Loans versus Grants in Japanese Bilateral ODA - Evidence from
Panel Data
Tomohito Hata

1

Introduction

In the present paper, I will examine the determinants of grant-loan allocation in Japanese ODA,
focusing on the recipients’ characteristics. In the series of discussions about Oﬃcial Development
Assistance (ODA), many studies have contributed to ﬁnd the factors that determine the amountof aid, that is, ”Who gives aid and why? ”, or ”Who receives aid and why? ”. For example,
Berthelemy (2006) examines whether and to what extent the donors’ interest is accounted forthe allocation of bilateral aids, making use of the two-part models. He ﬁnds that donors’ interestsin the recipient countries, measured by economic ties such as trade amount, do aﬀect the aid
allocation. He also ﬁnds that the extent to which these ties aﬀect the aid allocation varies
across the donor countries, and he classiﬁes the donor countries into ”altruistic”, ”neutral” and”egoistic” countries. Schudel (2008) reviews the relationship between foreign aid allocation andthe recipients’ corruption, taking into account the variation among donor countries, using bothpooled OLS and ﬁxed-eﬀects estimations. The study shows that the amount of bilateral aid
would be reduced if the recipients have high levels of corruption, and that this tendency would
be alleviated if the donor country has relatively high levels of corruption.

These studies focus on the total aid allocation, irrespective of the kind of ODA. In manystudies, the types of ODA - grans, loans and other types - are not of much interest. However, I
argue that these types, especially the amount of grants, should exhibit much variation dependingon the recipients’ characteristics such as ﬁnancial capacity of repaying loans. In addition, fromthe perspective of the donor countries, as loans can be executed politically more easily than
grants, the amount or the proportion of grants should reﬂect the relative ”importance” of therelation with the recipient country, i.e. more grants would be allocated to relatively ”important”
countries. Thus, by examining how grants are allocated, we can know how the donor country1

uses the types of ODA in the context of the foreign assistance strategy, as well as the total
amount of ODA.

In this paper, I focus on the grants and loans allocation in Japanese ODA. Traditionally,
Japan has spent the majority of their ODA in loans, but recently following the trends of foreign
aids they are spending more grants than ever. In addition, as for the sector in which Japanese
ODA is used, there has been a concentration on economic infrastructure and service, rather
than social capital development. Also note that there has been a notable concentration of their
ODA on Asian countries. These particular characteristics of Japanese ODA strategy has been
criticized that Japan has been utilizing ODA to foster economic activities of Japanese industries.

In spite of the criticisms, it is argued that loans-based Japanese ODA would foster thedevelopment of recipients. Though I would not go into too much details, it is also argued thatloans are more suitable than grants for development of the recipient countries.1 On the other
hand, foreign assistances in grants would help countries with heavy external debts. Besides its
eﬀectiveness, it is of much signiﬁcance to know how the choices of grants versus loans (or loans
versus grants) in Japanese ODA are determined. The objective of this paper is to ﬁnd the factorsthat aﬀect the use of grants versus loans, depending on the recipients’ characteristics. Usingpanel data analysis with the data on Japanese ODA disbursements from 2004 to 2011, I ﬁndwhich countries would receive more grants compared to loans. Discovering how their grant-loan
allocation is determined will contribute to the arguments of foreign aids, in terms of political
economy in aid as well as its eﬀectiveness.

By random-eﬀects model and ﬁxed eﬀects model regression, I ﬁnd that the policy quality,
current account balance and external debt stocks of the recipients aﬀect the ratio of grants,
but not the volume. Better macroeconomic management would increase the rate of loans, while
better external debt status increases the rate of grants. Also, Asian counties are found to receive
more loans than grants, and more aid volume compared to other regions.

The composition of this paper is as follows. In the next section, I summarize the former
studies concerning grants versus loans in ODA, and present the hypotheses for this paper. In

1

For more details about comparative advantage of loans versus grants, see Odedokun (2003).

2

section 3, I provide the econometric model and the details of data. In section 4, the results ofregressions are shown and possible implications are discussed. The following section 5 concludes.

2

Former Studies and Hypothesis of the Research

As for the former studies concerning grants versus loans, Odedokun (2003) can be raised. He
examines the economic and political factors that have inﬂuenced donor s past grant-loan mix
and discusses the comparative advantages of grants, soft loans and non-concessional loans. In
the ﬁrst part of his research, he carries out empirical tests based on annual panel data over 1970
to 1999 for 22 donor countries and 72 recipient countries. He focuses on the factors from thedonors’ side, and ﬁnds that donors’ GDP growth is the most powerful factor that increases theratio of grants out of total aids, along with other economic and political variables.

