lWP5f71 POLICY RESEARCH WORKING PAPER 2701 Trade and Production, A new database eases the way for researchers analyzing 19 76-99 statistics on trade, production, and tariffs. Alessandro Nicita Marcelo Olarreaga The World Bank Development Research Group Trade H November 2001 POLICY RESEARCH WORKING PAPER 2701 Summary findings Nicita and Olarreaga have prepared this paper as a Solution, or WITS, software) and include imports and companion to the Trade and Production database, which exports. Data on mirror exports (reported by trading contains trade, production, and tariff data for 67 partners) were obtained using WITS. The trade data are industrial and developing countries at the industry level aggregated by region and income group, as defined by for 1976-99. The sector disaggregation in the database the World Bank. A separate data set provides bilateral follows the International Standard Industrial trade flows (by partner) at the industry level. Classification (ISIC), with data provided at the three- The data on average tariffs (most favored nation) are digit level (28 industries) for all 67 countries and at the from the Trains database maintained by the United four-digit level (81 industries) for 24 of these countries. Nations Conference on Trade and Development and The production data are from the United Nations from the World Trade Organization's Trade Policy Industrial Development Organization's Industrial Reviews and Integrated Database. Statistics Database at the three- and four-digit level of The database also provides an input-output table for ISIC. They include value added, total output, average each country using data from version 4 of the Global wages, capital formation, number of employees, number Trade Analysis Project (GTAP) database. of female employees, and number of firms. The database is available on request on CD-ROM in a The trade data are from the United Nations Statistics series of ASCII files and Microsoft Excel worksheets. It is Division's Commodity Trade (Comtrade) database also available on the Web at http://www.worldbank.org./ (through the World Bank's World Integrated Trade research/trade. This paper-a product of Trade, Development Research Group-is part of a larger effort in the group to study the determinants of trade patterns. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Lili Tabada, room MC3-333, telephone 202-473-6986, fax 202-522-1159, email address Itabada@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at anicita@worldbank.org or molarreaga@worldbank.org. November 2001. (23 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Policy Research Dissemination Center Trade and Production, 1976-99* Alessandro NicitaL Marcelo Olarreaga® ' This paper accompanies the Trade and Production Database which is available at www.worldbank.org/research/trade or on request in electronic format (CD-ROM). The database is provided on an "as is" basis. The construction of this database has been funded by the World Bank's Research Support Budget and the Export Promotion Thematic Group. 'Development Research Group, Trade, World Bank, 1818 H Street, NW, Washington, D.C. 20433, email:anicita@worldbank.org e DECRG, World Bank and CEPR, London, email:molarreaga@worldbank.org 1. Introduction The Trade and Production Database described herein produces a readily available and comprehensive set of data. The purpose of this database is to facilitate the long and cumbersome tasks which researchers periodically face when collecting and organizing data. The database merges trade, production and tariff data available from different sources into a common classification: the International Standard Industrial Classification (ISIC), Rev. 2. Data availability varies, but the database potentially covers 67 developing and developed countries over the period 1976-1999. In that respect this database is a complement to other existing datasets, such as those provided in Feenstra (1996, 2000) and Feenstra, Lipsey and Bowen (1997).' The United Nations Industrial Development Organization (UNIDO) is the source for production related data. It was obtained from the CD-ROM version of their Industrial Statitistics Databases at the 3 and 4 digit level of the ISIC classification.2 The main source of trade data is the United Nations Statistical Office, which collects data from individual countries, and then reports the data in the Commodity Trade Statistics (COMTRADE).3 The World Bank's World Integrated Trade Solution (WITS) was used to "mirror" trade using partners data, when countries did not report their trade statistics to the United Nations. Tariff data are provided by two international organizations: the World Trade Organization (WTO) through the Trade Policy Review series and the Integrated Data Base CD-ROM,4 as well as the United Nations Conference on Trade and ' Feenstra (1996) covers US imports from 1972 to 1974. Feenstra, Lipsey and Bowen (1997) covers trade, production and tariff data from 1970 to 1992, but production data is only available for OECD countries. On the other hand, it includes non-tariff barriers (coverage ratios) excluded here. Feenstra (2000) covers only trade, but at a much higher level of product disaggregation than the one followed here. 2For more information on all of UJNIDO's industrial databases please visit: http://www.unido.ore/doc/50215.htmls. 3 For more information on UN's COMTRADE database and other products of the United Nations Statistical office, please visit: http:H/esa.un.org/unsd/pubs. 4 For more information on WTO products, please visit http://www.wto.org 2 Development (UNCTAD) through the Trade Analysis & Information System (TRAIPS).5 The original source for input-output tables is the Global Trade Analysis Project (GTAP).6 The various agencies utilize different classifications in the collection and publication of the data. The main accomplishment of this database is the grouping of the data and the matching of the different classifications. In particular, the Standard International Trac.e Classification (SITC) or the Harmonised System (HS) generally used to report trade and tariff data do not match in any straightforward way the classification used for industrial output. To address this problem, the Organization for Economic Cooperation and Development (OECD) has developed a concordance that approximates quite effectively SITC codes within the ISIC classification. The OECD's filter is the one used in the production of this database to filter trade data. A concordance from HS to ISIC has also been used to filter tariff data into the ISIC classification. All filters are provided in the CD-ROM under the directory called "concordances". The next section describes the different dimensions of the database and data availabil ity across time, countries and sectors. Section 3 briefly underlines some important issues to be aware of when utilizing the kind of data provided here. Finally, section 4 illustrates the technical aspects of the database. Four appendices describe in more detail various aspects of the database. 