Living Standards Measurement Survey 2001 (Wave 1 Panel)
Bosnia and Herzegovina
In 1992, Bosnia-Herzegovina, one of the six republics in former Yugoslavia, became an independent nation. A civil war started soon thereafter, lasting until 1995 and causing widespread destruction and losses of lives. Following the Dayton accord, BosniaHerzegovina (BiH) emerged as an independent state comprised of two entities, namely, the Federation of Bosnia-Herzegovina (FBiH) and the Republika Srpska (RS), and the district of Brcko. In addition to the destruction caused to the physical infrastructure, there was considerable social disruption and decline in living standards for a large section of the population. Along side these events, a period of economic transition to a market economy was occurring. The distributive impacts of this transition, both positive and negative, are unknown. In short, while it is clear that welfare levels have changed, there is very little information on poverty and social indicators on which to base policies and programs.
In the post-war process of rebuilding the economic and social base of the country, the government has faced the problems created by having little relevant data at the household level. The three statistical organizations in the country (State Agency for Statistics for BiH –BHAS, the RS Institute of Statistics-RSIS, and the FBiH Institute of Statistics-FIS) have been active in working to improve the data available to policy makers: both at the macro and the household level. One facet of their activities is to design and implement a series of household series. The first of these surveys is the Living Standards Measurement Study survey (LSMS). Later surveys will include the Household Budget Survey (an Income and Expenditure Survey) and a Labor Force Survey. A subset of the LSMS households will be re-interviewed in the two years following the LSMS to create a panel data set.
The three statistical organizations began work on the design of the Living Standards Measurement Study Survey (LSMS) in 1999. The purpose of the survey was to collect data needed for assessing the living standards of the population and for providing the key indicators needed for social and economic policy formulation. The survey was to provide data at the country and the entity level and to allow valid comparisons between entities to be made.
The LSMS survey was carried out in the Fall of 2001 by the three statistical organizations with financial and technical support from the Department for International Development of the British Government (DfID), United Nations Development Program (UNDP), the Japanese Government, and the World Bank (WB). The creation of a Master Sample for the survey was supported by the Swedish Government through SIDA, the European Commission, the Department for International Development of the British Government and the World Bank.
The overall management of the project was carried out by the Steering Board, comprised of the Directors of the RS and FBiH Statistical Institutes, the Management Board of the State Agency for Statistics and representatives from DfID, UNDP and the WB. The day-to-day project activities were carried out by the Survey Mangement Team, made up of two professionals from each of the three statistical organizations.
The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows:
1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population’s living conditions, as well as on available resources for satisfying basic needs.
2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population’s living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household.
3. To provide key contributions for development of government’s Poverty Reduction Strategy Paper, based on analyzed data.
Kind of Data
Sample survey data [ssd]
Domains: Urban/rural/mixed; Federation; Republic
Producers and sponsors
Authoring entity/Primary investigators
State Agency for Statistics (BHAS)
Republika Srpska Institute of Statistics (RSIS)
Federation of BiH Institute of Statistics (FIS)
The World Bank
Department for International Development of the British Government
United Nations Development Program
A total sample of 5,400 households was determined to be adequate for the needs of the survey: with 2,400 in the Republika Srpska and 3,000 in the Federation of BiH. The difficulty was in selecting a probability sample that would be representative of the country's population. The sample design for any survey depends upon the availability of information on the universe of households and individuals in the country. Usually this comes from a census or administrative records. In the case of BiH the most recent census was done in 1991. The data from this census were rendered obsolete due to both the simple passage of time but, more importantly, due to the massive population displacements that occurred during the war.
At the initial stages of this project it was decided that a master sample should be constructed. Experts from Statistics Sweden developed the plan for the master sample and provided the procedures for its construction. From this master sample, the households for the LSMS were selected.
[This section is based on Peter Lynn's note "LSMS Sample Design and Weighting - Summary". April, 2002. Essex University, commissioned by DfID.]
The master sample is based on a selection of municipalities and a full enumeration of the selected municipalities. Optimally, one would prefer smaller units (geographic or administrative) than municipalities. However, while it was considered that the population estimates of municipalities were reasonably accurate, this was not the case for smaller geographic or administrative areas. To avoid the error involved in sampling smaller areas with very uncertain population estimates, municipalities were used as the base unit for the master sample.
The Statistics Sweden team proposed two options based on this same method, with the only difference being in the number of municipalities included and enumerated. For reasons of funding, the smaller option proposed by the team was used, or Option B.
Stratification of Municipalities
The first step in creating the Master Sample was to group the 146 municipalities in the country into three strata- Urban, Rural and Mixed - within each of the two entities. Urban municipalities are those where 65 percent or more of the households are considered to be urban, and rural municipalities are those where the proportion of urban households is below 35 percent. The remaining municipalities were classified as Mixed (Urban and Rural) Municipalities. Brcko was excluded from the sampling frame.
