Abstract
We demonstrate that R&D intensity is an appropriate measure of vertical differentiation, while the Rauch (J Int Econ 48:7–35, 1999) classification mainly captures horizontal differentiation. Product market characteristics vary considerably across R&D intensity-based “technology levels” of the OECD-STI taxonomy, as well as across categories of the Rauch classification. Both high technology and differentiated products display lower price elasticity of demand and longer quality ladders than do low technology and homogeneous products. However, variety proliferation decreases with the technology level and increases with the Rauch category, while price dispersion increases with technology but not with the Rauch. Additionally, Rauch categories do not differ in factor intensities, while higher tech industries are more capital intensive than lower tech ones.
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Notes
Empirical work in demand estimation seldom states clearly what type of product differentiation firms face in the market. This happens due to the generality of demand models–such as Berry (1994) and Berry et al. (1995), among many—that in most cases can encompass both types of differentiation. However, counter-factual simulations that use the demand estimates may vary depending on what kind of differentiation one assumes. According to Sutton (1998), not accounting for vertical differentiation, for example, may lead to a too large predicted number of product entries.
Based on Khandelwal (2010), a “quality ladder” is a measure of within-industry quality dispersion. “Quality” is here understood as a demand shifter: A certain product (which is exported by a certain country or supplied by a certain firm) is identified as higher quality when, conditional on price, it has a larger demanded quantity.
Our interest in using this database is solely in product market characteristics, and not in international trade patterns or comparative advantages. For example, we are interested in cross-industry differences in quality ladder lengths, and not in explaining what makes an exporting country occupy the top or the bottom of the quality ladder in a certain industry.
The product-country fixed-effect is important not only for directly explaining the exported quantity, but also to account for endogeneity in the number of varieties. For example, there may be fixed costs to developing or producing a different variety, which gives bigger countries an advantage. Or, as Khandelwal (2010) suggests, there may be “hidden varieties” that depend positively on country size. Finally, there may be “hysteresis” in countries’ industrial development that makes them large exporters of certain products. All of those effects are captured by βij.
For those not familiar with Khandelwal’s (2010) method, an example may be in order: Suppose that a demand system is estimated for the sedan-type automobile industry. The Toyota Corolla would sell a zero quantity at the price $500,000. Indeed, this is how much a Rolls-Royce Phantom costs, and yet it sells some positive quantity. We must therefore conclude that the Rolls-Royce is higher quality, being located on a higher demand curve than is the Toyota Corolla. That is why quality, the way it is estimated here, is called a “demand shifter”. We thank the editor for this example.
In our sample, the cross-industry correlation between price dispersion and the quality ladder is only 0.18.
To be realistic, the price elasticity tends to be higher when the level of disaggregation is greater, which reflects the fact that varieties of the same product are generally closer substitutes than are different products. But the spirit of the analysis here is: Given the level of disaggregation (in our case, the industry level), when industry A is less price elastic than is industry B, this is consistent with A’s being more horizontally differentiated than is B.
Lahmandi-Ayed (2007) challenges this view. According to him, finiteness may also arise in horizontally differentiated markets – as long as consumers agree on the ranking of products when these are sold at marginal costs.
The idea of moving up technology levels is straightforward: there is an increase in R&D intensity. On the other hand, moving up Rauch categories is more involved: According to Rauch (1999), when a product has a reference price (and is thus classified as “homogeneous”) it means that it is traded on an organized exchange. Conversely, when a product does not have a reference price (and is thus classified as “differentiated”), matching buying and sellers is something that depends on “relational aspects” and is favored by proximity and common language/colonial ties.
This database is described in detail in Feenstra et al. (2002).
