Russia Longitudinal Monitoring Survey - Higher School of Economics 1996
Other Household Survey [hh/oth]
The Russian Longitudinal Monitoring Survey-Round VII (RLMS-VII) was designed to measure the standard of living in the Russian Federation at the end of 1996. The Russia Longitudinal Monitoring Survey (RLMS) is a series of nationally representative surveys designed to monitor the effects of Russian reforms on the health and economic welfare of households and individuals in the Russian Federation. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data.
Data for RLMS have been collected since 1992. Since 1994, the team has collected a new round of data almost every year in the second phase of the project.
The Russia Longitudinal Monitoring Survey (RLMS) is a household-based survey designed to measure the effects of Russian reforms on the economic well-being of households and individuals. In particular, determining the impact of reforms on household consumption and individual health is essential, as most of the subsidies provided to protect food production and health care have been or will be reduced, eliminated, or at least dramatically changed. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Data have been collected since 1992.
As its name implies, the RLMS is a longitudinal study of populations of dwelling units. Rounds V-VII are designed to provide a repeated cross-section sampling. Barring the construction of major new housing structures, renewed contact with a fixed national probability sample of dwelling units provides high coverage cross-sectional representation. The repeat visit at each round to a static sample of dwelling units also introduces a correlation between successive samples that leads to improved efficiency in longitudinal analyses comparing aggregate statistics.
The repeated cross-section design is far and away the simplest alternative for the RLMS. The sampling is cost efficient, easy to maintain, and easy to update when needed. The design supports both efficient cross-sectional and aggregate longitudinal analyses of change in the Russian household population. Updates to the sample, including a full replenishment of the probability sample of dwelling units, will not seriously disrupt the longitudinal data series.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households and individuals.
The scope of the study includes:
- Use of time;
- Health status;
- Medical services;
- Child care;
- Family information;
- Housing conditions;
- Living conditions;
- Transportation and related information;
- Local municipal and other services;
- Cost of food products;
- Farming and animal husbandry;
Producers and sponsors
National Research University Higher School of Economics
Carolina Population Center
University of North Carolina at Chapel Hill
Institute of Sociology RAS
National Research University Higher School of Economics
US National Institutes of Health
The sample was designed to allow the analysis of household data, as well as of data on all individuals residing in those households. "Household" was defined as a group of people who live together in a given domicile, and who share common income and expenditures. Households were defined to include unmarried children, 18 years of age or younger, who were temporarily residing outside the domicile at the time of the survey.
In addition, however, naturally kept track of the identity of particular households and individuals so that it would be possible to conduct meaningful longitudinal analyses. Occasionally, this proved to be complicated. For example, several households in Round V split into two households without moving from their dwelling units in Round VI. They were no longer sharing income and expenditures, and therefore no longer qualified as a single household under the definition given above. Both households were interviewed, and the link to the common household in Round V is provided in the data set.
The same is true of split households in Round VII, as well as of two joined households in which people in different dwelling units married and continued to live in a dwelling in the sample. Furthermore, as of Round VII, we followed households who moved out of the sample of dwellings in order to maintain the quality of longitudinal studies as well as possible. These moved households and individuals are not part of the sample of households based on dwellings, and a convenient indicator variable allows analysts to omit them from cross-sectional analyses. However, they are part of the sample of Round V and VI households followed over time, and can legitimately be used in longitudinal analyses.
A multistage probability sample was employed to draw the sample of dwelling units. First, a list of 2,029 consolidated regions (similar to counties) was created from which to draw primary sample 4 units (PSUs). These were allocated into 38 strata based largely on geographical factors and level of urbanization, but also based on ethnicity where there was salient variability. As in many national surveys involving face-to-face interviews, some remote areas were eliminated to contain costs; also, Chechnya was eliminated due to armed conflict. From among the remaining regions (containing more than 95% of the population), three very large population units were selected with certainty: Moscow city, Moscow Oblast, and St. Petersburg city each constituted self-representing (SR) strata. The remaining non-self-representing regions (NSRs) were allocated to 35 equal-sized strata. One region was then selected from each NSR stratum using the method probability proportional to size@ (PPS). That is, the probability that a region in a given NSR stratum was selected was directly proportional to its measure of population size.
