Love Me, Love My Dog: Effects of Attitudes on Trade and FDI. University of Hong... · LOVE ME, LOVE...
Transcript of Love Me, Love My Dog: Effects of Attitudes on Trade and FDI. University of Hong... · LOVE ME, LOVE...
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LOVE ME, LOVE MY DOG: EFFECTS OF ATTITUDES ON
TRADE AND FDIXiaohua Bao, Qing Liu, Larry D. Qiu and Daming Zhu
June 13‐14, 2017
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Introduction
• “China's Xi turns to panda diplomacy to seek EU trade deal”, Reuters, March 29, 2014.
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• People have different attitudes towards foreign countries.
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Attitudes and our research question
• The share of people from the U.S. in the survey that chose “very favorable” towards Canada is 37.41%, but only 1.97% towards Iran in 2007.
• The share of people from the U.S. in the survey choosing “very favorable” towards Japan was 16.78% in 2005 but 23.4% in 2008.
• Research questions:• Does it matter how foreigners view us? • Does attitude affect international trade?
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This paper
• This paper aims at investigating the impact of attitudes on trade and FDI.
• By adding attitude as an explanatory variable to the traditional gravity model of international trade, we find that a more positive attitude of the reporting country toward a responding country raises bilateral trade between the two countries, and a more negative attitude reduces bilateral trade.
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• Specifically, a one‐percentage point increase in the share of positiveattitude of people in country A towards country B raises country A’s imports from country B by 0.38% and country A’s exports to country B by 0.49%;
• a one‐percentage point increase in the share of negative attitude of people in country A towards country B reduces country A’s imports from country B by 0.19% and country A’s exports to country B by 0.23%.
• Similar results but with different magnitudes are found for the effects of attitudes on bilateral FDI.
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Literature
• Our paper belongs to the growing literature which examines the impacts of cultural factors on economic and financial outcomes.
• The tradition factors in gravity model• Economic factor (e.g., GDP)• Connectivity (e.g., distance, common language)
• Now, culture• How to measure?
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• One group of studies, which the present study is most related to, explores the culture effects on bilateral economic activities such as international trade and FDI
• Another group investigates the cultural effects on individualcountries’ economic performance (e.g., economic growth) and non‐economic aspects (e.g. fertility)
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• The culture aspect in Guiso et al. (QJE 2009) is trust. • Surveys conducted by Eurobarometer which covers many but not all European countries.
• Lower bilateral trust between two European countries results in less trade, less FDI, and less portfolio investment between the two countries
• Michaels and Zhi (AEJ 2010) focus on public attitudes. • Measure changes in public attitudes using the decline in the fraction of US Gallup Poll respondents who viewed France favorably from 83% in February 2002 to 35% in March 2003, and a drop of US favorability viewed by France from 63% to 43% from the Pew Global Attitudes Project.
• Worsening attitudes reduce bilateral trade about 8 to 9 percentage points.
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• National conflicts
• Blomberg and Hess’s (REStat 2004)• Many types of violence such as war, terrorism, revolutions and inter‐ethnic fighting from 177 countries during 1968‐1999
• Equivalent to 30% tariff• Glick and Taylor (REStat 2010)
• Focus on wars using a sample covering a large number of countries over the period 1870‐1997.
• Wars have large, persistent, and negative effects of war on bilateral trade. • Che et al. (JIE 2015)
• The Japanese invasion of China during 1937‐1945 reduces contemporary trade and investment between China and Japan
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• More on measure of culture (internal vs external)• Tabellini (JEEA 2010) measures culture by aggregating individual responses collected in the opinion polls of the World Value Surveys (WVS) in the 1990s, including individual values and beliefs (such as trust), respect for others, and confidence.
• Guiso et al. (JME 2003) also make use of the WVS data, but focuses on religion.
• Barro and McCleary (ASR 2003) investigate the effects of church attendance and Religion beliefs on economic growth.
• Fernandez and Fogli (NBER 2005) use past female labor force participation and total fertility rates from the country of ancestry to proxy culture.
• Felbermayr and Toubal (EER 2010) construct a measure of cultural proximity based on bilateral score data from the Eurovision Song Contest.
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• A distinguishing feature of our paper is that it deals with attitudesand the largest set of countries to examine the effects on imports, exports and FDI.
• More general • Differential effects for different country groups
• Important policy implications• Barriers to trade
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Model Specification
• Gravity model with attitude
0 1 2 3
Importijt
Attitudeijt,
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.
is the log value of import
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Data
• Pew Research Center’s Global Attitude Projects. • Conducts public opinion surveys around the world with over 400,000 interviews.
• A lot of information but we extract information related to attitude. • In the surveys, respondents are asked to report how much they like a specific country or the citizen of that country:
• Please tell me if you have a (i) very favorable, (ii) somewhat favorable, (iii) somewhat unfavorable, (iv) very unfavorable opinion, or (v) don’t know or refuse.
• Based on the survey data, we construct our attitude variable as follows. • We give a value 2 to “very favorable”, 1 to “somewhat favorable”, ‐1 for “somewhat unfavorable”, and ‐2 for “very unfavorable”.
