Understanding poverty persistence in Spain

Aylloón, Sara Article Understanding poverty persistence in Spain SERIEs - Journal of the Spanish Economic Association Provided in Cooperation with: Spanish Economic Association Suggested Citation: Aylloón, Sara (2013) : Understanding poverty persistence in Spain, SERIEs - Journal of the Spanish Economic Association, ISSN 1869-4195, Springer, Heidelberg, Vol. 4, Iss. 2, pp. 201-233, https://doi.org/10.1007/s13209-012-0089-4 This Version is available at: https://hdl.handle.net/10419/77757 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/2.0/ SERIEs (2013) 4:201–233 DOI 10.1007/s13209-012-0089-4 ORIGINAL ARTICLE Understanding poverty persistence in Spain Sara Ayllón Received: 29 December 2010 / Accepted: 6 July 2012 / Published online: 7 August 2012 © The Author(s) 2012. This article is published with open access at SpringerLink.com Abstract The aim of this paper is to study the mechanisms behind poverty persistence in Spain. We examine the importance of past poverty experiences for explaining current poverty as opposed to observed and unobserved individual heterogeneity. Our results are based on the model proposed by Cappellari and Jenkins (J Appl Econometr 19:593–610, 2004a) that estimates poverty transitions while simultaneously controlling for attrition and initial conditions. We find that about 50% of aggregate state dependence is genuine: poverty in a given year increases in itself the chances of experiencing poverty again in the future. The remainder is explained, among other characteristics, by living with a head of household who has no educational qualifications, being an immigrant or cohabiting with teenagers. Our findings call for a comprehensive and coordinated strategy against poverty that should focus equally on income-support policies and on enhancing those characteristics that best protect against economic hardship. From a methodological point of view, we learn that unobservables affecting initial conditions and sample retention are exogenous to those related to poverty transience. However, results prove to be sensitive to the choice of poverty line. Keywords Poverty persistence ·State dependence ·Attrition ·Initial conditions JEL Classification I32 ·D31 ·C33 1 Introduction For several reasons, it is crucial to take a longitudinal perspective in the analysis of poverty in any given context. First, the study of the dynamic aspects of poverty leads to S. Ayllón (B ) Department of Economics and EQUALITAS, Universitat de Girona, C/Universitat de Girona 10, 17071 Girona, Spain e-mail: [email protected] 123 202 SERIEs (2013) 4:201–233 a better understanding of the nature of poverty and the type of individuals that suffer it. A more precise description of poverty is enhanced by learning from the determinants of poverty entries and exits. Second, dynamic analyses distinguish between chronic and transient poverty. In this regard, it is commonly agreed that experiencing poverty for a long period of time is worse than being temporarily below the poverty line. Third, poverty dynamics enable the study not only of the symptoms but also the processes that lead to economic deprivation (Jenkins 2011). And, fourth, results on poverty dynamics are informative for policy design. If poverty is transitory, emphasis should be given to short-term income-support policies that help to cope with temporary earnings shocks. However, if poverty is suffered chronically, poor individuals could be better helped by policies that enhance those characteristics that protect them from adversity—for example, employability through education or training. This paper studies poverty dynamics in Spain by focusing on transitions into poverty and, especially, persistence.1Compared to other European countries, poverty in Spain is characterised by high levels of incidence and major recurrence (OECD 2008; Cantó et al. 2012). This means that the poverty line is crossed more often than in countries with similar poverty rates. At the same time, a sizeable percentage of the population is persistently below the poverty line. Recent estimates by Jenkins and Van Kerm (2011) find that 11.0% of Spaniards are at-risk-of persistent poverty (at least 3years out of a 4-year window)—only below Estonia, Portugal, Latvia, Ireland and Italy out of 21 European countries analysed. In this paper, we are concerned with the mechanisms behind poverty chronicity. On the one hand, persistent poverty could be due to genuine state dependence: experiencing poverty in a given period increases in itself the chances of suffering poverty again in the future. On the other hand, certain observed and unobserved characteristics that persist over time could make someone more likely to be successively poor. Learning to distinguish between the two also has important policy implications. If poverty persistence is mostly due to past poverty experiences, policy design should focus on income transfers. Helping individuals to move above the poverty line will break the poverty spiral in the future. However, if poverty persistence is explained by heterogeneity, policies should centre on enhancing the individual and household characteristics that prevent poverty. Therefore, the main contribution of our paper is to examine the importance of the two sources of poverty persistence by measuring, for the first time, the degree of poverty genuine state dependence in Spain and derive policy recommendations. With this objective in mind, we apply to Spanish data a model proposed in the literature by Cappellari and Jenkins (2004a) that estimates poverty entries and persistence and deals with the initial conditions problem and the possibility of non-random attrition of the sample. It is important to account for initial conditions because the initially poor may be a non-random sample of the population and ignoring this may bias our poverty inflow and outflow estimates. Furthermore, estimates of poverty dynamics should control for the fact that transitions are only observed for those individuals in the survey at t−1 and at tand again these may be a selected group of the original sample. We assess the endogeneity of both processes to poverty transitions by freely estimating the correlations between unobservables affecting each outcome. As far as 1In this study, we use the terms poverty chronicity and persistence interchangeably. 