AsiaPacific region: empirical analysis of the effects
A study of trade patterns of a number of countries in the AsiaPacific region, with emphasis on its relations with the Russian Federation with the use of econometric estimates. The main factors affecting the cost of trade flows between countries.
Рубрика  Международные отношения и мировая экономика 
Вид  дипломная работа 
Язык  английский 
Дата добавления  02.10.2016 
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AsiaPacific region: empirical analysis of the effects
Introduction
trade relation econometric
Last decades can be characterized as the period of active international economic integration that covers almost all countries in the world. A complex of political, economic, geographical characteristics of countries determines intensity of this process in particular directions. Awareness of the extent of significance of these factors is crucial when defining international economic policy priorities of countries.
Economic interests of many large economies are now concentrated in AsiaPacific region due to its fast economic growth and intensive technological development in strategically important spheres. This attractive features of countries belonging to this region makes other countries to interact with them more intensively in order to enhance their own economic growth and intensify technology exchange occurring in the process of international trade for achieving the higher level of technological development.
Russian economy of XX century can be characterized as autarky based on almost complete selfsufficiency principle under control of government. It means that the dependence on the exchange with other countries was minimized. This closed economic regime negatively influenced trade and investment international flows between Russia (in this specified time  the Soviet Union of Socialist Republics) and capitalistic unit of countries. In 1980s the idea of economy based on the government plan discredited itself and the Soviet economy took a step forward transformation into market economy: privatization of property and market modernization began. After this and even more after the collapse of the Soviet Union the process of intensive economic integration of Russia in the world economy took place.
Previously economic relations between Russian Federation and AsiaPacific region were passive. However, increasing power of countries of this region, their active economic growth and technological development, geographic position of Russia regarding Asian countries and some other factors contributed to change of this situation. For realization of national economic interests of Russia it is crucial to raise the level of its international cooperation with countries of AsiaPacific region and to form clear foreign economic policy in this direction.
Some steps were taken forward increase of economic integration of Russia in AsiaPacific region. One of the most significant among them is the fact that since 1988 Russia is the participant of becoming increasingly significant APEC (AsiaPacific Economic Cooperation), the entry into which brought economic benefits through extending international trade geography of Russia and trade familiarization with such trade destinations as Singapore, HongKong, Thailand and other previously strange for RF trade destinations. The participation in APEC led to liberalization of international trade and investment activities and strengthening of trade ties due to reducing barriers between countriesparticipants of the forum. Nowadays, Russia is a powerful member of APEC: it intensively takes part in the process of modification of APEC and has a significant weight in discussing strategically important issues. Furthermore, the indicators of active integration of Russian Federation in APEC were such events as Asian economic forum in 2004, where perspectives of investment and energy cooperation in APEC region were discussed, and the summit APEC in Vladivostok in 2012.
Besides membership in APEC Russian Federation economically integrates in AsiaPacific region through participation in other international organizations: Shanghai Cooperation Organization (since 1996), BRICS (since 2006), Association of South East Asian Nations (ASEAN, since 1996) and  and cooperation with other economic blocks.
The shift of international activities of Russian Federation to AsiaPacific region is a weighty argument to investigate economic integration there. It is crucial in understanding of their trade patterns, which in turn is helpful for determining of main directions of foreign economic policies and predicting of perspectives of further economic cooperation with this region.
Besides analysis of the influence of participation in economic trade agreements of countries on exportimport trade flows, geographic factors are taken into consideration with use of gravity model approach.
The process of economic integration and priorities in international trade are determined by a complex of characteristics of countries. Geographic group of features, which is a focus of current study, indicate transportation costs of international trade, which are simultaneously the reason and the consequence of international economic integration.
Geographic group of characteristics includes area, distances, common border, access to seas, common language, colonial links and others. The reason of choice of these characteristics of countries as the focus of the research is that they do not only indicate transportation costs, but they are crucial when investigating trade patterns of Russian Federation. International trade of this country with other countries is highly dependent on geographic peculiarities due to a great length of Russian borders, its vast area, a large number of neighboring countries and other factors.
One of the most frequently used methods of investigation of influence of geographic factors on international trade flows is gravity approach. Initially it was used by J. Tinbergen (1962) as the way of determining the character of interconnection between trade values and economic size of trading countries and distances between them on the base of Newton's gravity law. Economic sizes are determined by values of gross domestic product. They can be described as economic weight of country in trade pattern.
For the purposes of the research data on international trade flows and geographic characteristics of 29 countries of AsiaPacific region was downloaded and consolidated into one database. The choice of countries is grounded on export and import values: countries with no or low trade flows were excluded from observation, because without them there is nothing to estimate.
Empirical part of the work contains estimation of data described above with use of one of the most common approaches to gravity model analysis  Poisson Pseudo Maximum Likelihood, that is considered to be suitable by majority of empirical works investigating international trade flows (Silva and Tenreyro, 2006; Melitz and Toubal, 2014) The appropriate method should contribute to avoidance of two main challenges that usually occur when analyzing international trade flows with appliance of gravity. The first problem that leads to biased estimations is heteroscedasticity, which occurs because data downloaded from different sources has different quality. Another challenge is sample selection, which takes place because of excluding of zero flows, that often occur in trade data, from observation. Research conducted by Silva and Tenreyro (2006) proves the fact that estimation of loglinear gravity regressions in multiplicative form in presence of heteroscedasticity and sample selection problems leads to distorted results.
