Is there a Causal Effect of High School Math on Labor Market Outcomes?

Size: px
Start display at page:

Download "Is there a Causal Effect of High School Math on Labor Market Outcomes?"

Transcription

1 Is there a Causal Effect of High School Math on Labor Market Outcomes? Juanna Schrøter Joensen Department of Economics, University of Aarhus Helena Skyt Nielsen Department of Economics, University of Aarhus May 5, Work in progress - Abstract In this paper we exploit a high school reform to identify the causal effect of advanced high school Math on labor market outcomes. Students who take advanced Math courses have more favourable labor market outcomes. We find some evidence of a causal relationship for the students who are induced to choose Math after being exposed to the reform which meant that they were able to combine advanced Math with a more flexible choice of additional advanced courses. The effect partly stems from the fact that those students end up with higher education and subjects of education leading to well-paid jobs. JEL Classification: I20, J24. Keywords: Math, High School Reform, Instrumental Variable, Local Average Treatment Effect. Financial support from the Danish Research Agency is gratefully acknowledged. We thank participants at CEPR/IFAU/Uppsala University-, DGPE- conferences, and seminar participants at University of Aarhus for comments on earlier drafts. The usual disclaimer applies. 1

6 earnings equation is assumed linear and additive in the explanatory variables: y i,13 = β 0 + β 1 X i,13 + β 2 W i,0 + δmatha i + ε i, (1) where X i,13 is a vector of individual characteristics of individual i as measured 13 years after starting high school, including highest completed education and actual labor market experience; W i,0 is a vector of individual and family background variables of individual i, including the mother s as well as the father s highest completed education and income as measured in the year before the individual entered high school. It also includes time invariant individual background characteristics such as gender and high school GPA. The underlying latent variable for the selection equation is also assumed linear and additive in the explanatory variables. Individual i chooses the Math level, j = A, B, that provides the greatest value, V ij, where A denotes the advanced level. That is, individual i chooses high level Math if and only if V ia > V ib. By normalizing the value of choosing lthe less advanced Math courses, V ib, to zero, we obtain the selection rule: MathA i = 1 [α 0 + α 1 X i,13 + α 2 W i,0 + θz i + u i > 0], (2) where Z i is an instrumental variable which is either a reform indicator or a continuous distance measure. We assume that: µ µµ εi 0 σ2 ρ N 2,. u i 0 ρ 1 Since we assume that the error terms are independently and identically distributed across individuals, the Stable Unit Treatment Value Assumption (SUTVA) is implicitly satisfied. This assumption implies that potential earnings of each individual i is unrelated to the Math level of other individuals. Hence, we assume away general equilibrium effects due to for instance a general rise in the Math level in the labor force. In our basic case, we apply the two step IV estimator, where the probit estimates of the Math selection equation are obtained in the first stage: P (MathA i =1 X i,13,w i,0,z i )=Φ (α 0 + α 1 X i,13 + α 2 W i,0 + θz i ) From these estimates, the hazard for each individual is computed as: 3 γ 1i = φ( α 0+ α 1 X i,13 + α 2 W i,0 + θz i) Φ( α γ i = 0 + α 1 X i,13 + α 2 W i,0 +, if MathA θz i) i =1 γ 0i = φ( α0+ α1xi,13+ α2wi,0+ θz i) 1 Φ( α 0 + α 1 X i,13 + α 2 W i,0 +, if MathA θz i) i =0 (3) Then consistent estimates of the structural parameters β 0, β 1, β 2,andδ are obtained in the second stage by augmenting the outcome equation (1) with the hazard and estimating it by Ordinary Least Squares (OLS). 3 This is equivalent to the generalized residuals of the probit model describing the high level Math choice, cf. Gourieroux, Monfort, Renault and Trognon (1987). 6

7 The question immediately arises, which variables should be included among the explanatory variables? Post secondary schooling is most likely affected by Math qualifications from high school. As discussed by Altonji (1995), Math courses may induce individuals to take longer educations. If Math courses make longer education more profitable, for instance because they are cheaper to acquire because lower efforts are needed, this effect should be attributed to the Math course. However, on the other hand, the opportunity cost of higher education should be deducted, which means a number comparable to the real interest should be deducted (Altonji uses 4% as a maximum on the real interest). By choosing the high level Math course in high school, individuals may enhance the probability of finalizing educations leading to high paying occupations, which would increase δ. First of all, it might be easier to complete an education within the fields of Engineering, Natural Science or Economics, and secondly, the high school graduates simply extend their choice set when it comes to higher education. 4 We are able to distinguish between the direct effect and the total effect (direct plus indirect effect) of Math. 5 The direct effect of Math on earnings stem from Math affecting for instance logical reasoning and increasing cognitive skills which are useful in most occupations. The indirect effect would go through an enhanced probability of finalizing favorable university educations, and this effect would disappear when we include length and subjects of education in X i,13. In order to give high level Math full credit for all these effects, the length and type of higher education should be left out of the regression. In order to obtain an overview of the relationships, we estimate δ with and without controls for length and type of higher education. 6 A similar issue arises with respect to testscores (GPA), therefore we also estimate δ with and without GPA, because they may induce a positive or negative bias depending on the relationship between GPA, Math courses and unobserved ability, cf. Levine and Zimmerman (1995). The GPA is measured during high school, with highest weight on the grades obtained in the last year of high school. Hence the GPA may be affected by high school courses attended. However, Alexander and Pallas (1984) show that test scores at high school graduation, i.e. in 12th grade, are dominated by the effect of aptitude and prior achievements up until 9th grade, rather than learning, experience and achievements during high school. Thus we are confident that high school GPA is a reliable measure of aptitude and initial ability at high school entry, and not to any severe extent directly affected by high school courses. Parental background variables are measured in the year of high school entry, thus we do not have the same concern that they are influenced by student 4 By passing high level Math courses they can be admitted to university educations within Natural Science and Engineering without supplementary coursework. Up until 1990, even students with medium level math would be admitted without supplementary coursework, although they might have had a harder time following the courses. 5 This was done by Ackerman (2000), who found that one third of the total effect of Math on earnings is an indirect effect running through further education. 6 This approach was also used by Levine and Zimmerman (1995) and Rose and Betts (2004). 7

