Journal of Family and Economic Issues

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Journal of Family and Economic Issues Individual pension plans in Spain: How expected change in future income and liquidity constraints shape the behavior of households --Manuscript Draft-Manuscript Number:

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Individual pension plans in Spain: How expected change in future income and liquidity constraints shape the behavior of households

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Individual private pension plans; Family finances; Logit models; Heckman two-step method

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Dolores Moreno-Herrero Universty of Granada Granada, Granada SPAIN

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Universty of Granada

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Dolores Moreno-Herrero

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Dolores Moreno-Herrero Manuel Salas-Velasco José Sánchez-Campillo

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Individual pension plans in Spain: How expected change in future income and liquidity constraints shape the behavior of households

Dolores Moreno-Herrero is a Senior lecturer at the University of Granada. Her research concentrates on the economics of education but she has also worked in other applied areas in economics including pensions, behavioral economics, and health economics. She obtained her Ph.D. from the University of Granada. Email: [email protected] University of Granada Department of Applied Economics Campus Cartuja ― 18071 Granada, Spain

Manuel Salas-Velasco is a Senior lecturer at the University of Granada. His research lines are mainly the economics of education, labor economics, and applied micro-econometrics. He has been a visiting professor at Columbia University, Stanford University, and the University of Oxford, among others. Email: [email protected] University of Granada Department of Applied Economics Campus Cartuja ― 18071 Granada, Spain

José Sánchez-Campillo is a Senior lecturer at the University of Granada. His work focuses on the economics of education but he has also worked in other areas including pensions, the economics of happiness, health economics, and applied macroeconomics. He obtained his Ph.D. from the University of Granada. Email: [email protected] University of Granada Department of Applied Economics Campus Cartuja ― 18071 Granada, Spain

Abstract

Understanding the motives that underlie Spaniards’ retirement saving decisions is important because many, if not most future retirees, will need to rely on personal savings to maintain a decent standard of living. The governor of the Bank of Spain has stated recently that the current public pension system will not guarantee an adequate pension to the citizens, advising to save now for retirement. In this debate on public pensions, and the complementary role that private pensions might play in Spain, this article has shed light on the decision of Spanish households to

engage in individual pension plans and it has identified which factors determine the total amount saved in such retirement plans. Using micro data from the Bank of Spain (Survey of Household Finances 2011), the analysis has revealed that the expectations of lower future income, along with preferences for the financial risk and education, exert an important influence on the likelihood of enrolling in a private pension plan. University education minimizes the myopic behavior of households in the sense of making them more forward-looking and cautious in the face of their future well-being. Additionally using Heckman's methodology to correct for the problem of selection bias, our results have revealed that liquidity constraints affect negatively the total amount of money saved for retirement. Keywords: individual private pension plans; family finances; logit models; Heckman two-step method JEL classification: J1; J26; I29; H31

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Individual pension plans in Spain: How expected change in future income and liquidity constraints shape the behavior of households Abstract

Understanding the motives that underlie Spaniards’ retirement saving decisions is important because many, if not most future retirees, will need to rely on personal savings to maintain a decent standard of living. The governor of the Bank of Spain has stated recently that the current public pension system will not guarantee an adequate pension to the citizens, advising to save now for retirement. In this debate on public pensions, and the complementary role that private pensions might play in Spain, this article has shed light on the decision of Spanish households to engage in individual pension plans and it has identified which factors determine the total amount saved in such retirement plans. Using micro data from the Bank of Spain (Survey of Household Finances 2011), the analysis has revealed that the expectations of lower future income, along with preferences for the financial risk and education, exert an important influence on the likelihood of enrolling in a private pension plan. University education minimizes the myopic behavior of households in the sense of making them more forward-looking and cautious in the face of their future well-being. Additionally using Heckman's methodology to correct for the problem of selection bias, our results have revealed that liquidity constraints affect negatively the total amount of money saved for retirement. Keywords: individual private pension plans; family finances; logit models; Heckman two-step method JEL classification: J1; J26; I29; H31

Introduction Since the mid-90s, the Spanish pension system has gone through a long reform process aimed at improving its long-term sustainability and redressing its main distortions. A Spanish population increasingly aging as a result of the lengthening of life expectancy, along with a relatively low labor force participation rate and high unemployment, make our current public pension system unviable in the medium/long term. Right now, there are fewer than two workers paying for every person who comes to retirement, an alarming sign in a generous pay-as-you-go scheme where a public pension is nowadays about 80% of the last salary earned, nearly twice the Organization for Economic Cooperation and Development average (OECD 2015). We must also note the required budgetary discipline from Brussels that mandates a significant reduction in public spending on public pensions

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along with a necessary reduction in spending on the elderly (such as medicines, health care services, etc.). Nevertheless, the concern about the financial viability of public pension systems does not focus only on the Spanish case (Banco de España 2010). There is also an alarm in Europe regarding the financial sustainability of public pension systems, given the strong pressure from the European institutions to their Member States, mainly to southern countries, to reform in depth their public pension systems. For example, the publication by the European Commission of the White paper: An agenda for adequate, safe and sustainable pensions outlined a series of recommendations globally which emphasized the need to reform public pensions both by demographic forecasts and by the sustainability of public finances (European Commission 2012). In particular, the White Paper suggested developing complementary private retirement schemes. The recent reforms of public pension systems along with the complex macroeconomic situation in the majority of advanced economies require citizens to be now much more aware of the risks and effects of their decisions on consumption, debt, and saving planning than in the past decades. In a context of unsustainable public pensions, individual private pension plans might be an alternative to Spanish families to save in the long term to complement the Social Security pension in retirement. Private pension plans are a way of saving for retirement and they cannot be considered a standard financial product, such as stocks or bonds, that households may buy and sell in the financial markets (Rosen and Wu 2004). In fact, individual private pension plans cannot be recovered until retirement. However, unlike the United States with the Traditional and Roth IRAs, or Riester pensions in Germany, individual private pension plans are underdeveloped in Spain because not only citizens so far have relied on the public pension system but also by the ideological connotations that private pension plans have among Spanish citizens. Actually, a considerable part of the Spanish population still associates the development of these plans with neoliberal ideologies (market forces) and a failure of the public sector. The degree of participation in private pension schemes varies significantly between countries (Holzmann et al. 2012). In general, participation tends to be lower in those countries where the level of benefits from the public pension system is higher (OECD 2012), but private schemes play an increasingly important complementary role. In Latin America, particularly in Chile, major structural reforms have meant generally the introduction of individual capitalization systems, resulting in the development of private pension plans. In most OECD countries, private pension plans are also an instrument of savings-forecast supplementary to the public pension system. However, they still have a marginal role in Spain, with fund assets worth US $116,355.2 million in 2011, representing 7.8% of GDP (OECD 2013). The importance of these funds in Spain is reduced in comparison with the

