non technical research summary

Guide to writing non-technical summaries

Posted: by Bella Williams on 23/07/18

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Non-Technical Summary

The non-technical summary may be the most important section of your project initiation and your reports. This will be accessed by legislators who make decisions about funding allocations, as well as the general public, community leaders, government staff, and other scientists.

The non-technical summary is your opportunity to sum up the importance of your project in terms that non-scientists can understand. A good non-technical summary is composed of 1-2 succinct paragraphs that cover three main points:

1. What is the current issue or problem that the research addresses and why does it need to be researched? When answering this question consider a perspective that goes beyond the primary end-users of the science you are conducting. Why is this topic important to the larger community in terms of economics, community and environment as well as agriculture?

2. What basic methods and approaches will be used to collect and produce data/results and subsequently inform target audiences? This should be different from your objectives list. Do NOT copy and paste the same text here. This section should explain, in plain, non-technical language what you intend to do.

3. Through the methods mentioned above, what ultimate goals do you hope the project will achieve and what is the general impact expected to be if this goal is met? What societal benefits may be realized?

In answering the above questions, make sure to provide enough detail so that you are touching upon the main purpose of the project, the expected accomplishments, and anticipated benefits of the research.

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How To Write A Research Summary

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

What is the research question and how is it addressed?
Is there a hypothesis in the introductory part?
What type of methods are being adopted?
What is the sample size for data collection and how is it being analyzed?
What are the most vital findings?
Do the results support the hypothesis?

Discussion/Conclusion

What is the final solution to the problem statement?
What is the explanation for the obtained results?
What is the drawn inference?
What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

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As part of the project licence application process, applicants must produce a summary (known as a Non-Technical Summary) [ASPA section 5A (1) (e) and (2) (a&b)] of their proposed programme of work. Non-Technical Summaries are published on the Home Office website annually once a licence has been granted. This mandatory requirement helps to put the debate on the use of animals in research and testing on a more informed footing.

The University of Cambridge publishes its Non-technical summaries on the University Biomedical Services website . The University will collate and release Non-Technical Summaries on a quarterly basis.

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Writing Data Science Report for Non-Technical Audiences ¶

As a data scientist, you’ll often be required to summarize your analyses and present them to non-data scientists. This type of translation of technical analyses to something of use to less-technical audiences is an absolutely critical part of being an effective data scientist – if you don’t communicate what you’ve done to decision makers, it often doesn’t matter how rigorous or careful your work has been up to that point.

With that in mind, here is an outline of one strategy for writing for non-technical audiences. Obviously different people may prefer slightly different approaches, but I think this is a good model to start with.

Also, note that this is the model I’d like you to use when writing your final report for this class, so there are a few notes that are specific to class expectations!

Identify your audience ¶

Before you write a single word, you should pause to reflect on exactly who you wish to address with your report, and their background. What follows are general guidelines, but the better you know your audience, the more precisely you can tailor the level of detail in your report.

For this class: At the top of your report, please specify the stakeholder to whom you are addressing your report – a product manager, a legislative aid, a policymaker, etc. This stakeholder should be relevant to your study, but should not be someone with data science training. You may assume they know about basic statistical concepts (means and standard deviations), but no more (no assumed understanding of potential outcomes, the theoretical underpinnings of experiments, specific designs like differences-in-differences, etc.). Obviously this is not something you’d put in a real write-up, but will be helpful for evaluation of your project.

Introduction / Executive Summary ¶

One of the most important things to remember when writing up an analysis is that the person your writing to has too many things to do, and is definitely less interested in your project than you are. With that in mind, it’s important that you write and organize your report in a way that catches their attention early and gets them invested so they keep reading. As a result, one generally wants to start with the most important parts of the analysis, then slowly draw back and lay out additional details.

You may have never noticed this before, but this is how most news articles are written: one of the first two or three paragraphs is what’s referred to as the “nut graf” (or nutshell paragraph) in which the journalist basically summarizes the entire news article in a single paragraph. In the words of Ken Wells from the Wall Street Journal , the nut graf is “a paragraph that says what this whole story is about and why you should read it. It’s a flag to the reader, high up in the story: You can decide to proceed or not, but if you read no farther, you know what that story’s about.

Thankfully you probably aren’t so pressed for time that you have to summarize everything in a single paragraph, but we will follow a similar structure in which we try and give the reader a full summary of why your project is important, how you do your analysis, and broadly what you conclude up front. In particular, I would argue that your introduction / executive summary should be organized as follows:

Identify the problem you wish to address

The first thing to do in any report is motivate your analysis – tell us about why you need to undertake this project. At this point in the report, keep this relatively brief – the motivation for the project is important, but you don’t want to drown the reader in background. This should probably be one-to-two solid paragraphs. But don’t draw it out – we can get more into background on the problem later, and you don’t want to get bogged down talking about the problem, you want to get to how you’re gonna help the reader.

What question will you try to answer, and how will it help you address

Here’s the linchpin of the report: announce the question you’re seeking to answer in your project and make it clear how this will help address the problem you’ve identified. This transition is where you will either get the reader to buy into the report and read it carefully, or lose their interest.

Summarize your strategy

Now in one to two paragraphs provide an overview of your project, your approach, and a preliminary summary of your results.

In all, you should have covered all this is about one page, maybe a page and a half, and hopefully now you’ve got your reader hooked!

Background ¶

OK, so at this point you’ve hopefully caught your readers interest, so now you can circle back and provide any additional background needed to help the reader better understand your motivation or the specific context you are analyzing (if you’re looking at a policy change, the details of the policy, the context in which it occurred, the players involved, etc.) The amount of background needed will vary across projects, but whatever you need goes here.

Your Design ¶

Here’s where you lay out how you plan to answer the question you laid out in your summary.

As you do so, bear in mind the difference between your goals in writing to a non-technical stakeholder and your goals when writing to a fellow data scientist (most of your professors).

When writing to a fellow data scientist, you’re generally writing to a skeptical audience. Your goal is to try and convince them that you did everything correctly – crossed every t and dotted every i. This is especially true when writing to professors in technical classes, since you’re usually trying to demonstrate your mastery of a technical skill, which means communicating very detail.

