In 2023, the number of child workers in Indonesia amounted to around 1.01 million people. This indicated a decrease of approximately 40 thousand people compared to 2021. Child labor in Indonesia is still an ongoing issue due to poverty and a lack of access to education in some parts of the country. The pandemic notably affected the problem, as the number of child workers increased in 2020. Although the numbers have decreased since then, they remain higher than the pre-pandemic level. The challenges of child labor in Indonesia The persisting issue of child labor in Indonesia stems from different factors such as economics, social norms, and education. Poverty acts as a crucial driving factor in the case of child labor practices. Many children are pushed to stop attending school and get to work to help the family’s income, as over nine percent of the Indonesian population still lives below the poverty line. The islands in the eastern part of the archipelago, such as Maluku and Papua, had the highest poverty rates of over 20 percent in 2022. It was also found that Papua had the highest share of students who had to attend school and work simultaneously. Moreover, in certain areas of the archipelago, cultural beliefs are linked to entering the labor force at an early age, with some believing this to help shape children to have better life opportunities in the future. The lack of awareness about the effects of child labor and some companies not complying with the laws against child labor further exacerbate the issue. Child labor in the Indonesian agricultural sector Child labor in Indonesia is more prevalent in rural areas. As of 2022, there has been an increase in the child labor rate in Indonesia’s rural areas in the agricultural sector, which most commonly offers informal employment with minimal employment protections. Child workers in this sector face higher risks of being exposed to harmful chemicals used in pesticides and fertilizers, causing raised concerns about their safety. Despite the efforts to overcome this issue, such as child protection laws, government allocations for infrastructure, and government allocations for education to improve living conditions and educational access, the need for strategic initiatives to combat child labor in Indonesia remains.

Abstract

This survey intends to: - · Measure the labour force or economically active population size in relation to the general population in the country. · Identify and analyse the factors leading to the emergence and growth of Labour Force in the country. · Monitor the labour force participation. · Identify and measure the informal sector from within the labour force. · Monitor other Key Indicators of the Labour Market such as employment rates,unemployment rates, hours of work, average income and/or wages etc.

Furthermore, the survey seeks to examine the relationships of socio-economic factors such as education, health, social security, employment within the labour force, and more importantly to measure the causes and effects of children’s involvements in economic activities with special focus on the conditions and environment under which affected children operate.

The main objective of the 2012 LFS was to collect data on the social and economic activities of the population, including detailed information on employment, unemployment, underemployment, wages, informal sector, general characteristics of the labour force and economically inactive population. The survey was designed to specifically measure and monitor Key Indicators of the Labour Market (KILM) such as employment levels, unemployment, income and child labour in Zambia. The measurement of the KILM was with a view to informing users and policy-makers for decision-making. The methodology used in carrying out the survey and the design of questionnaire conform to internationally acceptable standards.

Geographic coverage

The 2012 Labour Force Survey (LFS) was a nation-wide survey covering household population in all the ten provinces and, in both rural and urban areas.

The survey covered a representative sample of 11, 520 households, which were selected at two stages. In the first stage, 576 Standard Enumeration Areas (SEAs) were selected from a sampling frame developed from the 2010 Census of Population and Housing. In the second stage, households in each of the selected SEA were first listed/updated and then 20 households for enumeration were selected. The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results.

Analysis unit

The unit of analysis was Households and Individuals (men and women of 5 years and older).

Universe

The survey covered all de jure household members (usual residents) in non-institutionalised housing units, all women and men aged 5 years and older.

The survey excluded institutional populations such as those in hospitals, barracks, prisons or refugee camps. This is because the survey was intended only for usual members of the households, i.e. members who lived together as a household for at least six months or who intended to live together as a household for more than six months - who constituted a household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to allow separate estimates at national level for rural and urban areas. Further, it also allowed for provincial estimates. A cluster, which is equivalent to a Standard Enumeration Area (SEA), was the primary sampling unit in the first stage. In the second stage, a household was a sampling unit for enumeration purposes.

Zambia is administratively divided into ten provinces. Each province is in turn subdivided into districts. For statistical purposes each district is subdivided into Census Supervisory Areas (CSAs) and these are in turn demarcated into Standard Enumeration Areas (SEAs). The Census mapping exercise of 2006-2010 in preparation for the 2010 Census of Population and Housing, demarcated the CSAs within wards, wards within constituencies and constituencies within districts. As at the time of the survey, Zambia had 74 districts, 150 constituencies, 1,430 wards and about 25,000 SEAs. Information borne on the list of SEAs from the sampling frame also includes number of households and the population size as at the last update of the SEA. The number of households determined the selection of primary sampling units (PSU). The SEAs are stratified as urban and rural.

The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results. The proportional allocation does not however allow for reliable estimates for lower domains like district, ward or constituency. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. The sample was then selected using a stratified two-stage cluster design.

Sampling deviation

There was no deviation from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires (Form A and Forma B) were used to collect data from the household members. Form A was used in the first stage for listing purposes while Form B was used in the second stage for collecting detailed data from the selected households. It was a requirement for each household member to provide responses during the face-to-face interview to the questions that were asked.

The main questionnaire has ten sections namely: a. Demographic Characteristics b. Education, Literacy and Skills Training c. Economic Activity d. Employment e. Hours of Work and Underemployment f. Income g. Unemployment/Job Search h. Previous Work Experience i. Household Chores j. Working Conditions (i.e. Forced labour)

Cleaning operations

Data editing took place at a number of stages throughout the processing. These included: 1. Field editing 2. Office editing and coding 3. During data entry 4. Structure checking and completeness 5. Secondary editing 6. Strucural checking of SAS data files

Response rate

At the end of the field work and editing in the provinces, a total of at least 11,000 of completed questionnaires, representing a 99.8 percent response rate were sent to Head Office for data processing.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and in addition to this, external resources such as the 2012 Labour Force Survey report has been attached.

Abstract

The 1999 Zambia Multiple Indicator Cluster Survey (MICS) was a nationally and provincially representative survey of households, women, and children. The main objectives of the survey were to provide up-to-date information for assessing the situation of children and women in Zambia at the end of the decade; and to furnish the necessary data for monitoring progress toward the goals established at the World Summit for Children. This data will form the basis for future action.