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average in OECD (73.8%), and far from countries such as Holland (135.5%), Iceland (128.7%), UK (95.8%) and the US (72.2%) (OECD 2013). We explored in this paper the question of whether Spanish households were saving adequately for their future retirement. With microdata from the Survey of Household Finances of the Bank of Spain for 2011 (Encuesta Financiera de las Familias, hereafter EFF-2011), we studied saving decisions in individual private pension plans and the variables that explained this behavior. In Spain, the percentage of households that were saving for retirement in private pension plans was 20.25%, and the median amount was relatively low (€7,376). Many OECD countries have traditionally used various tax incentives to encourage people to engage in private pension plans. However, the analysis of our database revealed that the majority of Spanish households enrolled in an individual private pension plan mainly on the basis of future savings and future security (64.5%). We have labeled this behavior as precautionary saving for retirement. In fact, in the absence of complete insurance, expected shocks in disposable income lead prudent agents to save for smoothing the consumption path; e.g., under the given assumptions, saving does not only serve to finance consumption after retirement but also to insure households against income shocks (Shunk 2007). To our knowledge, this is the first study that has explained retirement saving decisions of the Spanish households using the EFF-2011. The aim of this paper has been twofold. On the one hand, we shed light on the decision of the households to engage or not in individual private pension plans and, on the other hand, for those households who chose to engage in them, we explained which factors determined the total amount saved in such plans. Controlling by socio-demographic variables of the households – such as the level of education, marital status, occupational status, age, and gender – and the levels of income of the families, our objective was to explain to what extent expected change in future income and liquidity constraints, along with other financial attributes of the households, shaped the behavior of the Spanish households in enrolling in private pension plans. This paper is organized as follows. In the second section, we review the literature that addresses household savings decisions with special reference to saving for retirement. Next, we introduce the econometric methodology used in the analysis. In particular, we explain the binomial logit model of the probability of enrolling in the individual private pension plan, and Heckman's methodology to explain the amount saved by households. In the fourth section, the data source and variables are described. Besides the typical variables of the households such as education and income, we have included the attitude towards the risk, future income expectations, and financial habits as well. In the fifth section, we present the main results. The analysis revealed that expectations of lower future income, along with preferences for the financial risk and education, exert an important influence on

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the likelihood of enrolling into a private pension plan. In addition, our results confirmed that liquidity constraints affected negatively the total amount of money saved for retirement. Finally, the article concludes with a summary of the main findings and policy recommendations. Literature review Economic explanations of household saving behavior are based on the hypothesis that rational households will anticipate changes in their future income and they will respond by smoothing the levels of consumption in order to maximize their expected lifetime utility. Lifecycle theories of resource allocation provide a useful framework for examining retirement saving decisions. Early versions of the lifecycle model explained the old-age provision motive as the central saving motive: Individuals save while working in order to counteract the drop in income at retirement (Friedman 1975; Modigliani and Brumberg 1954). The basic version of the lifecycle model was extended to include other saving motives, most prominently the precautionary saving motive (Carroll 1997; Gourinchas and Parker 2002). Further extensions included a housing motive (Hayashi 1988) and a bequest motive (Hurd 1987). Institutional differences across countries may also play a major role in different savings behaviors. High replacement rates after retirement may substitute the need for precautionary savings (Browning and Lusardi 1996). Nevertheless, countries with a higher degree of uncertainty regarding income and other future economic circumstances will most likely feature higher saving rates in the presence of a precautionary saving motive (Boersch-Supan and Lusardi 2003). Applied research has been interested in knowing, on the one hand, if households are saving adequately for their future retirement and, on the other hand, what the reasons are that shape this behavior. In this sense, Hershey and Jacobs-Lawson (2012) showed that American working adults expected significantly less income than they believed they would need to achieve a reasonable standard of living in retirement. However, it is striking that roughly half of working households in the US were not saving enough to be able to maintain their current spending after retirement (Hanna et al. 2016). There are several reasons why households might have a positive or negative attitude towards retirement saving. Findings from research range from those that have stressed the influence that certain sociological factors of the households exert in the decisions of saving for the retirement (e.g., Payne et al. 2014) to those studies that have focused mainly on variables of economicfinancial nature (Hanna et al. 2016, among others). In this regard, a wide variety of research in the field of behavioral economics has shown that individuals do not have enough saving discipline, which significantly affects the level of savings due to myopia that prevents them from considering the future (Foster 2015).

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The expectation of future income is perhaps the main factor in understanding the precautionary saving for retirement. Nevertheless, the primary difficulty of directly testing this feature is measuring ex-ante the household’s expectations of future income. In most empirical works, individual expectations and uncertainty are not available, leading authors to assume static, rational expectations (Attanasio and Rohwedder 2003; Feldstein 1974; Kapteyn et al. 2005). To avoid making such strict assumptions regarding the expectation formation process, several studies suggest using individuals’ subjective expectations of future income (Guiso et al. 1992, 1996, 2013; van Santen 2013). Households that expect a future income lower than their current one will have greater incentives to look for alternative forms of saving during working life, such as contracting individual private pension plans. However, it is striking that many households start too late to make contributions to those pension plans to maintain the purchasing power after retirement (Thaler and Sunstein 2008). In fact, age affects retirement behavior in the sense that savings for retirement purposes become significant only closer to retirement (Cagetti 2003). We can find several barriers that prevent households from making this type of savings. A vast literature has highlighted that liquidity constraints affect the amounts saved for retirement (Bryant and Zick 2006; Corsini and Spataro 2013; Magnussen 1994). In the lifecycle framework, the existence of credit constraints has, in fact, direct predictions for saving (Deaton 1991). Households that face binding borrowing constraints are prevented from smoothing consumption as they can only consume less than they would optimally like to. The work of Corsini and Spataro (2013), for example, showed that liquidity constraints affected the amount saved during the working life. Carroll and Kimball (2001) provided an explanation of the apparently contradictory results that have emerged from simulation studies, which sometimes seem to indicate that constraints intensify precautionary saving motives or sometimes find constraints and precautionary behavior to be substitutes. The decisions about retirement savings are also related to households’ behavioral characteristics such as risk attitude (Hariharan et al. 2000; Hilgert et al. 2003). Studies from the general investment literature have shown that risk-tolerant individuals prefer to invest in high-risk options (e.g., equities), whereas those who are risk averse prefer to invest in bonds and certificates of deposit (Jacobs-Lawson and Hershey 2005). Similar findings have emerged from studies focused on retirement investments (Sunden and Surette 1998). Likewise, Grable and Joo (1997) reported that risk tolerance was a significant predictor of retirement investment and saving strategies. Moreover, the allocation of assets for retirement was likely to be correlated with the willingness of households to trade risk for return (Sunden and Surette 1998).

