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Health efficiency and macroeconomic performance: A cross country comparison of EU member states

Gakis Konstantinos-Panagiotis

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Summary

In this current thesis we present a comparison based on the efficiency of the healthcare sector and the economies of the European Union’s member states for the year 2019. For the purposes of this cross-country comparison of both the healthcare sector and the economies of EU member states a crucial factor was the study of the meaning of efficiency and the choice of data which can best describe the current status and the problems that they are dealing with. The comparison and evaluation of the efficiency for both sectors was done using the Data Envelopment Analysis also known as DEA which Charnes , Cooper and Rhodes first introduced in their work in 1978 titled “Measuring the efficiency of decision making units” in order to compare and estimate the relative efficiency of different systems and organizations and its widely used to describe such problems ever since. The DEA method estimates the relative efficiency between units by using linear programming techniques and using as data the available resources which are being used during the production process (inputs) in order to achieve a goal or produce goods and services (outputs). These inputs and outputs are the quantitative characteristics of the systems and organizations under evaluation which are called decision making units(DMUs). With the use of DEA method we are in a position to determine each time the performance of each country that takes place in our study relative to the rest of them , which compose our sample and therefore we are able to rank our countries based on their efficiency. Furthermore DEA provides us with knowledge for each country under evaluation regarding the points which make it lags behind and also ways which we could increase their efficiency , if the country is judged to be inefficient. The way of which we chose to implement the DEA method was by using the R programming language and its integrated development environment R Studio which allowed us to run the script which we created in order to make use of the two basic models on DEA and whose difference is in the assumptions they use regarding their returns to scale. The conclusions which were exported during the analysis of both healthcare sector and the economies offered us a deep analysis as far as the performance of the countries which took place but also provided us with a set of points or factors for which policy makers should focus on and create a framework of actions for increasing the efficiency of inefficient units/countries in the future.

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