Other studies about grants and loans in ODA mainly focuses on its eﬀectiveness, such as Iimi
and Ojima (2008) and Cohen et al (2007). Taking into account these former studies, there will
be room for investigating the determinants for the choice of grants and loans from the recipientscharacteristics rather than donors when we think about achieving the feasibility of the aids.

Although the use of grants would highly depends on the donor’s capacity of ﬁnance, domestic
political and economic situation and so on, it is natural to think that the rate of grants accountfor the recipients’ economic and political characteristics. For instance, a recipient with heavy
external debts would be less likely to receive loans, due to higher risks of not repaying the loans.
I list the factors that would inﬂuence the rate of grants and their expected eﬀects.

External debt amount
As mentioned above, heavy external debts of the recipients are expected to reduce the incentive to provide loans. This variable will positively aﬀect grants.

GDP per capita and GDP growth rate
The economic outcomes of the recipients are expected to aﬀect grants. Grants are more likely
to given to poorer countries, hence GDP per capita will negatively correlate with grants. On
the other hand, the eﬀect of GDP growth rate is ambiguous. One can predict that more growthenables the recipients to receive more loans and therefore negatively aﬀects grants. However,3the contrary might also be true: more growth can result in more grants because of the donor’s
incentive to improve relation with that country for future economic or political ties.

Policy quality
Good governance is considered to be a precondition for eﬀective foreign assistance, or evenfor receiving more aids. As described later, here I use policy indices to represent the quality ofpolicy. Good policies are expected to aﬀect the amount of ODA positively, as the donor country
can more easily justify their aid. However, in terms of grants, we will have to look more carefullyat the eﬀects. Better policy quality are surely expected to increase loans, but how about grants?
One presumption is that the proportion of grants will decrease, as loans increase relatively morethan grants.

Political freedom and democratic status
These factors also aﬀect grants, like policy quality. The democratic status will aﬀect grants
negatively, as is the case of policy quality.

Economic ties with JapanODA is said to reﬂect the relationship between the donor and the recipient, as argued inBerthelemy (2006). A stronger tie with Japan should increase grants, in terms of both theamount and the proportion out of the total aids.

RegionAs mentioned earlier, Japan has provided relatively more aids on Asian countries. In the
regression analysis here, I include Asian dummy variable to see whether the region aﬀects the
proportion of grants as well as the total amount of ODA. Which of grants or loans is higher inthis region is unexpected, but observing the eﬀect of this variable one can see whether there isa trend in Asian countries in terms grants versus loans.

3

Data Description and the ModelIn this section I present the source of the data I use in the analysis, descriptive statistics of thevariables, and the model.

4

Table 1: Descriptive statistics of the dependent and independent variables

The data for the regression analysis was collected through online databases. Bilateral ODA
from Japan, FDI from Japan, developing country classiﬁcation by DAC were collected from DAC
databases of OECD. Most of the economic and political variables of the recipient countries, suchas external debts, GDP, GDP growth rate and policy quality indices rely on the World Development Indicator, obtained through the World Bank databases. As other political variables, I
use Polcon III of the political constraint database, political freedom index by Freedom House,and democracy index by Polity IV.

The descriptive statistics of the variables are described in Table 1.

The variables lgrantsratio and lgrants gdp denote the rate of grants received out of the total
ODA and the amount of grant disbursements divided by GDP (both in logarithm), respectively.
These two variables are used as the dependent variable in the regression models. The variable5grantsratio is of our primary interest, and lgrants gdp helps us when we think about whether
or not the eﬀects of the independent variables are also the same for aid volume.

The next three variables debtpolicy, macromanagement and transparency are the indicesof policy quality, generated by the assessment of the CPIA. The variable debtpolicy represents
the eﬀectiveness of the public sector to manage the debt account, macromanagement the rating of macroeconomic management, and transparency the accountability of the governmental
information to public, respectively. As these variables had fewer observations compared to theother variables, I test the models with and without these variables.

