An Overview on the Construction of North American Regional Supply-Use and Input-Output Tables and their Applications in Policy Analysis

Statistics Canada

Anthony Peluso

U.S. Bureau of Economic Analysis

Gabriel Medeiros

Jeffrey Young

U.S. International Trade Commission

Ross J. Hallren

Lin Jones

Richard Nugent

Heather Wickramarachi

 

ECONOMICS WORKING PAPER SERIES

Working Paper 2017-12-A

 

 

U.S. INTERNATIONAL TRADE COMMISSION

500 E Street SW

Washington, DC 20436

December 2017

 

Office of Economics working papers are the result of ongoing professional research of USITC Staff and are solely meant to represent the opinions and professional research of individual authors. These papers are not meant to represent in any way the views of the U.S. International Trade Commission or any of its individual Commissioners. Working papers are circulated to promote the active exchange of ideas between USITC Staff and recognized experts outside the USITC and to promote professional development of Office Staff by encouraging outside professional critique of staff research.

An Overview on the Construction of North American Regional Supply-Use and Input-Output Tables and their Applications in Policy Analysis

Ross J. Hallren, Lin Jones, Gabriel Madeiros, Richard Nugent, Anthony Peluso, Heather Wickramarachi, and Jeffrey Young

 

Office of Economics Working Paper 2017-12-A

December 2017

 

 

ABSTRACT

In section I, we introduce the trilateral and multiyear cooperative venture between Canada, Mexico, and the United States on constructing this North American Trade-in-Value Added (NA-TiVA) database, and the complementarities between this NA-TiVA project and similar APEC TiVA and OECD TiVA work. In section II, we introduce the conceptual methodology, data requirements, as well as technical issues for constructing regional supply-use and intercountry input-output tables. In section III, we discuss the ongoing trade statistics reconciliation work under the NA TiVA project. In section IV, we further describe in detail the features and architecture of a regional North American supply-use table (NASUT) and a regional North American inter-country input-output table (NAIOT). In section V, we highlight the immediate and future policy applications of the project’s output, a (NA-TiVA) database, and the kinds of research questions that will be answerable because of this new database. This white paper is the first in a series on the subject.

 

 

Lin Jones

Office of Economics, Country and Regional Division

Lin.Jones@usitc.gov

 

Heather Wickramarachi

Office of Economics, Country and Regional Division

Heather.Wickramarachi@usitc.gov

 

 

 

 

 

 

 

 

 

Section I

Introduction

 

 

 

 

 

 

 

 

Ross Hallren, Lin Jones, Richard Nugent, Heather Wickramarachi


 

Introduction

 

Trade-in-Value Added (TiVA) is a statistical approach used to measure the interconnectivity and marginal contribution in production of participating economies in global value chains (GVCs) (Degain and Maurer, 2015). The advantage of TiVA over traditional trade statistics is that TiVA measures trade flows consistent with internationally, vertically integrated global production networks, often called GVCs. TiVA statistics allow us to better analyze three aspects of international trade: measuring the contribution of domestic versus foreign intermediates in the exports, tracing production across countries to their final destination, and finally quantifying how individual industries contribute to producing exports (Lewis, 2013).

TiVA statistics allow us to map and quantify the interdependencies between industries and economies, and help us develop better estimates of the contribution from each country in the production processes and, consequently, better measure the impact from GVC engagement for domestic economies. However, it is necessary to highlight the underlying compilation methodology of TiVA in order to better understand the characteristics, scope and interpretation of TiVA. Hence, it is important to remember that TiVA statistics are estimated statistics that are derived, in part, from official statistics. TiVA statistics are meant to complement but not to replace official statistics.

