1. Hello and Welcome to… Data analysis
with your hosts: Erin Sills and Jerry Shively
INSERT IMAGE of RADIO
Maybe the radio program of interest in sources of protein - fishing vs. bushmeat vs. livestock - so start by listening for information about fishing: looking at your data

2. Description vs. Explanation
Describing a situation is good
Explaining why a situation exists is better
Example of description:
poor households are more reliant on forests
Example of explanation:
poor household have low agricultural capacity, and therefore must rely on forests
INSERT IMAGE of RADIO
Maybe the radio program of interest in sources of protein - fishing vs. bushmeat vs. livestock - so start by listening for information about fishing: looking at your data

3. Description vs. Explanation
Answering interesting questions:
which radio program?
Look at your data:
listening to the radio – what station are looking for?
Unconditional means vs. conditional means
Statistics vs. Economics
Signal vs. Noise
Is that static I hear?
INSERT IMAGE of RADIO
Maybe the radio program of interest in sources of protein - fishing vs. bushmeat vs. livestock - so start by listening for information about fishing: looking at your data

4. Explaining variation Variance is your friend (up to a point)
Variance in data = underlying variation in either behavior or constraints
Without variance, there is nothing to explain
But, love is like oxygen…

5. Tuning in
What is the relationship between an outcome and a key variable of interest (for policy or theory)?
What are the determinants of outcomes (as suggested by theory, literature, field experience, patterns in the data)
Causation vs. correlation

6. What is your story?
Find a story → try to change or undermine your story → new & potentially more interesting story
Subject your story to robustness checks
Embrace parsimony
What is the simplest story that is consistent with your data?
Simple stories are more appealing

7. Developing your story Example: Income from Fishing
Mean = $310/hh/ year
St. dev. Median
village 1
village 2 (+ outliers?)
village 3 (few fish?)
Mean in three villages is the same
But standard deviation suggests that there is much more variation in some villages
Median: half are below and half are above 300; half are 85 or lower – few high “outliers”?; half are zero or lower – maybe only a few people fish?

8. Village 1
Mean = 310, Stdev = 175, Median = 300

9. Village 2 Mean = 310, Stdev = 343, Median = 85
Looking at these data might lead you to start asking whether there are different typologies of households …

10. Village 3 Mean = 310, Stdev = 530, Median = 0
Looking at these data might lead you to develop a two part question: who fishes, of those who fish – how much?

11. Bonus round

12. Back to fishing Hypothesis: more educated HHs fish more
Estimate a bivariate regression
Y = income from fishing
X = yrs education
Y = *X
Tuning in
Parsimonious
→ Challenge the story
Ready to publish?
(1) Remember how you constructed your data
Is educ related to opp cost of time, which you used to construct price of fish?
(2) Theory, literature, field experience suggest omitted variables
Are you using education of hh head?
Educational opportunities have been increasing, so inversely related to age, so the real story is that younger women fish
(3) Reverse causality
Are you using average education of whole hh?
Fishing is lucrative activity, and women who can get into this market make enough money to send their kids to school

13. Back to fishing IncF = 5 + 0.65 yrs educ Source: NIST
Look at your data
Source:
NIST