Household Survey Data on Remittances in Sending Countries

Johan A. Mistiaen

International Technical meeting on Measuring RemittancesWashington DC - January 24-25, 2005

Sampling and Questionnaire Design: Options and Uses

World Bank - Development Data Group

OverviewWhy Collect Micro-Data from Remittance Senders?Sampling Frame Design Options Why is Sampling a Critical Issue? Plan A: Build a Representative Sampling Frame Plan B: Some Micro-Data is Better Than None On Sample Size

Questionnaire Design and Implementation A Core Module: Towards Data Consistency Implementation Challenges

Ideas for a Research Agenda

Sampling Design OptionsWhy is Sampling a Key Issue?

A representative sampling frame is the cornerstone of sample-based statistical analysis:

Without it we cannot obtain sample-based statistics or inferences that are representative of the population of interest.

For instance, representative sample data is needed to compute “propensity to remit” estimates.

Sampling frames of the population sub-groups that send remittances are non-existing need to build them

Need to define our target population (domain of analysis)

All persons above 18 years of age that were born in a foreign country.

Unlikely standard frames can be used…

Sampling Design OptionsPlan A: Build a Representative Sampling Frame

Option I: Finding Option I: Finding AllAll Needles in the Haystack Needles in the Haystack

Current Population Registers

Data systems that record selected info on the de jure population in a country; including data that identify residents by street address, age and country of birth.

Construct address referenced listings of all members in the respective target sub-population groups by geographical areas (asap) which

become the “clusters” of our sampling frame.

Sampling Design OptionsPlan A: Build a Representative Sampling Frame

Option I: Finding Option I: Finding AllAll Needles in the Haystack Needles in the HaystackCan apply standard techniques to select a representative (stratified) sample of each sub-group (i.e. by country of birth) with associated sampling weights (the inverse selection probability).Work ongoing to implement this approach in some EU member states.Already in design phase to draw samples of African-born residents in Belgium.Advantages: Representative sample Relatively easy to maintain sampling frame

Sampling Design OptionsPlan A: Build a Representative Sampling Frame

Option II: Finding the key HaystacksOption II: Finding the key HaystacksPopulation Census Data

Typically collect data on “country of birth” (sometimes also include street addresses)

Identify all geographical areas (as small as possible) from the census that contain target sub-population group members; these become “clusters” in our sample frame.

Examples: UK 2001 Population Census US 2000 Population

Census

From Population Census data From Population Census data it is possible to build a “frame” it is possible to build a “frame” of Enumeration Areas/Blocs of Enumeration Areas/Blocs (100?-150? hhs) in the UK that (100?-150? hhs) in the UK that contain people born in contain people born in specificspecific foreign countriesforeign countries

Data on “country of birth”was also collected via the

“long form” of the 2000 UScensus (1 out of 6 hhs)

Sampling Design OptionsPlan A: Build a Representative Sampling FrameOption II: Finding the key HaystacksOption II: Finding the key Haystacks

A Two-Step Sampling Approach

Step 1: Draw sample of clusters (can adjust probability of selection on the proportion of target sub-population).

Step 2: Conduct a “screening” or “re-listing” exercise to identify current incidence of the target population.

Draw sample based on screened clusters

If needed, adjust initial cluster sample ex-post (if step 2 conducted “on-the-go”) either via re-weighting methods or with supplementary sampling.

Sampling Design OptionsPlan A: Build a Representative Sampling Frame

Options I and II: Limitations and Caveats

Frame Errors: All Needles?…“illegal” immigrants… Population registers vs. population census data Pilot attempts to supplement main sampling frame

by “snowball” sampling (i.e. referrals), through relevant organizations, and at key likely contact points (Groenewold and Bilsborrow, 2004).

Population register approach potentially feasible in most EU member states; but few useable population registers elsewhere (Bilsborrow et al., 1997).

Sampling Design OptionsPlan A: Build a Representative Sampling Frame

Options I and II: Limitations and Caveats

“sensitive data”: Government cooperation critical

“updating” of population census based frames… without screening all relevant clusters will need to account for modeling

errors.

Sampling Design OptionsPlan B: Some Micro-Data is Better Than NoneAggregation Point Sampling

Listing of migrant (foreign-born) meeting points Religious venues, community centers, international phone

businesses, employment offices, etc…

Will capture both legal and undocumented immigrantsEx-post determination of respondent selection probabilities Based on “visit frequency” profiles (e.g., what aggregation

points in the sample are visited, how often, when, etc…)

Can yield a (representative) sampleApplied successfully to interview Ghanaian and Egyptian born persons in Italy (Groenewold and Bilsborrow, 2004).

Sampling Design OptionsOn Sample Size

Osili (2004): Sampled 112 Nigerian born residents in the Chicago area to study remittances

Average annual per capita remittances: $6,000Standard deviation: $11,250 95% confidence interval = [$3,750 ; $8,250]

Average annual per capita income: $25,500 Mean Propensity to Remit = 0.23 95% confidence interval = [0.15 ; 0.32]

Increasing sample size to 400 would halve the standard error

Optimal sample size will be a function of the distribution of the variable of interest and the targeted precision

Questionnaire Design and Implementation

A Core Module: Towards Data Consistency Core data collection Consistent across countries and within

countries Modular: stand alone or tag-on to other survey

Implementation Challenges Minimizing Non-Response

Questionnaire design, interviewer selection/training, collaboration with community groups, etc.

Understanding/Correcting for Non-Response

Ideas for a Research AgendaStatistical and econometric analysis to obtain better measures of the “propensity to remit” and its determinants; both household characteristics and market variables (e.g., transaction costs…).Small area estimation of the “propensity to remit” by combining survey and census data.