In practice, yardstick competition has not become a success story among recent regulatory approaches. England and Wales’ water and sewerage sec- tors are the most often cited examples of real-world applications. But many other studies and sectors have stayed at timid attempts to embrace yardstick competition, only in order to continue without it (examples of electricity in many countries, and public transport sectors). Assuming that information requirements may be one obstacle to the implementation of yardstick com- petition, we propose a modification of the yardstick competition mechanism introduced by Shleifer (16). Yardstick competition proposed by Shleifer can be interpreted as differentiated cost based price caps. Firms are allowed to charge a two-part tariff where the unit price is capped to average (marginal) costs and the fixed (access) charge 1
Nudge and its philosophy have undoubtedly led to greater understanding and use of BE by policymakers. A number of other popular books and academic articles have also exploited the intuitive and demonstrable nature of BE findings, highlighting the implications of BE for policy (see Section 1 for references), or providing systematic reviews or debates by policy area (e.g. Foote, Goette and Meier, 2009, presents a good series based on US policy). Various think-tanks and state agencies have also produced summaries of BE findings for policymakers, either of a general nature (e.g. New Economics Foundation, 2005) or specific to a policy area (e.g. Bennett et al., 2010). Yet there are significant problems to be overcome if BE findings are to be exploited beneficially. Some of these are common to the incorporation of any area of technical expertise into policymaking, such as communicating the findings to policymakers accurately and ensuring that they are relied upon only in appropriate contexts. However, two difficulties associated with BE are less common and, it is argued here, central to its successful exploitation by policymakers. First, it is a field with very many relevant empirical results and comparatively little overarching theory. Second, the research frontier is progressing at a rapid pace. Both of these properties of BE are inherent. By incorporating the methods of experimental psychology (and related disciplines) into economics, BE is rapidly generating a great number of fruitful empirical findings. But, as in psychology itself, simple, powerful theories are more difficult to come by. Meanwhile, the bulk of research effort continues to add to the list of empirical findings.
Macatangay (2002) identifies patterns in NP and PG's bidding which he suggests are . 3 Shares for 1995 and 2000 based on data described in Appendix A. The UK in October and November 2000 after the end of my sample period. This PDF book incorporate 0455 economics 2002 oct paper 3 conduct. To download free cambridge workingpapers in economics cwpe 0455 cmi you need to register.
role for taste shocks, and the estimated entry costs are also higher: $212,000 for repeat entrepreneurs and $246,000 for new entrepreneurs. These changes are driven by two patterns in the data. While exits from entrepreneurship are negatively correlated with relative entrepreneurial earnings, most individuals who persist in self-employment earn only modestly more as entrepreneurs than they would in paid work. This choice is incompatible with having a strong distaste for entrepreneurship. When all workers are constrained to have the same preferences, this pattern pushes the disutility from working in entrepreneurship towards zero. Meanwhile, for paid workers, the high variance of entrepreneurial ability generates a strong incentive to experiment with entrepreneurship; although the probability of being far in the right tail of entrepreneurial ability is low, the payoff to discovering that talent is high. Nonetheless, the majority of paid workers never enter entrepreneurship. Without heterogeneous preferences, only very high entry costs can justify this behavior. As we will see in the next section, this simpler model cannot predict movements between sectors as well as the full model with heterogeneity.
so any newly matched workers have disutility from working at their new employers of x i;m t = x L . Second, newly matched …rms make take-it-or-leave-it o¤ers to their matched workers, who can accept or reject. 7 If rejected, then the worker and the o¤ering …rm earn no revenue or pay in the period, and re-enter the matching market next period. Third, if the o¤er is accepted, or if the worker and …rm are already under contract from a prior period, then the …rm earns revenue of 1 and pays the agreed-upon wage. Fourth, with probability p 2 (0; 1), each employed worker who was satis…ed with her employer at the start of the period becomes dissatis…ed, and her disutility of working at that employer becomes x i;m t = x H . Once dissatis…ed, employees do not change types again while at their same employers. Fifth, workers tell …rms if they are leaving the …rm to enter the matching market in the next period. The series of events is shown in Figure 6.
Jonathan Berk, Nittai Bergman, Asaf Bernstein, V.V. Chari, Alex Edmans, Fred Malherbe, Vincent Glode, Radha Gopalan, Kyle Herkenhoff, Anastasia Kartasheva, Amir Kermani, Deborah Lucas, Asaf Manela, Holger Mueller, Alessandro Previtero, Adriano Rampini, Felipe Severino, Kelly Shue, Ngoc-Khanh Tran, Randy Wright and seminar participants at Berkeley Haas, the Bank of England, the 2015 Canadian Eco- nomic Association, the 2015 CFF Conference on Bank Stability and Regulation in Gothenburg (Sweden), the 2015 European Summer Symposium in Financial Markets at Gerzensee, the 2015 IDC Summer Finance Conference, the IMF, the 2015 Labor and Finance Group conference at Vanderbilt, the 2015 LBS Summer Symposium, the 2015 Midwest Macro conference, the 2015 SED Meetings, the Sixth Duke–UNC Corporate Finance Conference, the St Louis Fed, the Toulouse School of Economics, and Washington University in St Louis. We alone are responsible for any remaining errors.
its decision to acquire youtube.com, or due to a move to a state-of-the-art new headquarter building, there is a general increase in excitement on the prospect of working for the company, drawing more and more students toward a Computer Science major. In order to proxy for such attention-grabbing salient events about companies, we rely on various different measures of skewness. The idea is that when a few firms in the industry do exceptionally well, these firms usually prominently feature in the media and capture people’s attention. Given the difficulty in gathering and analyzing data on the actual distribution of work opportunities in different industries, people’s expectations about these opportunities – and hence, major choices – are disproportionately influenced by these salient, easy to recall events.
One common refrain from the interviews was that operating changes after buyouts often took the form of refocusing on core operations rather than dramatic changes in mission. This refocusing effort is generally described as a combination of eliminating non-core oper- ations, some of which were built up in a form of mission creep, and shifting more attention to the reliability and efficiency of core operations. One executive characterized the changes as “getting back to the basics” and focusing on the “boring stuff.” A number of the execu- tives explicitly identified improvements in workplace safety as a specific plank in a broader platform of core operational improvements. These executives typically pointed to the belief that a safer work environment would allow the firm to contain labor costs in the long run as the primary motive for efforts to improve safety. For example, a former PE executive in the energy industry who is now with Total Safety, a safety consultancy, characterized the view on improved workplace safety as follows: “Fewer compliance problems, less scrutiny from regulators, sure, but the really good companies recognized that safe working environments increase morale, decrease turnover, and impact wage negotiations.”
We thank Han Kim, David Matsa, Hyunseob Kim and seminar participants at the CSEF-EIEF-SITE Conference on Finance and Labor, Labor and Finance Group Meeting, Census RDC Annual Research Conference, Conference on Finance, Organizations and Markets, 12 th Annual Conference on Corporate Finance at Washington University, Nova School of Business and Economics, CEMFI, Center for Economic Studies at the Census Bureau, SAIF, HKUST, University of Maryland, George Mason University, University of North Carolina at Charlotte, and University of Texas at Austin for helpful suggestions. The research in this paper was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau. This research uses data from the Census Bureau's Longitudinal Employer Household Dynamics Program, which was partially supported by the following National Science Foundation Grants SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed.