olds since entry Local Competition under the 1996 Act in urban areas was advanced enough by 2001 to provide a very large share of households in these areas with at least one competitive choice.46 The model's independent variables are size of the metropolitan area, income, age of the head of household, number of persons in the house- hold, race or ethnicity, and two dummy variables indicating whether the household subscribes to AT&T or MCI for its long-distance service. The latter two variables are intended to capture the fact that AT&T and MCI are also new entrants into local exchange service and may therefore have an advantage in inducing their customers to switch from the incumbent local carrier. Because of the large number of local entrants, I was unable to identify a single price variable for the entrant's local service for each household in the sample. A given household may have a number of entrants to choose from, and their prices may vary. Instead of using any one entrant's price, I use the UNE-P wholesale rate that any entrant faces in the state in which the household resides. For the incumbent rate, I use the average ILEC rate in the state, stripped of the estimated prices of second lines and calling fea- tures. Hence my price variable is the ratio of the UNE-P rate to the aver- age ILEC rate for the state. For the first quarter of 2001, my sample includes 658 households, 52 of which used a new entrant for their local service. The chi-square goodness of fit score for the entire logit regression is 50.3, which is significant at the 2 percent level of confidence. The relative price coefficient is -0.379, which has the expected sign but is not statistically significant. For the sec- ond quarter of 2001, with 49 households of a total of 685 subscribing to an entrant's service, the goodness of fit improves to a chi-square of 57.6, which is significant at the 0.4 percent level of confidence. The estimated coefficient for the UNE-P/ILEC price variable in this quarter is -2.094, which is significant at the 0.5 p