A user-friendly method for estimating discrete choice land-use model in a panel data setting.

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Economic growth and electricity consumption: Fresh evidence of panel data for LAC.

This study reexamines the causal nexus among electricity consumption (EC) and economic growth (EG) for a panel of 31 countries in Latin America and the Caribbean between 1980 and 2021. We find that there are statistically significant feedback impacts among the research variables in the long run. A 1 percent augment in EC raises EG by 0.5 percent and a 1 percent augment in EG produces a 1.54 percent increase in EC which reflects the nature of the latter as a luxury good and implies a tradeoff between economy and environment, since although greater electrical infrastructure drives EG, the latter also increases the EC whose use in a non-responsible manner could lead to environmental degradation through higher CO2 emissions. Therefore, the main policy implication is that, it is necessary to promote EG based on infrastructure focused on sustainable development, ensuring the well-being of present and future generations.

Income change and sympathy for right-wing populist parties in the Netherlands: The role of gender and income inequality within households.

The global rise of right-wing populist [RWP] parties presents a major political concern. RWP parties' voters tend to be citizens who have either experienced or fear economic deprivation. Income change constitutes a viable measure of this deprivation. However, previous contributions examining effects of income change on support for RWP parties have yielded diverging conclusions. This paper challenges previous findings by incorporating considerations of gender and within-household inequality. We hypothesise a negative relationship between, on the one hand, personal and household income change and, on the other hand, sympathy towards RWP parties. Furthermore, we expect to find a stronger association between personal income change and RWP sympathy among men. Moreover, we expect the relationship between household income change and RWP sympathy to differ between genders. Finally, we hypothesise that this gender disparity can be interpreted by considering who contributes most to the household income. All these hypotheses are grounded in gender socialisation and economic dominance theories. Analysing Dutch LISS longitudinal data spanning from 2007 to 2021 (N = 7,801, n = 43,954) through fixed-effects multilevel linear regression models enables us to address various competing explanations. It appears that only for men, personal income change is negatively linked with sympathies towards RWP parties. However, considering who is the highest earner within households reveals that women are also affected by their personal income change if they earn the highest income. For both men and women, household income change is negatively linked with sympathies towards RWP parties. These results lend partial support to both the socialisation and economic dominance theories. The implications of these findings are discussed.

The impact of national savings on economic development: a focused study on the ten poorest countries in Sub-Saharan Africa.

The purpose of this study is to examine the impact of national savings on economic development, as measured by the Human Development Index (HDI), Inequality-adjusted HDI (iHDI), and Multidimensional Poverty Index (MPI), in ten of the poorest countries in Sub-Saharan Africa. The study employs a sequential Generalized Method of Moments (GMM) analysis to address potential endogeneity issues and account for the dynamic nature of the relationships, covering the period from 2009 to 2019. The findings reveal a complex relationship between national savings and the selected development indicators. While national savings exhibit positive impacts on HDI and iHDI, the results are not consistently statistically significant across all the sequential models. However, the analysis suggests that national savings have a positive influence on reducing multidimensional poverty, as measured by MPI, particularly when effectively channeled into productive investments. The study also highlights the significant positive impact of government expenditure and foreign direct investment (FDI) on human development, underscoring the importance of strategic public investments and foreign capital. The results suggest that while national savings are crucial, their effective utilization is essential for enhancing human development indices. Strategic investments in public goods and foreign capital are also important. The mixed effects of inflation and official development assistance (ODA) emphasize the need for stable economic policies and effective utilization of foreign aid. The modest positive impact of institutional quality suggests that improvements in governance and institutional frameworks can contribute to human development. The findings underscore the need for policies promoting financial inclusion, efficient public expenditure, foreign direct investment, and stable macroeconomic conditions to leverage national savings for economic development. The study's findings provide valuable insights for policymakers in Sub-Saharan Africa, highlighting the need for comprehensive strategies that leverage national savings, public expenditure, and foreign investment to drive sustainable economic development and poverty reduction.

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021.

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

Research on the effect of charitable donations on regional medical level.

