Philippines PH: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 40.100 % in 2015. This records a decrease from the previous number of 42.200 % for 2012. Philippines PH: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 42.200 % from Dec 1985 (Median) to 2015, with 11 observations. The data reached an all-time high of 46.000 % in 1997 and a record low of 40.100 % in 2015. Philippines PH: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Historical dataset showing Philippines income inequality - gini coefficient by year from N/A to N/A.

According to a survey conducted by Ipsos in the Philippines, the majority of the respondents believed that women would be paid the same as men in 2020. Only ** percent of the surveyed respondents were pessimistic on the gender pay gap in the country.

Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 11.700 % in 2021. This records a decrease from the previous number of 12.900 % for 2018. Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 15.850 % from Dec 2000 (Median) to 2021, with 8 observations. The data reached an all-time high of 17.100 % in 2006 and a record low of 11.700 % in 2021. Philippines PH: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Income share held by highest 20% of Philippines dipped by 2.29% from 48.00 % in 2021 to 46.90 % in 2023. Since the 0.38% rise in 2012, income share held by highest 20% dropped by 11.01% in 2023. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

Income share held by third 20% of Philippines grew by 2.10% from 14.30 % in 2021 to 14.60 % in 2023. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Philippines Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data was reported at 0.960 % in 2015. This records an increase from the previous number of 0.930 % for 2012. Philippines Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data is updated yearly, averaging 0.720 % from Dec 1997 (Median) to 2015, with 7 observations. The data reached an all-time high of 0.960 % in 2015 and a record low of 0.560 % in 2000. Philippines Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Weighted average;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data was reported at 8.820 Intl $/Day in 2021. This records an increase from the previous number of 8.410 Intl $/Day for 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data is updated yearly, averaging 8.615 Intl $/Day from Dec 2015 (Median) to 2021, with 2 observations. The data reached an all-time high of 8.820 Intl $/Day in 2021 and a record low of 8.410 Intl $/Day in 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Total Population: 2017 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Manila. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Manila. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Manila, the median household income stands at $153,028 for householders within the 25 to 44 years age group, followed by $63,815 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $39,951.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Manila median household income by age. You can refer the same here

The statistic shows the distribution of employment in the Philippines by economic sector from 2013 to 2023. In 2023, 22.37 percent of the employees in the Philippines were active in the agricultural sector, 18.47 percent in industry and 59.16 percent in the services sector.

The study is based on village full enumeration field surveys. The main purpose of the study is to examine the changes in the structure of rural household income and its distribution over the past decade with primary data generated through in-depth household surveys in four villages with contrasting production environments. This study also shed light on the changes in the poverty situation of rural households over the 1985-97 period.

This includes all measure of poverty among family and population at the regional level for the years 1991, 2006, 2009, 2012, and 2015. These are Poverty Incidence and Magnitude, Poverty and Food Thresholds, Poverty Gap, Income Gap, and Extent of Poverty. These data were derived from the result of Family Income and Expenditure Surveys and Labor Force Surveys.Map Displays at Scale: 1:12,000,000 to 1:147,000,000. Download detailed metadata about Philippine SDG 1.

Abstract

The 2003 Family Income and Expenditure Survey (FIES) had the following primary objectives:

1) to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines;

2) to determine the sources of income and income distribution, levels of living and spending patterns, and the degree of inequality among families;

3) to provide benchmark information to update weights for the estimation of consumer price index; and

4) to provide information for the estimation of the country's poverty threshold and incidence.

Geographic coverage

National coverage

Analysis unit

Household Consumption expenditure item Income by source

Universe

The 2003 FIES has as its target population, all households and members of households nationwide. A household is defined as an aggregate of persons, generally but not necessarily bound by ties of kinship, who live together under the same roof and eat together or share in common the household food. Household membership comprises the head of the household, relatives living with him such as his/her spouse, children, parent, brother/sister, son-in-law/daughter-in-law, grandson/granddaughter and other relatives. Household membership likewise includes boarders, domestic helpers and non-relatives. A person who lives alone is considered a separate household.