Measuring trade flows in value added as opposed to gross value of trade flows has become increasingly important as the influence that GVCs has on international trade continues to rise. (Johnson, 2014; Ahmad and Ribarsky, 2014). The proliferation of GVCs means that production has become increasingly fragmented and vertically integrated across countries (Jones and Kierzkowski, 1988; Hummels, Ishii, and Yi, 2001; OECD, 2013). At the micro level, this means that many firms in disparate countries are interconnected. Across international borders, these firms take part in particular stages of the production process, together forming a global supply chain. As a result, intermediate inputs may cross international borders several times before being used to produce final consumable goods. This matters for several reasons. First, when goods cross multiple borders multiple times, they are exposed to more trade costs, which accumulate and compound before the goods are sold for final consumption. Additionally, traditional gross trade flows are overstated because gross trade flows may count intermediates multiple times. Relatedly, gross trade flows obscure the marginal contributions of countries along GVCs. TiVA measures the flows related to the value that is added at each stage of production by each country and maps from where value is created, where it is exported, and how it is used, as final consumption or as an input for future exports. How we understand gains from trade from trade flows is fundamental, and value-added approaches lead to better understanding of GVCs and their role in international trade.

There are two ways to capture TiVA. The first method is a direct approach, which decomposes existing data on trade statistics. Johnson (2012) introduce a TiVA indicator using value-added to output ratios from the source country to compute the value-added associated with the implicit output transfer to each destination. Koopman, Wang, and Wei (2014) build on the literature in vertical specialization (e.g. Hummels, Ishii, and Yi 2001) and the literature on TiVA (e.g. Johnson and Noguera, 2012; Daudin, Rifflart, and Schweisguth, 2011) to implement a complete decomposition of a country's gross exports by value added components. This work has evolved into a second, indirect method of capturing TiVA. The indirect method is employed in the regional North American supply-use table (NASUT) and the regional North American inter-country input-output table (NAIOT). Estimating TiVA this way relies on national and international input-output tables as well as bilateral trade statistics to derive the international intermediate and final supply-demand matrices. These matrices reveal the origin and use of goods and services produced and exchanged among the countries and industries within the table domain. Other major international input-output tables include the Asian International Input-Output (AIO) Tables published by the Institute of Developing Economies Japan External Trade Organization (IDE-JETRO), the Inter-Country Input-Output (ICIO) Tables published by the OECD, the World Input-Output Tables (WIOT) published by the World Input-Output Database (WIOD) project, and the Eora Multi-region Input-Output Database (Eora MRIO).

The studies based on the above two approaches have revealed a trend of rising foreign value-added content in international trade flows and the resulting implications for trade policies. Johnson and Noguera (2016) find that value-added exports are falling relative to gross exports, which means that double-counting is increasingly more common in trade flows. This is consistent with increased GVC activity. Hummels, Ishii, and Yi (2001) show that vertical specialization has grown about 30 percent and accounts for about one-third of the growth in trade from about 1970 to 1990.

In recent years, more than half of global manufacturing imports are intermediate goods and more than 70 percent of global services imports are intermediate services (OECD, 2013). This is relevant because tariffs (and other trade costs) have a higher impact on the cost of GVC activity. Each time an intermediate input crosses an international border as part of the production process, the input incurs trade costs. As first observed by Yi (2003), trade costs are compounded when intermediate goods cross borders multiple times to complete the production process. Rouzet and Miroudot (2013) demonstrate that small tariffs can add up to a significant sum by the time a finished product reaches its consumers. Other trade costs such as non-tariff measures also have such accumulative effect on downstream products.

What the literature indicates the trends in GVCs mean for trade flows, generally, are two-fold. First, with the growth of GVC activity, gross value of trade flows will continue to be larger than the value of final goods that cross borders. Second, trade policy designed with respect to gross trade flows could have the potential to be overly restrictive or even impose costs indirectly on domestic production. Trade-in-Value Added thus provides a supplementary, relevant reference for evaluating the economic effect of trade policies.

In this paper, we introduce the North American Trade-in-Value Added (NA-TiVA) project, a trilateral, multiyear initiative that aims to produce a regional TiVA database that maps the value chains connecting Canada, the United States, and Mexico. Furthermore, we introduce and discuss the project’s deliverables, the agencies involved, how the NA-TiVA project complements other ongoing TiVA initiatives around the world, the technical framework for producing a regional inter-country input-output table for the NA region, and the value of this work to resolving open policy questions within international trade.

Ongoing TiVA Initiatives

 

Currently there are three major ongoing global and regional TiVA projects that are related to the North America TiVA project. They are the World Input-Output database (WIOD), OECD-WTO TiVA, and APEC TiVA initiatives.

