Unemployment Research Articles

Measuring Regional Competitiveness Through The Regional Development Index

This research aims to conduct an analysis focusing on measuring the level of regional competitiveness by utilizing the regional development index as a measure of regional development performance in Luwu Raya. This measurement examines several indicators considered influential, namely economic, social, infrastructure, and environmental aspects. The sample in this research includes all regions in Luwu Raya, comprising Palopo City, Luwu Regency, North Luwu Regency, and East Luwu Regency. The method employed in this research is quantitative analysis using the composite performance index as the analytical technique. The primary steps in constructing a composite index involve weighting, literature studies, trend determination, alternative index determination, and composite index determination. The results reveal that the regional competitiveness in Luwu Raya is unevenly distributed. Palopo City is the region with the highest level of competitiveness, while two regencies, North Luwu and Luwu, have the lowest levels of competitiveness. The most influential aspect of regional competitiveness is infrastructure, as measured by data on the condition of paved roads and clean water distribution. The problem that needs to be addressed in almost all regions of Luwu Raya is poverty. Infrastructure is a pioneering indicator that can have a significant positive or negative impact on development in Luwu Raya. Therefore, it is expected that the government can focus its development policies on infrastructure to provide multiplier effects on economic and social aspects through inclusive economic growth and job creation.

Prioritization of investment order in Brazil among renewable energy sources: an analysis using the SWARA-MOORA-3NAG method

Purpose This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development. Design/methodology/approach The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact. Findings The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs. Research limitations/implications While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness. Originality/value This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

Effects of SUDENE’s tax incentives on job creation in the Northeast region of Brazil

ABSTRACT This paper analyzes the effects of tax incentives on job creation by companies located in the Northeast region of Brazil, disaggregated by sector. The generalized synthetic control method is employed to handle this problem. The evidence indicates that the incentives have beneficial effects on increasing firm employment. Specifically, we observed positive impacts in the labour-intensive manufacturing sector. Therefore, the results provide positive evidence for using tax incentives for regional development, although the effects are concentrated in the manufacturing sector.

Pemodelan Teknologi dalam Aplikasi KitaLulus untuk Lowongan Pekerjaan Menggunakan Metode Technology Acceptance Model (TAM)

Technological advancements have increased the need for job vacancy information, while the unemployment rate in Indonesia remains high. The KitaLulus application is designed to facilitate job seekers in finding opportunities that match their qualifications and preferences. This study adopts the Technology Acceptance Model (TAM) to analyze user acceptance of this application, focusing on perceived usefulness and perceived ease of use. The objective of this research is to evaluate users' perceptions of the KitaLulus application and analyze the influence of perceived usefulness, perceived ease of use, and behavioral intention on actual system usage. The results indicate that perceived usefulness and perceived ease of use significantly influence the acceptance and use of the KitaLulus application. These findings emphasize the importance of intuitive and beneficial application design to enhance technology adoption, while contributing to the development of job search applications and the academic literature on technology acceptance.

Regional socioeconomic characteristics and density of general practitioners in Germany: A nationwide cross-sectional and longitudinal spatial analysis

ObjectivesLimited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics. Study designThis longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants. MethodsUnivariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective. ResultsIn contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. β = −2.31, p = 0.021; 2019: std. β = −4.14, p < 0.001) and higher unemployment rate (2015: std. β = −2.84, p = 0.005; 2019: std. β = −5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. β = −2.17, p = 0.030). ConclusionA higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care.

