Perception and willingness to contribute towards food banking in the Ashanti Region of Ghana

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nicholas Oppong Mensah ◽  
Ernest Christlieb Amrago ◽  
Jeffery Kofi Asare ◽  
Anthony Donkor ◽  
Frank Osei Tutu ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
The Body ◽  
Social Implications ◽  
Food Banks ◽  
Content Type ◽  
Ashanti Region ◽  
Quantile Regression Model ◽  
Positive Knowledge ◽  
Willingness To Contribute

PurposeThe purpose of this study is to examine the perception and willingness to contribute towards food banking in the Ashanti Region of Ghana.Design/methodology/approachStructured questionnaire was used to elicit primary data for the study from 385 respondents via the multistage sampling approach. The quantile regression model was used to analyse the factors that influence the willingness to contribute towards food banks across quantiles of contribution. Factor analysis was further used to examine the perception of food banking.FindingsGender, education and awareness influence the quantiles of contribution. Gender positively influences contribution at the 0.50 quantile. Education negatively affects contribution at the 0.25 and 0.50 quantiles whereas awareness influences contribution at the 0.75 quantiles. The benefit perception of the user and the social status perception of receiving food from food banks convey a sense of positive knowledge concerning what food banking should entail.Research limitations/implicationsThe study provides insights on the determinants affecting the contribution towards food banking across quantiles of contribution. However, it worth noting that, the study uses cross-sectional data which fail to account for the changes over time. A Longitudinal study would therefore be imperative concerning the implementation of food banking.Practical implicationsThe perceived positive knowledge of food banking is suggestive that, the Government of Ghana through the Ministry of Food and Agriculture (MOFA) should strengthen measures directed towards the implementation of food banking. Moving forward, non-governmental organisations on the verge of conducting a pilot implementation of food banks should give critical focus to the given area of study as the inhabitants are most likely to be attuned to such a course. Finally, to champion contribution amongst the inhabitants, leaders of food banking initiatives and other stakeholders should work in conjunction with residents that are aware of food banks at the high-income class. This procedure would aid in reducing the chances of low contributions to the implementation of food banking.Social implicationsThis paper provides empirical implications for the development of food banks in Ghana. The findings emanating from this study has substantial social implications, because it serves as an instrumental guide to the implementation of food banks by the MOFA, and when implemented would assuage the poor living conditions of individuals that do not meet a three-square meal per day.Originality/valueIn this research, the authors add to the body of knowledge by employing a quantitative approach. Moreover, the authors extend the frontiers of the methodological approach by using the quantile regression model to understand the factors that influence the contribution towards food banking across quantiles of contribution. Furthermore, several studies in the developed world have been geographically limited to UK, USA, Canada and Germany with few studies in Ghana. Besides, there is limited rigorous empirical study of the perception and willingness to contribute towards food banking in Ghana.

Non-linear impact of globalization on financial crimes: a case of developing economies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabia Muhammad Amjad ◽  
Abdul Rafay ◽  
Noman Arshed ◽  
Mubbasher Munir ◽  
Maryam Muhammad Amjad
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Money Laundering ◽  
Developing Economies ◽  
Financial Crime ◽  
Content Type ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
Financial Crimes ◽  
Non Linear

Purpose The Financial Action Task Force defines money laundering as “processing of these criminal proceeds to disguise their illegal origin”. This is the major portion of financial crime that has ties across borders and like all financial crimes which are well planned and camouflaged, this crime is difficult to detect and deter. Over the years, on one side, globalization has provided development opportunities, it has also become one reason for the pervasiveness of money laundering. This has led to a disturbance in the global financial system and social unrest as proceeds from money laundering are being used in terrorism. The purpose of this study is to explore the non linear effect of globalization on financial crime in the form of money laundering. Design/methodology/approach An investigation based on 119 developing countries from the time period of 1985 till 2015 is conducted in this study. The panel quantile regression model was used to estimate antecedents of money laundering. Findings The study confirmed that globalization follows an inverted U-shaped relationship with money laundering. Furthermore, indicators such as investment portfolio and socioeconomic conditions have a significant effect on money laundering. Originality/value The panel quantile regression model was used to estimate antecedents of money laundering.

A quantile regression forecasting model for ICT development

2014 ◽  
Vol 52 (7) ◽  
pp. 1263-1272 ◽  
Author(s):  
Tiffany Hui-Kuang Yu
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Information Criterion ◽  
Forecasting Model ◽  
Content Type ◽  
Quantile Regression Model ◽  
Information And Communication ◽  
Ict Development ◽  
Quantile Regression Method ◽  
Practical Implications

Purpose – Because quantile regression gets more popular and provides more comprehensive interpretations, it is important to advance quantile regression for forecasting. By extending the convention quantile regression, the purpose of this paper is to propose a quantile regression-forecasting model to forecast information and communication technology (ICT) development. Design/methodology/approach – This paper proposes an approach to forecasting based on quantile regression method. Findings – Via quantile information criterion, the proposed approach can identify whether the independent variables are predictable. For those which are predictable, the proposed approach can be used to forecast these variables. Practical implications – The proposed approach is used to forecast ICT development. It can also be used to forecast other problem domains. Originality/value – Based on the empirical results, the proposed approach advances the application of quantile regression model to forecast. The applicability of quantile regression model is greatly enhanced.

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Santi Gopal Maji ◽  
Rupjyoti Saha
Keyword(s):  
Firm Performance ◽  
Panel Data ◽  
Quantile Regression ◽  
Regression Model ◽  
Financial Performance ◽  
Positive Impact ◽  
Gender Diversity ◽  
Content Type ◽  
Quantile Regression Model ◽  
The Impact

Purpose This paper aims to examine the impact of gender diversity both at operational and leadership levels on the financial performance of firms in India. Design/methodology/approach The study is based on a panel data set of 100 large Indian corporate firms. This study uses the Blau index and Shannon index to compute gender diversity. First, this paper uses system generalized method of moments model to deal with the potential endogeneity issue in the association between gender diversity and firm performance. Second, to unveil heterogeneity in such a relationship, the study applies panel data quantile regression model. Finally, the study adopts a generalized estimating equation model to investigate such relationships for group affiliated and standalone firms. Findings This study finds a significant positive impact of workforce gender diversity and board gender diversity on the financial performance of firms. Further, the results of the quantile regression model indicate that the impact of gender diversity (workforce and board) on firm performance is more pronounced at higher quantiles of the conditional distribution of firm performance. However, the study fails to extricate any significant impact of audit committee gender diversity on firm performance. Finally, the study also finds a significant positive impact of gender diversity at both workforce and board level for a group affiliated, as well as standalone firms. Originality/value The present study makes a novel contribution to the extant literature on the association between gender diversity and financial performance of firms by examining such diversity at both operational and leadership levels in the context of an emerging country such as India that captures the complex realities pertaining to gender issues. Further, the study contributes to the empirical literature regarding the heterogeneous impact of gender diversity on firm performance in the Indian context.

scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

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