R. Fairlie, E. Reedy, Arnobio Morelix, Joshua Russell-Fritch
The Kauffman Index of Startup Activity is a comprehensive indicator of new business creation in the United States, integrating several high-quality sources of timely entrepreneurship information into one composite indicator of startup activity. The Index captures business activity in all industries and is based on both a nationally representative sample size of more than a half million observations each year and on the universe of all employer businesses in the United States — which covers approximately five million companies. This allows us to look at both entrepreneurs and the startups they create.This report presents trends in startup activity over the past two decades for the United States. Two upcoming reports look at these same trends in all fifty states and the forty largest U.S. metropolitan areas. Trends in startup activity also are reported at the national level for specific demographic groups for some of the Index components, when available.
考夫曼创业活动指数(Kauffman Index of Startup Activity)是美国新企业创建的综合指标，它将多个高质量的及时创业信息来源整合为一个创业活动的综合指标。该指数涵盖了所有行业的商业活动，并基于每年超过50万次的全国代表性样本和美国所有雇主企业(涵盖约500万家公司)。这让我们能够同时看待企业家和他们创建的初创公司。这份报告展示了过去二十年来美国创业活动的趋势。即将发布的两份报告着眼于所有50个州和美国最大的40个大都市区的相同趋势。如果有的话，还会在国家一级报告一些指数组成部分的特定人口群体的创业活动趋势。
{"title":"Kauffman Index of Startup Activity: National Trends 2016","authors":"R. Fairlie, E. Reedy, Arnobio Morelix, Joshua Russell-Fritch","doi":"10.2139/ssrn.2828359","DOIUrl":"https://doi.org/10.2139/ssrn.2828359","url":null,"abstract":"The Kauffman Index of Startup Activity is a comprehensive indicator of new business creation in the United States, integrating several high-quality sources of timely entrepreneurship information into one composite indicator of startup activity. The Index captures business activity in all industries and is based on both a nationally representative sample size of more than a half million observations each year and on the universe of all employer businesses in the United States — which covers approximately five million companies. This allows us to look at both entrepreneurs and the startups they create.This report presents trends in startup activity over the past two decades for the United States. Two upcoming reports look at these same trends in all fifty states and the forty largest U.S. metropolitan areas. Trends in startup activity also are reported at the national level for specific demographic groups for some of the Index components, when available.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127726562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul A. Gompers, W. Gornall, S. Kaplan, Ilya A. Strebulaev
We survey 885 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment selection; valuation; deal structure; post-investment value-added; exits; internal firm organization; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. We compare our results to those for CFOs (Graham and Harvey 2001) and private equity investors (Gompers, Kaplan and Mukharlyamov forthcoming).
我们调查了681家公司的885名机构风险投资家(vc)，以了解他们如何在八个方面做出决策:交易采购;投资选择;估值;交易结构;post-investment增值;出口;企业内部组织;以及与有限合伙人的关系。在选择投资时，风投认为管理团队比产品或技术等与业务相关的特征更重要。他们还将最终投资成功或失败的可能性更多地归因于团队，而不是企业。虽然交易来源、交易选择和投资后增值都有助于价值创造，但风投认为交易选择是三者中最重要的。我们还探索(并发现)不同行业、阶段、地理位置和过去成功的实践差异。我们将我们的结果与首席财务官(Graham and Harvey 2001)和私人股本投资者(Gompers、Kaplan和Mukharlyamov即将出版)的结果进行了比较。
{"title":"How Do Venture Capitalists Make Decisions?","authors":"Paul A. Gompers, W. Gornall, S. Kaplan, Ilya A. Strebulaev","doi":"10.2139/SSRN.2801385","DOIUrl":"https://doi.org/10.2139/SSRN.2801385","url":null,"abstract":"We survey 885 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment selection; valuation; deal structure; post-investment value-added; exits; internal firm organization; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. We compare our results to those for CFOs (Graham and Harvey 2001) and private equity investors (Gompers, Kaplan and Mukharlyamov forthcoming).","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131648521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Entrepreneurs need to accumulate different types of resources to take the initial steps to grow their ventures. While much is known about the configurations of resources that improve venture success, less is known on how ventures should initially accumulate resources to begin to exploit valuable opportunities. Using agent-based simulations, we classify resources by the functions (search and execution) that they provide. We find that acquiring search resources before execution resources leads to more valuable opportunities, but only under conditions of higher uncertainty. We contribute to the entrepreneurial resource acquisition literature by showing how resource order may affect an entrepreneur’s ability in opportunity discovery, evaluation, and exploitation. We draw inferences on contingencies that can increase the salience of resource order on venture success.