The Impact of the Opportunity Zone Program on Residential Real Estate

Problem definition: Opportunity zones (OZs) are designated census tracts in which real estate investments can gain tax benefits. Introduced by the U.S. Tax Cuts and Jobs Act of 2017, the goal of the OZ program is to foster economic development in distressed neighborhoods. In this paper, we investigate and optimize the OZ selection process and examine the impact of OZs by exploiting two data sets: a proprietary real estate data set that includes 36.1 million residential transactions spanning all 50 U.S. states and census-tract demographics data between 2010 and 2019. Methodology/results: We show that census tracts with higher poverty and unemployment rates were more likely to be selected. Counterintuitively, however, tracts with a higher average real estate price were also more likely to be selected. We then apply difference-in-differences, synthetic control, and matching techniques to rigorously assess the impact of the OZ program on two key real estate metrics: price and transaction volume. We find that the OZ program increased real estate prices by 4.03%–6.13% but do not observe a significant effect on the transaction volume. We also find that investors primarily targeted the high-end real estate market, namely, exhibiting a cherry-picking behavior. To better fulfill its intended societal and economic goals, we propose an optimization framework with fairness considerations for OZ assignment decisions. We show that the OZs assigned from our fairness-aware optimization formulation can better serve distressed communities and mitigate investors’ cherry-picking behavior. Managerial implications: Our paper underscores the importance of incorporating fairness in OZ designation to achieve a desirable real estate market reaction. Our large-scale empirical analysis provides a comprehensive assessment of the current government OZ assignment, and our fairness-aware optimization framework provides concrete recommendations for policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0746 .

Validation of a dispositional critical thinking scale using an original analytical methodology for addressing data from Google Trends

PurposeA methodological approach is required that complements studies based on surveys, providing a perspective with greater truthfulness and coverage. The study aims to develop a methodology to validate psychological/managerial constructs using data from Google Trends, taking as a case study a critical thinking (CT) scale in organizational domains previously supported by survey data.Design/methodology/approachThe developed methodology consists of eight stages, in which the following is integrated: (1) Internet search interest data (19 Spanish-speaking countries); (2) deductive research processes (e.g. theoretical model, linguistic manifestations, fieldwork, data matrix, analysis statistical, reporting); (3) psychometric properties (e.g. construct validity, criterion validity, reliability) and (4) objective data to examine criterion validity (e.g. unemployment rate).FindingsThe application of the methodology produces evidence that supports the reliability (Cronbach’s alpha, Guttman’s λ4), construct validity (intra-correlations and correlations with reference variables: “entrepreneurship,” “critical thinking,” “soccer,” “beer,” “pornography”) and criterion validity (prediction of unemployment rate) of the CT scale.Research limitations/implicationsThe methodology makes it possible to support or invalidate the quality of construct measurement scales by planning, capturing and processing data available on the internet.Practical implicationsThis manuscript is useful for research in business management (and related areas), which is intensive in the use of psychological/managerial constructs.Originality/valueThe methodology uses a new type of evidence; it is noninvasive, usually more truthful than responses to surveys, and has greater coverage of people participating indirectly in the study.

HOW DID THE PANDEMIC AFFECT THE RESIZING OF TELEVISION NEWSROOMS IN ALBANIA

The pandemic has brought great surprise by transforming many dimensions of our lives, but it has undoubtedly also found the entire media industry unprepared. It has led to the paralysis of every political and economic activity, as well as of media systems, not only in Albania but did not spare the information corporations all over the world. The immediate challenges faced by the media, particularly the traditional ones, were the collapse of information, job losses, decline in advertising, reduction of staff and budgets, biased information, and decreased access to resources. In this article, we aim to prepare a qualitative analysis, of how Covid-19 has re-dimensioned the editorial staff of information television in terms of organization and operation of daily basis of the journalist’s work. In the following, we have taken into consideration our study of three newsrooms of news television, such as: Syri Tv, Euronews, and News 24. The methodology is based on qualitative interviews of the three relevant chief editors of the aforementioned media, such as: Bledian Koka, Anta Muçaku, and Anila Jole. The survey of 23 journalists working in these newsrooms also provides quantitative data, concluding that today’s editorships bring many "innovations" and organizational "changes", that had not previously been embraced by classic TV editors, before the COVID-19.

