UNIVERSITÀ DEGLI STUDI DI MODENA E REGGIO EMILIA
Ph.D. Program in Labour, Development and Innovation XXX Cycle
Poverty and Policy Evaluation:
Traditional Methods and New Approaches
Ph.D. Candidate: Dr. Giovanni Gallo
Tutor: Professor Massimo Baldini
Co-Tutor: Professor Costanza Torricelli
Coordinator of the Ph.D. Program: Professor Tindara Addabbo
“If you never try you'll never know Just what you're worth”
Coldplay
3
Contents
Contents 3
Acknowledgements 5
Abstract 7
Abstract 9
Chapter 1 – Cash Transfers and Poverty in Europe: Comparing Exclusion and Targeting Across Welfare Regimes 12
1.Introduction 12
2.Literature review and motivation Errore. Il segnalibro non è definito.
3. Data and methodology 14
4. Poverty, poverty persistence, and non-receipt of cash transfers 18
4.1. Cash transfer non-receipt across groups 19
5. Econometric Analysis 21
5.1. Robustness checks 24
6. Conclusions 25
References 26
Appendix 29
Chapter 2 – Individual Heterogeneity and Pension Choices: How to Communicate an Effective Message? 35
1. Introduction 35
2. The Elaboration Likelihood Model of persuasion (ELM) 36
3. The severance pay transfer into a pension fund: the Italian reform and its main message 38
4. Data 41
4.1. Sample descriptives and the Tfr transfer decision 43
5. An assessment of the effectiveness of the reform message based on ELM 47
6. Regression models of the ELM outcomes 52
6.1. Modelling the ELM outcomes 54
6.2. Robustness checks and alternative specification of the ELM variables 57
7. Conclusions 58
References 60
Appendix 63
Chapter 3 – Past Income Scarcity and Current Perception of Financial Fragility 65
1. Introduction 65
2. Empirical strategy and data 67
3. Results 70
3.1. Scarcity effect by population income class and demographic characteristics 73
3.2. Alternative specifications of perceived financial fragility 75
3.3. Estimation of scarcity effects through the Coarsened Exact Matching 76
3.4. Robustness checks 77
4. Conclusions 78
References 79
Chapter 4 – Rotation Group Bias in the Estimation of EU Social Indicators 82
1. Introduction 82
2. Data 83
3. Methods 85
3.1. Definition of social indicators 85
3.2. Assessing rotation group bias 86
4. Results 88
4.1. Unconditional Effects (UE) 93
4.2. Conditional effects (CE): socio-demographic variables and main sampling characteristics 93 4.3. Can we link rotation group bias to main sampling design characteristics? 97
5. Conclusions 100
References 100
4
5
Acknowledgements
First and foremost, I would like to thank Professor Massimo Baldini for his understanding and expert advices throughout the PhD path; they helped my research countless times. Similarly, I would like to express my sincere gratitude to Professor Costanza Torricelli for her expertise and unfailingly support. I have benefited greatly from working with both of you.
I would like to thank Professor Arthur van Soest for his supervision and availability during my visiting at the Tilburg University. I am equally grateful to Professor Philippe Van Kerm and Dr. Alessio Fusco for the precious opportunity to work together, as well as Dr. Andrea Albanese, Dr. Francesco Andreoli, and all the other people I have had the pleasure to meet during my visiting at the Luxembourg Institute of Socio-Economic Research, for having made that very useful and doubtless special.
My thanks go to all the professors, researchers, and staff of the Department of Economics ‘Marco Biagi’ at the University of Modena and Reggio Emilia, in particular Professor Paola Bertolini and Professor Enrico Giovannetti for their as unique as constant support.
A special thanks to my office mate, Professor Giuseppe Fiorani, and to my fellow PhD students Dr. Antonella Cavallo and Dr. Luca Silvestri who have made these three years together astoundingly happy, less tiresome and, of course, unforgivable.
Finally, any word would not be sufficient to express how much grateful I am to my family, to my partner, and to all my friends who have been patient with me in the worst moments and have quietly accepted all my whims and oddities.
To conclude, I gratefully acknowledged the funding received towards my PhD from the Fondazione Giacomo Brodolini PhD fellowship and from the University of Modena and Reggio Emilia 2014 FAR Grant on ‘Individual heterogeneity and household choices: which implications for pension and financial products?’. Also, I am grateful to the third Network for the analysis of EU-SILC (Net-SILC3), funded by Eurostat, which has supported the work represented in Chapter 4.
The European Commission bears no responsibility for the analyses and conclusions, which are solely those of the authors.
6
7
Abstract
The evaluation of public policies is a research topic in which a well-defined methodology has been developed over time. The difference-in-differences technique, the propensity score matching, and other evaluation strategies more and more complicated under an econometric point of view jointly compose a methodological framework to obtain the best possible counterfactual in (almost) any kind of situation. Similarly, in the study of poverty and policies to fight against it a common methodological approach has been consolidated over time, generally coinciding with the Eurostat’s one at European level or the Italian National Institute of Statistics (Istat) at national level.
The PhD thesis starts from the application of some of ‘traditional’ methods described above and try to introduce new approaches to both evaluate public policies and study the poverty phenomenon.
It investigates the heterogeneity of elaboration processes behind an individual choice related to a pension reform and persistent psychological effects triggered by a poverty experience. Moreover, the thesis presents the application of very recent matching and econometric methods, such as the Coarsened Exact Matching and the Influence Function regressions.
The thesis consists of four chapters.
Chapter 1 studies whether there are systematic differences in the capability of cash social transfers, belonging to different European welfare systems, to reach both the transitory poor and the persistent poor. Based on bivariate Probit models applied to EU-SILC data, this chapter find that population subgroups at-risk-of poverty that do not receive cash transfers are quite similar among European welfare regimes, but with important differences in terms of exclusion rates. From both a cross- sectional and a longitudinal point of view, a common framework for social policies seems to be in place, which tends to exclude some categories of the poor (e.g. foreigners, self-employed, employed) from benefits receipt, while including others with much greater probability (e.g. disabled, minors).
The Mediterranean welfare system is, by far, the one that excludes the greater share of the poor from cash transfers, followed by the East-European countries.
