Cross-sectional Units Research Articles

Testing the Baumol Cost Disease Hypothesis in the Health Sector of Sub-Saharan Africa

ABSTRACT: This paper takes a closer look at the well-researched topic of the determinants of health expenditure. However, as an improvement over previous studies, the current study tested the cost disease hypothesis in the healthcare sector in sub–Saharan Africa to assess the sector’s productivity. The cross-sectional units in the current study comprise forty-eight sub-Saharan African countries; spanning the period 2000 to 2020. The data was sourced from two main sources; namely, World Development Indicators and the ILOSTAT . The main variable of interest ( Baumol variable) was constructed using a combination of various variables. The methodology relied on several econometric techniques i.e., fixed effect, random effects and two-stage least square estimations. The growth rate of gross fixed capital formation was used to control for the potential reverse causation between the growth rate of health expenditure per capita and the Baumol variable. Based on the empirical results from the various estimations, it appears that there is some evidence of the presence of the cost disease in the healthcare sector of sub–Saharan Africa. This effect became stronger when the endogeneity in the model was taken into consideration. However, this effect became greatly diminished after the growth rate of GDP per capita and the growth of the price level were introduced in the model. The statistical significance of the growth rate of these variables reinforces their important roles in the determination of the levels and growth in health expenditure. The confirmation of the basic Baumol model in determining the growth of health expenditure suggests that the growth of health expenditure in sub-Saharan Africa might be a result of health workers’ wages outstripping productivity in the sector. This suggests that the inelastic nature of the demand for health care is the main driver of the growth of health expenditure; thus might not be reflected in improved health care. Furthermore, policymakers need to consider changes in the price level as it may deflate the real value of the budgetary amount.

Crecimiento económico, turismo e incertidumbre de la política económica en los principales destinos turísticos (1998-2018)

Objective: to study the relationship among tourism demand and economic growth, real exchange rate, and economic policy uncertainty (EPU) in Australia, China, France, Germany, Italy, Japan, Spain, the United States, and the United Kingdom. Methodology: cross-section dependence, unit root, and cointegration tests were applied. The long-term model was estimated according to the Panel-Corrected Standard Error (PCSE), Feasible Generalised Least Squares (FGLS), and Fully Modified Ordinary Least Square (FMOLS). Results: the results show a positive relationship between economic growth and the real exchange rate concerning tourism demand. In the long term, the causality relationship is from uncertainty, exchange rate, and GDP to tourism. The feedback hypothesis is validated. Limitations: availability of data.Originality: it studies the joint influence of economic growth and the EPU on demand for tourism in the countries of the world’s leading destinations for international tourism according to international tourist income. Conclusions: tourism will drive general economic growth and, correspondingly, investment in other sectors of the economy will also influence an increase in tourism.

HOW DOES ECONOMIC FREEDOM AND GOVERNANCE QUALITY AFFECT ECONOMIC GROWTH: A PANEL DATA ANALYSIS FOR 26 EUROPEAN COUNTRIES

The purpose of this study is to examine the impact of economic freedom and governance quality on economic growth. Sustainable economic growth and the factors determining prosperity have long been of interest to researchers. Therefore, analyzing the impact of economic freedoms and governance quality on economic growth is crucial. This is particularly important in a region like the European Union, which has the world's largest economic network. Twenty six of the European Union countries were chosen for this purpose. The level of economic freedom by Fraser Institute, and the level of governance by Worldbank were used to analyze this relationship. The primary hypothesis of the study is that institutional factors will stimulate economic expansion. In order to examine this hypothesis, tests for cross-section dependency, heterogeneity, unit root, cointegration, and causality were conducted. Consequently, it has been determined that these factors contribute to economic growth throughout the period. It has been discovered that there are differences across nations, and the causes for these distinctions are discussed in the discussion section.

Influence of fiscal decentralization, fintech, and mineral resources on green productivity of G5 countries

The study explores how regional financial autonomy, financial technologies, and resource management strategies intersect to influence sustainable practices and environmental efficiency in G5 economies. The yearly data from 1990 to 2021 has been taken for Canada, Japan, Germany, the United Kingdom, and the United States of America. Empirical analysis of the Cross-sectional Augmented Distributed Lag (CS-ARDL) modeling framework has been performed after a preliminary investigation regarding the variables' cross-sectional dependence, slope heterogeneity, and unit root properties. The outcomes of long-run estimation reveal the positive impact of fintech and revenue decentralization, while mineral resources and expenditure decentralization negatively influence green productivity. Similar results are observed in the short-run; however, their marginal effects are relatively lower. Moreover, the error correction term is negative and significant, indicating an 81.4% speed of adjustment annually to restore the obtained model's equilibrium system. These estimates are validated by the Augmented Mean Group (AMG) and Common Correlated Effect Mean Group (CCEMG) estimators. A bi-directional causality of fintech and revenue decentralization is observed, which offers valuable policy implications.

Measuring Diffusion Over a Large Network

Abstract This article introduces a measure of the diffusion of binary outcomes over a large, sparse network, when the diffusion is observed in two time periods. The measure captures the aggregated spillover effect of the state-switches in the initial period on their neighbours’ outcomes in the second period. This article introduces a causal network that captures the causal connections among the cross-sectional units over the two periods. It shows that when the researcher’s observed network contains the causal network as a subgraph, the measure of diffusion is identified as a simple, spatio-temporal dependence measure of observed outcomes. When the observed network does not satisfy this condition, but the spillover effect is non-negative, the spatio-temporal dependence measure serves as a lower bound for diffusion. Using this, a lower confidence bound for diffusion is proposed, and its asymptotic validity is established. The Monte Carlo simulation studies demonstrate the finite sample stability of the inference across a range of network configurations. The article applies the method to data on Indian villages to measure the diffusion of microfinancing decisions over households’ social networks.

How does institutional quality determine energy consumption? Empirical evidence from the South Asia region

Purpose Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific energy types. Among others, institutional quality (INQ) is another factor that can determine energy consumption. Given this, the current study aimed to investigate the impact of INQ on fossil fuel energy (FFE) and renewable energy consumption (REC).Design/methodology/approach The empirical analysis was conducted on 20 years (2000–2019) of data from South Asian economies, and regression among variables was established by employing the dynamic ordinary least square and fully modified ordinary least square models. The selection of both techniques is subject to the existence of cointegration identified by the Johansen cointegration test. Other pre-estimation techniques include cross-section dependence and unit root testing validating the estimation of coefficients in the long run.Findings The analysis mainly reveals the negative impact of INQ on FFE and the positive impact of INQ on REC. The authors further find the asymmetric impact of control variables including foreign direct investment inflow, economic growth, inflation rate, financial sector development and energy investment on the consumption of both types of energy.Research limitations/implications Given the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically those that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts to improve the INQ.Practical implications In view of the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts for improving the INQ.Originality/value This study offers robustness to the empirical findings of existing literature on the INQ-REC nexus and complements the underdeveloped literature on the INQ-FFE relationship.

The impact of financial innovation and infrastructure on inbound tourism in Central Asia

This empirical study investigates the effect of financial innovation and infrastructure on inbound tourism in Central Asia, along with trade openness as a control variable. This study developed an infrastructure index for Central Asia comprised of different parameters of infrastructure. The data has been assessed for cross-sectional dependence, unit root and coefficients of parameters were obtained by pooled mean group autoregressive distributed lagged model and simultaneous quantile regression. Results show that financial innovation and trade openness have a significant and positive effect on inbound tourism in the long run whereas infrastructure has an insignificant effect. Dynamic stability analysis confirms that the model of the study is stable and will adjust itself from any imbalance within three years of span. The results of quantile regression highlight that financial innovation is positively associated with tourism development in Central Asia irrespective of quantile (Q25, Q50, Q75). Policy implications are provided based on research findings.

