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Measuring Economic Policy Uncertainty Scott R

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1 Measuring Economic Policy Uncertainty Scott R
Measuring Economic Policy Uncertainty Scott R. Baker (Northwestern) Nick Bloom (Stanford & NBER) Steve Davis (Chicago & NBER) AEA, January 2015

2 This paper tries to investigate two questions
Uncertainty: Does policy uncertainty matter? News: Can text search create data (back to 1880s)?

3 We approach policy uncertainty methodically
1) Measuring policy uncertainty 2) Evaluating our measure 3) Estimating the impact of policy uncertainty

4 Our policy uncertainty index is based on computer search of Newspapers
For 10 major US papers get monthly counts of articles with: {economic or economy}, and {uncertain or uncertainty}, and {regulation or deficit or federal reserve or congress or legislation or white house} Divide the count for each month by the count of all articles Normalize each to SD=1, then sum all 10 papers to get the U.S monthly index

5 Constructing our US News-Based EPU Index
Newspapers: Boston Globe New York Times Chicago Tribune SF Chronicle Dallas Morning News USA Today Los Angeles Times Wall Street Journal Miami Herald Washington Post Note: We use Access World News Newsbank Service when constructing a daily EPU Index, because the daily index requires a higher density of news sources.

6 US News-based policy uncertainty index: Jan 1985-Aug 2014
50 100 150 200 250 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Debt Ceiling; Euro Debt 9/11 Shutdown Fiscal Cliff Lehman and TARP Gulf War II Bush Election Gulf War I Black Monday Stimulus Debate Russian Crisis/LTCM Clinton-Election Euro Crisis and 2010 Midterms Source: “Measuring Economic Policy Uncertainty” by Scott R. Baker, Nicholas Bloom and Steven J. Davis, all data at Data normalized to 100 prior to 2010.

7 Category EPU analysis – look for category terms alongside our economic policy uncertainty terms
Small newspapers like the Modesto Bee or the Oregon Emerald, Seattle Times Note: Analysis uses Newsbank coverage of around 1000 US national and local newspapers See Table 1 in the Baker, Bloom and Davis (2013) for a more detailed analysis.

8 Policy Uncertainty Index
Can run the index back to 1900 using 6 newspapers (Jan 1900 – Dec 2012) Lehman and TARP Great Depression, New Deal and FDR Great Depression Relapse Debt Ceiling 300 9/11 and Gulf War II Gold Standard Act Gulf War I Black Monday Asian Fin. Crisis Assassination of McKinley OPEC II Truman-Dewey election OPEC I Versailles conference 200 Start of WW I Watergate McNary Haughen farm bill Policy Uncertainty Index Berlin Conference 100 Political fighting: Taft v Roosevelt over Conservatives vs Progressives in Republican Party ( ) The November 1912 spike is the election of Woodrow Wilson over Roosevelt (second) and Taft (third) Close elections: Wilson v Hughes in 1916 Truman vs Dewey in 1948 JFK-Nixon 1960 Major economics policies: Woodrow Wilson’s New Freedom progressive agenda ( ) Calvin Coolidge ( ) – very laissez-faire and inactive (“no new experiments”), reduced size of government and debt, and “scientific taxation” (minimize tax changes – see New Deal 1932 plus last year under Hoover JFK – New Frontier starts in 1960 LBJ – Great Society from 1964 (like New Frontier uses task forces of outsiders, including lots of economists…) 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Notes: Index of Policy-Related Economic Uncertainty composed of quarterly news articles containing uncertain or uncertainty, economic or economy, and policy relevant terms (scaled by the total number of articles) in 6 newspapers (WP, BG, LAT, NYT, WSJ and CHT). Data normalized to 100 from

9 India Economic Policy Uncertainty Index
Exchange Rate Fluctuations and Worry 100 200 300 2003 2005 2007 2009 2011 2013 Lokpal Bill Lehman Bros Congress Party wins National Election India-US Nuclear Deal Price Hikes Bear Sterns India Based Policy Uncertainty Index Source: Data from 7 Indian newspapers (Economic Times, Times of India, Hindustan Times, Hindu, Statesman, Indian Express, and Financial Express)

10 China Economic Policy Uncertainty Index
Political Transition and new National Congress 100 200 300 400 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Eurozone Fears and Protectionism Inflation and Export Pressure 9/11 China Deflation and Deficit China Based Policy Uncertainty Index Rising Interest Rates China Stimulus Source: Data until August Based on newspaper articles from the South China Morning Post.

