Examination of the poverty-environmental degradation nexus in Sub-Saharan Africa

The relationship between environmental degradation and poverty has increasingly become the focus of national strategic decision-making in recent years.However,despite several theoretical explorations on the nexus,a dearth of empirical literature on the poverty-environmental degradation nexus,specifically on Sub-Saharan Africa(SSA),still exists.In this study,we investigated the poverty-environmental degradation nexus in SSA.We hypothesized that poverty is both a cause and effect of environmental degradation,and this relationship is explained as a vicious cycle.Unlike previous studies,we employed several alternative indicators of environmental degradation to examine the poverty-environmental degradation nexus in this study.We used data from 41 countries of SSA between 1996 and 2019 and employed the generalized method of moments(GMM)approach.The findings suggest a cyclical relationship between poverty and environmental degradation in SSA,which confirms that an increase in poverty leads to an increase in environmental degradation,especially in deforestation and PM2.5 emissions.Similarly,the increase in environmental degradation positively affects poverty in SSA.We also confirmed that exogenous conditioning factors such as population growth rate,education,industrialization,and income inequality,institutional quality indicators such as governance effectiveness,control of corruption,freedom ad civil liberty,and democracy,and endogenous factors including fossil fuel energy use,fuelwood energy use,household health expenditure,infant mortality rate,and agriculture productivity influence the nexus between poverty and environmental degradation.The findings on the relationship between poverty and environmental degradation in SSA are a testimonial evidence that both poverty and environmental degradation are significant issues in SSA.Hence,poverty alleviation policies in SSA should not lead to PM2.5 emissions and deforestation,which may as well force people into a poverty-environmental degradation trap.Instead,poverty reduction policies should simultaneously achieve environmental conservation.

Alternative measure of financial development and investment-cash flow sensitivity:evidence from an emerging economy

This study examines the impact of financial development on corporate investment in terms of their influence on financing constraints.This study also tries to find the effect of financial development on the investment-cash flow sensitivity across the size,degree of financial constraints and group affiliation of the firm.This study employs dynamic panel data model or more specifically system generalized method of moments(GMM)estimation technique.The estimation results reveal that cash flow affects the investment decision of the company positively,which implies that Indian firms are financially constrained.Also,we observe that financial development reduces the investment-cash flow sensitivity and the effect of financial development is more prominent for small size and standalone firms.The results are robust across the period and,for both financially constrained and unconstrained firms.This study contributes to the existing literature by analyzing the impact of financial development on the role of cash flow in determining investments undertaken by the Indian firms,which is an unexplored issue from an emerging market perspective.

Financial constraints and investment decisions of listed Indian manufacturing firms

Background:The purpose of the study is to understand the role of cash flow sensitivity to investment as a measure of financial constraints among listed Indian manufacturing firms.It also analyses the role of tangibility in alleviating financial constraints.Further,the role of other financial factors in investment decisions is explored.Methods:The study is conducted using the generalized method of moments(GMM)estimator on dynamic panel data for the period of(2009–2015)on 768 listed manufacturing firms.Results:The analysis finds that cash flow sensitivity is a valid measure of financial constraints in the Indian manufacturing sector.Results according to splitting criteria found that investment decisions of standalone firms are more sensitive to cash flow than group affiliated firms.Further,splitting the firms according to market capitalization and tangible net worth reveals a higher degree of cash flow sensitivity by firms with lower market capitalization and asset tangibility.The results for the effects of tangibility of assets on easing financial constraint were found significant only in the case of firms with low tangible net worth and medium market capitalization.Conclusions:The study confirms cash flow sensitivity to investment as a valid measure of financial constraints.It will confirm pooling of internal funds by financially constrained firms to accept profitable investment opportunities in future.Further,it also reports that asset tangibility eases the financial constraints faced by firms.

The mechanism and heterogeneity of environmental regulations'impact on the technological progress of dairy farming

The study analyses the theoretical mechanism through which environmental regulation affects the dairy industry’s technological progress,with a particular focus on how the effect is conditional on farm size.Using the input–output data of dairy farms of different sizes from 2009 to 2019 in 10 Chinese provinces/autonomous regions in China and the quantitative measurement index of environmental regulation,the study estimates environmental regulation’s heterogeneous influences on the dairy industry’s technological progress by dynamic panel data models.The empirical results suggest that,first,environmental regulation has a U-type influence on the technological progress of dairy farming.The U-type influence means moving from pollution control’s high cost and low technology progress to the high profit and high innovation input generated by optimizing the breeding structure.Second,the promotion of dairy farming technology depends on farm size.The effect of environmental regulation on technological progress in moderately large-farms showed a U-type relationship.In contrast,the effect in free-range and large-size dairy farms showed a linear and positive relationship.The government should further strengthen environmental regulation based on advancing moderately large-farms in compliance with market mechanisms in the long run.Particular attention should be paid to the forms of environmental regulation so that dairy cattle breeding technology can break through the inflection point of the“U”curve as soon as possible and ensure the significance of the rising stage.Along the way,technical support should be provided for realizing environmental protection and economic growth.

