economic analysis research paper example

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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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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Kristin A. Van Gaasbeck

Department of economics, college of social sciences and interdisciplinary studies, california state university, sacramento, writing in economics :: components of a research paper.

An economics research paper includes the parts listed below. Some of these may be, and often are, combined into sections of the research paper. Depending on the nature of the research question, some parts may be emphasized more than others.

I've condensed information from several different sources. This is cursory content on how to write in economics, please make use of the additional resources . Also, every researcher has his or her own opinion about the best way to proceed. The information I've collected below is one of many possible ways to approach an undergraduate or graduate research project in Economics.

The abstract is a description of your research paper. The writing style of the abstract is very condensed - it should be no more than 350 words (or 5-6 sentences). The abstract is designed to identify the following to a potential reader:

• The research question What is the question that is the focus of your research? A good research question is one that (i) doesn't have an obvious answer (otherwise, why bother researching it?) and (ii) is testable using data.
• Your contribution to the research on the subject What has the previous literature found and what is your contribution to general understanding of the economic problem/question.
• How you answer the research question How you use theoretical and/or empirical analysis to answer the research question.
• Results Your findings based on the aforementioned analysis

The abstract is written when the paper is completed. It should not be the same as your introduction - the audience is different.

Introduction

The introduction is designed to both identify and motivate your research question. Like an essay you would write in other subjects, the introduction begins with a broad statement, and then narrows down to your specific research question.

In the end, make sure that you've done the following in your introduction:

• State your research question
• Motivate why the subject of your research is important to economists and other stakeholders
• Explain to the reader where your research fits into the subject.
• Identify your contribution to general understanding on the subject/research question
• Summarize how you intend to answer the research question
• State your general results and answer to your research question.

The first paragraph of the introduction is used to motivate why this research is important and of interest to economists and other stakeholders (e.g., parents and teachers in education economics, central bankers in monetary policy, and residents and businesses affected by pollution). It may conclude with a statement of your research question, followed by a discussion of who is affected by the economic issue under study. It is not appropriate to include personal anecdotes in a written research paper. Remember, you are motivating why the research should be of interest to the reader.

The second paragraph typically has more detail about how you plan to answer the research question, possibly citing other work closely related to your own research. In fact, many authors combine the literature review with the introduction in order to streamline this discussion. This paragraph may conclude with your general findings.

You should be able to write the first paragraph when you begin your research. The second paragraph can be written as you are concluding your research, as it draws on information from subsequent sections of your paper.

Literature Review

The literature review serves two main purposes:

• motivate why your research question is important in the context of the broader subject
• provide the reader with information on what other researchers have found (highlighting your contribution)

If someone has done a similar analysis to yours, tell us, and then explain how yours is different. Explain their findings, and then follow up with what you expect to find in your own research, and compare.

Some things to keep in mind for your literature review:

• Conduct a comprehensive search of the research on your subject Familiarize yourself with search engines in Economics (ECONLit is the most comprehensive) - do not rely on Google or other general search engines because they will link to you information that is not peer-reviewed research. A good general rule is as follows: if it is a paper not listed on ECONLit, it is probably not appropriate for a research paper in economics. Of course, there are exceptions. See my ECON 145 resources for more information on search engines .
• Create an annotated bibliography for the papers you plan to cite in your research paper. More information on annotated bibliographies is given below . This is a good step to take early on in your literature review search because it helps you keep track of the papers you plan to cite, and helps you to summarize information in one place. This will help you with the subsequent steps below.
• Identify which papers are most relevant to your research question It is easy to find lots of articles on one topic, but difficult to sort out which ones are important and relevant to your specific topic. You need to find the most relevant articles for your topic, and tell the reader why these are relevant articles for your topic specifically.
• Make an outline of your literature review Write an outline of your literature review. When writing your literature review, you want to organize the research of others into themes that you want to convey to the reader. Do not simply list papers chronologically and summarize the results of others. You should group papers by common themes.
• Critically read research papers You cannot read research papers like novels or the newspaper. Economics research papers are often dense and technical, requiring carefully reading. If you are not actively engaged as a reader, taking notes and writing questions to yourself as you go along, you are making poor use of your time and will not get much out of your literature review. See my page on Critical Reading for more information on strategies for how to read economics research papers.
• Be aware of plagiarism. This is very difficult for the novice researcher because some information is generally taken as known, while other information is not. The best way to get a sense for how to appropriately cite and attribute material is to read economics research articles. Avoiding plagiarism doesn't mean rewriting someone else's ideas in your own words. If you are using someone else's idea, whether in quotes or not, you must cite it. When in doubt, cite.
• common research questions in the subject (introduction),
• economic models used to answer related research questions (economic model),
• empirical methodologies common in the field (empirical methodology),
• data sources you may use in your analysis (data description),
• how to report your results (empirical analysis), and
• how to identify your contribution to understanding of the research question/subject (conclusion/analysis).

Economic Model/Empirical Methodology

This section (or sections) or your paper are designed to show how you intend to answer your research question using economic theory (economic model) and empirically (using statistical tests). For the novice researcher, it is useful to think of these two approaches as separate. This avoids the temptation to confuse them.

Economic Model

This is what you have studied in most of your other economics classes. For example, what happens to the price of housing when the population increases? Using demand-supply model, we know that an increase in population leads to an increase in the demand for housing, increasing the equilibrium price. In reading economics research papers, the economic model is often not identified because it is assumed the reader (economic researchers) are familiar with the underlying model. However, to the novice researcher, the model may not be obvious, so it is important to outline the model and include it in your research paper.

Your economic model is how you make predictions of what you expect to find in the data. Based on the simple example above, we'd expect to see a positive relationship between housing prices and population, ceteris paribus (e.g., holding all other variables in the demand-supply model unchanged).

Another important point is that your economic model is what implies a causal relationship between the economic variables. While you may detect a positive or negative relationship in the data, this alone tells you nothing about which variable is causing a change in the other variable. The economic model can be used to model this relationship. In the example above, we assume that in the model, a change in population causes a change in the housing price.

The economic model should make no mention of data, regression analysis, or statistical tests. The model is a purely theoretical construct, based on an abstract notion of how the world works. The empirical methodology section of your paper is how you plan to test these relationships in the data. An economic model is NOT a regression equation.

Finally, you should use an economic model that is common in the literature on your subject. Unless you are proposing a new model, you should rely on those used by other researchers in the field. This will allow you to use your literature review to justify your choice of model. Also, this is why the economic model is often embedded in the literature review of the paper. For novice researchers, I recommend keeping it separate, to make sure you understand how to use your economic model to conduct theoretical analysis.

Empirical Methodology

This is where you describe to the reader how you plan to test the relationships implied by your economic/theoretical model. First, you want to identify your dependent variable. This is the variable you are seeking to explain the behavior of. Next, you want to identify possible explanatory variables. These are the variables that could potentially affect your dependent variable.

Often in economic models, there are abstract notions of how some variables affect others. For example, human capital affects production, but how would we measure human capital in the data? You can find suitable proxies for a variable like human capital by familiarizing yourself with the literature.

So, how could a researcher go about testing the relationship between housing prices and population? First, we know that housing price is the dependent variable. Population is one explanatory variable, but are there others that affect housing prices? Yes. We know this from the demand and supply model that there are other variables that shift demand for housing (income, prices of substitutes and complements, expectations, tastes and preferences, etc.) and the supply of housing (input costs, expectations, the number of sellers, etc.). In order to isolate the effect of population on house price, we need to control for these other factors.

The most common strategy for empirical work regression analysis because it allows the researcher to isolate the correlation between two variables, while holding other explanatory variables constant (e.g., ceteris paribus from the model above). Often in the empirical methodology section, the researcher will point out potential estimation issues, highlighting the need for more advanced econometric techniques that go beyond ordinary least squares (OLS).

This section does not actually do any statistical analysis, but it may include a description of the data (see below). In advising students on research papers, I usually recommend the following breakdown for the empirical methodology section:

• Data description This is a description of the data you plan to use for your analysis. It usually includes a citation of the primary source, data frequency, how the data are measured, the frequency of the data, etc. The amount of detail depends on the nature of the data. Also, this is the section where you would report any modifications you make to the data.
• Preliminary data analysis This section reports summary statistics, histograms, time series plots, and other similar information. This section is designed to give the reader a sense of what your sample looks like. In reporting this information, you should be selective - more is not always better. You need to decide which information you need/want to convey to the reader and how to best convey it. See my Empirical Methods in Economics page for ideas on basic statistical analysis.
• Regression Equation Now, you're ready to remind the reader of your particular test and how you are going to go about using regression to test it. This section should include a regression equation, a discussion justifying this equation, and a description of the expected signs on the coefficients for each of the explanatory variables (spending more time on those that are of particular interest for your study). Remember, the regression coefficient measure the marginal effects of the explanatory variable on the dependent variable (holding the other variables constant, ceteris paribus). When justifying your regression equation and discussing the expected signs for the coefficients, you should make some clear connections back to your theory section and the literature review section of your paper. Also, make sure that you are using your regression equation to answer your research question. What is the testable hypothesis? Does this test answer your research question? See my Empirical Methods in Economics page for a simple primer on regression analysis.