As for Chapter 2, a psychological model, called Elaboration Likelihood Model, is used to explain how communication influences pension choices in a heterogeneous population. According to this model, individuals follow either a “central route” or a “peripheral route” depending on their motivation and ability to process, and eventually change or retain their initial attitude. The chapter exploits the 2007 Italian reform that allowed transferring future severance pay contributions into a pension fund. Although this was accompanied by a government information campaign clearly advising employees to make the transfer, only a minority of the employees did so. Based on micro panel data from the Bank of Italy Survey on Household Income and Wealth, the analysis shows two main findings. First, the decision to transfer and pension fund participation after the reform are more likely for more (financially) educated and older individuals, with high household income and wealth, and less likely for employed women, in the South, and in small firms. Second, cognitive processes underlying the same final decision on pension fund participation appeared to be quite different. A particularly interesting finding is that decision consciousness is lower for employees working in small firms, where employers have an incentive to stimulate their workers to deny the transfer. These results have policy implications for the effectiveness of pension reforms and the information campaign
8
accompanying it. For instance, treating smaller firms in a similar way as larger firms would significantly affect the outcome of the reform.
The analysis developed in Chapter 3 wants to question a common assumption in public policies to fight poverty, according to which helping people with financial difficulties to transit out of poverty is enough to solve this social problem. In fact, leaving a condition of income scarcity does not necessarily lead to a change in individual’s poverty perception, since the individual may remain mentally stuck in a long-lasting status of perceived financial fragility. This has important implications for future levels of poverty and inequality, because of its related (negative) effects on cognitive capabilities, behaviors, and decisions. The aim of the chapter is to test whether and to what extent an income scarcity condition experienced in the past affects the individual’s assessment of financial fragility over time. Using EU-SILC longitudinal data, main results highlight that individuals who transited out of poverty tend to record a lower subjective ability to make ends meet than those who never experienced poverty, even after three years left and at the same level of household income. To test the robustness of these findings, beyond the standard checks on both the dependent variable and the variable of interest, they are also estimated through the new Coarsened Exact Matching approach, but conclusions overall remain the same.
Finally, Chapter 4 examines to what extent the rotating panel design adopted by Eurostat in EU- SILC (European Union Statistics on Income and Living Condition) survey influences the estimates of social indicators such as income poverty or income inequality, in other words whether a “rotation group bias” is observed. To this end, this chapter applies new Influence Function regressions. The analysis on the 2014 EU-SILC cross-sectional data highlights that the estimated poverty and income inequality measures for newer rotation groups are often higher than for older ones. “Fresh” rotation groups exert an influence that is significantly and frequently different from other rotation groups in 7 of the 28 countries examined, but also other countries are (less frequently) affected by rotation group bias. These impacts remain overall significant even when accounting for different socio-demographic characteristics of households and main characteristics of the sampling. Not all countries are affected by the bias however. The analysis cannot isolate the source of the bias, but it raises attention to an issue that may affect the reliability of important social indicator estimates.
9
Abstract
La valutazione delle politiche pubbliche è un ambito di ricerca nel quale si è sviluppata una metodologia ben definita nel tempo. La tecnica della differenza-nelle-differenze, il propensity score matching e altre strategie valutative più e più complesse dal punto di vista econometrico compongono complessivamente un supporto metodologico atto a ottenere il miglior controfattuale possibile in qualsiasi tipo (o quasi) di situazione. Allo stesso modo, nello studio della povertà e delle politiche di contrasto di questo fenomeno si è consolidato nel tempo un comune approccio che coincide con quello definito dall’Eurostat a livello europeo oppure dall’Istat a livello nazionale.
La presente tesi di dottorato muove dall’applicazione di alcuni dei metodi tradizionali appena descritti e tenta di introdurre dei nuovi approcci da attuare sia nella valutazione delle politiche pubbliche sia nell’analisi della povertà. Essa investiga sull’eterogeneità dei processi cognitivi dietro la scelta individuale collegata a una riforma pensionistica e sui perduranti effetti psicologici innescati da un’esperienza di povertà. Inoltre, la tesi presenta un’applicazione di recenti tecniche econometriche e di matching, quali sono il Coarsened Exact Matching e le Influence Function regressions.
La tesi si sviluppa in quattro capitoli.
Nel primo capitolo, seguendo un approccio più “tradizionale”, viene illustrata e valutata la capacità dei trasferimenti monetari dei diversi sistemi europei di welfare di raggiungere i soggetti in condizioni di povertà transitoria o persistente. Tramite l’applicazione di un modello Probit bivariato su dati EU- SILC, emerge che le categorie della popolazione a rischio di povertà che non ricevono trasferimenti monetari sono molto simili tra i sistemi europei di welfare, sebbene permangano forti differenze nei tassi di esclusione. Da un punto di vista sia trasversale che longitudinale, sembra essere presente una comune tendenza nelle politiche sociali ad escludere dai benefici certe categorie di poveri (stranieri, lavoratori autonomi, occupati) rispetto ad altre (disabili, minori). Il sistema di welfare mediterraneo risulta essere quello che non include la quota maggiore di poveri nel beneficio dei trasferimenti monetari, seguito dal sistema dei paesi del Centro-Est Europa.
Nel secondo capitolo viene usato un modello di analisi cognitiva, denominato Elaboration Likelihood Model, per spiegare in che modo la comunicazione influenza le scelte pensionistiche nel contesto di una popolazione eterogenea. Secondo questo modello, gli individui seguono un percorso
“centrale” o uno “periferico” in base alla loro motivazione e abilità di elaborazione ed eventualmente cambiano o mantengono la loro attitudine iniziale. Il capitolo sfrutta la riforma del sistema pensionistico complementare del 2007 che ha permesso ai lavoratori italiani del settore privato di trasferire i contributi futuri del proprio TFR in un fondo pensione. Benché la riforma sia stata accompagnata, per volere del governo, da una campagna informativa che consigliava chiaramente di effettuare il trasferimento, solo una minoranza dei lavoratori lo ha fatto. Basato sui micro dati dell’Indagine della Banca d’Italia sui bilanci delle famiglie, l’analisi mostra due importanti risultati.
