How Large is the Retirement Consumption Drop in Italy?

1. How Large is the Retirement Consumption Drop in Italy? Erich Battistin Agar Brugiavini Enrico Rettore Guglielmo Weber

2. Motivation • According to the life-cycle permanent income Hp consumers decide how much to consume, keeping in mind their future prospects • They form intertemporal plans aimed at smoothing the (discounted) marginal utility of consumption over the life cycle • Any period to period change in the actual level of the marginal utility of consumption is uncorrelated with past information available to the household. That is, it should be a result of unpredictable shocks.

3. Motivation • This holds true also around retirement age: any change in the marginal utility of consumption should be uncorrelated with planned retirement behaviour. • Recent micro evidence has emphasized that there is a one-off drop in consumption at the time of retirement that might be hard to reconcile with life-time optimizing behaviour (see for example Banks et al., 1998, Bernheim et al., 2001). • This is known as the retirement consumption puzzle

4. Motivation Some possible reasons mentioned in the literature: • changes in preferences due to increased leisure • shocks inducing retirement and affecting the level of consumption • reduction in work-related expenditures (transport, meals out, clothing) • increase in home production of services and/or more efficient purchases • unexpectedly low pensions or liquidity problems (not in Italy, though – think of severance pay - liquidazione!)

5. What Others Have Done • Banks, Blundell and Tanner (1998) use repeated cross section data from the FES – they estimate log-linear Euler equations from cohort data by IV (using lagged interest rates, consumption and income growth as instruments) and find unexplained negative residuals around typical male retirement ages (60-67). • The largest residual obtains at age 63 (1.5%). Altogether, cumulated residual are in the 8-10% region. • Non-separabilities between leisure and consumption can explain only part of the drop.

6. What Others Have Done • Bernheim, Skinner and Weinberg (2001) use panel data from the PSID to estimate Euler equations. Retirement status is instrumented by taking age-specific predicted probabilities conditional on demographics (however cannot explain spikes at ages 62 and 65). • Median drop is 14%, but higher for low wealth • Sample is split in groups: low wealth-to-income households drop their consumption most. • “31% of households reduce their consumption by at least 35 percentage points at retirement”.

7. What Others Have Done Possible explanations and related literature: • Many workers are surprised by inadequate resources when they retire (not consistent with life-cycle model & rational expectations). • Work related expenses. • Home production and/or more efficient shopping (Aguiar and Hurst, 2005, Hurd and Rohwedder, 2006). • Miniaci et al (2003) estimate by OLS the Italian retirement consumption drop at 5.4%.

8. What We Do • An alternative identification strategy: we estimate the change in consumption at retirement by exploiting the exogenous variability in the retirement decision induced by the eligibility rules of the Italian pension system. • Information on consumption expenditures, eligibility for retirement and retirement status is obtained from the Bank of Italy Survey on Household Income and Wealth (SHIW). No need of panel data to achieve identification.

9. Punch-line • Key result: household non-durable consumption drops by 9.8% because of male retirement. A larger drop estimated for total food (14.1%). • Our strategy provides non-parametric identification only for a subpopulation of those who retire (those who retire at the time they become eligible). • We estimate smaller drops for “poverty sample”. • Our estimates can be reconciled with utility optimization - in the cross section, drop in work-related expenses and leisure substitutes is large enough to explain changes in consumption.

10. The Causal Problem • Let S* be a variable denoting time to/fromeligibility for retirement, negative values indicate that the subject is not yet eligible. • Let R be the retirement status, R=1 for the retired and R=0 otherwise. Since retirement is an option available only to the eligible workers, the probability to retire is zero if S*<0 (and it is thus discontinuous at S*=0 ). • Let (Y1,Y0) be the two potentialhousehold consumption expenditures corresponding to the head being retired or not retired, respectively, and let β=Y1-Y0 . • Let Y = Y0+Rβ be observed consumption, where Y≡Y1 for households whose head is retired andY≡Y0 otherwise.

11. Identification in a nutshell Start by comparing expenditures for households marginally close toS*=0; since Y = Y0+Rβ we have that Consider the difference around eligibility:

12. Identification in a nutshell Key identifying restriction (the mean consumption profile under the no-retirement alternative is smooth enough at zero): The result rests upon a weak regularity condition: if none of the heads were to retire no discontinuity in household consumption would take place at the time they become eligible (i.e. at S*=0) – see Hahn et al. (2001) and Battistin and Rettore (2006). This amounts to assuming that any idiosyncratic shocks relevant to the retirement choice and correlated with Y0 (e.g. health shocks) do not occur selectively at either side of the eligibility threshold.

