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David Levine's Cheap Advice for Writing a Dissertation

David Levine's Cheap Advice for Presenting Results

David Levine's Cheap Advice for Going on the Job Market

David Levine's Cheap Advice for Starting as a New Faculty

David Levine's Cheap Advice:

Going on the Job Market

To go on the academic job market requires that by late November you have one pretty good paper, and two paper ideas. When first sending your packet to departments, ask for a faculty list with research interests, so you will know with whom you are dealing.

Questions you are likely to be asked many times

1. Tell me about your thesis.

§  Prepare a five minute talk. Sound enthusiastic! Realize that most of your interviewers know little about your field, so provide lots of motivation for your study. Practice the talk with friends before the meetings.

§  Prepare answers to the three or four most frequent questions and criticisms of your work.

§  Prepare a few thoughts about further research concerning your thesis.

2. When do you think you will finish your dissertation?

Your odds of landing a job are higher if you can convincingly argue you will be done by June (best) or at least by September. Employers do not like new assistant professors to arrive with unfinished dissertations (although many such assistant professors exist).

3. What are you thinking about working on next?

Describe one or two research projects that you would like to work on next. You don't need to carry out these exact projects. You can include extensions of your thesis: ideas it has spawned, or tests of it you will someday do. At least one idea should be distinct from your thesis. On the other hand, don't be scattered. Most of your ideas should hang together, as if you were writing a book or two.

4. What courses would you like to teach?

Be sure to know the needs of the department before answering this one. Mention undergrad and grad courses, and perhaps an advanced grad course. The advanced courses are sought after; don't sound as if you demand to teach one. Mention a service course (intro, micro, or macro) that you are willing to teach.

Know the text you might use in the course, particularly for undergraduates. (You can ask around for plausible texts, and perhaps reading lists.) Think about an outline of topics for one or two courses.

Questions to ask

The interviews are also for you to find out things. Also, asking questions shows you are interested, and fills gaps in the conversation that the incompetent interviewers leave when they run out of things to say. Do not discuss money. There is lots of time for that.

§  Facilities: library, research assistants, computers, research institutes, etc.

§  Teaching:

o   Load: anything above four quarters or 3 semesters a year is heavy, below that is light.

o   Likely courses you would teach

o   The choice you would have

§  Possible to create your own course?

o   Quality of students: undergrad and grad

§  Faculty:

o   Size, interests, mix of fields

§  Visit faculty web pages to find out who is in your field beforehand.

§  Department: direction it is going, research vs. teaching,

§  Later in the process you can ask about:

o   Class size, TA's, readers, undergrad vs. grad

o   Summer money and travel money

o   Tenure patterns (% assistants who get tenured)

o   Secretary, photocopy and phone rules, parking

§  These sound minor, but can be important in the long run

o   Personal computer and printer; access to a larger research machine

o   Housing help: search, mortgage, faculty housing, flybacks to look for housing

o   Research assistant

o   Reduced teaching load first year

o   Minimize # of preparations (separate courses) you teach in one year

o   Any grants that are available. (Some may have deadlines before your arrival.)

Helpful links[1]

Job Sites

§  Jobs for Economists

§  Academy of Management (AOM)

§  Chronicle Careers

o   Mgmt:

o   Policy:

§  Association for Public Policy Analysis and Management (APPAM)

§  Academy of International Business

Other Useful Sites

§  On the market for junior economists at the JOE website:


§  The importance of asking good questions

§  Landing Your First Job

§  On negotiating your offer

Final Hint

The key to the interview is to be enthusiastic. Interviewers are looking for (1) people who would accept an offer, and (2) nice colleagues. The first consideration implies that you should have a reason why the interviewer's school is really your first choice ("Oh, I just love -40 in winter and searing heat in summer," she explained to the Minnesotans). The second point implies that you must be friendly, witty, and easygoing (during the high-pressure meetings!). Nothing is more important than being friendly and collegial. They are looking for someone they would like to have lunch with every day-this dominates most other criteria.

This is just one in a series of David Levine's Cheap Advice. Collect them all! Dazzle your friends! Feel free to circulate to anyone who might find them useful.

David Levine's Cheap Advice for Writing a Dissertation.

David Levine's Cheap Advice for Presenting Results

David Levine's Cheap Advice for Going on the Job Market

David Levine's Cheap Advice for Starting as a New Faculty

Write to David Levine, Haas School of Business, University of California, Berkeley CA 94720; for additional Cheap Advice, or to give Cheap Advice back to the source. Feedback is always appreciated.

David Levine's Cheap Advice:

Writing a Dissertation

I was trained as a graduate student to read other people's research. Then I finished my special field exams and was told to go write some research. I compare my experience to learning how to read literature, then being asked to do creative writing. Not unrelated, but hardly the same set of skills. Here is some advice I found helpful when I was searching for a topic.