123 SERIEs (2013) 4:201–233 203 we know, a similar application to the Spanish case does not exist in the literature and we will therefore be able to assess whether the determinants of poverty dynamics are robust to the methodology used. Our main findings show that about 50 % of the probability of being poor in a given period is due to past poverty experiences in Spain: there is a sizeable scarring effect by which poor individuals enter a vicious circle from which it is difficult to escape. The remaining state dependence is positively associated with a head of household having low educational qualifications, being of immigrant origin and teenagers being in the household, and is negatively associated with the number of workers or cohabiting with young people. This means that anti-poverty policies in Spain should equally focus on income-support policies—breaking the poverty spiral—and on social policies that enhance protective factors—education, training, housing, etc. From a methodological point of view, we show that unobserved heterogeneity affecting poverty status in the base year and/or sample retention are exogenous to unobservables related with poverty transitions when using the standard poverty line. These results support previous estimates for poverty in Spain based on the European Community Household Panel that did not account for possibly correlated unobservables between transience, attrition and initial conditions. However, when the poverty line is set at 40 or 50% of the median of equivalent household income we find that individuals that are more likely to be initially poor are less likely to remain poor compared to the non-poor—an example of Galtonian regression towards the mean. Similarly, retained individuals are less likely to remain poor or fall into poverty. Failure to account for both endogeneities would underestimate our poverty persistence and entry estimates when poverty is more extremely defined. The paper is structured as follows. Section 2, after this introduction, briefly revises the existing literature on poverty dynamics devoted to the Spanish case. Section 3 presents the data and some methodological choices, while Sect. 4shows poverty transitions on a descriptive level. Section 5explains the model by Cappellari and Jenkins (2004a) used in this work and Sect. 6discusses the empirical results. Section 7concludes. 2Review Part of the literature on poverty transience in the Spanish case focuses on the description of trends. Cantó et al. (2003)usingtheEncuesta Contínua de Presupuestos Familiares (ECPF) for the 1985–1995 period find that the decline in poverty of the late 1980s may be associated with high exit rates rather than a major improvement in the situation of those at risk of falling into it. However, the increase in poverty risk experienced by Spaniards in the early 1990s was due both to the increase in poverty entries and, especially, to the reduction in poverty exits. Bárcena et al. (2006) update these results by running a similar descriptive analysis for the 1993–2000 period with data from the ECHP.2 2The first estimates of poverty entries and exits in Spain using data from the ECHP are given in García Mainar and Toharia (1998). 123 204 SERIEs (2013) 4:201–233 As for the characterization of poverty dynamics, Cantó (2003) assesses the importance of demographic and socio-economic characteristics for the probability of poverty exit. She finds that less than 10% of the transitions out of poverty are linked to demographic events while the remainder are related to changes in the labour market or the receipt of social assistance benefits. In relation to methodological questions, Cantó et al. (2006) show that the choice of quarterly or annual income has important consequences for poverty estimates. The exit rate is fairly similar for both income definitions but the entry rate is higher for quarterly income. Furthermore, they show that only half of those classified as leavers by one income definition are equally classified by the other, and the misclassification is even stronger when considering poverty entries. Moreover, Cantó et al. (2012) study the profiles of poor individuals according to the length of their poverty experiences. They distinguish between the chronic and transitory poor and among the latter, those that are recurrent (more than one spell in poverty) or not. They argue that Spain has relatively low levels of chronicity but a high percentage of recurrent poor—especially among households with a head below 65 years of age, with low educational qualifications, and that is self-employed or cohabits with young children. However, note that they also find that 15.5% of Spaniards lived below the poverty line at least 4 out of 7years during the same period than the one analysed in this paper—the highest percentage of all the countries they study except for Portugal. Researchers have also studied duration dependence in the poverty status. Cantó (2002) assesses the importance of time in poverty on the measurement of entries and exits. She proposes a discrete time duration-dependent n-order Markov process with heterogeneity jointly estimating exits and (re)entries. Results show that one third of households