Is Joining the European Union a Good Thing?
A Synthetic Control Testing the Effects on the Slovak Republic
Nicole Smith
Abstract
After the fall of the Soviet Union in 1989, many Soviet states and satellites adopted more
neoliberal and democratic policies to catch up with the rest of the world. This paper analyzes
three effects associated with the Slovak Republic joining the European Union by using the
synthetic control approach proposed by Abadie, Diamond, and Hainmueller. The three
treatments are (1) adopting the Copenhagen Criteria, which alter the social, political, and
institutional policies of the state and propose an application for membership, (2) joining the
European Union in the 2004 Enlargement, and (3) adopting the euro as official currency and
joining the monetary policies of the European Monetary Union. The synthetic control method
shows that the Copenhagen Criteria had adverse effects on GDP per capita in the Slovak
Republic, but the actual membership of the EU and the EMU increase economic growth of the
Slovak Republic.
1
Economists have frequently debated the idea that poorer countries will have a higher
growth rate once certain intervention measures are met in order to catch up with, or converge,
with the rest of the developed world, specifically Western countries. 1 The former Soviet states of
Eastern Europe are no exception to this theory, as observed through higher growth in the years
following the abolishment of a centrally planned economy. Under communist rule, many of these
countries missed out on a significant portion of economic growth compared to the rest of Europe,
who conducted policies under neoliberalism and market freedoms, as well as receiving aid
through the US Marshall Plan. Not only did these countriesspecifically the Slovak Republic in
this studymiss out on longer periods of economic liberalization and democratization that is
often associated with growth, but they also had a disadvantage in terms of cultural convergence,
an idea from world polity theory, as they abided by communist economics during the formation
of neoliberalism and the European Union.
During the last two decades, many economic and structural changes have occurred in
former Soviet states and satellite states that lead to an increase in GDP per capita (in terms of
purchasing power parity) as seen in Figure 1. These changes include (1) the agreement of the
conditions on monetary and fiscal policy in order to achieve EU candidacy, (2) accession into the
European Union, and (3) the adoption of the euro and monetary policies under the European
Monetary Union. Aside from leaving central planning, the Slovak Republic adopted three policy
treatments to increase economic growth; the purpose of this paper is to address which, if any, had
a positive impact and the potential to lead to convergence. By conducting three separate
synthetic control analyses, this paper will address issues of economic growth at each step of the
way; synthetic control attempts to provide the counterfactual to what would have happened
1
Robert J.Barro, and Xavier Sala-i-Martin, “Convergence,” Journal of Political Economy 100.2 (1992): 223.
2
Figure 1
without treatment. In other words, I am trying to isolate the effects of each step on economic
growth in the Slovak Republic. Three potential hypothesis exist:
Hypothesis 1: The removal of barriers, due to agreement with the Copenhagen Criteria, found in
a central planned economy provided the greatest increase in economic growth that still has
impacts today.
Hypothesis 2: Joining the European Union provides the best explanation for increase in
economic growth in Slovak Republic as it provided a safety net and better access to markets.
3
Hypothesis 3: Adopting a common monetary policy under the European Monetary Union and the
Maastricht Treaty, as well as better exchange rates under the euro provided a positive and
significant impact on economic growth in the post-Soviet states.
In theory, all three hypotheses will have a significant effect on GDP per capita in the
Slovak Republic. The outline for the rest of the paper will be an extensive literature review in
Section 2. Section 3 will show the methodology of synthetic control. In Section 4, I will present
the results. The last section will conclude.
Literature Review
There is a vast literature on economic growth in post-Soviet states and EU accession, and
it will be presented for each treatment in terms of before and after in order to better assess the
predictions and theories of neoclassical economics. Slovak Republic was chosen for this analysis
because of its proximity to the rest of the European Union and its previous relationship with the
Soviet Union. Slovak Republic has the following timeline for treatments: agreement to
candidacy and policy changes in 1995, as well as EU accession during the 2004 Eastern
enlargement, and the adoption of the euro and monetary policy under the European Monetary
Union in 2009.
Copenhagen Criteria and Other Agreement Conditions
In order to understand how market conditions changed with the fall of communism, we
must analyze the existing Socialist structure in Slovak Republic. Under this economic system,
physical assets and capital goods cannot be privately owned, investment is manipulated by the
state through bank credit and rate of investment via market mechanisms, and “the right to
4
manage the firm is in the hands of its employees.” 2 Essentially, in a centrally planned economy,
there is the absence of neoliberalism as all markets are controlled and regulated by the state.
There is little debate in economic theory that centrally planned economies are harmful to
economic growth, and GDP per capita in previous communist states, “would have been at least
three times higher on average by 1992, if they had continued the capitalist way of development
after World War II.” 3 Because of this, one could assume that economic growth would greatly
increase with re-entry into a capitalist market, which occurred in the early 1990s for most former
Soviet states and satellites.
Before achieving actual accession into the European Union, candidate countries must
sign an agreement that outlines criteria for monetary and fiscal policies. Specifically, the
Copenhagen Criteria are concerned with political, economic, and institutional aspects of the
candidate country. The political criteria require that a country have democratic stability and basic
human rights for all peoples. The economic criteria outline a functioning market economy and
ability to address EU competitiveness. The institutional criteria enforce the ability to maintain
the existing institutional structure established by other EU members, including certain laws and
administrative functions. 4 Many of these criteria challenge the existing norms of the post-Soviet
Slovak Republic, and they were adequately implemented before full membership was agreed
upon.