Institutional population is not within the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2003 MS considers the country's 17 administrative regions as defined in Executive Orders (EO) 36 and 131 as the sampling domains. A domain is referred to as a subdivision of the country for which estimates with adequate level of precision are generated. It must be noted that while there is demand for data at the provincial level (and to some extent municipal and barangay levels), the provinces were not treated as sampling domains because there are more than 80 provinces which would entail a large resource requirement. Below are the 17 administrative regions of the country:

National Capital Region Cordillera Administrative Region Region I - Ilocos Region II - Cagayan Valley Region III - Central Luzon Region IVA - CALABARZON Region IVB - MIMAROPA Region V - Bicol Region VI - Western Visayas Region VII - Central Visayas Region VIII - Eastern Visayas Region IX - Zamboanga Peninsula Region X - Northern Mindanao Region XI - Davao Region XII - SOCCSKSARGEN Region XIII - Caraga Autonomous Region in Muslim Mindanao

As in most household surveys, the 2003 MS made use of an area sample design. For this purpose, the Enumeration Area Reference File (EARF) of the 2000 Census of Population and Housing (CPH) was utilized as sampling frame. The EARF contains the number of households by enumeration area (EA) in each barangay.

This frame was used to form the primary sampling units (PSUs). With consideration of the period for which the 2003 MS will be in use, the PSUs were formed/defined as a barangay or a combination of barangays with at least 500 households.

The 2003 MS considers the 17 regions of the country as the primary strata. Within each region, further stratification was performed using geographic groupings such as provinces, highly urbanized cities (HUCs), and independent component cities (ICCs). Within each of these substrata formed within regions, the PSUs were further stratified, to the extent possible, using the proportion of strong houses (PSTRONG), indicator of engagement in agriculture of the area (AGRI), and a measure of per capita income (PERCAPITA) as stratification factors.

The 2003 MS consists of a sample of 2,835 PSUs. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the total PSUs; a half sample contains one-half of the four sub-samples or equivalent to all PSUs in two replicates.

The final number of sample PSUs for each domain was determined by first classifying PSUs as either self-representing (SR) or non-self-representing (NSR). In addition, to facilitate the selection of sub-samples, the total number of NSR PSUs in each region was adjusted to make it a multiple of 4.

SR PSUs refers to a very large PSU in the region/domain with a selection probability of approximately 1 or higher and is outright included in the MS; it is properly treated as a stratum; also known as certainty PSU. NSR PSUs refers to a regular too small sized PSU in a region/domain; also known as non-certainty PSU. The 2003 MS consists of 330 certainty PSUs and 2,505 non-certainty PSUs.

To have some control over the sub-sample size, the PSUs were selected with probability proportional to some estimated measure of size. The size measure refers to the total number of households from the 2000 CPH. Because of the wide variation in PSU sizes, PSUs with selection probabilities greater than 1 were identified and were included in the sample as certainty selections.

At the second stage, enumeration areas (EAs) were selected within sampled PSUs, and at the third stage, housing units were selected within sampled EAs. Generally, all households in sampled housing units were enumerated, except for few cases when the number of households in a housing unit exceeds three. In which case, a sample of three households in a sampled housing unit was selected at random with equal probability.

An EA is defined as an area with discernable boundaries within barangays consisting of about 150 contiguous households. These EAs were identified during the 2000 CPH. A housing unit, on the other hand, is a structurally separate and independent place of abode which, by the way it has been constructed, converted, or arranged, is intended for habitation by a household.

The 2003 FIES involved the interview of a national sample of about 51,000 sample households deemed sufficient to gather data on family income and family expenditure and related information affecting income and expenditure levels and patterns in the Philippines at the national and regional level. The sample households covered in the survey were the same households interviewed in the July 2003 and January 2004 round of the LFS.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The 2003 FIES questionnaire contains about 800 data items and a summary for comparing income and expenditures. The questionnaires were subjected to a rigorous manual and machine edit checks for completeness, arithmetic accuracy, range validity and internal consistency.

The major steps in the machine processing are as follows: 1. Data Entry 2. Completeness Check 3. Matching of visit records 4. Consistency and Macro Edit (Big Edit) 5. Generation of the Public Use File 6. Tabulation

Steps 1 to 2 were done right after each visit. The remaining steps were carried out only after the second visit had been completed.

Steps 1 to 4 were done at the Regional Office while Steps 5 and 6 were completed in the Central Office.

After completing Steps 1 to 4, data files were transmitted to the Central Office where a summary file was generated. The summary file was used to produce the consistency tables as well as the preliminary and textual tables.

When the generated tables showed inconsistencies, selected data items were subjected to further scrutiny and validation. The cycle of generation of consistency tables and data validation were done until questionable data items were verified.