So that, if country j exports four HS goods and country l exports two HS goods that are all classified under the 5-digit SITC product ZZZZZ tag, then we will say that j exports twice as many ZZZZZ varieties as l. If only countries j and l produce and export ZZZZZ – and j exports the varieties ZZZZZ1, ZZZZZ2, ZZZZZ3, and ZZZZZ4, and l exports ZZZZZ4 and ZZZZZ5 – then we will say that there exist five ZZZZZ varieties. This way of interpreting a “variety” is consonant with the approach of Hummels and Klenow (2005), but is not unique. For example, Broda and Weinstein (2006) define a “variety” as a disaggregated-product/exporting-country pair. In Appendix D, we give examples of 4-digit industries and all of their component 5-digit products and all of their component 10-digit HS varieties.
Because the most recent year that is available in the U.S. import database is 2006, we cannot estimate product market characteristics for a period around 2011, which is the base year for Galindo-Rueda and Verger’s revision. Since our sample period is 2002-2006, we have alternatively tried the older revision in http://www.oecd.org/sti/ind/48350231.pdf, based on the OECD Science, Technology and Industry Scoreboard 2003. The results (available on request) are practically identical to the results that are presented in the following sections.
Throughout the paper, we take the identification highly R&D intensive = high tech as given. As Galindo-Rueda and Verger (2016) warn, R&D intensity is an indicative but insufficient measure of high technology, which could also be detected by patenting, skill intensity, etc. However, until their working paper appeared, all OECD-STI publications made the above-mentioned identification, which is still prevalent nowadays.
According to Rauch (1999), the difference between the “conservative” and the “liberal” versions arises because of a few products that cannot be unambiguously classified. The conservative version is simply the version that is less lenient in classifying products as homogeneous. Indeed, of the 502 4-digit industries that are in our final sample, 374 industries are classified as “differentiated” by the Rauch “conservative” version, and 364 industries are so classified by the Rauch “liberal” version.
As Rauch (1999) defines, “organized” products are those that are traded on an organized exchange, such as a commodity exchange. He gives as an example Lead and Lead Alloys, Unwrought (SITC 6851), which is globally traded on the London Metal Exchange. Although “reference priced” products are not traded on an organized exchange, their prices are listed in trade publications without mentioning the name of the manufacturer. Rauch gives as an example Polymerization and Copolymerization Products (SITC 583), the price of which is quoted weekly in Chemical Marketing Reporter. In contrast, Rauch Differentiated products (defined by default as those that are neither Organized nor Reference Priced)–such as “shoes”–would require a very high level of disaggregation to be properly characterized. In the limit, this process of disaggregation would lead to products that have only one manufacturer: “branded products”.
In some industries (as chemicals) quantities are usually reported in liters; in other industries (as plastics) in kilograms; and in other industries (as automobiles) quantities are reported in units. This is not a problem because our import demand functions are estimated separately for each industry. A problem arises, though, when two or more products belonging in the same industry have their quantities reported in different, incompatible units of measurement. In those (59) cases, we excluded the whole 4-digit industry from our sample.
In our webpage (https://sites.google.com/view/eduardocorreiadesouza?usp=sharing), under this paper’s title, we provide the list of 4-digit industries in our sample, classified by technology level and by Rauch category, and with the number of SITC 5-digit products, the number of exporting countries, and the total value of U.S. imports (2002–2006) by industry.
Factor endowments data are also from Cadot et al. (2010). Physical capital is in U.S. dollars per worker, and human capital is in average years of schooling for the working age population. Because some SITC products appear in the U.S. import database but not in Comtrade’s, we were able to estimate factor intensities for 411 4-digit industries only.
The real exchange rates relative to the U.S. are from http://bruegel.org/2012/03/real-effective-exchange-rates-for-178-countries-a-new-database/.
In Table 10 of Appendix B, we re-estimate Eq. (6) in a panel that pools all industries belonging to a certain technology level or Rauch category together, instead of first estimating (6) for each industry, and then taking cross-industry averages. Because the different price-elasticities are estimated jointly in this panel, the correlations across industries are accounted for, providing a sense of their importance. The implied t-statistics of equal price-coefficients in Table 10B (the square root of the F-test statistics of equal coefficients) are higher than the respective coefficients in Table 7, which indicates the conservativeness of our inferences.
