Download - Remaining Weeks

Remaining Weeks

• Next week: Diff-n-Diff

• Nov. 17: Power calculations.

• Nov. 24: summary, in class presentations.

• Dec. 1: Guests, more presentations.

Motivation: Causality.

AP Headline Today:

Teen pregnancies tied to tastes for sexy TV shows

Real-World Complications

Attrition

Data Quality

Cars Stuck in the Mud, Employees Robbed

What type of day are you having?

Practical ProblemsLanguage

Culture

Being around the same four Westerners 24/7 without going crazy.

Solutions:

Having had a real job?

Management skills

Actual Organizations

• CEGA (Our Sponsor)

http://cega.berkeley.edu• Poverty Action Lab (J-PAL)

http://www.povertyactionlab.org

• Innovations for Poverty Actionhttp://www.poverty-action.org

• Blum Center for Developing Economieshttp://blumcenter.berkeley.edu

http://cega.berkeley.edu/

http://cega.berkeley.edu/

http://www.poverty-action.org/

CEGA-related Faculty

• Alain de Janvry

• Paul J. Gertler

• David I. Levine

• Edward Miguel

• Nancy Padian

• Elisabeth Sadoulethttp://cega.berkeley.edu/template.php?page=people

Larger NGO-types

• The World Bank

• Center for Global Development

• International Food Policy Research Institute

many, many more

Human Subjects

UC Berkeley Committee for the Protection of Human Subjects

http://cphs.berkeley.edu

In-country organization as well, for example:

Kenya Medical Research Institute

http://www.kemri.org

Attrition

Randomized trials often require that we get data from the subjects twice--once before the experiment and once after.

What if we can’t find them afterwards?

Worksheet

How might you expect people we couldn’t find to differ from those we could easily find?

What could cause people to go missing?

Attrition

Create Lower/Upper Bound for our estimates by assuming the worst about the people we couldn’t find.

(Ummm, I can’t remember this reference. Sorry.)

In our case, we’ll just say it’s important to find as many people as possible to get good data.

Attrition in KLPS

Kenyan Life Panel Survey

2003-2005 follow-up to Deworming (1998-2000)

7500 of the original 30,000 were randomly selected to be surveyed.

Attrition in KLPS

First, go their old school and ask around.

Second, try and go find their house.

Third, travel far and wide.

Attrition in KLPS

Using two-part regular and intensive tracking just like in “Moving to Opportunity.”

After finding as large a portion as you can, select random sub-sample of everyone remaining.

ERR=MRR+SRR*(1-MRR)

Attrition in KLPS

End Results:

84% successfully contacted

83% successfully surveyed

Attrition in KLPS

4 different types of being “found,” by treatment and gender

QuickTime™ and a decompressor

are needed to see this picture.

Where’d we find them?

--19% Outside Busia--14% Outside Neighboring Areas--25% Overall (Non-Snapshot)



So, We Got 84%, Are We Cool?

• Is treatment correlated with attrition?

So, We Got 84%, Are We Cool?

• Is treatment correlated with attrition?Probably Not. We found 83.9% to 85.0% in all treatment groups.



Was it worth it?

• We spent a lot of money to find the emigrants. QuickTime™ and a

decompressorare needed to see this picture.

Did we need to bother?

• Migrants are 1.7 cm shorter than non-migrants, and an additional year of treatment increased migrant height by .4 cm and only .1 cm for the full sample.



The Nuts & Bolts of Building the Dataset

• Written on hard-copy of survey.

• Sub-sample checked for mistakes.

• Data-entry place double enters.

• We check for correlation of two entries.

• We re-enter 5% sample and check against their work, accept if error rate below threshold.

• That’s the “raw” data

The Nuts & Bolts of Building the Dataset

• Depressed grad students spend whole summers in windowless Unix lab on the 6th floor of the 2nd ugliest building on campus writing cleaning files, which checks for blanks and skip-pattern violations.

• Send the list of flagged entries to location of hard copies

• Hard-copies checked against soft-copy. Soft-copy corrected, mistake flag lowered.

• Feel free to use the data.

Data Quality

Fine, we correctly recorded what the respondent said, but should we really trust what they said?

That is, if you were 16 and had a miscarriage a year ago, would you really want to tell an older man that’s a stranger about it?

GenderQuickTime™ and a decompressor


TribeQuickTime™ and a

decompressorare needed to see this picture.

Do Kids Know What They’re Talking About?

• Disregard the respondent/enumerator relationship. Do the kids really know what they’re talking about?

• Depends on the question.

What’s Reliable?

• We sample 5% to be resurveyed, successully resurveyed about 4%. 3 months later on average.

Baseline: If we ask “what tribe are you?” It stays the same 95% of the time.

Pretty Decent



I Can’t Throw This Very Far.



Fraction Matching

• Sub-Tribe 95%

• Age in 1998 76%

• Grade in 2002 86%

• Ever left local area 91%

• Mom/Dad Education 51-53%

What Determines Remembering?

• Tables 22 and 23 show what characteristics are correlated with giving the same answer about Mom/Dad’s education in both survey and re-survey.

Conclusion

• Field work is great; go do some.• Try and find everyone.• Especially if you’re more/less likely to find

them thanks to your intervention.• Do your Field Officers effect the answers

given?• Does the respondent really know the right

answer in the first place?

Download - Remaining Weeks

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