comparative living standards project
DESCRIPTION
Comparative Living Standards Project. Kinnon Scott Diane Steele DECPI, April 27, 2010. Two Products. Meta Data Describing Content of LSMS Surveys Comparative Data Base of LSMS actual data (variables/indicators). Why?. Increase the use of LSMS data Meet expressed demand from - PowerPoint PPT PresentationTRANSCRIPT
Comparative Living Standards Project
Kinnon ScottDiane Steele
DECPI, April 27, 2010
Two Products Meta Data Describing Content of
LSMS Surveys
Comparative Data Base of LSMS actual data (variables/indicators)
Why? Increase the use of LSMS data Meet expressed demand from
Existing users Potential users
What are LSMS surveys? Multi-topic Household Surveys
Relationships between/among topics Strong money-metric welfare measure
Demand driven relevant to a country at given time
(comparability issue) Coverage has large gaps Timing is not consistent
Designed for policy analysis and research
Getting Data Used Document and archive the 60+ LSMS
survey data bases Improvements in data access
policies/agreements Provide data and documentation to
researchers Each data set has
Data set (3 formats) Basic information document Questionnaire Additional Documentation
All in electronic format (and hardcopy) In-country activities
(collaboration,training)
LSMS Web Site
Key problems in further dissemination/use of data
1. No easy way to determine the content of all the surveys
2. Not accessible to non-specialists (trained in micro-data analysis)
3. Start up costs for doing cross-country analysis
So how to meet the needs of these users, researchers and non-researchers?
Problem 1: Researchers need to know which
surveys have the topics they need There is no source for this Need to go through all
questionnaires (or consult ‘institutional memory’
Solution 1: Meta Data of LSMS Surveys
Create web-based tool containing meta data describing the contents of existing LSMS data sets
Searchable Data Base Update continually May need to add new details
(LSMS-ISA)
Meta data search engine site
Key Decisions: Content Topics to include
Identify the universe Level of disaggregation
Module (Education) Submodule (preschool, general,
training) Topics (preschool costs, type,
distance) Variables (cost of supplies, cost of
transport, cost of food) Interlinking
(ed->level->costs) vs. (exp.->educationlevel
Key Decisions: Search Results
Actual question vs Questionnaire? Depends on purpose ADP, IHSN question banks
Consistency in survey design Questionnaire development
LSMS- research data sets Context matters Need to know respondent, ages,
additional information
Development Path Drafted list of topics (subtopics) Created first web interface Tested Substantially revised the interface Revised and expanded the list of
topics ‘Populated’ data base
Problem 2: Many potential users do not have
skills to analyze micro-data Many potential users do not have
time to analyze multiple data bases
Under-utilization of the data
Solution 2: Comparative Data Base (CLSP)
Database of a subset of variables/indicators from LSMS Surveys
Focus is on comparability across countries
Detailed documentation Allow ‘on-the-fly’ tables/statistics within
and among countries Respecting sampling (weights,
representat.) Respecting confidentiality
Key Decisions: Content List of variables
Needs vs Comparability Present vs Future
Define ‘Comparable’ Standard Definitions for Indicators When not to include a survey
(100% of all variables, 80%, 10%?) Test set of data- (issues in certain
regions, multi-year surveys)
Evolution Consumption Aggregates
Best possible, best comparable, existing
Completely non-intuitive to users Requires redefinition of poverty lines Stick with existing consumption
aggregates (well documented) Use existing poverty measures
Evolution On-the-fly analysis
Basic statistics can be constructed by user
Need for advanced statistical ability Using public domain statistical software-
all on our server (Qinghua Zhao’ adaptation of R)
Need for very straightforward abilities Created some ‘canned variables’ Commonly used/mis-used
Documentation Tie to output
Comparative data base site
Evolution Platform to build on:
RIGA: with FAO, collaborated in the construction of income aggregates and variables
LMD: with PREM and DEC integrating labor variables
Integrate or stand alone
Development Path Built on
Sub-national data base Africa Standardized files
DDP Not interactive Costly to user Not maintained
Created new interface completely Iterative process
Lessons learned Lessons learned
Search engine for data sets very- maintaining/ updating needs to be done
Time and resources costs (LIS example) Comparability/harmonized is easier said
than done Learning curve Documentation of process, decisions
Funding from KCP and GAP