2. Description of the Database on Trade and Production The database is constructed using the ISIC classification and includes trade, production, and tariff data. Depending on the country, the database covers a time span from the late '70s to the late '90s. When available, the database is accompanied by data on tariffs, and an input/output table, provided for each country. The Trade and Production Database is 5For more information on UNCTAD's TRAINS database, please visit http ://www.unctad.org/trains/index.htm 6For more information on GTAP's dataset, ptease visit http://www.agecon.purdue.edu/gtap/ 3 divided in two independent databases. The first database covers 67 countries and reports data at the 3 digit ISIC classification for a total of 28 manufacturing sectors. A second database covers a subset of 24 countries and reports the data at the more disaggregated 4 digit ISIC classification, covering 81 manufacturing sectors. Each data set is extensively quality controlled and examined for anomalies. Appendix A illustrates the different dimensions of the database and data availability. 2A. Production Data The production data are collected by UNIDO and OECD through the joint annual collection program of general industrial statistics and published in the UNIDO annual commercial publication, the International Yearbook of Industrial Statistics. They are also available in electronic format (CD-ROM).7 UNIDO provides internationally consistent data by collecting annual data directly from all non-OECD member countries through UNIDO's country questionnaire. OECD collects data for its member states and provides the information to UNIDO. The data are usually obtained from industrial census statistics and then compiled into ISIC categories. The industrial data cover only the manufacturing sector and is published at two different levels of detail. The three digit level of aggregation covers 28 manufacturing sectors, while 81 manufacturing sectors are covered at the four digit level. A complete list of those sectors is provided in Appendix C. For each sector, the data on production report yearly values in thousands of US dollars on total output, value added, gross fix capital formation and average wages. The other variables, number of enterprises, total employees and female employees, have values expressed in units.8 The data published by UNIDO are by no means complete. Some countries may report few indicators, and some time series may not be complete across all years or industries. Following the usual notation, each missing observation is reported as a dot or a blank. Zeroes are reported as by UNIDO. For further details on data availability across variables see tables in appendix A. 7 The source for this database is UNIDO's CD-ROM version. 8 Appendix A describes industrial variables in more detail. 4 2B. Trade Data The trade data are collected and organized by the United Nations Statistical Office and reported in the COMTRADE database. For the purpose of the Trade and Production Database, the data are first downloaded in the SITC rev. 2 classification and then transformed into ISIC. This process utilizes the concordance filters developed by the OECD, which provides two slightly different concordance tables: one for exports anc, one for imports. These tables do not follow a one-to-one correspondence, but matching is achieved through a method involving a series of carefully estimated weights. The Trade and Production database is balanced and reports values for imports and exports. Data on mirrored exports, i.e., exports calculated using import data reported by partner couniries, are also provided.9 The World Bank's WITS system was used to mirror missing trade data. To make the database manageable, the trade flows are aggregated according to World Bank regions and by income level. The database also reports data on trade flows with particularly interesting markets such as E.U., Japan, U.S.A. and world totals, producing a total of 34 region groups. 10 Appendix D illustrates the country composition of each region/income group. For more detailed studies, we also provide bilateral trade flows by partner country in a series of separate ASCII files."1 The trade data are quite complete: there are very few country periods for which data are missing. Whenever the data are not available, the missing observations are reported as dots or blanks. All trade values are reported in thousands of US dollars. 2C. Tariff Data The tariff data utilized in the database originate from two sources: the WTO and U7NCTAD. The WTO data are published in WTO publications such as the Trade Policy Review. WTO data are also available in a CD-ROM (the Integrated Data Base CD-ROM of the WTO). The time series available from WTO sources are limited, and data are 9 Generally, import data is of better quality than import data for fiscal reasons. However, mirror data need to be use with caution as suggested by Yeats (1995). ' Due to rounding errors and aggregation issues there may be very slight differences (usually less than 0.01 %) between the sum of the different regions and totals. Users should be aware of these possible discrepancies. 5 available only for countries which have joined the WTO. The WTO reports the MFN applied tariff data already in the ISIC classification. Therefore, no further manipulation of the data is required in this case. Tariff data are also available through the TRAINS database maintained by UNCTAD. TRAINS is a comprehensive computerized information system at the HS-based tariff line level that covers tariff, para-tariff and non- tariff measures as well as import flows by origin for more than 100 countries. In the best cases, the TRAINS data start in the late '80s. UNCTAD reports the tariff data utilizing the six digit HS classification. The conversion from HS classification into ISIC is achieved using a one-to-one concordance table. TRAINS data are far from complete. While there are only a few countries for which no tariff data are available, the time series are quite sparse. Whenever data are available the Trade and Production Database reports tariff collected both by UNCTAD and by the WTO. Small discrepancies may appear mostly due to the use of different filters by the two institutions and different methods used to calculate tariff averages at the six digit level of the HS. In particular, UNCTAD calculates the simple averages using as the denominator only the actual number of dutiable lines, while the WTO includes all lines. The tariff data reported in the Trade and Production Database are MFN simple averages at the 3 or 4 digit level of the ISIC. 2D. Input/Output tables. Input and output tables are based upon the Global Trade Analysis Project (GTAP) database version 4. The GTAP database utilizes data from the early '90s in constructing its input and output tables. Only one table is provided for each country in the database. GTAP aggregates some countries in regions; therefore, those countries have the same input and output tables.12 Those interested in how GTAP constructs each particular input/output table should refer to the GTAP database or consult the GTAP web site (www.gtap.org). The tables reported in the Trade and Production Database are aggregated at the three digit level of the ISIC classification. To facilitate the use of the tables within the database, the data reflecting input and output have been broken down " The data in the bilateral trade flow files are not balanced. That is, the number of years, product groups and/or partner countries may differ across reporters. 