Trade and Foreign Direct Investment in Uzbekistan
201
and is statistically significant, which confirms the results of a series of other
empirical studies (Head and Mayer 2013, Kang and Dagli 2018).
Finally, the GDP and distance are combined with other measures to estimate
the export determinants of Uzbekistan. We use the extended gravity model
6
similar to Kang and Dagli (2018), which takes the following log-linear form:
lnX
it
=
β
0
+
β
1
lndist
i
+
β
2
lnGDP
t
+
β
3
lnPOP
it
+
β
4
C
i
+
β
5
lnRER
it
+
φ
i
+
γ
t
+
ε
it
,
where
ln
denotes log,
X
it
is the value of deflated exports of Uzbekistan to its
trading partner
i
at year
t
,
dist
i
is the population-weighted distance between
Uzbekistan and its importers, and
GDP
t
is the value of deflated GDP of
Uzbekistan.
POP
it
is the population of partner destinations that vary over time.
C
i
are the common control variables that include dummies
7
for geographical
contiguity, membership in the EU, and WTO membership.
RER
it
is the bilateral
real exchange rate, calculated by the consumer price index weighted nominal
exchange rate (Kang and Dagli 2018).
φ
i
and
γ
t
are importer and year fixed
effects, respectively. Together they control for endogeneity arising from
omitted variables, such as tariff and nontariff barriers. With the inclusion of
importer fixed effects, the initial GDP variables of importing countries drop
out. Finally,
ε
it
is the error term. The dataset on control variables as well as the
population and distance were extracted from CEPII. The nominal exchange
rates were retrieved from the Thomson Reuters DataStream platform,
and consumer price indexes were retrieved from the World Bank’s World
Development Indicators. The data cover 47 importers and 17 years (2000–
2016), generating 799 observations.
In practice, the estimated parameters of a gravity equation are interpreted
in terms of elasticities. For example, the distance parameter (in logarithms)
is the elasticity of trade to distance, reflecting the percentage variation in
trade following a 1% increase in distance. The distance parameter typically
serves as a proxy for trade costs. Intuitively, the longer the bilateral distance,
the higher the associated trade costs. A geographical contiguity variable in
the analysis is used to capture information costs. In our dataset the countries
include dummies for Kazakhstan, the Kyrgyz Republic, and Tajikistan, which
share common cultural and language features. Such features help to decrease
information costs because firms share similar business environments. The
dummies for membership in the EU and the WTO reflect the importer’s
6
The naive form of the gravity equation includes the GDP of country of origin and country of destination, and
their physical distance.
7
Dummies are variables that take a value of 0 or 1. For example, a dummy takes the value 1 if an importing
country is a member of the WTO, and 0 otherwise.
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