Morgan Foy at the NBER Digest:
High-Speed Rail Expansion and German Worker Mobility: Starting in the late 1990s, Germany expanded its high-speed rail network (HSR), connecting outlying locales to large urban areas. In The Effect of Infrastructure on Worker Mobility: Evidence from High-Speed Rail Expansion in Germany (NBER Working Paper No. 24507), Daniel F. Heuermann and Johannes F. Schmieder study how this large-scale infrastructure investment affected commuter behavior. They find that the expansion reduced travel times and increased commuting, as workers moved to jobs in smaller cities while keeping their places of residence in larger urban areas.
Until the late 1990s, the HSR system connected the largest cities of Germany. The connected cities were located in just three of the 16 German states. Areas between the large cities, through which the tracks ran, campaigned for stations, and in a second wave of expansion, the government added stops in many of these cities.
The researchers analyze the effects of this infrastructure improvement by comparing cities granted stops in the second wave of expansion with other small German cities that were not added to the rail network. They note that new rail stops were not placed due to economic conditions, such as connectedness to urban centers, but because of political factors. Moreover, unlike infrastructure investments in roads and highways, the HSR system exclusively carries passengers. It does not transport goods, so it affects labor but not product markets.
The researchers create a dataset that includes travel times, train schedules, and administrative employment data, which contain the region of work and residence for each traveler.
For the average pair of cities in their study, the high-speed rail expansion reduced travel time by 13 minutes, or about 10 percent of the pre-expansion time. The number of commuters rose by 0.25 percent for each 1 percent decrease in travel time. Reductions in commuter time and the corresponding increase in passengers followed an inverted U-shaped pattern, with the largest impacts occurring on routes of 200 to 500 kilometers in length.
The researchers estimate that 840,000 people started to use rail transportation for commuting during their 1994-2010 sample period. Twelve percent of the increase in ridership was attributable to the 10 percent reduction in commuting time as a result of HSR expansion.
The researchers find that the number of commuters from small cities to large cities is 40 percent larger than the number commuting from large to small cities. The opposite pattern, however, commuting from large cities to small, was much more sensitive to the reduction in travel time from HSR expansion. This supports the view that workers enjoy living in large urban areas and are not there solely for employment.
The study concludes that the gains from the investment in infrastructure accrued mainly to smaller cities. Commuters are twice as likely as non-commuters to be college graduates, which suggests that building HSR networks may be one way to engage relatively high-skilled workers in the economies of peripheral regions.
Posted by Mark Thoma on Tuesday, July 31, 2018 at 04:52 PM in Academic Papers, Economics |
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Posted by Mark Thoma on Saturday, June 23, 2018 at 09:07 AM in Economics, Links |
There was a problem with comments I didn't notice until today (don't think anyone could comment). Hopefully it is fixed.
Posted by Mark Thoma on Thursday, June 21, 2018 at 09:15 AM in Weblogs |
Posted by Mark Thoma on Wednesday, June 20, 2018 at 10:47 AM in Economics, Links |
Posted by Mark Thoma on Thursday, June 14, 2018 at 10:54 AM in Economics, Links |
Posted by Mark Thoma on Tuesday, June 12, 2018 at 11:29 AM in Economics, Links |
Posted by Mark Thoma on Monday, June 11, 2018 at 09:20 AM in Economics, Links |
Posted by Mark Thoma on Friday, June 8, 2018 at 10:14 AM in Economics, Links |
Just as I was trying post more often once again I broke my upper right arm. Thought I had broken my left wrist too but that's just a severe sprain. I can hardly type for now.
Posted by Mark Thoma on Thursday, June 7, 2018 at 09:10 AM in Links |
Oh, What a Stupid Trade War (Very Slightly Wonkish), by Paul Krugman, NY Times: So, the trade war is on. And what a stupid trade war it is. …
The official – and legal – justification for the steel and aluminum tariffs is national security. That’s an obviously fraudulent rationale... But Trump and co. presumably don’t care about telling lies with regard to economic policy... They would see it as all fair game if the policy delivered job gains Trump could trumpet. Will it?
