as it disccused

 

Use Google Scholar to find a peer-reviewed journal article related to the topic we will be discussing over the next few weeks: Higher education.

In bullet point format, summarize the main points of the article in your own words to the best of your ability. If you desire, feel free to include your thoughts on the article in the final bullet points.

Format (an example is attached):

On the top line of the first page, insert the citation of your chosen paper in 

APA reference style format

.

Bullet points shall be double-spaced from each other, but each bullet point shall be single-spaced. (Again, see attached example.)

12 pt., Times New Roman font.

      This is a low-stakes assignment, but you are required to format your summary as instructed.

Suggestions:

1. If you must do a research project/paper for another course, then note you are welcome to use this assignment to augment that effort.
2. It is highly unlikely you will understand the entirety of 99% of peer-reviewed journal articles (that are worth reading, at least). 
3. Try using varieties of search terms. Google Scholar is sensitive to even the slightest variations in search terms. For example: Let’s say you want to find a paper on the effect of marijuana usage on college students’ academic performance. Searching “marijuana college students” returns 95,500 results and the top hit is “The residual cognitive effects of heavy marijuana use in college students”. Searching “marijuana university students” returns 112,000 results and the top hit is “Alcohol and drug use in UK university students”.
4. Find an article title that seems interesting? I suggest you follow these steps:No more than two pages. Upload your summary on Blackboard.

   Okay, now read the abstract. If it fails to interest you, or seems too “mathy” or otherwise unintelligible, then keep searching. There are tens of thousands of articles on any topic you can fathom that you should find fascinating, somewhat intelligible, and relevant to your interests. You are significantly more likely to enjoy this assignment and learn exponentially more if you choose your articles with care.
   Note the number of citations–Google Scholar results report “Cited by ___” and automatically presents search results with the most highly cited papers up top. More citations mean the journal is highly respected, the article’s findings are very influential, and professors are more likely to recognize the journal/article/author(s)—undergraduates are forgiven for citing lame articles, but usually highly rewarded when professors realize you’re grappling with the highest caliber of respected research.
   Note the date of publication. Whether the date of publication matters depends on context. An article presenting statistics on marijuana use amongst college students conducted in 1985 is probably not relevant, but an article on the “influence of spirituality on substance abuse by college students” from 2001 is still very much relevant. Use common sense.
   If using Google Scholar, click “Cited by” and “Related articles” and quickly scan the top results. Very often you’ll find better articles published more recently. Note that newer articles usually have fewer citations, which makes sense. If a “Cited by” or more “Recent article” has more citations, that’s powerful signal it’s a much better and more influential paper. Check it out.However, the most important criteria I suggest for picking papers: Is it interesting—does it immediately appeal to your interests? Can you understand most of the abstract? Do the main findings sound important, and perhaps contrary to your current beliefs?
   You’ve chosen a paper. Now scan the article and make sure it’s not 90% equations that you’d need a PhD to comprehend and it isn’t ridiculously long. (Top journals usually limit papers to 40 pages—don’t freak out, that’s an upper bound and most papers have lots of graphs, tables, endnotes, etc.)
   Now, read the introduction, then skip to the conclusion. A quality paper gives you all important points in these two sections.
   Now, read the rest of the article to the best of your ability. On the first pass just read without stopping even if you don’t understand most of it. Scholars often rely on specialized methods only very few specialists can comprehend. Don’t sweat it, just plow through. Exposure to this portion of the paper is one of my main objectives for this assignment, which I will explain several times during class. Now, do something else to take your mind of the paper for a while. After at least a few hours, take another shot at reading the guts of the paper. You’ll be surprised to discover how much you can learn from high-level research it you have faith in yourself and use a disciplined approach.
   Download the paper.
   Now, open a Word document. Make sure it’s set to Times New Roman, 12-point font If you use any other font or style I will set you on fire.
   Click the “References” tab, select “APA”, click “Manage Sources”, then “New”, and then “Journal Article”. Fill out the fields using information from the article.
   At the top of the first page, insert your citation by clicking “Bibliography” and “Insert Bibliography”.
   In bullet point format and using your own words, summarize the article using the same order as the article. Underline one or two main bullet points that you think are the most interesting and/or important, please—this is for my benefit.
   The last bullet point is an opportunity for you to tell me your opinion of the article, its value (if any), and what you learned from the article and the assignment. Please be honest. I often use honest feedback to recalibrate assignments, and even the harshest criticism is welcome if it’s clearly articulated and supported.

