scholarly journals Plant clinics, farm performance and poverty alleviation: Panel data evidence from Rwanda

2020 ◽  
Vol 129 ◽  
pp. 104881 ◽  
Author(s):  
Justice A. Tambo ◽  
Bellancile Uzayisenga ◽  
Idah Mugambi ◽  
Mary Bundi ◽  
Silvia Silvestri
Keyword(s):  
Panel Data ◽  
Poverty Alleviation ◽  
Farm Performance ◽  
Plant Clinics

CO2 emissions and poverty alleviation in China: An empirical study based on municipal panel data

2018 ◽  
Vol 202 ◽  
pp. 883-891 ◽  
Author(s):  
Gui Jin ◽  
Rao Fu ◽  
Zhihui Li ◽  
Feng Wu ◽  
Fan Zhang
Keyword(s):  
Panel Data ◽  
Empirical Study ◽  
Co2 Emissions ◽  
Poverty Alleviation

Tourism and poverty alleviation: An empirical analysis using panel data on Peru's departments

2017 ◽  
Vol 19 (6) ◽  
pp. 746-756 ◽  
Author(s):  
Carmen María Llorca-Rodríguez ◽  
Amalia Cristina Casas-Jurado ◽  
Rosa María García-Fernández
Keyword(s):  
Panel Data ◽  
Empirical Analysis ◽  
Poverty Alleviation

Reflections on Microfinance

2019 ◽  
pp. 188-207
Author(s):  
Sovik Mukherjee ◽  
Ramesh Chandra Das
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Poverty Alleviation ◽  
Poverty Reduction ◽  
Poverty Line ◽  
Infant Mortality Rate ◽  
Self Sufficiency ◽  
Socio Economic Development

Microfinance has become the latest buzzword in the credit markets where it shoulders the responsibility of alleviating poverty coupled with socio-economic development. Dealing with microfinance coupled with the issue of poverty reduction, the first concern is to handle the twin objectives of poverty alleviation and achievement of financial self-sufficiency, which are often at loggerheads. To begin with, the present chapter constructs a five-dimensional human poverty index (HPI) in terms of the rate of unemployment, state wise illiteracy rate, state-wise infant mortality rate, state-wise percentage of population below the poverty line, and the percentage of population not having an access to electricity for the states across India. Thus, this will serve as an index for the extent of poverty. Consequently, a fall in the value of the index actually implies poverty alleviation. This empirical model does not justify the hypothesis that “microfinance reduces poverty” at the macro level using cross-state panel data for India.

scholarly journals The Impact of Microfinance on Poverty Alleviation: The Case of Pakistan

2019 ◽  
Vol 5 (3) ◽  
pp. 16
Author(s):  
Wook Sohn ◽  
Laila Ume
Keyword(s):  
Panel Data ◽  
Household Income ◽  
Poverty Alleviation ◽  
Positive Impact ◽  
Fixed Effect ◽  
District Level ◽  
Efficient Tool ◽  
Panel Regressions ◽  
Household Assets ◽  
The Impact

This paper examines the effect of microfinance on poverty alleviation in Pakistan using district-level panel data. We conduct fixed effect panel regressions to show a statistically significant positive impact of microfinance on household income, ownership of household assets, education, and expenditure. These results suggest that the development of microfinance is an efficient tool for both welfare improvement and poverty alleviation in Pakistan.

scholarly journals STRUKTUR PENDAPATAN PERIKANAN TANGKAP KELUARGA NELAYAN DAN IMPLIKASINYA: Analisis Data Panel Kelautan dan Perikanan Nasional

2016 ◽  
Vol 11 (2) ◽  
pp. 237 ◽  
Author(s):  
Rikrik Rahadian ◽  
Maulana Firdaus ◽  
Andrian Ramadhan
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Poverty Alleviation ◽  
Panel Data Analysis ◽  
Income Family ◽  
Expenditure Data ◽  
Total Effort

