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Methods Used in Economic Research: An Empirical Study of Trends and Levels

The methods used in economic research are analyzed on a sample of all 3,415 regular research papers published in 10 general interest journals every 5th year from 1997 to 2017. The papers are classified into three main groups by method: theory, experiments, and empirics. The theory and empirics groups are almost equally large. Most empiric papers use the classical method, which derives an operational model from theory and runs regressions. The number of papers published increases by 3.3% p.a. Two trends are highly significant: The fraction of theoretical papers has fallen by 26 pp (percentage points), while the fraction of papers using the classical method has increased by 15 pp. Economic theory predicts that such papers exaggerate, and the papers that have been analyzed by meta-analysis confirm the prediction. It is discussed if other methods have smaller problems.

1 Introduction

This paper studies the pattern in the research methods in economics by a sample of 3,415 regular papers published in the years 1997, 2002, 2007, 2012, and 2017 in 10 journals. The analysis builds on the beliefs that truth exists, but it is difficult to find, and that all the methods listed in the next paragraph have problems as discussed in Sections 2 and 4. Hereby I do not imply that all – or even most – papers have these problems, but we rarely know how serious it is when we read a paper. A key aspect of the problem is that a “perfect” study is very demanding and requires far too much space to report, especially if the paper looks for usable results. Thus, each paper is just one look at an aspect of the problem analyzed. Only when many studies using different methods reach a joint finding, we can trust that it is true.

Section 2 discusses the classification of papers by method into three main categories: (M1) Theory , with three subgroups: (M1.1) economic theory, (M1.2) statistical methods, and (M1.3) surveys. (M2) Experiments , with two subgroups: (M2.1) lab experiments and (M2.2) natural experiments. (M3) Empirics , with three subgroups: (M3.1) descriptive, (M3.2) classical empirics, and (M3.3) newer empirics. More than 90% of the papers are easy to classify, but a stochastic element enters in the classification of the rest. Thus, the study has some – hopefully random – measurement errors.

Section 3 discusses the sample of journals chosen. The choice has been limited by the following main criteria: It should be good journals below the top ten A-journals, i.e., my article covers B-journals, which are the journals where most research economists publish. It should be general interest journals, and the journals should be so different that it is likely that patterns that generalize across these journals apply to more (most?) journals. The Appendix gives some crude counts of researchers, departments, and journals. It assesses that there are about 150 B-level journals, but less than half meet the criteria, so I have selected about 15% of the possible ones. This is the most problematic element in the study. If the reader accepts my choice, the paper tells an interesting story about economic research.

All B-level journals try hard to have a serious refereeing process. If our selection is representative, the 150 journals have increased the annual number of papers published from about 7,500 in 1997 to about 14,000 papers in 2017, giving about 200,000 papers for the period. Thus, the B-level dominates our science. Our sample is about 6% for the years covered, but less than 2% of all papers published in B-journals in the period. However, it is a larger fraction of the papers in general interest journals.

It is impossible for anyone to read more than a small fraction of this flood of papers. Consequently, researchers compete for space in journals and for attention from the readers, as measured in the form of citations. It should be uncontroversial that papers that hold a clear message are easier to publish and get more citations. Thus, an element of sales promotion may enter papers in the form of exaggeration , which is a joint problem for all eight methods. This is in accordance with economic theory that predicts that rational researchers report exaggerated results; see Paldam ( 2016 , 2018 ). For empirical papers, meta-methods exist to summarize the results from many papers, notably papers using regressions. Section 4.4 reports that meta-studies find that exaggeration is common.

The empirical literature surveying the use of research methods is quite small, as I have found two articles only: Hamermesh ( 2013 ) covers 748 articles in 6 years a decade apart studies in three A-journals using a slightly different classification of methods, [1] while my study covers B-journals. Angrist, Azoulay, Ellison, Hill, and Lu ( 2017 ) use a machine-learning classification of 134,000 papers in 80 journals to look at the three main methods. My study subdivide the three categories into eight. The machine-learning algorithm is only sketched, so the paper is difficult to replicate, but it is surely a major effort. A key result in both articles is the strong decrease of theory in economic publications. This finding is confirmed, and it is shown that the corresponding increase in empirical articles is concentrated on the classical method.

I have tried to explain what I have done, so that everything is easy to replicate, in full or for one journal or one year. The coding of each article is available at least for the next five years. I should add that I have been in economic research for half a century. Some of the assessments in the paper will reflect my observations/experience during this period (indicated as my assessments). This especially applies to the judgements expressed in Section 4.

2 The eight categories

Table 1 reports that the annual number of papers in the ten journals has increased 1.9 times, or by 3.3% per year. The Appendix gives the full counts per category, journal, and year. By looking at data over two decades, I study how economic research develops. The increase in the production of papers is caused by two factors: The increase in the number of researchers. The increasing importance of publications for the careers of researchers.