As the main vehicle for the tertiary distribution, charity has a certain regulating effect on regional medical level. However, the improvement of regional medical effect of charity has yet to be tested. Based on provincial panel data from 1997 to 2019, this study analyzes the impact of charitable donations on regional medical level. The empirical results show that charitable donations widen the gap of overall regional medical level in China, which not only results from the current period but also from charity accumulation in the past. The regional heterogeneity analysis show that charitable donations have expanded the regional medical level of the eastern and western regions, while have no significant effect on the regional medical level gap in the central region. The widening effect in the eastern region of charitable donations is the largest. In addition, charitable donations expand the regional medical level gap between urban and rural areas in China. Charity, as the regional medical development mechanism, has not yet played its due role and advantages in regulating regional medical level gap. Formulating and adjusting the corresponding charity promotion policies is necessary.

Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure.

This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.

Empirical analysis of trade costs and their impact on carbon emissions in RCEP countries.

Global economic integration and environmental issues have attracted widespread attention in recent years. As one of the world's most significant free trade agreements, the Regional Comprehensive Economic Partnership (RCEP) significantly impacts trade and the environment. However, research on the relationship between trade costs and carbon emissions still needs to be completed. This study explores the relationship between trade costs and carbon emissions within the framework of the Trade Benefit Theory, which posits that trade liberalization and openness generate economic benefits through increased efficiency, technological advancement, and economic growth. This study analyzes panel data from 12 RCEP countries from 2001 to 2014, employing static and dynamic panel models to examine the relationship between trade costs and carbon emissions. The analysis utilizes mixed regression, fixed (random) effects models, and the systematic GMM method. The results indicate that decreases in trade costs are associated with reduced environmental pollution, aligning with the Environmental Kuznets Curve (EKC) hypothesis, which posits an N-shaped relationship between trade costs and carbon emissions. Implementing RCEP facilitates a decrease in trade-related pollution, suggesting that reducing trade costs can help mitigate environmental pollution. Furthermore, the observed N-shaped EKC for trade costs and carbon emissions highlights the potential of RCEP to reduce the impact of trade-related pollution.

Domestic and international impacts of rice export restrictions: The recent case of indian non-basmati rice.

This study examines the impact of India's export restrictions on domestic retail rice prices using a dynamic panel GARCH model. The findings suggest that export restrictions are not a sufficient condition to lower domestic prices. Export restrictions are associated with lower retail price volatility in the East Zone. Moreover, the international price transmission to a sample of Asian and African economies shows that all countries are vulnerable, but the degree and kinds of vulnerability differ. Rice exporters appear to be the most susceptible as domestic prices increase in these countries. Rice importers are also vulnerable because of price increases, but the increases are less than in countries where the private sector decides on import quantities.

Social and Economic Determinants of Life Expectancy at Birth in Eastern Europe.

Life expectancy at birth is considered a parameter of the social development, health system, or economic development of a country. We aimed to investigate the effects of GDP per capita (as the economic factor), health care expenditure, the number of medical doctors (as social factors), and CO2 emissions (as the environmental factor) on life expectancy. We used panel data analysis for 13 Eastern European countries over the 2000-2020 period. After performing the analysis, we used a cross-country fixed-effects panel (GLS with SUR weights). According to our model, a one percent increase in health expenditure (as % of GDP) increases life expectancy at birth by 0.376 years, whereas each additional medical doctor per 10,000 inhabitants increases life expectancy at birth by 0.088 years on average. At the same time, each additional 10,000 USD per capita each year would increase life expectancy at birth by 1.8 years on average. If CO2 emissions increase by 1 metric ton per capita, life expectancy at birth would decrease by 0.24 years, suggesting that higher carbon emissions are capable of reducing longevity. Every European country has to make special efforts to increase the life expectancy of its inhabitants by applying economic and health policies focused on the well-being of the population.

A study on the factors influencing the vulnerability of women of childbearing age to health poverty in rural western China.

