Annex_9_-_Nesstar_MCC_Evaluation_Metadata_Template_1.3.NesstarTemplate

Adaptation of template IHSN_1.6_en from the International Household Survey Network

The only differences are:

- the study title was removed from the Document Description section (it was redundant with "Title" in the Study description"

- the CESSDA topics classification was replaced by the WB topics classification as controlled vocabulary.

- added "Holdings URI" and "Collection" in the document description section

- integrated the Impact Evaluation fields into the main template for World Bank use

- added "Impact Evaluation Methods" using "2.1.6.2 Version Responsibility Affiliation" field

Name of the person(s) or organization(s) who documented the dataset. Use the "role" attribute to distinguish different stages of involvement in the production process.

MCC Clarification:

Although Survey Firms will be expected to provide information related to data collection in a Data Collection Report, the Independent Evaluator is ultimately responsible for the information in the Metadata.

Content and Format:

Name: Evaluation Individual or Firm Name Abbreviation: Only include if applicable (e.g., NORC) Affiliation: For individuals, list the university or institution that they are affiliated with. Leave blank if a firm is the metadata producer.

Role: Independent Evaluator or MCC in the case that MCC created the Nesstar metadata file.

Examples:

Name: National Statistics Office Abbreviation: NSO Affiliation:

Role: Documentation of the study

Name: International Household Survey Network Abbreviation: IHSN Affiliation:

Role: Review of the metadata

This is the date (in ISO format YYYY-MM-DD) the DDI metadata was produced (not distributed or archived). This date will be automatically input when you save the file.

Documenting a dataset is not a trivial exercise. Producing "perfect" metadata is probably impossible. It may therefore happen that, having identified errors in a DDI document or having received suggestions for improvement, you decide to modify the Document even after a first version has been disseminated. This element is used to identify and describe the current version of the document. It is good practice to provide a version number (and date), and information on what distinguishes this version from the previous one(s) if relevant.

Metadata maybe updated continuously and we therefore need to track versions. The Version number will be a sequential number based on the number of versions since the original, with the original version being number 1. The Version number is followed by the original date of the metadata production, and an explanation for the change(s) since the original version.

Content and Format:

Version X (Original YYYY-MM-DD): Explanation

Example:

Version 1.1 (July 2006). This version is identical to version 1.0, except for the section on Data Appraisal which was updated.

The ID number of a DDI document is a unique number that is used to identify this DDI file. Define and use a consistent scheme to use.

Content and Format:

DDI-country-producer-survey-year(s)

- country is the 3-letter ISO country abbreviation

- producer is the abbreviation of the producing agency

- survey is the survey abbreviation

- year is the reference year (or the year the survey report was received)

- DDI document version number

A metadata file for a rural water supply activity survey in Mozambique created by Stanford and Virginia Tech in 2011 could have the following ID: DDI-MCC-MOZ-IE-WASH-2011-v01.

If the same survey is documented by a staff from the IHSN, this would be DDI-MCC-MOZ-IHSN-RWSA-2011-v01.

Include the word 'Compact' or 'Threshold' as applicable, as well as the compact or threshold number.

Examples:

Moldova Compact Cape Verde Compact II Tanzania Threshold Niger Threshold II

Brief description of the Compact components being evaluated (Project/Activity/Sub-Activity). This is essentially the first paragraph in the MCC Summary of Findings.

Most threshold evaluations fall under Cap Bldg & Inst Dev.

Overview of Projects/Activities/Sub-Activities objectives and a description of the causal chain from inputs, outputs, to outcomes in order to achieve objectives.

Please define the program participant in terms of specific selection criteria for participation in the program.

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Enter the country name, even in cases where the survey did not cover the entire country. In the field "Abbreviation", we recommend that you enter the 3-letter ISO code of the country. If the dataset you document covers more than one country, enter all in separate rows.

Name: Full country name Abbreviation: Three-letter country code

MCC Clarification: Country or Countries where evaluation was conducted.

