There are large variations in both public and private health spending per capita across states in India. shows public and private health spending by state; the states being placed in descending order according to the government’s per capita expenditure on health as a share of total health spending. shows the relationship between total health spending and the percentage that is publically funded. There is little relationship between these variables indicating that that low public expenditure may play a role in generating higher levels of private expenditure, so there is not a one to one mapping from public health spending to total health spending. This “crowding out” of private health spending is a potential reason why public health spending may be ineffective.
Per capita health spending (in Rupees)
public and total health spending
We focus on the effect of state level public health spending. It is noteworthy that there are rural-urban differentials in public spending, with per capita spending in urban areas generally much higher than in rural areas for most states (Peters et al., 2002
), which may produce differential results in terms of health outcomes in urban and rural areas.
Public health expenditures seems to have a direct impact on the infant mortality rate (Bhalotra, 2007
). shows the relationship per capita health spending of the State government and infant mortality7
. Richer states tend to spend more per capita, and generally have lower infant mortality rates (IMRs). However, this is not always the case. Some of the richer states, such as Maharashtra and Haryana, have comparatively low per capita public expenditure on health. On the contrary, Sikkim, with relatively low per capita income, has high per capita public spending on health.
Infant mortality rate and health spending
reports summary statistics, based on data for individuals, with household and state level variables being linked to each individual. Overall, the probability of dying in the two year period under study in this sample is about 2 percent. Average per capita public health spending is 125 Rupees, ranging from 49 (Bihar) to 406 Rupees (Goa). Rural population comprises 69% of the sample population. Only 45% of the population under study has access to a toilet (28% to a flushed toilet and 17% to a latrine). Less than half (42%) of the people have access to piped water. Note that the 20% of households are in each asset quintile, but the percentage of people in each quintile may differ due to different household sizes across quintiles.
presents the results of the probit model of mortality displayed in the methods section. We use the log of public health spending in the state for 1997-98 as the main explanatory variable. This model shows the inverse association between public health spending and mortality. Public health spending has a significant effect and reduces mortality rates. We estimate that a 10 percent increase in per capita public health spending, i.e., about 13 Rupees, in an average state with all variables at their mean values, decreases the probability of death for an average individual by about 0.0004. Given the overall mortality rate of 2% in the sample, this is a reduction in mortality rates of about 2%, giving an elasticity of mortality with respect to public health spending of about −0.2.
Effect of health spending at state-level on probability of death at individual-level (Probit model)
The estimated effect of the log of private household spending on health has a similar impact on health outcomes. Most health spending comes from private sources8
shows that 82 percent of people in rural areas and 79 percent in urban areas go to private providers for primary care. Our results suggest that the same percentage increase in either public or private spending has similar effects. Given that public spending is much less than private spending this means that each rupee of public health spending is about 4 times more effective at reducing mortality as private health spending.
In we find that the log of per capita net state domestic product is not a statistically significant predictor of mortality. It contrast to the cross country literature that stresses the importance of average income in determining mortality (Carrin and Politi, 1995
, Demery and Walton, 1998
, Filmer and Pritchett, 1999
), our findings indicate a more important role of household economic status, as proxied by the asset index level. We find higher mortality rates among households in the lower quintiles of the asset index, with mortality being more than 1.1 percentage points higher in the bottom quintile than the top quintile (the baseline). Given an overall mortality rate of around 2%, this means mortality is about 50% higher among the poorest quintile.
We find higher mortality in rural areas as compared with urban areas. We find access to a flush toilet or a latrine significant in reducing mortality, though there are no significant effects of water sources on mortality which his is consistent with prior evidence in the literature (Lee et al., 1997
). We find no significant effects of caste or religion on mortality.
We find significantly higher mortality among men as compared to women. We take as our reference group infants aged zero to one year. Children aged one to five years have significantly lower mortality than infants, and mortality tends to fall with age at first. The lowest mortality is among those aged 14-40. Mortality rises with age after age forty.
reports effects of interaction terms between public health spending and gender, place of residence (urban, rural), and socio-economic group of the household as well as with the age group of the individual. This interaction effects allow us to investigate if the effect of public spending on health is uniform across groups or is concentrated on specific populations.