All things considered, the major purpose of current research is to estimate the effects of international economic integration of Russia in Asia Pacific region through explanation of differences and changes in exportimport trade flows with use of gravity model approach. One more side purpose is to choose the most appropriate method of applying gravity model to explanation of international trade flows.
Main hypothesis of the work can be formulated as following:
1 Geographic characteristics are significant factors influencing exportimport flows between countries in AsiaPacific region.
2 Exportimport trade flows are the inverse function of distance between trading partners.
3 Exportimport trade flows are proportional to economic sizes of trading countries that are expressed in gross domestic product values.
4 Remoteness from other countries in the world positively influences exportimport trade flows.
5 Poisson PseudoMaximum Likelihood is an appropriate method of gravity model estimation.
The rest of the paper is organized in the following way: Section 2 provides the existing, mainly empirical literature concerning observed issue, Section 3 contains description of data and variables, its empirical analysis with help of OLS and PPML and estimation results, Section 4 concludes.
1.Literature review
The pioneer in applying Newton's physical gravity law to explanation of trade flows between countries was Tinbergen (1962). He made an analogy between masses of objects in gravity law and economic sizes of trading countries expressed in gross domestic product values. What is more, he stated that international trade flows are proportional to GDP values and inversely dependent on distance between countriespartners. This statement in the form of the formula is expressed in the following way:
where ,  economic sizes of trading partners i and j expressed in GDP,  distance between them, G  constant.
Since then, this idea has been intensively applied to analysis of international trade flows because empirical results of estimations with use of gravity model are intuitively evident. It is logically understandable that countries that are more contiguous and have greater GDP values trade more with each other comparatively to their trade with other countries.
After Tinbergen`s work many economists criticized his choice of empirical method of estimation (ordinary least squares estimator) due to its inability to cope with existence of zero trade flows and heteroscedasticity, that are frequent problems when estimating trade data. Furthermore, only GDP and distance cannot fully explain changes in exportimport trade flows.
In further works a wider set of explanatory variables and more appropriate methods were used. Besides GDP values and distance variables researchers introduced some more geographic characteristics, such as common borders, colonial links, common language and others. Rose (2002) used trade agreements variable in order to estimate whether membership in international agreements influences values of trade between countries. What is more, to the method of ordinary lest squares (OLS) employed by Tinbergen such methods as Poisson PseudoMaximum Likelihood estimation (PPML)(Silva and Tenreyro, 2006), fixed effects (FEM) and random effects (REM) models for panel data, HausmanTaylor estimation (HT) (Nguyen, 2009), Tobit model (Eaton and Tamura, 1994), Heckman sample selection model (Heckman, 1979), 2step method of moments (TSMM) (Xiong and Chen, 2014) and many others were added.
The great variety of existing methods of econometric analysis of trade data with use of gravity model can be explained by two challenges faced by researchers. The first frequent problem leading to biased estimations of trade flows is presence of heteroscedasticity. This inconstancy of variance of random errors of regressions appears because data for estimation of gravity model is usually downloaded from different sources that means the varying data quality.
Another challenge, that should be solved in order to name the method appropriate for estimation, is socalled problem of sample selection, which occurs due to existence of zero flows. The traditional gravity model assumes that countries have positive export and import international flows, because zerovariables cannot be included in traditionally used loglinear OLS equation due to undefined logarithm of zero. In trade and gravity datasets zero and missing values are frequent and in some empirical works they are excluded reasoning that if they equal zero they are not significant as there is nothing to estimate. However, their exclusion results in biased estimations of coefficients. Consequently, in case of zero or missing explanatory variables their influence on international trade flows becomes underestimated, and in case of zero or missing export and import flows their exclusion leads to omission of reasons for no or low trade between countries.
The initial specification with only GDP and distance variables suggested by Tinbergen (1962) was estimated by OLS method, the gist of which is minimizing the residual when estimating difference between observed and predicted by the linear approximation values. Majority of researchers criticize application of this method to gravity because it is unable to cope with two main problems described above. Furthermore, only GDP values and distance between countries are not able to fully explain international trade flows between countries. It leads to the problem of omitted variables and consequently biased estimations. Further papers used more explanatory variables in their modifications of gravity model. For instance, they introduced common borders, common language, colonial links etc. Moreover, it is a disputable question whether trade flows are linearly dependent on incomes of trading countries and other parameters and theoretical foundations of gravity model, which usually employ multiplicative form of equation, do not support use of OLS method.
In order to avoid various problems associated with use of OLS method researchers introduced a range of approaches that suit better for gravity estimation.
One of the most frequently used method for estimation gravity model is Poisson PseudoMaximum Likelihood (PPML), adequacy of which was explained in many papers, particularly in details by Silva and Tenreyro (2006). They tested OLS, NLS (nonlinear least squares estimator) and PPML in order to define factors influencing bilateral trade flows for 136 countries in 1990. Silva and Tenreyro (2006) estimations showed that role of geodesic distance is larger under OLS than under PPML. Concerning estimation methods, authors made a conclusion that first two estimators does not suit gravity model estimation: OLS estimator cannot solve both problems described above and NLS estimator ignores problem of data heteroscedasticity. Thus, PPML method, which copes with both challenges, was chosen. Silva and Tenreyro (2006) stated that PPML results are robust to heteroscedasticity because it employs logarithm of error term that includes its overdispersion. Furthermore, besides authors mentioned above, Xiong and Chen (2014) proved that it naturally deals with the problem of zero trade values. Solution of these two problems gives consistent estimations of coefficients. Some specifications of PPML method were introduced by Burger et al. (2009): negative binomial PPML (nbPPML), zero inflated PPML, zero inflated nbPPML. Despite such advantages of these methods as allowing use of zero flows and dialing with problem of excessive dispersion, this specifications are hard to interpret due to explaining of estimations through levels, but not elasticities. However, in later work Silva (2011) pointed out that PPML estimator leads to some problems, though they can be easily solved. Silva and Tenreyro (2010) stated if data configuration is inappropriate (for example in the case of perfect collinearity) PPML estimations does not exist. Furthermore, Martin and Pham (2008) criticized this estimation method due to the fact that when zeros are not random, use of PPML results can prove to be biased.
