Regression-Discontinuity Design - Institute for Policy Research

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Creating A Regression Discontinuity • Choose Arkansas to be the RDD group. – Choose an assignment variable and a cut‐off value – Delete treatment group cases below the cut‐off – Delete control group cases above the cut‐off. • Two assignment variables: – AGE with cut‐off = 50 – Pre‐Test Medicaid Expenditures with cut‐off = $7000 • Two Outcomes: – Number of Unpaid Workers – Post‐Test Medicaid Expenditures • Two Non‐Equivalent Control Groups: – Control group from New Jersey – Control group from Florida
What Is The Target Parameter? • Average untreated outcome above the cut‐off score. – The corresponding experiment is random assignment among people above the cut‐off. – Here, that means random assignment of people over age 50 for one assignment variable and random assignment of people with expenditures over $7000 last year. • We also look at the functional forms that are extrapolated beyond the cut‐off. This gives a sense of the RD estimates that would result if different cut‐off values were selected.
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Regression‐Discontinuity Design D
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Design Overview Design Examples Vis
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Real Examples of RD Reading First b
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RDD Visual Depiction Comparison Tre
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Two Rationales for RDD 1. Selection
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Two Rationales for RDD 1. Selection
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1. Overrides to the cutoff: Sharp,
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Fuzzy RD Designs • Requires a dis
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Requirements for Estimating Treatme
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Implementation Threats to RD Design
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Example: AYP Data from Texas Densit
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What to do? • This is a big probl
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Analytic Threats to RD Design: Miss
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Nonlinearities in Functional Form
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Here we see a discontinuity between
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Functional Form: Interactions • S
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If we superimpose the regression li
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Here we see an example where the tr
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Adding Nonlinear Terms to the Model
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Adding Nonlinear and Interaction Te
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Adding Terms, Continued • When in
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Example of Parametric Plot used aga
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Non‐parametric Approach lim x↓c
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Non‐parametric plot Local linear
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Semi‐Parametric Plot RD data from
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What to do? • Check to see whethe
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Shadish, Galindo, Wong, V., Steiner
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RD Design assumption 2 • Continui
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Implementation assumption 2 • No
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Analytic assumption 1 continued •
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Shadish et al. (under review) Vocab
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RDD Example 2
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Best case scenario - regression lin
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Correct specification of functional
Page 69 and 70: What to do (2) • Parametric appro
Page 71 and 72: What to do (4) • State‐of‐art
Page 73 and 74: What to do (5) • Move to a tie br
Page 75 and 76: Example Cocaine Project • Include
Page 77 and 78: What to do (7) • Estimation throu
Page 79 and 80: 2. Dealing with Overrides to the cu
Page 81 and 82: 2. Overrides to the cutoff • Mult
Page 83 and 84: Specification of Functional Form Pa
Page 85 and 86: Specification of Functional Form No
Page 87 and 88: Overrides to cutoff (2) Alternative
Page 89 and 90: Final estimates Function al form Pa
Page 91 and 92: Addressing Potential Issues with us
Page 93 and 94: IS Wong et al. a Fallible RD or Ins
Page 95 and 96: African‐American Home language Sp
Page 97 and 98: Fallible RD? • Now we look for di
Page 99 and 100: McCrary Test for New Jersey
Page 101 and 102: Summarizing Checks - Conditional Me
Page 103 and 104: Other Kinds of Threats with RD 1. L
Page 105 and 106: Variance of Impact Estimator in RA
Page 107 and 108: Variance of Impact Estimator in RDD
Page 109 and 110: Power Considerations Unique to RDD
Page 111 and 112: Generalizing Beyond The Cut‐off I
Page 113 and 114: Observed vs Counterfactual Outcomes
Page 115 and 116: Questions About Generalizing From R
Page 117 and 118: What Do We Mean By Comparison Group
Page 119: Our Purpose • Use experimental da
Page 127 and 128: What About The Above The Cut‐Off
Page 129 and 130: Root Mean Square Error In Experimen
Page 131 and 132: Post-Treatment Medicaid Expenditure
Page 133 and 134: Results For Number of Unpaid Worker
Page 135 and 136: Results For Expenditures With Expen
Page 137 and 138: Conclusions re Using a Comparison R
Page 139 and 140: Implications for Practice • Which
Page 141 and 142: RD with Multiple Cutoffs Design (as
Page 143 and 144: Analyzing RDs with Multiple Cutoffs
Page 145 and 146: Examples • Black, Galdo, and Smit
Page 147 and 148: Issue 2: To pool or not pool across
Page 149 and 150: Pooling is Already Common in Social
Page 151 and 152: What is Special about Pooling RD Es
Page 153 and 154: Estimating Treatment Effects across
Page 155 and 156: Estimating Treatment Effects across
Page 157 and 158: Generalizing Across Multiple Assign
Page 159 and 160: Multivariate RDD with Two Assignmen
Page 161 and 162: Recent Education Examples of RDDs w
Page 163 and 164: Frontier‐specific Effect (τ R )
Page 165 and 166: Requirements for a valid Multivaria
Page 167 and 168: Frontier Approach Estimates the dis
Page 169 and 170: Univariate Approach Addresses dimen
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Monte Carlo Study Wong, V., Steiner
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Implications for Practice • Which
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