Step-families and blended families, dependent children living in step-families, and households where an additional child stays for more than 30 days a year.

This statistic shows the number of families with children under the age of 18 in France in 2021, by type of family (in thousand). It appears that 329,000 minor children were living in a blended family in France in 2021.

Financial overview and grant giving statistics of Extended Family Services

In Turkey, the share of extended-family households continuously declined from 2014 to 2022. In 2023, there was a slight increase in the figures, which added up to **** percent. In comparison, extended-family households held a share of nearly ** percent in 2014.

Data on census family structure, number of children, average number of children and age of youngest child for census families with children, Canada, provinces and territories, census metropolitan areas and census agglomerations, 2021, 2016 and 2011 censuses.

Extended family members can help women with childcare. Using data from 32 sub-Saharan African countries, we find that the prevalence of nuclear families relative to extended families is increasing over time. The overall share of nuclear families is 56 percent and is higher in rural areas. We then use detailed time use data on childcare provision from 110 rural villages in the Democratic Republic of the Congo. We find that while women do receive childcare assistance from their extended family, they provide 84 percent of childcare hours. These results highlight the need for formal childcare provision, particularly in rural areas.

Dataset contains counts and measures for families and extended families from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for families and extended families in households in occupied private dwellings:

• Count of families
• Family type
• Number of people in family
• Average number of people in family
• Total family income
• Median ($) total family income
• Count of extended families
• Extended family type
• Total extended family income
• Median ($) total extended family income.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

The dataset is a file of the raw interview scripts with my interviewees during the fieldwork conducted between 2021.6 to 2022.2.

This thesis investigates how urban middle-class working women with two children make sense of work, childcare, and self under the universal two-child policy of China. This thesis also explores how the idea of individual and family interact in these women's construction of a sense of self. On January 1st, 2016, the one-child policy was replaced by the universal two-child policy, under which all married couples in China are allowed to have two children. In the scholarships of motherhood, it is widely documented across cultures that it is a site of patriarchal oppression where women are expected to meet the unrealistic ideal of intensive mothering to be a good mother, suffer from the motherhood wage penalty and face more work-family conflict than fathers. Emprical studies of China also came to similar conclusions and such findings are not only widely regonized in scholarship but is also widespread in popular discourse in China. Despite that marriage and having children is still universal for the generation of the research target, women born in the 1970s and 1980s, due to compounding influence fo the one-child policy, increasing financial burden of raising a child etcs, having only one child has become widely acceptable and normal. Given this context, this study intend to investigate how these middle-class women, who are relatively empowered and resourceful, come to a decision that is seemingly against their own interest. Moreover, unlike in the west where the issue of childbearing and childcaring is mainly an issue of the conjugal couple and the gender realtions is at the center of the discussion, in China, extended family, especially grandparents also play a role in both the decision making process and the subsequent childcare arrangement. Therefore, to study the second-time mothers’ childcare and work experiences in contemporary urban China, we also need to situate them, as individuals, in their family. To investigate how they make sense of childcare and work is also to understand the tension between individual and family. By interviewing twenty-one parents from middle-class family in Guangzhou with a second child under six years old, this study finds that these urban working women with two children consider themselves as an individual unit and full-time paid employment is something that cannot be given up since it is the means of securing that independent self . However, they did not prioritize their personal interest to that of other family members, especially the elder child and thus the decision of having a second child is mainly for the sake of the elder child. Moreover, grandparents played an essential role to provide a childcare safety net, without which, these urban working women would not be able to work full-time and maintain the independent self as they defined it. The portrayal of these women’s experiences reflected the individualization process in China where people are indivdualized without individualism, and family are evoked as strategy to achieve personal as well as family goals. The findings of this study contributs to theories of motherhood by adding an intergenerational perspective to the existing gender perspective and also contributes to the studies of family by understanding the relation and interaction between individual and family in thse women’s construction of sense of self in the context of contemporary China.

