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ECON 626: Applied Microeconomics Lecture 4: Instrumental Variables Professors: Pamela Jakiela and Owen Ozier Compliance with Treatment How High Is Take-Up? Even free programs are costly for participants, and take-up is often low

  1. Wald - extending to endogeneity Data generating process: Z ∼ U (0 , 2) ν 1 , ν 2 , ν 3 ∼ N (0 , 1) i . i . d . ξ = 2 ν 3 + 0 . 2 ν 1 η = − 3 ν 3 + 0 . 2 ν 2 ξ and η not independent; strongly negatively correlated. UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 23

  2. Wald - extending to endogeneity Data generating process: Z ∼ U (0 , 2) ν 1 , ν 2 , ν 3 ∼ N (0 , 1) i . i . d . ξ = 2 ν 3 + 0 . 2 ν 1 η = − 3 ν 3 + 0 . 2 ν 2 ξ and η not independent; strongly negatively correlated. X = Z + ξ Y = X + η UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 23

  3. Wald - extending to endogeneity Data generating process: X Y Z ∼ U (0 , 2) ν 1 , ν 2 , ν 3 ∼ N (0 , 1) i . i . d . ξ = 2 ν 3 + 0 . 2 ν 1 η = − 3 ν 3 + 0 . 2 ν 2 ξ and η not independent; ν 3 strongly negatively correlated. X = Z + ξ Y = X + η UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 23

  4. Wald - extending to endogeneity Data generating process: Z X Y Z ∼ U (0 , 2) ν 1 , ν 2 , ν 3 ∼ N (0 , 1) i . i . d . ξ = 2 ν 3 + 0 . 2 ν 1 η = − 3 ν 3 + 0 . 2 ν 2 ξ and η not independent; ν 3 strongly negatively correlated. X = Z + ξ Y = X + η UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 23

  5. Wald - extending to endogeneity Data generating process: Z X Y Z ∼ U (0 , 2) ν 1 , ν 2 , ν 3 ∼ N (0 , 1) i . i . d . ξ = 2 ν 3 + 0 . 2 ν 1 η = − 3 ν 3 + 0 . 2 ν 2 ξ and η not independent; ν 3 strongly negatively correlated. X = Z + ξ Y = X + η Begin Wald approach by considering a split based on whether Z > 1. UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 23

  6. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 24

  7. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 24

  8. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 24

  9. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 24

  10. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 24

  11. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 25

  12. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 25

  13. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 26

  14. Wald - extending to endogeneity UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 26

  15. Instrumental variables scenarios UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  16. Instrumental variables scenarios Problem: measure the causal casual effect of X end on Y . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  17. Instrumental variables scenarios Problem: measure the causal effect of X end on Y . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  18. Instrumental variables scenarios Problem: measure the causal effect of X end on Y . Inconsistency of least-squares methods when: measurement error in regressors, simultaneity, or when causal equation ( Y ) error term is correlated with X end (omitted variables). Discussion in Cameron and Trivedi, section 6.4, and Angrist and Pishke chapter 4. UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  19. Instrumental variables scenarios Problem: measure the causal effect of X end on Y . Inconsistency of least-squares methods when: measurement error in regressors, simultaneity, or when causal equation ( Y ) error term is correlated with X end (omitted variables). Discussion in Cameron and Trivedi, section 6.4, and Angrist and Pishke chapter 4. Example: X end is schooling; Y is wage; “ability” drives both Y and X end , so may bias cross-sectional regression of Y on X end . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  20. Instrumental variables scenarios Problem: measure the causal effect of X end on Y . Inconsistency of least-squares methods when: measurement error in regressors, simultaneity, or when causal equation ( Y ) error term is correlated with X end (omitted variables). Discussion in Cameron and Trivedi, section 6.4, and Angrist and Pishke chapter 4. Example: X end is schooling; Y is wage; “ability” drives both Y and X end , so may bias cross-sectional regression of Y on X end . Example: X end is number of children; Y is labor force participation; “inclination to remain outside the formal labor force” drives Y down and X end up, so may bias cross-sectional regression of Y on X end . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  21. Instrumental variables scenarios Problem: measure the causal effect of X end on Y . Inconsistency of least-squares methods when: measurement error in regressors, simultaneity, or when causal equation ( Y ) error term is correlated with X end (omitted variables). Discussion in Cameron and Trivedi, section 6.4, and Angrist and Pishke chapter 4. Example: X end is schooling; Y is wage; “ability” drives both Y and X end , so may bias cross-sectional regression of Y on X end . Example: X end is number of children; Y is labor force participation; “inclination to remain outside the formal labor force” drives Y down and X end up, so may bias cross-sectional regression of Y on X end . Example: X end is medical treatment; Y is health; prior illness drives Y down and X end up, so may bias cross-sectional regression of Y on X end . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 27