13. Identification in a nutshell By using simple algebra we have: • Estimators of the causal effect of retirement on consumption are analogue estimators obtained by replacing the quantities in the last expression by their empirical counterparts. • Following Imbens and Angrist (1994) and Hanh et al. (2001), it can be shown that this expression coincides with the IV estimator obtained by instrumenting the endogenous variable R with the eligibility status defined from S*.

14. Endogeneity of S* • The S* variable may be the outcome of individual choices (time to enter the labour market, temporary exits, etc). This might casts doubts that our identification strategy is marred by an endogeneity problem. • Consider the regression we use to get the numerator of the IV estimate (the reduced form): Y= δ0 + δ1 S* + δ2 S*2 + δ3 1(S*>0) +ε The mean of Y conditional on S* is: E{Y|S*} = δ0 + δ1 S* + δ2 S*2 + δ3 1(S*>0) + E{ε|S*} where the last term does not vanish if S* is endogenous.

15. Endogeneity of S* • Nonetheless, the numerator of the IV estimand: E{Y|S* =0+}-E{Y|S* =0-} is not biased for δ3, the drop in consumption at the eligibility cut-off point, provided that: E{ε|S*=0+}=E{ε|S*=0-}. • Our identifying restriction is that the dependence between the unobservables ε and S* is not discontinuously changing at the cut-off for eligibility.

16. Data • We use data from the Bank of Italy “Survey oh Household Income and Wealth” - SHIW. This is a survey of repeated cross sections running since 1987 to 2004. It contains a panel component, that is only exploited for consistency checks . • We focus the attention on waves 1993 to 2004. • Consumption is based on retrospective questions on • Food at home plus meals regularly consumed out of the home (food) • Total spending, net of rent and key durable goods purchases (non-durable consumption) • Retirement is based on the answer to two questions: if the person reports that he was not working for the most part of the year, and then that he was a “job-pensioner”, he is considered to be retired from work.

17. The Reform Process • Two major reforms in 1992 (Amato) and 1995 (Dini) • Gradually moving from defined benefit to (notionally) defined contribution • Lots of additional minor changes have been made nearly every year since 1992 • Further changes will take place in 2008 (restrictions on early retirement)

19. The measurement of eligibility The eligibility variable S* has been derived from SHIW data for the period 1993-2002 using self-reported information on age,gender, seniority (i.e. accrued years of contributions), retirement status and age at retirement. In the two-dimensional space defined by seniority and age we calculated for all individuals in the sample the distance from eligibility accounting for changes in the eligibility rules introduced by reforms over time (by gender and separately for private sector, public sector and self-employed). We use observations referring to household heads within a 10-year band to/from eligibility. Observations on subjects at S*=0 are dropped because their retirement status is not uniquely identified.

20. The measurement of eligibility For retired individuals: time elapsed since eligibility has been calculated using the rule operating at the time they retired (basically using information on age at retirement). For workers: time to eligibility has been calculated using the rule operating at the time they are interviewed. Accrued years of contributions have been imputed, when missing, either by a consistency check exploiting the panel dimension of the data or by using self-reported age of entry in the labour market. In 1993 we dropped all non-panel observations, because of missing information on both contributions and age of labour market entry (for the retired)

21. distance to/from eligibility

22. distance to/from eligibility by retirement status

23. Retirement by Eligibility Status

24. Measurement Error • We observe a non-negligible fraction of retired individuals amongst the ineligibles (this regardless of having imputed the eligibility variable for some individuals): this we take as evidence of measurement error in the data. • Measurement error bias in the estimation of causal parameters can be severe (see, for example, Battistin and Chesher, 2004). • Misclassification of the retirement status R is unlikely to be important, as retired individuals are asked a detailed set of questions on their pension. • Measurement error in the eligibility variable S* is most likely to be the explanation.

25. Measurement Error • Based on what we observe in the data, measurement error in S*can not be classical. If S=S*+u, with u a zero-mean error orthogonal to S* we would not observe any discontinuity in the proportion of retired individual s at the cut-off point. • A type of measurement error consistent with the discontinuity in the raw probability of R=1 we observe in the data is: where Z is an indicator for having S= S*and U is a classical measurement error.