Financial overview and grant giving statistics of Next Step Family Home

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

The National Survey of America's Families (NSAF) is a household survey that provides a comprehensive look at quantitative measures of the well-being of children, adults, and their families. While the focus of the survey is at the state level, the scope is national -- with a primary emphasis on low-income families. NSAF information was gathered from interviews conducted with the Most Knowledgeable Adult (MKA), the person in the household who was most knowledgeable about the questions being asked about the respondent, their spouse/partner (if applicable) and the focal child (or children). Data were collected from more than 40,000 families in two stages. First, a screener interview was administered to determine whether a household was eligible to complete the second, extended interview. Two types of extended interviews were administered. Option A interviews were used in households with children under age 18. Option B interviews were used in childless adult households and also with emancipated minors. The extended interview was divided into several sections and is labeled A through P below: A. Student Status. This section contained two questions that asked whether the respondent was a student and whether that household was the respondent's usual residence. B. Health Status and Satisfaction. These questions asked about the respondent's satisfaction with health care, access to health care, the health status of the focal children, and the health status of the respondent. It also covered questions about the respondent's awareness of specific insurance programs such as Medicaid, and those associated with the Children's Health Insurance Program (CHIP). C. Parent/Child/Family Interaction and Education. This series of questions asked about education for focal children. Questions addressed the focal child's current grade (or the last grade completed) and the child's attitudes toward school and schoolwork, skipping school, suspensions, and changing schools. Questions were also asked about children over 11 years old working for pay and attending summer school. D. Household Roster. In this section, the name, age, and sex of all persons living in the household were recorded, and relationships between all household members were investigated. E. Health Care Coverage. Information was gathered about current health insurance coverage for the respondent, the respondent's spouse/partner, and the focal children. Questions were also asked about characteristics of that coverage and of periods in which family members had no insurance coverage. F. Health Care Use and Access. This section gathered information about health status, health care services received, and necessary health care services that were postponed during the preceding 12 months. Questions on routine care, overnight stays in hospitals, dental care, mental health care, women's health care, well-child care, and prescription medicines were also included. G. Child Care. In this section, respondents were queried as to child care arrangements including Head Start, child care centers, before- or after-school care, and babysitters. Questions were asked about the total number of hours per week in each care situation, the typical number of children cared for, the typical number of adult child care providers, and child care costs. H. Nonresidential Parent/Father. These questions determined whether a focal child had a nonresident parent, how often the child saw his/her nonresident parent, whether the nonresident parent provided financial support, and whether nonresident parents were required by child support orders to provide financial support. I. Employment and Earnings. This section contained a series of questions about the employment and earnings of the respondent and the spouse/partner for the current and preceding year. Topics included employment status, occupation, industry, employer-provided health insurance, hours worked, and earnings. Some questions were also asked about the earnings of other family members. J. Family Income. Family income was also identified from a wide variety of sources other than earnings from employment. These sources included public assistance (e.g., Temporary Assistance for Needy Families [TANF], General Assistance, Emergency Assistance, or vouchers), Food Stamps, child support, foster care payments, financial assistance from friends or relatives, unemployment c

The Project on Human Development in Chicago Neighborhoods (PHDCN) was a large-scale, interdisciplinary study of how families, schools, and neighborhoods affect child and adolescent development. One component of the PHDCN was the Longitudinal Cohort Study, which was a series of coordinated longitudinal studies that followed over 6,000 randomly selected children, adolescents, and young adults, and their primary caregivers over time to examine the changing circumstances of their lives, as well as the personal characteristics, that might lead them toward or away from a variety of antisocial behaviors. Numerous measures were administered to respondents to gauge various aspects of human development, including individual differences, as well as family, peer, and school influences. One such measure was the Extended Family Health instrument. It was administered to subjects' primary caregivers in Cohorts 0, 3, 6, 9, 12, and 15 and obtained information about the physical health, mental health, criminal history, and drug use history of anyone who ever lived in the same household as the subject for any period of time.

Knowledge Networks conducted the New Family Structures Survey on behalf of University of Texas at Austin. Specifically, the study examined the experiences of the respondents (ages 18-39) growing up in unconventional families where:

a. Parents are of the same sex (N=248)
b. Biologically unrelated parents adopted the respondent (N=210)
c. Parents were unmarried but cohabiting (N=657)
d. Biological mother had a romantic relationship with another man (N=52)
e. Biological mother did not have a romantic relationship with another man (N=122)

In addition, the survey also collected data among respondents who did not grow up in those unconventional families as a control/comparison group (N=1898).

The study seeks to understand how young adults (ages 18-39) raised by same-sex parents fare on a variety of social, emotional, and relational outcomes when compared with young adults raised in homes with their married biological parents, those raised with a step-parent, and those raised in homes with two adoptive parents. Questions on the survey cover a variety of topics including: parent-child relationship quality, romantic relationships, sexual orientation, public assistance, criminal activity, Facebook, bullying, charity, religious views, religious practices, life satisfaction, first sex, condom use, sexually transmitted diseases, rape, molestation, abortion, masturbation, suicide and alcohol use.

Financial overview and grant giving statistics of Step Family Foundation

The data are families in Hingham, Massachusetts, reconstituted by Daniel Scott Smith for his PhD dissertation from printed genealogies, vital, church and tax records, and censuses, along the lines of previous French and English work (e.g. Fleury, M. and L. Henry, Nouveau manuel de depouillement et d'exploitation de l'etat civil ancien (1965) and Wrigley, E.A, "Family Reconstitution," in E.A. Wrigley, ed. An Introduction to English Historical Demography (1966)). Family reconstitution is a method for studying demographic behavior in the absence of modern censuses and vital registration, providing for both observation of demographic events as well as the population and time at risk. In his dissertation, Population, Family and Society in Hingham, Massachusetts, 1635-1880 (University of California-Berkeley, 1973) Smith refers to the data as "statistical genealogy". The data were intended to be used to examine demographic patterns, family structure and social stratification in the past, and to generalize these patterns across the New England region. This dataset includes information on 1727 marriages. Variables include information about birth, death, marriage, fertility and wealth of husbands and wives, their parents and children. Naming practices are also represented in the data. There are no direct observations of children, but rather summary-type measures of characteristics of children and the couple's fertility history. A variable indicating the quality of the reconstitution is included. However, the meaning of the variable values has been lost.

As of 2019, the most popular household type in Russia was an extended family, referring to a household type that included relatives other than children and partners. The share of such households in the country was measured at 37 percent. Nine percent of total households in Russia were represented by a family of one or two parents and an adult child aged over 18 years.

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

The cities that are covered are La Paz, El Alto, Cochabamba and Santa Cruz de la Sierra.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members 15 to 64 years old. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the population 15-64 years old, living in urban areas, as defined by each country's statistical office. The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc.) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc. - Persons living outside the country at the time of data collection

Kind of data

Sample survey data [ssd]

Sampling procedure

Stratified 3-stage sample design was implemented in Bolivia. The stratification variable is city-wealth category. There are 20 strata created by grouping the primary sample units (PSUs) into the 4 cities, i.e.,1- La Paz, 2-El Alto, 3-Cochabamba, 4-Santa Cruz de la Sierra, and 5 wealth categories, i.e., 1-Poorest, 2-Moderately Poor, 3-Middle Wealth, 4-Moderately Rich, 5-Rich.

The source of the sample frame of the first stage units is the 2001 National Census of Population and Housing carried out by the National Institute of Statistics. The primary sample unit (PSU) is a Census Sector. A sample of 218 PSUs was selected from the 10,304 PSUs on the sample frame. This sample of PSUs was comprised of 160 'initial' PSUs and 58 'reserve' PSUs. Of the 218 sampled PSUs, there were 169 activated PSUs consisting of 155 Initial Sampled PSUs and 14 Reserve sampled PSUs. Among the 160 'initial' PSUs, 5 PSUs were replaced due to security concerns; also, 14 reserve PSUs were activated to supplement the sample for initial PSUs where the target sample of 15 interviews was not achieved due to high levels of non-response; thus, only 169 PSUs were actually activated during data collection. The PSUs were grouped according to city-wealth strata, and within each city-wealth stratum PSUs were selected with probability proportional to size (PPS), where the measure of size was the number of households in a PSU.