that escape poverty soon return to it while if they manage to be out of poverty for 1year, the chances of falling back into it strongly decrease. Similarly, Bárcena et al. (2004) argue for the need to consider not only poverty status at tconditional on poverty status at t−1, but also the time spent in the same poverty status as t−1. The probability of poverty transition prove to be smaller when accounting for time inertia. More recently, Arranz and Cantó (2011) proposed a multi-state multiple transition discrete hazard regression model that controls not only for observed and unobserved heterogeneity but also for the length of the current poverty spell, the time between spells, the occurrence of multiple spells and the accumulation of poverty spells. They find evidence of negative duration dependence—longer poverty spells reduce the probability of exit and increase the rixk of (re)entry. Among other results, second and third poverty spells are found to be shorter than the first, and non-poverty spells are of longer duration than poverty spells. Amuedo-Dorantes and Serrano-Padial (2010), on the other hand, are concerned with feedback effects and examine the poverty implications for past and current temporary employment in Spain. They find that having a temporary contract not only increases the probability of current poverty but also of future poverty via an indirect effect that increases the chances of having a type of contract in the future with a higher poverty risk (while no direct effect of a past temporary contract on poverty is found). However, in most studies referred to Spain, initial conditions are not explicitly modelled together with poverty transitions with the exception of Gradín and Cantó (2011). In this case, the authors study the difference in the probability of being poor 123 SERIEs (2013) 4:201–233 205 depending on the presence of children in the household by means of a Heckman model and a random effects dynamic probit that allows the authors to control for unobserved heterogeneity and initial conditions. However, the paper does not offer a measure of genuine state dependence or an explicit control for attrition. Moreover, in Arranz and Cantó (2011), initial conditions are taken into account by merely adding three variables to their estimation related to the health of the members of the household, the presence of working-age females and the head’s unemployment spells in the last 5years. We believe the methodology used in this paper offers a more precise control. As for attrition, Cantó et al. (2003) and Cantó (2003) do take into account potential non-randomness of sample reduction through the construction of their own sample weights (also see, for a similar strategy, Ayala et al. 2006). The inconvenience of this strategy is that the control is over observed heterogeneity only. Finally, Cantó et al. (2007) estimate poverty exits using a Heckman selection model that controls for retention. With data from the ECPF for the 1985–1995 period, they find that retention and poverty exit equations are independent in most of their model specifications. Nevertheless, we are still left with the question as to whether there is a correlation between unobservables affecting attrition that also influence poverty transience for the later period between 1994 and 2000. Finally, and regarding the application of the same econometric strategy, apart from the original article by Cappellari and Jenkins (2004a) that studies the British case, Buddelmeyer and Verick (2008) have also used it for Australia, Fusco and Islam (2011) for Luxembourg and Faye et al. (2011) in the case of Nairobi’s slums. Their results are commented throughout the paper. Variations in the model for the analysis of poverty can be found in Van Kerm (2004) for the case of Belgium and in Nilsson (2012)fora study of poverty state dependence among twins in Sweden. 3 Data and definitions 3.1 Data The data set used in the analysis is the Spanish component of the European Community Household Panel (ECHP) which is a harmonised cross-national longitudinal survey collected across all members of the (former) European Union-15. The panel runs from 1994 to 2001. Data is based on a standardised questionnaire that collects information related to income, education, employment, household structure, housing, health, social relations and individual satisfaction. The target population consists of all private households throughout the national territory in every country and hence, indigenous households are left out of the analysis. 3.2 Unit of analysis and sample size Although the household is the unit of measurement for income, we examine poverty dynamics at the individual level. As argued in OECD (2001), this methodological choice offers the advantage of giving greater weight to larger families and makes it possible to track the poverty status of individuals when family structure changes 123 206 SERIEs (2013) 4:201–233 Table 1 Number of sample observations Wave Individuals Total observations 1 (1994) 9.443 9.433 2 (1995) 8.914 18.347 3 (1996) 8.243 26.590 4 (1997) 7.691 34.281 5 (1998) 7.319 41.600 6 (1999) 6.926 48.526 7 (2000) 6.791 55.317 Total 55.317 Individuals between 25 and 64 years old (included) Source: Own construction using the ECHP, 1994–2001. Note that the last poverty transitions take place between 1999 and 2000 and therefore the total number of transitions observed is 48.526 when allowing transitions to missing (e.g. divorce, marriage, leaving parental home, etc.). Furthermore, and following previous literature, we restrict our analysis to the population between 25 and 64years old. As indicated by Arranz and Cantó (2007), it is among the Spanish working-age population that transitory or short-term poverty mainly takes place—and this is explicitly what we model in this study. See also, OECD (2001) for similar evidence. Moreover, we exclude individuals aged 65 or over in order to avoid the impact of retirement decisions on poverty dynamics. Table 1shows the number of sample observations used for the empirical analysis. Note that apart from the age of the individuals, no other restrictions are imposed on our working sample.3We allow individuals into the panel even if we know their poverty status for one single year and they transit to missing in the following one. Thus, our panel is unbalanced and maximizes the use of the information available in the survey. We consider this feature of our analysis to be a major advantage because it does not incur possible sample selection problems and neither raises questions about representativeness. 