Table 3.

Average Treatment Effects - Comparison of Estimates from Our Model to Those from Simpler Methods

HS Grad.	Linear Regression				Matching		Model
	OLS	OLS-P	OLS-F	OLS-FI	NNM(3)-F	PSM-F	${\text{ATE}}_j^k$*
Wages	0.205	0.073	0.155	0.159	0.098	0.132	0.094
SE	(0.025)	(0.026)	(0.025)	(0.035)	(0.037)	(0.051)	(0.056)
PV-Wage	0.380	0.213	0.318	0.277	0.196	0.226	0.173
SE	(0.030)	(0.031)	(0.030)	(0.041)	(0.053)	(0.058)	(0.059)
Smoking	−0.327	−0.246	−0.281	−0.301	−0.260	−0.271	−0.263
SE	(0.028)	(0.029)	0.028	0.041	(0.058)	(0.060)	(0.056)
Health-Limits-Work	−0.178	−0.115	−0.151	−0.150	−0.048	−0.095	−0.108
SE	(0.022)	(0.024)	(0.023)	(0.033)	(0.029)	(0.036)	(0.042)
Coll. Enroll	OLS	OLS-P	OLS-F	OLS-FI	NNM(3)-F	PSM-F	${\text{ATE}}_j^k$
Wages	0.223	0.121	0.186	0.190	0.177	0.207	0.134
SE	(0.023)	(0.024)	(0.024)	(0.023)	(0.029)	(0.031)	(0.025)
PV-Wage	0.221	0.109	0.176	0.171	0.188	0.226	0.137
SE	(0.027)	(0.029)	(0.028)	(0.027)	(0.030)	(0.032)	(0.029)
Smoking	−0.177	−0.138	−0.165	−0.170	−0.129	−0.144	−0.139
SE	(0.026)	(0.028)	(0.027)	(0.028)	(0.029)	(0.058)	(0.028)
Health-Limits-Work	−0.085	−0.037	−0.066	−0.057	−0.029	−0.042	−0.037
SE	(0.020)	(0.022)	(0.021)	(0.021)	(0.022)	(0.029)	(0.022)
Coll. Grad	OLS	OLS-P	OLS-F	OLS-FI	NNM(3)-F	PSM-F	${\text{ATE}}_j^k$
Wages	0.210	0.146	0.184	0.185	0.173	0.143	0.114
SE	(0.032)	(0.034)	(0.033)	(0.035)	(0.041)	(0.051)	(0.037)
PV-Wage	0.243	0.163	0.208	0.228	0.191	0.269	0.171
SE	(0.037)	(0.040)	(0.038)	(0.037)	(0.039)	(0.042)	(0.040)
Smoking	−0.209	−0.171	−0.195	−0.192	−0.132	−0.161	−0.172
SE	(0.032)	(0.035)	(0.033)	(0.035)	(0.039)	(0.039)	(0.043)
Health-Limits-Work	−0.085	−0.069	−0.078	−0.077	−0.048	−0.051	−0.064
SE	(0.024)	(0.026)	(0.025)	(0.026)	(0.026)	(0.027)	(0.031)

Notes: We estimate the ATE inclusive of continuation values for each outcome and and educational choice using a variety of methods. All models are estimated for populations that reach the node being analyzed (Q_j = 1), inclusive of those who go on to further schooling in order to make them comparable to the ATE from our model that includes continuation values (Equation (21)). All OLS models use the full set of controls listed in Table 1. “OLS” estimates a linear model using a schooling dummy (Q_j+1), and controls (Y = Q_j+1b_j + X′β + ε). “OLS-P” estimates a linear model using a schooling dummy, a vector of controls, and three measures of abilities arrayed in a vector A: summed ASVAB scores, GPA, and an indicator of risky behavior (Y = Q_j+1b_j + X′β + A′α + ε). All models ending in “-F” are estimated using Bartlett factor scores (Bartlett, 1937, 1938) estimated using our measurement system, but using the built-in routine for estimating factor models in STATA via maximum likelihood, not accounting for schooling at the time of the test. “OLS-F” estimates the model Y = Q_j+1b_j + X′β + θ̂′α+ ε where θ̂ are the Bartlett factor scores described above. “OLS-FI” is similar to “OLS-F” except that Q_j+1 is interacted with the X and θ̂ allowing the coefficients on the controls and abilities to vary by education level. “NNM(3)-F” is the estimated treatment effect from nearest-neighbor matching with 3 neighbors. Neighbors are matched on their Bartlett cognitive factor, Bartlett non-cognitive factor, and an index constructed from their observed characteristics (Z) generating choices as described in Web Appendix A.18. “PSM-F” presents the estimated average treatment effect from propensity score matching where propensity scores are estimated using Bartlett cognitive factors, Bartlett non-cognitive factors, the full set of control variables, and the full set of node-specific instruments. “ ${\text{ATE}}_j^k$” presents the estimated average treatment effect from the model presented in this paper (inclusive of continuation value), corresponding to Equation (13).