Shripad Tuljapurkar

Overview

Shripad Tuljapurkar is a mathematical demographer and population biologist at Mountain View Research (Mountain View, CA) and Stanford University. He is best known for co-developing (with Ronald Lee) the stochastic U.S. population forecasting framework that combined Lee-Carter stochastic mortality with stochastic fertility to produce full joint probability distributions over future population age structures. The Lee-Tuljapurkar collaboration produced the first fully probabilistic projections of Social Security finances, identifying fertility — not mortality — as the dominant source of 75-year solvency uncertainty.

Key Contributions

• Stochastic fertility forecasting validation (Tuljapurkar and Boe 1999) — International Journal of Forecasting 15: 259–271. Validated Lee's constrained ARMA fertility model via historical launch experiments (1945–1975); showed that the long-run TFR average (F*) operates on 30–50 year timescales not probed by time-series estimation, creating structural uncertainty irreducible by data; demonstrated that Bayesian empirical priors over F* widen prediction intervals ~15% relative to benchmark. Explains why fertility uncertainty dominates 75-year Long-Term Actuarial Balance uncertainty: mortality forecasting has bounded LC variance, fertility forecasting inherits both ARMA and F* structural uncertainty.
• Stochastic population forecasts for the US (Lee and Tuljapurkar 1994) — JASA 89(428): 1175–1189. Extended Lee-Carter stochastic mortality to incorporate stochastic fertility, generating full probability distributions over future U.S. population size and age structure. Mean old-age dependency ratio in 2070: 0.47 with 95% CI 0.26–0.68, vs. SSA's 0.41 (0.31–0.57). Adopted by the Congressional Budget Office for stochastic federal budget projections (1996).
• Stochastic Social Security finance projections (Lee and Tuljapurkar 1998b) — AER Papers and Proceedings 88(2): 237–241. First fully probabilistic forecast of OASDI trust fund solvency. Mean LTAB = −3.3 pp (vs. SSA's −2.2 pp); median payroll tax in 2070 = 21%; identified fertility > productivity > interest > mortality as 75-year uncertainty ranking, reversing SSA's ordering. See Long-Term Actuarial Balance.
• Companion chapter (Lee and Tuljapurkar 1998a) — "Stochastic Forecasts for Social Security." In D. Wise (ed.), Frontiers in the Economics of Aging. Chicago: University of Chicago Press. More complete methodological exposition of the stochastic SS finance model.
• G7 universal mortality pattern (Tuljapurkar, Li and Boe 2000) — Nature 405: 789–792. Established that the LC single-factor structure holds in all G7 countries (>94% of temporal variance in log death rates explained by one factor in every country); stochastic median $e_0$ forecasts exceed official central projections by 1.3–8.0 yr by 2050; dependency ratios 6–40% higher than official by 2050. See Lee-Carter Model and Long-Term Actuarial Balance.

Related

• Ronald Lee
• Nan Li
• Carl Boe
• Stochastic Fertility Forecasting
• Lee-Carter Model
• Long-Term Actuarial Balance
• SSA Mortality Forecasting
• Social Security Administration

Sources

• Lee Anderson and Tuljapurkar 2003 — Stochastic Forecasts of the Social Security Trust Fund
• Lee and Tuljapurkar 1998 — Uncertain Demographic Futures and Social Security Finances
• Tuljapurkar and Boe 1999 — Validation, Probability-Weighted Priors, and Information in Stochastic Forecasts
• Tuljapurkar, Li and Boe 2000 — A Universal Pattern of Mortality Decline in the G7 Countries
• Denton Feaver and Spencer 2005 — Time Series Analysis and Stochastic Forecasting
• Lee and Tuljapurkar 1994 — Stochastic Population Forecasts for the United States Beyond High Medium and Low
• Lee and Tuljapurkar 1999 — Population Forecasting for Fiscal Planning Issues and Innovations
• Meseguer 2010 — Forecasting Vital Rates and Population with Bayesian Vector Autoregressions