Economic Modelling

This study uses monthly housing prices data of the four cities, Seoul, Hong Kong, Tokyo, and New York, from January 1993 to April 2016. We consider Tokyo and Hong Kong residential property prices, obtained from the International Monetary Fund (IMF) Global Housing Watch; housing purchase price composite indices for Seoul, from the Korea Appraisal Board; and the S&P/Case-Shiller home price index for New York which, from the Federal Reserve Economic Data of Federal Reserve Bank of St. Louis.

This is the dataset which we used for our econometric analysis.

Data-set for spatial fiscal interactions using SDM. Paper: The Role of Infrastructure Investment and Factor Productivity in International Tax Competition.

Our dataset is annual and comprehends the period from 1980 to 2014 for 20 Latin American and 14 Asian countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Guatemala, Honduras, Haiti, Jamaica, Mexico, Nicaragua, Panama, Peru, Paraguay, El Salvador, Uruguay, Venezuela; Bangladesh, China, Indonesia, India, South Korea, Sri Lanka, Myanmar, Mongolia, Malaysia, Nepal, Pakistan, Philippines, Singapore, Thailand). The time span was chosen given data availability. Export and import series were obtained from the Atlas of Economic Complexity that uses raw data for goods, as reported to the United Nations Statistical Division (COMTRADE), and for services, from the International Monetary Fund (IMF). We chose a 11-sector level of aggregation because it allows us to address sectoral differences keeping the analysis as simple as possible. Gross Domestic Product (GDP) series and price deflators were obtained from the Penn World Table (PWT) 9.0. For each country, output of the rest of the world corresponds to the sum of GDP of all countries in the PWT minus domestic GDP. Finally, ECI and COI indexes also come from the Atlas of Economic Complexity. The last columns of both files report our estimates of Thirlwall's law.

This paper identifies the key problem in studies related to the global value chain, which is estimating the import matrix. Here, a dual trade regime is incorporated into a new set of China's input-output tables, using data for the period 1997-2015. This study re-estimated the domestic content in China's exports, based on a special input-output (IO) database. Contrasting this database with alternative generic databases suggests the special IO database has promising features for evaluating the domestic content of exports.

This is the code to replicate the analysis in the paper "Mixed Frequency GVAR analysis of macro-uncertainty and financial stress spillovers in the Eurozone" by Andrea Cipollini and Ieva Mikaliunaite. # CLIFS.txt contains the Country-level index of financial stress from ECB database # GDP_uncertainty.txt contains GDP growth uncertainty index, by Rossi and Sekhposyan (2017) # weights_trade.txt contains the trade weights from BIS. # The file Rstudio_code replicate the results for full sample MF-GVAR model, in Tables 3-6 (Panels A, Full sample, h=4). # Please choose a working directory using setwd("set working directory")

Bank for international Settlements -Consolidated Banking Statistics

The dataset on business churning, productivity and product market regulation uses publicly available data from Eurostat, Ameco and OECD. Data on business churning are retrieved from the Eurostat’s Business Demography Database, which provides statistics on firms’ birth and death rates. The birth (death) rate is defined as the number of enterprise births (deaths) in the reference period (t) divided by the number of enterprises active in t. The business ‘’churn’’ – or firm turnover – is computed as the sum of the birth and death rates. Additional data from Eurostat are retrieved to compute a decomposition of labour productivity, as well as to create a measure for allocative efficiency across and within groups of firms classified by size, for country-year-sector combinations. Total factor productivity (TFP) growth is defined as the portion of output that is not explained by the amount of inputs used in production, and therefore referred to as a representation of technological progress. TFP is computed on the basis of a neo-classical Cobb-Douglas production function, as a residual of the gross domestic product after the contributions of labour and capital have been taken into account. Its level is determined by how efficiently and intensely the inputs are utilised in production. As such, the computation of TFP requires some assumptions. In particular, we assume that the elasticities of labour and capital are equal to 2/3 and 1/3, respectively. Moreover, using aggregate values of total employment in millions of persons and consumption of fixed capital in millions we assume constant skill composition of the employed skill force and constant composition of the capital stock. TFP variables are obtained using Ameco data and are available with a sectoral breakdown. Product market regulation is measured by the OECD Regulation in Energy, Transport and Communications Index (PMR ETCR). Finally, we construct an indicator which captures the cyclical position of a given sector. Following Bartelsman et al. (1994), the indicator is constructed using the growth of downstream sectors, i.e. sectors that buy inputs from the sector of interest. The cyclical indicator is computed using World Input-Output Tables, providing data in years 2000-2014 (Timmer et al. 2015), and deflated by the GDP deflator. The overall (slightly unbalanced) dataset covers 28 European Union countries over the period 2000-2014.

-ISSP data, year 2006, enriched with information from -World Bank, World Development Indicators 2006 -OECD, Education at a Glance 2012

include the data, code and figure of the paper