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Analyzing postsecondary returns: does educational loan default play a role?

I. INTRODUCTION

Leslie and Brinkman (1988) provide comprehensive surveys of the large and well established literature on the economic returns of college degrees relative to high school educations. Little is known, however, about how the economic benefits vary with the type of postsecondary education received. As enrollments grow in vocational programs, an evaluation of the effectiveness of these programs and of the federal policies used to subsidize them becomes imperative. Although government statistics only recently have included data on enrollments in vocational programs, McPherson and Schapiro (1991, pp. 139144) provide anecdotal evidence that enrollments grew substantially throughout the 1980s. The limited research in this area shows clear economic advantages for baccalaureate educations relative to both vocational and associate programs. In most of these income studies, researchers control for variables that directly influence earnings as well as variables that control for elements of discrimination or capture personal traits of the student that also affect earnings. This paper extends the existing literature by further controlling for individual characteristics that typically go unmeasured. Using a micro-data base of students who participated in the federally guaranteed student loan (GSL) program - also known as the Stafford Loan program - the analysis here estimates annual incomes and includes the status of the educational loan as a regressor. Intrinsic personal attributes of commitment and responsibility that contribute to the decision not to repay the student loan also significantly influence one's earnings. Therefore, guaranteed student loan defaulters, ceteris paribus, should have significantly lower incomes than those who either still are repaying or have paid in-full their educational loans.

Previous income analyses suggest that graduating from the postsecondary program partially captures these characteristics of motivation and initiative (see Grubb 1992, 1993). Graduating not only adds to an individual's productive capability but also signals intrinsic personal traits that employers prefer and also reward with higher incomes. Yet, these studies make no clear distinction between the productivity and signaling elements that graduation confers on incomes. The empirical model in the analysis here includes whether the individual completed the postsecondary program as well as whether he/she defaulted on the educational loan that helped finance the education, thus clarifying the distinction.

Although the analysis here includes only past participants of the GSL program, the empirical findings confirm that post-schooling incomes vary dramatically depending upon the postsecondary program attended. Of particular interest, the results show that students who default on their educational loans earn significantly lower incomes than do non-defaulters.

II. METHODOLOGY

A. Data

The data used to analyze postsecondary labor market returns are from the National Postsecondary Student Aid Study - Student Loan Recipient Survey (NPSAS). The National Center for Educational Statistics (NCES) collected this data base in 1988. The NCES surveyed past participants of the GSL program who had left their institutions between 1976 and 1985 and who were still servicing, had paid in-full, or had defaulted on their GSLs.

The NPSAS data are especially valuable because they contain a vast array of information about the student's personal background, type of postsecondary education, and employment record. The full data base contains information on over 8,000 guaranteed student loan borrowers. However, the empirical work in this paper uses only those students who attended either non-degree or degree vocational, associate, or baccalaureate programs. Since economic returns differ among undergraduate and graduate degree holders, the analysis eliminates individuals who had borrowed for a post-undergraduate program such as a Master's, Ph.D., or professional degree. Furthermore, the analysis excludes observations if data were missing on one or more of the key variables of interest. The final data base used in this study includes 2,219 students: 767 who attended a vocational program; 311 associate degree students; and 1,141 baccalaureates.

B. Income Differences

Table 1 presents the mean annual incomes in 1986 for the former students, segregated by the type of postsecondary institution attended and by gender and race. Column 1 shows that annual earnings for bachelors students are higher than those for vocational and associates, with B.A. and B.S. students earning $27,456 annually on average. Moreover, for all three postsecondary programs, students who had defaulted on the educational loan earn significantly lower incomes, at the 1 percent level, than do non-defaulters. The differential is greatest for the vocational student ($7,685) and smallest in baccalaureate programs ($4,745), with defaulters from associate programs receiving $5,040 less on average.