Exploring the impact of natural Resource utilization on human capital development: A sustainable development perspective

The COVID-19 pandemic has had a profound impact on societies worldwide, highlighting the need for sustainable development practices. This study focuses on the N-11 countries, aiming to investigate the influence of coal rent, oil rent and mineral rent on human capital by controlling the role of economic growth and financial development from 1990 to 2021. The study analysis begins by conducting preliminary tests to address cross-section dependency, slope heterogeneity, unit root, and cointegration to ensure robust and accurate findings. The study employed the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) techniques for the panel data analysis. The LM cointegration method results reveal the existence of long-run relationship among the study variables. These test findings revealed that natural coal rent, oil rent and mineral rent are positively and significantly associated with human capital. We observe that economic growth and financial development also helpful to increase the human development level in these group of countries. Based on these finding, this study suggests different policy implication for these group of countries which will help policymakers to reach on consensus. Exploring the impact of natural resource utilization on human capital development from a sustainable development perspective during COVID-19 highlights the need for informed policy decisions. By considering the economic, social, and environmental dimensions, policymakers can create a resilient and sustainable future. Integrating sustainable development principles, embracing technology and innovation, and fostering international cooperation are essential to ensure a positive impact on human capital development in the face of resource utilization challenges.

Does Financial Development in Relation to Remittances influence Economic Growth in Sub-Saharan Africa?

Abstract This study investigates the influence of financial development on the relationship between remittances and economic growth within the Sub-Saharan Africa region. The study utilised panel data encompassing 42 countries, spanning the time frame 1980 to 2021. The research employed a cross-sectional augmented panel unit root test, as well as the Im-Pesaran-Shin unit-root test, to conduct the unit root analysis. The regression estimations employed the Pooled Mean Group and Mean Group/ARDL methodologies. The research findings demonstrate a significant association between the development of financial systems and sustained economic growth over an extended period. The findings from the empirical analysis indicate that there exists a substitutive relationship between financial development and the association between remittances and economic growth. The study proposes that it would be advantageous for financial policies in the Sub-Saharan Africa (SSA) region to give priority to the implementation of efficient financial reforms with the goal of improving the overall depth of the financial sector. Keywords: Remittances, Financial development, ARDL, Economic growth, Sub-Saharan Africa.

Can minimizing risk exposures help in inhibiting carbon footprints? The environmental repercussions of international trade and clean energy

Since bettering environmental conditions has acquired significant interest globally, discovering factors that may facilitate the establishment of environmental sustainability is currently of foremost importance. Hence, this study considers a sample of 33 members of the Organization for Economic Cooperation and Development and checks whether reducing exposure to different forms of country risks, in the presence of international trade and clean energy consumption, can reduce their respective carbon footprint levels. Utilizing annual data from 2000 to 2018 and employing methods that handle problems related to dependence across cross-sectional units and heterogeneity of slope coefficients, the findings endorse that (a) reducing financial and political risks abate carbon footprints, (b) economic risk exposure does not influence carbon footprints, (c) international trade exerts carbon footprint-boosting effects, and (d) undergoing unclean to clean energy transition curbs carbon footprints. Accordingly, the concerned governments should these findings into account while conceptualizing green environmental policies in the future.

The Effect of R&D on Economic Growth: Evidence from Cross-Country Panel Data

ABSTRACT: Research and Development (R&D) in particular and innovation in general are the best drivers of long-run sustainable economic growth and spending in R&D has been recognized as one of the means to the economic accomplishment of both developed and developing countries. However, the sustainability of the association between R&D & economic growth exclusively depends upon the performance of other macroeconomic determinants. Hence, we examine the effect of R&D on economic growth in the existence of five important macroeconomic determinants. The study focusses on 151 selected countries, including both developed and developing, over the period 1996-2020 and focused on the macro-level analysis. Our research is based on the deployment of system Generalized Method of Moments (GMM) technique. The GMM technique encompasses longitudinal data; and is gathered by pooling time-series across a pool of cross-sectional units, specifically country-wise. The biggest advantage of using the GMM technique is that it allows for addressing the endogeneity problem using lagged explanatory variables as instrumental variables. We have three samples in our analysis, namely pool of developed countries, pool of developing countries, and the pool of both developed and developing countries. Our study deploys four proxies of R&D to work the nexus between R&D and economic growth in presence of five important macroeconomic determinants. In each of these three samples, we run four GMM models, depending upon the deployment of four R&D activities that we used in this study. We observe an evidence that R&D plays a significant role in economic growth when the selected macroeconomic determinants are used as control variables. Our findings are mostly robust to R&D specifications, country specifications, model specifications, and estimation strategies. Most importantly, the effect of lagged economic growth is highly positive, signifying that economies with higher economic growth in the previous years tend to grow faster in presence of R&D intensity. The behaviour of other macroeconomic variables is having diverse nature. Some of these variables behave positively, while others behave negatively. This study suggests that policymakers should focus on improving R&D activities and strengthening the chosen macroeconomic determinants to facilitate economic development. Hence, it contributes to the ongoing policy debate, that is with reference to R&D effect on economic growth, on the finest strategies for attaining sustainable economic growth in all these selected countries. Hence, we can use these findings to develop a targeted policy that promotes R&D intensity in the phases of economic development.