Under the stipulation that potential member countries will converge with the rest of the
European Union, additional measures in relation to health, education, agriculture, infrastructure,
2
Svetozar Pejovich, “The Firm, Monetary Policy, and Property Rights in a Planner Economy,” Western Economic
Journal, 7.3 (1969): 193.
3
Marek Dabrowski and Jacek Rostowski, ed. Eastern Enlargement of the EU, (New York: Springer
Science+Business Media, LLC, 2001): 3.
4
"Economic Accession Criteria." European Commission. September 10, 2014. Accessed December 17, 2015.
http://ec.europa.eu/economy_finance/international/enlargement/criteria/index_en.htm.
5
transportation, privatization, and welfare such as pension plans will also need to be met. 5 Even
unspecified policy norms were implemented into candidate countries as those on the outskirts of
the European Union adopt its rules based on a rational cost-benefit analysis. If the rule is
beneficial for both domestic and EU relationsor if the EU pressures the country to adopt a
policy as a condition for EU membershipthen it will most likely be implemented. 6 As outlined
above, one can infer that joining the European Union is an expensive investment and requires
government spending to make up for lack of the private sector at first; however, we are testing
this assumption by investigating how GDP per capita changes with or without EU membership.
New European Union Countries
In 2004, the European Union underwent the largest expansion of its members by adding
ten countries, primarily those in Eastern Europe who were previously under Soviet rule. In
theory, this would greatly contribute to high economic growth rates in member countries because
liberalization occurred on industrial products only in Eastern European countries attempting to
join the EU. Countries who are seeking to join the EU improve greatly in terms of industry
building, employment rates, and openness to trade. 7 EU membership also provided an advantage
as new EU members receive income transfers from “payments into the recipient countries’
public budgets originate from the extension of EU structural policies to new members, and
payments to suppliers in the agricultural and food sector.” 8
5
Dabrowski and Rostowski, 24.
F. Schimmelfennig and U. Sedelmeier, “Governance by conditionality: EU rule transfer to the candidate countries
of Central and Eastern Europe,” Journal of European Public Policy 11.4 (2004): 661–79.
7
Hubert Gabrisch. “Eastern Enlargement of the European Union: Macroeconomic Effects in New Member States.”
Europe-Asia Studies 49 (1997): 567-590.
8
Ibid., 575.
6
6
Dabrowski and Rostowski claim, “there is a widespread expectation that these economies
will grow faster than the current EU members, gradually closing the development gap” based on
the analysis presented by Barro and Sala-I-Martin. 9 Their reasons for fast-growing economies in
former communist countries are: they have ended central planning typically considered to be a
negative economic policy, the use of “learning by doing” in market economy institutions,
structural reforms, and other EU benefits. 10 This can be observed in the charts displaying GDP
growth in previously communist countries, as the GDP greatly increases in the Slovak Republic
after joining the European Union. This accelerated growth rate is partially due to being a
candidate for EU accession as countries often receive pre-accession aid. Joining the EU is an
expensive investment, as countries will have to meet the standards set in place by the Union,
including environmental regulations, infrastructure investment, public schools, as well as relying
on the private sector to provide other needs. 11 The economic growth also improves from high
deficit countries that lessen their debt to GDP ratio in order to better their chances at accession.
Countries often move away from agriculture and more towards industrial and financial markets;
before accession, Slovak Republic had larger agricultural sectors than the EU average. 12 After
accession into the EU, we can observe increased stability in the financial sectors of the Eastern
European countries, especially as trust is built with the international community and a bond
market emerges. 13
Broadman uses regressions to show that investment in most countries in the EU or in EU
accession countries has a positive coefficient, whereas population growth has a negative
9
Dabrowski and Rostowski, 5.
Ibid., 5.
11
Ibid., 24.
12
Ibid., 177.
13
Corneliu Stirbu, “Financial Market Integration in the Wider European Union,” Hamburg Institute of International
Economics Discussion Paper 297 (2004: 34
10
7
coefficient for non-EU countries and a positive coefficient for some EU countries. The authors
summarize the effects of several factors on economic growth: investment, population growth,
human capital, inflation, and government consumption. The paper argues, “the accumulation of
human capital is extremely important for enhancing economic growth.” 14 Through this, the paper
finds convergence in economic growth. They also argue that inflation appears to have a negative
impact on growth, whereas government consumption and trade have an unclear result.
European Monetary Union and the Maastricht Treaty
Most EU members agree to join the European Monetary Union (EMU) and adopt the
euro eventually. Slovak Republic adopted the euro in 2009. The Maastricht Treaty has several
requirements for candidates countries that wish to adopt the euro: (1) low long-term interest rates,
(2) government budget deficit, (3) low inflationcannot exceed +1.5 percentage points, and (4)
nominal exchange rates in fluctuation with the euro. 15 These criteria would encourage higher
economic growth rates for countries, which we observe in Figure 1.
Because the euro adoption occurred either slightly before or after the financial crisis, the
effects are difficult to observe. However, in theory, the adoption of a single currency in a
monetary union would lead to drastic changes in both the private and public sector. 16 This can be
observed in the Slovak Republic by looking at the existence of private firms and institutions
14
Harry G. Broadman, Tiiu Paas, and Paul J.J. Welfens, ed,. Economic Liberalization and Integration Policy:
Options for Eastern Europe and Russia. (Berlin: Springer, 2006), 293.
15
Anna Lipinska, “The Maastricht Convergence Criteria and Optimal Monetary Policy for the EMU Accession
Countries,” European Central Bank: Working Paper Series. No. 896 (2008): 7.