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Of great concern in this analysis is whether households have the adequate financial skills to save effectively. A lack of financial knowledge may cause individuals to start saving too late in life to attain their desired retirement goals. In this regard, the economic literature has revealed that individuals with low levels of financial knowledge find it difficult to make retirement planning decisions (Alessie et al. 2011; van Rooij et al. 2012). Financial knowledge is statistically linked to financial practices related to credit management and saving (Guiso and Jappelli 2008; Hilgert et al. 2003; Lusardi and Tufano 2009; van Rooij et al. 2011). But the idea that financial literacy affects the behavior of individuals in relation to saving, and, in particular, their decisions regarding retirement, is not new (Bernheim 1998), although applied studies began to take a greater interest in this issue a couple of decades ago. Most recent works have corroborated these results, demonstrating that financial literacy has a positive impact on planning for retirement savings. For example, Lusardi and Mitchell (2007a, 2007b, 2011) concluded that the financially literate are more likely to plan for retirement. In other words, financial literacy is closely tied to retirement planning and retirement wealth accumulation. In the context of the increasing reliance on private provision for retirement, the importance of individuals having the financial literacy to successfully navigate complex financial decisions late in life should not be underestimated (Banks et al. 2015). However, numeracy literacy is also important. Banks and Oldfield (2007) showed that numeracy levels were strongly correlated with measures of retirement saving and investment portfolios, even when controlling for other dimensions of cognitive ability as well as educational attainment. However, in the absence of direct information on financial literacy and/or financial knowledge, applied studies have used formal education to approximate financial literacy, assuming that formal education levels affect the behavior of individuals in relation to saving, in particular, their decisions regarding retirement, because individuals with higher levels of education have a higher ability to absorb complex information. For example, Bernheim and Sholz (1992) showed that those who had more formal education were more likely to engage in more sophisticated financial planning. After comparing the saving behavior of people with and without a college degree, the authors reported that individuals with college degrees were likely to save more adequately for retirement, that is, they made decisions that were more consistent with the lifecycle model. Other studies have also addressed the positive relationship between education and savings (Ameriks et al. 2003; Attanasio 1998; Mitchell and Moore 1998; van Rooij et al. 2012). In the same line, Sunden and Surette (1998) included indicator variables for levels of schooling. According to their estimates, college graduates were more likely than high-school graduates to have a defined contribution (DC) plan, whereas workers without a high school diploma were much less likely than high school graduates to have a DC plan.

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Self-employment may also play an important role in explaining the behavior of individuals with regard to their retirement savings. By taking responsibility for their own business, the selfemployed demonstrate initiative and interest in business issues. They may, therefore, be more likely than other workers to plan for their retirement. Moreover, self-employed workers are more heavily represented among the planners (Brucker and Leppel 2013). Knoef et al. (2016) also showed that the self-employed in the Netherlands were less likely to maintain their standard of living during retirement. Self-employed households have relatively low occupational pension rights, but relatively high voluntary pensions, private savings, and net housing wealth. The median selfemployed household is expected to replace only 50% of current income; this figure is 71% for all working-age households (Knoef et al. 2016). In comparing pension contributions and accumulations between employed men and women, Bajtelsmit and Jianakoplos (2000) emphasized that the lower earnings of women implied a reduced ability to contribute to retirement savings. They highlighted the fact that women, on average, were more concentrated in the lowest income categories compared with men. Differences in earnings are important because they inevitably magnify the gender differences in retirement income. If gender differences persist, women may end up accumulating less wealth for retirement regardless of how they invest their DC assets (Sunden and Surette 1998). The effect of household financial management practices on household saving patterns, and in particular decisions regarding retirement savings, is more recent and has been corroborated in the economic literature by the results of several research papers such as Lee et al. (2000) and Hilgert and Hogarth (2003), among others. The financial habits of households, such as the use of the Internet, is one of the most recent variables in the study of the determinants of household financial decisions (Rubin and Rubin 2010), particularly the decision to save for retirement (Oehler and Werner 2008). Technological changes and the Internet have increased the information and choices available to consumers without geographical limitations. But a certain level of knowledge and understanding is required for consumers to benefit from this development. After controlling for a number of relevant factors, Bogan (2008) showed that households which used computers/the Internet increased their stock market participation rates substantially more than households that did not use computers/the Internet. Finally, this section of literature review discusses other factors that determine the likelihood of enrolling in private pension plans by the families. For example, Lum and Lightfoot (2003) found that health had a large and significant effect on both the probability that a person nearing retirement age will contribute to an IRA and the amount of money that a person will hold in IRAs. In line with this finding, using data from the HRS, Dwyer and Mitchell (1999) found that health conditions

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influenced retirement planning more strongly than economic variables. The future time perspective is a psychological variable that has received a good deal of attention in the financial planning literature as well. It is a measure of the extent to which individuals focus on the future, rather than the present or past. Among psychological studies, the construct is typically referred to as FTP or future orientation, whereas in the economic literature it is often referred to as one’s level of patience, time preference, or planning the horizon (Jacobs-Lawson and Hershey 2005). Similarly, one’s level of patience (e.g., the willingness to postpone spending to save) is related to retirement saving tendencies (Bernheim et al. 2001; Burtless 1999). Taken together, these findings convincingly reveal that one’s future orientation is likely to have a significant impact on saving behaviors. Econometric Methodology Modeling Household Saving Behavior: Probability of Enrolling in Private Pension Plans Private pensions do not represent the main source of retirement financing among Spanish households. This subsection briefly describes the methodological framework used to model the behavior of retirement saving among Spanish families. In this regard, we characterized households in their decisions to enroll or not in an individual private pension plan. Under standard assumptions about the utility function and combined with the fact that income is usually lower after retirement than before, classical lifecycle theory essentially captures an old-age provision motive (Schunk 2007). If we assume that households choose the most attractive alternative from two options, then the choices reveal the households’ preferences (Jiménez and Salas-Velasco 2000). For a household (designated i) that chose an individual private pension plan, it would be verified that Ui1 > Ui0, where Ui1 and Ui0 are the utilities that i associates with enrolling or not into a private pension plan, respectively. The utility Uij that the alternative j (j = 1 enrolling in a private pension plan; j = 0 not enrolling in a private pension plan) given household i, is composed of two parts: a systematic term, which depends on an attributes vector X (expected income in the future for the household, education, age, etc.), and another random one ∈ij ̅𝑖𝑗 + 𝜖𝑖𝑗 𝑈𝑖𝑗 = 𝑈

