Living Standards Survey 2001
Timor-Leste, 2001
National Statistics Directorate
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Identification
Survey ID Number
TLS_2001_LSS-W1_v01_EN_M_v01_A_OCS
Title
Living Standards Survey 2001
Country
| Name | Country code |
|---|---|
| Timor-Leste | TLS |
Study type
Living Standards Measurement Study [hh/lsms]
Series Information
This is the first LSS conducted in Timor-Leste.
Abstract
Timor-Leste experienced a fundamental social and economic upheaval after its people voted for independence from Indonesia in a referendum in August 1999. Population was displaced, and public and private infrastructure was destroyed or rendered inoperable. Soon after the violence ceased, the country began rebuilding itself with the support from UN agencies, the international donor community and NGOs. The government laid out a National Development Plan (NDP) with two central goals: to promote rapid, equitable and sustainable economic growth and to reduce poverty. Formulating a national plan and poverty reduction strategy required data on poverty and living standards, and given the profound changes experienced, new data collection had to be undertaken to accurately assess the living conditions in the country. The Planning Commission of the Timor-Leste Transitional Authority undertook a Poverty Assessment Project along with the World Bank, the Asian Development Bank, the United Nations Development Programme and the Japanese International Cooperation Agency (JICA).
This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001. A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyse policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital. Data collection was undertaken between end August and November 2001. The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.
This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001. A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyse policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital. Data collection was undertaken between end August and November 2001. The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households
Scope
Notes
1 Household information
A: Household Roster
B: New members since the violence in 1999
C: Persons leaving household after violence in1999
D: Information on parents of household members
2 Housing
A: Description of the dwelling
B: Housing state
C: Services
D: Ownership and expenditure
3 Access to facilities
4 Expenditures and consumption
A: Weekly food consumption
B: Monthly and annual non-food expenditure
C: Durable goods
5 Education
A: General education
B: Attendance school years 1998-1999 and 2001-2002
6 Health
A: Health care use
B: Children health
7 Fertility and maternity history
8 Employment
A: Labour force participation
B: Job information
C: Individual time use
9 Farming and livestock
A: Plots
B: Crops harvested
C: Agricultural inputs
D: Forestry
E: Farming equipment
F: Labour and farm produce
G: Livestock
H: Fishing and aquaculture
10 Transfers, borrowing and savings
A: Transfers given and loaned
B: Transfers received
C: Borrowing
D: Aid assistance
E: Savings
11 Other income
12 Social capital
13 Subjective wellbeing
A: Individual
B: Household
C: Vulnerability
A: Household Roster
B: New members since the violence in 1999
C: Persons leaving household after violence in1999
D: Information on parents of household members
2 Housing
A: Description of the dwelling
B: Housing state
C: Services
D: Ownership and expenditure
3 Access to facilities
4 Expenditures and consumption
A: Weekly food consumption
B: Monthly and annual non-food expenditure
C: Durable goods
5 Education
A: General education
B: Attendance school years 1998-1999 and 2001-2002
6 Health
A: Health care use
B: Children health
7 Fertility and maternity history
8 Employment
A: Labour force participation
B: Job information
C: Individual time use
9 Farming and livestock
A: Plots
B: Crops harvested
C: Agricultural inputs
D: Forestry
E: Farming equipment
F: Labour and farm produce
G: Livestock
H: Fishing and aquaculture
10 Transfers, borrowing and savings
A: Transfers given and loaned
B: Transfers received
C: Borrowing
D: Aid assistance
E: Savings
11 Other income
12 Social capital
13 Subjective wellbeing
A: Individual
B: Household
C: Vulnerability
Topics
| Topic | Vocabulary |
|---|---|
| Food (production, crisis) | FAO |
| Health | FAO |
| Population & Reproductive Health | FAO |
| Access to Finance | FAO |
| Payment Systems | FAO |
| Labor | FAO |
| Livestock | FAO |
Coverage
Geographic Coverage
National coverage
Producers and sponsors
Primary investigators
| Name |
|---|
| National Statistics Directorate |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| World Bank | WB | Financial assistance |
Sampling
Sampling Procedure