Segregating the sample based upon gender and race (columns 2-5) confirms the same general results for all demographic groups except males. Male bachelors students earn significantly more on average, and there still exists a premium for repaying the educational loan. The differential, however, between defaulters and non-defaulters is smallest among male associate recipients. Even though the default [TABULAR DATA FOR TABLE 1 OMITTED] rate among blacks is nearly four times the rate for non-blacks (51 percent vs. 14 percent), table 1 reveals that for blacks who do not repay their student loans, the earnings differential is smaller than for non-blacks in all three educational groups. Non-black defaulters earn roughly 8 to 29 percent less (statistically significant at five percent or lower), on average, than do non-defaulters. However, among African-Americans, the income differential between defaulters and non-defaulters is 20 percent for vocational and associate students and only three percent for baccalaureates (none is statistically significant at the conventional levels). Before controlling for other variables that influence earnings, the NPSAS data indicate that defaulting on the educational loan may cause systematic differences among African-Americans. Blacks earn less than non-blacks on average. Among ethnic backgrounds, the income gap is smaller the more advanced the degree. A black vocational student earns roughly 62 percent of a non-black's income (statistically significant at one percent), yet for an African-American baccalaureate, earnings are 82 percent of the non-black's, annually. This difference in the means is not statistically significant.

Annual incomes also vary greatly among genders even though the default rate does not - it is approximately 17 percent for both genders. Again, before controlling for other variables, the NPSAS data indicate that males have higher income levels than do females, except for defaulters in bachelors programs and all students in associate programs. Analysts have attributed this latter result to the different type of associate education that men and women receive. Men typically train for less marketable "upper status" jobs such as accounting or computer programming whereas women learn "lower status" skills that are highly marketable in positions such as secretary or dental hygienist (Wilms and Hansen, 1982).

C. Model

The formal model follows Mincer's (1974) functional form for estimating earnings except that here the dependent variable is annual income and not the natural logarithm of income. As Grubb (1992, 1993) notes, the log-linear form is not always appropriate. Since the data are segregated by the educational degree, the number of years of education is not included as an explanatory variable, and hence a rate of return to education is not being estimated. Furthermore, these data probably are not skewed to the right as are most aggregate earnings data that justify the use of a semi-log function. Since post-undergraduate students are excluded and all observations are young and relatively the same age, disproportionately high wage earners in this data base do not exist.

The basic model is

[Y.sub.i] = [[Beta].sub.0] + [[Beta].sub.1][X.sub.1i] + [[Beta].sub.2][X.sub.2i] + [[Beta].sub.3][X.sub.3i] + [[Beta].sub.4][X.sub.4i] + [[Epsilon].sub.i]

where Y is annual income in 1986 measured in thousands of dollars, X1 is a vector of schooling variables, X2 is a vector of employment variables, X3 is a vector of demographic variables, and X4 is a vector of family background variables. The disturbance term, [Epsilon], is normally distributed with a zero mean. Table 2 lists the names and descriptions of the variables. Table 3 includes the mean and standard deviation for each variable based upon the postsecondary schooling program attended.

Separating the NPSAS sample by postsecondary degrees reveals the differences in characteristics of the former students. Bachelor students were most likely to be high school graduates who completed their postsecondary education and repaid their educational loans. Although a clear majority of vocational and associate students also were high school graduates, a greater percentage of them either received a high school graduate equivalency diploma or dropped out of high school. Likewise, fewer vocational and associate students had completed their postsecondary program (59 and 54 percent, respectively) compared to baccalaureates, and more had defaulted on their educational loans. Only 12 percent of the bachelor students had defaulted, whereas 27 and 18 percent of the vocational students and associates, respectively, defaulted.

The employment variables show little variation in the number of hours worked per week and years of potential work experience among the three groups. The analysis here measures the experience variable differently than do other labor market studies. Experience is the number of years since the individual left the postsecondary program and hence measures potential work experience. Although it ignores any wage advantage awarded for pre-postsecondary schooling experience, it better captures the labor market returns from the postsecondary education, regardless of the individual's age. Furthermore, because national averages show that whites and males have more continuous work lives, this variable will tend to be more accurate for whites than for blacks and for males than for females.

[TABULAR DATA FOR TABLE 2 OMITTED]

Differences do emerge in post-schooling occupations. Vocational and associate students are twice as likely to be employed in the service or administrative support sector than are bachelor students. The most common occupations for baccalaureates are in the professional and executive sectors. These two sectors employ 46 percent of baccalaureates but only 20 percent of vocational and the associate candidates. Furthermore, given the variation in course work provided by the three different postsecondary programs, a larger percentage of the vocational and associate students, compared to baccalaureates, work in the trade sector or as technicians.

