برآورد پتانسیل تجارت محصولات سرامیکی و مصنوعات شیشه‌ای در گروه کشورهای cis (براساس مدل جاذبه)

با توجه به روند رو به رشد تجارت ایران با کشورهای cis در کالاهای محصولات سرامیکی و مصنوعات شیشه ای و به‌منظور آگاهی سیاست‌گذار از مهم‌ترین عوامل موثر بر تجارت ایران با کشورهای موردبررسی، در این مقاله سعی شده است با استفاده از داده های تلفیقی سال های 2019-2009 و بر اساس مدل جاذبه، پتانسیل تجارت ایران با کشورهای گروه cis به تفکیک گروه های hs68 (محصولات سرامیک، شیشه و مصنوعات از شیشه)، hs69 (محصولات سرامیکی) و hs70 (شیشه و مصنوعات شیشه ای) بررسی شود. تجزیه‌وتحلیل اطلاعات مرتبط با کشورهای طرف تجاری نشان داد که بزرگ‌ترین شریک تجاری ایران در گروه کالایی hs68 کشورهای قزاقستان، آذربایجان و روسیه و در گروه کالایی hs69 و hs70 کشورهای آذربایجان، ارمنستان، قزاقستان و روسیه می باشند. نتایج برآورد نشان داد که قدرت توضیح دهندگی مدل برای هر سه گروه کالایی با استفاده از روش های حداقل مربعات معمولی، اثرات ثابت و اثرات تصادفی بالا بوده و متغیرهای اندازه و ابعاد اقتصادی تاثیر مثبت و معنی دار و عدم توازن تجاری و مسافت تاثیر منفی و معنی داری بر جریان تجاری کشورهای گروه cis داشته اند.

Estimating the trade potential of ceramic and glass products in Iran and CIS(Based on the gravity model)

1 IntroductionAn overview of the main suppliers of important imported goods in the CIS group shows that Iran is one of the main producers of ceramic products and glass products in the growing market of this region due to its comparative advantage and Iranian companies have a large capacity to meet the needs of CIS countries. In this regard, the present article analyzes the factors affecting the trade of ceramic products and glass products by HS codes as described in HS68, HS69 and HS70 in CIS trade partners (which includes the Republic of Azerbaijan, Armenia, Russia, Kyrgyzstan and Kazakhstan). The data covers the 20092019 period, and a panel data model is estimated by using methods of ordinary least squares, fixed effects and random effects.2 Theoretical FrameworkAfter the end of World War II, international trade grew faster, so that in recent years the world trade has grown largely faster than world production. Meanwhile, the share of developed countries in trade has been growing more than the total trade. Analysis of trade flows between countries showed that the exports with an emphasis on industrial goods is increasing in all countries. As trade growth increased, various models were introduced to explain business flows, the most practical of which was the Gravity model, which is widely used in international trade to explain business flows, to determine business potential, and to examine the effects of integration on Bilateral trade, etc. The gravity model is a simple model for analyzing bilateral business flows between geographic entities. In the 1980s, gravity models showed that economic growth, productivity, human capital, and economic freedom were among the factors influencing trade. They also showed that trade is affected by factors such as conditions in the origin country, economic scale, differences in the stock of factors of production or technology.3 MethodologyThe general form of the gravity model is:Where Tijt is the trade volume of ceramic products and glass products from country i  to country j, yit is the GDP of the exporting country, Yjt is the GDP of the importer country, Zijt denotes variables affecting the flow of trade such as distance between countries (in kilometers), trade imbalances, etc., uijt is a random disturbance term iid (normally and independently distributed). In order to facilitate the estimation, the above model was linearized as follows. represent elasticities. The logarithmic form of the formulated gravity model is: where Tijt is the trade volume of ceramic products and glass products of country i to country j. yit is the GDP of the exporter country. This variable represents the size of the economy of the exporting countries. yjt is the GDP of the importer country. This variable represents  the size of the economy of the importer country. is the degree of trade imbalance between the exporting country and the importing countries:Where () export (import) of country i to (from) country j at time t.  is the distance between the exporter country and the importer country and  represents the random disturbance term iid (normally and independently distributed ).4 Results DiscussionThe model is estimated by conventional least squares method, fixed effects and random effects by commodity groups HS68 (ceramic products, glassware and glass products), HS69 (ceramic products) and HS70 (glassware and glass products), for CIS countries, by using STATA14 software. the estimation results are presented in the following three tables. Table1. Results of gravity model estimation for HS68 group by different panel methodsvariableMethod        OLS  FE  RE  Coef.SECoef.SECoef.SE 1.580.33 1.330.33 1.940.34 1.450.34 1.230.33 1.940.26 0.060.04 0.020.04 0.030.03 0.170.04 0.160.04 0.160.04Constant0.070.04 0.050.03 0.020.03 Table2. Results of gravity model estimation for HS69 group by different panel methodsvariableMethod        OLS  FE  RE  Coef.SECoef.SECoef.SE 0.530.09 0.550.13 0.560.12 1.230.33 1.320.24 1.440.26 0.160.04 0.240.04 0.210.04 0.230.04 0.240.06 0.230.06Constant0.030.03 0.030.03 0.040.04 Table3. Results of gravity model estimation for HS70 group by different panel methodsvariableMethod        OLS  FE  RE  Coef.SECoef.SECoef.SE 0.560.22 0.620.25 0.620.52 1.220.16 1.140.33 1.310.32 0.120.04 0.150.04 0.220.04 0.020.04 0.160.04 0.040.04Constant0.070.03 0.050.03 0.040.035 Conclusions SuggestionsTo estimate the value of trade between countries, a differential gravity model of bilateral trade flows was formulated and estimated with panel data from 2009 to 2019 for each of the commodity groups HS68 (ceramic products, glass and glass products), HS69 (ceramic products) as well as HS70 (glass and glass products). The parameters were estimated with a large database by using ordinary least squares, fixedeffects and randomeffects methods. For the three commodity groups, the results were stable across methods. For HS68, exports were elastic with respect to the gross domestic product (GDP) of exporters and importers GDP. For HS69, exports were inelastic with respect to the exporters GDP and elastic with respect to importers GDP. Exports of HS70 were inelastic with the exporters GDP and elastic with respect to the importers. Results show that geographical distance and trade imbalance is negative and significant; trade increases if the transportation costs decrease. We also introduce the economic dimension and income percapita; these proxies confirm the positive effects in bilateral trade.