the role of attitude in learning economics: race and gender differences

Journal of Economics and Finance �9 Volume 18, Number 2 �9 Summer 1994 �9 Pages 139-151

The Role of Attitude in Learning Economics: Race and Gender Differences

Lu ther D. Lawson

ABSTRACT

This study identifies and analyzes factors that affect a learner's knowledgel comprehension, and application of economics by racial and gender grouping. A decomposition model is used to evaluate the imPact of attitudinal effects and other exogenous variables on economic cognition. Preliminary findings suggest that the attitude of black students towards economics instruction differs from their white cohorts while no gender differences are found.

Introduction

The literature is rich with studies that focus on traditional learning-production- functions where the selection of independent variables is classified by major types. For example, Davisson and Bonello (1976) chose three general categories for their production function: (1) human capital, (2) utilization rates, and (3) technology. Other studies categorize input variables according to student-teacher relationships (Lawson and O'Donnell 1986). For the two racial groups, blacks and whites, the present study parallels these approaches for variable selection, but adds another dimension: attitudinal effects. The paper identifies four categories of exogenous variables: Student Attributes, Institutional Factors, and Human Capital, plus Attitudinal Observations which are the focus of this study. Specifically, the first part of this study is an attempt to identify differences between black and white high school students in their attitudes toward learning economics once student attributes, institutional factors, and human capital are taken in to account. The second part of this study focuses on any learning differences between males and females.

Differences in the learning of economics by gender is extensive in the literature. Many studies suggest that the learning of economics is male-dominated, particularly when multiple choice tests are involved. Recent studies showing gender specific

Luther D. laWson is Associate Professor of Economics, The University of North Carolina at Wilmington, Wilmington, NC.

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JOURNAL OF ECONOMICS AND FINANCE Volume 18 Number 2 Summer 1994

differences in learning are Heath 1989 and Watts 1987 who confirm the earlier findings of Siegfried 1979. The explanation hinges on the belief that males have a relative advantage in quantitative skills and females excel in verbal areas. Social and cultural conditioning might also contribute to score differences. Conversely, some studies find no difference between males' and females' learning of economics (Rhine 1989; Buckles and Freeman 1984; Williams et al. 1992).

The present study investigates further the possibility of gender differences in learning economics. Some advantages of the present study over other studies is that it incorporates race and gender, and then employs statistical tests to determine'if any differences found are significant.

The set of independent variables selected for this study includes many used in previous studies, but also includes variables seldom used in previous work. Surprisingly, few studies consider the effect of the learner's age on his or her learning of economics. Since it is reasonable to assume learning is a cumulative process, a positive sign is expected in the present study.

Similar to age, the exogenous variable family income has been explored very little for its effects on the learning of economics. This fact is not surprising since income data for individual families is difficult to obtain. In a manner similar to those students sitting for the California Achievement Test (CAT), the present investigation relies on income information estimated by the classroom teacher. A positive correlation is expected between nonpoverty homes (e.g., family income above $13,000) I where the parental influence reinforces the child's need to learn and the monetary reward from enhanced earnings and the learner's posttest score (Castenell and Castenell 1988). While there have been several efforts to measure institutional constraints on the production function (e.g., school size: Eberts et al. 1990; class size: McConnell and Sosin 1984), no studies have measured what effect the hour in which the student takes the economics course has on the student's knowledge and comprehension. The sign of CLASTIME is indeterminate.

The variables RESCLASS, POSTEAS and POSTATE control for attitude. It is hypothesized that if a student has a positive attitude towards economics - from whatever source - that student will outperform his or her peers who have little or no interest in the discipline. A review of the literature shows there have been several recent attempts to measure the relationship between achievement and attitude (Phipps and Clark 1993, Soper and Walstad 1983, and Walstad and Soper 1983) where there exists some extrinsic factor (e.g., knowledgeable and interesting teachers, school systems that are committed to economics instruction, outside programs or speakers, etc.). These findings suggest that factors that influence student learning do make a difference. With respect to minority students' attitude and achievement, Walstad and Soper (1989) found that although blacks score about 1.46 points lower than whites on the cognitive test, minority students showed a significantly more positive attitude towards economics than did whites (2.66 over whites).

