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Do Poor Institutions Create More Losers from Globalisation?

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Christopher A. Hartwell
President, CASE
10 October 2017
Open seminar at Eesti Pank

Published in: Economy & Finance
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Do Poor Institutions Create More Losers from Globalisation?

  1. 1. Christopher A. Hartwell President, CASE 10 October 2017 Do Poor Institutions Create More Losers from Globalisation? www.case-research.eu
  2. 2. Outline 1. Some Background on Trade 2. The Institutional Link 3. Hypotheses 4. Identification Strategy and Data 5. Results 6. Discussion and Conclusions
  3. 3. Background • The benefits of trade liberalization one of the least controversial maxims of economics – Theoretically, empirically, morally trade is an unmitigated good • However, political and economic effects occur in the opening to trade – Disruptions affects specific sectors, creating “losers” – Governments may try to compensate the “losers” via policies funded by the “winners”
  4. 4. Trade and “Losers” Figure 1 – Trade as a % of GDP in the World and High/Low Income Countries 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.4 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 AverageGiniCoefficient Tradeas%ofGDP Average Gini Coefficient (rhs) World High income Low income Source: World Development Indicators and Author’s Calculations from Solt (2016)
  5. 5. Background (II) • Compensatory policies may be less effective in convincing the populace of trade’s value if the number of losers reaches a critical mass – The dispersed economic benefits of trade may be overwhelmed by the concentrated costs • Indeed, if distortions in an economy generate an artificially large number of losers: – Fiscal policies could be strained or, in the worst case, unable to cope with the compensation process – Backsliding could occur in future trade liberalization
  6. 6. The Institutional Link What factors may create abnormal levels of “losers” from trade? Poor institutions, first and foremost! • Complex interdependencies between trade and institutions – Acemoglu et al. (2005) detail how trade influenced institutional development in Europe in the 16th through the 19th centuries – Good institutions contribute to trade openness itself, creating a virtuous circle (Greif 1993, Dollar and Kraay 2003)
  7. 7. Hypotheses H1: Countries with poor institutions create more losers from trade than those with good institutions • Poor institutions can generate “unnecessary” losers from trade by creating distortions in an economy H2: Countries with more losers from trade have slower trade liberalisation progress • Effects of poor institutions are dynamic, can create a vicious cycle where increased inequality feeds back into institutional deterioration and this slows further openness
  8. 8. Hypotheses – A Graphic Representation Trade Openness/Globalization Institutions Losers from Globalization
  9. 9. First Things First: Who is a “Loser?” • No real accepted definition – O’Brien and Leichenko (2003) note that terms “winners” and “losers” have both political and economic meanings • Economists generally focus on class, income level, and/or source of income – Effect of trade on income distributions or on segments of society • Common usage of poorest 1% or 5% as a reference point for potential losers – Poorest often located in informal economy and cannot reap gains from globalisation (alternately more exposed to shocks) – Unfortunately, empirical evidence is less uniform on the effects of trade on the poorest – Milanovic (2013) finds that the losers from globalisation are those between the 75th and the 90th percentile in income globally, whose incomes grew much more slowly than other percentiles (bottom 5% grew much more quickly!) – Other research (Dollar and Kraay 2004, Topalova 2010) shows that the poor are a diverse lot who tend to benefit from trade
  10. 10. First Things First: Who is a “Loser?” (II) • Trade literature instead focuses on skill levels – Tend to be correlated with but do not exclusively overlap with income levels • Davidson and Matusz (2006) an example – two groups of losers from liberalisation: “stayers” who are stuck in the low-tech sector and “movers” who go through costly training to switch from the low- to the high-tech sector. • However, effects of globalisation on workers conditional on level of development – Low-skilled workers in high-income countries have greater bargaining power than low-skilled workers in low- income countries (Rudra 2005) – This means wages remain high (Lawrence and Slaughter 1993)
  11. 11. First Things First: Who is a “Loser?” (III) • Losers not necessarily the poor but the relatively disadvantaged – Graham (2001): losers are newly vulnerable members of the middle class who perceive that gains from have gone disproportionately to top incomes – Kriesi et al. (2006): individuals who have a strong sense of identity with their national community likely to perceive themselves as losers under globalisation due to “de- nationalisation” which accompanies trade liberalisation.
