nep-age New Economics Papers
on Economics of Ageing
Issue of 2015‒08‒07
six papers chosen by
Claudia Villosio
LABORatorio R. Revelli

  1. Seniority Wages and the Role of Firms in Retirement By Frimmel, Wolfgang; Horvath, Gerard Thomas; Schnalzenberger, Mario; Winter-Ebmer, Rudolf
  2. The financial support for long-term elderly care and household savings behaviour By Asako Ohinata; Matteo Picchio
  3. Strategic Intelligence Monitor on Personal Health Systems Phase 3 (SIMPHS 3) – DREAMING (Spain) Case Study Report By Ramon Sabes-Figuera
  4. Forecasting Leading Death Causes in Australia using Extended CreditRisk$+$ By Pavel V. Shevchenko; Jonas Hirz; Uwe Schmock
  5. The Welfare State and Migration:Coalition-formation dynamics By Assaf Razin
  6. Which Models Can We Trust to Evaluate Consumer Decision Making? Comment on “Choice Inconsistencies among the Elderly” By Jonathan D. Ketcham; Nicolai V. Kuminoff; Christopher A. Powers

  1. By: Frimmel, Wolfgang; Horvath, Gerard Thomas; Schnalzenberger, Mario; Winter-Ebmer, Rudolf
    Abstract: In general, retirement is seen as a pure labor supply phenomenon, but firms can have strong incentives to send expensive older workers into retirement. Based on the seniority wage model developed by Lazear (1979), we discuss steep seniority wage profiles as incentives for firms to dismiss older workers before retirement. Conditional on individual retirement incentives, e.g., social security wealth or health status, the steepness of the wage profile will have different incentives for workers as compared to firms when it comes to the retirement date. Using an instrumental variable approach to account for selection of workers in our firms and for reverse causality, we find that firms with higher labor costs for older workers are associated with lower job exit age.
    Keywords: firm incentives; retirement; seniority wages
    JEL: H55 J14 J26 J31
    Date: 2015–07
  2. By: Asako Ohinata; Matteo Picchio
    Abstract: We analyse how the financial support for long-term elderly care affects the level of household savings. Using a difference-in-differences estimator, we investigate the 2002 Scottish reform, which introduced free formal personal care for all the elderly aged 65 and above residing in Scotland. Our semiparametric estimation technique allows the policy effects to be flexibly estimated across age groups. We find that the Scottish policy reduced the average household saving by about £7,200. Moreover, the estimated effects are heterogeneous across age groups of the head of household: these effects are particularly strong among those aged between 40 and 60. The largest effect is observed at age 49 with the reduction in the average household saving by £12,764.
    Keywords: Long-term elderly care; ageing; means tested financial support; saving; wealth; difference-in-differences.
    JEL: C21 D14 I18 J14
    Date: 2015–07
  3. By: Ramon Sabes-Figuera (European Commission – JRC - IPTS)
    Abstract: DREAMING (ElDeRly-friEndly Alarm handling and MonitorING) was a large-scale pilot project that took place in 6 sites over a period of around 4 years, starting in 2008. It aimed to demonstrate new services that could help elderly people live independently in their home environment as long for as their physical and mental conditions allow. The technologies deployed were a combination of health and environmental monitoring systems. The data collected was processed by a decision support system and handled by a call centre. None of the 6 DREAMING sites developed further or integrated the services into the package of health and social care benefits offered to the population covered. Nevertheless, the innovation Unit of Barbastro Health Care Area (Spain) relied on the lessons and experiences from DREAMING and previous projects to design and test the implementation of telemonitoring services with a stronger integrated care approach.
    Keywords: SIMPHS, eHealth, Remote Monitoring, ageing, integrated care, independent living, case studies, facilitators, governance, impact, drivers, barriers, integration, organisation
    JEL: I11 I18 O33 O38
    Date: 2015–07
  4. By: Pavel V. Shevchenko; Jonas Hirz; Uwe Schmock
    Abstract: Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk$^+$ methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. These common factors introduce dependence between policyholders in annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. Moreover this framework allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include improvement in health treatments or better medication. In this paper, using publicly available data for Australia, we estimate the model using Markov chain Monte Carlo method to identify leading death causes across all age groups including long term forecast for 2031 and 2051. On top of general reduced mortality, the proportion of deaths for certain certain causes has changed massively over the period 1987 to 2011. Our model forecasts suggest that if these trends persist, then the future gives a whole new picture of mortality for people aged above 40 years. Neoplasms will become the overall number-one death cause. Moreover, deaths due to mental and behavioural disorders are very likely to surge whilst deaths due to circulatory diseases will tend to decrease. This potential increase in deaths due to mental and behavioural disorders for older ages will have a massive impact on social systems as, typically, such patients need long-term geriatric care.
    Date: 2015–07
  5. By: Assaf Razin (tel aviv university)
    Abstract: We develop a dynamic political-economic theory of welfare state and immigration policies, featuring three distinct voting groups: skilled work- ers, unskilled workers, and old retirees. The essence of inter - and intra- generational redistribution of a typical welfare system is captured with a proportional tax on labor income to nance a transfer in a balanced- budget manner. We provide an analytical characterization of political- economic equilibrium policy rules consisting of the tax rate, the skill com- position of migrants, and the total number of migrants. When none of these groups enjoy a majority (50 percent of the voters or more), political coalitions will form. With overlapping generations and policy-determined influx of immigrants, the formation of the political coalitions changes over time. These future changes are taken into account when policies are shaped.
    Date: 2015
  6. By: Jonathan D. Ketcham; Nicolai V. Kuminoff; Christopher A. Powers
    Abstract: Neoclassical and psychological models of consumer behavior often make divergent predictions for the welfare effects of paternalistic policies, leaving wide scope for researchers’ choice of a model to influence their policy conclusions. We develop a framework to reduce this model uncertainty and apply it to administrative data on consumer decision making in Medicare Part D. Consumers’ choices for prescription drug insurance plans can be explained by Abaluck and Gruber’s (AER 2011) model of utility maximization with psychological biases or by a neoclassical version of their model that precludes such biases. We evaluate these competing hypotheses using nonparametric tests of utility maximization and a trio of model validation tests. We find that 79% of enrollment decisions in Medicare Part D from 2006-2010 satisfied basic axioms of consumer preference theory under the assumptions of full information, zero transaction cost, and no measurement error. The validation tests provide evidence against widespread psychological biases. In particular, we find that precluding psychological biases improves the structural model’s out-of-sample predictions for consumer behavior.
    JEL: D12 I11 I38
    Date: 2015–07

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