But a stakeholder reading your analysis is generally someone who mostly decided to put their trust in you when they hired you, and at this point your job is not to convince them every technical nuance of the project is right – by definition, most non-technical audiences wouldn’t be able to read a balance table showing that your randomization created balanced samples – but rather to communicate to them the key take-aways of the analysis.

That’s not to say you don’t need to engage with some technical aspects of your project. For example, if you’re using a good causal design, it’s critical the reader know why your causal research design is better than just looking at observational data in a regular regression (especially since someone else may try and argue with your results using that type of data). And of course they need to know about any limitations of what you’ve learned. But you don’t have to put every bit of due deligience you’ve done in the main report.

With that in mind, one thing that’s crucial to this section if you’re doing causal inference is to help the reader understand why you’re using a specific causal design without using technical language. To do so, you want to lay out specific, concrete reasons that just using observational data might lead to erroneous conclusions (e.g. do the same thing you did on the homework assignments / midterm when asked about how people were interpreting observational studies.)

For example, if you are doing an experiment to see how sending people coupons would impact consumer behavior, you want to explain that “we can’t just use data on sales from stores that chose to send out coupons to evaluate whether we should be sending out coupons to all our customers because it’s possible that the stores that sent out coupons did so precisely because they knew that their customers were struggling financially, and thus needed coupons to be able to afford products. As a result, if we compared sales to customers who got coupons to those who did not, we might inadvertently assume the lower sales to customers who got coupons was the result of the coupons, when in fact it actually reflected the fact that the coupons went to customers who were less well-off financially to begin with.”

“But if we run an experiment in which we randomly assign customers to either receive a coupon or not, then we know that on average the people getting coupons will be the same as the people not getting coupons (since who gets coupons is random, and not related to anything like customer income). As a result, we can compare sales to customers who got coupons and those that did not, and infer with confidence that any difference we see is the result of getting coupons, not other differences in the customers with or without coupons.”

(See? No discussion of potential outcomes or use of terms like “baseline differences!”

Your Results ¶

Now it’s time for results! As with your design, remember your goal is to emphasize the key take-aways of your analysis, which means both what the data can tell you and what it can’t . Remember that honest humility is a key part of being a good data scientist – don’t over-sell your results!

Conclusions ¶

Now the final part of the project – quickly recapitulate the problem you wanted to address, the question you sought to answer, the answer you reached, and the implications of this result. In this discussion, make sure you talk a lot about external validity : where are these results likely applicable? Where are they not? What other research could be done to learn more? Do you have concrete recommendations?

Remember when I said that in writing to a non-technical stakeholder, you don’t have to detail all the nuances of your analysis? Well… that’s true. BUT: it’s often good to put the details of all the careful analyses of robustness and diagnostic tests you completed in appendices. That way you can reference them in the body of your report (communicating in broad terms that you were careful without boring your reader), but then also include them in case your stakeholder wants to share your report with another data scientist for a second opinion.

So you probably want ( and for this class, should have ) an appendix with things like balance tests, A/A tests, evidence of parallel trends, discussion of why you chose certain sample restrictions, alternate specifications, etc., depending on what’s appropriate for your particular research design.

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Research Summary – Structure, Examples and Writing Guide

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Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Jun 17, 2019

EARA sets out its guidance on improving Non-Technical Summaries for the general public

Updated: Apr 29, 2021

The European Animal Research Association (EARA) has submitted a guidance document on Non-Technical Summaries (NTS) to the EU Commission on how NTS can be made more understandable for the ordinary reader.

The details were presented by Javier Guillén, (pictured below) a member of the EARA working group that produced the guidance document, at the 14th FELASA Congress, held in Prague, Czech Republic, last week.

Javier told the Congress that as part of its strategy to improve openness and transparency on the use of animals in research in Europe, EARA has been working closely with the EU to help improve the information provided to the general public.

It is understood that the Commission will produce additional guidance on NTS for Member States using some of the EARA guidance document findings.

Every research project application, that intends to use animals, is required to include a publicly available NTS which includes a simple explanation of the project’s objectives, predicted harms, benefits and number and types of animals used. It must also demonstrate compliance with the 3Rs (replacement, reduction and refinement).

EARA Executive Director, Kirk Leech, said: “We are very pleased to hear that the Commission has found the EARA working group’s observations useful.

“NTS are a small part of the overall need to improve openness and transparency on animal research, but they could be a valuable resource, in particular, for the media and other influencers who communicate with the public directly, in explaining issues such as animal welfare and the benefits that biomedical research can bring for society.”

NTS are widely seen as a positive development in improving transparency on animal research to the public. However, it is widely agreed that there are a number of problems in the compilation, accuracy, standardisation and accessibility of NTS.

In November 2017, the EU Commission published its Review of Directive 2010/63/EU on the protection of animals used for scientific purposes. The Commission reported that there had been some progress on transparency, but suggested that further improvements were needed. In particular further work is needed on the publication of statistical information on animal use and on non-technical project summaries (NTS).

EARA identified clear opportunities to improve NTS for the general public and set up its working group in 2018. The working group brought together representatives from the user community, with a range of experts from backgrounds in animal welfare, communications and private and public biomedical research, including membership of institutional ethics committees and welfare organisations.

As an example of its guidance, the EARA working group suggested that, in the Adverse Effects section of the NTS, rather than saying what will be done to animals (e.g. ‘rats will be injected’), researchers should try to describe in everyday language what the animal’s experience is likely to be.

For example, ‘Rats will likely experience some discomfort, mild pain and bruising to the skin from being injected on four occasions’.

The members were Javier Guillén, (AAALAC International, chair), Michael Addelman (University of Manchester), Peter Janssen (FENS_CARE), Serban Morosan (GIRCOR) , Barney Reed (RSPCA) , Kirsty Reid (EFPIA) , Bob Tolliday (EARA) and Hanna-Marja Voipio (FELASA).

** The guidance is based on the current proposed template by the European Commission. Since a new version of this template is expected before the end of 2019, the Working Group may consider a future update of the guidance to make it fit better with the new template, although the opinion of the Working Group is that the main concepts of the presented guidance will be still valid.

#EaraBriefing #PressRelease #EU #3Rs

EARA Briefing
Press Release

Statistics and non-technical project summaries

Improving transparency through open access to information on the use of animals for scientific purposes.

To progress towards the ultimate goal of full replacement, it is crucial to understand where, how and why animals are still required to be used for scientific purposes.