26. Measurement Error Parameter of interest Identification result: if the two groups Z=0 and Z=1 are not systematically different with respect to (Y,S*,U), the following ratio correctly identifies the parameter of interest As an implication, under the assumptions made on the measurement error, the IV estimator obtained by instrumenting R by 1(S>0) recovers the causal effect of interest.

27. Estimation • Select couples and single males, set the household head to the male and define retired households as those whose male head is retired (we do not consider retirement of the spouse at this stage). • Use observations for heads within a 10-year band to/from eligibility. Observations on households at S=0 are not used in the estimation because their eligibility status can not be uniquely identified. • Take averages of household consumption on non-durables from SHIW and proportions of retired heads by S (120 cells from -10 to 10, excluding 0) and by year. • Get IV estimates instrumenting retirement with eligibility status, the latter being defined as 1(S>0). Pool different waves adding time dummies and use polynomials in S throughout. Adjust standard errors for clustering and to account for differences in cell size.

28. A key feature of the Italian pension system is that many individuals retire as soon as they become eligible

29. First StageE{R|S} = α0 + α1 S + α2 S2 + α3 1(S>0)

30. Reduced FormE{Y|S} = δ0 + δ1 S + δ2 S2 + δ31(S>0)

31. Estimation results IV estimates using logged expenditure on non-durables

32. Estimation results IV estimates using logged expenditure on food

33. Specification tests • Identification strategy requires no change at S* = 0 in variables that affect consumption but are not affected by eligibility status. • We show that this condition is met by education, age, size of the main residence and proportion of couples • Exclusion restriction: family size. This is negatively affected by retirement induced by eligibility (-0.30). In particular, number of grown children cohabiting with their parents falls (-0.25). • Possible explanation: individuals retire as soon as they become eligible as a way to let their children move out (they give them part of their severance pay) • Hence actual consumption drop is even smaller than 9.8%!

34. Economic Interpretation • In the US, consumption drop is largest among the low pre-retirement wealth (BSW). • We estimate a pre-eligibility wealth equation, and use it to predict for the whole sample (w_fit). We show this measure does not change at S*=0. • We select those households who w_fit is in the bottom third (w_poor). We call this “poverty sample” • We estimate small and insignificant effects of eligibility-induced retirement for this poverty sample • Our estimated consumption drop is unlikely to be due to lack of financial resources!

35. Back of the Envelope Stuff • A causal effect of retirement on consumption expenditures is not surprising per se. The question is whether this is consistent with life-time optimizing behavior. • A consumption drop can occur if utility is not additively separable in consumption and leisure: since leisure increases abruptly at retirement, consumption increases or decreases depending on how leisure affects the marginal utility of consumption. • For instance, if utility is Cobb-Douglas in male leisure and non-durable consumption, and individuals work full time prior to retirement, our estimated 9.8% consumption drop implies an elasticity of intertemporal substitution of 0.84

36. Work-Related Expenses • One good model is restrictive: Some goods are leisure substitutes (e.g. food out) or work-related (e.g. travel, clothing), other leisure complements (food in, home heating). • We explore which components of household expenditure drive the fall that we have documented. • We use data from the 2002 Survey of Family Budgets: this contains no information on eligibility, but detailed information on household expenditures.

37. Work-Related Expenses • We compare expenditures for households whose head’s age is 50-54 and 65-69. Heads in the latter group are mostly retired, mostly employed in the former group. • The comparison is corrected for composition differences with respect to region of residence, number of equivalent adults and size of the main residence. Support issues turn out to be of no concern. • The overall drop is 15.6% : 50% larger than the estimated retirement consumption drop (9.8%). A third of the drop is due to age, two thirds to retirement.

39. Work-Related Expenses • Total difference is - 241 euros (-15.6%). Mostly accounted for by meals out (-36), clothing (-58), transport (-76). • Overall 170 out of 241 “drop” is accounted for by “work-related expenses”. Our estimates imply that consumption should fall by 151 Euros because of eligibility-induced retirement. • Work-related expenses are less important for manual workers (canteen meals and overalls normally provided by the employer – public transport is heavily subsidized). This may explain why there is no drop for the poverty sample!

40. Conclusions • We estimate that non-durable consumption falls by 9.8% in Italy because of retirement. • This drop is lower than in the US (14 %) but comparable to the UK (8%-10%, non-durable consumption). • Our estimates can be reconciled with utility optimization: in the cross section, drop in work-related expenses is large enough to explain it.