Siliverstovs and Schumacher (2009), who compared OLS and PPML estimators, also stated that PPML is an appropriate approach to apply to gravity model estimation of trade patterns. They built 29 regression models that were estimated not only on the aggregate countrylevel, but also on the industrylevel. Furthermore, they provided detailed formulation of the process of estimation with use of PPML estimator. These authors found an important difference between OLS and PPML: elasticities, particularly for countries incomes, are smaller when estimating data by PPML. That is explained by the fact that heteroscedasticity of error term, which cannot be solved by OLS estimation procedure, significantly influences OLS results and biases them.
Adequacy of applying PPML method to panel dataset was proved by Proenca, Sperlich and Savasci (2015). They provided an investigation of semimixed effects gravity model for EU25 countries between 2004 and 2007 and employed a range of dummy variables for geography bonds of countries like: common border, landlockedness and etc for avoiding the problem of omitted variables that correlate with covariates. Their method can be considered as the extension of Silva and Tenreyro's (2006) PPML estimation model.
Fally (2015) also preferred PPML estimator for analysis of bilateral trade data. He introduced exporter and importer fixed effects and multilateral resistance term that was proposed by Anderson and Wincoop (2003) in their work, containing the way of solution of McCalums (1995) Border Puzzle for Canada and US. Furthermore, Fally revealed the unique peculiarity of PPML estimator: fitted output of bilateral trade flows are equal to their observed output. Other methods cannot provide such property. That is why, author made a conclusion, that coincides with majority of previous authors  PPML estimator fits gravity data.
Head (2003) introduced the formula for remoteness and provides justifications for inclusion of such variables as common border, common language, colonial links and trade agreements into the estimated gravity equation.
The work that thoroughly described gravity model theoretical and empirical justifications and its' application to estimation of bilateral trade flows was written by Frankel (1997). He explained the importance of variables for international trade organizations, geographical proximity and grounded the inclusion of latter in gravity analysis by transportation costs. Though Frankel estimated his data using only OLS method, he contributed a lot into gravity model theory, because he suggested a great variety of specifications that can be used in the process of estimation under other more appropriate methods. Moreover, he justificated the adequacy of foreign policy variables, particularly, the effects of participation in trade agreements. Cameron and Trivedi (2013) provided explanation of pseudo maximum likelihood methods in the base which PPML estimator lies. Particularly, it employs populationaveraged effects that are like pooled Poisson, but with averaged individual effects.
All things considered, a majority of relatively contemporary authors, who investigated the issue of estimation of bilateral exportimport trade flows with appliance of gravity model, came to a conclusion that Poisson PseudoMaximum Likelihood estimator is one of the most suitable. Though they tried to compare it with other models (Tobit, NLS, OLS and etc), but none of these estimators is able to solve challenges of trade data employed in gravity models. They give wrong results due to heteroscedasticity of trade data and existence of zero trade flows. Furthermore, all authors regardless specifications of model used or estimation method, proved the initial statement of Tinbergen (1962) that trade flows are proportional to economic sizes of trading countries and inversely depend on distance between them.
2. Methodology
2.1 Estimation methods description
Absence of consensus among economists about the most appropriate method of econometric estimation of gravity equations led to existence of a wide variety of methods, which all have their own shortcomings. The right estimator should be able to cope with problems, associated with trade data: heteroscedasticity and existence of zero trade flows.
Empirical gravity has always been presented in multiplicative form:
where , , ,  parameters and  an error term.
The transformation it into logarithmic form gives more common form of traditionally used in empirical literature gravity model:
2.1.1 OLS estimator
Firstly, the initially applied OLS method of explanation of exportimport trade flows with use of gravity model is used for analysis of our dataset. Estimations differ from those by Tinbergen (1962) as we include a wider set of variables. There is a set of assumptions concerning data characteristics in order to consider OLS a suitable estimator. Firstly, data should have a normal distribution of large dataset. Furthermore, variance of residuals should be constant (no heteroscedasticity). Secondly, relations between dependent and explanatory variables should be characterized by linear equation:
where is dependent variable,  coefficients of explanatory variables,  explanatory variables,  the error term.
The gist of the method is minimizing the sum of differences between predicted and observable values. Thus, the goal of the method is to minimize the sum of squared and to define the equation, characterizing linear dependence:
Loglinear OLS regression equation looks as follows:
The method suffers from considerable problems that makes it inappropriate in majority of cases, especially in analysis of macroeconomic data, such as in our paper. Trade data usually satisfies only the one data requirement  macroeconomic studies usually employ enormous datasets. However, the amount of missing, extremely high or extremely low values is also large.
Application of OLS method to gravity model gives biased results. Firstly, it cannot cope with heteroscedasticity problem, which occurs due to significant up or down deviation of variable value from the average value. Secondly, this method obviously cannot deal with problem of zero flows either as it automatically excludes zeros because the logarithm of zero is undefined. Even if all trade values are positive, the error value is correlated with covariates, that leads to inconsistence of estimations.
2.1.2 PPML  Poisson Pseudo Maximum Likelihood Estimator
