Search Results (13)

This paper explores the effects of the cessation of forest commodity exports and the implementation of an industrialization strategy in Gabon, drawing on traditional theories of regional growth. The creation of the Nkok Special Economic Zone (SEZ) in 2012, accompanied by its strategic location and significant infrastructure investments, illustrates the application of Rosenstein-Rodan’s “Big Push” and Douglass-North’s “export base” theories. These initiatives also led to a polarization process consistent with the work of Perroux and other theorists of unbalanced regional growth. The study assesses the impact of this SEZ on regions external to the SEZ and the macroenvironment during the period 2014–2022. It highlights the industrial and commercial mechanisms that promote agglomeration economies, technological diffusion, the creation of economic connections, and the structuring into “core-periphery” zones, in accordance with the concepts of Hirschman. The results show a strong positive correlation between industrial income, exports (excluding raw materials), and industrial production. However, the ban on the export of wood raw materials led to a negative relationship between industrial income and exports of these products. Furthermore, the local processing of forest products has promoted industrial diversification, generated new products, and gradually increased added value. The process of economic and geographical polarization is described as a transitional phase of imbalances whose long-term implications require in-depth studies, particularly in the context of countries in the South and underdeveloped environments. Full article

Solar photovoltaic power plants typically consist of rows of solar panels, where the accurate estimation of solar irradiance on inclined surfaces significantly impacts energy generation. Existing practices often only account for the first row, neglecting shading from subsequent rows. In this work, ten transposition models were assessed against experimental data and a transposition model for inner rows was developed and validated. The developed model incorporates view factors and direct and circumsolar irradiances shading from adjacent rows, significantly improving global tilted irradiance (GTI) estimates. This model was validated against one-minute observations recorded between 14 April and 1 June 2022, at Évora, Portugal (38.5306, −8.0112) resulting in values of mean bias error (MBE) and root-mean-squared error (RMSE) of −12.9 W/m² and 76.8 W/m², respectively, which represent an improvement of 368.3 W/m² in the MBE of GTI estimations compared to the best-performing transposition model for the first row. The proposed model was also evaluated in an operational forecast setting where corrected forecasts of direct and diffuse irradiance (0 to 72 h ahead) were used as inputs, resulting in an MBE and RMSE of −33.6 W/m² and 169.7 W/m², respectively. These findings underscore the potential of the developed model to enhance solar energy forecasting accuracy and operational algorithms’ efficiency and robustness. Full article

In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets yield consistent estimates from conditional logit estimators for binary response panel data models. When faced with a missing covariates problem, researchers adopt various imputation techniques to complete the data and without loss of generality; consistent estimates still suffice asymptotically. For maximum likelihood estimates of the parameters for logistic regression in cases of imputed covariates, the optimal choice of an imputation technique that yields the best estimates with minimum variance is still elusive. This paper aims to examine the behaviour of the Hessian matrix with optimal values of the imputed covariates vector, which will make the Newton–Raphson algorithm converge faster through a reduced absolute value of the product of the score function and the inverse fisher information component. We focus on a method used to modify the conditional likelihood function through the partitioning of the covariate matrix. We also confirm that the positive moduli of the Hessian for conditional estimators are sufficient for the concavity of the log-likelihood function, resulting in optimum parameter estimates. An increased Hessian modulus ensures the faster convergence of the parameter estimates. Simulation results reveal that model-based imputations perform better than classical imputation techniques, yielding estimates with smaller bias and higher precision for the conditional maximum likelihood estimation of nonlinear panel models. Full article