16
Carlo Monticelli and Oreste Tristani, “What does the single monetary policy do?: A Svar benchmark for the
European Central Bank,” European Central Bank Working Paper Series (1999): 15.
8
before and after joining the European Unionaccording to the World Development Indicators,
Slovenia’s domestic credit to the private sector was approximately 71% of GDP in 2013. 17
If we analyze economic growth in the Slovak Republic after the financial crisis in 2008,
we observe that overall economic growth continued in the country but joblessness increase. This
may be due to the continued help of foreign investment from the rest of the EU members, and the
new challenge for the country is how to continue increased economic growth based solely on
domestic sources rather than FDI. 18 Domestic and private investments were emphasized earlier
as a means of observing economic growth in accession countries, and I argue its importance
again here in a recovering EU economy.
The literature that exists on new member EU countries is vast and not specifically
oriented to one subject of the financial sector; however, by looking at data and regressions before
and after European Union accession will provide an idea of both predictions and results. This
will be crucial for understanding how economic growth changed due to the joining of one central
bank, and hopefully a synthetic control or matching approach will yield results that show the
difference in growth between a hypothetical Slovak Republic and the actual.
Methodology: Synthetic Control
This is a technique developed by Abadie, Diamond, and Hainmueller in 2010, a synthetic
unit, a country in my example, is created using similarities in the entire dataset, where other units
are weighted. 19 The actual country receives the treatmentadoption of Copenhagen criteria, EU
17
World Development Indicators “Domestic credit to private sector (% of GDP).”
http://data.worldbank.org/topic/private-sector?display=graph
18
Jarko Fidrmuc, Caroline Klein, Robert Price, and Andreas Worgotter, “Slovakia: A Catching Up Euro Area
Member In and Out of the Crisis,” OECD Economics Department Working Papers, No. 1019, (2013).
19
Alberto Abadie, Alexis Diamond, and Jens Hainmueller, “Comparative Politics and the Synthetic Control
Method,” American Journal of Political Science 00.0 (2014): 1-16.
9
accession, and EMU membership in this examplewhereas the synthetic country, modeled after
the actual country pre-treatment and the addition of weighted countries, does not receive the
treatment. This is essentially a counterfactual to what would have happened if the Slovak
Republic had continued as normal. The donor units for the synthetic country are taken from the
sample of countries in the world with data for GDP per capita, but exclude any possible countries
that have the same treatment. Using the real gross domestic product divided by the population, I
test the effect of each treatment on GDP per capita in terms of purchasing power parity (PPP),
using 2005 USD from the Penn World Tables. 20 The predictor variables for each synthetic
control vary by treatment; they are elaborated on in the appendix.
For the Copenhagen criteria, the pre-treatment period begins in 1990 and the posttreatment period ends in 2003, which is one year before the next treatment is given. The data
begins in 1990 due to data constraints from the Former Soviet Union; however, I think that it is
sufficient to analyze the effects of applying for European Union membership. This treatment
period is testing the effects of two policy changes that coincide with one another: the adoption of
neoliberal economics and the Copenhagen criteria (which is based off of an open market
economy) to replace the preexisting socialist state. There is a possibility that the results from this
treatment are more due to the end of the socialist regime rather than the desire to join the EU;
however, the ending of old market and the beginning of the new market are one in the same
transformation.
The 2004 enlargement of the European Union is captured by the time period 1996-2008
for Slovak Republic, due to the differing years in the other two treatments; this time period is
forced in order to avoid the confounding events of Copenhagen criteria and the adoption of the
20
Robert C. Feenstra, Robert Inklaar and Marcel P. Timmer, "The Next Generation of the Penn World Table,"
forthcoming American Economic Review, (2015) available for download at www.gdpc.net/pwt
10
euro. It would be more revealing if the post-treatment period could be extended more than three
years, but the third treatment interferes with this desire. Because I am testing the three different
treatments, I can infer that no other manipulative events occurred during this treatment period.
The existing treatment period (2005) and the final data collected by Penn World tables
restrict the period of euro membership (2011). This is the shortest period, and continually
updated data will be able to further elaborate the effects. The financial crisis of 2008 occurred
during this time period, but it was a global effect. While it may not have equally affected each
country, the treatment should not be altered by the simultaneity.
Results
Treatment 1: Application
The first treatment tests the effect of applying for membership to the European Union and
the conditionality of the Copenhagen criteria. All countries that were already members of the
European Union or previously applied for membership are excluded from the synthetic. The
predictor indicators are lagged values of GDP per capita for pre-treatment years (1990, 1992, and
1994), gross capital formation, percent of urban population, fertility rate, Polity Score to measure
the democratic level of Slovak Republic 21 and ethnolinguistic fractionalization measure (elf15). 22
Simply, by looking at the graph presented in Figure 2, GDP per capita in the Slovak
Republic has fallen behind the predictor found in the synthetic control country, which is
21
Systemic Peace Project, Polity IV. http://www.systemicpeace.org/inscrdata.html
William Easterly and Ross Levine. “Africa’s Growth Tragedy: Policies and Ethnic Divisions,” The Quarterly
Journal of Economics 112.4 (1997): 1203–50.
22
11
composed of Albania, Belarus, Canada, and Qatar. 23 The synthetic control country matches very
well pretty well on the predictor indicators.
Figure 2:
While this gap between the treated country and the synthetic may seem large and significant, it is
important to understand what this effect actually means through placebo tests of changing the
year and country of the treatment, which will be elaborated on in future sections.