OK, here’s the point where being a card-carrying economist gets me into a bit of trouble. The proper answer about the job-creation or -destruction effect of a trade policy – any trade policy, no matter how well or badly conceived – is basically zero. ….Why? The Fed... Even if tariffs were expansionary, that would just make the Fed raise rates faster, which would in turn crowd out jobs in other industries...
But I think this is a case where macroeconomics, even though I believe it’s right, gets in the way of useful discussion. We do want to know whether the Trump trade war ... would add or subtract jobs holding monetary policy constant, even though we know monetary policy won’t be constant.
And the answer, almost surely, is that this trade war will actually be a job-killer, not a job-creator, for two reasons.
First, Trump is putting tariffs on intermediate goods…, some of which themselves have to compete on world markets. Most obviously, cars and other durable manufactured goods will become more expensive to produce, which means that we’ll sell less of them; and whatever gains there are in primary metals employment will be offset by job losses in downstream industries.
Playing with the numbers, it seems highly likely that even this direct effect is a net negative for employment.
Second, other countries will retaliate against U.S. exports, costing jobs in everything from motorcycles to sausages. …
Finally – and I think this is really important – we’re dealing with real countries here, mainly democracies. Real countries have real politics; they have pride; and their electorates really, really don’t like Trump. This means that even if their leaders might want to make concessions, their voters probably won’t allow it. ...
So this is a remarkably stupid economic conflict to get into. And the situation in this trade war is likely to develop not necessarily to Trump’s advantage.
Posted by Mark Thoma on Thursday, May 31, 2018 at 03:26 PM in Economics, International Trade, Politics, Unemployment |
Posted by Mark Thoma on Thursday, May 31, 2018 at 10:10 AM in Economics, Links |
From an Economic Letter at the FRBSF:
Is GDP Overstating Economic Activity?, by Zheng Liu, Mark M. Spiegel, and Eric B. Tallman: Two common measures of overall economic output are gross domestic product (GDP) and gross domestic income (GDI). GDP is based on aggregate expenditures, while GDI is based on aggregate income. In principle, the two measures should be identical. However, in practice, they are not. The differences between these two series can arise from differences in source data, errors in measuring their components, and the seasonal adjustment process.
In this Economic Letter, we evaluate the reliability of GDP relative to two alternatives, GDI and a combination of the two known as GDPplus, for measuring economic output. We test the ability of each to forecast a benchmark measure of economic activity over the past two years. We find that GDP consistently outperforms the other two as a more accurate predictor of aggregate economic activity over this period. This suggests that the relative weakness of GDI growth in recent years does not necessarily indicate weakness in overall economic growth.
Discrepancies between GDP and GDI
What drives the discrepancies between GDP and GDI is not well understood. The source data for the components that go into GDP and GDI are measured with errors, which may lead to discrepancies between the two. Further discrepancies can arise because those different components are adjusted for seasonality at different points in time (see, for example, Grimm 2007).
The differences between these two series can be large. For example, in the last two quarters of 2007, inflation-adjusted or “real” GDI was declining whereas real GDP was still growing. The year-over-year growth rate of GDP exceeded that of GDI by almost 2.6 percentage points. Over long periods, however, final measures of growth in GDP and GDI tend to yield roughly equivalent assessments of economic activity. Since 1985, real GDP grew at an average annual rate of about 3.98%, while real GDI grew at a similar average rate of 4.02%.
Since late 2015, the two series have diverged, with real GDP growth consistently exceeding real GDI growth (Figure 1). The differences in growth are significant in this period. For example, if we used GDI growth to assess overall economic activity since July 2015, then the size of real aggregate output by the end of 2017 would be $230 billion smaller than if GDP growth were used. This divergence between the two sends mixed signals regarding the strength of recent economic activity.
Mixed signals from GDP and GDI growth
Source: Bureau of Economic Analysis.
Evaluating GDP, GDI, combination
Researchers often debate which of these series measures economic activity more accurately. Nalewaik (2012) argues that GDI outperforms GDP in forecasting recessions. GDI does appear to exhibit more cyclical volatility than GDP. One reason may be that GDI is more highly correlated with a number of business cycle indicators, including movements in both employment and unemployment (Nalewaik 2010). On the other hand, the Bureau of Economic Analysis has resisted this conclusion, arguing that GDP is in general based on more reliable source data than GDI is (Landefeld 2010).