The Impact of Education and Employment on Economic Competitiveness: A Comparative Analysis of South African Provinces

This study examines the economic competitiveness of South Africa’s nine provinces in terms of job creation and the role of education in enhancing economic development. Utilizing data from the 2010–2019 Quarterly Labour Force Survey and GDP Quarterly Estimates per Region Reports, regression and correlation analyses were conducted. Results revealed a significant correlation between education, economic competitiveness and employment in Western Cape, Gauteng, Mpumalanga and Limpopo. These results highlight the importance of investing in education. By prioritizing educational investments in provinces where significant positive correlations have been observed, targeted interventions can effectively drive regional economic development and job growth.

Optimal Short-Term Inflation Rate Forecasting Model in Kenya: in Depth Analysis

Purpose: The Kenya economy experienced an increase in the price of basic commodities, increased unemployment rates and consequently reduced real wage levels due to job cuts. An accurate forecast of this unprecedented inflation rate could have cushioned the Kenyan population from its effects. Uncertainty over future inflation forecasting has caused detrimental and negative impact not only globally but also in the Kenyan economy. Combining several techniques of forecasting is an instinctual way to improve prediction performance as the limitations of one method are compensated by the strength of the other model. The general objective of the study was to develop an optimal model of forecasting short term inflation rate in Kenya. The specific objectives were to establish models of forecasting short term inflation rate using SARIMA, GARCH and hybrid SARIMA-GARCH family Models, select optimal model amongst the three models and Predict 12 months ahead inflation rate using the optimal model. Scope of the study from 2005 January to 2024 July. The study was anchored on monetary theory of inflation, Keynesian theory of inflation as well as rationale expectation theory of inflation. Methodology: Data was sourced from KNBS and CBK. The study was guided by positivism research philosophy. Explanatory research design was used in this study. Target population was 230 monthly observations. Sample and sampling techniques used was time series. PACF, ACF Dfuller, Kpss test, Philips perron test indicated the data was stationary after 1ST differencing. Findings: Statistic validation test results indicated SARIMA’s (1,1,1)(1,1,)12 adjusted 2 R was perfectly 1 indicating all variations in squared residuals were explained by lagged residuals. P- value for lagged squared residuals were significant at (0.00). F-statisticvalue was significant (0.020) suggesting overall model fit. Model stability test AR roots polynomial lied outside the unit circle. eGarch(1,1) model with lowest aic -0.534 was best in Garch Family models. Hybrid Sarima((1,1,1),(1,1,1) eGARCH(1,1) was identified. Comparison of Sarima(1,1,1)(1,1,1)12, eGARCH (1,1) and Hybrid Sarima((1,1,1),(1,1,1)12 eGARCH(1,1) using forecast accuracy revealed that hybrid model was the optimal model with lowest MAE 0.166 and RMSE 0.259. Diagnostic checks The Ljung- Box test (LL) and Q2 indicated non-significant autocorrelation p- value were greater than 05% indicating that the models residuals were white noise. The DOF/GED parameter (4.144466*, 1.101958, 6.977499) represented the degrees of freedom for the tdistribution and the coefficients where significant at 0.05% meaning the model’s assumptions for normal distributions were met. The coefficients of AR Normal 0.048140, T-Student −0.031854, GED −0.02396 and MA Normal 0.055167 ,T-Student 0.059645, GED 0.051390 terms were significant at 5%. Recommendations: The results implied that the model predicted a decrease in the Kenya’s inflation rate for the next 12 months. The study recommended that inflation rate would be hovering below an average rate of 10 within the next 12 months with high volatility up to July 2025 and policy makers should use this prediction for planning in order to maintain Kenya’s macroeconomic stability. Policy makers were also advised to use the hybrid model to forecast short term inflation rate 12 months ahead and in future years. The benefits of the study were added knowledge of hybridizing to researchers and to policy makers. This study therefore provided better inflation forecasts (Kenya) to be used for strategical planning for short -term effects of inflation by the government.

