Labor Market Panel Survey, TLMPS 2014
Tunisia, 2014 - 2015
Economic Research Forum
Identification
Survey ID Number
TUN_TLMPS_2014_V2
Title
Labor Market Panel Survey, TLMPS 2014
Country
| Name | Country code |
|---|---|
| Tunisia | TUN |
Study type
Labor Market Panel Surveys [hh/LMPS]
Series Information
Similar to both the Egypt Labor Market Panel Surveys (ELMPSs) of 1998, 2006, and 2012 and Jordan Labor Market Panel Survey (JLMPS) of 2010, and as part of the same seried, the Tunisia Labor Market Panel Survey (TLMPS) of 2014 is the first wave of what will eventually become a longitudinal survey of the Tunisian labor market.
Abstract
The Egypt Labor Market Panel Surveys (ELMPSs) of 1998, 2006, and 2012 and Jordan Labor Market Panel Survey (JLMPS) of 2010 have become well-recognized data sources for labor market studies in the Middle East and North Africa (MENA). These two surveys have been used in numerous research endeavors including peer reviewed academic publications, dissertations, and international organization reports. As part of the same series of surveys, the Tunisia Labor Market Panel Survey (TLMPS) of 2014 is the first wave of what will eventually become a longitudinal survey of the Tunisian labor market. Being far richer than any currently available data, the TLMPS 2014 is a much-needed addition in a landscape of otherwise scarce publicly-accessible data on the Tunisian labor market. The TLMPS 2014 was collected in partnership between the Economic Research Forum (ERF) and the Tunisian National Institute of Statistics (INS).
Similarly to its Egyptian and Jordanian counterparts, the TLMPS 2014 is a nationally representative survey that features detailed information on households and individuals, especially in regards to labor market characteristics. As in other countries in the MENA region, Tunisia suffers from high unemployment, particularly for university graduates, youth, and women, and from low female labor force participation.
The survey allows for an in-depth investigation of current employment characteristics as well as analyses of broader labor market dynamics. For instance, analyses have already revealed the particularly long unemployment durations Tunisian youth experience, long even in comparison to other countries in the region.
For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).
Similarly to its Egyptian and Jordanian counterparts, the TLMPS 2014 is a nationally representative survey that features detailed information on households and individuals, especially in regards to labor market characteristics. As in other countries in the MENA region, Tunisia suffers from high unemployment, particularly for university graduates, youth, and women, and from low female labor force participation.
The survey allows for an in-depth investigation of current employment characteristics as well as analyses of broader labor market dynamics. For instance, analyses have already revealed the particularly long unemployment durations Tunisian youth experience, long even in comparison to other countries in the region.
For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).
Kind of Data
Sample survey data [ssd]
Unit of Analysis
1- Households.
2- Individuals.
2- Individuals.
Version
Version Description
-----> V2.0: This version includes the following data files:
TLMPS 2014 v2.0 all.dta (contains all variables)
---OR---
TLMPS 2014 v2.0 pt 1.dta (contains variables in questionnaires 1 & 2, in addition to created variables)
TLMPS 2014 v2.0 pt 2.dta (contains variables in questionnaire 3, in addition to created variables)
TLMPS 2014 v2.0 all.dta (contains all variables)
---OR---
TLMPS 2014 v2.0 pt 1.dta (contains variables in questionnaires 1 & 2, in addition to created variables)
TLMPS 2014 v2.0 pt 2.dta (contains variables in questionnaire 3, in addition to created variables)
Production
Date
2016-07
Version Notes
----> Datafiles
The "TLMPS 2014 v2.0 all.dta" data file contains more than 2,000 variables.
If you are using STATA IC, this file has too many variables to open.
Use "TLMPS 2014 v2.0 pt 1.dta" and "TLMPS 2014 v2.0 pt 2.dta" instead.
If you can open a file with more than 2,000 variables, you can just use "TLMPS 2014 v2.0 all.dta".
----> Identifiers
hhid: unique household identifier
indid: unique individual identifier
----> Weight variables
expan_hh: household questionnaire expansion factor
expan_roster: household roster expansion factor
expan_indiv: child/adult questionnaire expansion factor
expan_migr_ent: household migration/enterprise questionnaire expansion factor
----> Notes on weight
Data is not self-weighted.
Weights need to be used to get representative statistics.
Weights should be used in STATA as analytical or probability weights.
The weight used should be selected based on which questionnaire the dependent variable comes from.