Further analysis of table 3 shows little variation among gender and age; however, ethnic status varies greatly among the three postsecondary programs. A larger proportion of the vocational students are African-Americans - 16 percent, compared to 11 percent in associate programs and 7 percent in baccalaureate colleges and universities. (Given the small sample size of ethnic groups, the race variable is not further segregated from whites.) Moreover, bachelor students are more likely to have an executive or professional father and a working mother. Finally, comparing family income reveals that exactly half of the bachelor students come from a family with incomes of $30,000 or more, whereas roughly only a third of the vocational and associates students do.(1) However, more than a fifth of the vocational students have family incomes of less than $11,000.
TABLE 3


Means and Standard Deviations


VARIABLE VOCATIONAL ASSOCIATE'S BACHELOR'S


SCHOOLING


HSDIPLOMA 0.852 0.932 0.981
 (0.356) (0.251) (0.137)
HSGED 0.111 0.061 0.017
 (0.314) (0.240) (0.131)
HSDROPOUT 0.038 0.006 0.002
 (0.191) (0.080) (0.042)
COMPLETE 0.593 0.543 0.721
 (0.492) (0.499) (0.449)
DEFAULT 0.265 0.183 0.117
 (0.442) (0.388) (0.318)


EMPLOYMENT


HOURS/WK 40.646 38.714 41.184
 (9.436) (10.294) (9.991)
EXPER 3.334 3.222 3.207
 (2.307) (2.125) (2.268)
EXEC 0.092 0.080 0.221
 (0.290) (0.272) (0.415)
PROF 0.103 0.119 0.239
 (0.304) (0.324) (0.426)
TECH 0.074 0.109 0.046
 (0.262) (0.313) (0.210)
SALES 0.062 0.068 0.133
 (0.242) (0.251) (0.339)
TRADE 0.203 0.174 0.051
 (0.402) (0.379) (0.219)
CLER/SERV 0.325 0.309 0.167
 (0.469) (0.463) (0.373)
OTHER 0.143 0.140 0.142
 (0.357) (0.346) (0.349)


DEMOGRAPHICS


MALE 0.494 0.486 0.551
 (0.500) (0.501) (0.498)
BLACK 0.155 0.106 0.067
 (0.362) (0.308) (0.251)
AGE86 27.822 27.309 26.817
 (5.788) (5.766) (4.280)


FAMILY BACKGROUND


DADEXEC/PROF 0.202 0.199 0.328
 (0.401) (0.400) (0.470)
DADTRADE 0.304 0.344 0.219
 (0.460) (0.476) (0.413)
MOMWORK 0.564 0.559 0.589
 (0.496) (0.497) (0.492)
PARINC1 0.203 0.158 0.087
 (0.402) (0.365) (0.283)
PARINC2 0.152 0.113 0.089
 (0.359) (0.317) (0.285)
PARINC3 0.159 0.158 0.131
 (0.366) (0.365) (0.338)
PARINC4 0.150 0.219 0.194
 (0.358) (0.414) (0.396)
PARINC5 0.335 0.352 0.500
 (0.472) (0.478) (0.500)
OBSERVATIONS 767 311 1,141


III. EMPIRICAL FINDINGS

Table 4 presents the OLS estimates and t-statistics of three different specifications of the income equation given above where vocational and associate degrees appear as dummy variables. The first regression includes all the variables except the educational loan's default status. The second regression contains all the schooling variables, including a dummy variable indicating whether the student had defaulted on his or her guaranteed student loan, as well as the same employment, and demographic and family background variables that appear in the first specification. The third regression in table 4 endogenously controls for default by using the residuals generated when estimating the probability of default in place of the 0-1 dummy variable.

A. Specification (1)

The results of the income equation for the NPSAS data base verify that for past participants of the GSL program, baccalaureates receive a significant earnings advantage. After controlling for schooling, employment, and demographics and family background (column 1), vocational students earn $2,724 less than bachelors, and associate incomes are $2,566 less, annually. Although these differentials are substantially lower than those reported in other studies (e.g., Grubb, 1993), one must keep in mind that this data base is composed of relatively recent graduates and includes only those who financed their educations with GSLs.
TABLE 4


Income Equation
Dep. Var.: Thousands of 1986 Dollars


VARIABLES (1) (2) (3)


