Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographics GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

How high is the brand awareness of Stop & Shop in the United States?When it comes to grocery store customers, brand awareness of Stop & Shop is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Stop & Shop in the United States?In total, ** of U.S. grocery store customers say they like Stop & Shop. However, in actuality, among the *** of U.S. respondents who know Stop & Shop, *** of people like the brand.What is the usage share of Stop & Shop in the United States?All in all, ** of grocery store customers in the United States use Stop & Shop. That means, of the *** who know the brand, *** use them.How loyal are the customers of Stop & Shop?Around ** of grocery store customers in the United States say they are likely to use Stop & Shop again. Set in relation to the ** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around Stop & Shop in the United States?In August 2022, about ** of U.S. grocery store customers had heard about Stop & Shop in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Stop & Shop in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Sourcing accurate and up-to-date map data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps Map Data uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographics data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

GapMaps Map Data also includes the latest Point-of-Interest (POI) Data for leading retail brands across a range of categories including Fast Food/ QSR, Health & Fitness, Supermarket/Grocery and Cafe sectors which is updated monthly.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps Map Data for Asia and MENA can be utilized in any GIS platform and includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Map Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.
6. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
7. Customer Profiling
8. Target Marketing
9. Market Share Analysis

Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

Global Second Hand Apparel Market size was worth around USD 42.5 billion in 2023 and is grow to around USD 113.2 billion by 2032, a CAGR of 11.5%.

The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because it is more expensive to pay using a card or phone. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

This dataset contains a number of files containing hearth and population censuses for the Duchy of Brabant and Lordship of Mechelen, starting from 1374. The dataset is linked to the GIS dataset Historical Atlas of the Low Countries (1350-1800).

The data was collected and digitised with the great help of Bastiaan van den Akker, who worked in 2018/19 as an intern at the International Institute of Social History and, as part of the NWO-funded project 'Imagining a Territory' (led by Dr. Mario Damen), at the University of Amsterdam.

The files in the folder "summaries" refer to data files prepared for further analysis, by disaggregating counts of combined areas using known ratios of other years, by interpolating missing values using nearest-neighbour-analysis of growth rates, and by creating a consistent time series by relating each figure to a predefined spatial part of the duchy which remains fixed.
The methodology behind these files is described in A.E. Oostindiër and R.J. Stapel, 'Demographic Shifts and the Politics of Taxation in the Making of Fifteenth-Century Brabant', in: M.J.M. Damen and K. Overlaet eds., Constructing and Representing Territory in Late Medieval and Early Modern Europe (Amsterdam: Amsterdam University Press), which is currently under review.

For users not familiar with creating spatial joins in GIS, we would like refer to this related dataset. It contains a number of specially prepared GIS layers for the Duchy of Brabant, that store some of the data available below.

The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because of some other reason. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

How high is the brand awareness of Penny in Germany?When it comes to grocery store customers, brand awareness of Penny is at *** in Germany. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Penny in Germany?In total, *** of German grocery store customers say they like Penny. However, in actuality, among the *** of German respondents who know Penny, *** of people like the brand.What is the usage share of Penny in Germany?All in all, *** of grocery store customers in Germany use Penny. That means, of the *** who know the brand, *** use them.How loyal are the customers of Penny?Around *** of grocery store customers in Germany say they are likely to use Penny again. Set in relation to the *** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around Penny in Germany?In August 2022, about *** of German grocery store customers had heard about Penny in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's some buzz around Penny in Germany.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

The percentage of respondents who report not using a mobile phone or card to pay for instore purchase in the past year. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

Characteristics of apps included in a systematic review of apps providing information about abortion.

Snapshot img { margin: 10px !important; } Outlook of the children’s furniture market in the US

In this market research report, Technavio’s analyst has estimated the children’s furniture market in the US to grow steadily at a CAGR of more than 4% during the forecast period. The rise in demand for multifunctional children’s furniture is one of the key growth drivers for this market. A sharp increase in real estate prices in the US has led to a considerable reduction in house sizes. With smaller rooms, consumers prefer children’s furniture items that are compact and easy-to-move with storage facilities. Most contemporary children’s furniture is available with storage spaces in tables, under beds, and in entertainment consoles. Moreover, consumers consider such multifunctional furniture as value for money which will lead to an augmented demand for children’s furniture in the coming years.