Il primo è che la decisione di trasferire e la partecipazione ai fondi pensione dopo la riforma sono stati più frequenti tra gli individui più educati (finanziariamente), più anziani e con alti livelli di reddito e ricchezza familiare, mentre sono stati meno frequenti tra le lavoratrici donne, i residenti del Mezzogiorno e coloro che lavorano nelle aziende con meno di 50 dipendenti. Il secondo risultato è che i processi cognitivi, pur conducendo alle stesse decisioni finali sulla partecipazione ai fondi
10
pensione, sono stati talvolta molto diversi. Un risultato particolarmente interessante è che la consapevolezza della decisione è stata inferiore tra coloro che lavorano nelle aziende con meno di 50 dipendenti, dove i datori di lavoro hanno avuto un incentivo a spingere i loro lavoratori a rifiutare il trasferimento del TFR. Queste evidenze hanno rilevanti implicazioni di policy per l’efficacia delle riforme pensionistiche e la campagna informativa ad esse associata. Per esempio, eliminare la diversa regolamentazione riservata alle aziende con meno di 50 dipendenti influenzerebbe significativamente il risultato della riforma.
L'analisi ad oggetto del terzo capitolo vuole mettere in discussione il presupposto, comune nelle politiche pubbliche di contrasto alla povertà, secondo il quale aiutare le persone in difficoltà finanziaria a transitare fuori dallo stato di povertà sia sufficiente a risolvere il problema sociale.
Infatti, abbandonare la condizione di scarsità di reddito spesso non si traduce automaticamente in un cambiamento della propria percezione di povertà, poiché gli individui potrebbero rimanere bloccati in un stato mentale nel quale ci si continua a percepire finanziariamente fragili. Ciò ha delle rilevanti implicazioni per i futuri livelli di povertà e disuguaglianza a causa degli effetti negativi sulle abilità cognitive, i comportamenti e le decisioni familiari a questo stato connessi. L’obiettivo del capitolo è quindi verificare se e con quale intensità l’esperienza di una condizione di scarsità reddituale nel passato incide sulla valutazione personale della fragilità finanziaria nel tempo. In base alle elaborazioni svolte sui dati panel EU-SILC, sembra che le persone con esperienze di povertà tendano a percepirsi finanziariamente più fragili rispetto a chi non ha vissuto momenti di scarsità in passato, anche dopo che tre anni che l’esperienza si è conclusa e a pari livello di reddito familiare. Per testare la robustezza dei risultati, oltre ai controlli ordinari sulla variabile dipendente e la variabile di interesse, questi sono stati stimati nuovamente attraverso il nuovo approccio del Coarsened Exact Matching, ma le conclusioni sono rimaste le stesse nel loro complesso.
Infine, il quarto capitolo esamina con quale intensità il design di panel ruotato adottato dall’Eurostat nell’indagine EU-SILC (European Union Statistics on Income and Living Condition) influenza la stima degli indicatori sociali come, ad esempio, quelli per la povertà o la disuguaglianza nella distribuzione del reddito. In altre parole, esso cerca di verificare se una “distorsione da gruppi di rotazione” è osservata. A tal fine, questo capitolo applica le recenti Influence Function regressions.
L’analisi sui dati trasversali EU-SILC 2014 mostra che gli indicatori di povertà e disuguaglianza stimati per i nuovi gruppi di rotazione sono spesso più elevati di quelli stimati per i gruppi presenti da più tempo nel panel. I gruppi “giovani” esercitano un’influenza che è frequentemente e significativamente diversa da quella degli altri gruppi di rotazione in 7 dei 28 paesi analizzati, sebbene anche altri paesi registrino (meno di frequente) la stessa distorsione. Questi impatti rimangono complessivamente significativo anche quando vengono presi in considerazione le diverse caratteristiche socio-demografiche delle famiglie e le principali caratteristiche del campionamento.
Non tutti i paesi, comunque, rilevano la distorsione da gruppi di rotazione. L’analisi non riesce ad isolare con precisione la fonte della distorsione, ma accresce l’attenzione su un problema che potrebbe inficiare l’affidabilità delle stime di indicatori molto importanti nell’ambito delle politiche sociali.
11
12
Chapter 1 – Cash Transfers and Poverty in Europe: Comparing Exclusion and Targeting Across Welfare Regimes 1
1. Introduction
One of the principal aims of welfare state systems has traditionally been the protection of all individuals from the risks of poverty and social exclusion. The primary instrument conceived for this function is the provision of public support in the form of social transfers, i.e. cash benefits or in-kind services. Given the final objective of social transfers, an ideal ‘first best’ solution should consist of a reality where poverty rates are substantially reduced by public intervention and no poor person is left alone.
In the last few years, many studies have attempted to evaluate the role of cash transfers in the fight against poverty. Some of them analyze the overall effect of transfers on coverage rates and poverty reduction (see, for instance, Huber and Stephens, 2001, Matsaganis et al., 2008, Figari et al., 2013, Marx et al., 2013, Cantillon et al., 2014, and Marx et al., 2014), emphasizing in particular a trade-off on poverty alleviation between targeting and universalism. In fact, while some authors argue that, particularly in cases of budget constraints, targeting is the most efficient and effective way to reduce poverty, others underline that, in the long term, targeting weakens the electorate’s acceptance of an active and massive involvement of government in the welfare state (Huber and Stephens, 2001;
Pierson, 2004). According to Ferrarini et al. (2016), however, it is not just a matter of trade-off between universalism and targeting, since the major contribution to poverty reduction comes from the size of transferred income. This may explain why high poverty rate reductions are observed both in systems traditionally oriented toward universalism (e.g. Denmark, Sweden) and in countries with a strong emphasis on targeted measures (e.g. United Kingdom, Ireland). Marx et al. (2013) reach the same conclusion confirming that the best performing countries in term of redistributive impact employ “targeting within universalism”, although there is no guarantee of a relevant redistribution without a high level of spending.
This “macro” approach to the effects of cash transfers, however, does not recognize that the impact of transfers on poverty may vary according to individual or household characteristics, which can also influence the probability of receiving social transfers and of transiting out of poverty. For this, a more micro-level based approach is necessary. One of the main objectives of this approach is to study the determinants of non-take-up, i.e. why a part of the poor population remains excluded from social transfers. Riphahn (2001), using German EVS data of 1993, shows that social benefits non-take-up depends on the application costs, the amount and duration of the expected benefits, and the individually perceived stigma. Other authors like Frazer and Marlier (2009) and Warin (2014) add
1 This work is currently submitted for publication with the title “Cash transfers and poverty in Europe: Comparing exclusion and targeting across welfare regimes” (joint with Massimo Baldini, Manuel Reverberi, and Andrea Trapani).