The health of women of childbearing age in rural areas is crucial for the development of individuals, families, and society. Research on the identification and influencing factors of health vulnerability in impoverished and disadvantaged groups is important for adjusting and implementing health poverty alleviation policies. However, there is limited research on the health vulnerability of women of childbearing age in rural Western China. Based on panel data from the Rural Residents' Family Health Status Survey in 2019 and 2022, the vulnerability to health poverty of women of childbearing age in rural areas was constructed using the three-stage feasible generalized least squares method. Variables from four dimensions-physical capital, financial capital, social capital, and human capital-were included in the sustainable livelihood analysis framework for analysis. The Tobit model was used to analyze the influencing factors of vulnerability to health poverty among women of childbearing age in rural Western China, and the contribution rates of various factors were studied using the Shapley value decomposition method. In 2019 and 2022, under the poverty line standards of $1.90 and $2.15, respectively, the vulnerability to health poverty among rural women of childbearing age exceeded 20%. Tobit regression analysis revealed that the type of drinking water being well water significantly increased the vulnerability to health poverty of rural women of childbearing age (P < 0.05), whereas the separation of housing and kitchen, registered poor households, household loans, annual per capita household income, expenditures on social interactions, educational level, self-assessed health status, respondent age, and the utilization of hospital services significantly reduced the vulnerability to health poverty of rural women of childbearing age (P < 0.05). Shapley's decomposition shows that annual per capita household income, expenditures on social interactions, respondent age, and household loans are the factors contributing most to the vulnerability to health poverty of rural women of childbearing age, while other variables have a smaller contribution rate. The health poverty situation of women of childbearing age in rural Western China is not optimistic. Preintervention for health poverty should be strengthened among rural women of childbearing age, early warning mechanisms for the risk of falling back into poverty due to illness should be established, the precise identification of highly vulnerable rural women of childbearing age should be improved, and the medical insurance system for rural women of childbearing age should be enhanced to help improve their current health poverty situation.

The hidden cost of sophistication: economic complexity and obesity.

Obesity has become a global health crisis, affecting people of all ages, regions, and socio-economic backgrounds. While individual behaviour and genetic factors contribute to obesity, the role of economic complexity in the evolution of obesity rates has not yet been empirically studied. Using a large panel of 110 countries over the period 1976-2015, this article estimates the linear and non-linear links between obesity and economic complexity. According to baseline results, an improvement in economic complexity will lead to an increase in obesity up to a certain threshold. Beyond this turning point, any further increase in economic complexity will significantly contribute to obesity reduction. The issue of simultaneity is tackled using the two-stage instrumental variable method. Our findings support the Obesity Kuznets Curve (OKC) pattern, which suggests that economic progress and obesity have an inverted U-shaped relationship. Our results suggest that greater embeddedness of knowledge in the products produced and exported by a country increases the likelihood of obesity in society, at least up to a threshold. From these results, some important policy implications are discussed.

Does medial tourism promote growth in healthcare sector?

The purpose of this study is to investigate the effect of medical tourism revenues on the growth of healthcare sector across 49 emerging and developed economies from 2008 to 2022. Using panel GMM and PMG/ARDL estimation methods, the results show that higher levels of medical tourism revenues promote growth in the healthcare sector. This finding remains robust across different sample periods, alternative measure of healthcare sector performance, and model specifications.

A synthetic approach to the Holiday Climate Index for the Mediterranean Coast of Türkiye.

Climate change affects the comfort level of tourists visiting coastal areas. Researching these impacts is important for a more comprehensive understanding of climate change and for developing effective adaptation solutions. Considering this fact, the study derived the Holiday Climate Index (HCI: Coast, HCI: Urban, and HCI: Combined) in the Mediterranean coastal provinces of Türkiye from 1976 to 2020. Utilizing the derived indices, the effects of climate-related holiday comfort on the number of tourist arrivals as well as on overnight stays between 1976 and 2020 were examined by panel data analysis. The study examined how comfort patterns could change during the period 2026-2050 under the pessimistic RCP8.5 scenario. It was detected that the comfort level significantly and positively affects the number of arrivals and overnight stays of tourists. Besides, comfort levels were found to deteriorate in the period 2026-2050 compared to the reference period, 1976-2020. As the comfort conditions get worse, the number of tourist arrivals and overnight stays is expected to decline in the future. It is envisaged that the results of the study can be useful for tourists, tourism professionals, operators, other stakeholders, and policymakers.

Competitiveness of citrus export and its determinants: a two-way fixed effect panel data model approach.

Pakistan has a conducive condition for the development of a wide range of scrumptious fruits. As a result, the country grows a diverse assortment of tropical and subtropical fruits; the most prized and top-ranked fruit among all fruits grown in Pakistan is citrus. Citrus is the principal fruit that contributes significantly to Pakistan's export earnings and national income. In this study, the cross-border determinants influencing Pakistan's citrus exports to its topmost 22 trading partners are examined using a gravity model technique. This is the first large study from Pakistan by using gravity model to check the impact of various cross-border factors on citrus fruit export. The analysis is based on a panel dataset covering the years 2003 to 2021. To estimate the results, the study used fixed effect regression with time and country fixed effects. The results signify that per capita income, population, and some regional dummies are positively associated with citrus exports from Pakistan. Citrus price, distance, exchange rate, and other regional dummies are observed to have an adverse effect on citrus exports. Trade agreements between Pakistan and trade partners such as free trade agreements, preferential trade agreements, and SAFTA, have been observed as important determinants of citrus exports. Citrus exporters in Pakistan can also benefit from understanding the factors that influence export markets. By addressing the challenges identified in this study, Pakistan can enhance its citrus exports and boost its agricultural sector.

