Economic Policy Uncertainty in China Since 1949: The View from Mainland Newspapers
Steven J. Davis, Dingqian Liu and Xuguang S. Sheng
Fourth Annual IMF-Atlanta Fed Research Workshop on China’s Economy
Atlanta, 19 September 2019
Overview1. Construct monthly indices of Economic Policy Uncertainty (EPU) &
Trade Policy Uncertainty (TPU) for China:– Follow BBD approach, but use NLP methods to select P terms– Use two leading mainland Chinese newspapers, Renmin Daily
and Guangming Daily, from 1949 onwards2. Display and discuss indices
– Historic context, info content, comparison to other China EPU3. Recent role of trade policy as source of uncertainty4. Use indices to estimate dynamic relationship of policy uncertainty
to output, employment, and investment.5. In progress: TPU and firm-level equity returns in China
What Do Our Policy Uncertainty Measures Seek to Capture?
All of the following:• Uncertainty about who will make economic policy decisions –
e.g., who will win the next election?• Uncertainty about what economic policy actions decision makers
will undertake, and when.• Uncertainty about the economic effects of policy actions – past,
present and future actions• Economic uncertainty induced by policy inaction • Uncertain economic ramifications of national security and other
policy matters that may not be mainly economic in character 2
Selecting Term Sets and Quantifying EPU• Like BBD, use scaled frequency counts of newspaper articles that
contain selected terms about Economics, Policy and Uncertainty.• Like BBD, we rely on a combination of expert judgment and
informal auditing to select the Economy and Uncertainty terms.• We differ in our approach to the selection of Policy terms.
– BBD rely on human readings of 12,000 randomly sampled articles to populate a list of candidate Policy terms. They select the permutation of candidate terms to minimize (false positives + false negatives) in computer-automated classifications compared to human classifications.
– We use NLP tools to select our Policy terms. The NLP approach is much less labor intensive than the BBD approach.
3
How We Select Policy Terms, 1
4
We extract Policy terms from the Annual Government Work Reports (������), which are available for 50 individual years: 1954-1960, 1964, 1975, 1978-2018.
– Delivered by the Premier of China to the public. – Reviews economic policy and its impact in past year and discusses
outlook for economic and social development in the coming year. – See http://english.gov.cn/govtworkreport2019/ for more
information about these reports.
How We Select Policy Terms, 2
5
1. Segment Chinese character strings in the Annual Reports into
individual words, phrases and names.
In written Chinese, the smallest text unit is a character that typically represents
one syllable of a spoken word or a whole word. Chinese sentences are written as a
continuous stream of such characters. (In contrast, the smallest text unit in English
and other Latin languages is an alphabet letter, letters are strung together to form
words, and words in a sentence are separated by spaces.) New words and named
entities (organizations, persons, locations, and so on) are formed by particular
character sequences. There can be more than one way to render a particular
sequence of Chinese characters into specific words, phrases and entities. To deal
with this matter, we first slice the Chinese-language document into words and
multi-word names and phrases following the algorithm of Sun, Wang and Li (2012).
We tried two Chinese word segmentation packages in Python, jieba and pkuseg,
which yielded very similar results.
How We Select Policy Terms, 3
6
2. After word segmentation, we apply a standard NLP algorithm to identify highly relevant terms.
– Apply TextRank algorithm of Mihalcea and Tarau (2004) to identify policy-relevant words and phrases (hereafter, “terms”). TextRank is related to the Brin and Page (1998) algorithm for ranking webpages. It assigns a value to each term based on its overall correlation with other terms in the same underlying document or library.
– Using this algorithm, we identified highly relevant sets of terms in each Annual Report. We then took the union of these term sets.
3. We judgmentally pruned this list of terms to remove non-policy terms and terms with high potential for false positives.
How We Select Policy Terms, 4
7
We are exploring three modifications and alternatives to our current approach for selecting terms in the P set:1. We plan to experiment with allowing the P set to vary across
the three eras described above. 2. We are working with other NLP algorithms for term set
selection. 3. As a robustness check, we plan to construct an expert-driven
choice of P terms by consulting authoritative books and articles that discuss economic policy developments in China.
