The importance of accounting for endogenous health inputs is demonstrated in two early studies of ROSENZWEIG and SCHULTZ (1983) and SCHULTZ (1984) who provided a generic approach to the estimation of health production and input equations. Based on the data from the USA and using the instrumental variables technique, they obtained parameters of a production function related to children’s birth weight. Thereby, they recognised that the choice of inputs (e.g., prenatal medical care in SCHULTZ, 1984) is influenced by unobservable variables, which also have an impact on health outcomes. That is, unobservable health endowments such as difficulties prior to pregnancy may induce a higher demand for prenatal care. SCHULTZ (1984) showed that when the endogeneity of prenatal care was ignored (treated as exogenous in an estimation of production function by OLS), its effect of child mortality was positive. In contrast, in the 2SLS estimation that accounted for endogeneity of prenatal care, this health input was found to reduce the mortality significantly. Similar findings can be found in the work of ROSENZWEIG and SCHULTZ (1983). They demonstrated that not accounting for health heterogeneity leads to an underestimation of the positive effect of early prenatal care on the weight of a new-born and of the negative impact of the mother's smoking while pregnant.
In recent decades the problem of rising overweight and obesity induced active research in the health field, with a number of studies investigating the determinants of these negative health outcomes (e.g., CHOU et al., 2004; CHOU et al., 2008; SCHROETER et al., 2005; NAYGA, 2000). Due to a frequent lack of longitudinal data (e.g., especially on prices) the estimations based on a single-period data are prevailing (STRAUSS and THOMAS, 1995). An example of a longitudinal study is the one by CHOU et al. (2004). Based on household production theory they investigated the determinants of BMI and obesity in the USA using the data from the Behavioural Risk Factor Surveillance System for the years 1984–1999. They estimated reduced-form equations for the BMI and the probability of being obese by the OLS method.
The equations contained a number of relevant price variables, e.g., a real fast-food meal price, the number of fast-food and full-service restaurants per 10,000 persons in respondent’s state of residence, the real full-service restaurant meal price, real cigarette and alcohol prices. In addition, household income, individual characteristics and environmental variables were included. The study revealed a positive relationship between the increasing per-capita number
of restaurants, declining food prices, anti-smoking campaigns (higher cigarette prices) and an upward trend in weight. This is in line with results of other studies that found a relationship between higher fast-food prices and lower BMI as well as obesity among children and adolescents (see e.g., POWELL et al., 2007 and CHOU et al., 2008). Further, CHOU et al. (2004) argue that it is a priority for further research to develop a structural model of obesity with caloric intake, energy expenditure, and smoking being endogenous determinants of weight.
HUFFMAN et al. (2006) investigated based on household production function obesity-related mortality among 18 OECD countries using panel data for the years 1971 to 2001. The mortality rates attributed to cardiovascular diseases and diabetes were used as proxies for obesity. First, the health production function to analyse links between mortality and diet were specified. According to the results, higher intake of calories and sugar increased mortality (10% rise in consumed calories increases mortality by 7%). Conversely, higher intake of fruits and vegetables, dietary information, technical change in medicine, and a better healthcare system reduced mortality. The second model estimates the household health supply function in reduced form that depends on prices for foods and other goods, real salary, schooling, share of the employed as a dummy for health system and trend. Negative effects of food prices were found (10% decrease in the price increases mortality by 1.5%); of non-food prices, real wage and labour participation. However, the effect of education and income on obesity-related mortality was not significant.
Using the data of first, second, and third National Health and Nutrition Examination Surveys, RASHAD et al. (2006) investigated an influence of a number of community (e.g., gasoline tax, smoking tax, availability of restaurants) and individual characteristics (e.g., age, gender, marital status) on BMI and obesity among US citizens. The reduced equations for BMI and obesity were estimated with the exogenous variables mentioned above. It was assumed in the study that changes in the environment (such as a rise in a number of fast food restaurants) led to changes in habits. The results suggested an increase in obesity due to a higher per-capita number of restaurants and a higher BMI among females as a response to the campaign on smoking reduction.
Demand relations for health inputs and production technology are both considered in the study by RASHAD (2006). To investigate the determinants of adult obesity among US citizens he constructed a structural model of obesity among US citizens, relating individual BMI to their energy intake, activity level and smoking. First, the OLS model is estimated.
Further, he treats the behavioural variables as a subject of individual choice and controls for
their potential endogeneity by applying the 2SLS method. This included an estimation of reduced-form equations for behavioural variables with a set of state-level characteristics as instrumental variables. In the second stage, the parameters of the production function of BMI were estimated. In contrast to the results of the single-equation procedure, the strong effects of caloric intake and smoking disappeared (except the impact of energy intake for females) in the two-stage least squares models.
CHEN et al. (2002) provided empirical evidence that accounting for endogeneity in the modelling of health outcomes and its determinants can lead to changes in the direction and intensity of the postulated relations. In their study blood pressure is modelled to be dependent on the person’s nutrient intake, physical activity and medication use. When the endogeneity of these inputs was controlled (by modelling these choices as dependent on prices, wages and income), the effect of sodium intake on blood pressure turned out to be negative. They argue that this result is supported by the biomedical view on this relationship.
The problem of the quality of instruments in the IV approach is discussed in the work of KENKEL (1995), who aimed to estimate the impact of a number of behaviours on adults’ health.
However, he evaluates the results from his two-stage model as implausible and attributes the failure to account for the endogeneity partly to the lack of explanatory power of the instruments such as money prices. He argues that prices might be of low relevance for many behavioural choices. ROSENZWEIG and SCHULTZ (1983), who used an instrumental-variable technique to examine the effect of endogenous health inputs such as medication, smoking, and fertility on birth weight, also indicated that the instruments employed in their two-stage estimation approach had little explanatory power13.
To summarise, the first category of the estimation methods reviewed in this chapter is related to the single-equation approach, which presumes an exogenous nature of all regressors.
The second type involves an estimation of the equation in several steps that allows to account for potential endogeneity of the model variables and to produce consistent estimates (e.g., 2SLS). Finally, as discussed above, the full-information estimation methods can be employed that offer a number of advantages including a possibility to test complex relationships and to estimate all parameters of the system simultaneously. In the next section SEM as one of the full-information approaches is discussed and its features and strengths are presented taking into account the goals of the study. The section discusses the nature of the method and provides a rationale for its application in the actual project.
13 Some of the study examples presented in this chapter are also discussed in DEMYDAS (2013).