Carbon emission is the focus of global climate change concerns. Population aging changes the level of labor structure, which directly affects the industry adjustment and will also have a long-term impact on carbon emissions. Uncovering the complex association among population aging, labor allocation, and CO₂ emission is crucial for developing effective policies for low-carbon and sustainable development in China. Therefore, this study aims to analyze whether population aging contributes to reducing carbon emission intensity by regulating labor allocation. Based on provincial panel data from 2000 to 2019, the Systematic Generalized Method of Moments (Systematic GMM) model and the Bias Corrected Least Squares Estimation with Nonsymmetric Dependence Structure (Bias Corrected LSDV) model are adopted in this study. The results show that nationwide as a whole, population aging objectively inhibits human capital accumulation and, to some extent, weakens its positive carbon emission reduction effect. Meanwhile, population aging helps to mitigate the increase in carbon emissions caused by the capital-labor endowment structure. Due to the dual impact of aging and population migration, the emission reduction effect of human capital accumulation is significant in the East. The brain drain in the central and western regions further inhibits the positive effect of regional human capital accumulation. Promoting the rationalization of population mobility nationwide, reducing the brain drain in less developed regions, and directing capital into technology-intensive industrial sectors are the core keys to achieving optimal labor allocation in an aging society. This will help China meet its carbon neutrality target on schedule. Full article

Researchers conducting longitudinal data analysis in psychology and the behavioral sciences have several statistical methods to choose from, most of which either require specialized software to conduct or advanced knowledge of statistical methods to inform the selection of the correct model options (e.g., correlation structure). One simple alternative to conventional longitudinal data analysis methods is to calculate the area under the curve (AUC) from repeated measures and then use this new variable in one’s model. The present study assessed the relative efficacy of two AUC measures: the AUC with respect to the ground (AUC-g) and the AUC with respect to the increase (AUC-i) in comparison to latent growth curve modeling (LGCM), a popular repeated measures data analysis method. Using data from the ongoing Panel Study of Income Dynamics (PSID), we assessed the effects of four predictor variables on repeated measures of social anxiety, using both the AUC and LGCM. We used the full information maximum likelihood (FIML) method to account for missing data in LGCM and multiple imputation to account for missing data in the calculation of both AUC measures. Extracting parameter estimates from these models, we next conducted Monte Carlo simulations to assess the parameter bias and power (two estimates of performance) of both methods in the same models, with sample sizes ranging from 741 to 50. The results using both AUC measures in the initial models paralleled those of LGCM, particularly with respect to the LGCM baseline. With respect to the simulations, both AUC measures preformed as well or even better than LGCM in all sample sizes assessed. These results suggest that the AUC may be a viable alternative to LGCM, especially for researchers with less access to the specialized software necessary to conduct LGCM. Full article

An issue with estimating the impact of industrial support is that the firms that receive support may be politically connected, introducing omitted variable bias. Applying fixed-effects regressions on Vietnamese panel data containing several proxies for political connectedness to correct this bias, we find that firms that receive industrial support in the form of tax holidays experience more rapid productivity growth, particularly in R&D-intensive industries, and less so among politically connected firms. These findings do not appear to be due to the presence of financing constraints. We then develop a second-generation Schumpeterian growth model with many industries, and show that tax holidays disproportionately raise productivity growth in R&D-intensive industries. These results are significant and important for governments, especially those in transition and developing countries, in better targeting their industrial policy to facilitate higher productivity growth. Full article

This paper proposes a new variant and reinvestigates the validity of the Balassa–Samuelson (BS) hypothesis for nine East and South Asian countries under new specifications. The BS hypothesis is often criticized for one of its fundamental, but oversimplified assumptions related to Purchasing Power Parity (PPP) holding which can be confirmed for cronss-country tradables’ prices, implying nontraded-sector prices are solely responsible for inducing trend deviations in real exchange rate. The assumption, when empirically tested, does not always hold valid, revealing a price difference in tradables for Asian countries against the world (U.S.), a potential driver of their trend in real exchange rate deviations (appreciation). A new approach based on Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) is used to estimate the long-run BS coefficients, while the error correction mechanism is employed to estimate the short-run estimates. These results motivated us to allow for the inexistence of PPP for cross-country tradables; the standard form of the BS model is then tested in its relaxed form using time-series and panel data econometric tests. Despite a relaxing of the BS model in favor of tradables’ price deviation from PPP, the results are not sufficiently supportive of the BS hypothesis. These findings hold strong economic implications for Asia, suggesting that intercountry sectoral productivity bias of regional economies with the world does not necessarily exert substantial effects on their long-run real exchange rates. Additionally, contrary to the core belief of the BS model, intercountry tradables’ price differentials are found to substantially explain real exchange rate movements away from their long-run equilibrium. Full article