SAMPLE SIZE AND ANALYTIC DOMAINS
A survey relies on identifying a subgroup of a population that is representative both for the underlying population and for specific analytical domains of interest. The main objective of the TLSS is to derive a poverty profile for the country and salient population groups. The fundamental analytic domains identified are the Major Urban Centers (Dili and Baucau), the Other Urban Centers and the Rural Areas. The survey represents certain important sub-divisions of the Rural Areas, namely two major agro-ecologic zones (Lowlands and Highlands) and three broad geographic regions (West, Center and East). In addition to these domains, we can separate landlocked sucos (Inland) from those with sea access (Coast), and generate categories merging rural and urban strata along the geographic, altitude, and sea access dimensions. However, the TLSS does not provide detailed indicators for narrow geographic areas, such as postos or even districts. [Note: Timor-Leste is divided into 13 major units called districts. These are further subdivided into 67 postos (subdistricts), 498 sucos (villages) and 2,336 aldeias (sub-villages). The administrative structure is uniform throughout the country, including rural and urban areas.] The survey has a sample size of 1,800 households, or about one percent of the total number of households in Timor-Leste. The experience of Living Standards Measurement Surveys in many countries - most of them substantially larger than Timor-Leste - has shown that samples of that size are sufficient for the requirements of a poverty assessment. The survey domains were defined as follows. The Urban Area is divided into the Major Urban Centers (the 31 sucos in Dili and the 6 sucos in Baucau) and the Other Urban Centers (the remaining 34 urban sucos outside Dili and Baucau). The rest of the country (427 sucos in total) comprises the Rural Area. The grouping of sucos into urban and rural areas is based on the Indonesian classification. In addition, we separated rural sucos both by agro-ecological zones and geographic areas. With the help of the Geographic Information System developed at the Department of Agriculture, sucos were subsequently qualified as belonging to the Highlands or the Lowlands depending on the share of their surface above and below the 500 m level curve. The three westernmost districts (Oecussi, Bobonaro and Cova Lima) constitute the Western Region, the three easternmost districts (Baucau, Lautem and Viqueque) the Eastern Region, and the remaining seven districts (Aileu, Ainaro, Dili, Ermera, Liquica, Manufahi and Manatuto) belong to the Central Region.
SAMPLING STRATA AND SAMPLE ALLOCATION
Our next step was to ensure that each analytical domain contained a sufficient number of households. Assuming a uniform sampling fraction of approximately 1/100, a non-stratified 1,800-household sample would contain around 240 Major Urban households and 170 Other Urban households -too few to sustain representative and significant analyses. We therefore stratified the sample to separate the two urban areas from the rural areas. The rural strata were large enough so that its implicit stratification along agro-ecological and geographical dimensions was sufficient to ensure that these dimensions were represented proportionally to their share of the population. The final sample design by strata was as follows: 450 households in the Major Urban Centers (378 in Dili and 72 in Baucau), 252 households in the Other Urban Centers and 1,098 households in the Rural Areas.
SAMPLING STRATEGY
The sampling of households in each stratum, with the exception of Urban Dili, followed a 3-stage procedure. In the first stage, a certain number of sucos were selected with probability proportional to size (PPS). Hence 4 sucos were selected in Urban Baucau, 14 in Other Urban Centers and 61 in the Rural Areas. In the second stage, 3 aldeias in each suco were selected, again with probability proportional to size (PPS). In the third stage, 6 households were selected in each aldeia with equal probability (EP). This implies that the sample is approximately selfweighted within the stratum: all households in the stratum had the same chance of being visited by the survey. A simpler and more efficient 2-stage process was used for Urban Dili. In the first stage, 63 aldeias were selected with PPS and in the second stage 6 households with equal probability in each aldeia (for a total sample of 378 households). This procedure reduces sampling errors since the sample will be spread more than with the standard 3-stage process, but it can only be applied to Urban Dili as only there it was possible to sort the selected aldeias into groups of 3 aldeias located in close proximity of each other.
