What determines when undergraduates complete their
theses? Evidence from two economics departments
Curt Lo¨fgren
a, Henry Ohlsson
b,*aDepartment of Economics, Umeå University, SE-901 87 Umeå, Sweden bDepartment of Economics, Uppsala University, Box 513, SE-751 20 Uppsala, Sweden
Received 1 December 1996; received in revised form 3 November 1997
Abstract
Most economics students at Uppsala and Umeå do not complete their undergraduate thesis within the intended time. We find that coauthoring, compared to writing alone, increases the probability of completing a thesis. A second thesis is less likely to be completed than a first. The two departments also differ in completion time. The probability of completing decreases over time. There is also some weaker evidence that students with high grades are more likely to
complete and that women take a longer time to complete their theses. [JEL A22, I20].1998 Elsevier Science Ltd.
All rights reserved.
Keywords: Undergraduate economics education; Thesis completion; Event history analysis; Co-operative learning; Gender; Ability; Preferences
1. Introduction
There are two reasons why students may fail a univer-sity course: they choose not to make the effort necessary to succeed and/or they don’t have the ability necessary for success given any effort. In this paper we first present a model of how students’ preferences and ability simul-taneously determine educational output. We use the model to illustrate how to capture the differences between preferences and ability effects. Second, we present empirical results in light of the theoretical model. We focus on a particular educational production pro-cess: the completion of an undergraduate thesis. The
the-sis is a substantial part of college education in Sweden.1
At a Swedish university the length of a semester is approximately 20 weeks. A thesis is needed for the Bachelor’s degree (C-thesis) and another thesis is written for the Master’s degree (D-thesis). The workload for
* Corresponding author. E-mail: [email protected] 1See Appendix A for a more extensive description of the courses in economics.
0272-7757/98/$ - see front matter1998 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 ( 9 8 ) 0 0 0 0 5 - 3
each of the theses is expected to be 10 weeks (the full-time equivalent of half a semester).
Many students, however, do not complete their theses within this expected time. This is the case for more than half of the students in our data set. One out of five stu-dents have still not completed seven semesters after they first registered for the thesis course. The important ques-tions are: What are the determinants of thesis com-pletion? Are the effects on the probability of completion that we can trace due to preferences and/or ability?
Our main result is that coauthoring, compared to writ-ing alone, increases the probability of completwrit-ing a the-sis. Moreover, a student writing a second thesis (D-thesis) is less likely to complete than a student writing a first thesis. The two departments also differ in com-pletion time. The probability of completing decreases over time. There is also some weaker evidence that stu-dents with high grades are more likely to complete and that women take longer time to complete their theses.
associated with ability. The negative effect of writing a second thesis has to do with preferences and so does the decreasing probability over time. The lower probability of women is because of preferences, not ability.
The paper is structured as follows: In Section 2 we present a theoretical model of how the decision to com-plete is made. The choice of variables and the descriptive facts are discussed in Section 3. Section 4 described the estimation method. Empirical results are reported and discussed in Section 5. In Section 6 the main results are summarized and discussed.
2. Theoretical framework
The educational achievement of a student is determ-ined both by the student’s ability and his or her prefer-ences for making an effort studying instead of doing something else. To structure our discussion on the deter-minants of thesis completion time we have extended a model of student behavior from Costrell (1994). In the model, students are assumed to differ in preferences as well as in ability. Suppose that student i has the utility function:
Ui5Ui(Li,wi) (1)
where Liis the effort studying and wiis future earnings.
It is assumed that ∂Ui/∂Li , 0 and ∂Ui/∂wi > 0. The
student’s educational achievement, which is defined as the student’s future productivity, is given by the edu-cational production function:
yi5yi(Li) (2)
assuming∂yi/∂Li> 0 and ∂ 2y
i/∂L 2
i ,0. The production
function reflects how the ability of the student deter-mines his or her educational achievement/future ductivity. The zero level of study effort yields the
pro-ductivity y05yi(L0). This is assumed to be the same for
all students.
