The Effect of Political Leaders ’ Educational and Professional Background on Trade Liberalization Evidence from Tariff Rates, Marek Hlaváč, MPP Harvard University Political Economy and Government Bratislava Economic Meeting June 8 th, 2012 Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Overview I examine the effect of political leaders ’ educational and professional backgrounds on trade liberalization, as measured by the level of tariff rates imposed on imported goods and services. I find that, during the time period from 1988 until 2005, countries with university-educated chief government executives imposed lower tariffs than countries whose leaders did not have a university education. Leaders ’ educational background in economics is associated with greater reductions in tariff rates than a background in law. A professional background in the military or as a union leader is associated with higher tariff rates. These results suggest that the educational and professional backgrounds of government leaders can have important effects on trade policy. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Three Hypotheses Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Three Hypotheses Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Three Hypotheses Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Hypothesis 2: A government leader ’ s educational background in economics will be associated with a lower level of tariff rates than an educational background in law. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Three Hypotheses Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Hypothesis 2: A government leader ’ s educational background in economics will be associated with a lower level of tariff rates than an educational background in law. Hypothesis 3: A government leader ’ s professional background in scientific economics will be associated with a lower level of tariff rates than a professional background in law. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypothesis 1 Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypothesis 1 Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Stolper-Samuelson Theorem (1941): gain/loss from trade based on factor abundance (e.g., Mayda, 2005; Mayda and Rodrik, 2005) Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypothesis 1 Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Stolper-Samuelson Theorem (1941): gain/loss from trade based on factor abundance (e.g., Mayda, 2005; Mayda and Rodrik, 2005) Reevaluation by Hainmueller and Hiscox (2006) Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypothesis 1 Hypothesis 1: A government leader ’ s university-level educational qualification will be associated with a reduction in the level of tariff rates, compared to the baseline of not having a university education. Stolper-Samuelson Theorem (1941): gain/loss from trade based on factor abundance (e.g., Mayda, 2005; Mayda and Rodrik, 2005) Reevaluation by Hainmueller and Hiscox (2006) Lack of labor market pressures for government leaders: Ideational channel more likely Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypotheses 2 and 3 Hypothesis 2: A government leader ’ s educational background in economics will be associated with a lower level of tariff rates than an educational background in law. Hypothesis 3: A government leader ’ s professional background in scientific economics will be associated with a lower level of tariff rates than a professional background in law. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypotheses 2 and 3 Hypothesis 2: A government leader ’ s educational background in economics will be associated with a lower level of tariff rates than an educational background in law. Hypothesis 3: A government leader ’ s professional background in scientific economics will be associated with a lower level of tariff rates than a professional background in law. Economics: survey experiment by Hiscox (2006); consensus about efficiency benefits of trade liberalization; experiments about the effect of economics education on attitudes and behavior Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 1: Hypotheses and Theoretical Background Hypotheses 2 and 3 Hypothesis 2: A government leader ’ s educational background in economics will be associated with a lower level of tariff rates than an educational background in law. Hypothesis 3: A government leader ’ s professional background in scientific economics will be associated with a lower level of tariff rates than a professional background in law. Economics: survey experiment by Hiscox (2006); consensus about efficiency benefits of trade liberalization; experiments about the effect of economics education on attitudes and behavior Law: This needs work. So far, Murphy, Shleifer and Vishny (1991) on rent-seeking and growth. Looking for survey data about lawyers ’ attitudes towards trade, regulation in general. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 2: Data and Empirical Strategy Dependent Variable: Tariff Rates Most Favored Nation (MFN) vs. Applied Rates Simple vs. Import Share-Weighted Product Coverage: All, Manufacturing, Primary Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 2: Data and Empirical Strategy Independent Variable of Interest: Leader ’ s Background set of dummies from Dreher et al. (2008) Education: 7 categories Profession: 11 categories Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 2: Data and Empirical Strategy Control Variables Level of Economic Development: real GDP per capita Rate of Economic Growth: lagged real GDP growth Political Regime: democracy dummy based on Polity IV Government Idelogy: omitted due to data availability: suggestions? Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 2: Data and Empirical Strategy List of Countries Included in the Sample AlgeriaChinaIndia Argentina ColombiaIreland Australia Costa RicaIsrael AustriaCzech Republic Italy Bangladesh DenmarkJapan Belgium EcuadorKenya MexicoPortugalSwitzerland MoldovaRomaniaSyria Netherlands Russian Federation Tanzania New Zealand Saudi ArabiaThailand Nicaragua SingaporeTogo Norway SlovakiaTunisia BoliviaEgyptLebanon PanamaSloveniaTurkey BrazilFinlandMadagascar ParaguaySouth AfricaUnited Kingdom BulgariaFranceMalaysia PeruSpainUnited States CanadaGermanyMaliPhilippines Sri LankaUruguay ChileGreeceMauritius PolandSwedenVenezuela Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 2: Data and Empirical Strategy Summary Statistics VariableSourceUnitObservationsMeanStandard Deviation GDP per capitaWDI (2011)constant2005 inter- 1, national dollars, PPP, thousands GDP growth rate t−1 WDI (2011)percent, annual1, DemocracyPolity IV (2011)dummy (0/1)1, EducationDreher et al.(2008)dummy (0/1) — Unknown1, — Not University1, — Economics1, — Law1, — Politics1, — Natural Science1, — Other University1, ProfessionDreher et al.(2008)dummy (0/1) — Unknown/None1, — Entrepreneur1, — White Collar1, — Blue Collar1, — Union Executive1, — Science (Economics)1, — Science (Other)1, — Law1, — Military1, — Politician1, — Other1, Note: One observation represents a country-year. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, /18