Category English Terms In Chinese Characters
Uncertainty
uncertain/uncertainty/not certain/unsure/not sure/hard to tell/unpredictable/unknown
�%�/��%/���/��/
2�/2�4*/2��*/2�4!/2�4�/�$
Economics economy/business '"/��
Policy
fiscal/monetary/ China Securities Regulatory Commission/ China Banking Regulatory Commission/ Ministry of Finance/ The People's Bank of China/National Development and Reform Commission / Opening-up/ Reform/Ministry of Commerce/legislation/tax/ national bonds/government debt/central bank/ Ministry of Commerce/ tariff/governmental deficit
,�/-�/+#�/1#�/,�0/��1(/��
��/ ��/�3 /�0/ �/ )/&�/��/���/�(/ '.0/�&/�
�/�
Table 1. Term Sets for Economic Policy Uncertainty in China
8
Table 2. Term Sets for Trade Policy Uncertainty in China
9
0
20
40
60
80
100
120
140
Figure A.1. Articles Per Day by Newspaper and Year
0
10
20
30
40
50
60
70
80
90
Renmin Daily Guangming Daily
1949-1978: Central Planning
1979-1999: Reformand Opening Up
2000-2018:Globalization
10
1949-1978: Central Planning
1979-1999: Reformand Opening Up
2000-2018:Globalization
Renmin Guangming Renmin Guangming Renmin Guangming(a) Articles Per Day 46 29 93 54 109 75(b) Percent that contains a term in E
21.70% 16.75% 34.56% 23.18% 34.17% 30.47%
(c) Percent of E articles that contain a term in U
0.86% 0.93% 0.46% 0.84% 2.27% 3.06%
(d) Percent of E articles that contain a term in U and a term in P
0.36% 0.43% 0.41% 0.57% 1.92% 2.36%
Table 3. Articles by Newspaper and Era
11
Mechanics of EPU Index Construction1. Flag articles in each paper that contain at least one
term in each of the E, P and U term sets.2. Get raw monthly counts of these EPU articles.3. For each newspaper and month, scale the raw EPU
article count by the count of all articles.4. Standardize each newspaper’s series of scaled EPU
counts to the same variability over time.5. Average over papers by month to get EPU index. 6. Multiplicatively normalize the EPU index to 100. Do so
separately in three periods: 1949-1978, 1979-1999, 2000-2018
13
Concerns about Eurozone Insufficient
Domestic Demand
U.S.– Japan Trade Dispute on Autos
(July)
German Reunification
(February)Price Reform and
High Inflation
Declaration of Chinese Enterprise Law
(February)
Political Battle over the Role for Market Forces
Regan Election Victory
(November)
Contradiction between Market and Excessive Centralization
�Oct. - Nov.)
Political Battle over the Role for Market Forces
Regan Election Victory
(November)
Contradiction between Market and Excessive Centralization
�Oct. - Nov.)
Price Reform and High Inflation
Declaration of Chinese Enterprise Law
(February)
Political Battle over the Role for Market Forces
Regan Election Victory
(November)
Contradiction between Market and Excessive Centralization
�Oct. - Nov.)
0
100
200
300
400
500 Concerns about Eurozone Insufficient
Domestic Demand
U.S.– Japan Trade Dispute on Autos
(July)
German Reunification
(February)Price Reform and
High Inflation
Declaration of Chinese Enterprise Law
(February)
Political Battle over the Role for Market Forces
Regan Election Victory
(November)
Contradiction between Market and Excessive Centralization
�Oct. – Nov.)
Figure 2. Economic Policy Uncertainty in China: Reform and Opening-up (1979-1999)
14
15
0
50
100
150
200
250
300
350
400
EPU EPU_SCMP EPU_HL
Figure 4. Comparison of China EPU Indexes
16
0
200
400
600
800
1000
1200
1400
1600
China Accession to WTO(March)
SOEs Reform& Layoffs(March)
Global Financial Crisis(November – March)
Europe Debt Crisis, Downgrade of U.S.