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Methodology and Collection of the Data

The present study is an ex post facto design which examines a cohort of ninth grade students, by racial grouping, from a large southeastern United States school system. All students completed a year-long social studies requirement of which one- third was economics. For a 12 week period, economics instruction was given to 1,012 ninth graders. During this period, approximately one-half of the students were taught by the inclass teacher four days per week; the fifth day's instruction was provided by a person from the business community. The remaining students were denied any community resource person and were taught by their normally assigned classroom teacher during the typical five-day school week. 2 As indicated above, this economics learning study is ex post facto (or quasi-experimental) in nature; that is, a designed experiment where there were limited controls prior to data collection. Although a prescribed curriculum was followed by all classes, and there was uniformity in the concepts discussed, measurement of teaching effectiveness was possible only for the in-class instructors; it was not possible for the community resource person. 3 Hence, a "true" classical experimental design in this study was not possible. Nonetheless, several ex post facto experimental designs have been used with qualified success (Buckles and Freeman 1984; Palmer et al. 1979; Becker et al. 1975).

At the beginning of the grading period, all students sat for two pretesting instruments: the Survey on Economics Attitudes (SEA) 4 and the Test of Economic Knowledge (TEK) 5. The extensively used SEA is a nationally-normed affective- domain instrument consisting of two parts. The first part, identified as Attitudes Toward Economics (ATE), measures the individual's attitude towards economics as a course of study. Sample questions might include "I enjoy reading articles about economic topics," or "Studying economics is a waste of time." The second part, Economic Attitude Sophistication (EAS), attempts to quantify the young person's attitudes toward economic issues. Sample questions would include "Government should control the price of gasoline," or "People should not have to pay taxes." Each part of the SEA consists of fourteen statements to which students respond on a five-point Likert-type scale. In sum, the EAS measure simply assesses the degree of or tendency of a student to hold opinions consistent with the current state of knowledge (Soper and Walstad 1983, 14). The Test of Economic Knowledge is a nationally-normed economics achievement test available through the National Council on Economic Education.

Near the end of the grading period, the students sat for a posttest using the same two testing instruments. The purpose of the pretests was to measure the student's general level of economic literacy (as measured by the TEK) and their initial level of attitude towards economics (as measured by the SEA). The purpose o f the posttests was to measure any change in the learner's original position. Furthermore, at the time the student was sitting for the posttest, socioeconomic data was collected

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from the classroom teacher. The data collected were then incorporated into a decomposition model for analysis.

Model Specification

One approach to measuring differences between groups is to pool the data using a dummy variable to indicate group status. A statistically significant value for the dummy variable would infer differences between the two groups. However, this approach ignores interaction effects and the effects of other socioeconomic variables. An alternative method is to decompose group mean differences into background 6 and residual differences. The residual effect and its components, the constant effect and the coefficient effect can then be tested using a Chow-type test to determine if there are significant differences.

Early use was made of a decomposition model by Blinder, 1973; Flanagan, 1974; and Oaxaca, 1973 to measure the extent of wage differentials between two groups, but they did not ~employ statistical tests for any differences. More recent use of the decomposition model to investigate wage disparities (Jackson and Lindley 1989 and Lindley et al. 1992) relies on Chow-type tests for statistical differences. Although the focus of this paper is on test score distinctions between blacks and whites (males and females) rather than wage differences, the decomposition model is appropriate.

Model

To test the hypotheses that there are differences between blacks and whites (males and females), the following behavioral model is developed:

Y/ = a + 2] ~3jXji + ei i=1 ..... n; j--2 ..... k (1)

Where Yi is the posttest, the Test of Economic Knowledge (TEK) Score received by the i t~ individual, the vector X: i contains the three values taken by the j (j =2 . . . . . k) explanatory variables for the i t~ individual, e i is a stochastic disturbance, and c~ and /3j are the parameters to be estimated by ordinary least squares. For a sample composed of blacks and whites:

Blacks: ~n = a B + E bjn ~ s (2) J

Whites: ~ w = a w + Z bjW ~jw (3)

Where a and bj are estimates of ~ and 13j respectively, the B and W superscripts for the sample and the bar superscript denote means for blacks and whites respectively. For a model analyzing males and females the B and W superscripts would be changed to F and M respectively. If there were no differences between mean test

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The Role of Attitude in Learning Economics." Race and Gender Differences

scores, the average black posttest score on the Test of Economic Knowledge (TEK) could be written as:

~a = aW + ~ byW ~B (4) J

and yA would equal Y-~ and ~ would equal yB. To the extent that there are

differences between the mean test scores of blacks and whites and, if black mean scores are lower than white mean scores, then yA (the hypothetical score if black independent variables are multiplied by white coefficients) will lie between y w and

The total differences in mean test scores (~w_yB) can be expressed as the sum of the background effect (yW_yA) and the residual effect (ya_~-B). In turn, the

residual effect can be further decomposed into the constant effect and the coefficient effect. All of these effects can be tested for statistical difference from zero.