INTERCEPT -7.238 -8.070 -7.810
 (-2.645)(***) (-2.958)(***) (-2.737)(***)
VOCATIONAL -2.724 -2.610 -2.714
 (-3.439)(***) (-3.312)(***) (-3.435)(***)
ASSOCIATE -2.566 -2.622 -2.545
 (-2.501)(***) (-2.569)(***) (-2.485)(***)
HSGED -3.332 -2.154 -2.898
 (-2.268)(**) (-1.455) (-1.969)(**)
HSDROPOUT -4.797 -3.262 -3.942
 (-1.741)(*) (-1.183) (-1.427)
COMPLETE 2.054 1.626 1.876
 (2.900)(***) (2.290)(**) (2.646)(***)
DEFAULT (Dummy) -4.800
 (-4.943)(***)
DEFAULT (Residual) -3.106
 (-3.239)(***)
HOURS/WK 0.165 0.172 0.173
 (4.724)(***) (4.938)(***) (4.942)(***)
EXPER 2.369 2.712 2.511
 (4.428)(***) (5.054)(***) (4.688)(***)
EXPERSQ -0.088 -0.120 -0.103
 (-1.555) (-2.120)(**) (-1.814)(*)
EXEC 3.575 3.384 3.438
 (2.924)(***) (2.781)(***) (2.817)(***)
PROF 6.473 6.264 6.349
 (5.448)(***) (5.297)(***) (5.353)(***)
TECH 2.467 2.250 2.381
 (1.585) (1.452) (1.532)
SALES 1.604 1.840 1.694
 (1.180) (1.360) (1.249)
CLER/SERV -1.796 -1.470 -1.608
 (-1.600) (-1.314) (-1.433)
TRADE -2.162 -1.628 -1.877
 (-1.640)(*) (-1.238) (-1.424)


 Without With Default With Default
VARIABLES Default Dummy Residual


MALE -0.325 -0.392 -0.395
 (-0.452) (-0.548) (-0.551)
BLACK -3.751 -2.201 -3.747
 (-3.331)(***) (-1.892)(*) (-3.335)(***)
AGE86 0.748 0.772 0.764
 (10.013)(***) (10.366)(***) (10.231)(***)
DADEXEC/PROF 1.676 1.674 1.680
 (1.978)(**) (1.986)(**) (1.986)(**)
DADTRADE -0.035 -0.071 -0.044
 (-0.043) (-0.089) (-0.054)
MOMWORK -0.128 -0.159 -0.101
 (-0.188) (-0.236) (-0.150)
PARINC1 -6.888 -6.481 -6.836
 (-5.968)(***) (-5.631)(***) (-5.935)(***)
PARINC2 -4.573 -4.277 -4.446
 (-3.984)(***) (-3.740)(***) (-3.879)(***)
PARINC3 -4.145 -3.922 -3.993
 (-4.013)(***) (-3.814)(***) (-3.870)(***)
PARINC4 -3.377 -3.374 -3.365
 (-3.628)(***) (-3.644)(***) (-3.622)(***)
N 2,219 2,219 2,219
[R.sup.2] .241 .249 .245
F-RATIO 29.04 29.15 28.42


t-statistics are given in parentheses: *, **, and *** indicate
statistical significance at 10, 5 and 1 percent, respectively.


This analysis also finds that completing both high school and postsecondary educations significantly increases post-schooling incomes. High school graduates earn $3,300 more than those with graduate equivalency degrees and $4,800 more than dropouts. Furthermore, as hypothesized by both human capital economists and supporters of education signaling theories, students who complete their postsecondary programs earn more annually than do dropouts - $2,054 more according to these results.

Of the employment variables, both the number of hours worked per week and the years of work experience since terminating the postsecondary education significantly increase annual earnings, with the latter exhibiting the typical concave shape. Furthermore, executives and professionals earn $3,575 and $6,473 more, respectively, on average than do those in the excluded group. However, those employed in the trade sectors earn $2,162 less on average.

As found in most studies on earnings differentials, blacks earn significantly less than do individuals from other ethnic origins, and older individuals have significantly higher incomes. The literature ascribes the former finding to labor market discrimination. However, the effects of discrimination most often develop over time, through training and advancement. For these data, an alternative explanation for the income differential between the races is that African-Americans, particularly those in vocational programs, are more likely to attend lower quality schools and hence receive inferior training. Contrary to the typical literature, the results here show that men earn less than women, although the coefficient is insignificant. Again, given the limited job experience of this data base, this effect is likely to change over time. Moreover, even after controlling for family income, students whose fathers are executives or professionals have significantly higher incomes than do those whose fathers have other occupations, and students from families with the highest annual income level - $30,000 or more - earn significantly more than do students from all other groups. These two results might reflect parents' ability through job connections and wealth to access high paying jobs for their children.