Household job insecurity, food insufficiency, and children’s mental health needs: Household Pulse Survey in June 2023

BackgroundEconomic hardships have far-reaching implications for families with children, impacting their well-being and development during the COVID-19 pandemic. The pandemic has led to household financial problems following job loss and reduced income. As parents or caregivers face financial difficulties, children may experience emotional distress through heightened anxiety and a sense of helplessness. This study examined the effects of household job insecurity and food insufficiency due to the pandemic's aftermath on the mental well-being of children in the United States.MethodThis study utilized data from the Household Pulse Survey (HPS) during June 7–19, 2023 (Phase 3.9). The prevalence and effect of job insecurity, delineated by recent household job loss and employment status, and food insufficiency, measured through levels of food insufficiency, were analyzed across households with different age groups of children: under 5 years, 5–11 years, and 12–17 years, with an overall perspective on children under 18. We calculated unweighted observations, survey-weighted proportions, and survey-weighted 95% confidence intervals to analyze the impact of job insecurity and food insufficiency on children’s mental health treatment needs.ResultsThis study highlighted differences in economic hardships due to household job insecurity and food insufficiency across households with children in multiple age groups. There were almost twice as many respondents with no work in the last 7 days (33.68%) compared to those who experienced job loss within the last 4 weeks (14.82%). Additionally, 51.68% of respondents reported food insufficiency over the last 7 days, with more than half of the households experiencing food insufficiency due to the inability to afford more food. Mental health treatment needs among children were significantly higher in households experiencing job loss and food insufficiency. The need for mental health support for children increased with age in households facing job loss or no employment.ConclusionsThis study enhances our understanding of post-pandemic economic hardships and their influence on children’s mental health needs across different age groups. By identifying disparities and patterns, policymakers and practitioners can design targeted interventions to address employment opportunities and food access in the aftermath of the COVID-19 pandemic. Providing mental health treatments is crucial, especially for children aged 5–11 and 12–17, who are at higher risk of experiencing emotional distress due to household economic hardships.

Urban Agriculture: Exploring Its Potential, Challenges, and Socio-Economic Impacts

Urban agriculture, the practice of growing and cultivating food within urban and peri-urban areas, has garnered increasing attention in recent years due to its potential to address various challenges related to food security, environmental sustainability, and socio-economic development. This review article provides a comprehensive exploration of urban agriculture, examining its multifaceted potential, the challenges it faces, and its socio-economic impacts in greater detail. The article delves into the diverse forms of urban agriculture, ranging from rooftop gardens and community allotments to vertical farming and aquaponics, highlighting their contributions to local food production, green spaces, and community cohesion. Additionally, the review discusses the challenges and constraints associated with urban agriculture in more depth, including issues related to land availability, soil contamination, water management, and regulatory hurdles. Furthermore, it examines the socio-economic impacts of urban agriculture with a nuanced perspective, exploring how it can lead to improved access to fresh produce, job creation, poverty alleviation, and social empowerment at both individual and community levels. By shedding light on the complexities of urban agriculture and providing a more comprehensive analysis of its potential and challenges, this review aims to inform policymakers, urban planners, researchers, and practitioners about the transformative potential of urban agriculture and the need for supportive policies and interventions to foster its sustainable growth and maximize its benefits for urban communities, the review dives deeper into the various forms of urban agriculture, providing detailed insights into their unique characteristics, benefits, and challenges. Rooftop gardens, for example, are explored not only for their contribution to local food production but also for their role in mitigating urban heat island effects, improving air quality, and enhancing building energy efficiency. Community allotments are examined in the context of social cohesion, community engagement, and opportunities for education and skill-building. Vertical farming and aquaponics are discussed in terms of their innovative technologies, resource efficiency, and potential scalability to meet the growing demand for fresh produce in urban areas, the review delves into the challenges and constraints associated with urban agriculture, offering nuanced analyses of their underlying causes and potential solutions. Land availability issues are examined in relation to urban sprawl, land-use planning policies, and competing demands for space in densely populated cities. Soil contamination concerns are explored in light of industrial legacies, brownfield redevelopment, and strategies for soil remediation and regeneration. Water management challenges are discussed in the context of water scarcity, pollution, and the need for sustainable irrigation practices in urban agriculture. Regulatory hurdles are analyzed in terms of their impact on the viability and scalability of urban agriculture initiatives, highlighting the importance of supportive policy frameworks, zoning regulations, and land-use planning strategies. the review provides a comprehensive examination of the socio-economic impacts of urban agriculture, drawing on empirical evidence from case studies and research findings. It explores how urban agriculture can contribute to improved food access and nutrition outcomes, particularly for vulnerable populations living in food deserts and low-income neighborhoods.