8 achievements and course choices during high school. We employ a broad view of human capital investment and allow family background to influence both labor market ability and Math ability. Thus we include family background variables both among the observed abilities in the outcome equation, i.e. the factors that shift the marginal rate of return to investment in high level Math, and among the observed abilities affecting the selection into high level Math, i.e. the factors that shift the marginal supply of funds scedule. The explanatory variable of primary interest is the indicator for whether individual i passed the high level Math course in high school, MathA i. This variable is potentially endogenous, since there most likely exist unobserved variables affecting both future earnings and the choice of high level Math. Hence, endogeneity bias could arise due to individuals self-selecting into Math courses based on expected future earnings gains (selection on outcomes) or due to unobserved ability bias (selection on unobservables). Firstly, the choice of high level Math may be endogenous in the earnings equation if individuals who aspire to go into a high-paying occupation, e.g. as Engineers, choose the high level Math course in order to enhance the possibilities to succeed as an Engineer, cf. Levine and Zimmerman (1995). Secondly, unobserved ability bias arises if Math level depends on unobserved ability, e.g. if only the most talented individuals choose to attend the high level Math course and we fail to control for talent, then the estimate of δ will be upward biased. To the extent that GPA approximates the unobserved talent, including GPA as an explanatory variable corrects for the unobserved ability bias. However, this correction relies on the assumption that attending the high level Math course has no effect on GPA. 7 The instrumental variables approach that we use deals with both sources of endogeneity. 2.1 Instrumental Variables Estimation of LATE We use three alternative instrumental variables, Z j i,j =1, 2, 3, which are based on the policy reform of 1988 and the experimental curricula introduced prior to the reform. The reform and the experimental curricula work as exogenous cost shocks inducing more students to choose high level Math. Hence, the reform may be seen as a natural experiment from which we obtain a source of individual variation in students Math level, which does not influence the outcomes of interest. Before the reform, advanced Math was only supplied in a package with advanced Physics, which scared away a lot of potentially Math interested students. Some high schools got an exemption and were allowed to try out an experimental curriculum where advanced Math could be combined with Chemistry. We create three different instruments each of which exploits the exogenous variation in the exposure of students to the possibility of combining advanced Math courses with a various set of other advanced courses. Two of our instruments are binary reform indicators, whereas the third instrument is a continuous distance measure. In the following, we treat the instrumental variable, Z i, as a binary indicator knowing that the generalization to 7 See for example Hansen, Heckman and Mullen (2004) for a more comprehensive discussion of ability bias, and the effect of schooling on test scores. 8

9 the continuous case is straightforward. 8 For simplicity, we suppress superscript j, and let conditioning on the two matrices of explanatory variables, X i,13 and W i,0 be implicit. Associated with the two possible outcomes of Z i are counterfactual treatments, MathA i1 and MathA i0. These are the treatment statuses we would observe if i were exposed to the reform (Z i =1)ornot(Z i =0), respectively. Although, for each individual we only observe one of these. Likewise, there are counterfactual outcomes, y i1 and y i0, associated with the two possible treatment statuses. Where y i1 denotes the earnings of individual i if participating in the high level Math course (MathA i =1), and y i0 denotes the earnings if not (MathA i =0). Thus, the causal effect of the high level Math course on earnings is given by y i1 y i0. Since we observe only one of these (y i1 or y i0 )foreach individual we need to impose assumptions in order to identify the treatment effect of high level Math. To sum up, for each individual in a random sample of high school students, we observe (Z i,matha i,y i ), where the observed treatment indicator depends on whether the individual i was exposed to the reform or not, MathA i {MathA i1,matha i0 }, and the observed earnings depends on whether individual i has high level Math or not, y i {y i1,y i0 }. Instrumental variables estimation identifies the local average treatment effect (LATE), which is the causal effect of high level Math on earnings for those who are induced to choose high level Math because they were exposed to the reform. LATE has the advantage of being estimable using instrumental variables under weak conditions. But it also has two potential drawbacks: First of all, it measures the effect of treatment on a generally unidentifiable subpopulation, and secondly, the definition of LATE depends on the particular instrumental variable that we have available. In order to identify the LATE by use of a binary instrument, two conditions have to be satisfied: (a) Existence of instrument, and (b) Monotonicity. 9 In our framework, condition (a) is satisfied if the coefficient to Z i is strongly significant in the selection equation, and independent of ε i and u i. That is, the reform should influence earnings only through its effect on the probability of obtaining high level Math. This condition ensures that Z i is a proper instrument, and is very reasonable in our application, however, it is inherently untestable. Condition (b) guarantees identification of LATE by only allowing the instrument to affect the choice of Math level in a monotone way. More formaly, we assume that MathA i1 MathA i0. That is, since individuals on average are more likely to choose high level Math if they are exposed to the reform, E [MathA i Z i =1]>E[MathA i Z i =0], then anyone who would choose high level Math given that they were exposed to the reform would also have chosen high level Math had they not been exposed to it. In other words, when Z i switches from 0 to 1, we only have that students switch from not choosing the high level Math course to choosing the high level Math course (not the other way around). This seems reasonable because all pre-reform options 8 See e.g. Heckman, Lalonde and Smith (1999). 9 See e.g. Imbens & Angrist (1994) for technical details. 9

