Really Uncertain Business Cycles Nick Bloom Stanford NBER
Really Uncertain Business Cycles Nick Bloom (Stanford & NBER) Max Floetotto (Mc. Kinsey (the dark-side)) Nir Jaimovich (Duke & NBER) Itay Saporta-Eksten (Tel Aviv) Stephen J. Terry (Stanford) Hass University, 11/13/2014
Uncertainty as another driver of business cycles Many business people and policymakers believed this was a key factor in driving recent recessions: “participants reported that uncertainty about the economic outlook was leading firms to defer spending projects until prospects for economic activity became clearer. ” FOMC minutes, April 2008
Uncertainty as another driver of business cycles Many economists have also a taken a similar view “the main contribution to the decline in output and employment during the 2007 -2009 recession are estimated to come from financial and uncertainty shocks” Stock and Watson (2012, BPEA)
Uncertainty has also been in the media a lot recently
Although for some the best evidence is. . …
…. that Paul Krugman thinks it does not matter
So we study second moment (uncertainty) shocks ● Can generalize this to include idiosyncratic demand shocks (e. g. Hopenhayn and Rogerson, 1993)
Summary of what this paper does A. Provides empirics suggesting uncertainty is: 1. Counter-cyclical 2. Not primarily driven by first moment shocks B. Builds a DSGE model generalized with time-varying uncertainty, heterogeneous firms and non-convex adjustment costs, finding: 1. Uncertainty shocks generate drops and rebounds in labor, capital, TFP & output 2. Uncertainty shocks change the impact of policies
Intuition is adjustment costs for investment and hiring leads to Ss models (here for capitals) Density of units Disinvest (s) Invest (S) Innaction Productivity / Capital Disinvestment Investment
Increased uncertainty makes the SS thresholds move outwards Density of units Disinvest (s) Invest (S) Innaction Productivity / Capital
This leads net investment to fall, because investment drops more than disinvestment Invest (S) Density of units Disinvest (s) Productivity / Capital Drop in disinvestment Drop in investment
This leads to the: “Delay effect”: higher uncertainty leads firms to postpone decisions. So net investment (and hiring) falls ∂I/∂σ<0 where I=investment or hiring, σ=uncertainty
Higher uncertainty also reduces responsiveness to stimulus (like prices, taxes and interest rates) Density of units Disinvest (s) Invest (S) Marginal investing density at low uncertainty threshold Productivity / Capital Marginal investing density at high uncertainty threshold
This leads to the : “Delay effect”: higher uncertainty leads firms to postpone decisions. So net investment and hiring falls ∂I/∂σ<0 where I=investment or hiring, σ=uncertainty “Caution effect”: higher uncertainty reduces firms response to other changes, like prices or TFP ∂2 I/∂A∂σ<0 where I and σ as above, A=prices or TFP
Since this model has 2 -factors with adjustment costs it has a 2 -dimensional response box Low uncertainty High uncertainty
Measuring Uncertainty Model Simulation of an uncertainty shock Policy experiment
Macro and Micro Uncertainty appear countercyclical ● Counter cyclical macro uncertainty is by now a stylized fact – so I will just show some summary graphs (from Bloom 2014, JEP) ● Counter cyclical micro uncertainty is less clears
Volatility of the daily returns on the S&P 500 Stock returns realized volatility (back to 1950) Source: Monthly volatility of the daily returns on the S&P 500 at an annualized level. Grey bars are NBER recessions. Data spans 1950 Q 1 -2013 Q 4.
VIX, the 1 -month ahead implied S&P 500 volatility Source: VIX is the implied volatility on the S&P 500, averaged to the quarterly level, provided by the Chicago Board of Options and Exchange. The VIX is the markets implied level of stock-market volatility over the next 30 -days, where values are in standard-deviations on the S&P 500 at an annualized level. Grey bars are NBER recessions. Data spans 1990 Q 1 -2013 Q 4.