12 If the input and output tables are not country specific, the name of the region is also reported in each table. 6 into two tables. The first table reports each manufacturing sector's share that is sold to other industrial sectors and the second table reports the value of each manufacturing sector's originating from intermediate products from each manufacturing sector. Using, this GTAP data, the Trade and Production Database provides an intermediate import share table that demonstrates the import share of intermediates utilized by each sector in each country. These tables are available only in EXCEL format. 3. Problems and special considerations The data in the Trade and Production Database have been grouped and organized to facilitate its use for a large number of purposes. It is not aimed to produce quick answers, but rather to help researchers in the lengthy and cumbersome exercise of collecting and organizing data. In order to give to researcher the maximum degree of flexibility, the data have not been changed beyond the adaptations described in section two. Nevertheless, there are a few points that need to be emphasized. Monetary data are not deflated, and are expressed in thousands of USD. If the data were not supplied in USD. the common practice was followed of using the yearly average exchange rate to convert the domestic currency to the dollar. 13 Caution should also be used when analyzing data gathered from Germany (GER).14 To have a consistent time series, data on Germnany before and immediately after unification are constructed as the sum of the Federal Republic of Germany (DFA) and the Democratic Republic of Germany (DDR). Especially for the years immediately after unification, it is possible that some data on thie DDR are not reported, therefore producing a sudden shift in the time series. The production data from UNIDO are subject to differences in national classifications and assumptions are needed to convert from the national (country specific) industrial classification into the ISIC classification. For example, similar industries may be allocated in slightly different ISIC sectors in different countries, or industries of the sarrie size may have resulted too small to be reported in some countries while they may be fully 13 The researcher should keep this in mind and treat with caution cases where there has been a large and sudden change in the exchange rate. 14 In other databases the country code identifying Germany may be "DEU" instead of the one used here "GER". 7 reported in others. This kind of problems are generally most pronounced at the more disaggregated level.15 A common issue in trade data is the presence of the label "not classified" as a partner. This is the case when the country doesn't know, or doesn't want to disclose, the origin or the destination of a trade flow. The Trade and Production Database deals with this issue in the following way: data on not classified countries are not allocated to any particular region but are reported as a separate observations. Nevertheless, it could be that the "not classified" value results as negative due to concordance aggregation between the SITC and the ISIC classifications. In this case, the negative value is split across all regions according to weights calculated on the basis of existing documented trade. This problem affects only a minimal part of the data and is equivalent in assuming that exports or imports not classified by country of destination or origin are distributed to each region using as weight the documented trade flows. 16 In the even rarer case that, after the transformation from SITC to ISIC, the documented trade flows with the regions turns to be negative, the value of "not classified" is diminished by the amount of the sum of those negative values and the negative values are set to 0. These operations are performed only on the data aggregated by region and income level. To give maximum flexibility to the researchers, the reallocation of the negative values is not performed in the ASCII file reporting country by country trade flows. Another recurring issue with trade data, as discussed in detail by Feenstra (1996) and Gordon and Feenstra (2001), is the existence of entrepots, i.e. countries where transits of trade flows take place but which do not constitute the origin or the final destination of those. In many cases the country of origin (0) reports the entrep6t (T) as the destination of the shipment. Meanwhile the entrepot country does not report the import and the final importer (F) reports the original exporter (0) as the origin. This creates a surplus (between 0 and T) and a deficit (between 0 and F). In the example above, country (F) reports an import from (0), which is not reported (as an export to F) by country (0), 15 Note that the 4 digit codes starting with 312 are commonly collapsed into 3 digit category 311. 16 Negative values were encountered in less than 0.01% of the observations. 8 creating a discrepancy. The researcher should keep in mind when analyzing entrepots such as Hong Kong, Macao, Singapore and the Netherlands. For this reason, trade data also include values of mirrored exports."7 In many cases there are huge discrepancies which are attributable to a series of different reasons such as transport costs, different product classifications, entrep6ts and poor accounting methods. It is advisable to use mirrored export only in cases where there are serious doubts about the capability of tile reporting country in managing the collection of records on trade flows. 