The next two variables caccount and caccountsqrd represent the current account of therecipients, and square of the current account respectively. The variables ldebt, ltrade andlmilitaryex are the gross external debts, the total amount of trade and the military expenditure(again all in logarithm), respectively. The next two variables f di gdp and f disqrd are theamount of foreign direct investment from Japan and its squared form. These two variables areused as proxies of economic ties with Japan. gdp gr is the GDP growth rate and lgdp is theGDP per capita in logarithm of the recipients.

The next ﬁve variables are dummy variables. Asia means that the recipient country is an
Asian country, and the next four variables means the classiﬁcation of the developing country by
DAC of OECD. All developing countries are classiﬁed into 4 groups depending on the GDP per
capita, and in this dataset the dummy variables dac 1, dac 2, dac 3 and dac 4 are attached tothese groups, from the group with the lowest GDP per capital to the group with the highest.

The last three variables represent the democratic status of the recipients. P olconiii is thepolitical constraint index, pr is the political rights index by Freedom House, and polity is thedemocracy index by Polity IV.
For the econometric model, I basically follow the approach of Schudel (2008), which investigates the bilateral aid allocation using panel data method. To begin with, I assume the following
panel data model:

Yi,t = αX i,t + βZ i,t + γD i + νi,t6

(1)

where Yi,t denotes the amount of bilateral grants or the rate of grants out of the total bilateral
ODA amount from Japan, X i,t a set of recipients’ economic status variables, Z i,t a set ofrecipients’ political variables, D i a set of other dummy variables for the aid trends and νi,t =ai + ui,t a composite error term, respectively. As the diﬀerence from the approach of Schudel(2008), here I assume two types of regression models: the random-eﬀects (RE) model and theﬁxed-eﬀects (FE) model.

The RE model can be estimated by transforming the equation (1) as:∗∗
Yi,t= αX ∗i,t + βZ ∗i,t + γD ∗i + νi,t(2)

∗ = Y − λY , X ∗ = X
where Yi,ti,tii,t − λX i and so on, and λ can be estimated through thei,testimator of se(ui,t ) and se(ai ). This model allows us to include the time-constant variables likea region dummy in the regression, while alleviating the bias caused by the unobserved eﬀect ai
for each i. However, the drawback of this model is that we even have to suﬀer from the bias
caused by ai , unless we count for the desirable data on control variables. Thus, this model isused only to ﬁnd whether there is notable trends on the rate of grants or the grants volume,
depending on the region or other time-constant factors.

The FE model is deﬁned as follows:Ÿi,t = αẌ i,t + β Z̈ i,t + γ D̈ i + üi,t(3)where Ÿ = Yi,t − Y i and so on. The merit of using the FE model is that this allows us toeliminate the unobserved eﬀect ai , although in this model we cannot use the time-constantvariables. Hence, the objective of using this model is to see the pure eﬀects of the economic andpolitical variables on the grants.2

4

Results

The following Table 2 reports the results of the RE models.3

2

See Wooldridge (2008) for more details.
Note that here I also included year dummy variables for each year, but these are not shown in the results.
None of them is signiﬁcant.
3

7

debtpolicy
macromanagementtransparency
caccount
caccountsqrd
ldebt
ltradelmilitaryex
fdi_gdp
fdisqrdgdp_gr
lgdp
asia
dac_2
dac_3
dac_4

(1)

(2)

(3)

(4)

lgrantsratio

lgrantsratio

lgrants_gdp

lgrants_gdp

0.383(3.64)***-0.327(3.31)***-0.366(2.93)***
0.017(2.63)***
0.000(1.42)-0.163(2.61)***-0.056(0.33)-0.186(1.96)*-0.183(0.12)-2.394(1.79)*
0.005(0.32)-0.308(5.06)***-0.530(3.43)***
0.437(1.29)-0.157(0.91)
0.064(0.18)

polconiiiprpolity_cons
N
Number of groups
Wald Chi-squared

8.456(4.72)***
13240
142.200.019(2.86)***
0.000(1.12)-0.173(2.99)***-0.353(1.72)*-0.028(0.26)-1.239(6.91)***-0.754(3.71)***
0.026(2.11)**-0.260(3.88)***-0.631(3.24)***
0.537(0.93)-0.187(0.78)
0.028(0.10)-0.057(0.18)
0.008(0.18)
0.001(0.30)
7.363(3.58)***
323
73
120.67-0.423(1.94)*
0.243(1.18)
0.387(1.50)-0.006(0.49)
0.001(1.71)*
0.045(0.35)
0.317(0.91)-0.469(2.45)**
3.523(1.12)-1.186(0.42)-0.065(1.94)*-0.244(1.99)**
1.192(3.89)***
0.062(0.09)-0.633(1.83)*-3.437(4.67)***-14.849(4.11)***
13240
126.20p<0.1; ** p<0.05; *** p<0.01
T-statistics are given in parenthesis