Factors like the growing preference for hand-crafted children’s furniture in the US to impel the prospects for market growth until the end of the forecast period. To cope with the increasing demand for handcrafted furniture, vendors are now partnering with local manufacturers for the supply of these products. For instance, Just Kids Stuff is a US-based manufacturer of hand-crafted furniture for children. With several companies like IKEA partnering with social enterprises to launch hand-crafted children’s furniture, the growth prospects of the children’s furniture market in the US will have a positive outlook over the forecast period.

Segmentation by age group and analysis of the children’s furniture market in the US

Children aged 0-4 years
Children aged 5-12 years

In 2015, the furniture market for children aged 5-12 years dominated the market and accounted for more than 62% of the market share. The increasing population of children in this age group will drive the market during the predicted period. The population of children aged between 5 and 12 years has increased by 0.37% between 2010 and 2014 in the US. Additionally, product innovations and an augmented demand for eco-friendly furniture items will spur the growth of this market segment during the forecast period.

Segmentation by distribution channel and analysis of the children’s furniture market in the US

Traditional furniture stores
Specialty furniture stores
Hypermarkets, supermarkets, department stores, and clubhouses
E-retailers

The traditional furniture stores segment dominated by the children’s furniture market in the US and accounted for more than 36% of the market share during 2015. This distribution channel will experience slow growth over the next five years due to the mature nature of the market.

Competitive landscape and key vendors

The children’s furniture market in the US is fragmented and highly competitive owing to the presence of a few large global and several regional players. To survive and succeed in a highly competitive environment, vendors need to distinguish their product and service offerings through a clear and unique value proposition.

Key vendors in this market are -

Ashley Furniture HomeStores
Berkshire Hathaway
IKEA
Rooms To Go
Williams-Sonoma

Other prominent vendors analyzed in this report are Amazon, American Signature, ATG Stores, Cabela's, Costco Wholesale, Ethan Allen Global, Haverty Furniture, Herman Miller, JC Penney, Kroger, La-Z-Boy Furniture Galleries, Otto (Crate & Barrel), Overstock.com, Pier 1 Imports, Raymour & Flanigan Furniture, Restoration Hardware, Sears Holdings, Sleepy's, Target, Walmart, and Wayfair.

Key questions answered in the report include

What will the market size and the growth rate be in 2020?
What are the key factors driving the children’s furniture market in the US?
What are the key market trends impacting the growth of the children’s furniture market in the US?
What are the challenges to market growth?
Who are the key vendors in the children’s furniture market in the US?
What are the market opportunities and threats faced by the vendors in the children’s furniture market in the US?
Trending factors influencing the market shares in the US.
What are the key outcomes of the five forces analysis of the children’s furniture market in the US?

Technavio also offers customization on reports based on specific client requirement.

Related reports

Home Furniture Market in the US 2016-2020
Global Luxury Furniture Market 2015-2019
Global Office Furniture Market 2016-2020
Outdoor Furniture Market in the US 2015-2019

Abstract

The LFS 2001 focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

Geographic coverage

The data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates.

Analysis unit

Household, individual

Universe

The survey covered all the Palestinian households who are a usual residence in West Bank and Gaza.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consisted of a master sample of enumeration areas (EAs) selected from the population housing and estableshment census 1997, the master sample cosists of area units of relatively equal size (number of housholds), these units have been used as primary sampling units (PSUs).

Sampling Frame and Target Population:

Target Population: It consists of all individuals aged 10 years and older normally residing in their households in Palestine during 2000.

Sample Design: The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made: 1. Stratification by Governorates.

1. Stratification by type of locality which comprises: (a) Urban (b) Rural (c) Refugee Camps
2. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
3. Stratification by size of locality (number of households).

Sample Size: The sample size in the 20th round consisted of 7,559 households, which amounts to a sample of around 28,959 persons aged 10 years and over (including 22,874 aged 15 years and over). In the 21st round the sample consisted of 7,559 households, which amounts to a sample of around 28,922 persons aged 10 years and over (including 22,762 aged 15 years and over), in the 22nd round the sample consisted of 7,559 households, which amounts to a sample of around 28,380 persons aged 10 years and over (including 22,495 aged 15 years and over). which amounts to a sample of around 26,974 persons aged 10 years and over (including 21,240 aged 15 years and over). In the 23rd round the sample consisted of 7,559 households; which amounts to a sample of around 27,870 persons aged 10 years and over (including 21,868 aged 15 years and over). The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Each round of the Labor Force Survey covers all the 484 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

The LFS questionnaire consists of four main sections: Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 10 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

Data editing took place at a number of stages through the processing including: 1. Office editing and coding 2. During data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Response rate

The overall response rate for the survey was 84.2%

Sampling error estimates

Detailed information on the sampling Error is available in the Survey Report.