Nunn,N. (2008). The Long Term Effects of Africa’s Slave Trades. Quarterly Journal of Economics, 123(1), 139-176.
Introduction:
Nunn’s article begins by stating the need for the research. He states that parts of Africa have continuously faced economic underdevelopment (a trend that still continues to this day).
This economic underdevelopment has typically been attributed to Africa’s colonialist past (especially European colonialism) and Africa’s many waves of slave trading.
Citing a lack of empirical analysis on the latter topic, Nunn proceeds with the thesis of his paper: African countries more affected by the slave trades are more likely to face current economic underdevelopment and stagnation.
Nunn recognized that this negative relationship between countries most affected by slave trade and current GDP may not have a causal connection. For this reason, he goes on to rule out any other intervening variables that could serve as a better explanation.
One of the main possibilities of an alternative casual explanation is that the countries most affected were countries that were already struggling (before 1400 A.D.)
To rule out this rival hypothesis, Nunn consulted two different sources of information: (1) African Historical experts on why certain areas were selected over others to be involved in the slave trades, and (2) historic population data for various African countries.
Both seem to disprove the rival hypothesis because data shows that the areas most affected by the slave trades are the areas that were the most developed previously.
Historical Background:
The African Slave Trades lasted for about half of a millennium (1400-1900 approximately) and essentially cut the population of Africa in half.
There were four main waves of slave trading focusing on different regions at different times, but the most recognized is known as the Trans-Atlantic slave trade. (Slaves shipped from West/West Central Africa to Europe and the Americas)

· The three other waves include: the trans-Saharan slave trade, the Red Sea slave trade, and the Indian Ocean slave trade.

· The African slave trades are the most massive in history with approximately 12 million people exported in just the trans-Atlantic trade (6 million in the 3 others combined) (figures not including those who died in raids or travel).

· Another reason that Nunn stated for the uniqueness of these trades is that this was the first time that certain ethnicities began to enslave their own people.

· Nunn stated that this is a direct cause of increased “social and ethnic fragmentation” that weakened states and pre-trade institutions. (This also highly contributed to rampant amoral familism and the destruction of the African tribal system (C.f. George Ayittey))

· The slave trades caused much infighting between different African groups. This lead to a further deterioration of prior social, political, and judicial institutions. Corruption grew uncontrollably.

Slave Export Data:

· Nunn states that his mode of measurement for this study is the number of slaves taken from each country during the years 1400-1900.

· To obtain the total number of slaves exported from Africa, Nunn used shipping data from 34,584 voyages from 1514-1866 as well as other databases that have been extensively compiled over several years.

· In order to locate where the slaves originated from in Africa, Nunn looked at data that reported their ethnic identities. (Data from “records of sale, slave registers, slave runaway notices, court records, church records, and notarial documents.”)

· Nunn then created an algorithm to try and accurately show (as much as humanly possible) in which region each slave originated.

Basic Correlations: OLS Estimates

· This section is very math intensive, but basically Nunn runs several linear regression models showing that there is indeed a relationship between countries with a history of high slave trade and low GDP (2000 was the year used). He uses the same methods to show how other rival hypotheses are not adequate in addressing causality.

