Shamzaeffa Samsudin

Strengthening urban economic resilience is critical for sustaining stable economic growth amid frequent external shocks and ongoing structural economic transformation in China. As a core financial paradigm in the digital era, digital financial inclusion (DFI) requires systematic examination of its long-term macroeconomic stabilization effects. This study investigates the effect of DFI on urban economic resilience, explores its underlying transmission mechanisms, and determines heterogeneous effects across regional and urban characteristics. Based on panel data of 334 prefecture-level-and-above cities in China from 2011 to 2023, this study constructs a multi-dimensional urban economic resilience capacity index that reflects cities’ latent systemic capacity to resist, recover, adapt, and transform in response to exogenous shocks. Employing a double machine learning (DML) framework to address potential nonlinearities and complex functional relationships, this study provides robust empirical evidence of a positive relationship between DFI and urban economic resilience. The exploration of transmission mechanisms indicates that technological innovation, industrial structure upgrading, and consumption upgrading serve as potential paths. Heterogeneity analyses further indicate that the resilience-boosting effects of DFI vary across geographic regions, DFI dimensions, urban attributes and policy contexts, being notably larger in eastern, non-resource-based, core, and “Broadband China” pilot cities. These findings yield targeted policy implications, emphasizing the need for regionally differentiated DFI strategies and a balanced development paradigm between empowerment efficiency and risk governance. This study offers critical empirical insights and a reliable reference for leveraging DFI to foster high-quality, shock-resistant, and sustainable urban economic development.

The current public health crisis, namely the COVID-19 pandemic, is not confined to a single region but is a worldwide occurrence. A nationwide lockdown is the only viable option for containing and mitigating the effects of this pandemic. However, the after-effects of the lockdown, namely unemployment, have disturbed and destroyed the labor market. Malaysia has experienced three outbreaks of the COVID-19 pandemic and four lockdowns with high stringency. Hence, this paper examines the impact of the COVID-19 pandemic outbreaks and associated lockdowns on unemployment in Malaysia, and it is important to understand the power of each outbreak and lockdown on unemployment. This paper refers to the monthly unemployment rate from January 2016 to December 2021. The ARIMA-Intervention is adopted using the three outbreaks of COVID-19 and four types of Movement Control Order (MCO). The findings indicate that the second COVID-19 outbreak significantly impacted unemployment compared to the other two outbreaks. Additionally, among the four rounds of Movement Control Order (MCO), MCO 1.0 had a notable and significant effect on unemployment. Unemployment is exacerbated when a containment policy is implemented unexpectedly. It is suggested that the government establishes a policy to prepare for the future pandemic.

Job losses occur globally due to economic uncertainty, public health crises such as pandemics, or other factors beyond the employee’s control. Losing one’s employment can be an overwhelming and stressful experience, which can affect psychological and emotional health. Coping strategies are important for mitigating the impact of job loss, particularly during the COVID-19 pandemic, which is more pervasive and severe than typical economic downturns due to government-imposed mobility restrictions. This study aims to examine the coping strategies adopted by Malaysian formal workers who experienced involuntary job loss, with a specific focus on how the sequence and combination of these strategies evolved amid mobility restrictions. Using a qualitative research design, in-depth interviews were conducted with 10 job losers from five sectors most affected by the pandemic in Malaysia: accommodation and food services, transportation, professional and technical services, wholesale and retail trade, and arts, entertainment, and recreation. A deductive thematic analysis, grounded in the Integrative Control Theory Model, was adopted in this study. The findings revealed that various problem-focused and symptom-focused strategies applied by Malaysian job losers were driven by the duration of unemployment and lockdowns. An “integrated mode,” or a combination of both strategies, was also found to help them navigate the experience of job loss. In addition to the duration of unemployment and lockdowns, other demographic factors such as age, ethnicity, and religion also drove the observed patterns. These findings demonstrated the specific coping strategies adopted in response to job loss, addressing a gap in existing research, which has largely focused on the psychological impact of the COVID-19 pandemic on employed individuals. The findings offer critical insights for policymakers, highlighting the importance of developing targeted interventions beyond problem-focused strategies.

