This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level.

Short-time work (STW) is a widely used policy instrument that assists firms in times of crisis. It has been used in many OECD (Organisation for Economic Co-operation and Development) countries in the 2007–2009 Great Recession and recently as a response to the financial impacts of the COVID-19 pandemic. It allows employees to reduce their working hours with a slight salary reduction. This makes it possible for firms not to lay off workers during economic downshifts. However, there are few evaluation studies of the effectiveness of STW to meet the economic shock due to COVID-19. The present study contributes to filling this research gap by presenting the estimated effects of STW on several important outcome variables. Swedish firm-level data are used to evaluate the impact of the subsidy using different matching methods. The results suggest that firms that received STW support had fewer employees than those that did not receive STW support. This reduction in working hours was, on average, the equivalent of 0.6 full-time employees compared to firms that did not receive STW. Nevertheless, the results imply that the companies that received STW are more productive, as measured by turnover per employee, than those that did not. The main policy recommendations are that public funds should be spent carefully and that support initiatives such as STW must be evaluated.