Hints for finding paper topics: Choosing a paper topic requires balancing the importance and feasibility of the topic. Feasibility involves tractability for theoretical research, and data availability for empirical research. I recommend creating a number of one-page paper proposals, each with a topic sentence and data source.

There are several heuristics for finding researchable paper topics. One way is to list three of four papers you like. This list may hint at the style, political point of view, level of abstraction, and methods that are appropriate for you.

Other paper ideas are motivated by bad research, not good: find statements by policy-makers, analysts and journalists that make you angry, and find data sets to address the issue. When studying for exams, keep a list of stupid things you read to return to during the dissertation stage.

Don't be afraid of normal science: repeating in a longitudinal data set what others have done only in the cross section. Some paper ideas are data-driven: read codebooks from a variety of data sets and see if certain questions spark ideas or tests of theories you find interesting. For every paper you read, think what else you would have done with that dataset or methodology.

Write a paper, not a dissertation. A dissertation can be a daunting prospect. If possible, focus on a 25-page paper with a clear one-sentence topic. If you like this paper, then it can become the basis of a dissertation. I promise you that just about any topic can grow to 150 pages; nearly all topics students first choose are WAY too broad.

A dissertation is any stack of papers with three signatures on the top sheet. Paul Samuelson's dissertation was modestly entitled Foundations of Economics. It lived up to that title, and remains a classic to this day. I initially felt that my dissertation should be, if not that good, at least vaguely comparable in the sense of answering one of the Big Questions.

Do not fall prey, as I did, to the "magnum opus fallacy". A dissertation should be normal science that certifies your competency. Save the big thoughts in a file, and get finished! Write an opus when you are a professor, and are being paid for it. Meanwhile, negotiate the smallest useful chunk of the Big Project with your dissertation advisers, and do that single piece of research.

A dissertation group is essential. Having folks to bounce your ideas off of at an early stage is irreplaceable. The members of your dissertation group do not need to have similar fields; any smart person will be useful at each stage of a project, from vague ideas through research design, and write up. All you need are people who are willing to listen to raw ideas, and willing to read first "pre-drafts" of chapters.

This is just one in a series of David Levine's Cheap Advice. Collect them all! Dazzle your friends! Feel free to circulate to anyone who might find them useful.

David Levine's Cheap Advice for Writing a Dissertation

David Levine's Cheap Advice for Presenting Results

David Levine's Cheap Advice for Going on the Job Market

David Levine's Cheap Advice for Starting as a New Faculty

Write to David Levine, Haas School of Business, University of California, Berkeley CA 94720; for additional Cheap Advice, or to give Cheap Advice back to the source. Feedback is always appreciated.

David Levine's

Cheap Advice for New Faculty

When people evaluate you for tenure, they need to know (1) who you are, and (2) what (one or two sentences) results you are associated with. Thus, my basic advice is focus your research, get it read, and get seen. Sadly, the visibility of yourself is as important as the quality of your work in professional success.

Have a theme: You should think of your research as writing a book, not articles. The idea is that your first five years' research should involve one or two unifying themes.

The reason for sticking to a theme is that people evaluating you need to be able to say "This young scholar is one of the world's experts at _______." You get to fill the blank. However, your work must hold together so that there is some word or phrase they can stick in the blank.

In an ideal world, academics would be able to study any important topic they were competent to shed light on. In the real world, assistant professors are being evaluated; you don't need to play the game I describe, but now you know the rules.

Get read: List people who should read your papers. The list should include at least three groups: a dozen or so senior people in your field who are likely to write letters evaluating you when you go up for tenure; classmates and other buddies who are likely to actually read the stuff; and people who work on related topics and you would like to cite your stuff. Make up a master list that receives most papers, and modify it for each paper. Look at each paper's bibliography for ideas on who might want to see it. (Everyone wants to see papers that cite them -- that's why so many professors read papers from the back.)

Typically you should mail out papers in their working paper stage, one draft before submission.

Feedback is crucial. All research has flaws, and all drafts have easy-to-correct flaws. Make sure your research and writing has lots of feedback to fix both types. Especially for the clarity issues, be open minded. If your parents, friends, colleagues in unrelated disciplines, will read the sucker, send it to them!

Get seen: Travel every year to give seminars. If you are going to be near a university, call up six months early and tell them you are available and would like to give a seminar.

Don't be shy: figure out how to give a talk at your field's annual convention, and do it! Invite a mix of friends and more well-known people.

Learn how to be a Manager: Secretaries and research assistants are just a few of the people you need to learn to manage. The secrets are few: respect and communication. Respect is shown, for example, by asking their schedule when you propose work, and by always saying, "Thank you." In addition, if they have expertise in a job, only give advice on how to do it if they ask.

