The Philippine Statistics Authority (PSA)-La Union Provincial Statistical Office carries out the January 2025 quarterly round of the Labor Force Survey (LFS) from 08 to 31 January 2025 in sample enumeration areas in La Union.
In preparation for the activity, PSA-La Union conducted a two-day training on 02 to 03 January 2025 to its five hired Statistical Researchers (SRs) to equip them with the necessary skills and knowledge to perform their tasks effectively in conducting the interview following the standard procedures in collecting data.
The hired SRs will visit and interview a total of 390 randomly selected sample enumeration areas in La Union. Data items to be collected in this survey are information on the demographic and economic characteristics of all household members.
The LFS is a nationwide survey of households conducted by the PSA to gather data on the demographic and socio-economic characteristics of the population. Data from the survey provides information on major labor market trends such as labor force participation and unemployment rates, hours worked, past industries, and occupations of currently unemployed persons.
For this survey round, questions on functional difficulties are included. As defined by the International Classification of Functioning, Disability, and Health (ICF) of the World Health Organization, functioning is an umbrella term for body functions, body structures, activities, and participation. It denotes the positive aspects of the interaction between an individual (with a health condition) and that individual’s contextual factors (environmental and personal factors).
The success of the LFS highly depends on the quality of the data that will be collected. Hence, PSA-La Union encourages the sample households to participate in the survey by providing truthful and complete information. Respondents are assured that all gathered information will be held with the utmost confidentiality under Section 19 of Republic Act (RA) No. 10173, otherwise known as the Data Privacy Act of 2012, and Section 26 of RA 10625, otherwise known as the Philippine Statistical Act of 2013.
Meanwhile, the PSA released on 08 January 2025 the results of the LFS November 2024 round. Some of the highlights of the report in the country are as follows:
1. In November 2024, the country's employment rate was estimated at 96.8 percent. This was higher than the estimated employment rate of 96.4 percent in November 2023 and in October 2024 at 96.1 percent.
In terms of magnitude, the number of employed individuals in November 2024 was recorded at 49.54 million. This was lower than the estimated number of employed persons of 49.64 million in November 2023. In October 2024, the number of employed individuals was posted at 48.16 million.
2. The unemployment rate in November 2024 was recorded at 3.2 percent. This was lower than the unemployment rate in November 2023 at 3.6 percent and in October 2024 at 3.9 percent.
In terms of levels, the number of unemployed persons in November 2024 was recorded at 1.66 million. This was lower than the number of unemployed persons of 1.83 million in November 2023 and 1.97 million in October 2024.
3. The labor force participation rate (LFPR) in November 2024 was estimated at 64.6 percent. This was lower than the estimated LFPR in November 2023 at 65.9 percent. In October 2024, the LFPR was estimated at 63.3 percent.
The reported LFPR in November 2024 translates to a total of 51.20 million Filipinos aged 15 years old and over who were in the labor force, or those who were either employed or unemployed. The number of individuals aged 15 years old and over who were in the labor force was 51.47 million in November 2023 and 50.12 million in October 2024.
4. The underemployment rate in November 2024 decreased to 10.8 percent, from 11.7 percent in November 2023 and 12.6 percent in October 2024.
5. On average, employed persons worked 41.1 hours per week. This was higher than the average hours worked in a week in November 2023 at 40.2 hours and in October 2024 at 41.0 hours per week.
SGD. DANITES E. TEÑIDO, PhD
Chief Statistical Specialist
/RBP/MBL
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