Example:

Name: Burkina Faso Abbreviation: BFA

If Compact: Name of Compact Project/Activity/Sub-Activity If Threshold: Country Name Threshold Number

MCC Clarification:

Using the Compact hierarchy as a reference, write the Project/Activity/Sub-Activity/Threshold that is being evaluated. If the evaluation covers a combination of multiple Project/Activity/Sub-Activities, or anything below a sub-activity level, then use the term "component(s)."

Examples:

For the evaluation of the Water to Market Project's Farmer Training Activity, the name can be "Water to Market Farmer Training" For the first Malawi Threshold, the name can be "Malawi Threshold I"

Although providing a translated title is optional, a translated title may be ideal for evaluations done in countries that have official languages other than English. Special characters should be properly displayed (such as accents and other stress marks or different alphabets).

MCC Clarification: If the evaluation evolved from a performance to an impact or impact to a performance, indicate Independent Evaluation instead of specify type.

Select from controlled vocabulary options:

Independent Performance Evaluation, Independent Impact Evaluation, or Independent Evaluation

Raw data for internal use only, Edited clean data for internal use only, Licensed datasets, accessible under conditions, Anonymized dataset for public distribution, Not applicable to this evaluation; no quantitative data to be shared

- v01: Edited, anonymous dataset for public distribution.

Overview of Evaluation Design, includes evaluation type (performance/impact), evaluation questions, exposure to treatment. Upon completion of evaluation, include brief summary of results.

Keywords summarize the content or subject matter of the survey. As topic classifications, these are used to facilitate referencing and searches in electronic survey catalogs.

Keywords should be selected from a standard thesaurus, preferably an international, multilingual thesaurus. Entering a list of keywords is tedious. This option is provided for advanced users only.

Content and Format:

Select from controlled vocabulary options:

Randomization, Propensity Score Matching, Difference-in-Difference, Regression Discontinuity Design, Interrupted Time Series, Continuous Treatment, Pre-Post with Comparison Population, Pre-Post, Ex-Post, Independent Ex-Post ERR, Independent Ex-Post ERR and HDM-4, Other (Performance Evaluation)

MCC Clarification:

These are the Performance options listed above; all other options are only to be selected for Impact Evaluations:

Pre-Post with Comparison Population, Pre-Post, Ex-Post, Independent Ex-Post ERR, Independent Ex-Post ERR and HDM-4, Other (Performance Evaluation)

Choose from any of the following: individuals, households, community, administrative units, enterprise, school, health center, other.

This field is a broad classification of the data and it is associated with a drop down box providing controlled vocabulary. That controlled vocabulary includes 9 items but is not limited to them.

MCC Clarification: Only fill in this field if it is applicable.

List of questionnaires and short description of each (languages, modules)

Example:

The questionnaires for the Generic MICS were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes household characteristics, support to orphaned and vulnerable children, education, child labour, water and sanitation, household use of insecticide treated mosquito nets, and salt iodization, with optional modules for child discipline, child disability, maternal mortality and security of tenure and durability of housing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child.

The women's questionnaire include women's characteristics, child mortality, tetanus toxoid, maternal and newborn health, marriage, polygyny, female genital cutting, contraception, and HIV/AIDS knowledge, with optional modules for unmet need, domestic violence, and sexual behavior.

The children's questionnaire includes children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, malaria, immunization, and anthropometry, with an optional module for child development.

The questionnaires were developed in English from the MICS3 Model Questionnaires, and were translated into Mumbo-jumbo. After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Mumbo-jumbo version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators.

The English and Mumbo-jumbo questionnaires were both piloted as part of the survey pretest.

All questionnaires and modules are provided as external resources.

This field aims at describing at what geographic level the data are representative. Typical entries will be "National coverage", "Urban (or rural) areas only", "State of ...", "Capital city", etc.

