Data · dataset · 2010
Study on the Methodology, Framework and Field Investigation of the National Financial Literacy Survey
Listed in Harvard Dataverse
This is the first undertaken National Financial Literacy Survey in Taiwan with fieldwork on a population of 3,000 Taiwanese aged over 15 years old.
Description
The survey data was weighted to random sampling of population structure. The questionnaire is designed according to respondent’s national financial literacy that covers 9 tiers.
They are Money Management, Savings, Credit Management, Financial Planning, Insurance and Risk Control, Loan Management, Retirement Planning, Investment Know-How and Financial Information. Of those 9 tiers, they are categorized in two which are Action and Knowledge. The respondents are categorized into 9 cluster groups by age, marital status and occupation to gain a better understanding on whether there are significant differences on the national financial literacy based on the population structure.
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We discovered the “working and married” cluster accompanied the highest score, follows by “working and single”, “pre-retirees” and “working and divorced” cluster. The cluster scored the lowest is “older ages.” Student cluster, excluding on Loan Management and Financial Planning tires, scored below overall average, showing student cluster is in need of working on their financial knowledge. We also found age is strongly correlated with financial literacy level.
Aged over 60 and between 15 and 19 respondents demonstrated below average score while age between 30 and 39 well above the average. Education, personal income and household income are also correlated to financial literacy. Financial literacy advanced as the education level moves up.
According to the survey, the lower the personal income, the lower the financial literacy. Most respondents consider employers, government, and educational organizations are responsible to help the public on the financial education through media and publication means, especially for the housewives/househusbands, unemployed, older generation, and aborigine clusters. They often have no channel of finding assistance on their financial knowledge which our government needs to take this subject a serious concern and provides assistance.
In accord with the survey and public financial education, we suggest the short-term targets are: 1. in order to know the level of financial literacy and the improvement of financial education, this survey shuld undertake yearly in 3 years, 2.centre government should open a communicating window to avoid duplicating resources; 3. the government promotions of saving insurance and personal credit reports show little accurate rate that government promotion need a more effective way on public financial education; 4. student, older ages, unemployed, and aborigines clusters need more government concern as OEDC conclusion states that financial literacy are strongly related to educational level and social standard, same as our survey result.
The mid-term target is to implement financial education licenses program and promote peripheral financial organizations and training agencies offering classes to general public. In the long-term, building a financial education and learning system in schools to allow student building the financial knowledge since early age is the best resolution.
Links
Where it is published
- Dataverse dataset page dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.6141%2FTW-SRDA-D00089-1 ↗
landing page · from Harvard Dataverse
- DOI doi.org/10.6141/tw-srda-d00089-1 ↗
DOI / persistent id · from Harvard Dataverse
Catalogue records · 1
- Dataverse API dataverse.harvard.edu/api/datasets/:persistentId/?persistentId=doi%3A10.6141%2FTW-SR… ↗
metadata API · from Harvard Dataverse
Provenance · 1 source records, 5 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Harvard Dataverse | doi:10.6141/TW-SRDA-D00089-1 | 15 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| created_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| description | source · Harvard Dataverse | connector:dataverse@1.0.0 | /description |
| publication_date | source · Harvard Dataverse | connector:dataverse@1.0.0 | |
| title | source · Harvard Dataverse | connector:dataverse@1.0.0 | /name |
| updated_date | source · Harvard Dataverse | connector:dataverse@1.0.0 |