Geographic coverage

The 1999 Zambia Multiple Indicator Cluster Survey (MICS) was a nationally and provincially representative survey of households, women, and children.

Analysis unit

Household, Women, Child

Kind of data

Sample survey data [ssd]

Sampling procedure

The Zambia MICS was designed to provide estimates of MICS indicators at the national level, for urban and rural areas, and for nine provinces. The two-stage stratified probability proportional to size (PPS) cluster sampling method was applied in Zambia’s MICS survey. Each province was an independent stratum. Each province is stratified by urban and rural strata. The first stage involved the selection of the primary sampling units (Standard Enumeration Areas, SEA) based on the probability proportional to size method in each district. The second stage was the selection of households within the sample SEAs. Three hundred sixty SEAs were selected from total 13,000 SEAs in Zambia. Twenty five households in urban areas and 20 households in rural areas were selected from each sampled SEA by the systematic sampling method. A total of 8,000 households was drawn. Sample weights are used for reporting national and provincial level results. The full technical details of the sample design are included in Appendix A.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the Zambia MICS was based on the MICS Model Questionnaire with some modifications and additions. The questionnaire was administered in each household. As opposed to the MICS Model Questionnaire, the Zambian MICS used one unified questionnaire the comprised the household, women’s and child questionnaires. Appropriate instructions guided the enumeration process vis-à-vis which section of the questionnaire applied to what category of respondent. In each hous ehold, information was collected on household members including sex, age, literacy, marital status, and orphanhood status. Household level questions also included information on education, household expenditure, household income, labour force, child labour, water and sanitation, and salt iodization modules. The module on child labour was expanded to take into account the needs of the ILO child labour component. The module on education considered all members of the household greater than five years as opposed to the MICS standard of child level questions. In addition to questions at household level, questions were administered in each household for women age 12-49 and children under age five in contrast to the MICS standard of 15-49 years. For children, the questions were administered to the mother or primary caretaker of the child. All modules in the MICS model questionnaire were used for child level questions with the exception of the child mortality module. The optional modules of maternal mortality and child disability were not implemented in the Zambia MICS. Since the Zambian MICS was prepared ahead of the finalization of the MICS model questionnaire, some questions and their responses may not strictly follow the MICS model.

Response rate

In Zambia, 8,000 households were selected for the MICS sample. When a household refused to be interviewed or could not be found (non-contact), or dwelling could not be found or could not be interviewed due to some other problem, the household was replaced. The replacement was meant to improve the response rate. However, there were cases when even the replacement household could not be interviewed. In the end, a total of 7,915 households were successfully interviewed (see Table 1 of Appendix D) for a household response rate of 98.9 per cent. The response rate was higher in rural areas (99.1 per cent) than in urban areas (98.8 per cent). In the interviewed households, 10,128 eligible women aged 15-49 were identified. Of these, 9,639 were successfully interviewed, yielding a response rate of 95 per cent. In addition, 6,397 children under age five were listed in the household questionnaire. Of these, questionnaires were completed for 6,217 children for a response rate of 97 per cent.

The questions contained in SASAS questionnaires one and two for 2007 were asked of a half sample of approximately 3500 respondents each. The data set contains 3164 records and 385 variables. Topics included in the questionnaires are: democracy, intergroup relations, public services, moral issues, crime, voting, demographics and other classificatory variables. Rotating modules are: child poverty, poverty, household expenditure, climate change / global warming, soccer world cup, service delivery, Batho Pele principles, International Social Surveys Programme (ISSP) module: leisure time and sport and smoking and tobacco behaviour .

This dataset explores Early Care and Education Funding: Head Start Allocation and State-Funded Prekindergarten Participation. This data is state level and expresses the participation per state. Head Start and Early Head Start are comprehensive child development programs that serve children from birth to age 5, their families, and pregnant women. The overall goal of these programs is to increase the school readiness of young children in families earning low incomes. The Head Start program delivers comprehensive services including: education, health, nutrition, screening for developmental delays, and a variety of social services, if the family needs them. The program is designed to meet the social, emotional, physical and cognitive development of children. This data is from Latest Data: Fiscal Year 2004 (Head Start) and School Year 2002-2003 (State Funded Prekindergarten). This data is from National Child Care Information Center. Refer to NCCIC Child Care Database for detailed state information (http://nccic.org/IMS/Results.asp). Compiled by: National Association of Child Care Resources and Referral Agencies (http://www.naccrra.org/randd/head_start/expenditure.php)

Training Pants (Pull-Ups) Market Outlook

The global market size for Training Pants (Pull-Ups) was valued at approximately USD 6.5 billion in 2023 and is projected to reach USD 11.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.3% during the forecast period. The growth of this market is driven by factors such as rising awareness about hygiene, increasing disposable income, and the growing number of working parents who favor convenient childcare products. As the global population continues to expand, the demand for efficient and practical solutions like training pants is on the rise, further bolstering market growth.

One of the primary growth factors of the Training Pants (Pull-Ups) market is the increasing awareness about the importance of hygiene and sanitation among parents. With the constant rise in literacy rates and access to information, parents are becoming more conscientious about the health and well-being of their children. This awareness drives them toward products that offer better hygiene, comfort, and ease of use, such as training pants, which are designed to help with toilet training while providing the necessary protection against leaks and accidents.

Another significant growth factor is the rise in dual-income households, which has led to an increased demand for convenient baby care products. As more parents balance work and family life, the need for easy-to-use and efficient childcare solutions has surged. Training pants offer a practical solution for busy parents who are looking for ways to simplify the toilet training process while maintaining their children's comfort. The convenience of pull-ups, which can be easily worn and removed like regular underwear, makes them a popular choice among working parents.

Furthermore, the increasing disposable income of families in emerging economies is a notable driver of the Training Pants (Pull-Ups) market. As economic conditions improve in regions such as Asia Pacific and Latin America, families have more spending power, allowing them to afford higher-quality baby care products. This shift in economic landscape is expected to further fuel the demand for training pants, as parents in these regions seek products that offer better hygiene, comfort, and ease of use for their children.

On the regional front, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. This growth can be attributed to the large population base, increasing birth rates, and rising disposable incomes in countries like China and India. Additionally, the expansion of retail networks and the growing popularity of e-commerce platforms in the region are expected to boost the market for training pants. North America and Europe are also significant markets due to high awareness levels and established retail infrastructures, but their growth rates will be more moderate compared to the Asia Pacific region.

