Number of Families: Philippines - All Income Classes data was reported at 22,730,000.000 Unit in 2015. This records an increase from the previous number of 21,426,000.000 Unit for 2012. Number of Families: Philippines - All Income Classes data is updated yearly, averaging 15,874,827.500 Unit from Dec 1988 (Median) to 2015, with 10 observations. The data reached an all-time high of 22,730,000.000 Unit in 2015 and a record low of 10,533,925.000 Unit in 1988. Number of Families: Philippines - All Income Classes data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H014: Family Income and Expenditure Survey: No of Families: By Income Class and Main Source of Income.

Context

The Philippine Statistics Authority (PSA) spearheads the conduct of the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three (3) years, is aimed at providing data on family income and expenditure, including, among others, levels of consumption by item of expenditure, sources of income in cash, and related information affecting income and expenditure levels and patterns in the Philippines.

Content

Inside this data set is some selected variables from the latest Family Income and Expenditure Survey (FIES) in the Philippines. It contains more than 40k observations and 60 variables which is primarily comprised of the household income and expenditures of that specific household

Acknowledgements

The Philippine Statistics Authority for providing the publisher with their raw data

Inspiration

Socio-economic classification models in the Philippines has been very problematic. In fact, not one SEC model has been widely accepted. Government bodies uses their own SEC models and private research entities uses their own. We all know that household income is the greatest indicator of one's socio-economic classification that's why the publisher would like to find out the following:

1) Best model in predicting household income 2) Key drivers of household income, we want to make the model as sparse as possible 3) Some exploratory analysis in the data would also be useful