Jacques van der Gaag

The life-cycle hypothesis and the consumption behavior of the elderly

The life-cycle hypothesis (LCHO) is based on the common-sense idea that households do not make saving or dis saving decisions solely on the basis of their current income and wealth, but that they also take into account their expected future circumstances and are affected by their past experience. In particular, because people can anticipate that their incomes will fall sharply when they retire, they save when younger and dissave after retirement so as to maintain, more or less, their previous standards of living. This paper presents new data on income and consumption that are more appropriate for testing the LCHO than the data used in previous studies. Then we examine the average propensities to consume of the elderly and nonelderly and show how they do not accord with the LCHO. The LCHO predicts that the elderly dissave, or at least that they have a significantly higher average propensity to consume, at a given level of income, than the nonelderly. In fact, we found strong evidence to the contrary; i.e., the elderly not only do not dissave to finance their consumption during retirement, they spend less on consumption goods and services (save significantly more) than the nonelderly at all levels of income. Moreover, the oldest of the elderly save the most at given levels of income. After testing some adjustments that attempt to salvage the LCHO, we suggest alternative explanations for the observed higher average propensities to consume of the elderly.

Health as an unobservable: A MIMIC-model of demand for health care

Abstract This paper develops a model to analyze the demand for health care. It differs from current practice in that (1) it deals explicitly with the complex relation between income, health, health insurance, and the demand for health care, and (2) ‘health’ is treated as an unobservable variable. We present the Maximum Likelihood estimates of an eleven-equation, simultaneous, multiple-indicator, multiple-causes (MIMIC) model, containing two simultaneously determined unobservables and, in total, nine ‘indicators’. Data used stem from a health-care survey among 8000 households in The Netherlands. The results show, among other things, that health and permanent income have mutual, positive impacts. Both age and education have important direct and indirect (via permanent income) effects on health. The estimated impact of the availability of health care on individual demand confirms similar results based on aggregated data.

Estimating demand for medical care: health as a critical factor for adults and children

In an era when resources are increasingly directed toward medical care, understanding the factors that influence demand takes on greater importance. Most work in this area (see, for example, Newhouse and Phelps, 1974; Hyman, 1971; Rosett and Huang, 1973; Newhouse, 1981) is directed to understanding the role of personal income and health insurance on demand, with emphasis on the dimensions of insurance. The recent Health Insurance Study conducted by the Rand Corporation focused on measuring responsiveness to various coinsurance rates — the partial payment by the consumer. Other recent work has addressed the value of time, length of wait, and demand for medical care. Equity issues are implicitly or explicitly raised by many of these studies. For example, the Health Insurance Study suggests that persons with low incomes decrease their medical care usage more than higher-income individuals when coinsurance is imposed.

On measuring the cost of children

Abstract The primary objectives of this paper are to give a precise definition of the costs of a child and to compare various approaches in the literature to estimating these costs. Two alternative approaches are discussed at length. The first goes back to the work of Engel (1895) on household consumption patterns. The second is based on methods to directly estimate Individual Welfare Functions of Income (van Praag, 1968). Both approaches produce well-defined measures of levels of well-being. These measures are adopted to define the cost of a child. We will show that both approaches can be viewed as stemming from the same general methodological framework. A short discussion of methodologies that do not fit this framework is included. It is concluded that there exists in the literature no consensus on the exact value of the cost of a child. The estimates for a first child range from 0–42% of a households yearly income. However, the century-long development of this topic in the economic literature has produced a precise definition of the cost of a child that—once adopted—will enable researchers to improve their estimates and narrow down this disappointingly wide range.

What influences children's health?

Abstract According to generally available government statistics, child health is related to socioeconomic status. Mortality, days ill, most specific disease rates are all higher among “disadvantaged” children. However, the understanding of determinants of childrens health is hindered by the lack of comprehensive measures of health. Using a simultaneous structural equation model we develop an overall measure of underlying health status. In this model we find childrens health is improved by one socioeconomic factor, mothers education: but negatively influenced by income. Parents marital status, mothers working and race also impact a childs health.