The World Input-Output database (WIOD): The official WIOD project ran from May 1, 2009 to May 1, 2012, as a joint effort of eleven European research institutions. It was funded by the European Commission. Under the official WIOD project, the accounting framework and methodologies of constructing the TiVA databases, as well as the first version of the World Input-Output database were developed. The database was officially launched in April 2012. Since then, two additional versions of WIOD databases, namely the 2013 and 2016 Releases, were published. The 2016 Released database covers 28 EU countries and 15 other major economies in the world for years 2000-2014 with 56 industries.

The OECD-WTO TiVA database: The Organization for Economic Cooperate and Development (OECD) and World Trade Organization (WTO) undertook a joint initiative on TiVA in 2013. Since then, two versions of TiVA databases have been released (2013 and 2015 release). The 2015 release of OECD-WTO TiVA database covers 61 countries and 13 regions, with 34 industries, for years 1995, 2000, 2005, 2008-2011.

APEC TiVA initiative: In 2014, APEC economic leaders endorsed the APEC TiVA database initiative, a four-year project co-led by China and the United States. Under this project, an APEC TiVA database would be constructed by the end of 2018, covering 21 APEC economies.

Each of these three major global and regional TiVA initiatives include Canada, Mexico, and the United States. In the light of this, why is there still a need for constructing the NA TiVA database? What kind of additional value can the NA TiVA project bring to this global and regional network of TiVA initiatives?

The NA-TiVA project was motivated by regional statistical developments and continuous improvements in compiling TiVA databases. The 2003 Mexican input-output table distinguishes trade flows by domestic producers and production undertaken in Maquiladoras, a tax-free, tariff-free special processing zone, which allowed the estimates of separate production coefficients and thus TiVA measures for these two distinctive zones in Mexico (Koopman, Powers, Wang, and Wei, 2010; De la Cruz, Koopman, Wang, and Wei, 2011). The government of Canada further highlighted the importance and relevance of global value chains in the publication of a book assessing the impact and implication of GVCs (Foreign Affairs and International Trade Canada, 2011); and as of the 2015 edition of the OECD’s ICIO tables, Mexico is broken out as Mexico Global Manufacturers and Mexico Non-Global Manufacturers. This NA TiVA project builds off of these developments.

Constructing inter-country input-output tables, or so called TiVA databases, requires the harmonization of national supply-use tables (SUTs) or input-output tables (IOTs) as well as bilateral trade statistics from different countries. However, the data produced by countries often vary greatly in the level of detail and differ in industry and product classifications. Thus, the more countries are included in a global or regional TiVA project, the higher level of aggregation would be required for the purpose of harmonization. With only three countries involved, it is feasible for the NA TiVA database to include more products and sectors than other global and regional TiVA projects.

Moreover, other factors, such as all three countries adopt the same industry and product classifications (e.g. using the North American Industry Classification System (NAICS)), and produce SUTS at similarly detailed levels, would ensure the compatibility of data components, and thus lead to better quality of the resulting NA TiVA database.

Finally, the NA TiVA project could synthesize the ongoing trilateral trade statistics reconciliation effort and produce better-quality balanced bilateral trade data to feed into other global and regional TiVA initiatives. One of the key inputs for constructing TiVA databases is balanced bilateral trade statistics. However, countries rarely report symmetric bilateral trade statistics[!DSTAG SYMBOL ID=ch0001 MML=0 CHARID=ch INSUBSUPER=0/]one country’s reported exports rarely equals its trading partner’s reported imports, and vice versa. To reconcile such asymmetries to produce balanced bilateral trade statistics, joint effort by both trading countries is warranted, including investigating the causes of asymmetries at detailed product level and making corresponding adjustment mechanically. However, global and regional TiVA initiatives often have to consider an incredible number of country pairs, making such an elaborate reconciliation practice rather infeasible. Thus, global and regional TiVA initiatives often turn to economic modelling to balance bilateral trade statistics which could be applied in a systematic way to all countries. Although such approach can be mathematically sound, the resulting data often require additional scrutiny, validation, and adjustment, as they do not always reflect the reality accurately. Canada, Mexico, and the United States have ongoing bilateral trade reconciliation. This NA TiVA project provides additional motivation and framework for this effort.