With the ALURES Statistical EU Database and the ALURES Non-Technical Summary EU Database , the EU provides a unique level of transparency on animal-based research and testing. These tools will allow more targeted efforts for the development of alternative non-animal approaches.

The data contained in the ALURES database is collected by the Member States and submitted to the European Commission annually.

Statistical database

The ALURES statistical database consists of three sections.

Section 1 gives the numbers of animals (conventional and genetically altered) that are used for the first time for research, testing, routine production, and for education and training. It also shows the species and the origins of the animals. For total numbers of animals used in these procedures, please consult this section.

Section 2 gives numbers of all uses (first use and subsequent reuses) of animals for research, testing, routine production and for education and training. It also gives the reason for use (e.g. specific research area, type of testing), the actual severity (mild, moderate, severe) experience by animals, the animal’s genetic status, and the use of animals to meet legislative requirements.

Section 3 gives the numbers of genetically altered animals to support scientific research. These animals were used for the creation of new lines or for the maintenance of existing colonies. Section 3 provides the actual numbers of animals used for the first time and details of all uses, and the type of research for which new genetically altered lines have been created. These animals are not included in Sections 1 and 2.

The number of reporting countries varies. 2015-2017 data is compiled of data from 28 EU Member States. From 2018 onwards, also data from Norway is included and therefore direct year to year comparisons cannot be made between 2018 and previous years.

Currently ALURES allows for data mining at EU level. National data is published annually by the Member States and, from 2021 data onwards, also accessible through ALURES.

Leaflet on ALURES

Project summaries database

In addition to the provision of statistical data on the use of animals for scientific purposes, it is important that information on projects using live animals is made publicly available. Article 43 of the Directive establishes non-technical project summaries (NTS) to achieve this. Detailed content of NTS can be found in Annex I of Commission Implementing Decision 2020/569/EU .

These non-technical project summaries are available in the open access ALURES NTS EU Database .

Concerning the timing of publication in the EU database, Member States are required to submit NTS for projects authorised after 1.1.2021 within six months from the authorisation.

Concerning the update of NTS with the results of retrospective assessment, it is important to know that the Directive allows the possibility to inform about an upcoming retrospective assessment in the NTS. The following Member States have transposed this requirement in their national legislation : Belgium, Bulgaria, Denmark, Greece, Croatia, Cyprus, Lithuania, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Sweden and Finland.

The remaining Member States are not required to note in the NTS whether a project is selected for retrospective assessment. They are nevertheless free to use the EU database for the publication of the results of retrospective assessment on voluntary basis.

Until the end of 2020, Member States were required to publish NTS at national level. For projects authorised after January 2021, NTS are published in the Database. A list of national websites hosting NTS published before 2020 can be found below.

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The Economics of Technology Professor, Stanford Graduate School of Business

Non-tech research, recent papers.

Beyond Prediction: Using Big Data for Policy Problems , Science, Vol. 355, Issue 6324, pp. 483-485.
Economists (and Economics) in Tech Companies , working paper.
The Impact of Machine Learning on Economics , Forthcoming in book: The Economics of Artificial Intelligence: An Agenda.

A Non-technical summary of my research circa 2007

This document provides a somewhat chatty discussion of selected research papers that may be accessible to a broader audience. It starts with a discussion of different styles of research in economics, to try to better explain how some of my “basic research” fits in and how it might be used by other economists, and why it might be important even if it doesn’t directly relate to policy. The document then provides more in-depth discussion of a few selected papers in different areas of economics. At the end of each extended description of a paper or set of papers, I have included an “executive summary” that boils the ideas down to a few sentences.

Applied versus Basic Research in Economics
Comparative Statics Under Uncertainty
How have I ended up working at so many parts of the basic-to-applied continuum?
Diversity in Organizations
Skewed Bidding
Comparing Auction Formats
Policy Questions
Collusion and Price Rigidity
Sophisticated Collusion
Difference-in-Difference Models
Testing Theories about Complementarity

Research Styles in Economics: How My Work Fits In

I.A Applied versus Basic Research in Economics

I am often asked to describe common themes in my work, and to explain how I came to work on such a wide range of topics. One way to explain this is in terms of the distinction between “applied” and “basic” or “fundamental” research. Most academic economists work somewhere in the middle of this continuum, and one thing that makes me relatively unusual is that I have worked on both extremes.

Economists are ultimately trying to make predictions and evaluate alternative public policies. The most applied part of economics research consists of analyses that are of direct use to policy-makers. One example is advice that I give to governments about how to design auctions for public resources (such as timber), which is informed by theory and empirical research. Another example is a paper I wrote on evaluating the impact of enhanced 911 systems on health care outcomes. Although the paper was interesting to academic economists for other reasons, the results could be directly applied to help a policy-maker decide whether enhanced 911 was worth the investment.

In the middle part of the basic-to-applied continuum, some applied theory research papers are designed to provide insight towards real-world problems, but on their own they would not be used as the sole basis for making a business or public policy decision. For example, my research on mentoring and diversity in organizations (discussed in more detail below) identifies one set of forces that a firm might consider when setting its hiring and promotion policies; and it added a fresh element to the debate about the costs and benefits of affirmative action. But my coauthors and I intentionally crafted a model that abstracted from numerous real-world concerns. A well-designed applied theory model isolates the effects of particular forces, much as a scientist in a laboratory might try an experiment in a petri dish that has been sterilized and thus eliminates many microorganisms that are present in nature. Applied theory models ideally provide qualitative predictions that can be tested with data, or they may form the basis of a statistical model used to calibrate parameters of the theoretical model using real-world data.

Other kinds of papers in the middle of the continuum include empirical papers (that is, papers that analyze real-world data) that are of interest because they make a broader point, validate a more general class of theories, or estimate a parameter that is important for many economic applications (such as a parameter that describes risk preferences for a particular population). I analyze historical data from U.S. Forest Service timber auctions, and economists are interested because many of the conclusions shed light on what behavior we might expect (qualitatively) in other auction markets. For example, my research on “ skewed bidding ” (see below ) documents that subtle predictions of game theoretic models are borne out in the data, and it highlights unintended consequences of auction rules that are used in a wide variety of government procurement auctions. My work comparing open and sealed bids highlights the fact that the rules of an auction can have quantitatively important consequences for the set of bidders who participate (large or small bidders), and how competitive the bidding process is.