Data appraisal

Detailed information on the data appraisal is available in the Survey Report

The percentage of respondents who report the main reason they used cash to pay for instore purchase in the past year, is because they don't trust payments using a card or phone. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

How high is the brand awareness of Hy-Vee in the United States?When it comes to grocery store customers, brand awareness of Hy-Vee is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Hy-Vee in the United States?In total, *% of U.S. grocery store customers say they like Hy-Vee. However, in actuality, among the **% of U.S. respondents who know Hy-Vee, **% of people like the brand.What is the usage share of Hy-Vee in the United States?All in all, *% of grocery store customers in the United States use Hy-Vee. That means, of the **% who know the brand, **% use them.How loyal are the customers of Hy-Vee?Around *% of grocery store customers in the United States say they are likely to use Hy-Vee again. Set in relation to the *% usage share of the brand, this means that **% of their customers show loyalty to the brand.What's the buzz around Hy-Vee in the United States?In August 2022, about *% of U.S. grocery store customers had heard about Hy-Vee in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's little buzz around Hy-Vee in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2001 (LFS).

The importance of this survey lies in that it focuses mainly on labour force key indicators, main characteristics of the employed, unemployed, underemployed and persons outside labour force, labour force according to level of education, distribution of the employed population by occupation, economic activity, place of work, employment status, hours and days worked and average daily wage in NIS for the employees.

The survey main objectives are: - To estimate the labor force and its percentage to the population. - To estimate the number of employed individuals. - To analyze labour force according to gender, employment status, educational level , occupation and economic activity. - To provide information about the main changes in the labour market structure and its socio economic characteristics. - To estimate the numbers of unemployed individuals and analyze their general characteristics. - To estimate the rate of working hours and wages for employed individuals in addition to analyze of other characteristics.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

The Data are representative at region level (West Bank, Gaza Strip), locality type (urban, rural, camp) and governorates

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

Target Population:

All Palestinians aged 10 years or older living in the Palestinian Territory, excluding those living in institutions such as prisons or shelters.

Sampling Frame:

The sampling frame consisted of a master sample of Enumeration Areas (EAs) selected from the population housing and establishment census 1997. The master sample consists of area units of relatively equal size (number of households), these units have been used as Primary Sampling Units (PSUs).

Sample Design:

The sample is a two-stage stratified cluster random sample.

Stratification: Four levels of stratification were made:

1. Stratification by Governorates.
2. Stratification by type of locality which comprises: (a) Urban, (b) Rural, and (c) Refugee Camps
3. Stratification by classifying localities, excluding governorate centers, into three strata based on the ownership of households of durable goods within these localities.
4. Stratification by size of locality (number of households).

Sample Size:

The sample size in the first quarter consisted of 7,559 households, which amounts to a sample of around 28,959 persons aged 15 years and over (including 22,874 aged 15 years and over). In the second round the sample consisted of 7,559 households, which amounts to a sample of around 28,922 persons aged 10 years and over (including 22,762 aged 15 years and over), in the third round the sample consisted of 7,559 households, which amounts to a sample of around 28,380 persons aged 10 years and over (including 22,495 aged 15 years and over).which amount to a sample of around 26974 persons aged 10 years and over (including 21240 aged 15 years and over). In the fourth round the sample consisted of 7,559 households; which amounts to a sample of around 27,870 persons aged 10 years and over (including 21,868 aged 15 years and over).

The sample size allowed for non-response and related losses. In addition, the average number of households selected in each cell was 16.

Sample Rotation:

Each round of the Labor Force Survey covers all the 481 master sample areas. Basically, the areas remain fixed over time, but households in 50% of the EAs are replaced each round. The same household remains in the sample over 2 consecutive rounds, rests for the next two rounds and represented again in the sample for another and last two consecutive rounds before it is dropped from the sample. A 50 % overlap is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes). In earlier applications of the LFS (rounds 1 to 11); the rotation pattern used was different; requiring a household to remain in the sample for six consecutive rounds, then dropped. The objective of such a pattern was to increase the overlap between consecutive rounds. The new rotation pattern was introduced to reduce the burden on the households resulting from visiting the same household for six consecutive times.