18 4. Technical information The data in the Trade and Production Database are stored in ASCII files, which can bz read by any text editor or statistical software. In addition, part of the data is also available as MS Excel worksheets. The ASCII files are comma separated and include variable names in the first row. MS Excel files are usually self-explanatory. Table 1 describes the set of files present in the CD-ROM that encompasses the Trade and Production Database. The variables contained in each of the files of the database are illustrated in Appendix B. Table 1 Trade and Production Database Files |Directory Filename Size Description Database data4digit.csv -22mb Complete database at the 4 digit level (comma separated) database data3digit.csv -25mb Complete database at the 3 digit level (comma separated) database data4digit.xls -58mb Complete database at the 4 digit level (excel worksheets) database data3digit.xls -58mb Complete database at the 3 digit level (excel worksheets) database totalregion.cvs -2mb Total trade flows (comma separated) bilateral trade ???bilateral.csv -1-5mb Bilateral trade flows at 4 digit level (comma separated P bilateral trade totalbilateral.csv -6mb Bilateral total trade flows (comma separated) i_o tables i_o tables.xls -1 mb Input Output tables (excel worksheets) i_o tables intermediate_imports.xls <1 mb Intermediates products, sectors' imported share (excel worksheet) docs and tables trade and production.pdf -1mb The Paper (pdf format) " Mirrored data is available from 1980 to 1998. Many countries failed to report trade data before 1930 and for year 1999 therefore producing incomplete results for those years. 18 However, in some cases mirrored export may be considered more precise than exports because trade flows are usually better recorded in entrance (imports). Therefore, mirrored export carries useful and utilizable information, on a bilateral basis, in the cases where the partner countries have a good custom administration. 9 Directory Filename Size Description docs and tables regions and income.xIs <1mb Country classification to region and income (excel worksheet) docs and tables data avail.xIs <1 mb Comprehensive Data Availability Table (excel worksheet) docs and tables data avail production.xIs <1 mb Production Data Availability Table specific (excel worksheets) docs and tables othertables.xls <1mb Other tables as in the paper (excel worksheets) concordances ccode_region_incIvl.csv <1 mb concordances COMTRADE codes to World Bank Regions (text) concordances hs_isic.txt <1mb concordance table HS96 to ISIC (text) concordances sitc2_isic.txt <1mb Concordance table SITC rev2 to ISIC (text) Note: all data follow ISIC classification rev 2. 10 References Feenstra, Robert C., (1996), US. Imports, 19 72-1994. Data and Concordances. NATIONAL BUREAU OF ECONOMIC RESEARCH. WORKING PAPER SERIES (U.S.); No. 5515. Feenstra, Robert C (2000), World Trade Flows, 1980-1997, mimeo, University of California Davis, March 2000. Feenstra, Robert C., Robert E. Lipsey and Harry P. Bowen, (1997), World Trade F/lo"s, 1970-1992 with Production and Tarif Data. NATIONAL BUREAU OF ECONOMIC RESEARCH. WORKING PAPER SERIES (U.S.); No. 5910 Hanson, Gordon H. and Robert C. Feenstra, (2001), Intermediaries in entrep6t trade: Hong Kong re-exports of Chinese goods. NATIONAL BUREAU OF ECONOMIC RESEARCH. WORKING PAPER SERIES (U.S.); No. 8088 McDougall, Robert, Aziz Elbehri and Truong P. Truong, (1998), Global Trade, Assistance, and Protection: The GTAP 4 Data Base. Center for Global Trade Analysis, Purdue University. United Nations, (1975), Standard Industrial Trade Classification System, Revision 2. Statistical Papers Series M, no. 34/Rev.2, New York. United Nations Conference on Trade and Development (1999), TRAINS database. Geneva, Switzerland. United Nations Industrial Development Organization (several years), International yearbook of industrial statistics series (CD-ROM version). Vienna, Austria. World Trade Organization, Trade Policy Review Series. Geneva, Switzerland. Yeats, Alexander (1995), "Are partner country statistics useful for estimating "missing" trade data?", World Bank Policy Research Working Paper # 1501 11 Appendix A: Data Availability and Database Dimensions. Country Production Trade Tariffs Code Name 3 digit 4 digit Data WT04d WT03d TRAINS ARG Argentina 83-93(91-92) 80-99 98 92-99(94) ARM Armenia 94-97 95-99 AUS Australia 76-92 79-99 93,98 91-99(92,94-95) AUT Austria 76-98 78-99 97 88-99 BGD Bangladesh 76-92 77-99 93 89,94,99-99 BGR Bulgaria 80-97 92-99 97 BOL Bolivia 76-9B 88-95 80-99 96 96 93-99 CAN Canada 76-98 81-98 78-99 96 96 89,93-99(94) CHL Chile 76-98 85-98(87-88) 81-99 97 97 92-99(96) CHN China 77-97 84-99 92-98(95) CMR Cameroon 76-97(85-88) 90-97 76-98 94 94 94-95 COL Colombia 76-98 81-98(84) 78-99 96 96 91-99(93,98) CRI Costa Rica 76-97 80-97 81-99 94-95 94-95 95,99-99 CYP Cyprus 76-98 81-98 76-99 97 96 DNK Denmark 76-98 76-99 97 88-99 ECU Ecuador 76-97 84-95 79-99 96 96 93-99 EGY Egypt 76-96 80-95 81-99 98 98 95,98 ESP Spain 76-98 78-99 97 88-99 ETH Ethiopia 90-97 76-97 95 FIN Finland 76-98 76-99 97 88-99 FRA France 76-95 78-99 97 88-99 GBR United Kingdom 76-98 78-99 97 88-99 GER Germany 76-94 78-99 97 88-99 GRC Greece 76-98 76-99 97 88-99 GTM Guatemala 76-97(90-91&96) 83-87(90&96) 81-99 98 98 95,98 HKG Hong Kong 76-98 81-96 78-99 96 96 88.98 HND Honduras 81-96 81-96 81-99 95,99 HUN Hungary 76-98 76-99 96 91,93,96-97 IDN Indonesia 76-97 81-97 79-99 96 98 89-99(91-92,94,96-97) IND India 76-98 81-95 80-99 93,97 93,97 90,92,97,99-99 IRL Ireland 76-97 76-99 97 88-99 IRN Iran 76-93(78) 81-99 99 ITA Italy 76-94 77-99 97 88-99 JOR Jordan 76-97 86-96 81-99 99 JPN Japan 76-98 76-99 96 88-99 KEN Kenya 76-98 84-99 94 KOR Korea, Republic of 76-97 81-97 76-99 96 96 88-99(91,93-94,97-98) KWT Kuwait 76-97 81-97 81-99 LKA Sri Lanka 79-95 79-96 95 90-93-94,97 LVA Latvia 92-96 94-99 98 96-97 MAC Macau 78-97 76-99 96 MAR Morocco 76-97 76-98 95 93,97 MDA Moldova 91-96 9499 MEX Mexico 76-98 85-95 81-99 98 98 91,95,97-99 MWI Malawi 79-94 77-98 94-98(95) MYS Malaysia 76-98 81-98 78-99 97 97 88,91,93,96-97 NLD Netherlands 76-98 78-99 97 88-99 NOR Norway 76-98 76-99 95 88,93-99(94,97,99) NPL Nepal 77-96(78-85&92&95) 81-99 93,98-99 NZL New Zealand 76-96 79-99 96 92-93,96-99 PAK Pakistan 76-92 82-99 95 95,98 PAN Panama 76-97(95) 86-99 98 PER Peru 82-96 76-99 98 93,95,97-99 PHL Philippines 76-97 83-97 77-99 96 96 88-99(91,96-97) POL Poland 76-98 80-99 99 91-92,95,-96 PRT Portugal 76-97 79-99 97 88-99 ROM Romania 85-93 89-99 99 91,99 SGP Singapore 76-97 81-94 79-99 96 96 89,95 SWE Sweden 76-98 76-99 97 88-99 THA Thailand 76-94(78&80-81&83&B5&87&92) 76-99 95 89,91.93,95 TTO Trnidad and Tobago 76-95(79-80) 79-99 91-92,96,99 TUR Turkey 76-98 81-97 81-99 93,98 93,99 93,95,97 TvvN Taiwan 76-96 76-99 96 89,92,96,99-99 URY Uruguay 76-97 81-99 98 92,95-99 USA United States 76-98 81-98 78-99 96 96 89-99(94) VEN Venezuela 76-96 81-96(82-83) 81-99 94 94 92,95-99(96) ZAF South Africa 76-98 76-99 97 88,90-99(92,94-95,98 Note: For the years reported in parentheses the data are not available. The symbol '-' divides the first and last year of a time series, the symbol " divides single years. For example: 76-96,98(78-81,92) indicates that the data are available for the years 1976-1996 and 1998, excluding the years from 1978 to 1981 and the year 1992. 