Evolving waste management: The impact of environmental technology, taxes, and carbon emissions on incineration in EU countries.

Amid the urgent global imperatives concerning climate change and resource preservation, our research delves into the critical domains of waste management and environmental sustainability within the European Union (EU), collecting data from 1990 to 2022. The Method of Moments Quantile Regression (MMQR) results reveal a resounding commitment among EU member states to diminish their reliance on incineration, which is evident through adopting green technologies and environmentally conscious taxation policies, aligning with the European Union's sustainability objectives. However, this transition presents the intricate task of harmonizing industrial emissions management with efficient waste disposal. Tailoring waste management strategies to accommodate diverse consumption patterns and unique circumstances within individual member states becomes imperative. Cointegrating regressions highlighted the long-run relationship among the selected variables, while Feasible Generalized Least Squares (FGLS) and Panel-Corrected Standard Errors (PCSE) estimates roughly confirmed MMQR results. ML analyses, conducted through two ensemble methods (Gradient Boosting, GB, and Extreme Gradient Boosting, XGBoost) shed light on the relative importance of the predictors: in particular, environmental taxation, consumption-based emissions, and production-based emissions greatly contribute to determining the variation of combustible renewables and waste. This study recommends that EU countries establish monitoring mechanisms to advance waste management and environmental sustainability through green technology adoption, enhance environmental taxation policies, and accelerate the renewable energy transition.

Investigating the dynamic effects of service value added on CO2 emissions: Novel insights from a non-parametric approach.

Carbon dioxide (CO2) emissions are the primary contributors to climate change. Addressing and mitigating climate change necessitates the effective management and utilization of renewable energy consumption, which poses a substantial challenge for the forthcoming decades. This study explores the dynamic effects of service value added (SVA) and renewable energy on environmental quality, particularly focusing on CO2 emissions. Unlike previous studies, we employ a non-parametric modeling approach to uncover the time-varying influence of service sector growth on CO2 emissions. Specifically, we apply the local linear dummy variable estimation (LLDVE) method to a panel of the 17 highest-emitting nations over the period 1980-2021. Our study uncovers a non-linear relationship between CO2 emissions and SVA. From 1980 to 2003, we observe a negative correlation. However, starting from 2005 to 2020, we witness a shift towards a positive correlation, indicating a rise in energy consumption within the service sector. The results indicate that significant emitter economies have yet to achieve sustainability, with the service sector continuing to contribute to pollution. Addressing this issue necessitates more robust climate change policies and increased investment in clean energy, specifically targeting the service sector, including buildings and transport.

Nuclear energy generation's impact on the CO2 emissions and ecological footprint among European Union countries.

This study employs Fully Modified Ordinary Least Squares, Common Correlated Effects and Dumitrescu-Hurlin panel causality techniques to investigate the environmental impacts of nuclear energy generation in European Union countries from 1990 to 2022. The ongoing debate within the European Union and the empirical contradictions in the literature, coupled with the overall singular-dimensionality surrounding the impacts of nuclear energy on the environment, necessitate a broader and comprehensive examination of its effects across various environmental dimensions. These dimensions include the presence of CO2 emissions and the ecological footprint generated. The findings reveal that nuclear energy adoption by countries tends to affect CO2 emissions but this relationship goes from CO2 to nuclear energy consumption as per the causality test, while the ecological footprint variable does not exhibit a causal relationship with nuclear energy consumption. We estimated that a higher presence of air pollutants promotes the generation of nuclear energy as an alternative to fossil fuel energy sources. The study highlights that while nuclear energy generation produces no air pollution, it does impose significant land use requirements, potentially leading to ecosystem degradation. Factors such as uranium extraction, nuclear waste management, disposal, and accidents contribute to this impact. Further research is needed to understand the specific mechanisms and factors contributing to the observed environmental degradation associated with nuclear energy generation.

Doubly robust proximal synthetic controls.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors.

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types.