At the “basic research” end of the continuum are tools and methods that economists use to conduct research. This work is several steps removed from real policy questions, yet it can have a fairly broad impact, because it influences the way in which many different studies might be conducted. Microeconomic theory may be relevant to researchers who develop applied theory models, while Econometric theory provides tools and methods for analyzing data. Econometrics often combines economic theory with statistical theory. Fairly unusual among present-day economists, I have done significant “basic research” in both microeconomic theory and econometric theory.

I.B Comparative Statics Under Uncertainty

In several cases, the goal of my basic research has been to design methods that focus the attention of the modeler on the essential economic assumptions, and allow the modeler to dispense with simplifying assumptions that are used only to make a model tractable. When building an applied theory model, a researcher might assume that an agent faces uncertainty that follows a normal distribution. I developed methods that allow the modeler to consider arbitrary (and unspecified) distributions, and my results help identity exactly what assumptions are required about the distribution of uncertainty to make a desired prediction. Removing unnecessary assumptions (the specific functional form of a normal distribution, for example) allows the researcher to truly understand the content of the remaining assumptions. It can make models more elegant, more transparent, and thus more insightful.

My research on “comparative statics” under uncertainty falls into this category of basic research. Comparative statics is the study of how economic variables change when something in the environment changes. How do investors respond to an increase in uncertainty? How do bidders respond to information about the amount of oil in the ground? My research identifies the crucial economic assumptions on risk preferences and the nature of risk that allow the researcher to draw conclusions, such as the conclusion that investment decreases in response to an increase in uncertainty.

I applied these tools myself in several other “basic research” problems. For example, I showed that in a class of games such as auctions, if a bidder always places a higher bid when she has a higher valuation for winning, then we can be assured that a “pure strategy Nash equilibrium” exists. A Nash equilibrium has the property that if one bidder knows the opponent’s strategy (their “bidding plan” that specifies the bid they would place for each possible valuation), then the bidder does not want to alter her own strategy. In a pure strategy Nash equilibrium, the player does not “toss a coin” to determine her bid after seeing her own valuation. Knowing that such an equilibrium exists is a crucial first step towards analyzing outcomes in the auction, both theoretically and empirically.

I have also developed basic research tools to analyze informativeness of statistical information an economic agent might receive.

These tools are necessarily abstract, and they have little use for, say, financial analysts on Wall Street. They are designed to help academic economists craft more elegant and insightful theories with a minimum of distracting, extraneous assumptions.

I.C How have I ended up working at so many parts of the basic-to-applied continuum?

Many (but certainly not all) economists focus their research on one part of the basic-to-applied-research continuum. How did I end up working (together with my coauthors) in so many different parts of it, and what are some common themes? In several different instances, ideas for research have developed as a result of more applied research. For example, my first papers were applied theory models of organizations, including research on diversity and affirmative action, and through that work I became exposed to empirical work on organizations. In another example, I was working on an empirical paper about 911 systems that employed difference-in-difference methods (see below for more explanation of what these are). In each case, my desire to understand the problems lead me to “basic research.” I noticed common themes in a wide range of papers, but was dissatisfied with the conceptual frameworks and methods from the existing literature. I then went on to write more abstract papers that developed new tools and methods that could be used not only for my original applications, but also other applications as well.

Since the methods arose in direct response to my own applications, I could be sure that they would be useful. I was also able to exploit common themes across classes of problems: from my work on comparative statics to my work on statistical models, I have seen that monotonicity–the property that one variable always increases with another–plays a central role.

One reason I have liked working on the economics of auctions is that it is possible to study auctions from pretty much every point on the basic-to-applied continuum. Auction design policy questions arise all the time, and I have been fortunate to have a role in designing real-world auctions. I have also developed applied theory models and used them to guide empirical work. Finally, I have done “basic research” on the statistical analysis of auction data, as well as on theoretical questions such as whether an equilibrium to the game between bidders even exists at all, and if so how the equilibrium can be computed. Since auctions have had so many high-profile applications in recent years (see below for more discussion of these), it seems likely that they will continue to be an active area for all types of research in the future.

II. Economics of Organizations

II.A Diversity in Organizations

When I was starting out in graduate school, I noticed that some of my former students seemed to have particularly chummy relationships with some of the faculty. Before long, I learned that there was a regular basketball game. Now, I knew a fair bit about basketball, having graduated from Duke University, and I had played on co-ed intramural teams in college. However, I soon learned that women were not welcome. Nor, for that matter, were small or unathletic men. When summer rolled around, some of those same students who were playing basketball had landed prime RAships with the faculty in the group.

The debate in the media about affirmative action and diversity seemed almost entirely focused on fairness, and somewhat on role modeling. Although I agreed that having a great role model would be a big help, that wasn’t the only problem. Being different seemed to make a difference to my productivity. Yet, it also seemed to me that it didn’t have to be that way. For example, the guys in the economics department of my year (there was only one woman) were interested not just in basketball, but in weightlifting. Though weightlifting wouldn’t have been my first choice as a hobby, I could certainly participate fully, and so I did. And thus I wasn’t left out of the networking and informal conversations that can sometimes lead to collaborations. After these experiences, I was left puzzling over what economic theory would have to say about these experiences. What was missing from standard models that would account for these real effects of being a minority?

Together with two classmates, Chris Avery and Peter Zemsky, I crafted an “applied theory” model of diversity that captured the idea that similarities among workers of different “types”–gender or race, for example–make mentoring more productive. At the same time, the model incorporates a cost to homogeneity: talent is scarce, and if an organization restricts itself to hiring and promoting workers of just one type, they miss out on the best and brightest of the other type. (This model requires that labor markets are somewhat thin–workers have preferences about the companies they work for, due to location or firm idiosyncracies–and talent specific to a particular firms’ needs is scarce too. These assumptions apply well to highly skilled labor markets like academics.)