3.3 Poverty A person is considered poor if the equivalent income of the household where she/he lives is below the poverty line defined as 60% of the median of that distribution. The threshold is relative to time so there is a poverty line for each of the years analysed. We use the modified OECD equivalent scale as the scaling factor that takes into account the economies of scale within the household by giving a weight of 1 to the first adult, 0.5 to the remaining adult members of the household and 0.3 to children under 14 3For instance, Arranz and Cantó (2007) need to limit their analysis to individuals present in the survey in 1994. Bárcena et al. (2006) restrict their analysis to adults participating during the eight waves of the panel which considerably cuts their working sample. Cantó et al. (2003) with data from the ECPF limit the sample to those individuals answering at least five quarterly questionnaires. It is sometimes argued that results from bigger samples referred to shorter windows may be more representative and reliable than from smaller samples for longer windows (see Jenkins 2011). 123 SERIEs (2013) 4:201–233 207 years of age.4Moreover, we assume that all incomes are pooled together and shared equally among household members. 3.4 Income in the ECHP The income distribution used in our analysis is net annual household income which adds the income from all possible sources for all household members. As in other surveys, annual income variables are collected retrospectively in the ECHP. For instance, in wave 1, run in 1994, annual income variables refer to incomes obtained by household members in 1993. Neglecting this time lag between the period to which household income refers (year t−1) and the period to which household composition and other variables of interest relate (year t) would introduce some bias. Therefore, net household income in year t−1 is finally constructed as the sum of net personal income reported at tof the individuals that were present in the household at t−1 (see Debels and Vandecasteele 2005;Arranz and Cantó 2007).5This approach makes it possible to build household equivalent income at each year with the household composition (and equivalence scale) referring to the same year. Note, however, that the choice of this income distribution implies, on the one hand, that only seven waves of the panel can be used in our analysis, and, on the other, that a certain number of missing values arise when one of the members of the household does not report his/her income at t, either because of attrition or because this person refuses to collaborate with this part of the questionnaire. Table 9of the Appendix can be used to check how differences in the population headcount ratio are very small either using or not the income distribution with or without time lag in relation to the equivalence scale. Table 10, on the other hand, shows how the use of the corrected household income definition increases the number of transitions to missing by about 2.9 %. Nonetheless, we use an estimation technique that explicitly accounts for sample attrition (see below). 4 Poverty dynamics in Spain: a description The aim of this section is to briefly describe poverty transitions in Spain during the analysed period for the proposed sample. Table 2shows the poverty status of Spanish individuals aged 25–64 at time tconditional on their status at t−1. The first panel shows the results when missing income information is not taken into account and the second displays them when we do. First of all, it is worth noting the important difference in the probability of being poor at time tdepending on the poverty status at t−1. The chances of an individual being poor at twere 58.41% if s/he was already poor at t−1 but only 8.17% if 4These methodological options follow the recommendations of the European Commission for the analysis of poverty and social exclusion in the European Union (Laeken indicators). 5Debels and Vandecasteele (2008) propose a more accurate measure that accounts for changes in household composition within waves. 123 208 SERIEs (2013) 4:201–233 Table 2 Poverty status at t conditional of poverty status at t−1 in Spain without and with income missing data, 1994–2000 Source: Own construction using the ECHP, 1994–2001. Individuals aged 25–64. N=41.457 observations when not taking into account the missing information and N=48.526 observations when allowing transitions to missing Year t Not poor Poor Missing t-1 Not poor 91.83 8.17 – Poor 41.59 58.41 – Total 83.26 16.74 – t-1 Not poor 78.52 6.98 14.49 Poor 35.68 50.11 14.22 Total 71.23 14.32 14.45 not.6One of the objectives of this study is to address the possibility of an endogenous selection mechanism occurring by which individuals observed poor at tperhaps are over (or under) represented at t−1. Secondly, it is interesting to observe the pattern of transitions to missing. Results show that 14.49% of individuals that are not poor at t−1 are no longer observed at t. Among those