We analyze how the incidence of minimum wage increases falls on customers by studying two minimum wages in Los Angeles County that remained unequal for over five years. Because Los Angeles' municipal borders are porous, the case resembles a natural experiment. Using a novel 5-year price survey dataset, we show that the full incidence of the higher minimum wage fell on customers in high-income neighborhoods, and that none of the incidence fell on customers in low-wage neighborhoods. Additionally, exposure to competitors subject to a lower minimum wage mitigated these effects, and lower-wage restaurants exposed to higher-wage competitors received monopoly rents.

This paper proposes a method of forecasting US recessions beginning with data displays that contain the last 12 quarters of seven US expansions. These end-of-expansion displays allow observers to see for themselves what is different about the last year before recessions compared with the two earlier years. Using a statistical model that treats this historical data as draws from a 12-dimensional multivariate normal distribution, the most recent data are probabilistically inserted into these images where the recent data are most like the historical data. This is a recession forecast based not on presumed patterns but on patterns revealed by the data.

The paper finds determinants for the end of U.S. economic expansions since 1960 using a binary variable approach. Different from previous studies, we specify a left-hand side variable that is coded as one during the year prior to NBER dated recessions rather than during recessions. We thereby avoid confounding the occurrence of a recession with its length. We limit the sample by excluding recession periods and the recovery months during the subsequent expansions. This eliminates contamination of the conclusions by taking out observations for which subsequent recessions are unlikely. The resulting specification with the interest rate spread as a predictor outperforms the traditionally used equation and is robust against alternative specifications regarding the warning period.

In the restaurant industry, the incidence of an increase in the minimum wage may fall on restaurant owners, customers, landlords, and/or employees.We analyze the first two in this study, with implications for the incidence borne by landlords and employees.We exploit a geographical discontinuity in Los Angeles County, where in 2015 the City of Los Angeles passed a minimum wage law and in 2016 the State of California passed a different minimum wage law.This created two minimum wage schedules in the county that remained unequal for over five years.Using a novel data set from a multi-year price survey, our analysis shows that the incidence of Los Angeles City's higher minimum wage fell on customers in high-income neighborhoods, and on landlords and restaurant owners in low-income neighborhoods.We further show that the mix of responses at restaurants subject to the LA City minimum wage, including price increases, menu changes, and restaurant closures, was affected by proximity to restaurants subject to the lower California State minimum wage.The effect of neighborhood income levels and distance to lowerwage competition has important implications for designing minimum wage policies.

We explore how the opinions of economists regarding the impact of a fiscal stimulus have evolved over the last century. In describing the evolution of economists’ opinions, we contrast deductive methods based on words, pictures and math including calibrated models versus inductive methods using data images, tables of statistics and estimated models. The debate has between two theoretical tribes (interventionists and non-interventionists) both of whom are little affected by empirical studies because persuasive non-experimental data evidence is hard to find. Proposing an alternative metaphor to DSGE mechanical models, we posit that the economy should be thought to be an organic system (akin to a human being) which goes through healthy, unhealthy and recovery stages. With the illness metaphor in place, we briefly describe the theoretical basis of medieval medicine before the microscope and before clinical trials, which we suggest is analogous to current macroeconomics.

This paper provides theory and evidence that worker effort has played an important role in the increase in income inequality in the United States between 1980 and 2016.The theory suggests that a worker needs to exert effort enough to pay the rental value of the physical and human capital, thus high effort and high pay for jobs operating expensive capital.With that as a foundation, we use data from the ACS surveys in 1980 and 2016 to estimate Mincer equations for six different education levels that explain wage incomes as a function of weekly hours worked and other worker features.One finding is a decline in annual income for high school graduates for all hours worked per week.We argue that the sharp decline in manufacturing jobs forces down wages of those with high school degrees who have precious few high-effort opportunities outside of manufacturing.Another finding is that incomes rose only for those with advanced degrees and with weekly hours in excess of 40.We attribute this to the natural talent needed to make a computer deliver exceptional value and to the relative ease with which long hours can be chosen when working over the Internet.

Prior to 1440, a classroom was a place where the lecturer read the lecture and the students scribbled down notes. This was a critical part of the educational process because there were no books to ...

Under current policies the deficit may get to 7% of GDP in 10 years. Our political process is not coming to grips with responsible fiscal policy. We have both parties agreeing a deficit is a great way of attracting votes. Future increases in spending are coming from entitlements. We need incentives in health care to do things more efficiently and encourage competition. The biggest risk for the U.S. economy is the global bond market. We need to get moving. If not, eventually inflation, or a crisis of confidence, will be the wake-up call on the growth of U.S. debt.