If data is being used across multiple questionnaires, we still recommend using the weight based on the questionnaire the dependent variable comes from, but that covariates be created so that observations missing data on those covariates are included with dummies for missing.
For instance, if using receipt of remittances (covariate) to predict child schooling (dependent variable), create remittance receipt categorically/as a series of dummies for: 0 (did not receive), 1 (did receive), 2 (data missing).
----> Problems with the Data
There are a large number of issues with the data that users should be aware of:
(1) Observations Missing Corresponding Data:
There are individuals in the household questionnaire and household roster who should have answered a questionnaire (migration, adult, child) and did not. The variables stat_adult_quest, stat_child_quest, and stat_migr_quest indicate whether observations were matched successfully. Non-response to these questionnaires has been incorporated into the questionnaire specific weights.
(2) Skip pattern problems:
Skip patterns were not always obeyed as they should be. Created variables are based on the preceding information and applying skip patterns as in the questionnaire.
(3) Contradictory information:
Individuals often responded contradictorily in different sections (identified as a wage worker in one section, not in another). Generally, created variables were defined based on the earliest information in the questionnaire.
(4) Non-response:
There are substantial problems with non-response to different sections/questions. While we have created weights for total non-response to a questionnaire part (individual (adult/child) or migration/enterprise), there is a great deal of non-response and missing data on individual questions. There is therefore a lot of missing data in both raw and created variables. If a variable of interest to you has substantial missing data, it may be necessary to use missing data techniques (for instance multiple imputation) to achieve representative statistics. Non-response to full questionnaires was definitely non-random and it is therefore likely that missing data on specific questions is also non-random, which will bias results.
(5) Educational Attainment Data:
For children 6-14, in the education section those who entered basic as their highest level (304a) have been skipped over the question on the school year (304b). Likewise, all adults 15-45 who attended school were skipped over the question on the school year (308b), except those who attended the third cycle of higher education (in 308a). There were a few individuals who did not obey these skip patterns, but mostly the key year within-level data is missing. This problem is based on the questionnaire skip pattern, but is a problem for identifying years of schooling and educational attainment. We reconstructed educational attainment from the life events calendar as much as possible.
(6) Data cleaning philosophy:
As we generated created variables, we enforced the skip patterns that should have been applied, and when there were data contradictions, we treated the earlier information as correct. For example, individuals were asked if they had worked in the past 3 months. Those who had worked in the past 3 months were then supposed to be asked their employment status in a later section. In that later section, some individuals who had not worked in the past 3 months (per the earlier data) gave an employment status. In the created variables, we set the (later) employment status variable to missing if the (earlier) employment questions indicated the individual had not been working.
(7) For "raw" (h*, v*, c*, m*) variables, because we wanted to allow researchers the option to make their own case-by-case decisions on contradictory information or data that did not follow the skip patterns, we undertook only "light" cleaning. Invalid responses (e.g. 0 for a 1 "yes" 2 "no" variable, "2018" for a birth year) were all removed. Responses with clear and minor entry errors (e.g. 998 for a year of birth, presumably instead of 9998 for don't know) were recoded to correct codes. Due to the programming in data collection, unfortunately it was not always possible to distinguish between a missing response and a no or zero response for some variables. For example, when individuals were asked their highest year of schooling completed within a level, everyone who did not answer the question was given a zero, but zero would also be a valid response for someone who was just starting school. When there was a zero response, we set it to missing if it was given by an individual who, per the preceding raw variables and skip patterns, should not have answered the question. However, remaining zero responses (or "no" responses for some variables) might actually represent missing data, and should be treated with some caution.
The "TLMPS 2014 v2.0 all.dta" data file contains more than 2,000 variables.
If you are using STATA IC, this file has too many variables to open.
Use "TLMPS 2014 v2.0 pt 1.dta" and "TLMPS 2014 v2.0 pt 2.dta" instead.
If you can open a file with more than 2,000 variables, you can just use "TLMPS 2014 v2.0 all.dta".
----> Identifiers
hhid: unique household identifier
indid: unique individual identifier
----> Weight variables
expan_hh: household questionnaire expansion factor
expan_roster: household roster expansion factor
expan_indiv: child/adult questionnaire expansion factor
expan_migr_ent: household migration/enterprise questionnaire expansion factor
----> Notes on weight
Data is not self-weighted.
Weights need to be used to get representative statistics.
Weights should be used in STATA as analytical or probability weights.
The weight used should be selected based on which questionnaire the dependent variable comes from.