Table 1: Match of Treated and Synthetic Countries, Treatment 1
Treated
rgdppc (1990)
14930.8
rgdppc (1992)
10972.67
rgdppc (1994)
11587.36
grosscap
28.14407
urban
56.6496
fert
43.86028
polity2
7
elf15
0.3075
Synthetic
13660.73
11671.09
11614.5
20.71964
60.49301
28.56992
3.0176
0.4016629
23
Qatar and Canada are geographically and compositionally different from the Slovak Republic. However, each
country makes up less than one-fifth of the synthetic country. Qatar is probably a match due to the economic
indicators, rather than the non-economic (polity and ethnolinguistic), as it is a donor country in several models.
While Canada’s high GDP per capita may skew the results, the placebo test will reveal the significance.
12
Robustness Checks
In order to fully comprehend the composition of the synthetic control, we must alter the
predictor variables to test for robustness of the effects. For this reason, I run the same synthetic
control but simply drop the non-economic variables of world polity score and ethnolinguistic
Figure 3:
fractionalization. The results are consistent. The treated country has a lower GDP per capita than
the synthetic country; in this case, the gap between the two appears to be greater. Slovak
Republic may have been better off in the short run if they had refrained from abiding by the
policy changes in the Copenhagen Criteria. The placebo test, along with the statistical match of
the predictor variables, is presented in the Appendix, which reveals the significance of this gap
and robustness check.
Placebo tests
In order to analyze the significance of the results presented in previous sections, it is
essential to understand if the Slovak Republic is an outlier. For this, I run the treatment on every
country in the donor pool. The results of this test are presented in Figure 4. Because the Slovak
13
Republic is not the most extreme outlier but is also not perfectly aligned with the mean effect of
the treatment, I conclude that it is slightly significant, meaning that GDP per capita in the Slovak
Figure 4:
Republic would have been greater without the EU application process. The RMPSE for each
country in the donor pool are listed in the Appendix, but the average is 330.91 and the Slovak
Republic is 671.49.
Treatment 2: European Union Membership
In order to fully understand the effects of an open market and joining the European Union,
it is essential to analyze the actual EU membership, which provides many benefits such as
greater access to trade, Schengen borders that allow EU members to travel between borders, and
more stable financial markets. In theory, we would expect EU membership to be significantly
beneficial to the economy. We observe the effects in Figure 5. There seems to be the opposite
effect from Treatment 1, where the treated country exceeds the GDP per capita of the synthetic
country. This is good for the European Union, as it supports the foundation of cooperation
14
between independent nation-states. The donor countries selected for the synthetic are somewhat
counterintuitive as most are in East Asia and therefore, they are not geographically similar. 24
However, Slovak Republic experienced high economic growth rates, based on GDP per capita,
which is reflected by the synthetic composition of East Asian countries. Since the Slovak
Figure 5:
Republic is still transitioning from a communist to a democratic regime, the Polity IV index best
matches with East Asian countries. Once again, we must analyze robustness and a placebo test in
order to understand the significance of gap between treated and synthetic countries.
An interesting improvement can be observed when comparing the changes in predictor
variables between treatments, as there is a great increase in GDP per capita and fertility is nearly
halved. Over time, both economic and non-economic conditions are improving in the Slovak
Republic.
24
East Asian economies have had unprecedented high growth rates in recent history; this may skew the validity of
the synthetic control. However, robustness checks should provide statistical evidence in favor of either situation.
15
Table 2: Match of Treated and Synthetic Countries, Treatment 2
Treated
rgdppc (1996)
12406.68
rgdppc (2000)
12819.89
rgdppc (2003)
14384.53
grosscap
31.46
urban
56.23
fert
24.39
polity2
8.5
elf15
0.307
Synthetic
12398.43
12815.76
14379.47
31.44
56.71
30.42
3.0176
0.326
Robustness Checks & Placebo Tests
Presented below are the robustness and placebo tests for the second treatment, EU
membership. The theory is the same as above, and these tests help to explain the importance and
significance of Figure 5. The robustness check (Figure 6A), presents a conundrum as the noneconomic indicators added as predictor variables show the opposite result. The placebo test
(Figure 6B) is
Figure 6A and 6B:
similar to Treatment 1, in that it the effect is not extremely significant. The average RMSPE is
478.56, and the Slovak Republic is 862.46. Essentially, a potential conclusion from the statistical
analysis is that while Slovak Republic has had beneficial results from joining the European
16
Union, the effects on other countries in the area may differ and be contradictory to the Slovak
Republic; in other words, there is not necessarily external validity.
Treatment 3: European Monetary Union
One of the stipulations of becoming a member of the European Union is to eventually
abide by the monetary policies of the European Monetary Union (EMU) and adopt the euro as
the official currency of the country. Slovak Republic fulfilled this requirement in 2009,
immediately after the financial crisis of 2008. By joining the EMU during the crisis, Slovak
Republic fared better than if they had monetary policies of their own and a currency subject to
foreign exchange rates, which are sometimes volatile. Figure 7 illustrates how joining the euro
assisted the Slovak Republic economy. The composition of the synthetic country further supports
the effectiveness of joining the EMU, as a majority of the countries selected for the synthetic
control are from Europe and half belong to the European Union (although not the EMU). Croatia,
Estonia, and Romania all belong to the EU, and suffered more during the financial crisis than
those countries that were subject to the euro and the EMU.