Table 2: Results from RE models

8

0.001
(0.14)
0.000(1.63)
0.108(1.38)
0.423(1.44)-0.267(1.73)*
0.988(4.24)***
0.535(1.99)**-0.030(1.83)*-0.380(3.87)***
1.481(5.09)***-0.341(0.40)-0.766(2.16)**-2.501(5.96)***-0.244(0.58)-0.092(1.50)-0.001(0.12)-11.446(3.83)***
323
73
293.20Here I assumed four models, two of which (the model (1) and (2)) take the ratio of grants outof total ODA as the dependent variable, and the rest (the model (3) and (4)) take the amountof grants divided by GDP. In the model (1) and (3) the policy quality variables are used, and in
the model (2) and (4) Polcon III, political rights and polity are used instead of them, in orderto maintain the number of observations.4

From the results, one can observe some important factors for the grants. First, let’s look at
the model (1) and (2) that take the ratio of grants as the dependent variable. In both models,the variables caccount, ldebt, lgdp and asia turn out to be statistically signiﬁcant. The signs of
the coeﬃcients on caccount ldebt are somewhat contrary to the expectation. The result suggeststhat a country with better current account balance or with less external debts is more likely
to receive more grants, compared to loans. One explanation for this is that Japan might beproviding grants as a result of the recipients’ eﬀorts of improving their government accountbalances, not as a way of improving it. A country with better current account balance or withless external debts is considered to be awarded by higher proportion of grants.

The variable lgdp is also signiﬁcant for both models, with a negative sign of its coeﬃcient.
This means a country with higher GDP per capita would receive more proportion of loans than
grants. This result does not contradict to the expectation.

The most powerful independent variable is asia for both models, in terms of its absolutevalue of the coeﬃcient as well as statistic signiﬁcance. This variable suggests that Japan isproviding more loans than grants in Asian region. This might be because Japan is promoting
ODA for economic sector, such as infrastructure in Asia. For the projects of economic sector, it
seems easier to capture the expected outcome of the project, making it easier to plan loans.

The diﬀerence between the model (1) and (2) is the political variables. In model (1) policy
quality indices are used, while in model (2) they are altered by polconiii, pr and polity. Looking
at the policy quality variables in model (1), all of them turn out to be signiﬁcant. However, thesigns of the coeﬃcients should be observed carefully. The result shows that a country with good4

The t-statistics in the table are usual ones (i.e. not heteroskedasticity-robust ones). I also tried using
heteroskedasticity-robust t-statistics, but little changes were found. This is also the case for the results from FE
models.

9policy in managing external debts (represented in debtpolicy) is likely to receive more grants,
rather than loans. As is the case of external debts, this is contrary to the expectation. Thissuggests that a country with better at handling external debts would be awarded by higher
proportion of grants. The coeﬃcients on the other two variables suggest that a country withgood macroeconomic management or transparency is expected to have more loans than grants.

Another diﬀerence between the model (1) and (2) is the statistic signiﬁcance of the variablef di gdp, which is signiﬁcant in model (2) but not in model (1). This means that once we control
for the policy quality, economic tie plays little role in determining the rate of grants. In otherwords, the eﬀect of policy quality is more dominant than that of economic tie.

The political variables polconiii, pr, polity are not signiﬁcant at all in all the models. Thismight not be so surprising: the decision for ODA disbursements is made insider the governments,
irrespective of the voices of the public.

In the model (3) and (4), the dependent variable is the amount of grants, not the rates. By
comparing them with model (1) and (2), we can see whether or not each independent variableaﬀects the increase (or decrease) in the grants volume. One important implication may be drawn
for those variables that are signiﬁcant in model (1) and (2) but not in model (3) and (4). Thesevariables are considered to aﬀect the proportion of grants versus loans, but not the volume.
Hence, policy quality, current account balance and external debts do aﬀect the proportion of
grants, but do not increase or decrease the amount.