11 North Korean Economic Policy Uncertainty Index
250 200 Policy Uncertainty Index 150 100 50 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Source: Data from 0 North Korean newspapers

12 Russian Economic Policy Uncertainty Index (beta)
Orange Revolution in Ukraine Duma elections and protests against election fraud Kizlyar hostage crisis; PM Chubais resigns Constitutional Crisis Russian financial crisis First Chechen War Second Chechen War Acting PM Gaidar resigns Russian military exits Chechnya Timoshenko resigns; Terror attack in Nalchik Parliament dismissed In Ukraine Terror attacks in Nalchik & Stavropol Medveded election Putin becomes PM Lehman Brothers Failure Ukraine Conflict Taper Tantrum Putin election Kiev Euromaidan; Crimea annexation Source: Data from Kommersant daily newspaper ( )

13 We approach policy uncertainty methodically
1) Measuring policy uncertainty 2) Evaluating our measure 3) Estimating the impact of policy uncertainty 4) Why policy uncertainty changes over time

14 A) Market Use Market suggests informational value in the data
I) We have also tracked numerous institutions using the data like Goldmans, Citibank, JP Morgan, Blackrock, Wells Fargo, IMF, Fed, ECB etc (see II) This has led Bloomberg, FRED, Reuters and Haver to stream the data for their financial and policy users

15 B) Comparison: stock market implied volatility (the VIX)
10 20 30 40 50 60 VIX (red) 100 150 200 250 Economic Policy Uncertainty Index (Blue) 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Credit Crunch Correlation 1-month VIX and EPU index = 0.55 Correlation 10-year VIX synthetic and EPU index = 0.73 Debt Ceiling Obama Election, Banking Crisis 9/11 WorldCom & Enron Gulf War I LTCM default Clinton election Large interest rate cuts Gulf War II Asian crisis Source: Data until October 2012

16 C) Running Detailed Human Audits
10 undergraduates read ≈ 9,098 newspaper articles to date using a 63-page audit guide to code articles if they discuss “economic uncertainty” and “economic policy uncertainty”

17 Find humans and computers give similar results in large samples: yearly from 1900
Correlation=0.837 Computer Auditors typically 70 a year, papers typically 0.5m a year post 1985, 0.25m a year Human Human index based on audit of 3727 articles (ave=34 per year) in the LA Times and New York Times (the two papers we could audit from 1900 to 2012) versus the historical index for these two papers.

18 Find humans and computers give similar results in large samples: quarterly from 1985
Correlation=0.721 Computer Auditors typically 70 a year, papers typically 0.5m a year post 1985, 0.25m a year Human Human index based on audit of 3891 articles (34.7 per month) in the LA Times, New York Times, Miami Herald and SF Chronicle (the five papers we could audit from 1985 to 2012).

19 The human-computer differences are uncorrelated with real outcomes: e
The human-computer differences are uncorrelated with real outcomes: e.g. GDP growth Correlation=0.071 Yearly economic policy uncertainty index based on human audit of 3727 articles in the LA Times and New York Times (the two papers we could audit from 1900 to 2012) in a 3-year moving average to yield an average of 121 articles per year.

20 D Bias test: compare 5 most Republican and 5 most Democrat papers – they looks similar
Reagan, Bush I Clinton Bush II Obama Papers sorted into 5 most ‘Republican’ or ‘Democratic’ groups using the media slant measure from Gentzkow & Shapiro (2010).

21 We approach policy uncertainty methodically
1) Measuring policy uncertainty 2) Evaluating our measure 3) Estimating the impact of policy uncertainty - Firm-level regressions - Macro VARs

22 Microdata: Firm-level estimates exploit differences in industry exposure to government
Use the Federal Registry of Contracts and match this to Compustat firms (using Compustat parent & D&B subsid names) Generate average industry contracts/revenue (1999 to 2012)

23 Yit = Fi + Pt + α*Expj*Govt + β*Expj*EPUt + εi,t
Microdata: Run firm level panel regressions Firm fixed effects Period fixed effects Yit = Fi + Pt + α*Expj*Govt + β*Expj*EPUt + εi,t Firm stock price volatility Firm government exposure × government expenditure (1st moment effect) Firm government exposure × policy uncertainty (2nd moment effect) i=firm, j=industry, t=quarter Estimated firm by quarter , standard-errors clustered by j

24 Microdata: Firms with greater government exposure have higher stock vol uncertainty when EPU is high
Notes: Firm-clustered standard errors. Firm by quarter panel data from , using individual firm implied volatility from individual firm equity options.