Conclusions:

· The African slave trade is a significant contribution to current economic underdevelopment in the region. Ethnic fractionalization and the destruction of institutions are byproducts that continue to manifest themselves in modern day Africa. Data (qualitative and quantitative) also shows that more affected areas were previously more developed. The slave trade definitely contributed to stunted growth.

Journal of Economic Perspectives—Volume 7, Number 3—Summer 1993—Pages 167–174

Do Students Go to Class? Should They?

David Romer

L
ectures and other class meetings are a primary means of instruction in
almost all undergraduate courses. Yet almost everyone who has taught
an undergraduate course has probably noticed that attendance at these

meetings is far from perfect. There is surprisingly little systematic evidence,
however, about attendance and its effects. There are three natural questions.
What is the extent of absenteeism? How much, if at all, does absenteeism affect
learning? Should anything be done about absenteeism?

This article presents quantitative evidence on the first two of these ques-
tions, and speculative comments on the third. First, attendance counts in
economics courses at three relatively elite universities indicate that absenteeism
is rampant: usually about one-third of students are not at class. Second,
regression estimates of the relation between attendance and performance in
one large lecture course suggest that attendance may substantially affect learn-
ing: considering only students who do all of the problem sets and controlling
for prior grade point average, the difference in performance between a student
who attends regularly and one who attends sporadically is about a full letter
grade. In light of these results, steps to increase attendance, including making
attendance mandatory, may deserve serious consideration.1

1I have been unable to find any previous investigations of the extent of absenteeism. There have
been a few other studies of the relation between attendance and performance (for example,
Schmidt, 1983; Park and Kerr, 1990). These studies generally confirm the findings here that
attendance and performance are related even when a variety of student characteristics are
controlled for. The present study differs from this earlier work in focusing on the quantitative
magnitude of the relationship and on the issue of the extent to which the relationship reflects a
genuine effect of attendance.

• David Romer is Professor of Economics, University of California, Berkeley,
California.

168 Journal of Economic Perspectives

Do Students Attend Class?

Counts were made of the number of students attending one meeting of
every undergraduate economics class during a “typical” week of the spring
1992 semester at three schools. School A is a medium-sized (6000 undergradu-
ates) private university; School B is a large (20,000 undergraduates) public
university; and School C is a small (2500 undergraduates) liberal arts college.
The schools are intended to be representative of the upper echelons of Ameri-
can colleges and universities. All three are classified by Barron’s Profiles of
American Colleges (1991 edition) as “highly competitive,” the second highest of
their six categories.2

The attendance counts were made a few weeks before the end of the
semester at each school. This choice avoided both times when attendance is
generally thought to be unusually low (such as just after exams and immedi-
ately before and after vacations) and times when it is generally thought to be
unusually high (such as just before exams). Individuals at all three schools
independently suggested that attendance a few weeks before the end of the
semester was likely to be representative of average attendance. Attendance was
taken at one meeting of each class during the sample week. Current enrollment
figures were obtained from departmental offices.

Table 1 reports the results. The first row shows the overall absenteeism
rates at each school. At School A, 34 percent of students were absent; at School
B, 40 percent; and at School C, 25 percent.3 In short, on a typical day at a
typical elite American university, roughly one-third of the students in eco-
nomics courses are not attending class.

The remaining rows of the table break down the overall figures along
various dimensions. Course size appears to have an important effect on absen-
teeism. At all three schools, absenteeism is considerably lower in the smallest
third of classes than in the largest third. In addition, average class size is lowest
at School C and highest at School B, which is consistent with the fact that
absenteeism is lowest at C and highest at B. Absenteeism is also lower in courses
with a significant mathematical component (such as econometrics, honors
sections of intermediate theory, and field courses in theory). This pattern holds