Section 2: Data and Empirical Strategy Empirical Model Ordinary Least Squares (OLS) with the following specification: TariffRate i,t = αControls i,t + βBackground i,t + γYear t + i,t,(1) where Controls i,t is a vector of control variables, Background i,t is a vector of education or professional background dummies, and is a well-behaved stochastic error term. The subscripts i and t index countries and years, respectively. In all specifications, a set of year dummies (Year t ) is included to account for changes to tariff rate levels that affect all countries in a given year: attitudes towards governance, multilateral trade negotiation rounds? Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 3: Estimation Results Estimation Results: All Products, Education Dependent Variable: Tariff Rate, All Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.790 ∗∗∗ −0.527 ∗∗∗ −0.953 ∗∗∗ −0.610 ∗∗∗ (0.132)(0.107)(0.127)(0.108) (GDP per capita) ∗∗∗ −0.008 ∗∗∗ ∗∗∗ ∗∗∗ (0.003)(0.002)(0.003)(0.002) GDP growth rate t− ∗ ∗∗ (0.094)(0.088)(0.092)(0.089) Democracy −4.451 ∗∗∗ −2.861 ∗∗∗ −4.284 ∗∗∗ −3.365 ∗∗∗ (1.128)(1.013)(1.008)(1.015) Education — Economics −3.172 ∗∗∗ −5.219 ∗∗ −2.915 ∗∗∗ −4.965 ∗ (0.685)(2.593)(0.638)(2.597) — Politics −2.285 ∗∗∗ −4.243 ∗ −2.233 ∗∗∗ −3.985 (0.639)(2.460)(0.586)(2.463) — Law−0.893−2.862−0.137−2.723 (0.709)(2.435)(0.686)(2.438) — Natural Science −2.485 ∗∗∗ −4.764 ∗ −2.376 ∗∗∗ −4.549 (0.768)(2.752)(0.803)(2.769) — Other University −3.197 ∗∗∗ −4.297 ∗ −2.509 ∗∗∗ −3.930 ∗ (1.003)(2.204)(0.917)(2.209) Constant ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (1.697)(1.857)(1.504)(1.861) Year effectsYes Observations698 Adjusted R-squared Notes:Statistically significant at the ***1, **5, *10 percent level. Heteroskedasticity-robust standard errors are in parentheses. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 3: Estimation Results Estimation Results: All Products, Profession Dependent Variable: Tariff Rate, All Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.913 ∗∗∗ −0.645 ∗∗∗ −1.092 ∗∗∗ −0.728 ∗∗∗ (0.160)(0.118)(0.152)(0.119) (GDP per capita) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.004)(0.003) GDP growth rate t− ∗∗ ∗∗ (0.091)(0.088) (0.089) Democracy −3.189 ∗∗∗ −2.647 ∗∗∗ −2.966 ∗∗∗ −3.035 ∗∗∗ (1.116)(0.824)(1.025)(0.824) Profession — Entrepreneur (1.132)(1.006)(1.406)(1.097) — White Collar (0.712)(0.710)(0.648)(0.690) — Blue Collar ∗ ∗∗ (1.624)(1.607)(1.687)(1.511) — Union Executive ∗∗∗ ∗∗ ∗∗∗ ∗∗ (0.863)(1.008)(0.871)(1.009) — Science (Economics)−1.033−0.483 −1.426 ∗∗ (0.791)(0.877)(0.760)(0.880) — Science (Other)− − (0.732)(0.855)(0.799)(0.880) — Law ∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.661)(0.642)(0.668)(0.637) — Military ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (1.561)(1.423)(1.376)(1.419) — Politician ∗∗∗ ∗∗ ∗∗∗ ∗∗ (0.733)(1.392)(0.696)(1.389) Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, /18

Section 3: Estimation Results Estimation Results: Manufactured Products, Education Dependent Variable: Tariff Rate, Manufactured Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.835 ∗∗∗ −0.619 ∗∗∗ −0.994 ∗∗∗ −0.719 ∗∗∗ (0.138)(0.114)(0.130)(0.116) (GDP per capita) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.003) GDP growth rate t− ∗∗ ∗ (0.100)(0.087)(0.097)(0.089) Democracy −4.620 ∗∗∗ −3.767 ∗∗∗ −4.425 ∗∗∗ −4.237 ∗∗∗ (1.179)(0.959)(1.036)(0.966) Education — Economics −2.794 ∗∗∗ −2.410 ∗∗∗ −2.726 ∗∗∗ −2.161 ∗∗∗ (0.698)(0.537)(0.656)(0.546) — Politics −2.108 ∗∗∗ −1.676 ∗∗∗ −2.160 ∗∗∗ −1.473 ∗∗∗ (0.659)(0.543)(0.608)(0.551) — Law− (0.748)(0.564)(0.713)(0.576) — Natural Science −2.191 ∗∗∗ −1.528 ∗∗ −2.182 ∗∗∗ −1.355 ∗ (0.784)(0.700)(0.842)(0.757) — Other University −2.507 ∗∗ −1.879 ∗∗ −2.134 ∗∗ −1.516 ∗ (1.087)(0.867)(0.958)(0.881) Constant ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (1.845)(1.474)(1.567)(1.485) Year effectsYes Observations698 Adjusted R-squared Notes:Statistically significant at the ***1, **5, *10 percent level. Heteroskedasticity-robust standard errors are in parentheses. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 3: Estimation Results Estimation Results: Manufactured Products, Profession Dependent Variable: Tariff Rate, Manufactured Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.994 ∗∗∗ −0.782 ∗∗∗ −1.150 ∗∗∗ −0.882 ∗∗∗ (0.167)(0.146)(0.156)(0.147) (GDP per capita) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.004)(0.003)(0.004)(0.003) GDP growth rate t− ∗ ∗∗ ∗∗ (0.097)(0.084)(0.092)(0.085) Democracy −3.186 ∗∗∗ −2.478 ∗∗∗ −3.015 ∗∗∗ −2.848 ∗∗∗ (1.172)(0.956)(1.057)(0.961) Profession — Entrepreneur ∗ ∗∗ (1.205)(1.064)(1.495)(1.158) — White Collar ∗ ∗ (0.658)(0.585)(0.647)(0.582) — Blue Collar ∗∗ ∗∗ (1.659)(1.369)(1.768)(1.314) — Union Executive ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.802)(0.666)(0.805)(0.682) — Science (Economics)−0.833−0.453 −1.324 ∗ −0.253 (0.763)(0.708)(0.773)(0.720) — Science (Other)− − (0.758)(0.734)(0.838)(0.776) — Law ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.656)(0.597)(0.690)(0.617) — Military ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (1.667)(1.400)(1.426)(1.397) — Politician ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.708)(0.610)(0.701)(0.615) Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, /18

Section 3: Estimation Results Estimation Results: Primary Products, Education Dependent Variable: Tariff Rate, Primary Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.631 ∗∗∗ −0.415 ∗∗∗ −0.801 ∗∗∗ −0.475 ∗∗∗ (0.152)(0.148)(0.129)(0.147) (GDP per capita) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.004)(0.003) GDP growth rate t− ∗ ∗ (0.085)(0.118)(0.079)(0.118) Democracy −3.895 ∗∗∗ −0.648 −4.194 ∗∗∗ −1.321 (1.313)(1.615)(1.356)(1.606) Education — Economics −4.459 ∗∗∗ − ∗ −4.202 ∗∗∗ −9.798 ∗ (0.958)(5.825)(0.951)(5.826) — Politics −2.886 ∗∗∗ −9.002 −2.745 ∗∗∗ −8.574 (0.872)(5.548)(0.810)(5.546) — Law −3.674 ∗∗∗ −9.016 ∗ −3.265 ∗∗∗ −8.710 (0.900)(5.448)(0.897)(5.447) — Natural Science −3.383 ∗∗ − ∗ −3.571 ∗∗∗ − ∗ (1.317)(6.153)(1.077)(6.159) — Other University −5.578 ∗∗∗ −8.910 ∗ −5.008 ∗∗∗ −8.474 ∗ (1.161)(4.775)(1.229)(4.776) Constant ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (1.770)(3.431)(2.053)(3.427) Year effectsYes Observations698 Adjusted R-squared Notes:Statistically significant at the ***1, **5, *10 percent level. Heteroskedasticity-robust standard errors are in parentheses. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18

Section 3: Estimation Results Estimation Results: Primary Products, Profession Dependent Variable: Tariff Rate, Primary Products Most Favored Nation, Applied, simpleweightedsimpleweighted GDP per capita −0.629 ∗∗∗ −0.420 ∗∗∗ −0.827 ∗∗∗ −0.479 ∗∗∗ (0.165)(0.130)(0.142)(0.129) (GDP per capita) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ (0.004)(0.003) GDP growth rate t− ∗∗ ∗∗ (0.083)(0.128)(0.076)(0.128) Democracy −3.208 ∗∗ −2.601 ∗∗∗ −3.448 ∗∗∗ −3.053 ∗∗∗ (1.266)(0.931)(1.312)(0.928) Profession — Entrepreneur−0.120−3.166−0.401−2.614 (1.446)(2.191)(1.268)(2.180) — White Collar−1.539 −3.584 ∗ −1.670 −3.235 ∗ (1.317)(1.894)(1.021)(1.815) — Blue Collar (1.760)(3.035)(1.502)(2.790) — Union Executive2.050− −1.891 (1.534)(3.218)(1.965)(3.165) — Science (Economics)−1.651−2.524 −1.872 ∗ −2.180 (1.225)(1.981)(0.993)(1.933) — Science (Other) −1.910 ∗ −2.583 −1.574 ∗ −2.116 (1.146)(1.952)(0.946)(1.914) — Law −2.313 ∗∗ −3.170 ∗ −1.260 −3.159 ∗∗ (1.119)(1.683)(0.893)(1.598) — Military ∗∗∗ ∗∗∗ (1.772)(2.435)(1.785)(2.397) — Politician (1.155)(3.237)(0.942)(3.200) Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, /18

Conclusion Product Coverage Hypothesis All ManufacturedPrimary 1: university-level education leads to lower tariff rates yes 2: economics education associated with lower tariff rates than law educationyes no 3: economics profession associated with lower tariff rates than law professionyes no Avenues for future research: What is special about primary products? Other outcome variables: bound rates, binding coverage, etc. Marek Hlavac(Harvard)Leader Background & Trade LiberalizationJune 8, / 18