10 are also available post-reform options. Hence, the IV estimator of the causal effect of high level Math on earnings corresponds to LAT E = E [y i1 y i0 MathA i0 =0,MathA i1 =1]= While the estimated parameter has predictive power for the subpopulation complying with the instrument, there is no reason to believe that the LATE corresponds to the average treatment effect in the population, AT E E [y i1 y i0 ], i.e. the expected effect of participating in the high level Math course for a randomly drawn high school student, or to the impact of treatment on the treated, TT E [y i1 y i0 MathA i1 =1], i.e. the expected effect of participation in the high level Math course for those who actually participated. 10 The instrumental variables method is very effective in estimating average treatment effects if a good instrument for treatment is available, and the reform based instruments are indeed strong: they predict the treatment of high level Math, they are also likely to be unrelated to unobserved heterogeneity (e.g. Math ability), and therefore redundant in the structural model E [y X, W, Z], i.e. any earnings differences between the individuals who are exposed to the reform and those who are not are assumed to be captured by the observed variables X and W. The validity of the instruments and the identifying assumptions are discussed in the next subsection, and Appendix B contain additional evidence about the validity of the instruments. According to Heckman (1997), economically meaningful IV estimates can be found using instruments measuring policy interventions that induce some people to switch participation status while leaving non-switchers unaffected. A zero social cost would then allow us to interpret the LATE as the effect of the marginal policy change on per capita earnings. 2.2 Three reform-based instrumental variables Below we describe the three instruments in turn and discuss whether the assumptions are expected to be satisfied. The first instrumental variable, Zi 1,is equal to one if the individual started high school in 1988, and zero otherwise. 11 Students who start high school in 1988 are exposed to the reform which means that they are free to combine advanced Math with any other advanced course, e.g. Physics, Chemistry, English, Biology, Music, Political Science, etc. Students who start in high school in 1987 are allowed to take advanced Math if they also take the advanced Physics course, unless they are at a high school which has implemented an experimental curriculum in which case they can replace the advanced Physics course with advanced Chemistry. Hence, the IV approach using Zi 1 identifies the LATE for individuals who are induced to choose advanced 10 In a homogenous effects model or in a model with heterogenous effect which are not reacted upon by the individuals, the three parameters are identical. In the case of a continuous instrument, the LATE may recover the TT parameter if it is estimated for all possible values of the instrument, see Heckman, Lalonde and Smith (1999). 11 Using the terminology of Rosenzweig and Wolpin (2000), this instrument belongs to the group of instruments applying exogenous variation in birth dates. E[y i Z i =1] E[y i Z i =0] E[MathA i Z i =1] E[MathA i Z i =0]. 10

14 constant. In particular, 58% of the individuals who chose to enter high school in 1987 chose the traditional academic high school track, compared to 57% in However, the reform clearly affects the traditional high school students choice of high level Math. Only 27% of the students choose high level Math in the 1987 cohort as compared to 54% in the 1988 cohort. A higher proportion of male high school students choose high level Math. The proportion of males who start high school in 1987 and choose high level Math is 42%, as opposed to 73% of males in the 1988 high school cohort. For females these figures are 16% and 40%, respectively. Table 2 shows descriptive statistics categorized by the indicator of high level Math, and by high school cohort within each Math level. It reveals that high level Math students have very favorable labor market and educational outcomes. They have higher GPA from high school. More high level Math students attend and complete higher education at any level. Aside from having higher completion rates, high level Math students also complete a given educational level at a faster rate. Hence high level Math students seem to be more efficient in the higher educational system. In addition, they are also more successful after labor market entry where they are less unemployed and earn more. High level Math students log earnings are 0.28 higher than earnings of other high school students. As a point of reference, we note that the Math log earnings gap is as large as the gender log earnings gap for high school graduates. The Math earnings gap is larger within the 1987 high school cohort than within the 1988 cohort. Hence we set out to find out whether this huge earnings gap is due to the fact that students become more productive on the labor market because of the high level Math course, or it is only due to selection of the students with most favorable unobservable characteristics into the high level Math course. 4 Results The key explanatory variable in the empirical analysis is the indicator for completing the high level Math course in high school, MathA i. Aside from presenting the results using IV estimation with our three reform-based instruments, we replicate previous studies to the maximum feasible extent. This means that we also estimate the earnings equation (1) by OLS using the total estimation sample, the 1987 high school cohort, and the 1988 high school cohort, respectively. Furthermore, we estimate (1) including high school Fixed Effects (FE), hence controlling for unobserved differences across high schools, e.g. in quality of the student body and teachers. At last, we also apply a variant of the instrument suggested by Altonji (1995) which is the mean participation rate in the high level Math course at the students high school. 14

15 4.1 Estimation of the causal effect of Math on Earnings The key outcome variable is (yearly) log earnings 13 years after starting high school. 20 The preferred income measure would be lifetime income. However, since the individuals in our sample are relatively young (in their thirties), it is not possible to construct a sensible measure of lifetime income. We believe that the chosen income measure is sensible because individuals have on average been on the labor market for less than five years, and hence they are likely to have settled into careers. A separate note on variables reflecting higher education is needed. As argued in Section 2 above: On the one hand, higher education is a confounder of the direct effect of high school Math on earnings. On the other hand, it is important to control for it, since otherwise we might give high school Math credit for income increases that result from investments of time (and money) in higher education. To take this into account we run all our estimations with and without controlling for further education. We sequentially include two sets of mutually exclusive indicator variables for highest completed education - one for the level of education categories and one for the level-subject categories, since the cost of adegreeaswellastheeffect of high level Math is presumably not independent of subject of education. 21 Similarly, we also run all estimations with and without the GPA proxy for aptitude, cf. discussion in Section 2 above. To account for differences in earnings profiles, we control for (linear quadratic) actual labor market experience in all specifications with additional explanatory variables. In principle, actual labor market experience could also be considered a confounder of the direct effect of high level Math (to the extent that high level Math affects employment). High level Math students are indeed more employed, cf. Table 2, but in the estimations the treatment effect of the high level Math course is not significantly affected by including (or excluding) actual labor market experience Results of estimation Estimation results for the total estimation sample of high school graduates entering high school in 1987 and 1988 are shown in Table 3. Estimates of the effect of high level Math on log earnings for all specifications and all estimation strategies are presented along with standard errors (in parantheses) and indications of significance. In addition to OLS, FE and estimation by use of Altonji s instrument, Table 3 presents the results which applies instrument #1, which is discussed in the next subsection. The results from OLS reveal that students who complete the high level Math course in high school receive 0.28 log points higher earnings. Some of this effect 20 We have done the analysis using three different income measures: gross income, net income, and labor market income. Furthermore, we have looked at income 12, 13 and 14 years after starting high school, respectively. Our qualitative results are robust to the change of income measure. 21 We have also estimated a specification with years of education, these results were similar, hence not reported. 15