Sovereign Credit Rating(October – November)
Trump Takes Office,U.S. Withdraws from TPP
(March)
Trade War between U.S. and China
Trump Election Victory(January)
China’s 2nd White Paper on US-China Trade Conflict,Rising Brexit Uncertainty
(June)
Figure 5.A. China Trade Policy Uncertainty Index, January 2000 to July 2019
Normalized to 100 from 2000 to 2018
17
Trump Election Victory(January)
Trump Takes Office,U.S. Withdraws from TPP
(March)
US-China’s Tariff Escalation,China Published White Paper: The Facts and China's Position on China-US Trade Friction
(July-October)
Trump’s Tariffs Effective,China’s Retaliation
(March-April)
Escalated Tense on Korean Peninsula and Geopolitics
(March)
0
200
400
600
800
1000
1200
1400
1600
Trump Election Victory(January)
Trump Takes Office,U.S. Withdraws from TPP
(March)
China’s 1st White Paper of Policy Position
(July-October)
Trump’s Tariffs Effective,China’s Retaliation
(March-April)
Escalated Tense on Korean Peninsula Nuclear
Issue & Geopolitics(March)
Surging Protectionism, Unilateralism & Anti-Globalization
(November)
0
200
400
600
800
1000
1200
1400
1600
Trump Election Victory(January)
Trump Takes Office,U.S. Withdraws from TPP
(March)
China’s 1st White Paper on US-China Trade Conflict,
IMF’s Concerns on Global Economy (July-October)
Trump’s Tariffs Become Effective, China’s Retaliation
(March-April)
Escalated Concerns on Korean Peninsula & Geopolitics Tension
(March)
China’s 2nd White Paper on US-China Trade Conflict,
Rising Brexit Uncertainty(June)
Detrimental Effect of Protectionism, Unilateralism and Anti-globalization on
International Market, Firms and Global Economy (November)
Figure 5.B. China Trade Policy Uncertainty Index, January 2015 to July 2019Normalized to 100 from 2000 to 2018
18
19
0
100
200
300
400
500
600
700
800
900
1000
1100
Jan-8
5
Jan-8
7
Jan-8
9
Jan-9
1
Jan-9
3
Jan-9
5
Jan-9
7
Jan-9
9
Jan-0
1
Jan-0
3
Jan-0
5
Jan-0
7
Jan-0
9
Jan-1
1
Jan-1
3
Jan-1
5
Jan-1
7
Jan-1
9
Trad
e Po
licy
Unce
rtain
ty In
dex V
alue
U.S. Trade Policy Uncertainty Index, January 1985 to July 2019
Source: "Measuring Economic Policy Uncertainty" by Scott R. Baker, Nicholas Bloom and Steven J. Davis, as updated at www.policyuncertainty.com. Monthly data normalized to 100 from 1985 to 2009.
Negotiation, Ratification &
Implementation of NAFTA
Brexit
Referendum
Trade Policy Conflicts
Intensify in 2018-19,
Especially Between
the U.S. and China,
Trump
Election
Victory
U.S. Pulls
Out of TPP
Table 4. Trade Policy Share of EPU Articles, Selected Times Periods for Three Major Economies
Time PeriodUnited States Japan China
1987-2015 4 8 162000-2015 2 7 20
NAFTA: January 1992 to June 1995 11 11 10China WTO Accession: Jan 2000 to Dec 2002 3 5 36
November 2016 to December 2018 9 20 39March-December 2018 15 27 48
January-July 2019 12 29 42Note: Table entries report the percent of articles about Economic Policy Uncertainty that discuss trade policy matters in leading newspapers for the indicated countries. They are tabulated from data developed by Baker et al. (2016) for the United States, Arbatli et al. (2019) for China and Davis et al. (2019) for China.
Trade Policy Jolted the U.S. Stock Market in 2018 and 2019
Number of Daily Stock Market
Jumps Greater than |2.5%|
Number Attributed
to Trade Policy News Percent
1900 to 2017 1,103 7 0.6%
2018 to
August 2019 13 5 38.5%
21
Note: This table is a tabulation of results in Baker, Bloom, Davis and Sammon (2019), who consider all daily
jumps in the U.S. stock market greater than 2.5%, up or down, since 1900. They classify the reason for each
jump into 16 categories based on human readings of next-day (or same evening) accounts in the Wall Street Journal. The table reports the number of jumps and the number attributed primarily to news about trade
policy. The five jump dates in the recent period attributed primarily to trade policy, and the corresponding
value-weighted returns on the S&P 500, are 22 March 2018, -2.52%; 26 March 2018, 2.72%; 4 December 2018,
-3.24%; 5 August 2019, -2.98%; and 23 August 2019, -2.59. All but one of the earlier jumps attributed primarily
to trade policy occurred in the 1930s.