25 Microdata: Stock vol uncertainty results are very robust to different measures, samples and controls
Notes: Firm-clustered standard errors. Firm by quarter panel data from , using individual firm implied volatility from individual firm equity options.

26 Microdata: Firms in sectors with higher government exposure cut investment & hiring when EPU is high
Notes: Firm-clustered standard errors. Firm by quarter panel data for investment and firm by year for employment from

27 Magnitude for Investment and Employment v
Magnitude for Investment and Employment v. large in exposed sectors (health, defense & construction) Consider EPU increase from 2005/6 to 2011/12 (84%) for firm with govt. exposure of 0.25 (health, defense & constr.). Results suggest reduce investment by 9% (similar to average recessionary drop in NIPA investment of 8.5%) Results suggest reduce employment by 12% (much larger than average recessionary drop in employment of 2%)

28 We approach policy uncertainty methodically
1) Measuring policy uncertainty 2) Evaluating our measure 3) Estimating the impact of policy uncertainty - Firm-level regressions - Macro VARs

29 VAR for US industrial production and employment after a 2005/6 to 2011/12 sized EPU shock
Notes: The impulse response function for Industrial Production and Employment to a rise in the policy-related uncertainty index from the average value to the average value. The central (black) solid line is the mean estimate, the dashed (red) outer lines are the one SE bands. Estimated using a monthly Cholesky Vector Auto Regression on: the EPU index, log(S&P 500), federal funds rate, log employment, log industrial production. Monthly data from 1985M1 to 2012M12, using 3 lags. Industrial Production, (%) Employment Impact, (%) Months after the economics policy uncertainty shock 29

30 VAR robustness Industrial Production Impact (% deviation)
Six months of lags Adding EU (after EPU) Adding VIX (after EPU) Industrial Production Impact (% deviation) Reverse bivariate (industrial production & EPU) historical data Baseline Bivariate (EPU and industrial production) Months after the policy uncertainty shock Notes: This shows the impulse response function for GDP and employment to an increase in the policy-related uncertainty index from the average to the average. Estimated using a monthly Cholesky Vector Auto Regression (VAR) of the uncertainty index, log(S&P 500 index), federal reserve funds rate, log employment and log industrial production with 3 lags unless otherwise specified. Data from 1985 to 2012, except for the pre-1985 data spec which uses EPU and IP data from 1920 to 1984. 30

31 11 Country Panel VAR, with Country & Period FEs
Notes: Shows the impulse response function for Industrial Production and employment to an increase in the policy-related uncertainty index from the average value to the average value. The central (black) solid line is the mean estimate while the dashed (red) outer lines are the one-standard-error bands. Estimated using a monthly Cholesky Vector Auto Regression (VAR) with 3 lags on the EPU index, log(S&P 500 index), unemployment rate, and log industrial production, plus a full set of country, year and month fixed-effects. Country data weighted by the number of newspapers used to make the EPU series. Fit to monthly data from 1985M1 to 2012M12 where available. Estimated on data from Canada, China, France, Germany, India, Italy, Japan, Russia, Spain, UK and the USA. Industrial Production, (%) Unemployment Impact, (%) Months after the economics policy uncertainty shock 31

32 11 Country Panel VAR robustness
Six months of lags Industrial Production Impact (% deviation) Adding stock volatility Reverse bivariate (industrial production & EPU) Baseline No country or time FEs Dropping stock-price Bivariate (EPU and industrial production) Months after the policy uncertainty shock Notes: XXXX This shows the impulse response function for GDP and employment to an increase in the policy-related uncertainty index from the average to the average. Estimated using a monthly Cholesky Vector Auto Regression (VAR) of the uncertainty index, log(S&P 500 index), federal reserve funds rate, log employment and log industrial production with 3 lags unless otherwise specified. Data from 1985 to 2012, except for the pre-1985 data spec which uses EPU and IP data from 1920 to 1984. 32

33 Conclusions Policy uncertainty fluctuates at a high frequency, driven by the business cycle, the political factors, & shocks (e.g. wars) Policy uncertainty appears to have risen since the 1960s (maybe from political polarization & larger government) Firm-level (and VAR) evidence suggests EPU can: Substantially increase stock-volatility and reduce hiring & investment, in defense, healthcare & construction Moderately reduce overall output and employment

