ECONOMIC IMPACT OF AUTOMATION ON FILIPINO WORKERS
Description
The study employed a descriptive-quantitative research design with secondary data analysis to examine the economic impact of automation on Filipino workers. This design proved appropriate, as it allowed the researcher to systematically review labor market statistics, economic reports, and workforce data from government agencies and international institutions. Descriptive methods were used to demonstrate and interpret trends related to automation, employment, productivity, and workforce changes, while quantitative methods facilitated the analysis of numerical data and labor market indicators relevant to the study.
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Steps to reproduce
The data for this study were gathered through a systematic secondary data collection approach using credible and publicly accessible sources related to labor economics and automation. The researcher identified relevant information from institutions such as the Philippine Statistics Authority, Department of Labor and Employment, International Labor Organization, World Bank, and OECD. These sources were selected based on their reliability, relevance to the study variables, and availability of updated statistics on employment trends, automation exposure, industry vulnerability, and workforce skills. The process began with the identification of key research variables, followed by the extraction of corresponding indicators such as unemployment rates, sectoral employment distribution, and automation risk estimates. After data collection, all information was organized, compared, and validated across multiple sources to ensure consistency and accuracy. No primary data collection instruments such as surveys or interviews were used; instead, the study relied entirely on published reports and official datasets. Data analysis and processing were conducted using standard spreadsheet software such as Microsoft Excel or Google Sheets to generate tables, compute descriptive statistics, and conduct trend analysis. This structured workflow ensures transparency and allows replication of the study using the same datasets and procedures.
Institutions
- Biliran Province State UniversityEastern Visayas, Naval