scholarly journals Analytical Tools for Measuring Poverty Dynamics: An Application Using Panel Data in the Philippines

2016 ◽  
Author(s):  
Arturo Martinez, Jr.
Keyword(s):  
Panel Data ◽  
The Philippines ◽  
Poverty Dynamics ◽  
Analytical Tools

The Dynamics of Poverty

2017 ◽  
Author(s):  
Anirudh Krishna
Keyword(s):  
Panel Data ◽  
Social Mobility ◽  
Mixed Method ◽  
Poverty Reduction ◽  
Poverty Dynamics ◽  
Future Directions ◽  
Chronic Poverty ◽  
Ethnographic Studies ◽  
The Social

This article examines the dynamics of poverty and explains why poverty dynamics studies are necessary: to estimate the risk of impoverishment and the probability of escaping poverty; to identify the reasons associated with poverty descents and escapes; to distinguish between transient and chronic poverty; and to elucidate the social mobility prospects of individuals in different economic situations. The article begins with an overview of three types of approaches used in investigating poverty dynamics: panel data studies, participatory poverty assessments and ethnographic studies, and mixed-method studies. This is followed by a discussion of key findings from poverty dynamics studies; one finding is that poverty creation and poverty reduction occur everywhere in tandem. The article concludes by outlining future directions for research into poverty dynamics.

scholarly journals Fermatean Fuzzy DEMATEL and MMDE Algorithm for Modelling the Barriers of Implementing Education 4.0: Insights from the Philippines

2022 ◽  
Vol 12 (2) ◽  
pp. 689
Author(s):  
Gamaliel Gonzales ◽  
Felix Costan ◽  
Decem Suladay ◽  
Roselyn Gonzales ◽  
Lynne Enriquez ◽  
...  
Keyword(s):  
Developing Economies ◽  
Threshold Value ◽  
The Philippines ◽  
Resource Scarcity ◽  
Implementation Barriers ◽  
Fuzzy Dematel ◽  
Learning Platforms ◽  
Training Resources ◽  
Analytical Tools ◽  
The Impact

Technological transitions in the education sector of developing economies are faced with a range of barriers, involving resource scarcity, socio-cultural concerns, and issues related to management and policy. The popularity of Industry 4.0 has prompted Education 4.0 (EDUC4), an approach to learning that involves transformation using advanced technologies. While a recent work reported a comprehensive list of barriers to EDUC4 implementation, particularly in developing economies, further analysis to identify those priority barriers remains a gap. Thus, this work addresses this gap by introducing a novel methodological extension of the decision-making trial and evaluation laboratory (DEMATEL) method following the integration of Fermatean fuzzy sets (FFS). The FFS, compared to other fuzzy environments, could capture higher levels of uncertainties that are associated when eliciting judgments necessary for the DEMATEL. Such integration is aided by the maximum mean de-entropy (MMDE) algorithm, which analytically determines the threshold value crucial for constructing the prominence-relation map of the DEMATEL. Following its application in evaluating the implementation of EDUC4 in Philippine universities, the critical barriers are the lack of training resources, costs, insufficiency of available technologies, skills gap of human resources, knowledge gap, and the complexity of the learning platforms. Among this set, barriers related to cost and lack of training resources are deemed the most prominent ones. The statistical test on the impact of addressing the two prominent barriers shows that addressing the barrier related to costs yields statistically more favorable results regarding the mitigation of other EDUC4 implementation barriers. Although these insights may contain idiosyncrasies, they can serve as starting points of discussion in other relevant developing economies. These methodological and practical contributions advance the development of analytical tools under uncertainty that can handle pressing problems such as the EDUC4 implementation.

scholarly journals FINANCIAL DEEPENING AND INTEREST RATE RELATIONSHIP IN FACING THE ASEAN ECONOMIC COMMUNITY 2015: VECM AND PANEL DATA APPROACH

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Lukman Hakim
Keyword(s):  
Panel Data ◽  
Interest Rate ◽  
Interest Rates ◽  
Negative Impact ◽  
The Philippines ◽  
Financial Deepening ◽  
Economic Community ◽  
Deposit Rate ◽  
Asean Economic Community ◽  
The Interest Rate

The relationship of the financial deepening to the interest rate has become an important study for the Southeast Asia countries, especially preparation forentering the ASEAN Economic Community (AEC) in 2015. This study will explore the effect of interest rates on deposits and credit to the financial deepening in ASEAN 5. By using VECM showed that Indonesia, the Philippines and Singapore possessed a similar pattern where lending rates negatively affect financial deepening, while the deposit rate positive effect. In contrast to Malaysia and Thailand, deposit rates had a negative impact on financial depth, while the loan interest rate was positive. Meanwhile, using panel data for the ASEAN 5 showed that the effect of interest rates on loans to the depth of the financial sector is negative, whereas the effect of deposit rate was positive

scholarly journals Economic Growth Determinants of Asean Economic Community: Feasible Generalized Least Square Approach