The 3,415 papers

2.1 (M1) Theory: subgroups (M1.1) to (M1.3)

Table 2 lists the groups and main numbers discussed in the rest of the paper. Section 2.1 discusses (M1) theory. Section 2.2 covers (M2) experimental methods, while Section 2.3 looks at (M3) empirical methods using statistical inference from data.

The 3,415 papers – fractions in percent

The change of the fractions from 1997 to 2017 in percentage points

Note: Section 3.4 tests if the pattern observed in Table 3 is statistically significant. The Appendix reports the full data.

2.1.1 (M1.1) Economic theory

Papers are where the main content is the development of a theoretical model. The ideal theory paper presents a (simple) new model that recasts the way we look at something important. Such papers are rare and obtain large numbers of citations. Most theoretical papers present variants of known models and obtain few citations.

In a few papers, the analysis is verbal, but more than 95% rely on mathematics, though the technical level differs. Theory papers may start by a descriptive introduction giving the stylized fact the model explains, but the bulk of the paper is the formal analysis, building a model and deriving proofs of some propositions from the model. It is often demonstrated how the model works by a set of simulations, including a calibration made to look realistic. However, the calibrations differ greatly by the efforts made to reach realism. Often, the simulations are in lieu of an analytical solution or just an illustration suggesting the magnitudes of the results reached.

Theoretical papers suffer from the problem known as T-hacking , [2] where the able author by a careful selection of assumptions can tailor the theory to give the results desired. Thus, the proofs made from the model may represent the ability and preferences of the researcher rather than the properties of the economy.

2.1.2 (M1.2) Statistical method

Papers reporting new estimators and tests are published in a handful of specialized journals in econometrics and mathematical statistics – such journals are not included. In our general interest journals, some papers compare estimators on actual data sets. If the demonstration of a methodological improvement is the main feature of the paper, it belongs to (M1.2), but if the economic interpretation is the main point of the paper, it belongs to (M3.2) or (M3.3). [3]

Some papers, including a special issue of Empirical Economics (vol. 53–1), deal with forecasting models. Such models normally have a weak relation to economic theory. They are sometimes justified precisely because of their eclectic nature. They are classified as either (M1.2) or (M3.1), depending upon the focus. It appears that different methods work better on different data sets, and perhaps a trade-off exists between the user-friendliness of the model and the improvement reached.

2.1.3 (M1.3) Surveys

When the literature in a certain field becomes substantial, it normally presents a motley picture with an amazing variation, especially when different schools exist in the field. Thus, a survey is needed, and our sample contains 68 survey articles. They are of two types, where the second type is still rare:

2.1.3.1 (M1.3.1) Assessed surveys

Here, the author reads the papers and assesses what the most reliable results are. Such assessments require judgement that is often quite difficult to distinguish from priors, even for the author of the survey.

2.1.3.2 (M1.3.2) Meta-studies

They are quantitative surveys of estimates of parameters claimed to be the same. Over the two decades from 1997 to 2017, about 500 meta-studies have been made in economics. Our sample includes five, which is 0.15%. [4] Meta-analysis has two levels: The basic level collects and codes the estimates and studies their distribution. This is a rather objective exercise where results seem to replicate rather well. [5] The second level analyzes the variation between the results. This is less objective. The papers analyzed by meta-studies are empirical studies using method (M3.2), though a few use estimates from (M3.1) and (M3.3).

2.2 (M2) Experimental methods: subgroups (M2.1) and (M2.2)

Experiments are of three distinct types, where the last two are rare, so they are lumped together. They are taking place in real life.

2.2.1 (M2.1) Lab experiments

The sample had 1.9% papers using this method in 1997, and it has expanded to 9.7% in 2017. It is a technique that is much easier to apply to micro- than to macroeconomics, so it has spread unequally in the 10 journals, and many experiments are reported in a couple of special journals that are not included in our sample.

Most of these experiments take place in a laboratory, where the subjects communicate with a computer, giving a controlled, but artificial, environment. [6] A number of subjects are told a (more or less abstract) story and paid to react in either of a number of possible ways. A great deal of ingenuity has gone into the construction of such experiments and in the methods used to analyze the results. Lab experiments do allow studies of behavior that are hard to analyze in any other way, and they frequently show sides of human behavior that are difficult to rationalize by economic theory. It appears that such demonstration is a strong argument for the publication of a study.

However, everything is artificial – even the payment. In some cases, the stories told are so elaborate and abstract that framing must be a substantial risk; [7] see Levitt and List ( 2007 ) for a lucid summary, and Bergh and Wichardt ( 2018 ) for a striking example. In addition, experiments cost money, which limits the number of subjects. It is also worth pointing to the difference between expressive and real behavior. It is typically much cheaper for the subject to “express” nice behavior in a lab than to be nice in the real world.

(M2.2) Event studies are studies of real world experiments. They are of two types:

(M2.2.1) Field experiments analyze cases where some people get a certain treatment and others do not. The “gold standard” for such experiments is double blind random sampling, where everything (but the result!) is preannounced; see Christensen and Miguel ( 2018 ). Experiments with humans require permission from the relevant authorities, and the experiment takes time too. In the process, things may happen that compromise the strict rules of the standard. [8] Controlled experiments are expensive, as they require a team of researchers. Our sample of papers contains no study that fulfills the gold standard requirements, but there are a few less stringent studies of real life experiments.