Percent of Articles about Equity Market Volatility in Leading U.S. Newspapers that Discuss Trade Policy Matters, 1985 to 2018
0
5
10
15
20
25
30
35
40
1985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018
1985-2015 Mean: 2.7%
NAFTA Negotiations, Agreement,Ratification and Introduction; January1992 to June 1995 Mean: 6.7%
Trump Election, November 2016
Trump Takes Office, Pulls out of TPP,January 2017
Tariff Hikes, Trade Tensions, March-December 2018; Mean: 26.0%
Brexit Referendum, June 2016
Note: Computed from automated readings of newspaper articles about Equity Market Volatility and (Equity Market Volatility + Trade Policy) in 11 major U.S. newspapers. Source: Baker, Bloom, Davis and Kost (2019). 22
Policy Uncertainty Shocks and Economic Activity
• Fit VAR models to monthly and quarterly data for China to obtain dynamic responses of real investment, output, etc. to innovations in China EPU or China TPU.–2000M1 to 2019M3 for monthly data–2000Q1 to 2019Q1 for quarterly data– Select lag lengths based on Schwarz information criteria–All VAR systems include linear trends
• Shocks identified by Cholesky decompositions– We report estimated IRFs to unit standard deviation innovations and
85% confidence intervals23
Variables Considered in Monthly VAR Systems
Variable Transformation Mean Standard Deviation
Real Investment 100 ∗ $n(Nominal Gross InvestmentInvestment De5lator ) 702.93 98.64
Real Exports 100 ∗ ln(Nominal ExportGDP De5lator ) 651.44 47.10
Shanghai StockMarket Index 100 ∗ ln(;;<) 773.10 36.21
Our China EPU Our China EPU 100 68.74
BBD EPU (SCMP) BBD EPU 100 86.83
HL EPU HL EPU 100 34.79
Our TPU Our TPU 100 140.92
24
-5
-4
-3
-2
-1
0
1
2
3
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Our China EPU BBD China EPU (SCMP) HL China EPU
Figure 1. Percent Responses of Real Investment to Alternative China EPU Innovations, Monthly Data
25
Cholesky Ordering: ln(Shanghai Stock Index),China EPU, ln(Gross Real Investment)
Standard deviation of innovationsOur China EPU: 37.31BBD China EPU: 40.37HL China EPU: 17.10
One lag of each variable in VAR system
Figure 2. Percent Responses of Real Investment to Innovations in Our China EPU Measure
26
Baseline specification and Cholesky ordering follow Figure 1
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Baseline 3 Lags Alternative Order1 Alternative Order2
“Alternative Order 1” is China EPU, ln(Shanghai Stock Index), ln(Gross Real Investment)
“Alternative Order 2” isln(Shanghai Stock Index), ln(Gross Real Investment), China EPU
Figure 3. Percent Responses of Real Investment to Our China EPU and Our China TPU Innovations Compared, Monthly Data
27
Cholesky Ordering: ln(Shanghai Stock Index), China EPU, ln(Gross Real Investment)
Standard deviation of innovationsOur China EPU: 37.31Our China TPU: 88.71
One lag of each variable in VAR system
-5
-4
-3
-2
-1
0
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Our China EPU Our China TPU
Figure 4. Percent Responses of Real Exports to Alternative China EPU Innovations, Monthly Data
28
Standard deviation of innovationsOur China EPU: 38.91BBD China EPU: 40.86HL China EPU: 17.26
One lag of each variable in VAR system
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Our China EPU BBD China EPU HL China EPU
Cholesky Ordering: ln(Shanghai Stock Index),China EPU, ln(Real Exports)
Figure 5. Percent Responses of Real Exports to Innovations in Our China EPU Measure
29
Baseline specification and Cholesky ordering follow Figure 4
“Alternative Order 1” is China EPU, ln(Shanghai Stock Index), ln(Real Exports)
“Alternative Order 2” isln(Shanghai Stock Index), ln(Real Exports), China EPU
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Baseline Alternative Order 1 Alternative Order 2