The History, Scope, and Major Objectives of the NA TiVA Initiative

In October 2014, the representatives from the United States, Canada, and Mexico met and kicked off the idea of constructing the NA TiVA database at a UN conference in Mexico. The main objective of this project is to construct the NA TiVA database by 2021 covering three NA countries with more detailed industry and firm information, and to improve the quality of TiVA measures for the value chains in the NA region.

 

The NA-TiVA project involves eight government agencies across the three NA countries: for Canada, Statistics Canada (STATCAN) and Global Affairs Canada; for Mexico, Instituto Nacional de Estadística y Geografía (INEGI) and Banco de Mexico; and for the United States, the Bureau of Economic Analysis (BEA), the U.S. Census Bureau (CENSUS), the U.S. International Trade Commission (USITC), and the Office of the U.S. Trade Representative (USTR).

 

In addition, because the resulting NA-TiVA database would be eventually integrated into the OECD-WTO TiVA database to improve the quality of information on the North American region, participants of the NA-TiVA project regularly meet with OECD representatives to harmonize TiVA database compilation methodologies, exchange data to synthesize the effort and ensure consistency across countries, and discuss best practices. Other international organizations, such as United Nations Statistics Division (UNSD), and WTO, are often consulted as well for national account and trade statistics related issues.

 

Under the NA-TiVA initiative, three parallel work streams have been established: The trade in goods and services reconciliation team, which is tasked to produce balanced bilateral trade statistics for goods and services; the SUT team, whose goal is to harmonize the national SUTs and compile the regional NASUTs and NAIOTs; and the White Paper team, the goal of which is to produce documentation that outlines the conceptual methodology, identifies major technical issues, describes policy applications of a NA-TiVA initiative, and details project outputs as well as future work.


 










Section II

 

Overarching Conceptual Methodology

and Major Technical Issues[1]







Lin Jones


 

This section describes the overarching methodology framework, identifies major technical issues that need to be addressed, and proposes a three-stage flow charts and corresponding steps as the general guideline for constructing the NA TiVA databases.

The Overarching NA-IOT Methodology Framework

Underlying the NA TiVA databases are the NA input-output tables (NAIOTs). There are two types of NAIOTs that can be constructed, each with its own advantages as well as limitations.[2]

 

Table II-1. NAIOTs with endogenous Rest of World (RoW)

Intermediate Use

Final Demand

Output

Canada

(c)

Mexico

(m)

USA

(u)

RoW

Canada

(c)

Mexico

(m)

USA

(u)

RoW

(row)

 

Canada

(c)

IDci,j

IMc,mi,j

IMc,ui,j

IMc,rowi,j

FDci

FMc,mi

FMc,ui

FMc,rowi

Oci

Mexico

(m)

IMm,ci,j

IDmi,j

IMm,u i,j

IMm,rowi,j

FMm,ci

FDmi

FMm,ui

FMm,rowi

Omi

USA

(u)

IMu,ci,j

IMu,mi,j

IDui,j

IMu,rowi,j

FMu,ci

FMu,mi

FDui

FMu,rowi

Oui

RoW

(row)

IMrow,ci,j

IMrow,mi,j

IMrow,ui,j

IDrowi,j

FMrow,ci

FMrow,mi

FMrow,ui

FDrowi

Orowi

Value-added

Vcj

Vmj

Vuj

Vrowj

 

 

Output

Ocj

Omj

Ouj

Orowj

Source: author’s modification from Nadim Ahmad’s “Creating Global Input-output tables,” 2017

 

With this approach, RoW would be treated as a single economy, and its supply use tables (SUTs) and other “national” data would be estimated. The resulting NASUTs and NAIOTs include the input-output relationships between RoW and other countries at sector/product level.

 

Using this type of NAIOTs, trade between RoW and the NA countries is differentiated for intermediate and final uses. As a result, domestic value that initially is embodied in intermediate goods/services exports to RoW but eventually returns home can be captured.


