Η παραοικονομία στο χώρο της υγείας στην Ελλάδα: τάσεις - εξελίξεις - προοπτικές

Abstract

This dissertation investigates health service users' attitudes towards informal payments, regarding the greek socio-economic context and vicious cycle, as informal payments’ contribution to health expenditures. We develop shadow economy’s theoretical framework and explain estimation methods. Our analysis took place in 13 Greek Regions related to demographic, income, professional, family, and other users’ characteristics. The aim of our dissertation is informal payments’ analysis for health services users in Greece. We conducted field research to fulfill our goal. Participants were adults rejecting parents who answered for their children in different age categories, family, and financial profiles. The questionnaire focused on informal payment amounts for health incidents. We use primary data via a quantitative survey considering the questionnaire (with 39 questions), difficulty, requested information, their reliability, convenience sampling, time, and cost of the research. We apply statistical analyzes for reliable conclusions. Besides variables independence control, we focused on specific answers regarding users’ informal payments for health services. We investigate users’ profiles, households characteristics, health care services utilization during the last 4 months, health care problem type, specific health care services, and informal payment amount. Our research innovates as informal payments are latent (non-observable) variables operating based on independent variables, divided into causes and indicators. Independent variables were 24, divided into four categories focusing on health service demand (8 variables), informal payment behaviors (5 variables), patients' willingness to pay informally (4 variables), and demographic variables (7 variables). We performed regression analysis to examine the relationship between dependent and independent variables. Univariate analysis of the respondents is used to investigate users’ characteristics and responses via qualitative and categorical variables. In open questions, we conducted the content analysis and descriptive analysis measures of the used questions and Pearson linear correlation coefficient (χ2) applied to check variables’ relationships. We choose the conditional logistic regression method to assess the relationship between the dependent and independent variables. We choose multiple indicators-multiple causes flexible model adapted to informal health care payments requirements. We relied on the initial structural equation, including different vectors enclosing the previous variables. I used the aforementioned model for informal health payments assessment and estimation of principal component and regression analysis application. During the modeling process, we focused on informal health payments index construction and the Kaiser - Meyer - Olkin test, confirming our analysis’s statistical significance and helping us to construct the initial structural equation. Regarding index modeling about informal health payments, we implement regression analysis. Next, we identify the statistical relationship between our model’s questions, excluding variables that destroy our model’s dynamics and statistical significance, using the forward stepwise regression method. Based on the acceptable change R2 = 0.417 of our model, we chose six variables.