The background effect is the difference between the average test scores for whites and blacks due to differences between the vectors of students' characteristics can be

shown as:

= E b T , j - , , (5)

The constant effect is that portion of the total test score difference which cannot be accounted for by different student backgrounds is measured by the coefficient of the race dummy in a model with interaction terms. An insignificant race dummy variable in an interaction model suggests there are no unexplained differences in background attributes that affect the learner's level of economic literacy. The coefficient effect is a measure of the differential between-group changes to unit

changes in the independent variables for blacks. Together, the constant and coefficient effect can be shown as:

[ (constant effect) [ (coefficient effect) ]

Since the coefficient effect is the sum of the products of black means (YX B) times the respective coefficients of the interaction variable (bW-bBj), it can be tested by a Chow-type procedure. A statistically significant F-value suggests there are differences between blacks' and whites' backgrounds which affect their learning of

economics. Equation (1) and subsequent appertainments can be more formally specified:

3 3 2 1

Yi = a~-, b ) a o j i + ~ C k SAId + ~ d m IFmi + ~ fp HCpi + e* i i = 1 . . . . . n (7) j= l k=l m=l p=l

Where AOj (j = 1 . . . . . 3) are three attitudinal observations: the posttest Economic Attitudes Sophistication score (POSTEAS), the posttest Attitude Towards Economics score (POSTATE), and RSCLASS a dummy variable with a value of unity if the

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ninth grade class had a community resource person); SA k ( k = l . . . . . 3) are three student attributes (AGE, GENDER and FAMINC - a dummy variable if the student's family income is greater than $13,000 a value of one is assigned,); IF m (m= 1,. . . ,2) are two institutional factors (CLASSIZE and CLASTIME), and HCpi (p = 1 . . . . . 1) the student's pretest Test of Economics Knowledge score (PRETEK).

Equation (7) presupposes that the endogenous variable POSTEK is to be regressed against a human capital proxy (PRETEK) and the set of attitudinal observations, student attributes, and institutional factors whose values are predetermined. Using PRETEK as a regressor is similar to using the numerical difference between the tests, but without constraining the coefficient on the pretest score to a value of one (Eberts et al. 1990). The selection of the exogenous variables follows many previous studies. However, no economic education studies in the literature to date have used the more complex decomposition model.

Empirical Results

The results of the models are presented in the following tables. Table 1 records the relationship among the four sets of predictor variables and the Test of Economic Knowledge for blacks and whites. The mean test score for blacks is 21.28 while for whites the mean score is 24.89. This difference is significant at the 0.001 level (t statistic of 8.48). To determine which variables, if any, help explain the difference in mean scores, five regression equations are estimated and the various effects examined (Jackson and Lindley 1989; and Lindley, Fish and Jackson 1992). Table 1 contains the results of the estimates.

T A B L E 1

RELATIONSHIP BETWEEN PREDICTOR VARIABLES AND THE TEST OF ECONOMIC KNOWLEDGE (TEK): BLACKS AND WHITES

Pooled Pooled Pooled Model Model - No Model Race Dutmny White Black

Variable Whites Blacks Race Dummy Race Dummy & Interaction Means Means

CONSTANT -2.6980 3.6662 -1.0096 -1.3900 3.6662 (-0.80) (0.86) (-0.38) (-0.53) (0.85)

POSTEAS 0.0769*** 0.0924** 0.0788*** 0.0807*** 0.0924** 46.532 45.989 (2.79) (2.26) (3.45) (3.54) (2.24)

POSTATE 0.1075"** -0.0218 0.0683*** 0.0710"** -0.0218 45.554 45.979 (4.91) (-0.64) (3.70) (3.85) (-0.63)

PRETEK 0.7421"** 0.7359*** 0.7564*** 0.7438*** 0.7359*** 22.719 19.057 (23.97) (16.00) (30.28) (28.91) (15.87)

RSCLASS -0.1918 -0.3622 -0.2152 -0.2596 -0.3622 0.577 0.450 (-0.62) (-0.75) (-0.82) (-0.99) (-0.74)

