Search results

The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.

Purpose: FinTech is exploding all over the world. FinTech companies play a critical role in growing the banking industry. This chapter reviews existing literature on FinTech in banking, particularly its publication trend, journal productivity and impact, affiliated organisations and related themes.

Need for the Study: FinTech is reshaping the banking sector as banks move towards digitisation. FinTech has eliminated the need for paper, reduced the requirement for physical presence, and destroyed the necessity for cash. Several researchers have studied the features and benefits of FinTech technologies in the banking field. So, there is a need to analyse the available literature to identify the scope of further research in this field.

Methodology: For a comprehensive review, Bibliometric and content analysis of 77 open access green articles collected through the structured database of ‘Dimensions’ is done. These articles are published in the UGC Journal List Group II.

Findings: It is revealed that the execution of FinTech is continuously increasing in the banking sector, which has resulted in automation in various banking activities. The study revolves around technology and Banking, Financial Inclusion and Growth, and the Impact of the Financial Crisis on Banking and Peer-to-Peer (P2P) lending.

Practical Implications: The conclusions of this study can help academia and industry improve their understanding of FinTech in Banking, specifically its publication trend, geographical distribution, and creation of coherent themes. Careful analysis of collected articles will help to explore the scope of further research.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This chapter seeks to quantify the effects of geographic access to community health centers on the likelihood of an individual having a regular source of health care.

Utilizing survey and center location data, the analysis employs bivariate cross-tabulation with chi-square and multinominal logistic regression to quantify the relationship between variables.

While individuals living in close spatial proximity to community health centers were more likely to identify a community health center as a regular source of care as compared with those without proximal access, the effect of community health center access on the identification of any source of regular health care was generally insignificant or negative, except for populations with a chronic medical condition.

While these findings support current literature suggesting that spatial proximity to care is insufficient to transform at-risk populations into regular primary care users, it is important to note that it is possible that individuals prefer to access primary care services outside of their immediate neighborhoods, potentially mediating the observed effect of proximity to care on the likelihood of having a regular source of care. Also, because this analysis is based on cross-sectional survey data, it is impossible to make a causal argument about the relationship between variables. Only the observed association can be asserted and used to inform future studies.

Existing research supports a positive association between community health center utilization and measures of health for social groups traditionally facing barriers to care, but few studies isolate the effect of center availability and health, particularly when considering those living in the catchment area but are not regular users. Due to the complexity and prevalence of barriers to health care for vulnerable and at-risk populations, these findings suggest that improving geographic access to primary health care does not guarantee positive outcomes for target groups. The magnitude of social disadvantage on vulnerable and at-risk populations can have a devastating effect on health care outcomes that is not easily overcome by social programs.

The objective of this chapter is to discuss how different techniques in Regional Science and Peace Science and the emerging techniques in Management Science can be used in analysing Disaster Management and Global pandemic with special reference to developing countries. It is necessary for me to first discuss the subjects of Disaster Management, Regional Science, Peace Science and Management Science. The objective of this chapter is to emphasise that the studies of Disaster Management should be more integrated with socioeconomic and geographical factors. The greatest disaster facing the world is the possibility of war, particularly nuclear war, and the preparation of the means of destruction through military spending.

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

This chapter applies the five drivers of productivity framework to regional microdata for Egypt and extends it by introducing an index of industrial clusters as an explanatory factor of the productivity performance of local private sector firms. Applying structural equation models, the geographic concentration of sectoral economic activity is found to have a positive and statistically significant effect on labor productivity. The transmission mechanism is conjectured to be the positive spillovers that are created, which local firms can tap into. In contrast, a higher concentration of skilled workers in an industrial sector in a region is associated with lower levels of labor productivity – a finding that suggests there may be structural deficiencies in the allocation of skilled workers. Regional policy should focus on net investments in gross capital formation throughout the country, for which the national and regional governments should improve how public investments are managed and the institutional framework – including the rule of law, bureaucracy and red tape, conflict of interest, transparency, and governance – so that private investment (both local and foreign) may substantially increase.

Measuring the diffusion of protests, or more generally, the diffusion of events, is an ongoing task in social sciences. This paper proposes an inter-event approach to study what types of protests tend to diffuse or decline. We develop a standardized, five-step procedure to measure what we define as “event diffusion momentum” (EDM): (1) employ event-based data containing information on the time, location, and features of each protest; (2) define the temporal and spatial ranges of interest; (3) for each observation, count the number of events before and after it within the defined ranges; (4) predict the numbers of post-event and pre-event protests with appropriate count models; (5) calculate the ratios of predicted values for each predictor and confidence intervals using the delta method. The ratio is the EDM. Applying this method to Dynamics of Collective Action (DoCA) data, we identify several micro- and macro-level factors associated with protest diffusion in the United States, 1960–1995. We conclude with the implications and generalizability of the proposed method.