3D printed wood-fiber reinforced architected cellular composite beams with engineered flexural properties

This study explores the integration of cellulose-based compounds into a biobased polymer to create high-performance sustainable materials with improved properties. The elastic flexural characteristics of 3D printed composites, comprised of polylactic-acid (PLA) biopolymer reinforced by a variety of waste wood fiber weight percentage (2.5%−15%), are investigated. Initial steps involve producing wood-fiber reinforced PLA (WF-PLA) filaments and the 3D printing of test specimens. Experimental outcomes reveal enhanced flexural modulus (60% increase), rigidity (72% increase), strength (39% increase), and failure strain (21% increase) alongside reduced composite coupon density (5.2% decrease), attributed to wood fiber incorporation. To achieve lightweight and sustainable structural components with tunable attributes, architected composite cellular beams are introduced. These beams comprised of wood-fiber reinforced composites feature cross-sectional unit cells with one or two reflection symmetries, showcasing the synergy between architecture and material composition in enhancing quasi-isotropic flexural rigidity. Investigation on the flexural rigidity ratio ([EI]yy/[EI]xx) involves theoretical modeling, computational analysis, and experimental validation using 3D printed samples. WF-PLA filaments enable the 3D printing of engineered quasi-isotropic cellular beams, termed “Isoflex”, that demonstrate up to 130% enhanced specific flexural rigidity (bending rigidity-to-mass ratio) and up to 70% improvement in flexural rigidity ratio to have isotropic flexural properties, compared to pure PLA beams. This study introduces WF-PLA engineered cellular composites as a sustainable avenue for tuning structural properties, contributing to the realm of additive manufacturing.

The impact of public-private investment in energy on environmental degradation: evidence from major investment countries.

The objective of the present study is to explore the impact of public-private investment in energy, foreign direct investment, urbanization, and renewable and non-renewable energy consumption on environmental degradation in major investment countries during the period 1998Q4-2018Q4. In doing so, the cross-sectional dependence test and CIPS panel unit test were employed to identify the cross-sectionally dependency and the integrational properties/stationarity among the variables. Furthermore, we opted for Westerlund (2007) panel cointegration test to check the long-run association among the variables. To achieve the short-run and long-run elasticities, we have recommended cross-sectional-autoregressive distributive lag (CS-ARDL). The study outcomes revealed that public-private partnership in energy is negatively and significantly impacting CO2 emissions in both the short run and the long run. Furthermore, foreign direct investment and urbanization are negatively related to CO2 emissions, while renewable energy is positively affected it. However, the coefficients are insignificant. Moreover, non-renewable energy has a positive and substantial influence on CO2 emissions. Lastly, study outcomes offer several policy insights to develop investment in public and private partnerships in the energy sector to reduce CO2 emissions in major investment countries.

The roles of renewable energy, globalization, population expansion and deliberative democracy on Sustainable Development in South Asia.