-. 1 -. 2 -. 3 vol correlation -. 4 Correlation of stock volatility (or VIX) and industrial production growth 0 Stock-volatility and VIX lead and lag the cycle vix correlation -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 Lead (lag if negative) months on volatility (or VIX) Source: Industrial production monthly data from Federal Reserve Board data from 1970 onwards (VIX from 1990 onwards)
GDP GARCH(1, 1) conditional vol. 37% higher in recessions Source: Bloom (2013), “Fluctuations in Uncertainty”, NBER WP 19714 (auxiliary data do-file)
News-Based uncertainty indicators US Newspapers: • Boston Globe • Chicago Tribune • Dallas Morning News • Los Angeles Times • Miami Herald • • • New York Times SF Chronicle USA Today Wall Street Journal Washington Post Basic idea is to search for frequency of words like econom* and uncert* in newspapers 22
US Economic policy uncertainty Black Monday Bush Election Gulf War I Clinton. Election Gulf War II Lehman and TARP Stimulus Debate Russian Crisis/LTCM 100 150 200 250 9/11 Fiscal Cliff Shutdown Debt Ceiling; Euro Debt Source: “Measuring Economic Policy Uncertainty” by Scott R. Baker, Nicholas Bloom and Steven J. Davis, all data at www. policyuncertainty. com. Data normalized to 100 prior to 2010. 20 13 20 11 20 09 20 07 20 05 20 03 20 01 19 99 19 97 19 95 19 93 19 91 19 89 19 87 19 85 50 Euro Crisis and 2010 Midterms
0 -. 05 -. 15 -. 25 Correlation EPU & industrial production growth Policy Uncertainty also leads and lags the cycle -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 Lead (lag if negative) months on policy uncertainty news index Source: Economic Policy Uncertainty Index from www. policyuncertainty. com. Industrial production monthly data from Federal Reserve Board. Data from 1985 onwards.
200 Great Depression, Gold New Deal Standard Act and FDR Assassination of Mc. Kinley Versailles conference Great Depression Relapse Lehman Debt and TARP Ceiling Gulf Black War I Monday Post-War OPEC II Strikes, OPEC I Truman. Dewey Watergate Start of Mc. Nary WW I Haughen farm bill 9/11 and Gulf War II Asian Fin. Crisis 100 Berlin Conference 0 Policy Uncertainty Index 300 News based measures are useful back in time 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 smoothed total number of articles) in 5 newspapers (WP, BG, LAT, WSJ and CHT). Data normalized to 100 from 1900 -2011.
Russian Crisis/LTCM Asian Crisis Italy Rating Cut Papandreou calls for referendum, then resigns Lehman Bros. Treaty of Accession/ Gulf War II 9/11 Northern Rock & Ensuing Financial Turmoil German Elections Ongoing Eurozone Stresses 14 20 13 20 12 20 11 20 10 20 09 20 08 20 07 20 06 20 05 20 04 20 20 02 20 01 20 00 20 99 19 98 03 French and Dutch Voters Reject European Constitution 19 97 19 Greek Bailout Request, Rating Cuts Nice Treaty Referendum 50 Policy Uncertainty Index 100 150 200 European Economic Policy Uncertainty Index Source: www. policyuncertainty. com. Based on 10 paper (El Pais, El Mundo, Corriere della Sera, La Repubblica, Le Monde, Le Figaro, the Financial Times, Handelsblatt, FAZ. )
Exchange Rate Fluctuations and Worry Lokpal Bill Congress Party wins National Election 200 Lehman Bros Price Hikes India-US Nuclear Deal 100 150 Bear Sterns 14 20 13 20 12 20 11 20 10 20 09 20 08 20 07 20 06 20 05 20 04 20 20 03 50 India Based Policy Uncertainty Index 250 India Economic Policy Uncertainty Index Source: www. policyuncertainty. com. Data from 7 Indian newspapers (Economic Times, Times of India, Hindustan Times, Hindu, Statesman, Indian Express, and Financial Express)
Eurozone Fears and Protectionism Inflation and Export Pressure 9/11 300 Political Transition and new National Congress 200 China Deflation and Deficit Rising Interest Rates 100 China Stimulus 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 99 19 98 19 97 19 96 19 19 95 0 China Based Policy Uncertainty Index 400 China Economic Policy Uncertainty Index Source: www. policyuncertainty. com. Data until February 2014. Based on newspaper articles from the South China Morning Post.