2A total of 127 schools, enrolling about 675,000 undergraduates, are classified by Barron’s as
“most competitive” or “highly competitive.” Of these, 24 schools, with 140,000 undergraduates,
are private universities with between 4000 and 10,000 undergraduates; 14, with 270,000
u n d e r g r a d u a t e s , are public universities with over 10,000 undergraduates; and 56, with
100,000 undergraduates, are colleges or universities with minimal graduate programs and fewer
than 3500 undergraduates. T h e remaining schools are small and medium-sized public colleges and
universities (15 schools, with 70,000 undergraduates), large private universities (5 schools with
70,000 undergraduates), and small private universities (13 schools with 30,000 undergraduates).
3 A t t e n d a n c e counts were inadvertently not made in a handful of classes at School C. These classes
do not appear to differ in any systematic way from the classes at which attendance was taken.

David Romer 169

Table 1
Absenteeism Rates in Economics Classes

at all three schools.4 Similarly, at all three schools absenteeism is somewhat
higher in core courses than in field courses.

Finally, it is generally perceived, not surprisingly, that students attend class
more often when the quality of instruction is higher. At School B, for example,
absenteeism is 34 percent for courses taught by regular faculty and 47 percent
for courses with other instructors. To investigate this issue more systematically,
course evaluation data for all undergraduate economics courses for one term
were obtained from a fourth school, School D. This school, like School B, is a
large public university. The two variables of interest are students’ average
rating of the overall effectiveness of the instructor and the fraction of the
students enrolled in the course who returned the course evaluation form
(which is a reasonably good measure of attendance at one of the last class
meetings of the term). The point estimates from a simple regression of the
fraction of students attending the class on the average rating imply that raising
the average rating from the 25th percentile to the 75th lowers absenteeism by
10 percentage points; the t-statistic on the rating variable is 3.4. Thus the
quality of instruction (or at least students’ perception of that quality) appears to
have an important impact on attendance.

4The figures for mathematical courses at School A are based on only one course. Thus this figure
should be given little weight.

170 Journal of Economic Perspectives

Other features of the data from School D generally confirm the findings
for the other schools.5 Absenteeism is high (45 percent across all courses), and
lower in small courses than in large (31 percent in the smallest third of courses
and 54 percent in the largest third). Again, absenteeism is lower in courses with
a mathematical emphasis (39 percent, versus 47 percent for other courses), and
higher in core courses (52 percent, versus 31 percent in field courses that only
require principles and 37 percent in advanced field courses).

A straightforward regression confirms these patterns of differences in
absenteeism across different types of courses. Specifically, using the data from
all four schools, I ran a regression (across courses) with the fraction of students
absent as the dependent variable, and a constant, the log of enrollment, and
dummies for mathematical content, for the two types of upper level courses,
and for three of the four schools as independent variables. The resulting
estimates imply that a doubling of enrollment is associated with a rise in
absenteeism of 4 percentage points; that mathematical content is associated
with a fall in absenteeism of 3 percentage points; and that moving from a core
course to either type of field course is associated with a fall in absenteeism of 5
to 7 percentage points. The coefficient on the enrollment variable is highly
statistically significant; those on the field course dummies are marginally so;
and those on the dummy for mathematical content and the three school
dummies are insignificant.

Should Students Attend Class?

These findings raise the question of whether absenteeism has a substantial
effect on learning. It is possible that students do not attend class because they
would learn relatively little if they did—because the instruction is of low quality,
or because they have already mastered the material, or because they can learn
the material better by spending the same time studying in other ways. Alterna-
tively, it is possible that learning is severely adversely affected by absences, but
that many students are absent anyway—because they have genuinely better
uses of their time, or because they mistakenly believe that attendance is not
important to learning, or because they attach relatively little importance to
learning.

Because student attendance is not exogenous—students choose whether to
attend class—it is not possible to isolate definitively the impact of attendance on

5The data from School D are not strictly comparable with those from the other schools, because
they reflect class meetings at the end of the term and because a few students are present but do not
return the evaluation form. It seems unlikely that these differences have any substantial effect on
the results.