Suppose that employers can identify the productivity of the individual. Future earnings would then, in a well-functioning market economy, correspond to productivity,
wi5 yi. Choosing effort to maximize utility subject to
the production function gives the first-order condition:
∂yi/∂Li5 2
∂Ui/∂Li
∂Ui/∂yi
(3)
where∂yi/∂Liis the marginal product of effort, ∂Ui/∂Li
is the marginal disutility of study effort, and∂Ui/∂yiis
the marginal utility of future earnings. According to Eq. (3) a student in the situation above will increase his or her study effort as long as the utility from one extra hour studying — through the resulting increase in future earn-ings — is larger than the disutility associated with the
extra hour of studies. In Fig. 1 a student with the
pro-duction function y1would choose to study L1hours
ther-eby reaching a maximum utility of U1
1.
Suppose instead that employers, because of infor-mation costs, cannot identify the productivity of the indi-vidual. What the employers can do is to distinguish between students who have earned a degree and those who have not. In this situation there will be no individual variation in productivity among those graduating. Future earnings will equal the productivity level associated with the degree. Students have no incentive to study more than necessary to graduate, since such extra efforts would not yield extra earnings. Suppose that the edu-cational achievement necessary to graduate is yˆ and that
the study effort resulting in this standard is Lˆi.
Assume also that the student has full information; i.e., he knows his production function. The student will then choose between two effort–earnings combinations:
max[Ui(Lˆi,yˆ),Ui(L0,y0)] (4)
The student, whose situation is depicted in Fig. 1, will
attain the standard yˆ by spending Lˆ1hours studying. The
maximum standard that the student will meet is y˜1. The
student would not choose to meet a higher standard than this because he or she would then experience a lower
utility than by choosing y0, which can be seen in Fig. 1.
The time a student studies, therefore, depends both on preferences and ability. However, the model produces the following important result: If we only study students who have completed their thesis (i.e., by excluding those
that have chosen (L0,y0)), the variation in effort Li can
be attributed to differences in ability only. This is seen
in Fig. 2, which shows the situation for three students. Student 1 meets the standard yˆ, completes his thesis, by
making an effort of Lˆ1 hours. Student 2, on the other
hand, does not complete her thesis. She chooses (L0,y0).
These two students have the same ability, i.e., the same production function, but different preferences, i.e., stud-ent 2 has higher preferences for doing something else than studying. Student 3 also chooses to complete her
studies with an effort of Lˆ3. This student differs from the
other two in both preferences and ability. It is obvious then that when comparing all three students’ preferences and ability both determine effort. But when only compar-ing completers — students 1 and 3 — the difference in
effort (Lˆ32Lˆ1) is attributable only to the different
pro-duction functions y3 and y1, i.e., to the difference in
ability.
Let us summarize:
Student Production Indifference Effort Thesis
function curve
1 y1 U1 Lˆ1 Completed
2 y2 U2 L0 Not
completed
3 y3 U3 Lˆ3 Completed
So far the assumption has been made that a student can only choose between meeting the standard yˆ or not. A more realistic description is that a student has a choice between different standards for different grades. Suppose there are two grades other than failing (as at Swedish
universities): passed (yˆp) and passed with distinction
(yˆpd). Compared to Eq. (4) the student now will choose
among three effort–earnings combinations:
Fig. 2. Choice of time for studies and future earnings under different production and utility functions.
max[Ui(Lˆ pd i ,yˆ
pd
),Ui(Lˆ p i,yˆ
p
),Ui(L0,y0)] (5)
Preferences and ability will determine the choice in the way illustrated by Fig. 3. The figure shows two stu-dents with identical production functions but different
indifference maps. Student 2 will choose to meet yˆp
while student 1 will choose yˆpd. The difference in study
effort between these two students is determined by pref-erences since they have the same production function. In general, it is the case that if we compare students who have completed with the same degree, the study effort is a matter of ability only. However, if we compare com-pleters (both those who have passed with distinction and those who have passed) it is not possible to attribute the difference in study effort to ability only.
In this study the focus is on study time (thesis
com-pletion time). Denote this by l*
i and say it is measured
in weeks. The completion time is by definition equal to
study effort, Li, divided by study intensity, Li/l*i (the
number of study hours per week) so that l*
i ;Li/(Li/l * i).
