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The present study aimed to examine the effect of received remittance inflows on the price level ratio of the purchasing power parity conversion factor to the market exchange rate in 36 developed and developing countries from 2004 to 2020.

The panel data conducted a comparative analysis and used panel least squares, regression with Driscoll-Kraay standard errors of fixed effect, random effect, feasible generalised least squares and maximum likelihood robust least squares to overcome the heterogeneity issue. Furthermore, the two-step difference generalised method of moments to overcome the endogeneity issue. Diagnostic tests were used to increase robustness.

In the studied countries, there was a statistically significant negative relationship between received remittance inflows and the price-level ratio of the purchasing power parity conversion factor to the market exchange rate. This relationship explains why remittance flows depreciate the real exchange rate. The study’s results also indicated that attracting investments can improve the quality of institutions despite high tax rates, leading to low tax revenue.

The current study findings enrich the understanding of policies of how governments should minimise tariff rates on capital imports and introduce export-oriented incentive programmes. The study also revealed that Dutch disease can occur due to differences in the demand structure and manufacturing development policy.

This article employs a panel vector autoregression (PVAR) model to examine the relationship between digital financial inclusion (DFI), economic growth (EG), and gender equality (GE) across different levels of financial development.

Based on the current financial development dynamics, this study applies the PVAR method to two groups of countries: the first group represents the high financial development group, and the second group represents the low financial development group, during the period from 2015 to 2021.

The findings from impulse response functions reveal that digital financial inclusion fosters economic growth in nations with advanced financial systems, while simultaneously mitigating gender inequality. Conversely, in countries with less developed financial infrastructures, digital financial inclusion stimulates economic growth but exacerbates gender disparities. Moreover, the variance decomposition analysis indicates that the linkage between economic growth, digital financial inclusion, and gender inequality is more intertwined in countries with limited financial development than in those with well-established financial systems.

Effective deployment of new technologies relies heavily on technological infrastructure. This policy focuses on constructing and developing information technology infrastructure to create favorable conditions for the implementation of new DFI technologies. This study also emphasizes promoting equitable education and training by ensuring that both women and men have equal opportunities to access quality education and training. This may involve investing in early childhood education, providing access to primary education, and offering scholarships to women in technology, science, and engineering fields.

Cryptoassets are a diverse category of digital assets that rely on blockchain technology. They encompass various categories, such as cryptocurrencies, utility tokens, security tokens, tokenized assets and securities, and stablecoins. Cryptocurrencies are decentralized digital units of value that enable secure and transparent transactions. Utility tokens provide access to specific services or products within a blockchain network. Security tokens offer rights and entitlements similar to traditional securities, representing ownership in real-world assets or participation in investment opportunities. Tokenized assets and securities are digital representations of tangible or intangible assets, allowing for fractional ownership and enhanced liquidity. Stablecoins are blockchain-based digital assets designed to maintain a stable value, often pegged to fiat currencies or physical assets. This chapter examines each category's characteristics, benefits, and risks; explores their implementations and current applications in the fintech ecosystem; and discusses relevant regulations and future development opportunities.

Rural indigenous schools in Malaysia can be far from equipped with facilities and conducive learning environments, especially in schools serving a large and diverse community. SK RPS (Rancangan Penempatan Semula/Resettlement Programme) Banun is an all-indigenous school located in the interiors of Gerik, Perak in Malaysia. Being the only school in the vicinity of an Orang Asli settlement comprising 18 villages of the Je hai and Temiar tribes, this public school is said to be a one stop hub for the community. Despite the school's existence, there are several challenges which hinders children of the Orang Asli community to attain a quality education or in some cases to even go to school. This paper is written based on the lived experiences of three teachers from mid-2022 to mid-2023 and amplifies the day-to-day challenges of the school and its community. The paper also discusses the initiatives implemented to tackle some of the challenges, highlighting key successes and suggestions to improve some areas particularly in enhancing and reimagining pedagogical approaches as well as learning environments in Orang Asli schools in Malaysia.

The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

This paper aims to evaluate the impact of the “Postliteracy” program on the qualification of beneficiaries for socioeconomic integration. This qualification is achieved first through the consolidation of the achievements of individuals freed from illiteracy, and then through their support in creating income-generating activities by providing them with technical, economic, legal and institutional knowledge to ensure their conscious and responsible participation in local and regional development efforts.