{"title":"Horse and Cart: The Role of Order in New Ventures","authors":"Nachiket Bhawe, Hans Rawhouser, J. Pollack","doi":"10.2139/ssrn.2919676","DOIUrl":"https://doi.org/10.2139/ssrn.2919676","url":null,"abstract":"Entrepreneurs need to accumulate different types of resources to take the initial steps to grow their ventures. While much is known about the configurations of resources that improve venture success, less is known on how ventures should initially accumulate resources to begin to exploit valuable opportunities. Using agent-based simulations, we classify resources by the functions (search and execution) that they provide. We find that acquiring search resources before execution resources leads to more valuable opportunities, but only under conditions of higher uncertainty. We contribute to the entrepreneurial resource acquisition literature by showing how resource order may affect an entrepreneur’s ability in opportunity discovery, evaluation, and exploitation. We draw inferences on contingencies that can increase the salience of resource order on venture success.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Population aging is expected to slow US economic growth. We use variation in the predetermined component of population aging across states to estimate the impact of aging on growth in GDP per capita for 1980–2010. We find that each 10 percent increase in the fraction of the population age 60+ decreased per capita GDP by 5.5 percent. One-third of the reduction arose from slower employment growth; two-thirds due to slower labor productivity growth. Labor compensation and wages also declined in response. Our estimate implies population aging reduced the growth rate in GDP per capita by 0.3 percentage points per year during 1980–2010. (JEL E23, E24, J11, J14, J31, O47)
{"title":"The Effect of Population Aging on Economic Growth, the Labor Force and Productivity","authors":"Nicole Maestas, Kathleen J. Mullen, David Powell","doi":"10.3386/w22452","DOIUrl":"https://doi.org/10.3386/w22452","url":null,"abstract":"Population aging is expected to slow US economic growth. We use variation in the predetermined component of population aging across states to estimate the impact of aging on growth in GDP per capita for 1980–2010. We find that each 10 percent increase in the fraction of the population age 60+ decreased per capita GDP by 5.5 percent. One-third of the reduction arose from slower employment growth; two-thirds due to slower labor productivity growth. Labor compensation and wages also declined in response. Our estimate implies population aging reduced the growth rate in GDP per capita by 0.3 percentage points per year during 1980–2010. (JEL E23, E24, J11, J14, J31, O47)","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134050018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although many studies consider the spatial pattern of manufacturing plants in developing countries, the role of services as a driver of urbanization and structural transformation is still not well understood. Using establishment level data from India, this paper helps narrow this gap by comparing and contrasting the spatial development of services with that in manufacturing. The study during the 2001-2010 period suggests that (i) services are more urbanized than manufacturing and are moving toward the urban and, by contrast, the organized manufacturing sector is moving away from urban cores to the rural periphery; (ii) manufacturing and services activities are highly correlated in spatial terms and exhibit a high degree of concentration in just a few states and industries; (iii) manufacturing in urban districts has a stronger tendency to locate closer to larger cities relative to services activity; (iv) infrastructure has a significant effect on manufacturing output, while human capital matters more for services activity; and lastly, (v) technology penetration, measured by the penetration of the Internet, is more strongly associated with services than manufacturing. Similar results hold when growth in activity is measured over the study period rather than levels. Manufacturing and services do not appear to crowd each other out of local areas.
{"title":"Spatial Development and Agglomeration Economies in Services -- Lessons from India","authors":"Syed Ejaz Ghani, A. G. Goswami, W. Kerr","doi":"10.1596/1813-9450-7741","DOIUrl":"https://doi.org/10.1596/1813-9450-7741","url":null,"abstract":"Although many studies consider the spatial pattern of manufacturing plants in developing countries, the role of services as a driver of urbanization and structural transformation is still not well understood. Using establishment level data from India, this paper helps narrow this gap by comparing and contrasting the spatial development of services with that in manufacturing. The study during the 2001-2010 period suggests that (i) services are more urbanized than manufacturing and are moving toward the urban and, by contrast, the organized manufacturing sector is moving away from urban cores to the rural periphery; (ii) manufacturing and services activities are highly correlated in spatial terms and exhibit a high degree of concentration in just a few states and industries; (iii) manufacturing in urban districts has a stronger tendency to locate closer to larger cities relative to services activity; (iv) infrastructure has a significant effect on manufacturing output, while human capital matters more for services activity; and lastly, (v) technology penetration, measured by the penetration of the Internet, is more strongly associated with services than manufacturing. Similar results hold when growth in activity is measured over the study period rather than levels. Manufacturing and services do not appear to crowd each other out of local areas.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122400066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Fafchamps, Måns Soderbom, Monique vanden Boogaart
Using a large administrate dataset covering the universe of phone calls and airtime transfers in a country over a four year period, we examine the pattern of adoption of airtime transfers over time. We start by documenting strong network effects: increased usage of the new airtime transfer service by social neighbors predicts a higher adoption probability. We then seek to narrow down the possible sources of these network effects by distinguishing between network externalities and social learning. Within social learning, we also seek to differentiate between learning about existence of the new product from learning about its quality or usefulness. We find robust evidence suggestive of social learning both for the existence and the quality of the product. In contrast, we find that network effects turn negative after first adoption, suggesting that airtime transfers are strategic substitutes among network neighbors.