If data is being used across multiple questionnaires, we still recommend using the weight based on the questionnaire the dependent variable comes from, but that covariates be created so that observations missing data on those covariates are included with dummies for missing.
For instance, if using receipt of remittances (covariate) to predict child schooling (dependent variable), create remittance receipt categorically/as a series of dummies for: 0 (did not receive), 1 (did receive), 2 (data missing).
----> Problems with the Data
There are a large number of issues with the data that users should be aware of:
(1) Observations Missing Corresponding Data:
There are individuals in the household questionnaire and household roster who should have answered a questionnaire (migration, adult, child) and did not. The variables stat_adult_quest, stat_child_quest, and stat_migr_quest indicate whether observations were matched successfully. Non-response to these questionnaires has been incorporated into the questionnaire specific weights.
(2) Skip pattern problems:
Skip patterns were not always obeyed as they should be. Created variables are based on the preceding information and applying skip patterns as in the questionnaire.
(3) Contradictory information:
Individuals often responded contradictorily in different sections (identified as a wage worker in one section, not in another). Generally, created variables were defined based on the earliest information in the questionnaire.
(4) Non-response:
There are substantial problems with non-response to different sections/questions. While we have created weights for total non-response to a questionnaire part (individual (adult/child) or migration/enterprise), there is a great deal of non-response and missing data on individual questions. There is therefore a lot of missing data in both raw and created variables. If a variable of interest to you has substantial missing data, it may be necessary to use missing data techniques (for instance multiple imputation) to achieve representative statistics. Non-response to full questionnaires was definitely non-random and it is therefore likely that missing data on specific questions is also non-random, which will bias results.
(5) Educational Attainment Data:
For children 6-14, in the education section those who entered basic as their highest level (304a) have been skipped over the question on the school year (304b). Likewise, all adults 15-45 who attended school were skipped over the question on the school year (308b), except those who attended the third cycle of higher education (in 308a). There were a few individuals who did not obey these skip patterns, but mostly the key year within-level data is missing. This problem is based on the questionnaire skip pattern, but is a problem for identifying years of schooling and educational attainment. We reconstructed educational attainment from the life events calendar as much as possible.
(6) Data cleaning philosophy:
As we generated created variables, we enforced the skip patterns that should have been applied, and when there were data contradictions, we treated the earlier information as correct. For example, individuals were asked if they had worked in the past 3 months. Those who had worked in the past 3 months were then supposed to be asked their employment status in a later section. In that later section, some individuals who had not worked in the past 3 months (per the earlier data) gave an employment status. In the created variables, we set the (later) employment status variable to missing if the (earlier) employment questions indicated the individual had not been working.
(7) For "raw" (h*, v*, c*, m*) variables, because we wanted to allow researchers the option to make their own case-by-case decisions on contradictory information or data that did not follow the skip patterns, we undertook only "light" cleaning. Invalid responses (e.g. 0 for a 1 "yes" 2 "no" variable, "2018" for a birth year) were all removed. Responses with clear and minor entry errors (e.g. 998 for a year of birth, presumably instead of 9998 for don't know) were recoded to correct codes. Due to the programming in data collection, unfortunately it was not always possible to distinguish between a missing response and a no or zero response for some variables. For example, when individuals were asked their highest year of schooling completed within a level, everyone who did not answer the question was given a zero, but zero would also be a valid response for someone who was just starting school. When there was a zero response, we set it to missing if it was given by an individual who, per the preceding raw variables and skip patterns, should not have answered the question. However, remaining zero responses (or "no" responses for some variables) might actually represent missing data, and should be treated with some caution.
Scope
Notes
The survey provides insight into jobs held across the individual's career trajectory, current income, as well as benefits and other non-pecuniary aspects of employment.
The TLMPS 2014 also includes retrospective information on educational trajectories, residential mobility patterns, migration history, marital and fertility history, and can thus be used to conduct in-depth analyses of the life course.
In addition, the TLMPS 2014 includes detailed information on the socio-economic status of individuals, such as parental background (education and employment when the individual was 15 years of age), as well as information on the assets and resources of the household. This information permits researchers to explore inter-generational dynamics even after individuals have left their natal households.
The TLMPS 2014 also includes retrospective information on educational trajectories, residential mobility patterns, migration history, marital and fertility history, and can thus be used to conduct in-depth analyses of the life course.
In addition, the TLMPS 2014 includes detailed information on the socio-economic status of individuals, such as parental background (education and employment when the individual was 15 years of age), as well as information on the assets and resources of the household. This information permits researchers to explore inter-generational dynamics even after individuals have left their natal households.