A selective review of statistical methods using calibration information from similar studies

In the era of big data,divide-and-conquer,parallel,and distributed inference methods have become increasingly popular.How to effectively use the calibration information from each machine in parallel computation has become a challenging task for statisticians and computer scientists.Many newly developed methods have roots in traditional statistical approaches that make use of calibration information.In this paper,we first review some classical statistical methods for using calibration information,including simple meta-analysis methods,parametric likelihood,empirical likelihood,and the generalized method of moments.We further investigate how these methods incorporate summarized or auxiliary information from previous studies,related studies,or populations.We find that the methods based on summarized data usually have little or nearly no efficiency loss compared with the corresponding methods based on all-individual data.Finally,we review some recently developed big data analysis methods including communication-efficient distributed approaches,renewal estimation,and incremental inference as examples of the latest developments in methods using calibration information.

Modeling the dynamic performance of transportation infrastructure using panel data model in state-space specifications

In this study,different modeling approaches used in panel data for performance forecast of transportation infrastructure are firstly reviewed,and the panel data models(PDMs)are highlighted for longitudinal data sets.The state-space specification of PDMs are proposed as a framework to formulate dynamic performance models for transportation facilities and panel data sets are used for estimation.The models could simultaneously capture the heterogeneity and update forecast through inspections.PDMs are applied to tackle the cross-section heterogeneity of longitudinal data,and PDMs in state-space forms are used to achieve the goal of updating performance forecast with new coming data.To illustrate the methodology,three classes of dynamic PDMs are presented in four examples to compare with two classes of static PDMs for a group of composite pavement sections in an airport in east China.Estimation results obtained by ordinary least square(OLS)estimator and system generalized method of moments(SGMM)are compared for two dynamic instances.The results show that the average root mean square errors of dynamic specifications are all significantly lower than those of static counterparts as prediction continues over time.There is no significant difference of prediction accuracy between state-space model and curve shifting model over a short time.In addition,SGMM does not obtain higher prediction accuracy than OLS in this case.Finally,it is recommended to specify the inspection intervals as several constants with integer multiples.

Impact of host country's business environment on China's OFDI in Belt and Road countries:Based on the perspective of investment motives

This study used a two-step system generalized method of moments to examine the impact of the business environment in the Belt and Road countries on outward foreign direct investment(OFDI)of China while presenting a deeper investigation into the spatial characteristics of OFDI through a spatial error model.The results revealed that the impact mentioned above varies with different investment motivations.If Chinese businesses are motivated by local consumer markets or seeking a human workforce to make an outward direct investment,they will choose countries with poor business environments.They will select countries with stable business environments for their natural resources or strategic assets.Significant spatial agglomeration exists in China's OFDI in the countries and regions along the routes,while substantial evidence is absent on the business environment investment effect with different motivations.Finally,relevant recommen-dations concluded according to the study.

A GMM approach in coupling internal data and external summary information with heterogeneous data populations

Because of advances in data collection and storage,statistical analysis in modern scientific research and practice now has opportunities to utilize external information such as summary statistics from similar studies.A likelihood approach based on a parametric model assumption has been developed in the literature to utilize external summary information when the populations for external and main internal data are assumed to be the same.In this article,we instead consider the generalized estimation equation(GEE)approach for statistical inference,which is semiparametric or nonparametric,and show how to utilize external summary information even when internal and external data populations are not the same.Our approach is coupling the internal data and external summary information to form additional estimation equations and then applying the generalized method of moments(GMM).We show that the proposed GMM estimator is asymptotically normal and,under some conditions,is more efficient than the GEE estimator without using external summary information.Estimators of the asymptotic covariance matrix of the GMM estimators are also proposed.Simulation results are obtained to confirm our theory and quantify the improvements by utilizing external data.An example is also included for illustration.

Regression Estimation for Longitudinal Data with Nonignorable Intermittent Nonresponse and Dropout

We mainly focus on regression estimation in a longitudinal study with nonignorable intermittent nonresponse and dropout.To handle the identifiability issue,we take a time-independent covariate as nonresponse instrument which is independent of nonresponse propensity conditioned on other covariates and responses to ensure the identifiability of nonresponse propensity.The nonresponse propensity is assumed to be a parametric model,and the corresponding parameters are estimated by using the generalized method of moments approach.Then the marginal response means are estimated by inverse probability weighting method.Furthermore,to improve the robustness of estimators,we derive an augmented inverse probability weighting estimator which is shown to be consistent and asymptotically normally distributed.Simulation studies and a real-data analysis show that the proposed approach yields highly efficient estimators.