(M2.2.2) Natural experiments take advantage of a discontinuity in the environment, i.e., the period before and after an (unpredicted) change of a law, an earthquake, etc. Methods have been developed to find the effect of the discontinuity. Often, such studies look like (M3.2) classical studies with many controls that may or may not belong. Thus, the problems discussed under (M3.2) will also apply.

2.3 (M3) Empirical methods: subgroups (M3.1) to (M3.3)

The remaining methods are studies making inference from “real” data, which are data samples where the researcher chooses the sample, but has no control over the data generating process.

(M3.1) Descriptive studies are deductive. The researcher describes the data aiming at finding structures that tell a story, which can be interpreted. The findings may call for a formal test. If one clean test follows from the description, [9] the paper is classified under (M3.1). If a more elaborate regression analysis is used, it is classified as (M3.2). Descriptive studies often contain a great deal of theory.

Some descriptive studies present a new data set developed by the author to analyze a debated issue. In these cases, it is often possible to make a clean test, so to the extent that biases sneak in, they are hidden in the details of the assessments made when the data are compiled.

(M3.2) Classical empirics has three steps: It starts by a theory, which is developed into an operational model. Then it presents the data set, and finally it runs regressions.

The significance levels of the t -ratios on the coefficient estimated assume that the regression is the first meeting of the estimation model and the data. We all know that this is rarely the case; see also point (m1) in Section 4.4. In practice, the classical method is often just a presentation technique. The great virtue of the method is that it can be applied to real problems outside academia. The relevance comes with a price: The method is quite flexible as many choices have to be made, and they often give different results. Preferences and interests, as discussed in Sections 4.3 and 4.4 below, notably as point (m2), may affect these choices.

(M3.3) Newer empirics . Partly as a reaction to the problems of (M3.2), the last 3–4 decades have seen a whole set of newer empirical techniques. [10] They include different types of VARs, Bayesian techniques, causality/co-integration tests, Kalman Filters, hazard functions, etc. I have found 162 (or 4.7%) papers where these techniques are the main ones used. The fraction was highest in 1997. Since then it has varied, but with no trend.

I think that the main reason for the lack of success for the new empirics is that it is quite bulky to report a careful set of co-integration tests or VARs, and they often show results that are far from useful in the sense that they are unclear and difficult to interpret. With some introduction and discussion, there is not much space left in the article. Therefore, we are dealing with a cookbook that makes for rather dull dishes, which are difficult to sell in the market.

Note the contrast between (M3.2) and (M3.3): (M3.2) makes it possible to write papers that are too good, while (M3.3) often makes them too dull. This contributes to explain why (M3.2) is getting (even) more popular and the lack of success of (M3.3), but then, it is arguable that it is more dangerous to act on exaggerated results than on results that are weak.

3 The 10 journals

The 10 journals chosen are: (J1) Can [Canadian Journal of Economics], (J2) Emp [Empirical Economics], (J3) EER [European Economic Review], (J4) EJPE [European Journal of Political Economy], (J5) JEBO [Journal of Economic Behavior & Organization], (J6) Inter [Journal of International Economics], (J7) Macro [Journal of Macroeconomics], (J8) Kyklos, (J9) PuCh [Public Choice], and (J10) SJE [Scandinavian Journal of Economics].

Section 3.1 discusses the choice of journals, while Section 3.2 considers how journals deal with the pressure for publication. Section 3.3 shows the marked difference in publication profile of the journals, and Section 3.4 tests if the trends in methods are significant.

3.1 The selection of journals

They should be general interest journals – methodological journals are excluded. By general interest, I mean that they bring papers where an executive summary may interest policymakers and people in general. (ii) They should be journals in English (the Canadian Journal includes one paper in French), which are open to researchers from all countries, so that the majority of the authors are from outside the country of the journal. [11] (iii) They should be sufficiently different so that it is likely that patterns, which apply to these journals, tell a believable story about economic research. Note that (i) and (iii) require some compromises, as is evident in the choice of (J2), (J6), (J7), and (J8) ( Table 4 ).

The 10 journals covered

Note. Growth is the average annual growth from 1997 to 2017 in the number of papers published.

Methodological journals are excluded, as they are not interesting to outsiders. However, new methods are developed to be used in general interest journals. From studies of citations, we know that useful methodological papers are highly cited. If they remain unused, we presume that it is because they are useless, though, of course, there may be a long lag.

The choice of journals may contain some subjectivity, but I think that they are sufficiently diverse so that patterns that generalize across these journals will also generalize across a broader range of good journals.

The papers included are the regular research articles. Consequently, I exclude short notes to other papers and book reviews, [12] except for a few article-long discussions of controversial books.

