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This paper aims to propose a dynamic stochastic general equilibrium (DSGE) model for the State Bank of Vietnam (SBV) to assess the response from the household sector to monetary policy shocks through the consumption function. Moreover, the transmission from monetary policy to household consumption and income distribution is experimented with through the vector autoregression (VAR) model.

In this study, the authors used the maximum likelihood estimation to estimate the DSGE and VAR models with the sample from 1996Q1 to the end of 2021Q4 (104 observations).

The DSGE model’s results show that the response of the household sector is as expected in the theory: a monetary policy shock occurs that increases the policy interest rate by 0.29%, leading to a decrease in consumer spending of about 0.041%, the shock fades after one year. Estimates from the VAR model give similar results: a monetary policy shock narrows income inequality after about 2–3 quarters and this process tends to slow down in the long run.

Based on the research results, the authors propose policy implications for the SBV to achieve the goal of price stability, and stabilizing the macro-economic environment in Vietnam.

The findings of the study have theoretical contributions and empirical scientific evidence showing the effectiveness of the implementation of the SBV’s monetary policy in the context of macro-instability, namely: flexibility, caution and coordination of different measures promptly.

The manufacturing sector is witnessing the need to continuously improve overall performance by eliminating inefficiencies in the supply chain. The adoption of lean concepts to address wasteful or non-value-adding activities in the supply chain is crucial. This article determines key factors of lean supply chain management (LSCM) for continuous improvement in the manufacturing sector.

The methodology comprises three steps. The first step identifies critical factors of LSCM in manufacturing from prior research and a series of expert consultations. Critical factors are identified and validated that industries can leverage to attain their lean goals. The second step uses the decision-making and trial evaluation laboratory (DEMATEL) method to determine the causal relationship among the factors. DEMATEL analysis categorizes factors into cause and effect, which will assist industry personnel in decision-making. The third step involves further data analysis to visualize the importance of the most critical factors. It develops a machine learning (ML) model in the form of a decision tree that helps in assessing the factors into cause or effect groups via a threshold value of expert ratings.

IT tools, JIT manufacturing and material handling and logistics form the most critical factors for LSCM implementation.

The analysis from DEMATEL and ML together will be beneficial for manufacturing practitioners to improve the supply chain performance based on the identified factors and their criticality towards LSCM implementation.

This paper investigates whether and to what extent operating in inner areas affects the profitability of innovative Italian small and medium-sized enterprises (SMEs) over 2012–2018.

Guided by the National Strategy for Inner Areas and the “Investment Compact,” this study distinguishes between inner and core innovative SMEs. It employs various econometric models to estimate a regression for the return on assets of SMEs, differentiating between firms operating in inner and non-inner areas of northwest, northeast, centre and south Italy.

Findings reveal that innovative SMEs in inner areas generally exhibit lower profitability compared to those in non-inner municipalities. However, huge heterogeneity in results is observed across the country. Specifically, innovative SMEs in the inner areas of the south register lower profitability than those operating in non-inner zones. Conversely, innovative SMEs located in the inner municipalities of northwest and northeast Italy show higher profitability than their peers in non-inner areas. The results imply that targeted policies for inner areas are crucial. However, due to the diversity of local impacts, a differentiated approach, depending on the geographic context, is necessary.

The study aims to explore the relationship between inner areas and the performance of innovative SMEs in Italy. More precisely, it examines the effect of operating in a municipality located within an inner area on the profitability of innovative SMEs. This issue has been overlooked in existing literature. Importantly, we aim to determine whether there is a heterogeneous impact based on geographical localisation, specifically in the Northwest, the Northeast, the Centre and the South of the country. Therefore, this paper contributes to the literature by investigating the factors influencing the performance of innovative SMEs and suggesting new policy recommendations for developing inner areas in Italy.

This paper investigates one of the potential costs of rising segregation in American cities by evaluating empirically the extent at which ethnic-based segregation contributes to the onset and the speed of propagation of the COVID-19 pandemic.

Regression analysis based on matched data on early incidence of COVID-19 cases, segregation and covariates. Identification resorts on variations in segregation across MSAs and heterogeneity in the geography and timing of stay-at-home orders.

One cross-MSA standard deviation increase in segregation leads to a significant and robust rise of COVID-19 cases of 8.7 per 100,000 residents across urban counties.

Combines spatial data on COVID-19 cases and segregation; use of a new segregation measure; focus on early incidence of the pandemic and its drivers.

The measurement of women's economic welfare and exploring its underlying factors have been undervalued in the context of Khyber Pakhtunkhwa, Pakistan. This study addressed this gap by focusing on assessing women's subjective economic welfare and its socioeconomic and cultural determinants in the education and health sectors within Mardan, Northern Pakistan.

The study used stratified random sampling techniques for the selection of sample respondents and collected data through a well-structured questionnaire. To measure women’s economic welfare, the study utilizes Lorenz curves, the Gini index, the Sen Social Welfare function and an individual's gross monthly income. Furthermore, the ordinary least squares method was utilized to analyze the determinants of economic welfare.

The findings show greater income inequality and a lower welfare level for women in the education sector compared to the health sector. Likewise, the study identifies several key determinants, such as age, educational qualification, job experience, respect for working women, outside and work-place problems and the suffering of family members of working women for their economic well-being.