  22. Instrumental variables basics UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  23. Instrumental variables basics Terminology of Instrumental Variables (“IV”) approach: UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  24. Instrumental variables basics Terminology of Instrumental Variables (“IV”) approach: First stage: Z affects X end UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  25. Instrumental variables basics Terminology of Instrumental Variables (“IV”) approach: First stage: Z affects X end Exclusion restriction: Z ONLY affects Y via its effect on X end UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  26. Instrumental variables basics Terminology of Instrumental Variables (“IV”) approach: First stage: Z affects X end Exclusion restriction: Z ONLY affects Y via its effect on X end Z : “instrument(s)” or “excluded instrument(s)” Y : “dependent variable” or “endogenous dependent variable” X end : “endogenous variable” or “endogenous regressor” UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  27. Instrumental variables basics Terminology of Instrumental Variables (“IV”) approach: First stage: Z affects X end Exclusion restriction: Z ONLY affects Y via its effect on X end Z : “instrument(s)” or “excluded instrument(s)” Y : “dependent variable” or “endogenous dependent variable” X end : “endogenous variable” or “endogenous regressor” What about other covariates? X ex : “covariates” or “exogenous regressors” (First stage and exclusion restriction now conditional on X ex .) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 28

  28. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  29. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  30. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  31. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  32. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  33. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Z i + X ex ′ Y i = π 21 π 20 + ξ 2 i i ���� ���� ���� ρπ 11 ( ρπ 10 + α ) ( ρξ 1 i + η i ) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  34. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  35. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i ˆ π 11 Z i + X ex X end ′ ˆ = ˆ π 10 (Estimated first stage) i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  36. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i ˆ π 11 + X ex X end = Z ′ ′ ˆ i ˆ π 10 (Estimated first stage) i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  37. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i ˆ π 11 + X ex X end = Z ′ ′ ˆ i ˆ π 10 (Estimated first stage) i i Y i = ρ ( ˆ − ˆ + X ex X end + ( X end X end ′ α + η i (plug into causal model) )) i i i i � �� � X end i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  38. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i ˆ π 11 + X ex X end = Z ′ ′ ˆ i ˆ π 10 (Estimated first stage) i i Y i = ρ ( ˆ − ˆ X end + ( X end X end )) + X ex ′ α + η i i i i i Y i = ρ ˆ − ˆ + X ex ′ α + ( η i + ρ ( X end X end X end )) (“Second stage”) i i i i UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  39. Instrumental variables basics = π 11 Z i + X ex ′ π 10 + ξ 1 i (“First stage”) X end i i + X ex ′ α + η i (causal model) Y i = ρ X end i i E [ η i | X ex i ] = 0; E [ ξ 1 i | X ex i ] = 0; E [ η i ξ 1 i | X ex i ] � = 0; E [ η i | Z i , X ex i ] = 0; Y i = ρ ( π 11 Z i + X ex ′ π 10 + ξ 1 i ) + X ex ′ α + η i i i Y i = ρπ 11 Z i + X ex ′ ( ρπ 10 + α ) + ( ρξ 1 i + η i ) i Y i = π 21 Z i + X ex ′ π 20 + ξ 2 i (“Reduced form”) i ˆ π 11 + X ex X end = Z ′ ′ ˆ i ˆ π 10 (Estimated first stage) i i Y i = ρ ( ˆ − ˆ X end + ( X end X end )) + X ex ′ α + η i i i i i Y i = ρ ˆ − ˆ + X ex ′ α + ( η i + ρ ( X end X end X end )) (“Second stage”) i i i i Hence: “Two-stage least squares,” “2SLS” or “TSLS” UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 29

  40. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  41. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument X end is schooling (endogenous regressor); Y is wage (dependent var.); how do we find variation in education that is not driven by the common (unobserved) causes of education and wage (“ability”)? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  42. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument X end is schooling (endogenous regressor); Y is wage (dependent var.); how do we find variation in education that is not driven by the common (unobserved) causes of education and wage (“ability”)? Z is quarter of birth (instrument). Exclusion restriction? First stage? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  43. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument X end is schooling (endogenous regressor); Y is wage (dependent var.); how do we find variation in education that is not driven by the common (unobserved) causes of education and wage (“ability”)? Z is quarter of birth (instrument). Exclusion restriction? First stage? Born in Q4: start school just before you turn 6. At age 16, you have completed 10+ years of school. Born in Q1: start school September after you turn 6. At age 16, you have completed 9 years and a few months of school. UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  44. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument X end is schooling (endogenous regressor); Y is wage (dependent var.); how do we find variation in education that is not driven by the common (unobserved) causes of education and wage (“ability”)? Z is quarter of birth (instrument). Exclusion restriction? First stage? Born in Q4: start school just before you turn 6. At age 16, you have completed 10+ years of school. Born in Q1: start school September after you turn 6. At age 16, you have completed 9 years and a few months of school. UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  45. Instrumental variables scenarios Example: quarter of birth / compulsory schooling instrument X end is schooling (endogenous regressor); Y is wage (dependent var.); how do we find variation in education that is not driven by the common (unobserved) causes of education and wage (“ability”)? Z is quarter of birth (instrument). Exclusion restriction? First stage? Born in Q4: start school just before you turn 6. At age 16, you have completed 10+ years of school. Born in Q1: start school September after you turn 6. At age 16, you have completed 9 years and a few months of school. Finding: wage returns to education via 2SLS slightly larger than OLS. (Angrist and Krueger 1991) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 30