We analyzed the optimal promotion for firms in this setting, when firms have limited “senior” positions in a hierarchy and select among lower-level employees who have experienced mentoring during their “junior” phase. A lack of diversity among senior workers will imply that junior workers of the minority type acquire less human capital. The problem is inherently dynamic because “biasing” promotions–by considering type as well as ability in promotions–is a long-term investment that pays off when the firm achieves the the optimal ratio of different types from the perspective of mentoring. We show that when there are diminishing returns to having more same-type mentors, the initial conditions of a firm can matter: a firm that starts out homogeneous may actively bias promotions in favor of the majority, since only a handful of minority junior candidates can ever hope to overcome the disadvantage of less effective mentoring. Having a few minority senior agents isn’t worth it, since the candidates they mentor never get promoted anyway. On the other hand, if by chance or initial conditions, a firm achieves critical mass of the minority type in the senior ranks, the firm implements a “voluntary affirmative action” policy, investing in tipping the balance towards full diversity.

These types of phenomena can have long-term effects on the educational investment of workers. If individuals anticipate future opportunities for promotion may be limited by type-biased mentoring, and there are not sufficient firms in the economy that cater to minorities, investments in education may be suboptimal. Society may then prefer a more diverse set of firms. On the other hand, firms that are prevented by law from implementing voluntary affirmative action problems may experience long delays in reaching their preferred (and more profitable/productive) level of diversity.

This paper was my first economics paper (it was published later due to publication delays). It taught me about the power of formal models to help make clear and logically consistent arguments that contributed to a policy debate.

Executive summary of the mentoring and diversity paper : The paper considers promotion policies for firms when mentoring is more effective between senior and junior workers of the same type, and when talent is scarce. Firms face a tradeoff between maximizing the mentoring that goes to the majority type–where type-biased mentoring implies that majorities are more likely to be promoted–and implementing voluntary affirmative action programs that lead the firm more quickly to full diversity, which allows the firm to provide intermediate amounts of mentoring to the best and brightest workers of both groups.

III. Auctions and Market Design: Empirics, Econometrics, and Policy

Auctions are used in a wide variety of settings. eBay auctions may be the most familiar to today’s young people. High-stakes auctions for spectrum (to be used for mobile phones and related technologies) have made headlines by raising billions of dollars at a time around the world. One of my thesis advisors, Paul Milgrom, helped design the simultaneous ascending auction implemented by the FCC in 1994. This auction simultaneously allocated licenses in hundreds of cities around the country. Even before these “modern” examples, however, auctions have been used for large amounts of economic activity, including government procurement of goods and services, government allocation of natural resources such as oil and timber, and in the private sector for items as diverse as art, antiques, real estate, used cars, fresh fish, and fresh flowers. Auctions have been documented from biblical times.

Auctions are an example of markets where individuals, firms, or governments play an active role in specifying and enforcing the “rules of the game.” In contrast, markets for many products are more “organic,” in that firms and consumers interact without direct intervention from a central agent (except within broad legal constraints). The field of “market design,” which is fairly old in some ways but has only recently been popularized under that title, studies markets that are, or can be, actively designed. Examples outside of auction markets included matching markets, such as the mechanisms that assign children to public schools in New York and Boston, medical residencies to newly minted M.D.’s, roommates to rooms, kidney donors to recipients, or prospective members to sororities. My colleague Al Roth has studied many of these examples extensively.

The field of market design tends to focus on questions about how the rules of the game matter for efficiency, for distribution (what kinds of players win and lose with different rules), and in some cases for revenue (e.g. for auctioneers) and profits (for bidders). In some cases, economists are asked to design entirely new market institutions, in which case they typically do theoretical analyses of the properties of new mechanisms as well as experiments to test their performance with real players. Market design is a field where economists have been fairly successful in influencing policy: in many prominent examples (e.g. the FCC auctions for spectrum designed by my thesis advisor Paul Milgrom and his advisor, Bob Wilson), leading academic researchers have determined the main elements of new, high-stakes markets. I am part of a firm called “Market Design” that provides advice to governments around the world on auction design.

Personally, I got my start in auctions when I worked for a company that sold computers to the government through procurement auctions. I wrote an undergraduate thesis about the procurement process for computers under the direction of Professor Bob Marshall (now at Penn State). Bob went on to do research (of the “applied theory” variety) about the topic, and seeing him testify in front of Congress about his research inspired me about the ability of economic theory to influence public policy.

On Bob’s suggestion, I went on to study timber auctions. This turned into a long-standing area of research for me. Timber auctions in the U.S. Forest Service provide a rich laboratory to test fairly nuanced predictions of game theory about strategic behavior in games where players have private information. In addition, they provide the opportunity to show how the rules of the auction–the “market design”–matter for outcomes.

III.A Skewed Bidding

In joint work with Jonathan Levin , I analyze the strategic use of information by bidders in common value auctions (i.e., auctions where all bidders value the object equally, but each has a private signal about this value), with data from U.S. timber auctions. In these auctions, bidding is multi-dimensional.

In the “scale sale” auction format used by the U.S. Forest Service (FS), a sale begins with the FS estimating the proportion of each species on a tract. These estimates are publicly announced, at which point potential bidders may conduct their own estimates. Firms bid a per-unit price for each species of timber. The winner is the firm with the highest “average” bid, computed by multiplying the unit prices by the proportions announced by the FS. The winner then has a number of years to remove all designated timber. The FS measures timber as it is removed, and the winner pays for the timber as it is removed at the rates specified in the bid. Thus, there may be a significant gap between the average bid, weighted by FS estimates, and the average payment, weighted by the true quantities. Such a gap is typical: on tracts with two main species of timber, the FS estimate of the proportion of timber that is the primary species is within 5% of the actual proportion removed on only half of the sales.

In a scale sale, if a firm has private information about the composition of the tract, it can structure its bid so that in expectation, its average payment is less than its average bid. This can be done by “skewing” one’s bid onto the species the bidder believes has been over-estimated by the FS. To see this, suppose there are two species, Douglas Fir and Western Hemlock, and the FS estimates they are present in equal proportions. Suppose that based on its own cruise and the FS announcement, a firm estimates that 60% of the timber is Douglas Fir. The two bid vectors ($100,$100) and ($50,$150) yield the same average bid, $100. However, the firm expects to make an average payment of $100 under the first bid and only $90=.6*$50+.4*$150 under the second. Similarly, a bidder who believes that 65% of the timber is Douglas Fir will expect to pay an average of only $85 under the second bid.