Note that we do not describe here where the data was collected. For example, as sample survey could be declared as "national coverage" even in cases where some districts where not included in the sample, as long as the sampling strategy was such that the representativity is national.

This description should indicate where the program is being implemented (nationally, regionally, etc) with specifically defined regions.

A topic classification facilitates referencing and searches in electronic survey catalogs. Topics should be selected from a standard thesaurus, preferably an international, multilingual thesaurus.

The IHSN recommends the use of the thesaurus used by the Council of European Social Science Data Archives (CESSDA). The CESSDA thesaurus has been introduced as a controlled vocabulary in the IHSN Study Template version 1.3 (available at www.surveynetwork.org/toolkit <http://www.surveynetwork.org/toolkit>).

See CESSDA Vocabulary menu options (or "Topics") in the MCC Evaluation Catalog: http://data.mcc.gov/evaluations/index.php/catalog

Text: Keywords to identify evaluation with in the public Evalaution Catalog Vocabulary: Leave blank until we have a controlled vocabulary selection to choose from.

Vocabulary URI: Leave blank

Words or phrases that describe salient aspects of a data collection's content. Can be used for building keyword indexes and for classification and retrieval purposes. A controlled vocabulary can be employed. Maps to Dublin Core Subject. The vocab attribute is provided for specification of the controlled vocabulary in use, e.g., LCSH, MeSH, etc. The vocabURI attribute specifies the location for the full controlled vocabulary.

Name: Independent Evaluator Firm (or individual) Name Affiliation: For individuals, list the university or institution that they are affiliated with. Leave blank for evaluation firms.

Provide the full unabbreviated name

List the organizations (national or international) that have contributed, in cash or in kind, to the financing of the survey.

Agency: Millennium Challenge Corporation Abbreviation: MCC

MCC should already be listed here. This optional field can also be used to acknowledge any other people and institutions that have in some form contributed to the survey.

Define study population.

Parents of and children 0-5 years old living in rural Tanzania.

This field only applies to sample surveys. Information on sampling procedure is crucial (although not applicable for censuses and administrative datasets).

Description of power calculations, sample sizes, selection process, level of representation, etc.

MCC Clarification:

Information on sampling procedure is crucial (although not applicable for censuses and administrative datasets). This section should include summary information that includes though is not limited to:

Required sample size and actual sample size, Selection process (e.g., probability proportional to size or over sampling), Stratification (implicit and explicit), Stages of sample selection, Level of representation, Strategy for absent respondents/not found/refusals (replacement or not), Sample frame used, and listing exercise conducted to update it

5000 households were selected for the sample. Of these, 4996 were occupied households and 4811 were successfully interviewed for a response rate of 96.3%. Within these households, 7815 eligible women aged 15-49 were identified for interview, of which 7505 were successfully interviewed (response rate 96.0%), and 3242 children aged 0-4 were identified for whom the mother or caretaker was successfully interviewed for 3167 children (response rate 97.7%). These give overall response rates (household response rate times individual response rate) for the women's interview of 92.5% and for the children's interview of 94.1%.

This field only applies to sample surveys.

Sometimes the reality of the field requires a deviation from the sampling design (for example due to difficulty to access to zones due to weather problems, political instability, etc). If for any reason, the sample design has deviated, this should be reported here.

Response rate provides that percentage of households (or other sample unit) that participated in the survey based on the original sample size. Omissions may occur due to refusal to participate, impossibility to locate the respondent, or other. Sometimes, a household may be replaced by another by design. Check that the information provided here is consistent with the sample size indicated in the "Sampling procedure" field and the number of records found in the dataset (for example, if the sample design mention a sample of 5,000 households and the data on contain data on 4,500 households, the response rate should not be 100 percent).

This field only applies to sample surveys.

Provide here the list of variables used as weighting coefficient. If more than one variable is a weighting variable, describe how these variables differ from each other and what the purpose of each one of them is.