Mode of data collection

Face-to-face [f2f]

Research instrument

One of the main survey tools is the questionnaire, the survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

1. Identification Data:

The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

2. Quality Control:

This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

3. Household Roster:

This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

4. Employment Part:

This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

Raw Data

The data processing stage consisted of the following operations: 1. Editing before data entry All questionnaires were then edited in the main office using the same instructions adopted for editing in the field.

1. Coding At this stage, the Economic Activity variable underwent coding according to West Bank and Gaza Strip Standard commodities Classification, based on the United Nations ISIC-3. The Economic Activity for all employed and ever employed individuals was classified at the fourth-digit-level. The occupations were coded on the basis of the International Standard Occupational Classification of 1988 at the third-digit-level (ISCO-88).

2. Data Entry In this stage data were entered into the computer, using a data entry template BLAISE. The data entry program was prepared in order to satisfy the following requirements:

• Duplication of the questionnaire on the computer screen.
• Logical and consistency checks of data entered.
• Possibility for internal editing of questionnaire answers.
• Maintaining a minimum of errors in digital data entry and fieldwork.
• User-friendly handling

Accordingly, data editing took place at a number of stages through the processing including: 1. office editing and coding 2. during data entry 3. structure checking and completeness 4. structural checking of SPSS data files

Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The overall response rate for the survey was 84.2%

More information on the distribution of response rates by different survey rounds is available in Page 11 of the data user guide provided among the disseminated survey materials under a file named "Palestine 2001- Data User Guide (English).pdf".

Sampling error estimates

Since the data reported here are based on a sample survey and not on a complete enumeration, they are subjected to sampling errors as well as non-sampling errors. Sampling errors are random outcomes of the sample design, and are, therefore, in principle measurable by the statistical concept of standard error.

A description of the

Abstract

Tonga Household Income and Expenditure Survey 2009 (HIES), undertaken by the Tonga Statistics Department during the period from 1 January 2009 to 31 December 2009. This is the second survey of its kind in Tonga. The last one was carried out in 2000/01, and the results were used in November 2002 to rebase the Consumer Price Index (CPI). A report from that survey was produced in December 2002, and where possible, results from this report will be made to be comparable to the previous report.

• To provide updated information for the expenditure item weights for the CPI; • To provide some data for the components of National Accounts; and • To provide information on the nature and distribution of household income and expenditure for planners, policy makers, and the general public.

Geographic coverage

National Coverage and Island Division

Analysis unit

Private Household, individual, income and expenditure item

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design was done in such a way that promoted estimates primarily at the national level, but also at the island division level. For that reason a higher sample fraction was selected in the smaller island divisions.

Rural Tongatapu received the smallest sample fraction (8.3%) as it had the highest population. On the other hand the Ongo Niua received the largest sample fraction (21.5%) as their population was the smallest. Overall a sample of roughly 10 per cent was selected for Tonga.

The sample was selected independently within each of the 6 target areas. Firstly, extremely remote areas were removed from the frame (and thus not given a chance of selection) as it was considered too expensive to cover these areas. These areas only represented about 3.5 per cent of the total population for Tonga, so the impact of their removal was considered very minimal.

The sampling in each area was then undertaken using a two-stage process. The first stage involved the selection of census blocks using Probability Proportional to Size (PPS) sampling, where the size measure was the expected number of households in that block. For the second stage, a fixed number (twelve) of households were selected from each selected census block using systematic sampling. The household lists for all selected blocks were updated just prior to the second stage of selection.

Given the sample was spread out over four quarters during the 2009 calendar year, every 4th selected census block was allocated to a respective quarter. To ensure an equally distribution of sample to each quarter, the number of census blocks selected for each of the six target group was made divisible by four. This therefore meant the sample size for each target group was adjusted so that it was divisible by (4*12)=48, as can be seen in Table 1 of Section 1 of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were 4 main survey schedules used to collect the information for the survey: 1) Household Questionnaire 2) Individual Questionnaire - Part 1 3) Individual Questionnaire - Part 2 4) Individual Diary (x2)

Household Questionnaire This questionnaire is primarily used to collect information on large expenditure items, but also collects information about the dwelling characteristics. In total there are 14 sections to this questionnaire which cover: 1 Dwelling Characteristics 2 Household Possessions 3 Dwelling Tenure 4 Construction of Dwellings 5 Household Bills 6 Transport Expenses 7 Major Consumer Durables 8 Education/Recreation 9 Medical & Health 10 Overseas Travel 11 Special Events 12 Subsistence Activity Sales 13 Remittances 14 Contributions to Church/Village/School As stated above, the first section is devoted to collecting information about key dwelling characteristics, whereas the second section collects information on household possessions. Sections 3-11, and Section 14, focus on expenses the household incurs, whereas Section 13 focuses on remittances both paid by and received by the household. Finally, Section 12 collects information from households about the income they generate from subsistence activities. This section is the main question collecting income from the household questionnaire, as was included here as it was considered more appropriate to collect this data at the household level. The front page of this Questionnaire is also used for collecting the Roster of Household Members.