Data Description

An alternative to the ordering mentioned above is as follows. You can begin with a regression equation, then provide a detailed description of the data, along with some preliminary data analysis. It is most common to have the data description as its own section of the paper - mainly to make it easier for readers to reference it if they plan to do similar research. You could then follow this Data section with an Empirical Methodology section that consists of the #3 Regression equation described above.

Empirical Analysis

This section is often titled "Results" in economic research papers, as it reports the results from your regression analysis above. There are commonly-used templates for reporting regression results. The best way to familiarize yourself with these templates is from the papers you cite in your literature review. You will see that it is common to report multiple regressions in one table, with the explanatory variables listed vertically on the left. See my page on Empirical Methods in Economics for more details.

The empirical analysis should include a table with your regression results, and your written analysis of these results. Note, this does not mean repeating the information in your regression tables. It means interpreting these coefficients in light of your economic model and comparing your findings to other papers from your literature review.

The conclusion usually consists of about three paragraphs. The first begins with a restatement of the research question, followed by a description of what we know about this research question from the literature (very concisely). Then the paragraph concludes with a brief description of the theoretical answer to the question.

The second paragraph begins with an answer to the research question, based on your empirical analysis. The researcher then proceeds to compare his/her findings to the consensus in the literature, pointing out possible reasons for differences and similarities. For example, perhaps you studied a different time period, or a different country. Perhaps you used a different measure of the dependent or explanatory variables.

In the final paragraph, it is common to draw policy implications from your research. In a practical sense, who cares about this research question (remember the stakeholders from the introduction..) and what can they do with this knowledge? Often the conclusion will point toward directions for future research, based on possible extensions to your research.

Bibliography/References

The bibliography contains complete references of the works that cited and referred to in your research.

It is essential that you give proper credit to all works that you cite, even if they are not included in your literature review. For example, if you obtained data from a publication that is not easily available, it would be appropriate to cite it in your data description and include it in your bibliography. Incomplete or inaccurate citations are akin to plagiarism, so please be sure to carefully check your references and keep track of them while completing your literature review.

In economics, it is most common to use APA style in citing references in the text of your paper and in creating a bibliography. For more information, see the APA style guide provided by the Library , or simply pick up a copy of the APA style guide if you will be using it frequently.

Annotated Bibliography An annotated bibliography is one that includes the reference (mentioned above), along with a few sentences describing the research and how it relates to your research paper. Often the description will begin with a statement of what the research found, followed by one or two sentences that are relevant to the research question you are studying.

Even though APA style calls for a double-spaced annotated bibliography, many researchers prefer a single spaced one. The Library has information on annotated bibliographies and I have posted an outstanding example from undergraduate Economic Research Methods .

The best annotated bibliographies are those written by students who have read the literature critically. See my page on Critical Reading for more information on strategies for how to read economics research papers. Even if an annotated bibliography is not assigned as part of your research project, it is a useful exercise for you to engage in, especially if you have to present your research orally or using a poster. If you are unable to write an annotated bibliography, then you are probably writing a poor review of the literature on your subject and a less than satisfactory research paper.

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This page is meant to help you create a literature review for academic projects and publications. Each tab outlines a different aspect of what a literature review is and how to build one. If you need help finding sources for your literature reviews, check out How To pages.

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What is a Literature Review?

A literature review is a comprehensive summary and analysis of previously published research on a particular topic. Literature reviews should give the reader an overview of the important theories and themes that have previously been discussed on the topic, as well as any important researchers who have contributed to the discourse. This review should connect the established conclusions to the hypothesis being presented in the rest of the paper.

What a Literature Review Is Not:

• Annotated Bibliography: An annotated bibliography summarizes and assesses each resource individually and separately. A literature review explores the connections between different articles to illustrate important themes/theories/research trends within a larger research area. 
• Timeline: While a literature review can be organized chronologically, they are not simple timelines of previous events. They should not be a list of any kind. Individual examples or events should be combined to illustrate larger ideas or concepts.
• Argumentative Paper: Literature reviews are not meant to be making an argument. They are explorations of a concept to give the audience an understanding of what has already been written and researched about an idea. As many perspectives as possible should be included in a literature review in order to give the reader as comprehensive understanding of a topic as possible.

Why Write a Literature Review?

After reading the literature review, the reader should have a basic understanding of the topic. A reader should be able to come into your paper without really knowing anything about an idea, and after reading the literature, feel more confident about the important points.

A literature review should also help the reader understand the focus the rest of the paper will take within the larger topic. If the reader knows what has already been studied, they will be better prepared for the novel argument that is about to be made.

A literature review should help the reader understand the important history, themes, events, and ideas about a particular topic. Connections between ideas/themes should also explored. Part of the importance of a literature review is to prove to experts who do read your paper that you are knowledgeable enough to contribute to the academic discussion. You have to have done your homework.

A literature review should also identify the gaps in research to show the reader what hasn't yet been explored. Your thesis should ideally address one of the gaps identified in the research. Scholarly articles are meant to push academic conversations forward with new ideas and arguments. Before knowing where the gaps are in a topic, you need to have read what others have written.

What does a literature review look like?

As mentioned in other tabs, literature reviews should discuss the big ideas that make up a topic. Each literature review should be broken up into different subtopics. Each subtopic should use groups of articles as evidence to support the ideas. There are several different ways of organizing a literature review. It will depend on the patterns one sees in the groups of articles as to which strategy should be used. Here are a few examples of how to organize your review:

Chronological

If there are clear trends that change over time, a chronological approach could be used to organize a literature review. For example, one might argue that in the 1970s, the predominant theories and themes argued something. However, in the 1980s, the theories evolved to something else. Then, in the 1990s, theories evolved further. Each decade is a subtopic, and articles should be used as examples. 

Themes/Theories

There may also be clear distinctions between schools of thought within a topic, a theoretical breakdown may be most appropriate. Each theory could be a subtopic, and articles supporting the theme should be included as evidence for each one. 

If researchers mainly differ in the way they went about conducting research, literature reviews can be organized by methodology. Each type of method could be a subtopic,  and articles using the method should be included as evidence for each one.

Preliminary Steps for Literature Review

• Define your research question
• Compile a list of initial keywords to use for searching based on question
• Search for literature that discusses the topics surrounding your research question
• Assess and organize your literature into logical groups
• Identify gaps in research and conduct secondary searches (if necessary)
• Reassess and reorganize literature again (if necessary)
• Write review

Here is an example of a literature review, taken from the beginning of a research article. You can find other examples within most scholarly research articles. The majority of published scholarship includes a literature review section, and you can use those to become more familiar with these reviews.

Source:  Perceptions of the Police by LGBT Communities

• ISU Writing Assistance The Julia N. Visor Academic Center provides one-on-one writing assistance for any course or need. By focusing on the writing process instead of merely on grammar and editing, we are committed to making you a better writer.
• University of Toronto: The Literature Review Written by Dena Taylor, Health Sciences Writing Centre
• Purdue OWL - Writing a Lit Review Goes over the basic steps
• UW Madison Writing Center - Review of Literature A description of what each piece of a literature review should entail.
• USC Libraries - Literature Reviews Offers detailed guidance on how to develop, organize, and write a college-level research paper in the social and behavioral sciences.
• Creating the literature review: integrating research questions and arguments Blog post with very helpful overview for how to organize and build/integrate arguments in a literature review
• Understanding, Selecting, and Integrating a Theoretical Framework in Dissertation Research: Creating the Blueprint for Your “House” Article focusing on constructing a literature review for a dissertation. Still very relevant for literature reviews in other types of content.

A note that many of these examples will be far longer and in-depth than what's required for your assignment. However, they will give you an idea of the general structure and components of a literature review. Additionally, most scholarly articles will include a literature review section. Looking over the articles you have been assigned in classes will also help you.

• Sample Literature Review (Univ. of Florida) This guide will provide research and writing tips to help students complete a literature review assignment.
• Sociology Literature Review (Univ. of Hawaii) Written in ASA citation style - don't follow this format.
• Sample Lit Review - Univ. of Vermont Includes an example with tips in the footnotes.
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Economics Research Paper

This sample economics research paper features: 7800 words (approx. 26 pages), an outline, and a bibliography with 36 sources. Browse other research paper examples for more inspiration. If you need a thorough research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Evolutionary Economics Research Paper

Introduction, relationship between theories of biological and sociocultural evolution, the scope and methods of evolutionary economics, marxist models of evolution, original institutional economics, the new institutional economics, whither evolutionary economics.

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Evolutionary economics has gained increasing acceptance as a field of economics that focuses on change over time in the process of material provisioning (production, distribution, and consumption) and the social institutions that surround that process. It is closely related to, and often draws on research in, other disciplines such as economic sociology, economic anthropology, and international political economy. It has important implications for many other fields in economics, including, but not limited to, growth theory, economic development, economic history, political economy, history of thought, gender economics, industrial organization, the study of business cycles, and financial crises.