Diabetes Mellitus and its Association with Work Patterns and Characteristics: A Narrative Review.

Diabetes mellitus is a leading cause of disability with adverse effects on the quality of life. It also affects occupational health by impacting several work-related parameters. This review discusses the relationship between diabetes and absenteeism, presenteeism, work impairment and unemployment. The association between work and diabetic complications such as neuropathic pain, diabetic foot, psychological issues and hypoglycemia due to treatment is also examined. Evidence points to a relationship between diabetes and absenteeism, reduced work productivity, and, thus, overall work impairment. A stronger negative impact on work performance is mediated by painful diabetic neuropathy and diabetic foot. In addition, psychological distress has been positively correlated with total workdays lost and frequency of absence. Depression in the diabetic population has also been linked with increased absenteeism, presenteeism, and work disability. Moreover, hypoglycaemia induced by antidiabetic medication may affect work attendance and performance. Finally, diabetes has been associated with inequality in the work environment, lower job satisfaction and higher unemployment rates, mainly because of its complications.

Realization of Short-Term Fertility Intentions in a Comparative Perspective: Which Macro-Level Conditions Matter?

This is the first large-scale comparative study to search for macro-level determinants of the realization of short-term fertility intentions across European countries. Using the unique follow-up feature of the Generations and Gender Survey and adopting rigorous definitions of intentions and outcomes, it reports on the level of fulfilment and finds clear heterogeneity across European countries. Using a multilevel, multivariate approach, it examines the nature of macro-level factors that may explain differences in the realization of fertility intentions. Based on our analysis, we conclude that labour market stability (as measured by fluctuations in the unemployment rate), price stability, heavy welfare state involvement, and the dominance of certain attitudinal conditions all support the greater realization of short-term fertility intentions.

Social gradients in employment during and after the COVID-19 pandemic

AbstractWe examine employment effects of the COVID-19 crisis in Norway from March 2020 through June 2022: during the initial lockdown, through the subsequent recovery, and after the dust had settled. While we identify large and socially skewed effects of the crisis through its early phases, we find no long-term effects on employees exposed to early risk of job loss. For those employed at the onset of the pandemic, both the level and the socioeconomic composition of employment quickly returned to normal. In contrast, we find considerable negative long-term employment effects on people who were neither in employment nor in education when the crisis hit. We argue that these patterns can be explained by social insurance policies that gave priority to protecting existing jobs and to distribute benefits to those who were temporarily laid off. Given the extreme increase in the social insurance caseload, an almost unavoidable side-effect was reduced capacity for providing services to the already non-employed.

Integrating artificial intelligence into ERP systems: advantages, disadvantages and prospects

Objective: to identify the key benefits and potential risks associated with the use of artificial intelligence in ERP systems to improve decision-making processes, management efficiency and operational performance of various sectors, including commercial and non-profit organizations. Methods: systematic literature review, empirical data analysis, analytical and experimental research methods. Results: the key directions of artificial intelligence implementation in ERP-systems are reflected, providing improvement of operational efficiency, customer relations, as well as optimization of business processes, data management, supply chain and personnel management, automation of operations related to finance, optimization of customer relations; implementation of artificial intelligence in ERP-systems reduces inventory management costs, improves the accuracy of forecasting andinventory optimization, accelerates financial analysis and increases the accuracy of budgeting, resulting in reduced budget planning time; it also increases productivity by optimizing necessary production processes and reducing equipment downtime. However, there are also risks of confidential data leakage, unauthorized access to data; job losses due to automation of tasks; and vulnerability to cyberattacks. Scientific novelty: the little-studied directions of artificial intelligence integration in ERP-systems are analyzed; an integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency. Practical significance: the formulated directions for solving the identified problems of artificial intelligence integration in ERP-systems can be implemented in practice, as they will enable to better take into account local requirements and laws.