The failure of OLS estimator to solve main gravity challenges makes us to choose another estimator. The most frequently used in empirical literature estimator that is used for analysis of our models is Poisson Pseudo Maximum Likelihood estimator (PPML). The adequacy of it for gravitybased models was explained by Silva and Tenreyro (2006). They stated that method gives consistent estimations of gravity model regressions unlike OLS estimator under only one assumption  correctly chosen variables for model specification.
The Poisson regression estimates expected values according with following:
According to Silva and Tenreyro (2006) taking into account fulfillment of Jensen's inequality of logarithm of expected value of variable to expected value of its logarithm (E[ln ln(E[)) loglinear estimator gives biased results when heteroscedasticity of errors takes place.
In current paper we apply the PPML with population averaged effects as in the paper by Cameron and Trivedi (2013). The model averages individual effects, and the main condition of results consistence is the same as in the pooled OLS:
There are three main properties of PPML estimator that makes it suitable for estimation of gravity models. Firstly, it should be noted that Poisson distribution of data is not necessary for PPML method application, so it can be used in analysis of not only count data, but also of more extended nonlinear models with use of dummies such as gravity model. Secondly, as it employs the logarithm of the error it includes variance, thus it is able to cope with its heteroscedasticity, which makes it impossible to obtain consistent unbiased results due to correlation of error term with explanatory variables. Thirdly, it naturally include zero values, which are automatically dropped from the process of estimation by OLS method because logarithm of zero cannot be counted. However, trade data abounds in zero values because not all countries are willing to export some goods to all countries. Third property was stated by Khosla (2014): PPML coefficients of explanatory variables can be interpreted as semielasticities, that makes interpretation as simple as in the case of OLS.
2.2 Data description
2.2.1 Variables description and expected signs
In order to achieve the aim of the paper empirical analysis of panel dataset, compiled from several gravity and trade data sources, was conducted. Data was collected for 29 countries of AsiaPacific region listed in Appendix A between 1996 and 2012.
The list of variables, their short descriptions, measure values and sources of data on them are presented in Appendix B. There are six sources, employed for compiling our dataset: Trade Statistics Database (UN Comtrade), World Integrated Trade Solution (WITS) website, the World Bank website, Research and Expertise on the World Economy website CEPII, Kellogg Institute for International Studies exploring democracy and human development website and UN Department of Economic and Social Affairs website.
Countries are organized in pairs (every country of the region with every country of the region) and their own characteristics and geographical bonds between them are included for every year in the observed period.
In models analyzed in further sections logarithms of export and import values are used as dependent variables. They are main indicators of international trade intensity between countries in pairs.
In the first interpretation of gravity model by Tinbergen (1962) only variables, expressing economic sizes of trading countries and circle distance between them, were employed for explanation of trade patterns between countries. We also use this variables and make an accent on their significance and interpretation in the process of result estimation. Economic sizes of trading countries are expressed in Gross Domestic Product values, the indicator of cost of final goods and services that are produced on the territory of the country in observed year. This measure was brought for both countries in pairs. Tinbergen (1962) stated that trade flows positively depend on economic sizes of both countries, because higher GDP value of countryreporter means greater ability to export goods and services abroad whereas GDP value of countrypartner is the indicator of value of demand for these exported goods. Thus, empirical analysis conducted in further sections is expected to reveal positive signs of coefficients for both of these variables.
Another explanatory variable that is of great importance is distance between countries in pairs. Hummels (2001) stated that distance between destinations is a proxy of costs on transportation of goods and services from countryexporter to countryimporter. In the CEPII dataset four approaches to estimation distance were employed, however we took only two of them as different specifications of models: geodesic distance between capitals and weighted by population distance. According to Tinbergen (1962) again, trade flows should negatively depend on distance. Thus, we expect that regardless the way of distance calculation, the estimations of this variable will give negative sign.
For calculation of geodesic distance between capitals the great circle formula is used:
)
where R=6371  radius of the Earth,  latitudes of capitals of countries i and j respectively,  longitudes of cities in countries i and j respectively. Latitudes and longitudes of cities should be in radians, so that if they are in degrees, they should be multiplied by 57.3.
However, there are some problems with use of distance calculated in such way. Firstly, the North Pole is not taken into consideration, because there are no any trade roots. Furthermore, if area of countries is large enough calculation of distances only between capitals can lead to wrong results. Moreover, according to Head (2003) costs of transportation include also cost of loading goods in the transport and other activities that do not depend on distance between destinations.
The approach of weighted by population distances uses share of population in capitals in the overall population of each country in the pair. The formula for weighted distance calculation introduced by Head and Mayer (2002) is as follows:
where  weighted distance between countries i and j,  population of city k in the country I,  overall population of the country i,  population of city l in the country j,  overall population of the country j,  sensitivity of bilateral trade flows to distance between cities k and l in countries i and j respectively (). is considered to be equal 1.
It is crucial for those who investigate trade patterns to estimate distance between destinations for several reasons. Firstly, as it was said above, it indicates the amount of possible transportation costs. Secondly, more distant countries might have greater cultural differences, that makes an obstacle for international trade. Thirdly, it is more problematically to get information about markets of distant countries. One more explanatory variable that is often used in contemporary empirical literature is remoteness, that was counted with use of formula suggested by Head (2003):
where  distance between countries i and j,  share of GDP value of countrypartner of country i in the world GDP.
According to formula above remoteness is distance weighted by GDP. So it may be considered as one more approach to distance calculation. Initially, another formula of remoteness calculation was suggest by Helliwell (1998):