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Historically, evolutionary economics was the province of critics of the mainstream, neoclassical tradition. Both Marxist and original institutional economists (OIE) have long asserted the importance and relevance of understanding change over time and critiqued the standard competitive model for its abstract, ahistorical, and static focus. In recent years, however, the rise of the new institutional economics (NIE) as well as game theory has resulted in wider acceptance of evolutionary explanations by the mainstream (Hodgson, 2007b, pp. 1-15; North, 1990). Consequently, it is now possible to identify three major traditions in evolutionary economics: the Marxist (Sherman, 2006), the OIE (Hodgson, 2004), and the NIE (North, 1990). Each of these major traditions encompasses multiple strands within it. As a general rule, Marxists and OIEs seek to replace the standard competitive model of mainstream economics, while NIEs seek to complement the standard competitive model, although the growing acceptance of game theory may make this less of an important distinction. Despite their differences, it is possible to identify some common themes that are shared by each of these disparate traditions. For example, authors in each tradition have exhibited a concern with how the interaction of technology, social institutions, and ideologies leads to changes in economic and social organization over time.

The goal of this research paper is to introduce the reader to a few of the major concerns, themes, and important authors of each respective tradition. In doing so, it will first address some general issues in evolutionary economics, including its relationship to evolutionary biology as well as some conceptual, definitional, and taxonomic issues. It will then proceed to provide a brief overview of the evolution of each respective tradition. Unfortunately, the length of this research paper precludes discussion of many worthy contributions to each tradition as well as important topics that can and should be addressed by evolutionary economics. For example, space does not permit a discussion of how evolutionary economics could be applied to gender economics or how economists who write on gender often incorporate the contributions of evolutionary economists. Nor will this research paper attempt to assess the extent of empirical or conceptual progress in evolutionary economics within or between respective traditions. In addition, the reader should be aware that evolutionary economics itself is an evolving field and that the boundaries between the three traditions are often fluid.

General Issues in Evolutionary Economics

Taken at face value, the word evolution simply means change. But Darwin’s theory of gradual (step-by-step) evolution by variation of inherited characteristics and natural selection (differential survival based on the level of adaptation) removed both theological and teleological explanations from the process of biological evolution and placed humans firmly in the natural world. The modern neo-Darwinian synthetic theory of evolution combines Darwin’s focus on gradual (step-by-step) change based on variation of inherited characteristics and natural selection with modern population genetics. Both Darwin’s original theory and the modern synthetic theory of evolution explain change within a species, the rise of new species, and the more dramatic kinds of change such as the rise of mammals, primates, and eventually human beings as a result of the same step-by-step process (Mayr, 2001, 2004).

At the risk of oversimplifying slightly, it should be noted that the neo-Darwinian synthesis formulated by Thedosius Dobzhansky and Ernst Mayr in the 1950s has given rise to two sometimes opposing strands within the overarching frame of the synthesis (Mayr, 2004, pp. 133-138). One strand, exemplified by Richard Dawkins, who has written many widely read books on evolution, focuses on the role of genes in building organisms and on the tendency of natural selection to result in highly adapted organisms. This approach is sometimes referred to as the strong adaptationist program in evolutionary biology. It is closely related to fields such as sociobiology and evolutionary psychology, which explain many human behaviors in terms of their evolutionary origins.

Other evolutionary biologists have de-emphasized the role of natural selection and emphasize the importance of understanding biological evolution in terms of emergence, chance, path dependence, satisficing, and punctuated equilibrium. Richard Lewontin and the late Stephen J. Gould are two widely read authors who have advocated this position. Both Gould and Lewontin have been strongly critical of biologically based explanations for human behavior.

Although these two differing approaches to evolution are sometime viewed as rivals, they are in actuality complementary to each other. It is important to understand both aspects of biological evolution. In addition, biological evolution is a very complex process, and evolutionary biologists continue to push their field forward. Contemporary research in evolutionary biology focuses on the important interactions between genes, organisms, and their interaction with the environment in the process of development. Evolutionary biologists have also become more aware of the importance of lateral gene transfer and endo-symbiosis in bacteria evolution. However, there is still widespread consensus among evolutionary biologists that the synthetic theory of evolution is a true theory. Evolutionary biologists reject theories that incorporate teleological explanations or inheritance of acquired characteristics because these theories have been discredited empirically. Evolutionary biologists reject theories that are premised on or seek to find evidence of supernatural design as this adds nothing to the explanation and draws the focus of science away from understanding and explaining natural law.

Evolutionary economists often draw on and incorporate concepts developed by evolutionary biologists to explain how economic evolution occurs. For example, many evolutionary economists view economic evolution as a nondirected step-by-step process that is non-teleological (it lacks a specific goal or predetermined endpoint). Many, although not necessarily all, evolutionary economists agree that humans have at least some genetically based cognitive and social predispositions that are a result of genetic evolution. Some examples include the ability to learn a language, to learn social norms, to cooperate in groups, and to develop complex tool kits with which to transform nature into useable goods and services. In addition, the use of the Darwinian concepts of inheritance, variation, and selection as analogs to explain outcomes is pervasive in evolutionary economics. Evolutionary economists also distinguish between specific or microevolution (change that occurs within a sociocultural system) and general or macroevolution (change from one sociocultural system to another).

Some evolutionary economists view the market as natural and as an extended phenotype. Other evolutionary economists argue that evolutionary economics should be viewed as a generalization of the Darwinian concepts of variation, inheritance, and natural selection with each case specifying additional, relevant detail (Hodgson, 2007a; Hodgson & Knudsen, 2006). Others have argued that while Darwinian concepts often provide useful analogies for understanding sociocultural evolution, aspects of sociocultural evolution are distinctly non-Darwinian (Poirot, 2007). For example, in at least some instances, social and economic evolution results from the conscious decisions of groups of purposive agents who intentionally design or redesign human institutions. Also, in the process of socio-cultural evolution, we can pass on cultural traits that we acquire through the process of learning. Biological evolution results in a branching pattern and barriers between different species. But human cultures can always learn from each other. The more emphasis that is placed on purposive design of social institutions and cultural learning as well as the abruptness (instead of the step-by-step nature) of social change, the less Darwinian a model of sociocultural evolution becomes. However, it would be difficult to identify anyone today who argued for a strong teleological concept of sociocultural evolution or who sought to explain sociocultural evolution in terms of divine or supernatural intervention.

Two other important concepts borrowed from the natural sciences, emergence and complexity, also play a key role in evolutionary economics. Emergence means that an observed system results from the complex interaction of the components of the subsystems. This process of interaction gives rise to patterns that would not be predicted from and cannot be reduced to the behaviors of the individual components. However, understanding the system still requires an understanding of its components and the interaction of the components. So it is important to understand what individuals do. And it is also important to understand how individual choices and habits interact with social institutions in a dynamic way. It is often easier to think in mechanical terms. But if we are careless with mechanical analogies, then we can be easily misled.

This raises the question of what it is that evolves in sociocultural evolution. In evolutionary biology, selection takes place at multiple levels but logically requires changes in the gene pool of a population over time (Mayr, 2004, pp. 133-158). This has led some evolutionary economists to suggest that institutions and/or organizational routines provide us with an analog to the gene. Others argue that there is not a precise analog. To understand this debate, we first have to understand what an institution is.

It is popular to define institutions as “rules of the game.” This is a good start, but it confuses the function of institutions with a definition of institutions. A more extensive definition of institution defines an institution as any instituted process, or in other words a shared, learned, ordered, patterned, and ongoing way of thinking, feeling, and acting. Institutions may be tacit and informal or highly organized and structured. By this latter definition, modern firms, medieval manors, technology, nation-states, political ideologies, and even technology are all institutions. In other words, virtually everything that humans do is an instituted process. Institutions are component parts of a sociocultural system.

But to just call everything an “institution” can make it difficult to conduct analysis. So it is useful to draw a distinction between entities such as social ideologies (e.g., Calvinism and democracy), social institutions (e.g., class, caste, kinship, the family, the nation-state), organizations (e.g., the modern firm, the International Monetary Fund, the medieval manor), organizational routines of actors within specific organizations, and technology (the combined set of knowledge, practices, and tool kits used in production). So in that sense, everything in sociocultural systems is constantly evolving. There is no precise analog in sociocultural evolution to the gene pool of a population.

As suggested above, social institutions are part of more general wholes, which it is convenient to term sociocultural systems. A sociocultural system includes the direct patterns of interaction of a society with the ecosystem (its subsistence strategy, technology, and demographic patterns), its social institutions, and its patterns of abstract meaning and value. Many anthropologists classify sociocultural systems by their scale, complexity, and the amount of energy captured by their subsistence strategy. Standard classification includes bands, tribes, chiefdoms, agrarian states, and industrial states, each of which corresponds roughly to subsistence strategies of foraging, horticulture, pastoralism and fishing, settled agriculture, and modern industrial technology. This classification system provides a useful scheme with which to understand the rise of large agrarian empires in the neolithic era and, ultimately, the Industrial Revolution in northwestern Europe. It also provides a useful classificatory schema with which to understand the interaction of multiple kinds of contemporary societies in a globalizing world. However, care must be taken to emphasize the multilinear and dynamic nature of socio-cultural evolution rather than rigidly applying these concepts as a universal and unilinear schema (e.g., see Harris, 1997; Wolf, 1982).

