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Dynamic Assessment of Agriculture and Economic Growth Nexus in Morocco: Evidence from Structural VAR and Directed Acyclic Graphs
Ouahiba Elalaoui,
Khalil Allali,
Aziz Fadlaoui,
Nassreddine Maatala,
Abdelouafi Ibrahimy
Issue:
Volume 10, Issue 4, August 2022
Pages:
150-165
Received:
31 May 2022
Accepted:
27 June 2022
Published:
5 July 2022
DOI:
10.11648/j.ijefm.20221004.11
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Abstract: The recurrence of international crises and their negative impact on the economy and household food security has stimulated a strong revival of interest in the role of the agricultural sector and its relationship with the national economy. Recently, a macro-econometric model has shown a well-established bidirectional causality nexus between the agricultural sector and the Moroccan economy. However, the assessment of the magnitude of effects in both directions and their historical evolution are crucial topics that have not yet been explored. The current study empirically examines the dynamic interrelationships between Moroccan agriculture and GDP using the structural VAR model. The data set consists of the annual macroeconomic time series covering the period 1980-2019, namely: GDP per capita, agricultural GDP, investment rate, money supply and trade openness. This paper exploits recent advances in artificial intelligence to determine the over-identifying restrictions, through Directed Acyclic Graphs. Impulse response functions reveal that the Moroccan economy is very sensitive to agricultural shocks compared to shocks due to other endogenous variables, meanwhile the agricultural sector is very reactive to its shocks. The results from the variance decomposition show that the agricultural shocks are the most important driver of economic growth fluctuations and account for almost 69% of the forecast error variance for the first year. The share of GDP shocks in the variance of the forecast error of agricultural GDP does not exceed 7% for a ten-year horizon, while agricultural shocks dominate the decomposition variance profile and never fall below the 74% threshold. These results highlight the predominance of the Agriculture-Led Growth hypothesis in comparison with the Growth-Led Agriculture hypothesis. The findings resulting from the historical decomposition reconfirm the historical dependence between the national economy and agriculture. This sector sometimes acts as a shock absorber, counteracting the poor performance of other sectors of the economy. Under the Structural VAR model, the historical analysis illustrates that the national economy is increasingly resilient to agricultural shocks because of the improved resilience of Moroccan agriculture to climate shocks. Although the impact of agriculture is historically prominent, the magnitude of its impact has significantly reduced by 22% between 1982-1999 and 2000-2019. Given the strong potential of the agricultural sector to promote economic growth, policymakers should continue to create favorable conditions to support the development of the sector.
Abstract: The recurrence of international crises and their negative impact on the economy and household food security has stimulated a strong revival of interest in the role of the agricultural sector and its relationship with the national economy. Recently, a macro-econometric model has shown a well-established bidirectional causality nexus between the agri...
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Using Machine Learning for the Development of a Maintenance Management System: Case Study of Kenya
Manoj Kumar Jha,
Hellon G. Ogallo
Issue:
Volume 10, Issue 4, August 2022
Pages:
166-172
Received:
31 May 2022
Accepted:
29 June 2022
Published:
12 July 2022
DOI:
10.11648/j.ijefm.20221004.12
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Abstract: A Maintenance Management System (MMS) was first developed in the 1982 for implementation in the Arizona Department of Transportation in the United States. It allows for a forecast of future maintenance activities for a road network which deteriorates over time. Successive enhancements to the original MMS have been made over the years by different researchers, including some by the first author. The primary enhancements have been in the formulation and solution algorithms. The initial solution algorithms were Linear Programming (LP) and Dynamic Programming (DP), which, in some previous works, were replaced by genetic algorithms due to their efficiency over LP and DP. In this paper, we propose a Machine Learning (ML) framework for the development of a MMS, which can be a better approach than previously developed approaches. The ML framework uses a Python-based solution methodology in conjunction with geo-spatial modeling, which appears more attractive and efficient in working directly with GIS maps and databases. With respect to application, the attention is focused on African countries using Kenya as a case study example. A recent report on state of Kenyan roads found over 35 percent of Kenyan roads to be still in poor condition even though a comparison of the condition of the roads between 2003 and 2018 showed a successive improvement in road condition over the years. Poor road condition affects mobility and, in turn affects the country’s economy. We adopt a Markov Decision Process to predict the maintenance actions to be undertaken for the Kenyan road network in order to keep an acceptable level of service quality over a specified planning horizon. A budget can then be estimated based on the cost of maintenance actions. A case study using Geographic Information System maps and databases demonstrates the effectiveness of the approach. The result shows that an MMS for Kenyan roads can help forecast the maintenance activities to be undertaken over a planning horizon. For more realistic practical applications, using some of our previous works as a guide, an algorithm to decide on the level of deterioration over time can be developed in future works which could consider factors like weather, vehicle mix, and traffic load.