Urban, Rural and Mixed Municipalities: It is worth noting that the urban-rural definitions used in BiH are unusual with such large administrative units as municipalities classified as if they were completely homogeneous. Their classification into urban, rural, mixed comes from the 1991 Census which used the predominant type of income of households in the municipality to define the municipality. This definition is imperfect in two ways. First, the distribution of income sources may have changed dramatically from the pre-war times: populations have shifted, large industries have closed and much agricultural land remains unusable due to the presence of land mines. Second, the definition is not comparable to other countries' where villages, towns and cities are classified by population size into rural or urban or by types of services and infrastructure available. Clearly, the types of communities within a municipality vary substantially in terms of both population and infrastructure.
However, these imperfections are not detrimental to the sample design (the urban/rural definition may not be very useful for analysis purposes, but that is a separate issue). [Note: It may be noted that the percent of LSMS households in each stratum reporting using agricultural land or having livestock is highest in the "rural" municipalities and lowest in the "urban" municipalities. However, the concentration of agricultural households is higher in RS, so the municipality types are not comparable across entities. The percent reporting no land or livestock in RS was 74.7% in "urban" municipalities, 43.4% in "mixed" municipalities and 31.2% in "rural" municipalities. Respective figures for FbiH were 88.7%, 60.4% and 40.0%.]
The classification is used simply for stratification. The stratification is likely to have some small impact on the variance of survey estimates, but it does not introduce any bias.
Selection of Municipalities
Option B of the Master Sample involved sampling municipalities independently from each of the six strata described in the previous section. Municipalities were selected with probability proportional to estimated population size (PPES) within each stratum, so as to select approximately 50% of the mostly urban municipalities, 20% of the mixed and 10% of the mostly rural ones. Overall, 25 municipalities were selected (out of 146) with 14 in the FbiH and 11 in the RS. The distribution of selected municipalities over the sampling strata is shown below.
Note: Mi is the total number of municipalities in stratum i (i=1, … , 6); mi is the number of municipalities selected from stratum i;
As the selection of the specific municipalities in the Master Sample was made PPES within strata, for each municipality, the probability of selection was:
Pj = mi X (Nij / Ni*)
Mi is the total number of municipalities in stratum i (i=1, … , 6);
mi is the number of municipalities selected from stratum i;
Nij is the estimated number of households in municipality j in stratum i (j = 1, …, M i
Ni* is the estimated total number of households in stratum i.
(See the resulting porobabilities in document "BASIC INFORMATION DOCUMENT", Table 3)
In each of the selected municipalities a full listing of households ("microcensus") was carried out. The work was carried out in a decentralized approach, wherein the FIS and the RSIS were responsible for carrying out the fieldwork under the general guidance of the BHAS. The municipalities cooperated by providing temporary office and storage space and recruitment of enumerators and controllers for the survey. The fieldwork was supervised by the staff of the two entity institutes, and these were trained in their respective institutes. This involved three phases:
Preparatory Phase: The tasks carried out during this phase included updating of maps with respect to street names, street numbers and buildings, defining the boundaries of the municipalities, and the enumeration areas within them. This was done by the geodesic institutes of the two entities. The next step was identifying enumerators, controllers and supervisors, training them and assigning them to specific areas. The other tasks during this phase were the printing of questionnaires and instructions, defining the codes to be used and informing the municipalities about their specific responsibilities. While the controllers were selected by municipalities, the supervisors were provided by the entity institutes.
Listing Phase: Enumerators were provided maps of their areas and the questionnaires and instruction manuals They collected information on the households in their assigned areas using a short questionnaire which gathered information on the identify of the head of household, address, and the number of members in the household by sex and age. If no one was home, the household was visited again to record the information. If, after three such visits, no one was home, the information was obtained from the neighbors. The controllers supervised the fieldwork, checked the filled-in schedules and completed a report form on the fieldwork. They also assisted the interviewers whenever there were difficulties. The supervisors of the entity institutes conducted spot checks and ensured completeness and accuracy of data collection and the transfer of all the filled schedules to the entity institutes.
Data-entry Phase: The data entry was performed at the entity institutes using a custom data entry system based on ACCESS software. Forty data entry operators (18 in the RS and 22 in the Federation) were selected and trained by the institute staff. The data entry was performed in two shifts and was supervised by two programmers of the entity institutes. The data were checked for logic and coding errors and tabulated to provide the essential information such as number of enumeration areas covered, number of households covered, number of members in the households by sex, number of refusals, number of households whose members were absent even after three visits etc. These tabulations were made by municipality and enumeration areas and formed the basis for the second stage sampling.