observed as poor, the percentage is 14.22. At first sight, it seems that sample retention is exogenous to poverty status at t−1. In the following section, however, we explicitly address the question of potential non-random selection of the sample.7 Table 3sheds additional light on the different transition probabilities year by year. As can be observed, entry rates fluctuate between 6.17 and 8.18% while exit rates do so between 31.09 and 41.15%. However, as similarly pointed out by Arranz and Cantó (2007), there is no clear trend of poverty outflows and inflows throughout the period and it is difficult to distinguish, on a descriptive level, a different sample retention process for those who are poor and not poor at t−1.8 Finally, Table 4presents household income level as a percentage of the median of those individuals entering or escaping poverty, which enables assessment of the income level that is the starting point for a transition. As shown, the chances of falling into poverty are highest for those with incomes between 60 and 70% of the median while they are much smaller as income increases. Interestingly though, such a clear pattern does not emerge in the case of poverty exits: the opportunities for escaping poverty are greater for those individuals with an income below 20 % of the median than for those between 20 and 40%.9We give another explanation for this finding below. 6These results are fairly similar to those obtained by Bárcena et al. (2006) with the same dataset as they estimate an entry rate of 8.07% and an exit rate of 39.80 %. Note, however, that they base their estimates on a balanced panel for individuals that are in the panel for eight consecutive waves and are aged 16 or above. 7In OECD (2001) it is argued that “Attrition bias may be particularly acute for the ECHP since attrition rates are quite high for some of the participating countries (…) and the poverty population appears to drop out of the sample at a disproportionate rate in most of these countries” (OECD 2001, p. 43). However, from this descriptive analysis we do not find such a clear pattern in the Spanish case. 8Arranz and Cantó (2007) with data from the ECHP also argue that “[…], the probability of attrition does not appear to be determined by the individual poverty situation” (Arranz and Cantó 2007, p. 13). 9Bárcena et al. (2006) in their descriptive analysis find an even greater similarity between the exit rate for Spanish households with income below 10 % of the median and those between 50 and 60% of the same. 123 SERIEs (2013) 4:201–233 215 6 Empirical results We present the empirical findings by focusing first on the model specification results in order to assess how well the model fits the data.23 Second, we introduce the findings relative to state dependence, to move on to a discussion of the model coefficients and marginal effects. Finally, some robustness checks are commented. 6.1 Model specification Table 5first presents the probability predictions of the model computed as shown in Eqs. (10) and (11). Poverty persistence is 0.611 which compares closely to the (unweighted) raw probability of 0.596 (or 0.605 if we compare to those retained).24 Similarly, the predicted proportion of individuals that enter poverty status at t(given that they were not poor at t−1) is 0.089, which is close to the 0.081 from the raw data. As for the probability of exit, a predicted 0.388 compares to 0.404. And finally, for the proportion of individuals being retained, the value of the predicted probability and the raw value are both 0.855. The same occurs for the proportion of initially poor (0.171). Second, we test for possible ignorability of initial conditions and attrition. Table 5 also presents the results relative to the correlations when the poverty threshold is set at 60% of the median of the household equivalent income distribution. As shown, none of the estimated correlations is significantly different from zero. Thus, unobserved heterogeneity affecting retention is not related to that which affects initial poverty status or poverty transience which implies that the retention and the initial conditions equations could actually have been estimated separately from that of transience.25,26 The exogeneity tests of the two selection processes considered could not be rejected by the Wald tests conducted. These results necessarily support previous poverty dynamics estimates for the Spanish case derived from the ECHP that did not consider unobserved heterogeneity related to attrition and initial conditions when using the standard poverty line. On the other hand, it strengthens the use of simpler computational strategies such as that proposed by Wooldridge (2005) for the estimation of genuine state dependence. As commented, in order to identify the model, it is crucial to find valid instruments. Wald tests indicate that the fact that the head of household suffers from a chronic disease could be excluded from the transitions equation, and the same occurs for the original sample member dummy and the type of questionnaire answered. On the contrary, both sets of instruments increase the precision of the initial conditions and retention equations, respectively. 23 Model assumptions such as, for instance, normality of random effects, presented in the previous section are presumed from this point onwards. 24 Note that the comparison is always with an unweighted probability as we do not have weights for those individuals that attrit. Thus, the unweighted probabilities differ slightly from those presented in Table 2. 25 Differences in the predicted probabilities of persistence and entry among all individuals and the retained sample are negligible which confirms the ignorability of retention. 