16 is an indirect effect through parental background variables, labor market experience and gender. Controlling for these background variables, the Math earnings gap is reduced to Likewise, some of the total effect is due to aptitude. Controlling for GPA further reduces the income premium by another four percentage points. However, if further educational choices are also controlled for, theincomepremiumisnotaffected by controlling for GPA. This is what would have been expected given the consistent evidence of individuals self-selecting into educational levels based on ability, see e.g. Willis and Rosen (1979). Furthermore, GPA is used as an admission criteria to the universities and other institutions of higher education. Since GPA is used by educational institutions to screen individuals, it can directly affect further educational choices. The effect goes down from 0.17 to 0.10 after controlling for higher educational level choices, and further down to 0.07 when also controlling for educational subjects. Hence, more than half of the total effect of high level Math on earnings is an indirect effect running through higher education. This indicates that the potential gains from having high level Math are not independent of which length and subject of higher education individuals choose. The result is intuitively clear, since individuals with high level Math will probably be more successful in completing a Science education and other technical educations that traditionally lead to a better paid job. On the other hand, high level Math students are probably also less likely to choose postsecondary education within Humanities that traditionally lead to lower paid jobs. 22 However, it is not clear to which extent, high level Math students have learned skills that make them better able to successfully complete postsecondary education within the Humanities. Some of the skills learned in the high level Math course like clarity in expressions, logical reasoning and inference, as well as imagination and ingenuity, should prove to be powerful tools for completing a higher education in any field. 23 The separate OLS regressions for each high school cohort, reveal that the high level Math earnings premium is higher for the 1987 high school cohort. The high level Math students in the 1987 experience an earnings gain of 34 percent, which diminishes to 11 percent when controlling for all observable background variables. On the other hand high level Math students in the 1988 high school cohort have a significant earnings premium of 29 percent, which is reduced to 7 percent when controlling for all observable background variables. The estimated effects of the high level Math course from the FE are slightly lower than the OLS estimates. Nevertheless, the pattern is similar. The income premium from having high level Math is 27 percent in total, but diminishes to 7 percent when we control for all background variables, including level-subject categories of higher education. This shows that selection of the most talented students into the best quality high schools is not that important. This was also to be expected, since the centralized control of the high schools means that the course quality should be similar across high schools, cf. Section 3 above. 22 Descriptive statistics confirm these two presumptions (not reported, but available upon request). 23 See the elaboration by Ackerman (2000), and Appendix A in this paper for further details on the objectives of the high level Math course. 16

17 Applying the proportion of high level Math students in the year group in the student s high school as an instrument for the student s own Math level we find smaller estimates of the effect of high level Math on earnings. The estimated IV coefficient measures the effect of those who are induced to take the advanced Math course because they move to another high school where marginally more students attend the high level Math course. The total effect is 0.11, but diminishes to 0.05 when controlling for parental background caracteristics, gender and experience, and becomes insignificant when controlling for either GPA or further education. Although the instrument has a high preditive power of attending the high level Math course, it is not considered a clean instrument for several reasons (cf. Section 1) Instrument #1 The last (lower right) panel in Table 3 presents the results which applies instrument #1. Instrument #1 is an indicator for belonging to cohort 1988 and hence being exposed to the actual reform of Table 4 shows a full set of estimation results for one of the specifications. The estimated coefficients of the selection equation show that the instrument is very strong with a t-statistic above 50. Therefore the precision on the estimate of the effect of the endogenous variable is accordingly high. The IV estimates of the causal effect of high level Math on earnings using our instrument, show no effect of high level Math on earnings. Thus the high school students who are induced to choose high level Math because of the wider set of non-science options do not gain (in terms of earnings) from attending the extra Math course. Hence, the seemingly large effect of high level Math on earnings seems to be a selection effect of individuals with more favorable unobservable characteristics into the high level Math course, and not acausaleffect. 24 To explore the selection effect in more detail, and since part of the indirect effect of Math on earnings runs through gender, we estimate all specifications on the subsamples of males and females, respectively. The results for males are shown in Table 5a and for females in Table 5b. We find high estimated coefficients to the high level Math course indicators for males and much smaller coefficients for females. The OLS estimates for males range from 0.28 (with no explanatory variables) to 0.10 (controlling for all background variables), and similarly for the FE estimates. While the OLS and FE estimates are large and significant, the IV estimates are small, negative and most often insignificant. Hence yet again, the apparent effect of Math on earnings is not a causal effect. The gender difference in the estimated coefficients indicates that the selection of males with more favorable unobservable characteristics into the high level Math course seems to be much more prevalent than the similar selection process for females. 24 Sensitivity analysis of the IV estimates corroborates that these results are very robust to estimation strategy and model specification, cf. Appendix B. 17

ABILITY SORTING AND THE IMPORTANCE OF COLLEGE QUALITY TO STUDENT ACHIEVEMENT: EVIDENCE FROM COMMUNITY COLLEGES

ABILITY SORTING AND THE IMPORTANCE OF COLLEGE QUALITY TO STUDENT ACHIEVEMENT: EVIDENCE FROM COMMUNITY COLLEGES Kevin Stange Ford School of Public Policy University of Michigan Ann Arbor, MI 48109-3091

A Comparison of Charter Schools and Traditional Public Schools in Idaho

A Comparison of Charter Schools and Traditional Public Schools in Idaho Dale Ballou Bettie Teasley Tim Zeidner Vanderbilt University August, 2006 Abstract We investigate the effectiveness of Idaho charter

College Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics

College Pricing Ben Johnson April 30, 2012 Abstract Colleges in the United States price discriminate based on student characteristics such as ability and income. This paper develops a model of college

Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice

Megan Andrew Cheng Wang Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice Background Many states and municipalities now allow parents to choose their children