Figure 7
17
Once again, we observe an improvement in GDP per capita, a reduction in the fertility
rate (typically correlated with improvements in development), and a Polity IV score of that is
close to 10 (the highest score of the index). Not only did joining the euro and the EMU help the
Slovak Republic during the financial crisis, but also the combination of the treatments has
yielded an overall improvement, both economically and non-economically.
Table 3: Match of Treated and Synthetic Countries, Treatment 3
Treated
rgdppc (2005)
15720.95
rgdppc (2007)
19477.67
rgdppc (2008)
20996.15
grosscap
28.99984
urban
55.33675
fert
20.704
polity2
9.75
elf15
0.3075
Synthetic
15695.46
19443.78
20960.94
28.36325
59.61485
29.37299
8.459
0.2699542
Robustness Checks
The robustness (Figure 8A) and placebo tests (Figure 8B) show that removing the noneconomic variables (Polity IV and ethnolinguistic fractionalization) show an even greater gap in
GDP per capita between the treated and the synthetic country. This is mostly likely due to the
fact that the composition of the synthetic country is made up of nearly every country in the
dataset, which all performed differently during the financial crisis. The placebo test illustrates
that the treated country is not statistically significant, as it is not an outlier. The average RMSPE
is 526.14, and the Slovak Republic RMSPE is 467.89. The difference between the root mean
squared percentage errors is small, and this effect is less significant than the other treatments.
18
Figure 8A and 8B:
Conclusion
Overall, there are mixed results from the synthetic control tests for the three treatments
on the effects of joining the European Union. The first treatment, abiding by the policy
recommendations of the Copenhagen Criteria, has a negative effect on the GDP per capita of the
Slovak Republic. It would have been better for the Slovak Republic to develop without the
stipulations of the Copenhagen Criteria, which are aligned with neoliberal ideologies. This
negative impact is probably due to the downsides of a transitioning economy as different sectors
understand their new role in investment and development of the national economy. However,
after joining the European Union and the adopting the euro as official currency, the neoliberal
policies and the politico-economic union policies of the European Union are helpful in
improving gross domestic product for the Slovak Republic.
Because of these conclusions, one might assume that the benefits of joining the EU are
valid for countries elsewhere. However, the robustness checks and placebo tests question the
external validity of the joining the EU. In order to fully understand the positives associated with
19
joining the European Union and adopting its official currency, synthetic control would need to be
constructed for several other countries. Yet, we can conclude that it was beneficial to the Slovak
Republic and its policies helped the country to develop after years of centrally planned
economics under the Soviet Union. One might hypothesize that the effects of joining the
European Union would be similar and positive for countries that have similar histories and
composition to the Slovak Republic. Nonetheless, we know that the Slovak Republic has
benefited from its membership in the European Union, and this support is crucial for current
situations as there are rumors of the EU dissolving after the financial crisis and refugee crisis
damage relationships internally.
20
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22
Appendix
Table A1: Description of variables
Variable
Description
rgdppc
Real gross domestic product
(rgdpce) divided by the
population (pop) to account
for purchasing power parity
grosscap
Gross capital formation
(percent of GDP); measures
fixed assets and inventories
urban
Urban population (percent of
total population)
fert
Fertility rate, total (births per
woman); number of children
that would be born if a woman
lived to the end of
childbearing years
polity2
Polity IV score; measures
democratic and autocratic
regimes on an index with the
range: -10 to +10.
elf15
Ethnolinguistic
fractionalization, which is the
probability that two randomly
chosen people will belong to
the same ethnic or linguistic
family
reserves
Total reserves holdings
including gold, foreign
exchange, and reserves of IMF
members
unemp
Unemployment, total (percent
of total labor force); person
who is without work but
available and searching for
employment
Source
Penn World Tables, version
8.1
World Development
Indicators (2015)
World Development
Indicators (2015)
World Development
Indicators (2015)
World Polity Score, Systemic
Peace Project
Easterly and Levine (1997)
World Development
Indicators (2015)
World Development
Indicators (2015)
Table A2: Descriptive statistics for dependent and independent variables
Variable
Obs
Mean
Std. Dev.
Min
rgdppc
3,036
10837.76
13181.11
184.8175
grosscap
2,941
23.14909
11.5964
-2.42358
urban
3,036
54.01074
23.16527
5.416
fert
3,036
66.44664
51.94273
1.857
polity2
3,016
3.577586
6.474695
-10
elf15
3,036
.4818783
.2992333
.003
Max
134040.4
219.0694
100
228.642
10
.9647
23
Table A3: Synthetic Composition for Treatment 1
Country
Percent
Albania
0.439
Belarus
0.195
Canada
0.191
Qatar
0.175
Table A4: Root Mean Square Prediction Error (RMSPE) for Treatment 1 Placebo
Country
RMSPE
Country
RMSPE
Country
Albania
718.26
Gabon
348.59
Nigeria
Argentina
1124.45
Gambia
24.61
Oman
Armenia
490.69
Ghana
32.92
Pakistan
Australia
98.90
Guatemala
80.35
Panama
Azerbaijan
644.77
Guinea
15.84
Paraguay
Bahrain
807.92
Guinea-Bissau
132.58
Peru
Bangladesh
11.37
Honduras
14.37
Philippines
Belarus
317.75
India
13.99
Qatar
Benin
6.47
Indonesia
122.93
Russia
Bhutan
54.90
Iran
281.56
Rwanda
Bolivia
10.05
Israel
440.07
Saudi Arabia
Botswana
112.57
Jamaica
62.46
Senegal
Brazil
39.99
Japan
542.69
Sierra Leone
Burkina Faso
29.37
Jordan
160.40
Singapore
Burundi
24.65
Kazakhstan
638.10
Slovak Rep.