34 Finally, should note all the data is online
Data available at:

35 Back-Up

36 Future Work: working on firm-level surveys
Projecting ahead over the next twelve months, please provide the approximate percentage change in your firm's SALES LEVELS for: The LOWEST CASE change in my firm’s sales levels would be: % The LOW CASE change in my firm’s sales levels would be: % The MEDIUM CASE change in my firm’s sales levels would be: % The HIGH CASE change in my firm’s sales levels would be: % The HIGHEST CASE change in my firm’s sales levels would be: % Numbers in red are the average response from the pilot on 300 firms

37 Piloting results look good from testing on a monthly survey on 300 firms: change in sales

38 Can also ask about probabilities
Please assign a percentage likelihood to these SALES LEVEL changes you selected above (values should sum to 100%) 10 % : The approximate likelihood of realizing the LOWEST CASE change 18 % : The approximate likelihood of realizing the LOW CASE change 40 % : The approximate likelihood of realizing the MEDIUM CASE change 23 % : The approximate likelihood of realizing the HIGH CASE change 9 % : The approximate likelihood of realizing the HIGHEST CASE change Numbers in red are the average response from the pilot on 300 firms

39 Piloting results look good from testing on a monthly survey on 300 firms: probabilities

40 Stock market data: More similar to 10 year index of implied volatility on the S&P500 (correlation 0.73) 10 20 30 40 50 60 100 150 200 250 Economic policy uncertainty 2002 2004 2006 2008 2010 2012 Implied volatility Correlation EPU and 1 month=0.578 Correlation EPU and 10 years=0.855 Economic Policy Uncertainty (•) 1 Month Implied Volatility (♦) 10 Year Implied Volatility (+) Notes: Data from “The buzz: Links between policy uncertainty and equity volatility”, by Krag Gregory and Jose Rangel, Goldman Sachs, November 12, 2012.

41 The key sources of policy uncertainty from 1985
Fiscal cliff, Europe and Debt Ceiling Gulf War II Gulf War I Black Monday Lehman and TARP 9/11 Clinton Election Gingrich Shutdown Note: This quarterly chart shows the 5 most important sources of economic policy uncertainty based on frequency counts of newspaper articles.

42 Why is not fully clear – but looking into this along with Jonathan Rodden and Brandice Canes-Wrone

43 Also tested by fitting events we know - VIX
Correlation=0.733 Notes: Frequency of the triple of “economy/economic”, “uncertain/uncertainty” and one of a collection of financial market terms (stock price, equity price, stock market) in 10 major US papers and normalized by the total number of articles, by month and paper. Both series scaled to same mean. Each series set to mean of 100 over entire period.

44 Surveys: e.g. compare to the FOMC Beige Book’s mentions of uncertainty and policy uncertainty
Correlation with our EPU index=0.72 Note: Plots the frequency of the word “uncertain” in each quarter of the Federal Open Market Committees’ (FOMC) Beige Book. Data from 1983Q4 (when the Beige book started) to 2013Q1. The Beige Book is an overview of economic conditions of about 15,000 words in length prepared two weeks before each FOMC meeting. The count of “Policy Uncertainty” uses a human audit to attribute each mention of the word uncertain to a policy context (e.g. uncertainty about fiscal policy) or a non-policy context (e.g. uncertainty about GDP growth). See the paper for full details.

45 Beige Book breakdown also points to similar factors
1990 Q Q1 Gulf War I 1993 Q Q3 Clinton Tax Reforms 2001 Q Q2 9/11 Attacks 2002 Q Q2 Gulf War II 2004 Q Q4 Bush/Kerry Election 2008 Q Q4 Lehman's and recession 2010 Q Q1 Debt-ceiling crisis 1983 Q3 – 2013 Q1 Overall Average Overall Economic Uncertainty 11 8.8 7.7 13.5 5.2 10.2 15.8 5.5 Economic Policy Uncertainty 6.3 1.2 4.8 2.8 0.8 6.8 1.7 All Fiscal Matters 1 1.5 0.4 3.3 1.0 Taxes Only 0.2 0.3 1.4 Spending Only 0.5 Monetary Policy Health Care 2 0.1 National Security and War 5.3 Financial Regulation Sovereign debt, currency crisis U.S. Elections and Leadership Changes 2.2 0.9 Other Specified Policy Matters 0.7 Politics, Unspecified 3 1.6 Sum of Policy & Politics Categories 9.3 3.0 10.0 2.5


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