Not shown in Table 4, we note that the t-statistic of the difference in means between -1.32 (Top-tech mean) and -1.61 (Bottom-tech mean) is 6.70.
Khandelwal (2010) also finds that quality ladders are increasing with the industry R&D intensity.
Not shown in Table 4, we note that the t-statistic of the difference in means between 1.94 (Medium-high mean) and 1.56 (Medium-low mean) is 6.01. And the t-statistic of the difference in means between 1.97 (Top-tech mean) and 1.49 (Bottom-tech mean) is 8.68.
The quality ladder and price dispersion patterns across technology levels and Rauch categories are the same if these characteristics are measured by the standard deviation instead of by the interquartile range, for example. Results can be provided upon request.
Notice that our results concerning variety proliferation corroborate Kugler and Verhoogen’s (2012) choice of the Rauch classification as a proxy for horizontal differentiation, and of R&D and advertising intensity as a proxy for vertical differentiation. Drawing on Shaked and Sutton (1987), Beath and Katsoulacos (1991) further associate finiteness (a limited number of varieties) and vertical differentiation with R&D intensive goods.
Not shown, the t-statistic of the difference in mean between the High- and the Medium-low-tech groups is -4.94.
Khandelwal (2010) also finds a positive correlation between the industry’s quality ladder length and its physical capital intensity.
Our conclusions here depend on the assumption that quality ladders are the main indicator of vertical differentiation. But we must admit that some of the increase in price dispersion as we move from low-techs to high-techs within the Rauch-homogeneous category may reflect an increase in vertical differentiation that is not captured by the quality ladders. Also, one may find the fact that price dispersion exists within Rauch-homogeneous industries to be at odds with the Law of One Price (LOP). However, as is known, many empirical exceptions to LOP arise because of things such as preferential trade agreements that cause the importer to buy not from the cheapest exporting country.
Because Rauch-differentiated industries represent 75% of the industries in our dataset, this type of R&D is the prevalent one, which justifies our conclusions with regard to the OECD-STI classification at the end of Sect. 4: That real-world R&D efforts are directed mainly at improving quality.
In Appendix D, the reader will find how some of the 4-digit industry examples given here subdivide into 5-digit products, and how these subdivide into 10-digit HS varieties.
Another example–based only on its long (4.69) quality ladder and very high (26) number of varieties–is SITC 7812 (Motor vehicles for transport of persons): This is a Medium high-tech, Rauch-Differentiated industry.
One might conjecture whether the means in Table 7 are not hiding heterogeneity within groups, and if these patterns hold for important sectors and industries as well. In Appendix C, we group the 4-digit SITC industries into 20 ISIC sectors, and report average product market characteristics for them. Broadly, the patterns that are detected for the four-group grid analysis also hold for the ISIC sectors classified by technology level and Rauch category.
The Spearman correlation ranks each 4-digit SITC industry by the product market characteristic in question and by the R&D intensity. Given that the OECD-STI taxonomy is an ordinal variable, this is the natural correlation to use for a comparison with alternative R&D intensity measures.
See Barro-Lee Educational Attainment Data (barrolee.com).
Here, the number of countries that are product i’s exporters, Mit, coincides with the number of countries for which we have factor endowments data, from Cadot et al. (2010).
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CNPq, 310360/2016-1, Ricardo Brito.
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None of the authors has any financial or non-finalcial interest that is directly or indirectly related to this work.
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We appreciate the helpful comments and suggestions of Bernardo Blum, Emanuel Ornelas, and Luciana Ferreira, and seminar participants at Insper and USP. Ítalo Franca provided excellent research assistance. All errors are our own. Ricardo Brito additionally thanks to the financial support of CNPq Grant no. 310360/2016-1.