The evolutionary biologist Ernst Mayr (2004) argued that biologists who study genetic evolution ask “why” questions while biologists who study things such as biochemistry ask “how” questions. Similarly, many mainstream economists ask “how” questions while evolutionary economists ask “why” questions. While the study of evolutionary economics does not preclude the use of formal mathematical models or quantification, most of its practitioners employ qualitative and interpretive methods. Also, as suggested above, some evolutionary biologists focus on changes that occur at the level of species, while others focus on more dramatic kinds of change. Similarly, evolutionary economists are interested in the study of sociocultural evolution on a grand scale, such as the rise of agrarian empires or modern capitalism, as well more specific, micro-level evolution such as changes in the organizational routines of individual firms.

Consequently, the kinds of issues that evolutionary economists are interested in overlap with the focus of other social sciences and even, in some instances, with the fields of ecology and evolutionary biology. Evolutionary economics reflects a tendency to counter the fragmentation of political economy into disparate social sciences that occurred in the late nineteenth and early twentieth centuries. Evolutionary economists, like their counterparts in economic sociology, economic anthropology, and political economy, focus more directly on those institutions with the strongest, most immediate, direct relevance to the process of material provisioning. So there may still be a need for some division of labor in the social sciences. What is of direct relevance will vary according to what is being analyzed in any particular study. An economic historian studying the rise of capitalism may, following Weber, find an understanding of Calvinist theology to be essential. Someone studying financial innovation in twenty-first-century industrialized societies would most likely find the religious affiliation of modern banking executives to be of little interest or relevance.

Research Traditions in Evolutionary Economics

Evolutionary economics is composed of three rival but sometimes overlapping major traditions: the Marxist, the OIE, and the NIE. While there is some degree of ideological overlap between the schools, each of the respective schools tends to share a common overarching ideology. Marxists seek to replace capitalism, OIEs seek to reform capitalism, and NIEs generally view capitalism as beneficent. This is not, notably, to argue that the ideology necessarily determines the empirical and theoretical analysis. Also, as previously noted, Marxists and OIEs seek to replace the standard competitive model while NIEs seek to complement the standard model. However, the reader should be aware that the boundary between the three traditions is often fuzzy, and there is sometimes overlap between the three traditions. Similarly, each of these three schools is composed of multiple strands and has undergone significant change over time.

The remainder of this research paper will focus on outlining in very broad terms a few of the significant themes and concerns of each respective tradition, how these traditions have changed over time, and the contributions of a few representative authors of each of the three traditions. The reader may note that despite the differences between the traditions, there is a strong interest in all three in understanding how technology, social institutions, and cognitive models interact in the process of sociocultural evolution. The division made between the three traditions may be of greater interest and relevance in the United States, where there is a strong correlation between specific organizations and schools of thought. For example, the Association for Evolutionary Economics (AFEE) has been the primary promoter of OIE in the United States. In contrast, the European Association for Evolutionary Political Economy (EAEPE) has a much wider umbrella. So there may be hope someday for a grand synthesis of the three respective traditions.

There are, of course, many different Marxist and quasiMarxist models of sociocultural evolution. For the purposes of this research paper, it is convenient to make the differentia specifica of a Marxist model of sociocultural evolution a focus on class struggle: the conflict between social groups defined in terms of differential access to the productive resources of a given society (Dugger & Sherman, 2000). This way of understanding sociocultural evolution is often referred to as historical materialism. While Darwinian reasoning may at times be employed in Marxist theories of sociocultural evolution, Marxists have generally emphasized the non-Darwinian aspects of sociocultural evolution as well as sharp discontinuities between human and infrahuman species. At the same time, it is hard to think of any academic Marxists writing today who would advocate Lysenkoism or Lamarckian theories of inheritance as valid explanatory concepts for understanding genetic evolution.

To understand historical materialism, we must begin with Marx’s concept of the mode of production (for extended discussions, see Wolf, 1982, chap. 3, and also Fusfeld, 1977). A mode of production includes the techno-environmental relationships (e.g., agriculture based on a plough or factories using steam engines) and the social relationships of production (e.g., warlords and peasants or factory owners and workers) or, in Marxist jargon, the forces of production and the social relations of production, respectively. These relationships between groups of people in Marx’s view are characterized by unequal relations of power, domination, subordination, and exploitation. This gives rise to social conflict over the terms of access to and the distribution of the productive resources of society. Social conflict requires the creation of a coercive entity to enforce the interests of the dominant social class (i.e., a state). In addition, human beings develop complex ideologies with which to justify their positions. Thus, the entire civilization (or what above is termed a sociocultural system) rests on a given mode of production, with the mode of production distinguished by the primary means of mobilizing labor (e.g., slavery, serfdom, wage labor).

In his analysis of Western history, Marx distinguished between the primitive commune, the slave mode of production of the ancient Roman Empire, the Germanic mode of production, the feudal mode of production of medieval Europe, and the modern capitalist mode of production. In analyzing Western history, Marx argued that each successive mode of production had produced technological advance, thus elevating the material level of human existence.

Capitalism, in Marx’s view, is qualitatively different from extended commodity production. Capitalism requires that land, labor, and capital are fully treated as commodities. This means that labor is “free” in the sense of not being legally bound to perform labor for the dominant class and “free” in the sense that it has no claim to the resources needed to produce goods and services. Therefore, capital is used as a means to finance innovation in production, and labor is compelled by economic circumstances to sell its labor power. Because capitalism promotes endless accumulation of capital, it is thus far the most successful in a material sense. However, the dynamic of capitalist accumulation gives rise to periodic crises, and it is therefore unstable. In addition, it is often destructive of human relationships. So a relationship of apparent freedom is in actuality a relationship of power, subordination, and domination that will give rise to social conflict. The only way to end this conflict, in Marx’s view, is to redesign social institutions so as to pro-mote both development of the forces of production and social cooperation (i.e., replace capitalism with socialism). There is disagreement among scholars who study Marx as to whether Marx thought that the triumph of socialism over capitalism was inevitable.

Insofar as one seeks to explain the historical origins of capitalism and the Industrial Revolution, two historical epochs are of particular relevance. Marxist historians and Marxist economists (and many others) with a particular interest in economic history thus often refer to two transitions (one from antiquity to feudalism and the other from feudalism to capitalism) as giving rise to modern capitalism. Howard Sherman (1995, 2006), a well-known Marxist economist, has summarized and synthesized much of this existing literature.

Sherman traces Western economic history from tribal organization through the rise of modern capitalism. Sherman is a materialist who analyzes societies by starting with the material base of human existence and examines the interaction between technology, economic institutions, social institutions, and ideologies. Technology and technological innovation as well as social conflict between classes are key variables in Sherman’s analysis. But overall, Sherman’s schema is holistic and interactive, rather than mechanical or reductionist.

In analyzing the breakdown of feudalism, Sherman focuses on the tripartite class conflict between peasants, nobles, and monarchs and the ability of each of the respective classes to force an outcome on the other classes. As a consequence of this conflict, a new pattern of relationships based on private property and production for profit in a market, as well as increasingly organized around new sources of mechanical power, gave rise to a unique and extremely productive system referred to as capitalism. This system of production encourages constant cost cutting, innovation, and capital accumulation, thus leading to the potential for the progressive material elevation of human society.

However, capitalist society is still riven by conflict between property-less workers and property-owning capitalists. Because the capitalist has a monopoly over the productive resources of society, the capitalist is still able to compel the worker to produce a surplus for the capitalist. This creates social conflict between the capitalist and worker and also forces the capitalist into an ultimately self-defeating boom-and-bust cycle of rising profits and increasing concentrations of capital, followed by falling rates of profit, leading to cycles of recession and crisis. The institutional structure of capitalism also magnifies other social conflicts and problems such as environmental degradation and destruction, as well as relations between racial and ethnic groups and genders. The solution to this social conflict, in Sherman’s view, is to replace the institutions of capitalism with economic democracy (i.e., democratic socialism).

Sherman, who has long been a critic of Stalinist-style socialism, also extends his analysis to change in Russia and the Soviet Union. The October Revolution of 1917 occurred because neither the czar nor the Mensheviks were able to satisfy the material aspirations of the vast majority of Russians. But industrialization in the Soviet Union became a nondemocratic, elite-directed process due primarily to the particular circumstances surrounding the Bolshevik Revolution, the ensuing civil war, and the problems of the New Economic Policy. In time, factions among the elites developed as the Soviet economy proved unable to satisfy the material aspirations of the majority of the Soviet population. This created new pressure for change as elites were able to capture this process. Due also to pressure from the West, change in the former Soviet Union took the direction of restoring capitalism rather than developing greater economic democracy.

