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LT7: Regression Discontinuity Sam Marden

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Basic Idea Explain in no more than three sentences why we think about regression discontinuities as natural experiments. Remember that throughout the first part of the term, we are looking for ways to construct a valid counterfactual. In the case of the example regarding college scholarships and the PSAT test that we discussed in lecture, what is the counterfactual we are trying to construct? Explain using either math or words, how the arbitrary eligibility rule of the National Merit Scholarship helps us construct this counterfactual.

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Q1 scatter emp age if year==1986 & region=="Quebec“, xline(29.5)

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Q2 & 3 twoway (scatter emp age if year==1986 & region=="Quebec" & age>=25) /// (lfitci emp age if year==1986 & region=="Quebec" & age>=25 & age<30, fcolor(none)) /// (lfitci emp age if year==1986 & region=="Quebec" & age>=25 & age>=30, fcolor(none))

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Q4 reg emp treat age if year==1986 & region=="Quebec" & age>=25, r outreg2 using "C:\Users\marden\Desktop\EC455\lt6.d oc", word replace reg emp treat age age0treat if year==1986 & region=="Quebec" & age>=25, r outreg2 using "C:\Users\marden\Desktop\EC455\lt6.d oc", word append (1)(2) VARIABLESemp treat *** *** (0.0103)( ) age *** ( )( ) age0treat ** ( ) Constant0.882***0.723*** (0.0284)(0.0643) Observations15 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Q5 (1)(2) VARIABLESemp treat (0.0242)(0.0149) age ** *** ( )( ) age0treat *** ( ) Constant0.826***0.360*** (0.0772)(0.0603) Observations15 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Q6 (1)(2) VARIABLEShours treat-1.419***-1.782*** (0.442)(0.342) age-0.310*** (0.0562)(0.0852) age0treat * (0.106) Constant34.50***29.32*** (1.540)(2.414) Observations15 R-squared Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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