To evaluate the relative reliability of GDP versus GDI for measuring economic output, we compare their abilities to forecast a benchmark measure of economic activity. We focus on the Chicago Fed National Activity Index (CFNAI) as the benchmark, since it is publicly available. The CFNAI is a monthly index of national economic activity, generated as the common component of 85 monthly series in the U.S. economy. These underlying series include a wide variety of data covering production and income, employment and unemployment, personal consumption and housing, and sales and orders. The CFNAI has been shown to help forecast real GDP (Lang and Lansing 2010). We use the CFNAI as a benchmark activity indicator to evaluate the relative forecasting performances of GDP and GDI and their combinations. Since the discrepancy between these two series has persisted for several years, we focus on the final releases of the GDP and GDI series.
Some have argued that, because the GDP and GDI series contain independent information, it may be preferable to combine the two series into a single more informative activity indicator. One series that uses such a combination is the Philadelphia Fed’s GDPplus series, which is a weighted average of GDP and GDI, with the weights based on the approach described by Aruoba et al. (2016). As a weighted average, GDPplus indicates activity levels between the two individual series. We therefore also consider the forecasting performance of the GDPplus series over this period of extended discrepancy between reported GDP and GDI growth.
To confirm the accuracy of our approach, we repeated our investigation with two alternative series constructed using methodologies similar to the CFNAI. The first alternative is an aggregate economic activity index (EAI) we constructed by extracting the common components of 90 underlying monthly time series. The EAI covers a broader set of monthly indicators than the CFNAI, since we also include information from goods prices and asset prices.
The second alternative indicator we considered is an activity index constructed by Barigozzi and Luciani (2018), which we call the BL index. Like our index, the BL index includes price indexes and other measures of labor costs. The authors base their estimates on the portions of GDP and GDI that are driven by common macroeconomic shocks under the assumption that they have equivalent effects on GDP and GDI. This restriction implies that deviations between GDP and GDI are transitory, and that the two series follow each other over time.
The EAI and the BL index are both highly correlated with the CFNAI and thus yielded similar conclusions. We describe the source data and our methodology for constructing the EAI as well as the analysis using both it and the BL index in an online appendix.
To examine the relative performances of GDP, GDI, and GDPplus for forecasting the CFNAI, we first estimate an empirical model in which the CFNAI is related to four lagged values of one of these measures of aggregate output. Ideally, we would have used the full sample of postwar data in our model, but there are some structural breaks in the data related to factors such as changes in the monetary policy regime since the mid-1980s and the Great Moderation that make this challenging. We therefore choose to focus on the sample starting from the first quarter of 1985 in this discussion; our results using the full sample are similar, as we report in the online appendix.
To examine how well each of the measures of aggregate output are able to forecast the CFNAI, we estimate the model using the sample observations up to the end of 2015, the period before GDP and GDI diverged. Once we determine the estimated coefficients that describe each relationship, we use those values to estimate forecasts for the period when discrepancies developed, from the first quarter of 2016 to the end of 2017. We then calculate the prediction errors, measured by the root mean-squared errors, for each measure of aggregate output. The smaller the prediction error, the better the forecasting performance.
In addition to examining the forecasting performance of GDP, GDI, and GDPplus for predicting the CFNAI economic activity indicator, we also examined their forecasting performance for the unemployment rate as reported by the Bureau of Labor Statistics.
Figure 2 displays the prediction errors from 2016 to 2017 for each of the alternative output measures—GDP, GDI, and GDPplus—estimated from our model for CFNAI and unemployment. For ease of comparison, we normalize the prediction errors from the model with GDP to one. The figure shows that the prediction errors over this period based on the GDP series are substantively lower than those based on GDI or GDPplus. This finding holds true not just for these proxies for economic activity but also for our EAI and the BL index (see the online appendix). Moreover, formal statistical tests of forecasting performance indicate that the forecasts based on GDP are significantly better than those based on GDI or GDPplus at the 95% confidence level. This result suggests that, in recent periods, GDP has been a more reliable independent indicator of economic activity than either GDI or GDPplus.