B. Specification (2)

Column 2 of table 4 again contains earnings estimates for the full sample but also includes a dummy variable indicating whether the student defaulted on his/her guaranteed student loan. Adding the default dummy generates two interesting changes. First, as table 1 indicates, defaulting on the educational loan significantly reduces one's annual income by more than $4,800, even after controlling for educational attainment and performance, employment, and demographics including family background. The default dummy variable captures personal attributes of the individual that other income studies typically leave unmeasured. Characteristics such as commitment and dedication that contribute to the decision not to repay a loan are likely to be transferred to other personal endeavors such as schooling and employment and hence significantly reduce one's income.

A second effect is a change in relative size and significance of the coefficients of several other variables. Not controlling for motivational differences with the default variable appears to bias estimates for the schooling performance variables. Neither HSGED nor HSDROPOUT statistically influences incomes once the analysis controls for default, although both coefficients still are negative. Apparently, high school education, per se, does not influence earnings. However, a high school dropout or GED recipient is less committed toward his or her secondary education. Therefore, high school performance appears to significantly reduce income later without directly controlling for these motivational attributes. Furthermore, the impact that completing the postsecondary program has on one's post-schooling income is less significant. This result supports the claim that graduating at least partially signals traits that the labor market later rewards.

Controlling for the loan default-status reveals that experience has a stronger influence on earnings. Traits that influence a student to default on an educational loan appear to negatively affect an employer's incentive to invest in training this individual (as the experience variables typically reflect). The only other variation in the employment variables is for those employed in the trade sector. Although these individuals still earn the lowest incomes, the coefficient no longer is statistically significant.

Column 2 indicates that even after the analysis controls for default, women earn more than men. However, the coefficient still is insignificant. More striking is that the black/non-black income differential now is smaller. The decline in both the impact and significance of the black coefficient lends further support to the theory that characteristics that the default variable controls for influence part of the earnings gap among races. These characteristics include the quality of school attended and hence the training received, not race per se.

Finally, the estimated income equation in column 2 indicates that baccalaureates do enjoy an income advantage, even after the analysis controls for the default of the educational loan. Vocational and associate students still earn roughly $2,600 less on average than do bachelor recipients.

C. Specification (3)

The final specification endogenously includes the default status of the GSL in the income equation. Although the dependent variable is a measure of income after the default has occurred for most students in this data base, one may argue that defaulting is a function of income in the repayment period, which is correlated with subsequent earnings. (Boyd, 1991, finds that most GSL defaults occur within a year and a half after the student terminates his/her postsecondary education. Therefore, the 1986 annual incomes data likely reflect most defaults among students in the NPSAS.) Controlling for the potential correlation between the default dummy variable and the error term involves re-estimating specification (2) using a two-equation analysis. The first equation estimates the predicted probability of default. (The model used to predict default probability is the following: Default= f[Loan amount, Income in the repayment period, Mortgage dummy, Year of graduation, Degree type, Graduation, Race, Gender, Marital status, Parental income]. Boyd, 1994, discusses the results.) The residual from the default equation is used as a regressor in place of the default dummy variable in the income equation. As an anonymous referee points out, using the residual rather than the predicted probability of default isolates the character attributes from ability to pay. Performing a two-equation analysis of post-schooling incomes does not qualitatively change the results from those found in the first specification. (The t-ratio reported for the predicted probability of default variable has been corrected for the dependence across observations of the two equations.)

Defaulters still earn significantly lower incomes than do non-defaulters. Clearly, unobservable characteristics of the individual such as initiative and commitment negatively influence income even after controlling for ability to pay. Moreover, using the exogenously generated 0-1 default variable rather than the residual from the model predicting the probability of default biases some of the coefficients since the default dummy not only captures initiative but also ability to pay. The effects that the other schooling, employment, and demographic and family background variables have on post-schooling incomes are for the most part qualitatively the same as those in specification (1).