In addition to training pants, Baby Swim Pants Diaper have emerged as a popular choice among parents who want to ensure their children's comfort and safety during water activities. These specialized diapers are designed to prevent leaks while allowing the child to move freely in the water. As families increasingly engage in recreational activities like swimming, the demand for baby swim pants has seen a significant rise. They offer a practical solution for parents who want to introduce their children to water environments without the worry of accidents. The convenience and peace of mind provided by baby swim pants make them an essential item for many parents, contributing to their growing popularity in the market.

Product Type Analysis

The Training Pants (Pull-Ups) market is segmented based on product type into disposable and reusable training pants. Disposable training pants dominate the market due to their convenience and ease of use. These products are designed for single-use and can be easily discarded after being soiled, making them an attractive option for busy parents. The convenience of not having to wash and dry the pants after each use is a significant factor driving the demand for disposable training pants. Additionally, advancements in materials and absorbent technologies have improved the effectiveness and comfort of disposable training pants, further boosting their popularity.

On the other hand, reusable training pants are gaining traction among environmentally conscious consumers. These tra

Abstract

The phone survey was conducted to gather data on the socio-economic impacts of COVID-19 crisis, as well as the Hunga Tonga-Hunga Ha'apai volcanic eruption and tsunami in Tonga. Round 2 interviewed 2,503 households both in urban and rural regions of the country from July 2022 to August 2022. Survey topics included employment and income, food security, coping strategies, access to health services, asset ownership, and preparedness. Purpose of Round 2 survey was to continue tracking the impact of the crises after Round 1, which was completed in April, 2022 - May, 2022. Additionally, round 2 survey besides the household information, gathers data on individual level that was not included in Round 1. Two individual datasets explore adult employment and child education. While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, etc. to develop an initial picture of the impacts of the crises on the population.

Geographic coverage

National urban and rural (5 islands): Tongatapu, Vava'u, Ha'apai, Eua, Ongo Niua

Analysis unit

Household, Individual

Universe

All respondents must be at least 18 years of age to undertake the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tonga HFPS Round 2 sample was generated in three ways. The first method is Random Digit Dialing (RDD) process covering all cell telephone numbers active at the time of the sample selection. Approximately 16% of the sample was generated through RDD.

The RDD methodology generates virtually all possible telephone numbers in the country under the national telephone numbering plan and then draws a random sample of numbers. This method guarantees full coverage of the population with a phone.

First, a large first-phase sample of cell phone numbers was selected and screened through an automated process to identify the active numbers. Then, a smaller second-phase sample was selected from the active residential numbers identified in the first-phase sample and was delivered to the data collection team to be called by the interviewers. When a cell phone was called, the call answerer was interviewed as long as he or she was 18 years of age or above and knowledgeable about the household activities.

It was initially planned to stratify the sample by island group based on the phone number prefixes. However, this was not feasible given the high internal migration across islands and the atypical assignment of phone number prefixes across islands in Tonga. The sample is overrepresenting urban areas and the population of Tongatapu.

Approximately, 56% of the Round 2 sample was made up of the returning respondents from Round 1 who were recontacted.

The remaining 28% of the R2 respondents was taken from Tonga's Household Income and Expenditure Survey (HIES).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire was developed in both English and Tongan. Sections of the Questionnaire: 1. Interview Information 2. Basic Information 3. Vaccine Information 4. Health 5. Education 6. Food Insecurity 7. Employment 8. Income 9. Coping Strategies 10. Assets 11. Digital 12. Recontact

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the survey firm and the World Bank team. Data cleaning mainly included formatting, relabeling, and excluding survey monitoring variables (e.g., interview start and end times). Data was edited using the software STATA.

Response rate

Total number of households interviewed for round 2 survey was 5,085 out of which 2,503 finished the interview - about 50% success rate. More specifically, response rate for R1 recontacted households was 60.8%, and the response rate for RDD sample was 24%.

Abstract

The Government of Madagascar (GOM), through the Ministry of Population, Social Protection and Promotion of Women (MPSPPW) and the Social Development Fund (FID) is implementing a Human Development Cash Transfer (HDCT) program in partnership with the World Bank (WB). The program targets selected geographical areas and provides bi-monthly cash transfers to extreme poor households (bottom 30th percentile in the income distribution) with children through primary school age (0-10 years). The cash transfer is designed to provide both short-term income support and to leverage longer-term family investments in children’s human development, notably core elements of early childhood development, nutrition and formal education. A portion of the cash transfer is conditioned on regular primary school attendance while an unconditional transfer coupled with encouragement to attend child nutrition and development sessions is provided for households with younger children not yet in primary school. The cash transfer also accompanies various community leader-led informational modules on family well-being, health, nutrition, and sanitation. ideas42 is partnering with the World Bank’s Madagascar Social Protection Team to support the GOM in designing and implementing a rigorous impact evaluation that meets its policy needs by clarifying the effectiveness of the HDCT program. An important focus of the evaluation is in piloting certain community participation, behavioral science, and motivational interventions that may improve the effectiveness of the program and help guide its ultimate scalability.

Geographic coverage

51 communes across Madagascar

Analysis unit

Households

Universe

Extremely poor households (bottom 30th percentile of income) with children ages 0-10 (primary school age)

Kind of data

Sample survey data [ssd]

Sampling procedure

ideas42 is running this impact evaluation of a conditional cash transfer program in Madagascar for 51,000 households from 2016-2019. It is a three-level cluster randomization design. There are three intervention effects we want to measure:

1. The effect of the Cash Transfer (commune level)
2. The additional effect of the Mother Leader (ML) group program (village level)
3. The additional effect of the Nudges (village level)

4,485 households were sampled for the baseline survey. For the MDAT, 3,366 households were eligible (have at least one eligible child) and surveyed using MDAT. The endline will be the full 8,222 households, but the baseline enabled us do basic randomization checks and provides some information on the vulnerable population for the government before the 3-year experiment ends. Given these constraints, our approach to baseline sampling was as follows:

1. Sampled from all 309 treatment villages (surveyed 7-8 randomly selected eligible households per village).

2. For pure control, randomly sampled 65 villages total. This meant sampling 5 villages from each of the 13 control communes, with 9-10 people from each village. This is because cash is evaluated at the commune level – there are 110 control villages, and we did not necessarily need to go into all of them. Since there are 13 control communes, we needed to sample from about 5 villages from each commune, with 9-10 people from each village. That is roughly 600 people from 65 total villages. Other important notes are that four of the 13 control communes had only five eligible villages. One of the communes only had three villages, so five villages per commune was the only realistic sampling ceiling.