Most attempts at communication are not completely successful. (In the classroom this problem is reflected in my maxim: "I can teach faster than they can learn.") Thus, the golden rule of communication is "Say unto others what you believe others have said unto you." After giving your instructions, always ask your RA to explain what she thought you asked her to do. (Do this in a way that makes it clear you consider yourself at fault for any miscommunication. Perhaps: "I didn't explain that very well. Can you tell me what you are planning to do?") If they don't repeat back to me their understanding of my instructions, then it is unlikely the job will be accomplished as I expect.

Budget your research time: In economics research begun in year t usually leads to a first draft in year t+1 or t+2. Circulating it and presenting it at a seminar or two brings us to year t+3. Submit it to a journal and they will (if you are lucky) ask for a revision in year t+4, which you might complete that year. If so, they could accept it as fast as year t+5, but it might not come out till year t+6. On the one hand, many papers can move from conception to publication in less than six years; on the other hand, many papers take more.

Count back from the time you need to submit your tenure file. If you are three years away and your case is less than 100% solid, don't begin any new research! At this point your priority must be to move working papers and papers with requested revisions into the Accepted pile.

This is just one in a series of David Levine's Cheap Advice. Collect them all! Dazzle your friends!

David Levine's Cheap Advice for Writing a Dissertation

David Levine's Cheap Advice for Presenting Results

David Levine's Cheap Advice for Going on the Job Market

David Levine's Cheap Advice for Starting as a New Faculty

Write to David Levine, Haas School of Business, University of California, Berkeley CA 94720; for additional Cheap Advice, or to give Cheap Advice back to the source. Feedback is always appreciated.

David Levine's Cheap Advice:

How to Present Results

Presenting Your Results

Authors must work hard to make their results easy to follow. Thus, tables must be comprehensible by themselves. That is, a reader should be able to understand a table without reference to the text (and preferably without reference to other tables). The converse rule is that the text should stand by itself, even if a reader ignores the tables.

Variable names should be self-explanatory. The text should flow without ever showing that the reader has encountered a variable name. For example, "Each plant visit by a social scientist corresponded to 3.1% higher voluntary turnover per month (SE = .9%, P < .05)" is much better than "The coefficient of SCISTUD on VOLTUR was .031 (.009)," which is better than "S and V were significantly related." In short, nobody should know what passed between you and your computer in the middle of the night. Name your dummy variable Male or Female, but not Sex.

Often variables are proxies for conceptual variables. If you want to measure complexity, but your variable is log(employment), name the variable employment (if all variables are expressed in logs) or log(employment), not complexity.

Each table has number and a meaningful title: "Wages Do Not Rise with Tenure," not "Wages."

All statistical tests include the name of the test, the test statistic, and the significance level. Include a brief description of complex procedures at the bottom of the table. Remind readers of the meaning of complicated variables.

Each estimated equation lists the dependent variable, sample size and functional form. If one of these is constant for an entire table, list it only once. Explain any adjustments to standard errors such as clustering (and the clustering unit(s)). Always include measures of goodness of fit such as R˛ or log-likelihood. Usually include a test of the significance of the entire regression (such as an F test for ordinary least squares), and appropriate residual diagnostics (such as Durbin-Watson statistics and heteroskedasticity tests).

Coefficients should include standard errors or t statistics, and some indication of statistical significance (P values or * for P < .05, ** for P < .01). A note explains whether there are standard errors or t statistics. Report P < .10 only if the sample size is small.  For very large datasets it makes sense to demand a higher degree of certainty, so only report P < .01.

Include a meaningful number of digits, usually at two or three significant places. Neither .34849560 (3.132534) nor 0.00 (SE = 0.00, P < .001) is useful. Sometimes multiply or divide a variable by 10 so the coefficient is in a meaningful range.

Always in the text, and often in the tables, translate the coefficients of interest into words. For example, convert logit coefficients to measure how a one-unit change in the variable affects the probability of the occurrence of the event (dP/dX). Translate hazard rate coefficients to show how a one unit change in the variable affects the expected time until some event occurs: "If the union local is in a city, its expected lifetime was four months shorter than if it was rural."

Also, always explain whether the effect is large or not, perhaps by comparing its size to something readers understand. For example, the importance of marital status on wages can be expressed as how many years of education are required to raise wages by as much. Sometimes standardized coefficients (expressed in standard deviation units) are useful: "A one month increase in training (approximately one standard deviation) corresponds to 4 percent lower turnover (approximately one half of a standard deviation)."

Effect sizes from Lisrel, factor analysis, path analysis, ordered logits, etc., are hard to understand. Thus, explain the size as well as the statistical significance of the important effects. 