(1)

But utility Uij is not observable directly. What we observed was the decision Yi, which is worth 1 if the household i had a plan and 0 otherwise. If a rational household chose the alternative that gave it the greatest utility, then we would have Probability [Yi = 1] = Probability [Ui1 > Ui0] Probability [Yi = 0] = Probability [Ui0 > Ui1] McFadden (1974) proved that the probability that household i chose alternative 1 was

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𝑃𝑟𝑜𝑏[𝑌𝑖 = 1] =

𝑒 𝑋𝑖 𝛽 ′

1 + 𝑒 𝑋𝑖 𝛽

(2)

This would be the reduced form for the binomial logit model, where the X'i row vector of explanatory variables for the ith household contains the independent or explanatory variables considered in Table 4 (and including a constant) and where we assume that the non-observed ∈’s follow a distribution of logistic probability. The logit model can be seen as a special case of a general model of utility maximization (Cramer 1991). Savings for Retirement: A Sample Selection Model The second objective of this paper was to explain the total amount saved by Spanish households in individual private pension plans. We could think a priori about a linear regression; however, a problem of sample self-selection emerges in the sense that households, as mentioned above, previously decided whether or not to subscribe a pension plan. Statistical analyses based on non-randomly selected samples can lead to erroneous conclusions and poor policy. The Heckman correction (1979), a two-step statistical approach, has offered a means of correcting for non-randomly selected samples. Therefore, the behavior of Spanish households about the decision to enroll or not in an individual private pension plan, on the one hand, and the variables that explain the accumulated wealth in those plans, on the other hand, were analyzed in this paper using this procedure, often called the “Heckit model.” In the first stage, using a binomial probit model for the entire sample of Spanish households, we estimated the participation equation that determines the decision to enroll or not in a private pension plan, with the following econometric specification

Z i*   ' Wi  ui

(3)

where the latent variable (not observed) Z i* is defined by the variable Z i (dummy)  1 ( the household enrolled into at least one private pension plan ) if z *i  0 Zi   ( the household did not enroll into a private pension plan ) if z *i  0  0

In Equation (3), Wi is a vector of observed characteristics that influence the probability of having enrolled in an individual private pension plan;  a vector of parameters to be estimated; and ui, the error term with mean zero. In the first stage, for each observation of the sample, we computed the inverse Mills ratio    ˆWi       ˆWi 

ˆi  

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where  (.) and Φ(.) are the density functions and distribution of a Normal typified, respectively. In the second stage, for the subsample of households that engaged in individual private pension plans, we were interested in knowing what factors explained the retirement wealth (accumulated savings in pension plans). The equation of interest was

Yi   ' X i   'ˆi   i

(4)

In Equation (4), Yi measures the retirement wealth in pension plans (in logarithmic terms); Xi is a vector of variables of financial habits and behavior, and other control variables (socioeconomic characteristics) that affect the amount of the retirement wealth; ˆi is the inverse Mills ratio; β is the vector of parameters to be estimated ( ˆ 's are now consistent and distributed approximately normal); and εi is a random variable with mean zero and constant variance which reflects unobservable characteristics that affect retirement wealth in pension plans. In a sample selection model, we must estimate simultaneously an equation of participation or selection mechanism (Equation (3)) and the main equation of interest (Equation (4)). Heckman (1979) presented a method for obtaining a consistent estimate of β when the selection mechanism had a dichotomous outcome and the subsequent equation of interest had a continuous dependent variable. As long as at least one explanatory variable in the selection equation is not in the equation of interest, this technique is a good one. However, the researcher must identify such a variable, called an exclusion restriction. If all variables influencing selection were posited to influence the subsequent process, then the Heckman method would be of dubious value (Sartori 2003; Wooldridge 2012). In Equation (4), the coefficient on the inverse Mill’s ratio indicates whether or not there is selection bias. If the coefficient is statistically significant, then there is selection bias. In our econometric estimation, at least one of the explanatory variables considered in the analysis must be a source of exogenous variation in the sense that influences the likelihood of having engaged in an individual pension plan but does not affect the amount thereof – it could be used as an extra variable in the Heckman methodology.

Data source and variables Data Source The data used in this paper come from the EFF-2011, which provides detailed information on income, assets, debts, consumption, and households’ socio-demographic characteristics (Bover 2008, 2011). It was conducted by the Bank of Spain in collaboration with the National Institute of Statistics and the Inland Revenue and is the only statistical source in Spain that relates those 10

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variables for each household. We would like to emphasize that this dataset is the only one available for Spain which contains the information necessary for the hypotheses to be tested.1 In order to get representative information about the population, the population elevation factors take mainly into consideration the characteristics of the sample design and response rates for different economic levels (higher economic levels respond less). That is, the distribution of wealth is not symmetrical, and some classes of assets are held by a small fraction of the population; therefore, it is appropriate to incorporate an oversampling of households with higher levels of wealth. In this type of survey, the lack of response to certain items of the questionnaire is frequent, so any analysis based solely on fully completed questionnaires would lead to significant bias and considerable loss of information. For this reason, the Bank of Spain provided five separate imputations for each value of the survey that was not observed. In this paper, using the statistical software Stata14 and weights of each household according to the instructions on the Bank of Spain website (Banco de España 2008), we have combined the information obtained from these multiple imputations together in one file. The reasons for imputations and the choice of the imputation methods can be seen in Barceló (2006), Banco de España (2008), and Bover (2011). It would have been desirable to know the educational level of each family member, and the type of pension plan held by each of them, but the unavailability of such information required the use of personal characteristics of household head in the statistical analysis such as his/her educational level, age, labor status, etc. In any case, it is likely that the pattern of household head in relation to pension plans would be followed by other members of the family unit, given the influence that he/she usually exerts on the family. Variables The objective of this work was twofold. The first aim was to identify which factors explained the behavior of the Spanish households in their decisions of enrolling in private pension plans, with special emphasis on the role of expected change in household's future income. The second goal was, for those households that decided to save on private pension plans, to identify the variables that explained the total amount of money saved in these plans and, in particular, the effect of liquidity constraints on the amount that Spanish households could save for retirement. In order to achieve those objectives, we put into practice the methodology discussed in the previous section. In this regard, the dependent variable in Equation (2), and also in Equation (3), has taken the value of 1 if the household enrolled in private pension plans, and 0 otherwise. In Equation (4), the dependent variable was the natural logarithm of the total retirement saving in private pension plans. 1

The total number of households in the sample of the EFF-2011 of 6,106.