It should be noted that the standard Marxist model of historical materialism focuses on the ability of capitalism to elevate the material capacity of human societies. This focus has been challenged by the rise of world systems and dependency theory. Theorists who follow this line of thinking focus on the uneven nature of development and the tendency of core economies to place boundaries on the development of formerly colonized areas of the world. Some theorists in this tradition have been justly accused of having a rather muddled conception of the term capitalism, insofar as they claim inspiration from Marx. The late Eric Wolf (1982), a well-known economic anthropologist, resolved many of these conceptual issues in his book Europe and the People Without History. So rather than assume that capitalism leads uniformly to material progress, Wolf extended the historical materialist model to analyze the process of uneven development in the world system as a whole. In their textbook on economic development, James Cypher and James Dietz (2004) provide an excellent history and exposition of classical Marxism, dependency theory, and extended analysis and discussion of the new institutional economics, original institutional economics, and modernization theory.

Thorstein Veblen (1898) was the founder of OIE, and his influence on OIE continues to be prevalent (Hodgson, 2004). Veblen was strongly influenced by Darwin’s theory of biological evolution and held evolutionary science as the standard for the social sciences, including economics, to emulate. He was also deeply influenced by the evolutionary epistemology of the American pragmatists Charles Saunders Peirce and John Dewey. In addition, he incorporated the contrasting positions of nineteenth-century evolutionist anthropology, as exhibited by the work of Tylor and Morgan, and the historical particularism of Franz Boas. Although he was strongly critical of Marx and of Marxism, there are both parallels as well as differences in the writings of Marx and Veblen.

Like Marx, Veblen focused on the importance of understanding the interaction of changes in technology, social institutions, and social ideologies as well as social conflict. Veblen also had a stage theory of history, which he borrowed from the prevailing anthropological schemas of his day. However, where Marx focuses on concepts such as class and mode of production, Veblen focuses on instituted processes and the conflicts created by vested interests seeking to reinforce invidious distinctions. Veblen’s model of sociocultural evolution is a conflict model in that it focuses broadly on social conflict that arises in the struggle for access to power, prestige, and property. But it is not a class-based model in the sense that Marxists use class.

In “Why Is Economics Not an Evolutionary Science?” (1898) and in “The Preconceptions of Economic Science” (1899) , Veblen developed a critique of the mainstream economics of his day. In developing this critique, Veblen was critical of the abstract and a priori nature of much of mainstream economic analysis. In articulating this point, he contrasted the “a priori method” with the “matter of fact method.” This particular aspect of Veblen’s criticism has often led some to view both Veblen and later OIEs as “atheoretical.” But this misses the point for at least two reasons.

Veblen did not eschew theoretical analysis per se. He was however, critical of theory that divorced itself from understanding actual, real-world processes of material provisioning. But most important, in Veblen’s view, economics was not up to the standards of evolutionary science because economics continued to implicitly embrace the concepts of natural price and natural law by focusing on economics as the study of economizing behavior and the adjustment of markets to equilibrium. In contrast, Veblen argued that the process of material provisioning entailed a constant process of adaptation to the physical and social environment through the adjustment of institutions or deeply ingrained social habits based on instinct. Veblen’s understanding of the term institution was broad enough to encompass any instituted process. Yet he drew a sharp distinction between institutions and technology. He was sharply critical of the former and strongly in favor of the latter.

When Veblen wrote about deep-seated and persistent social habits developing on the basis of genetically based instincts, he did in fact appear to mean something similar to contemporary theories of gene-culture evolution. Social habits are not consciously thought-through, purposive behaviors—they develop out of the complex “reflex arc” of enculturation based on genetically based propensities to act in the presence of environmental stimuli. Instincts are acquired through genetic evolution and social habits through enculturation. Both are inherited, vary in nature, and may therefore be selected for or against in the process of sociocultural evolution (Hodgson, 2004, Part III). However, Veblen also borrowed from Dewey a view of socialization in which individuals are active participants in socialization, a concept that was later more clearly articulated by Meade. In addition, Veblen also emphasized the ability of humans to conceptualize and engage in purposive behavior.

Veblen drew a sharp dichotomy between the instinct of workmanship and the instinct of predation. He associated the instinct of workmanship with a focus on adaptive, problem-solving, tinkering, and innovative behavior. In contrast, he associated predation with a focus on brute force, ceremonial displays of power, emulative behavior, conspicuous consumption, financial speculation, and the power of vested interests. Veblen argued that the instinct of workmanship arose in the primitive stage of human history (roughly corresponding to what contemporary anthropologists would term bands and tribes) and that the instinct of predation emerged during the stage of barbarism (roughly corresponding to the rise of chiefdoms). These instincts gave rise to deep-seated social habits. Both instincts continued to be present during the rise of civilization (agrarian states) and persisted in modern civilization (industrial states). But because modern civilization is based on the rise and extensive application of machine technology, further progress would require the triumph of the instinct of workmanship over the instinct of predation.

But in Veblen’s view, there was no reason to expect this would necessarily occur. Vested interests were often capable of instituting their power to reinforce the instinct of predation. Hence, institutions often served to encapsulate and reinforce the instinct of predation. The behaviors of predation were primarily exhibited by the new “leisure class” or, in other words, the robber barons of the late nineteenth century. In contrast, workmen and engineers often exhibited the instinct of workmanship. Consequently, Veblen tended to view institutions in general as change inhibiting and the instinct of workmanship as change promoting.

In later works, Veblen extended this kind of analysis to study other topics such as changes in firm organization and the business cycle. Veblen argued that as modern firms became larger and more monopolistic, a permanent leisure class arose, thus displacing technological thinking among this new class. In addition, increasing amounts of time and energy were channeled into financial speculation, leading to repeated financial crises. Emulative behavior in the form of conspicuous consumption and ceremonial displays of patriotism and militarism served to reinforce the instinct of predation. In his analysis of the rise of militarism in Prussia, Veblen noted the socially devastating impact of the triumph of the instinct of predation. Thus, Veblen tended to identify institutions with imbecilic behaviors that serve to block the triumph of technological innovations.

Veblen’s focus on the conflict between the instinct of workmanship and predatory and pecuniary instincts is often referred to as the instrumental-ceremonial dichotomy. Ayres (1938) in particular reinforced the tendency of the OIE to focus on the past binding and ceremonial aspects of institutions and on the scientific and progressive nature of instrumental reasoning. This dichotomy was, at one point in time, a core proposition of the OIE.

Most contemporary OIEs, however, recognize and accept that at least some institutions can promote and facilitate progressive change and that technology itself is an institution. This rethinking of the ceremonial-instrumental dichotomy is also reflected in the incorporation of Karl Polanyi’s (1944) dichotomy between habitation and improvement. Polanyi noted that the need for social pro-tection may actually serve a noninvidious purpose. Some improvements destroy livelihoods and reinforce invidious distinctions while others promote the life process. So the distinction might better be thought of in terms of “invidious versus noninvidious.”

One OIE who had a more positive understanding of the role of institutions is J. R. Commons (Commons, 1970; Wunder & Kemp, 2008). Commons in particular focused on the need for order in society and thus addressed the evolution of legal systems and the state. Commons’s theory is primarily microevolutionary insofar as he focuses on the evolution of legal arrangements and shifting power alignments in modern industrial states. Commons is not as critical of existing arrangements as Veblen. Institutions, including the state, in Commons’s view, are clearly both necessary and potentially beneficial. For example, with the rise of big business, labor conflict, and the problems inherent in the business cycle, there is a need for a strong state to manage this conflict. At the same time, Commons developed a theory of the business cycle that has strong elements in common with some of Keynes’s analysis.

The Veblenian strand as expressed by Commons is, by the standards of American politics, moderately left of center in that it expresses support for much of the regulatory framework and expanded role of government in managing the business cycle that came out of the New Deal and the publication of Keynes’s (1936) The General Theory of Employment, Interest and Money. Not surprisingly, a number of OIE economists have begun to attempt to synthesize OIE and Keynes, relying to a large degree on the work of Hyman Minsky (1982). This project, often referred to as PKI (post-Keynesian institutionalism), is microevolution-ary in nature in that it focuses on the problems of financial instability created by financial innovation and deregulation. The goal of PKI is wisely managed capitalism (Whalen, 2008). PKI clearly has a focus on the possibility of designing effective institutions, which logically implies that at least some institutions can embody instrumental reasoning.

In contrast to the direction taken by some OIEs, Hodgson (2004) has argued that Veblen’s focus on technological thinking and the Commons-Ayres trend in OIE was a wrong turn for OIE. He has sought to revivify OIE by reinterpreting Veblenian economics as generalized Darwinism. Generalized Darwinism, according to Hodgson, generalizes the basic principles of Darwin’s biological theory of evolution (inheritance, variation, and selection) to sociocultural evolution. In Hodgson’s view, the mechanisms of inheritance, variation, and selection are not just analogies or metaphors to explain outcomes in social evolution—they are ontological principles that describe any entity that evolves. As noted above, because institutions and organizational routines are inherited through cultural learning and vary, they are subject to selection. Social evolution is therefore a special case of the more general case of evolution.

