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Despite the importance of digital technology in mitigating the adverse effects of the COVID-19 pandemic and related containment measures, limited research attention has been devoted to the impact of movement restrictions on digital business transformation in North Africa. This paper investigates the impact of mobility restrictions on firms' decisions to adopt digital technologies across sectors, emphasizing the challenges associated with accessing both customers and suppliers.

The study uses the ERF COVID-19 MENA Monitor Enterprise survey (2021), covering 3,978 enterprises across three North African countries: Egypt, Tunisia and Morocco. The analysis employed the linear probability model (LPM) to account for observable and unobservable heterogeneity across countries and over time.

The results indicated that mobility restrictions have a positive impact on firms' decisions to adopt digital solutions during the COVID-19 pandemic across most industry sectors. Notably, firms operating in manufacturing, trade, retail and services demonstrated a higher likelihood to adopt technologies. However, the analysis revealed some variations in the impact of mobility restrictions across sectors and countries.

This study has several contributions. First, this study is unique in utilizing firm-level data gathered during the COVID-19 pandemic to investigate the impact of mobility restrictions on firms' decisions to adopt digital solutions. Second, the study examines the influence of mobility restrictions on digitalization across industry sectors, to the best of our knowledge, no empirical study has specifically focused on the digital business transformation across sectors.

This paper investigates the key determinants affecting household spending for university degree in Vietnam. This paper serves as empirical evidence for policymakers to select appropriate factors to estimate financial needs for university students in Vietnam.

We employ an innovative variable selection approach with adaptive LASSO for Tobit Regression Model.

The results suggest income, region and ethnicity play significant roles in determining higher education expenditure at Vietnamese households. Gender-related indicators (such as gender of household head, student’s gender), distance to school, occupations, etc. are empirically insignificant.

This study proposes a data-driven method by interfering regulation and Tobit regression to understand the divergence in higher education spending. Especially, adaptive LASSO was first introduced to identify key determinants of higher education expenditure at household level in Vietnam. It hopes to tackle over-fitting problems of traditional OLS regression in previous literature.