State accountability systems have been a primary school reform initiative in the US for the past 20 years, but often produce unintended negative consequences. In 2004, the Texas Education Agency (TEA) implemented the Performance Based Monitoring and Analysis System (PBMAS), which included an accountability indicator focused on the percentage of students found eligible for special education under the Individuals with Disabilities Education Act (IDEA), the nation’s special education law. From 2004 through 2016, the percentage of students found eligible for special education in Texas declined significantly, while the national rate held constant. Eventually, the U.S. Department of Education (ED) investigated TEA and the statewide implementation of IDEA. The purpose of this study is two-fold: (a) to evaluate the potential impact of the the PBMAS indicator on manipulation of special education identification practices; and (b) to describe how the indicator may have influenced school and district personnel. We highlight several concerning trends in state and district data and, through an analysis of publicly available reports from the ED, show how district and school personnel knowingly and unknowingly acted in ways that delayed and denied special education to potentially eligible students. We conclude with recommendations for TEA and implications for future research and policy. © 2019, Arizona State University. All rights reserved.

We evaluate the cost-effectiveness of two early childhood interventions that use instructional coaching and parent coaching as levers for improvement. The study design allows us to compare the individual effects of each intervention as well as their combined effect on student outcomes. We find that teachers receiving instructional coaching improve their use of evidence-based instructional practices, while families receiving parent coaching show increases in numerous responsive parenting behaviors associated with positive child outcomes. Both interventions demonstrate positive impacts on students, but only parent coaching shows statistically significant effects across a range of student outcomes. Instructional coaching alone is substantially less costly and may therefore be the most cost-effective of the three treatment conditions; however, small sample sizes limit our ability to reach definitive conclusions. Policy simulations suggest that implementing these interventions could raise the overall cost-effectiveness of Head Start by at least 16 percent. © 2019 by the Association for Public Policy Analysis and Management

Scholars have not reached consensus on the best approach to measure state school finance equity. The regression-based approach estimates the relationship between district poverty rate and funding level, controlling for other district cost factors. A second commonly used approach involves estimating the weighted average funding level for low-income students or other subgroups. Meanwhile, policymakers have preferences for their own data systems and poverty indicators when reading reports and assessing progress. We constructed parallel, district-level panel data sets using data from the California Department of Education and the U.S. Census. We estimated changes over time in district-level school finance equity under California's Local Control Funding Formula, using multiple school finance measurement approaches, with each of the two data sets. Our results show that different methods and analytic choices result in policy-relevant differences in findings. We discuss the implications for policy and future research.

Price differentials, among other factors, persuade many residents of Northern Mexico to shop in the Southwestern United States border region. Employment patterns in the latter region are studied using a set of control variables and two indicators that are likely to influence cross-border shopping patterns. The first is a real exchange rate index, which captures changes in relative prices in the United States and Mexico. The second is real per capita gross state product in Mexican states adjacent to the international boundary. Both of these variables are found to impact retail and restaurant employment in the United States border zone, confirming that cross-border shopping influences labor market conditions in that region. Furthermore, the estimated elasticities vary across retail sub-sectors in ways that are generally consistent with prior research. Overall, the results suggest that economic setbacks in Northern Mexico and real peso depreciations are likely to have adverse consequences for important sectors of border economies in the United States.

Links between electricity consumption and economic growth are fairly well documented for national economies, but less so for urban economies. The analysis of such relationships at the sub-national level of aggregation can potentially offer a useful complement to national-level research. This study examines the electricity-growth nexus in El Paso, Texas, while also considering the roles of capital stocks and employment. Testing suggests the presence of cointegrating relationships and a vector error correction model is estimated. Granger causality tests reveal the absence of causality between electricity consumption and personal income, implying that energy conservation efforts will have a neutral effect on economic growth. Furthermore, the results indicate that causality runs from the capital stock and employment to both personal income and electricity consumption. This echoes previous research regarding the importance of accounting for capital and labour factors of production in studies of aggregate electricity utilization and economic performance. The methodology used in this analysis to develop a broad synthetic measure of the urban capital stock, including various categories of public infrastructure, can also be applied to other regions and urban economies.

