The causal effect of income on health: An original microsimulation approach

The causal effect of income on health: An original microsimulation approach

Table of Contents

Introduction: An original microsimulation approach

The relationship between health and individual income is a topic at the center of health economics, with the broad finding that higher socioeconomic status(original microsimulation approach) is associated with better health (Preston [1975]). This relationship has been reviewed in many countries and concerning a wide variety of health outcomes (see for instance Wilkinson et al. [1996]; Van Doorslaer et al. [1997]; Kawachi and Kennedy [1999]). While this relationship appears to be well-known, this is not always true concerning its causal interpretation. There are many possible pathways through which earnings fluctuations can impact health. Indeed, income or more generally socioeconomic status is correlated with health in a causal way of the former on the latter. However, we can also think of the reverse association by stating that poor health status may influence income, by reducing the ability to work (Apouey and Clark [2015]). This lack of a clear understanding is an important omission and the direction of the impact of an income shock on health does not seem obvious. Policymakers who aim at improving general health or narrowing health inequalities in a society, need to consider the true direction of causality between income and health. In the absence of randomized controlled experiments, which are not feasible in this context, the difficulty in disentangling causes and effects is due to endogeneity. Indeed, if the health and the income determine each other simultaneously, then there is an endogeneity issue in their relationship. As a result, since a simultaneous causality in both directions may exist, testing the causal impact of income on health implies assessing the exogeneity of income.

Moreover, identifying the factors that influence the age profile of self-perceived health is a difficult undertaking, which justifies further investigation. In this way, we intend to do an original microsimulation method (following the methodology of Dormont et al. [2006]) to analyze health changes over time. Thanks to this method, we will be able to separately identify changes that are due to changes in morbidity or due to the age of individuals on one hand, and to others changes due to individual characteristics on the other. Concerning morbidity, we will consider a vector of chronic illnesses and disability indicators. This microsimulation method apprehends to identify the impact of income changes while controlling for the age, the morbidity, and the technological progress on the self-perceived health status.

To correctly identify these factors, one need to control for their possible endogeneity towards the self-perceived health status. Indeed, individuals take into account several elements of their health and transcribe them into this subjective measure of health. Functional limitations for which an individual is treated, diseases, diagnosed health problems as well as interactions with health professionals are factors which influence the self-rated health (Tubeuf et al. [2008]). This measurement even if it is 4 subjective, is a good predictor of an individual’s health (Benitez-Silva et al. [2004]). Thus, it interprets factors which are not always observed by health professionals since it integrates personal expectation of a level of health.

This paper adds a contribution to these subjects by estimating changes in the self-perceived health status following an income shock. As a result, it contributes to the literature concerning causality issues since we control for some aspects that could influence the causal link using an original microsimulation approach. Moreover, we use an instrumental variable approach as well as exogenous shocks to get rid of the exogeneity issues related to income.

In section 1 we present the theoretical framework of the causal relationship between income and health. Section 2 describes the econometric framework as well as the microsimulation approach. Then, in section 3 we detail our data. Section 4 reports the results of the empirical analysis. Section 5 concludes the paper.


A heavily researched topic in health economics is the relationship between income and health and more specifically the direction of causality between the two. This paper highlights whether income causally implies health. While it seems well-known that people with higher incomes enjoy better health, it is far more difficult to establish the direction of the causality of this relationship. The definition of causality which is chosen here is the Granger one implying a persistence phenomenon in the relationship. Factors such as age, the morbidity or the technical progress have to be controlled since they could influence this relationship. We use a rich longitudinal database (SHARE survey) which covers a statistically representative sample of European individuals aged 50 and over and reports detailed information on income and health as well as health behaviors.

The microsimulations performed above enable us to identify the components of the health-income relationship and to control for the endogeneity issues which can arise. Indeed, we assess that this approach allows us to get rid of the endogeneity of the morbidity indicators since we fix these factors to the previous date and look at their impacts on the current health status of individuals. Thus, instantaneous endogeneity of morbidity on health is then no more an issue. Moreover, to get rid of the income endogeneity issue which can bias our estimates, we implement an instrumental variables’ method as well as exogenous income shocks to the estimation.

The results presented here have underlined a central point in the analysis of health economics and income. Researchers need a clear understanding of the direction of the causality in this relationship. Indeed, our method and results ensure the Granger causality of income on health. In other words, we show that income has a permanent effect on subjective health status. We thus get rid of the possible reverse 29 causation in this relationship since our results appear to be robust.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

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SHARE Project acknowledgement - Research Data Center and Data Access- original microsimulation approach

∗ThEMA, University of Cergy-Pontoise. Email:

†ThEMA, University of Cergy-Pontoise. Email:

The causal effect of income on health: An original microsimulation approach

SHARE Project acknowledgment – Research

Data Center and Data Access

“This paper uses data from SHARE Waves 1, 2, 4, and 5 (DOIs: 10.6103/SHARE.

w1.260, 10.6103 – SHARE.w2.260, 10.6103 – SHARE.w4.111, 10.6103 – SHARE.w5.

100), see B¨orsch-Supan et al. [2013] for methodological details.

The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193,

COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7

(SHARE-PREP: N 211909, SHARE-LEAP: N 227822, SHARE M4: N 261982). Additional funding from the German Ministry of Education and Research, the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30

AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11, OGHA 04-064) and

From various national funding sources is gratefully acknowledged (see”

Tubeuf et al. [2008]). … Data set. DOI: 10.6103/SHARE.w1.500. 2016a. A. Börsch-Supan.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Maryam kakaei was born in 1984 in Arak. She holds a Master's degree in Software Engineering from Azad University of Arak.