GDP outperforms GDI, GDPplus in predicting activity
Note: Figure shows prediction errors with GDP indexed to 1.
While GDP and GDI are theoretically identical measures of economic output, they can differ significantly in practice over some periods. The differences between the two series have been particularly pronounced in the past two years, when GDP growth has been consistently stronger than GDI growth. Based on this observation, some analysts have claimed that GDP might be overstating the pace of growth and that GDI, or some combination of GDP and GDI, should be used to evaluate the levels and growth rate of economic activity.
To evaluate the validity of this claim, we compared the relative performances of GDP, GDI, and a combined measure, GDPplus, for forecasting the CFNAI, which we use as a benchmark measure of economic activity over the past two years. We find that GDP consistently outperforms both GDI and combinations of the two, such as GDPplus, in forecasting aggregate economic activity during the past two years. In this sense, GDP is a more accurate predictor of aggregate economic activity than GDI over this period. Therefore, the relative weakness of GDI growth observed in recent years does not necessarily indicate weakness in overall economic growth.
Zheng Liu is a senior research advisor in the Economic Research Department of the Federal Reserve Bank of San Francisco.
Mark M. Spiegel is a vice president in the Economic Research Department of the Federal Reserve Bank of San Francisco.
Eric B. Tallman is a research associate in the Economic Research Department of the Federal Reserve Bank of San Francisco.
Aruoba, S. Boragan, Francis X. Diebold, Jeremy Nalewaik, Frank Schorfheide, and Dongho Song. 2016. “Improving GDP Measurement: A Measurement-Error Perspective.” Journal of Econometrics 191(2), pp. 384–397.
Barigozzi, Matteo, and Matteo Luciani. 2018. “Do National Account Statistics Underestimate U.S. Real Output Growth?” Board of Governors FEDS Notes, January 9.
Grimm, Bruce T. 2007. “The Statistical Discrepancy.” Bureau of Economic Analysis Working Paper 2007-01, March 2.
Landefeld, J. Steven. 2010. “Comments and Discussion: The Income- and Expenditure-Side Estimates of U.S. Output Growth.” Brookings Papers on Economic Activity, Spring, pp. 112–123.
Lang, David, and Kevin J. Lansing. 2010. “Forecasting Growth Over the Next Year with a Business Cycle Index.” FRBSF Economic Letter 2010-29 (September 27).
Nalewaik, Jeremy J. 2010. “The Income- and Expenditure-Side Estimates of U.S. Output Growth.” Brookings Papers on Economic Activity, Spring, pp. 71–106.
Nalewaik, Jeremy J. 2012. “Estimating Probabilities of Recession in Real Time Using GDP and GDI.” Journal of Money, Credit, and Banking 44, pp. 235–253.
Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System.
Posted by Mark Thoma on Tuesday, May 29, 2018 at 02:42 PM in Economics, Monetary Policy |
Posted by Mark Thoma on Tuesday, May 29, 2018 at 09:36 AM in Economics, Links |
Does economics matter?: Does economics matter? I ask because I suspect I would understand political debate better if I realized that it doesn’t.
Everybody tends to over-rate the importance of their profession: it’s part of deformation professionelle. Lawyers over-rate the importance of the law, artists of the arts and so on. Maybe economists do the same. Perhaps we should realize that most people who are interested in politics just aren’t interested in economics.
If we adopt this perspective, a lot falls into place. ...