Sample weights for the household data were computed as the inverse of the probability of selection of the household, computed at the sampling domain level (urban/rural within each region). The household weights were adjusted for non-response at the domain level, and were then normalized by a constant factor so that the total weighted number of households equals the total unweighted number of households. The household weight variable is called HHWEIGHT and is used with the HH data and the HL data.

For sampling surveys, it is good practice to calculate and publish sampling error. This field is used to provide information on these calculations. This includes:

- A list of ratios/indicators for which sampling errors were computed.

- Details regarding the software used for computing the sampling error, and reference to the programs used (to be provided as external resources) as the program used to perform the calculations.

- Reference to the reports or other document where the results can be found (to be provided as external resources).

Example:

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the 2005-2006 MICS sample is the result of a multi-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the 2005-2006 MICS. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

DATE MUST BE ENTERED IN THE ISO FORMAT YYYY-MM-DD

YYYY-MM-DD (Baseline); YYYY-MM-DD (Interim); YYYY-MM-DD (End-line)

In some cases, data collection for a same survey can be conducted in waves. In such case, you should enter the start and end date of each wave separately, and identify each wave in the "cycle" field. If exact dates are unavailable, then include the month and year only.

Include any additional information and direct users to Data Collection Reports if available.

The pre-test for the survey took place from August 15, 2006 - August 25, 2006 and included 14 interviewers who would later become supervisors for the main survey.

Each interviewing team comprised of 3-4 female interviewers (no male interviewers were used due to the sensitivity of the subject matter), together with a field editor and a supervisor and a driver. A total of 52 interviewers, 14 supervisors and 14 field editors were used. Data collection took place over a period of about 6 weeks from September 2, 2006 until October 17, 2006. Interviewing took place everyday throughout the fieldwork period, although interviewing teams were permitted to take one day off per week.

Interviews averaged 35 minutes for the household questionnaire (excluding salt testing), 23 minutes for the women's questionnaire, and 27 for the under five children's questionnaire (excluding the anthropometry). Interviews were conducted primarily in English and Mumbo-jumbo, but occasionally used local translation in double-Dutch, when the respondent did not speak English or Mumbo-jumbo.

Six staff members of GenCenStat provided overall fieldwork coordination and supervision. The overall field coordinator was Mrs. Doe.

This element is provided in order to record information regarding the persons and/or agencies that took charge of the data collection. This element includes 3 fields: Name, Abbreviation and the Affiliation. In most cases, we will record here the name of the agency, not the name of interviewers. Only in the case of very small-scale surveys, with a very limited number of interviewers, the name of person will be included as well. The field Affiliation is optional and not relevant in all cases.

Name: Name of data collection firm(s) or survey firms.

Abbreviation: Abbreviation of Data collection firm name(s) or survey firm name(s).

This is the data collection firm responsible for administering the questionnaire or interview or compiling the data. This refers to the entities collecting the data, not to the entities producing the documentation.

Name: Central Statistics Office Abbreviation: CSO

Please describe how data collection teams were organized (how many team members per team, how many teams per supervisor, how was data entry managed)

Interviewing was conducted by teams of interviewers. Each interviewing team comprised of 3-4 female interviewers, a field editor and a supervisor, and a driver. Each team used a 4 wheel drive vehicle to travel from cluster to cluster (and where necessary within cluster).

The role of the supervisor was to coordinator field data collection activities, including management of the field teams, supplies and equipment, finances, maps and listings, coordinate with local authorities concerning the survey plan and make arrangements for accommodation and travel. Additionally, the field supervisor assigned the work to the interviewers, spot checked work, maintained field control documents, and sent completed questionnaires and progress reports to the central office.

The field editor was responsible for reviewing each questionnaire at the end of the day, checking for missed questions, skip errors, fields incorrectly completed, and checking for inconsistencies in the data. The field editor also observed interviews and conducted review sessions with interviewers.

Responsibilities of the supervisors and field editors are described in the Instructions for Supervisors and Field Editors, together with the different field controls that were in place to control the quality of the fieldwork.