26 Buddelmeyer and Verick (2008) did not find significant correlations in the Australian case either, but argue that joint estimation improves efficiency. 123 216 SERIEs (2013) 4:201–233 Table 5 Predicted probabilities, estimates of model correlations, exogeneity of initial conditions and sample retention tests, validity of instruments, state dependence estimates and poverty duration predictions(p-values in brackets) Predicted probabilities Poverty persistence 0.611 Poverty exit 0.388 Poverty entry 0.089 Initially poor 0.171 Sample retained 0.855 Correlations between unobservables ρ10.0165 (0.353) ρ2−0.2849 (0.285) ρ3−0.1360 (0.191) Tests for correlations ρ1=ρ21.85 (0.3963) ρ1=ρ32.58 (0.2749) ρ1=ρ2=ρ33.47 (0.3250) Tests for instruments Exclusion of ‘hh. suffers a chronic disease’ 4.57 (0.1017) from transitions equation (2 d.f.) Exclusion of OSM and interview 5.06 (0.7515) type from transitions equation (8 d.f.) Exclusion of all the instrument variables 9.44 (0.4906) from transitions equation (10 d.f.) Inclusion of ‘hh. suffers a chronic 18.42 (0.0000) disease’ in IC equation (1 d.f.) Inclusion of OSM and interview type 56.77 (0.0000) in retention equation (4 d.f.) State dependence α 1=α 2(64 d.f.) 2445.24 (0.0000) Aggregate state dependence (ASD) 0.523 Genuine state dependence (GSD) 0.271 Poverty duration prediction Poverty spell duration (in years) 1.91 (mean); 0.93 (median) Non-poverty spell duration (in years) 28.17 (mean); 19.16 (median) Steady-poor probability 20.2% Source: Own construction using the ECHP, 1994-2001. Individuals aged 25 to 64 6.2 State dependence Cappellari and Jenkins (2004a) define aggregate state dependence (ASD) as the simple difference between the probability of being poor at tfor those being poor at t−1 and the probability of being poor at tfor those who were not poor at t−1. Thus, 123 SERIEs (2013) 4:201–233 217 ASD =i∈(Pit−1=1)(Pr(Pit =1|Pit−1=1)) iPit−1 −i∈(Pit−1=1)(Pr(Pit =1|Pit−1=0)) i(1−Pit−1)(12) The estimated value of the aggregate state dependence is 0.523—very close to that obtained by Cappellari and Jenkins (2004a) for Britain (0.526) and by Buddlemeyer and Verick (2007) for Australia (0.520). However, and as already argued, it is important to distinguish between state dependence that is the result of heterogeneity and that of genuine (or true) state dependence (GSD). The former occurs because certain individual characteristics increase the probability of being poor and those characteristics may exhibit persistence over time. The latter occurs when the experience of poverty at the base year increases in itself the chances of being poor the following year. In the framework of the model presented here, a simple Wald test of absence of genuine state dependence can be formulated as H0:α 1=α 2. If the null hypothesis is not rejected, it means that poverty status at tdoes not depend on poverty status at t−1 since the overall effect of poverty entry is the same as that of poverty persistence and no sign of GSD exists. Our estimates prove that this is not the case. The null hypothesis of no genuine state dependence is rejected with a p-value of 0.000. Moreover, we compute GSD by averaging throughout the sample the predicted probability of being poor at tgiven poor at t−1 minus the probability of being poor at tgiven not poor at t−1. Note that GSD, as opposed to ASD, controls for individual observed and unobserved heterogeneity. Formally, it can be computed as follows: GSD =1 NN  i=1[Pr(Pit =1|Pit−1=1)−Pr(Pit =1|Pit−1=0)](13) Based on our model estimates, GSD in the Spanish case amounts to 0.271—which is between that obtained in the British case (0.310) by Cappellari and Jenkins (2004a) and in the Australian one (0.260) by Buddelmeyer and Verick (2008).27 Thus, in Spain, poverty in a given period increases the probability of being poor in the following period relative to another individual with identical characteristics that was not initially poor. About half the aggregate state dependence is genuine.28 Our findings highlight the 27 Fusco and Islam (2011) obtain a value of 0.38 and 0.70 for GSD and ASD, respectively for the case of Luxembourg. And, Faye et al. (2011) estimate GSD to be 90 % of ASD in Nairobi’s slums. However, note that their data set consists of only two waves. 28 In order to be able to compare these results, and following Wooldridge (2005), we also estimate a dynamic random-effects probit model for poverty at tthat controls for state dependence, unobserved heterogeneity and initial conditions, which assumes a certain correlation between time-varying covariates and the specific-effect (see Stewart 2007) and with explanatory variables referred to t−1. We obtain fairly similar results with an ASD of 0.472, GSD of 0.227 and a percentage GSD/ASD of 48%. (Results available from the author on request.) Note, however, that the more generally used specification by which explanatory variables refer to the same period as the dependent variable (as in Gradín and Cantó 2011) results in a lower degree of genuine state dependence which questions whether the fulfilment of the strict exogeneity assumption overstates GSD. 123 218 SERIEs (2013) 4:201–233 balanced need for tax-benefit policies and those that centre on education, training and the acquisition of skills—both types of policy are called for given that individual heterogeneity and genuine state dependence are similarly relevant for explaining poverty in Spain. Our findings in Table 6are completed by computing the mean duration of a poverty spell (1/1−sit), the median duration of a poverty spell (log(0.5)/log(si)), the mean duration of a non-poverty spell (1/ei), the median duration of a non-poverty spell (log(0.5)/log(1−ei)) and the unconditional probability of being poor (1/ei+1−si) (see Boskin and Nold 1975, for a full derivation of this formulae and an application to welfare subsidies recidivism). As shown, a poverty spell in Spain lasts an average of nearly two years, while non-poverty spells around 14 times longer. 