3. We randomly selected 3,000 eligible households and only did the MDAT (the early childhood cognitive development measure instrument) for households that had children ages 2-6. That said, once we had the household list for the baseline, we could pre-select with which of the children in a given household we could conduct the MDAT.

For absentee respondents/refusals/not found, field enumerators had a backup list of randomly selected households from which could replace the absentee household. Field followed order of the randomly generated backup list systematically to prevent the introduction of any bias into the sampling process.

For additional information on the sampling, please refer to the 'Human Development Cash Transfer Sampling Approach Brief' available for download.

Mode of data collection

Face-to-face [f2f]

Research instrument

The HDCT Baseline and Midline Surveys: The baseline and midline surveys were designed by pulling key questions used in other national-level surveys carried out in Madagascar by INSTAT (Institut National de laStatistique de Madagascar), as well as in other relevant surveys conducted in Africa (e.g., World Bank surveys). They covered topics such as household demographic information, education, food and non food-related household consumption, general household expenses, agricultural production, sources of household revenue, women’s empowerment, parenting practices and food security. The HDCT baseline survey was approved by the National Institute of Statistics (INSTAT) in charge of the technical secretariat of the CCISE (Committee of Coordination of the Statistical and Economic Information) in Madagascar before it was implemented. The baseline survey was published in English and French and is available to download. The midline survey is provided in French and is also available for download.

The Malawi Developmental Assessment Tool (MDAT): The MDAT (Gladstone et al 2010) is a child development assessment survey specifically designed for a rural African context and is publicly available for adaptation to multiple countries. We (1) adapted this tool to a Malagasy context, creating a Madagascar-specific tool that can be used by future researchers and early childhood development specialists to assess developmental status and (2) used this tool to conduct a baseline assessment of child developmental status for the HDCT program. The MDAT assesses child developmental status across four domains: gross motor, fine motor, language, and social abilities. The MDAT survey received a non-objection from the Ethical Committee of the Ministry of Public Health before implementation. This survey was also published in English and Malagasy and is available for download.

Cleaning operations

CAETIC développement, the survey implementing agency, led the data processing for this study.

Response rate

TMDH Baseline: 99.6% (4,484/4,485) MDAT: 99.9% (3,365/3,366); 2,629 surveys were completed

Abstract (en): This special topic poll, conducted April 30 to May 6, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. This poll sought Americans' views on the most important problems facing the United States, their local communities and their own families. Respondents rated the public schools, crime, and drug problems at the national and local levels, their level of optimism about their own future and that of the country, and the reasons they felt that way. Respondents were asked whether they were better off financially than their parents were at their age, whether they expected their own children to be better off financially than they were, and whether the American Dream was still possible for most people. Respondents then compared their expectations about life to their actual experiences in areas such as job security, financial earnings, employment benefits, job opportunities, health care benefits, retirement savings, and leisure time. A series of questions asked whether the United States was in a long-term economic and moral decline, whether the country's main problems were caused more by a lack of economic opportunity or a lack of morality, and whether the United States was still the best country in the world. Additional topics covered immigration policy and the extent to which respondents trusted the federal, state, and local governments. Demographic variables included respondents' sex, age, race, education level, marital status, household income, political party affiliation, political philosophy, voter registration and participation history, labor union membership, the presence of children in the household, whether these children attended a public school, and the employment status of respondents and their spouses. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was at home at the time of interview. 2009-10-29 First names were removed from the data file. A full product suite including online analysis with question text has been added. The location of the weight variable was also corrected. telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis. The data collection was produced by Chilton Research Services of Radnor, PA. Original reports using these data may be found via the ABC News Polling Unit Website.According to the data collection instrument, code 3 in the variable Q909 (Education Level) included respondents who answered that they had attended a technical school.The original data file contained four records per case and was reformatted into a data file with one record per case. To protect respondent confidentiality, respondent names were removed from the data file.The CASEID variable was created for use with online analysis.

Abstract

The main objectives of the survey were: 1- To provide reliable data on the situation of women and children in order to prepare the National Report on the End-Decade Goals set at the World Summit for Children held in 1990. 2- To provide information that can be used in the evaluation of the joint projects between the Government of Lebanon and UNICEF 3- To provide data on the largest number of indicators that can be used in developing future plans of cooperation between the Government of Lebanon and UNICEF 4- To develop the database available and build-up the technical capacities of the Central Administration of Statistics in planning and implementing surveys according to international standards.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected to provide data on health, education, and social indicators related to the situation of children in Lebanon as a whole and in each of the five governorates (South , North, Beqaa, Beirut, Mount Lebanon), as well as in the four most underserved districts as group (Akkar, Minyeh/Dannieh, Baalbeck, Hermel). The sample was determined at 8125 households distributed on the five governorates. The sampling was done in two stages. The first stage included random selection of 65 ilots from each of the five governorates based on the sampling frame set by the Census of Buildings undertaken in 1995-1996. The second stage was through the selection of 25 households systematically from each of the 25 ilots. For those ilots that had less than 25 households, the total number of households was considered.

Mode of data collection

Face-to-face [f2f]

Research instrument

The national committee of the survey reviewed the questionnaires developed by UNICEF at the international level. The necessary modifications were introduced in order to meet the requirements of the government bodies and to fit the local conditions. A number of questions were introduced on the households, the equipment used, the monthly income, employment, education, reasons behind illiteracy and drop-out from school. The questionnaire on the under five children was amended to include more information on early child development. Information was also collected on home injuries and child labor among children aged 6 18 years inside and outside the home. The questionnaires were field tested and modified accordingly.

Cleaning operations

Data entry was made between 1 August and 31 October 2000. Data entry was made using ten computers following ORACLE. In order to guarantee the quality of data entry, the questionnaires were entered twice in parallel and were counterchecked. Data processing started from the beginning of November until the end of 2000, while the present report was prepared in January 2001.

Response rate

out of 7784 households in the sample, a total of 7231 were found occupied while the rest were vacant. From those information was obtained for 6834 households, hence yielding a response rate of 94.5 percent at national level. There were disparities however in the response rate, highest in Beqaa (98.8 percent) and lowest in Beirut (88.2 percent). It is noteworthy here that the low response rate in Beirut is attributed to the fact that the survey took place in the summer, where many of its inhabitants and of the inhabitants of major cities leave on holidays. It also coincided with the liberation of the regions of South Lebanon and West Beqaa and the subsequent population movements. In addition, there was a relatively high rate of rejection among the target group in Beirut, compared to the other governorates.

A total of 4245 women from the target group (i.e. women in child bearing age 15 49 years) have been identified in the households targeted. Of those, 4244 have been interviewed, yielding a response rate of about 100 percent. In addition, information was obtained on 2786 children under five out of 2803 identified in the households, equivalent to a response rate of 99.2 percent.

The number of questions for which answers are missing is very low, hence indicating the high quality of the data.

BackgroundDue to the increased magnitude of overweight/obesity in many countries, the World Health Organization (WHO) has identified it as a significant public health crisis, particularly affecting women of reproductive age in developing nations. Despite obesity/overweight among women of reproductive age being widely acknowledged as a pressing public health issue, there has been limited investigation into its pooled prevalence and various associated factors in low and middle-income countries (LMICs) with high maternal mortality. Thus, the objective of our study was to assess the pooled prevalence and associated factors of overweight/obesity among reproductive-age women in low and middle-income countries with high maternal mortality.MethodsWe analyzed secondary data using recent Demographic and Health Survey datasets from 21 low and middle-income countries with high maternal mortality. A weighted sample of 64,076 women of reproductive age was included in the analysis. The variables were extracted from the IR file, and the data were cleaned, recoded, and analyzed using STATA version 14.2 software. A multilevel binary logistic regression model was applied, and adjusted odds ratios (AOR) with 95% confidence intervals and a p-value of ≤ 0.05 were used to identify statistically significant associated factors. Model fitness and comparison were assessed using the ICC, MOR, PCV, and deviance (−2LLR).ResultIn this study, the pooled prevalence of overweight/obesity among women of reproductive age was 32% (95% CI: 27% − 37%), with a significant variation between countries, ranging from 10% in Burundi to 53% in Mauritania. Women of reproductive age with overweight/obesity showed a significant positive association with various factors compared to those with a normal BMI. Accordingly, women’s age, women’s educational status, women’s occupation, women’s marital status, households’ income levels, number of living children, frequency of watching television, using the internet, sex of household head, and sources of drinking water were identified as individual-level factors. On the other hand, residence, community poverty, and community-level media usage were found to be significantly associated with community-level variables.Conclusions and recommendationsMore than three out of ten women of reproductive age were overweight/obese in low and middle-income countries with high maternal mortality. Individual-level and community-level factors were associated with overweight/obesity. Special attention is recommended to older women, those with formal education, non-working women, individuals who spend time watching television and using the internet, urban residents, and female household heads. Furthermore, since higher household income is associated with an increased likelihood of weight gain, it is important to provide appropriate health interventions for women from the wealthiest households.

This dataset displays the number of Research and development researchers in their respective countries. This data is on an annual time line, and was formed by: UNESCO Institute for Statistics. http://www.uis.unesco.org, Core Theme: Science and Technology. Montreal. The United Nations Statistical Yearbook is an annual compilation of a wide range of international economic, social and environmental statistics on over 200 countries and areas of the world, compiled from sources including UN agencies and other international, national and specialized organizations. The 50th issue contains data available to the Statistics Division as of March 2006 and presents them in 76 tables on topics such as: agriculture; balance of payments; culture and communication; development assistance; education; energy; environment; finance; industrial production; international merchandise trade; international tourism; labor force; manufacturing; national accounts; nutrition; population; prices; research and development; transport; and wages. The number of years of data shown in the tables varies from one to ten, with the ten-year tables covering 1994 to 2003 or 1995 to 2004. Accompanying the tables are technical notes providing brief descriptions of major statistical concepts, definitions and classifications. For publication information please visit https://unp.un.org/details.aspx?entry=B06SYH&title=Statistical+Yearbook+2005 Data Availability: http://unstats.un.org/unsd Access Date: November 29, 2007

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives:

CSES 2013 will continue the work started through CSES 2004 and the annual CSES 2007 to 2014 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives:

Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

Households Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2013 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2. One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3. In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2013"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. . For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2013 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2012 will continue the work started through CSES 2004 and the annual CSES 2007 and 2008 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives:

Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2012 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2012"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2012 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the "Rectangular Strategy of Cambodia".

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Among specific objectives, the following deserve special mention: - Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. - Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. - Collect data on migration - Collect data on utilization of education, housing and land ownership - Collect data on household assets and outstanding loans. - Collect data on household's construction activities. - Collect information on maternal health, child health/care. - Collect information on health of the household members related to illness, injury and disability. - Collect information on economic activities including the economic activities for children aged between 5 and 17 years. - Collect information on victimization by the household - Collect information on the presence of the household members. - Collect information on household income and receipts, expenditure and consumption of own production (also in diaries).

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO's. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

Households Individuals

Universe

The target population for CSES is all “normal” households in Cambodia. The term normal is defined in the Population Census 2008 as households that are not institutional households, homeless households, boat population households or households of transient population. (Institutional households are boarding houses, military barracks, prisons, student dormitories, etc.).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design

The sample of villages for CSES 2011 is just a simple random 50 % subsample from the CSES 2009 sample of villages, just like for 2010. Consequently, the description of the CSES 2011 sample design will by necessity begin with a description of the CSES 2009 design.

The sample design for CSES 2010 is basically the same as the CSES 2009 design. For the 2010 and 2011 survey a subsample of 360 EAs (stage 2 units) was selected from the CSES 2009 sample of 720 EAs. The selection was done by simple random sampling within strata. The selection resulted in 136 urban EAs and 224 rural EAs.