This distinctive book sets forth, on an advanced level, various methods for the quantitative measurement of important relationships at issue in areas of the balance of payments and international trade and welfare. The results achieved in recent studies are presented and the directions for new research are indicated. This book is composed of two main parts.Part I deals with the balance of payments and consists of the first half of the book. One of the longest and almost important chapters of this part talks about, at length the time-series analysis of the demand for imports and exports from the point of view of an individual country. This subject has a long and somewhat checkered history dating from the 1940's, when a number of estimates using least squares multiple regression methods were made of import and export demand functions for the interwar period. The noteworthy feature of many of these estimates was that they suggested relatively low price elasticities of demand in international trade. The implication was thus drawn that the international price mechanism could not be relied on for balance-of payments adjustment purposes.This book talks about the topics of theory and measurement of the elasticity of substitution in international trade, estimating the international capital movements, and forecasting and policy analysis with econometric models. Part II deals with international trade and welfare. While, there are many other books dealing with trade theory, this title focuses on a narrower range of topics that are not always mentioned or understood by individuals, such as the theory and measurement of trade dependence and interdependence, the analysis of the component factors a country has that affects how its export growth is over time, and the welfare effects of trade liberalizationThis book serves as a guide and reference work for economics graduate students, academicians, and practicing economists in private and governmental circles. They will find this book

Proof of antitrust impact and estimation of damages are central elements in antitrust cases. Generally, more is needed for these purposes than simple observational evidence regarding changes in price levels over time. This is because changes in economic conditions unrelated to the behavior at issue also may play a role in observed outcomes. For example, prices of consumer electronics have been falling for several decades because of technological progress. Against that backdrop, a successful price-fixing conspiracy may not lead to observable price increases but only slow their rate of decline. Therefore, proof of impact and estimation of damages often amounts to sorting out the effects on market outcomes of illegal behavior from the effects of other market supply and demand factors.

This paper proposes a context-minimal range of alternative regression models that is used to generate a range of alternative estimates. A prior distribution is assumed with a zero mean but an ambiguous covariance matrix. The choice of the prior covariance matrix is facilitated by transformation to standardized variables which makes the prior expected R2 equal to the sum of the prior variances. Three different ranges of the prior expected R2 are used to define three different intervals of prior covariance matrices which are used to produce three different sets of s-values.

This paper compares and contrasts Bayesian variable-exclusion methods proposed by Eduardo Ley and coauthors with methods proposed by Raftery and Sala-i-Martin et al. and with the s-values proposed by myself. A distinction is drawn between estimation uncertainty which is the focus of Ley׳s research and model ambiguity which arises in Ley׳s work and is the focus of my own recent proposal. The discussion is organized around the prior covariance matrix, which needs to be diagonal to support all-subsets regressions. The basic question addressed here is: what aspects of the prior covariance matrix can be taken as known, what aspects can be estimated and what aspects require a sensitivity analysis because they are neither known nor estimable. When diagonality is in doubt, we are more-or-less forced into a model ambiguity sensitivity mode because the data are never rich enough credibly to estimate the full prior covariance matrix. When diagonality is assumed, the data evidence, though very limited, can help to estimate the diagonal elements, but this literature has not yet produced a compelling conventional treatment which will necessarily include both estimation uncertainty and model ambiguity as they relate both to the diagonal values and to the rest of the prior covariance matrix. But there has been a lot of progress.

Journal of Money, Credit and BankingVolume 47, Issue S1 p. 295-299 Article Discussion of Ferrero EDWARD LEAMER, EDWARD LEAMERSearch for more papers by this author EDWARD LEAMER, EDWARD LEAMERSearch for more papers by this author First published: 29 March 2015 https://doi.org/10.1111/jmcb.12203Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume47, IssueS1March/April 2015Pages 295-299 RelatedInformation

The downturn of 2008–09 has confirmed that: (i) housing is the single most critical part of the U.S. business cycle, (ii) the proper conduct of monetary policy needs to be cognizant that choices made at one point in time affect the options later, and (iii) the best time to intervene in the housing cycle is when the volume of building is above normal and growing more so. What was different this time was the rapid and substantial decline in home prices.

Most data sets used by economists are collected with after-the-fact surveys and the time aggregation is done by the survey respondents who produce, for example, monthly aggregates not actual transactions. 21st century digital transaction technologies will increasingly allow the collection of actual transactions, which will create an important new set of opportunities for forming time aggregates. This paper uses a transaction-by-transaction data set on purchases of diesel fuel by over-the-road truckers to form amonthly diesel volume index from 1999 to 2012 purged of weekday, holiday and calendar effects. These high-frequency data allow new and more accurate ways to correct for (1) the variability in the weekday composition of months and (2) the drift of holiday effects between months. These corrections have substantial effects on month-to-month comparisons.

A review of the Heckscher–Ohlin framework prompts a noted economist to consider the methodology of economics. In this spirited and provocative book, Edward Leamer turns an examination of the Heckscher–Ohlin framework for global competition into an opportunity to consider the craft of economics: what economists do, what they should do, and what they shouldn't do. Claiming “a lifetime relationship with Heckscher–Ohlin,” Leamer argues that Bertil Ohlin's original idea offered something useful though vague and not necessarily valid; the economists who later translated his ideas into mathematical theorems offered something precise and valid but not necessarily useful. He argues further that the best economists keep formal and informal thinking in balance. An Ohlinesque mostly prose style can let in faulty thinking and fuzzy communication; a mostly math style allows misplaced emphasis and opaque communication. Leamer writes that today's model- and math-driven economics needs more prose and less math. Leamer shows that the Heckscher–Ohlin framework is still useful, and that there is still much work to be done with it. But he issues a caveat about economists: “What we do is not science, it's fiction and journalism.” Economic theory, he writes, is fiction (stories, loosely connected to the facts); data analysis is journalism (facts, loosely connected to the stories). Rather than titling the two sections of his book Theory and Evidence, he calls them Economic Fiction and Econometric Journalism, explaining, “If you find that startling, that's good. I am trying to keep you awake.”