The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each of the initial PSU sample, resulting in a final initial sample of 2,400 interviews. At the second stage of sample selection, 45 households were selected in each PSU using a systematic random method. The 45 households were randomly divided into 15 'Initial' households, and 30 'Reserve' households that were ranked according to the random sample selection order. Note: Due to higher than expected levels of non-response in some PSUs, additional households were sampled; thus, the final actual sample in some PSUs exceeded 45 households.

The third stage sample unit was an individual 15-64 years old (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include:

• The background questionnaire developed by the World Bank (WB) STEP team
• Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP technical standards: two independent translators adapted and translated the STEP background questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The survey instruments were piloted as part of the survey pre-test.

The background questionnaire covers such topics as respondents' demographic characteristics, dwelling characteristics, education and training, health, employment, job skill requirements, personality, behavior and preferences, language and family background.

The background questionnaire, the structure of the Reading Literacy Assessment and Reading Literacy Data Codebook are provided in the document "Bolivia STEP Skills Measurement Survey Instruments", available in external resources.

Cleaning operations

STEP data management process:

1) Raw data is sent by the survey firm 2) The World Bank (WB) STEP team runs data checks on the background questionnaire data. Educational Testing Services (ETS) runs data checks on the Reading Literacy Assessment data. Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check if the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the background questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information on data processing in STEP surveys is provided in "STEP Guidelines for Data Processing" document, available in external resources. The template do-file used by the STEP team to check raw background questionnaire data is provided as an external resource, too.

Response rate

An overall response rate of 43% was achieved in the Bolivia STEP Survey. All non-response cases were documented (refusal/not found/no eligible household member, etc.) and accounted for during the weighting process. In such cases, a reserve household was activated to replace the initial household. Procedures are described in "Operation Manual" that is provided as an external resource.

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

This depository was used to analyse how socially close (e.g. parents, children, siblings) and distant (e.g. cousins, and siblings' spouse's families) connections within households' within-village extended family networks influence their informal insurance in rural Mexico. Socially close connections are considered to be more effective in enforcing informal risk sharing arrangements, but may be more economically similar and less numerous than socially distant connections, and thereby provide fewer risk sharing opportunities. Insurance is measured using information on household consumption and income; while extended family connections were identified using an algorithm that exploits the Mexican naming system. Based on the identified connections, the full within-village extended family map was constructed and a graph theory based measure of social distance was used to identify socially close and distant connections. A second related collection titled "Characterising risk sharing in extended family networks: Group Size and Informal Risk Sharing" includes files associated with another study conducted under the same grant using data on extended family in rural Malawi (see Related Resources).

Risk is extremely prevalent in rural areas of developing countries, but markets for credit and insurance are undeveloped and government-provided social insurance is very rare. Households in these contexts resort to informal tools, such as gifts and inter-personal transfers, to share idiosyncratic risk. Such informal risk sharing occurs within social networks, with family networks, particularly, forming a “natural” risk sharing institution. This research aims to deepen our understanding of risk sharing in family networks, focusing explicitly on the role of network structure (representation of who is related to who). This is likely important because many informal tools employed for risk sharing rely on bilateral relationships. Therefore, who one is linked with and who their links are further linked with will shape both how risk is shared and the amount shared. Specifically, this research will use unique data from Mexico with information on family ties and socio-economic variables to: (1) How do socially close connections (i.e. parents, siblings and children of the head and spouse residing outside the household) and socially distant connections (i.e. the family of one’s siblings’ spouses; as well as uncles, aunts, cousins) affect how well a household’s consumption is protected against idiosyncratic fluctuations in its income? (2) To what extent does the quality of a connection matter in its effectiveness in providing risk sharing?