12 Production Data availability' 3 digit LSIC Country I Output Value Added Gross Fix #Firms #Employees #Female Wages Capital FormI_______ Employees AG 83-3 9 1-923 183-93 91-92 3:85,93-94 471419 8,483-93 81-92 ARM 94-97 _______ _______ 86-97 86-97 94___97 AUS 76-9 -9 2 6-58-9 1926 76-9~5 86-9 8-06)76-95 AUT 76997 9 6-94 81-94 76-98 t83-94 i76-98 ____ F3GD 76-92 176 31-92 8~~~~ ~ ~~~~~~~~1-9369 1-92 76-92 6GR -80-97 -___ 96 6-97 91-97 76-97 _____93-97 76-97 _ BOL 76-9 769 6-94 81-95 7-876-98 ~~~~~~~~~~~~~78-80,95-87) __ _ _ _ _ __ _ _ _ _ _ CAN 76-98 76-98-- 7690994 76-98 ___3 19 76-98 CH-L 76-98 ____ '6-99 76-95(87 E8) 1-9 97-88 _76-98 ___ 5-96 87-88) 76-98 CHN 77,-97 - - 80-9 ____ -28-777-97 77-66 CMR 799,~~~~ 88, ~ 176 97(85-89' ~6-978 ~ 8-9 76-97 85-89 7-9 COL 76-9 7-98 76-968-6 76-98 ____ 87-94(90-92) 76-98 CRI1 ~ 76- 97 30 84-97 84-97 76-97 GYP 76- 7698 ___ 76-97 .8-77-6-99 81 -94769 DNK 76-39 98 4618-276-98 8928-9 76-98 ECU _____76-- 969 19 76-97 76-97 EGY 7-679676-95 91I-95 - 7 6 - 96 91-95 76-96 __ ESP 76-98 76-9 ~ 7-7 819 3-95 - 76-98 ___76-98 ETH -90-9 190 9 90-97 90-97 90-97 ____ 91-97 9-7 __ FIN 76-9 7698 ___ 7-981-97 76-98 81-85 76-98 FRA 76-9 -_76-95 __ 76-96 -76-96 77-92 GBR_____76-98 76-98 76-9591 _8-7 95, 76-98 81-95 1,3-4 76-98 GER__ 76__ 94 7 6-______ 76-93 81-94 -- 76-94 81-90769 GRC__ 76__ 96 7 6 98 6-2779 81-92 76-98 69 GTM 76~ ~-78906 768 17-8(9)_ 11-97390,963 7-,91-9.97 76-97 89-9C,6)) HKG_____76-9 98 *76-96 81-96 76-98 ____ 4_______76-98 HND_____81-9 ~ 68-48-58-1 76-95 _______ 81-96 HUN____ 7-876-98 i76-98 81-93 76-9818921468 IDN 76-97 76-97 76-97 81-97 76-97 193-97 76-97 IND 76-98 31698 ___ 6-95 81-95 80-98 193-95 76-98 EL 7-__ _________ 7-19-176-97 ___ 88-91 76-9 IRN 76-93(78 __76-93378, 79-93 91-83 91-93 ____7-3739-37-37J _ ITA 76-94 76-94 76-94 81-94 76-94 8__ 7-94(91-94) 76-94 JOR 76-, 7 6-97 76-97 81-97 76-97 85-97 76-97 jPN 76-98 76-98 8697"1-97 - 76-98 85.95-9 76-98 __ KEN 76-9 8 9376-988384 76-98 87-97(95) 76-98 KOR 76-97 76-97 : 7-16-97 82-97 76-97 KoST 76-97 !76-97 _76-96 81677_97 B2-96 76-97 LKA 79-95 7993818 _i995 8731 81-95 76-95 90-95 80-95 _ LVA ____92-9 ~ 93-96 _____ 92-96 86-96 8-6_____ MAC 78-97 78-97 79--97 87 -t81 -9797 78-97 4-7887 78-97 MAR 76--9' 76-9 841fi. S_5-97 __-__- 98297 ____76-97 92-96 76-97 MO7__ 19 3-68-6169 94-95 9-95 92j MDA 91 86--996-9 7-9 7-9 MEX -T6-9i3 7~6-98 7-181-95 76-98 76-98 MYS 76-98 ~~~76-98 76-96 81-96 76-98 83-96 -76-98 ______ __ __ ~~~~~~~~7 9~0-82 984 _ _8 _ NLD ______ ___ 6-98 76-93 49 7-9 69 NOR 7d6 -76-98 -9 76-97 81-97769.7-8 __ NPL 77 86-96 76-96 77,89-90,96 82-96 77.82,86-96 86-96 ~ 77,86-96 ____________ 92 95,) 1~~81-85.92,95~ 383-8~5,92,95' 39,9)92,95) 192,95) NZL 76-9___ 6-96 -___ __ 7-90 _____ 81-96l823 -76-96 88-93 176-96 PAK D6~~~~2 76-92 76-91 81-91 -76-92 76-92 PAN - 76 97395 ____ 6-97 96- 97 -9 096958-7 69 76-97(95) 82-90(86-88) 76-97(9 ____ PER 829493 82~~~~~ ~~-9439 9A4(9) 8-49 999 2993 4___ 6-97 6-9578 81-95 76-97 92-95 76-97 -__ POL 82848998 ~~~~~~76-98 .76-93 -81-93 - 76-98 ___________________ PET 76 07 ~~~~~ ~ ~~~~~~~76-97 -76-97 81-95 7-7___ 1938-893 76-899 88-93 ' 76-94 -81-94 __ 76-94 1_ I90-94 7_____ 6-97 7 .7-7W19 69 76-97 SWE 76--98 76-9 .67 - 81-9~4 76-98 91-90 76-98 THA 76-94)78 80- 76-94(78,80.- 8-4 82-94 76-94(78,80- 82-94 76-94(78,80- ______ 3 83,858'792) 81.83.85,87,923 3 2 (8__,'3,85,87,92 81,83,85.87,92) 383,85,87,92 ~ _81,83,85.87, 92 TTO _76-_9937,9803 76-95 -199-9 8195 76-95 ______ 6-95 79-8ij TUR 76___ 98__ 76-98 7-98-94 76-98 83-90 76-98 ~6-9676-97 81-97769 UKR_____ _9 ____ _________90-98 87-98 98-98 90--98 - URY____ ____ 6-7899 8 76-97 76-97 JUSA 7e6 9 6T9 7-9 828,2 76-98 92-95 76-95 I VEN 76___ _______ -676-96 80) 82-96 76-96 76-96 __ IZAF T6~~~~-96-9879-93 855.91 76-98 85 76-98 Note Por t1he years reported irn parentheses the data are not available. The symbol- divides the first and last year of a time series, the symbol ., divides single years For example 76-96,98(78-8' 923 indicates t31a1 toie data are available for tne years 1976-1 996 and 1999. excluding the years from 19781to1981 ard the year '992. 13 Production Data availability 4 digit ISIC Country Output Value Added Gross Fix #Firms #Employees #Female Wages Capital Fofnn Employees BOL 88-95 88-95 88-94 88-95 88-95 CAN 81-98 81-98 81-94 81-98 81-91 81-98 CH L 86-98(87-88) 85-98(87-88) 85-96(8788) 85-98 87-88 85-96(87-88 85-98 87-88 CMR 90-97 92-97 91-97 91-97 92-97 91-97 COL 81-98(84) 8 87-96(89-90) 87-96 81-9884 87-94(90,92) 81 98(84) CR1__8___ 97 880-97 84-97 84-97 834-97 CYP 81-98 81-98 89-97 81-98 81-98 81-94 81-98 ECU 84-95 84-95 91-95 84-95 84-95 84-95 EGY 83-95 80-95 83-95 91-95 83-95 GTM 83-97(89-90,96) 83-882 83-97(90,96 83-88,91-95,97 83-97(89-90,96) HKG_____77-96 77-96 92-96 77-96 77-96 77-96 H-ND_ 77-96 7 7-9 83-9589-91 83-9586-89 77-96 IDN 81-97 81-97 90-9796 81-97 81-97 93-97 81-97 IND 81-95 81-95 89-95 81-95 81-95 93-95 81-95 JOR 86-96 86-96 95-96 86-96 86-96 85-96 86-96 KOR 81-97 81-97 92-95 81-97 81-97 82-95 81-97 K1T___ 77-97 77-97 89-96 77-96 77-97 77-97 MEX 87-97 85-94 84-91 87-97 87-97 _ |87 97 MYS 81-98 81-98 93-96 81-97 81-98 83-95(84) 81-98 PHL 83-97 83-97 90-95 83-97 83-97 92-95 83-97 SGP 81-94 81-94 89-94 81-94 81-94 81-94 TUR 81-97 81-97 90-97 81-94 81-97 83-89 81-97 USA 81-98 81-98 92-95 82,87,92 81-98 92-95 81-98 IVEN 811-96(82-83) 81-96(82-83) 96 81-96(82-83) 81-96(82-83) 81-96(82-83 Note: For the years reported in parentheses the data are not available. The symbol '-' divides the first and last year of a time series, the symbol ',' divides single years For example: 76-96,98(78-81,92) indicates that the data are available for the years 1976-1996 and 1998, excluding the years from 1978 to 1981 and the year 1992. The variable "Gross fix capital formation" refers to the value of purchases and own account construction of fixed assets during the reference year less the value of corresponding sales. The fixed assets covered are the one (whether new or used) with a productive life of one year or more. The variable "#Firms" refers to the number of Establishments or Enterprises. Establishment is a unit which engages, under a single ownership or control, in one or predominantly one, kind of activity in a single location. An enterprise is a legal entity possessing the right to conducting business in its own name. The variable "#Employees" refers to the number or employees or persons engaged. Number of persons engaged includes employees plus self employed. The variable "Wages" refers to wages and salaries paid to employees. The variables "Output" and "Value Added" are reported using different accounting methods. Some countries reports them in factor values and some others in producer prices, moreover some other countries fails in reporting the accounting methods used in the calculation. The database reports the information on how the production data has been constructed in a series of "flag" variables. Those variables are illustrated, among the others, in Appendix B. 14 Appendix B: Variables Variable Names and Descriptions Filename:data4digit.csv - data3digit.csv Column Variable Name Unit Description 1 ccode 3 digit code Country Code 2 year 4 digit year Year 3 pcode 3 or 4 digit ISIC code Product Code 4 vIOUTP (000 USD) Output Value (see variable flago) 5 vIVADD (000 USD) Value Added (see variable flagv) 6 vIFIRMS units Number of Establishment or Enterprises (see variable flagF) 7 vILABOR units Number of Employees or Persons Engaged (see variable flagl) 8 