Do Students Go To Class? Should They? 171

learning. But this section presents some suggestive evidence. In the fall 1990
semester, I took attendance at six meetings of my large intermediate macroeco-
nomics course. The resulting data can be used to investigate the relation
between attendance and performance.

As in other courses, overall absenteeism was high (25 percent). Twelve
percent of the students missed four or more of the meetings where attendance
was taken; 28 percent missed two or three; and 59 percent missed none or one.
Thus, absenteeism appears to be a mixture of some students missing most
classes and many students missing a smaller number of classes.

Student performance is measured as the overall score on the three exams
in the course. For ease of interpretation, the scores are converted to the usual
4-point grading scale: 3.84 and above represents an A; 3.50 to 3.83 an A ;
and so on down to 1.50 to 1.83 for a C . Because no D + ‘s or D ‘s were
assigned, 1.17–1.49 represents a D and 1.16 and below an F.

The first column of Table 2 reports the results of a simple regression of
performance on the fraction of lectures attended.6 The regression reveals a
statistically significant and quantitatively large relation between attendance and
performance. The t-statistic on attendance is 6.2; the point estimates imply that
a student who attends only a quarter of the lectures on average earns a 1.79
(C ), while a student who attends all of the lectures on average earns a 3.44
(B + ). Attendance alone accounts for 31 percent of the variance in
performance.

Students who are more interested in the material, or more skilled academi-
cally, or more focused on academics are almost certain to attend class more
often than students who are less interested, less skilled, or less focused (other
factors held constant). If this is the case, then the results in Column 1 of

Table 2

to some extent reflect a general impact of motivation on performance rather
than a true effect of attendance.

I attempt to address this problem in three ways. First, I restrict the sample
to the 60 percent of the students who did all nine problem sets. It seems likely
that most of the students who were not devoting serious effort to the course did
not complete all of the problem sets. In addition, the lowest problem set score
was dropped in computing the course grade; thus the students who completed
all nine may have been especially motivated. On both grounds, this restricted

6There is one econometric complication worth mentioning: because attendance was not taken at
every class meeting, some of the variation across students in measured attendance is due to
measurement error rather than to true differences in attendance over the whole semester. If the
class meetings at which attendance was taken were a random sample of all the meetings—which
appears to be a good approximation—it is straightforward to estimate the size of the measurement
error. This procedure implies that 28 percent of the variation in measured attendance represents
measurement error. This estimate can be used to correct the regression coefficients, standard
errors, and R2’s for the bias that would otherwise be introduced by the measurement error. All of
the results reported in Table 2 have been corrected in this way.

172 Journal of Economic Perspectives

Table 2

The Relationship Between Attendance and Performance

sample may be more homogeneous in terms of general motivation than the full
class. But as the second column of Table 2 shows, the relation between
attendance and performance in this sample is actually slightly stronger than for
the class as a whole.

Second, doing the problem sets is arguably as good a proxy as attending
the lectures for motivation. But Column 3 shows that there is a much stronger
relation between attendance and performance than between doing the problem
sets and performance: when both variables are entered in the regression, the
coefficient on the fraction of lectures attended is almost three times as large as
the coefficient on the fraction of problem sets completed. Thus, either atten-
dance is a much better proxy than completing the problem sets for motivation,
or attendance has a large additional impact on performance.

Third, data were obtained on students’ grade point averages as of the
beginning of the semester. Including GPA as a control variable in the regres-
sion serves to control for some of the differences across students in general
ability and motivation. In fact, because students’ academic performance in
previous classes depends in part on their attendance in those classes, the
coefficient on prior GPA will capture some of the effect of attendance on
performance; as a result, including GPA as a control variable could cause the
coefficient on attendance to understate the true impact of attendance on
performance.