Appendices
Appendix
Appendix A: Variables Construction
The variables that are available at CID-econ U.S. import database are explained in detail in Feenstra et al. (2002): Xijt is the quantity of 5-digit SITC product i that is exported by country j to the U.S.. in year t; Vijt is the corresponding total value (in current US$) deflated by the U.S. GDP deflator (from http://www.econstats.com/weo/V005.htm), not including transportation costs or tariffs; \({charge}_{ijt}\) corresponds to transportation costs (in constant US$); and \({duty}_{ijt}\) is the total value (also in constant US$) that is levied by import tariffs. With these variables, we construct the unit value variable that we call “price” in the main text:
And, dividing by cross-country averages, we get the relative price that is used as a trimming criterion, as is explained in Sect. 3:
where: n denotes an exporter country (to the U.S.), and there are Mit different exporters of product i in year t.
Using the variable \({charge}_{ijt}\) from the CID-econ database, we also construct a measure of unit transportation costs, to be used as an instrument in the estimation of Eq. (6):
We take countries’ factor endowments from the Cadot et al. (2010) database: PHYSKj and HUMKj denote respectively country j’s physical capital (in constant dollars) per worker and human capital per worker; the latter is actually the average schooling years for the population aged 15 or more, as in the Barro and Lee database.Footnote 37
In order to construct 5-digit products’ factor intensities, we use the Cadot et al. (2010) revealed factor intensity index:
where:
so that 5-digit product i's physical capital intensity is a weighted average of the physical capital endowments of i’s exporting countries. The weights ωij are such that Vij/Vj is the value of i’s exports by country j divided by the total value of j’s exports (of all products). In order to calculate the weights ωij we resort to the UN-Comtrade database, so here we are considering countries’ exports to the whole world, not only to the United States, as well as US exports to other countries.Footnote 38 Notice also that \(\sum_{j=1}^{{M}_{it}}{\omega }_{ijt}=1\).
Analogously, for human capital intensities:
Appendix B: Demand Estimates
For each SITC 4-digit industry panel, we first estimate Eq. (6) by Instrumental Variables (IV) with transportation costs and real exchange rates as instruments to account for price endogeneity;and also by Ordinary Least Squares (OLS) for reference.
Table 9 presents the estimates under the labels of “OLS” and “Unrestricted IV”. It reports averages and medians, across all 4-digit industries, of the estimated coefficients (\({\sigma }_{N}\) and \({\sigma }_{P}\)) and some descriptive statistics. We see that exported quantities depend positively on variety, with coefficients that are close to one, confirming the trade literature assumption of “love of variety”. As in Erkel-Rousse and Mirza (2002), as we move from the OLS to the IV estimates, we get higher price elasticities. Since our price variable is the CIF price plus tariffs, it approximates what consumers pay for; and price elasticities that are greater than one are indeed expected, so long as exporting firms operate on the elastic portion of the demand curve in equilibrium.
By imposing the restriction \({\sigma }_{N}=1\) in (6), labeled simply as “IV Price Coeff” in Table 9, the IV price coefficient estimates almost do not change, and 91% of the 4-digit industries’ price elasticity estimates have absolute t-statistics that are greater than 1.96. The rejections of H0s in under-identification and weak-identification tests confirm that this “restricted” IV model is identified and not weakly identified.
Because there are alternative ways to test for heterogeneity across groups in the price elasticities of demand–in addition to our choice of estimating elasticities for each 4-digit industry and then testing differences in means across technology or Rauch levels–Table 10 tests differences in the elasticities of the different groups in a panel of all observations from 4-digit industries for which the IV price coefficient is significant in Table 9. Common price coefficients by group are estimated jointly. Because the correlations across industries and between groups are accounted for in the panel estimation, hypothesis testing of price coefficients significance and equality are the standard t-test and F-test. To facilitate comparison with the difference in means tests from Table 7, in Panel B we compute t-statistics in parentheses that are the square root of the chi2(1) from the F-statistics of equal price elasticity.
