HFA-DA. The probability for beneficiaries to seek acute surgeries/inpatient care is 1 per 100 over six months, as presented in Column 2, which is close to the national average level. Columns 3–6 present parametric estimations of the results, including OLS with a dummy variable defined as equal to 1 if beneficiary (Column 3), OLS with the dummy variable and a fourth order polynomial in welfare scores (Column 4), OLS with the dummy and other covariates to control for additional differences between the beneficiaries right below the threshold and nonbeneficiaries right above the threshold (Column 5), and finally a Probit model with marginal probability presented (Column 6). The last column presents the impact size based on the first OLS estimation.

Different specifications yield very similar results, and they all indicate that the beneficiaries of the MAP utilized more acute surgeries than the non-beneficiaries in the neighborhood of the threshold. The impact of the MAP, i.e. beneficiaries are nine times more likely to have acute surgeries than non-beneficiaries with a similar poverty status, seems unusually high. One needs to interpret this result with great caution. Possible reasons for such high impact are proposed below.

First, the health services utilization rate is very low in Georgia, and the main reason for not seeking care is financial, especially among the poor. Lifting the financial constraint removes the most significant barrier to poor people seeking care and would naturally precipitate a huge increase in health services utilization by this sub-group of the poor population. This finding is consistent with the literature, which shows that when costs of medical care are reduced by health insurance, people tend to use more health services (Holly, Gardiol et al. 1998; Harmon and Nolan 2001; Hofer 2006). A significant increase in utilization of health services has also been observed when user fees are removed (Deininger and Mpuga 2003; Yates 2007). However, the literature on price elasticity of demand for medical care yields changes in demand of a smaller magnitude when cost of care changes.<sup>1112</sup> A strict comparison with the literature is difficult due to the different methodologies and measurements used in the various studies. The most compatible case is the Rand Health Insurance Experiment study (HIE), which assessed responses in demand to various cost-sharing schemes as compared with free care and showed a 20%-39% deduction in hospital admissions due to cost-sharing.<sup>13</sup> The extremely high increase in utilization of surgeries seen in this study of the MAP may be partly attributable to accumulated demand, given that the study only covers the first six months of the program. Pent up demand for health services may have caused the poor who qualified for benefits to rush to take advantage of them before yet another change in health policy could result in their revocation, thus causing a dramatic spike in utilization in the first six months. One may observe a gradual decline in the surge once the cumulated demand is exhausted.

Second, as mentioned before, the SUSIF data may suffer from a misclassification problem. The increased use of acute surgeries may have resulted from classifying non-acute surgeries as acute

> 11  P. Zweifel and W. Manning, 2000. “Moral Hazard and Consumer Incentives in Health Care” in Handbook of Health Economics, Vol. 1 Elservier Science B.V.

> 12  W. Manning et al. 1987 “Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment”, The American Economic Review, Vol. 77 No. 3

> 13 Rand Health, 2006 “The Health Insurance Experiment: A classic RAND study speaks to the current health care reform debate”, Joseph Newhouse and the Insurance Experiment Group. _Free for All? Lessons from the RAND Health Experiment_ .  Cambridge. Mass.: Harvard University Press, 1993.

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