdom
1 Source: Adapted from Figure 1 and Figure 2 in Trifiletti [15]. 2 Plus and minus were in the original Figure 2 of Trifiletti [15] and they indicate the depth of effort provided by the different welfare’s models.
Table 2. Final sample, observations not weighted 1.
Table 2. Final sample, observations not weighted 1.
CountryIndividuals (Men and Women)Single Women 2Women in a Couple 3
Czech Republic79838863186
Denmark63604952708
Spain13,03613685330
France11,12513444342
Italy17,32121056747
United Kingdom886713903267
total64,692758825,580
1 Source: Author’s own elaboration on EU SILC (2013), 2 and 3 final sample for logistic analysis.
Table 3. Economic insecurity by number of dimensions involved, weighted percentages (by gender and couples), 20–64 1.
Table 3. Economic insecurity by number of dimensions involved, weighted percentages (by gender and couples), 20–64 1.
Economic InsecurityCzech Rep.DenmarkSpainFranceItalyUKTotal
Well-being53.7%67.2%49.1%61.3%47.2%57.6%54.6%
single man48.9%53.9%45.2%54.0%50.9%45.9%49.7%
single woman35.0%42.4%41.1%39.5%37.1%31.6%37.2%
couples56.7%76.8%50.6%66.6%48.3%63.6%58.1%
Moderate risk 221.9%21.5%19.5%19.5%20.4%22.7%20.6%
single man22.9%33.0%21.9%23.0%19.7%27.1%23.3%
single woman23.1%31.0%20.0%26.3%24.5%29.5%25.7%
couples21.6%16.1%19.2%17.7%19.8%20.9%19.4%
High risk 321.1%9.1%24.1%14.4%22.4%14.1%18.2%
single man22.3%11.2%26.2%16.7%21.2%17.7%19.6%
single woman34.5%19.2%30.6%25.3%28.1%25.8%27.0%
couples19.1%6.0%23.0%11.9%21.7%11.6%16.6%
Extreme risk 43.3%2.2%7.3%4.7%9.95%5.7%6.5%
single man6.0%1.9%6.7%6.4%8.3%9.3%7.3%
single woman7.4%7.5%8.3%8.9%10.3%13.1%10.1%
couples2.5%1.0%7.3%3.7%10.1%3.9%5.8%
Total100%100%100%100%100%100%100%
Gender gap in well-being (single) 513.9%11.5%4.1%14.5%13.8%14.3%12.5%
Gap in well-being between single women and couples 621.7%34.5%9.5%27.0%11.2%32.0%21.0%
1 Source: Author’s own elaboration on EU SILC (2013). 2 Individuals and households report one factor of economic insecurity; 3 they report two factors of economic insecurity; 4 they report all factors of economic insecurity; 5 gap is calculated male—women. The gap in wellbeing between gender is not significant; 6 gap is calculated couples—women. The gap between women in a couple and single women is significant at 95% in all countries, with p < 0.001.
Table 4. Logistic regression on household economic insecurity—Single Women (Odds Ratio) 1.
Table 4. Logistic regression on household economic insecurity—Single Women (Odds Ratio) 1.
CZDKESFRITUK
part-time2.2780.9130.8191.2201.2461.050
more than 40 h0.9001.5180.7360.654 *0.627 **0.795
unemployed3.0823.698 +1.0493.459 *3.634 **1.985
inactive1.8392.2790.6821.6190.9641.006
low work intensity3.060 +1.3841.4072.423 *2.556 **1.230
ref. Full-Time
40 s2.322 ***1.866 *1.805 ***1.579 *1.0151.908 **
30 s1.2642.164 *2.191 ***1.2891.2732.356 ***
20 s1.6891.4891.4591.525 +1.452 +1.688
ref. Over50
migrant background0.2050.3891.5111.1842.326 *1.744
migrant # age1.4511.0791.0551.1171.0830.734 +
(upper) secondary2.658 ***1.1312.038 ***1.676 ***1.307 *2.118 ***
up to lower secondary4.175 ***1.0093.401 ***1.790 **2.846 ***3.705 ***
ref. tertiary
number of months not in paid work0.9721.0211.0390.9920.9900.976
Living with other adults3.580 ***3.081 **2.819 ***2.660 ***2.497 ***3.543 ***
Single Parent2.297 ***2.544 **1.407 +2.524 ***1.723 ***3.143 ***
ref. One person household
Experienced a separation or a divorce1.367 +1.661 *2.318 ***1.396 *1.735 ***1.198
Presence of at least one child under 31.8420.248 +1.0251.5371.4081.777
Presence of dependent adults1.2290.7681.2190.6101.2992.004