Our paper first derives an equilibrium model of bidding in scale sale auctions. Then, we gather ex post data on the actual amount of timber harvested from a set of timber tracts, thus allowing for an unusual opportunity to present direct evidence supporting the hypothesis that bidders are privately informed ex ante (at the time they place bids) about the composition of the tract. We find that bidding behavior is consistent with the strategic use of private information, and that players follow the subtle predictions of the theory.

In particular, on average, bidders bid more aggressively on species where it turns out less timber is harvested than the FS estimated. Thus, the revenue collected by the FS is systematically lower than what the FS estimated at the time of bidding. We also find that it is the bidders who skew the most aggressively that tend to win auctions, as predicted by theory. Even though firms could choose to insure themselves by bidding the same profit margin on each species of timber, doing so would be a handicap in competitive bidding. Other firms can bid more, but expect to pay less, by skewing.

At times, skewing can be extreme, with bidders bidding several times more the selling value (gross of harvesting costs) for some species of trees. Occasionally, a large gamble like this goes wrong for bidders, and they actually have to pay the (very unprofitable) price per tree on a large volume of timber. Thus, one unintended consequence of the rules of the auction is that bidders must contend with bad results from risky bidding strategies.

While scale auctions may appear peculiar to the FS, similar mechanisms are employed widely in procurement. For instance, “unit-price” construction contracts specify per-unit prices for different items, and payment is made based on realized quantities. In contract auctions, bids are scored using pre-announced quantity estimates. In these contexts, “unbalanced bidding” is common. In some cases, contracts explicitly protect against extreme skewing by stating that a unit price may be renegotiated if the realized quantities differ from the initial estimates by more than some fixed amount. Many government procurement contracts also have a similar “unit-price” structure. The Government Purchasing Office (GPO) recently included new provisions to address unbalanced bidding, reserving the right to reject “materially unbalanced” bids.

III.B Comparing Auction Formats

In another recent project with Jonathan Levin and Enrique Seira, I analyze the effect of market design, in particular the auction format, on competition in auctions when entry is endogenous. In timber auctions, potential bidders are heterogeneous, including both small logging operations and large mills. I derive and test comparative statics predictions comparing entry and bidding behavior in two different types of auction formats. One is the open ascending auction, where bidders meet in a room, and bidders raise one anothers’ bids in an unstructured way until only one bidder remains. In the first-price, sealed-bid auction, bidders submit sealed bids, which are opened simultaneously. The highest bid wins, and the winner pays their own bid.

We show that weaker bidders participate more in first-price auctions. We further show that when comparing first-price and ascending auctions, the effect of auction format on participation is much larger than the effect of format on bidding conditional on participation. In other words, how many bidders come is more important than how they bid once they get there. In addition, using bidding behavior from first-price auctions as a benchmark, we provide evidence that suggests that bidders are not bidding competitively in ascending auctions. Theory suggests that tacit collusion should be easier in ascending auctions, and our results confirm this prediction.

Jonathan Levin and I have also analyzed the effects of small-business set-aside programs, showing that additional entry by small businesses can offset much of the potential revenue loss from excluding large bidders, and analyzing the efficiency of alternative policies to promote small businesses, such as subsidies.

III.C Policy Questions

My work on timber auctions extends to the policy arena as well. For five years, I worked with the British Columbia Ministry Forests to design a major deregulation of the timber industry and a new auction-based method for pricing government timber, which comprises most timber in British Columbia and is responsible for about a quarter of the province’s economic activity.

IV. Collusion in Auctions and Dynamic Pricing Games

Collusion among firms is a central problem in the economics of industrial organization. Anti-competitive prices may harm consumers, and further, colluding firms may sacrifice productive efficiency by failing to allocate market share to the firms with the lowest production costs.

My research looks as questions such as the following: How can we understand the use of fairly simple-minded collusive schemes (such as firms setting equal prices while maintaining stable market shares over time) when such schemes clearly sacrifice a lot of the potential profit from tailoring market shares to changes in relative productivity, inventories, input costs, and other factors over time? How does the ability to communicate influence prices and productive efficiency? And what would change if firms were able as well to make direct side-payments to one another (perhaps at some cost, if side-payments are illegal but detection occurs with modest probability)? Do predictions about collusive behavior have any macroeconomic implications, for example for how prices change with the business cycle?

In a series of papers with Kyle Bagwell, I address these questions in an “applied theory” model of firm behavior that has the following central features: (i) firms interact repeatedly over time; (ii) monetary transfers among firms are restricted or prohibited; and (iii) the firms have private information about time-varying cost or local demand conditions. Private information about cost may arise due to confidential labor or supply contracts; incremental process innovations; or changing inventory levels. Collusive profits are highest if the firms can implement a self-enforcing collusive scheme whereby prices are close to monopoly levels and productive efficiency is attained. Productive efficiency–allocating production to the most efficient firm– requires that private information is truthfully revealed through market conduct, as when firms with lower cost levels charge lower prices and thus receive greater market share. One theme of our analysis is that colluding firms tend to sacrifice productive efficiency instead of lowering prices, and some types of anti-trust enforcement may lead firms to decrease productive efficiency without significantly affecting prices.

This research program also has some “basic research” components. Collusion among firms entails subtle strategic incentives in a dynamic game, and when firms are privately informed, there is another layer of complexity in the strategies. The key challenge is how to provide incentives for firms to reveal their private information, so that the information can be used to maximize profits for the cartel, when all firms have the incentive to pretend to be low-cost in order to gain more market share.

We were among the first to develop models of dynamic games with hidden information, and further the approach we took in our analysis is applicable to a much broader set of problems than collusion, some of which I have explored myself in other work. We showed how tools from one area of basic research in microeconomic theory–“mechanism design”–could be fruitfully combined with tools from another area–“repeated games”–to yield elegant and tractable analyses of the way in which agents in a dynamic game provide incentives for revelation of private information and for cooperative behavior. Our earlier papers started with games that were stationary–the environment is stable, and firms receive new realizations of private information each period that are independent over time–and later we went on to analyze games where firms have private information that is serially correlated over time, so that play in one period can reveal information relevant to future periods.