Abstract: A Maintenance Management System (MMS) was first developed in the 1982 for implementation in the Arizona Department of Transportation in the United States. It allows for a forecast of future maintenance activities for a road network which deteriorates over time. Successive enhancements to the original MMS have been made over the years by different r...
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Human Capital and Labour Productivity: Empirical Evidence from Developing Countries
Mohamed Fathy Abdelgany,
Amira Abdelmoez Saleh
Issue:
Volume 10, Issue 4, August 2022
Pages:
173-184
Received:
22 June 2022
Accepted:
9 July 2022
Published:
18 July 2022
DOI:
10.11648/j.ijefm.20221004.13
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Abstract: Human capital is the main driving force of labour productivity. Thus, this study aims to investigate the impact of both education and health as crucial dimensions of human capital on labour productivity in 39 developing countries. To achieve this objective, the study employs a dynamic Generalized Method of Moment (GMM) estimator on panel data from 2000 to 2019. This study utilizes two separate models. The first model focuses on estimating the effect of education on labour productivity. While the second one focuses assessing on the effect of health status on labour productivity and the study examines both models through three separate steps. The findings reveal that education positively and significantly affects labour productivity. Also, the correlation between health status and labour productivity is statistically significant and positive. Additionally, the study shows that physical capital, trade openness, inflation, and the level of advanced technology are meaningful determinants of labour productivity in developing countries. These results are in line with economic theory and many empirical studies. Furthermore, the results show that education has a more significant impact on labour productivity than health. The study suggests that policymakers in developing countries should target primary education as an approach to increase labour productivity and adopt appropriate measures to enhance workers’ health.
Abstract: Human capital is the main driving force of labour productivity. Thus, this study aims to investigate the impact of both education and health as crucial dimensions of human capital on labour productivity in 39 developing countries. To achieve this objective, the study employs a dynamic Generalized Method of Moment (GMM) estimator on panel data from ...
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Determinants of Tax Compliance Behavior: The Case of Small and Medium Enterprises in Burao City, Somaliland
Issue:
Volume 10, Issue 4, August 2022
Pages:
185-197
Received:
30 May 2022
Accepted:
11 July 2022
Published:
22 July 2022
DOI:
10.11648/j.ijefm.20221004.14
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Abstract: For Somaliland government, to increase the collection of different tax revenue from the various taxable citizens, it is very crucial to explore why people comply or not comply with tax rules and regulations of the country and consequently, achieve a level where people voluntarily comply with tax laws and file tax return on time without underestimating the income statement. Hence, the goal of this study was to look into the factors that influence tax compliance behavior in Burao, Somaliland, using small and medium-sized enterprises (SMEs) as a case study. The study focused on factors such as tax audit, tax rate, tax payers' perception of government spending, penalty, fairness and equity of the tax system, referent group influence, tax knowledge and awareness, and tax authority efficiency. The study employed a quantitative research approach and a descriptive study in design. The study's target population was the SMEs in Burao, which numbered 1069, according to the Ministry of Finance Development and the Ministry of Trade and Tourism, and the researcher selected a sample of 171 SMEs using stratified random sampling. Self-administered questionnaire was used as a data collection instrument for the study. In addition to that, the author utilized both descriptive and econometric analysis; multiple regression was used as an econometric technique to determine the effect of a predictor on the predicted. The findings from the regression indicated that tax audit, tax knowledge and awareness, perception of government spending, the influence of referent groups, and the efficiency of tax authorities are the main determinants that have a statistically significant positive impact on the tax compliance behavior of the SME taxpayers in Burao City. On the other hand, it has been identified that tax rates, the fairness and equity in the tax system, and penalties/fines are found to have a statistically insignificant positive influence on the tax compliance behavior of SME taxpayers. Finally, to boost the tax compliance of the Burao SME taxpayers, the study put forward some recommendations according to the study’s findings, including enhancing the auditing/investigation capabilities of tax authorities, boosting the tax knowledge and awareness of the SME taxpayers, improving the perception of taxpayers towards government spending, and strengthening the effectiveness and efficiency of tax authorities in terms of their service delivery, enforcing laws, creating awareness, and offering consulting services to clients.