 

Table II-2. NAIOTs with exogenous RoW

 

Intermediate Use

Final Demand

Exports[3]

Output

Canada

(c)

Mexico

(m)

USA

(u)

Canada

(c)

Mexico

(m)

USA

(u)

ROW

(row)

 

Canada

(c)

IDci,j

IMc,mi,j

IMc,ui,j

FDci

FMc,mi

FMc,ui

FMc,rowi

Oci

Mexico

(m)

IMm,ci,j

IDmi,j

IMm,u i,,j

FMm,ci

FDmi

FMm,ui

FMm,rowi

Omi

USA

(u)

IMu,ci,j

IMu,mi,j

IDui,j

FMu,ci

FMu,mi

FDui

FMu,rowi

Oui

Imports from ROW

IMrow,ci,j

IMrow,mi,j

IMrow,ui,j

FMrow,ci

FMrow,mi

FMrow,ui

 

 

Value-added

Vcj

Vmj

Vuj

 

 

Output

Ocj

Omj

Ouj

Source: author’s modification from Nadim Ahmad’s “Creating Global Input-output tables,” 2017

 

The second approach treats RoW as exogenous, and assumes exports to RoW are for final use only. It does not differentiate exports to RoW between intermediate and final uses, and it does not require the estimation of input-output relationships between countries and RoW. However, as a result, domestic value embodied in intermediate exports to RoW and eventually returned home could not be separately estimated and captured.

The second approach may be preferable when a) data for estimating input-output relationship between countries at a detailed sector/product level are available and b) examining inter-country production at such detailed level is desirable, however, c) estimating input-output relationship for RoW at such detailed level proves to be infeasible, or the accuracy of the estimation could suffer and thus is undesirable.

One of the major advantages from the NA TiVA initiative is that all three NA countries use similar classification systems with high quality statistics, which allow the construction of the NA TiVA database at more detailed product and industry levels than any other ongoing global and regional TiVA initiatives. To preserve this advantage without losing the quality, constructing NAIOTs with exogenous RoW might be the suitable choice. For more detailed discussion on the NA IOT, please see Section IV.

 

The Basic Underlying Data

Conceptually, constructing NAIOTs is not complicated. The basic underlying idea is to link three NA countries’ national supply-use tables with bilateral trade statistics through import use matrices to derive the data required to build inter-industry-country input-output relations.

To construct NASUTs and NAIOTs, the following data need to be derived from the above three datasets:[4]

·         A domestic transactions matrix, IDkij, showing the value of domestically produced intermediate consumption in basic prices[5] for country k used by domestic industry j of output produced by domestic industry i.

·         An import transaction matrix, IMki,j, showing the value of imported intermediate consumption at CIF price for country k, used by domestic industry j of output produced by foreign industry i located abroad.

·         A column of domestic final demand, FDki, showing the value at basic prices of domestic final consumption (by households, non-profit institutions serving households, and government) for country k, as well as fixed capital (including investment and changes in inventories) of output produced by domestic industry i.

·         A column of imported final demand[6], FMki, showing the value at CIF price of imported final consumption (including by households, non-profit institutions serving households, and government) for country k, as well as fixed capital (including investment and changes in inventories) of output produced by foreign industry i located abroad.

Note: for both FDki and FMki, separate columns for each final demand category are in principle available.

·         Bilateral trade matrices[7], in ‘free on board’ (f.o.b) prices, Xs,ri = Ms,ri, showing the value of exports in FOB price sent by industry i in sourcing country s to receiving country r. Or, in other words, because bilateral trade data is coherent[8], it equals to the value of imports by receiving country r from industry i in sourcing country s.

·         A vector of gross output by industry, Okj, at basic prices, and value-added[9], Vkj , by industry for country k, where Okj = j IDki,j + FDkj+ Ekj and Vkj = Okj - ∑i (IDki,j + IMki,j).

With the data listed above, to produce a NAIOT, the additional work needed to be done is to disaggregate IMki,j and FMki into IMs,ri,j and FMs,ri, by industry i in sourcing countries s, as follows

                          IMs,ri,j = Ms,ri / (∑world=c,m,u, row Ms,worldi ) * IMri,j                                                                              (I)    

                                      FMs,ri = Ms,ri / (∑world=c,m,u, row Ms,worldi ) *  FMri                                                                                 (II)

IMs,ri,j: imported intermediate by industry j in receiving country r from industry i in sourcing country s;

Ms,ri: total imports by receiving country r from industry i in sourcing country s;

world=c,m,u,row Ms,worldi: the world’s total imports from industry i in sourcing country s;

IMri,j: total imported intermediate by industry j in receiving country r from foreign industry i located abroad;

FMri: total imported final demand in receiving country r from foreign industry i located abroad.