vINFEME units Number of Female Employees 9 vlWAGES (000 USD) Wages and Salaries Paid to Employees 10 vlFCAPF (000 USD) Gross Fix Capital Formation (see note) 11 tarWTO percentage Tariffs WTO (simple average) 12 tarTRAINS percentage Tariffs UNCTAD (simple average) 13 impTOTALTOT (000 USD) Total Import 14 expTOTALTOT (000 USD) Total Export 15 mexpTOTALTOT (000 USD) Total Mirrored Export 16 impEU_EU (000 USD) Imports European Union 17 expEU_EU (000 USD) Exports European Union 18 mexpEU_EU (000 USD) Mirrored Exports European Union 19 impJPN_JPN (000 USD) Imports Japan 20 expJPN_JPN (000 USD) Exports Japan 21 mexpJPN_JPN (000 USD) Mirrored Exports Japan 22 impUSA_USA (000 USD) Imports United States 23 expUSA_USA (000 USD) Exports United States 24 mexpUSA_USA (000 USD) Mirrored Exports United States 25 impAMERCHOE (000 USD) Imports Americas High Income OECD 26 expAMERCHOE (000 USD) Exports Americas High Income OECD 27 mexpAMERCHOE (000 USD) Mirrored Exports Americas High Income OECD 28 impAMERCHOT (000 USD) Imports Americas High Income not OECD 29 expAMERCHOT (000 USD) Exports Americas High Income not OECD 30 mexpAMERCHOT (000 USD) Mirrored Exports Americas High Income not OECD 31 impAMERCMUP (000 USD) Imports Americas Upper Middle Income 32 expAMERCMUP (000 USD) Exports Americas Upper Middle Income 33 mexpAMERCMUP (000 USD) Mirrored Exports Americas Upper Middle Income 34 impAMERCMLW (000 USD) Imports Americas Lower Middle Income 35 expAMERCMLW (000 USD) Exports Americas Lower Middle Income 36 mexpAMERCMLW (000 USD) Mirrored Exports Americas Lower Middle Income 37 impAMERCLOW (000 USD) Imports Americas Low Income 38 expAMERCLOW (000 USD) Exports Americas Low Income 39 mexpAMERCLOW (000 USD) Mirrored Exports Americas Low Income 40 impEECASHOT (000 USD) Imports East Europe and Central Asia High Income not 0ECD 41 expEECASHOT (000 USD) Exports East Europe and Central Asia High Income not CECD 42 mexpEECASHOT (000 USD) Mirrored Exports East Europe and Central Asia High Inco-ne not OECD 43 impEECASMUP (000 USD) Imports East Europe and Central Asia Upper Middle Inccirne 44 expEECASMUP (000 USD) Exports East Europe and Central Asia Upper Middle Inco!ne 45 mexpEECASMUP (000 USD) Mirrored Exports East Europe and Central Asia Upper Middle Income 46 impEECASMLW (000 USD) Imports East Europe and Central Asia Lower Middle Income 47 expEECASMLW (000 USD) Exports East Europe and Central Asia Lower Middle Income 48 mexpEECASMLW (000 USD) Mirrored Exports East Europe and Central Asia Lower Middle Income 49 impEECASLOW (000 USD) Imports East Europe and Central Asia Low Income 50 expEECASLOW (000 US D) Exports East Europe and Central Asia Low Income 15 Column Variable Name Unit Description 51 mexpEECASLOW (000 USD) Mirrored Exports East Europe and Central Asia Low Income 52 impEPASIHOE (000 USD) Imports East Asia and Pacific High Income OECD 53 expEPASIHOE (000 USD) Exports East Asia and Pacific High Income OECD 54 mexpEPASIHOE (000 USD) Mirrored Exports East Asia and Pacific High Income OECD 55 impEPASIHOT (000 USD) Imports East Asia and Pacific High Income not OECD 56 expEPASIHOT (000 USD) Exports East Asia and Pacific High Income not OECD 57 mexpEPASIHOT (000 USD) Mirrored Exports East Asia and Pacific High Income not OECD 58 impEPASIMUP (000 USD) Imports East Asia and Pacific Upper Middle Income 59 expEPASIMUP (000 USD) Exports East Asia and Pacific Upper Middle Income 60 mexpEPASIMUP (000 USD) Mirrored Exports East Asia and Pacific Upper Middle Income 61 impEPASIMLW (000 USD) Imports East Asia and Pacific Lower Middle Income 62 expEPASIMLW (000 USD) Exports East Asia and Pacific Lower Middle Income 63 mexpEPASIMLW (000 USO) Mirrored Exports East Asia and Pacific Lower Middle Income 64 impEPASILOW (000 USD) Imports East Asia and Pacific Low Income 65 expEPASILOW (000 USD) Exports East Asia and Pacific Low Income 66 mexpEPASILOW (000 USD) Mirrored Exports East Asia and Pacific Low Income 67 impESAFRMID (000 USD) Imports East and Southern Africa Upper Middle Income 68 expESAFRMID (000 USD) Exports East and Southern Africa Upper Middle Income 69 mexpESAFRMID (000 USD) Mirrored Exports East and Southern Africa Upper Middle Income 70 impESAFRLOW (000 USD) Imports East and Southern Africa Low Income 71 expESAFRLOW (000 USD) Exports East and Southern Africa Low Income 72 mexpESAFRLOW (000 USD) Mirrored Exports East and Southern Africa Low Income 73 impMEASTHOT (000 USD) Imports Middle East High Income not OECD 74 expMEASTHOT (000 USD) Exports Middle East High Income not OECD 75 mexpMEASTHOT (000 USD) Mirrored Exports Middle East High Income not OECD 76 impMEASTMUP (000 USD) Imports Middle East Upper Middle Income 77 expMEASTMUP (000 USD) Exports Middle East Upper Middle Income 78 mexpMEASTMUP (000 USD) Mirrored Exports Middle East Upper Middle Income 79 impMEASTMLW (000 USD) Imports Middle East Lower Middle Income 80 expMEASTMLW (000 USD) Exports Middle East Lower Middle Income 81 mexpMEASTMLW (000 USD) Mirrored Exports Middle East Lower Middle Income 82 impMEASTLOW (000 USD) Imports Middle East Low Income 83 expMEASTLOW (000 USD) Exports Middle East Low Income 84 mexpMEASTLOW (000 USD) Mirrored Exports Middle East Low Income 85 impNNAFRMUP (000 USD) Imports North Africa Upper Middle Income 86 expNNAFRMUP (000 USD) Exports North Africa Upper Middle Income 87 mexpNNAFRMUP (000 USD) Mirrored Exports North Africa Upper Middle Income 88 impNNAFRMLW (000 USD) Imports North Africa Lower Middle Income 89 expNNAFRMLW (000 USD) Exports North Africa Lower Middle Income 90 mexpNNAFRMLW (000 USD) Mirrored Exports North Africa Lower Middle Income 91 impRSTEUHOE (000 USD) Imports Rest of Europe High Income OECD 92 expRSTEUHOE (000 USD) Exports Rest of Europe High Income OECD 93 mexpRSTEUHOE (000 USD) Mirrored Exports Rest of Europe High Income OECD 94 impRSTEUHOT (000 USD) Imports Rest of Europe High Income not OECD 95 expRSTEUHOT (000 USD) Exports Rest of Europe High Income not OECD 96 mexpRSTEUHOT (000 USD) Mirrored Exports Rest of Europe High Income not OECD 97 impRSTEUMDL (000 USD) Imports Rest of Europe Lower Middle Income 98 expRSTEUMDL (000 USD) Exports Rest of Europe Lower Middle Income 99 mexpRSTEUMDL (000 USD) Mirrored Exports Rest of Europe Lower Middle Income 100 impSOASIHOT (000 USD) Imports South Asia High Income not OECD 101 expSOASIHOT (000 USD) Exports South Asia High Income not OECD 102 mexpSOASIHOT (000 USD) Mirrored Exports South Asia High Income not OECD 103 impSOASIMLW (000 USD) Imports South Asia Lower Middle Income 104 expSOASIMLW (000 USD) Exports South Asia Lower Middle Income 105 mexpSOASIMLW (000 USD) Mirrored Exports South Asia Lower Middle Income 106 impSOASILOW (000 USD) Imports South Asia Low Income 107 expSOASILOW (000 USD) Exports South Asia Low Income 108 mexpSOASILOW (000 USD) Mirrored Exports South Asia Low Income 16 Column Variable Name Unit Description 109 impWWAFRHOT (000 USD) Imports West Africa High Income not OECD 110 expWWAFRHOT (000 USD) Exports West Africa High Income not OECD 111 mexpWWAFRHOT (000 USD) Mirrored Exports West Africa High Income not OECD 112 impWVvAFRMUP (000 USD) Imports West Africa Upper Middle Income 113 expWWAFRMUP (000 USD) Exports West Africa Upper Middle Income 114 mexpWWAFRMUP (000 USD) Mirrored Exports West Africa Upper Middle Income 115 impWWAFRMLW (000 USD) Imports West Africa Lower Middle Income 116 expWWAFRMLW (000 USD) Exports West Africa Lower Middle Income 117 mexpWWAFRMLW (000 USD) Mirrored Exports West Africa Lower Middle Income 118 impWWAFRLOW (000 USD) Imports West Africa Low Income 119 expWWAFRLOW (000 USD) Exports West Africa Low Income 120 mexpWWAFRLOW (000 USO) Mirrored Exports West Africa Low Income 121 impNOTCLNCL (000 USD) Imports Not Specified 122 expNOTCLNCL (000 USD) Exports Not Specified 123 flago Flag variable for vIOUTP (output value): the variable can take 3 values accordinc how its value is espressed: "pprice" for producer prices, "fvalue" for factor value and "nocls" fou not specified. 