  12. 12. Settling on a Definition • If “loss” under globalisation is a relative phenomenon, then relative metrics are needed to measure it • This paper uses within-country income inequality, measured by Gini coefficient (taken from Solt 2016), as proxy for losses due to trade • Several benefits: – Workers or industries disadvantaged by globalisation will fall behind, creating a widening gap with those who have successfully taken advantage of globalisation – Mitigates reality that development level of an economy matters for determining the impact of trade; benchmark is not against an idealised representative worker, but against the country’s own income distribution – Econometrically, there are numerous sources of income inequality but they may be controlled for (leaving much of any widening gap attributable directly to trade)
  13. 13. Institutions and “Losers”: the Theory • Globalisation is a process, usually a discrete policy change but with long-term effects – Way in which an economy reacts depends upon the incentives prevalent throughout the country • Institutions are the creators, enforcers, and guarantors of various incentive structures in a country – Institutions mediate returns to factors of production precisely via power they exert on incentives, altering relative prices through information dispersion or negatively via transaction costs or cultural and organisational barriers – Poor institutions have an adverse effect on incentives and thus retard the gains from trade (Kapstein 2000). • Strong empirical connection between poor institutional quality and inequality globally (Chong and Calderon 2000; Chong and Gradstein 2007; Lin and Fu 2016)
  14. 14. The Reality of “Institutions” • Institutions are not an amorphous lump or a black box – differentiated by form and function: – Political: Pertaining to distribution of political power – Economic: Designed or arising to maximize the utility of principals in the economic sphere, by solely influencing and mediating economic outcomes pertaining to distribution of resources. – Social: Institutions not explicitly concerned with political power or economic incentives but geared towards behavior and norms outside these spheres
  15. 15. Which Institutions would Mitigate Trade- Related Losses? • Labour market institutions a good candidate for affecting gains from trade – Rigid markets and EPL can impact reallocation of resources – Minimum wage and EPL could also help lower poverty for insiders • Property rights another important guarantor of incentives – Contract enforcement allows poor to extract rents as well as the rich – Property rights allows for income mobility (collateralizing assets) and actual mobility (sale and disposal of assets) – Inequality can decrease property rights as rich cling to their spoils (Sonin 2003)
  16. 16. Which Institutions would Mitigate Trade- Related Losses? (II) • Democracy, a key political institution, also has a role to play – Polities choose their labour market institutions, as noted – Also allows for choice of fiscal policies for redistribution and “credible commitment” for future redistribution • Empirical evidence mixed – Simpson (1990) found a U-shaped relationship between democracy and inequality – Rodrik (1998) shows stronger linear association between democracy and lessened inequality – Inequality may actually be lower in authoritarian regimes, in order to buy off social unrest (Gradstein and Milanovic 2004) – Reuveny and Li (2003) show that democracy in the presence of economic openness reduces income inequality over a sample of 69 countries
  17. 17. Feedback Effects Democracy • Chong and Gradstein (2007) demonstrate feedback effects between institutions and inequality, showing inequality is directly tied to poorer-quality political institutions • Democracy may also amplify the effects of trade-related losses onto future trade liberalization – too many losers means a polity less willing to vote for future liberalization Property Rights • Keefer and Knack (2002) note that social polarisation is bad for property rights, showing that inequality leads to a more interventionist government with short time-horizons • Politically-created inequality creates further barriers to entry in the form of weak property rights
