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Value-Added Trade and Empirical Distributions of RCA Indices

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Abstract

Revealed Comparative Advantage (RCA) indices aid in identification of the sectors in which countries reveal comparative advantage or disadvantage. Apart from serving such a dichotomous measure, the RCA indices are frequently employed as cardinal or ordinal measures over time. Application of the indices for comparative analyses calls attention towards the distributions of RCA indices, which must reasonably be stable over time, sectors and countries. Stability of index distributions facilitates the usage of indices as cardinal or ordinal measures over time. The present paper therefore analyses the empirical distributions of RCA indices to determine their suitability. However, such an analysis would be incomplete if the implications for RCA indices due to growing significance of global supply chains are not recognized. Hence apart from analyzing the distributions of gross trade based RCA indices, the distributions of domestic value-added in export based indices are also examined, and the differences are noted. Similar extensive analyses on the distributions of RCA indices are lacking in the literature. In this sense, the present paper makes an important contribution to the existing literature on RCA indices.

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Notes

  1. See Ballance et al. (1987).

  2. As it does not necessarily follow that some other country has lost comparative advantage in that same product.

  3. The details on the derivation of expression (1) can be provided on request.

  4. For calculating the index values for each country or sector in a year, the reference group comprises of 36 countries and 16 sectors.

  5. Calculations are based on the data on merchandise exports from 36 countries to USA for the year 2011, obtained from WIOD.

  6. Lucid representations of the signed rank and rank sum test can be found in Conover (1999).

  7. Since rank sum test examines two sets of sample observations at a time, considerations of all 36 countries or 16 sectors will imply analyzing \(^{36}\)C\(_{2}\) or \(^{16}\)C\(_{2}\) cases for country or sectoral stability. We instead assume that the considered set of countries or sectors will be able to represent the entire sample efficiently.

  8. These facts will also be evident from the results of the rank sum test reported in Tables 14, 15, 16 and 17. Different values for Log-of-Balassa may be reported in some cases due to zero exports of a sector by a country.

  9. The results for the stability of country/sectoral index distributions over sectors/over countries for the year 2011 are further reaffirmed by the non-parametric Kolmogorov Smirnov (KS) test. KS is devised for unpaired samples and hence is not applicable for analyzing the shifts in empirical distributions over time. The test determines whether two samples are drawn from two populations with identical distribution functions, by comparing two cumulative distribution functions. KS test detects shifts in distributions due to changes in means, standard deviations, presence of outliers, differences in skewness or kurtosis or number modes etc. In the context of this paper, however, KS does not produce results significantly different from the rank sum test. The results can be obtained from the authors on request. Since the usage of the index as a cardinal or ordinal measure in any given year is based on the stability of index distribution through stability of sectoral mean (for a country) or country mean (for a sector), rank sum test, which is based on shifts of median and/or mean is more appropriate in this paper.

  10. Summarized in “Review of the Literature” section.

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Author information

Authors and Affiliations

  1. Department of Humanities and Social Sciences, Indian Institute of Information Technology Guwahati, Guwahati, Assam, 781001, India

    Kaveri Deb

  2. Department of Humanities and Social Sciences, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Bodhisattva Sengupta

Authors
  1. Kaveri Deb
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  2. Bodhisattva Sengupta
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Correspondence to Kaveri Deb.

Appendices

Appendix A

36 countries included in the sample for each year (also constituting reference group for calculation of RCA indices)—Australia, Austria, Belgium, Bulgaria, Brazil, Canada, China, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Greece, Hungary, Indonesia, India, Ireland, Italy, Japan, Korea, Lithuania, Mexico, Malta, Netherlands, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Turkey, Taiwan and Rest of the World (excluding Cyprus, Latvia, Luxembourg, Sweden and USA which had to be deleted at various stages of calculation).

Appendix B

Description of 16 merchandise sectors

Sector code

Sector description

1

Agriculture, hunting, forestry and fishing

2

Mining and quarrying

3

Food, Beverages and tobacco

4

Textiles and textile products

5

Leather, leather and footwear

6

Wood and products of wood and cork (excluding furniture)

7

Pulp, paper, printing and publishing

8

Coke, refined petroleum and nuclear fuel

9

Chemicals and chemical products

10

Rubber and plastics

11

Other non-metallic minerals manufactures (e.g., glass and glass products, ceramic ware, cement, lime and plaster, articles of asbestos, asphalt etc.)

12

Manufacture of basic metals and fabricated metals excluding machinery and equipment

13

Machinery (not elsewhere classified)

14

Electrical and optical equipments

15

Transport equipments

16

Manufacturing (not elsewhere classified); recycling. Manufactures under this category includes furniture, jewellery, musical instruments, sports goods, toys etc.

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Deb, K., Sengupta, B. Value-Added Trade and Empirical Distributions of RCA Indices. J. Quant. Econ. 16, 235–264 (2018). https://doi.org/10.1007/s40953-017-0071-x

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