If bilateral trade could be broken down into end-use categories of intermediates (I), capital goods (K), and goods for final consumption (C), they can be rewritten as

              IMs,ri,j = IMs,ri / (∑world=c,m,u,row IMs,worldi ) *  IMri,j                                                                                       (III)

              FMs,ri  =  Ms,ri [!DSTAG SYMBOL ID=ch0002 MML=1 CHARID=ch INSUBSUPER=0/]   IMs,ri                                                                                                                                                                                           (IV)

IMs,ri: total intermediate imports by receiving country r from industry i in sourcing country s;

world=c,m,u,row IMs,worldi: the world’s total intermediate imports from industry i in sourcing country s.

Although the underlying conceptual methodology of constructing NAIOTs is simple, in practice, there are quite a few technical data issues that need to be addressed.

 

Major Technical Issues

 

·         Major technical issues in harmonizing national input-output accounts

Several data compatibility issues in the three countries’ national I-O accounts require harmonization before they could be linked together.

 

o   Incompatible classification systems

 

Canada, Mexico, and the United States jointly developed the North American Industry Classification System (NAICS) in 1997 to allow for a high level of comparability among the North American countries. Since then, NAICS has been the basis of these three countries’ economic and business statistics, including national I-O accounts. As a result, the compatibility issue in industry classification has been minimized.

 

Since 2003, the three countries also began to work on developing the North American Product Classification System (NAPCS) as the united classification system for products produced in the North America. However, the adoption of NAPCS in these three countries varies. Currently, the United States and Mexico still use a NAICS-based product classification system by mapping source data into the NAICS system. The implementations of NAPCS in both countries are a few years away. Canada, on the other hand, has implemented NAPCS based product classification system for its 2009[!DSTAG SYMBOL ID=ch0003 MML=1 CHARID=ch INSUBSUPER=0/]2013 SUTs, but used the Standard Industrial Classification (SIC) and NAICS based classification systems for SUTs from previous years.

 

o   Various industry/product detail levels and available years

 

The United States publishes the 1997-2015 time series SUTs at 71 industry and 72 product levels. Estimates at more detailed level (389 industries and 387 products) are only available for the benchmark years, which are compiled every five years in years ending in 2 and 7, corresponding to the occurrence of the U.S. Economic Census. The last benchmark year data available is 2007; and the next benchmark year data (2012) is expected to be published in the fall of 2018.

 

Canada produces annual benchmark SUTs based on annual surveys. Currently it has SUTS for 1961-2013. The most recent SUTs (2009-2013) have 230 industries and 490 products. The SUTs from earlier years (e.g. 1997-2008) have up to 300 industries and 700 products.

 

Mexico compiles SUTs every five years in years ending in 3 and 8. Currently, Mexico has SUTs for years 2003, 2008, and 2013. Its 2003 SUTs have 79 industries and products (3 digit at the NAICS 2002); its 2008 and 2013 SUTs have 262 industries and products (4 digit at the NAICS 2007). (Note: for the purpose of the APEC and NA TiVA projects, Mexico put additional resources and effort to produce the 2012 SUTs, which was completed in September of 2017.)

 

o   Different currency and unit value

 

The United States reports its national I-O account data in millions of U.S. dollars. Canada reports its national I-O account data in thousands of Canadian dollars. Mexico reports its national I-O account data in millions of Mexican Pesos.

 

Before any harmonization between these national I-O account data takes place, to address the above data compatibility issues, it is necessary to define the standard NA TiVA industry and product classifications at the most detailed feasible levels, at standard currency, for all years.

 

·         Major technical issues in balancing bilateral trade statistics

 

Trade statistics are another crucial input for constructing NAIOTs. However, some inherent issues in trade data would require additional actions before they could be used. These issues include reconciling discrepancies in bilateral merchandise trade statistics; estimating missing bilateral services trade statistics; and aligning balanced external trade statistics to national account trade data.