However, this formula contains a significant mistake: if distance is high and the relative economic size of country is small, the remoteness can overestimated. So, the formula by Head (2003) is a more appropriate way of distance estimation. Remoteness was counted for both types of distances described above. The sign of this variable is expected to be positive according to statement of Silva and Tenreyro (2006) that trade flows between countries are larger for countries that have larger distances to all other countries observed, reflected by the remoteness variable.
Besides distance, the analysis employed other variables showing geographic bonds of countries in the pair are:
1. Contiguity or existence of common border  dummy, that is equal 1 if countries in the pair share common border and 0 if they do not. The expected sign for this variable is plus as transaction costs of transportation goods to neighboring countries are lower than to distant countries. Furthermore, it is more possible, that these countries are culturally similar, that increases the willingness to export there.
2. Common official language  dummy, that is equal 1 if population in both countries speaks common official language and 0 if they do not. It shows cultural identity of countries and facilitates better interaction due to ability to communicate using common language. Consequently, it is expected that sign will be positive.
3. Dummies for being the part of the same continent: dummy for being a part of America, dummy for being a part of Asia and dummy for being a part of Pacific. Countries are more often trade with countries belonging to the same continent due to its lower remoteness. The sign of this variables is expected to be positive, however there can be exceptions in some specifications.
4. Dummy for being part of one country in the past or now. It equals one if countries in the pair have ever been united by one country and 0 if not. The expected sign is positive as the variable also indicates cultural bonds of countries. Moreover, it is more possible that population speaks common language in such countries.
5. Colonial dummy variable equals 1 if countries have ever had a common countrycolonizer and equal 0 if not. The same justification as for the previous variable is acceptable. The sign is expected to be positive.
Another block of geographic variables includes:
1. Area of countrypartner  the inclusion of this variable can be justified also by increase of transportation costs with greater area of countrypartner as the citydestination may be distant from the border. Furthermore, Silva and Tenreyro (2006) stated that smaller countries are usually more open to international trade flows. Sign of the variable is expected to be negative for this reason.
2. Landlockedness of both countries  dummies for countrypartner and for countryreporter that equal 1 if there is no access to seas and 0 if the country is not landlocked. The expected sign is negative, as absence of access to seas increases transportation costs. The only one country Mongolia  is landlocked among those that we included into our analysis. So, we can investigate influence of its landlockedness on trade of AsiaPacific countries with it.
Geographic variables are crucial when analyzing international trade flows. Distance, common language, common border, access to seas and others are indicators of transaction costs for movement of goods and services. These variables explain why some countries trade with these particular countries. It is expected that weighted distances better fit our regression analysis due to the fact that in the list of countries some have a considerable area and estimating distance between only capitals or the most populated cities can give biased results.
The last block of variables reflects existence of agreements between countries in pairs. The dataset for agreements was downloaded from Kellogg Institute for International Studies exploring democracy and human development website and initially included types of agreements: 1  preferential trade agreement, 2  free trade agreement, 3  customs union, 4  common market, 5  economic union. However, among observed in our research countries there is no any pair that had 4^{th} and 5^{th} and the 3^{rd} one have only 3% of country pairs, so it is not significantly influence integration in the region. For 1 and 2 types dummy variables, that equal 1 if countries have this type of agreement and 0 if they do not, were created. Participation in agreements liberalizes international trade by lowering tariffs for countriesmembers, consequently signs of coefficients are positive.
2.2.2 Data heteroscedasticity and descriptive statistics
Firstly, it should be decided whether the problem of heteroscedasticity of trade variables exists in order to prove that OLS method is not appropriate estimator for gravity model. For this purpose, BreuschPagan and White's tests were conducted. The null hypothesis for both of them is that variances are constant, that means that there is no heteroscedasticity problem. Results of tests are presented in tables in Appendix C.
The null hypothesis about homoscedasticity of trade variables is rejected since chisquare statistics has pvalue less than even 1%. It means that problem of heteroscedasticity of trade variables exists. Thus, OLS estimations are inefficient and standard errors are biased. This can lead to erroneous interpretation of regression results. That is the reason for choice of another method of estimation gravity model, which is able to cope with problems associated with use of OLS.
Table with descriptive statistics of continuous variables is presented below.
Some crucial points can be revealed from this table. Firstly, the number of observations for import and export is less for export and import values: we have some missing variables. Secondly, standard deviation form average value is significantly high and there can be outliers both to up and down direction. Thirdly, minimum values of both variables are low. These 3 points show that our data suffers from the problem of zero values, which was described above. Moreover, according to areas minimum and maximum values, both extremely large countries (like Russia) and extremely small (like Singapore).
Descriptive statistics for binary variables is presented in appendix D. We can observe that the majority of countries included into our research is situated in Asia, and the situation when both countries in the pair are situated is most frequent for Asia too. Only 5.7% of countries have common borders and 17% of pairs of countries have common official language. There are 14.53% pairs of countries that have free trade agreements and 16.02% that have preferential trade agreements.
2.3 Models and estimation results
2.3.1 Ordinary least squares estimations
The first step of our empirical analysis is to estimate dataset employing method of OLS as it was suggested in the first work applying gravity law to explanation of trade flows between countries by Tinbergen (1962). The set of variables is the same in his work, though one more specification was added  distance weighted by population is included into the set of variables estimated.
Equation estimated is the following:
where  the trade flow from country i to country j (export or import), ,  gross domestic products of countries i and j respectively,  distance between countries i and j (geodesic between capitals or weighted by population).
Table below presents results of estimation. (1) and (3) columns present results for specification with geodesic distances between capitals and (2) and (4) columns are results for specification with weighted by population distances.
(1) 
(2) 
(3) 
(4) 

