Financial Inclusion, P2P Lending, and MSMEs

In Indonesia after the Asian financial crisis of 1997–1998, wide reforms were carried out, and “inclusive” economic development were adopted. One component of inclusive economic development is “financial inclusion.” This implies an absence of barriers that might deter micro, small, and medium enterprises (MSMEs) from obtaining financial services. However, the portion of bank credit received by MSMEs is still small. Therefore, financial technology (FinTech) is welcome as an alternative source of funding for MSMEs. This chapter discusses three related issues, namely financial inclusion, MSMEs, and P2P lending. It concludes that Indonesia still has a long way to go to achieve full financial inclusion. This chapter suggests that with the presence of P2P lending, the number of MSMEs, especially MSEs, in Indonesia that have access to formal financing will increase. Even though aggregate data are not available, the interviews with a small number of owners of MSEs who received P2P loans suggest that the presence of P2P lending companies give some benefits for MSEs.

The Effect of Rural Development on Poverty Gap, Poverty Severity and Local Economic Growth in Indonesia

The effect of rural development in reducing the poverty gap and economic growth has not been much analyzed in recent studies. This study examines the effects of rural development (as calculated by using the Village Development Index, VDI) on poverty and economic growth. Precisely, poverty is measured by the depth of poverty (as measured by Poverty Gap Index, P1) and poverty severity (as measured by Poverty Severity Index, P2) using the aggregate data at the district level in Indonesia. Understandably, many factors influence the effort to reduce the poverty gap in rural areas, and it can be started by improving rural economic development. The result of this study indicates that regions with the VDI categorized as “self-sufficient” and “developed” villages have the potential to reduce the depth of poverty and poverty severity in its areas and to increase economic growth. In contrast, underdeveloped and very underdeveloped regions in their VDI category experienced a more significant gap in the depth and severity of the poverty. This result implies that the Indonesian government must accelerate and improve the development of rural areas, especially in less developed regions. Thus, a better rural development status will attract more opportunities to grow rural economic activities and improve the community welfare.

Evaluation of Profits of Securities Firms Listed on the Vietnam’s Stock Market

The paper investigates gross profit, net profit and profit after tax of securities firms listed on the Vietnam’s stock market. The paper employs a set of aggregate data from 22 securities firms listed on the Vietnam’s stock market, and comments from experts in this research field. The authors used descriptive statistics, comparison with the support of Stata13 software to evaluate and measure gross profit (GP), net profit (NP) and profit after tax (PAT) of securities firms listed on the Vietnam’s stock market. The results show that there is a difference in the gross profit, net profit and profit after tax of securities firms listed on the Vietnam’s stock market. Enterprises had 10 years old posted up or more have a larger profit than the rest of firms.

Data and code availability statements in systematic reviews of interventions are often missing or inaccurate: a content analysis

Objectives: To estimate the frequency of data and code availability statements in a random sample of systematic reviews with meta-analysis of aggregate data, summarise the content of the statements and investigate how often data and code files were shared. Methods: We searched for systematic reviews with meta-analysis of aggregate data on the effects of a health, social, behavioural or educational intervention that were indexed in PubMed, Education Collection via ProQuest, Scopus via Elsevier, and Social Sciences Citation Index and Science Citation Index Expanded via Web of Science during a four-week period (between November 2nd and December 2nd, 2020). Records were randomly sorted and screened independently by two authors until our target sample of 300 systematic reviews was reached. Two authors independently recorded whether a data or code availability statement (or both) appeared in each review and coded the content of the statements using an inductive approach. Results: Of the 300 included systematic reviews with meta-analysis, 86 (29%) had a data availability statement and seven (2%) had both a data and code availability statement. In 12/93 (13%) data availability statements, authors stated that data files were available for download from the journal website or a data repository, which we verified as being true. While 39/93 (42%) authors stated data were available upon request, 37/93 (40%) implied that sharing of data files was not necessary or applicable to them, most often because "all data appear in the article" or "no datasets were generated or analysed". Discussion: Data and code availability statements appear infrequently in systematic review manuscripts. Authors who do provide a data availability statement often incorrectly imply that data sharing is not applicable to systematic reviews. Our results suggest the need for various interventions to increase data and code sharing by systematic reviewers.

EXPRESS: Bayesian Consumer Profiling: How to Estimate Consumer Characteristics from Aggregate Data

Firms use aggregate data from data brokers (e.g., Acxiom, Experian) and external data sources (e.g., Census) to infer the likely characteristics of consumers in a target list and thus better predict consumers’ profiles and needs unobtrusively. We demonstrate that the simple count method most commonly used in this effort relies implicitly on an assumption of conditional independence that fails to hold in many settings of managerial interest. We develop a Bayesian profiling introducing different conditional independence assumptions. We also show how to introduce additional observed covariates into this model. We use simulations to show that in managerially relevant settings, the Bayesian method will outperform the simple count method, often by an order of magnitude. We then compare different conditional independence assumptions in two case studies. The first example estimates customers’ age on the basis of their first names; prediction errors decrease substantially. In the second example, we infer the income, occupation, and education of online visitors of a marketing analytic software company based exclusively on their IP addresses. The face validity of the predictions improves dramatically and reveals an interesting (and more complex) endogenous list-selection mechanism than the one suggested by the simple count method.