Use of Stata in modeling the determinants of work engagement

Two primary datasets were used provided by Eurofound in the European Working Conditions Survey. Data were gathered before and during the COVID-19 pandemic, which allowed me to include the pandemic context in the analysis. Additionally, some macroeconomic and other social variables were included, such as GDP per capita, labor force participation rate, unemployment rate, the level of social trust, Doing Business Index, and European Quality of Government Index. Stata, with its potential for data cleaning and checking, allowed me to merge all variables from complex datasets into one set with 115,608 observations and over 100 variables from 34 European countries. When I prepared the data, some repetitive tricks of commands were applied. Stata programmability helped in preparing the model using the logistic regression method. A dichotomous outcome (dependent) variable was modeled—engaged or not engaged into work. The predictor variables of interest were those related to work, such as working conditions, occupational characteristics, and the level of human capital. The logistic command in Stata produced results in terms of odds ratios, which were interpreted to calculate the effect of chosen predictors on the response variable and consequently to take or reject the constructed research hypothesis. The innovation of presented analysis lies in including macroeconomic or macrosocial variables and consideration of the international and intersectoral analysis. The presented logit model provided by Stata possibilities fills the research gap in the area of the work engagement phenomenon.