Households were selected in the same way as in CSES 2009. For CSES 2010 and 2011 only 10 households are selected in each rural EA, as compared to 20 households in 2009. In urban areas 10 households were selected, just as in 2009.

The sampling resulted in a sample of 3,600 households, 1,360 urban households and 2,240 rural households.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

Form 4: Diary form on daily household expenditure and income

Cleaning operations

The NIS team commenced their work of checking and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. SIDA project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionnaires on delivery.

Response rate

The CSES 2011 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Snapshot img

China Preschool Or Childcare Market Size 2024-2028

The China preschool or childcare market size is forecast to increase by USD 9.47 billion at a CAGR of 12.2% between 2023 and 2028.

The preschool and childcare market in China is experiencing significant growth, driven by established and international players holding pricing leverage. This trend is fueled by the increasing educational expenditure in Chinese households, reflecting the growing importance of early childhood education. The COVID-19 pandemic and subsequent scenarios, such as school closures and remote learning, pose potential hindrances to market expansion. These trends and challenges create a complex landscape for preschool and childcare providers, necessitating strategic adaptations and innovative solutions. However, the market may face challenges due to COVID-19 pandemic-like scenarios, which could hinder growth by disrupting operations and increasing safety concerns. As the Chinese economy continues to expand and urbanization progresses, the demand for quality preschool and childcare services is expected to rise. This market analysis report delves into the key factors shaping the growth and challenges of the preschool and childcare market in China, providing valuable insights for stakeholders and investors.

What will be the size of the China Preschool Or Childcare Market during the forecast period?

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The preschool and childcare market in China is experiencing robust growth due to rapid urbanization, the rise of nuclear families, and increasing numbers of working women. With disposable income on the rise, organized players are capitalizing on this trend through franchise-based models, offering comprehensive care that includes physical, cognitive, and emotional growth for children aged less than 2, as well as infants. Holistic child development is a priority, with learning tools and digital classrooms becoming increasingly common. Bilingual education is also gaining popularity. The market encompasses a range of offerings, from anganwadi centers and preprimary sections in publicly-owned preschools to standalone, privately-owned preschools and full day care and afterschool care programs.
Tech-savvy parents seek innovative technology in education, such as e-learning tools, to enhance their children's learning experience. Overall, the market is dynamic and expanding, with a focus on providing high-quality care that addresses the diverse needs of China's growing population of young children.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Service

  Full-time preschool or childcare
  On-demand preschool or childcare


Age Group

  Children aged below 3 years
  Children aged between 3 and 6 years


Ownership

  Public preschool or childcare
  Private preschool or childcare


Geography

  China

By Service Insights

The full-time preschool or childcare segment is estimated to witness significant growth during the forecast period.

In China, preschools and childcare centers have gained prominence as nuclear families and working women prioritize comprehensive early education for their children. Rapid urbanization and increasing disposable income have led to the emergence of organized players In the market, adopting franchise-based models and incorporating advanced learning tools such as digital classrooms. The holistic development of children, focusing on physical, cognitive, and emotional growth, is a priority. Bilingual education and international curricula are increasingly popular, with offerings from institutions like Kidzee, Bachpan Global, Shemrock Play School, Kangaroo Kids, Hello Kids, and Little Millennium. Maintaining quality standards and tech-driven learning approaches are essential, despite high operational costs.

The Ministry of Education is implementing educational policies to address the investment gap, with a focus on technology in education and e-learning tools. Urban population growth and infrastructure development have created demand for preschools, full day care, and afterschool care. Ownership models include publicly and privately owned preschools, catering to infants, young toddlers, and preschoolers. Tech-savvy parents seek personalized education approaches, emphasizing professional care and early formal schooling.

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The full-time preschool or childcare segment was valued at USD 7.09 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of dri

Abstract

The CSES is a household survey with questions to households and the household members. In the household questionnaire there are a number of modules with questions relating to the living conditions, e.g. housing conditions, education, health, expenditure/income and labour force. It is designed to provide information on social and economic conditions of households for policy studies on poverty, household production and final consumption for the National Accounts and weights for the CPI.

The main objective of the survey is to collect statistical information about living standards of the population and the extent of poverty. Essential areas as household production and cash income, household level and structure of consumption including poverty and nutrition, education and access to schooling, health and access to medical care, transport and communication, housing and amenities and family and social relations. For recording expenditure, consumption and income the Diary Method was applied for the first time. The survey also included a Time Use Form detailing activities of household members during a 24-hour period.

Another main objective of the survey is also to collect accurate statistical information about living standards of the population and the extent of poverty as an essential instrument to assist the government in diagnosing the problems and designing effective policies for reducing poverty, and in evaluating the progress of poverty reduction which are the main priorities in the "Rectangular Strategy" of the Royal Government of Cambodia.

Geographic coverage

National

Urban/Rural

Analysis unit

1. household

2. individual

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The Cambodia Socio-Economic Survey 2003-04 (CSES) is conducted in a nationwide representative sample of 15,000 households within 900 sampling units (villages). It is divided into 15 monthly representative samples of 1000 households in 60 villages.

The sampling design and implementation was made in March 2003. A three-stage sample design was devised. Since NIS already had a master sample based on the Population Census 1998, consisting of 600 villages, it was used. But in order to reach the preferred number of 900 villages, the sample was extended to include an additional 300 villages.

In the first stage, a sample of villages was selected in the head office. The villages were initially stratified into 45 strata (province*urban/rural). The villages were selected using systematic sampling with probabilities proportionate to size (PPS). The size measures used for the selection were number of households in the village according the 1998 Census. The resulting sample thus consisted of 900 villages, of which 600 are in rural areas and 300 in urban areas.

In the second stage one Census Enumeration Area (EA or alternatively PSU) was selected randomly also in the head office. At the beginning of the fieldwork, all households in the selected EA were listed using a household listing form, and following internationally recommended procedures. A systematic sample of households was then drawn in a third stage. The third stage sample was 20 households in rural areas and 10 households in the urban areas.

Design work

The work on sample design was carried out in the following areas:

• Estimation of sampling errors and design effects in the CSES 1999

• Calculation of optimal sample size within primary sampling units

• Sample size and sample allocation for CSES 2003

The work was done in a group of NIS staff in the form of expert assisted hands-on training in sampling design and calculation of sampling errors.