  18. 18. Methodology and Data • A three-legged triangle of influence needs an appropriate system of equations • This paper uses a 3SLS approach to model the interlinked influences of trade, inequality, and institutions • Bootstrapped standard errors and country fixed effects • Data for 196 countries from 1960-2015 – Compiled from a large number of publicly available sources, including Solt (2016), the World Bank’s World Development Indicators (WDI), the International Country Risk Guide (ICRG), the IMF’s International Financial Statistics (IFS), previous research, and others – Gaps in the data and in institutional metrics generally leave us with a set of between 1,000 – 2,000 observations
  19. 19. Methodology (II) 3SLS approach has a separate equation for each leg of the triangle (1) 𝐼𝑁𝐸𝑄𝑖𝑡 = 𝛼𝑇𝑅𝐴𝐷𝐸𝑖𝑡 + 𝛽𝐼𝑁𝑆𝑇𝐼𝑇𝑈𝑇𝐼𝑂𝑁𝑆𝑖𝑡 + 𝛾𝑇𝑅𝐴𝐷𝐸 ∗ 𝐼𝑁𝑆𝑇𝐼𝑇𝑈𝑇𝐼𝑂𝑁𝑆𝑖𝑡 + 𝛿𝑋𝑖𝑡 ′ + 𝜇 𝑡 + 𝜖𝑖𝑡 (2) 𝐼𝑁𝑆𝑇𝐼𝑇𝑈𝑇𝐼𝑂𝑁𝑆𝑖𝑡 = 𝛼𝑇𝑅𝐴𝐷𝐸𝑖𝑡 + 𝛽𝐼𝑁𝐸𝑄𝑖𝑡 + 𝜌𝑌𝑖𝑡 ′ + 𝜇 𝑡 + 𝜖𝑖𝑡 (3) 𝑇𝑅𝐴𝐷𝐸𝑖𝑡 = 𝛼𝐼𝑁𝑆𝑇𝐼𝑇𝑈𝑇𝐼𝑂𝑁𝑆𝑖𝑡 + 𝛽𝐼𝑁𝐸𝑄𝑖𝑡 + 𝜏𝑍𝑖𝑡 ′ + 𝜇 𝑡 + 𝜖𝑖𝑡
  20. 20. Methodology (III): Measuring Inequality Two approaches to capturing inequality • Within-country inequality – Mentioned before, use of Gini coefficient from Solt (2016) – Solt generates 100 country observations per year to encapsulate uncertainty, averages used here • Sigma convergence (Sala-i-Martin 1996) – Li et al. (1998) note much variation in inequality does not actually occur within-country but across countries – Dispersion metric equal to the standard deviation of a country’s log per capita GDP versus all other countries in that particular year – Used to capture divergence of entire country due to trade-related losses or gains
  21. 21. Methodology (IV): Measuring Institutions • Property Rights – ICRG Investor Protection • Risk of expropriation, contract enforcement, and repatriation of profits, scored on a scale from 0-12 with higher numbers indicating better protection – Contract-intensive money • Proportion of money held outside the formal banking sector: 𝑀2 − 𝐶 𝑀2 • Higher numbers indicate more money in the formal financial sector, and thus higher property rights • Clague et. al (1999:200) note, “Where citizens believe that there is sufficient third-party enforcement, they are more likely to allow other parties to hold their money in exchange for some compensation.” • Democracy – ICRG Democratic Accountability indicator • Extent of responsiveness of a government to its people, rated from 1 to 6, with higher number indicating more democracy
  22. 22. Methodology (V) Equation 1: Inequality • GDP per capita (level and squared, to capture Kuznets-type effects) • Labour market efficiency (national unemployment rate) • Democracy • Government spending • Natural Resource Rents • Female Mortality • Human Capital (schooling and WEF HCI) Equation 2: Property Rights • Natural Resource Rents • Latitude • Level of democracy • Population size • Financial market development • Labour market efficiency • Initial GDP per capita • Initial levels of education (gross secondary enrollment) • Dummies for legal origin (La Porta et al. 2008) Equation 3: Trade Openness • Country Size • Population • Latitude • Landlockedness • Resource Endowment • Human Capital • Labour Market Efficiency • Investment Potential (initial levels of schooling) • Government Spending • Access to Finance and/or Financial Depth (bank deposits to GDP) • Structure of the Economy (agriculture as a percentage of GDP). Different Control Set for Each Equation
  23. 23. Results: Baseline/Headline Average Gini Coefficient 1 2 3 4 Trade openness 0.02 -0.001 0.005 0.002 4.61*** 1.22 3.36*** 1.25 INSTITUTIONAL VARIABLES Property rights (contract-intensive money) 0.21 0.05 5.11*** 3.30** Property rights (Investor Protection) 0.02 0.02 2.10** 1.46 Democratic accountability -0.03 -0.01 -0.01 0.10 6.45*** 2.53*** 0.59 2.48** Contract-intensive money*Trade Openness -0.02 -0.004 4.53*** 2.29** Investor Protection*Trade Openness 0.0001 0.0001 0.53 0.41 Democratic Accountability*Trade Openness -0.0003 -0.0005 2.38** 1.80* C -1.27 -0.20 -0.52 -0.55 4.23*** 1.84* 3.51*** 4.63*** n 1105 1932 918 1594 R-squared 0.22 0.18 0.57 0.38
  24. 24. Interpretation of Results Within-country inequality • Trade openness has a marginal positive impact on inequality but only with inclusion of contract- intensive money • Both forms of property rights increase inequality (theoretically a good and proper result) • Interaction of contract-intensive money and openness and democracy and openness both appear to mitigate inequality – Investor protection has no effect – Democratic accountability a more fragile result and scale is very small