Statistical significance: Most authors focus on whether coefficients are statistically significantly different from zero at the 5 percent level.  Thus, if b1 is statistically significant from zero at the 5% level, but b2 is not, some authors write as if b1 is different from and more important than b2.  At a minimum, authors should focus on confidence intervals, and explicitly test if b1 and b2 differ by statistically significant amounts. 

Consider the power of the test. With a million observations, unimportant effects are usually statistically significant.  With 30 observations, incredibly important results are often not statistically significant at the 5% level.  Thus, never use the word “significance” without a qualifier; do you mean economically or statistically significant?

Summary statistics should be included for the main variables (and for all variables for the referees). Usually include means, standard deviations, units, sources, geographical region covered, and sample size. Include the statistics for the un-logged version of any important variables you use in log form. If your paper computes important and meaningful intermediate results (residuals from a first-stage equation, for example), include their summary statistics.

Often give the referee a table of correlations of either the main variables or of all the variables.

Use simple statistical procedures to complement fancier analyses. Many papers have a simple comparison of means as their main result (to be formalized and measured more precisely in later sections). If your paper has such a comparison at its core, then present the unadjusted means (e.g., men vs. women, or U.S. vs. Japan) before the fancier statistical analysis.

Figures and graphs, like tables, are supposed to be comprehensible without reference to the text, and the text without reference to the figure. Place labels (in words, not symbols or variable names) and units on axes.

Equations worthy of being in your paper can be expressed as a sentence. Do so. Many economic theories generate an interesting first-order condition. Restate it in words: "At the optimum, a dollar spent today must bring the same utility as a dollar spent tomorrow, suitably discounted."

Abstracts present the question the paper will answer. Since social scientists are not mystery writers, abstracts also summarize the results. Inability to write an abstract of fewer than 100 words often indicates deeper problems with the paper.

Robustness checks and limitations of the study

No research is perfect. This section presents a checklist of common imperfections in empirical research. Discuss the limitations of your paper and plausible alternate explanations for the results.

Surveys face problems with the match between the actual sample and the perfect sample. Always mention the response rates and possible response biases. Are responses likely to be sensitive to changes in the wording of the questions? Do respondents have incentives to hide their attitudes? Are attitudes likely to match behaviors?

A naming issue appears when data undergo factor analysis or are summed into an index. Do not merely name the factors or indices, but present a sample of the questions along with the factor weights. Differentiate exploratory from confirmatory factor analysis.

Econometric and statistical analyses are subject to all the difficulties of surveys. They also fall prey to the "textbook chapters:" simultaneity, sample selection, measurement error, outliers, and so forth.  Almost all analyses should use methods that adjust for clustering of the sample (as in most common datasets) and that are robust to the presence of heteroskedasticity.  Re-analyze the data several times to check the specification, and make sure that your results are not sensitive to changes in the estimating technique, functional forms, control variables, and so forth. If all is well, allude to these results briefly, but do not include them in the paper. Often include copies of these tables for the referee.

Experiments almost always have questionable external validity. Is there any reason to believe people in actual organizations involved in complex social networks making decisions that affect their careers over many years act the way the college sophomores do in your one-hour treatment?

Is the name you have given your treatment appropriate? If you tell one group of people they are not worth the wage they receive, this treatment may affect self-esteem as well as perceived equity. Thus, naming the treated group perceived inequity can mislead readers.

Case studies have limited generalizability. Explain in what sense this case study generalizes, and outline the limits of its generalizability. What are the likely biases due to subjective perception and reporting?

Causality is almost never clear in the social sciences. Common problems include reverse causality, selection effects, and omitted variable bias. Discuss alternative causal explanations for the results.  These alternative channels often suggest additional tests you might perform.

When using instrumental variables or multi-equation methods such as two-stage least squares and maximum likelihood, always list your instruments or other identifying assumptions. Include these lists in both the text and the tables. Justify why it is plausible that your instruments are exogenous. Both identification by functional form (as in many sample selection models) and instrumenting with lagged variables require strong justification.

Economic theory: Explain the robustness of the results in "assumption space." Differentiate assumptions that are crucial from those that merely simplify. Even if you do not prove, explain what happens if preferences are not Cobb-Douglas, or if agents are not identical. Usually include detailed derivations in an appendix for the referee.

Stylistic issues

Go for clear exposition of substance as opposed to trying to impress readers with big words, hard statistics, or fancy theory. Present the main conclusions so that they will be accessible to the nonspecialist reader in the final section.

Use footnotes sparingly, and Latin almost never. Don't rely on variable names if you can spell them out. Remind readers often of the meaning of your symbols: often replace “q” with "the elasticity of substitution." Readers have to expand out the entire phrase--help them out.

Final words

No article has ever suffered from too much helpful advice from colleagues. Be sure many readers look at your article. Use family, friends, colleagues from other departments: most of your paper should be understandable even to the nonspecialist.

[1] From the BPP class of 2006.