Class Size and Class Heterogeneity

DISCUSSION PAPER SERIES IZA DP No. 4443 Class Size and Class Heterogeneity Giacomo De Giorgi Michele Pellizzari William Gui Woolston September 2009 Forschungsinstitut zur Zukunft der Arbeit Institute for

Earnings Functions and Rates of Return

DISCUSSION PAPER SERIES IZA DP No. 3310 Earnings Functions and Rates of Return James J. Heckman Lance J. Lochner Petra E. Todd January 2008 Forschungsinstitut zur Zukunft der Arbeit Institute for the Study

The Effect of Income on Educational Attainment: Evidence from State Earned Income Tax Credit Expansions

The Effect of Income on Educational Attainment: Evidence from State Earned Income Tax Credit Expansions Katherine Michelmore Policy Analysis and Management Cornell University km459@cornell.edu September

The Effects of Ability Tracking of Future Primary School Teachers on Student Performance

The Effects of Ability Tracking of Future Primary School Teachers on Student Performance Johan Coenen, Chris van Klaveren, Wim Groot and Henriëtte Maassen van den Brink TIER WORKING PAPER SERIES TIER WP

How and Why Has Teacher Quality Changed in Australia?

The Australian Economic Review, vol. 41, no. 2, pp. 141 59 How and Why Has Teacher Quality Changed in Australia? Andrew Leigh and Chris Ryan Research School of Social Sciences, The Australian National

BASIC EDUCATION IN GHANA IN THE POST-REFORM PERIOD

BASIC EDUCATION IN GHANA IN THE POST-REFORM PERIOD By Abena D. Oduro Centre for Policy Analysis Accra November, 2000 Please do not Quote, Comments Welcome. ABSTRACT This paper reviews the first stage of

Research Update. Educational Migration and Non-return in Northern Ireland May 2008

Research Update Educational Migration and Non-return in Northern Ireland May 2008 The Equality Commission for Northern Ireland (hereafter the Commission ) in 2007 contracted the Employment Research Institute

UPPER SECONDARY CURRICULUM OPTIONS AND LABOR MARKET PERFORMANCE: EVIDENCE FROM A GRADUATES SURVEY IN GREECE

UPPER SECONDARY CURRICULUM OPTIONS AND LABOR MARKET PERFORMANCE: EVIDENCE FROM A GRADUATES SURVEY IN GREECE Stamatis Paleocrassas, Panagiotis Rousseas, Vassilia Vretakou Pedagogical Institute, Athens Abstract

PEER EFFECTS IN THE CLASSROOM: LEARNING FROM GENDER AND RACE VARIATION *

PEER EFFECTS IN THE CLASSROOM: LEARNING FROM GENDER AND RACE VARIATION * Caroline M. Hoxby NBER Working Paper 7867 August 2000 Peer effects are potentially important for understanding the optimal organization

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District

An Empirical Analysis of the Effects of Mexican American Studies Participation on Student Achievement within Tucson Unified School District Report Submitted June 20, 2012, to Willis D. Hawley, Ph.D., Special

GCSE English Language 2012 An investigation into the outcomes for candidates in Wales

GCSE English Language 2012 An investigation into the outcomes for candidates in Wales Qualifications and Learning Division 10 September 2012 GCSE English Language 2012 An investigation into the outcomes

BENCHMARK TREND COMPARISON REPORT:

National Survey of Student Engagement (NSSE) BENCHMARK TREND COMPARISON REPORT: CARNEGIE PEER INSTITUTIONS, 2003-2011 PREPARED BY: ANGEL A. SANCHEZ, DIRECTOR KELLI PAYNE, ADMINISTRATIVE ANALYST/ SPECIALIST

School Size and the Quality of Teaching and Learning

School Size and the Quality of Teaching and Learning An Analysis of Relationships between School Size and Assessments of Factors Related to the Quality of Teaching and Learning in Primary Schools Undertaken

NCEO Technical Report 27

Home About Publications Special Topics Presentations State Policies Accommodations Bibliography Teleconferences Tools Related Sites Interpreting Trends in the Performance of Special Education Students

Examining the Earnings Trajectories of Community College Students Using a Piecewise Growth Curve Modeling Approach

Examining the Earnings Trajectories of Community College Students Using a Piecewise Growth Curve Modeling Approach A CAPSEE Working Paper Shanna Smith Jaggars Di Xu Community College Research Center Teachers

The effects of home computers on school enrollment

Economics of Education Review 24 (2005) 533 547 www.elsevier.com/locate/econedurev The effects of home computers on school enrollment Robert W. Fairlie Department of Economics, University of California,

IS FINANCIAL LITERACY IMPROVED BY PARTICIPATING IN A STOCK MARKET GAME?

21 JOURNAL FOR ECONOMIC EDUCATORS, 10(1), SUMMER 2010 IS FINANCIAL LITERACY IMPROVED BY PARTICIPATING IN A STOCK MARKET GAME? Cynthia Harter and John F.R. Harter 1 Abstract This study investigates the

w o r k i n g p a p e r s

w o r k i n g p a p e r s 2 0 0 9 Assessing the Potential of Using Value-Added Estimates of Teacher Job Performance for Making Tenure Decisions Dan Goldhaber Michael Hansen crpe working paper # 2009_2

DO CLASSROOM EXPERIMENTS INCREASE STUDENT MOTIVATION? A PILOT STUDY

DO CLASSROOM EXPERIMENTS INCREASE STUDENT MOTIVATION? A PILOT STUDY Hans Gremmen, PhD Gijs van den Brekel, MSc Department of Economics, Tilburg University, The Netherlands Abstract: More and more teachers

School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne

School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne Web Appendix See paper for references to Appendix Appendix 1: Multiple Schools

ANALYSIS: LABOUR MARKET SUCCESS OF VOCATIONAL AND HIGHER EDUCATION GRADUATES

ANALYSIS: LABOUR MARKET SUCCESS OF VOCATIONAL AND HIGHER EDUCATION GRADUATES Authors: Ingrid Jaggo, Mart Reinhold & Aune Valk, Analysis Department of the Ministry of Education and Research I KEY CONCLUSIONS

