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This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

This study aims to measure the level of satisfaction among women with childbirth services provided at public health facilities. Further, to analyse the impact of their socio-economic and obstetric characteristics on their level of satisfaction.

To accomplish these objectives a cross-sectional survey was conducted in two districts of an Indian state, Bihar. Structured questionnaire was developed based on the scale proposed by Okumu and Oyugi (2018) both for vaginal and caesarean birth patients. For empirical analysis multiple linear regression model was employed.

Results suggest that majority of mothers are satisfied with the care they received during childbirth, regardless of whether they chose a caesarean (55%) or vaginal delivery (53%). Women report the lowest levels of satisfaction with postpartum care and the privacy that was preserved by healthcare personnel at health facility. Further the study also confirms the association between patient’s socio-economic characteristics and their satisfaction level.

This is the first study of its kind to highlight the situation of public healthcare system in Bihar, which is the third most populated state in India with poor social and health indicators.