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The EFF-2011 dataset recorded detailed information on both, financial variables (such as income, savings, and assets held by families) and socio-demographic characteristics of households. Table 4 has shown the explanatory variables, grouped into two blocks. 1. Variables related to the head of household According to the EFF-2011, the household member who answered the survey providing information on the whole family is called reference person (that normally coincides with the person handling economic matters). The information on socio-demographic variables used in the econometric analysis is referred to the householder that coincided with the reference person if this was a male or the partner of the reference person if this was a female. In the empirical analysis, we considered the level of education, marital status, socio-occupational status, age, and gender. 2. Variables related to the household Along with households' expectations of future income and liquidity constraints of the household, the following variables were considered: (i) the level of per capita household income corrected by the OECD equivalent scale (in logs); (ii) the logarithm of real assets (e.g., cars, jewelry); (iii) if the family had its own home or not; and (iv) if the household made monthly payments for debts. In addition, explanatory variables have been incorporated into the model relating to financial products and habits of families, which could be associated with the probability of having individual pension plans and/or the amount accumulated in them, such as private pension plans, pension insurance, and life insurance. Financial habits of families were collected through a variable that tells us whether the household regularly used telephone or Internet banking, which is an indicator of the level of attention devoted to the families to financial matters and to the costs of the transaction for financial operations. Finally, we also incorporated the attitude of the family against the decision to take financial risks – exogenous innate trait of household members.2 Table 1 shows the percentage of households with individual private pension plans and the main descriptive statistics according to several householders' characteristics. It can be seen that 20.25% engaged in at least one individual pension plan, which means that, of out 17,429,825 families, only 3,529,175 engaged in individual pension plans. Considering only households with private pension plans, the median value of its assets stood at €7,376, which proves the marginal role of individual pension plans in Spain.

2

For the selection of variables, and in order to avoid multicollinearity, we used the command post estat vif available in Stata. With this command, it is proved that the final model has no multicollinearity and it was verified that, for example, age and age squared cannot be entered in the model as continuous variables along with the other variables finally selected because of multicollinearity.

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According to the age of the householder, as expected, the percentage of households with private pension plans increased as the householder aged until the range of age between 55 and 64, for which almost 32% of households enrolled in individual pension plans. The relative number of households with private pension plans dramatically decreased after 65 years of age. But for those who maintained individual pension plans in the age brackets above the usual retirement age, the median amount of retirement savings did not decrease after 65 years. This is consistent with the fact that many households, whose householder retires, decide to withdraw the funds gradually. In addition, retirees could continue making contributions to individual pension plans and enjoy the corresponding tax relief.

Insert Table 1 here

The educational level of the householder affected the decisions of the households to save in private pension plans as expected. Significant increases in both the percentage of households with pension plans, and the median amount accumulated in the plan were associated with higher level of education. For households with higher education, 35.17% of them engaged in individual pension plans, and median amount reached €10,000. According to the employment status of the householder, the self-employed were those who, in relative terms, were most likely to be enrolled into private pension schemes, probably because they are more vulnerable to the public pension system. There were also few households with individual private pension plans whose householder was a retiree (8.83%), but the median amount in this financial product was €19,000. Likewise, the tenure of the housing acquired with a mortgage and making monthly payments for debts affected both enrolling into an individual pension plan as well as the amount saved. We must emphasize that household that makes monthly payments for debts contributed more often to individual pension plans than those who do not make such payments, but the median saved amount was much lower. This result could be explained by the usual business practices undertaken by financial institutions that offer loans with lower interest rates to their customers if they enroll in a private pension plan. In relation to Spanish households that expected a lower future income, 24.82% of them enrolled in a private pension plan. It also highlights the fact that households with liquidity constraints saved less than households without such restrictions, specifically, €3,000 compared to €7,500.

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Regarding the rest of variables, we note that two of them, attitude toward financial risk and regular use the telephone or online banking services, significantly affected, in the expected direction, enrollment into individual pension plans; however, they did not affect the median amount thereof. In the absence of a specific test in econometrics to identify the variables that explain the possible self-selection, these two variables, which have a more exogenous nature – in the sense that capture attitudes and skills with a high innate component – are potential variables of selection (extra variables) in the Heckman' methodology.

Results of the Econometric Analysis Determinants of the Likelihood of Enrolling in a Pension Plan First, to analyze the likelihood of enrolling in individual pension plans we ran successively three binomial logit models. The values of the odds ratio and the level of significance of the variables in each of the three models appear in Table 2.3 In Model 1 – without controlling for the level of income, wealth, and financial products and habits of families – education is the variable that most influenced the likelihood of enrolling into individual private pension plans. Thus, secondary education increases the likelihood of enrolling into individual plans by 84%, and higher education by 264%. This positive relationship between education and retirement saving is not new as it has also been found in other applied studies in economics (Bernheim 1998; Bernheim and Sholz 1992; Lusardi and Mitchell, 2014; McMahon 2009). Other variables that also increased the probability of enrolling in a private pension plan were marital status and employment status, but more moderately. Being married increased the probability by 27% compared to the reference category; and the selfemployed, compared to wage earners, were 30% more likely to enroll in pension plans. However, unlike other studies (Holzmann et al. 2012), gender had no statistically significant effect on the likelihood of enrolling in individual pension plans. Finally, with regard to age, it was observed that, with respect to the reference category (55 to 64), the likelihood of having individual pension plans was reduced for the rest age groups. For example, households whose head was aged below 35 years were 74% less likely to be enrolled in individual pension plans, and those aged between 35 and 44 years were 59% less likely to be enrolled in a private pension plan. This result is consistent with the findings of applied work that show that individuals start late to save for their retirement (Thaler and Sunstein 2008).

Insert Table 2 here 3

An odds ratio greater than one tells us that an explanatory variable increases the probability of having a pension plan. And an odds ratio between 0 and 1 reduces probability.