Match Quality, Worker Productivity, and Worker Mobility: Direct Evidence From Teachers

Match Quality, Worker Productivity, and Worker Mobility: Direct Evidence From Teachers C. Kirabo Jackson 1 Draft Date: September 13, 2010 Northwestern University, IPR, and NBER I investigate the importance

LANGUAGE DIVERSITY AND ECONOMIC DEVELOPMENT. Paul De Grauwe. University of Leuven

Preliminary draft LANGUAGE DIVERSITY AND ECONOMIC DEVELOPMENT Paul De Grauwe University of Leuven January 2006 I am grateful to Michel Beine, Hans Dewachter, Geert Dhaene, Marco Lyrio, Pablo Rovira Kaltwasser,

Sector Differences in Student Learning: Differences in Achievement Gains Across School Years and During the Summer

Catholic Education: A Journal of Inquiry and Practice Volume 7 Issue 2 Article 6 July 213 Sector Differences in Student Learning: Differences in Achievement Gains Across School Years and During the Summer

DOES OUR EDUCATIONAL SYSTEM ENHANCE CREATIVITY AND INNOVATION AMONG GIFTED STUDENTS?

DOES OUR EDUCATIONAL SYSTEM ENHANCE CREATIVITY AND INNOVATION AMONG GIFTED STUDENTS? M. Aichouni 1*, R. Al-Hamali, A. Al-Ghamdi, A. Al-Ghonamy, E. Al-Badawi, M. Touahmia, and N. Ait-Messaoudene 1 University

Accessing Higher Education in Developing Countries: panel data analysis from India, Peru and Vietnam

Accessing Higher Education in Developing Countries: panel data analysis from India, Peru and Vietnam Alan Sanchez (GRADE) y Abhijeet Singh (UCL) 12 de Agosto, 2017 Introduction Higher education in developing

Work Environment and Opt-Out Rates at Motherhood Across High-Education Career Paths

Work Environment and Opt-Out Rates at Motherhood Across High-Education Career Paths Jane Leber Herr Catherine Wolfram Industrial and Labor Relations Review 2012, 65(4) : 928 950 November 2011 Abstract

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany

Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Jana Kitzmann and Dirk Schiereck, Endowed Chair for Banking and Finance, EUROPEAN BUSINESS SCHOOL, International

Universityy. The content of

WORKING PAPER #31 An Evaluation of Empirical Bayes Estimation of Value Added Teacher Performance Measuress Cassandra M. Guarino, Indianaa Universityy Michelle Maxfield, Michigan State Universityy Mark

Social and Economic Inequality in the Educational Career: Do the Effects of Social Background Characteristics Decline?

European Sociological Review, Vol. 13 No. 3, 305-321 305 Social and Economic Inequality in the Educational Career: Do the Effects of Social Background Characteristics Decline? Marianne Nondli Hansen This

Evaluation of a College Freshman Diversity Research Program

Evaluation of a College Freshman Diversity Research Program Sarah Garner University of Washington, Seattle, Washington 98195 Michael J. Tremmel University of Washington, Seattle, Washington 98195 Sarah

More Teachers, Smarter Students? Potential Side Effects of the German Educational Expansion *

More Teachers, Smarter Students? Potential Side Effects of the German Educational Expansion * Matthias Westphal University of Paderborn, RWI Essen & Ruhr Graduate School in Economics October 2017 Abstract

EDUCATIONAL ATTAINMENT

EDUCATIONAL ATTAINMENT By 2030, at least 60 percent of Texans ages 25 to 34 will have a postsecondary credential or degree. Target: Increase the percent of Texans ages 25 to 34 with a postsecondary credential.

The Talent Development High School Model Context, Components, and Initial Impacts on Ninth-Grade Students Engagement and Performance

The Talent Development High School Model Context, Components, and Initial Impacts on Ninth-Grade Students Engagement and Performance James J. Kemple, Corinne M. Herlihy Executive Summary June 2004 In many

Teacher intelligence: What is it and why do we care?

Teacher intelligence: What is it and why do we care? Andrew J McEachin Provost Fellow University of Southern California Dominic J Brewer Associate Dean for Research & Faculty Affairs Clifford H. & Betty

Effective Pre-school and Primary Education 3-11 Project (EPPE 3-11)

Effective Pre-school and Primary Education 3-11 Project (EPPE 3-11) A longitudinal study funded by the DfES (2003 2008) Exploring pupils views of primary school in Year 5 Address for correspondence: EPPSE

learning collegiate assessment]

[ collegiate learning assessment] INSTITUTIONAL REPORT 2005 2006 Kalamazoo College council for aid to education 215 lexington avenue floor 21 new york new york 10016-6023 p 212.217.0700 f 212.661.9766

Learning But Not Earning? The Value of Job Corps Training for Hispanics

Learning But Not Earning? The Value of Job Corps Training for Hispanics Alfonso Flores-Lagunes The University of Arizona Department of Economics Tucson, AZ 85721 (520) 626-3165 alfonso@eller.arizona.edu

American Journal of Business Education October 2009 Volume 2, Number 7

Factors Affecting Students Grades In Principles Of Economics Orhan Kara, West Chester University, USA Fathollah Bagheri, University of North Dakota, USA Thomas Tolin, West Chester University, USA ABSTRACT

Lecture 1: Machine Learning Basics

1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

Gender, Competitiveness and Career Choices

Gender, Competitiveness and Career Choices Thomas Buser University of Amsterdam and TIER Muriel Niederle Stanford University and NBER Hessel Oosterbeek University of Amsterdam and TIER July 3, 2013 Abstract

Estimating returns to education using different natural experiment techniques

ARTICLE IN PRESS Economics of Education Review 27 (2008) 149 160 www.elsevier.com/locate/econedurev Estimating returns to education using different natural experiment techniques Andrew Leigh, Chris Ryan

VOL. 3, NO. 5, May 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

Exploratory Study on Factors that Impact / Influence Success and failure of Students in the Foundation Computer Studies Course at the National University of Samoa 1 2 Elisapeta Mauai, Edna Temese 1 Computing