In the model the student is assumed to make a choice of the utility-maximizing effort. But there is also a choice of how to allocate this effort between study intensity and study time. This means that for two students with the same ability (the same production function and the same
Lifor a given standard) one of them may choose a
rela-tively low effort per week (Li/l
*
i) and thereby have to use
more weeks to complete the thesis. This is a question of differences in preferences, not in ability. It follows that to fully distinguish, in accordance with the theoretical model, between preference and ability effects one needs
information on study effort, Li.
3. Descriptive facts
Data have been collected for the 181 students who were registered in undergraduate theses courses in eco-nomics at Uppsala and Umeå during 1993. They were observed for three and a half years, which means a total of seven semesters. It is well known in Sweden that the-sis work often stretches out into at least a second sem-ester although the intention in theses courses is that the-ses should be completed within the first semester. Table 1 shows that at the end of the seventh semester, counting from when thesis work was started, the completion rate has converged to slightly less than four-fifths of the stu-dents. The completion process may vary between differ-ent groups of studdiffer-ents. Table 1 indicates that the time to complete is longer in Umeå than in Uppsala and longer for women than for men in Uppsala.
The information about these students available to us is the data recorded at the study registers maintained at the two departments. The variables extracted from these registers are the following (see Table 2): characteristics of students’ background (gender and age) and measures of earlier educational experiences (grade point average from secondary school and choice of the science study
program in secondary school).2 The students’ previous
record of finishing courses in (the intended) time is rep-resented by the time that has elapsed since they first passed an introductory course of economics. The stu-dents’ prior knowledge of economics is represented by the share of courses in economics where the student has received the highest grade: passed with distinction.
Students follow different study programs. This choice
Table 1
Completion rate of students
University Gender Cumulative share Share not Number of
completing students
1 2 3 4 5 6 7
Uppsala women 46 67 79 80 82 82 82 18 61
men 64 74 77 80 82 83 86 14 83
total 56 71 78 80 82 83 84 16 144
Umea¨ women 14 71 79 79 86 86 86 14 14
men 9 48 61 74 78 78 78 22 23
total 11 57 68 76 81 81 81 19 37
Total women 40 68 79 80 83 83 83 17 75
men 52 68 74 78 81 82 84 16 106
total 47 68 76 79 82 82 83 17 181
2Appendix B gives the exact definitions of the variables.
might reflect important differences in students’ aptitude for thesis work. Study programs may for instance vary in the number of papers that the student has had to write prior to the undergraduate thesis. Not all students are enrolled in study programs. Some students choose not to enroll in a study program, instead choosing to apply for one course at a time.
The thesis may be coauthored. Both advantages and disadvantages can be expected as a result. Having two or more (more unusual) authors is accompanied by the necessity of adjustment and cooperation in preparation, reading, and writing which, if not handled properly, might slow the work down. However, this potential dis-advantage might turn into an dis-advantage if handled suc-cessfully. A likely increase in the discipline of work, as well as other benefits of collaboration, could increase the pace of writing. Undergraduate theses are written on two levels: the C- and the D-thesis. For the more advanced D-level one would expect the work to take longer but this might be balanced out by the experience D-students have from their C-thesis. In the estimations there is an indicator variable for whether the thesis is on the D-level or not.
possi-Table 2
Arithmetic means
Uppsala Umeå Both
Not Pass Pass with Total Not Pass Pass with Total Total
completed distinction completed distinction
Background
Women, % 48 41 43 42 29 43 39 38 41
Age, years 26.5 25.6 24.7 25.5 27.9 27.0 26.4 26.8 25.8
Science program, 35 33 41 35 57 17 30 33 35
secondary school, %
Grade point average, 3.7 3.7 4.1 3.8 3.5 3.7 3.7 3.7 3.8
secondary school
Study time, 3.9 2.5 2.2 2.7 2.2 2.0 2.4 2.2 2.6
economics, years
High grades, 17 21 31 23 40 29 58 49 28
economics, % Study programs
Public administration, 26 36 19 30 14 14 22 19 28
%
Business economics, 35 25 30 28 29 43 44 40 30
%
Social science, % 9 6 14 8 14 0 22 16 10
International 9 6 11 8 – – – – 6
economics, %
Other programs, % 9 6 14 8 0 0 0 0 7
Single subject 13 21 14 18 43 43 13 24 19
courses, % Thesis
Coauthored, % 17 44 60 44 0 29 57 41 43
D-level thesis, % 35 13 14 17 29 0 22 19 17
Applied econometrics, 0 13 24 14 – – – – 11
%
Spring 1993, % 30 32 24 30 29 29 9 16 27
Number of students 23 84 37 144 7 7 23 37 181
(Number of students, (19) (72) (36) (127) (6) (4) (22) (32) (159)
grade point average)
bility of using the summer vacation for thesis work which would shorten the time for spring students when compared to the fall students.