South Asian region is extremely vulnerable to climate change which hampers its attainment of the sustainable development goals (SDGs). This study explores how sustainable development of South Asian nations is affected by the clean or renewable energy consumption, globalization, population growth and deliberative democracy. To tackle the effects of shocks within the cross-sectional units as well as to account for endogeneity, this study utilizes Common Correlated Effects Mean Group-Generalized Method of Moments (CCE-GMM) estimation technique proposed by Neal (2015). Common Correlated Effects Mean Group (CCE-MG) of Pesaran 2006 and Augmented Mean Group (AMG) by Eberhardt and Teal (2010) and Eberhardt and Bond (2009) techniques are also utilized as robustness checks. The empirical results reveal that the consumption of renewable or clean energy can significantly and positively affect sustainable development, implying that deploying clean energy technologies is helpful to achieve SDG agenda in South Asia. Population growth is found to be hampering sustainable development while deliberative democracy ensures this development. The impact of globalization on sustainable development was found to be negative yet insignificant. Bidirectional causal relationship was discovered between sustainable development and renewable energy, between population and sustainable development, between deliberative democracy and sustainable development and between deliberative democracy and globalization. Finally, the study provides policy directions to achieve sustainable development in South Asia via enhanced integration of renewable energy in the region's energy mix.

Heterogeneous effects of energy productivity improvement on consumption-based carbon footprints in developed and developing countries: The relevance of improving institutional quality

Economic expansion often causes multifaceted damages to environmental well-being. Thus, the persistent degradation in the global environment has become a concerning issue for both developed and developing regions across the world. Against this backdrop, this study checks how the per capita carbon footprint figures of the developed Group of Seven (G7) and the developing Emerging Seven (E7) countries are influenced by energy productivity improvement, institutional quality, and other key macroeconomic factors. Considering annual data from 2000 to 2020, advanced panel data estimation techniques that are robust against data-related problems such as endogeneity, heterogeneous slope coefficients, and dependency among the cross-sectional units are utilized. Overall, the results certify that making productive use of energy reduces consumption-based carbon footprints in E7 countries but not in G7 countries. Besides, improving institutional quality is seen to not only reduce carbon footprints in both samples directly but also indirectly mediate and moderate the energy productivity-carbon footprint nexus in G7 and E7 countries, respectively. Moreover, financial globalization is observed to minimize carbon footprints in G7 countries while boosting them in E7 countries. Contrastingly, financial development is observed to homogeneously boost the carbon footprint levels of both G7 and E7 countries. Lastly, international trade is found not to influence the carbon footprint levels in both samples of countries. Therefore, taking these major findings into consideration, a set of policies apart from making optimal use of energy are recommended for inhibiting the consumption-based carbon footprint figures of the concerned countries.

Bitcoin trading, economic growth, energy use, and CO2 emissions: An advanced panel study of emerging market economies

Carbon dioxide emissions pose a severe threat to the global eco-system and human health, yet at the same time the exponential growth of Bitcoin trading, unsustainable mode of current economic development, and the low efficient energy usage have become burgeoning issues in emerging market economies. By applying advanced econometric techniques, this research empirically tests the dynamic impacts of Bitcoin trading, economic growth, and energy use on CO2 emissions in the top 20 Bitcoin-trading emerging market economies from 2013 to 2020, highlighting results on the existence of cross-section dependence, slope heterogeneity, and unit roots in the panel data. Moreover, Bitcoin trading, economic growth, and energy use have a significantly positive effect on CO2 emissions from a long-term perspective. Findings further show that CO2 emissions have a bi-causal correlation with all three determinants. Relevant implications are offered as follows. For academic researchers, cross-section dependence and slope heterogeneity are important considerations, and ignoring them would lead to significantly biased inferences. Emerging market governments should introduce eco-friendly technology for Bitcoin trading, modify the present extensive mode of economic growth, and guard against unsustainable energy consumption.

Xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models

In this article, we introduce a new community-contributed command, xtnumfac, for estimating the number of common factors in time-series and panel datasets using the methods of Bai and Ng (2002, Econometrica 70: 191–221), Ahn and Horenstein (2013, Econometrica 81: 1203–1227), Onatski (2010, Review of Economics and Statistics 92: 1004–1016), and Gagliardini, Ossola, and Scaillet (2019, Journal of Econometrics 212: 503–521). Common factors are usually unobserved or unobservable. In time series, they influence all predictors, while in paneldata models, they influence all cross-sectional units at different degrees. Examples are shocks from oil prices, inflation, or demand or supply shocks. Knowledge about the number of factors is key for multiple econometric estimation methods, such as Pesaran (2006, Econometrica 74: 967–1012), Bai (2009, Econometrica 77: 1229–1279), Norkute et al. (2021, Journal of Econometrics 220: 416–446), and Kripfganz and Sarafidis (2021, Stata Journal 21: 659–686). This article discusses a total of 10 methods to estimate the number of common factors. Examples based on Kapetanios, Pesaran, and Reese (2021, Journal of Econometrics 221: 510–541) show that U.S. house prices are exposed to up to 10 common factors. Therefore, when one fits models with house prices as a dependent variable, the number of factors must be considered.

None-linear nexus between natural resources dependency, foreign direct investment, and environmental sustainability in newly industrialized countries

This study explores the non-linear effect of natural resource rents and foreign direct investment (FDI) on consumption-based carbon emissions in Newly Industrialized Economies (NIEs) from 1995 to 2020. Furthermore, this study examines the interactive effect of mineral resources and FDI, coal resources, and FDI using the method of moments quantile regression (MMQR). It also apply dynamic ordinary least square (DOLS), fully modified ordinary least square (FMOLS), and fixed effect-ordinary least square (FE-OLS) for the robustness. The preliminary findings confirm that cross-sectional units are interdependent with slope heterogeneity and panel cointegration. The subsequent results through MMQR reflect that mineral rents and coal rents tend to increase the carbon emissions across all the quantiles (0.10th to 0.90th); however, the average change in the dependent variable is higher at higher-order quantiles (0.60th-0.90th) and lower at lower order quantiles (0.10th to 0.30th). FDI tends to control trade-adjusted carbon emissions in all three ranges of quantiles. The interactive effect of mineral resources and FDI and coal resources and FDI support a significant reduction in carbon emissions. These findings suggest promoting the international flow of investment in the energy-efficient and natural resource industry for sustainable development.

Interconnecting Sustainable Development Goals 7 And 13: The Role of Renewable Energy Innovations Towards Combating the Climate Change

This research examines the trends in environmental footprints through energy innovations, digital trade, economic freedom, and environmental regulation from the context of G7 economies. Quarterly observations from 1998-2020 have been utilized for the advanced-panel model entitled Method of Moments Quantile Regression (MMQR). The initial findings confirm slope heterogeneity, interdependence between the cross-sectional units, stationarity properties, and panel cointegration. The results through FM-OLS, D-OLS, and FE-OLS justify that energy innovations, digital trade, and environmental regulations control ecological damages. In contrast, economic freedom and growth are causing more damage to nature, like ecological footprints (EFP). Similarly, the results through MMQR confirm that the impact of energy innovations, digital trade, and environmental regulations is accepted as a panacea to control environmental degradation in G7. However, the magnitude of the coefficient varies across different quantiles. More specifically, the findings show that the impact of energy innovations is highly significant at 0.50th quantile. In contrast, through digital trade, the impact on EFP is only significant under medium and higher order quantiles (i.e., 0.50th, 0.75th-1.0th). Contrarily, economic freedom is causing more EFP across all the quantiles, where the findings are highly significant at 0.75th quantile. Besides, a few other policy implications are also discussed.

Excited-state absorption in light-scattering Er:CaSc2O4 ceramic

Excited-state absorption (ESA) in visible from the I13/24 level in the upconversion phosphor Er:CaSc2O4 is investigated. ESA spectra are recorded in the spectral range 400 - 750 nm in ceramic optically-scattering Er(2 at.%):CaSc2O4. The ESA spectra are calibrated to cross-section units and separated from other processes (luminescence, stimulated emission) taking place in this spectral range. ESA from the I11/24 level is also noticed and discussed. The cross-sections of the various ESA processes in the visible spectral range are found. The precision of the calibration method is discussed.