Abstract

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

Areas are classified as urban based on each country's official definition.Some STEP surveys had narrower urban sampling. In Yunnan Province the sample covered the urban areas of Kunming. - Detailed information is provided in the weighting documentation.

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The STEP target population is the urban population aged 15 to 64 included, living in urban areas, as defined by each country's statistical office. The target population for the China-Yunnan STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of Kunming at the time of data collection.

The following are excluded from the sample: - Residents of institutions (prisons, hospitals, etc) - Residents of senior homes and hospices - Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc - Persons living outside the country at the time of data collection In some countries, extremely remote villages or conflict-ridden regions could not be surveyed. These cases are listed in the weighting documentation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The China-Yunnan survey firm implemented a partial literacy assessment design. The partial assessment required each selected person to attempt to complete a General Booklet comprising Reading Components and a set of Core Literacy Items. The partial assessment sampling objective was to have a minimum of about 2000 selected persons attempt the General Booklet. The target population for the China-Yunnan STEP survey comprised all non-institutionalized persons 15 to 64 years of age (inclusive) living in private dwellings in urban areas of Kunming at the time of data collection. The sample frame for the selection of first stage sample units was the Excel file 'sampling frame for STEP _CHINA' that was provided by the China-Yunnan survey firm. The frame is a complete list of first stage sampling units in the urban areas of Kunming. The source of this sample frame is the National Population Census, November, 2010. The sample frame includes 5564 PSUs in 299 Census Enumeration Areas. According to the sample frame, there are 1,067,256 households in the 5564 PSUs.

The China-Yunnan sample design was a 3 stage cluster sample design.

First Stage Sample The primary sample unit (PSU) is a Census Enumeration Area (CEA) Block. The sampling objective was to conduct interviews in 135 CEA Blocks. At the first stage of sample selection, 27 additional PSUs were also selected as reserve PSUs to be used in the event that it was impossible to obtain any interviews in one or more of the initial PSUs. A total of 162 PSUs were selected with probability proportional to size, where the measure of size was the number of households in a PSU. Subsequently, from the file of 162 sampled PSUs, a PPS sample of 135 PSUs was selected to be the 'Initial' PSU sample. Note that none of the 27 reserve PSUs was activated during data collection.

Second Stage Sample The second stage sample unit (SSU) is a household. The sampling objective was to obtain interviews at 15 households within each selected PSU. At the second stage of sample selection, 30 households were selected in each PSU using a systematic random method. The 30 households were randomly divided into 15 'Initial' households, and 15 'Reserve' households that were ranked according to the random sample selection order.

Third Stage Sample The third stage sample unit was an individual aged 15-64 (inclusive). The sampling objective was to select one individual with equal probability from each selected household.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: - The background Questionnaire developed by the WB STEP team - Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator.

The WB STEP team and ETS collaborated closely with the Chinese survey firm during the process and reviewed the adaptation and translation to Mandarin using a back translation.

The survey instruments were both piloted as part of the survey pretest.

The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

STEP Data Management Process: 1) Raw data is sent by the survey firm 2) The WB STEP team runs data checks on the Background Questionnaire data. - ETS runs data checks on the Reading Literacy Assessment data. - Comments and questions are sent back to the survey firm. 3) The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data. 4) The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm. 5) Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies. 6) ETS scales the Reading Literacy Assessment data. 7) The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Detailed information data processing in STEP surveys is provided in the 'Guidelines for STEP Data Entry Programs' document provided as an external resource. The template do-file used by the STEP team to check the raw background questionnaire data is provided as an external resource.

Response rate

The response rate for Yunnan Province (urban) was 98% (See STEP Methodology Note Table 4)

Sampling error estimates

A weighting documentation was prepared for each participating country and provides some information on sampling errors. All country weighting documentations are provided as an external resource.

A Family Team Meeting (FTM) is a family-centered rapid response meeting CPI uses to try and prevent a removal by engaging caregivers, parents and extended family and friends to address child safety concerns. An FTM is not limited to an investigation and can occur at any point or stage in which CPI or CPS is involved with a family. More information at www.dfps.texas.gov