Table 2 displays means of the variables at the two universities. The numbers are similar. According to the table it seems that higher theses grades are associated with younger students, with higher previous grades, with coauthoring, and with C-theses. One of the variables stands out more than others: Coauthors constitute a far smaller share of the students not completing their theses than of the total group of students. In Uppsala only 17% of noncompleters were coauthors while 44% of students that passed and 60% of those who passed with distinction
came from this group. The pattern in Umeå is similar.3
The students in the applied econometrics course in
Uppsala also seem to come out better than other groups of students, as far as the averages in Table 2 can lead us. Data in the table must be interpreted with great caution. Specific cells in the table may represent a small number of students, particularly for the case of Umeå.
4. Econometric specification
Our data have two drawbacks. First, we do not have information on the study intensity of students. We would
Table 3
Semester when the thesis is completed
Semester Number of students that Hazard rate, %
complete could potentially complete
1 85 181 47
2 38 96 40
3 14 58 24
4 6 44 14
5 5 38 13
6 1 33 3
7 2 32 6
Total 151 482 31
> 7 ? 30 ?
have preferred to have information about the total num-ber of days and the total numnum-ber of hours spent on the thesis work. Bearing this mind, we assume that study intensity is constant over students or at least does not vary systematically with regard to the explanatory vari-ables.
Second, our data are discrete. We know during which semester that thesis work was completed but we do not know the exact date when the thesis was completed.
Grouped data regression analysis (e.g., Bra¨nna¨s, 1987)
and discrete time hazard analysis (e.g., Allison, 1984) are two empirical methods that can be used to study data of this type. These methods provide a measurement of the probability of both the occurrence and the timing of an event. The empirical analysis presented here is based
on event history analysis.4
A time profile of thesis completion is given in Table 3. A data set consisting of 482 cases has been structed in the following way: The first 181 cases con-sists of the total number of observed students. They were all potential completers in their first semester of thesis work. Less than half of them did actually complete dur-ing the first semester (the hazard rate is 47%) leavdur-ing 96 students to potentially complete during for the second semester. Of these 38 did complete, leaving 58 potential completers for the third semester. Over the seven sem-esters the sum of potential completers in each semester is 482, which constitutes our data set.
Among the first 181 cases in the data set the dependent variable is coded 1 for the 85 completers and 0 for the 96 noncompleters. These represent the next 96 cases for
4We have also estimated grouped data regression models. The results are similar to those presented in this paper. The results are available on request.
which the dependent variable is coded 1 for the 38 com-pleters and 0 for the 58 noncomcom-pleters in the second semester. Working this way up to the seventh semester gives a total of 151 cases being coded 1 for the depen-dent variable equaling the number of studepen-dents completing their thesis during the observed period. In this way the data set gives information of whether a student has com-pleted his thesis or not. It also gives information of the time it has taken to complete. A student who has com-pleted during the first semester is only represented among the first 181 cases, thereby contributing 1 “per-son-semester” to the data set. A student completing dur-ing the third semester is represented as well among the first 181 and the second 96 cases (with a 0 in both cases for the dependent variable) as among the third 58 cases (with a 1 for the dependent variable) thereby contributing 3 “person-semesters” to the data set.
The last column of Table 3 shows that the hazard rate decreases over time. Less than 10 of the remaining stu-dents complete during the 6th and the 7th semester. We do not know if, and if so when, the remaining 30 students will complete. The following model has been estimated:
For the dependent variable, yit, we have
yit
5
H
1 if student i has completed during semester t,0 if student i has not completed during semester t.
