Neighborhood Effects, Public Housing and Unemployment in France

	Simple probit	Seemingly unrelated probits^		Simultaneous probits^
Public housing		No	Yes	No	Yes	Yes	Yes
in neighborhood eq. Dropped variable	None	None	None	None	None	Occupational	Spouse’s
in unemployment eq.	(ɪ)	(2)	(3)	(4)	(5)	status (6)	nationality (7)
Deprived neighborhood Coefficient 0.173***				0.631**	0.788***	0.869	0.805
Correlation	(0.042)	0.0992***	0.0604**	(0.270) -0.274*	(0.116) -0.390***	(0.107) -0.426***	(0.114) -0.396***
Marginal effect	0.0213	(0.0255) 0.0205	(0.0257) 0.0125	(0.155) 0.0194	(0.065) 0.0118	(0.060) 0.0136	(0.064) 0.0125
	(0.00593)	(0.00602)	(0.00587)	(0.00700)	(0.00626)	(0.00630)	(0.00624)
	[0.00591]	[0.00596]	[0.00563]	[0.00705]	[0.00611]	[0.00627]	[0.00612]
Public housing Marginal effect					0.0315	0.0386	0.0327
Log likelihood	-2351.34	-8164.32	-7883.84	-8162.24	(0.00632) [0.00677] -7865.29	(0.00658) [0.00709] -7932.85	(0.00628) [0.00676] -7881.14
Pseudo-R2	5.88	9.55	12.66	9.57	12.86	12.11	12.68

Notes: Control variables include, unless otherwise mentioned, individual’s age, nationality, education level, occupational status, spouse’ age,
nationality, education level and number of children, (a) Neighborhood variable is not in the unemployment equation, (b) Neighborhood variable
is in the unemployment equation. Mean marginal effects are calculated as 1/N ∑(P(yn = 1∣⅜2 = I)^- Р(.Уи = l∣!∕i2 = 0)) for neighborhood type
and l∕N∑(P(y_il = l,τ∕_i2 = ⅛₃) + P(yn = ¹>⅜2 = t¾₃) - P(y_il = 0, y_i2 = ⅛3) ^- P(yn = 0, y_i2 = 0∣⅜₃)) for public housing. Predicted
probabilities are calculated for each individual vector of characteristics. Standard errors of marginal effects are calculated by the delta method.
Standard errors between square brackets are corrected for dependencies within neighborhoods.

Table 6: Effect of public housing and neighborhood on unemployment probability

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