Selection of EAs
The municipalities are divided into geographic areas called enumeration areas (EAs). In theory, each enumeration area consists of the number of households that can be interviewed in a census by an enumerator in one day. The EAs in BiH are based on the 1991 Census. But, at the time the Master Sample listing operation was carried out, many of the enumeration areas actually contained many fewer households (in some cases, zero). As enumeration areas were to be the primary sampling unit for the LSMS survey, the first step was to combine contiguous EAs until a new enumeration area with a minimum of 50 households was formed. These newly constructed EAs were called groups of enumeration areas (GNDs) and replaced the original small EAs. Thus the primary sampling units (PSUs) were actually a mix of the original EAs of sufficient size and the new constructed GNDs. For simplicity, the remaining discussion will use the term EA to refer to both. Based on the population figures from the Master Sample microcensus, 250 EAs were selected with PPS from the municipalities in the FBiH and, and 200 EAs were selected with PPS in the municipalities of the RS.
Detailed information on the calculation of the number of EAs to select in each municipality is available in document "BASIC INFORMATION DOCUMENT")
Selection of Households
Within each of the 450 selected EAs, 12 households were selected systematically. Detailed information on household selection and overall selection probabilities is available in document "BASIC INFORMATION DOCUMENT")
Overall, the response rate in the survey was 82 percent. For each enumeration area, four replacement households were selected prior to the field work. Using these replacement households as needed (a total of 938 households), the final sample size was 5,402 households interviewed.
To produce unbiased estimates for LSMS, each sample household should be weighted by the inverse of its selection probability. Detailed information is available in document "BASIC INFORMATION DOCUMENT (2001)".
An important point about the LSMS weights is that they have considerable variability.
Dates of Data Collection (YYYY/MM/DD)
Mode of data collection
Type of Research Instrument
The LSMS in Bosnia-Herzegovina is a multi-topic household survey covering a wide range of topics that affect welfare: housing, education, health, labor, migration, credit, vouchers, social assistance, consumption, agricultural and non-agricultural activities. The LSMS was designed to collect the information required for an assessment of living standards and to provide the key indicators required for social and economic planning. Inter alia, the LSMS in BiH was designed to measure welfare in both monetary and non-monetary terms. Detailed information was collected on household consumption (expenditures, home production, use value of housing and durables), on social assistance such as old age pensions, war veterans pensions, assistance received by orphans, widows, and on sources of income. Non-monetary measures include detailed information on housing, and access to, and the use of, public services such as education and health.
In addition to the household questionnaire, a price questionnaire was also administered to identify the variations in price levels of key food products in the different municipalities covered by the survey.
The overall content of the household questionnaire and the individual questions included in it were designed to address the specific situation of the country and the data needs of policymakers. In addition, several sections of the questionnaire were based on draft questionnaires for future surveys (the HBS and the LFS) and/or older surveys and thus will be helpful in allowing some tracking of indicators over time. The process of designing the questionnaire was lengthy and involved an inter-institutional team from the three statistical organizations of the country—the Survey Management Team. Although efforts to create a formal data users’ group of line ministries and other users were not successful, several ministries did provide detailed comments and suggestions on the modules relevant to their ministries.
It is worth noting the importance of several of these in the BiH context. First, the migration module collected information on present status: given the dislocation of the population by the war and the legal ramifications of present status this module was considered to be of great importance. Second, a module on non-agricultural household businesses was used as the existing administrative data in the country cannot provide any information for assessing the prevalence or size of this sector. Third, in the health module questions pertaining to depression were added to determine how prevalent this ailment was given the post-conflict situation. Fourth, a module on anthropometric measurement of children was not included: a recent Multiple Indicators Cluster Survey (MICs) done by UNICEF had shown that malnutrition was negligible in the country.
The Price Questionnaire
A price questionnaire was administered in each group of enumeration areas covered by the survey. Three locations where food is sold (market, shop, etc.) were visited in each area and prices were collected for 39 commonly consumed food items. Limited information on the point of sale was also collected. It should be noted that a community questionnaire, usually standard in an LSMS survey to collect data on the presence of services and social infrastructure in the areas in which households selected for the survey are situated, was not done in the BiH LSMS.
An integrated approach to data entry and fieldwork was adopted in Bosnia and Herzegovina. Data entry proceeded side by side with data gathering to ensure verification and correction in the field. Data entry stations were located in the regional offices of the entity institutes and were equipped with computers, modem and a dedicated telephone line. The completed questionnaires were delivered to these stations each day for data entry.