AGE -0.0421 -0.0268 -0.0331 -0.0305 -0.0269 86.597 88.293 (-1.43) (-0.75) (-1.46) (-1.35) (-0.74)

GENDER -0.2158 0.1100 -0.1170 -0.1398 0.1100 0.456 0.396 (-0.69) (0.22) (0.44) (-0.52) (0.22)

FAMINC 1.6568*** 0.4102 0.9927** 0.7993* 0.4102 0.948 0.750 (2.36) (0.70) (2.29) (1.80) (0.69)

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The Ro le o f At t i tude in Learning Economics." Race and Gender Di f ferences

TABLE 1 continued

Pooled Pooled Pooled Model Model - No Model Race Dummy White Black

Variable Whites Blacks Race Dummy Race Dummy & Interaction Means Means

CLASSIZE 0.1568"** 0.0673 0.1268"** 0.1261"** 0.0673 (2.93) (0.98) (3.00) (2.99) (0.97)

CLASTIME 0.9187"** 1.6249"** 1.1659"** 1.1559"** 1.6249"** (2.83) (3.29) (4.30) (4.27) (3.26)

RACE 0.6137"* -6.3642 (1.98) (-1.16)

RACE-POSTEAS -0.0154 (-0.31)

RACE-POSTATE 0.1292"** (3.17)

RACE-PRETEK 0.0062 (o.11)

RACE-RSCLASS 0.1704 (0,29)

RACE-AGE -0.0152 (-0.33)

RACE-GENDER -0.3258 (-0.54)

RACE-FAMINC 1.2466 (1.36)

RACE-CLASSIZE 0.0896 (1.o2)

RACE-CLASTIME -0.7062 (-1.19)

25.778 25.354

0.533 0.507

R 2 0.576 0.572 0.596 0.597 0.604 SEE 10290,08 4 2 0 1 . 6 1 1 5 1 1 9 . 6 7 15056 .85 14791.69 N 675 280 955 955 955

t MEAN TEST SCORE 24.8889 21.2857 23.8325 23.8325 23.8325

t Test of Economic Knowledge (TEK)

Percent Absolute Difference Difference

yw = 24.8889 Total Effect = yW_yB = 3.6032 17.0 yA = 21.7234 Background Effect = yW_ya = 3.1655 14.6 yB = 21.2857 Residual Effect = y&yB = 0.4377 02.1

Constant Effect ~stafistic

Coefficient Effect

= -6.3642 (RACE Coefficient of Interaction Model) = (-1.16) (not significant) = 6.8019 (0.4377 - (41.3642)) [Residual - Constant]

F Value for Residual Effect

F ' = (15119.67 - 14791.69)/9 = 2.304; 14791.69/(955-18)

F Value for Coefficient Effect

F ' - (15056.85 - 14791.69)/8 = 2.096 14791.69[(955-18)

*Significant at the 0.10 level or better. **Significant at the 0,05 level or better. ***Significant at the 0.01 level or bettor.

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Examining the models run separately for blacks and whites (models one and two), three of the variables are significant and appropriately signed (POSTEAS, PRETEK, and CLASTIME) for both groups. FAMINC, CLASSIZE, and the student's post attitude towards economics as a course of study (POSTATE) are significant for whites, but not for blacks.

As noted, the mean test scores for whites are 17 percent higher than black mean test scores. This difference between the mean scores represents the total effect. The total effect can be decomposed into the background effect and the residual effect. The background effect here is analogous to the endowment effect in labor rriodels. It reflects the difference in the mean results that is due to the differences in the mean characteristics of blacks and whites taking the test. If blacks and whites on average were the same age, had the same family income, and had the same values for the other variables in the model, then we would expect black and white scores not to differ. If they did differ, it would be because there are differences between the two groups that are not captured by the variables in the model.