3.2 Creating space in journals

As mentioned in the introduction, the annual production of research papers in economics has now reached about 1,000 papers in top journals, and about 14,000 papers in the group of good journals. [13] The production has grown with 3.3% per year, and thus it has doubled the last twenty years. The hard-working researcher will read less than 100 papers a year. I know of no signs that this number is increasing. Thus, the upward trend in publication must be due to the large increase in the importance of publications for the careers of researchers, which has greatly increased the production of papers. There has also been a large increase in the number of researches, but as citations are increasingly skewed toward the top journals (see Heckman & Moktan, 2018 ), it has not increased demand for papers correspondingly. The pressures from the supply side have caused journals to look for ways to create space.

Book reviews have dropped to less than 1/3. Perhaps, it also indicates that economists read fewer books than they used to. Journals have increasingly come to use smaller fonts and larger pages, allowing more words per page. The journals from North-Holland Elsevier have managed to cram almost two old pages into one new one. [14] This makes it easier to publish papers, while they become harder to read.

Many journals have changed their numbering system for the annual issues, making it less transparent how much they publish. Only three – Canadian Economic Journal, Kyklos, and Scandinavian Journal of Economics – have kept the schedule of publishing one volume of four issues per year. It gives about 40 papers per year. Public Choice has a (fairly) consistent system with four volumes of two double issues per year – this gives about 100 papers. The remaining journals have changed their numbering system and increased the number of papers published per year – often dramatically.

Thus, I assess the wave of publications is caused by the increased supply of papers and not to the demand for reading material. Consequently, the study confirms and updates the observation by Temple ( 1918 , p. 242): “… as the world gets older the more people are inclined to write but the less they are inclined to read.”

3.3 How different are the journals?

The appendix reports the counts for each year and journal of the research methods. From these counts, a set of χ 2 -scores is calculated for the three main groups of methods – they are reported in Table 5 . It gives the χ 2 -test comparing the profile of each journal to the one of the other nine journals taken to be the theoretical distribution.

The methodological profile of the journals –  χ 2 -scores for main groups

Note: The χ 2 -scores are calculated relative to all other journals. The sign (+) or (−) indicates if the journal has too many or too few papers relatively in the category. The P -values for the χ 2 (3)-test always reject that the journal has the same methodological profile as the other nine journals.

The test rejects that the distribution is the same as the average for any of the journals. The closest to the average is the EJPE and Public Choice. The two most deviating scores are for the most micro-oriented journal JEBO, which brings many experimental papers, and of course, Empirical Economics, which brings many empirical papers.

3.4 Trends in the use of the methods

Table 3 already gave an impression of the main trends in the methods preferred by economists. I now test if these impressions are statistically significant. The tests have to be tailored to disregard three differences between the journals: their methodological profiles, the number of papers they publish, and the trend in the number. Table 6 reports a set of distribution free tests, which overcome these differences. The tests are done on the shares of each research method for each journal. As the data cover five years, it gives 10 pairs of years to compare. [15] The three trend-scores in the []-brackets count how often the shares go up, down, or stay the same in the 10 cases. This is the count done for a Kendall rank correlation comparing the five shares with a positive trend (such as 1, 2, 3, 4, and 5).

Trend-scores and tests for the eight subgroups of methods across the 10 journals

Note: The three trend-scores in each [ I 1 , I 2 , I 3 ]-bracket are a Kendall-count over all 10 combinations of years. I 1 counts how often the share goes up. I 2 counts when the share goes down, and I 3 counts the number of ties. Most ties occur when there are no observations either year. Thus, I 1 + I 2 + I 3 = 10. The tests are two-sided binominal tests disregarding the zeroes. The test results in bold are significant at the 5% level.

The first set of trend-scores for (M1.1) and (J1) is [1, 9, 0]. It means that 1 of the 10 share-pairs increases, while nine decrease and no ties are found. The two-sided binominal test is 2%, so it is unlikely to happen. Nine of the ten journals in the (M1.1)-column have a majority of falling shares. The important point is that the counts in one column can be added – as is done in the all-row; this gives a powerful trend test that disregards differences between journals and the number of papers published. ( Table A1 )

Four of the trend-tests are significant: The fall in theoretical papers and the rise in classical papers. There is also a rise in the share of stat method and event studies. It is surprising that there is no trend in the number of experimental studies, but see Table A2 (in Appendix).

4 An attempt to interpret the pattern found

The development in the methods pursued by researchers in economics is a reaction to the demand and supply forces on the market for economic papers. As already argued, it seems that a key factor is the increasing production of papers.

The shares add to 100, so the decline of one method means that the others rise. Section 4.1 looks at the biggest change – the reduction in theory papers. Section 4.2 discusses the rise in two new categories. Section 4.3 considers the large increase in the classical method, while Section 4.4 looks at what we know about that method from meta-analysis.

4.1 The decline of theory: economics suffers from theory fatigue [16]

The papers in economic theory have dropped from 59.5 to 33.6% – this is the largest change for any of the eight subgroups. [17] It is highly significant in the trend test. I attribute this drop to theory fatigue.

