The Unassuming Economist

From Econbrowser:

“Update of The Data Will Set You Free  (in preparation for a new semester!)

In an era of easily accessible databases, am constantly amazed that people write stuff that is easily falsifiable.  Or ask me for the “raw” data when it’s freely available via a aggregating database I’ve provided the hyperlink (most galling is when they then accuse me of misquoting data sources).

Just to remind the frequent commenters on this blog, freely available and documented data available here:

St. Louis Fed economic database Thousands of time series on economic activity, in an easily downloadable form.

IMF International Financial Statistics

IMF World Economic Outlook databases.

World Bank World Development Indicators.

BIS

ECB

DBnomics (a “European FRED”)

YCharts Macro and equity market data series.

ino.com Futures data.

Federal Reserve Board data Monetary, financial and output data collected by the Nation’s central bank.

Bureau of Economic Analysis, Dept. of Commerce Data on GDP and components (the national income and product accounts) as well as other macroeconomic data.

Bureau of the Census, Dept. of Commerce Data on the characteristics of the US population  US firms, as well as other data.

Bureau of Labor Statistics, Dept. of Labor Data on wages, prices, productivity, and employment and unemployment rates.

Energy Information Agency, Dept. of Energy Data on energy (electricity, gas, petroleum) production, consumption and prices.

Economic Report of the President, various years. The back portion of this annual publication contains about 70 tables of government economic data.

Economic Indicators CEA and JEC Compilation of economic data in tabular form.

Economic Time Series page A large collection of economic time series.

NBER Data Specialized economic databases created by economists associated with the National Bureau of Economic Research.

Penn World Tables v.10.

Netherlands Bureau for Economic Policy Analysis World Trade Monitor

World Bank, A Global Database of Inflation.

Jordà-Schularick-Taylor Macrohistory Database

Usually I cite FRED or BEA and/or BLS via FRED, or from the above data sources. In certain cases, I have written papers using specialized data sources. Below are links to those data sources.”

Continue reading here.

From Mark J. Perry (AEI):

“As I wrote in the summer of 2018 on CD, I’ve probably created and posted more than 3,000 graphics on CD, Twitter, and Facebook including charts, graphs, tables, figures, maps, and Venn diagrams over the last 16 years. Of all of those graphics, I don’t think any has gotten more attention, links, re-Tweets, re-posts, and mentions than previous versions of the chart above, which was once referred to as “the Chart of the Century.” Here are some examples of the attention that past versions of the chart above have gotten:

*Marketwatch has featured the chart twice here and here and made this comment “When this chart’s creator, econ professor Mark Perry and the man behind the Carpe Diem blog, first posted it on Twitter, it was hailed as “stunning” and “one of the most important charts about the economy this century.“

*Barry Ritholz has featured various versions of the chart three times on his Big Picture Blog here, here, and here.

*Bloomberg published an article in July 2018 titled “Chart of Century Gives Powell Gloomy Glimpse of Trade-War World,” with this opening:

A multi-colored graphic that’s made the rounds at the Federal Reserve hints at what Chairman Jerome Powell could face if President Donald Trump succeeds in throwing globalization into reverse: Higher prices for many goods and potentially faster inflation.

Plugged as possibly the chart of the century by economist and originator Mark Perry, it shows that prices of goods subject to foreign competition — think toys and television sets — have tumbled over the past two decades as trade barriers have come down around the world. Prices of so-called non-tradeables — hospital stays and college tuition, to name two — have surged.”

From a paper by Tamás Kiss, Hoang Nguyen and Pär Österholm:

“In this paper, we have analysed the relevance of taking non-Gaussianity into account when empirically modelling Okun’s law in Australia, the euro area, the United Kingdom and the United States. Our results based on Bayesian VAR models with stochastic volatility suggest that heavier-than-Gaussian tails find support in some cases. Taking skewness into account is, however, less beneficial in this context considering our baseline sample. Our results confirm that it is important to account for heavy tails in the distribution of macroeconomic variables, an argument put forward by Fagiolo et al. (2008) and Ascari et al. (2015) among others.

It should be noted though that our results to some extent depend on whether data from the corona pandemic are included or not. We believe that including them might be problematic since they should probably be treated as outliers (see the discussion in Carriero et al., 2021). If they nevertheless are treated as regular observations, our analysis indicates that the evidence of non-Gaussianity strengthens. In addition, it can be noted that accounting for non-Gaussianity not only improves the model fit in several cases but it also captures the large swings in the variables without causing large swings in the stochastic volatility.

Apart from the modelling perspective, our analysis has also provided updated international empirical evidence concerning Okun’s law. We find that the dynamic relationship between the variables in all four economies is such that a shock to GDP growth has robustly negative effects on the change in the unemployment rate. This finding is robust to whether we include the period associated with the corona pandemic or not. It confirms Ball et al. (2017) and Ball et al. (2019) who argue that Okun’s law continues to be a robust relationship in empirical macroeconomics. This should be highly relevant information to the central banks of the economies studied here, suggesting that Okun’s law – which has been an important empirical relationship when modelling the economy continues to be useful regardless of modelling choices and time periods.”

Source: Structural Change, Productive Development, and Capital Flows: Does Financial “Bonanza” Cause Premature Deindustrialization?

A recent working paper by A. Botta et al (2022) of the Levy Economics Institute analyzes factors that may have hindered productive development for over four decades prior to the COVID-19 pandemic.

Abstract: We investigate the role of (non-FDI) net capital inflows as a potential source of premature deindustrialization. We consider a sample of 36 developed and developing countries from 1980 to 2017, with major emphasis on the case of emerging and developing economies (EDE) in the context of increasing financial integration. We show that periods of abundant capital inflows may have caused the significant contraction of manufacturing share to employment and GDP, as well as the decrease of the economic complexity index. We also show that phenomena of “perverse” structural change are significantly more relevant in EDE countries than advanced ones. Based on such evidence, we conclude with some policy suggestions highlighting capital controls and external macroprudential measures taming international capital mobility as useful tools for promoting long-run productive development on top of strengthening (short-term) financial and macroeconomic stability.

From EconBrowser:

“[On] January 20^th, EconoFact celebrates its 5 year anniversary, providing non-partisan, incisive analyses on timely and important economic and social policy issues. It does so by bringing to the public debate the expertise of leading economists and social scientists via memos and podcasts.

Topics:

Employment

Debt and Deficits

The Covid-19 Pandemic

The Safety Net

With contributions from a network of economists and social scientists. (full disclosure: I’m one of the contributors.)

The entire topics list A-Z is here.”