124 flagv Flag variable for vlVADD (value added): the variable can take 3 values according how the value is espressed: "pprice" for producer prices, "fvalue" for factor value and "nocis" for not specified. 125 flagf Flag variable for vIFIRMS (number of firms): the variable can take 2 values: "estab" number of establishments and "entpr" number of enterprises (see note) 126 flagl Flag variable for vlLABOR (labor force): the variable can take 2 values: "emply" number of employees and "perse" number of persons engaged (see note) Notes: Gross fixed capital formation refers to the value of purchases and own account construction of fixed assets durng the reference year less the value of corresponding sales. The fixed assets covered are the one (whether new or used) with a productive life of one year or more. Establishment is a unit which engages, under a single ownership or control, in one or predominantly one, kind of activity in a single location. Enterprise is a legal entity possessing the right to conducting business in its own name. Number of persons engaged includes employees plus self employed. filename: CCCbilateral.csv Column Variable Name Unit Description 1 year 4 digit year Year 2 ccode 3 digit code Country Code 3-84 e???? (000 USD) Exports 84-166 i???? (000 USD) Imports 167-248 mexp???? (000 USD) Mirrored Exports Note: CCC refers to country code and ???? refers to the 4 digit ISIC product group. filename: totalregion.csv Column Variable Name Unit Description 1 year 4 digit year Year 2 reporter 3 digit code Country Code 3 region 5 digit code Region Code 4 incivl 5 digit code Income Code 5 imports (000 USD) Imports 6 exports (000 USD) Exports 17 Filename: totalbilateral.csv Column Variable Name Unit Description 1 year 4 digit year Year 2 reporter 3 digit code Country Code 3 partner 3 digit year Partner Code 4 imports (000 USD) Imports 5 exports (000 USD) Exports Regions an Income levels abbreviations. Region Code Description Income Code Description AMERC Americas HOECD High Income OECD EECAS East Europe and Central Asia HOTHR High Income Others EEU European Union MIDUP Upper Middle Income EPASI East Asia and Pacific MIDLW Lower Middle Income ESAFR East and Southern Africa LOW Low Income JPN Japan MEAST Middle East NNAFR North Africa NOTCL Not Specified RSTEU Rest of Europe SOASI South Asia USA USA WLD World Total WWAFR West Africa 18 Appendix C: The ISIC Classification ISIC 3 digit description 311 Food products 313 Beverages 314 Tobacco 321 Textiles 322 Wearing apparel except footwear 323 Leather products 324 Footwear except rubber or plastic 331 Wood products except furniture 332 Furniture except metal 341 Paper and products 342 Printing and publishing 351 Industrial chemicals 352 Other chemicals 353 Petroleum refineries 354 Miscellaneous petroleum and coal products 355 Rubber products 356 Plastic products 361 Pottery china earthenware 362 Glass and products 369 Other non-metallic mineral products 371 Iron and steel 372 Non-ferrous metals 381 Fabricated metal products 382 Machinery except electrical 383 Machinery electric 384 Transport equipment 385 Professional and scientific equipment 390 Other manufactured products ISIC 4 digit description 3111 Slaughtering preparing and preserving meat 3112 Manufacture of dairy products 3113 Canning and preserving of fruits and vegetables 3114 Canning preserving and processing of fish crustacea and similar foods 3115 Manufacture of vegetable and animal oils and fats 3116 Grain mill products 3117 Manufacture of bakery products 3118 Sugar factories and refineries 3119 Manufacture of cocoa chocolate and sugar confectionery 3121 Manufacture of food products not elsewhere classified 3122 Manufacture of prepared animal feeds 19 ISIC 3 digit description 3131 Distilling rectifying and blending spirits 3132 Wine industries 3133 Malt liquors and malt 3134 Soft drinks and carbonated waters industries 3140 Tobacco manufactures 3211 Spinning weaving and finishing textiles 3212 Manufacture of made-up textile goods except wearing apparel 3213 Knitting mills 3214 Manufacture of carpets and rugs 3215 Cordage rope and twine industries 3219 Manufacture of textiles not elsewhere classified 3220 Manufacture of wearing apparel except footwear 3231 Tanneries and leather finishing 3232 Fur dressing and dyeing industries 3233 Manufacture of products of leather and leather substitutes except footwear and 3240 Manufacture of footwear except vulcanized or moulded rubber or plastic footwear 3311 Sawmills planing and other wood mills 3312 Manufacture of wooden and cane containers and small cane ware 3319 Manufacture of wood and cork products not elsewhere classified 3320 Manufacture of furniture and fixtures except primarily of metal 3411 Manufacture of pulp paper and paperboard 3412 Manufacture of containers and boxes of paper and paperboard 3419 Manufacture of pulp paper and paperboard articles not elsewhere classified 3420 Printing publishing and allied industries 3511 Manufacture of basic industrial chemicals except fertilizers 3512 Manufacture of fertilizers and pesticides 3513 Manufacture of synthetic resins plastic materials and man-made fibres except gi 3521 Manufacture of paints varnishes and lacquers 3522 Manufacture of drugs and medicines 3523 Manufacture of soap and cleaning preparations perfumes cosmetics and other toi 3529 Manufacture of chemical products not elsewhere classified 3530 Petroleum refineries 3540 Manufacture of miscellaneous products of petroleum and coal 3551 Tyre and tube industries 3559 Manufacture of rubber products not elsewhere classified 3560 Manufacture of plastic products not elsewhere classified 3610 Manufacture of pottery china and earthenware 3620 Manufacture of glass and glass products 3691 Manufacture of structural clay products 3692 Manufacture of cement lime and plaster 3699 Manufacture of non-metallic mineral products not elsewhere classified 3710 Iron and steel basic industries 3720 Non-ferrous metal basic industries 3811 Manufacture of cutlery hand tools and general hardware 3812 