The Impact of Business Obstacles on Firm Growth and Job Stability in East Asia and Pacific Nations

Economic sustainability is closely linked to firm growth and employment stability, making them of great interest to policymakers and business leaders. Insights into the factors that impact employment growth and employment stability aid decision makers to develop policies that encourage economic growth and economic sustainability. This study used World Bank Enterprise Survey data to examine the effect of the business obstacles of financing, labour regulation, and under-skilled workforce on firm growth and on employment stability, estimated by the proportion of permanent to non-permanent workforce in East Asia and Pacific nations. The instrumental variables (IV) method was used with two-stage least squares (2SLS) to account for potential endogeneity between the business obstacles and employment growth and the proportion of permanent to non-permanent workers employed by firms. In addition, the quantile method was applied to capture the partial effect of the reported obstacles across different segments of firm growth. Findings included a significant negative effect of the financing obstacle on employment growth and therefore firm growth, particularly at the lowest levels thereof. In addition, financing and labour regulations obstacles have a significant, negative effect on the proportion of permanent employees in a firm’s workforce.

Flood susceptibility analysis of the Panjkora Valley Northern Pakistan, using frequencyratio approach

Purpose Floods are considered as one of the most lethal natural disasters having the potential to cause havoc to entire communities. Pakistan is the land of wide topographic and climatic variations which make it vulnerable to floods. The purpose of this paper is to identify flood susceptible zones in the Panjkora Basin using frequency ratio model. Design/methodology/approach A total of seven parameters or flood conditioning factors were considered, and weights were assigned according to the frequency ratio technique. For the preparation of layers, satellite imageries and digital elevation model data were used. Frequency ratio was calculated using correlation between these parameters and flood. Flood susceptibility index map was divided into five zones through quantile method in ArcMap. Findings Findings of the study reveal that near half of the area (43%) is located in the very high susceptible zone, while only 20% area is classified as low to very low susceptible. Originality/value This paper is entirely based on original research. The approach used in this study has not been applied to the study area before.

A Compound Class of the Inverse Gamma and Power Series Distributions

In this paper, the inverse gamma power series (IGPS) class of distributions asymmetric is introduced. This family is obtained by compounding inverse gamma and power series distributions. We present the density, survival and hazard functions, moments and the order statistics of the IGPS. Estimation is first discussed by means of the quantile method. Then, an EM algorithm is implemented to compute the maximum likelihood estimates of the parameters. Moreover, a simulation study is carried out to examine the effectiveness of these estimates. Finally, the performance of the new class is analyzed by means of two asymmetric real data sets.

Análise espacial dos focos de calor no município de São Félix do Xingu, Estado do Pará, Brasil

O uso do fogo pelas práticas agrícolas e pecuárias, as atividades antrópicas, e o desmatamento são os principais fatores pelos altos índices de focos de queimada na região amazônica, atividade que causa grandes prejuízos ambientais, biológicos, e até mesmo de saúde pública. Nesse contexto, o objetivo do trabalho foi quantificar e analisar os focos de calor no município de são Félix do Xingu, entre os anos de 2009 e 2018, utilizando dados do Instituto Nacional de Pesquisas Espaciais (INPE). Com dados obtidos do satélite Aqua – T, equipado com o sensor Moderate Resolution Imaging Spectroradiometer (MODIS), e analisados no software ArcGis 10.5, utilizando o Sistema de Coordenadas Geográficas e datum SIRGAS 2000, através do processo Kernel Density para densidade e do método Quantile Method. A metodologia utilizada permitiu encontrar 34.685 focos de calor no município durante o período analisado, desses, foi observado picos de incidência nos anos de 2010 e 2017, e os meses de julho a outubro como o período de maior ocorrência. Dessa forma, o estudo mostrou-se relevante na identificação e análise dos focos de calor na área, podendo auxiliar no combate e fiscalização de queimadas.

A Questionnaire Survey on the Sense of Educational and Social Equities among College Students in China

The sense of equity reflects how fair a domain is as evaluated by those engaging in that domain. It is very meaningful to explore the sense of equity among college students, which are a special group of people. This study carried out a questionnaire survey on the sense of equity among 982 college students in China, from the perspectives of educational equity and social equity. The questions were validated by the 27/73 quantile method, and the survey results were analyzed through one-way analysis of variance (one-way ANOVA). The results showed that the college students could evaluate the sense of equity rather accurately and generally had a higher sense of equity, but failed to sense the social outcome equity well; their sense of social equity was lower than the sense of educational equity; the sense of equity varied between college students in different majors: the science majors had a lower sense of equity than those majoring in liberal arts; some college students had a misunderstanding of equity.

Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression

The purpose of this study is to compare the ability of the Classical Quantile Regression method and the Bayesian Quantile Regression method in estimating models that contain autocorrelated error problems using simulation studies. In the quantile regression approach, the data response is divided into several pieces or quantiles conditions on indicator variables. Then, The parameter model is estimated for each selected quantiles. The parameters are estimated using conditional quantile functions obtained by minimizing absolute asymmetric errors. In the Bayesian quantile regression method, the data error is assumed to be asymmetric Laplace distribution. The Bayesian approach for quantile regression uses the Markov Chain Monte Carlo Method with the Gibbs sample algorithm to produce a converging posterior mean. The best method for estimating parameter is the method that produces the smallest absolute value of bias and the smallest confidence interval. This study resulted that the Bayesian Quantile method produces smaller absolute bias values and confidence intervals than the quantile regression method. These results proved that the Bayesian Quantile Regression method tends to produce better estimate values than the Quantile Regression method in the case of autocorrelation errors. Keywords: Quantile Regression Method, Bayesian Quantile Regression Method, Confidence Interval, Autocorrelation.