Revisiting the impact of corruption on income inequality worldwide

AbstractThe relationship between corruption and income inequality has been widely studied, but there is no consensus on whether corruption increases or reduces inequality. We conduct an extreme bounds analysis (EBA) to test the robustness of the explanatory variables proposed in the literature. Using a sample of up to 150 countries, with data mostly going back to 1980, we find that corruption does not appear to have a clear positive effect on inequality or may even reduce it. Also, contrary to what is sometimes suggested in the literature, the results do not support an inverted U‐shaped effect of corruption on income distribution. A more important role in explaining income distribution seems to be played by the level of financial development, the old‐age dependency ratio, the unemployment rate, the capital stock to GDP ratio and the population growth rate. These are often found to be significant drivers of inequality, regardless of the set of control variables and the definition of corruption used.

Treating abandoned mine drainage can protect streams cost effectively and benefit vulnerable communities

Some communities endure environmental hazards from abandoned mines and job loss from the energy transition away from coal. Recent US legislation provides a historic appropriation for abandoned mine hazards like the acidic water that often drains from them. Who the investment will benefit and what it will accomplish is unclear. Here we provide answers for the case of abandoned mine drainage in Pennsylvania. We find that communities most exposed to mine drainage have incomes 30 percent below that of unaffected communities and are twice as vulnerable to the energy transition. Within affected communities, exposure is associated with a greater urban, non-white, renter population. Analytical modeling using data from past treatment efforts shows that they have been relatively cost effective, protecting streams for about $5700 per kilometer per year. Federal appropriations for Pennsylvania could address all impaired streams for 25 years but would leave insufficient funding for other abandoned mine hazards.

Inflation’s Reduction of the Real Minimum Wage and Unemployment in the USA: 1987 to 2021

&lt;p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.&lt;/span&gt;&lt;/p&gt;

The Interplay of Dietary Habits, Economic Factors, and Globalization: Assessing the Role of Institutional Quality

Background: Dietary habits are pivotal for population health and well-being, yet remain a pressing global issue, particularly in Sub-Saharan Africa (SSA), where economic instability and institutional challenges exacerbate dietary problems. Despite extensive research, there is a notable gap in the literature regarding the direct and interactive effects of institutional quality and inflationary shocks on dietary habits. Methods: This study delves into these complex interplays across 44 SSA nations from 2002 to 2022. Employing an innovative entropy method (EM) and the generalized autoregressive conditional heteroskedasticity (GARCH) modeling, the study introduces an inclusive institutional quality index and an inflationary shock predictor as crucial determinants of dietary habits in the literature. Results: The results from the panel-corrected standard error (PCSE) method and feasible generalized least squares (FGLS) model reveal that per capita GDP, school enrollment rate, government expenditures, globalization index, and urbanization are positively associated with population dietary habits, while inflationary shock, food insecurity, and unemployment rate exert negative influences. Notably, institutional quality acts as a catalyst, amplifying the positive effects of the former group and absorbing the negative impacts of the latter on population dietary habits. Additionally, a dynamic panel causality analysis confirms a bidirectional causality nexus between population dietary habits and all variables, except for inflationary shocks, which demonstrate a unidirectional causality link. Conclusions: These findings carry significant policy implications, underscoring the complex dynamics between institutional quality, inflationary shocks, and dietary habits in the region. The bidirectional causality highlights the need for holistic interventions that address economic, social, and institutional factors simultaneously. Moreover, the unidirectional causality of inflationary shocks on dietary habits suggests that stabilizing inflation is critical to protecting dietary habits. These results provide critical insights for policymakers to design targeted interventions aimed at improving nutrition, bolstering institutional frameworks, and ensuring public health resilience in the face of economic and social shocks.