A working paper version can be found in the CAPPapers Series (n. 145/2016). It has been presented at XXVIII Annual Conference of the Italian Society of Public Economics (Lecce, Italy, 2016) and LISER Research Seminar (Esch-Belval, Luxembourg, 2017).
13
further causes of non-take-up, such as the lack of information or awareness, the complexity of the system, and the indifference towards or even rejection of the social security system. Estimating the extent of non-take-up of means-tested pensions in Greece and Spain, Matsaganis et al. (2010) find that even when benefits are awarded for life and are targeted to the elderly population (that should suffer less from stigma than working age people), transfers present incomplete take-up rates.
Summing up, the literature seems to show that no income maintenance system is perfectly able to reach all the poor, and a proportion of them, although small, runs the risk of being excluded.
A further important aim of this kind of studies is to evaluate the effects of transfers on individuals’
transitions in or out of poverty over time. For instance, using the 1991-1996 British Household Panel Survey data, Jenkins (2000) highlights that a significant number of poverty transitions are due to changes in benefit income and also that the persistently poor report the greatest share of total expenditure for social benefits. An alternative approach to evaluate the effectiveness of social transfers against poverty is provided by Fabrizi et al. (2014), in which a comparison between before and after social benefits scenarios replaces the panel analysis. They find that in Italy some groups of the population are more likely to be left behind by national social policies, arguing that this could be due to a chronic lack of policy coordination, as well as to targeting rules based on occupational and demographic characteristics of individuals and not on the economic conditions of households.
Another relevant issue concerns poverty measurement. As some authors like Ozdemir and Ward (2010) argue, the at-risk-of poverty concept used by the European Commission may not be the most appropriate one to represent poverty status. For this purpose, under the hypothesis that a temporary income loss is not necessarily associated with a condition of real difficulty, they distinguish between persistent (calculated with EU-SILC longitudinal data) and temporary poverty. By comparing the two different concepts of poverty, an important aspect is to check whether the persistently and temporary poor have different characteristics (Biewen, 2014), as well as to identify whether some socio- demographic characteristics can be associated with the probability of transitions in and out of poverty (Andriopoulou and Tsakloglou, 2011).
Finally, there is some evidence that the probability of receiving cash transfers is strongly influenced not only by individual characteristics, but also by specific features of social protection systems, such as the degree of targeting (Anderson and Meyer, 1997; Fabrizi et al., 2014) and their generosity (Lohmann, 2009). Therefore, the identification of categories of the poor suffering exclusion from cash transfers enables the existence of a relationship between transfers exclusion, specific individual profiles, and welfare regimes features to be verified.
Among all the above-mentioned researches, only a few of them identify who is excluded in the fight against income poverty. Nonetheless, when they do this, these studies focus on specific social transfers, on specific countries, or only on the temporary poor individuals/households. The novelty of our paper relies on filling this gap through an analysis: I) that considers all types of cash social transfers; II) based on a comparison among 24 European countries belonging to different welfare systems; and III) that explores the probability of being excluded from any social transfer for both the (temporary) poor and the persistently poor. Considering all types of cash transfers regardless of their target (e.g. unemployed, family, disability) and main characteristics (e.g. eligibility criteria, means- test), rather than a specific sub-sample, allows to guarantee a definition as wide as possible of benefit receipt, and thus avoid observing an incomplete framework of income support systems. Also, the comparative study of European welfare systems allows to take into account the significant historically
14
founded dissimilarities and verify whether they are still present among them. Lastly, persistently poor may be pretty different from those who are poor only for one year or short spells in terms of both socio-economic vulnerability and reasons of benefit non-receipt. Therefore, comparing these two categories of poor people may help to better understand the relationship between poverty persistence and exclusion from cash transfers.
To be sure, not receiving benefits despite being poor may depend on different factors, such as the technical and administrative design of each transfer or the inability and unwillingness to request it even though the entitlement is guaranteed. However, separating these factors would require a detailed analysis of each single transfer, an impossible task for a multi-country comparative study like the present one. For this reason, we always refer to the combined effect of eligibility criteria and other potential determinants of non-take-up of cash transfers, not deriving any data-driven conclusion about their relative importance.
Based on the EU-SILC data, our main results show that the European welfare systems we analyzed have turned out to be more similar than we expected regarding the target characteristics of their cash transfers. Indeed, a common framework for social policies seems to be in place, which tends to exclude some categories of the poor (e.g. foreigners, self-employed, employed) from benefit receipt, while including with much greater probability other groups (e.g. disabled, minors). What seems to be different among the European welfare regimes is, instead, the degree of inclusion or exclusion for each category of the poor (or the persistently poor), i.e. the magnitudes of the determinants on the non-receipt of cash transfers. Specifically, our evidences emphasize large, still present differences in the universalism degree.
The paper is organized as follows. Section 2 describes the EU-SILC data and defines our methodology. Section 3 presents a descriptive analysis of poverty, poverty persistence and exclusion rates from cash transfers across welfare regimes, while Section 4 reports the econometric results and robustness checks. Last Section concludes.
2. Data and methodology
Our analysis across different European welfare regimes relies on data from the European Union Statistics on Income and Living Condition (EU-SILC). This survey provides comparable and detailed data on income, labour, numerous demographic and socio-economic variables (e.g. health, education, perceived poverty), and cash transfers at both individual and household level. Moreover, EU-SILC allows for both a cross-sectional and a longitudinal analysis. As for the former, we use the 2014 EU- SILC cross-sectional dataset, which contains incomes received in 2013, while for the latter the 2010- 2013 EU-SILC longitudinal dataset is used, which contains incomes received in 2009-2012 period.2
Cash transfers are very different from each other, especially in an international context, but they may be divided into two main categories: cash transfers and ‘in kind’ transfers (i.e. services provided for free or with a price lower than the market one). However, since EU-SILC data do not provide
2 In particular, we use the cross-sectional EU-SILC UDB 2014 – version 1 of January 2016 and the longitudinal EU-SILC UDB 2013 – version 2 of January 2016.