The hazard rate, Pit, is the probability that student i
completes his/her thesis in semester t given that the the-sis is not completed in the preceding semester, or
Pit5Pr(yit51|yi(t21)50)
rep-resents the possibilities to complete the thesis for each of the students in semester t who did not complete before. These possibilities are determined both by
prefer-ences and ability. Suppose also that Zitis a linear
func-tion of the explanatory variables xit. We have
Zit5a 1 bxit
Assume also that the larger Zit, the larger the student’s
possibilities to complete in semester t. Say more specifi-cally that if Z > 0 the thesis will be completed. This
means that Pit 5F(Zit). Let F be a cumulative logistic
distribution function. Then
Pit5
1 11e−(a 1 bxit)
This results in the following likelihood function:
L5 P
which has been maximized with respect to the
para-meters a and b using the LIMDEP package (Greene,
1995). The estimation results are presented in Section 5.
5. Evidence
When describing the data we found that coauthors do much better than single authors. This still holds in the hazard estimation when simultaneously controlling for the effects of all other variables, as can be seen from Table 4. A coauthor is more likely to complete. The esti-mations also suggest that coauthors complete because of
ability, not preferences.5The coefficient in the estimation
for all is almost identical to that for completers. The theoretical model in Section 2 suggests that, given that adequate data are available, the estimated effects for stu-dents receiving a specific grade will capture differences in ability (or in educational production functions).
For D- (Master’s) thesis a conclusion also about the preference effect is possible. Students writing D-theses are less likely to complete than those writing C-theses (column 1 in Table 4). However, if the D-students do complete, and particularly if they do so passing with dis-tinction, they do it in a shorter time than the C-students who complete (column 4 in Table 4). An explanation of this result is that the preference and the ability effects work in opposite directions. The D-students are at the end of their education. They have a choice of giving up
5Ability should be broadly interpreted. In the theoretical model differences in ability are defined as differences in the educational production functions.
their plans for a D-thesis and graduating with a Bach-elor’s degree or completing their D-theses and gradu-ating with a Master’s degree. This is a question of prefer-ences. But if they do decide to complete, and particularly if they strive for the highest grade, the reason they do it in a shorter time than the C-students is because of higher ability (higher educational output for given study effort). They already have the training in thesis writing from their C-thesis experience.
Students in Umeå show a longer completion time than students in Uppsala. A reason for this could be the stricter rules for thesis work in Uppsala. At this univer-sity students are only allowed to present their theses dur-ing a two-week period at the end of each semester. There is a long wait for the next occasion if you do not finish in time. In Umeå students are allowed to present their theses whenever they are finished. It is possible that the Umeå students in this situation take a little longer time to complete. The way we have interpreted the theoretical model this will show up as an ability effect — the edu-cational production functions differ between the two uni-versities.
The hazard rate for thesis completion varies with time. The probability of completing is lower from the 4th sem-ester to the 7th semsem-ester compared to the 1st semsem-ester. If we restrict the sample to those completing and those passing, there are no significant time effects. This sug-gests that the decreasing probability over time has to do with preferences rather than ability. For the students who pass with distinction the hazard rate is higher in the 2nd and 3rd semesters compared to the 1st semester. This ability effect says that more effort is needed to receive the highest grade.
There are also some empirical results with borderline significance worth mentioning: High grades in prior eco-nomics courses increases the probability of completing. If we restrict the sample to those passing, the estimated effect is significant while it is still positive but insignifi-cant for the full sample. This suggests that the effect has more to do with ability than preferences.
We noted in Section 3 that women in Uppsala seem to take a longer time completing than men. The esti-mation results in Table 4 show a negative effect (significant at the 10% level) for women when compar-ing among all students but no such effect within the group of completers. So if women take longer time to complete than men this seems to be because of
prefer-ences and not ability.6
Table 4
The probability of completing a thesis, logit estimations
All Completing Passing Passing with dist.
