Minimum Wages and Teen Employment: A Spatial Panel Approach

The authors employ spatial econometrics techniques and Annual Averages data from the U.S. Bureau of Labor Statistics for 1990-2004 to examine how changes in the minimum wage affect teen employment. Spatial econometrics techniques account for the fact that employment is correlated across states. Such correlation may exist if a change in the minimum wage in a state affects employment not only in its own state but also in other, neighboring states. The authors show that state minimum wages negatively affect teen employment to a larger degree than is found in studies that do not account for this correlation. Their results show a combined direct and indirect effect of minimum wages on teen employment to be -2.1% for a 10% increase in the real effective minimum wage. Ignoring spatial correlation underestimates the magnitude of the effect of minimum wages on teen employment.minimum wage, teen employment, spatial econometrics

Spatial Econometric Issues for Bio-Economic and Land-Use Modeling

We survey the literature on spatial bio-economic and land-use modelling and review thematic developments. Unobserved site-specific heterogeneity is common in almost all of the surveyed works. Heterogeneity appears also to be a significant catalyst engendering significant methodological innovation. To better equip prototypes to adequately incorporate heterogeneity, we consider a smorgasbord of extensions. We highlight some problems arising with their application; provide Bayesian solutions to some; and conjecture solutions for others.spatial econometrics, bio-economic and land-use modelling, Bayesian solution, Land Economics/Use,

Local and global spatial effects in hierarchical models

Hierarchical models have a long history in empirical applications; recognition of the fact that many datasets of interest to applied econometricians are nested; counties within states, pupils within school, regions within countries, etc. Just as many datasets are characterized by nesting, many are also characterized by the presence of spatial dependence or spatial heterogeneity. Significant advances have been made in developing econometric techniques and models to allow applied econometricians to address this spatial dimension to their data. This paper fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models

A Note on Partitioning Effects Estimates Over Space

In this paper we provide an applied example for calculating the so-called effects estimates of LeSage and Pace (2009) for partitions of the impacts over space. While the partitioning of the impacts by orders of neighbors over space for the spatial autoregressive (SAR) model is a relatively straightforward procedure, care must be taken in the case of the spatial Durbin model (SDM). We provide an illustration of these calculations for both models using a widely available data set on voter turnout for the 1980 United States presidential election

Minimum Wages and Teen Employment: A Spatial Panel Approach

The authors employ spatial econometric techniques and Annual Averages data from the U.S. Bureau of Labor Statistics for 1990-2004 to examine how changes in the minimum wage affect teen employment. Spatial econometric techniques account for the fact that employment is correlated across states. Such correlation may exist if a change in the minimum wage in a state affects employment not only in its own state but also in other, neighboring states. The authors show that state minimum wages negatively affect teen employment to a larger degree than is found in studies that do not account for this correlation. Their results show a combined direct and indirect effect of minimum wages on teen employment to be -2.1% for a 10% increase in the real effective minimum wage. Ignoring spatial correlation underestimates the magnitude of the effect of minimum wages on teen employment

Bayesian estimation of the spatial Durbin error model with an application to voter turnout in the 2004 presidential election

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, we formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model

Impact Evaluation of Investments in the Appalachian Region: A Reappraisal

We evaluate the impact of the Appalachian Regional Commission’s investments on its members counties over almost fifty years. We apply different propensity score methods to find the most appropriate matching and to identify the effect of policy implementation in the most accurate way possible. The general evidence is that counties that received ARC funding had higher per-capita income growth compared to the control counties. Per-capita income growth rate in ARC counties grew an average of 5.5 percent over the entire study time period compared to the control counties. Employment grew significantly faster in ARC counties compared to the control counties for most of the study period. The average difference in growth rates between the counties that obtained ARC investments and those matched counties that did not receive ARC investments was approximately 4.2 percent

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN