Conexiones Flexibles Cuando sea necesario emplear conexiones flexibles,

My aim in this study is to analyze the change in older workers’ retirement

decision in response to recent changes in Social Security policies, and to pursue this aim I employ a reduced form strategy. Because of the diverse pathways into retirement actually chosen by older individuals leaving work, previous literature has emphasized the

inadequacy in relying on a single definition of retirement to capture retirement

behavior.27 I, therefore, define retirement age in two ways: the age at which individual withdraws from labor force; or the age at which the individual assesses himself to be retired. This approach allows me to explore the sensitivity of the estimation results to the different definitions of retirement.

I focus on the first observed retirement among individuals who are working and have not previously retired. Both the retirement definitions include only individuals at work (or looking for work) at the time of sampling wave. I condition on work for two reasons. First, even though many older workers reenter the labor force after retirement, there are many others for whom the transition costs to work following retirement may be very high either due to loss of skills, or high search costs. If I do not condition on work and include all individuals regardless of their current labor force status, then the response of older workers to policy changes may be difficult to discern. Second, to analyze the

27 _{Diamond and Hausman (1984), Quinn, Burkhauser, and Myers (1990), Ruhm (1990), Blau}

change in the retirement behavior of older women in response to these policy changes, it is useful to focus on women retiring conditional on working at a prior date. This is beneficial because even though the work attachment of older women has increased substantially over time, there are still many older women with no work history. Other researchers have also noted the importance of reentry behavior among older men, the idea that individuals return to work at a later date after retiring.28 Following the first observed retirement from work, I, however, ignore any subsequent return to the labor market by the individual.29

1.4.1. DATA

Survey of Income and Program Participation (SIPP): To empirically assess the validity of the theoretical predictions, the data used are from the four most recent panels 1996-2008 of the Survey of Income and Program Participation (SIPP), which include data from years 1996-2013. The SIPP has a rotating panel design, and individuals within a panel are followed for a period of 3 to 4 years. Table 1.5 summarizes the length of each panel used in the study along with the reference period for which the data are available. Beginning in 1996, the SIPP was redesigned to include a larger initial sample than earlier panels. Panel members are randomly assigned to four different rotation groups, and each month members of one of the rotation groups are interviewed. These interviews are scheduled at intervals of four months; at each interview individuals are asked questions about their activity in the preceding four months. The SIPP oversamples households from

28 _{Diamond and Hausman (1984), Quinn, Burkhauser, and Myers (1990), and Maestas (2010).} 29 _{I do this for two reasons. First, as Diamond and Hausman (1984) note, allowing for reentry}

behavior will require a more elaborate formal model. Second, the SIPP dataset that I use for the empirical analysis follows individuals for a relatively short duration of 3 to 5 years, which does not provide enough time to observe un-retirement behavior following retirement from work.

areas with high poverty concentration.30 Due to Census budget cuts the sample for the 2004 panel was cut in half at the end of the eighth wave (reference period June 2006- December 2007).31

In comparison to the two other recently used datasets (the Health and Retirement Study and the Current Population Survey) the SIPP has both strengths and weaknesses. The main strengths of the SIPP relative to the Health and Retirement Study (HRS) are its larger sample size and the shorter duration between interviews.32 Compared to the Current Population Survey (CPS) the SIPP has more accurate birth year information.33 This is useful because both the normal retirement age and delayed retirement credit policy changes are assigned by birth year. Moreover, unlike the CPS, the SIPP follows individuals when they relocate. The main weaknesses in using the SIPP relative to the HRS are its shorter panel length and the unavailability of detailed pension plan incentives (defined benefit or defined contribution) information, and information on retiree health insurance. The SIPP contains information on both these variables in its topical wave modules which are administered once in a panel.34 _{I do not include these variables}

because of the infrequency of the topical modules and the associated decrease in sample

30 _{Survey of Income and Program participation User Guide Chapter 2, 2009.}

31 _{After the eighth wave fifty percent of the sample was dropped and not interviewed in}

subsequent months. The data for wave 1 through wave 8 was collected for the full sample.

32 _{In the Health and Retirement Study individuals are interviewed every two years while in the}

SIPP individuals are interviewed every four months.

33 _{The birth year information had some inconsistencies for individuals in panel 2004 and 2008. In}

such cases, for the affected individuals I kept only those observations where the individual responds to the interview personally and his birth year and month are not imputed.

34 _{The data on both these variables are available in the following topical modules of the SIPP:}

size as fewer individuals are present in survey at the time of data collection on these variables.

1.4.2. RETIREMENT MODEL SPECIFICATION

I analyze the retirement decision using a hazard model framework. An individual is at risk of retiring between ages 60 to 75. The discrete-time retirement hazard at any age is the probability of retiring at that age conditional on not having already retired. The retirement hazard at a particular age depends on various explanatory variables that make retirement more or less appealing than staying at work, I discuss these variables in detail below. The discrete-time retirement hazard hit for an individual i at any age t (in the

interval (t-1, t])

ℎ𝑖𝑖𝛿𝛿 = Pr(𝑁𝑁𝑒𝑒𝑑𝑑𝑅𝑅𝑟𝑟𝑒𝑒𝑖𝑖𝛿𝛿 = 1 | 𝑁𝑁𝑒𝑒𝑑𝑑𝑅𝑅𝑟𝑟𝑒𝑒𝑖𝑖𝛿𝛿−1 = 0 𝑥𝑥𝑖𝑖𝛿𝛿,𝑦𝑦𝑖𝑖)