Cambodia
52.43
Kenya
71.53
South Africa
Cameroon
35.07
Korea, Rep.
750.96
Sri Lanka
Canada
247.46
Kuwait
7063.35
Sudan
Central Afr.
27.17
Kyrgyz Republic
265.68
Suriname
Chad
94.24
Lesotho
33.84
Swaziland
Chile
60.71
Macedonia, FYR
178.80
Syria
China
141.53
Madagascar
11.58
Tanzania
Colombia
42.16
Malawi
35.49
Thailand
Comoros
50.92
Malaysia
19.91
Togo
Congo, Dem.
28.72
Mali
46.45
Trinidad & Tob.
Congo, Rep.
150.47
Mauritania
60.79
Tunisia
Costa Rica
36.70
Mauritius
36.06
Uganda
Cote d’Ivoire
23.54
Mexico
388.98
Ukraine
Dominican Rep
72.74
Moldova
539.44
United States
Ecuador
55.20
Mongolia
366.23
Uruguay
Egypt
60.10
Mozambique
36.72
Venezuela
El Salvador
30.94
Namibia
163.37
Vietnam
Eq. Guinea
57.62
Nepal
52.89
Yemen
Ethiopia
17.17
New Zealand
669.10
Zimbabwe
Fiji
80.40
Niger
19.58
RMSPE
80.86
153.90
12.02
127.98
19.90
47.80
42.27
862.57
1577.85
138.44
1041.01
4.70
158.46
1973.95
671.49
85.44
27.00
45.30
41.75
479.38
37.07
7.85
199.28
51.57
154.24
91.54
53.76
221.68
5635.80
123.27
313.36
17.83
14.16
321.59
24
Average RMPSE = 330.91
Figure A1: Placebo test for robustness check (change variables)
This robustness test is significant because the actual treated company is mostly an outlier, when
compared to other donor countries that receive the same treatment. In Table A4, we observe that
some of the donor countries are similar throughout the models.
Table A5: Synthetic Composition for Treatment 1, Robustness Change Variables
Country
Percent
Albania
0.532
Canada
0.032
Qatar
0.436
Table A6: RMSPE for Treatment 1, Robustness Change Variables
Country
RMSPE
Country
RMSPE
Albania
726.76
Gabon
298.04
Argentina
814.50
Gambia
12.30
Armenia
410.78
Ghana
40.02
Australia
421.02
Guatemala
49.22
Azerbaijan
744.08
Guinea
16.61
Country
Nigeria
Oman
Pakistan
Panama
Paraguay
RMSPE
80.80
197.48
9.25
449.93
41.56
25
Bahrain
Bangladesh
Belarus
Benin
Bhutan
Bolivia
Botswana
Brazil
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Central Afr.
Chad
Chile
China
Colombia
Comoros
Congo, Dem.
Congo, Rep.
Costa Rica
Cote d’Ivoire
Dominican Rep
Ecuador
Egypt
El Salvador
Eq. Guinea
Ethiopia
Fiji
233.23
12.11
319.26
18.59
104.06
18.29
213.95
23.89
28.72
7.67
44.27
137.63
244.64
20.30
83.62
54.97
129.85
56.58
27.55
26.24
158.47
236.49
18.77
67.85
56.29
9.56
39.11
56.81
18.77
30.40
Guinea-Bissau
Honduras
India
Indonesia
Iran
Israel
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kuwait
Kyrgyz Republic
Lesotho
Macedonia, FYR
Madagascar
Malawi
Malaysia
Mali
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Mozambique
Namibia
Nepal
New Zealand
Niger
76.39
26.11
42.87
153.54
153.54
987.34
72.40
558.53
127.70
627.42
66.13
1109.00
7050.28
329.77
83.72
132.85
17.94
35.06
4.80
34.04
53.71
534.43
37.15
472.51
283.99
33.15
161.57
51.32
748.83
24.80
Peru
Philippines
Qatar
Russia
Rwanda
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Rep.
South Africa
Sri Lanka
Sudan
Suriname
Swaziland
Syria
Tanzania
Thailand
Togo
Trinidad & Tob.
Tunisia
Uganda
Ukraine
United States
Uruguay
Venezuela
Vietnam
Yemen
Zimbabwe
38.61
169.93
861.16
1574.80
139.88
295.97
14.61
194.60
2214.55
643.40
121.12
36.44
35.00
337.09
283.90
21.30
12.27
722.88
79.98
221.95
10.12
81.10
347.21
5635.80
78.93
175.25
17.90
12.91
412.03
Average RMSPE = 343.24
Table A7: Synthetic Composition for Treatment 2
Country
Percent
Equatorial Guinea
0.028
Jamaica
0.075
Japan
0.109
Korea, Rep.
0.326
Mongolia
0.092
Thailand
0.37
26
Table A8: RMSPE for Treatment 2 Placebo
Country
RMSPE Country
Albania
228.43 Gabon
Angola
274.57 Gambia
Argentina
288.06 Georgia
Armenia
72.57
Ghana
Australia
1851.31 Guatemala
Azerbaijan
107.6
Guinea
Bahrain
889.64 Guinea-Bisseau
Bangladesh
25.73
Honduras
Belarus
208.25 India
Benin
30.12
Indonesia
Bhutan
312.11 Israel
Bolivia
106.29 Jamaica
Botswana
949.17 Japan
Brazil
137.02 Jordan
Bulgaria
227.24 Kazakhstan
Burkina Faso
28.15
Kenya
Burundi
26.23
Korea, Rep
Cambodia
49.08
Kuwait
Cameroon
136.57 Kyrgyz Rep
Canada
923.04 Lao PDR
Central Afr.
