Remaining Weeks

1
Remaining Weeks

• Next week Diff-n-Diff
• Nov. 17 Power calculations.
• Nov. 24 summary, in class presentations.
• Dec. 1 Guests, more presentations.

2
Motivation Causality.

• AP Headline Today
• Teen pregnancies tied to tastes for sexy TV shows

3
Real-World Complications

• Attrition
• Data Quality
• Cars Stuck in the Mud, Employees Robbed

4
(No Transcript)
5
(No Transcript)
6
(No Transcript)
7
What type of day are you having?
8
Practical Problems

• Language
• Culture
• Being around the same four Westerners 24/7
  without going crazy.
• Solutions
• Having had a real job?
• Management skills

9
Actual Organizations

• CEGA (Our Sponsor)
• http//cega.berkeley.edu
• Poverty Action Lab (J-PAL)
• http//www.povertyactionlab.org
• Innovations for Poverty Action
• http//www.poverty-action.org
• Blum Center for Developing Economies
• http//blumcenter.berkeley.edu

10
CEGA-related Faculty

• Alain de Janvry
• Paul J. Gertler
• David I. Levine
• Edward Miguel
• Nancy Padian
• Elisabeth Sadoulet
• http//cega.berkeley.edu/template.php?pagepeople

11
Larger NGO-types

• The World Bank
• Center for Global Development
• International Food Policy Research Institute
• many, many more

12
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

13
Attrition

• Randomized trials often require that we get data
  from the subjects twice--once before the
  experiment and once after.
• What if we cant find them afterwards?

14
Worksheet

• How might you expect people we couldnt find to
  differ from those we could easily find?
• What could cause people to go missing?

15
Attrition

• Create Lower/Upper Bound for our estimates by
  assuming the worst about the people we couldnt
  find.
• (Ummm, I cant remember this reference. Sorry.)
• In our case, well just say its important to
  find as many people as possible to get good data.

16
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.

17
Attrition in KLPS

• First, go their old school and ask around.
• Second, try and go find their house.
• Third, travel far and wide.

18
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.
• ERRMRRSRR(1-MRR)

19
Attrition in KLPS

• End Results
• 84 successfully contacted
• 83 successfully surveyed

20
Attrition in KLPS

• 4 different types of being found, by treatment
  and gender

21
Whered we find them?--19 Outside Busia--14
Outside Neighboring Areas--25 Overall
(Non-Snapshot)
22
So, We Got 84, Are We Cool?

• Is treatment correlated with attrition?

23
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.

24
Was it worth it?

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

25
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.

26
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.
• Thats the raw data

27
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.

28
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 thats a stranger about it?

29
Gender
30
Tribe
31
Do Kids Know What Theyre Talking About?

• Disregard the respondent/enumerator relationship.
  Do the kids really know what theyre talking
  about?
• Depends on the question.

32
Whats 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.

33
Pretty Decent
34
Pretty Decent
35
I Cant Throw This Very Far.
36
I Cant Throw This Very Far.
37
Fraction Matching

• Sub-Tribe 95
• Age in 1998 76
• Grade in 2002 86
• Ever left local area 91
• Mom/Dad Education 51-53

38
What Determines Remembering?

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

39
Conclusion

• Field work is great go do some.
• Try and find everyone.
• Especially if youre 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?