However, Hodgson (2004) also acknowledges that human agents are purposive and that culture is an emergent phenomenon. So Hodgson is not seeking to biologize social inequality or to reduce the social sciences to genetic principles such as inclusive fitness. Indeed, as Hodgson states, “more is needed” than just the principles of inheritance, variation, and natural selection. This would appear to be an understanding of how social institutions, in concert with instincts and human agency, generate outcomes in a complex, emergent process of social evolution. To this end, Hodgson has incorporated some elements of structure agency theory into his analysis.

Hodgson’s program could be taken as an injunction to OIEs to build models of change that incorporate both Darwinian principles as well as more complex concepts of structure and agency. Hodgson has used this model to explain how changes in firm organization can be selected for or against by changes in market structure. So there are strong parallels between the work of Hodgson and that of Nelson and Winter (1982), who could notably be placed in either the OIE or NIE camp. As noted in the preceding section, Hodgson’s view of evolutionary economics as “generalized Darwinism” is controversial, even among his fellow OIEs.

One competing strand of Veblenian economics is the radical strand as advocated by Bill Dugger (Dugger & Sherman, 2000). Dugger focuses on the role of technology, instrumental reasoning, and institutions as providing the capacity for improving the material condition of humans. The full application of instrumental reasoning, however, in Dugger’s view is blocked by the key institutions of capitalism. These institutions are reinforced by ceremonial myths. Dugger also puts more emphasis on the social and ideological implications of the respective traditions and has been sharply critical of the NIE. He has also notably been instrumental in promoting dialogue between Marxists and OIEs and has often copublished works on sociocultural evolution with Howard Sherman. Dugger also tends to emphasize the non-Darwinian nature of sociocultural evolution.

It can be fairly argued that Adam Smith was the first evolutionary economist, even though his contributions predate any significant consideration of biological evolution by naturalists. Adam Smith provides an account of how an increasingly complex society arises out of the natural propensity of humans to truck, barter, and exchange (Fusfeld, 1977; Smith, 1776/1937). Ironically, some of Smith’s concerns with specialization and division of labor, as well as the writings of another political economist, Thomas Malthus, influenced Darwin. Many Social Darwinists in the late nineteenth century drew on Darwinian reasoning to explain how competitive markets work and to justify social inequality. Some twentieth-century theorists such as Frederick Hayek and Larry Arnhart have tended to view the market as a natural outgrowth of human genetic endowments.

Taken as a whole, however, evolutionary explanations fell out of favor among economists in the twentieth century. In the late nineteenth century, the social sciences became increasingly fragmented, and the new field of economics increasingly lost its evolutionary focus. With the triumph of the standard competitive model in the mid-twentieth century, economics became narrowly focused on providing formal mathematical proofs of narrowly defined “how” questions. However, there are some signs that the standard competitive model is in the process of being displaced by game theory. There is also widespread recognition that it is necessary to supplement the standard competitive model with an evolutionary account. These developments have led to an increased acceptance of evolutionary explanations among mainstream economists and renewed attention to the importance of institutions in framing economic outcomes.

Some strands of the NIE, particularly the version espoused by Coase (1974) and Williamson (1985), view institutions primarily as providing “solutions” to the problems of asymmetric information and transactions costs. This strand of NIE does not significantly challenge the standard competitive model or its underlying behavioral assumptions. To the contrary, it is a complement to the standard competitive model. It is also to a large degree a micro-oriented theory of sociocultural evolution.

A more dynamic view of economic evolution is that of Joseph Schumpeter (1908, 1950). Schumpeter focused on the individual entrepreneur and his role in promoting technological innovation. This technological innovation disturbs the equilibrium and leads to gales of creative destruction. However, with the rise of the modern, bureaucratically organized firm, the role of the entrepreneur was lessened, leading to a static and moribund organization. Schumpeter thought that this would eventually lead to the destruction of capitalism, an outcome that, in contrast to Marx, Schumpeter viewed in a negative way. Schumpeter, however, drew a strong distinction between statics, exemplified by the Walrasian model of his day, and dynamics, exemplified by theories of economic evolution. Thus, “dynamics” was intended to complement “statics” (Andersen, 2008). Many contemporary mainstream models of economic growth, often referred to as new growth theory, explicitly incorporate Schumpeterian analysis.

Some of the richness of Schumpeter’s focus on technological innovation as gales of creative destruction has been recaptured by the economic historian Joel Mokyr (1990) in his masterful work on technological progress. Mokyr adapts Gould’s concept of “punctuated equilibrium” to the history of technology. He also draws a distinction between invention (the rise of new techniques and processes) and innovation (the spread of these new techniques). The Industrial Revolution, in Mokyr’s view, is ongoing but is nevertheless a clear instance of a dramatic change in technological and social organization. Similarly, the work of Nelson and Winter (1982), previously cited, which acknowledges the contributions of Veblen, can also be considered neo-Schumpeterian. There are, it should be noted, significant parallels between Marx, Schumpeter, and Veblen, as well as differences.

The most prominent and most successful NIE, of course, is Douglas North. North’s career has spanned several decades, during which his contributions to multiple fields in economics have been voluminous. Notably, North’s own views themselves have undergone significant evolution. North’s (1981) earlier work on economic evolution was an application of the work of Coase (1974) and Williamson (1985) to the problem of economic evolution and did not significantly challenge the standard competitive model. North viewed economic evolution as taking place due to changing resource constraints in response to the growth in population as rational agents calculated the marginal costs and marginal benefits of shifting from foraging to farming.

North’s later work (1990, 1991, 1994), however, has challenged many aspects of the standard competitive model. North has focused specifically on the role institutions play in cognitive framing of decision making. Notably, North has explicitly abandoned the theory of strong rational choice in favor of models of human behavior that focus on the limited ability of humans to obtain, process, and act on information. In most textbook models of market behavior, price is the primary means of providing information. But in North’s view of markets, information encompasses much more than price. In addition, norms, values, and ideology can blunt the ability of humans to obtain and interpret some information. North is not arguing that humans are “irrational” as his approach still logically implies some degree of calculation and conscious decision making based on self-interest. But he has abandoned the strong view of rationality, which implies humans are lightning rods of hedonic calculation. In that sense, his view of human behavior is much closer to that of the Austrians in focusing on the purposiveness of human behavior.

For the most part, North tends to see institutions as constraints on human action, though he acknowledges that institutions can provide incentives both in terms of the things we actually do, as well as the things that we do not do. Thus, institutions that reward innovative behavior, risk seeking, and trade will lead to efficient outcomes. Institutions that reward rent seeking and prohibit innovation and trade will lead to inefficient outcomes. Once an institutional structure is set, there is a strong degree of inertia that perpetuates the existing institutional structure. In other words, evolutionary paths, in North’s view, tend to be path dependent. Clearly, the kinds of institutions in North’s view that promote efficient outcomes are those that clearly define the rules of the game in favor of the operation of markets. This does not necessarily imply laissez-faire as the state may still be necessary to perform multiple functions. It does serve to distinguish between states, such as Great Britain in the seventeenth and eighteenth centuries or South Korea in the past several decades, that were able to define an institutional framework that promoted innovation and growth as opposed to states such as Spain in the sixteenth and seventeenth centuries or in the Congo (Zaire) today that destroy any incentive for innovation and economic growth.

This raises two very interesting questions. How does a particular type of path become established, and how does it change? North’s explanation is one that is rooted in a metaphor of variation and selection. Greater variation will allow for a higher probability that a particular path will be successful. Greater centralization will reduce variation and increase the chances that the state will adopt or promote institutions that blunt technological and social innovation. North explains the greater success of Europe versus the rest of the world as a result of the relative decentralization of Europe in the early modern period. Arbitrary authoritarian states that destroyed incentives for growth such as Spain existed. But Spain was unable to impose its will on Europe or on the emerging world market. Consequently, this enabled states such as England, where the power of the Crown became limited as Parliament enacted laws to protect commercial interests and innovation, to industrialize rapidly and emerge as world leaders. These contrasting paths were transferred to the New World. The United States inherited and successfully modified the institutional framework of Britain and therefore developed. Latin America inherited and failed to successfully modify the institutional frame-work of absolutist Spain and developed much more slowly.

Evolutionary economics clearly has a future. Economists in general are becoming more attuned to the importance of understanding how humans organize the economy through institutions and how institutions change over time. This entails extensive borrowing of concepts from evolutionary biology and a reconsideration of the underlying behavioral assumptions of mainstream economics. Understanding how institutions permit or inhibit changes in technology, as well as how changes in technology in turn require changes in institutions, is a concern of all three schools of evolutionary economics. As NIE economists push the boundaries of the mainstream, at least some have increasingly asked heterodox questions, and a few have been willing to acknowledge heterodox contributions. Some Marxist and OIE scholars have also begun to note that at least some versions of NIE, if not necessarily entirely new, are at least genuinely institutional and evolutionary. Any grand synthesis seems distant, but there is at least a basis for further argumentation and even dialogue.