Individual Questionnaire - Part 1 This questionnaire collects basic demographic information about each individual in the household, including: • Relationship to Household Head • Sex • Age • Ethnicity • Marital Status

Also collected in this form is information about health problems each individual may have encountered in the last 3 months, followed by education information. For the education section, if a person is currently attending an education institution, then current level is asked, whereas if the person attended an education institution but no longer attends, then the highest level completed is collected. The last main section of this form collects information about labour force and is only asked of individuals aged 10 years and above. These questions aim to classify each person in scope for this section as either: • In the Labour Force - Employed • In the Labour Force - Unemployed • Not in the Labour Force

Individual Questionnaire - Part 2 This questionnaire is focused on collecting information from individuals regarding their income. There are eight sections to this questionnaire of which six are devoted to income. They include: 1 Wages and Salary 2 Self-Employment
3 Previous Jobs
4 Ad-hoc Jobs 5 Pensions/Welfare Benefits 6 Other Income 7 Loan Information 8 Contributions to Benefit Schemes

As stated above, the first six sections of this questionnaire focus on income. Section 7 collects information pertaining to loans for i) households, ii) cars, iii) special events and iv) other, and finally the last question is an expense related question covering contributions to benefit schemes which was considered best covered at an individual level.

Individual Diary The last form used for the survey was the Individual Diary which each individual aged 10 years and over was required to fill in for two weeks (two one-week diaries).

Each diary had 4 sections covering the following: 1) Items Purchased: This section had a separate page for each day and was for recording all items bought in a store, street vendors, market or any other place (including credit) 2) Home Grown/Produced Items: This section was for recording home grown/produced items consisting of items such as food grown at home or at the family plantation, self caught or gathered fish and homemade handicrafts and other goods grown and produced at home. Information is recorded for these items consumed by the household which they produced themselves, these items they gave away as a gift, and these items they received as a gift. 3) Gifts Given and Received: This section of the diary is for recording gifts given and received including both cash and purchased goods (but not home produced). If any member of the household receives a gift that meets this criteria during the diary keeping period from someone who is not a member of their household it is recorded here. 4) Winnings from Gambling: The last section of the Diary is for recording all winnings from gambling during the diary keeping period.

Cleaning operations

Batch edits in CSPro were performed on the data after data entry was completed. The batch edits were aimed at identifying any values falling outside acceptable ranges, as well as other inconsistencies in the data. As this process was done at the batch level, questionnaires were often referred to and manual changes to the data were performed to amend identified errors.

One significant problem which was identified during this process was the incorrect coding of phone card purchase to the purchase of actual phones. As there were many such cases, an automatic code change was applied to any purchase of phones which was less than $40 - recoding them to purchase of phone cards.

Response rate

The final Response Rates for the survey was high, which will assist in yielding statistically significant estimates. Across all six target groups the response rate was in excess of 95 per cent, with the exception of Ongo Niua who only reported 50 per cent. The reason the number was so low in the Ongo Niua was because this target area was only visited in the 2nd quarter, where half the total sample were enumerated (to make up for the sample loss in the first quarter), and was not visited again in quarter 3 and 4.

The reason behind the high response rates in other areas was due to the updated lists for selected census blocks excluding vacant dwellings. As such, it was mostly refusals that impacted on the final response rates.

Sampling error estimates

Sampling errors refer to those errors that are implicit in any sample survey, where only a portion of the population is covered. Non-sampling errors refer to all other types of error. These can arise at any stage of the survey process. Examples of activities that are likely to increase the level of non-sampling error are: failing to select a proper sample, poor questionnaire design, weak field supervision, inaccurate data entry, insufficient data editing, or failure to analyze or report on the data correctly. If a census of all the households in Tonga were carried out, there would be no sampling error (but probably increased non-sampling

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