David Romer 173

Column 4 of Table 2 shows the effects of including grade point average in
the regression. Prior GPA has an extremely strong relation with performance.
But the inclusion of GPA has little impact on the relation between attendance
and performance. The coefficient on attendance is two-thirds as large as it is in
the basic regression in Column 1, and it remains highly significant. The point
estimates imply that a student with the mean prior GPA earns on average a
2.13 (C) if he or she attends a quarter of the lectures but a 3.27 (B + ) if his or
her attendance is perfect.

Finally, Column 5 shows the results of both restricting the sample to the
students who did all nine problem sets and controlling for prior GPA. Even in
this case, the relation between attendance and performance remains large and
significant. The estimates imply that a student with the mean prior GPA earns
on average a C + if he or she attends only a quarter of the classes, compared to
a B + if attendance is perfect.

None of these ways of attempting to address the problem that attendance is
not exogenous is definitive. Nonetheless, they all give similar results: simple
ways of controlling for motivation and other omitted factors have only a
moderate impact on the relationship between attendance and performance.
Thus, although the possibility that the relationship reflects the impact of
omitted factors rather than a true effect cannot be ruled out, it seems likely that
an important part of the relationship reflects a genuine effect of attendance.

Should Attendance Be Mandatory?

Absenteeism is rampant in undergraduate economics courses at major
American universities. In addition, there is a very strong statistical relationship
between absenteeism and performance, and the evidence is consistent with the
view that this relationship has an important causal component.

These results raise the question of whether measures should be taken to
combat absenteeism. At the very least, exhortations to attend class seem called
for, and those exhortations can be backed up with data. But stronger measures
might be preferable. A generation ago, both in principle and in practice,
attendance at class was not optional. Today, often in principle and almost
always in practice, it is. Perhaps a return to the old system would make a large
difference to learning. There is no way to find out but to try. I believe that the
results here both about the extent of absenteeism and its relation to perfor-
mance are suggestive enough to warrant experimenting with making class
attendance mandatory in some undergraduate lecture courses.

One could also use mandatory attendance to perform a genuine controlled
experiment that could isolate the true impact of attendance on mastery of the
material. Specifically, one could randomly divide the students in a course into
two groups, an experimental group whose grading was based in part on

174 Journal of Economic Perspectives

attendance and a control group whose grading was not. By comparing the

attendance and the performance of the two groups, one could learn both the

impact of mandatory attendance on absenteeism and the impact of attendance

on performance.7 Unless either the impact of mandatory attendance on absen-

teeism or the size of the class were very large, the results of carrying out this

experiment for a single class would not allow one to estimate the impact of

attendance on performance with much precision. But the pooled results from

several such experiments could.

• I am grateful to Caroline Fohlin, Matthew Jones, and Costas Tsatsaronis for excellent

research assistance, and to Robert Cox, Roger Farmer, Steven Fazzari, Alan Krueger,

Christina Romer, Paul Ruud, Joseph Stiglitz, Timothy Taylor, and Robert Turner for

useful comments.

7Such an experiment would presumably require appropriate approval. Students could be given the
right to opt out of the experiment by being allowed to choose (before the class is divided into the
experimental and control groups) to have their grade based on a formula that gave attendance half
the weight used in the grading formula for the experimental group. Fairness could be ensured by
assigning grades to all students using all three procedures (experimental, control, and opting out),
and making the mean grade for the full class the same under all three procedures, before the
allocation of the students to the three groups was known to the person assigning grades.

References

Park, Kang H., and Peter M. Kerr, “De-
terminants of Academic Performance: A
Multinomial Logit Approach,” Journal of Eco-
nomic Education, Spring 1990, 21, 101–11.

Schmidt, Robert M., “Who Maximizes
What? A Study in Student Time Allocation,”
American Economic Review, May 1983, 73:2,
23–28.

http://pubs.aeaweb.org/action/showLinks?crossref=10.2307%2F1181978

Do Students Go to Class? Should They?
Do Students Attend Class?
Should Students Attend Class?
Should Attendance Be Mandatory?
References