Dataset on Measuring the Unmeasurable Multidimensional Rural Poverty for Economic Development: Analysis from the Poorest District of the Poorest Province in the Poorest Region of Luzon, Philippines

Published: 18 December 2023| Version 1 | DOI: 10.17632/s76nh7dm4v.1
Emmanuel Onsay,


The oldest societal issue that has ever been is poverty, which is also the hardest to overcome. It is both unmeasurable and multidimensional. Decomposing rural multidimensional poverty is therefore a crucial method of measurement. The majority of poverty studies are typically designed for macroeconomic considerations, are general, and are subject to significant sampling errors. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. A processed and analyzed dataset from Goa, Camarines Sur's extensive community-based monitoring system is presented in this work. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.


Steps to reproduce

The dataset included 36 indicators at magnitude and proportion measurements of the municipality from 6 multidimensional poverty classifications of 4 sectors separated into 34 barangays. It was taken from the massive community-based monitoring system and cleaned, filtered, converted, coded, analyzed, and processed. Many of the variables in the system are not pertinent to the formulation of policy. The researchers generated the necessary data for economic development successfully and efficiently using a variety of software programs, including R, STATA, Python, SPSS, and MS Excel. In addition, models that are diagnostic, prescriptive, predictive, and descriptive were used to extract and analyze data that will be used as input for economic development. Multidimensional poverty is thought to be unmeasurable because it is hard to measure, particularly in rural areas and certain localities. The dataset is helpful in giving information for developing policies and programs that are specifically aimed at the poorest regions in order to reduce poverty and promote economic development. Other developing nations and underdeveloped areas of the world can use the statistics to create baseline data and multidimensional poverty indicators when creating plans for economic growth. Subsequent investigators could utilize the data analytics protocols, variables, techniques, policy recommendations, and computational approaches to develop comparable study on quantifying the intangible elements of multidimensional poverty in other impoverished areas. Poverty incidence, poverty gap, severity statistics, watts index, and classifications for different industries and localities in impoverished regions may all be calculated, verified, and simulated using the poverty analytics dataset. The datasets that have been processed offer both descriptive and diagnostic analytics that assess various aspects of society such as population dynamics, health and nutrition, housing and settlement, water and sanitation, basic education from elementary school to senior high school, income classifications, employment and livelihood, peace and order, and preparedness for disaster risk reduction. The prescriptive analytics dataset will be helpful to local government units, national government agencies, scholars, researchers, extensionists, policy-makers, academicians, charitable institutions, and social entrepreneurs in developing programs and tracking their effects for socioeconomic advancement, not only in the Philippines but globally as well. The econometric models and predictive analytics dataset may offer empirical crumbs of proof to support poverty theories in rural research, contributing to the body of knowledge on the limited availability of multidimensional poverty measures in the Philippines and other developing nations.


Partido State University, De la Salle University, University of the Philippines Los Banos Graduate School


Econometrics, Economic Development, Rural Economics, Poverty Alleviation, Analysis of Poverty, Measurement of Poverty, Data Analytics


Partido State University

2023 Research Grant for Poverty Alleviation and Economic Development Projects