Topics
| Topic | Vocabulary |
|---|---|
| Background, parental, and sibling characteristics | ERF |
| Labor Force | ERF |
| Unemployment | ERF |
| Migration & Remittances | ERF |
| Education | ERF |
| Earnings | ERF |
| Empowerment | ERF |
| Job dynamics | ERF |
| Marriage, Fertility, and women status | ERF |
Coverage
Geographic Coverage
The sample covered urban/rural areas of each of Tunisia's governorates
Universe
The survey covered a national sample of households and all households members.
Producers and sponsors
Primary investigators
| Name |
|---|
| Economic Research Forum |
Producers
| Name |
|---|
| Tunisian National Institute of Statistics |
Sampling
Sampling Procedure
The initial sample frame included around 5,160 households drawn from a larger sample that is regularly used to conduct the quarterly survey on population and employment in Tunisia. This larger sample contained 18,000 households as of the last quarter of 2012. The drawing of the sample was done in two stages. In the first stage, 258 enumeration areas were randomly drawn according to the principle of probability proportional to size from the list of enumeration areas drawn up in the 2004 Census. This first sampling stage was carried out using 46 strata comprised of the urban/rural components of each of Tunisia's governorates. The final sample was made up of 253 clusters (out of a possible 40,377 nationally). In the second stage, 20 households were supposed to be drawn at random from each cluster. This procedure was, however, not strictly followed in the field.
Response Rate
There were several different problems with non-response during the fielding. First, households often refused to respond entirely. Second, in completing the household survey, some individuals were not captured and some households refused or failed to answer the migration/enterprise questionnaire. In this section we discuss the patterns of non-response, which are incorporated into the weights, discussed below:
1. Non-response of the entire household
While the initial goal was to collect data from 5,160 households, time pressures reduced the intended sample to 4,986 households. Of the 4,986 households initially selected, interviews were completed with only 4,521, generating an overall household non-response rate of 9.3%. Additionally, because several clusters were found not to have the requisite twenty households at the end of the data collection stage, additional households were added to some clusters to improve the response rate, leading to wide variation in the number of the households per cluster. The minimum number of households interviewed in a cluster was 8 and the maximum was 34. The mean was 19.7, and the median was 20, with the interquartile range going from 17 to 22 households.
After this additional work to add households to the sample, non-response rates at a cluster level ranged from 0% (complete response), which occurred for 29% of clusters, to a maximum of 62.5%. The mean non-response at the cluster level was 10.2%, the median was 6.7%, the 75th percentile was 13.3%, and the 90th percentile was 24.8%. This household non-response is incorporated in the weights at a cluster level, with the households that did respond within a cluster representing those that did not.
2. Non-response to child, adult, and migration/enterprise questionnaires
As well as problems with non-response on the household level, there were problems with completing the child, adult, and migration/enterprise questionnaires. We developed weights to account for non-response to each of these questionnaires in their entirety. However, individuals often stopped answering partway through a questionnaire, suffered from incorrect skips, or other data problems, such that data is sometimes missing for a particular question within a questionnaire that contains some data. Additional data imputation techniques, implemented on a question-by-question basis, are required for these problems.
1. Non-response of the entire household
While the initial goal was to collect data from 5,160 households, time pressures reduced the intended sample to 4,986 households. Of the 4,986 households initially selected, interviews were completed with only 4,521, generating an overall household non-response rate of 9.3%. Additionally, because several clusters were found not to have the requisite twenty households at the end of the data collection stage, additional households were added to some clusters to improve the response rate, leading to wide variation in the number of the households per cluster. The minimum number of households interviewed in a cluster was 8 and the maximum was 34. The mean was 19.7, and the median was 20, with the interquartile range going from 17 to 22 households.
After this additional work to add households to the sample, non-response rates at a cluster level ranged from 0% (complete response), which occurred for 29% of clusters, to a maximum of 62.5%. The mean non-response at the cluster level was 10.2%, the median was 6.7%, the 75th percentile was 13.3%, and the 90th percentile was 24.8%. This household non-response is incorporated in the weights at a cluster level, with the households that did respond within a cluster representing those that did not.
2. Non-response to child, adult, and migration/enterprise questionnaires
As well as problems with non-response on the household level, there were problems with completing the child, adult, and migration/enterprise questionnaires. We developed weights to account for non-response to each of these questionnaires in their entirety. However, individuals often stopped answering partway through a questionnaire, suffered from incorrect skips, or other data problems, such that data is sometimes missing for a particular question within a questionnaire that contains some data. Additional data imputation techniques, implemented on a question-by-question basis, are required for these problems.