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Model 2, in addition to the explanatory variables of Model 1, incorporates the per capita income (based OECD equivalence scale and in logarithmic terms). As expected, household income increased the likelihood of enrolling in a private pension plan. Once income was introduced into the model, the influence of education on the likelihood of enrolling in individual pension plans was decreased, but not eliminated. In Model 2, having secondary education increased the probability only by 45%, and having the higher education increased this probability by 108%. The most significant finding was the loss of significance of the age group between 45 and 55 years. Model 3 includes, in addition to the socio-demographic characteristics of households, variables of financial habits and behavior of households. We observe in Table 2 that the estimated coefficients associated with the variables education, marital status, and age remained statistically significant. However, self-employed status along with the gender, did not show statistical significance. These results are of great interest since even with controlling for income and wealth, education did not lose its significance, which shows that the educational level of the household captures more than the income level. In other words, education, especially university education, minimizes the myopic behavior of households in the sense of making them more forward-looking and cautious in the face of their future welfare. It also confirms that university education generates monetary benefits such as higher wages (Salas-Velasco 2006), but also non-monetary benefits since they know how to save for old age – they have more and better information. In relation to the new variables included in Model 3, we would highlight the positive effect that a lower expected future income exerted on the probability of contracting a private pension plan (by 21%). For those households expecting a decrease in their future income, enrolling in a private pension plan is a supplementary source of income in retirement. Moreover, households that revealed a preference for the financial risk increased the likelihood of engaging in a private pension plan by 23% compared with households that have risk aversion. This latter finding is in line with research findings reporting that risk tolerance is a significant predictor of investment for retirement and saving strategies for the future (Grable and Joo 1997; Sunden and Surette 1998). In relation to the composition of the household portfolio, there were two financial products that influenced the probability of enrolling individual private pension plans. Life insurance appeared as a complementary product to pension plans, while pension insurance was shown as a substitute product to pension plans. However, other pension plans (not individual), such as those sponsored by the companies, have not shown statistical significance. Some financial habits of Spanish households, such as the use of the Internet and telephone banking, had a positive effect on the probability of enrolling in private pension plans. This finding

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is in line with other studies that show that households with this type of financial habits were more likely to participate in financial markets and in contracting financial products such as pension plans (Rubin and Rubin 2010). As expected, and all other things being equal, those households with monthly debt payments had less saving capacity and, therefore, less probability of enrolling in private pension plans. But Spanish households that had purchased a home with a mortgage were more likely to enroll in private pension plans. Behind this finding is the banking practice of offering mortgage rates more favorable to individuals who engage in additional financial products such as individual pension plans. In addition, as expected, the higher household wealth, as measured by real assets, the greater the likelihood of engaging in a private pension plan. Nevertheless, in the logit analysis, the coefficient associated with liquidity constraints showed no significance. Explaining the Amount Saved in Individual Pension Plans Using the Heckman Correction for Sample Selection After analyzing the likelihood of enrolling in private pension plans, the goal was to identify the variables that explained the total amount of money saved in these plans and, in particular, the effect that liquidity constraints had on the amount Spanish households saved for retirement. First, in Table 5 appear the results of estimation of Heckman's model, without using any variable exclusion. It should be noted that if all the variables that influenced the probability of enrolling into individual pension plans were included as explanatory variables of the amount saved, the results would be of questionable validity (Sartori 2003; Wooldridge 2012). However, this first analysis can be used to detect possible variables that influence the probability of enrolling in a private pension plan but not on the total amount saved in it. Indeed, we can see that the variables preference for risk and use of Internet and telephone banking affected positively the probability of enrolling in a pension plan, but did not influence significantly the amount saved. In the absence of a specific econometric test to identify the variables that explain the possible self-selection, these three variables, which have a more exogenous nature in the sense that they capture attitudes and skills with a high innate component, are potentially selection variables in Heckman’s model (extra variables). Therefore, these are variables that have been finally introduced in the model as selection variables in Heckman’s model, as shown in Table 3. The estimated coefficient associated with lambda was statistically significant, so there is evidence of a problem of sample selection. This confirms that the two-stage model proposed to explain the amount that families have accumulated in individual pension plans is appropriate, and therefore would not be correct to explain the precautionary saving for retirement using directly a linear regression model (OLS estimation) exclusively with families that have enrolled in individual private pension plans.

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The results of the first stage of the Heckman methodology broadly confirmed the findings shown in Table 2.4 We should highlight, again as expected, change in future income affected the probability of enrolling in a private pension plan, specifically those households that expected less income in the future increased the likelihood of enrolling in a private pension plan. Focusing on the coefficients of the second stage shown in Table 3, it is confirmed the hypothesis that liquidity constraints affect negatively the total amount saved. The estimated coefficient of this variable is negative and statistically significant. Those households that were denied a loan in the past two years had pensions worth 62.2% less than those households without liquidity constraints. This interpretation is not entirely wrong; however, if the explanatory variable is dichotomous and not continuous, as in our case for the variable liquidity constraints, the correct interpretation is that proposed by Halvorsen and Palmquist (1980, p. 474), according to which we must take the antilog of dichotomous estimated coefficient (base e) and subtract 1. Reinterpreting therefore, the estimated coefficient would e-0.622 - 1 = -0.463. Therefore, the percent reduction in total savings in individual pension plans associated with households with liquidity constraints was 46.3 per 100. This result is in line with that obtained in the work of Corsini and Spataro (2013) in which it was shown that liquidity constraints affected the amount saved during working life, but did not affect the decision of which pension plan to choose. Carroll and Kimball (2001) provided an explanation for the apparently contradictory results that have emerged from simulation studies, which have sometimes seemed to indicate that constraints intensify precautionary saving motives, and sometimes have found constraints and precautionary behavior to be substitutes. As expected, income and wealth in real assets have positive effects (statistically significant coefficients) in the total amount saved by households in these plans. Being in logarithms both the dependent variable and two of the explanatory variables – income and wealth in real assets – the estimated coefficients of these two latter variables can be interpreted directly as elasticities. Both variables – income and wealth in real assets – have an influence on the amount saved that is quite similar. Thus, an increase of 1% in per capita family income (in the equivalent OECD scale) caused an increase in the amount saved on individual pension plans of 0.25%; and an increase in wealth in real assets of 1% raised the amount saved by 0.26%. Regarding the age of the household head, it is observed that having a more advanced age and/or being retired positively influenced the amount that households saved in individual pension plans. This result is consistent with the idea that once the retirement age is reached, around 65 4

Although logit and probit models are often used interchangeably in applied research to explain qualitative dichotomous variables, this study opted to use the logit model, being based on utility models (and easy interpretation of the odds ratio), as discussed in the second section. However, Heckman methodology demands to correct the sample selection estimating a probit.

