A Benefit-Cost Analysis of Tulsa Pre-K, Based on Effects on High School Graduation and College Attendance

This paper presents new benefit-cost estimates for the Tulsa universal preschool program. These calculations are based on estimated effects from previous papers of Tulsa pre-K on high school graduation rates and college attendance rates of students who were enrolled in kindergarten in Tulsa Public Schools in the fall of 2006. In this paper, educational effects from these prior papers are used to infer lifetime earnings effects and are compared with program costs. Our conservative estimates of earnings effects suggest that per pre-K participant, the present value of earnings effects in 2021 dollars is $25,533, compared with program costs of$9,628, for a benefit-cost ratio of 2.65. Compared to prior benefit-cost studies of Tulsa pre-K, this benefit-cost ratio is below what was predicted from Tulsa pre-K’s estimated effects on kindergarten test scores, but above what was predicted from Tulsa pre-K’s estimated effects on grade retention by ninth grade. This fading and recovery of predicted pre-K effects as children go through K–12 and then enter adulthood is consistent with prior research. It attests to the importance of the “soft skill” effects of pre-K and reminds us that short-term studies of pre-K provide useful information for public-policy decisions

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Does early childhood education help to improve high school outcomes? Results from Tulsa

Early childhood education contributes to improved school readiness but impacts on high school remain unclear. This study estimates the effects of Tulsa, Oklahoma\u27s universal pre-K and Head Start programs through the junior year of high school (in 2018/2019; N = 2902; M = 16.52, SD = .39; 48% female; 28% white, 34% Black, 27% Hispanic, 8% Native American). Propensity score weighted regressions suggest students who attended pre-K, but not Head Start, missed less school, were less likely to fail courses and be retained in grade, were more likely to take an Advanced Placement/International Baccalaureate course, but did not have higher test scores or grades. Subgroup analyses by race/ethnicity demonstrated some differences in the pattern of associations favoring students of color

Monitoring

The first requirement for successful implementation of precision agriculture in the plant production sector is to measure and map within-field spatial and temporal variability. This can be achieved by means of two main sensing categories, namely, remote sensing and proximal sensing, for characterizing both soils and crops. Each of these two categories has advantageous and shortcomings. This chapter discusses the potential of different sensing technologies to characterize within-field variability of soils and crops, by providing high sampling resolution data necessary for site-specific management of farm input resources (e.g., fertilizers, water for irrigation, seeds and pesticides). Each of the sensing methods presented are discussed in terms of (1) a brief introduction of a technology, (2) list of properties and associated accuracy and practicality and (3) application case studies for agricultural management