Bruce D. Baker

Do Principals with Stronger Academic Backgrounds Hire Better Teachers? Policy Implications for Improving High-Poverty Schools.

Principals play an important role in determining the quality of their schools by the selection of teachers. A preponderance of evidence from the economic and education policy literature indicates that teachers with stronger academic backgrounds produce better student outcomes. This article hypothesizes that school principals with certain attributes are likely to favor teachers with similar attributes to their own. This study uses the Schools and Staffing Surveys from 1993 to 1994 to test whether school administrators who attended more selective universities are more or less likely to hire teachers who attended more selective undergraduate institutions. Findings suggest that principals’undergraduate background matters when it comes to their recruitment, selection, and perhaps retention of teachers with strong academic undergraduate backgrounds, especially in high-poverty schools. Principals in high-poverty schools who attended highly or the most selective undergraduate institutions were 3.3 times more likely to hire teachers who attended similar institutions.

Academic Drift, Institutional Production, and Professional Distribution of Graduate Degrees in Educational Leadership:

Purpose: This article sheds light on some basic questions about the distribution of educational leadership preparation degree programs among different types of institutions and the distribution of advanced degrees, by type, exploring change over time and the relationship to regional labor market estimates. Method: We used data from five major national data sets (Institutional Postsecondary Education Data System, Survey of Earned Doctorates, Schools and Staffing Survey, National Center for Education Statistics Common Core of Data, and Census data) to explore the production of graduate degrees in educational leadership by institutions of higher education and the distribution of graduate degrees across building level leaders in K-12 public education systems. We used two institutional classification systems— the Carnegie Classification and the U.S. News & World Report higher education classification—to group postsecondary institutions by resources and rank. The time period for our analysis is 1990 to 2003. Findings: On the production side, we found that the number of graduate degree programs and degrees granted in educational leadership increased considerably from 1993 to 2003, with masters degree programs increasing by 16% and the number of masters degrees granted increasing by 90%. Degree production shifted by institutional type, with the role of research universities in producing masters, specialist, and doctoral degrees declining dramatically and Comprehensive colleges and universities showing over a four-fold increase in the share. Degree production fluctuates widely among states, unrelated to school population estimates, suggesting areas for further research and policy analysis.

Charter Schools, Teacher Labor Market Deregulation, and Teacher Quality: Evidence from the Schools and Staffing Survey.

This study uses teacher-level data from the Schools and Staffing Survey of 1999 to test whether teachers in charter schools have stronger academic backgrounds than their peers working in conventional public schools and whether state regulatory policies are associated with differences between public and public charter school teachers. Specifically, the authors estimate whether school type (public, public charter, independent private, private Catholic) is associated with the likelihood that a teacher attended a highly or most competitive undergraduate college. They find compelling evidence both that charter schools generally hire more teachers from more competitive undergraduate institutions than do conventional public schools and, furthermore, that relaxation of state teacher certification policies for charter schools increases the likelihood that charter schools hire teachers from more competitive undergraduate institutions. Findings are highly dependent on supply of teachers from competitive undergraduate institutions.

A comparison of conventional linear regression methods and neural networks for forecasting educational spending

This study presents an application of neural network methods for forecasting per pupil expenditures in public elementary and secondary schools in the United States. Using annual historical data from 1959 through 1990, forecasts were prepared for the period from 1991 through 1995. Forecasting models included the multivariate regression model developed by the National Center for Education Statistics for their annual Projections of Education Statistics Series, and three neural architectures: (1) recurrent backpropagation; (2) Generalized Regression; and (3) Group Method of Data Handling. Forecasts were compared for accuracy against actual values for educational spending for the period. Regarding prediction accuracy, neural network results ranged from comparable to superior with respect to the NCES model. Contrary to expectations, the most successful neural network procedure yielded its results with an even simpler linear form than the NCES model. The findings suggest the potential value of neural algorithms for strengthening econometric models as well as producing accurate forecasts. [JEL C45, C53, I21]

State Policies and Equal Opportunity: The Example of Gifted Education

This study explores the relationship between state policies, including state mandates and state aid allocations, and the distribution of educational opportunities. Specifically, we analyze the availability of and participation rates in programs for gifted and talented students using data from the Common Core of Data 1993–94 and the Schools and Staffing Survey 1993–94. Analyses herein suggest that program mandates and funding may be effective tools for increasing the distribution of opportunities for gifted children. However, models of both aid distribution and opportunity distribution indicate a tendency of states more significantly involved in gifted education, as indicated by mandates and funding, to promote regressive distributions of opportunities (greater availability in schools with fewer low-income students) through regressive distributions of aid (higher levels of aid to districts with fewer children in poverty). More specific case analyses, however, reveal that some states like Virginia may be taking steps to promote more neutral distributions of opportunities through more progressive allocations of state aid.

