The capital city of Manila had a cost of living index score of 37.9 in 2023, indicating a decrease from the previous year. In the Asia Pacific region, Seoul in South Korea had the highest cost of living among other megacities as of 2023.

At 8.07 U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the July 2024 Big Mac index. Concurrently, the cost of a Big Mac was 5.69 dollars in the U.S., and 6.06 U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

Abstract

Seaweed has become one of the most promising commodities in the country because of its wide industrial and commercial uses. It is a major contributor to foreign currency earnings. Seaweed culture requires more labor inputs, thus, it offers more employment opportunities than other fishery activities, especially, for those idle labor forces in the coastal areas.

Due to the large potentials of seaweed, it has become necessary to ascertain the profitability of venturing into the production of this commodity. Its large contribution to the overall fishery subsector has also made it one of the top priorities for development. The Costs and Returns Survey of Seaweed Production was conducted to generate the needed information for promoting the sustained expansion of the seaweed industry in the Philippines.

The main objective of this study was to generate production costs and returns structure of seaweeds. Specifically, it was conducted to determine production cost structures, indicators of profitability such as gross and net returns, returns above cash costs, net profit - cost ratio, etc., information on the use of materials and labor inputs; and other related socio-economic variables including information on new production technologies.

Geographic coverage

The survey was conducted in five (5) provinces, namely; Palawan, Bohol, Zamboanga Sibugay, Maguindanao and Tawi-Tawi.

Analysis unit

Seaweed farm operators and seaweed farms with harvests during the reference period as the units of analysis

Universe

The survey covered all seaweed farms with harvest during the last completed production cycle in 2007

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Design, Sample Size and Sample Selection Procedure

A two-stage sampling design was employed with the barangay as the primary sampling unit and seaweed farm operator as the secondary sampling unit. The list of seaweed producing barangays which was generated through the Aquaculture Farms Inventory was used as the sampling frame. The sample barangays were drawn using simple random sampling from the list of barangays with at least 90 percent cumulative share of seaweed harvested area and with more than five seaweed operators. Ten (10) barangays were drawn from each province except for Maguindanao, which has less than ten seaweed producing barangays. In this case, all barangays were covered by the survey. The number of sample seaweed operators was proportionately allocated to the number of operators in the sample barangay. In each sample barangay, sample seaweed farm operators were identified using snowball sampling approach. Names and addresses of seaweed operators living in the barangay were obtained from the barangay council or from seaweed farmers association. From this list, the enumerator selected a seaweed farm operator at random. A set of screening questions was asked from the operator to determine if he/she met the criteria set for the survey. Whether the operator was qualified or not, he/she would then be asked to recommend others who they thought could qualify as survey respondents. From these names, the enumerator again selected a seaweed farm operator as the second potential sample for the survey. The process continued until the required number of samples was attained.

The survey was able to enumerate the following sample seaweed farm operators by province and variety planted.

Palawan
COTTONII 71 Bohol
COTTONII 51 SPINOSUM 24 Zamboanga Sibugay COTTONII 30 ALVAREZII 20 Maguindanao COTTONII 50 Tawi-Tawi
COTTONII 49 ALVAREZII 1

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was a structured questionnaire written in English. It was designed in tabular form and some in question type format.

The questionnaire consisted of 8 pages covering 13 blocks as follows:

A. GEOGRAPHIC INFORMATION includes the location of the seaweed farm such as the name of the region, province, city/municipality and barangay.

B. SAMPLE IDENTIFICATION such as the name, age, sex, highest educational attainment, main occupation and seaweed farming experience of the sample farmer/operator and the name of the respondent.

C. FARM CHARACTERISTICS such as area of seaweed farm, variety planted, culture method, loaction of seaweed farm, month of planting and harvesting, number of croppings and harvests.

D. FARM INVESTMENTS such as inventory of farm investments used, year and cost of acquisition, repairs and improvement cost and estimated life and usage in the focus farm.

E. MATERIAL INPUTS AND SUPPLIES contain the quantity and cost of material inputs used.

F. LABOR INPUTS such as labor utilization (in terms of mandays) and labor cost by type of farming activity, by source of labor and by sex and food cost incurred.

G. OTHER PRODUCTION COSTS cover cash and non-cash payments for salaries of employees abd caretakers, cooperative fees, rentals of machine and tools, fuel and oil, transport costs of inputs, license, interest payment on crop loan and other production costs.

H. PRODUCTION AND DISPOSITION such as volume of seaweed production (fresh or dry) and its disposition in terms of sold, harvesters' share, caretakers' share, other laborers' share, for home consumption, given away, used for seedlings, wastage and other purposes.

I. BUYER INFORMATION includes the major buyer of seaweeds and the percentage of seaweed sold to each buyer.

J. PROBLEMS ENCOUNTERED such as problems affecting production and marketing of seaweeds.

K. ACCESS TO CREDIT such as the amount and source of loan and interest rate per annum.

L. OTHER INFORMATION such as membership in seaweed-related association and benefits derived, access to extension services, future plans and recommendations to improve seaweed industry.

M. INTERVIEW/SURVEY PARTICULARS contain the name and signature of interviewer, field supervisor/editor and PASO and date accomplished.

Cleaning operations

Manual editing was initially done at the Provincial Operations Center during and after data collection using the CRS editing guidelines prepared by the Central Office. The edited questionnaires were again checked at the Central Office. Coding and encoding were likewise done at the Central Office.

Refer to Technical Documents for the Editing Guidelines.

Response rate

Response rate of 98.67%

Sampling error estimates

Not applicable.

Data appraisal

The quality of the survey results were reviewed in terms of reliability and acceptability. A comparison with other related reference materials on input usage, labor utilization, production cost and return structure of seaweed was done.

Based on the 2021 census conducted in the Philippines, households in Metro Manila or the National Capital Region (NCR) had an annual average expenditure of 322,000 Philippine pesos. This indicates a decrease from the average annual expenditure from the 2018 census, which amounted to 369,000 Philippine pesos.

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

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

Analysis unit

The reporting unit was the household which implied that the statistics emanating from this survey referred to the characteristics of the population residing in private households. Institutional population is not within the scope of the survey.

For FIES, the concept of family was used. A family consists of the household head, spouse, unmarried children, ever-married children, son-in-law/daughter-in-law, parents of the head/spouse and other relatives who are members of the household.

In addition, two or more persons not related to each other by blood, marriage or adoption are also considered in this survey. However, only the income and expenditure of the member who is considered as the household head are included.

Universe

The survey involved the interview of a national sample of about 51,000 sample households deemed sufficient to provide reliable estimates of income and expenditure at the national and regional level.

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.

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 interview

Research instrument

Refer to the attached 2003 FIES questionnaire in pdf file (External Resources)

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

According to a 2024 survey in the Philippines, 51 percent of respondents identified the rising energy prices and the cost of living as their leading concern about the impact of transitioning to renewable energy or cutting fossil fuels. Energy shortages and loss of jobs were also among their main concerns.

Informal traditional retailers accounted for nearly half of the total food retail sales in the Philippines in 2023, making them the leading food retail channel. This was followed by supermarkets, which contributed to about a quarter of those sales. What makes informal traditional retailers popular?  With easily collapsible stalls found in very accessible spots, informal traditional retailers are usually spread out in various locations in the Philippines. These retailers typically provide fresh food items such as fruits, vegetables, fish, and meat that could be sold at a much cheaper price as they are not regulated. These sellers also have lower operating costs as they do not need to pay rent. In addition, such retailers can offer better deals, depending on the availability of the products they are selling. Recovering from losses brought by the pandemic, the retail sales of informal traditional retailers increased by about 430 percent between 2021 and 2022. Impact of inflation on grocery shopping  As the average inflation rate of all commodities in the Philippines continues to rise, households facing difficulties being able to afford necessities. A March 2023 survey revealed that most Filipino consumers check prices first before buying anything due to inflation. Meanwhile, due to rising living costs/inflation, etc., some other consumers have chosen to reduce the frequency of doing any leisure activities, such as dining out and going to bars or cinemas.