Manufacture of furniture and fixtures primarily of metal 3813 Manufacture of structural metal products 20 ISIC 3 digit description 3819 Manufacture of fabricated metal products except machinery and equipment not - el 3821 Manufacture of engines and turbines 3822 Manufacture of agricultural machinery and equipment 3823 Manufacture of metal and woodworking machinery 3824 Manufacture of special industrial machinery and equipment except metal and - woo 3825 Manufacture of office computing and accounting machinery 3829 Machinery and equipment except electrical not elsewhere classified 3831 Manufacture of electrical industrial machinery and apparatus 3832 Manufacture of radio television and communication equipment and apparatus 3833 Manufacture of electrical appliances and housewares 3839 Manufacture of electrical apparatus and supplies not elsewhere classified 3841 Shipbuilding and repairing 3842 Manufacture of railroad equipment 3843 Manufacture of motor vehicles 3844 Manufacture of motorcycles and bicycles 3845 Manufacture of aircraft 3849 Manufacture of transport equipment not elsewhere classified 3851 Manufacture of professional and scientific and measuring and controlling equipm 3852 Manufacture of photographic and optical goods 3853 Manufacture of watches and clocks 3901 Manufacture of jewellery and related articles 3902 Manufacture of musical instruments 3903 Manufacture of sporting and athletic goods 3909 Manufacturing industries not elsewhere classified Note: when collapsing the 4 digit categories into 3 digit the standard practice is to aggregate 312x and 311 x into 31 1. 21 Appendix D: Country Region Income Concordances. East and West Africa East Asia and South Asia East Europe Rest of Middle East North Africa Americas Southern Africa Pacific Central Asia Europe Ethiopia Benin Cambodia Afghanistan Armenia Yemen Haiti Angola Burkina Faso Indonesia Bangladesh Azerbaijan Yemen DR Nicaragua Burundi Cameroon Korea, DPR Bhutan Georgia Comoros Central Afr. Rep. Laos India Kyrgyzstan Eritrea Chad Mongolia Nepal Moldova Ethiopia Congo Myanmar Pakistan Tajikistan Kenya Cote D'Ivoire Solomon Islands Turkmenistan Lesotho Gambia Viet Nam Ukraine Madagascar Ghana Uzbekistan Low Income Malawi Guinea Mozambique Guinea-Bissau Rwanda Liberia Somalia Mali Sudan Mauritania Tanzania Niger Uganda Nigeria Zambia Sao Tome and Principe Zimbabwe Senegal Sierra Leone Togo Western Sahara Zaire Cape Verde China Maldives USSR Turkey Iran Algeria Belize Equatorial Guinea Fiji Sri Lanka Albania Iraq Djibouti Bolivia Kiribati Belarus Jordan Egypt Colombia Marshall Islands Bosnia And Herzcgowina Syria Morocco Costa Rica Micronesia Bulgaria Tunisia Cuba Middle Income Papua New Guinea Kazakhstan Dominican Rep. Lower Philippines Latvia Ecuador Samoa Lithuania El Salvador Thailand Macedonia Guatemala Tonga Romania Guyana Vanuatu Russian Federation Honduras Serbia Jamaica Yugoslavia Paraguay Peru Saint Vincent Suriname 22 East and West Africa East Asia and South Asia East Europe Rest of Middle East North Africa Americas Southern Africa Pacific Central Asia Europe Botswana Gabon American Samoa Czechoslovakia Bahrain Lybia Antigua and Barbuda Mauritius Korea, Republic of Germany DDR Lebanon Malta Argentina Mayotte Malaysia Croatia Oman Barbados Seychelles Palau Czech Republic Saudi Arabia Brazil South Africa Estonia Chile Middle Income Hungary Dominica Upper Poland Grenada Slovakia Mexico Panama Puerto Rico Saint Kitts and Nevis Saint Lucia Trinidad and Tobago Uruguay Venezuela Australia Austria Canada Japan Benelux United States New Zealand Denmark Finland France High Income Germany OECD Greece Iceland Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom St. Helena Brunei Slovenia Andorra Israel Aruba French Polynesia Cyprus Kuwait Bahamas High Income Guam Gibraltar Qatar Bermuda Non-OECD Hong Kong Greenland United Arab Emirates Cayman Islands Macau Monaco Falkland Islands New Caledonia Netherlands Antilles NUithim M01d'id ibidid$ Vil9ill Iisands (U.S.) Singapore Taiwan 23 Policy Research Working Paper Series Contact Title Author Date for paper WPS2681 On the Duration of Civil War Paul Collier September 2001 P. Collier Anke Hoeffler 88208 Mans Soderbom WPS2682 Deposit Insurance and Financial Robert Cull September 2001 K. Labrie Development Lemma W. Senbet 31001 Marco Sorge WPS2683 Financial Policies and the Prevention Frederic S. Mishkin October 2001 R. Vo of Financial Crises in Emerging 33722 Market Economies WPS2684 From Monetary Targeting to Inflation Frederic S. Mishkin October 2001 R. Vo Targeting: Lessons from Industrialized 33722 Countries WPS2685 Monetary Policy Strategies for Frederic S. Mishkin October 2001 R. Vo Latin America Miguel A. Savastano 33722 WPS2686 Education, Earnings, and Inequality Andreas Blom October 2001 S. Benbouzid in Brazil, 1982-98: Implications for Lauritz Holm-Nielsen 88469 Education Policy Dorte Verner WPS2687 Geographic Patterns of Land Use Kenneth M. Chomitz October 2001 S. Hendrickson and Land Intensity in the Brazilian Timothy S. Thomas 37118 Amazon WPS2688 Aid, Shocks, and Growth Paul Collier October 2001 A. Kitson-Walters Jan Dehn 33712 WPS2689 Global Trade and Food Safety: John S. Wilson October 2001 L. Tabada Winners and Losers in a Fragmented Tsunehiro Otsuki 36896 System WPS2690 Ringing in the 20Th Century: Scot Wallsten October 2001 P. Sintim-Aboagye The Effects of State Monopolies, 37644 Private Ownership,and Operating Licenses on Telecommunications in Europe, 1892-1914 WPS2691 Evolution of Earnings and Rates of Gladys L6pez-Acevedo October 2001 M. Geller Returns to Education in Mexico 85155 WPS2692 Introduction to Property Theory: David Ellerman October 2001 B. Mekuria The Fundamental Theorems 82756 Policy Research Working Paper Series Contact Title Author Date for paper WPS2693 Helping People Help Themselves: David Ellerman October 2001 B. MekLuria Toward a Theory of Autonomy- 82756 Compatible Help WPS2694 Financial Development and Financing Inessa Love October 2001 K. Labrie Constraints: International Evidence 31001 from the Structural Investment Model WPS2695 Trade, Credit, Financial Intermediary Raymond Fisman October 2001 K. Labria Development, and Industry Growth Inessa Love 31001 WPS2696 Firms as Financial Intermediaries: Asli Demirguc-Kunt October 2001 K. Labrie Evidence from Trade Credit Data Vojislav Maksimovic 31001 WPS2697 Regional Integration and Industrial Dorsati H. Madani October 2001 L. Tabada Growth among Developing Countries: 36896 The Case of Three ASEAN Members WPS2698 Foreign Bank Entry: Experience. George Clarke October 2001 P. Sintirri-Aboagye Implications for Developing Countries. Robert Cull 38526 and Agenda for Further Research Maria Soledad Martinez Peria Susana M. Sanchez WPS2699 Benefits and Costs of International Pierre-Richard Agenor October 2001 M. Gosiengfiao Financial Integration: Theory and Facts 33363 WPS2700 Business Cycles, Economic Crises. Pierre-Richard Agenor October 2001 M. Gosiengfiao and the Poor: Testing for Asymmetric 33363 Effects