In previous surveys PSUs have been villages. It was decided to use village as PSU also for the CSES 2004 mainly because the communes were considered too large (and too few) to serve efficiently as PSUs. Another factor weighing in favor of villages was the fact that there already exists a master sample of villages at NIS.

The master sample consists of 600 villages (88 urban and 512 rural villages). The selection of villages was made with PPS sampling, hence facilitating an approximately self-weighing design with equal workloads in the villages. It was discussed whether a further stratification on 3-4 crude income-level strata should be done in urban Phnom Penh in order to secure a good spread of the sample over different income levels. It was decided not to do such stratification. Phnom Penh has a large sample (90 villages) selected with systematic sampling over a geographically ordered sample frame; this will in itself secure a reasonably good spread of PSUs.

The master sample is allocated over the strata proportionally to the total number of households in the strata. A problem with the master sample is that due to the proportional allocation the urban sample is too small to provide for good estimates in the urban domain. It was therefore decided to expand the sample to include 600 rural villages and 300 urban villages.

Secondary Sampling Units (SSU)

The 600 villages in the master sample are divided in small segments containing approximately ten households each by using census enumeration area maps. As a consequence the boundaries of the segments would be difficult to identify in the field. There would be a risk that housing units constructed after the census will be missed when households are listed within segments during the fieldwork. It was therefore decided not to use the segments in the second stage sampling. The available options are in this situation either (a) to select households directly on stage in the village or (b) to use the enumeration areas as secondary sampling units. Selecting households directly would require a listing of all households in the village prior to the fieldwork. Such a listing would become time-consuming in large villages. It was therefore decided that enumeration areas would be used as SSUs, and that one enumeration area is selected within each sampled village.

Implementation

Villages were selected with a systematic PPS procedure within each stratum. For each sampled village one census enumeration area (EA) was selected. As the enumeration areas are roughly of the same size, the selection was done with equal probability sampling.

Ten (10) households were selected in each sampled village in the CSES 99. Calculations indicated that this sample size was close to optimum. Since the optimum is rather flat, the loss in efficiency from sample sizes of 12-15 is fairly small.

From a purely sampling efficiency point of view, a larger sample than 15 households per village should not be taken. However, factors relating to interviewers' security and well-being weighed in favor of having two interviewers per village in the rural areas. A workload of 10 households between the two interviewers in the village was considered too small. A workload of 15-20 households would be reasonable. All things taken together resulted in a sample of 10 households in urban areas (with one interviewer per village) and 20 households in rural areas.

The resulting sample consisted of 300 urban PSUs and 600 rural PSUs. From the urban PSUs 10 households were selected while 20 households were selected from rural PSUs. The sample thus contained 15000 households to be interviewed during 15 fieldwork months with 1000 different households each month.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

Form 4: Diary form on daily household expenditure and income

Form 5: Time use form detailing activities of household members during one 24-hour period.

Questionnaire design

The questionnaire is one of the first items in a strategy for quality control in data collection through surveys. Any piece of information to be collected must be formulated as a question so that all interviewers can be trained to read the questions in the same way. The questions must be formulated in such a way that all interviewers feel comfortable reading the questions aloud and that all respondents understand the questions in the same way. The layout of the questionnaire must be done so that the interviewer immediately understands how the respondent's answer should be recorded. A lot of work is normally needed to meet these requirements that are built into the process of communication in the interview situation. This is the kind of work in which final perfection is elusive and further improvements can always be made.

The initial work on questionnaire design resulted in a first draft prepared by NIS in early 2003. With expert assistance from Statistics Sweden in March the same year, a systematic walk-through question by question was done. A number of essential problems to be solved were then identified while errors or minor problems

Abstract

In 2008, the South African Presidency embarked on an intensive effort to track changes in the well-being of South Africans by closely following about 28 000 people - young and old, rich and poor - over a period of years. This was undertaken through initiating the National Income Dynamics Study (NIDS). The NIDS survey is the first national panel study to document the dynamic structure of a sample of household members in South Africa and changes in their incomes, expenditures, assets, access to services, education, health, and other dimensions of well-being. A key feature of the panel study is its ability to follow people as they move out of their original 7 305 households. In doing this, the movement of household members as they leave and/or return to the household or set up their own households will be adequately captured in subsequent waves of this panel study.

The first “baseline” wave of NIDS was conducted by the Southern Africa Labour and Development Research Unit (SALDRU) based at the University of Cape Town's School of Economics. The first wave of fieldwork commenced in February 2008, and data and report released in July 2009. The design of NIDS envisaged data collection every two years.

Elsewhere in the world such surveys have been invaluable in promoting understanding of who is making progress in a society and who is not and, importantly, what factors are driving these dynamics. In addition, panel data is invaluable for the purposes of evaluating and monitoring the efficacy of social policies and programmes. This is because the panel allows researchers and policy analysts to see how households and individuals are impacted when they become eligible for these programmes.

Completed and non-response interviews in the NIDS Data: The NIDS datasets contain both completed and non-response interviews (e.g. Refusals). It is recommended that researchers limit their research to completed interviews to avoid item non-response from non-response interviews. The completed interviews can be identified by making use of the wx'_y'_outcome variables, where x' represents the wave andy' represents the relevant data file/outcome type indicator. These outcome variables can be found in each of the following data files, Adult, Child, Proxy, HHQuestionnaire and Link File. The only exception to this is Wave 1 where no outcome variable exists. This is because at a household level, all of the interviews are completed. However this does not apply at an individual level where non-response interviews can be identified by making use of the "Reason for refusal" variables, namely w1_a_refexpl or w1_c_refexpl in the Adult and Child data files respectively.

Geographic coverage

The NIDS data is nationally representative. The survey began in 2008 with a nationally representative sample of over 28,000 individuals in 7,300 households across the country. The survey is repeated every two years with these same household members, who are called Continuing Sample Members (CSMs). The survey is designed to follow people who are CSMs, wherever they may be in SA at the time of interview. The NIDS data is therefore, by design, not representative provincially or at a lower level of geography (e.g. District Council).

Analysis unit

The units of analysis in the NIDS 2008 survey are individuals and households.