Kemiskinan merupakan sebuah kata yang sangat melekat dengan keluarga Nelayan, sehingga banyak usaha telah dilakukan oleh pemerintah untuk menanganinya, umumnya melalui pemberian bantuan serta pemberdayaan usaha keluarga nelayan. Penelitian ini dilakukan untuk melihat kondisi struktur pendapatan perikanan keluarga Nelayan selama ini melalui analisis ketergantungan pendapatan perikanan keluarga Nelayan terhadap tingkat usaha penangkapan. Analisis dilakukan dengan menggunakan analisis data Panel, dengan dua variabel berupa data rata-rata pendapatan keluarga Nelayan dan rata-rata pengeluaran BBM di enam lokasi tipologi penangkapan pada penelitian PANELKANAS – Bitung, Sampang, Sambas, Sibolga, OKI dan Purwakarta – sepanjang periode 2010-2013. Hasil analisis data sepanjang periode pengamatan menunjukkan beberapa fenomena berikut ini: 1). Peningkatan usaha penangkapan berpengaruh signifikan meningkatkan Pendapatan Perikanan Keluarga sebesar 15% dari nilai usaha yang dilakukan; 2). Terjadi rata-rata pendapatan perikanan non-penangkapan yang positif di semua lokasi penelitian; dan 3). Terdapat kesenjangan nilai rata-rata pendapatan perikanan non-perikanan antar lokasi penelitian. Berdasarkan hasil yang diperoleh, maka untuk membantu keluarga nelayan meningkatkan kesejahteraannya akan diperlukan kebijakan yang dapat mendorong peningkatan usaha penangkapan dan diversifikasi usaha perikanan.Title: Structure of Capture Fisheries Income Family Fisherman and Implications: Panel Data Analysis of The National Marine and Fishery Poverty is a word closely associated with fisher’s households, thus far numerous government efforts – such as grants and empowerment programs – have been conducted to tackle this problem. This paper is aimed at scrutinizing the income structure of fisher’s households by analyzing the dependency of their income to their Catching Effort. The panel data analysis conducted is based on the average Household’s Fisheries Income and its average Gasoline Expenditure data of six PANELKANAS’ Captured Fisheries locations – Bitung, Sampang, Sambas, Sibolga, OKI and Purwakarta – which were observed through out the 2010-2013 periode. The result shows several phenomena, such as: (1). Catch effort is a significant factor positively affecting the household’s fisheries income, as much as 15% of the total effort value; (2). There has been a positive average non-captured fisheries income in every location observed; and (3). There have been disparities of the average non-captured fisheries income among different locations. With such results, poverty alleviation of fisher’s households would still require both catch-effort enhancing as well as livelihood diversifying policies.

scholarly journals Fiscal Capacity and Poverty Alleviation: A Panel Data Analysis for Yogyakarta Special Province, Indonesia

2015 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
Jaka Sriyana
Keyword(s):  
Panel Data ◽  
Poverty Alleviation ◽  
Local Governments ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
Public Spending ◽  
Poverty Rate ◽  
Fixed Effects Model ◽  
Fiscal Capacity

This study attempts to analyze the role of fiscal capacity in poverty alleviation in Yogyakarta for the period of 2006-2013. For this purpose, this study uses panel data approach to estimate the empirical model involving four regencies and a city. This research f ound that fixed effects model is the best model to explain the role of fiscal capacity to the poverty rate. Overall, the results present that all independent variables are good predictors for poverty rate model. Based on the complete fixed effects model, the research shows that public spending and fiscal transfer are significant predictors for poverty rate. In contrast, government own revenue has negative impact to poverty rate. These results indicate fiscal capacity in among regencies and city in Yogyakarta has well managed to improve social welfare. This study also reveals that local governments need to improve their public spending as well as to strengthen their frameworks on public services policies.

scholarly journals STRUCTURAL TRANSFORMATION AND POVERTY ERADICATION IN EAST JAVA (A PANEL DATA APPROACH OF 38 COUNTIES)

2021 ◽  
Vol 6 (1) ◽  
pp. 114
Author(s):  
Achmad Rifa'i ◽  
Listiono Listiono
Keyword(s):  
Panel Data ◽  
Structural Transformation ◽  
Poverty Alleviation ◽  
Service Sector ◽  
Industrial Sector ◽  
Transformation Process ◽  
Poverty Eradication

The characteristic of structural transformation is a decrease in the share of agriculture followed by an increase in the industry share in the economy. Sometimes, the share of services to the economy increases more rapidly than the share of the industry, called immature structural transformation. This study aims to analyze the structural transformation process in East Java and its impact on poverty alleviation. Panel data for 38 districts/cities used from the Statistics Indonesia (BPS) during the 2012-2015 period. The estimation results revealed empirically that the service sector has a significant impact on reducing poverty in East Java. This research argues that East Java has experienced immature structural transformation seen from the stagnation of the industry’s share of the economy. It is supposed that the role of the industrial sector is not significant, while the service sector is better to reduce poverty.Keywords: Structural Transformation, Poverty, East Java, Panel DataJEL: B22, I32, L16 

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