Integrating mobile phone technologies into labor-market intermediation: a multi-treatment experimental design

IZA Journal of Labor & Development

Table 4 Labor market intermediation on employment outcome. Labor-Market Intermediation Program, Lima 2009-2010

	Month # 1		Month # 2		Month # 3
Dep variable: employment
Overall treatment	0.066**	0.062**	0.056*	0.055*	0.003	−0.002
	(0.032)	(0.027)	(0.032)	(0.031)	(0.031)	(0.033)
Type of treatment
Traditional treatment (D^T1)	0.041	0.034	0.046	0.040	−0.018	−0.034
	(0.039)	(0.035)	(0.038)	(0.036)	(0.038)	(0.044)
Restricted-SMS treatment (D^T2)	0.081*	0.083*	0.057	0.060	0.034	0.034
	(0.046)	(0.042)	(0.046)	(0.046)	(0.045)	(0.045)
Unrestricted-SMS treatment (D^T3)	0.086**	0.081**	0.067*	0.069*	0.008	0.008
	(0.040)	(0.032)	(0.039)	(0.037)	(0.039)	(0.035)
p-value of F-test: D^T1 = D^T2 = D^T3	0.478	0.381	0.876	0.712	0.493	0.334
N	1118	1118	1118	1118	1118	1118
Covariates	No	Yes	No	Yes	No	Yes
Experimental groups FE	No	Yes	No	Yes	No	Yes

Notes: Standard errrors in parenthesis. Estimates based on a parametric cross-sectional estimator. The treatment indicator takes the value 1 for those benefiting from labor-market intermediation, 0 otherwise. Socio-demographic covariates include age, gender, marital status, poverty index, whether individual has children, number of children, whether individual is migrant, number of years since migration, years of schooling. Labor-market covariates include whether individual had ever worked, whether individual had pension plan, health insurance, accident insurance, formal contract as well as blue-collar and white-collar worker indicators. Clustered standard errors by day are considered when including experimental groups fixed effects in columns 2, 4, and 6. ***statistically significant at 1%, **statistically significant at 5%, *statistically significant at 10%.