Posted by Mark Thoma on Sunday, May 27, 2018 at 10:04 AM in Economics, Politics |
David Glasner at Uneasy Money:
Neo- and Other Liberalisms: Everybody seems to be worked up about “neoliberalism” these days. A review of Quinn Slobodian’s new book on the Austrian (or perhaps the Austro-Hungarian) roots of neoliberalism in the New Republic by Patrick Iber reminded me that the term “neoliberalism” which, in my own faulty recollection, came into somewhat popular usage only in the early 1980s, had actually been coined in the early the late 1930s at the now almost legendary Colloque Walter Lippmann and had actually been used by Hayek in at least one of his political essays in the 1940s. In that usage the point of neoliberalism was to revise and update the classical nineteenth-century liberalism that seemed to have run aground in the Great Depression, when the attempt to resurrect and restore what had been widely – and in my view mistakenly – regarded as an essential pillar of the nineteenth-century liberal order – the international gold standard – collapsed in an epic international catastrophe. The new liberalism was supposed to be a kinder and gentler — less relentlessly laissez-faire – version of the old liberalism, more amenable to interventions to aid the less well-off and to social-insurance programs providing a safety net to cushion individuals against the economic risks of modern capitalism, while preserving the social benefits and efficiencies of a market economy based on private property and voluntary exchange. ...
Posted by Mark Thoma on Saturday, May 26, 2018 at 08:59 AM in Economics |
Barbara Biasi and Petra Moser at VoxEU:
Effects of copyrights on science: Summary Copyrights grant publishers exclusive rights to content for almost a century. In science, this can involve substantial social costs by limiting who can access existing research. This column uses a unique WWII-era programme in the US, which allowed US publishers to reprint exact copies of German-owned science books, to explore how copyrights affect follow-on science. This artificial removal of copyright barriers led to a 25% decline in prices, and a 67% increase in citations. These results suggest that restrictive copyright policies slow down the progress of science considerably.
Posted by Mark Thoma on Saturday, May 26, 2018 at 08:58 AM in Economics, Market Failure, Productivity, Regulation |
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From the NBER Digest. "Two studies suggest that an increase in employers' monopsony power is associated with lower wages.":
Employer Concentration and Stagnant Wages: Stagnant wages and a declining share of labor income in GDP in recent decades have spawned a number of explanations. These include outsourcing, foreign competition, automation, and the decline of unions. Two new studies focus on another factor that may have affected the relative bargaining position of workers and firms: employer domination of local job markets. One shows that wage growth slowed as industrial consolidation increased over the past 40 years; the other shows that in many job markets across the country there is little competition for workers in specific job categories.
In Strong Employers and Weak Employees: How Does Employer Concentration Affect Wages? (NBER Working Paper No. 24307), Efraim Benmelech, Nittai Bergman, and Hyunseob Kim analyzed county-level census data for industrial firms for the period 1977 to 2009 to study the impact of employer concentration on wages in local labor markets. By focusing on manufacturing, they were able to control directly for worker productivity. The researchers found that, although there was substantial cross-sectional and time series variation in concentration, average local-level employer concentration increased between 1977-81 and 2002-9, based on the Standard Industrial Classification four-digit code for industry groups. Their measure of concentration is the Herfindahl-Hirschman Index (HHI), which is defined as the sum of the squares of the employment shares for all of the firms in a given industry. The employment-weighted mean value of this index rose from 0.698 to 0.756 during the study period, an increase of 5.8 percent. Forty percent of the plant-year observations were associated with manufacturing facilities in counties dominated by just a few firms. The researchers found a negative relationship between employer concentration and wages; it was twice as strong in the second half of their data sample as in the first half; a one standard deviation increase in the HHI was associated with a wage reduction of between 1 and 2 percent. They estimate that a firm operating in a labor market in which it was the only employer would pay wages 3.1 percent lower than those of a firm that operated in a less concentrated market. Most of the decline in wages appeared to occur as labor markets approached the pure monopsony case, namely the situation in which only one firm is hiring workers. In addition to finding lower wages in monopsony markets, the researchers also found that, over time, firms that dominate their labor markets were less likely to share productivity gains with employees. A one standard deviation decline in the HHI mapped to an increase in the elasticity of wages with respect to productivity of about 25 percent, from 0.38 to 0.47. Over the course of the study period, U.S. imports from China increased. The researchers found that import competition from China, which was associated with the closure or relocation of plants in a number of industries, accelerated the trend toward greater employer concentration in some local labor markets. This finding suggests that import competition not only reduced the demand for workers who previously produced the now-imported products, but that it may also have depressed wages for workers in other industries in affected labor markets as a result of increased labor market concentration. The only employees who did not experience wage stagnation in markets with high plant concentration were those who belonged to unions. About one quarter of the plants studied were unionized; the fraction was lower in the later than in the earlier years. Because this study focuses on workers employed by industrial firms, the fraction of workers who are union members is higher than for the U.S. labor market more broadly. To assess the robustness of their results, the researchers compared plants in the same industry owned by the same company but operating in different locations; they found that "those located in a more concentrated local labor market pay significantly lower wages."