Weighting
Data is not self-weighted.
For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).
For more information, see the paper(s) cited in the "Citations" section: (Assaad, Ragui, Samir Ghazouani, Caroline Krafft, and Dominique J. Rolando, 2016).
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2014 | 2015 | - |
Data Collection Mode
Face-to-face [f2f]
Data Collection Notes
An important aspect of data collection was the use of tablets and digitized versions of the questionnaires. These digitized questionnaires were produced using software tailored specifically for this project. For INS, this was a major innovation and the first time tablets rather than paper questionnaires were used to record data in a household survey. A number of challenges in the fielding and data processing stages, which we discuss below, arose from the process of transitioning from a paper to digital questionnaire model. Prior to data collection, the software and the questionnaires were tested. The pre-test was conducted over a period of three days in the governorate of Ben Arous, and covered about 100 households.
For the purpose of fieldwork, 25 teams were appointed by INS from its own field staff. Each team was composed of three interviewers and one supervisor. A training session, which lasted 10 days, was organized in advance of fielding. This session included Tunisian ERF members in charge of the project, INS staff, and an expert from the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS), the fielding partner for the ELMPS. As part of the training, enumerators received a manual with detailed information on the questions and the design of the questionnaire. During the training, all the trainees had to implement applied exercises, partly using the tablets, to familiarize themselves with the new digitized data collection process.
Fieldwork started in February of 2014, and the majority of it was completed within one month. However, due to difficulties in getting households to respond in certain areas, and the need to pause fielding while the Tunisian Population Census was underway, fieldwork continued until November of 2014. Further, due to problems in fielding, a number of households from the initial fielding were recontacted by phone in the spring of 2015 to complete their interviews. Given the length of the questionnaire and because of the need to interview the individual him or herself, quite often more than one interview session was needed per household. Once the data were collected, they were transmitted daily, and stored in central servers. The different questionnaires were saved as different files and linked in central processing based on household identifiers .
For the purpose of fieldwork, 25 teams were appointed by INS from its own field staff. Each team was composed of three interviewers and one supervisor. A training session, which lasted 10 days, was organized in advance of fielding. This session included Tunisian ERF members in charge of the project, INS staff, and an expert from the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS), the fielding partner for the ELMPS. As part of the training, enumerators received a manual with detailed information on the questions and the design of the questionnaire. During the training, all the trainees had to implement applied exercises, partly using the tablets, to familiarize themselves with the new digitized data collection process.
Fieldwork started in February of 2014, and the majority of it was completed within one month. However, due to difficulties in getting households to respond in certain areas, and the need to pause fielding while the Tunisian Population Census was underway, fieldwork continued until November of 2014. Further, due to problems in fielding, a number of households from the initial fielding were recontacted by phone in the spring of 2015 to complete their interviews. Given the length of the questionnaire and because of the need to interview the individual him or herself, quite often more than one interview session was needed per household. Once the data were collected, they were transmitted daily, and stored in central servers. The different questionnaires were saved as different files and linked in central processing based on household identifiers .
Data Collectors
| Name | Abbreviation |
|---|---|
| Tunisian National Institute of Statistics | INS |
Questionnaires
Questionnaires
The survey incorporates questionnaires to be administered at both the household and individual levels. At the household level, there was a general household questionnaire, as well as a questionnaire specifically about current migration, transfers, and agricultural and non-agricultural enterprises. At the individual level, there was a detailed questionnaire for working age individuals (15+) and an abbreviated version of the questionnaire for those 6-14 years old.
The main household questionnaire and the migration/enterprise questionnaire were designed to be answered by the most knowledgeable individual in the household, usually the head or the spouse of the head. Along with information on the characteristics of the dwelling, access to public services, and ownership of durables, the household questionnaire includes a full household roster with information on basic demographic characteristics, such as age, sex, and relationship to the head of household. The migration/enterprise questionnaire includes information on any family members currently abroad, remittances, and other transfers, such as child support and pensions. Data were gathered on both non-agricultural and agricultural enterprises, including assets used and net revenues.
The ELMPS and JLMPS had a single questionnaire for all individuals regardless of age. However, in Tunisia, a distinct questionnaire for individuals 6-14 was designed in order to more carefully incorporate measures of child labor. As very little child labor was detected even with this special design, in future LMPSs we plan to revert to a single questionnaire with a few additional questions targeted to children 6-14.