6.3 Observed heterogeneity Table 6shows the coefficients and marginal effects for the probability of poverty entry and persistence and Table 7presents those of retention and base year poverty. It is worth noting that more covariates are significant in the case of entries as opposed to persistence which is consistent with the fact that recurrent poverty affects more heterogeneous individuals which, in turn, highlights how policies should not only be targeted at poor individuals but also at those at risk of falling into it (Jenkins 2011). Most characteristics of heads of households are statistically significant in explaining poverty entry. Having no formal qualifications, or being self-employed, unemployed or of immigrant origin are positively related to it. Thus, labour market income instability is associated with entries while being an immigrant is clearly the most important risk factor—it increases the chance by 14%. However, age is negatively related to poverty entry but this effect reverses when getting older (which probably reflects upward mobility in the labour market) and also having completed at least secondary school. For instance, living in a household whose head has a university degree reduces the probability of entry by 18.8% in comparison to someone who only completed primary school. As for demographic characteristics of the household, the presence of children older than 2 increases the chances of entry while cohabiting with older adults or there being other workers in the household reduces it.29 Low educational qualifications and having a head of household of immigrant origin are positively associated with poverty persistence. Interestingly, cohabiting with people aged 19–24 reduces the probability of being permanently poor as opposed to thereby having individuals aged 12–15. In Spain, during the analysed period nearly 40% of individuals aged 19–24 that were living with their parents were working. The help-effect provided by young people to their families has been documented before by Cantó and Mercader (2001) and Ayllón (2009). Again, the number of workers in 29 Remember that our model includes individuals from 25 to 64 years of age. Therefore, it does not reflect the economic conditions of elderly households, which are normally in greater economic hardship than the overall population. However, the covariate reflects the effect of the presence of an elderly individual in a younger household possibly income pooling his/her retirement pension. 123 SERIEs (2013) 4:201–233 219 Table 6 Poverty entry and poverty persistence coefficients and marginal effects, Spain (1994–2000) Poverty entry Poverty persistence Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) Individual characteristics Ref. Male Female 0.0413∗∗∗ 0.0112 (3.37) 0.0414∗∗ 0.0019 (2.55) Age −0.0008 −0.0028 (−0.08) −0.0210∗0.0002 (−1.58) Age20.0006 0.0040 (0.25) 0.0003∗∗ −0.0000 (2.39) Head of household characteristics Demographic characteristics Ref. Male Female 0.0600 0.0216 (1.39) 0.0792 0.0106 (1.24) Age −0.0552∗∗∗ −0.0039 (−6.43) 0.0180 −0.0029 (1.49) Age20.0004∗∗∗ 0.0024 (5.10) −0.0001 0.0025 (−1.43) Ref. Spanish Immigrant 0.4747∗∗∗ 0.1393 (3.63) 0.5465∗∗∗ 0.0502 (2.72) Education Ref. Completed primary school No studies 0.2445∗∗∗ 0.0489 (4.10) 0.2230∗∗∗ 0.0419 (2.95) Secondary school −0.3781∗∗∗ −0.0885 (−8.38) −0.1445∗−0.0416 (−1.81) University degree −0.7723∗∗∗ −0.1881 (−10.54) −0.3930∗∗ −0.0867 (−2.24) Labour market characteristics Ref. Inactive Employed (salary) −0.1116 0.0085 (−1.63) 0.0540 −0.0174 (0.60) Employed (self-employed) 0.5815∗∗∗ 0.1048 (7.61) 0.0766 0.0365 (0.83) Unemployed 0.4151∗∗∗ 0.0398 (4.78) −0.0321 0.0356 (−0.37) 123 220 SERIEs (2013) 4:201–233 Table 6 continued Poverty entry Poverty persistence Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) Household characteristics Demographic characteristics Children aged 0–2 (presence of) 0.0231 0.0108 (0.42) 0.0800 0.0126 (1.02) Children aged 3–5 0.1240∗∗ 0.0417 (2.16) 0.1552∗0.0220 (1.96) Children aged 6–11 0.1599∗∗∗ 0.0322 (3.05) 0.1112 0.0263 (1.53) Children aged 12–15 0.1733∗∗∗ 0.0524 (3.40) 0.2167∗∗∗ 0.0314 (3.08) Children aged 16–18 0.1611∗∗∗ 0.0154 (3.22) 0.0210 0.0221 (0.29) Youth aged 19–24 0.0241 −0.0420 (0.52) −0.1930∗∗∗ −0.0023 (−2.85) Older adults aged 65-74 −0.2400∗∗∗ −0.0435 (-4.06) −0.0840 −0.0379 (−0.80) Older adults aged +75 −0.1797∗∗ −0.0331 (−1.98) −0.1314 −0.0542 (−0.80) Household size 0.0591∗∗∗ 0.0147 (2.66) 0.0615∗0.0048 (1.89) Lone family with children 0.2851 0.0394 (1.49) 0.1226 0.0375 (0.72) Labour market characteristics Number of workers in the household −0.2125∗∗∗ −0.0845 (−6.38) −0.2704∗∗∗ −0.0367 (−5.37) Housing Ref. Own housing (no mortgage) Own housing, mortgage −0.1394∗∗∗ −0.0673 (−3.33) −0.2220∗∗∗ −0.0228 (−3.34) Rent −0.0129 0.0060 (−0.23) 0.1131 0.0120 (1.52) Subsidized or rent free 0.0198 −0.0041 (0.29) 0.0717 0.0187 (0.93) 123 SERIEs (2013) 4:201–233 221 Table 6 continued Poverty entry Poverty persistence Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) Time (t) Ref. 1995 1996 0.0884∗0.0643 (1.78) 0.2430∗∗∗ 0.0138 (3.42) 1997 −0.0444 0.0048 (−0.88) 0.0995 0.0056 (1.45) 1998 0.0002 0.0243 (0.00) 0.1432∗∗ 0.0096 (1.96) 1999 0.0447 0.0275 (0.87) 0.1309∗0.0119 (1.72) 2000 0.1101∗∗ 0.0432 (2.12) 0.1439∗0.0167 (1.87) Constant 0.0306 (0.14) Correlations ρ1=0.0165(0.93);ρ2=−0.2849(−1.07);ρ3=−0.1306(−1.35) Log-pseudolikelihood −48937.4 Wald chi-square 402.10 (p≺0.000) Number of observations / Clusters 47.965/5830 Source: Own construction using the ECHP, 1994–2001. Individuals aged 25–64. Marginal effects refer to a head of household who is a Spanish male aged 40 that lives alone, is