Referencing the Danish Qualifications Framework for Lifelong Learning to the European Qualifications Framework

Referencing the Danish Qualifications for Lifelong Learning to the European Qualifications Referencing the Danish Qualifications for Lifelong Learning to the European Qualifications 2011 Referencing the

Australia s tertiary education sector

Australia s tertiary education sector TOM KARMEL NHI NGUYEN NATIONAL CENTRE FOR VOCATIONAL EDUCATION RESEARCH Paper presented to the Centre for the Economics of Education and Training 7 th National Conference

ROA Technical Report. Jaap Dronkers ROA-TR-2014/1. Research Centre for Education and the Labour Market ROA

Research Centre for Education and the Labour Market ROA Parental background, early scholastic ability, the allocation into secondary tracks and language skills at the age of 15 years in a highly differentiated

Grade Dropping, Strategic Behavior, and Student Satisficing

Grade Dropping, Strategic Behavior, and Student Satisficing Lester Hadsell Department of Economics State University of New York, College at Oneonta Oneonta, NY 13820 hadsell@oneonta.edu Raymond MacDermott

Estimating the Cost of Meeting Student Performance Standards in the St. Louis Public Schools

Estimating the Cost of Meeting Student Performance Standards in the St. Louis Public Schools Prepared by: William Duncombe Professor of Public Administration Education Finance and Accountability Program

Preprint.

http://www.diva-portal.org Preprint This is the submitted version of a paper presented at Privacy in Statistical Databases'2006 (PSD'2006), Rome, Italy, 13-15 December, 2006. Citation for the original

Fighting for Education:

Fighting for Education: Veterans and Financial Aid Andrew Barr University of Virginia November 8, 2014 (Please Do Not Distribute Outside of Your Institution) Abstract The Post-9/11 GI Bill brought about

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics

Essays on the Economics of High School-to-College Transition Programs and Teacher Effectiveness. Cecilia Speroni

Essays on the Economics of High School-to-College Transition Programs and Teacher Effectiveness Cecilia Speroni Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Mathematics Program Assessment Plan

Mathematics Program Assessment Plan Introduction This assessment plan is tentative and will continue to be refined as needed to best fit the requirements of the Board of Regent s and UAS Program Review

EDUCATIONAL ATTAINMENT

EDUCATIONAL ATTAINMENT By 2030, at least 60 percent of Texans ages 25 to 34 will have a postsecondary credential or degree. Target: Increase the percent of Texans ages 25 to 34 with a postsecondary credential.

Cross-Year Stability in Measures of Teachers and Teaching. Heather C. Hill Mark Chin Harvard Graduate School of Education

CROSS-YEAR STABILITY 1 Cross-Year Stability in Measures of Teachers and Teaching Heather C. Hill Mark Chin Harvard Graduate School of Education In recent years, more stringent teacher evaluation requirements

15% 10 +5 0 5 Tuition and Fees 10 Appropriations per FTE ( Excluding Federal Stimulus Funds) 15% 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93

Summary results (year 1-3)

Summary results (year 1-3) Evaluation and accountability are key issues in ensuring quality provision for all (Eurydice, 2004). In Europe, the dominant arrangement for educational accountability is school

Multiple regression as a practical tool for teacher preparation program evaluation

Multiple regression as a practical tool for teacher preparation program evaluation ABSTRACT Cynthia Williams Texas Christian University In response to No Child Left Behind mandates, budget cuts and various

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

Practical Research. Planning and Design. Paul D. Leedy. Jeanne Ellis Ormrod. Upper Saddle River, New Jersey Columbus, Ohio

SUB Gfittingen 213 789 981 2001 B 865 Practical Research Planning and Design Paul D. Leedy The American University, Emeritus Jeanne Ellis Ormrod University of New Hampshire Upper Saddle River, New Jersey

Teaching to Teach Literacy

Teaching to Teach Literacy Stephen Machin*, Sandra McNally**, Martina Viarengo*** April 2016 * Department of Economics, University College London and Centre for Economic Performance, London School of Economics

Professional Development and Incentives for Teacher Performance in Schools in Mexico. Gladys Lopez-Acevedo (LCSPP)*

Public Disclosure Authorized Professional Development and Incentives for Teacher Performance in Schools in Mexico Gladys Lopez-Acevedo (LCSPP)* Gacevedo@worldbank.org Public Disclosure Authorized Latin

College Pricing and Income Inequality

College Pricing and Income Inequality Zhifeng Cai U of Minnesota and FRB Minneapolis Jonathan Heathcote FRB Minneapolis OSU, November 15 2016 The views expressed herein are those of the authors and not

Introduction to Causal Inference. Problem Set 1. Required Problems

Introduction to Causal Inference Problem Set 1 Professor: Teppei Yamamoto Due Friday, July 15 (at beginning of class) Only the required problems are due on the above date. The optional problems will not

The Impact of Group Contract and Governance Structure on Performance Evidence from College Classrooms

JLEO 1 The Impact of Group Contract and Governance Structure on Performance Evidence from College Classrooms Zeynep Hansen* Boise State University and NBER Hideo Owan 5 University of Tokyo Jie Pan Loyola

The Netherlands. Jeroen Huisman. Introduction

4 The Netherlands Jeroen Huisman Introduction Looking solely at the legislation, one could claim that the Dutch higher education system has been officially known as a binary system since 1986. At that

Descriptive Summary of Beginning Postsecondary Students Two Years After Entry

NATIONAL CENTER FOR EDUCATION STATISTICS Statistical Analysis Report June 994 Descriptive Summary of 989 90 Beginning Postsecondary Students Two Years After Entry Contractor Report Robert Fitzgerald Lutz

Rote rehearsal and spacing effects in the free recall of pure and mixed lists. By: Peter P.J.L. Verkoeijen and Peter F. Delaney

Rote rehearsal and spacing effects in the free recall of pure and mixed lists By: Peter P.J.L. Verkoeijen and Peter F. Delaney Verkoeijen, P. P. J. L, & Delaney, P. F. (2008). Rote rehearsal and spacing