Bibliography:

• Andersen, E. S. (2008). Appraising Schumpeter’s “essence” after 100 years: From Walrasian economics to evolutionary economics. In K. K. Puranam & R. Kumar Jain B. (Eds.), Evolutionary economics. Hyderabad, India: ICFAI University Press.
• Ayres, C. (1938). The problem of economic order. New York: Farrar and Rinehart.
• Coase, R. H. (1974). The new institutional economics. Journal of Institutional and Theoretical Economics, 140, 229-231.
• Commons, J. R. (1970). The economics ofcollective action. Madison: University of Wisconsin Press.
• Cypher, J., & Dietz, J. (2004). The process of economic development. London: Routledge.
• Dugger, B. (1988). Radical institutionalism: Basic concepts. Review of Radical Political Economics, 20, 1-20.
• Dugger, B. (1995). Veblenian institutionalism. Journal of Economic Issues, 29, 1013-1027.
• Dugger, B., & Sherman, H. (Eds.). (2000). Reclaiming evolution: A dialogue between Marxism and institutionalism on social change. London: Routledge.
• Dugger, B., & Sherman, H. (Eds.). (2003). Evolutionary theory in the social sciences: Vol. 1. Early foundations and later contributions. London: Routledge.
• Fusfeld, D. R. (1977). The development of economic institutions. Journal of Economic Issues, 11, 743-784.
• Harris, M. (1997). Culture, people, nature (7th ed.). New York: Addison Wesley Longman.
• Hodgson, G. M. (2004). The evolution of institutional economics: Agency, structure and Darwinism in American institutionalism. London: Routledge.
• Hodgson, G. M. (Ed.). (2007a). The evolution of economic institutions: A critical reader. Cheltenham, UK: Edward Elgar.
• Hodgson, G. M. (2007b). Introduction. In G. M. Hodgson (Ed.), The evolution of economic institutions: A critical reader (pp. 1-15). Cheltenham, UK: Edward Elgar.
• Hodgson, G. M., & Knudsen, T. (2006). Why we need a generalized Darwinism and why a generalized Darwinism is not enough. Journal of Economic Behavior and Organization, 61, 1-19.
• Keynes, J. M. (1936). The general theory of employment, interest and money. Cambridge, UK: Cambridge University Press.
• Mayr, E. (2001). What evolution is. New York: Basic Books.
• Mayr, E. (2004). What makes biology unique? Cambridge, UK: Cambridge University Press.
• Minsky, H. (1982). Can “it” happen again? Essays on instability and finance. Armonk, NY: M. E. Sharpe. Mokyr, J. (1990). The lever of riches: Technological creativity and economic progress. New York: Oxford University Press.
• Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: Harvard University Press.
• North, D. (1981). Structure and change in economic history. New York: W. W. Norton.
• North, D. (1990). Institutions, institutional change and economic performance. Cambridge, UK: Cambridge University Press.
• North, D. (1991). Institutions. Journal of Economic Perspectives, 5, 97-112.
• North, D. (1994). Economic performance through time. American Economic Review, 84, 359-367.
• Poirot, C. S. (2007). How can institutional economics be an evolutionary science? Journal of Economic Issues, 51, 155-179.
• Polanyi, K. (1944). The great transformation. New York: Farrar and Rinehart. Schumpeter, J. (1908). Das Wesen und der Haupterhault der theoretischen Nationolokonomie [The nature and essence of theoretical economics]. Leipzig, Germany: Dunckerund Humboldt.
• Schumpeter, J. (1950). Capitalism, socialism and democracy (3rd ed.). New York: Harper.
• Sherman, H. (1995). Reinventing Marxism. Baltimore: Johns Hopkins University Press.
• Sherman, H. (2006). How society makes itself. New York: M. E. Sharpe.
• Smith, A. (1937). An enquiry into the nature and causes of the wealth of nations. New York: Modern Library. (Original work published 1776)
• Veblen, T. (1898). Why is economics not an evolutionary science? Quarterly Journal of Economics, 12, 373-397.
• Veblen, T. (1899). The preconceptions of economic science. Quarterly Journal of Economics, 13, 121-150.
• Whalen, C. J. (2008). Toward wisely managed capitalism: Post-Keynesianism and the creative state. Forum for Social Economics, 37, 43-60.
• Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press.
• Wolf, E. (1982). Europe and the people without history. Berkeley: University of California Press.
• Wunder, T., & Kemp, T. (2008). Institutionalism and the state: Founding fathers   re-examined.   Forum  for Social Economics, 37, 27-42.

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Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment

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Global Energy Crisis

How the energy crisis started, how global energy markets are impacting our daily life, and what governments are doing about it

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What is the energy crisis?

Record prices, fuel shortages, rising poverty, slowing economies: the first energy crisis that's truly global.

Energy markets began to tighten in 2021 because of a variety of factors, including the extraordinarily rapid economic rebound following the pandemic. But the situation escalated dramatically into a full-blown global energy crisis following Russia’s invasion of Ukraine in February 2022. The price of natural gas reached record highs, and as a result so did electricity in some markets. Oil prices hit their highest level since 2008. 

Higher energy prices have contributed to painfully high inflation, pushed families into poverty, forced some factories to curtail output or even shut down, and slowed economic growth to the point that some countries are heading towards severe recession. Europe, whose gas supply is uniquely vulnerable because of its historic reliance on Russia, could face gas rationing this winter, while many emerging economies are seeing sharply higher energy import bills and fuel shortages. While today’s energy crisis shares some parallels with the oil shocks of the 1970s, there are important differences. Today’s crisis involves all fossil fuels, while the 1970s price shocks were largely limited to oil at a time when the global economy was much more dependent on oil, and less dependent on gas. The entire word economy is much more interlinked than it was 50 years ago, magnifying the impact. That’s why we can refer to this as the first truly global energy crisis.

Some gas-intensive manufacturing plants in Europe have curtailed output because they can’t afford to keep operating, while in China some have simply had their power supply cut. In emerging and developing economies, where the share of household budgets spent on energy and food is already large, higher energy bills have increased extreme poverty and set back progress towards achieving universal and affordable energy access. Even in advanced economies, rising prices have impacted vulnerable households and caused significant economic, social and political strains.

Climate policies have been blamed in some quarters for contributing to the recent run-up in energy prices, but there is no evidence. In fact, a greater supply of clean energy sources and technologies would have protected consumers and mitigated some of the upward pressure on fuel prices.

Russia's invasion of Ukraine drove European and Asian gas prices to record highs

Evolution of key regional natural gas prices, june 2021-october 2022, what is causing it, disrupted supply chains, bad weather, low investment, and then came russia's invasion of ukraine.

Energy prices have been rising since 2021 because of the rapid economic recovery, weather conditions in various parts of the world, maintenance work that had been delayed by the pandemic, and earlier decisions by oil and gas companies and exporting countries to reduce investments. Russia began withholding gas supplies to Europe in 2021, months ahead of its invasion of Ukraine. All that led to already tight supplies. Russia’s attack on Ukraine greatly exacerbated the situation . The United States and the EU imposed a series of sanctions on Russia and many European countries declared their intention to phase out Russian gas imports completely. Meanwhile, Russia has increasingly curtailed or even turned off its export pipelines. Russia is by far the world’s largest exporter of fossil fuels, and a particularly important supplier to Europe. In 2021, a quarter of all energy consumed in the EU came from Russia. As Europe sought to replace Russian gas, it bid up prices of US, Australian and Qatari ship-borne liquefied natural gas (LNG), raising prices and diverting supply away from traditional LNG customers in Asia. Because gas frequently sets the price at which electricity is sold, power prices soared as well. Both LNG producers and importers are rushing to build new infrastructure to increase how much LNG can be traded internationally, but these costly projects take years to come online. Oil prices also initially soared as international trade routes were reconfigured after the United States, many European countries and some of their Asian allies said they would no longer buy Russian oil. Some shippers have declined to carry Russian oil because of sanctions and insurance risk. Many large oil producers were unable to boost supply to meet rising demand – even with the incentive of sky-high prices – because of a lack of investment in recent years. While prices have come down from their peaks, the outlook is uncertain with new rounds of European sanctions on Russia kicking in later this year.

What is being done?

Pandemic hangovers and rising interest rates limit public responses, while some countries turn to coal.

Some governments are looking to cushion the blow for customers and businesses, either through direct assistance, or by limiting prices for consumers and then paying energy providers the difference. But with inflation in many countries well above target and budget deficits already large because of emergency spending during the Covid-19 pandemic, the scope for cushioning the impact is more limited than in early 2020. Rising inflation has triggered increases in short-term interest rates in many countries, slowing down economic growth. Europeans have rushed to increase gas imports from alternative producers such as Algeria, Norway and Azerbaijan. Several countries have resumed or expanded the use of coal for power generation, and some are extending the lives of nuclear plants slated for de-commissioning. EU members have also introduced gas storage obligations, and agreed on voluntary targets to cut gas and electricity demand by 15% this winter through efficiency measures, greater use of renewables, and support for efficiency improvements. To ensure adequate oil supplies, the IEA and its members responded with the two largest ever releases of emergency oil stocks. With two decisions – on 1 March 2022 and 1 April – the IEA coordinated the release of some 182 million barrels of emergency oil from public stocks or obligated stocks held by industry. Some IEA member countries independently released additional public stocks, resulting in a total of over 240 million barrels being released between March and November 2022.