11.16
Lesotho
Chad
105.24 Liberia
Chile
342.09 Macedonia
China
94.26
Madagascar
Columbia
140.93 Malawi
Comoros
46.09
Malaysia
Congo, Dem
205.88 Mali
Congo, Rep
415.22 Mauritania
Costa Rica
388.49 Mauritius
Cote d’Ivoire
159.92 Mexico
Croatia
604.45 Moldova
Dominican Rep 451.56 Mongolia
Ecuador
218.53 Morocco
Egypt
47.53
Mozambique
El Salvador
33.15
Namibia
Eq. Guinea
1935.62 Nepal
Ethiopia
56.11
New Zealand
Fiji
168.93 Niger
RMSPE
624.01
52.19
104.12
137.99
109.46
148.10
102.12
83.63
74.13
153.27
487.90
146.01
2470.02
164.24
130.65
57.27
492.20
3906.12
169.02
51.99
202.25
139.32
114.60
33.35
34.26
218.87
44.59
59.80
237.12
400.33
135.31
43.61
164.61
40.42
79.53
25.72
1460.53
36.25
Country
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Qatar
Romania
Russia
Rwanda
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Rep
South Africa
Sri Lanka
Sudan
Suriname
Swaziland
Switzerland
Syria
Tanzania
Thailand
Togo
Trinidad & Tob
Tunisia
Turkey
Uganda
Ukraine
United States
Uruguary
Venezuela
Vietnam
Yemen
RMSPE
143.13
1213.87
438.50
57.84
578.77
190.61
135.53
153.18
14117.68
275.45
504.51
23.96
916.25
28.92
121.57
744.51
862.46
268.83
146.99
121.17
400.39
915.82
787.70
70.07
16.71
276.02
41.50
776.14
84.95
283.80
35.31
33.36
2999.95
159.16
482.34
100.89
85.73
Average RMSPE = 478.56
27
Figure A2: Placebo test for robustness check (change variables)
This robustness test is also somewhat significant, as the Slovak Republic is somewhat of an
outlier, but not extremely.
Table A9: RMSPE for Treatment 2, Robustness Change Variables
Country
RMSPE Country
RMSPE
Country
Albania
228.43 Gabon
624.01
Nigeria
Angola
274.57 Gambia
52.19
Norway
Argentina
288.06 Georgia
104.12
Oman
Armenia
72.57
Ghana
137.99
Pakistan
Australia
1851.31 Guatemala
109.46
Panama
Azerbaijan
107.60 Guinea
148.10
Paraguay
Bahrain
889.64 Guinea-Bissau
102.12
Peru
Bangladesh
25.73
Honduras
83.63
Philippines
Belarus
208.35 India
74.13
Qatar
Benin
30.13
Indonesia
153.37
Romania
Bhutan
312.11 Israel
487.90
Russia
Bolivia
106.29 Jamaica
146.01
Rwanda
Botswana
949.17 Japan
2470.02
Saudi Arabia
Brazil
137.02 Jordan
164.24
Senegal
Bulgaria
277.24 Kazakhstan
130.65
Sierra Leone
Burkina Faso
28.15
Kenya
57.27
Singapore
Burundi
26.22
Korea, Rep
492.20
Slovak Rep
Cambodia
49.98
Kuwait
3906.12
South Africa
RMSPE
143.13
1213.87
438.50
57.84
578.77
190.61
135.53
153.18
14117.68
275.45
504.51
23.96
916.25
38.92
121.58
744.51
862.46
268.83
28
Cameroon
Canada
Central Afr.
Chad
Chile
China
Columbia
Comoros
Congo, Dem
Congo, Rep
Costa Rica
Cote d’Ivoire
Croatia
Dominican Rep
Ecuador
Egypt
El Salvador
Eq. Guinea
Ethiopia
Fiji
136.58
923.04
11.16
105.24
342.09
94.26
140.93
46.07
205.88
415.22
388.49
159.92
604.45
451.56
218.53
47.53
33.15
1935.62
56.11
168.93
Kyrgyz Rep
Lao PDR
Lesotho
Liberia
Macedonia
Madagascar
Malawi
Malaysia
Mali
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
New Zealand
Niger
169.02
51.99
202.25
139.32
114.60
33.35
34.26
318.87
44.59
59.80
237.12
400.33
135.31
43.61
164.61
40.42
79.53
25.72
1460.53
36.25
Sri Lanka
Sudan
Suriname
Swaziland
Switzerland
Syria
Tanzania
Thailand
Togo
Trinidad & Tob
Tunisia
Turkey
Uganda
Ukraine
United States
Uruguay
Venezuela
Vietnam
Yemen
146.99
121.17
400.39
915.82
787.69
70.07
16.71
276.02
42.50
776.14
84.96
283.80
35.305
33.36
2999.95
159.16
483.34
100.89
85.73
Country
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
RMSPE
48.16
65.26
114.89
1470.10
7.91
89.60
45.39
21.12
12.16
Average RMSPE = 479.54
Table A10: Synthetic Composition of Countries, Treatment 3
Country
Percent
Burundi
0.011
Croatia
0.065
Estonia
0.151
Kuwait
0.039
Romania
0.608
Switzerland
0.0125
Table A11: RMSPE for Treatment 3 Placebo
Country
RMSPE Country
Albania
1.22
Gabon
Angola
35.73
Gambia
Argentina
16.72
Georgia
Armenia
97.66
Ghana
Australia
230.81 Guatemala
Azerbaijan
883.92 Guinea
Bahrain
1584.61 Guinea-Bissau
Bangladesh
5.33
Honduras
Belarus
1.43
Hungary
RMSPE
171.23
36.98
48.03
20.57
73.79
18.94
26.59
22.85
91.37
29
Benin
Bhutan
Bolivia
Botswana
Brazil
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Central Afr.
