Symmetric Revealed Comparative Advantage Details
Quantifying the differences between given countries' comparative advantage in producing a good is challenging for several reasons. Among them are problems stemming from data aggregation, along with the fact that factors unrelated to comparative advantage may be influencing trade flows. It is also difficult to define comparative advantage in terms of product prices in the absence of trade, since a lack of trade renders product prices unavailable. Two tools that are used to quantify differences in comparative advantages are the revealed comparative advantage (RCA) index and the symmetric revealed comparative advantage (SRCA) index (also called the revealed symmetric comparative advantage in some literature).
The RCA was developed in 1965 by Balassa using observed export statistics to reveal the underlying pattern of comparative advantage.[1] For example, the RCA of a country's processed raspberry industry is expressed as a function of the country's exports of processed raspberries divided by its total exports of processed fruits, divided by world exports of processed raspberries divided by total world exports of processed fruits (equation G.1). In the equation, 
is the value of exports of processed fruit 
from country 
, 
is the value of exports of all goods from country , 
is the value of global exports of processed fruit , and 
is the value of global exports of all goods.
Equation G.1: Revealed comparative advantage
In 1995, Laursen and Engedal expanded upon the RCA index and developed the SRCA index, which generates scores ranging between −1 and +1 and is symmetric around zero.[2] Countries with SRCA scores close to +1 have a higher revealed comparative advantage, and countries with scores close to −1 have a lower one. The SRCA index is a function of the RCA for a country and product pair where 
is the revealed comparative advantage for processed fruit from country (equation G.2).
Equation G.2: Symmetric revealed comparative advantage
The SRCA has been used extensively to quantify comparative advantage. Commission publications in 2005 and 2006 used these measures to compare the competitiveness of the industries of a number of different countries in the global marketplace.[3] More recently, in 2015, Laursen compared the RCA to the SRCA and made the case for using the SRCA.[4] He then compared these to other measures of comparative advantage, including the Michaely index, the contribution to trade balance (CTB) index, the chi square measure, and Bowen's net trade index (NTI). Laursen concluded that among those that he evaluated, the SRCA was the best measure of comparative advantage.[5] In their 2020 paper titled "Implied Comparative Advantage (ICA)," Hausmann, Stock, and Yildirim constructed an index based on Balassa's RCP, but replaced the global export shares with population shares.[6] The authors demonstrated that their index was a highly significant predictor of international export flows. The authors also completed their analysis using Balassa's 1965 construction and determined that the results were qualitatively the same.
This report presents two versions of the SRCA, one focusing on competitiveness in the global market, and one on competitiveness in the U.S. market. The global market competitiveness calculations are represented in equations G.1 and G.2 above. For these calculations, is the value of exports of processed raspberries (Harmonized System (HS) 0811.20) from country , is the value of exports of all goods from country , is the value of global exports of processed raspberries, and is the value of global exports of all goods. Processed raspberry export data must be harmonized for all countries so the 6-digit HS level of trade is used. As such, they include trade of all products classified under HS 0811.20 (raspberries, blackberries, mulberries, loganberries, black, white or red currants and gooseberries, uncooked or cooked by steaming or boiling in water, frozen, whether or not containing added sugar or other sweetening matter).
To analyze U.S. market competitiveness, U.S. imports of processed raspberries were used in place of export data for processed raspberries. U.S. import data at the 10-digit statistical breakout level of the Harmonized Tariff Schedule of the United States (HTS) makes it possible to construct an index that considers only processed raspberries (e.g., excludes blackberries, mulberries, loganberries, currants or gooseberries) and that includes a wider range of processed raspberry products (e.g., fresh raspberries for processing, raspberry puree, raspberries in frozen fruit mixes, and raspberry juice and concentrate).[7] While the same method of calculating the RCA was used, U.S. import data were used to calculate the variables (equation G.3). In this equation, 
is the value of U.S. imports of processed raspberries (calculated using a share of imports under HTS statistical reporting numbers 0810.20.1020 and 0810.20.9020, all imports under 0810.20.1024, 0810.20.9024, 0811.20.2025, shares of 0811.90.8080 and 0811.90.8085, and all imports under 2007.99.6510, 2008.99.2120, 2009.89.6055, and 2009.89.7055) from country , 
is the value of U.S. imports of all goods from country , 
is the value of total U.S. imports of processed raspberries, and 𝐼 is the value of U.S. imports of all goods.
Equation G.3: U.S. market competitiveness RCA
For both the U.S. market and the global market SRCAs, the denominator used in the country and global trade ratios ( and 
was all goods. For robustness, this was compared to calculations using a narrower product group for the denominator (HS 0811). Results for both categories were similar.
The data used for the U.S. market RCA indexes were adjusted before making the calculations.[8] Data for U.S. imports for consumption are official statistics from the Census Bureau of the U.S. Department of Commerce, accessed using USITC's DataWeb trade data querying tool. We adjusted these import data because DataWeb data did not reflect the most recent revisions issued by the Census Bureau at the time of these calculations.[9] For the 10-digit statistical reporting numbers with revisions, the revised data were used. For 10-digit statistical reporting numbers that did not have revisions, original DataWeb data were used. Additionally, the 2018 value of imports of fresh raspberries for processing (HTS 0810.20.9024, fresh raspberries in containers of 5 kg or greater) from Mexico was also adjusted. Since this statistical breakout came into effect in July 2018, imports for full year 2018 may be underestimated. Full-year 2018 imports were therefore estimated based on ratios of imports in 2019 and 2020. Total U.S. imports of this statistical breakout were adjusted accordingly. This was also done for frozen berry mixes (HTS 0811.90.8085), for which the statistical breakout came into effect in July 2019. Values of frozen berry mixes in 2018 and 2019 were estimated based on the share of frozen berry mixes in all frozen fruit and nut mixes (HTS 0811.90.8080). The value of frozen berry mixes was adjusted to reflect only the value of mixed raspberries based on estimates of the share of raspberries in the mixed bags (7/24).[10]
Export data for countries other than the United States used for RCA calculations are from IHS Markit's Global Trade Atlas database.[11] Again, U.S. domestic export and import data are official statistics from the U.S. Census Bureau accessed using DataWeb.[12] All RCA calculations used two-year data averages (2018–19) to reduce the effect of potential annual trade fluctuations and include the time period when the U.S. statistical breakouts for fresh raspberries in containers of 5 kg or greater and frozen berry mixes were in effect.
Bibliography
Balassa, Bela. "Trade Liberalisation and 'Revealed' Comparative Advantage." The Manchester School 33, no. 2 (1965): 99–123. https://doi.org/10.1111/j.1467-9957.1965.tb00050.x.
Hausmann, Ricardo, Daniel P. Stock, and Muhammed A. Yıldırım. "Implied Comparative Advantage." Research Policy, article 104143, 2020. https://doi.org/10.1016/j.respol.2020.104143.
Laursen, Keld. "Revealed Comparative Advantage and the Alternatives as Measures of International Specialisation." Eurasian Business Review 5 (February 5, 2015): 99–115. https://doi.org/10.1007/s40821-015-0017-1.
Laursen, Keld, and Carsten Endegal. "The Role of the Technology Factor in Economic Growth: A Theoretical and Empirical Inquiry into New Approaches to Economic Growth." MS thesis, University of Aalborg, 1995.
