distribution does not provide a good fit for one to three apprehensions. The numerical values for the probability of non-apprehension on the first crossing attempt, as estimated by each of the four distribution classes, are shown close to the vertical axis. (The values of the distributions at zero apprehensions are off the scale of this graph.) The probabilities for zero apprehensions for the three good-fitting distributions are close together (74 to 75 percent), while that of the poorer-fitting Poisson distribution is substantially lower (54 percent). Because the sample sizes are large, the nominal confidence intervals (not plotted) for the probability of non-apprehension on the first crossing are narrow, but these estimates are not adjusted for possible model misspecification. A variant of this distribution-fitting approach is the Good-Turing frequency model (Good, 1953). The simplest version of this approach estimates the probability of non-apprehension as the proportion of those apprehended who were apprehended exactly once. For the 2009 EMIF-N data, this estimate is 75.4 percent, which is in close agreement with the first three good-fitting distributions.
At least three agencies within DHS collect administrative data on apprehensions of unauthorized immigrants. USBP collects data on apprehensions between ports of entry, OFO collects data on apprehensions at ports of entry, and ICE collects data on apprehensions in the interior of the United States. Because fingerprints on those apprehended are collected in all three data sources, DHS’s ENFORCE database can integrate data across the three sources at the individual level. However, conversations with representatives from DHS suggest that the linkages between the apprehensions records controlled by USBP, OFO, and ICE in the ENFORCE database are limited to uses that relate specifically to enforcement. Linkages across the data sources for broader analytical purposes would require approval from each of the three agencies, and the full database has not been widely used for analysis.
If one wants to analyze apprehensions at the border, integrating USBP and OFO apprehensions records is essential.4 To understand how U.S. enforcement, either at the border or in the interior, affects attempts at unauthorized entry, integration of the ICE and USBP databases is necessary. As discussed in Chapter 2, increasing enforcement in one border sector may
4 See also National Research Council (2011:50-51) for a discussion of how the immigration enforcement data published in the widely used DHS Yearbook of Immigration Statistics do not completely reflect the immigration enforcement activities undertaken by all relevant DHS agencies.