The FAME (FIES computer-Aided Consistency and Macro Editing), an interactive Windows-based application system was used in data processing. This system was used starting with the 2000 FIES round. The interactive module of FAME enabled the following activities to be done simultaneously. a) Matching of visit records b) Consistency and macro edit (big edit) c) Range check

The improved system minimized processing time as well as minimized, if not eliminated, the need for paper to generate the reject listing.

Note: For data entry, CSPro Version 2.6 was used.

Response rate

The response rate for this survey is 95.7%. The response rate is the ratio of the total responding households to the total number of eligible households. Eligible households include households who were completely interviewed, refused to be interviewed or were temporarily away or not at home or on vacation during the survey period.

Sampling error estimates

As in all surveys, two types of non-response were encountered in the 2003 FIES: interview non-response and item non-response. Interview non-response refers to a sample household that could not be interviewed. Since the survey requires that the sample households be interviewed in both visits, households that transferred to another dwelling unit, temporarily away, on vacation, not at home, household unit demolished, destroyed by fire/typhoon and refusal to be interviewed in the second visit contributed to the number of interview non-response cases.

Item non-response, or the failure to obtain responses to particular survey items, resulted from factors such as respondents being unaware of the answer to a particular question, unwilling to provide the requested information or ENs’ omission of questions during the interview. Deterministic imputation was done to address item nonresponse. This imputation is a process in which proper entry for a particular missing item was deduced from other items of the questionnaire where the non-response item was observed. Notes and remarks indicated in the questionnaire were likewise used as basis for imputation.

Data appraisal

Refer to the

Philippines PH: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data was reported at 17.800 % in 2021. This records a decrease from the previous number of 18.300 % for 2018. Philippines PH: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data is updated yearly, averaging 34.950 % from Dec 2000 (Median) to 2021, with 8 observations. The data reached an all-time high of 39.400 % in 2006 and a record low of 17.800 % in 2021. Philippines PH: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data was reported at 3.730 Intl $/Day in 2021. This records an increase from the previous number of 3.130 Intl $/Day for 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data is updated yearly, averaging 3.430 Intl $/Day from Dec 2015 (Median) to 2021, with 2 observations. The data reached an all-time high of 3.730 Intl $/Day in 2021 and a record low of 3.130 Intl $/Day in 2015. Philippines PH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

Abstract

A. Objective To generate statistics for wage and salary administration and for wage determination in collective bargaining negotiations.

B. Uses of Data Inputs to wage, income, productivity and price policies, wage fixing and collective bargaining; occupational wage rates can be used to measure wage differentials, wage inequality in typical low wage and high wage occupations and for international comparability; industry data on basic pay and allowance can be used to measure wage differentials across industries, for investment decisions and as reference in periodic adjustments of minimum wages.

C. Main Topics Covered Occupational wage rates Median basic pay and median allowances of time-rate workers on full-time basis

Geographic coverage

National Capital Region

Analysis unit

Establishment

Universe

The survey covered non-agricultural establishments employing 50 or more workers except national postal activities, central banking, public administration and defense and compulsory social security, public education services, public medical, dental and other health services, activities of membership organizations, extra territorial organizations and bodies.

Kind of data

Sample survey data [ssd]

Sampling procedure

Statistical unit: The statistical unit is the establishment. Each unit is classified to an industry that reflects its main economic activity---the activity that contributes the biggest or major portion of the gross income or revenues of the establishment.

Survey universe/Sampling frame: The sampling frame used for the survey was taken from the List of Establishments of the National Statistics Office. On a partial basis, this is regularly updated based on the responses to other surveys of the BLES, establishment reports on retrenchments and closures submitted to the Regional Offices of the Department of Labor and Employment and other establishment lists.

Sampling design: The OWS is a complete enumeration survey of non-agricultural establishments employing 50 persons or more in the National Capital Region.

Sample size: For OWS 2002, number of establishments covered was 5,954 of which, 3,974 were eligible units.

Note: Refer to Field Operations Manual

Sampling deviation

Not all of the fielded questionnaires are accomplished. During data collection, there are reports of permanent closures, non-location, duplicate listing and shifts in industry and employment outside the survey coverage. Establishments that fall in these categories are not eligible elements (three consecutive survey rounds for "can not be located" establishments) of the frame and their count is not considered in the estimation. Non-respondents are made up of refusals, strikes or temporary closures, can not be located (less than three consecutive survey rounds) and those establishments whose questionnaires contain inconsistent item responses and have not replied to the verification queries by the time output table generation commences.