As mentioned in Section 2.1, the ideal theory paper presents a (simple) new model that recasts the way we look at something important. However, most theory papers are less exciting: They start from the standard model and argue that a well-known conclusion reached from the model hinges upon a debatable assumption – if it changes, so does the conclusion. Such papers are useful. From a literature on one main model, the profession learns its strengths and weaknesses. It appears that no generally accepted method exists to summarize this knowledge in a systematic way, though many thoughtful summaries have appeared.

I think that there is a deeper problem explaining theory fatigue. It is that many theoretical papers are quite unconvincing. Granted that the calculations are done right, believability hinges on the realism of the assumptions at the start and of the results presented at the end. In order for a model to convince, it should (at least) demonstrate the realism of either the assumptions or the outcome. [18] If both ends appear to hang in the air, it becomes a game giving little new knowledge about the world, however skillfully played.

The theory fatigue has caused a demand for simulations demonstrating that the models can mimic something in the world. Kydland and Prescott pioneered calibration methods (see their 1991 ). Calibrations may be carefully done, but it often appears like a numerical solution of a model that is too complex to allow an analytical solution.

4.2 Two examples of waves: one that is still rising and another that is fizzling out

When a new method of gaining insights in the economy first appears, it is surrounded by doubts, but it also promises a high marginal productivity of knowledge. Gradually the doubts subside, and many researchers enter the field. After some time this will cause the marginal productivity of the method to fall, and it becomes less interesting. The eight methods include two newer ones: Lab experiments and newer stats. [19]

It is not surprising that papers with lab experiments are increasing, though it did take a long time: The seminal paper presenting the technique was Smith ( 1962 ), but only a handful of papers are from the 1960s. Charles Plott organized the first experimental lab 10 years later – this created a new standard for experiments, but required an investment in a lab and some staff. Labs became more common in the 1990s as PCs got cheaper and software was developed to handle experiments, but only 1.9% of the papers in the 10 journals reported lab experiments in 1997. This has now increased to 9.7%, so the wave is still rising. The trend in experiments is concentrated in a few journals, so the trend test in Table 6 is insignificant, but it is significant in the Appendix Table A2 , where it is done on the sum of articles irrespective of the journal.

In addition to the rising share of lab experiment papers in some journals, the journal Experimental Economics was started in 1998, where it published 281 pages in three issues. In 2017, it had reached 1,006 pages in four issues, [20] which is an annual increase of 6.5%.

Compared with the success of experimental economics, the motley category of newer empirics has had a more modest success, as the fraction of papers in the 5 years are 5.8, 5.2, 3.5, 5.4, and 4.2, which has no trend. Newer stats also require investment, but mainly in human capital. [21] Some of the papers using the classical methodology contain a table with Dickey-Fuller tests or some eigenvalues of the data matrix, but they are normally peripheral to the analysis. A couple of papers use Kalman filters, and a dozen papers use Bayesian VARs. However, it is clear that the newer empirics have made little headway into our sample of general interest journals.

4.3 The steady rise of the classical method: flexibility rewarded

The typical classical paper provides estimates of a key effect that decision-makers outside academia want to know. This makes the paper policy relevant right from the start, and in many cases, it is possible to write a one page executive summary to the said decision-makers.

The three-step convention (see Section 2.3) is often followed rather loosely. The estimation model is nearly always much simpler than the theory. Thus, while the model can be derived from a theory, the reverse does not apply. Sometimes, the model seems to follow straight from common sense, and if the link from the theory to the model is thin, it begs the question: Is the theory really necessary? In such cases, it is hard to be convinced that the tests “confirm” the theory, but then, of course, tests only say that the data do not reject the theory.

The classical method is often only a presentation devise. Think of a researcher who has reached a nice publishable result through a long and tortuous path, including some failed attempts to find such results. It is not possible to describe that path within the severely limited space of an article. In addition, such a presentation would be rather dull to read, and none of us likes to talk about wasted efforts that in hindsight seem a bit silly. Here, the classical method becomes a convenient presentation device.

The biggest source of variation in the results is the choice of control/modifier variables. All datasets presumably contain some general and some special information, where the latter depends on the circumstances prevailing when the data were compiled. The regression should be controlled for these circumstances in order to reach the general result. Such ceteris paribus controls are not part of the theory, so many possible controls may be added. The ones chosen for publication often appear to be the ones delivering the “right” results by the priors of the researcher. The justification for their inclusion is often thin, and if two-stage regressions are used, the first stage instruments often have an even thinner justification.

Thus, the classical method is rather malleable to the preferences and interests of researchers and sponsors. This means that some papers using the classical technique are not what they pretend, as already pointed out by Leamer ( 1983 ), see also Paldam ( 2018 ) for new references and theory. The fact that data mining is tempting suggests that it is often possible to reach smashing results, making the paper nice to read. This may be precisely why it is cited.

Many papers using the classical method throw in some bits of exotic statistics technique to demonstrate the robustness of the result and the ability of the researcher. This presumably helps to generate credibility.