In the model (3) and (4), the Asian dummy variable turns out to be signiﬁcant, too. Taking
account its positive sign, one can say that Japan is providing larger amount of grants in Asia
compared to other regions, as well as loans. This means that Japan is providing more ODA to
Asian countries in both grants and loans, and the majority of their assistance is loans.

Looking at other variables enables us to ﬁnd the factors that aﬀect the aid volume of grants.
However, it is probable that the factors that aﬀect the volume diﬀer from the ones that aﬀect
the ratio of grants, so other variables should be needed. Finding the determinants of the aid
volume is outside the scope of this paper.

10

The following Table 3 refers to the results from FE models.5

debtpolicy
macromanagementtransparency
caccount
caccountsqrd
ldebt
ltradelmilitaryex
fdi_gdp
fdisqrdgdp_gr
lgdp

(5)

(6)

(7)

(8)

lgrantsratio

lgrantsratio

lgrants_gdp

lgrants_gdp

0.436(3.02)***-0.231(2.09)**-0.048(0.28)
0.016(1.76)*
0.000(1.42)-0.226(2.57)**-0.422(1.29)-0.113(0.54)-0.841(0.40)-2.237(1.43)
0.004(0.24)-0.381(2.29)**

polconiiiprpolity_cons
N
Number of groups
R-squared

9.956(2.12)**
132
40
0.39

0.021
(2.49)**
0.001(1.99)**-0.187(2.55)**-0.621(1.88)*-0.093(0.41)-1.257(6.75)***-0.661(3.00)***
0.029(2.70)***-0.000(0.00)-0.304(0.82)-0.023(0.32)
0.001(0.15)
2.107(0.52)
323
73
0.22-0.340(1.04)
0.307(1.23)
0.063(0.16)
0.014(0.67)
0.001(2.20)**
0.187(0.94)
1.121(1.53)-1.295(2.74)***
4.407(0.93)-0.993(0.28)-0.068(2.04)**-1.099(2.93)***

2.431(0.23)
132
40
0.41

0.006
(0.59)
0.000(1.39)
0.152(1.60)
0.491(1.14)-0.638(2.18)**
1.168(4.82)***
0.734(2.56)**-0.025(1.74)*-1.212(6.58)***
0.103(0.21)-0.089(0.94)
0.002(0.21)
7.725(1.46)
323
73
0.33

p<0.1; ** p<0.05; *** p<0.01
T-statistics are given in parenthesis

Table 3: Results from FE models

These models allow us to see the pure eﬀects of each variable, controlling for the individual5Note that the dummy variables like asia, dac 2 and so on are excluded from the model, because they are
constant over time.

11

country’s characteristics. Comparing the results from FE models with RE models, we canconﬁrm if the results obtained by RE models is credible or not. The dependent and independent
variables are the same with RE models, so we can compare directly two models, such as model(1) and (5), model (2) and (6) and so on.

Comparing model (5) and (6) with model (1) and (2), one can see there are not so much
diﬀerences, although for some variables statistic signiﬁcance is less in the FE models. For mostof the independent variables, the signs of the coeﬃcients are the same. This would support thecredibility of the results obtained by RE models. However, there are some diﬀerences between
the FE and RE models.

Looking at the model (5) and (1), one can see that the policy quality index for transparency
turns out to be insigniﬁcant in FE model. This means that once we control for the recipients’
individual characteristics, transparency has nothing with the ratio of grants. The other two
policy quality variables exhibit almost the same eﬀects. Therefore, we can say that in terms ofpolicy quality, external debt management and macroeconomic management matter.

As for other variables, the results are quite similar, except for the GDP per capita in model(6).This variable somehow turns out to have almost no eﬀect in the model (6). This weakens
the credibility of the results of model (2), but in model (1) and (5) this variable exhibits almost
the same eﬀect.

Even looking at the model (7) and (8), the results are quite similar to the results from RE
models. One interesting point is that military expenditure is quite signiﬁcant in all of the model(3), (4), (7), (8). This suggests that the recipients’ military expenditure reduces grants volume,
but again ﬁnding the determinants for the aid volume is outside the scope of this paper.

In summary, the regression models show that policy quality aﬀects the ratio of grants, especially the quality of external debt management and macroeconomic management. Current
account balance and external debt stocks also aﬀect the ratio, but somewhat in unexpected
ways. One can say that a country with better current account balance or less external debts
would be awarded by more grants. However, these eﬀects are not the case in terms of aid volume.