Respondents are post-stratified as to geographic, industry and employment size classifications. Non-respondents are retained in their classifications. Sample values of basic pay and allowances for the monitored occupations whose basis of payment is an hour or a day are converted into a standard monthly equivalent, assuming 313 working days and 8 hours per day. Daily rate x 26.08333; Hourly rate x 208.66667.

Mode of data collection

Other [oth] mixed method: self-accomplished, mailed, face-to-face

Research instrument

The 2002 OWS questionnaire is made up of the following sections:

Cover page (Page 1) This contains the address box for the establishment and other particulars.

Survey Information (Page 2) This section provides information on the purpose of the survey, coverage, reference period, collection authority, authorized field personnel, confidentiality clause, due date, availability of results and assistance available.

Part A: General Information (Page 3) This part inquires on the main economic activity, major product/s, goods or services, total employment, ownership (with foreign equity or wholly Filipino), spread of operations (whether establishment is a multinational), market orientation (for manufacturing only, engaged in export or domestic market only), presence of a union and existence of a collective bargaining agreement in the establishment.

Part B: Employment and Wage Rates of Time-Rate Workers on Full Time Basis (Pages 4 - 5) It inquires data on the distribution of time-rate workers on full-time basis by time unit (hourly, daily, monthly) and basic pay and allowance intervals;

Part C: Employment and Wage Rates of Time-Rate Workers on Full-Time Basis in Selected Occupations (Pages 6 - 11) For each occupation covered, the establishment is asked to report the time unit of work (hourly, daily, monthly), corresponding basic pay per worker and number of workers. Similar data are also asked for workers in the occupation that are given regular allowances. The total number of workers disaggregated by sex in each monitored occupation is likewise requested

Part D: Key and Representative Occupations in the Establishment (Page 12) This asks for the occupations and corresponding employment of those considered as unique to the industry/sector to which the establishment belongs, employs the most number of works, historically important in the wage structure or emerging/has a high growth potential.

Survey Results (Pages 13 - 14) Selected statistical tables from the previous two (2) survey rounds are provided for information of the respondents.

Part E: Certification of Respondent (Page 15) This box is provided for the respondent’s comments or suggestions (on the data it provided for the survey, results of previous survey rounds and improvements on the design/contents of the questionnaire) and for the name and signature, position, and telephone/fax numbers and e-mail address of the person responsible for filling out the form.

Part F: Survey Personnel (Page 15) This portion is allocated for the names of personnel involved in collection, editing and review of each questionnaire and dates when the activities were completed.

Part G: Industries with Selected Occupations (Page 16) This lists the selected 43 industries whose occupational wage rates and employment are being monitored.

Note: Refer to Questionnaire.

Cleaning operations

Data are manually and electronically processed. Upon collection of accomplished questionnaires, enumerators perform field editing before leaving the establishments to ensure completeness, consistency and reasonableness of entries in accordance with the field operations manual. The forms are again checked for data consistency and completeness by their field supervisors.

The BLES personnel undertake the final review, coding of information on classifications used, data entry and validation and scrutiny of aggregated results for coherence. Questionnaires with incomplete or inconsistent entries are returned to the establishments for verification, personally or through mail.

Note: Refer to Field Operations Manual Chapter 1 Section 1.10.

Response rate

The response rate in terms of eligible units was 78.7%.

Data appraisal

The survey results are checked for consistency with the results of previous OWS data and the minimum wage rates corresponding to the reference period of the survey.

Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 5.800 % in 2017. Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 5.800 % from Dec 2017 (Median) to 2017, with 1 observations. The data reached an all-time high of 5.800 % in 2017 and a record low of 5.800 % in 2017. Philippines Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;

Philippines PTE: Clothing & Footwear data was reported at 2.400 % in 2015. This stayed constant from the previous number of 2.400 % for 2012. Philippines PTE: Clothing & Footwear data is updated yearly, averaging 2.400 % from Dec 1997 (Median) to 2015, with 7 observations. The data reached an all-time high of 3.300 % in 1997 and a record low of 2.200 % in 2009. Philippines PTE: Clothing & Footwear data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H026: Family Income and Expenditure Survey: Percentage Distribution of Family Expenditure: By Income Class.

Philippines PTE: House Rent data was reported at 12.200 % in 2015. Philippines PTE: House Rent data is updated yearly, averaging 12.200 % from Dec 2015 (Median) to 2015, with 1 observations. Philippines PTE: House Rent data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H026: Family Income and Expenditure Survey: Percentage Distribution of Family Expenditure: By Income Class.