TY - JOUR
T1 - Self-assessed health: What does it mean and what does it hide?
AU - Au, Nicole
AU - Johnston, David William
PY - 2014
Y1 - 2014
N2 - Self-assessed general health (SAH) is one of the most frequently employed health measures in social science research. Its generic nature means it captures elements of health that more guided measures cannot, and its brevity makes it possible for health information to be included in crowded multifaceted surveys. However, a shortcoming of SAH is that it provides little guidance to researchers as to what individuals are thinking of when they assess their health - when a survey respondent reports that their health is poor , is it because they are in pain, tired, depressed, unable to climb stairs, or something else entirely? This limits the possible inference from empirical research. It also means that important determinants and consequences of health can be missed if they are only weakly reflected in SAH. Given the continued use of SAH, it is important to better understand its structure. In this paper we use household panel data from Australia to answer two related questions: (i) what components of health does SAH most strongly represent? and (ii) does the use of SAH conceal important health effects? To answer the first question, we use a detailed health instrument and take a rigorous econometric approach to identify the health dimensions most strongly reflected in SAH. To answer the second question, we estimate the causal effects of income on SAH and on disaggregated health measures using instrumental-variables models. We find that some health dimensions - especially vitality - are consistently important to an individual when they assess their health, while other dimensions are inconsequential. We demonstrate that this fact provides insight in to why some studies find weak income gradients in SAH. Instrumental-variable regression results show that shocks to household income have no effect on SAH, but strongly improve several dimensions of health that are less commonly measured.
AB - Self-assessed general health (SAH) is one of the most frequently employed health measures in social science research. Its generic nature means it captures elements of health that more guided measures cannot, and its brevity makes it possible for health information to be included in crowded multifaceted surveys. However, a shortcoming of SAH is that it provides little guidance to researchers as to what individuals are thinking of when they assess their health - when a survey respondent reports that their health is poor , is it because they are in pain, tired, depressed, unable to climb stairs, or something else entirely? This limits the possible inference from empirical research. It also means that important determinants and consequences of health can be missed if they are only weakly reflected in SAH. Given the continued use of SAH, it is important to better understand its structure. In this paper we use household panel data from Australia to answer two related questions: (i) what components of health does SAH most strongly represent? and (ii) does the use of SAH conceal important health effects? To answer the first question, we use a detailed health instrument and take a rigorous econometric approach to identify the health dimensions most strongly reflected in SAH. To answer the second question, we estimate the causal effects of income on SAH and on disaggregated health measures using instrumental-variables models. We find that some health dimensions - especially vitality - are consistently important to an individual when they assess their health, while other dimensions are inconsequential. We demonstrate that this fact provides insight in to why some studies find weak income gradients in SAH. Instrumental-variable regression results show that shocks to household income have no effect on SAH, but strongly improve several dimensions of health that are less commonly measured.
U2 - 10.1016/j.socscimed.2014.10.007
DO - 10.1016/j.socscimed.2014.10.007
M3 - Article
SN - 0277-9536
VL - 121
SP - 21
EP - 28
JO - Social Science & Medicine
JF - Social Science & Medicine
ER -