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Hello and Welcome to… Data analysis

Hello and Welcome to… Data analysis. with your hosts: Erin Sills and Jerry Shively. 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:

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Hello and Welcome to… Data analysis

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  1. Hello and Welcome to…Data analysis with your hosts: Erin Sills and Jerry Shively

  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

  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?

  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 • 175 300 village 1 • 343 85 village 2 (+ outliers?) • 530 0 village 3 (few fish?)

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

  9. Village 2 Mean = 310, Stdev = 343, Median = 85

  10. Village 3 Mean = 310, Stdev = 530, Median = 0

  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 = 5 + 0.65*X Tuning in • Parsimonious → Challenge the story

  13. Back to fishing IncF = 5 + 0.65 yrs educ Source: NIST

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