1. Nick Bloom, Applied Econometrics, Winter 2010 APPLIED ECONOMETRICS Lecture 1 - Identification

2. Nick Bloom, Applied Econometrics, Winter 2010 Defining Identification Experiments Natural Experiments Instrumental variables Econometric Identification

3. Nick Bloom, Applied Econometrics, Winter 2010 WHAT IS IDENTIFICATION? Graduate and professional economics mainly concerned with identification in empirical work Concept of understanding what is the causal relationship behind empirical results: This is essential for learning from empirical research Time-series example: Interest rates and GDP Cross-section example: Management & Productivity

4. Nick Bloom, Applied Econometrics, Winter 2010 WHAT IS DRIVING THIS RELATIONSHIP? Correlation = 0.233

5. Nick Bloom, Applied Econometrics, Winter 2010 REASONS FOR CORRELATION Imagine variables Y t and X t are correlated: There can be three reasons for this, which are not mutually exclusive: Cause: Changes in X t drive changes in Y t Reverse Cause: Changes in Y t drive changes in X t Correlated variable: Changes in Z t drives X t and Y t

6. Nick Bloom, Applied Econometrics, Winter 2010 WHAT IS DRIVING THIS RELATIONSHIP?

7. Nick Bloom, Applied Econometrics, Winter 2010 SO HOW DO WE GET IDENTIFICATION Four broad approaches for identification Experiments – you generate the variation Natural Experiments – you know what generated the variation Instrumental variables – you have a variable that can provide you variation Econometric Identification – you rely on (testable) econometric assumptions for identification

8. Nick Bloom, Applied Econometrics, Winter 2010 Defining Identification Experiments Natural Experiments Instrumental variables Econometric Identification

9. Nick Bloom, Applied Econometrics, Winter 2010 EXPERIMENTS (1) Experiments are totally standard in Science & Medicine For example: Set up a treatment and control group for a new drug, making sure these are comparable (or randomly selected) Ensure the sample sizes are large enough to obtain statistical significance Ensure the experiment is unbiased – i.e. the drug and the placebo are as similar as possible Run the experiment

10. Nick Bloom, Applied Econometrics, Winter 2010 EXPERIMENTS (2) Economists like to use the language of Science For example the UK considered introducing an Education Maintenance Allowance, to pay kids to stay on at school. But want to test first to see if this would this work. Set up a treatment and control regions to match these in characteristics Select enough regions to get large sample sizes Observe agents actions to evaluate impact (rather than self reported outcomes) Run the experiment

11. Nick Bloom, Applied Econometrics, Winter 2010 EXPERIMENTS (3) Experiments are rare in economics because they are expensive, although they becoming more popular: Typical areas for running experiments include: Development economics – cheaper to run experiments in the third World (water supply or management practices) Consumer economics – small stakes experiments that are easy to administer (credit cards) Individual business applications – firms can finance these (retail store layout) But some fields will never have experiment – for example macroeconomics

12. Nick Bloom, Applied Econometrics, Winter 2010 Defining Identification Experiments Natural Experiments Instrumental variables Econometric Identification

13. Nick Bloom, Applied Econometrics, Winter 2010 NATURAL EXPERIMENTS (1) Natural experiments are where fortunate situations create good underlying identification: Typically several approaches: Tax e.g. Response of R&D to the cost of capital (Bloom, Griffith & Van Reenen, 2002), (Chetty and Saez, 2003) Discontinuity (see over) Shock - financial crisis and Kibutzim (Abramitzky, 2007) Disasters - Ethiopian Jews airlift (Gould, Levy & Passerman, 2004)

14. Nick Bloom, Applied Econometrics, Winter 2010 NATURAL EXPERIMENTS (2) Natural experiments are almost the holly grail of modern applied economics In the absence of true experiments they provide the best way to provide simple identification Couple of standard way to use natural experiments in practice –Discountinunity analysis and/or –Difference in differences

15. Nick Bloom, Applied Econometrics, Winter 2010 DISCONTINUITY ANALYSIS – example 1 Region A (no tax) Region B (50% tax) Imagine a 50% tax is levied on investment in the rich coastal region A but not in the poor inland region B. If you saw the graph below could you say what the impact of the tax is on investment? Investment Estimated impact of the tax

16. Nick Bloom, Applied Econometrics, Winter 2010 DISCONTINUITY ANALYSIS – example 2 Impact of telephones on price of fish in Kerala (India)