IV.A Collusion and Price Rigidity

In joint work with Kyle Bagwell and Chris Sanchirico , we address some “stylized facts” about collusion: prices are often more rigid, and market shares more stable, in concentrated industries. In addition, there are many examples of real-world cartels that followed simple “rules-of-thumb,” such as requiring all firms to charge the same price (which might be indexed to some commonly observable cost variable.) When firms collude in this manner, the collusive price is independent of the private cost positions (beyond the wholesale price) of the member firms, and productive efficiency is not achieved. We formalize the idea that price rigidity is prevalent because of the costs of private information: in order to dissuade a high-cost firm from pricing as if it had low cost, price wars and pricing distortions are required. There is a tradeoff between high prices and efficiency in production, and we show that under some conditions, the tradeoff is always resolved in the same direction. This theme arises in other applications I have studied as well: in some cases, it just isn’t worth it to provide incentives for agents to reveal their private information.

Our analysis of this model focuses on the case where firms have limited observability of one anothers’ behavior: market-wide prices are observable, but not individual firm behavior. As a result, price wars take place at an industry-wide level, and firms can not effectively tailor rewards and punishments to individual firms.

We identify a tradeoff that is associated with collusive pricing schemes in which each firm’s price is strictly increasing in its cost level: such fully-sorting schemes use prices to allocate market share to the lowest-cost firm, but they also imply an informational cost, since a higher-cost firm must be deterred from charging a low price and claiming that its costs are low. The informational costs of collusion may be manifested in two ways. First, the prices of lower-cost firms may be distorted to low levels. Second, following the selection of lower prices, the collusive scheme may sometimes call for an equilibrium-path price war in future periods.

In the alternative we focus on, the rigid-pricing scheme , each firm selects the same price in each period, regardless of its current cost position. Getting firms to reveal private information is not necessary in this scheme, because productive efficiency is sacrificed. We show that when demand is inelastic, the best collusive equilibrium for firms has the following properties: (a) there are no price wars in equilibrium, and (b) if firms are sufficiently patient and the distribution of costs satisfies weak regularity conditions (in particular, it is log-concave), firms use a rigid-pricing scheme.

The first finding contrasts strikingly with the previously existing literature on collusion where firms do not have private information about costs, but rather firms can engage in secret price-cutting (Green-Porter (1984)). In the secret-price-cutting literature, price wars are an integral component of collusion (they are the only instrument to deter secret price cuts). The second finding offers an equilibrium interpretation of the empirical association between rigid pricing and industry concentration. In broad terms, when firms are able to effect transfers only with inefficient instruments (i.e., industry price wars), then incentive-compatible productive efficiency is more trouble than it is worth. Instead, the firms settle on a rigid price and accept inefficient allocation of production.

We also study an extension of the model where there are i.i.d. demand shocks in each period. In a high-demand state, the future looks unattractive relative to the present, and thus firms are more tempted to undercut the collusive price, serve the entire market today, and suffer the future price war.

If firms are fairly impatient, when a firm draws a lower-cost type, the temptation to undercut the assigned price is severe, since the resulting market-share gain is then especially appealing. For impatient firms, a collusive scheme thus must ensure that lower-cost types receive sufficient market share and select sufficiently low prices in equilibrium, so that the gains from cheating are not too great. If firms are not sufficiently patient to go along with the rigid-pricing scheme, they may still support a partially-rigid collusive scheme, in which lower-cost types price substantially below higher-cost types, reducing the gain from an off-schedule deviation. This amounts to an escape clause for low-cost firms: equilibrium collusion enables a firm that has especially low costs an “escape valve” as an alternative to deviating from the agreement and sending the industry into a price war. Instead undercutting the collusive price just a little bit, which all firms would like to do (and thus would trigger a price war), the escape valve requires a deep price cut. All firms know that only a very low-cost firm (or a firm with excessive inventories) would gain from such a price cut, and so a temporary price cut of this sort does not induce retaliation.

The need for escape valves is especially great when firms see today’s opportunities to grab market share as great relative to expected future profits from collusion. Thus, symmetric collusion between impatient firms may be marked by occasional (and perhaps substantial) price reductions by individual firms. Such departures are most likely to occur when today’s demand shock is high, so that the future looks bleak relative to the present, and when one firm receives a favorable cost shock. Although we might interpret this price reduction as a sort of price war, note that it does not induce retaliation: the price decrease represents a permitted escape clause (i.e., an opportunity to cut prices and increase market share) within the collusive scheme.

IV.B Sophisticated Collusion

The research on price rigidity focuses on models in which collusion between firms proceeds in a symmetric fashion, in the sense that each firm’s expected value is the same at the beginning of each period, before any uncertainty is realized. Collusion among firms can be more successful when firms can use asymmetric rewards and punishments .

Kyle Bagwell and I consider a model similar to the “Collusion and Price Rigidity” model outlined above, but with the following features: (i) firms can communicate with regard to current cost conditions; (ii) firms can use market-share favors, which requires that the identities of individual firms can be followed over time, and whereby individual firms can be treated asymmetrically as a reward or punishment for past behavior; (iii) firms can not make explicit monetary transfers (use bribes). After studying this model in some detail, we will analyze the way in which optimal collusion changes as restrictions (i) and (iii) are imposed or removed. In a subsequent paper , we allow for firm costs to be correlated over time.

Our first main result is that firms can achieve the highest possible level of profits–monopoly prices and efficient production–by exchanging “market share favors.” First consider how “inefficient” market-share favors might work. One day, each firm announces a cost. If firm 1 is low-cost and firm 2 is high-cost, firm 1 serves the market today, but in the next period, no matter what happens firm 2 takes a turn. (I often illustrate this idea in class with an example about two roommates negotiating over dish-washing duty–one roommate claims to have a big problem set due, but promises to wash the dishes tomorrow.) This gets efficient allocation of production in the first period, but in the second period, it might be inefficient.

Now consider “efficient market share favors.” In the first period of the game, if firm 1 has low cost and firm 2 has high cost, firm 1 serves the entire market; but, in the second period, firm 2 receives more than half the market in the event that the two firms have the same cost type, while otherwise the firms are efficient again. The prospect of tomorrow’s gain induces firm 2 to admit having high costs; but the reward is still efficient. In this case, future market-share favors have no efficiency loss. We show that when firms are sufficiently patient, first-best profits can be attained using efficient market-share favors. The finding of exact efficiency depends on a model with only two possible cost types, but the qualitative results can be extended.