15
enough information about the quality and quantity of in-kind services benefiting each individual, we focus only on cash transfers.3
EU-SILC data separate cash transfers in the following types:
• Unemployment benefits;
• Old-age and survivor’s benefits;
• Sickness benefits;
• Disability benefits;
• Education-related allowances;
• Family/children related allowances;
• Housing allowances;
• Social exclusion not elsewhere classified.
Such a broad definition of transfers may be questionable because several of the benefits listed above are not properly ‘pro-poor’. For example, since poor people generally have low levels of both income and work intensity, non-means-tested and/or contributory cash transfers (e.g. old-age and survivor’s benefits, unemployment benefits) can be considered less ‘pro-poor’ than other types of transfer.4 One solution consists in selecting the subset of cash transfers which are more ‘pro-poor’, such as the family/children related allowances and benefits dealing with social exclusion.
Nevertheless, policymakers of different countries may decide to pursue the same objective with different social policies, so if we rule out some benefits we run the risk of losing precious information for our analysis. An alternative solution may be to remove the elderly from the sample in order to reduce the weight of less ‘pro-poor’ benefits. In fact, statistics on the cross-sectional and longitudinal data regarding old-age benefits point out that: I) they represent the category of cash transfers with the greatest share of total expenditure; II) the share of the poor among their recipients is the lowest; III) very few poor elderlies are excluded from them or any other type of social transfer (details are available upon request). Therefore, we decided to adopt the same definition as Eurostat, thus considering all the above-mentioned cash transfers regardless of the strictness of their eligibility conditions, but restricting the analysis to households without retired individuals or persons aged more than 65 years.
The main interest of this paper lies in identifying the categories of the poor who are left behind by the various welfare systems, i.e. individuals who despite being poor are not reached by cash transfers.
However, given the universalistic structure of some systems and the broad definition of cash transfers that we use, the number of households with zero transfers in the sample is extremely low in some countries.5 Consequently, we decided to adopt a stricter definition of transfer receipt, considering as
3 In-kind transfers generally consist of universal programs in which everyone is eligible regardless of the income, such as health insurance or education. Therefore, the risk of excluding any population subgroups from them is negligible. The imputation rules that are usually followed by the empirical studies that focus on the distributional impact of these transfers assure that no one is excluded from general transfers such as health or education (Aaberge et al., 2013). In-kind transfers may sometimes consist also in means-tested targeted programs where a specific good is provided to a selected group of poor people, such as housing assistance. However, several both theoretical and empirical studies (Currie and Gahvari, 2008; Paulus et al., 2010) confirm for these in-kind transfers the same results we find for cash ones: most of them are in favor of the elderly, minors, and disabled.
4 The 2014 version of EU-SILC data, for the first time, contains information on cash benefits distinguishing for their means-test condition and contributory requirements, but they are still not available for all European countries.
5 In the cross-sectional sample, poor households receiving no cash transfers (i.e. total amount equals to zero) are less than 3% in three countries (Denmark, Finland, and Malta) and less than 5% in ten. In the longitudinal sample, the persistently
16
recipient an individual living in a household that receives an amount of benefits representing at least 4% of the total household disposable income (for detailed information on the definition choice of social transfer receipt see Section 4). In this way, we can focus on people for whom cash transfers are zero or represent a negligible share of their income.
To identify poor people, we use for both the cross-sectional analysis and the longitudinal one a modified version of the European Commission’s income poverty definition. The standard definition affirms that an individual is at-risk-of income poverty (or simply poor) when she lives in a household whose total equivalised income, after direct taxes and transfers, is below the standard at-risk-of poverty threshold, defined as 60 percent of the yearly national median equivalised income. This is obtained, for each country and year, by correcting total disposable household income for the modified OECD equivalence scale, which gives a value of 1 to the household head,6 0.5 and 0.3 to each additional adult and child (less than 14 years of age), respectively. As for poverty persistence, the European Commission defines the persistent risk of income poverty as ‘having an equivalised disposable income below the at-risk-of poverty threshold in the current year (i.e. the last year for which income data are available) and in at least two of the preceding three years’.
The population of poor individuals as defined above may be divided into two categories: the poor who receive no transfer and those who do receive them but with a total amount of transfers that is not enough to transit out of poverty. However, there is actually a third category of poor people: those who thanks to the transfers manage to overcome the poverty threshold, and so transit out of poverty.
Using the standard definitions of income poverty, we risk losing part of information about the poor because we would miss this third category. We therefore define as poor those living in a household whose total equivalised disposable income before all cash transfers is below the standard at-risk-of poverty threshold; similarly, we define poverty persistence as having a total equivalised disposable income before all cash transfers below the standard at-risk-of poverty threshold in 2013 and in at least two of the preceding three years.
The EU-SILC sample contains data for all 28 EU-Member countries and for 2 EU Associate Members (Iceland and Norway). Although a comparative analysis of transfers by country would be more precise and detailed, we prefer here to group countries by welfare regime. Many theoretical and empirical studies affirm that, within the European borders, there are at least four different welfare systems: the Scandinavian or social democratic regime, the Anglo-Saxon or liberal regime, the Continental or corporatist regime, and the Mediterranean one (Esping-Andersen, 1990; Ferrera, 1996;
Whelan and Maître, 2010; Urbé, 2012). Most recent studies add to the previous four regimes an additional one, arguing that Central and Eastern European countries are developing a welfare model of their own, combining Bismarckian social insurance, former communist egalitarianism, and a liberal market orientation (Cerami, 2006; Urbé, 2012).
Following the above-mentioned researches, we decided to exclude the following from our samples: Bulgaria, Croatia, and Romania because generally not included in any welfare system; and the Baltic countries (Estonia, Latvia, and Lithuania) because the welfare regime they compose (Whelan and Maître, 2010; Urbé, 2012) numbers too few observations to enable an econometric analysis. The adopted welfare regime classification shown in Table 1 almost coincides with the one
poor households receiving no cash transfers are in 2013 less than 3% in eight countries (Denmark, Malta, Netherlands, France, Iceland, Hungary, Finland, and Sweden).