Gifted Children in the Current Policy and Fiscal Context of Public Education: A National Snapshot and State-Level Equity Analysis of Texas

Should we be concerned if educational resources for gifted and talented children vary widely from school to school, district to district, or state to state? Does it matter whether those resources are distributed unevenly by race or social class? This article begins by addressing the basic underlying question: Do gifted and talented children require supplemental resources at all? Two alternate theoretical perspectives are discussed. Under one, the standards-based cost function, there is no need to provide supplemental resources to gifted children, whereas under the alternative resource-cost model, there may be a reasonable need. Accepting the resource-cost model assumption that gifted children do require supplemental resources, this article then explores the distribution of gifted and talented programming opportunities across a national sample of students(The National Educational Longitudinal Study of 1988) and the distribution of fiscal and human resources to gifted education within the Texas, a state long considered a national leader in gifted education. National results show that Hispanic and Native American students are less likely to have access to gifted programs in the eighth grade than Asian/Pacific Island students. Also, students in the lowest socioeconomic status (SES) quartile are far less likely than students in the highest two SES quartiles to have access to eighth-grade gifted and talented programs. Students in districts that are large or suburban, or both, are more likely than those in urban districts to have access, and students in Southern and Western states far more likely to have access than those in the Northeast. Texas results show substantial variance (CV = 125%) in spending per gifted pupil across districts. They further show that district level fiscal resources and community economic characteristics influence spending and the availability of specialized personnel.

Evaluating State Funding for Gifted Education Programs.

As a follow‐up to a previous article in which Baker and Friedman‐Nimz (2002a) recommended that gifted education advocates should focus on improving state funding of gifted education, this article provides an overview of state school finance policies for gifted education and frameworks for evaluating those policies. The frameworks are then applied for evaluating state school finance policies as of 1998–99 and state aid allocated to local districts for gifted education in 2000. In that year, only Florida provided both sufficient and equitable support for gifted education, assuming general education conditions to be adequate as well as equitable in that state. Gifted education funding in Virginia, while less adequate than supplemental funding in Florida, continues to be a model of equitable distribution.

Measuring the Outcomes of State Policies for Gifted Education: An Equity Analysis of Texas School Districts:

Policy research in gifted education continues to maintain a relatively narrow focus, dealing primarily with policy inputs like the presence and design of written mandates or the existence or lack of state finding. Yet, little attention has been paid to the actual distribution of programming opportunities for gifted children. There continues to be a vast disconnect between policy analysts and gifted educators. The objective of this article is to help bridge that gap by (1) introducing a set of policy tools for measuring the outcomes of gifted education policies and (2) applying those tools to data on the distribution of gifted education spending and designated personnel in the state of Texas. According to generally accepted policy benchmarks, the analyses performed indicate (a) continued vast inequities across school districts in the availability of resources and (b) unacceptable correlations between student population characteristics, community wealth, and the availability of opportunity.

Can Flexible Non-Linear Modeling Tell Us Anything New about Educational Productivity?.

Abstract The objective of this study is to test, under relatively simple circumstances, whether flexible non-linear models — including neural networks and genetic algorithms — can reveal otherwise unexpected patterns of relationship in typical school productivity data. Further, it is my objective to identify useful methods by which “questions raised” by flexible modeling can be explored with respect to our theoretical understandings of educational productivity. This study applies three types of algorithm — Backpropagation, Generalized Regression Neural Networks (GRNN) and Group Method of Data Handling (GMDH) — alongside linear regression modeling to school-level data on 183 elementary schools. The study finds that flexible modeling does raise unique questions in the form of identifiable non-linear relationships that go otherwise unnoticed when applying conventional methods.

Tricks of the Trade: State Legislative Actions in School Finance Policy that Perpetuate Racial Disparities in the Post-Brown Era.

This article discusses the tricks of the trade that legislatures in formerly de jure segregated states might use to maintain racial funding disparities in existence before Brown and whether such ploys might be vulnerable to legal challenges. The first section provides an overview of modern school finance formulas and explains how legislatures in de jure segregated states might use state aid policies to produce racial funding disparities. The second section, through macrolevel, quantitative analyses, identifies those states where state aid is allocated such that minority populations are disadvantaged and where that disadvantage is subsequently reflected in total state and local revenue. The third section presents in‐depth case analyses of racial disparities in Kansas and Alabama that were identified through macrolevel analyses. The final section analyzes possible legal challenges that plaintiffs in those formerly de jure segregated states identified in our case analyses may bring to racially neutral state aid policies that place minority school districts at a financial disadvantage.