Assessing Policies for Retirement Income Needs for Data, Research, and Models (1997) / Chapter Skim
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5 Development of Projection Models
5 Development of Projection Models
Pages 132-164
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From page 132... ...
Outputs from analytical models of the behavior of individuals and employers that receive general consensus should be inputs to projection models. Otherwise, when policy proposals are expected to elicit a behavioral response, projection models will fall short in estimating the likely effects.
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From page 133... ...
Some of the major types are time-series models, cell-based models, microsimulation models, macroeconomic models, and computable general equilibrium models. Some models incorporate multiple approaches: for example, in the Macroeconomic-Demographic Model (MDM)
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From page 134... ...
Such models usually employ stochastic or probabilistic methods to generate distributions for such outcomes as retirement decisions. DYNASIM2 and PRISM are two dynamic microsimulation models that have been used to evaluate alternative Social Security and employer pension policies.
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From page 135... ...
Dimensions of Models There are three key dimensions on which to evaluate projection models: · the degree to which the model provides accurate estimates of policy outcomes, in that the estimates approximate what would occur if a proposed policy change were implemented; · the amount of information provided in the model output on the uncertainty of the model estimates and their sensitivity to key assumptions; and · the degree to which the model incorporates best current professional judgment about the underlying behavior for example, the best judgment about the appropriate functional form and parameter values with which to estimate changes in personal savings in response to changes in employer pensions or Social Security. Obviously, it is highly desirable that models be accurate, provide estimates of uncertainty and sensitivity to key assumptions, and incorporate best professional judgment (or be able to use alternative specifications to reflect important disagreements)
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From page 136... ...
. Other dimensions of projection models include: · scope, in terms of the types of policies that a model can simulate and the kinds of outcome measures for which it can provide information; · detail, in terms of how well a model can represent all of the provisions of current law and estimate the effects of changes to one or more provisions; · extent of disaggregation of model outputs, that is, the extent to which the model can estimate the distributions of outcomes for groups of interest, variously defined, in addition to average tendencies; · time period, whether the model can develop projections for 5 years or 75 years; · extent of feedback loops, which permit estimating not only the first-order effects of a policy change, but also the second-order effects, taking into account behavioral responses to the new policy; · ability to link to other models, either by supplying outputs for other models to use as inputs or by accepting other models' outputs as inputs; · amount of elapsed time that is required to obtain outputs from the model on one or more policy options, including options that are close variants of current policies and options that differ markedly in one or more respects; · extent of openness and transparency, that is, how usable and understandable the model is to people other than the developers; · ease of use and modification as needed; and · extent of portability, from one computing hardware and software environment to another.
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From page 137... ...
Also, modeling of feedback effects is quite difficult, both operationally and because of the uncertainty of behavioral responses to policy changes. Hence, it may be cost-effective to have different models for answering questions about particular types of policy changes, as well as different models for estimating means versus distributions, effects over shorter versus longer periods, and second-order as distinct from first-order effects.
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From page 138... ...
Also, our review does not extend to health care financing projection models, although such models are very important to a full assessment of retirement income security, nor to tax revenue models, such as that used by the U.S. Treasury Department.1 Rather, we have looked primarily at models that can address somewhat broader retirement-income-related policy questions, for example, the effects of tax policy changes on accumulations in Individual Retirement Accounts (IRAs)
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From page 139... ...
They could be very useful for retirement-income-related policy analysis, but they generally suffer from several deficiencies: they have not been adequately validated and are not designed to facilitate validation; they are based on old or limited data sets; they do not reflect best current professional judgment about underlying behavior; they have little facility for implementing alternative behavioral specifications when there are important disagreements; their provision for feedback loops between policy changes and behavioral responses is weak; they generally have little provision for ready exchange of inputs and outputs with other models; they are not well documented; and they are not open and transparent or readily portable, although they now are generally implemented with personal computing technology. CGE models that have been developed to answer such questions as the intergenerational benefits and costs of alternative Social Security financing systems are impressive in their treatment of the interactions of the household, government, and private sectors of the economy and in their use of up-to-date information and research knowledge.
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From page 140... ...
and for a new general-purpose individual-level microsimulation model. Other retirement income modeling tools are needed as well, but it is clearly important to have the projection capabilities that employer models and a new individual-level microsimulation model can provide.
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From page 141... ...
A cell-based model would be easier to implement and have fewer data requirements than a microsimulation model, but it would have more limited capability for analysis and for simulation of behavioral response. It could also be useful to bring together researchers and people with experience in developing projection models to consider possible model designs on the basis of anticipating what research results may show.
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From page 142... ...
Dynamic microsimulation can in principle forecast the long-term effects of policy changes, including behavioral responses: for example, how a change in Social Security benefits will likely change retirement and savings behavior and, ultimately, affect retirement income levels for different cohorts of workers. We believe that a general-purpose, dynamic microsimulation model is needed to project the distributional effects of policy alternatives on the retirement income security of individuals and families over the medium term and for modeling interactions among policies.
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From page 143... ...
. More important, because they were originally implemented with costly and cumbersome mainframe computer technology, these models are not well structured to simulate the effects of policy changes on key behaviors, such as labor force participation.
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From page 144... ...
When new data and research results become available with which to build new models, then choices can be made about how best to take advantage of the latest technological developments for improved model functionality and design.4 Of course, there will always be new knowledge and methods that make any current state of the art of projection modeling obsolete in one or more respects. We are not thereby arguing that new microsimulation model development should be deferred indefinitely.
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From page 145... ...
Detailed Projection Capabilities A new individual-level microsimulation model requires detailed demographic, labor supply and retirement, pension coverage and benefits, and savings projection capabilities as integral components. Existing dynamic microsimulation models have well-developed demographic modules that could likely be adapted for a new model.