Field visits were also made by a team of central staff on a periodic basis during fieldwork. The senior staff of GenCenStat also made 3 visits to field teams to provide support and to review progress.

Evaluator please describe the data cleaning process. E.g., consistency checking, wildcode checking, etc. Include the role of the data collection firm (if any) and the process used to produce the cleaned, analysis file.

The data editing should contain information on how the data was treated or controlled for in terms of consistency and coherence. This item does not concern the data entry phase but only the editing of data whether manual or automatic.

- Was a hot deck or a cold deck technique used to edit the data?

- Were corrections made automatically (by program), or by visual control of the questionnaire?

- What software was used?

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding

b) During data entry

c) Structure checking and completeness

d) Secondary editing

e) Structural checking of SPSS data files Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Use this field to provide as much information as possible on the data entry design. This includes such details as:

- Mode of data entry (manual or by scanning, in the field/in regions/at headquarters)

- Computer architecture (laptop computers in the field, desktop computers, scanners, PDA, other; indicate the number of computers used)

- Software used

- Use (and rate) of double data entry

- Average productivity of data entry operators; number of data entry operators involved and their work schedule

Data entry was conducted by 12 data entry operators in tow shifts, supervised by 2 data entry supervisors, using a total of 7 computers (6 data entry computers plus one supervisors' computer). All data entry was conducted at the GenCenStat head office using manual data entry. For data entry, CSPro version 2.6.007 was used with a highly structured data entry program, using system controlled approach that controlled entry of each variable. All range checks and skips were controlled by the program and operators could not override these. A limited set of consistency checks were also included in the data entry program. In addition, the calculation of anthropometric Z-scores was also included in the data entry programs for use during analysis. Open-ended responses ("Other" answers) were not entered or coded, except in rare circumstances where the response matched an existing code in the questionnaire.

100% verification of all variables was performed using independent verification, i.e. double entry of data, with separate comparison of data followed by modification of one or both datasets to correct keying errors by original operators who first keyed the files. score and quintiles were included in the datasets for use in tabulations.

All microdata from MCC-financed evaluations must be anonymized at the individual level to protect the confidentiality of survey respondents. Any requests for non-anonymized data must go through the MCC Disclosure Review Board.

Each dataset should have an "Access policy" attached to it. The IHSN recommends three levels of accessibility:

- Public use files, accessible to all

- Licensed datasets, accessible under conditions

- Datasets only accessible in a data enclave, for the most sensitive and confidential data.

The IHSN has formulated standard, generic policies and access forms for each one of these three levels (which each country can customize to its specific needs). One of the three policies may be copy/pasted in this field once it has been edited as needed and approved by the appropriate authority. Before you fill this field, a decision has to be made by the management of the data depositor agency. Avoid writing a specific statement for each dataset.

If the access policy is subject to regular changes, you should enter here a URL where the user will find detailed information on access policy which applies to this specific dataset. If the datasets are sold, pricing information should also be provided on a website instead of being entered here.

If the access policy is not subject to regular changes, you may enter more detailed information here. For a public use file for example, you could enter information like:

The dataset has been anonymized and is available as a Public Use Dataset. It is accessible to all for statistical and research purposes only, under the following terms and conditions:

1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the [National Data Archive].

2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.

3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the [National Data Archive].

4. No attempt will be made to produce links among datasets provided by the [National Data Archive], or among data from the [National Data Archive] and other datasets that could identify individuals or organizations.

5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the [National Data Archive] will cite the source of data in accordance with the Citation Requirement provided with each dataset.

6. An electronic copy of all reports and publications based on the requested data will be sent to the [National Data Archive].

The original collector of the data, the [National Data Archive], and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.

Citation requirement is the way that the dataset should be referenced when cited in any publication. Every dataset should have a citation requirement. This will guarantee that the data producer gets proper credit, and that analytical results can be linked to the proper version of the dataset. The Access Policy should explicitly mention the obligation to comply with the citation requirement. The citation should include at least the primary investigator, the name and abbreviation of the dataset, the reference year, and the version number. Include also a website where the data or information on the data is made available by the official data depositor.

