Table A6

Tests of matching quality

Variable% Bias after different algorithm matching
Q(1)Q(2)Q(3)Q(4)
Lnhhinc18.4*** (12.52)2.0 (0.68)1.2 (0.55)3.1 (1.03)
Age−4.4* (−1.71)2.5 (0.83)2.1 (0.55)2.1 (0.41)
Gender9.8*** (4.01)0.2 (0.05)3.8 (1.09)3.5 (0.72)
Married4.0 (1.53)4.2 (1.44)2.9 (0.8)0.2 (0.04)
Hhsize0.4 (0.16)3.5 (1.23)−0.4 (−0.1)−2.4 (−0.49)
Fafh2.8 (1.18)0 (0.00)−3.3 (−0.89)−3.1 (−0.59)
Educ
Middle2.1 (0.85)−0.1 (−0.03)1.4 (0.41)3.4 (0.69)
High or technical8.4*** (3.22)−2.6 (−0.86)−1.5 (−0.38)0 (0.00 )
University or higher−5.2** (−2.35)−0.1 (−0.07)−1.9 (−0.58)−1.3 (−0.35)
Phyact
Moderate−4.6* (−1.78)7.3** (2.48)3.7 (1.01)0.2 (0.05)
Heavy3.8 (1.58)−2.5 (−0.91)−1.3 (−0.46)−0.6 (−0.18)
Year
20043.3 (1.53)3.8 (1.35)−2.6 (−0.72)0.9 (0.20 )
2006−2.1 (−0.8)3.2 (1.12)2.6 (0.74)1.1 (0.23)
20090.1 (0.05)−1.3 (−0.45)2.9 (0.88)−1.3 (−0.25)
20111.4 (0.54)−1 (−0.35)0.1 (0.04)0.1 (0.02)
Pseudo R20.0230.0030.0020.001
LR χ2211.8417.136.983.27

Note(s): Matching quality is from nearest neighbor matching with five partners. Matching quality with other algorithm produces close results. We created the treated and control groups based on the level of urbanicity index – we divided the sample into five urban quintiles. Q(1)–Q(4) refer to the quintiles; T-statistics are in the parentheses. *p < 0.1, **p < 0.05, ***p < 0.01

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