Sequential Bayesian approach to estimate the proportion of people in retirement conditions in Argentina

Abstract

One of the dimensions of a pension system’s performance is coverage. In most countries, pension systems require a minimum number of years of contribution as part of their eligibility requirements. For example, in Argentina, a minimum of 30 years of contributions is required. Therefore, it is not enough to study what percentage of the target population is covered at a given time; it is also necessary to study employment histories over a considerable period of time. Sometimes, developing countries do not have sufficient information for this purpose, but they incorporate new information as they digitize their records. The Bayesian approach can be useful in these cases where information is limited but regularly updated. The objective of this paper is to demonstrate the usefulness of the sequential Bayesian approach for estimating the proportion of workers eligible to retire in Argentina year after year. It is observed that as more information is incorporated, the proportion of people who remain active contributors (and, therefore, eligible for a pension) decreases. This implies that, for any individual, the probability of meeting the contribution requirement decreases as time passes from the first month of contribution. At the end of the process (once all available information has been incorporated), the Bayesian proportion estimate is different from that obtained with a frequentist approach, which is explained by the importance of a priori information provided by prior knowledge about the phenomenon. This type of sequential estimation exercise may be of interest to social security decision-makers.