Appendix C: Going more Disaggregated-Product Market Characteristics for ISIC Sectors
In Table 11, we group 469 of the 502 4-digit SITC industries from the sample into 20 ISIC sectors and report their average product market characteristics, technology level, and Rauch category. Given that the OECD-STI taxonomy sorts ISIC industries, each ISIC sector is unambiguously in a tech-level. However, the Rauch classification applies to SITC 4-digit industries, and an ISIC sector can gather both homogeneous and differentiated industries. This is the case for 12 sectors, for which we present market characteristics for both Rauch categories. The numbers in parentheses of column 4 are the proportions of 4-digit industries that belong to each Rauch category within the ISIC sector.
Table 11 broadly conveys the same “grid patterns” as in Table 7. Within ISIC sectors, as we move from Rauch homogeneous to differentiated, in general, we get lower price elasticities (in nine out of 12 sectors), longer quality ladders (in eight sectors), and bigger price dispersions (in 9 sectors). Among the Rauch-differentiated industries, we notice that the shortest quality ladder is for “leather and related products” (a Bottom-tech ISIC sector), and the longest is for “machinery and equipment, nec” (a Top-tech ISIC sector). Among the Rauch-homogeneous industries, the shortest quality ladder is for “fabricated metal products except weapons and ammunition” (a Bottom-tech ISIC sector), and the longest is for “machinery and equipment, nec” (a Top-tech ISIC sector).
Among Rauch-differentiated industries, the highest price elasticity is for a Bottom-tech ISIC sector (“leather and related products”). Among the Rauch-homogeneous industries, the highest price elasticities are for “fabricated metal products except weapons and ammunition” and “textiles”; both are Bottom-tech ISIC sectors.
Appendix D: The Industry-Product-Variety Structure
Here we provide a few familiar examples of 4-digit industries and all of the component 5-digit products and all of their component 10-digit HS varieties. The first example is SITC 6521 (Cotton gauze, pile and chenille fabrics): a homogeneous goods industry. The second is SITC 6643 (Drawn and blown glass in sheets, not worked): a case of horizontal differentiation. The third is SITC 7373 (Electric, laser etc. soldering machinery), a case of vertical differentiation. And the fourth is SITC 7812 (Motor vehicles for transport of persons), a case of mixed (horizontal and vertical) differentiation.
SITC 6521 (Cotton gauze, pile and chenille fabrics)
SITC 65211 | HS 5803100000 (Cotton gauze, other than narrow or special fabrics and trimmings) |
SITC 65212 | HS 5802110000 (Cotton terry toweling and similar terry woven fabrics, unbleached, other than narrow or special fabrics) |
SITC 65213 | HS 5802190000 (Cotton terry toweling and similar terry woven fabrics, bleached, other than narrow or special fabrics) |
SITC 6643 (Drawn and blown glass in sheets, not worked)
SITC 66431 Glass, colored throughout the mass (body tinted), opacified, flashed or having an absorbent or reflecting layer | HS 7004201000 (DRAWN/BLOWN GLS,IN SHEETS,ABSORB/REFL/NON-REFL LAY) HS 7004202010 (DRN/BLN GLS,REC SHTS LT 5MM THK,NT ABS/REF/NON-REF) HS 7004202020 (DRWN/BLWN GLS,REC SHTS = > 5MM THK,NT ABS/REF/NON-REF) HS 7004205000 (DRWN/BLWN GLS,NONRECT SHTS,NT ABS/REF/NON-REF LAY) |