4.4 Knowledge about classical papers reached from meta-studies

Individual studies using the classical method often look better than they are, and thus they are more uncertain than they appear, but we may think of the value of convergence for large N s (number of observations) as the truth. The exaggeration is largest in the beginning of a new literature, but gradually it becomes smaller. Thus, the classical method does generate truth when the effect searched for has been studied from many sides. The word research does mean that the search has to be repeated! It is highly risky to trust a few papers only.

Meta-analysis has found other results such as: Results in top journals do not stand out. It is necessary to look at many journals, as many papers on the same effect are needed. Little of the large variation between results is due to the choice of estimators.

A similar development should occur also for experimental economics. Experiments fall in families: A large number cover prisoner’s dilemma games, but there are also many studies of dictator games, auction games, etc. Surveys summarizing what we have learned about these games seem highly needed. Assessed summaries of old experiments are common, notably in introductions to papers reporting new ones. It should be possible to extract the knowledge reached by sets of related lab experiments in a quantitative way, by some sort of meta-technique, but this has barely started. The first pioneering meta-studies of lab experiments do find the usual wide variation of results from seemingly closely related experiments. [25] A recent large-scale replicability study by Camerer et al. ( 2018 ) finds that published experiments in the high quality journal Nature and Science exaggerate by a factor two just like regression studies using the classical method.

5 Conclusion

The study presents evidence that over the last 20 years economic research has moved away from theory towards empirical work using the classical method.

From the eighties onward, there has been a steady stream of papers pointing out that the classical method suffers from excess flexibility. It does deliver relevant results, but they tend to be too good. [26] While, increasingly, we know the size of the problems of the classical method, systematic knowledge about the problems of the other methods is weaker. It is possible that the problems are smaller, but we do not know.

Therefore, it is clear that obtaining solid knowledge about the size of an important effect requires a great deal of papers analyzing many aspects of the effect and a careful quantitative survey. It is a well-known principle in the harder sciences that results need repeated independent replication to be truly trustworthy. In economics, this is only accepted in principle.

The classical method of empirical research is gradually winning, and this is a fine development: It does give answers to important policy questions. These answers are highly variable and often exaggerated, but through the efforts of many competing researchers, solid knowledge will gradually emerge.

Home page: http://www.martin.paldam.dk

Acknowledgments

The paper has been presented at the 2018 MAER-Net Colloquium in Melbourne, the Kiel Aarhus workshop in 2018, and at the European Public Choice 2019 Meeting in Jerusalem. I am grateful for all comments, especially from Chris Doucouliagos, Eelke de Jong, and Bob Reed. In addition, I thank the referees for constructive advice.

Conflict of interest: Author states no conflict of interest.

Appendix: Two tables and some assessments of the size of the profession

The text needs some numbers to assess the representativity of the results reached. These numbers just need to be orders of magnitude. I use the standard three-level classification in A, B, and C of researchers, departments, and journals. The connections between the three categories are dynamic and rely on complex sorting mechanisms. In an international setting, it matters that researchers have preferences for countries, notably their own. The relation between the three categories has a stochastic element.

The World of Learning organization reports on 36,000 universities, colleges, and other institutes of tertiary education and research. Many of these institutions are mainly engaged in undergraduate teaching, and some are quite modest. If half of these institutions have a program in economics, with a staff of at least five, the total stock of academic economists is 100,000, of which most are at the C-level.

The A-level of about 500 tenured researchers working at the top ten universities (mainly) publishes in the top 10 journals that bring less than 1,000 papers per year; [27] see Heckman and Moktan (2020). They (mainly) cite each other, but they greatly influence other researchers. [28] The B-level consists of about 15–20,000 researchers who work at 4–500 research universities, with graduate programs and ambitions to publish. They (mainly) publish in the next level of about 150 journals. [29] In addition, there are at least another 1,000 institutions that strive to move up in the hierarchy.

The counts for each of the 10 journals

Counts, shares, and changes for all ten journals for subgroups

Note: The trend-scores are calculated as in Table 6 . Compared to the results in Table 6 , the results are similar, but the power is less than before. However, note that the results in Column (M2.1) dealing with experiments are stronger in Table A2 . This has to do with the way missing observations are treated in the test.

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The Young Economist’s Short Guide to Writing Economic Research

Attributes of writing economics.

• The discourse is often mathematical, with lots of formulas, lemmas, and proofs.
• Writing styles vary widely. Some authors are very dry and technical while a few are quite eloquent.

Economics writing is different from many other types of writing. It is essentially technical, and the primary goal is to achieve clarity. A clear presentation will allow the strength of your underlying analysis and the quality of your research to shine through.

Unlike prose writing in other disciplines, economics research takes time. Successful papers are not cranked out the night before a due date.

General Guidelines for Quality Research

Getting started.

The hardest part of any writing assignment is starting. Economics research usually begins with a strong understanding of literature, and papers require a section that summarizes and applies previous literature to what the paper at hand. This is the best way to start.

Your writing will demonstrate that you understand the findings that relate to the topic.

Economists use the first few paragraphs to set up research questions and the model and data they use to think about it. Sure, it can be dry, but this format ensures the write and reader have strong grasp on the subject and structure of the work that follows.