• We calculate the fraction of each type. • We use the fractions as the weights to calculate the weighted value.
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• Specifically, let a1, a2, a3 and a4 denote the fractions of responses in “very favorable”, “somewhat favorable”, “somewhat unfavorable” and “very unfavorable”, respectively.
• We construct three different attitude variables. • The first one is the overall attitude, defined as Attitude = 2a1 + a2 ‐ a3 ‐ 2a4, which is our core attitude variable.
• The second one is the favorable (or positive) attitude variable, defined as Attitude‐P = a1 + a2, which is the sum of the fractions of “very favorable” or “somewhat favorable”.
• And the third is the unfavorable (or negative) attitude variable, defined as Attitude‐N = a3 + a4, which is the sum of the fractions of “very unfavorable” or “somewhat unfavorable”.
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• Example: • US towards India in 2008• a1 = 0.17, a2 = 0.449, a3 = 0.12, and a4 = 0.028. • Attitude = 0.613, Attitude‐P = 0.619, and Attitude‐N = 0.148.
• The Pew Research Center’s Global Attitudes Projects cover 59 reporting countries and 27 corresponding countries starting from the year 2002.
• We use the period from 2002 to 2013.
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(1) (2) (3) (4) (5)
Variable Obs Mean Std. Dev. Min Max
lnImportij 1321 21.7403 2.4010 6.9679 26.6342
lnGDPi 1519 28.3083 1.4286 24.5497 30.4505
lnGDPj 1519 26.7620 1.8726 21.9301 30.4505
lnDistij 1519 8.4396 0.8649 4.7104 9.8755
a1 1519 0.1247 0.1254 0 0.8892
a2 1519 0.3548 0.1507 0.0080 0.7543
a3 1519 0.2318 0.1184 0.0033 0.5629
a4 1519 0.1573 0.1464 0.0033 0.9350
Attitude 1519 0.0577 0.6185 -1.8880 1.8317
Attitude-P 1519 0.4794 0.2117 0.0120 0.9553
Attitude-N 1519 0.3891 0.2052 0.0067 0.9880
Table 1: Summary statistics
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Regression Results
• Due to zero bilateral trade issue, we follow Silva and Tenreyro (2006) to use Poisson Pseudo Maximum Likelihood (PPML) Estimator in our main analysis.
• However, the results are also robust to the use of the OLS method.
• Result (Table 2):• Positive and significant• As in column (5), a one‐standard‐deviation increase in the attitude raises imports by 6.54% (= 0.1058*0.6185 as the standard deviation of Attitude is 0.6185 from Table 1)
• Columns (6) and (7) use different Attitude variables.
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Robustness Checks
• Table 3• Define and use
Attitude‐count = (n1 +2n2 +3n3 + 4n4)/(n1 +n2 +n3 + n4).• Also use
ln(Attitude‐count) • In column (3), we include importer‐year and exporter‐year fixed effects
• Column (4): OLS• Column (5): data of j’s export to i.
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(FEs)
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Endogeneity and IV Estimation
• We propose three methods to address the issue of possible reverse causality.
• First, we use the one‐year‐lagged attitudeAttitude‐lag or Attitudeij(t‐1)
• We can also use the average value of attitude over the entire period 2002‐2013
Attitude‐avg or Attitudeij(2002‐13)
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• Second, we further check the potential endogeneity problem with a “falsification” test. We use k’s attitude towards j as an “IV” for i’s attitude towards j. That is, we substitute Attitudeijt in model (1) by the “IV” Attitudekjt. We construct the “IV” for country i using the attitude of the country which has the import value most similar to country i. The regression results are reported in column (3): not significant.
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• Third, we follow Martin, et al. (2010) to construct our IV using military conflicts between two countries.
• The Correlates of War project (http://www.correlatesofwar.org/) maintains a database on Militarized Interstate Disputes (MID) which lists all bilateral interstate conflicts from 1816 to 2010, and quantifies their intensity on a scale from 1 to 5
• 1 = no militarized action, 2 = threat to use force, 3 = display of force, 4 = use of force, and 5 = war.
• Our IV is the 20‐year lagged cumulated number of MID since a given time, which we use 1914 (the first year of WWI) and 1939 (the first year of WWII), respectively.
• We restrict the MID to those with scale above 3.
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• We first find that MID is negatively correlated with Attitude in the first stage, then in the second stage use Attitude instrumented by MIDto run the regression.
• Our IV estimation results are reported in column (4) for the MID with the starting year of 1939 and column (5) for the MID with the starting year of 1914.
• We also check whether the effect of MID works through attitude: Yes, columns (6)‐(7).
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Heterogeneous Effects
• Different goods trade
• Different country groups
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Other Impacts
• On exports
• On outward FDI
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(FEs) (MID IV)
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Agenda
• Introduction• Empirical model• Data• Basic result• Robustness and IV• Heterogeneous effects• Other effects• Conclusion
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Conclusion
• Cultures affect economic exchanges between countries.
• This paper focuses on one aspect of cultures, and that is, attitudes.
• We find that good attitudes towards another country raise bilateral trade (both imports and exports) as well as FDI.
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