Russian Economic Policy Uncertainty Index (beta) 250 Russian financial crisis 200 Timoshenko resigns; Terror attack in Nalchik Orange Revolution in Ukraine Parliament dismissed In Ukraine Putin becomes PM 150 Terror attacks in Nalchik & Stavropol Ukraine Conflict 300 Russian military exits Chechnya Kiev Euromaidan; Crimea annexation Duma elections and protests against election fraud Taper Tantrum 350 Constitutional Crisis 400 Acting PM Gaidar resigns Second Chechen War Putin election 450 Medveded election Lehman Brothers Failure Kizlyar hostage crisis; PM Chubais resigns 100 50 1992 10 1993 3 1993 8 1994 1 1994 6 1994 11 1995 4 1995 9 1996 2 1996 7 1996 12 1997 5 1997 10 1998 3 1998 8 1999 1 1999 6 1999 11 2000 4 2000 9 2001 2 2001 7 2001 12 2002 5 2002 10 2003 3 2003 8 2004 1 2004 6 2004 11 2005 4 2005 9 2006 2 2006 7 2006 12 2007 5 2007 10 2008 3 2008 8 2009 1 2009 6 2009 11 2010 4 2010 9 2011 2 2011 7 2011 12 2012 5 2012 10 2013 3 2013 8 2014 1 2014 6 0 First Chechen War Source: Data from Kommersant daily newspaper (1992 -2014) 29
200 150 100 Source: www. policyuncertainty. com. Data from 0 North Korean newspapers 14 20 13 20 12 20 11 20 10 20 09 20 08 20 07 20 06 20 05 20 04 20 20 03 50 Policy Uncertainty Index 250 North Korean Economic Policy Uncertainty Index
Mean Forecaster disagreement GDP growth (mean forecast ) GDP growth uncertainty and disagreement (same scale) Forecaster disagreement and uncertainty: GDP Forecaster uncertainty Notes: Data from the probability changes of GDP annual growth rates from the Philadelphia Survey of Professional Forecasters. Mean forecast is the average forecasters expected GDP growth rate, forecaster disagreement is the cross-sectional standarddeviation of forecasts, and forecaster uncertainty is the median within forecaster subjective variance. Data only available on a consistent basis since 1992 Q 1, with an average of 48 forecasters per quarter. Data spans 1992 -20013.
Macro and Micro Uncertainty appear countercyclical ● Counter cyclical macro uncertainty is by now a stylized fact – so I will just show some summary graphs (from Bloom 2013) ● Counter cyclical micro uncertainty is less clears
We use census data to measure micro uncertainty ● (Micro) uncertainty is hard to measure ● We use Census ASM manufacturing data on about 50, 000 plants per year from 1972 -2010 ● Primary sample: plants with 25+ years of data ● Secondary samples: plants 2+ and 39 years of data ● Also show Census based uncertainty measures very correlated with other popular uncertainty measures 33
Define uncertainty as the variance of TFP ‘shocks’ Shocks are the forecast error in TFP, where TFP measured using standard SIC 4 -digit factor share approach log(TFP) Plant Year fixed effects effect Lagged log(TFP) TFP ‘shock’ Is this a shock? At least partially as this residual (ei, t) is correlated with parents’ stock returns (for plants with a publicly listed parent firm) over the same period (so contains news) Also looks very similar if condition on multiple lags of investment, employment, materials, sales, TFP expansions etc 34
Density Counter-cyclical: micro-uncertainty, the variance of plant TFP shocks, increased by 76% in the Great Recession TFP shock Notes: Constructed from the Census of Manufactures and the Annual Survey of Manufactures using the balanced panel of all 15, 752 establishments active in 2005 -06 and 2008 -09. Moments of the distribution for non-recession (recession) years are: mean 0 (-0. 166), variance 0. 198 (0. 349), coefficient of skewness -1. 060 (-1. 340) and kurtosis 15. 01 (11. 96). The year 2007 is omitted because according to the NBER the recession began in December 2007, so 2007 is not a clean “before” or “during” recession year.
