Publications by Author: Samir Soneji

This is a response to Peter Diamond's comment on a two paragraph passage in our article, Konstantin Kashin, Gary King, and Samir Soneji. 2015. “Systematic Bias and Nontransparency in US Social Security Administration Forecasts.” Journal of Economic Perspectives, 2, 29: 239-258. 

The accuracy of U.S. Social Security Administration (SSA) demographic and financial forecasts is crucial for the solvency of its Trust Funds, other government programs, industry decision making, and the evidence base of many scholarly articles. Because SSA makes public little replication information and uses qualitative and antiquated statistical forecasting methods, fully independent alternative forecasts (and the ability to score policy proposals to change the system) are nonexistent. Yet, no systematic evaluation of SSA forecasts has ever been published by SSA or anyone else --- until a companion paper to this one (King, Kashin, and Soneji, 2015a). We show that SSA's forecasting errors were approximately unbiased until about 2000, but then began to grow quickly, with increasingly overconfident uncertainty intervals. Moreover, the errors are all in the same potentially dangerous direction, making the Social Security Trust Funds look healthier than they actually are. We extend and then attempt to explain these findings with evidence from a large number of interviews we conducted with participants at every level of the forecasting and policy processes. We show that SSA's forecasting procedures meet all the conditions the modern social-psychology and statistical literatures demonstrate make bias likely. When those conditions mixed with potent new political forces trying to change Social Security, SSA's actuaries hunkered down trying hard to insulate their forecasts from strong political pressures. Unfortunately, this otherwise laudable resistance to undue influence, along with their ad hoc qualitative forecasting models, led the actuaries to miss important changes in the input data. Retirees began living longer lives and drawing benefits longer than predicted by simple extrapolations. We also show that the solution to this problem involves SSA or Congress implementing in government two of the central projects of political science over the last quarter century: [1] promoting transparency in data and methods and [2] replacing with formal statistical models large numbers of qualitative decisions too complex for unaided humans to make optimally.

The financial stability of four of the five largest U.S. federal entitlement programs, strategic decision making in several industries, and many academic publications all depend on the accuracy of demographic and financial forecasts made by the Social Security Administration (SSA). Although the SSA has performed these forecasts since 1942, no systematic and comprehensive evaluation of their accuracy has ever been published by SSA or anyone else. The absence of a systematic evaluation of forecasts is a concern because the SSA relies on informal procedures that are potentially subject to inadvertent biases and does not share with the public, the scientific community, or other parts of SSA sufficient data or information necessary to replicate or improve its forecasts. These issues result in SSA holding a monopoly position in policy debates as the sole supplier of fully independent forecasts and evaluations of proposals to change Social Security. To assist with the forecasting evaluation problem, we collect all SSA forecasts for years that have passed and discover error patterns that could have been---and could now be---used to improve future forecasts. Specifically, we find that after 2000, SSA forecasting errors grew considerably larger and most of these errors made the Social Security Trust Funds look more financially secure than they actually were. In addition, SSA's reported uncertainty intervals are overconfident and increasingly so after 2000. We discuss the implications of these systematic forecasting biases for public policy.

The financial viability of Social Security, the single largest U.S. Government program, depends on accurate forecasts of the solvency of its intergenerational trust fund. We begin by detailing information necessary for replicating the Social Security Administration’s (SSA’s) forecasting procedures, which until now has been unavailable in the public domain. We then offer a way to improve the quality of these procedures due to age-and sex-specific mortality forecasts. The most recent SSA mortality forecasts were based on the best available technology at the time, which was a combination of linear extrapolation and qualitative judgments. Unfortunately, linear extrapolation excludes known risk factors and is inconsistent with long-standing demographic patterns such as the smoothness of age profiles. Modern statistical methods typically outperform even the best qualitative judgments in these contexts. We show how to use such methods here, enabling researchers to forecast using far more information, such as the known risk factors of smoking and obesity and known demographic patterns. Including this extra information makes a sub¬stantial difference: For example, by only improving mortality forecasting methods, we predict three fewer years of net surplus, $730 billion less in Social Security trust funds, and program costs that are 0.66% greater of projected taxable payroll compared to SSA projections by 2031. More important than specific numerical estimates are the advantages of transparency, replicability, reduction of uncertainty, and what may be the resulting lower vulnerability to the politicization of program forecasts. In addition, by offering with this paper software and detailed replication information, we hope to marshal the efforts of the research community to include ever more informative inputs and to continue to reduce the uncertainties in Social Security forecasts.

This work builds on our article that provides forecasts of US Mortality rates (see King and Soneji, The Future of Death in America), a book developing improved methods for forecasting mortality (Girosi and King, Demographic Forecasting), all data we used (King and Soneji, replication data sets), and open source software that implements the methods (Girosi and King, YourCast).  Also available is a New York Times Op-Ed based on this work (King and Soneji, Social Security: It’s Worse Than You Think), and a replication data set for the Op-Ed (King and Soneji, replication data set).

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public pensions, private pensions, and health care financing systems. In part because existing methods seem to forecast worse when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too.