Chad
Chile
China
Colombia
Comoros
Congo, Dem.
Congo, Rep.
Costa Rica
Cote d’Ivoire
Croatia
Czech Rep.
Denmark
Dominican R
Ecuador
Egypt
El Salvador
Eq. Guinea
Estonia
Ethiopia
26.93
205.83
68.10
212.01
30.09
199.52
4.05
61.31
32.62
8.95
23.32
32.18
78.62
200.20
2.48
41.17
23.54
156.35
228.52
151.96
60.24
393.53
269.14
129.10
104.87
24.83
29.53
20.80
944.48
144.09
15.01
India
Indonesia
Israel
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kuwait
Kyrgyz Rep.
Lao PRD
Latvia
Lesotho
Liberia
Lithuania
Macedonia
Madagascar
Malawi
Malaysia
Mali
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Morocco
Mozambique
Namibia
Nepal
New Zealand
1.20
3.23
151.48
79.01
73.78
10.62
33.24
20.83
7.32
1053.29
34.85
78.09
173.98
2.99
6.85
250.44
85.01
6.13
27.64
142.53
7.69
183.22
141.88
20.57
15.00
63.81
5.61
5.24
71.44
8.07
513.78
Poland
Qatar
Romania
Russia
Rwanda
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Rep.
South Africa
Sri Lanka
Sudan
Swaziland
Sweden
Switzerland
Tanzania
Thailand
Togo
Tunisia
Turkey
Uganda
Ukraine
United King.
United States
Uruguay
Venezuela
Vietnam
Zimbabwe
339.28
42823.29
115.83
312.13
19.79
185.15
37.02
4.42
1857.57
467.89
11.31
8.55
59.32
241.40
400.44
1120.91
9.62
11.94
23.08
9.80
65.47
7.05
117.82
94.19
888.84
95.66
70.09
8.78
232.54
Average RMSPE = 536.13
Figure A3: Placebo test for robustness check (change variables)
30
This robustness check for the placebo test is the least significant of all the robustness checks
because it is near the average of the effect of treatments.
Table A12: Placebo Test for Treatment 3, Robustness
Country
RMSPE Country
RMSPE
Albania
1.22
Gabon
171.23
Angola
35.73
Gambia
36.98
Argentina
16.72
Georgia
48.03
Armenia
97.66
Ghana
20.57
Australia
230.81 Guatemala
73.79
Azerbaijan
883.92 Guinea
18.94
Bahrain
1584.61 Guinea-Bissau
26.59
Bangladesh
5.33
Honduras
22.85
Belarus
1.43
Hungary
91.37
Benin
26.93
India
1.20
Bhutan
205.84 Indonesia
3.23
Bolivia
68.10
Israel
151.48
Botswana
212.01 Jamaica
79.01
Brazil
30.09
Japan
73.78
Bulgaria
199.52 Jordan
10.62
Burkina Faso
4.05
Kazakhstan
33.24
Burundi
61.31
Kenya
20.83
Cambodia
32.62
Korea, Rep.
7.32
Cameroon
8.96
Kuwait
1053.29
Canada
23.32
Kyrgyz Rep.
34.85
Central Afr.
32.18
Lao PRD
78.98
Chad
78.62
Latvia
173.98
Chile
200.20 Lesotho
2.99
China
2.48
Liberia
6.85
Colombia
41.17
Lithuania
250.44
Comoros
23.54
Macedonia
85.01
Congo, Dem.
156.35 Madagascar
6.13
Congo, Rep.
228.52 Malawi
27.64
Costa Rica
151.96 Malaysia
142.53
Cote d’Ivoire
60.24
Mali
7.69
Croatia
393.53 Mauritania
183.22
Czech Rep.
269.10 Mauritius
141.88
Denmark
129.10 Mexico
20.57
Dominican R
104.87 Moldova
15.00
Ecuador
24.83
Mongolia
63.81
Egypt
29.53
Morocco
5.61
El Salvador
20.80
Mozambique
5.34
Eq. Guinea
944.48 Namibia
71.44
Country
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Qatar
Romania
Russia
Rwanda
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Rep.
South Africa
Sri Lanka
Sudan
Swaziland
Sweden
Switzerland
Tanzania
Thailand
Togo
Tunisia
Turkey
Uganda
Ukraine
United King.
United States
Uruguay
Venezuela
Vietnam
Zimbabwe
RMSPE
48.16
65.26
114.89
1470.10
7.91
89.60
45.39
21.12
12.16
339.28
42823.29
115.83
312.13
19.79
185.15
37.02
4.42
1857.57
467.89
11.31
8.55
59.32
241.40
400.44
1120.91
9.62
11.94
23.08
9.80
65.47
7.05
117.82
94.19
888.84
95.66
70.09
8.78
232.54
31
Estonia
Ethiopia
144.09
15.01
Nepal
New Zealand
8.07
513.78
Average RMSPE = 526.14
32