Effect of health spending at state-level on probability of death at individual-level with interactions
Model 1 in investigates the effect of public health spending on mortality by sex. The default group is women and we find that after adding this interaction the direct effect of public spending becomes twice as great as the model with no interaction (in ) while the interaction term between public health spending and being male is positive. This indicates that public spending on health has a significantly larger impact on the mortality of women than men.
In Model 2 we investigate the effect of interacting public health spending with rural residence. We do not find a significant effect of the interaction term, indicating similar impacts of public health spending across urban and rural areas. Navaneetham and Dharmalingam (2002)
also show that in the case of antenatal care, there is no significant rural-urban gap, due to the role played by the multipurpose health workers posted in the rural areas to provide maternal health care services.
Similarly, in Model 3, we find no differential impact of public health spending by the economic level of the household, proxied by the asset index quintiles. While we find that lower socio-economic status households have higher mortality rates we find no evidence that they benefit disproportionally from public health spending. The results of Model 3 contrast with previous studies which used more aggregated data and showed that the poor benefit more than the rich from public spending on health (Bidani and Ravallion, 1997
, Gupta et al., 2003
). Public spending in India has shifted significantly toward primary care (Mahapatra and Berman, 1995), resulting in improved access to public facilities by all income groups. However, the results of the India’s National Sample Surveys (NSS) of 1986-87 and 1995-96 showed a considerable decline in the utilization of public health services by the poor, especially the rural poor (Selvaraju, 2001
). They show that the rich consumed public services at three times the rate the poor uses and had a much higher chance than the poor of admission to public health facilities.
Model 4 examines the effect of public health spending by age group. We find that the interaction term between the young adult (18-40 years old) age group and public health spending is statistically significant. The coefficient on this interaction is positive and almost completely offsets the overall reduction in mortality caused by public health spending in Model 4. This means that the 18-40 age group appears to receive little or no benefit in mortality from public health spending, with larger than average effects accruing to younger and older people. There is little differential in health care utilization by age group in India (Berman, 1998
) but generally better health, and lower mortality of the 18-40 year olds may make the effects of health care less noticeable in this group.
The results in Tables and treat public spending on health as exogenous. It may be that there is a feedback from health needs to health spending, or some hidden variable that affects both health spending and health outcomes. Either of these would mean that the relationship between health spending and mortality rates we observe in tables and was not causal. To address this concern our estimation strategy in this section is to identify the causal effect of public health spending on mortality using an instrumental variable. The instrument needs to be a variable that is correlated with public health spending but exogenous with respect to health outcomes, and it also needs to be a variable that is not driven by any unobserved “third” variable we might suspect might be causing both changes in public health spending and health improvement. Evidence from other countries suggests that whenever there is a fiscal consolidation and stress, social sectors like health and education are targeted for reduced budget allocations (Tanzi, 2000
). In India, since public health at the state level is financed through general tax and non-tax revenue resources (the cost recovery from the services delivered has been negligible, at less than 2%), resource allocation is influenced by the general fiscal situation of the respective state governments (Selvaraju, 2001
). As a result, an increase in the fiscal deficit and a general resource crunch results in budget cut in the health sector (NCMH, 2005
). We use the state’s gross fiscal deficit as an instrument for its public health spending. We find that the deficit is indeed negatively correlated with the public spending on health and it seems unlikely that the fiscal deficit will be associated with mortality rates through other mechanisms9
presents the results of IV estimation of Equation (1)
using the state gross fiscal deficit in 1997-1998 as the instrument10
. Compared to the probit results without instrumentation (), the coefficients we estimate (−0.114 versus −0.120) and the marginal effects (−0.0044 versus −0.0045) are very similar. The similarity of the results with and without instrumentation indicates that our results are reasonably robust, though our first stage F-test of 5.51 indicates with our instrument may not be very strong (Wooldridge, 2002
, Cameron and Trivedi, 2005
probability of dying and public health spending, instrumental variable estimator