Environmental pollution, rapid economic growth, and other social factors have adverse effects on public health, which have consequently increased the burden of health expenditures during the last two decades. This paper provides a comprehensive analysis of carbon dioxide (CO₂) emissions and the environment index, as well as economic and non-economic factors such as Gross Domestic Product (GDP) growth, foreign direct investment, population aging, and secondary education impacts on per capita government and private health expenditures in 13 emerging economies for the time period of 1994–2017. We employ robust econometric techniques in this endeavor of panel data analysis to account for the issues of heterogeneity and cross-sectional dependence. This study applies the Lagrange Multiplier (LM) bootstrap approach to investigate the presence of panel cointegration and empirical results underscore the existence of cointegration among variables. For the execution of long-run analysis, we incorporate the two latest estimators, i.e., continuously updated-fully modified (CUP-FM) and continuously updated- bias corrected (CUP-BC). Findings of long-run elasticities have documented that the air-pollution indicators, i.e., CO₂ emissions and the environment index, have a positive and significant influence on government health expenditures, while in contrast, both factors negatively influence private health expenditures in emerging economies. We find that economic factors such as GDP growth consistently show a positive impact on both government and private health expenditures, whereas, foreign direct investment exhibits a significant negative and positive impact on government and private health expenditures respectively. Findings of non-economic factors can be used to argue that population aging increases health expenditures while secondary education lowers private health spending in emerging markets. Furthermore, empirical analysis of heterogeneous causality indicates that CO₂ emissions, the environment index, GDP growth, foreign direct investment, and secondary education have a unidirectional causal relationship with government and private health expenditures. Population aging has a strong relationship of bidirectional causality with government health expenditures and unidirectional causal relationship with private health expenditures. Findings of this paper put forward key suggestions for policy makers which can be used as valuable instruments for better understanding and aiming to maximize public healthcare and environmental quality gains which are highly connected with sustainable GDP growth and developments in emerging economies. Full article

The present study applies a bootstrapped data envelopment analysis (DEA) procedure to compute bias-corrected measures of agricultural total factor productivity (TFP) change and its components (technical change and technical efficiency change) using a panel data of 19 regions of Bangladesh covering a 23-year period (1987–2009), thereby overcoming the limitation of the lack of statistical inference of the conventional non-parametric DEA. Results revealed that overall productivity grew at a modest rate of 0.03%, mainly powered by technological progress at 0.03% and a negligible decline in technical efficiency at 0.004% with large disparities amongst regions. Six regions in the middle order shifted ranks with regard to TFP change following bias correction. The estimated confidence intervals demonstrated that many regions underwent either progress or regress in productivity performance over time. Investments in research and development (R&D), agricultural extension, and crop diversification are suggested to improve regional inequality and declining technical efficiency. Full article

Previous studies have investigated how government policies on renewable energy technology (RET) affect economic performance at the industrial level. However, each firm in the RET industry is heterogeneous in terms of their capacities, resources, and the amount of public subsidies they receive. Considering the context in which public subsidies are provided to firms, this study econometrically investigates the effects of government policies on firms’ financial performance using panel data from the Korean RET industry. We consider the results of various panel framework tests; establish a panel vector autoregressive model in first differences; and test the dynamic relationships between firms’ financial performance, government subsidies (R&D- and non-R&D-related), firm size and age, and organizational slack, using a bias-corrected least squares dummy variable estimator. We find that R&D- and non-R&D-related subsidies positively affect firms’ financial performance in the long run. In the short run, there are bidirectional positive causal relationships between firms’ financial performance and organizational slack (and non-R&D-related subsidy), and firm size and non-R&D-related subsidy. A positive short-run relationship runs from R&D-related subsidy to firms’ financial performance, from firm age to non-R&D-related subsidy, and from firm size to firm age. Further, there are dynamic effects in all estimations, demonstrating that the dependent variables of the previous period enhance their values in the current period. The results provide some policy and strategic implications. Full article