Background
Woman 20.46 (1.65) 0.02 (0.06) 0.13 (0.25) 20.12 (0.18)
Age 20.07 (1.05) 20.11 (1.16) 20.12 (0.67) 0.03 (0.15)
Science program, 20.04 (0.13) 0.10 (0.29) 0.17 (0.31) 0.02 (0.02)
secondary school
Grade point average, 0.06 (0.18) 20.33 (0.73) 0.25 (0.32) 0.09 (0.12)
secondary school
Study time, economics 20.09 (0.70) 20.08 (0.41) 20.04 (0.14) 20.02 (0.05) High grades, economics 1.02 (1.51) 1.65 (1.94) 4.35* (2.36) 20.70 (0.46) Study program
Public administration 0.16 (0.41) 20.08 (0.15) 20.06 (0.08) 20.70 (0.47) Business economics 20.31 (0.66) 20.74 (1.29) 22.33* (2.24) 0.44 (0.44)
Social science 0.08 (0.14) 20.54 (0.82) 21.80 (1.18) 20.89 (0.90)
International economics 0.46 (0.66) 0.13 (0.15) 21.05 (0.74) 21.30 (0.76)
Other programs 20.14 (0.21) 20.78 (1.04) 20.40 (0.38) 22.91 (1.94)
Thesis
Coauthored 1.35* (4.56) 1.30* (3.72) 1.44* (2.80) 1.14 (1.64)
D-thesis 20.78* (2.13) 0.55 (1.16) 0.36 (0.52) 2.42* (2.43)
Applied econometrics 1.54* (2.60) 0.80 (1.32) 0.42 (0.46) 1.27 (1.10)
Fixed effects
Spring 1993 20.48 (1.44) 20.72 (1.87) 20.84 (1.70) 0.31 (0.33)
Umeå 21.00* (2.50) 21.57* (3.28) 21.34 (1.42) 22.13* (2.68)
2nd semester 0.21 (0.70) 0.66 (1.79) 20.16 (0.31) 2.20* (3.42)
3rd semester 20.41 (1.02) 0.48 (0.94) 20.35 (0.52) 3.90* (2.89)
4th semester 21.55* (2.34) 20.38 (0.49) 20.35 (0.43) 27.20 (0.04)
5th semester 21.12 (1.91) 0.98 (1.13) 0.61 (0.65)
6th semester 22.48* (2.37) 0.04 (0.03) 0.18 (0.14)
7th semester 21.68* (2.17)
Constant 1.19 (0.54) 4.14 (1.26) 2.59 (0.44) 20.52 (0.10)
Log likelihood 2197.50 2138.40 273.69 247.57
Restricted log likelihood 2258.69 2159.72 293.32 266.27
x2, significance level 0.0000 0.0035 0.0091 0.0071
Number of observations 409 234 136 98
Note: Absolute t-values in parentheses.
Students from the applied econometrics course are more likely to complete their theses than other students. This appears to be more an effect of preferences than ability since there is no such effect when comparing only among completers.
Students who first registered for thesis work in the spring semester of 1993, compared to their counterparts in the fall semester, are likely to take longer completing given that they do complete during the observed seven semesters. This is shown by the estimation results of models 2 and 3. It is a result opposite to the one we expected. Our hypotheses, described in the preceding
6. Concluding remarks
The reasons why some college students do not finish their studies are important. It can be a matter of prefer-ences, e.g., when a student chooses not to complete a
thesis because he or she has something better to do.7But
if the reason instead is lack of ability there is an edu-cational problem. Let us point to four main findings.
First, students that coauthor their thesis are more likely to complete than single authors. This appears to be because of ability and not preferences.
One would expect that there exist preference effects since the students choose if to coauthor and their coauthors. A student who expects to take a long time to complete a thesis may choose to be a single author because he does not want to risk slowing a coauthor down or because he does not want to be pushed to work by the coauthor. However, it seems in the literature on cooperative learning at the college level that ability increasing effects also can be expected as a result of stu-dents collaborating (e.g., Bartlett, 1995; Light, 1992;
Maier and Keenan, 1994). Our finding supports
employing ideas of cooperative learning at colleges, for
ability reasons, also in thesis work.8
Second, D-students have a better training in writing than C-students for obvious reasons. If they choose to complete (at least for those that receive the highest grade) they are found to do so in a shorter time than C-students. This finding of a positive effect of the experi-ence of thesis writing on subsequent attempts may seem obvious. However, the education of economists (with a Master’s degree) in Sweden today is organized so that only one half of the final year is set aside for thesis work, while there is little if any writing assignments in prior courses. That a large portion of the students either take longer to complete or do not complete their thesis is not surprising in light of our finding concerning the impor-tance of writing practice.