Density Counter-cyclical: micro uncertainty, proxied by the variance of plant sales growth, rose in the recession Sales growth rate Notes: Constructed from the Census of Manufactures and the Annual Survey of Manufactures using a balanced panel of all 15, 752 establishments active in 2005 -06 and 2008 -09. Moments of the distribution for non-recession (recession) years are: mean 0. 026 (-0. 191), variance 0. 052 (0. 131), coefficient of skewness 0. 164 (-0. 330) and kurtosis 13. 07 (7. 66). The year 2007 is omitted because according to the NBER the recession began in December 2007, so 2007 is not a clean “before” or “during” recession year.
Higher (third and fourth) moments are not cyclical
Same in other micro measures – economy seems ‘fractal’: uncertainty rises at every level in recessions Idiosyncratic shocks appear more volatile in recessions at all levels: - industry - firm - plant - product
Industry level quarterly output growth rate (%) Industry growth dispersion (by month) 99 th percentile 95 th percentile 90 th percentile 75 th percentile 50 th percentile 25 th percentile 10 th percentile 5 th percentile 1 st percentile Note: 1 st, 5 th, 10 th, 25 th, 50 th, 75 th, 90 th, 95 th and 99 th percentiles of 3 -month growth rates of industrial production within each quarter. All 196 manufacturing NAICS sectors in the Federal Reserve Board database. Source: Bloom, Floetotto and Jaimovich (2009)
Inter Quartile range of sales growth rate Firm growth dispersion (by quarter) Across all firms (+ symbol) Across firms in a SIC 2 industry Note: Interquartile range of sales growth (Compustat firms). Only firms with 25+ years of accounts, and quarters with 500+ observations. SIC 2 only cells with 25+ obs. SIC 2 is used as the level of industry definition to maintain sample size. The grey shaded columns are recessions according to the NBER. Source: Bloom, Floetotto, Jaimovich, Saporta and Terry (2011)
Density Plant growth dispersion pre & during great recession Sales growth rate Source: “Really Uncertain Business Cycles” by Bloom, Floetotto, Jaimovich, Saporta and Terry (2012) Notes: Constructed from the Census of Manufactures and the Annual Survey of Manufactures using a balanced panel of 15, 752 establishments active in 2005 -06 and 2008 -09. Moments of the distribution for non-recession (recession) years are: mean 0. 026 (-0. 191), variance 0. 052 (0. 131), coefficient of skewness 0. 164 (-0. 330) and kurtosis 13. 07 (7. 66). The year 2007 is omitted because according to the NBER the recession began in December 2007, so 2007 is not a clean “before” or “during” recession year.