 

o   Reconciling discrepancies in bilateral merchandise trade statistics

 

Although the three countries maintain detailed merchandise trade statistics and they are compatible at the 6 digit Harmonized System (HS) level, one country’s reported import or export values rarely equal its trading partner’s reported export or import values at either the aggregate or detailed product level. A number of factors could contribute to such asymmetries in bilateral merchandise trade statistics, including valuation differences for exports and imports[10], re-exports, misclassifications, or under-reporting. Reconciling discrepancies and balancing bilateral merchandise trade statistics are required before merchandise trade statistics could be used in the NA TiVA database construction.[11]

 

o   Estimating missing bilateral services trade statistics

 

Compiling detailed bilateral services trade statistics is challenging. Internationally, there are substantial differences across countries in the availability and level of detail in reported bilateral services trade data. Currently, the United States publishes services trade statistics with the world, as well as roughly 88 trading partners by country or region. Most of U.S. bilateral services trade statistics are reported by the Balance of Payment and International Investment Position Manual (BPM) major categories;[12] in some cases, more detailed data are available by the extended Balance of Payments (EBOP) classifications.[13] In the case of U.S. reported bilateral services trade with Canada and Mexico, about 9 major BPM6 categories[14] and 39 sub-categories are available.

 

Canada publishes services trade statistics with the world, as well as roughly 80 trading partners by country or region. In the case of Canada reported bilateral services trade with the United States and Mexico, three major categories are available (travel, commercial services, transportation and government services).

 

The Central Bank of Mexico reports Mexican services trade statistics with the world for 8 categories: maintenance and repair services, transport, travel, insurance and pension services, financial services, charges for the use of intellectual property, telecommunications, and other services.[15] In addition, using surveys, the Bank of Mexico reports bilateral travel services trade with Canada and the United States.

 

·         Major technical issues in linking trade statistics with national input-output accounts

 

o   Harmonizing product classifications

 

Products in merchandise trade statistics are classified by the Harmonized Commodity Description and Coding Systems, also known as the Harmonized System (HS). Products in services trade statistics are classified by either the BPM or EBOPS classifications. Products in national I-O accounts are classified by either NAICS or NAPCS based product classification systems. Thus, harmonizing these three data sets in product classification is required before linking them through import use matrices.

 

o   Align merchandise trade statistics with national accounts

 

Merchandise trade statistics cover goods “which add to or subtract from the stock of materials resources of a country by entering (imports) or leaving (exports) its economic territory.” Thus, merchandise trade statistics capture the physical movements of goods across borders. Most of these international movements of goods pass through the customs administration of the exporting and importing countries, and are subject to customs procedures. Therefore, the main source for merchandise trade statistics is customs records.[16]

 

Merchandise trade statistics are usually the main data source for goods trade in national accounts, in addition to other data sources such as administrative data, and surveys of traders. However, since national accounts are balance of payment (BoP) based, which requires the change of economic ownership of goods between residents and nonresidents, additional adjustment to source data are commonly needed to account for coverage, timing, valuation, and classification that do not meet BoP guidelines.[17]

 

In addition, merchandise trade statistics and national accounts data are usually compiled by different statistical agencies.  Sometimes two data sets differ in their geographical coverage. For instance, the U.S. SUTs and national account data exclude the U.S. territories, such as Puerto Rico and the U.S. Virgin Island. Yet, U.S. merchandise trade statistics include these two U.S. territories. Thus, it is necessary to adjust all three countries’ merchandise trade statistics to be aligned with the U.S. national accounts’ geographical coverage first, before carrying out any bilateral merchandise trade statistics reconciliation exercises. For detailed discussions on merchandise trade statistics adjustment, please see section III.

 

o   Aligning different price valuations

 

In national accounts, data usually are reported in one of the following three price concepts: the basic price, the producer’s price, and the purchaser’s price. These price concepts reflect different valuations concerning whether specific underlying price components, such as taxes and subsidies, and trade and transport margins, are included.[18] Merchandise trade statistics are usually reported in either free on board (fob) or cost, insurance and freight (cif) value. Import use matrices are usually reported in cif, which is the equivalent of the basic price for imported products.

