The Impact of Legal Status on Immigrants’ Earnings and Human Capital: Evidence from the IRCA 1986

This paper analyzes the impact of IRCA 1986, a U.S. amnesty, on immigrants’ human capital development and labor market outcomes. Because of IRCA, the 1975- 1981 arrivals were all legalized by 1990. However, many of the 1982-1986 arrivals remained illegal. Using the California Latino immigrants in Census 1990, I find that the 1975-81 arrivals on average outperform the 1982-86 arrivals in men’s wage, women’s labor force participation rate, and English-speaking ability. This finding is not a general trend of labor market conditions, because the analysis using refugees and U.S.-born Latinos, which are two comparison groups without legal status issue, indicate no difference in outcomes between pre-1982 and post-1982 cohorts.

Does Latino Population Induce White Flight? Evidence from Los Angeles County

Whether local minority population induces white flight to suburbs or private schools is a question of interest to many researchers. However, empirically identifying the causality is difficult due to residential sorting. Relying on a residential sorting model, I assume that people live in the same neighborhood are homogenous. I identify the effect of local Latino population on white flight by using the cohort-to-cohort change in the ethnic composition within each neighborhood, which is a credibly idiosyncratic variation. Using Los Angeles Family and Neighborhood Survey, I find that for every 10 percentage points increase in the share of Latinos in a white child’s cohort and neighborhood, she is more likely to attend private school by 3 percentage points, or her household is more likely to move to a less Latino neighborhood in the next two years by 6-8 percentage points. Estimates imply that 88% of decrease in public school enrollment rate in California during 1990-2000 can be explained by white flight from Latinos.

On the Performance of Spectrum Sensing Algorithms using Multiple Antennas

In recent years, some spectrum sensing algorithms using multiple antennas, such as the eigenvalue based detection (EBD), have attracted a lot of attention. In this paper, we are interested in deriving the asymptotic distributions of the test statistics of the EBD algorithms. Two EBD algorithms using sample covariance matrices are considered: maximum eigenvalue detection (MED) and condition number detection (CND). The earlier studies usually assume that the number of antennas (K) and the number of samples (N) are both large, thus random matrix theory (RMT) can be used to derive the asymptotic distributions of the maximum and minimum eigenvalues of the sample covariance matrices. While assuming the number of antennas being large simplifies the derivations, in practice, the number of antennas equipped at a single secondary user is usually small, say 2 or 3, and once designed, this antenna number is fixed. Thus in this paper, our objective is to derive the asymptotic distributions of the eigenvalues and condition numbers of the sample covariance matrices for any fixed K but large N, from which the probability of detection and probability of false alarm can be obtained. The proposed methodology can also be used to analyze the performance of other EBD algorithms. Finally, computer simulations are presented to validate the accuracy of the derived results.Comment: IEEE GlobeCom 201

The Laplacian Eigenvalues and Invariants of Graphs

In this paper, we investigate some relations between the invariants (including vertex and edge connectivity and forwarding indices) of a graph and its Laplacian eigenvalues. In addition, we present a sufficient condition for the existence of Hamiltonicity in a graph involving its Laplacian eigenvalues.Comment: 10 pages,Filomat, 201