The IEA has also published action plans to cut oil use with immediate impact, as well as plans for how Europe can reduce its reliance on Russian gas and how common citizens can reduce their energy consumption . The invasion has sparked a reappraisal of energy policies and priorities, calling into question the viability of decades of infrastructure and investment decisions, and profoundly reorientating international energy trade. Gas had been expected to play a key role in many countries as a lower-emitting "bridge" between dirtier fossil fuels and renewable energies. But today’s crisis has called into question natural gas’ reliability.

The current crisis could accelerate the rollout of cleaner, sustainable renewable energy such as wind and solar, just as the 1970s oil shocks spurred major advances in energy efficiency, as well as in nuclear, solar and wind power. The crisis has also underscored the importance of investing in robust gas and power network infrastructure to better integrate regional markets. The EU’s RePowerEU, presented in May 2022 and the United States’ Inflation Reduction Act , passed in August 2022, both contain major initiatives to develop energy efficiency and promote renewable energies. 

The global energy crisis can be a historic turning point

Energy saving tips

1. Heating: turn it down

Lower your thermostat by just 1°C to save around 7% of your heating energy and cut an average bill by EUR 50-70 a year. Always set your thermostat as low as feels comfortable, and wear warm clothes indoors. Use a programmable thermostat to set the temperature to 15°C while you sleep and 10°C when the house is unoccupied. This cuts up to 10% a year off heating bills. Try to only heat the room you’re in or the rooms you use regularly.

The same idea applies in hot weather. Turn off air-conditioning when you’re out. Set the overall temperature 1 °C warmer to cut bills by up to 10%. And only cool the room you’re in.

2. Boiler: adjust the settings

Default boiler settings are often higher than you need. Lower the hot water temperature to save 8% of your heating energy and cut EUR 100 off an average bill.  You may have to have the plumber come once if you have a complex modern combi boiler and can’t figure out the manual. Make sure you follow local recommendations or consult your boiler manual. Swap a bath for a shower to spend less energy heating water. And if you already use a shower, take a shorter one. Hot water tanks and pipes should be insulated to stop heat escaping. Clean wood- and pellet-burning heaters regularly with a wire brush to keep them working efficiently.

3. Warm air: seal it in

Close windows and doors, insulate pipes and draught-proof around windows, chimneys and other gaps to keep the warm air inside. Unless your home is very new, you will lose heat through draughty doors and windows, gaps in the floor, or up the chimney. Draught-proof these gaps with sealant or weather stripping to save up to EUR 100 a year. Install tight-fitting curtains or shades on windows to retain even more heat. Close fireplace and chimney openings (unless a fire is burning) to stop warm air escaping straight up the chimney. And if you never use your fireplace, seal the chimney to stop heat escaping.

4. Lightbulbs: swap them out

Replace old lightbulbs with new LED ones, and only keep on the lights you need. LED bulbs are more efficient than incandescent and halogen lights, they burn out less frequently, and save around EUR 10 a year per bulb. Check the energy label when buying bulbs, and aim for A (the most efficient) rather than G (the least efficient). The simplest and easiest way to save energy is to turn lights off when you leave a room.

5. Grab a bike

Walking or cycling are great alternatives to driving for short journeys, and they help save money, cut emissions and reduce congestion. If you can, leave your car at home for shorter journeys; especially if it’s a larger car. Share your ride with neighbours, friends and colleagues to save energy and money. You’ll also see big savings and health benefits if you travel by bike. Many governments also offer incentives for electric bikes.

6. Use public transport

For longer distances where walking or cycling is impractical, public transport still reduces energy use, congestion and air pollution. If you’re going on a longer trip, consider leaving your car at home and taking the train. Buy a season ticket to save money over time. Your workplace or local government might also offer incentives for travel passes. Plan your trip in advance to save on tickets and find the best route.

7. Drive smarter

Optimise your driving style to reduce fuel consumption: drive smoothly and at lower speeds on motorways, close windows at high speeds and make sure your tires are properly inflated. Try to take routes that avoid heavy traffic and turn off the engine when you’re not moving. Drive 10 km/h slower on motorways to cut your fuel bill by around EUR 60 per year. Driving steadily between 50-90 km/h can also save fuel. When driving faster than 80 km/h, it’s more efficient to use A/C, rather than opening your windows. And service your engine regularly to maintain energy efficiency.

Analysis and forecast to 2026

Fuel report — December 2023

Europe’s energy crisis: Understanding the drivers of the fall in electricity demand

Commentary — 09 May 2023

Where things stand in the global energy crisis one year on

Commentary — 23 February 2023

The global energy crisis pushed fossil fuel consumption subsidies to an all-time high in 2022

Commentary — 16 February 2023

Fossil Fuels Consumption Subsidies 2022

Policy report — February 2023

Background note on the natural gas supply-demand balance of the European Union in 2023

Report — February 2023

Analysis and forecast to 2025

Fuel report — December 2022

How to Avoid Gas Shortages in the European Union in 2023

A practical set of actions to close a potential supply-demand gap

Flagship report — December 2022

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Biomarkers in SHARE: Documentation of Implementation, Collection, and Analysis of Dried Blood Spot (DBS) Samples 2015 – 2023

SHARE, the “Survey of Health, Ageing and Retirement in Europe”, is a large population-based panel survey among people aged 50 and over with data from 28 European countries and Israel. It investigates individual, economic, health-related, and social life-course circumstances in order to shed light on the challenges of population aging for individuals and society as a whole. Understanding aging per se, and how we age differently over the life course given our individual backgrounds, current health, and socio-economic factors, are the aims of SHARE. In order to maintain intertemporal, international and intercultural comparability, SHARE has adopted collection of objective data in the health domain. SHARE measures physical performance, such as grip strength, peak expiratory flow, walking speed, chair stand, word recall, and Euro-D depression among others in the physical, cognitive and mental health modules. In 2015, SHARE collected dried blood spot (DBS) samples as an additional objective measure of health. Eleven European countries and Israel participated in the DBS collection. The collection was harmonized in terms of designing documents, gaining consent, procuring blood-collection material, and training interviewers how to collect DBS samples while observing the national ethic and administrative regulations in all countries. Altogether, approximately 27,200 respondents consented, resulting in an overall participation rate of 67% with considerable differences between countries and interviewers. This report describes the carefully monitored processes of gaining consent for DBS collection and for the implementation, collection and evaluation of the to-date largest DBS-based study of a representative adult population in Europe. We also describe the choice of blood biomarkers, the assays employed to determine the blood biomarker concentrations in DBS collected in the home of survey respondents and the validation and adjustment of the laboratory results for the impact of sample collection in a non-medical environment. Finally, we present the data obtained for seven out of 17 blood biomarkers. The data for the remaining ten biomarkers analyzed at the Statens Serum Institut will follow in a separate release.

The EU-Commission’s contribution to SHARE through the 7th framework program (SHARE-M4, No. 261982) and the H2020 (SHAREDEV3, No. 676536) is gratefully acknowledged. Substantial co-funding for collection of HRS-harmonized biomarkers was granted by the US National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11, OGHA 04-064, BSR12-04 and R01 AG052527-02). The analysis work documented here has been supported specifically by the US National Institute on Aging grant R01 AG063944. We thank John Phillips for his enduring support and acknowledge late Richard Suzman for his decisive role to integrate blood-biomarker collection into SHARE. We thank the country teams and survey agencies of the SHARE countries participating in the DBS collection for the implementation of the new module in SHARE Wave 6, for training their interviewers and supporting them to collect DBS. In addition, our appreciation goes to the interviewers, who convinced the respondents to participate in this project and collected the blood during the survey. Special thanks go to the SDU staff and students at the Biobank in Odense, DK: Susanne Knudsen, Stine O. Høgh and, foremost, Nynne E. Ustrup for receiving, registering, safely storing, and later preparing thousands of DBS cards for shipment to the laboratories. Thanks to Mads Nybo (SDU and OUH, Odense, DK) for valuable discussions. A great thank goes to Nicklas Petersen at Statens Serum Institut, Copenhagen, DK, for sorting and punching all DBS samples. We thank our former colleagues Sabine Friedel (now Fact Field GmbH, Munich, Germany) and Julie Korbmacher (now KBO, Munich, Germany) for the collaboration until they left the SHARE biomarker team. We thank the colleagues from SHARE Database Management for supporting intermittent and the final release of data. Many colleagues supported us with ideas and fruitful critic, read and commented the manuscript versions and, last not least, encouraged us during the big project. Thanks to all of them. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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