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years, the plan holder does not withdraw as a lump sum in one payment. This latter result is not surprising since it is entirely consistent with that obtained in Tables 1 and 2. There were few families whose householder was retired, or elderly, that had individual pension plans. But those households that retained plans after retirement  even after taking into account income, wealth and age groups  kept a relatively large amount of savings. Finally, Table 3 shows that if the households made monthly payments for debts, this fact affected negatively the amount saved in individual plans; in particular, they saved 42.2% less on individual plans than those households that did not make such monthly payments (-34% as proposed by Halvorsen and Palmquist (1980)). Insert Table 3 here

Conclusion In Spain, like in other OECD countries, individual private pension plans are an instrument for additional savings to the public pension system. The last wave of microdata from the Survey of Household Financial (Bank of Spain) showed that only 20.25% of Spanish households had at least one individual private pension plan, with a median value of only €7,376, which confirms the marginal role that private pension plans have yet in Spain. Precisely, the governor of the Bank of Spain has stated recently that the current public pension system will not guarantee the level of pension that citizens expect, advising to save, mainly to young people, because their pensions will be lower in the future (Linde, 2015). In this debate on public pensions and the complementary role that private pensions should play in Spain, this article sheds light on the decision of Spanish households to engage or not in individual pension plans. For those households who chose to engage in them, our research explained what factors determined the total amount saved in such retirement plans. The likelihood of having individual pension plans was estimated with logit models, while the total amount saved in them was explained using a Heckman model to correct for the problem of sample selection bias. The results suggest that there are three key factors that influence the likelihood of engaging in an individual private pension plan by the Spanish families. First, the expectation of lower future income increased the probability of engaging in private pension plans. Second, households that revealed a preference for the financial risk increased the likelihood of engaging in an individual private pension plan compared with households that have risk aversion. Third, education exerted an important influence on the likelihood of enrolling in a private pension plan. Moreover, this paper reveals that liquidity constraints affect negatively the total amount of money saved for retirement,

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but the both level of per capita income of Spanish families, along with their wealth in real assets, have positive influences on total retirement savings. The main limitation of this research is that the analysis has been conducted only for 2011, which implies a fixed photo of the behavior of households for that year. We expect in the near future to have a new wave of information from the Bank of Spain which, if the same households continued to be surveyed, would allow us to construct a panel of data whose exploitation would give us a dynamic view of the behavior of Spanish households. In any case, this work is important for two reasons. First, because in a scenario of a smaller amount of a public pension it is important to know which families are ahead in engaging in private pension plans. Second, because as far as we know, this is the first work that studies this subject with the Financial Survey of the Families of the Bank of Spain. Regarding the implications for practice, the results of our work can help policy makers to raise awareness of the importance of saving for retirement. In Spain, unlike other European countries such as Sweden, the Netherlands, and Germany, there is still a lack of social awareness that future public pensions will not be as generous as were for past generations, mainly because there are (and there will be) more and more pensioners due to the increase in life expectancy. This excessive reliance on public pension plans, very characteristic of the countries of southern Europe, can be seen as a market failure in the sense that individuals voluntarily do not save for retirement. Public authorities, aware of the difficulties in the long-term sustainability of the public pension system, and the biases that affect human behavior in the face of saving for the future, should promote a culture of saving for retirement in the Spanish population, particularly in the less-foresight groups. The government should take some measures to raise awareness and encourage citizens to save for retirement so a decent standard of living can be retained in retirement. An alternative could be the automatic enrolment of workers in private pension plans, a strategy that has met with success in other countries. In this context, it may be very appropriate to increase the level of financial education and literacy of families to ensure that citizens have more knowledge about the most rational way to realize their long-term savings and diversify risks.

Compliance with Ethical Standards Conflict of Interest: Dolores Moreno-Herrero declares that she has no conflict of interest. Manuel Salas-Velasco declares that he has no conflict of interest. José Sánchez-Campillo declares that he has no conflict of interest. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

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Bernheim, B. D., Skinner, J., & Weinberg, S. (2001). What accounts for the variation in retirement wealth among US households? American Economic Review, 91(4), 832–857. Retrieved from http://www.jstor.org/stable/2677815 Boersch-Supan, A., & Lusardi, A. (2003). Saving: A cross-national perspective. In A. BoerschSupan, (Ed.), Life-cycle savings and public policy: A cross-national study in six countries, pp. 1 Retrieved from 32. New York: Academic Press. Bogan, V. (2008). Stock market participation and the internet. Journal of Financial and Quantitative Analysis, 43(1), 191–211. doi: http://dx.doi.org/10.1017/S0022109000002799 Bover, O. (2008). Dinámica de la renta y la riqueza de las familias españolas, resultados del panel de la Encuesta Financiera de las Familias (EFF) 2002-2005 (Documentos Ocasionales del Banco de España No. 0810). Madrid: Banco de España. Retrieved from https://studylib.es/doc/5576998/din%C3%A1mica-de-la-renta-y-la-riqueza-de-las-familias Bover, O. (2011). The Spanish Survey of Household Finances (EFF): Description and methods of the 2008 wave (Bank of Spain Occasional Documents No. 1103). doi: http://dx.doi.org/10.2139/ssrn.2514206 Browning, M., & Lusardi, A. (1996). Household saving: micro theories and micro facts. Journal of Economic Literature, 34(4), 1797–1855. Retrieved from http://www.jstor.org/stable/2729595 Brucker, E., & Leppel, K. (2013). Retirement plans: Planners and nonplanners. Educational Gerontology, 39(1), 1–11. doi: http://dx.doi.org/10.1080/03601277.2012.660859 Bryant, W. K., & Zick, C. D. (2006): The economic organization of the household (2nd ed.). New York, NY: Cambridge University Press. Burtless, G. (1999). An economic view of retirement. In H.J. Aaron (Ed.), Behavioral dimensions of retirement (pp. 7–42). Washington, D.C.: Brookings Institution Press. Cagetti, M. (2003). Wealth accumulation over the life cycle and precautionary savings. Journal of Business and Economic Statistics, 21(3), 339–353. doi: http://dx.doi.org/10.1198/073500103288619007 Carroll, C. (1997). Buffer-stock saving and the life cycle/permanent income hypothesis. Quarterly Journal of Economics, 112, 1–56. doi: http://dx.doi.org/10.1162/003355397555109 Carroll, C. D., & Kimball, M. S. (2001). Liquidity constraints and precautionary saving (NBER WP No. 8496). Retrieved from http://www.nber.org/papers/w8496 Corsini, L., & Spataro, L. (2013). Savings for retirement under liquidity constraints: A note. Economics Letters, 118(2), 258–261. doi: http://dx.doi.org/10.1016/j.econlet.2012.11.001 Cramer, J.S. (1991). The Logit model. An introduction for economists. London: Edward Arnold. Deaton, A. (1991). Saving and liquidity constraints. Econometrica, 59, 1221–1248. doi: http://dx.doi.org/10.2307/2938366 Dwyer, D., & Mitchell, O. (1999). Health problems as determinants of retirement: Are self-rated measures endogenous? Journal of Health Economics, 18(2), 173–193. doi: http://dx.doi.org/10.1016/S0167-6296(98)00034-4 European Commission (2012). White paper: An agenda for adequate, safe and sustainable pensions (COM 2012 55 final). Brussels: European Commission. Retrieved from http://www.esneu.org/news/79/index.html