This study evaluates the accuracy of previously published econometric forecasts for seven lodging sector variables that measure hotel activity in El Paso, Texas. The hotel forecasts have been generated annually using an econometric model of the El Paso metropolitan economy from 2006 forward. Predictive accuracy is evaluated relative to random walk benchmarks. Assessment is completed using both descriptive forecast error summary statistics as well as formal statistical tests. The econometric model outperforms the random walk benchmarks for a majority of the variables analyzed. However, statistical tests of forecast error differentials do not yield conclusive evidence in favor of the econometric historical track record. Tests of directional forecast accuracy also produce mixed results. Although the structural econometric model of hotel business conditions appears to provide useful predictive information, analysts and planners should also monitor recent history closely.

Since the emergence of middle schools as distinct educational settings in the 1960s, proponents of the model have advocated for structures and approaches that best meet the particular developmental needs of young adolescents. Middle school researchers have developed frameworks of best practices for schools that have been widely, if not uniformly, adopted. However, there is a paucity of large-scale quantitative research on the efficacy of such best practices. In this study we used state-level administrative data from Texas to estimate the school-level contribution to standardized test scores in math and language arts, along with absenteeism. We then regressed these value-added quantities on indicators of middle school structures, along with research-supported predictors of school efficacy. Results showed that schools with fewer classes in the school day and higher quality teachers performed better, among other indicators. Findings from models using the campus contribution to absenteeism were similar. These results indicate that while elements of the middle school model may help transform individual schools, the equitable distribution of resources and the undoing of de facto segregation are vital to the success of all young adolescents.

Abstract Although funding STEM graduate students in the United States is a multibillion-dollar endeavor annually, we have little information on how students' experiences and outcomes differ across funding mechanisms. The purpose of this NSF-funded research project is to understand how to spend graduate student funding wisely to ensure a variety of student experiences and an optimal set of outcomes, including equal access for all students to the financial, academic, professional and social resources that support success in graduate study. Specifically, the five-year research project is addressing the following research questions: 1. How do graduate students' funding mechanisms vary across their incoming characteristics (i.e., demographics and bachelor's or master's institutional type, location, or affiliation) and STEM discipline? 2. What is the relationship between graduate students' funding mechanisms and their post-doctoral outcomes, including time to degree, field of first job, job placement, and salary of first job? 3. How does the relationship between graduate students' funding mechanisms and their post-doctoral outcomes vary across their incoming characteristics (i.e., demographics and bachelor's or master's institutional type, location, or affiliation) and STEM discipline? 4. What do STEM graduate students, faculty members, and administrators perceive to be the benefits and drawbacks of various graduate student funding mechanisms? How does each group make decisions about offering or accepting offers of different funding mechanisms? 5. How does funding mechanism impact STEM graduate students' experiences, socialization, identity formation, and other factors previously shown to contribute to overall success? We are following a mixed methods approach to address these research questions. Our quantitative data are comprised of the complete Survey of Earned Doctorates, which is a restricted-use data set that is characterized by a greater than 90% response rate of all U.S. doctorate earners for the past several decades. The data set includes variables that will address the first three research questions. To address the final two research questions, the team is conducting a case study analysis of three STEM departments at each of eight different institutions. This qualitative aspect of the study seeks to understand how funding mechanisms enable or inhibit experiences and outcomes for students from the perspectives of department administrators, faculty advisors, and graduate students. We have been developing variables to quantify engineering graduate programs' doctoral student funding portfolios and to determine the extent to which funding portfolios vary. Our poster will illuminate how there is considerable variation in graduate student funding across engineering—institutions cluster into different “modes” of funding portfolios that do not neatly map onto common institutional categories, such as Carnegie classification and institutional control. We delve deeper into exploring this variation by comparing funding portfolios across engineering disciplines. These early research findings will inform future studies on understanding how graduate student funding corresponds to student experiences and outcomes, including access for students to the financial, academic, professional and social resources that support success.