In addition, however, the interviewer conducted individual interviews with as many household members 14 and older as possible, acquiring data about their individual activities and health. Data for children 13 and younger were obtained from adults in the household, and were entered in children's questionnaires. In the relatively small percent of cases where adults refused or were absent, surrogate adults in the family were not used to supply information for the missing adult. By virtue of the fact that virtually all members of households were interviewed, the sample constitutes a proper probability sample of individuals as well as of households, without any special weighting beyond that used for dwellings or households.
The sample was designed in the effort to obviate the need for weighting as much as possible. In general, this aim was achieved. It is unlikely that using weights will affect substantive results. Nevertheless, two kinds of weights have been calculated to compensate for imperfections in the sample procedure. First, though the sample procedure aimed at giving all dwelling units equal probability of selection, in practice this goal was not perfectly met. One set of weights, then, corrects for the fact that some strata were slightly larger than others, and that some SSUs selected with equal probability (rather than with PPS) turned out to be larger than others within the same PSU. It also corrects for disparate response rates across PSUs and SSUs. The second set of weights matches the sample of households and individuals to the 1989 census. The household sample is matched by urban-rural distribution and by household size; the individual sample is matched by the joint distribution of age, sex, and urban-rural location.
The general observation is that the combined influence of nonresponse attrition and household turnover does not seriously distort the geographic distribution of the sample or its size or household-head characteristics. The distributions for the geographic variables indicate that, between Round V and Round VII, there is a decline in the nominal representation of households in the Moscow/St. Petersburg region, reflected in a decline in the proportion of sample households from the urban domain. Households with a male head aged 18-59 may be subject to slightly higher than average attrition/net loss in replacement. If we focus only on these characteristics, the problem is not serious.
In summary, the net effect of nonresponse attrition and change in dwelling unit occupants across rounds on the marginal characteristics of the observed cross-sectional samples is modest. Loss in nominal "sample share" between Rounds V and VII is greatest for residents of Moscow/St. Petersburg--a loss in representation that is readily corrected with the combined sample selection/nonresponse adjustment factors that have been computed for each round. It is important to note that the simple analysis described here cannot demonstrate that no uncorrected attrition bias remains. The potential for uncorrected nonresponse bias can be specific to the dependent variable under study. Nevertheless, it appears that, with the nonresponse and post-stratification adjustments developed by Michael Swafford, the potential for serious attrition bias in repeated cross-section analysis is small.
On the basis of a probability sample of 3,591 households, as well as some 10,000 members of those households, the RLMS-Round VII provides more than 3,000 variables from which to construct many indices of material well-being at several levels of measurement: individual, household, and community. Since the files are linked, it is possible to study contextual effects on the welfare of individuals and households, as well as change over time among households and individuals.
Weights in Descriptive Analysis of RLMS Data.
Analysis weights are essential for unbiased sample-based estimation of RLMS descriptive statistics such as population and subclass means, proportions, and totals. The construction of a descriptive weight for cross-sectional analysis involves a simple sequence of steps:
(1) determine the probability of selection for each sample household;
(2) based on geographic and other known characteristics of sample households, compute an adjustment for nonresponding sample households;
(3) compute a nonresponse-adjusted weight as the product of the reciprocal of the sample selection probability and the nonresponse adjustment.
Since the RLMS attempts to interview all individuals within sample households, the selection probability for an individual equals that for his household. An individual in a cooperating household may, however, choose not to give an interview. If data on individuals-- both cooperating and not--are known from household listings, the nonresponse adjustment factor in the analysis weight can be computed at the level of the individual. Fortunately, the majority of RLMS nonresponse at the individual level corresponds to noncooperation by the entire household, and the household nonresponse adjustment factor will capture most of the sample attrition loss at both levels.
If recent census data on households and individuals are available, a fourth post-stratification step can be added: scaling analysis weights so that the sum of weights for a defined subpopulation matches the corresponding census proportion (e.g., the weighted sample proportion of females, age 45 and older, in the Moscow/St. Petersburg region matches the corresponding proportion from the most recent census). The post-stratification of analysis weights serves two functions:
(1) it can reduce the sampling variance of weighted estimates; more importantly
(2) it may correct noncoverage biases in the frame used to derive the original sample of dwellings and individuals.
