We take the CEPII GeoDist dataset and create binary variables for several. It also provides a pre and post crisis analysis of the trade partnerships which might have future implications for trade policy and trade relations for the two countries. Comment on Data Handling of variables retrieved from the V-Dem Dataset. Data for these additional variables have been collected from the CEPII Geodist dyadic dataset (Head et al., 2010) and the CEPII gravity dataset (Head and.
When crisis is introduced in the analysis, post crisis, common colony became an important influencer of trade for India.Īpplications/Improvements: This study helps in identifying the key determinants of India and China’s trade flows. China’s trade is influenced by higher per capita income of the trading partner and common language. The GeoDist webpage provides two distinct files: a country-specific one (geocepii)and a dyadic one (distcepii) including a set of different distance and common dummy variables used in gravity equations to identify particular links between countries such as colonial past, common languages, contiguity. Additionally, India’s trade flows are with countries having higher GDP but with lower per capita income. It compares the determinants of trade for India with China.įindings: The findings of the empirical analysis are in accordance with past literature indicating that India and China trade flows are mostly with geographically closer countries. There are two distinct files: a country-specific one (geocepii.xls or geocepii.dta) and a bilateral one (distcepii.xls or distcepii.dta), including the set of different distance variables and common dummy variables used in gravity equations to identify particular links between countries such as colonial past, common languages, contiguity. The study employs random effects panel regression model to establish the relationship between trade flows and different variables including distance, gross domestic product, per capital income, contiguity, common language and common colonizer. GeoDist provides several geographical variables, in particular bilateral distances measured using citylevel data to assess the geographic distribution of population inside each nation. GeoDist makes available the exhaustive set of gravity variables used in Mayer and Zignago (2005).
The data has been collected from International Monetary Fund (IMF), CEPII’s Geodist database and United Nations Conference on Trade and Development (UNCTAD). Notes on CEPII’s Distances Measures: The GeoDist Database. Methods/Statistical Analysis: To achieve the objective, gravity model has been applied on a panel dataset for the two countries. This study analyses the trade flows of two of the emerging economies namely China and India with an objective to draw a comparison between the determinants of bilateral trade flows of the two nations using data for a period of 9 years (2004-2013). Emerging economies gained a lot of momentum in world trade. Background/Objectives: According to the World Trade Organization data for 2013, China became world’s largest trading nation and India became the 15th largest trading nation.
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