Staying or Leaving? Analyzing the Rationality of Rural-Urban Migration Associated with Farm Income of Staying Households: A Case Study from Southern Ethiopia

Table 3

Second stage estimation results.

= + + + +
Comparison 1: nonmigrant sending against temporary migrant sending households
Number of Obs. = 212 HHs
Adj.	-stat.							Const.
		Coeff.		Coeff.		Coeff.		Coeff.
0.842	(3,208) = 275.7^***	0.26^***; (0.09)	2.97	1.13^***; (0.072)	15.77	0.23^***; (0.058)	4.08	−1.32; (0.835)	−1.59
= + + + +
Comparison 2: nonmigrant sending against permanent migrant sending households
No. Obs. = 177 HHs
Adj.	-stat.							Const.
		Coeff.		Coeff.		Coeff.		Coeff.
0.911	(3,173) = 279.4^***	0.14^*; (0.080)	1.72	1.68^***; (0.075)	22.39	0.11^***; (0.039)	2.79	−4.35^***; (0.666)	−6.53

refers to significant at 10% (); ^**refers to significant at 5% () and ^***refers to significance at 1% ().
Bootstrap standard errors (as noted by Petrin and Train [17] and Karaca-Mandic and Train [18] bootstrapping methods applied for the entire two-step estimators provide a valid estimator of the covariance matrix which is similar to the estimation done to correct the asymptotic standard errors by programming the asymptotic formula of covariance estimates. Bootstrapping is a convenient way of obtaining the covariance matrix estimators with two-step estimators and it also provides better parameter estimates particularly for conditions when asymptotic sampling distribution is too difficult to drive in multistage estimations [19, 20]. In this research, bootstrapping method is applied for the entire procedure of the two-step estimators with 1000 replications using STATA software package) are in parenthesis, source: author’s estimation.