Use of the dataset must be acknowledged using a citation which would include:

- the Identification of the Primary Investigator

- the title of the survey (including country, acronym and year of implementation)

- the survey reference number

- the source and date of download

Chicago (16th edition) (author-date)

Smith, Tom W., Peter V. Marsden, and Michael Hout. 2011. General Social Survey, 1972-2010 Cumulative File. ICPSR31521-v1. Chicago, IL: National Opinion Research Center. Distributed by Ann Arbor, MI: Inter-university Consortium for Political and Social Research. doi:10.3886/ICPSR31521.v1

Use of the dataset must be acknowledged using a citation which would include:

- the Identification of the Primary Investigator

- the title of the survey (including country, acronym and year of implementation)

- the survey reference number

- the source and date of download

Users of the data may need further clarification and information. This section may include the name-affiliation-email-URI of one or multiple contact persons. Avoid putting the name of individuals. The information provided here should be valid for the long term. It is therefore preferable to identify contact persons by a title. The same applies for the email field. Ideally, a "generic" email address should be provided. It is easy to configure a mail server in such a way that all messages sent to the generic email address would be automatically forwarded to some staff members.

All inquiries should be directed to this generic address, rather than to specific MCC contacts.

Content and Format:

Name: Monitoring & Evaluation Division of the Millennium Challenge Corporation Affiliation: Leave blank Email: impact-eval@mcc.gov

A data filename usually provides little information on its content. Provide here a description of this content. This description should clearly distinguish collected variables and derived variables. It is also useful to indicate the availability in the data file of some particular variables such as the weighting coefficients. If the file contains derived variables, it is good practice to refer to the computer program that generated it.

Examples:

- The file contains data related to section 3A of the household survey questionnaire (Education of household members aged 6 to 24 years). It also contains the weighting coefficient, and various recoded variables on levels of education.

- The file contains derived data on household consumption, annualized and aggregated by category of products and services. The file also contains a regional price deflator variable and the household weighting coefficient. The file was generated using a Stata program named "cons_aggregate.do" available in the external resources.

Put the name of the agency that produced the data file. Most data files will have been produced by the survey primary investigator. In some cases however, auxiliary or derived files from other producers may be released with a data set. This may for example include CPI data generated by a different agency, or files containing derived variables generated by a researcher.

A data file may undergo various changes and modifications. These file specific versions can be tracked in this element. This field will in most cases be left empty. It is more important to fill the field identifying the version of the dataset (see above).

Use this element if needed to provide information about the types of checks and operations that have been performed on the data file to make sure that the data are as correct as possible, e.g. consistency checking, wildcode checking, etc. Note that the information included here should be specific to the data file. Information about data processing checks that have been carried out on the data collection (study) as a whole should be provided in the "Data editing" element at the study level.

You may also provide here a reference to an external resource that contains the specifications for the data processing checks (that same information may be provided also in the "Data Editing" filed in the Study Description section).

Missing data can be given certain coding. A common convention is to iterate the number "9"to fill a field. This value needs to be defined as missing in the data set and can be explained in detail in this element.

This field, aiming to provide information to the user on items not covered elsewhere, will in most cases be left empty.

This element provides a space to describe the variable in detail. Not all variables require definition. The following variables should always be defined when available in a questionnaire:

- Household (attach this definition to the "household ID" variable

- Head of household (attach this definition to the variable "relationship to the head"

- Urban/rural

Greater description on the nature of the variable can be placed in this element. For example this element can provide a clearer definition for certain variables (i.e. a variable that provides information on whether a person is a household member). In the case of household membership, a conceptual definition can be provided.

A household member is defined as any person who has been resident in the household for six months or more in a given year and takes meals together OR by default the head of household, infants under 6 months, newly wedded couples etc.