RLMS data sets contain post-stratification weights - weights that adjust not only for design factors but also for deviations from the census characteristics. For households, we have produced post-stratification weights that fit our data to the known distribution of household size and location of residence (urban or rural). For individuals, our weights fit our data to the multivariate distribution of location, age, and gender. Of course, depending on the subject of one's analysis, it might be appropriate to compute post-stratification weights that adjust to other variables, and all analysts are free to compute their own.
There is considerable debate over the value of using weights in multivariate analysis. For example, in estimating linear or generalized linear models, many software programs allow the specification of weights for model fitting. Some statisticians argue that using weights is not necessary if the fixed effects that explain the variation in weights are included in the model. In RLMS data, the household characteristics that explain the greatest variation in weights are the geographic region and the urban/rural character of the civil division in which the dwelling is located. Variation in individual weights will reflect the geographic effects for households as well as differentials due to post-stratification of the sample by major geographic regions, age, and sex. Researchers who are interested in exploring the impact of RLMS weights on a multivariate analysis should consider the following test. Fit the model omitting the weights but including as fixed effects the household (region, urban/rural) or individual (region, urban/rural, age, and sex) characteristics. Without changing the specification, also estimate the model using the analysis weights. Compare the results to see if there are important differences in model parameters and/or interpretation. Differences in the unweighted and weighted versions could be due to added sampling variability introduced by the weighted estimation or could indicate that the model is not correctly specified.
At each round, the data contain some households with a sampling weight of zero (0). This values was assigned to the households who moved out of the sample area between rounds. They were located and interviewed to provide a group of respondents for longitudinal analyses. They were assigned weights of zero to keep analysts from inadvertently including them in cross-sectional analyses that are intended to be representative of Russia. (Only respondents with non-zero weights are part of the representative sample.)
Dates of Data Collection
Data Collection Mode
The questionnaire are English-language translations of the original Russian questionnaires. The English versions have been translated as literally as possible. The order of the questions and the layout of the pages have been preserved in the English versions.
The questionnaires are also designed to function as codebooks. The variable names, as they appear in the data sets, are usually listed below or to the left of the questions. If the abbreviation (char) appears with a variable name, then the responses to that question are stored in a character variable. If there is no variable name associated with a particular question, then the responses to that question do not appear in the data set. Some questions in the questionnaires are color coded. Pink means that the question was added. Green indicates changes from the previous round (e.g., year). Gray means that the questions were asked, but the data are not available for public use - the questions were added at the request of the Pension Office and are for their use only.
In Phase II (Rounds V - XX), when questionnaires were returned to local supervisors, those supervisors were required to examine them to locate problems that could best be remedied in the field, e.g., by returning to get key demographic information or cleaning ID numbers so that the roster of individuals located in the household questionnaire matched those on the individual questionnaires from that household. The questionnaires were then transported to Moscow, where yet another ID check was performed.
In Moscow, coders looked through all questionnaires to code so-called "other: specify" responses. However, open-ended questions (e.g., occupation questions) were not coded at this time. Instead, their texts were fully entered as long string variables. Entering the open-ended answers as character variables offered several advantages. First, it allowed data entry to begin immediately, with no delay for coding. Second, it permited the use of computer programs to assist in coding the string variables. Third, the method allowed any user of the original data sets to recode the character variables to suit his or her purposes without going back to the paper copies of the questionnaires.
All data entry was handled in-house using the SPSS data entry program on PCs.
Source: "Russia Longitudinal Monitoring survey, RLMS-HSE", conducted by Higher School of Economics and ZAO "Demoscope" together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS.
RLMS-HSE sites: http://www.cpc.unc.edu/projects/rlms-hse, http://www.hse.ru/org/hse/rlms.
Location of Data Collection
Carolina Population Center, the University of North Carolina at Chapel Hill
Archive where study is originally stored
Carolina Population Center, the University of North Carolina at Chapel Hill
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.