CrossRef Google Scholar 4. This effect was highest for respondents in age group of 65 years and above regression coefficient 0. Mullahy J: Econometric modeling of health care costs and expenditures: a survey of analytical issues and related policy considerations. Households where the head is in a white collar profession and male heads of the household negatively predicted OOP payments. Pakistan Institute of Development Economics.
J Health Econ. Households where the head is in a white collar profession and male heads of the household negatively predicted OOP payments. Anderson MR: Revisiting the behavioral model and access to medical care: does it matter?. Pak Dev Rev. Enacting mandatory social health insurance legislation can be a suitable option in the backdrop of rapid economic growth in the country since last decade. Our Projects.
Literacy rate increases in all provinces, Khyber Pakhtunkhwa High school enrolment, at 3. Noting that the last meeting of the Governing Council was held in April last year, the Minister directed to convene meeting initially every month and later quarterly in order to clear all the backlog and ensure smooth and efficient functioning of organization. Rous and Hotchkiss reported positive age related influences on OOP payments of all age groups except age years. The influence of the profession of the head of household on OOP payments is another important innovation in our analysis that should be further explored theoretically and empirically.
We included literacy of household head and spouse in our model and assessed their joint influence on OOP payments. These comparisons are constrained by differences in the selection of econometric model, the choice of independent variables and their interaction. We adapted various determinants of OOP payments from the literature review to our model considering data availability and our understanding of socio-economic and cultural factors in Pakistan.
This kind of time series research would be worth undertaking in Pakistan in future, but only when similar data is made available for later years. J Health Econ.
Conclusion Our findings strengthen the argument that multiple factors influence OOP payments. Automation of data a must for authentic and reliable economic statistics for better policy making. The Lancet.
Noting that the last meeting of the Governing Council was held in April last year, the Minister directed to convene meeting initially every month and later quarterly in order to clear all the backlog and ensure smooth and efficient functioning of organization. In the regression model male head predicted negative influence on log of OOP than female heads. The comparison of our results with earlier research is not conclusive. One important aspect from the health policy perspective is the provincial differences in OOP payments. J Polit Econ.
Enrolment was 7. Our findings show that urban households made higher OOP expenditures on healthcare than rural households. A one year recall and aggregate health expenditure for the entire household would likely reveal fewer zero responses. Their sampling unit and recall period of health expenditure was similar to our data set, i. Health System Performance Assessment.
CrossRef Google Scholar 4. It is equally important for the government to understand beneficiaries of public provision of healthcare services. Our findings also encourage provincial level analysis of OOP-Payments, especially in KPK such analysis are important from health policy perspective.