6 The household head is defined as the individual responsible for the accommodation. She must be aged 16 and over.
17
proposed by Whelan and Maître (2010), where the Scandinavian system is called ‘social democratic’, the Anglo-Saxon is the ‘liberal’, the Continental is the ‘corporatist’, the Mediterranean is the
‘southern European’, and the Central and Eastern European (CEE) system is called ‘post-socialist corporatist’ regime. There is only one exception: the Netherlands. According to Whelan and Maître (2010), the Netherlands belong to the social democratic regime, but the proper allocation of the Netherlands is actually debated. We decided here to follow Bukodi and Róbert (2007) and include the Netherlands in the Continental system. As for Malta, which is not considered by Whelan and Maître (2010), also the proper allocation of its welfare system is under debate. In fact, the Maltese regime represents a hybrid model between the Beveridge model and the Mediterranean one (Bukodi and Róbert, 2007; Urbé, 2012). In this analysis we decided to include Malta, together with Ireland and United Kingdom, in the Anglo-Saxon welfare system (i.e. the Beveridge’s one). However, we stress the reliability of the adopted welfare regime classification in Section 4.1.
Table 1 – Countries and sample observations by welfare system Welfare system Countries
No. Observations Cross-sectional
sample
Longitudinal sample Scandinavian DK, FI, IS, NO, SE 63,989 43,196
Anglo-Saxon IE, MT, UK 34,132 14,248
Continental AT, BE, DE, FR, LU, NL 82,051 66,060 Mediterranean CY, EL, ES, IT, PT 79,306 51,020
CEE CZ, HU, PL, SI, SK 72,987 58,196
Total 24 332,465 232,720
Notes: German observations are missing in the longitudinal dataset.
After dropping observations with missing values in our variables of interest,7 Table 1 shows that the cross-sectional sample consists of 332,465 individual observations, while the balanced panel sample consists of 232,720 observations (i.e. 58,180 individuals per wave). Tables A.2 and A.3 provide descriptives of the cross-sectional sample and the longitudinal one by poverty status and welfare regime. The unit of analysis is the individual; we therefore apply individual sample weights to all descriptives.
In general, the socio-demographic characteristics of the total population in the five welfare systems are often similar. Looking at the cross-sectional sample (Table A.2), the population in the Mediterranean system, with respect to the others, tends to live more within households where the household head is male, older, and less educated, the members is two or more but with few minors, and with at least one unemployed member. On the other hand, the CEE system shows that a very low number of household heads do not have local citizenship and there are more individuals living in an owned house. Looking at the poor population, several common points among the regimes still emerge.
In all welfare systems, poor individuals are more likely to live in households in which the household head is female, young (less than 30 years old), foreign, or with low education, the tenure status is different from ownership, and there is at least one unemployed or one disabled. There are also some differences in the composition of the poor among the European systems. For instance, the poor are
7 However, they represent only 0.83% of the original cross-sectional sample and 0.97% of the original panel sample (details are available upon request).
18
more concentrated among single persons in the Scandinavian and Continental systems, rather than among households with minors such as in the other regimes.
Table A.3 considers the longitudinal sample and presents some characteristics of the persistently poor for the year 2013. With respect to what appears regarding the cross-sectional sample, there are no significant differences, except for the fact that male household heads are here more frequent in the Anglo-Saxon system. Comparing socio-demographic characteristics of individuals at persistent risk of poverty to those of the sample as a whole, Table A.3 shows that the persistent poor tend to be concentrated in specific groups of the population as well. Many of their characteristics are like those shown in Table A.2, with some exceptions. For example, poverty persistence does not seem to regard young households alone, but also households with older heads: the middle-age groups (40-49) in the Mediterranean and CEE systems, and the old-age groups (50 or more) in the Scandinavian and Continental ones.
3. Poverty, poverty persistence, and non-receipt of cash transfers
As mentioned above, the main purpose of this work consists in highlighting potential dissimilarities in the degree of exclusion from cash transfers between welfare systems, as well as in checking whether there may be significant differences between them if we move from a cross- sectional to a longitudinal definition of poverty. To this aim, Table 2 reports both at-risk-of poverty rates (computed on disposable incomes that do not include transfers) and exclusion rates from cash transfers for all welfare regimes analyzed. In the latter case both for poor individuals and for the entire population.
Table 2 – Risk of poverty and cash transfer (CT) non-receipt by welfare system.
Cross-sectional sample (year 2014) Welfare system
At-Risk-of Poverty rate
before CT
CT Non-Receipt rate Poor Total
population
Scandinavian 28.3% 4.9% 33.3%
Anglo-Saxon 31.7% 7.3% 46.1%
Continental 27.6% 6.6% 35.8%
Mediterranean 32.5% 30.2% 55.3%
CEE 27.2% 16.0% 54.0%
Total 29.5% 14.5% 44.6%
The before-transfers risk of poverty is quite similar among the five welfare systems, in which more than one individual out of four is poor (almost one out of three in Mediterranean countries).8 Differences among the European regimes are much greater if we consider the exclusion rates from
8 To be clear, at-risk-of poverty rates illustrated in Table 2 are those calculated before cash transfers. Taking into account household disposable incomes including cash transfers, we observe that the risk of poverty is particularly widespread in Mediterranean countries (24.1%), whereas it is much lower in the Scandinavian and Continental systems (respectively 12.5% and 15.1%). The difference between the rates calculated after cash transfers and those calculated before somehow represents the overall effectiveness of social benefits in the fight against poverty. However, in this study we are only interested in poverty rates before cash transfers, because we want to understand who is excluded regardless of the effectiveness and efficiency of cash transfers operating in the welfare regimes.
19
cash transfers (Table 2, right panel). Indeed, 30.2% of the Mediterranean poor do not receive benefits of any kind, whereas very small shares of the poor are excluded in the Scandinavian and Continental countries. However, the best targeting performances shown by the latter may be related to the peculiarly universalistic approach of their cash transfers. In fact, when considering the total population, the Scandinavian system has the lowest exclusion rate from cash transfers (33.3%), followed by the Continental one (35.8%).
As for the persistent risk of poverty, Table 3 shows that the poverty persistence rates also feature few differences among the welfare systems, moving from 16.2% in the Scandinavian regime to 23.8%
in the Anglo-Saxon one. In Anglo-Saxon countries, poverty persistence also affects a large share of people who are at-risk-of poverty in 2013 (80.9%), similarly to the Continental system (81.2%).