Principal vacancies and appointments

Principal vacancies and appointments 2009 10 Sally Robertson New Zealand Council for Educational Research NEW ZEALAND COUNCIL FOR EDUCATIONAL RESEARCH TE RŪNANGA O AOTEAROA MŌ TE RANGAHAU I TE MĀTAURANGA

DEMS WORKING PAPER SERIES

DEPARTMENT OF ECONOMICS, MANAGEMENT AND STATISTICS UNIVERSITY OF MILAN BICOCCA DEMS WORKING PAPER SERIES Is it the way they use it? Teachers, ICT and student achievement Simona Comi, Marco Gui, Federica

The Effects of Statewide Private School Choice on College Enrollment and Graduation

E D U C A T I O N P O L I C Y P R O G R A M R E S E A RCH REPORT The Effects of Statewide Private School Choice on College Enrollment and Graduation Evidence from the Florida Tax Credit Scholarship Program

Iowa School District Profiles. Le Mars

Iowa School District Profiles Overview This profile describes enrollment trends, student performance, income levels, population, and other characteristics of the public school district. The report utilizes

MGT/MGP/MGB 261: Investment Analysis

UNIVERSITY OF CALIFORNIA, DAVIS GRADUATE SCHOOL OF MANAGEMENT SYLLABUS for Fall 2014 MGT/MGP/MGB 261: Investment Analysis Daytime MBA: Tu 12:00p.m. - 3:00 p.m. Location: 1302 Gallagher (CRN: 51489) Sacramento

(ALMOST?) BREAKING THE GLASS CEILING: OPEN MERIT ADMISSIONS IN MEDICAL EDUCATION IN PAKISTAN

(ALMOST?) BREAKING THE GLASS CEILING: OPEN MERIT ADMISSIONS IN MEDICAL EDUCATION IN PAKISTAN Tahir Andrabi and Niharika Singh Oct 30, 2015 AALIMS, Princeton University 2 Motivation In Pakistan (and other

Early Warning System Implementation Guide

Linking Research and Resources for Better High Schools betterhighschools.org September 2010 Early Warning System Implementation Guide For use with the National High School Center s Early Warning System

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4

Chapters 1-5 Cumulative Assessment AP Statistics Name: November 2008 Gillespie, Block 4 Part I: Multiple Choice This portion of the test will determine 60% of your overall test grade. Each question is

University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Journal of the National Collegiate Honors Council - -Online Archive National Collegiate Honors Council Fall 2004 The Impact

Value of Athletics in Higher Education March Prepared by Edward J. Ray, President Oregon State University

Materials linked from the 5/12/09 OSU Faculty Senate agenda 1. Who Participates Value of Athletics in Higher Education March 2009 Prepared by Edward J. Ray, President Oregon State University Today, more

Teacher Quality and Value-added Measurement Dan Goldhaber University of Washington and The Urban Institute dgoldhab@u.washington.edu April 28-29, 2009 Prepared for the TQ Center and REL Midwest Technical

Building People. Building Nations. GUIDELINES for the interpretation of Kenyan school reports

Building People. Building Nations. GUIDELINES for the interpretation of Kenyan school reports 1 Education is the great engine of personal development. It is through education that the daughter of a peasant

A Study of Metacognitive Awareness of Non-English Majors in L2 Listening

ISSN 1798-4769 Journal of Language Teaching and Research, Vol. 4, No. 3, pp. 504-510, May 2013 Manufactured in Finland. doi:10.4304/jltr.4.3.504-510 A Study of Metacognitive Awareness of Non-English Majors

ReFresh: Retaining First Year Engineering Students and Retraining for Success

ReFresh: Retaining First Year Engineering Students and Retraining for Success Neil Shyminsky and Lesley Mak University of Toronto lmak@ecf.utoronto.ca Abstract Student retention and support are key priorities

Detailed course syllabus

Detailed course syllabus 1. Linear regression model. Ordinary least squares method. This introductory class covers basic definitions of econometrics, econometric model, and economic data. Classification

Role Models, the Formation of Beliefs, and Girls Math. Ability: Evidence from Random Assignment of Students. in Chinese Middle Schools

Role Models, the Formation of Beliefs, and Girls Math Ability: Evidence from Random Assignment of Students in Chinese Middle Schools Alex Eble and Feng Hu February 2017 Abstract This paper studies the

Probability and Statistics Curriculum Pacing Guide

Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

Vocational Training Dropouts: The Role of Secondary Jobs

Vocational Training Dropouts: The Role of Secondary Jobs Katja Seidel Insitute of Economics Leuphana University Lueneburg katja.seidel@leuphana.de Nutzerkonferenz Bildung und Beruf: Erwerb und Verwertung

Calculators in a Middle School Mathematics Classroom: Helpful or Harmful?

University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Action Research Projects Math in the Middle Institute Partnership 7-2008 Calculators in a Middle School Mathematics Classroom:

College Pricing and Income Inequality

College Pricing and Income Inequality Zhifeng Cai U of Minnesota, Rutgers University, and FRB Minneapolis Jonathan Heathcote FRB Minneapolis NBER Income Distribution, July 20, 2017 The views expressed

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and Planning Overview Motivation for Analyses Analyses and

Linking the Ohio State Assessments to NWEA MAP Growth Tests *

Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA

Evidence for Reliability, Validity and Learning Effectiveness

PEARSON EDUCATION Evidence for Reliability, Validity and Learning Effectiveness Introduction Pearson Knowledge Technologies has conducted a large number and wide variety of reliability and validity studies

The Effect of Written Corrective Feedback on the Accuracy of English Article Usage in L2 Writing

Journal of Applied Linguistics and Language Research Volume 3, Issue 1, 2016, pp. 110-120 Available online at www.jallr.com ISSN: 2376-760X The Effect of Written Corrective Feedback on the Accuracy of

THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY

THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY William Barnett, University of Louisiana Monroe, barnett@ulm.edu Adrien Presley, Truman State University, apresley@truman.edu ABSTRACT