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Table 1. Percentage of households enrolled into individual pension plans according to different variables and median amount, 2011 % of households

Median amount (€)*

Mean

Standard deviation

Minimum

Maximum

14.34 23.66 31.67 31.99 7.01 1.58

3,000 4,000 8,000 14,000 19,900 20,000

6,422.52 7,031.67 15,152.26 28,569.47 36,128.42 34,844.71

9,592.70 9,909.22 29,539.56 47,338.02 56,183.91 43,759.66

99.00 89.00 100.00 60.00 10.00 950.00

79,030.00 130,745.00 5,000,000.00 1,320,000.00 896,344.00 308,786.00

13.50 25.00 35.17

5,500 7,245 10,000

10,714.61 17,543.68 25,374.18

17,783.28 34,322.65 47,035.23

100.00 99.00 60.00

606,131.00 500,000.00 5,000,000.00

27.67 33.64 8.83 14.56

7,000 8,000 19,000 2,800

14,567.24 16,121.47 35,598.33 14,922.11

30,006.34 33,431.92 48,224.31 36,035.93

60.00 90.00 10.00 10.00

5,000,000.00 1,320,000.00 896,344.00 785,525.00

27.95 11.74

7,063 7,636

17,788.52 16,533.17

32,497.49 40,534.17

10.00 10.00

896,344.00 5,000,000.00

12.31 20.59

3,000 7,500

8,019.63 17,684.38

12,025.69 35,259.14

89.00 1.00

200,000.00 5,000,000.00

21.20 18.11 24.82

3,900 8,000 8,000

15,959.62 17,582.18 18,308.99

35,798.37 31,815.71 39,373.10

10.00 10.00 60.00

549,072.00 1,320,000.00 5,000,000.00

21.06 15.97

5,495 10,000

13,026.39 23,979.62

25,751.98 44,362.23

10.00 30.00

1,320,000.00 5,000,000.00

18.44 33.36

6,900 10,000

27,839.75 14,849.94

43,472.63 31,889.61

90.00 10.00

676,750.00 5,000,000.00

45.80 19.21

6,900 7,500

17,466.08 17,440.34

24,834.74 35,720.30

200.00 10.00

424,000.00 5,000,000.00

Variables related to the head of household

Age

Variables related to the household

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

     

Under 35 years Between 35 and 44 years Between 45 and 54 years Between 55 and 64 years Between 65 and 74 years Older than 74 years Education  Less than high school  High school  Higher education Work status  Employee  Self-employed  Retired  Another type of inactive or unemployed Main house  Housing acquired with mortgage  Otherwise Liquidity constraints  The household has liquidity constraints  The household does not have liquidity constraints Expected income in the future for the household  Higher future income  Equal future income  Lower future income Monthly payments for debts  The household makes monthly payments  The household does not make payments Attitude to risk  The household does not like to take risks  Risky household Other pension plans not individual  The household has them  The household does not have them Pension insurance

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13 14 15 16 17 18 19  The household has them 8.10 9,000 14,045.43 20  The household does not have them 20.57 7,300 17,478.21 21 Life insurance by own decision 22  The household has insurance 23 37.85 8,000 18,316.60 24  Any member of the household has insurance 17.70 7,000 17,172.88 25 Using the telephone or internet banking regularly 26  Used by the household 38.30 9,000 22,558.05 27  Not used by the household 15.40 6,800 14,032.73 28 Total number of households 20.25 7,376 17,442.60 29 * For most of the relevant variables, such as those related to assets and liabilities, distributions show very high values for a relatively small 30 approximation to the typical values of the distribution than average, so this is the statistic that is included in the Table. 31 Source: EFF-2011 and authors' calculations 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 28 64 65

30,789.53 34,939.47

1,500.00 10.00

386,391.00 5,000,000.00

43,108.11 31,942.91

100.00 10.00

5,000,000.00 896,344.00

38,917.40 31,487.79 34,899.63

10.00 1.00 1.00

629,493.00 5,000,000.00

number of families. In this case, the median is a better

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 2. Logit models of the probability of having enrolled into individual private pension plan (odds ratio and significance) Model 1 1.84 3.64 1.27 0.68 0.82 1.30 0.50 0.51 0.91 0.26 0.41 0.73 0.42 0.12

Educational level (High School) Educational level (University) Marital status (married) Marital status (divorced) Marital status (widow/widower) Employment status (self-employed) Employment status (unemployed) Employment status (retired) Gender (female) Age < 35 35 ≤ Age < 45 45 ≤ Age < 55 65 ≤ Age < 75 Age ≥ 75 Household Income (in logs)–OECD scale Logarithm real assets Housing acquired with mortgage Liquidity constraints Higher future income Lower future income Monthly debt payments Other pension plans (not individual) Pension insurance Acquisition of life insurance Use of Internet and telephone banking Preference for risk Log pseudolikelihood Wald chi2 Prob > chi2 Pseudo R2

-2870.1263 920.36 0.0000 0.1808

Model 2 1.45 * 2.08 * 1.43 * 0.70 * 0.79 1.19 † 0.67 * 0.62 * 1.12 0.33 * 0.54 * 0.89 0.36 * 0.11 * 1.81 *

* * * * * * * * * * * *

-2775.5608 953.59 0.0000 0.2052

Model 3 1.29 1.58 1.15 0.64 0.67 1.01 0.66 0.59 1.17 0.37 0.55 0.93 0.36 0.11 1.48 1.19 1.22 0.92 1.02 1.21 0.83 1.19 0.19 1.35 1.48 1.23

* * * * * * * * * * * * *

* * * * * *

-2586.3589 965.36 0.0000 0.2258

Dependent variable: = 1 if the household enrolled into individual private pension plans; 0 = otherwise. The reference category for the dummy variables with two or more categories was a household whose head of household had less than High School, was single; was wage-earner, and aged between 55 and 64 years. OR stands for Odds Ratio No. of observations used in the analysis = 5,673

* Significant at 5% level (p