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From page 146... ...
Finally, the personal savings components of existing microsimulation models are very undeveloped. DYNASIM2 and PRISM project IRA contributions but not other kinds of personal savings.5 Research with HAS/AHEAD and other sources will be required to provide clearer answers about the appropriate analyti 5The long-term care submodel of PRISM assigns asset portfolios to people when they reach age 65, and then in each year of the projection it makes a simple adjustment of housing asset values by an assumed inflation rate and of nonhousing asset values by an assumed constant rate of dissaving.
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From page 147... ...
It would simply be infeasible to try to incorporate a health care policy simulation model into a new individual-level retirement income model. However, a new retirement model could usefully include a component perhaps off-line from the main model that estimates the portion of retirement income that remains after expected health care expenditures for different groups of the elderly population.
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From page 148... ...
outlines a relatively straightforward macroeconomic model that could accept outputs from a dynamic microsimulation model to use to estimate the effects on gross output and national savings of policy changes that affect Social Security taxes and benefits, employer pensions, and other personal savings. He notes that current projections of the Social Security and Medicare trust fund (from cell-based models)
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From page 149... ...
Such policies increase the potential for unexpected gains and losses in retirement income security for particular age cohorts and other groups due to such factors as variation in price inflation, ups and downs in the economy, and stock market booms and busts. Database To Be Used The choice of a primary database on which simulations are performed is critical to the development of a new individual-level microsimulation model with a full range of capabilities.
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From page 150... ...
Further improvements would need to be made to the SIPP questionnaire to make it suitable for use as a database for a new individual-level dynamic microsimulation model. For example, assuming that the HAS/AHEAD questions on subjective probabilities (e.g., of one's own life expectancy)
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From page 151... ...
Finally, SIPP would need to be matched with Social Security earnings records. SSA is currently using exact matches of the 1984 and 1990 SIPP panels with SSA data for limited kinds of retirement income modeling (see "Near-Term Modeling Strategies," below)
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From page 152... ...
If HRS/ AHEAD is to be merged with other panel surveys for projection modeling purposes, it will be critically important to review the survey questionnaires to identify ways to make them as compatible as possible. Design Considerations Each of the requirements and features discussed above stretches the capabilities of existing dynamic microsimulation models.
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From page 153... ...
15. The relevant federal agencies should consider the development of a new integrated, individual-level microsimulation model for retirement-income-related policy analysis as an important long-term goal, but construction of such a model would be premature until advances are made in data, research knowledge, and computational methods.
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From page 154... ...
For complex models, like dynamic microsimulation models, sample reuse techniques can be used to measure the uncertainty in a model's estimates due to the two kinds of sampling variability. However, the uncertainty due to the two kinds of errors may be difficult to put into a probabilistic context.
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From page 155... ...
. Moreover, there is no comparable record of regularly projecting the long-term funding status of employer pensions, even though this source of retirement income may be more uncertain than Social Security (see Burtless, 1993; also, Schieber and Shoven, 1993, develop such a projection for defined benefit and defined contribution plans)
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From page 156... ...
Also, given that relatively little work has been done to date to conduct external validation studies or sensitivity analyses of models, the extent to which a model incorporates best professional judgment is often the only indicator of the likely level of model performance. SSA Model Validation The Social Security actuarial cost model, which uses cell-based methods to carry forward the results of time-series analysis, is an exception to our statement that model validation and assessment of uncertainty are rarely performed.
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From page 157... ...
The SSASIM model, recently developed with support from SSA, uses Monte Carlo techniques to characterize uncertainty in the Social Security trust fund balance
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From page 158... ...
about the dynamics of each variable over time and in association with other variables. For example, a policy analyst can assume that such variables as rates of mortality decline follow a consistent trajectory over the span of the projection period (as does the SSA actuarial cost model)
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From page 159... ...
Admittedly, limited ad hoc models may not provide very reliable answers to policy questions, and, in particular, they are not likely to be able to handle important policy interactions. Also, special-purpose models may be able to model behavioral responses to policy changes only very crudely, although they should incorporate such responses when reasonable parameters are available and feasible to implement.
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From page 160... ...
, whose analysts regularly prepare ad hoc, special-purpose models, often using spreadsheet tools, to develop estimates of the likely costs and other effects of proposed policy changes. CBO staff combine data and parameter estimates in their ad hoc models from a wide range of sources, sometimes including as inputs the outputs of larger, more formal projection models with the results adjusted as seems appropriate.
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From page 161... ...
The SSA analysts propose to extend their models to project pension income, as well as Social Security benefits, on the basis of earnings histories and other characteristics of SIPP sample members. If successful, and if a savings income projection capability is also added, this approach could offer some useful policy modeling capability without requiring a full-fledged dynamic microsimulation model (assuming that policy changes can be factored into the projections in some manner)
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From page 162... ...
Similarly, work that PWBA analysts may do with employer pension issues should be well known and available to analysts in other relevant agencies. It is probably not feasible to establish a cross-agency retirement income modeling group with authority to prepare estimates on behalf of the executive branch, but perhaps it is possible to establish coordinating mechanisms.
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From page 163... ...
Outputs of the SSA actuarial cost model are so widely used for inputs to other models that it would be very helpful if outside analysts could more readily learn about and have access to the model. Such access would also facilitate outside reviews of the model's properties to identify other improvements.
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From page 164... ...
This model provides useful outputs for other models and helps frame the overall retirement income security policy debate. Priority improvements include the following: · replacing the scenario approach with probabilistic estimates of uncertainty for the model's projections that take into account covariances among the various time series (mortality, labor force participation, wage growth, etc.~; · adding a capability for limited distributional analysis of the effects of current and proposed Social Security provisions (e.g., of costs and benefits for low, middle, and high earners)
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