Twenty data entry operators (10 from Federation and 10 from RS) were trained in two training sessions held for a week each in Sarajevo and Banja Luka. The trainers were the staff of the two entity institutes who had undergone training in the CSPro software earlier and had participated in the workshops of the Pilot survey. Prior to the training, laptop computers were provided to the entity institutes, and the CSPro software was installed in them. The training for the data entry operators covered the following elements:
- Introduction to the LSMS Survey questionnaire; Introduction to the personal computers/ lap top computers; Copying data on diskette and printing of output;
- The Data entry programme (CSPro). Understanding of the Round 1 data entry screens (Modules 1-10);
- Practice of Round 1 (data entry trainees enter questionnaires completed by interviewer trainees during practice interviews);
- Understanding of Round 2 Data entry screen (Modules 11-13)
- Practice of Round 2 Data entry screens (data entry trainees entered the questionnaires completed by interviewer trainees)
- Control Procedures; Copying data on diskette and printing lists of errors; Transfer of the data through email to the institutes.
The data entry programme was fine-tuned during the training. Some unexpected responses during the interviews had to be accommodated and a few skip patterns fixed. The training emphasized the role of the data entry operator as a member of the survey team, and how the outputs of the programme (error lists) were to be provided to the supervisors and interviewers for necessary correction.
The goal was to produce high quality data. Several of the key features of this were:
1. Pre-coded verbatim questionnaires ;
2. Error detection at the time of data entry;
3. Data entry that was concurrent with fieldwork;
4. Correction of suspected errors in the field.
The following checks were incorporated in the data entry software:
1. Value Range: The program checked to ensure that the values entered were within the valid range for each variable;
2. Reference tables: Where appropriate, the entered data were checked against reference values ( e.g. the price of a kilo of tomato could not exceed 10 KM
3. Skip checks: The program checked that all appropriate skips were followed, both within and between different units of observation;
4. Checks for consistency between different responses: The program checked for internal consistency. For example, whether the age of a person was sufficient for the education level attained, if a filter question for agriculture had a positive response that the module had all relevant information entered and the like.
After the data entry was completed in the field, the data were transferred through email to the central offices in Sarajevo and Banja Luka with the help of PCAnywhere Software. The data entry programme was designed to detect many of the errors even at the stage of data entry, thereby minimizing the need for ex-post facto data editing. Once all data was compiled in the entity offices, a check was made to ensure the structural consistency of data files, i.e. that no records were duplicated or omitted.
When the RS and FBiH data files were merged it became apparent that a last minute decision on the treatment of decimal places in several modules had been different in the two entities. Thus the two data bases were not compatible. A correction was made and data from these modules were re-entered. Once this was done, the data sets were compatible and a countrywide data set was created. During this process some additional double entry was carried out to correct any data entry operator errors that had occurred.
It is important to note what is meant by ‘data cleaning’ in terms of the BiH-LSMS data set. In the sense that the data set is a faithful reflection of the responses of all interviewees the data set can be considered ‘cleaned’. Every effort was made to ensure that the information provided during the interviews was correctly entered in electronic format. As in any survey, this does not mean that the data set is perfect. As participation in the survey is voluntary, informants had the option to refuse to answer specific questions, and may have provided information that is not always consistent. The interviewers resolved as many inconsistencies as possible with the informants but there are, of course, limits.
However, given the widely differing needs of the range of analysts who will use the BiHLSMS data, nothing further has been done to the original data. While some data sets are processed so that all missing values are imputed, all outliers revalued and all inconsistencies fixed based on some set of assumptions, this has not been done here. The reason being that there is no correct way to resolve the problems of missing data, outliers and inconsistencies.
Each person will need to make his or her own decision on how to treat such data problems based on the type of analysis being carried out. For some analyses, the information in outlier values is key while for others, such outliers would distort findings and would need to be dropped or provided an imputed value. The same for missing values. Some analysts will chose to drop cases with missing values for the variables of interest to them while others will impute such values, using medians, mean or complex multi-variate techniques. In order to ensure the usefulness of the data set for all users, no attempt has been made to impute missing values, reconcile inconsistencies, re-value outliers, or in any way alter the responses provided by the respondents.
n 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:
State Agency for Statistics of BiH
TRG Bosne i Hercegovine 1
Statistical Institute of the Federation of BiH
Republika Srpska Statistical Institute
Veljka Mlaðenovica bb
78000 Banja Luka
Bosnia and Herzegovina
http://www.rzs.rs.ba The World Bank
Development Economics Research Group
LSMS Database Administrator
1818 H Street, NW
Washington, DC 20433, USA
tel: (202) 473-9041
fax: (202) 522-1153
3. The researcher will refer to the 2001 Bosnia and Herzegovina Living Standards Measurement Study Survey as the source of the information in all publications, conference papers, and manuscripts. At the same time the statistical institutions of Bosnia and Herzegovina are 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.
Disclaimer and copyrights
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.