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The National Center for Education Statistics (NCES) sponsored the National Education Longitudinal Study (NELS). The purpose of the NELS was to collect information that would help social scientists to determine the factors that affect the educational, social, and personal outcomes of children. The NELS is known for having exceptional measures of family background, characteristics of schools, and measures of educational attainment.
The study started with the survey of a nationally representative sample of eighth-graders in 1988. The survey was made nationally representative by first choosing a representative sample of schools (1,052) and then by randomly sampling students in each of the schools. One of the advantages of the NELS versus other social science datasets is that the NELS contains information from student questionnaires, teacher questionnaires, parent or guardian questionnaires, and school administrator questionnaires. The original survey contained information from 24,599 students, 5,193 teachers, 22,651 parents, and 1,052 schools (Curtin, Ingels, Wu, et al. 2002). Follow-up surveys of the students in the original survey were conducted in 1990, 1992, 1994, and 2000. Since there was attrition in the follow-up surveys, the NELS also surveyed some new students who were not in the original survey in order to maintain a nationally representative sample of students in each of the years. In addition to the information contained in the questionnaires, the NELS also contains transcript information, test scores, and zip code information.
Prior to the NELS, the NCES conducted the National Longitudinal Study of the High School Class of 1972 (NLS-72) and the High School and Beyond Survey (HS&B). Many of the components of the NELS were designed to facilitate comparisons between the surveys.
Major Studies and Findings
Hundreds of social science studies have used the NELS to evaluate educational outcomes, social outcomes, and personal outcomes. What follows is a discussion of a few strands of research where the NELS has proven to be extremely beneficial.
The literature on school choice has benefited a great deal from the availability of the NELS. Caroline Hoxby (2000) used the NELS to evaluate whether school choice improves school quality. She evaluates the effects of school choice by measuring the effects of Tiebout choice. Tiebout choice is the process by which individuals choose where to live based on the public-good characteristics of the area. She finds that increases in the amount of Tiebout choice leads to increased productivity, higher achievement, and less spending. Her results have recently been criticized by Jesse Rothstein (2005). She replied to these criticisms in Hoxby (2005).
The NELS has also been used as a resource for evaluating private-school enrollment. Robert Fairlie and Alexandra Resch (2002) use the NELS to evaluate enrollment in private schools and test whether private-school enrollment is related to the fraction of minority students enrolled in the local public school. They use several unique features of the NELS, in particular the information on the student’s zip code and information on the racial attitudes of the respondents. The authors find some evidence of “white flight” from public schools to private schools when the public schools enroll a large proportion of minority students.
Given the rich amount of information available on family background in the NELS, several studies have measured the effects of particular family characteristics on educational outcomes. Gary Painter and David Levine (2000) use the NELS to test whether divorce or remarriage of the student’s parents affects educational attainment. The authors found that divorce increased high school dropout rates and also affected out-of-wedlock births for young women.
The NELS has also been used to evaluate whether teacher characteristics affect student learning outcomes and student evaluations. Ronald G. Ehrenberg, Daniel D.
Goldhaber, and Dominic J. Brewer (1995) used the information in the teacher questionnaires to analyze whether the match between the teacher’s demographic information and the student’s demographic information influenced the amount the student learned in the classroom or the teacher’s evaluation of the student. The authors found that there was little correspondence between the teacher’s demographics and learning outcomes but that there appeared to be a relationship between the teacher’s demographics and the teacher’s evaluations of demographically dissimilar students. Thomas Dee (2005) revisited the information in the teacher questionnaires and found that teachers who were demographically dissimilar to the student being evaluated were more likely to rate the student unfavorably.
The NELS has also been used to document the extent of drug usage by teenagers. Philip DeCicca, Donald Kenkel, and Alan Mathios (2002) used the NELS to evaluate the extent of teenage smoking and the effects of cigarette taxes on teenage smoking. After utilizing the panel nature of the NELS, they found that taxes were not strongly related to teenage smoking. Thomas Dee and William Evans (2003) used the information contained in the NELS to document the amount of teenage drinking and the correlation of teenage drinking to educational attainment.
The most recent and final wave of the NELS was conducted in 2000. The 2000 data include information on family formation, educational attainment, and earnings. Future studies will most likely analyze the data included in the most recent follow-up and compare the outcomes of respondents of the NELS to the outcomes of the respondents to the HS&B survey and the NLS-72 survey.
Future studies may also use the large amount of information on educational outcomes available in the NELS. In particular, studies will most likely consider the determinants of whether a student attends college, whether the student persists in college, whether the student graduates from college, and what types of curriculum the student chooses to study in college. In addition, these studies will also most likely analyze the effects of these educational attainment measures on earnings.
In the future, we might also see studies that investigate how characteristics of the student’s family and area where the student grew up affect where the student chooses to live and work as a young adult. The one limitation of the NELS is that there are no other planned follow-ups, so the ability to measure the effects of factors during childhood on outcomes in adulthood will be limited to only those outcomes that are observed when the individual is approximately twenty-five years old.
- Curtin, Thomas , Steven J. Ingels, Shiying Wu, et al. 2002. User’s Manual National Education Longitudinal Study of 1988 Base-Year to Fourth Follow-Up Data File User’s Manual. U.S. Department of Education. Office of Educational Research and Improvement. NCES 2002-323.
- DeCicca, Philip, Donald Kenkel, and Alan M 2002. Putting Out the Fires: Will Higher Taxes Reduce the Onset of Youth Smoking? Journal of Political Economy 110 (1): 144–169.
- Dee, Thomas 2005. A Teacher Like Me: Does Race, Ethnicity or Gender Matter? American Economic Review 95 (2): 158–165.
- Dee, Thomas , and William N. Evans. 2003. Teen Drinking and Educational Attainment: Evidence from Two-Sample Instrumental Variables Estimates. Journal of Labor Economics 21 (1): 178–209.
- Ehrenberg, Ronald , Daniel D. Goldhaber, and Dominic J.Brewer. 1995. Do Teachers’ Race, Gender, and Ethnicity Matter? Evidence from the National Education Longitudinal Study of 1988. Industrial and Labor Relations Review 48 (3): 547–561.
- Fairlie, Robert W., and Alexandra Resch. 2002. Is There “White Flight” into Private Schools? Evidence from the National Education Longitudinal Survey. Review of Economics and Statistics 84 (1): 21–33.
- Hoxby, Car 2000. Does Competition Among Public Schools Benefit Students and Taxpayers? American Economic Review 90 (5): 1209–1238.
- Hoxby, Car 2005. Competition Among Public Schools: A Reply to Rothstein (2004). National Bureau of Economic Research Working Paper No. 11216.
- Painter, Gary, and David Levine. 2000. Family Structure and Youths’ Outcomes: Which Correlations Are Causal? Journal of Human Resources 35 (3): 524–549.
- Rothstein, Jesse. 2005. Does Competition Among Public Schools Benefit Students and Taxpayers? A Comment on Hoxby (2000). National Bureau of Economic Research Working Paper No. 11215.
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