Universe

The target population for NIDS 2008 was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified, two-stage cluster sample design was employed in sampling the households to be included in the base wave. In the first stage, 400 Primary Sampling Units (PSUs) were selected from Stats SA's 2003 Master Sample of 3000 PSUs. This Master Sample was the sample used by Stats SA for its Labour Force Surveys and General Household Surveys between 2004 and 2007 and for the 2005/06 Income and Expenditure Survey. Each of these surveys was conducted on non-overlapping samples drawn within each PSU.

The sample of PSUs for NIDS is a subset of the Master Sample. The explicit strata in the Master Sample are the 53 district councils (DCs). The sample was proportionally allocated to the strata based on the Master Sample DC PSU allocation and 400 PSUs were randomly selected within strata. It should be noted that the sample was not designed to be representative at provincial level, implying that analysis of the results at province level is not recommended.

Sample of dwelling units

At the time that the Master Sample was compiled, 8 non-overlapping samples of dwelling units were systematically drawn within each PSU. Each of these samples is called a "cluster" by Stats SA. These clusters were then allocated to the various household surveys that were conducted by Stats SA between 2004 and 2007. However, two clusters in each PSU were never used by Stats SA and these were allocated to NIDS.

It was sometimes necessary to re-list a PSU when the situation on the ground had drastically changed to an extent that the information recorded on the listing books no longer reflected the situation on the ground. In these cases, the PSU was re-listed and a new sample of dwelling units selected. However, the downside of re-listing a PSU is that the chance of sample overlap with dwelling units that are in other surveys is increased. The extent of this overlap cannot be quantified as the lists are no longer comparable. There is anecdotal evidence that sample overlap might have occurred in some PSUs.

Individual respondent selection

Fieldworkers were instructed to interview all households living at the selected address/dwelling unit. If they found that the dwelling unit was vacant or the dwelling no longer existed they were not permitted to substitute the dwelling unit but recorded this information on the household control sheet.

The household control sheet is a two page form. This form was completed for every dwelling unit that was selected in the study, regardless of whether or not a successful interview was conducted. Where more than one household resided at the selected dwelling unit, a separate household control sheet was completed for every household and they were treated in the data as separate units. In order to qualify as separate households they should not share resources or food. Lodgers and live-in domestic workers were considered separate households.

All resident household members at selected dwelling units were included in the NIDS panel, providing that at least one person in the household agreed to participate in the study. The household roster in the household questionnaire was used to identify potential participants in the study. Firstly, respondents were asked to list all individuals that have lived under this "roof" or within the same compound/homestead at least 15 days during the last 12 months OR who arrived in the last 15 days and this was now their usual residence. In addition the persons listed should share food from a common 'pot' and share resources from a common resource pool. All those listed on the household roster are considered household members.

All resident household members became NIDS sample members. In addition, non-resident members that were "out of scope" at the time of the survey also became NIDS sample members. Out-of-scope household members were those living in insititutions (such as boarding school hostels, halls of residence, prisons or hospitals) which were not part of the sampling frame. These individuals had a zero probability of selection at their usual place of residence and were thus included in the NIDS sample as part of the household that had listed them as non-resident members. These two groups constitute the permanent sample members (PSMs) and should have had an individual questionnaire (adult, child or proxy) completed for them. These individuals are PSMs even if they refused to be interviewed in the base wave.

An initial sample of 9600 dwelling units was drawn with the expectation of realizing 8000 successful interviews. However, during the initial round of fieldwork for Wave 1 we did not achieve the target number of households. Therefore we went back to the field to attempt to overturn refusals in 48 PSUs and to visit 24 new dwelling units in 32 of these areas. Stats SA drew an additional 24 dwelling units from their Master Sample in predominantly White and Asian PSUs in order to improve representation of these population groups in the data.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were administered for the National Income Dynamics Study 2008:

HOUSEHOLD QUESTIONNAIRE: This covered household characteristics, household roster, mortality history, living standards, expenditure, consumption, negative events, positive events, agriculture ADULT QUESTIONNAIRE: This was administered to all people in sampled households who were 15-years old or older on the day of the interview. The Adult Questionnaire collected data on demographics, education, labour market participation, income, health, well-being, numeracy and anthropometric measurements CHILD QUESTIONNAIRE: This asked questions of household members who were 14-years old or younger, and covered education, health, family support, grants and numeracy and anthropometric data PROXY QUESTIONNAIRE: These were completed where possible for adults who were unavailable or unable to answer their own adult questionnaire

Cleaning

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives:

CSES 2014 will continue the work started through CSES 2004 and the annual CSES 2007 to 2013 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan. In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives:

Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural 1 Banteay Meanchey
2 Kampong Cham/Tbong Khmum
3 Kampong Chhnang 4 Kampong Speu
5 Kampong Thom
6 Kandal
7 Kratie
8 Phnom Penh
9 Prey Veng
10 Pursat
11 Siem Reap
12 Svay Rieng
13 Takeo
14 Otdar Meanchey
15 Battambang/Pailin
16 Kampot/Kep
17 Preah Sihanouk/Koh Kong 18 Preah Vihear/Stung Treng
19 Mondul Kiri/Ratanak Kiri

Analysis unit

Households Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2014 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2. One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3. In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2014"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. . For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2014 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of

This dataset explores the Statistics Canada data from the 2001 Census of earnings by highest level of education completed. Average earnings of the population 15 years and over by highest level of schooling, by province and territory (2001 Census) Definitions Highest level of schooling: Refers to the highest grade or year of elementary or secondary (high) school attended, or to the highest year of university or other non-university education completed. University education is considered to be a higher level of schooling than other non-university education. Also, the attainment of a degree, certificate or diploma is considered to be at a higher level than years completed or attended without an educational qualification. Earnings (employment income): Refers to total income received by persons 15 years of age and over during calendar year 2000 as wages and salaries, net income from a non-farm unincorporated business and/or professional practice, and/or net farm self-employment income. High school graduation certificate and/or some postsecondary: Includes persons who have attended courses at postsecondary institutions and who may or may not have a high school graduation certificate. Excludes persons with a postsecondary certificate, diploma or degree. Since 1981, "postsecondary" refers to years of schooling completed at university or at institutions other than a university, a secondary (high) school or an elementary school. Examples of postsecondary institutions include community colleges, institutes of technology, CEGEPs, private trade schools, private business colleges and schools of nursing.