Nevertheless, in the reference period, the persistent poor individuals represent most of the cross- sectionally poor in 2013 for all the welfare regimes. Polin and Raitano (2014) seem to reach the same conclusion in their study based on pre-recession longitudinal data (EU-SILC 2005-2007), pointing out that this phenomenon already existed in all European countries before the Great Recession.
Table 3 – Poverty persistence and cash transfer non-receipt by welfare system.
Longitudinal sample (year 2013)
Welfare system
Persistent Poverty rate
before CT CT Non-Receipt rate
% % relative to the
temporary poor Persistent poor Total population
Scandinavian 16.2% 69.0% 2.8% 38.1%
Anglo-Saxon 23.8% 80.9% 3.6% 43.2%
Continental 21.1% 81.2% 2.4% 39.0%
Mediterranean 21.9% 74.3% 25.7% 58.0%
CEE 17.5% 74.1% 12.0% 55.3%
Total 20.9% 78.6% 7.6% 44.2%
Similarly to what we have seen in Table 2, a high share of the persistently poor who do not receive transfers appears to be a prerogative of the Mediterranean countries (Table 3, right panel) and, though to a lesser extent, of Eastern Europe. In other welfare systems this condition involves only a very small share of people at persistent risk of poverty, especially in the Continental one (2.4%). In this case, too, the regimes showing the lowest non-receipt rates among of cash transfers among the persistent poor also show the lowest rates in the total population.
3.1. Cash transfer non-receipt across groups
In the previous Section we observed some clear differences in terms of exclusion rates from transfers among the welfare systems, concerning not only the poor but also the population as a whole.
However, the presence of this gap in targeting capabilities does not automatically mean that an overall high coverage rate of transfers among poor individuals should be associated with high coverage rates among all subgroups of the poor population.
In order to capture possible dissimilarities in social transfer non-receipt within the poor population, before introducing the econometric analysis, Charts 1 and 2 provide a focus on the degree of exclusion from cash benefits for specific categories of poor individuals. Obviously, exclusion rates of the
20
considered subgroups across welfare systems must be compared to the overall exclusion rate among the poor of each welfare regime.
Chart 1 – Share of the temporary poor people excluded from cash transfers by subgroup of population and welfare system. Cross-sectional sample (year 2014)
Chart 2 – Share of persistent poor people excluded from cash transfers by subgroup of population and welfare system. Longitudinal sample (year 2013)
In general, Chart 1, for the 2014 cross-sectional dataset (2013 incomes), always shows very low non-receipt rates among the poor living in households with at least one disabled, while those who have a foreign head or at least an employed household member (especially if self-employed) are more likely to be excluded from cash transfers in all welfare systems. The Mediterranean system is the one with the highest exclusion rates for all socio-demographic groups of poor individuals considered, followed by the CEE one (except for the poor households with disabled, for which the Anglo-Saxon system reports a higher non-receipt rate). On the other hand, the Scandinavian and Continental countries have relatively low exclusion rates for almost all categories of poor people.
21
Turning to the longitudinal analysis, descriptive results reported in Chart 2 show that cash transfers are not in favour of households with workers at persistent risk of poverty.9 Indeed, individuals living in a household with at least one employed or self-employed who are persistently poor are those with the higher probability of exclusion from any kind of cash benefit in almost all welfare regimes. The only exception is again the Mediterranean system, in which households with minors and single parent households in poverty persistence status report higher non-receipt rates than those with employed members.
Similarly to the cross-sectional analysis, households with disabled components seem to be the most protected categories among the persistently poor population: across all welfare systems, the share of persistent poor excluded from cash transfers for this group does not exceed 0.4%. According to the data reported in Chart 2, the Mediterranean countries have also the highest rates of social transfer non-receipt in all categories of people at persistent risk of poverty, while the Anglo-Saxon countries show several ‘zeros’ in exclusion rates. Therefore, some categories of persistent poor (e.g.
households with disabled members or minors) seem to be particularly supported by social protection in these welfare regimes.
4. Econometric Analysis
Following previous empirical studies on the same topic (see, for instance, Fabrizi et al., 2014), in order to evaluate the impact of individual and household characteristics on the conditional probability of social transfer non-receipt given the poverty status, we estimate a bivariate Probit model (BPM).
We thus define the two following dependent variables:
𝑦1 = {1
0 if non-recipient of cash transfers otherwise
𝑦2 = {1
0 if at-risk-of income poverty otherwise
The model specification for the non-receipt of cash transfers despite being poor is defined as follows, for each individual i:
𝑦𝑖1∗ = 𝛽1𝑋𝑖1+ 𝜀𝑖1 𝑦𝑖1 = 1 𝑖𝑓 𝑦𝑖1∗ > 0; = 0, otherwise 𝑦𝑖2∗ = 𝛽2𝑋𝑖2+ 𝜀𝑖2 𝑦𝑖2 = 1 𝑖𝑓 𝑦𝑖2∗ > 0; = 0, otherwise
𝜀𝑖1, 𝜀𝑖2 ~
𝑖𝑖𝑑𝑁(0,1) , independent of 𝑋𝑖; 𝐶𝑜𝑣(𝜀𝑖1, 𝜀𝑖2) = 𝜌12
Where X is a vector of individual and household characteristics in 2014 and ρ12 is the correlation between the two errors. Individual characteristics (gender, age, citizenship, and education level), for each household member, are referred to the household head. Given the comparative aim of this analysis, we replicate the same model for each European welfare regime to find any structural difference.
Table 4 shows the marginal effects, at the sample means and weighted with individual sample weights, on the conditional probability of not receiving any social transfer given the poverty status
9 With respect to Chart 1, Chart 2 does not report foreigners because the EU-SILC longitudinal dataset does not provide this individual variable.
22
(Tables A.4 and A.5 illustrate Probit estimations of single equations). Since the correlation between the two residuals (ρ12) may be insignificant (i.e. estimating a BPM may not be necessary because there is no sample selection bias), first we test it, reporting the estimated correlations for Rho together with the results of the Wald test at the bottom of Table 4, confirming that using a BPM is here appropriate because Rho is significant (at the 1% level) in all European welfare systems.