In Concentration in U.S. Labor Markets: Evidence from Online Vacancy Data (NBER Working Paper No. 24395), José A. Azar, Ioana Marinescu, Marshall I. Steinbaum, and Bledi Taska found that in most locations employers have substantial monopsony power. The researchers studied job vacancies in the 709 federally delineated commuting zones, which depict the bounds of local economies. Drawing on a database compiled by Burning Glass Technologies from 40,000 employment websites, they calculated the level of labor market concentration by commuting zone, occupation, and quarter for the year 2016. They selected the top 200 occupations as classified by the Bureau of Labor Statistics' six-digit code, capturing 90 percent of the job postings in the database. As a yardstick for labor market concentration, the study calculated the Herfindahl-Hirschman Index measure, similar to the application in Working Paper 24307. The results suggested that the higher the market concentration, the stronger an employer's bargaining position. The average market had the equivalent of 2.5 recruiting employers. Under the standards that federal antitrust officials use when determining whether product markets exhibit excessive levels of concentration, 54 percent of the markets were highly concentrated, meaning they had the equivalent of fewer than four firms recruiting employees. Eleven percent of markets were moderately concentrated, and only 35 percent had low concentration. Nationwide, among the 30 largest occupations, marketing managers, web developers, and financial analysts faced the least favorable job markets; markets were most favorable for registered nurses, tractor-trailer drivers, and customer service representatives. The actual picture for job seekers, however, was brighter than these figures would indicate because commuting zones vary widely in employment levels. Commuting zones encompassing large cities had lower levels of labor market concentration than those around small cities or in rural areas. Accounting for the unequal distribution of employment, the researchers found that 23 percent of the national workforce is in highly or moderately concentrated labor markets. They argue that traditional market concentration thresholds underestimate workers' loss of bargaining power over time. They point out that those thresholds are geared to gauging the impact of mergers on the consumer marketplace, and that while consumers can buy products without the producers' explicit agreement, workers must find employers who agree to hire them.
Posted by Mark Thoma on Friday, May 4, 2018 at 10:37 AM in Academic Papers, Economics, Income Distribution, Market Failure |
Posted by Mark Thoma on Friday, May 4, 2018 at 10:31 AM in Economics, Links |
Posted by Mark Thoma on Tuesday, May 1, 2018 at 10:27 AM in Economics, Links |
Posted by Mark Thoma on Friday, April 27, 2018 at 07:42 PM in Economics, Links |
This is by Seppo Honkapohja and Kaushik Mitra (very wonkish):
Price Level Targeting with Evolving Credibility, by Seppo Honkapohja and Kaushik Mitra: Abstract We examine global dynamics under learning in a nonlinear New Keynesian model when monetary policy uses price-level targeting and compare it to inflation targeting. Domain of attraction of the targeted steady state gives a robustness criterion for policy regimes. Robustness of price-level targeting depends on whether a known target path is incorporated into learning. Credibility is measured by accuracy of this forecasting method relative to simple statistical forecasts. Credibility evolves through reinforcement learning. Initial credibility and initial level of target price are key factors influencing performance. Results match the Swedish experience of price level stabilization in 1920's and 30's.
Posted by Mark Thoma on Monday, April 23, 2018 at 12:31 PM in Academic Papers, Monetary Policy |
Posted by Mark Thoma on Monday, April 23, 2018 at 12:24 PM in Economics, Links |
Posted by Mark Thoma on Thursday, April 19, 2018 at 12:53 PM in Economics, Links |
Posted by Mark Thoma on Tuesday, April 10, 2018 at 10:02 AM in Economics, Links |
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Posted by Mark Thoma on Monday, April 2, 2018 at 10:32 AM in Economics, Links |