The questionnaire includes a variety of modules on labor market experience and outcomes and related issues. On the labor market side, it elicits information on the current labor market status of the individual, detailed job characteristics (for the employed), wage earnings and non-wage benefits (for wage workers) and participation in domestic and subsistence work. Those who work were asked about both primary and secondary jobs (if any). The questionnaire also includes a detailed labor market history starting from the first labor market status after leaving school and moving forward towards the present for those who ever worked. Further, there is a detailed section on return migration for those who ever worked abroad.
The labor market intersects with a number of other important life experiences, such as education, fertility, and marriage, which are also captured in the TLMPS individual questionnaire. For instance, there are modules on family background (parents and siblings), educational experiences, health, and residential mobility. For women, a section is devoted to fertility issues, the status of women in the household, and work-family issues such as child care and maternity leave. Data were also collected from both men and women on marriage and decisions around marriage, such as the incidence of kin marriage and living arrangements at marriage. Finally, there are modules on financial decision-making, with specific questions about savings and borrowing, as well as on the use of information technology.
The main household questionnaire and the migration/enterprise questionnaire were designed to be answered by the most knowledgeable individual in the household, usually the head or the spouse of the head. Along with information on the characteristics of the dwelling, access to public services, and ownership of durables, the household questionnaire includes a full household roster with information on basic demographic characteristics, such as age, sex, and relationship to the head of household. The migration/enterprise questionnaire includes information on any family members currently abroad, remittances, and other transfers, such as child support and pensions. Data were gathered on both non-agricultural and agricultural enterprises, including assets used and net revenues.
The ELMPS and JLMPS had a single questionnaire for all individuals regardless of age. However, in Tunisia, a distinct questionnaire for individuals 6-14 was designed in order to more carefully incorporate measures of child labor. As very little child labor was detected even with this special design, in future LMPSs we plan to revert to a single questionnaire with a few additional questions targeted to children 6-14.
The questionnaire includes a variety of modules on labor market experience and outcomes and related issues. On the labor market side, it elicits information on the current labor market status of the individual, detailed job characteristics (for the employed), wage earnings and non-wage benefits (for wage workers) and participation in domestic and subsistence work. Those who work were asked about both primary and secondary jobs (if any). The questionnaire also includes a detailed labor market history starting from the first labor market status after leaving school and moving forward towards the present for those who ever worked. Further, there is a detailed section on return migration for those who ever worked abroad.
The labor market intersects with a number of other important life experiences, such as education, fertility, and marriage, which are also captured in the TLMPS individual questionnaire. For instance, there are modules on family background (parents and siblings), educational experiences, health, and residential mobility. For women, a section is devoted to fertility issues, the status of women in the household, and work-family issues such as child care and maternity leave. Data were also collected from both men and women on marriage and decisions around marriage, such as the incidence of kin marriage and living arrangements at marriage. Finally, there are modules on financial decision-making, with specific questions about savings and borrowing, as well as on the use of information technology.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| Economic Research Forum | ERF | erfdataportal@erf.org.eg | www.erf.org.eg |
Contacts
| Name | URL | |
|---|---|---|
| Economic Research Forum (ERF) - 21 Al-Sad Al-Aaly St., Dokki, Giza, Egypt | erfdataportal@erf.org.eg | www.erf.org.eg |
Confidentiality
To access the micro data, researchers are required to register on the ERF website and comply with the data access agreement.
The data will be used only for scholarly, research, or educational purposes. Users are prohibited from using data acquired from the Economic Research Forum in the pursuit of any commercial or private ventures.
Access conditions
Licensed datasets, accessible under conditions.
Citation requirements
The users should cite the Economic Research Forum as follows:
OAMDI, 2016. Labor Market Panel Surveys (LMPS), http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; TLMPS 2014. Egypt: Economic Research Forum (ERF).
OAMDI, 2016. Labor Market Panel Surveys (LMPS), http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; TLMPS 2014. Egypt: Economic Research Forum (ERF).
Disclaimer and copyrights
Disclaimer
The Economic Research Forum has granted the researcher access to relevant data following exhaustive efforts to protect the confidentiality of individual data. The researcher is solely responsible for any analysis or conclusions drawn from available data.
Copyright
(c) 2016, Economic Research Forum
Metadata production
DDI Document ID
TUN_TLMPS_2014_V2
Producers
| Name | Abbreviation |
|---|---|
| Economic Research Forum | ERF |
Date of Metadata Production
2016-07
DDI Document version
Version 2.0