income in deciles for regression1.443 ***1.545 ***1.286 ***1.291 ***1.327 ***1.346 ***
Household rent the house1.906 **2.265 ***1.756 **2.670 ***2.248 ***2.932 ***
Household is economic dependent from welfare2.611 +1.1172.170 **2.526 *2.017 **3.840 ***
Observations8864951368134421051390
Pseudo R-squared0.2240.1740.2270.2340.2020.360
1 Source: Author’s own elaboration on EU SILC (2013). Significance level: + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 5. Logistic regression on household economic insecurity—Women in a couple (Odds Ratio) 1.
Table 5. Logistic regression on household economic insecurity—Women in a couple (Odds Ratio) 1.
CZDKESFRITUK
part-time0.8770.8361.278 *1.1200.838 +0.709 **
more than 40 h1.0021.0941.0280.9310.769 *0.981
unemployed2.913 ***2.540 **1.633 **1.2921.3020.515
inactive1.608 *1.4200.9951.0790.9910.256 ***
low work intensity1.2981.996 *1.474 **1.454 *1.347 +0.482 **
ref. Full-Time
40 s1.943 ***1.498 **1.1161.357 *1.0301.299 *
30 s1.893 ***1.2681.0701.706 ***1.0071.494 **
20 s1.698 **1.2291.2071.1811.1181.940 ***
ref. Over50
migrant background1.7001.5791.918 +2.020 +1.778 +0.626
migrant#age0.8891.0551.0240.9421.1011.018
(upper) secondary2.028 ***1.2001.723 ***2.082 ***1.658 ***1.606 ***
up to lower secondary4.372 ***1.484 *2.957 ***2.979 ***2.968 ***2.551 ***
ref. tertiary
number of months not in paid work0.9720.9750.9991.0001.0091.100 ***
Couple with children1.537 ***2.148 ***1.307 ***2.278 ***1.479 ***2.103 ***
ref. Couple without children
dummy presence of at least one child under 31.0620.7831.1791.0280.9081.092
dummy presence of dependent adults1.680 **1.2071.2591.2162.182 ***3.207 ***
dummy for living in homogamous couples1.205 +1.0970.890 +0.9500.840 **1.088
Economic dependency from partner0.703 *1.5121.0551.489 *1.1360.965
income in deciles for regression1.275 ***1.272 ***1.258 ***1.317 ***1.330 ***1.290 ***
Household rent the house1.592 ***2.251 ***2.294 ***2.115 ***2.182 ***2.619 ***
Economic dependency from welfare1.1162.401 *3.500 ***4.242 ***2.965 ***2.299 ***
Observations318627085330434267473267
Pseudo R-squared0.1390.1370.210.2210.2070.219
1 Source: Author’s own elaboration on EU SILC (2013). Significance level: + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
Table 6. Comparing the results with the hypothesis derived from Trifiletti (1999)’s theory.
Table 6. Comparing the results with the hypothesis derived from Trifiletti (1999)’s theory.
Source of Economic InsecurityCzech Republic.DenmarkSpainFranceItalyUnited Kingdom
Single women
Women’s LM participationOnly low work intensityYesNoYesYesNo
Economic dependency from welfareLimited significanceNoYesYesYesYes
Women in a couple
Women’s LM participationYesYesYesOnly low work intensityNoNo
Economic dependency from partnerReduce the riskNoNoYesNoNo
Economic dependency from welfareNoTo a reduced extentYesYesYesYes

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Maestripieri, L. A Job of One’s Own. Does Women’s Labor Market Participation Influence the Economic Insecurity of Households? Societies 2018, 8, 7. https://doi.org/10.3390/soc8010007

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Maestripieri L. A Job of One’s Own. Does Women’s Labor Market Participation Influence the Economic Insecurity of Households? Societies. 2018; 8(1):7. https://doi.org/10.3390/soc8010007

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Maestripieri, Lara. 2018. "A Job of One’s Own. Does Women’s Labor Market Participation Influence the Economic Insecurity of Households?" Societies 8, no. 1: 7. https://doi.org/10.3390/soc8010007

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