In the public health research domain, a large amount of literature draws attention to the identification of public health determinants using several evidence based approaches, where estimation of a Health Production Function (HPF) is an established economic approach (Ferda 2010, Shaw et al. 2005). A brief review of literature on public health determinants using the HPF approach is presented in this section. Evidence is drawn from both developed and developing countries, though the number of studies estimating public health determinants using HPF approach for low income economies is limited. In addition, HPF has been estimated at both micro and macro levels. For example, Kiiskinen (2003) used evidence from Finnish health survey to estimate a HPF at micro level and Shaw et al. (2005) estimated aggregate HPFs for 29 high income OECD countries.
Becker (1965) first introduced households as “producers of commodities” in his theory of the allocation of time and Grossman (1972) used this household production framework to develop a model of the demand for health. He defined health as a durable capital stock, where the end products were the health services that resulted from health capital goods. From this, the concept of a Health Production Function (HPF) emerged, implying a functional relationship between health status
(output) and its determinants (inputs). Grossman’s model assumed the use of medical care services as the most important input in the health production function, and generally ignored other health related inputs such as housing conditions, diet, recreation, smoking and alcohol consumption. Thereafter, other researchers improved Grossman’s theoretical model by incorporating the effects of socio- economic determinants such as education, life-style, health knowledge, environmental factors (Muurinen 1982, Kiiskinen 2003). Later on, Shaw et al. (2005) and Fayissa and Gutenna (2005) developed and estimated an aggregate HPF based on the Grossman (1972) theoretical model, where they transposed Grossman’s micro-model of individual health to a macro level of population health. These studies, irrespective of their approach type, have suggested that a health policy focussed only on health care services, excluding socioeconomic factors, may be ineffective in improving the population health status of a region or an economy.
Despite its limited appearance in the health economics literature, empirical evidence on a population production function for health can be traced back to1969 (Auster et al 1969). This study presented a regression model of state-level mortality rates as a function of medical care and environmental variables. Based on the approach of Auster et al. (1969), quite a few later studies estimated HPFs to provide evidence for a functional relationship between population life expectancy (or mortality) and various environmental measures (e.g. wealth, education, safety regulation, infrastructure), lifestyle measures like tobacco or alcohol consumption, and health care consumption measures such as medical or pharmaceutical expenditures (Thornton 2002, Shaw et al. 2005, Spinks and Hollingsworth 2005, Hakkinen et al. 2006). A review of selected empirical studies on HPF is provided in Table 3.2.
Table 3.2: Selected Empirical Studies Estimating a HPF
Source: Compiled from various studies
Researcher Health Status Period Data Method Country
Auster et al (1969) DR 1967 CS OLS USA
Rodgers (1979) LE and DR NS CS OLS 56 Countries
McAvinchey (1988) M 1960-1982 TS ADL 5 European Countries
Peltzman (1987) DR 1970-1980 CS GLS 22 Countries
Babazono and Hillman (1994) IM 1988 CS OLS 21 OECD Countries Barlow and Vissandjee (1999) LE at birth 1988 CS OLS OECD Cremiux et al. (1999) LE at birth 1978-1992 PTS GLS Canada Filmer and Pritchett (1999) IM 1990 CS OLS/IV 119 Countries Ngongo et al. (1999) DR at AG 1980-1992 Panel DS Italy Miller and Frech (2000) LE at AG 1996 CS OLS 21 OECD Countries Martinez-Sanchez et al. (2001) DR at AG 1991 CS DS Spain
Audrey (2004) M 1948-1996 TS ECM USA
Licthenberg (2002) LE at birth 1960-1997 TS ML USA
Thorton (2002) DR 1990 CS 2SLS USA
Wang (2003) DR at IM 1990 CS IV 60 Low Income Countries
Murthy et al (2003) SD, VD 2002 HS, Pooled 3SLS and
GMM Two Cities in India Fayissa and Gutema (2005) LE at birth 1990-2000 CS GLS 31 African Countries
Shaw et al. (2005) LE at AG 1990 CS OLS OECD
Nixon and Ulmann (2006) IM 1980-1995 CS OLS 15 EU Countries
Kabir (2008) LE at birth 2002 CS OLS 91 Countries
Chang and Ying (2008) DR at AG 1982-1999 Panel GMM Taiwan
Martin et al (2008) DSM 2006-2007 HS OLS, 2SLS UK
Shin-Jong (2009) M 1976-2003 Panel PEM Asia-Pacific Region
Ferda (2010) LE at birth 1965-2005 TS ARDL Turkey
Fayissa and Traian (2011) IM 1997-2005 Panel OLS 13 East European Countries Chowdhury (2011) LE at birth 1999-2008 Panel OLS 5 South Asian Countries Nasib et al (2013) LE at birth 1995-2009 Panel OLS
Islamic Conference Organization Member
Countries Bayati et al (2013) LE at birth 1995-2007 Panel OLS 21 East Mediterranean
Countries Indicators:
LE (Life Expectancy), IM (infant Mortality), M (All Cause Mortality), DSM (Disease Specific Mortality), DR (Death Rate) NS (Not Specified), SD (no. of sick days), VD (no. of visits to doctor), AG (Age Groups)
Data: CS (Cross-Section), TS (Time Series), PTS (Pooled Time Series), HS (Household Survey) Methods:
DS (Descriptive Statistics), OLS (Ordinary Least Squares), 2SLS (Two-Stage Least Squares), GLS (Generalized Least Squares), ECM (Error Correction Model), GMM (Generalized Method of Moments), ML (Maximum Likelihood), IV (Instrumental Variables), PEM (Panel Econometric Methods),, ADL (Almon Distributed Lag), ARDL (Auto Regressive Distributed Lag)
Table 3.2 shows that there is an increasing attention to a macro-level HPF (Bayat et al. 2013, Nasab et al. 2013, Fayissa and Traian 2011, Chowdhury 2011, Ferda 2010, Fayissa and Gutema 2005), though empirical estimation of HPF is dominated by micro level analysis using cross-section, panel or survey data. For example, while Fayissa and Traian (2011) estimated a macro level HPF for 13 East European countries in order to determine the most efficient way of allocating limited resources for improving the overall health status of countries considered, Ferda (2010) estimated a macro level HPF to find the long run determinants of longevity and mortality in Turkey’s population.
The growing interest in macro level HPF indicates that the current evidence- based policy decision process requires extensive knowledge on the long run behaviour of policy variables influencing public health. As the focus of this current thesis is to investigate the key factors for estimating and valuing health impacts of public investment policies, estimating a HPF for the Australian economy at an aggregate level over a sufficiently long period is considered most appropriate.