Product level price dispersion (by quarter) Source: Joe Vavra (2014, QJE) “Inflation dynamics and time varying volatility”
Suggests recessions can be characterized as a negative first moment and positive second moment shock Normal distribution of TFP shocks Recessionary distribution of TFP shocks
Measuring uncertainty Model Simulation of an uncertainty shock Policy experiment
Technology ● Large number of heterogeneous firms ● Macro productivity and micro productivity follow an AR process with time variation in the variance of innovations ● Uncertainty (σA and σZ) follows a 2 -point markov chain
Capital and labor adjustment costs ● Capital and labor follow the laws of motion: where i: investment δk: depreciation s: hiring δn: attrition ● Based on micro data allow for the full range of adjustment costs ● Fixed – lump sum cost for investment and/or hiring ● Partial – per $ disinvestment and/or per worker hired/fired ● Quadratic – to invest/disinvest and/or hire/fire more rapidly ● Assumed adjustments costs paid on all investment and hiring (even replacement investment and hiring)
Households
Firm’s value function
General equilibrium solution overview ● We have a recursive competitive equilibrium ● Solve numerically as no analytical solution ● Numerical solution approximates μ (the firm-level distribution over z, k and n) with moments, building particularly on Krusell and Smith (1998) and Khan and Thomas (2008)
Baseline calibration of the parameters
Calibration of the six uncertainty parameters
Calibrate six uncertainty parameters to match eight uncertainty moments (four micro and four macro)
Proxies for uncertainty Model Simulation of an uncertainty shock Policy experiment
Simulating an uncertainty shock Simulation: ● Simulate the economy with 20, 000 firms ● Repeat this 500 times and take the average Shock: ● Let the model run for 100 periods ● Then have 10 pre-periods with low uncertainty. Then move to high uncertainty in period 1, then allow uncertainty to evolve as normal ● Simulates the typical business cycle of a temporary increase in uncertainty following a few years of low uncertainty
Output deviation (in logs from value in period 0) An uncertainty shock causes an output drop of about 3. 5%, and a recovery to almost level within 1 year Quarters (uncertainty shock in quarter 1) Source: “Really Uncertain Business Cycles” by Bloom, Floetotto, Jaimovich, Saporta and Terry (2014)
Labor and investment drop and rebound, while TFP slowly drops and rebounds Deviation (in percent from value in quarter 0) Labor Investment “Delay effect” Labor Allocative Efficiency “Delay effect” Consumption “Caution effect” Quarters (uncertainty shock in quarter 1) ?
The pure uncertainty shocks model looks good, but still has some problems in fitting the data Duration: Output below trend for only 4 quarters (too rapid) Consumption: Overshoots in first period Labor: Firing falls in recessions (Ss bands move out) So also add in a 2% negative TFP shock to examine the impact of a negative first moment and positive second moment shock Results looks better and also frankly more realistic (even I doubt recessions are all driven by uncertainty shocks)
Figure 5: Adding a -2% first moment shocks increases the duration and helps to address consumption and firing issues Deviation (in logs from value in period 0) Uncertainty shock Uncertainty & -2% TFP shock Quarters (uncertainty shock in quarter 1)
What is driving this uncertainty effect? Uncertainty matters to the extent there is curvature in the model. Turns out we have curvature in three places: A) Production function (Cobb-Douglas) - Positive medium–run effect (Oi-Hartman-Abel) B) Adjustment costs (lumpy, irreversible) - Negative short-run effect (real options) C) Consumption function (log-linear utility) - Negative medium-run effects (consumption smoothing) Consumer durables or financial constraints would add more sources of curvature (ignored in this paper)
Decomposing the impact of uncertainty on output The positive Oi. Hartman-Abel effect (mostly medium run) 1) Partial Equilibrium, no adjustment costs Output deviation Consumption smoothing dampening the rebound (mostly medium run) 2) Partial Equilibrium, adjustment costs 3) General Equilibrium, adjustment costs The negative real options effect (mostly short run) Quarters (uncertainty shock in quarter 1)
Proxies for uncertainty Model Simulation of an uncertainty shock Policy experiment
Uncertainty alters effectiveness of policy ● What is the impact of stimulus during periods of high uncertainty? ● Analyze the effects of a wage subsidy ● Such a policy is not optimal in our model ● Study how uncertainty alters effectiveness of policy