  25. 25. Results: Sigma Convergence Sigma convergence 1 2 3 4 Trade openness -0.01 -0.01 -0.01 -0.004 5.25*** 2.92*** 4.86*** 3.21*** INSTITUTIONAL VARIABLES Property rights (contract-intensive money) -1.70 -1.20 7.38*** 4.80*** Property rights (Investor Protection) -0.07 -0.03 2.51** 2.12** Democratic accountability 0.02 0.03 -0.05 -0.002 5.85*** 3.58*** 1.59 0.08 Contract-intensive money*Trade Openness 0.02 0.01 5.47*** 4.45*** Investor Protection*Trade Openness 0.0007 0.0003 2.78*** 2.17** Democratic Accountability*Trade Openness 0.0004 0.0001 2.18** 0.39 C 3.51 2.58 3.41 2.48 19.17*** 16.57*** 16.81*** 23.50*** n 1105 1956 918 1594 R-squared 0.31 0.34 0.48 0.67
  26. 26. Interpretation of Results (II) Between-country inequality (Sigma convergence) • Trade openness unequivocally reduces between-country inequality • Property rights have a strong negative association with between-country inequality (no matter which metric of rights is used) – Interacting property rights and trade openness shows divergence in incomes across countries • Democratic accountability has little effect
  27. 27. Extension: Granger Causality Null Hypothesis Lags Obs F- Statistic Prob. Trade Openness does not Granger Cause Income Inequality 4 3485 2.87139 0.022 Income Inequality does not Granger Cause Trade Openness 2.02025 0.089 Income Inequality (-5 years) does not Granger Cause Trade Openness 4 3132 3.55478 0.007 Trade Openness does not Granger Cause Income Inequality (-5 years) 0.83373 0.504 Income Inequality (-10 years) does not Granger Cause Trade Openness 4 2549 4.00152 0.003 Trade Openness does not Granger Cause Income Inequality (-10 years) 0.04032 0.997
  28. 28. Results: Trade Openness and Staggered Inequality (H2) Trade Openness 1 2 3 4 5 6 Average Gini Coefficient 61.40 26.20 6.16*** 3.46*** Gini 5-year lag 13.31 14.26 5.71*** 7.19*** Gini 10-year lag 10.59 11.85 5.16*** 6.52*** INSTITUTIONAL VARIABLES Property rights (contract-intensive money) -13.59 -19.40 -0.15 5.24*** 1.37 0.01 Property rights (Investor Protection) 9.40 0.41 0.45 7.73*** 0.63 0.82 Democratic accountability 15.71 27.89 -2.65 1.72 -5.24 -0.41 3.32** 8.18*** 0.83 0.72 1.97** 0.19 C -216.77 -229.74 -36.87 -78.32 -22.37 -57.71 5.22*** 5.50*** 2.82*** 5.83*** 1.64 4.05*** n 918 1594 884 1522 823 1397 R-squared 0.28 0.35 0.41 0.42 0.57 0.42
  29. 29. Results: Trade Openness and Staggered Inequality (II) Dependent Variable: Trade Openness 1 2 3 4 5 6 Average Gini Coefficient 168.59 102.01 116.87 68.23 168.59 102.06 6.74*** 5.68*** 5.42*** 3.84*** 7.22*** 5.62*** Gini Coefficient*Democracy -29.49 -17.43 6.21*** 5.11*** Gini 5-year lag* Democracy -17.41 -9.59 4.74*** 3.02*** Gini 10 year lag*Democracy -29.49 -17.43 6.65*** 4.92*** INSTITUTIONAL VARIABLES Property rights (contract- intensive money) -13.27 -11.64 -13.27 4.77*** 4.57*** 5.52*** Property rights (Investor Protection) 1.36 1.04 1.36 2.32** 1.91* 2.82*** Democratic accountability 115.43 64.42 68.46 34.63 115.43 64.42 5.83*** 4.84*** 4.29*** 2.62*** 6.42*** 4.64*** C -556.85 -410.96 -386.57 -286.00 -556.85 -410.96 6.62*** 5.72*** 5.18*** 3.93*** 6.94*** 5.60*** n 918 1594 884 1522 918 1594 R-squared 0.50 0.27 0.47 0.30 0.47 0.27
  30. 30. Interpretation of Results (III) • Longer lags of the Gini coefficient have smaller scale but still have a positive effect on trade openness • Investor protection has a positive effect but overall property rights have a (puzzling) negative effect) • Interacting the Gini coefficient with democracy shows that prolonged inequality can reduce trade openness
  31. 31. Tentative Conclusions • Trade has not had a massive effect on income inequality but has created some losers • Better institutions in the face of trade seem to mitigate trade-related losses within a country • Property rights might help poor countries become rich but also helps rich countries become richer (sigma results) • Prolonged inequality does indeed stifle a democracy’s appetite for future trade liberalization
  32. 32. Still left to do… • Robustness tests/extension of the baseline regressions – Additional controls (Democracy equation experienced some problems) – BMA analysis to narrow down control set? – Other institutional metrics which might give more observations – Country sub-groups as in Braga de Macedo et al. (2013) • Other facets of globalization? – Trade only examined here, perhaps financial globalization as well • Comments welcome!
  33. 33. Dziękuję bardzo! Aitäh! Thank you! @CASE_research CASE-Network CASE – Center for Social and Economic Research

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