To evaluate the impact of the “Postliteracy” program, this paper uses quasi-experimental methods with a control group (participants of the “Literacy” program 2020 / 2021) and a treatment group (participants of the “Postliteracy” program 2021 / 2022). Skill acquisition is measured through pretest and posttest evaluations using a questionnaire aligned with the National Agency for the Fight Against Illiteracy (ANLCA)-adopted curriculum. The survey occurred at the beginning and at the end of the program, providing sufficient time for skill development. The questionnaire includes three sections covering socioprofessional characteristics, technical and economic domains and legal and institutional aspects. These sections contribute to a score reflecting the acquired skills for successful socioeconomic integration.

The results of the study demonstrate that the “Postliteracy” program has a positive impact on the acquisition of competences necessary for improved socioeconomic integration of the beneficiaries. The various matching techniques reveal a score difference ranging from 12 to 14 points in favor of program participants compared to those who did not participate. The Difference-in-Differences method confirms the positive and significant impact of the program.

The findings highlight the importance of the “Postliteracy” program in national literacy policy, underlining the need to further strengthen its presence within the programs deployed by ANLCA, notably by increasing the number of beneficiaries targeted by this program. To achieve this, it would be advisable to increase the funds allocated to it within ANLCA's budget.

The originality of this work is a unique research of the case of Morocco based on a microeconometric study for which the authors evaluate the impact of adult education by applying impact evaluation methods in the field of adult literacy.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

This paper provides novel evidence on the role of gender in the performance of university students, which is particularly relevant to the debate on the performance of female students in science, technology, engineering and mathematics (STEM) subjects.

Our approach relies on the metafrontier approach proposed by Huang et al. (2014), which measures students' efficiency within a given faculty and the impact of the faculty’s technology on students’ efficiency. We use a sample of 53,159 first-year students in 8 faculties from a large university in southern Italy from 2002–2003 to 2010–2011.

Students’ efficiency is relatively low, reflecting an essential role of unobserved heterogeneity. The different technologies of somewhat similar faculties have minimal impact on efficiency. There is a performance gap against women in five faculties, which on average is strongest for the faculties in the pure and applied science area. This gap increases with the proportion of female students and decreases with female lecturers.

The metafrontier has the benefit of providing relevant policy information on the drivers of student success by relying on data that universities routinely generate and preserve.

The stochastic metafrontier approach allows us to separate the group-specific frontiers from the metafrontier, yielding a decomposition of the efficiency scores of various faculties into technical efficiency scores and technological gaps.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

This study examines why low-wealth women entrepreneurs forgo mobile enabled money services and government supported micro finance for informal, community-based revolving loans in rural Nigeria.

Thematic analysis of 25 interviews with women in rural, south-west Nigeria. Entrepreneurial ecosystem theory, in the gendered context of micro finance and community-based lending, is employed.

This study explains the paradox of forgoing seemingly accessible mobile enabled credit, and formal credit schemes (e.g. micro-finance programs) for informal, one-on-one borrowing. Convenience and trust-based relationships with respected community members ease the burden of time scarcity and vulnerability associated with formal capital. Flexible terms, autonomy, self-reliance and knowing who one is dealing with make Esusu a preferred source of finance. Findings are discussed in the context of gendered entrepreneurial ecosystems in which participants conduct business.

The sample is not representative of women entrepreneurs in rural Nigeria. Survivorship bias is acknowledged. Further research is needed on the psychological risks of informal capital and the benefits of community-based lending.

Measures to scale mobile enabled credit, without commensurate interventions to address time management and other structural issues that confront women traders, limit their utility and impacts. Power differentials between women traders and lenders must also be considered in the design of lending products. Training of women traders and formal lenders should incorporate curricula about gender gaps in capital markets and systematic gender challenges to support entrepreneurs who seek to grow beyond subsistence enterprises.

This study documents decision criteria that motivate informal rural women traders to employ community-based revolving credit or Esusu. Findings inform measures to increase women entrepreneurs' access to capital in a rural sub-Saharan Africa contexts.