When firms are less patient, “perfect collusion” with efficient market share favors may no longer be possible. We show that in a profit-maximizing collusive scheme, firms tend to give up on trying to induce full productive efficiency, and instead share the market among low- and high- cost firms, rather than attempt to provide incentives using instruments like low prices in the present, price wars in the future, or inefficient production in the future. Efficient market-share favors allowed firms to provide incentives to reveal private information “for free”–market share was transferred from one firm in the cartel to another, but there was no loss in total profits to the cartel. Once incentives require things like low prices, that hurt the cartel as a whole, the cartel quickly abandons the goal of productive efficiency. Intuitively, productive inefficiency entails a loss of total profits, but at least the inefficient members of the cartel stand to gain from it, rather than consumers.

Consider next the role of communication. Standard collusion folklore holds that collusive firms frequent smoke-filled rooms; but the theoretical literature addressing the role of communication is sparse. In our model, communication among firms can offer benefits as well as costs. The benefit is that, when firms communicate their costs to one another, they can allocate market shares in a smooth, state-contingent fashion. On the downside, communication can make it especially temping to undercut the collusive price in a given period. Firm 1 may be especially tempted to undercut its assigned price if it learns that firm 2 has low costs and the collusive scheme assigns firm 2 larger market share; such a firm has less incentive to undercut if it is unsure of firm 2’s cost, as it harbors some hope of serving the market without cheating.

Weighing these costs and benefits, we find that for firms of moderate patience, the optimal collusive equilibria will entail communication in some periods and none in others.

Further, an anti-trust policy restricting communication reduces the collusive profits of moderately patient firms, potentially lowering productive efficiency but without affecting prices. The idea is that firms that cannot communicate end up allocating production less efficiently; but they choose inefficient production rather than low prices.

On the other hand, if firms are sufficiently patient, they are still able to achieve first-best profits in every period without communication.

In more recent research about this class of models, we ask how the results change when costs are correlated over time. This introduces a whole new host of incentive problems, since information a firm reveals in one period can be used against it in the future. Part of this analysis is “basic research”: how can we even analyze such a complicated dynamic game? After developing some tractable approaches for analysis, we provide two main insights. First, when costs are highly correlated over time relative to the patience of firms, “price rigidity” is optimal and firms sacrifice efficient production, but when firms are relatively patient, a more complex version of efficient market-share favors (described above) can be used to achieve perfect collusion.

Second, when firms are less patient relative to persistence and when cost distributions are “irregular” (e.g. low and high costs are more likely than intermediate costs), optimal collusion may involve an initial “signaling” phase, where one firm charges a very low price for a period of time in order to credibly reveal that it has low costs today and expects to remain low cost in the future; and then, firms return to monopoly prices, but the low-cost firm gets more market share. We can interpret this behavior as a form of “bargaining” over market share. All of the firms would like to claim that they should get more than an even share of the market; but the initial signalling behavior of extremely aggressive pricing provides a credible signal that earns a truly low-cost firm greater market share in the future. Higher cost firms would not find the initial signaling as worthwhile. Elements of this type of behavior have been observed in some real-world high-stakes cartels.

Executive Summary of These Papers : Privately informed colluding firms can achieve perfect collusion–with production allocated to the most efficient firm, and prices at monopoly levels–by using efficient market share favors. High-cost firms are induced to admit high costs (and accept low market shares in the present) by the promise of getting extra market share in future periods when the firms have similar costs. However, if costs are highly correlated over time, today’s high-cost firm will also be high-cost in the future, and such schemes will not work as well. Then, price rigidity may be optimal. In addition, “signaling” behavior can emerge, whereby a low-cost firm initiates very low prices for a period of time, in order to capture an unequal share of the market at monopoly prices in the future. Communication can help firms allocate more efficiently, and banning it may end up reducing productive efficiency without lowering prices.

V. Nonparametric Identification of Structural Econometric Models

The study of econometric identification asks the following question: if we had a large dataset containing specified data elements (e.g. all bids from a series of first-price auctions), would it be possible to learn the economic primitives (e.g. the distributions from which bidders draw their valuations) from the data, given some assumptions about individual behavior. I have analyzed econometric identification of a variety of models, including auctions , models of the impact of government policy on populations of individuals, models of organizational design, and models of consumer choice.

V.A Difference in Difference Models

I have also worked (together with Guido Imbens) on non-parametric identification of a non-linear structural model that can be used to analyze data in a setting that is suited for difference-in-difference models: data is available for a large group of individuals in at least two groups and at least two time periods, with some groups being subjected to a treatment (e.g. a minimum wage change) in a later period, and other groups (the “control groups”) not subject to the change. Difference-in-difference models are widely used in applied economics, in order to control for the underlying time trend that would have occurred in the absence of a treatment; the goal is to isolate a “treatment effect.” Our paper identifies key assumptions–assumptions that are interpretable in terms of economics–that are sufficient for identification of the treatment effect, showing that common functional form assumptions are not necessary. Our model allows for treatment effect to vary across individuals, and for the average effect of the treatment to vary across groups. This is useful because the traditional approach rules out important economic phenomena, such as a case where a state with higher average returns to a public program is the one that adopts it.

V.B Testing Theories About Complementarity

A fairly large literature at the intersection of economics, strategy, and management is concerned with the question of how different organizational design practices–such as training programs, job security, job rotation, information technology adoption–interact in affecting firm performance. Milgrom and Roberts’ (1990) study of modern manufacturing focused attention on complementarity among different organizational design practices, and a number of empirical papers began documenting correlations among these practices. In joint work with Scott Stern , I provide a formal model that shows how a whole battery of tests for complementarity that were proposed in the literature could be confounded by unobserved heterogeneity–that is, unobserved factors that might affect the costs and benefits of adopting different practices, that might themselves be correlated. Although economists have long understood the importance of unobserved heterogeneity in empirical work, this paper provides a rigorous analysis of the direction of biases that might arise, and how the same underlying model of unobserved heterogeneity can lead to a consistent, and thus especially misleading, pattern of results across a battery of different tests for complementarity that have been proposed in the literature.

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Guide to writing non-technical summaries
UAR helps people to understand why animals are used in scientific research, and the best way to do that is through openness and transparency. In 2018 we have worked with partners to develop tips that will help researchers write better and more engaging non-technical summaries of their ASPA licensed projects. These summaries are the only section ...
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