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