{"title":"Adoption with Social Learning and Network Externalities","authors":"M. Fafchamps, Måns Soderbom, Monique vanden Boogaart","doi":"10.3386/W22282","DOIUrl":"https://doi.org/10.3386/W22282","url":null,"abstract":"Using a large administrate dataset covering the universe of phone calls and airtime transfers in a country over a four year period, we examine the pattern of adoption of airtime transfers over time. We start by documenting strong network effects: increased usage of the new airtime transfer service by social neighbors predicts a higher adoption probability. We then seek to narrow down the possible sources of these network effects by distinguishing between network externalities and social learning. Within social learning, we also seek to differentiate between learning about existence of the new product from learning about its quality or usefulness. We find robust evidence suggestive of social learning both for the existence and the quality of the product. In contrast, we find that network effects turn negative after first adoption, suggesting that airtime transfers are strategic substitutes among network neighbors.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers? We present the results of a large-scale real-effort experiment with 18 treatment arms. We compare the effect of three motivators: (i) standard incentives; (ii) behavioral factors like present-bias, reference dependence, and social preferences; and (iii) non-monetary inducements from psychology. In addition, we elicit forecasts by behavioral experts regarding the effectiveness of the treatments, allowing us to compare results to expectations. We find that (i) monetary incentives work largely as expected, including a very low piece rate treatment which does not crowd out incentives; (ii) the evidence is partly consistent with standard behavioral models, including warm glow, though we do not find evidence of probability weighting; (iii) the psychological motivators are effective, but less so than incentives. We then compare the results to forecasts by 208 experts. On average, the experts anticipate several key features, like the effectiveness of psychological motivators. A sizeable share of experts, however, expects crowd-out, probability weighting, and pure altruism, counterfactually. This heterogeneity does not reflect field of training, as behavioral economists, standard economists, and psychologists make similar forecasts. Using a simple model, we back out key parameters for social preferences, time preferences, and reference dependence, comparing expert beliefs and experimental results.
{"title":"What Motivates Effort? Evidence and Expert Forecasts","authors":"Stefano DellaVigna, Devin G. Pope","doi":"10.1093/RESTUD/RDX033","DOIUrl":"https://doi.org/10.1093/RESTUD/RDX033","url":null,"abstract":"How much do different monetary and non-monetary motivators induce costly effort? Does the effectiveness line up with the expectations of researchers? We present the results of a large-scale real-effort experiment with 18 treatment arms. We compare the effect of three motivators: (i) standard incentives; (ii) behavioral factors like present-bias, reference dependence, and social preferences; and (iii) non-monetary inducements from psychology. In addition, we elicit forecasts by behavioral experts regarding the effectiveness of the treatments, allowing us to compare results to expectations. We find that (i) monetary incentives work largely as expected, including a very low piece rate treatment which does not crowd out incentives; (ii) the evidence is partly consistent with standard behavioral models, including warm glow, though we do not find evidence of probability weighting; (iii) the psychological motivators are effective, but less so than incentives. We then compare the results to forecasts by 208 experts. On average, the experts anticipate several key features, like the effectiveness of psychological motivators. A sizeable share of experts, however, expects crowd-out, probability weighting, and pure altruism, counterfactually. This heterogeneity does not reflect field of training, as behavioral economists, standard economists, and psychologists make similar forecasts. Using a simple model, we back out key parameters for social preferences, time preferences, and reference dependence, comparing expert beliefs and experimental results.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120993863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In 2010, the U.S. estate tax expired and executors of wealthy decedents were not required to file estate tax returns. In the absence of the estate tax, beneficiaries received assets with carryover rather than stepped-up basis. Unrealized capital gains accounted for 44 percent of the fair market value of non-cash assets in estates that chose the carryover basis regime, and an even higher percentage for some asset categories. Many of the largest gains were on assets that had been held for at least two decades.