= Pr(𝑇𝑇𝑖𝑖 ∈(𝑑𝑑 −1,𝑑𝑑] | 𝑇𝑇𝑖𝑖 > 𝑑𝑑 −1,𝑥𝑥𝑖𝑖𝛿𝛿,𝑦𝑦𝑖𝑖)

where T is the random variable denoting the age at retirement (beginning at age 60), and xit, yi are a set of time varying and time constant explanatory variables respectively that

affect the retirement hazard rate. Following Allison (1982), I use a logit model to specify the dependence between the retirement hazard and the explanatory variables.

log�_{(1− ℎ}ℎ𝑖𝑖𝛿𝛿

𝑖𝑖𝛿𝛿)�= 𝛼𝛼𝛿𝛿+ 𝛽𝛽 ′_𝑥𝑥

𝑖𝑖𝛿𝛿 + 𝛾𝛾′𝑦𝑦𝑖𝑖

αt is a set of constants (age dummies) denoting the non-parametric baseline hazard; this

specification allows the retirement hazard to vary by age while holding other explanatory variables constant.

The explanatory variables included in xit and yi that affect an individuals’

retirement hazard include both Social Security policy variables and socio-economic variables. I capture the effect of changes in the Social Security policies by including a variable for the normal retirement age which indicates (in months) the NRA assigned to individuals by their birth cohort, and another variable for the delayed retirement credit which indicates (in percent) the DRC assigned to each birth cohort. The impact of these two policy changes can be separately identified because the DRC changes started

affecting cohorts born after 1924 and were implemented every other year for individuals born in odd years. The full impact of the earnings tests being in place among individuals above the NRA relative to not having the earnings test is identified through the inclusion of two variables: an earnings test in place dummy (equals 1 if individual is within the age range covered by the earnings test) and the difference between the earnings test threshold amount (in thousands of real 2013 dollars) and the amount at which the threshold was before the 2000 repeal.

Recent evidence by Mastrobuoni (2009) and Blau and Behaghel (2012) notes a shift in the hazard for retirement and claiming social security benefits at the new NRA. The life-cycle model predicts the NRA as the optimal retirement age only in the presence of a convex kink in the lifetime budget constraint, but with an actuarially fair or more than fair delayed retirement credit there is no such kink and hence, no reason to observe the spike at the new NRA. The recent evidence, thus, points in the direction of a norm effect or reference dependence effect of the normal retirement age. Under norm effects, workers view the NRA as a focal point at which to retire. I examine the strength of the

influence of these norm effects on the retirement probability by including dummy variables for being below, at, or above the NRA.

Aside from these policy variables, there are two other variables that could potentially cause retirement probabilities to differ among individuals by changing the value of their SSW: marital status and education. The retirement probability of married workers can differ from single individuals because for married men the benefit from a change in the SSW from an additional year of work is not restricted only to them but also extends to their spouse through spousal (if she claims spousal benefits) and/or survivor benefits.35 The probability of retirement among older individuals may also differ by educational attainment for two reasons: difference in opportunities available for

continued work and education may be correlated with mortality. To assess differences in retirement probabilities arising due to marital status and education, I include dummy variables for being married and for educational attainment.

Health insurance and the availability of pension are two other factor affecting older workers retirement probability. Rust and Phelan (1997) find that men with employer provided health insurance and no retiree insurance are less like to leave labor force before age 65 than those with retiree health insurance. They also note that even after age 65, these men have a lower retirement hazard suggesting that employer provided health insurance may be more generous.36 To control for differences in the retirement

35 _{A workers’ wife is entitled to 50 percent of his PIA, and a surviving spouse is entitled to 100}

percent of the workers’ PIA. If the wife has an earnings record, she can choose to claim the higher of the benefits either based on her own record or as a dependent spouse.

36 _{Madrian and Beaulieu (1998) suggest one reason for this generosity could be the availability of}

coverage of dependents that is provided by employer health insurance but not by Medicare. Another reason could be that Medicare coverage is not comprehensive. Older individuals covered

hazard arising from the availability of employer provided health insurance I include two dummy variables. The first variable is a lagged dummy variable indicating whether an individual is covered by employer provided health insurance, intended to capture the difference in the retirement probability among individuals with and without employer provided health insurance at all ages 60 to 75. The second variable captures the differential effect of employer provided health insurance on those below age 65. The availability of a pension may also impact the retirement decision of older workers.

Without knowledge regarding the detailed pension plan incentives I am unable to account for the precise incentives faced by a worker.37 I try to capture the effect of availability of a pension plan by the inclusion of a union dummy variable which indicates whether an individual is covered by a union.

The race of an individual may also affect the retirement hazard. Past researchers have suggested that differences in retirement probabilities by race may arise due to difference in preference for leisure, but Gustman and Steinmeier (2004) attribute these differences in retirement to difference in time preference rates. I include race dummies to capture these differences. Other factors affecting the probability of retirement may

include difference in opportunities for work available at older ages; to allow the hazard to vary by these differences I include region dummies and the state specific unemployment rate. Personal responsibilities and financial needs may also cause otherwise similar individuals to have varying retirement propensities. I capture the impact of these factors

by retiree health insurance, however, will not be affected by health insurance availability and eligibility for Medicare in making their retirement decisions.

by including three variables: a dummy variable indicating whether an individual has children under 18, a dummy variable for ownership of home, and the number of members in the household.