Clear and Concise Work

Clarity is hard to achieve, but revising and reworking a paper ensures it is easy to read

• Organize your ideas into an argument with the help of an outline.
• Define the important terms you will use
• State your hypothesis and proceed deductively to reach your conclusions
• Avoid excess verbiage
• Edit yourself, remove what is not needed, and keep revising until you get down to a simple, efficient way of communicating
• Use the active voice
• Put statements in positive form
• Omit needless words (concise writing is clear writing)
• In summaries, generally stick to one tense

Time Management

Poor time management can wreck the best-planned papers. Deadlines are key to successful research papers.

• Start the project by finding your topic
• Begin your research
• Start and outline
• Write a draft
• Revise and polish

The Language of Economic Analysis

Economic theory has become very mathematical. Most PhD students are mathematicians, not simply economics majors. This means most quality economic research requires a strong use of mathematical language. Economic analysis is characterized by the use of models, simplified representations of how economic phenomena work. A model’s predictions about the future or the past are essentially empirical hypotheses. Since economics is not easily tested in controlled experiments, research requires data from the real world (census reports, balance sheets), and statistical methods (regressions and econometrics) to test the predictive power of models and hypotheses based on those models.

The Writing Process

Finding a topic.

There are a million ways to find a topic. It may be that you are writing for a specific subfield of economics, so topics are limited and thus easier to pick. However, must research starts organically, from passive reading or striking news articles. Make sure to find something that interests you. Be sure to find a niche and make a contribution to the subfield.

You will also need a project that can be done within the parameters of the assignment (length, due date, access to research materials). A profoundly interesting topic may not be manageable given the time and other constraints you face. The key is to just be practical.

Be sure to start your research as soon as possible. Your topic will evolve along the way, and the question you begin with may become less interesting as new information draws you in other directions. It is perfectly fine to shape your topic based on available data, but don’t get caught up in endlessly revising topics.

Finding and Using Sources

There are two types of economic sources: empirical data (information that is or can be easily translated into numerical form), and academic literature (books and articles that help you organize your ideas).

Economic data is compiled into a number of useful secondary sources:

• Economic Report of the President
• Statistical Abstract of the United States
• National Longitudinal Survey
• Census data
• Academic journals

The Outline

A good outline acts as an agenda for the things you want to accomplish:

• Introduction: Pose an interesting question or problem
• Literature Review: Survey the literature on your topic
• Methods/Data: Formulate your hypothesis and describe your data
• Results: Present your results with the help of graphs and charts
• Discussion: Critique your method and/or discuss any policy implications
• Conclusions: Summarize what you have done; pose questions for further research

Writing a Literature Review

The literature review demonstrates your familiarity with scholarly work on your topic and lays the foundations for your paper. The particular issues you intent to raise, the terms you will employ, and the approach you will take should be defined with reference to previous scholarly works.

Presenting a Hypothesis

Formulate a question, problem or conjecture, and describe the approach you will take to answer, solve, or test it. In presenting your hypothesis, you need to discuss the data set you are using and the type of regression you will run. You should say where you found the data, and use a table, graph, or simple statistics to summarize them. In term papers, it may not be possible to reach conclusive results. Don’t be afraid to state this clearly and accurately. It is okay to have an inconclusive paper, but it is not okay to make overly broad and unsupported statements.

Presenting Results

There are essentially two decisions to make: (1) How many empirical results should be presented, and (2) How should these results be described in the text?

• Focus only on what is important and be as clear as possible. Both smart and dumb readers will appreciate you pointing things out directly and clearly.
• Less is usually more: Reporting a small group of relevant results is better than covering every possible statistical analysis that could be made on the data.
• Clearly and precisely describe your tables, graphs, and figures in the text of your results section. The first and last sentence in a paragraph describing a result should be “big picture” statements, describing how the results in the table, graph or figure fit into the overall theme of the paper.

Discussing Results

The key to discussing results is to stay clear of making value judgments, and rely instead on economic facts and analyses. It is not the job of an economist to draw policy conclusions, even if the research supports strong evidence in a particular direction.

Referencing Sources

As with any research paper, source referencing depends on the will of a professor a discourse community. However, economists generally use soft references in the literature review section and then cite sources in conventional formats at the end of papers.

This guide was made possible by the excellent work of Robert Neugeboren and Mireille Jacobson of Harvard University and Paul Dudenhefer of Duke University.

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• Policy Brief - The Writing Center at UNC. This link Includes specifics on formatting.
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• Guidelines for Writing a Policy Brief

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Yale Economic Growth Center

Agte et al.: "Gender Gaps and Economic Growth: Why Haven't Women Won Globally (Yet)?"

EGC Discussion Paper No. 1105, May, 2024, by members of the Gender and Growth Gaps project team: Patrick Agte, Orazio Attanasio, Pinelopi K. Goldberg, Aishwarya Lakshmi Ratan, Rohini Pande, Michael Peters, Charity Troyer Moore, and Fabrizio Zilibotti.