Phaseolus vulgaris is an important grain legume for human consumption. Recently, association mapping studies have been performed for the species aiming to identify loci underlying quantitative variation of traits. It is now imperative to know whether the linkage disequilibrium (LD) reflects the true association between a marker and causative loci. The aim of this study was to estimate and analyze LD on a diversity panel of common beans using ordinary $r^2$ and $r^2$ extensions which correct bias due to population structure ( $r_S^2$ ), kinship ( $r_V^2$ ), and both ( $r_{VS}^2$ ). A total of 10,362 single nucleotide polymorphisms (SNPs) were identified by genotyping by sequencing (GBS), and polymorphisms were found to be widely distributed along the 11 chromosomes. In terms of $r^2$ , high values of LD (over 0.8) were identified between SNPs located at opposite chromosomal ends. Estimates for $r_V^2$ were lower than those for $r_S^2$ . Results for $r_V^2$ and $r_{VS}^2$ were similar, suggesting that kinship may also include information on population structure. Over genetic distance, LD decayed to 0.1 at a distance of 1 Mb for $r_{VS}^2$ . Inter-chromosomal LD was also evidenced. This study showed that LD estimates decay dramatically according to the population structure, and especially the degree of kinship. Importantly, the LD estimates reported herein may influence our ability to perform association mapping studies on P. vulgaris. Full article

This study examines how various actors influence the transition to a renewable-energy economy. We employ a conceptual framework derived from a literature review and text-mining analysis and establish a panel data model for an empirical test using unbalanced panel data from 25 member countries of the Organization for Economic Co-operation and Development (OECD), for the period from 1990 to 2014. We establish a panel vector autoregressive (VAR) model in the first differences and use a bias-corrected least squares dummy variable (LSDVC) estimator to test complex dynamic relationships between government, the public, markets, the traditional energy sector (i.e., the sector that uses nuclear power, oil, coal and natural gas as sources for electricity) and the contribution of renewables to the total energy supply. We also perform Wald tests on the coefficients of variables estimated by LSDVC estimator to determine causal relationships between the variables. The results of this study reveal that government and markets directly promote the transition to renewable energy, whereas the traditional energy sector negatively and directly affects the transition. By contrast, the public does not directly influence the transition to a renewable-energy economy. This study also shows that the government and public have positive indirect effects on the transition, by interacting with the market. We also find convincing evidence of significant dynamic-path dependence in all estimations. Finally, we discuss some implications based on the findings of this study. Full article

In response to the global financial crisis of 2007–2009, risk-based capital requirements have been reinforced in the new Basel III Accord to counter excessive bank risk-taking behavior. However, prior theoretical as well as empirical literature that studies the impact of risk-based capital requirements on bank risk-taking behavior is inconclusive. The primary purpose of this paper is to examine the impact of risk-based capital requirements on bank risk-taking behavior, using a panel dataset of 21 listed commercial banks of Pakistan over the period 2005–2012. Purely regulatory measures of bank capital, capital adequacy ratio, and bank assets portfolio risk, risk-weighted assets to total assets ratio, are used for the main analysis. Recently developed small N panel methods (bias corrected least squares dummy variable (LSDVC) method and system GMM method with instruments collapse option) are used to control for panel fixed effects, dynamic dependent variables, and endogenous independent variables. Overall, the results suggest that commercial banks have reduced assets portfolio risk in response to stringent risk-based capital requirements. Results also confirm that all banks having risk-based capital ratios either lower or higher than the regulatory required limits, have decreased portfolio risk in response to stringent risk-based capital requirements. The results are robust to alternative proxies of bank risk-taking, alternative estimation methods, and alternative samples. Full article