17. Nick Bloom, Applied Econometrics, Winter 2010 DIFFERNCES IN DIFFERENCES t 0 denotes pre-treatment periods for which data are available t 1 denotes post-treatment periods for which data are available Average change in outcome (pre and post-treatment) for treatment group minus average change in outcome for control group Identification comes from the differential change between the two groups pre and post-treatment –difference out unobserved fixed effects –difference out common time effects Key assumption of common time effects for the two groups

18. Nick Bloom, Applied Econometrics, Winter 2010 POLICY EXAMPLE OF “DIFF-IN-DIFF” Small firms R&D tax credit introduced in 2000 for firms with 250 or less employees So could look at firms before and after credit –But other things also changing (2000 peak of dotcom boom etc…) –So need to set up a control group of companies look similar to firms getting the credit except don’t get the credit Compare firms with 240 employees to those with 260 This is double-diff (or diff in diffs) to compare differences: –Between pre and post the credit (1999 versus 2001) –Between the treated (240 employees) and untreated firms (260 employees)

19. Nick Bloom, Applied Econometrics, Winter 2010 Defining Identification Experiments Natural Experiments Instrumental variables Econometric Identification

20. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES (1) Want to look at effect of schooling (S i ) on earnings (Y i ) Assume the true model is : Y i = α + β 1 S i + β 2 A i + v i where A i is (unobserved) ability which is positively correlated with S i, and v i is random independent noise What would happen if we estimated the following instead? Y i = a + b 1 S i + e i where e i = β 2 A i + v i

21. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES (2) ------Background Assume estimating equation below in Ordinary Least Squares Y = α + βX + e The estimate of β= E(Y’X)/E(X’X) = E((βX + e )’X)/E(X’X) = β + E(e’X)/E(X’X) = β only if e and X are independent But if e and X are correlated then the estimated is biased, and X is called “endogenous” (correlated with the error) ---------------------

22. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES (3) Thus, estimation of the following would be biased: Y i = a + b 1 S i + e i because S i and e i are correlated as e i is a function of ability E[b 1 ]=E[Y’S]/E[S’S] =E[(β 1 S i +β 2 A i +v i )’S] / E[S’S] = β 1 + E[(β 2 A i +v i )’S] / E[S’S] = β 1 + β 2 E[A i ’S] / E[S’S] > β 1 So because ignore ability, which is correlated with schooling, we overestimate the impact of schooling on earnings

23. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES (4) Imagine we had a variable – called an instrument Z – that was correlated with schooling but not ability. We could then use this to explain variation in schooling as it is not correlated with ability One example of this would be if the Government paid everyone born on even days to stay in school Then “born on an even day” would be an instrument for schooling – correlated with schooling but not ability In practice instruments are often hard to find

24. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES (5) Assume that Z is correlated with S but not A. Then the following instrumental variable estimator is consistent E[b 1 IV ]=E[Y’Z]/E[S’Z] =E[(β 1 S i +β 2 A i +v i )’Z] / E[S’Z] =E[β 1 S i ’Z + β 2 A i ’Z +v i ’Z] / E[S’Z] = β 1 + (β 2 E[A i ’S] + E[v i ’Z]) / E[S’Z] = β 1 Stata will calculate this for you. All you need to find is a variable that only affects your dependent variable via the variable you are interested in

25. Nick Bloom, Applied Econometrics, Winter 2010 INSTRUMENTAL VARIABLES Any questions on this? Imagine you wanted to evaluate the impact of crop yields on farmers behavior – can anyone suggest a good instrument

26. Nick Bloom, Applied Econometrics, Winter 2010 Defining Identification Experiments Natural Experiments Instrumental variables Econometric Identification

27. Nick Bloom, Applied Econometrics, Winter 2010 ECONOMETRIC IDENTIFICATION Another way to obtain identification is try to model everything For example, we claim we know how ability is correlated with schooling and so model the whole system The problem with this is: It is a lot more complicated It requires strong assumptions Thus, this is usually only undertaken when there is no obvious instrument or natural experiment

28. Nick Bloom, Applied Econometrics, Winter 2010 SUMMARY Identification – understanding the causality in a regression – is essential for generating meaningful results There are a range of approaches – but they all need some prior economic thought (i.e. is their a natural experiment?)