12

Also, GDP per capita aﬀects the ratio: poorer countries are more likely to receive grants, interms of both ratio and volume. Finally, Asian countries are much more likely to receive loans,compared to grants. Moreover, they are receiving more grants and loans in volume, and theamount of loans overwhelms the amount of grants.

5

Conclusion

In the present paper, I examined which factors aﬀect the rate of grants in Japanese ODA,
focusing on the recipients’ characteristics. This paper would shed a light on the unfamiliar topic
among the studies of foreign assistance: the determinants of grants versus loans. The mainﬁnding is that the policy quality of the recipients aﬀect the ratio of grants, but not the volume.
Then, one question arises: if the improvement in policy or government deﬁcit doesn’t aﬀect theaid volume, what is the importance of improving them? However, I argue that the types of
ODA matter when we think about the eﬀectiveness of aid as well as the government’s ﬁnancial
capacity, as argued in other studies. An interesting ﬁnding of this paper would be that Japanis providing more grants because a recipient makes good eﬀorts of improving current account or
reducing external debts. Also I ﬁnd that Asian countries receive more loans rather than grants,
but in terms of aid volume, they are receiving relatively larger amount of Japanese ODA.

Finally, I raise some drawbacks of this paper. First, as a result of limiting the donor to
Japan, the regressions suﬀered from a lack of observations. For model (1), there were only 40
groups of recipient countries. This might have resulted in unstable results of the regressions.
Also due to this small number of the observations, it was diﬃcult to try more variables, forexample, interaction terms and lagged variables as well as other possible variables.6 With more
observations, I would be able to allow more variables in the models.

Second, the model does not include the multilateral aid, which consists of a large amount ofgrants. I would expect diﬀerent results by including this, though it would be more diﬃcult tointerpret the results.

6

I would like to add that for available variables like GDP growth, I took their lags but there were little changes
in results.

13

To conclude, I would raise a new way of thinking ODA allocation. As is in this paper, it isworth considering the proportion of grants versus loans as well as aid volume, when we think
about the aid allocation.I argue that the quantity of ODA consists of two dimensions: volume
and grant-loan ratio. It may be useful to observe the ratio of grants, in order to see more detailsof the aid allocation.

References[1] Berthelemy, Jean-Claude. ”Bilateral Donors Interest Vs. Recipients Development Motives
in Aid Allocation: Do All Donors Behave the Same?”. Review of Development Economics
10, no. 2 (2006): 179-94.
[2] Chong, Alberto, and Mark Gradstein. ”What Determines Foreign Aid? The Donors’ Perspective.” Journal of Development Economics 87, no. 1 (8// 2008): 1-13.
[3] Cohen, Daniel, Pierre Jacquet, and Helmut Reisen. ”Loans or Grants?” [In English]. Review
of World Economics 143, no. 4 (2007/12/01 2007): 764-82.
[4] Cordella, Tito, and Hulya Ulku. Grants Versus Loans. 2004.
[5] Iimi, Atsushi, and Yasuhisa Ojima. ”Complementarities between Grants and Loans.” Journal of the Japanese and International Economies 22, no. 1 (3// 2008): 109-41.
[6] Odedokun, Matthew. Economics and Politics of Oﬃcial Loans Versus Grants: Panoramic
Issues and Empirical Evidence. WIDER Discussion Papers//World Institute for Development Economics (UNU-WIDER), 2003.
[7] Schudel, Carl Jan Willem. ”Corruption and Bilateral Aid: A Dyadic Approach.” Journalof Conﬂict Resolution 52, no. 4 (2008): 507-26.
[8] Wooldridge, Jeﬀery M. Introductory Econometrics: A Modern Approach, International
Edition, 4th Edition. South Western College, 2008.

Online Databases[9] DAC database: http://www.oecd.org/dac/stats/ (accessed on July 20, 2013)[10] World

Development

Indicator

http://data.worldbank.org/data-catalog/world-development-indicators (accessed on July 20, 2013)14

[11] Political Constraints Database http://mgmt5.wharton.upenn.edu/henisz/POLCON/ContactInfo.html(accessed on July 20, 2013)[12] Freedom House Database http://www.freedomhouse.org/report-types/freedom-world (accessed on July 20, 2013)[13] Polity Database http://www.systemicpeace.org/polity/polity4.htm#top (accessed on July20, 2013)

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