Variables Considered in Quarterly VAR Systems
Variable Transformation MeanStandard Deviation
Real GDP 100 ∗ $n('()$ *+, ) 1128.36 63.64
M2 Growth Rate Shocks from Atlanta Fed based on Higgins
and Zha (2015)
M2 Growth Rate Shocks 0 0.01
Shanghai StockMarket Index
100*ln (SSE) 336 16
Our China EPU Our China EPU 100 64.91
BBD EPU (SCMP) BBD EPU 100 82.96
HL EPU HL EPU 100 33.02
Our TPU Our TPU 106 135.98 30
Figure 6. Percent Responses of Real GDP to Alternative China EPU Innovations, Quarterly Data
31
Standard deviation of innovationsOur China EPU: 28.29BBD China EPU: 37.83HL China EPU: 14.55
One lag of each variable in VAR system
Cholesky Ordering: China EPU, ln(Shanghai Stock Index), Exogenous M2 Growth Rate,ln(Real GDP)
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Our China EPU BBD China EPU (SCMP) HL China EPU
Figure 7. Percent Responses of Real GDP to Innovations in Our China EPU Measure
32
Baseline specification and Cholesky ordering follow Figure 6
Alternative Ordering 1: ln(SSE), China EPU, M2 variable, ln(Real GDP)
Alternative ordering 2: ln(SSE), M2 variable, ln(Real GDP), China EPU
Bivariate VAR orders ln(Real GDP) first-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Baseline Alternative Order1 Alternative Order2
2 Lags Bivariate
Figure 8. Percent Responses of Real GDP to Our China TPU Innovations, Quarterly Data
33
Cholesky Ordering: China TPU, ln(Shanghai Stock Index), Exogenous M2 Growth Rate, ln(Real GDP)
One lag of each variable in VAR system
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Trade Policy Uncertainty and the Stock Return Behavior of Chinese Firms
In Progress
We are investigating the impact of trade policy news events and trade policy uncertainty on equity returns and their volatility for listed Chinese firms as a function of their exposure to U.S. trade and other factors.
34
Extra Slides
35
Figure A.2. Percent of Articles that Contain a Term in E
36
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%Renmin Daily Guangming Daily
Figure A.3. Percent of E Articles that Contain a Term in U and a term in P
37
0%
1%
2%
3%
4%
5%
6%
0%
1%
2%
3%
4%
5%
6%Renmin Daily Guangming Daily
050
100
150
200
250
300
350
1985 1990 1995 2000 2005 2010 2015
Polic
y U
ncer
tain
ty In
dex
Figure 1. US Economic Policy Uncertainty Index, 1985 to July 2019
Source: "Measuring Economic Policy Uncertainty" by Scott R. Baker, Nicholas Bloom and Steven J. Davis, as updated at www.policyuncertainty.com. Monthly data normalized to 100 from 1985 to 2009.
BlackMonday
Gulf War I
Russian Crisis/LTCM
Bush Election
9/11
Gulf War II
Stimulus Debate
Lehmanand TARP
Debt-Ceiling Crisis
Fisc
al C
liff
Eurozone Crises, U.S. Midterm Elections in Novemer 2010
Brex
it
Gov
t. Sh
utdo
wn Trump
Election
Rising tariffs & trade policy tensions, esp. between U.S. and China; scope of conflict broadens
Stee
l& A
lum
inum
Tar
iffs
U.S. Pulls out of TPP
39
25
75
125
175
225
275
325
375
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Figure 2. Global Economic Policy Uncertainty Index, January 1997 to July 2019
Notes: Using data for 21 countries that account for 80% of global GDP at current prices. Normalized to 100 from 1997 to 2015. Sources: Baker, Bloom and Davis (2016), Davis (2016) and PolicyUncertainty.com.
Asian & Russian
Financial Crises
9/11 Gulf War II
Global Financial
Crisis
Eurozone Crises, U.S. Fiscal Fights, China Leadership
Transition European Immigration Crisis
Brexit ReferendumTrump Election
Political turmoil in Brazil, France, South Korea,..., U.S. pulls out of TPP
Brexit confusion continues; political unrest in Italy,France, Turkey; Chinaslowdown worries
U.S.-China trade tensions begin to intensify