{"title":"Revenue and Incentive Effects of Basis Step-Up at Death: Lessons from the 2010 \"Voluntary\" Estate Tax Regime","authors":"Robert N. Gordon, David Joulfaian, J. Poterba","doi":"10.1257/AER.P20161037","DOIUrl":"https://doi.org/10.1257/AER.P20161037","url":null,"abstract":"Abstract In 2010, the U.S. estate tax expired and executors of wealthy decedents were not required to file estate tax returns. In the absence of the estate tax, beneficiaries received assets with carryover rather than stepped-up basis. Unrealized capital gains accounted for 44 percent of the fair market value of non-cash assets in estates that chose the carryover basis regime, and an even higher percentage for some asset categories. Many of the largest gains were on assets that had been held for at least two decades.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"591 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123941131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Glaeser, Andrew N. Hillis, S. Kominers, Michael Luca
Can open tournaments improve the quality of city services? The proliferation of big data makes it possible to use predictive analytics to better target services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. This paper provides a simple model suggesting that open tournaments dominate consulting contracts when cities have a reasonable tolerance for risk and when there is enough labor with low opportunity costs of time. We also illustrate how tournaments can be successful, by reporting on a Boston-based restaurant hygiene prediction tournament that we helped coordinate. The Boston tournament yielded algorithms—at low cost—that proved reasonably accurate when tested “out-of-sample” on hygiene inspections occurring after the algorithms were submitted. We draw upon our experience in working with Boston to provide practical suggestions for governments and other organizations seeking to run prediction tournaments in the future.
{"title":"Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy","authors":"E. Glaeser, Andrew N. Hillis, S. Kominers, Michael Luca","doi":"10.1257/AER.P20161027","DOIUrl":"https://doi.org/10.1257/AER.P20161027","url":null,"abstract":"Can open tournaments improve the quality of city services? The proliferation of big data makes it possible to use predictive analytics to better target services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. This paper provides a simple model suggesting that open tournaments dominate consulting contracts when cities have a reasonable tolerance for risk and when there is enough labor with low opportunity costs of time. We also illustrate how tournaments can be successful, by reporting on a Boston-based restaurant hygiene prediction tournament that we helped coordinate. The Boston tournament yielded algorithms—at low cost—that proved reasonably accurate when tested “out-of-sample” on hygiene inspections occurring after the algorithms were submitted. We draw upon our experience in working with Boston to provide practical suggestions for governments and other organizations seeking to run prediction tournaments in the future.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124248722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Workers respond to the output choices of their peers. What explains this well documented phenomenon of peer effects? Do workers value equity, fear punishment from equity-minded peers, or does output from peers teach them about employers’ expectations? We test these alternative explanations in a series of field experiments. We find clear evidence of peer effects, as have others. Workers raise their own output when exposed to high-output peers. They also punish low-output peers, even when that low output has no effect on them. They may be embracing and enforcing the employer’s expectations. (Exposure to employer-provided work samples influences output much the same as exposure to peer-provided work.) However, even when employer expectations are clearly stated, workers increase output beyond those expectations when exposed to workers producing above expectations. Overall, the evidence is strongly consistent with the notion that peer effects are mediated by workers’ sense of fairness related to relative effort.
{"title":"The Causes of Peer Effects in Production: Evidence from a Series of Field Experiments","authors":"J. Horton, R. Zeckhauser","doi":"10.2139/ssrn.1652993","DOIUrl":"https://doi.org/10.2139/ssrn.1652993","url":null,"abstract":"Workers respond to the output choices of their peers. What explains this well documented phenomenon of peer effects? Do workers value equity, fear punishment from equity-minded peers, or does output from peers teach them about employers’ expectations? We test these alternative explanations in a series of field experiments. We find clear evidence of peer effects, as have others. Workers raise their own output when exposed to high-output peers. They also punish low-output peers, even when that low output has no effect on them. They may be embracing and enforcing the employer’s expectations. (Exposure to employer-provided work samples influences output much the same as exposure to peer-provided work.) However, even when employer expectations are clearly stated, workers increase output beyond those expectations when exposed to workers producing above expectations. Overall, the evidence is strongly consistent with the notion that peer effects are mediated by workers’ sense of fairness related to relative effort.","PeriodicalId":325993,"journal":{"name":"Ewing Marion Kauffman Foundation Research Paper Series","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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