 

Since TiVA calculation seeks to capture the ultimate input-output economic relationships across multiple countries, building NAIOTs at the basic price would be the apparent choice, which would allow us to evaluate the contributions of margin sectors, such as wholesale and transport, to global value chains, while minimize any distortions that could arise from tax and subsidy policies of each country imposes. However, the concept of the basic price at the international setting is different from the national setting. For instance, cif value is considered as the equivalent to the basic price for imported goods at the national setting, but at the international setting, since cif contains international transport and insurance, as well as the domestic margins and net taxes from the exporting countries, it can no longer be treated as the basic price. The similar consideration is extended to fob value as well, as it contains exporting countries’ domestic margins and net taxes. Thus, to construct ICIO tables at the basic prices, international transactions require additional price adjustments. For detailed discussions on price adjustment, please see section IV.

 

General Workflow and Methodologies of Constructing TiVA Databases

 

To address the major technical issues listed above, this section outlines the general workflow and the underlying methodologies to construct the NAIOTs. It is based upon the eight-step approach developed by the OECD to create a coordinated global input-output table, while taking into account the alignment with the APEC TiVA database methodology. The proposed workflow and methodologies serve as the general guideline to construct the NASUTs and NAIOTs, as we should allow flexibility and freedom for revisions during the implementation. The OECD’s overall conceptual framework is applied to constructing an ICIO with endogenous RoW. As discussed under “The Overarching NA-IOT Methodology Framework,” there is an advantage of constructing NAIOTs with exogenous RoW, and thus is preferred by this NA TiVA project. Therefore, in our exercises, we only need to derive the input-output relations between the three NA economies without concerning such relations with RoW.

 

Stage one: harmonize and benchmark national SUTs (Figure II-1)

 

1.       Estimate SUTs at purchaser’s price (PP) if not available; and adjust the 2008 SNA based SUTs to the 1993 SNA based concept of processing trade and merchanting, if applicable;

2.       Harmonize SUTs (PP) to the NA TiVA industry and product Classifications and benchmark with the national account data of the corresponding years  (exports, imports, output, value added, final demand, margins, taxes):

                     i.            Estimate national account constraints at the NA TiVA standard industry/product level[19];

                   ii.            Harmonize SUTs to the NA TiVA industry and product Classifications with national account constraints;

                 iii.            If feasible, evaluate the treatment of re-exports in SUT compilation. In principal, economies should include re-exports in imports and exports in SUTs;

3.       For non-benchmark year SUTs, update them with the benchmark year national accounts data;

 

Figure II-1: Harmonize and benchmark national SUTs at purchaser’s prices

Title: Figure 1: Harmonize and benchmark national SUTs at purchaser's prices - Description: This flow chart describes the three-step process to harmonize national supply use tables at purchaser’s prices, and benchmark to national account data.

 

Stage two: prepare and process trade statistics (Figure II-2)

 

1.       For merchandise trade statistics, convert import value to the same FOBpp price, harmonize geographical coverages, and adjust for re-exports if such data are available;

2.       Use official services trade data, as well as other sources of services trade data to estimate missing bilateral services trade data;

3.       Determine the final step to balance bilateral merchandise and services trade statistics. If applicable, estimate the Symmetric Indices for each reporting NA economies as exporter and importer based on the reconciled trade statistics, and use them as the weight to generate balanced bilateral trade statistics (note: the Symmetric Indices for merchandise and services trade would be estimated separately);

4.       Harmonize balanced trade statistics to the NA TiVA product classifications; Step 3 and 4 could be completed simultaneously or separately.

5.       If applicable, adjust product exports in balanced trade statistics to be aligned with product exports in use tables introducing known adjustments to harmonize with national accounts concepts and introduce a column reflecting unallocated exports reflecting differences between SUT exports by product and the equivalent estimates derived from the alignment steps above.  Adjust product imports of corresponding trading partners’ accordingly and impose these within the import column - introducing, in turn, an item for unallocated imports.

 

Note: Step 3, 4 and 5 could be completed simultaneously or separately, depending on the methodologies and balancing models used.

 

Figure II-2: Prepare and process trade statistics