inactive, completed primary school, stays in his own property, does not have a chronic disease, is an original sample member and replied to a face-to-face interview. Significance level: * if p≺.05, ** if p≺.01 and *** if p≺.001 123 222 SERIEs (2013) 4:201–233 Table 7 Retention and poverty status at base year coefficients and marginal effects, Spain (1994–2000) Retention Poverty status at base year Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) Individual characteristics Ref. Male Female 0.0124 0.0027 (1.36) 0.0073 0.0016 (0.66) Age 0.0051 0.0011 (0.72) 0.0222∗∗ 0.0050 (2.09) Age2−0.0000 −0.0008 (−0.56) −0.0002∗∗ −0.0046 (−2.10) Head of household characteristics Demographic characteristics Ref. Male Female −0.0504∗−0.0131 (-1.78) 0.3257∗∗∗ 0.0500 (8.32) Age 0.0084 0.0019 (1.64) −0.0773∗∗∗ −0.0166 (−10.01) Age2−0.0000 −0.0011 (−1.17) 0.0008∗∗∗ 0.0157 (9.93) Ref. Spanish Immigrant −0.2345∗∗∗ −0.0789 (−2.81) 0.2878∗∗ 0.1076 (2.21) Education Ref. Completed primary school No studies 0.0277 0.0087 (0.72) 0.5656∗∗∗ 0.1981 (11.61) Secondary school 0.0501∗0.0152 (1.71) −0.4159∗∗∗ −0.1647 (−9.59) University degree 0.0283 0.0086 (0.85) −0.9955∗∗∗ −0.3667 (−16.52) Labour market characteristics Ref. Inactive Employed (salary) 0.1201∗∗∗ 0.0357 (2.34) −0.3246∗∗∗ −0.1287 (−5.93) Employed (self−employed) 0.0996∗∗ 0.0297 (1.62) 0.2044∗∗∗ 0.0776 (3.54) Unemployed −0.0310 −0.0097 (−0.55) 0.4829∗∗∗ 0.1729 (7.83) 123 SERIEs (2013) 4:201–233 223 Table 7 continued Retention Poverty status at base year Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) Household characteristics Demographic characteristics Children aged 0−2 (presence of) 0.1203∗∗∗ 0.0356 (3.11) 0.1801∗∗∗ 0.0686 (3.84) Children aged 3−50.1286∗∗∗ 0.0381 (3.20) 0.2179∗∗∗ 0.0825 (5.14) Children aged 6−11. 0.1429∗∗∗ 0.0420 (4.16) 0.3388∗∗∗ 0.1254 (8.20) Children aged 12−15 0.1085∗∗∗ 0.0323 (3.15) 0.3391∗∗∗ 0.1255 (8.54) Children aged 16−18 0.0992∗∗∗ 0.0297 (2.88) 0.3388∗∗∗ 0.1254 (8.63) Youth aged 19−24 −0.0078 −0.0024 (−0.24) 0.1298∗∗∗ 0.0498 (3.19) Older adults aged 65−74 −0.0344 −0.0108 (−0.95) −0.5031∗∗∗ −0.1983 (−9.15) Older adults aged +75 −0.2563∗∗∗ −0.0865 (−6.02) −0.8395∗∗∗ −0.3185 (−10.21) Household size −0.0914∗∗∗ −0.0215 (−6.11) 0.0812∗∗∗ 0.0190 (4.14) Lone family with dependent children −0.2098∗−0.0696 (−1.93) 0.5247∗∗∗ 0.1858 (4.21) Labour market characteristics Number of workers in the household −0.0272 0.0086 (−1.56) −0.3390∗∗∗ −0.1345 (−13.33) Housing Ref. Own housing (no mortgage) Own housing, mortgage −0.0102 −0.0032 (−0.36) −0.1477∗∗∗ −0.0583 (−3.82) Rent −0.1577∗∗∗ −0.0515 (−4.65) 0.1900∗∗∗ 0.0723 (3.71) Subsidized or rent free 0.0335 −0.0102 (0.70) 0.3677∗∗∗ 0.1352 (6.92) Time (t) Ref. 1995 1996 −0.1007∗∗∗ −0.0324 (−2.94) −0.0092 −0.0036 (−0.29) 123 224 SERIEs (2013) 4:201–233 Table 7 continued Retention Poverty status at base year Coefficient Marginal effect (t-ratio) Coefficient Marginal effect (t-ratio) 1997 −0.0882∗∗ −0.0282 (−2.53) 0.0794∗∗ 0.0307 (2.44) 1998 0.0038 0.0012 (0.10) 0.0722∗∗ 0.0278 (2.05) 1999 −0.0874∗∗ −0.0278 (−2.38) 0.0981∗∗∗ 0.0380 (2.78) 2000 0.0907∗∗ 0.0273 (2.37) 0.1184∗∗∗ 0.0455 (3.18) Instruments Ref. Participates 1st. time after w1 Original sample member 0.2121∗∗∗ 0.0419 (5.20) Ref. Face−to−face interview Self−administered interview −0.1710∗∗∗ −0.0417 (−3.03) Telephone interview −0.1913∗∗∗ −0.0474 (−2.88) Proxy interview −0.0817∗∗∗ −0.0192 (−3.67) Ref. HH does not have a chronic disease HH has a chronic disease 0.2569∗∗∗ 0.0657 (4.26) Constant 0.7961∗∗∗ (4.71) 0.1250 (0.50) Source: Own construction using the ECHP, 1994–2001. Individuals aged 25–64. Marginal effects refer to a head of household who is a Spanish male aged 40 that lives alone, is inactive, completed primary school, stays in his own property, does not have a chronic disease, is an original sample member and replied to a face-to-face interview. Significance level: * if p ≺.05, ** if p≺.01 and *** if p≺.001 123 SERIEs (2013) 4:201–233 231 Table 9 Headcount ratio (whole population) and number of individual-wave observations by income definition Wave Income refers to Income definition [1] Income definition [2] Poor Obs Poor Obs. 1 (1994) 1993 19.59 22.837 – 2 (1995) 1994 18.98 20.458 18.66 18.677 3 (1996) 1995 17.97 19.278 18.21 17.488 4 (1997) 1996 20.34 17.916 20.63 16.183 5 (1998) 1997 18.18 16.598 18.31 15.026 6 (1999) 1998 18.89 15.863 18.89 14.168 7 (2000) 1999 18.02 14.784 18.48 13.349 8 (2001) 2000 18.82 14.270 18.86 12.935 Source: Own construction using the ECHP, 1994–2001. [1] Household income refers to t−1butthe equivalence scale refers to t. [2] Household income and equivalence scale refer to t. Cross-sectional weights used. Notice that the headcount ratio for 1993 cannot be computed as we do not know household composition for this year. In few cases where there is missing income information for one household members, we were able to impute to that individual the income information given in the survey for within household nonresponse (0.37% of the individuals-waves sample). Also, note that household income for year t−1 cannot be computed in those households where one of the household members dies at t. In these cases, we proxied his/her personal income at twith the one reported at t−1 (0.30 % of the individuals-wave sample) Table 10 Poverty status at tconditional of poverty status at t−1 in Spain with missing income information and by income definition (whole population) Year t Not poor Poor Missing [1] t−1 Not poor 80.80 7.73 11.46 Poor 34.50 53.79 11.71 Total 72.05 16.44 11.51 [2] t−1 Not poor 78.22 7.36 14.42 Poor 32.83 53.06 14.12 Total 69.65 15.98 14.37 Source: Own construction using the ECHP, 1994–2001. [1] Household income refers to t−1butthe equivalence scale refers to t(1993–2000). 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