2021 ◽  
Vol 316 ◽  
pp. 01019
Author(s):  
Imamudin Yuliadi ◽  
Wahdi Salasi April Yudhi
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Random Effect ◽  
The Philippines ◽  
Least Square ◽  
Economic Community ◽  
Brunei Darussalam ◽  
Least Square Approach ◽  
Asean Economic Community

This study aims to analyze the factors determining economic growth in ASEAN countries, which are the ASEAN economic community members as a potential center for world economic growth. The research method applied in this study was a panel data analysis model with a feasible generalized least square approach. The research period was from 2015 – 2019 in all ASEAN member countries: Indonesia, Myanmar, Malaysia, Singapore, Thailand, Laos, Vietnam, Cambodia, Brunei Darussalam, and the Philippines. Testing the data used the Chow and Hausman tests to determine the analysis method: fixed effect, random effect, or common effect. The results of panel data regression estimation with a feasible generalized least square approach uncovered that the variables of the number of the labor force, currency exchange rates, money supply (M1), exports, imports, Gini index, foreign debt, corruption perception index, financial literacy index, and foreign investment (PMA) significantly affected the economic growth of the ASEAN economic community, including develop agriculture sector. Meanwhile, the variables of domestic investment and financing credit did not affect the economic growth of the ASEAN economic community. The conclusion and recommendation from this study’s results are each ASEAN country’s efforts to encourage economic growth by utilizing its comparative advantages and strategic cooperation to create market opportunities and increase the economic efficiency of the ASEAN economic community.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERTUMBUHAN EKONOMI ASEAN MEMBER COUNTRIES PADA TAHUN 2011-2016

2018 ◽  
Vol 11 (1) ◽  
pp. 24-29
Author(s):  
Agatha Christy Permata Sari ◽  
David Kaluge
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Government Expenditure ◽  
Least Square ◽  
Estimation Model ◽  
Asean Member ◽  
Brunei Darussalam ◽  
Growth Phenomenon ◽  
Effect Estimation ◽  
Analytical Tools

Economic Growth becomes the important factor to measure how success economic state. Therefore, many country will always try to increase their economic growth for the priority target that must be reached.this research has purpose to present which factor that can influence economic growth in ASEAN member countreias.t There is 10 countries being the sampe country, which is Indonesia, Thailand, Malaysia, Singapore, Philippines,Brunei Darussalam, Vietnam, Laos, Myanmar,and Cambodia. This research use Panel Data Regression, Generalized Least Square (GLS) method and fixed-effect estimation model by using analytical tools help in processing the data which uses Eviews 9 programm. Data is panel data of all ASEAN member countries in 6 year period (2011-2016). Varible that used are : GDP, Import, Export, Foreign Direct Inverstment, Competitiveness Index, Government Expenditure, and Labor force. The result is each independent variable can explain 99,4126% of dependen variable. Which is, IM (import) variable is the only one variable that has positive significant influence to economic growth. The finding from this research is economic growth phenomenon that occurred in ASEAN is greatly influenced by consumption rather than investment and production. So, almost the entirety of economic growth can be measured by consumption side.

Switch in Livelihood Strategies and Social Capital Have a Role to Play in Deciding Rural Poverty Dynamics: Evidence from Panel Data Analysis from Eastern India

2019 ◽  
Vol 55 (1) ◽  
pp. 76-94
Author(s):  
Sunil Khosla ◽  
Pradyot Ranjan Jena
Keyword(s):  
Social Capital ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Rural Households ◽  
Rural Poverty ◽  
State Level ◽  
Multinomial Logit Model ◽  
Poverty Dynamics ◽  
Eastern India

Rural households continuously move into and out of poverty due to various factors; and in response to this phenomenon, these rural households adopt several strategies. The purpose of the present paper was to examine the role of livelihood diversification and social capital in the movement of these households into and out of poverty in Eastern rural India. The present study classified households into four poverty groups (called poverty dynamics) based on the panel data gathered from 1353 rural households between 2004–2005 and 2011–2012. The study used the Sustainable Livelihoods Approach (SLA) and the multinomial logit model (MLM) to examine the poverty outcome between 2004–2005 and 2011–2012. As per the data collected, at the state level, 25.26% of households were chronic poor and 37.04% of households ascended out of poverty, while 8.20% of households descended into poverty between 2004–2005 and 2011–2012. Further, it was found out from the SLA that there is a positive relationship between the phenomena of non-farm activities and escaping poverty. The result from the MLM shows that social capital in the form of group membership in different saving schemes and social groups helps to ascend out of poverty.

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