The residual effect is the combination of the constant effect and the coefficient effect. The constant effect is the difference between the intercepts of the white and black models [race variable in equation (5)]. The coefficient effect is the sum of the differences between the coefficients of the white and black models. In Table 1, the constant effect is insignificant so it can be assumed that explanations for the differences in test scores are embodied in the variables in the model. That is, scores differ because there are differences in backgrounds. The F-Value of the residual effect (2.304) is significant at the 0.01 level. The combination of the constant effect and the coefficient effect is significant. However, the t-statistic on the race variable in equation (5) is not significant. All of the difference is embodied in the coefficient effect which is statistically significant at the 0.01 level (F-Value=2.096). However, of all the interaction terms, only the attitude variable (POSTATE) has a significant coefficient underscoring the major role attitude plays in determining expected test scores. The positive and statistically significant variable POSTATE shows that whites' economics test scores are affected by an affirmative attitude towards economics. In other words, the study shows that blacks are learning less economics than whites because of a less positive attitude towards economics as a study or discipline.

Table 2 records the relationship among the four sets of predictor variables and the Test of Economic Knowledge for males and females. A student t test of 1.32 reveals no statistical difference between males and females and their posttest economics score (24.24 and 23.51 respectively).

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The Role of Attitude in Learning Economics." Race and Gender Differences

T A B L E 2

R E L A T I O N S H I P B E T W E E N P R E D I C T O R V A R I A B L E S A N D T H E T E S T OF

E C O N O M I C K N O W L E D G E (TEK): M A L E S A N D F E M A L E S

Pooled Pooled Pooled No-Gender G e n d e r Gender

Variable Males Females Dummy D u m m y Interaction Males Females

CONSTANT -1.4845 -1.0683 -1.2078 -1.3900 -1.0683 -0.38 -0.28 -0.46 -0.53 -0.27

POSTEAS 0.0587* 0.1065"** 0.0813"** 0.0807*** 0.1065"** 1.68 3.52 3.57 3.54 3.30

POSTATE 0.0855*** 0.0572** 0.0706*** 0.0710"** -0.0572** 2.90 2.44 3.83 3.85 2.28

PRETEK 0.7721"** 0.7231"** 0.7428*** 0.7438*** 0.7231"** 18.88 22.02 28.98 28.91 20.65

RSCLASS -0.1385 -0.3218 -0.2577 -0.2596 -0.3218 -0,33 -0.97 -0.98 -0.99 -0.91

AGE -0.0063 -0.0555* -0.0326 -0.0305 43.0555 -0.19 -1.71 -1.47 -1.35 -1.60

RACE 0.4422 0.7599** -0.6068** -0.6137'* 0.7599* 0.87 1.97 1.96 1.98 1.85

FAIVlINC 1.1206 0.7088 0.7929 0.7993** 0.7088 1.50 1.30 1.79 1.80 1,22

CLASSIZE 0.0170 0.1985"** 0.1252"** 0.1261"** 0.1985"** 0.24 3.80 2.97 2.99 3.56

CLASTIME 1.3867"** 0.8881"** 1.1477"** 1.t559"** 1.8881"* 3, ! 1 2.63 4,25 4.27 2,46

GENDER 0.1398 -0.4162 0.52 -0,08

GENDER-POSTEAS -0.0478 -1,04

GENDER-POSTATE 0.0284 0.76

GENDER-PRETEK 0.0490 0.95

GENDER-RSCLASS 0.1833 0.35

GENDER-AGE 0.0492 1.07

GENDER-RACE -0.3177 -0.51

GENDER-FAMINC 0.4117 0.45

GENDER-CLASSIZE -0.1816** -2.12

GENDER-CLASTIME 0.4987 0.91

46.3174 46.4160

46.1885 45.2799

22.2243 21.1922

0.5370 0.5429

88.2015 86.2294

0.7351 0.6847

0.8973 0.8843

25.7255 25_5970

0.5513 0.5056

R 2 0.576 0.630 0,597 0.597 0.602 SEE 7519.94 7362.90 15061.22 15056.85 14882.85 N 4.9 536 955 955 955

tMEAN TEST SCORE 24.2458 23.5093 23.8325 23.8325 23.8325

t Test of Economic Knowledge (TEK)

Percent Absolute Difference Difference

yM = 24,2458 Total Effect = yM yF = .7365 03,0 yh = 23.2856 Background Effect = yM_ya = .9602 04.1 yF = 23.5093 Residual Effect = yA_yF = .2237 00.9

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TABLE 2 continued

Constant Effect = - .4162 (GENDER Coefficient of Interaction Model) t-statistic = -.08 (not significan0

Coefficient Effect = .1925 (-.2237 - (-.4162)) [Residual - Constant]

F Value for Residual Effect

F * = ( 1 5 0 6 1 . 2 2 - 14882.85) /9 = 19.819

1 4 8 8 2 . 8 5 / ( 9 5 5 - 1 8 ) 15.884

F Value for Coefficient Effect

- - = 1.248; (15056.85 - 14882.85)/8 = 21.75__._.~0 = 1.369 14885 .85 / (955-18) 15 .884

*Significant at the 0 .10 level or better. **Significant at the 0.05 level or better. ***Significant at the 0.01 level or better.