A survey relies on identifying a subgroup of a population that is representative both for the underlying population and for specific analytical domains of interest. The main objective of the TLSS is to derive a poverty profile for the country and salient population groups. The fundamental analytic domains identified are the Major Urban Centers (Dili and Baucau), the Other Urban Centers and the Rural Areas. The survey represents certain important sub-divisions of the Rural Areas, namely two major agro-ecologic zones (Lowlands and Highlands) and three broad geographic regions (West, Center and East). In addition to these domains, we can separate landlocked sucos (Inland) from those with sea access (Coast), and generate categories merging rural and urban strata along the geographic, altitude, and sea access dimensions. However, the TLSS does not provide detailed indicators for narrow geographic areas, such as postos or even districts. [Note: Timor-Leste is divided into 13 major units called districts. These are further subdivided into 67 postos (subdistricts), 498 sucos (villages) and 2,336 aldeias (sub-villages). The administrative structure is uniform throughout the country, including rural and urban areas.] The survey has a sample size of 1,800 households, or about one percent of the total number of households in Timor-Leste. The experience of Living Standards Measurement Surveys in many countries - most of them substantially larger than Timor-Leste - has shown that samples of that size are sufficient for the requirements of a poverty assessment. The survey domains were defined as follows. The Urban Area is divided into the Major Urban Centers (the 31 sucos in Dili and the 6 sucos in Baucau) and the Other Urban Centers (the remaining 34 urban sucos outside Dili and Baucau). The rest of the country (427 sucos in total) comprises the Rural Area. The grouping of sucos into urban and rural areas is based on the Indonesian classification. In addition, we separated rural sucos both by agro-ecological zones and geographic areas. With the help of the Geographic Information System developed at the Department of Agriculture, sucos were subsequently qualified as belonging to the Highlands or the Lowlands depending on the share of their surface above and below the 500 m level curve. The three westernmost districts (Oecussi, Bobonaro and Cova Lima) constitute the Western Region, the three easternmost districts (Baucau, Lautem and Viqueque) the Eastern Region, and the remaining seven districts (Aileu, Ainaro, Dili, Ermera, Liquica, Manufahi and Manatuto) belong to the Central Region.
SAMPLING STRATA AND SAMPLE ALLOCATION
Our next step was to ensure that each analytical domain contained a sufficient number of households. Assuming a uniform sampling fraction of approximately 1/100, a non-stratified 1,800-household sample would contain around 240 Major Urban households and 170 Other Urban households -too few to sustain representative and significant analyses. We therefore stratified the sample to separate the two urban areas from the rural areas. The rural strata were large enough so that its implicit stratification along agro-ecological and geographical dimensions was sufficient to ensure that these dimensions were represented proportionally to their share of the population. The final sample design by strata was as follows: 450 households in the Major Urban Centers (378 in Dili and 72 in Baucau), 252 households in the Other Urban Centers and 1,098 households in the Rural Areas.
SAMPLING STRATEGY
The sampling of households in each stratum, with the exception of Urban Dili, followed a 3-stage procedure. In the first stage, a certain number of sucos were selected with probability proportional to size (PPS). Hence 4 sucos were selected in Urban Baucau, 14 in Other Urban Centers and 61 in the Rural Areas. In the second stage, 3 aldeias in each suco were selected, again with probability proportional to size (PPS). In the third stage, 6 households were selected in each aldeia with equal probability (EP). This implies that the sample is approximately selfweighted within the stratum: all households in the stratum had the same chance of being visited by the survey. A simpler and more efficient 2-stage process was used for Urban Dili. In the first stage, 63 aldeias were selected with PPS and in the second stage 6 households with equal probability in each aldeia (for a total sample of 378 households). This procedure reduces sampling errors since the sample will be spread more than with the standard 3-stage process, but it can only be applied to Urban Dili as only there it was possible to sort the selected aldeias into groups of 3 aldeias located in close proximity of each other.
Weighting
See detailed information on selection probabilities and sampling weight calculations in document titled "Basic documentation".
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 2001-08 | 2001-11 |
Data Collection Mode
Face-to-face [f2f]
Data Processing
Data Editing
1. DATA ENTRY
A decentralized approach to data entry was adopted in Timor-Leste. Data entry proceeded side by side with data gathering with the help of laptops to ensure verification and correction in the field. The purpose of this procedure was twofold. First, it reduced the time of data processing because it was not necessary to send the questionnaires to the central office to be entered. More important, data were available for analysis very soon after the fieldwork was completed. And second, it allowed for immediate and extensive checks on data quality. Any inconsistency revealed at this stage was to be rectified by revisiting the households while still being in the village, and so, the need for later data editing was minimized. A second round of standard checks on data quality was also implemented in the project office in Dili upon retrieval of the data from the field teams. In general, with a few exceptions, the analysis has confirmed the high quality of the data entry and validation processes. The data entry program was designed to check for data entry errors, coding mistakes, as well as to search for incomplete or inaccurate data collection. It was based upon two major types of checks.
2. CHECKS
On the one hand, standard value-range checks were included. If the data entry operator entered data, which was outside the bounds of the programmed range, either because the number was not a pre-coded one or because it was extremely unlikely, the program would alert him. On the other hand, it also contained a series of checks to ensure that the data collected were internally consistent. The skip program used in the questionnaire was programmed into the data entry software to ensure that the information entered was consistent to the desired skip pattern. For instance, if the code “3” was entered by mistake in a question where the only valid responses were “1” or “2”, the program would alert the operator. Similarly, if the household reported having purchased a particular good, the program would check to see if information on quantities and expenditure was also reported. However, if the data entered into the computer matched the information provided in the questionnaires, the data entry operators were instructed not to make any changes to any of them. Such cases were brought to the attention of the supervisor, which either corrected the mistake based on another information collected in the questionnaire or decided if a visit to that household was necessary.