Does economic growth close labor market-linked gender gaps that disadvantage women? Conversely, do gender inequalities in the labor market impede growth? To inform these questions, we conduct two analyses. First, we estimate regressions using data on gender gaps in a range of labor market outcomes from 153 countries spanning two decades (1998-2018). Second, we conduct a systematic review of the recent economics literature on gender gaps in labor markets, examining 16 journals over 21 years. Our empirical analysis demonstrates that growth is not a panacea. While economic gender gaps have narrowed and growth is associated with gender gap closures specifically in incidence of paid work, the relationship between growth and labor market gaps is otherwise mixed, and results vary by specification. This result reflects, in part, the gendered nature of structural transformation, in which growth leads men to transition from agriculture to industry and services while many women exit the labor force. Disparities in hours worked and wages persist despite growth, and heterogeneity in trends and levels between regions highlight the importance of local institutions. To better understand whether gender inequalities impeded growth, we explore a nascent literature that shows that reducing gender gaps in labor markets increases aggregate productivity. Our broader review highlights how traditional explanations for gender differences do not adequately explain existing gaps and how policy responses need to be sensitive to the changing nature of economic growth. We conclude by posing open questions for future research.

P. Agte, O. Attanasio, P. Goldberg, A. Lakshmi Ratan, R. Pande, M. Peters, C. Troyer Moore, and F. Zilibotti. "Gender Gaps and Economic Growth: Why Haven't Women Won Globally (Yet)?" EGC Discussion Paper No. 1105. 

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• EGC Discussion Papers on EliScholar

The Simple Macroeconomics of AI

This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.

Paper prepared for Economic Policy. I am grateful to Can Yeşildere for phenomenal research assistance, and to Leonardo Bursztyn, Mert Demirer, Lauren Fahey, Roberto Galbiati, Isabelle Méjean, Shakked Noy, Sida Peng, Julia Regier, and Whitney Zhang as well as participants in the MIT Solow Memorial conference and the Economic Policy conference for useful comments. I am especially grateful to my discussants, David Hémous and Benoît Coeuré, for insightful comments and suggestions. I thank Pamela Mishkin and Daniel Rock for generously sharing their data on AI exposure. I am also heavily indebted to my collaborators on several projects related to these topics, David Autor, Simon Johnson and Pascual Restrepo, from whom I learned a great deal and who have also given me very useful comments on the current draft. Financial support from the Hewlett Foundation is gratefully acknowledged. All remaining errors are mine. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.

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The impact of environmental regulation and economic expectations on crop-livestock integration among hog farmers: a field study from China

• Research Article
• Published: 01 June 2024

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• Jing Cao 1 ,
• Jiapeng Xu 1 ,
• Huimin Cao 1 ,
• Fangfang Wang 1 ,
• Zhenyu Yan   ORCID: orcid.org/0000-0002-3154-9763 1 &
• Taimoor Muhammad 1  

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Decoupling of crop-livestock systems increases the risks of pollution, waste of nutrient resources, and biodiversity loss. Crop-livestock integration (CLI) is an effective solution to these problems, and motivating farmers to adopt CLI is the key. Many countries have implemented environmental regulations (ER) aiming to influence farmers’ CLI adoption decisions. Based on a field study of 316 hog farmers from Shaanxi Province of China, this paper applies the triple-hurdle model to empirically examine the impacts of economic expectations (EE) and ER on CLI adoption decisions. It also verifies the income effect of CLI. The results are as follows: 90.5% of farmers are willing to adopt CLI, but the adoption rate is only 40.8% and the average integration degree is only 0.236; CLI not been widely popularized. EE and ER promote farmers’ CLI adoption significantly, while the impact of interaction between EE and ER on CLI adoption differs. IER weakens the positive impact of EE on farmers’ CLI integration degree, which has a “crowding out effect.” GER negatively moderates the impact of EE on farmers’ adoption willingness of CLI. CER strengthens the positive effect of EE on farmers’ adoption behavior and CLI integration degree. CLI increases the farmers’ income. These results contribute to our understanding of the mechanisms of CLI adoption decisions and sustainable policy optimization for green agricultural development.

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This work was supported by Key Think Tank Research Project of Shaanxi Province on “Social Sciences Helping County Economies Develop in High Quality” (2023ZD0662); Humanities and Social Sciences Project of Fundamental Research Funds for the Central Universities in 2023 (452023307); Soft Science Research Program of Shaanxi Province (2022KRM032; 2023-CX-RKX-103); and Social Science Foundation of Shaanxi Province (2021D058; 2022D022). The authors would like to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper.

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Jiapeng Xu: conceptualization, supervision, project administration, article modification.

Huimin Cao: data, research methods.

Fangfang Wang: data, research methods.

Zhenyu Yan: conceptualization, supervision, project administration, article modification.

Taimoor Muhammad: data, research methods.

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Cao, J., Xu, J., Cao, H. et al. The impact of environmental regulation and economic expectations on crop-livestock integration among hog farmers: a field study from China. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33616-z

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