Examining the equations for males and females ( model one and two), four of the nine variables specified are significant and appropriately signed (POSTEAS, POSTATE, PRETEK, and CLASTIME) for both sexes. No other variables were found to be statistically significant for males; whereas for females, three additional variables emerged as significant at the 0.10 level or better. Indeed, for the model recording females only, seven of the nine variables were found to be significant. Only AGE yielded a sign other than one expected.

An examination of the bottom portion of Table 2 rejects any notion that economics is gender (male) specific. The total effect (the difference between males and females) as a percentage difference is only 3%. Furthermore, differences due to mean characteristics of males and females (background effect) are likewise slight at 4.1%. However, the difference between the intercepts of males and females (constant effect -.4162) is insignificant which suggests that some variables, but not necessarily one's sex, do account for the score difference between the sexes. Lastly, the F-values for the residual effect and the coefficient effect are found to be insignificant (1.248 and 1.369 respectively), further supporting the belief one's gender is not a factor in the learning of economics.

An examination of model five (pooled model with gender dummy and interaction terms) reveals that only the interaction term GENDER-CLASSIZE is significant at the 0.05 level or better. It appears that this variable accounts for some, if not all, of the difference between the economics test score between males and females. The negative sign implies that females do better in large classes than their male counterparts. Perhaps this finding will be surprising to some. Lastly, it should be noted that the interaction terms GENDER-POSTATE and GENDER-POSTEAS were not significant. Apparently, unlike that for whites, the learning of economics by females is not dependent on their attitude.

Conclusions

The primary purpose of the study has been to identify any differences in attitude and other variables between black and white high school students and their learning

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of economics. The null hypothesis that there is no difference in background attributes

between the two races which might affect their economics education was tested and

rejected. The major finding in the study suggests that a positive attitude towards

economics shared by white students, but lacking in black students, does affect their

level of economic literacy. To draw a linkage between attitude, economics

understanding, and future market participation is posit. If a poor attitude towards

economics is due to feelings of disenfranchisement, then school systems must make

greater efforts to point out that a firm understanding of how our system functions is

a necessary first step towards future market participation. The study found also that there is little evidence for the belief that economics is

a gender (male) specific discipline. There was no statistical evidence that

demonstrated that males did any better in economics than did females. The F-value

for both the residual effect and the coefficient effect were not significant, nor was

the pooled gender dummy variable. Only the variable GENDER-CLASSIZE emerged

as significant. Further, attitude was not a factor for females in their learning of

economics.

NOTES

1. The average poverty threshold for a family of four in the year in which the data was collected was $13,394 (Current Population Reports, Series P.60, No. 181, p.vii). The selection of $13,000 as the difference between "affluent" families and less "affluent" was based on this report.

2. The resource person experiment was part of Junior Achievement's Project Business, where a Business Consultant (community resource person) would teach an economics course, in conjunction with the teacher, one hour per week, for 12 weeks. The establishment of a treatment group and a control group was dependent on the hour that the resource person had available, and the time of day in which the classroom teacher was teaching the economics course. For example, a given resource person may have only Wednesday mornings available, say 10:00 a.m., and if the classroom teacher was teaching the economics course at 10:00 a.m., then a "match" was made. Hence a form of randomness was achieved.

3. To account for different teacher effectiveness, a Duncan Multiple Range Test was conducted. The findings of this test for mean variance, by teachers, suggested no difference in teaching effectiveness for fourteen of the seventeen in-class teachers participating in the study. "Three teachers and their classes were dropped from the study (78 students). Furthermore, an additional 136 respondents were eliminated from the study.

4. For the norming of the test and an explanation of its components see: John C. Soper and William Walstad, "On Measuring Economic Attitudes," Journal of Economic Education 14 (Fall 1983): 4-17.

5. For norming of the test and explanation of its components see: William B. Walstad and John C. Soper, "Test of Economic Knowledge, Examiner's Manual," National Council on Economic Education, (New York, 1987).

6. In a labor wage model, the background effect is known as the endowment effect.

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