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

1. Christopher A. Hartwell
President, CASE
10 October 2017
Do Poor Institutions Create
More Losers from
Globalisation?
www.case-research.eu

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. 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. 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. 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. 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. 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. Hypotheses – A Graphic Representation
Trade
Openness/Globalization
Institutions Losers from
Globalization

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Dziękuję bardzo!
Aitäh!
Thank you!
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