  46. Instrumental variables scenarios Example: same-sex and twins instruments (“human cloning”) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 31

  47. Instrumental variables scenarios Example: same-sex and twins instruments X end is number of children (endogenous regressor); Y is labor force participation (dependent variable); how do we find variation in family size that is not driven by the common (unobserved) causes of family size and labor force participation (“inclination to remain outside the formal labor force”)? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 31

  48. Instrumental variables scenarios Example: same-sex and twins instruments X end is number of children (endogenous regressor); Y is labor force participation (dependent variable); how do we find variation in family size that is not driven by the common (unobserved) causes of family size and labor force participation (“inclination to remain outside the formal labor force”)? Z = two indicators: twins at second birth; first two children same sex (instruments). Exclusion restriction? First stage? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 31

  49. Instrumental variables scenarios Example: same-sex and twins instruments X end is number of children (endogenous regressor); Y is labor force participation (dependent variable); how do we find variation in family size that is not driven by the common (unobserved) causes of family size and labor force participation (“inclination to remain outside the formal labor force”)? Z = two indicators: twins at second birth; first two children same sex (instruments). Exclusion restriction? First stage? Finding: family size decreases women’s labor force participation, but not by as much as OLS would suggest. (Angrist and Evans 1998, Mostly Harmless Table 4.1.4) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 31

  50. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  51. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  52. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery • Job Training Partnership Act (JTPA) randomized trial UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  53. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery • Job Training Partnership Act (JTPA) randomized trial • Ocean weather UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  54. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery • Job Training Partnership Act (JTPA) randomized trial • Ocean weather • Rainfall! (Paxson 1992; Miguel et al 2004: Maccini and Yang 2009; Madestam et al 2013; etc.) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  55. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery • Job Training Partnership Act (JTPA) randomized trial • Ocean weather • Rainfall! (Paxson 1992; Miguel et al 2004: Maccini and Yang 2009; Madestam et al 2013; etc.) • Electrification... UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  56. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? • Vietnam draft lottery • Job Training Partnership Act (JTPA) randomized trial • Ocean weather • Rainfall! (Paxson 1992; Miguel et al 2004: Maccini and Yang 2009; Madestam et al 2013; etc.) • Electrification... slope of land (Dinkelman 2011) UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 32

  57. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? Other kinds of scenarios UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 33

  58. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? Other kinds of scenarios • Y = Child IQ; X end = growing cotton; Z = born in US south UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 33

  59. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? Other kinds of scenarios • Y = Child IQ; X end = growing cotton; Z = born in US south • Y = “Happiness, 1-5;” X end = “Fair workplace, 1-5;” Z = variation in when a pay raise is announced to individuals UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 33

  60. Instrumental variables scenarios Likely source of OLS bias? Exclusion restriction? First stage? Other kinds of scenarios • Y = Child IQ; X end = growing cotton; Z = born in US south • Y = “Happiness, 1-5;” X end = “Fair workplace, 1-5;” Z = variation in when a pay raise is announced to individuals • Y = “Satisfied w/ govt services;” X end = city pruned tree branches over sidewalk recently; Z = city repaved street recently UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 33

  61. Instrumental variables: LATE (MHE Chapter 4.4) Consider a randomized trial with imperfect compliance (as in JTPA). Terminology: • Always-takers D 0 i = D 1 i = 1, so D i = 1 regardless of Z i • Never-takers D 0 i = D 1 i = 0, so D i = 0 regardless of Z i • Compliers D 0 i = 0; D 1 i = 1, so D i = Z i Under heterogeneous treatment effects, having not only compliers but also defiers would cause a problem. • Defiers : D 0 i = 1; D 1 i = 0, so D i = (1 − Z i ). We need monotonicity for an interpretable Local Average Treatment Effect when there are heterogeneous treatment effects: either D 1 i ≥ D 0 i ∀ i , or D 1 i ≤ D 0 i ∀ i . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 34

  62. Instrumental variables: Overidentification Terminology: • Exactly as many linearly independent instruments as endogenous regressors? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 35

  63. Instrumental variables: Overidentification Terminology: • Exactly as many linearly independent instruments as endogenous regressors? Just identified . UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 35

  64. Instrumental variables: Overidentification Terminology: • Exactly as many linearly independent instruments as endogenous regressors? Just identified . • More linearly independent instruments than endogenous regressors? UMD Economics 626: Applied Microeconomics Lecture 4: Instrumental Variables, Slide 35

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