Third, students in Uppsala are more likely to complete than the students in Umeå. A reason for this could be the stricter rules for theses work in Uppsala. Students are only allowed to present their theses during a two-week period at the end of each semester in Uppsala. In Umeå students are allowed to present their theses when they are finished. The threat of a long wait may induce students to complete faster. Our estimations suggest that
7Tinto (1987) argues that leaving college should not be regarded as a student failure. Instead, he argues, the environ-ment created by the college very much determines the prob-ability of finishing.
8Necessary steps of course must be taken to prevent free-riders from taking advantage of this arrangement. At the two departments studied the instructors and examiners supervise the process and both coauthors must defend their work satisfac-torily at a seminar before theses are passed.
the difference between the two departments has to do with differences in ability (or educational productions functions).
Fourth, the probability of completing decreases over time. This decrease has to do with preferences according to the estimations.
There is also some weaker evidence that students with high grades are more likely to complete and that women take longer time to complete their theses. The positive impact of high grades seems to be associated with ability.
In some studies of student activities gender has been treated as a taste variable (e.g., Chizmar, 1986). Our results appear to confirm this. The estimations suggest that if there is a lower probability for women to complete this is because of their preferences, not their ability.
Acknowledgements
We want to thank the Swedish Council for Studies of Higher Education for funding Curt Lo¨fgren’s partici-pation in the project. We are indebted to Kurt Bra¨nna¨s and Per Johansson for invaluable help. We also would like to thank two anonymous referees, Yngve Andersson, Roger Axelsson, Mats Bergman, Thomas Lindh, Karl-Gustaf Lo¨fgren, Jørn Stage, Eskil Wadensjo¨, and Maria Vredin for useful comments and suggestions. Helpful comments from seminar participants at the 1995 EEA Congress in Prague, at a Swedish National Agency for Higher Education 1997 conference, and at Umeå and Uppsala University are also gratefully acknowledged. We are also grateful to Berit Levin and Johan Lundberg for preparing the data.
Appendix A
Undergraduate economics in Sweden
the micro text while Mankiw (1994) Macroeconomics is used for the macro course. The other two courses are optional (finance, cost–benefit analysis, mathematics for economists, etc.) The advanced courses, the C-level and the D-level, start with micro and macro (five weeks each) and a thesis the first semester. The texts used are Grav-elle and Rees (1992) Microeconomics and Scarth (1988)
Macroeconomics. Students with C-level in economics
and six semester courses in total get a Bachelor’s degree. With this degree they are eligible to apply to Ph.D. pro-grams. Alternatively they can take the D-level in eco-nomics and another semester course to receive a Mas-ter’s degree (which is not a graduate degree). The D-level at Uppsala University consists of courses in public economics and finance (five weeks each) and a thesis.
Appendix B
Definition of variables
Woman, 1 for women and 0 for men.
Age, age of students when registered for the thesis course.
Science program, secondary school, 1 for students with a degree from the Natural Science Program (Natural Science or Technical branch) and 0 other-wise.
Grade point average, secondary school. Grades are given on a scale from 1 to 5, where 5 is the high-est grade.
Study time, economics. Number of years, when registered for the thesis course, since the student first passed in a course in economics.
High grades, economics. The share of five courses in economics where the student has received the grade pass with distinction. The five courses are introduc-tory and intermediate micro- and macroeconomics and advanced macroeconomics.
Public administration, 1 for students enrolled in the study program of public administration and 0 other-wise.
Business economics, 1 for students enrolled in the study program of business administration and eco-nomics and 0 otherwise.
Social science, 1 for students enrolled in the general study program of social sciences and 0 otherwise.
International business economics, 1 for students enrolled in the international study program of busi-ness administration and economics and 0 otherwise. Other programs, 1 for students enrolled in other study programs than those above and 0 otherwise. Single subject courses, 1 for students enrolled in sin-gle subject courses and 0 otherwise.
Coauthored, 1 if there are more than one author of the thesis and 0 for single authors.
D-theses, 1 for D-thesis and 0 for C-thesis. Applied econometrics, 1 for students enrolled in the course of applied econometrics and 0 otherwise. Spring 1993, 1 for those first registering for the thesis course during in the spring semester 1993 and 0 for the fall 1993 semester.
Umeå, 1 for Umeå students and 0 for Uppsala stu-dents.
1st, 2nd, 3rd semester etc., 1 for observations in the first, second, third semester etc. and 0 otherwise.
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