Purpose: This study aims to examine and compare the cyclical and structural factors influencing unemployment in Malaysia before and during the coronavirus disease pandemic. Design/methodology/approach: Using monthly time-series data from January 2016 to December 2022, this study employs gross domestic product (GDP) growth and interest rates as cyclical indicators, while job vacancy (JV) and long-term unemployment (LUE) represent structural factors. The autoregressive distributed lag model is applied to distinguish short-term and long-term effects. Findings: The results reveal that the cyclical factor from GDP growth significantly reduces unemployment both before and during the pandemic. Structural factors, particularly JV and LUE, played a significant role in influencing unemployment during the pandemic but were not significant in the pre-pandemic period. This underscores the significant role of structural factors in shaping labour market dynamics during a global health crisis. Originality/value: This study contributes to the literature by providing empirical insights into the distinct roles of cyclical and structural factors in unemployment, particularly during a global health crisis.

This study conducts a bibliometric analysis to delineate the intellectual structure and evolutionary trajectory of digital inclusive finance (DIF). Based on a dataset of 850 publications from the Web of Science Core Collection (2017–2025), we utilized CiteSpace 6.4.R1 to perform keyword co-occurrence, clustering, and burst detection analyses. Our findings indicate a rapidly growing body of literature, with China emerging as the leading contributor. Three primary research directions were identified: the theoretical underpinnings of DIF, its economic consequences, and its role in sustainable development. The field’s focus has shifted from foundational concepts to practical impacts, such as alleviating financing constraints and fostering innovation. Currently, scholarly attention is centered on high-quality development, with “efficiency” and “entrepreneurship” being prominent buzz terms. Future research is anticipated to concentrate on “poverty” and “industry.” This paper offers a systematic mapping of the DIF field, providing valuable insights into its current state and future directions. The implications are significant for scholars seeking to identify research gaps and for policymakers aiming to leverage DIF.

The government size is determined through the ratio between the government expenditure which including the operation and development with the Gross Domestic Product (GDP). This study with the intention to examine the long-run relationship between the determinants with the government size of Malaysia during the periods of year 1980 to year 2018. The determinants including the trade openness, country size, foreign direct investment openness, portfolio investment openness and the economic growth. The annual data are achieved from the World Bank and the Department Statistic of Malaysia (DOSM). Moreover, we adopted the Autoregressive Distributed Lag (ARDL) model, which proposed by Pesaran et al. (2001), to examine the long-run relationship. The result revealed that there are long-run negative relationship significantly between the determinants including the trade openness, country size, foreign direct investment openness and portfolio investment openness with the government size. On the other hands, the economic growth has a significant positive long-run relationship with the government size in Malaysia. Both trade openness and economic growth variables have the Granger Causality effects towards the government size variable. Therefore, it is essential for the government to maintain a balanced allocation of both operating and development expenditures, taking into account key influencing factors, to support sustainable long-term economic growth in Malaysia.

Unemployment, especially after the COVID-19 pandemic, is a critical issue for any country as it has economic and social ramifications. Consequently, forecasting unemployment becomes an essential task as it can guide government policy. Time series data are frequently influenced by outliers (unexpected events), and some outliers may exist with extreme observation to reduce the forecasting effectiveness of robust estimators. This study compared the performance of Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models in modelling and forecasting unemployment rates during the COVID-19 pandemic among the ASEAN-5 countries. These countries include Malaysia, Singapore, Thailand, the Philippines and Indonesia. The monthly unemployment data from January 2010 to December 2021 were applied for all cases, except Thailand, until December 2020. Each adequate model from both forecasting mechanisms underwent forecasting. Their performance was compared based on root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient and symmetric mean absolute percentage error (SMAPE). Static forecasting from the ARIMA and SARIMA models was found to perform better than the GARCH model in modelling and forecasting the unemployment rate among ASEAN-5 countries during the pandemic period.