Abstract: For Somaliland government, to increase the collection of different tax revenue from the various taxable citizens, it is very crucial to explore why people comply or not comply with tax rules and regulations of the country and consequently, achieve a level where people voluntarily comply with tax laws and file tax return on time without underestimat...
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Urban Inclusiveness and Perception of Class Mobility -- From the Perspective of Floating Population
Issue:
Volume 10, Issue 4, August 2022
Pages:
198-209
Received:
7 July 2022
Accepted:
23 July 2022
Published:
26 July 2022
DOI:
10.11648/j.ijefm.20221004.15
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Abstract: Improving people's sense of happiness and gain is the meaning of the title of inclusive city construction. Paying attention to the individual's subjective perception of class mobility can promote the orderly mobility of social classes and promote social structural changes. Based on the data of China Labor-force Dynamic Survey (CLDS) in 2016, this paper empirically analyzes the impact of urban inclusion on class mobility by using the Ordered Probit model. The results show that the probability of intra-generational upward mobility perception increases by 0.0897 and the probability of inter-generational upward mobility perception increases by 0.0617 for each unit of urban inclusion. This result is still robust after removing extreme values and changing the definition of perceived class mobility. Considering the endogeneity of urban inclusion, this paper uses Conditional Mixed Process (CMP) estimation to effectively reduce the bias caused by Ordered Probit model estimation. Further mechanism analysis shows that urban inclusion can positively affect the social network of floating population and then affect their sense of class mobility. Heterogeneity analysis found that the positive impact of urban inclusiveness on the perception of class mobility was the largest in the central region and the floating population of agricultural household registration type. Therefore, the relevant national departments still need to introduce some policies, pay attention to the accumulation of human capital, and continuously enhance the upward mobility perception of the eastern and western regions and non-agricultural Hukou groups while improving the urban inclusiveness, so as to enhance their "sense of access".
Abstract: Improving people's sense of happiness and gain is the meaning of the title of inclusive city construction. Paying attention to the individual's subjective perception of class mobility can promote the orderly mobility of social classes and promote social structural changes. Based on the data of China Labor-force Dynamic Survey (CLDS) in 2016, this p...
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Factors Influencing the Profitability of Commercial Banks: An Empirical Study on Listed Banks in Stock Exchange of Bangladesh
Md. Rashedul Azim,
Saifun Nahar
Issue:
Volume 10, Issue 4, August 2022
Pages:
210-216
Received:
30 May 2022
Accepted:
18 July 2022
Published:
4 August 2022
DOI:
10.11648/j.ijefm.20221004.16
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Abstract: The important aspect of the banking industry in Bangladesh is successfully helping to face the financial crisis at present and in the future. This paper explores some factors that influence the profitability of commercial banks. The main objective was to important factors analyze performance decisions as the profitability of commercial banks in Bangladesh. The necessary data was collected from secondary sources. Profitability is measured by return on assets (ROA). A sample of 16 commercial banks out of 32 listed on both DSE and CSE for the period 2016-2020. Profitability measures were calculated and using statistical tools SPSS 25 version through Pearson’s correlation, descriptive analysis of variance and regression analysis were utilized in testing the hypotheses and to measure sample banks accounting to their different characteristics. However, the results indicate that there is a significant positive between ROA of banks TCA, TIA and TOEI variables, as well as a negative correlation with the NLBS variable. The regression model reveals that banks specific factors such as TCA, TIA, TOEA and NLBS are influencing factors for the changes in ROA. Further, it is required to strengthen the recommendation that empirical studies should be undertaken in the same field to find out what more specific factors could affect the profitability of commercial banks in Bangladesh.
Abstract: The important aspect of the banking industry in Bangladesh is successfully helping to face the financial crisis at present and in the future. This paper explores some factors that influence the profitability of commercial banks. The main objective was to important factors analyze performance decisions as the profitability of commercial banks in Ban...