Table 4 – Marginal effects on benefit non-receipt given poverty status.
Cross-sectional sample (year 2014) Y = Non-Receipt of CT
given poverty status Scandinavian Anglo-Saxon Continental Mediterranean CEE REGRESSORS
Female -0.007*** -0.022*** -0.006*** -0.025*** -0.021***
Aged 30-39 -0.011*** -0.003 -0.016*** -0.025** -0.014
Aged 40-49 0.002 0.019* -0.019*** 0.044*** 0.007
Aged 50 or more -0.003 -0.013 -0.017*** 0.007 -0.074***
Foreign citizenship 0.029*** 0.102*** 0.020*** 0.027** 0.016 Lower secondary educ. -0.000 0.036*** 0.007 -0.015* -0.083***
Upper secondary educ. -0.001 0.056*** 0.007 0.062*** 0.012
Bachelor or more 0.001 0.090*** -0.003 0.107*** 0.022
Two adults -0.144*** -0.140*** -0.142*** -0.143*** -0.095***
HH without minors -0.174*** -0.240*** -0.290*** -0.146*** -0.188***
Single parent HH -0.189*** -0.422*** -0.328*** -0.141*** -0.328***
HH with 1-2 minors -0.195*** -0.379*** -0.328*** -0.188*** -0.297***
HH with >2 minors -0.203*** -0.423*** -0.338*** -0.291*** -0.388***
Owner -0.002 0.007 0.009*** -0.006 -0.005
At least one employed 0.003 -0.011 0.000 -0.098*** 0.012
At least one self-employed 0.017*** -0.002 0.031*** 0.091*** 0.079***
At least one unemployed -0.018*** -0.054*** -0.043*** -0.154*** -0.026***
At least one disabled -0.076*** -0.188*** -0.067*** -0.540*** -0.647***
Observations 63,989 34,132 82,051 79,306 72,987
Rho -0.716*** -0.671*** -0.668*** -0.410*** -0.587***
Notes: Robust Standard Errors; *** p<0.01, ** p<0.05, * p<0.1; Marginal Effects at Means and weighted with individual sample weights. The reference group is specified in the Appendix. All individual characteristics refer to the household head.
Results show that when the head is female, poor households have a much lower probability of non- receiving any transfer, independently of the welfare system considered. Households at-risk-of poverty have an overall greater probability of not receiving transfers also when their head is aged 40-49 (except for the Continental system), a foreigner (except for the CEE system) or highly educated (especially in the Anglo-Saxon and Mediterranean countries). The latter case may also be explained by the fact that individuals with high education are more susceptible to stigma (Eurofound, 2015) or because they trust in their own capabilities to overcome a temporary situation of income poverty.
According to the marginal effects reported in Table 4, living as a single person or in a household with at least one self-employed (in this case, except for the Anglo-Saxon system) determines a higher probability of not receiving any social transfer even in poverty condition. On the other hand, poor people who live in households with at least one unemployed or one disabled are more frequently recipients of benefits than the other individuals at-risk-of poverty, whereas means tests on the household wealth (i.e. having an owned house) seem important only in the Continental system.
23
Finally, contrary to the other systems, in the Mediterranean regime poor households with at least one employed have a significantly lower probability of being excluded from transfers, while this probability is higher for the self-employed.
One aspect stands out from these results: all welfare systems have a broadly similar propensity to include some groups of poor individuals and to exclude some others from social transfer receipt, according to specific socio-demographic characteristics. The signs of the estimated determinants are indeed often the same for each European regime. What seems to differ is the degree of inclusion or exclusion for each category, i.e. the intensity of the association between a characteristic and the probability of being excluded, not its direction.
Moving from the cross-sectional analysis to the longitudinal one, we now evaluate marginal effects on the conditional probability of social transfer non-receipt in the last wave of the panel (i.e. 2013) given the persistent risk of poverty (i.e. having a total equivalised disposable income before all cash transfers below the standard at-risk-of poverty threshold in 2013 and in at least two of the preceding three years), still estimating a BPM with the standard maximum likelihood procedure. Together with the previous y1, we thus need to define the following dependent variable:
𝑦3 = {1
0 if at persistent risk of income poverty otherwise
The model specification for the non-receipt of cash transfers despite the persistent poverty status is, for each individual i, as follows:
𝑦𝑖1∗ = 𝛽1𝑋𝑖1+ 𝜀𝑖1 𝑦𝑖1 = 1 𝑖𝑓 𝑦𝑖1∗ > 0; = 0, otherwise 𝑦𝑖3∗ = 𝛽2𝑋𝑖3+ 𝜀𝑖3 𝑦𝑖3 = 1 𝑖𝑓 𝑦𝑖3∗ > 0; = 0, otherwise
𝜀𝑖1, 𝜀𝑖3 ~
𝑖𝑖𝑑𝑁(0,1) , independent of 𝑋𝑖; 𝐶𝑜𝑣(𝜀𝑖1, 𝜀𝑖3) = 𝜌13
As well as in the econometric analysis, for each household member individual characteristics refer to the household head. In this case, too, we replicate the same model for each European welfare system. Table 5 reports marginal effects (at the sample means and weighted) on the conditional probability of transfer non-receipt given the persistent poverty status, as well as the Rho coefficients (Tables A.6 and A.7 illustrate estimations of single equations). The latter are significant according to a Wald test at the 1% level in all five European regimes, confirming the correctness of using a BPM rather than two separate Probit models.
Econometric results of the longitudinal analysis substantially agree with those from the cross-sectional one. Indeed, also among people at persistent risk of poverty, Table 5 shows a strong similarity among the five welfare systems of the population subgroups having a greater probability of being excluded from any transfer. More in detail, similarly to the cross-sectional analysis, living in a persistently poor household with a male, aged 35-49, or low-educated head, as a single person, or with at least one self-employed determines a higher probability of being non-recipient of any transfer. There are, however, some important differences with respect to the previous analysis. Firstly, given the persistent risk of poverty, having a high-educated household head remains a significant determinant of benefit non-receipt in the Mediterranean system only; the effect becomes even negative in the Continental one. Secondly, households with an owned dwelling are now more included in cash transfers in the Continental regime, but more excluded in the Anglo-Saxon one.