lnexp 
lnexp 
lnimp 
lnimp 

lngdp_p 
0.934^{***} 
0.934^{***} 
1.187^{***} 
1.188^{***} 

(0.00788) 
(0.00779) 
(0.00758) 
(0.00751) 

lngdp_rep 
1.166^{***} 
1.167^{***} 
0.973^{***} 
0.973^{***} 

(0.00814) 
(0.00805) 
(0.00772) 
(0.00764) 

lndistcap 
1.532^{***} 
1.429^{***} 

(0.0165) 
(0.0159) 

lndistw 
1.584^{***} 
1.474^{***} 

(0.0166) 
(0.0160) 

_cons 
22.28^{***} 
21.87^{***} 
24.65^{***} 
24.27^{***} 

(0.319) 
(0.316) 
(0.305) 
(0.303) 

N 
12683 
12683 
12910 
12910 

r2 
0.751 
0.756 
0.774 
0.778 

Standard errors in parentheses
^{*} p < 0.05, ^{**} p < 0.01, ^{***} p < 0.001
All coefficients are significant. Our results coincide with conclusions, made by Tinbergen: signs of coefficients for GDP values of trading countries are positive and signs for distances are negative. That means that international trade flows are proportional to economic sizes of trading countries and are an inverse function of distance between countryreporter and countrypartner.
Now more variables are included into regression, particularly we added remoteness variable dummies for common official language, contiguity, colonial ties, free trade agreements, preferential trade agreements, location in common continent America and location in common continent Asia. Furthermore, two different types of remoteness of countryreporter from other countries in the world are employed: calculated with use of geodesic distances and calculated with use of weighted by population distances.
Table for results is represented below. Columns (1) and (2) show specification with use weighted distances and (3) and (4) column represent specification with use of geodesic distance between capitals of countries in pairs.
(1) 
(2) 
(3) 
(4) 

lnexp 
lnimp 
lnexp 
lnimp 

lngdp_p 
0.959^{***} 
1.251^{***} 
0.957^{***} 
1.243^{***} 

(0.00906) 
(0.00973) 
(0.00917) 
(0.00980) 

lngdp_rep 
1.152^{***} 
0.980^{***} 
1.153^{***} 
0.975^{***} 

(0.00900) 
(0.00783) 
(0.00902) 
(0.00778) 

lndistw 
1.544^{***} 
1.446^{***} 

(0.0329) 
(0.0328) 

lnremdw1 
0.312^{***} 
0.160^{***} 

(0.0384) 
(0.0386) 

lnarea 
0.0947^{***} 
0.129^{***} 
0.0968^{***} 
0.128^{***} 

(0.00667) 
(0.00624) 
(0.00672) 
(0.00629) 

contig 
0.308^{***} 
0.238^{***} 
0.207^{**} 
0.145^{*} 

(0.0638) 
(0.0649) 
(0.0699) 
(0.0707) 