This study makes valuable contributions to the literature by focusing on the cultural perspective of Pakhtun women in Mardan and providing a context-specific understanding of subjective economic welfare. Additionally, the authors collected first-hand data, which gave an original outlook on working women's current economic welfare level. Furthermore, this study undertakes a comparative analysis of working women's welfare in the health and education sectors. This comparison offers a more accurate portrayal of the challenges and opportunities specific to these occupations.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0246

The primary purpose of this study is to explore the effect of technical changes on provincial-level income inequality in Vietnam. The authors also investigate whether the quality of institutions and human capital level moderate this relationship.

This research applies the fixed-effect and random-effect models on a balanced panel data set of 63 Vietnamese provinces/cities from 2010 to 2020.

The study’s empirical results show that technical improvement has a nonlinear influence on income disparity in Vietnamese localities. When the local level of technology is limited, technological change can mitigate income disparity. However, as local technological levels increase, inequality tends to rise. Moreover, the study also reveals that the quality of a province’s institutions and the level of human resources are factors that moderate the correlation between technological change and income inequality. For provinces with better institutional quality and/or better human resources, inequality tends to decline under the impact of technological change.

The results of this study suggest that while encouraging technology advancement, localities should also ensure sustainable development, reduce income inequality and focus on improving institutional quality and human resources development.

There are increasing concerns about the impact of technical change on inequality in income distribution; however, empirical evidence on this relationship in developing countries remains scarce. This study is among the few attempts to examine this issue at the provincial level of a developing country considering the moderation effect of institutional quality and human capital level.

This study assesses the probability of an OECD member country exhibiting high persistence in unemployment duration, considering income inequality, productivity, accumulation of human capital and labor income share in Gross Domestic Product (GDP) between the years 2013–2019.

To achieve the purpose of the study, a probabilistic analysis with panel data is employed, focusing on 20 OECD countries segmented into two groups: those with high persistence and low persistence in unemployment duration. Probit and Logit models are estimated, marginal changes are analyzed and the models are evaluated in terms of their classification accuracy. Finally, trends in probabilities over time are examined.

This paper exhibits that countries with higher human capital index, greater labor income share in GDP, and more relevant productivity for well-being reduce their probabilities of experiencing high persistence in unemployment duration. It is observed that Mexico (MEX), Greece (GRC), Italy (ITA), and Turkey (TUR) have elevated probabilities of experiencing high persistence in unemployment duration in the future, while Costa Rica (CRI), Estonia (EST), Slovakia (SVK), Czech Republic (CZE), Lithuania (LTU), Poland (POL), and Israel (ISR) show a marked downward trend in these probabilities. Lastly, countries like the United Kingdom (GBR), Denmark (DNK), Sweden (SWE), Norway (NOR), Netherlands (NLD), Germany (DEU), United States (USA), and Canada (CAN) present minimal risk of experiencing high persistence in unemployment duration in the future.

The measurement of the relationship between development outcomes and persistence in unemployment duration has been scarce. Generally, the literature has focused on the analysis of development and unemployment without delving into the duration of unemployment, let alone persistence in duration.

This paper provides a solid foundation for the formulation of policies aimed at promoting sustainable employment and inclusive economic growth.

Based on the findings of the study, two key development policies are proposed. Firstly, the implementation of investment programs in Human Capital to increase productivity is recommended. Resources should be directed towards initiatives that improve the necessary skills and competencies in the labor markets of OECD countries, especially in strategic economic sectors with higher production linkages. Additionally, incentivizing the application of active labor policies is proposed. This entails prioritizing policies aimed at increasing the labor income share in GDP through progressive fiscal reforms that strengthen social safety nets and ensure fair labor standards. Implementing employment programs targeted at vulnerable groups, such as long-term unemployed individuals, youth, female heads of households and marginalized communities, is also recommended to eliminate structural barriers to labor market participation and reduce disparities in unemployment persistence. Adopting these policies can help mitigate the risk of high unemployment duration persistence and foster sustainable and inclusive long-term economic growth.

This is the first study to analyze the probabilities of both developing and developed countries experiencing high persistence in unemployment duration. It specifically evaluates these probabilities over a period of time and also estimates potential outcomes if real investments were made to enhance their human capital, productivity and employability.

In this paper, the impact of the 2004 European Union accession on income inequalities within New Member States is analyzed.

An empirical analysis is conducted with nine New Member States over the period 1991–2015, with 55 economies serving as a control group. The newly introduced (by de Chaisemartin and D’Haultfœuille, 2023) method belonging to the family of difference-in-differences (DID) estimators is applied to allow for multiple non-binary treatments.

While accession to the European Union had a positive and significant impact on the market and net Gini coefficients in the treated countries, no evidence of the impact of accession on redistribution was found. Single-unit estimates signal that income inequalities rose due to EU membership in some member countries; the most convincing evidence shows that income distribution in Latvia was especially affected.

The author applied the method which addresses the presence of multiple non-binary treatments. Full-fledged membership was preceded by association status, and accession to the EU was accompanied or followed by engagement in other layers of integration (European Monetary Union and Schengen Area). Controlling for these features, the author was able to assess whether the pure EU effect contributed to increases in income inequalities.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.