A decentralized approach to data entry was adopted in Timor-Leste. Data entry proceeded side by side with data gathering with the help of laptops to ensure verification and correction in the field. The purpose of this procedure was twofold. First, it reduced the time of data processing because it was not necessary to send the questionnaires to the central office to be entered. More important, data were available for analysis very soon after the fieldwork was completed. And second, it allowed for immediate and extensive checks on data quality. Any inconsistency revealed at this stage was to be rectified by revisiting the households while still being in the village, and so, the need for later data editing was minimized. A second round of standard checks on data quality was also implemented in the project office in Dili upon retrieval of the data from the field teams. In general, with a few exceptions, the analysis has confirmed the high quality of the data entry and validation processes. The data entry program was designed to check for data entry errors, coding mistakes, as well as to search for incomplete or inaccurate data collection. It was based upon two major types of checks.
2. CHECKS
On the one hand, standard value-range checks were included. If the data entry operator entered data, which was outside the bounds of the programmed range, either because the number was not a pre-coded one or because it was extremely unlikely, the program would alert him. On the other hand, it also contained a series of checks to ensure that the data collected were internally consistent. The skip program used in the questionnaire was programmed into the data entry software to ensure that the information entered was consistent to the desired skip pattern. For instance, if the code “3” was entered by mistake in a question where the only valid responses were “1” or “2”, the program would alert the operator. Similarly, if the household reported having purchased a particular good, the program would check to see if information on quantities and expenditure was also reported. However, if the data entered into the computer matched the information provided in the questionnaires, the data entry operators were instructed not to make any changes to any of them. Such cases were brought to the attention of the supervisor, which either corrected the mistake based on another information collected in the questionnaire or decided if a visit to that household was necessary.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| LSMS Data Manager | The World Bank | [email protected] | http://go.worldbank.org/QJVDZDKJ60 |
Confidentiality
https://microdata.worldbank.org/index.php/terms-of-use
Access conditions
In receiving these data it is recognized that the data are supplied for use within your organization, and you agree to the following stipulations as conditions for the use of the data:
1. The data are supplied solely for the use described in this form and will not be made available to other organizations or individuals. Other organizations or individuals may request the data directly.
2. Three copies of all publications, conference papers, or other research reports based entirely or in part upon the requested data will be supplied to:
Manuel Mendonca
Director
National Statics Directorate
Caicoli, Dili, Timor Leste
e-mail: [email protected]
AND
The World Bank
Development Economics Research Group
LSMS Database Administrator
MSN MC3-306
1818 H Street, NW
Washington, DC 20433, USA
tel: (202) 473-9041
fax: (202) 522-1153
e-mail: [email protected]
3. The researcher will refer to the 2001 Timor Leste Survey of Living Standards as the source of the information in all publications, conference papers, and manuscripts. At the same time, the National Statistics Directorate is not responsable for the estimations reported by the analyst(s).
4. Users who download the data may not pass the data to third parties.
5. The database cannot be used for commercial ends, nor can it be sold.
1. The data are supplied solely for the use described in this form and will not be made available to other organizations or individuals. Other organizations or individuals may request the data directly.
2. Three copies of all publications, conference papers, or other research reports based entirely or in part upon the requested data will be supplied to:
Manuel Mendonca
Director
National Statics Directorate
Caicoli, Dili, Timor Leste
e-mail: [email protected]
AND
The World Bank
Development Economics Research Group
LSMS Database Administrator
MSN MC3-306
1818 H Street, NW
Washington, DC 20433, USA
tel: (202) 473-9041
fax: (202) 522-1153
e-mail: [email protected]
3. The researcher will refer to the 2001 Timor Leste Survey of Living Standards as the source of the information in all publications, conference papers, and manuscripts. At the same time, the National Statistics Directorate is not responsable for the estimations reported by the analyst(s).
4. Users who download the data may not pass the data to third parties.
5. The database cannot be used for commercial ends, nor can it be sold.
Citation requirements
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
- 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
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses
Metadata production
DDI Document ID
DDI_TLS_2001_LSS-W1_v01_EN_M_v01_A_OCS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Adoption of metadata for FAM |
| Development Economics Data Group | DECDG | World Bank | Documentation of the DDI |
DDI Document version
TLS_2001_LSS-W1_v01_EN_M_v01_A_OCS_v01