colony 
0.303 
0.124 
0.222 
0.0678 

(0.177) 
(0.201) 
(0.152) 
(0.173) 

smctry 
0.314^{***} 
0.580^{***} 
0.569^{***} 
0.823^{***} 

(0.0835) 
(0.0805) 
(0.0888) 
(0.0842) 

comlang_off 
0.879^{***} 
0.658^{***} 
0.903^{***} 
0.710^{***} 

(0.0356) 
(0.0346) 
(0.0359) 
(0.0351) 

fta 
0.591^{***} 
0.542^{***} 
0.680^{***} 
0.657^{***} 

(0.0386) 
(0.0384) 
(0.0385) 
(0.0381) 

pta 
0.273^{***} 
0.0381 
0.290^{***} 
0.0815^{*} 

(0.0432) 
(0.0374) 
(0.0436) 
(0.0382) 

as_comcont 
0.295^{***} 
0.500^{***} 
0.0516 
0.235^{***} 

(0.0550) 
(0.0542) 
(0.0538) 
(0.0538) 

am_comcont 
1.085^{***} 
0.783^{***} 
0.916^{***} 
0.711^{***} 

(0.0521) 
(0.0511) 
(0.0522) 
(0.0522) 

lndistcap 
1.372^{***} 
1.275^{***} 

(0.0315) 
(0.0317) 

lnremdcap1 
0.395^{***} 
0.246^{***} 

(0.0286) 
(0.0310) 

_cons 
24.18^{***} 
23.15^{***} 
26.55^{***} 
23.69^{***} 

(0.518) 
(0.488) 
(0.478) 
(0.460) 

N 
12683 
12910 
12683 
12910 

r2 
0.777 
0.797 
0.774 
0.794 

p 
0 
0 
0 
0 

Standard errors in parentheses
· p < 0.05, ^{**} p < 0.01, ^{***} p < 0.001
According to r2 and significance of coefficients the 1^{st} specification with weighted distances suits better for the purposes of the research.
Signs of all variables that are significant except common continent dummies coincide with expected signs. We have only one insignificant coefficient for variable reflecting previous and current colonial ties between countries (having the same colonizer) that may be connected with the fact that the low quantity of pairs of countries in this region that have ever had colonial bonds. Furthermore, cities like HongKong (a part of China), which have large trade turnover, were included in list of countries. That may bias the estimated parameter.
Application of OLS to our data revealed the same result for main variables as in Tinbergen's work: trade flows are proportional to economic sizes of countries and negatively depend on distance between them. As it was justified by Silva and Tenreyro (2006) the larger remoteness ratio from other countries in the world the greater exportimport flow in observed pair of countries. So, the sign coincides with their conclusion.
As it was stated before our dataset doesn't satisfy all limitations of OLS method. And application of this estimator gives wrong interpretations of results. For example, GDP of both partner and reporter in all described researches are close to 1. Coefficients in the table prove this peculiarity of OLS estimator, that shows that heteroscedasticity impact is greater in such models. It seams reasonable to move to another method of estimation. We have chose one of the most frequently used  Poisson Pseudo Maximun Likelihood estimator (PPML).
2.3.2 Poisson PseudoMaximum Likelihood estimations
Populationaverage effects PPML estimator was chosen for estimation of the model, because the variance of variable for areas of countries is large and population has uneven distribution. In such way it can be taken into consideration.
Another set of variables was chosen, comparatively to OLS estimation.
Firstly, the dependent variable is expressed in logarithm of trade turnover that is counted as sum of export and import flows. That variable expresses the total international trade activity of countrypartner. Dummies for common continents, being a part of the same country and having in the past or in the present common colonizer were excluded from observation.
The (1) column reflects specification that uses weighted by population distances and the (2)  that uses geodesic distances and remoteness ratios respectively.
(1) 
(2) 

lnexpimp 
lnexpimp 

lngdp_rep 
0.0771^{***} 
0.0770^{***} 

(0.00236) 
(0.00233) 

lngdp_p 
0.0730^{***} 
0.0731^{***} 

(0.00265) 
(0.00265) 

lndistw 
0.0889^{***} 

(0.00648) 

lnremdw1 
0.0437^{**} 

(0.0122) 

lnarea 
0.00780^{***} 
0.00809^{***} 

(0.00232) 
(0.00233) 

landlocked_rep 
0.0140^{***} 
0.0123^{***} 

(0.0565) 
(0.0564) 

landlocked_par 
0.0288^{***} 
0.0288^{***} 

(0.0382) 
(0.0383) 

contig 
0.00703^{***} 
0.0127 

(0.0234) 
(0.0237) 

comlang_off 
0.0605^{***} 
0.0591^{***} 

(0.0136) 
(0.0137) 

fta 
0.0108^{**} 
0.0123^{*} 

(0.00784) 
(0.00782) 

pta 
0.0343^{***} 
0.0338^{**} 

(0.00924) 
(0.00924) 

lndistcap 
0.0865^{***} 

(0.00643) 

lnremdcap1 
0.0441^{***} 

(0.0108) 

_cons 
0.313^{*} 
0.519^{***} 

(0.133) 
(0.128) 