Our stylized example policy ● Analyze a 1% wages subsidy ● Subsidy is unexpected and applies for 1 quarter ● Funded by lump sum tax on the representative consumer
Figure 8: Policy is about 50% less effective when uncertainty jumps up (the start of the recession) Output Deviation (in logs from value in period 0) 1% wage subsidy in normal times Wage subsidy increases output by 48% less over first 4 quarters if applied when uncertainty shock first hits 1% wage subsidy when uncertainty shock hits Quarters (uncertainty shock in quarter 1)
Proxies for uncertainty Model Simulation of an uncertainty shock Policy experiment Conclusion
Conclusions ● Uncertainty appears to be strongly counter cyclical (at macro and industry level) ● Realistically calibrated model shows: ● Uncertainty can lead to business cycle sized fluctuations in output, investment, hiring and TFP growth ● With additional first moment shock get very large fluctuations and can also match consumption ● Policy impact below-normal when uncertainty hits
One thing I am working on is 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: -9 % • The LOW CASE change in my firm’s sales levels would be: -3 % • The MEDIUM CASE change in my firm’s sales levels would be: 3 % • The HIGH CASE change in my firm’s sales levels would be: 9 % • The HIGHEST CASE change in my firm’s sales levels would be: 15 % Numbers in red are the average response from the pilot on 300 firms
Piloting results look good from testing on a monthly survey on 300 firms: change in sales
One thing I am working on is firm-level surveys 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
Piloting results look good from testing on a monthly survey on 300 firms: probabilities
BACKUP
Simplifying the problem
Old model: output – only micro / macro uncertainty Ignoring micro uncertainty reduces impact by about 40%, ignoring macro uncertainty by about 20%.
To note this paper does not (currently) analyze a number of other potentially important channels ● Consumer durables: (e. g. Romer 1990) ● Finance: (e. g. Christiano, Motto and Rostango (2010), Arrelano, Bai and Kehoe (2011) and Gilchrist, Sims and Zakrajek (2011)) ● Risk: (e. g. Fernandez-Villaverde, Guerron, Rubio-Ramriez and Uribe (2011) and Panousi and Papanikolaou 2011)
Proportion of plants firing Figure 4: The proportion of firms firing increases in recessions when these combine a first and second moment shock Quarters (uncertainty shock in quarter 1)
Deviation (in logs from value in period 0) Figure 6: Interestingly first moment shocks do not impact misallocation – this is driven entirely by uncertainty shocks Uncertainty shock Uncertainty & -2% TFP shock Quarters (uncertainty shock in quarter 1) Note: misallocation defined here as 1 -correlation(labor, TFP)
Figure 4: GE also moderates the fluctuations of investment by moving consumption, but leaves TFP pretty much unchanged Deviation (in logs from value in period 0) Partial Equilibrium General Equilibrium Quarters (uncertainty shock in quarter 1)
But is this simply output driving uncertainty? ? Test this using instruments to predict changes in output - we know β will be negative in OLS (they are correlated) - but is β negative in IV (does output drive uncertainty)? We use two different instruments (finding similar results for both) - Demand reduction for textiles after China joined the WTO - Industry exchange rate changes following Bertrand (2004) 78
Output does not appear to drive uncertainty (OLS is negative but the IV results are positive insignificant) 79
On the basis of the standard business cycle statistics the model looks pretty good
2% first moment shock only generates a smaller drop
The impact of uncertainty on labor hiring/firing thresholds
(back to Census data) We also find uncertainty is also higher within industries in “industry recessions” ● Measure industry uncertainty as the spread of TFP shocks within a SIC 4 -digit industry-year (average 27 plants per industry) ● Regress industry uncertainty on industry growth (output or TFP) ● Include full set of industry and year dummies, so remove all business cycle effects – so entirely a within industry relationship 83
Uncertainty is also higher in industry ‘recessions’ All regressions include full industry and year dummies 84
But SSA data on several million individuals shows rising 3 rd moment but flat 2 nd moment in recessions Guvenen, Ozkan & Song, “The nature of countercyclical income risk” (2014, JPE) Notes: Uses about 5 m obs per year from the US Social Security Administration earnings data
So firms and workers seem to differ in higher moments across recessions – not clear why? Macro, industry, firms, plants and prices Wages Working with Jae Song, David Price and Fatih Guvenen to investigate (David is presenting this on Saturday at 11: 30)
- Slides: 86