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Analysis of the Effect of Macro Variables on JCI Period of 2008 to 2020
Issue:
Volume 10, Issue 4, August 2022
Pages:
217-221
Received:
5 July 2022
Accepted:
29 July 2022
Published:
5 August 2022
DOI:
10.11648/j.ijefm.20221004.17
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Abstract: The Composite Price Index (JCI) often fluctuates from year to year, which should be expected by investors to increase, so that investors get certainty about the return on investment funds placed in the capital market. Analysis of economic factors shows a strong relationship between the macroeconomic and performance in Indonesian capital market. The purpose of this study was to analyze the effect of Macro Variables on the Composite Stock Price Index (JCI) for the period 2008 to 2020. The macro variables used were Inflation, Economic Growth, Dollar Exchange Rate and SBI or 7 Day Repo Rate against the JCI. The number of sample is 13 years, from 2008 to 2020. Multiple linear regression analysis, coefficient of determination test using SPSS Version 26 software. The conclusion is that Inflation has no significant effect on the JCI, while Economic Growth, Dollar Exchange and SBI have a significant effect on the JCI. Simultaneously, Inflation, Economic Growth, Dollar Exchange and SBI have a significant effect on the JCI.
Abstract: The Composite Price Index (JCI) often fluctuates from year to year, which should be expected by investors to increase, so that investors get certainty about the return on investment funds placed in the capital market. Analysis of economic factors shows a strong relationship between the macroeconomic and performance in Indonesian capital market. The...
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Predicting Stock Prices Using Stacking-Based Ensemble Learning and Seasonal and Trend Decomposition Using Loess
Issue:
Volume 10, Issue 4, August 2022
Pages:
222-228
Received:
19 July 2022
Accepted:
16 August 2022
Published:
17 August 2022
DOI:
10.11648/j.ijefm.20221004.18
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Abstract: With the development of the economy and the increasing awareness of people to invest in their own assets, stocks have become the most common way for people to manage their money. However, stocks also have strong risk and uncertainty. The emergence of artificial intelligence techniques has contributed to improving the stock forecast stability, so the stock market forecasting through artificial intelligence, in particular, the machine learning algorithms has become a popular research area. In this study, a hyper-parametric stacking-based ensemble learning model based on seasonal and trend decomposition using Loess (SEL-STL) is proposed. Firstly, the normalized preprocessing is performed on the raw data. Then, the preprocessed data is decomposed by means of seasonal and trend decomposition using Loess (STL). Subsequently, the Bayesian optimization algorithm is employed to optimize the hyper-parameters of the base prediction models. After that, the ensemble model is obtained by integrating the optimized base prediction models using the stacking-based ensemble learning method. Finally, the ensemble model is improved by further optimizing the model performance using the Adaptive Boosting. In the experiments, the datasets with three different stock exchange indices are used to evaluate the performance of the proposed model in stock price prediction. The experimental results show that the proposed model outperforms the other baseline prediction models in solving the stock price prediction problem.
Abstract: With the development of the economy and the increasing awareness of people to invest in their own assets, stocks have become the most common way for people to manage their money. However, stocks also have strong risk and uncertainty. The emergence of artificial intelligence techniques has contributed to improving the stock forecast stability, so th...
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A Study on Customers Perspective on Opportunities and Challenges of E-Commerce in India
Satheeshkumar Rangasamy,
Vetrivel Thiyagarajan,
Sampangi Gokula Krishnan,
Sushma Rawath,
Madhumitha
Issue:
Volume 10, Issue 4, August 2022
Pages:
229-236
Received:
2 July 2022
Accepted:
10 August 2022
Published:
24 August 2022
DOI:
10.11648/j.ijefm.20221004.19
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Abstract: E-Commerce refers to use of internet for commercial and/or business transactions. This articles focus on digitally enabled commercial and/or business transactions. Electronic commerce is a business model which allows an individuals and firms to buy and sell products over the web. E-commerce includes four main market segments like Business to Consumer, Business to Business, Consumer to Consumer and Consumer to Business. The purposes of the study are to identify the perspective of customers on opportunities and challenges in the e-commerce, to identify performance of e-commerce and also to address the challenges in the e-commerce market in India. Researcher has adopted descriptive research and presented the research work with the support of descriptive statistics and weighted average ranking score method. 42% of respondents said that Business to Consumer (B2C) and 38% of the respondents said that Consumer to Consumer (C2C) are the major business models that customer use in the e-commerce platform. No respondent said that they use the Government to Business (G2B) e-commerce model. It is suggested that e-commerce companies have to leverage their business transaction through the more added features like online payment, service availability of 24*7, faster transaction and timeliness of the transaction.
Abstract: E-Commerce refers to use of internet for commercial and/or business transactions. This articles focus on digitally enabled commercial and/or business transactions. Electronic commerce is a business model which allows an individuals and firms to buy and sell products over the web. E-commerce includes four main market segments like Business to Consum...
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