N 
13804 
13804 

chi2 
4285.7 
4280.1 

df_m 
11 
11 

Standard errors in parentheses
^{*} p < 0.05, ^{**} p < 0.01, ^{***} p < 0.001
All coefficients except contiguity in 2^{nd} specification are significant and have predicted signs. The 1^{st} specification has a more appropriate set of variables, particularly due to use of weighted distances. As it was stated in previous sections coefficients can be interpreted as semielasticities as in the case of OLS estimation.
The model proves results of all researchers investigating effects of geographic characteristics on values of exportimport trade flows: rise in GDP values of countriespartner leads to rise in their trade turnover (if GDP of countryreporter rises on 1% trade turnover rises on 7,7%) and distances have negative signs in both specifications (if weighted distance is 1% greater between countries than trade turnover is lower on 8,89%).
The increase of countryreporter's remoteness calculated with use of weighted by population distance on 1% means greater trade turnover on 4,37% as it has a positive sign. Consequently, the statement by Silva and Tenreyro (2006), that greater remoteness of the countryreporter from the rest of the world enlarges its trade turnover with the countrypartner, was proved.
If the value of variable for area of countrypartner, that has negative sign as predicted, is 1% higher the trade turnover is lower on 0,78%. This coincides with statement by Silva and Tenreyro (2006) that smaller countries are usually more open to international trade flows.
There is only one country that is landlocked  Mongolia. We can estimate an effect of its landlockedness on its trade turnover with other countries. If Mongolia is a reporting country, than its landlockedness lower trade turnover on 1,4% and if it is a partner, than on 2,88%. This appears due to higher transportation costs associated with no alternative means of transport to export or import goods.
Having a common border rises trade turnover on 0,7%. It is associated with lower transaction costs, because neighboring countries are less distant and they are more culturally similar to a countryreporter sue to its proximity.
Common official language spoken by population in both reporter and partner countries enlarges their trade turnover on 6,05%. The cause of this effect on bilateral trade flows is that trading units can easier understand each other as they speak the same language as their partner. Furthermore, this variable is the indicator of cultural similarity between countries too.
Having trade agreements between countries enlarges trade turnover, as it was stated by Anderson and Yotov (2016) leads to larger exportimport trade flows: if countries in the pair have preferential trade agreement, their trade turnover is on 3,43% larger, if free trade agreement  on 1,08%. This is associated with a greater integration of countries in AsiaPacific region.
All things considered, comparing two methods obviously leads to choice of PPML. Firstly, it was stated that it copes with two main challenges of gravity data: heteroscedasticity and zero trade flows. Secondly, it is generally assumed that logarithms of GDP of countryreporter and countrypartner should be close to 1. In OLS method this suggestion takes place, whereas PPML reveals semielasticities that are significantly less than 1. This also reflected in coefficients of all other explanatory variables, that are common in these methods: contiguity . This reflects that homoscedasticity of data significantly influences OLS estimations and leads to biased estimations. Estimations of data with use of PPML method with populationaveraged effects gives more realistic coefficients for variables.
2.3.3 Case of Russia
Russian Federation has unique geographic peculiarities that cannot be avoided when analyzing its trade patterns with other countries: enormous area, a great amount of neighboring countries, the location both in Asia and Europe and etc. After difficult for this country XXth century its extent of international trade liberalization have increased and now it is more opened for cooperation with other countries in the world. However, the trade accent moves from Europe countries to AsiaPacific countries that have grown economically a lot in last decades. That requires an empirical analysis. In the previous section, we have revealed that the 1^{st} specification gives better results. The results of its test for Russia are represented in the table below. However due to absence of free trade agreements between Russian Federation and countries of AsiaPacific region there is no sense to include this variable, furthermore the variable for landlockedness of countryreporter can also be deleted as it contains only of zeros, as we know that only Mongolia is landlocked in our list of countries. Furthermore, there is no sense to include remoteness as it is similar for all partners.
(1) 

lnexpimp 

lngdp_rep 
0.0131^{**} 

(0.00325) 

lngdp_p 
0.0521^{***} 

(0.00259) 

lndistw 
0.0961^{***} 

(0.00598) 

lnarea 
0.0105^{***} 

(0.00245) 

landlocked_par 
0.0003^{*} 

(0.00283) 

contig 
0.01621^{***} 

(0.0095) 

pta 
0.0749^{**} 

(0.00854) 

_cons 
0.313^{*} 

(0.125) 

Standard errors in parentheses
· p < 0.05, ^{**} p < 0.01, ^{***} p < 0.001
All coefficients are significant at least on 5% level. The increase of the value of GDP of Russia on 1% increases trade turnover on 1,31%, increase in GDP value of countrypartner increases it on 5,21%. The 1% greater distance weighted by population between Russia and countrypartner means the lower on 9,6% value of trade turnover. Greater on 1% partner's area is associated with lower Russia's trade turnover on 1,05%. Landlockedness of Mongolia lowers Russian trade turnover on 0,03%. Trade turnover with countriespartners that have common border with Russia is 1,62% higher. The participance in preferential trade agreements with USA and Canada increases trade turnover with them on 7,49% comparatively with other countries.
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