Determinants of Illegal Mexican Immigration into the US Southern Border States
A. Buehn, Stefan Eichler
Eastern Economic Journal,
No. 4,
2013
Abstract
We model illegal immigration across the US-Mexico border into Arizona, California, and Texas as an unobservable variable applying a Multiple Indicators Multiple Causes model. Using state-level data from 1985 to 2004, we test the incentives and deterrents influencing illegal immigration. Better labor market conditions in a US state and worse in Mexico encourage illegal immigration while more intense border enforcement deters it. Estimating the state-specific inflow of illegal Mexican immigrants we find that the 1994/95 peso crisis in Mexico led to significant increases in illegal immigration. US border enforcement policies in the 1990s provided temporary relief while post-9/11 re-enforcement has reduced illegal immigration.
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Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database
John M. Abowd, Ron S. Jarmin, Satkartar K. Kinney, Javier Miranda, Jerome P. Reiter, Arnold P. Reznek
International Statistical Review,
No. 3,
2011
Abstract
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments’ confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released establishment data are simulated from statistical models designed to mimic the distributions of the underlying real microdata. In this article, we describe an application of this strategy to create a public use file for the Longitudinal Business Database, an annual economic census of establishments in the United States comprising more than 20 million records dating back to 1976. The U.S. Bureau of the Census and the Internal Revenue Service recently approved the release of these synthetic microdata for public use, making the synthetic Longitudinal Business Database the first-ever business microdata set publicly released in the United States. We describe how we created the synthetic data, evaluated analytical validity, and assessed disclosure risk.
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Modelling Country Default Risk as a Latent Variable: A Multiple Indicators Multiple Causes Approach
A. Bühn, Stefan Eichler, Dominik Maltritz
Applied Economics,
No. 36,
2012
Abstract
We study the determinants of country default risk by applying a Multiple Indicators Multiple Causes (MIMIC) model. This accounts for the fact that country default risk is an unobservable variable. Whereas existing (regression-based) approaches typically use only one of several possible country default risk indicators as the dependent variable, the MIMIC model enables us to consider several indicators at once. The simultaneous consideration of sovereign yield spreads and Standard and Poor (S&P) ratings may help to improve the identification of the latent country default risk. Our results confirm most of the literature's main findings regarding important determinants of country default risk, refute others and provide new evidence to controversial questions.
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Smuggling Illegal versus Legal Goods across the U.S.-Mexico Border: A Structural Equations Model Approach
A. Buehn, Stefan Eichler
Southern Economic Journal,
No. 2,
2009
Abstract
We study the smuggling of illegal and legal goods across the U.S.-Mexico border from 1975 to 2004. Using a Multiple Indicators Multiple Causes (MIMIC) model we test the microeconomic determinants of both smuggling types and reveal their trends. We find that illegal goods smuggling decreased from $116 billion in 1984 to $27 billion in 2004 as a result of improved labor market conditions in Mexico and intensified U.S. border enforcement. Smuggling legal goods is motivated by tax and tariff evasion. While export misinvoicing fluctuated at low levels, import misinvoicing switched from underinvoicing to overinvoicing after Mexico's accession to the GATT and the North American Free Trade Agreement (NAFTA) induced lower tariffs.
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