Comparing the results in the third specification to the first two reveals that completing the postsecondary degree still increases post-schooling incomes. The impact is not so great in this specification as in the first, but it is larger than when the default dummy is included. The impact again is statistically significant at the one percent level. Likewise, the high school performance variables are smaller in absolute value and less significant once the default status is controlled for, yet the change is not as dramatic as when the dummy variable is used. These results indicate that the default residual captures individual characteristics that are somewhat different than those characteristics that determine both secondary and postsecondary school performance. Finally, the black coefficient is similar to that in the first specification. Even after controlling for character attributes, the analysis reveals that African-Americans earn significantly less on average. This implies that differences in the quality of the postsecondary program attended and hence in the training received - not character per se - at least partially generate the earnings differential among races.

IV. CONCLUSION

As the vocational sector has expanded throughout the 1980s, the number of vocational students receiving GSLs has increased. Associated with this growth has been an increase in the percentage of vocational loans that end in default. However, previous research revealed little about the economic benefits one receives from a vocational degree.

This analysis shows that, ceteris paribus, vocational and associate students earn significantly lower incomes than do bachelor students. The data base here focuses on post-schooling incomes one to 10 years after graduation and thus should provide policymakers with a better understanding of economic returns during the GSL repayment period. Previous studies using older and more experienced individuals show the economic advantages for postsecondary students. However, for many students with outstanding educational loans, initial income is a more critical determinant of the ability to repay the loan. Under present GSL policies, all loans enter repayment six months after the education is terminated, and equal payments typically are spread across a 10-year repayment period. Consequently, policymakers concerned with the relatively high rate of default among vocational students need to be especially well versed in the returns to vocational training in the period immediately after graduation.

Furthermore, as government officials consider expanding the role of vocational schooling in retraining the workforce in the face of large scale restructuring within the American economy, an understanding of the returns of this type of education in particular becomes increasingly important. This paper illustrates that personal motivational attributes are a significant contributor to future income. Therefore, an expanding pool of knowledge about all the various factors that affect returns to different types of education should lead to a more efficient match between students and educational type.

ABBREVIATIONS

GSL: Guaranteed Student Loan NCES: National Center for Educational Statistics NPSAS: National Postsecondary Student Aid Study

1. The family income variable is grouped into five continuous categories: $10,999 and less (PARINC1), $11,000-16,999 (PARINC2), $17,000-22,999 (PARINC3), $23,000-29,999 (PARINC4), and $30,000 and more (PARINC5). From 1978 to 1982, GSLs were not means tested. Since 1982, access was restricted to individuals whose family income was more than $30,000.

REFERENCES

Boyd, Laura A., "To Default or Not To Default? An Analysis of the Guaranteed Student Loan Program," unpublished manuscript, February 1994.

-----, Federally Subsidized Student Loans and the Economics of Default, unpublished Ph.D. Dissertation, The Ohio State University, 1991.

Grubb, W. Norton, "Postsecondary Education and the Sub-Baccalaureate Labor Market: New Evidence on Economic Returns," Economics of Education Review, 11:3, 1992, 225-248.

-----, "The Varied Economic Returns to Postsecondary Education," Journal of Human Resources, 28:2, 1993, 365-382.

Leslie, Larry L., and Paul T. Brinkman, The Economic Value of Higher Education, Macmillan Publishing, New York, 1988.

McPherson, Michael S., and Morton Owen Schapiro, Keeping College Affordable, The Brookings Institution, Washington, D.C., 1991.

Mincer, Jacob, Schooling, Experience, and Earnings, National Bureau of Economic Research, Columbia University Press, New York, 1974.

Wilms, Wellford W., and Stephen Hansell, "The Dubious Promise of Post-Secondary Vocational Education: Its Payoff to Dropouts and Graduates in the U.S.A.," International Journal of Educational Development, 2:1, 1982, 43-59.

Laura A. Boyd is an assistant Professor, Department of Economics, Denison University, Granville, Ohio. This is a revised version of a paper presented at the Western Economic Association International 69th Annual Conference, Vancouver, B.C., July 1, 1994. The author thanks the session participants, Patricia Reagan, Jayanthi Krishnan, David Boyd, and anonymous referees for their many helpful suggestions and comments.
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Author:Boyd, Laura A.
Publication:Contemporary Economic Policy
Date:Oct 1, 1995
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