SITC 66439 Drawn glass and blown glass, n.e.s., in sheets | HS 7004900500(OT DR A BLW GL RCT SH TH NO 1.5MM AR NO 0.26M2) HS 7004901000(OTH DRWN A BLWN GLS REC SHT THK NOV1.5MM AROV.26SM) HS 7004901500(OTH DRWN/BLWN GLS REC SHT 1.5–2MM THK NOV.26 SQ M) HS 7004902000(OTH DRWN/BLWN GLS REC SHT 1.5–2MM THK OV.26 SQ M) HS 7004902510(OTH DRWN/BLWN GLS REC SHT 2–3.5MM THK NOV.26 SQM A) HS 7004902520(OTH DRWN/BLWN GLS REC SHT 2–3.5MMTHK 0.26–0.58SM ARA) HS 7004902550(OTH DRWN/BLWN GLS REC SHT 2–3.5MM THK, AREAOV.58SM) HS 7004903010(OTH DRWN/BLWN GLS REC SHT 3.5–5MMTHK, AR NOV 0.26SM) HS 7004903020(OTH DRWN/BLWN GLS REC SHT OV5MM THK, AR NOV.26 SQM) HS 7004903050(OTH DRWN/BLWN GLS REC SHT OV3.5MMTHK AR.26-0.65 SQM) HS 7004904000(OTH DRWN/BLWN GLS REC SHT OV3.5MMTHK AREA OV.65SQM) HS 7004905000(OTH DRWN/BLOWN GLASS NESOI, NONRECT, NT TINTD ETC) |
SITC 7373 (Electric, laser etc. soldering machinery)
SITC73731 | HS 8515110000 (Soldering irons and guns) |
SITC 73732 | HS 8515190000 (Brazing or soldering machines and apparatus, n.e.s.) |
SITC 73733 | HS 8515210000 (Machines and apparatus for resistance welding of metal, fully or partly automatic) |
SITC 73734 | HS 8515290000 (Machines and apparatus for resistance welding of metal, n.e.s.) |
SITC 73735 | HS 8515310000 (Machines and apparatus for arc (including plasma arc) welding of metal, fully or partly automatic) |
SITC 73736 Machines and apparatus for arc welding of metal, n.e.s | HS 8515390020 (ELECTRIC WELDERS, ARC, AC TRANSFORMER TYPE,NON-ROT) HS 8515390040 (ELECTRIC WELDERS, ARC, NESOI, NON-ROTATING) HS 8515390060 (ELECTRIC ARC WELDING MACHINES, ROTATING TYPE) |
SITC 73737 Electric metalworking machines and apparatus, n.e.s | HS 8515800040 (ULTRASONIC WELDING MACHINES) HS 8515800080 (ELECTRIC HOT SPRAYING METAL MACHINES, NESOI) |
SITC 73739 Parts for electric laser, other light or photon beam, ultrasonic etc. soldering, brazing or welding machines and apparatus for hot metal etc. spraying | HS 8515901000 (PART WLD MACH;DIE ATTACH APPARATS;SEMICNDTR) HS 8515903000 (PARTS OF WELDING MACHINES AND APPARATUS, NESOI) HS 8515904000 (PARTS OF BRAZING AND SOLDERING EQUIP) |
SITC 7812 (Motor vehicles for transport of persons).
SITC 78120 Motor vehicles for the transport of persons (other than public transport), n.e.s | HS 8703210000 (PASS MTR VEH, SPARK IGN ENG, NOT OV 1,000 CC) HS 8703220000 (PASS MTR VEH,SPARK IGN ENG, > 1000CC BUT = < 1500CC) HS 8703230010 (MOTOR HOMES, SPARK IGNITION, 1500–3000 CC) HS 8703230022 (STA WGN UN 160CM HGT, SPARK IGN,4 CYL, 1500–3000CC) HS 8703230032 (STA WAG,NESOI,VANS, SPARK IGN,4 CYL, 1500–3000CC) HS 8703230042(PASS MTR VEH,NESOI, SPARK IGN,4 CYL, 1500–3000CC) HS 8703230062(PASS VEH,SPARK IGN,OV 4 BT NT OV 6 CYL,1500–3000CC) HS 8703230072(PASS VEH, SPARK IGN, OV 6 CYL, 1500–3000CC) HS 8703230074(3PASS VEH, SPARK IGN, OV 6 CYL, 1500–3000CC) HS 8703230076(PASS VEH,OV 6 CYL,1500–3000CC,INT GT 3.1 N/O 3.4) HS 8703230078(PASS VEH, SPARK IGN, OV 6 CYL, 1500–3000CC) HS 8703230090(PASS VEH, SPARK IGN, CYL CAP 1500–3000CC, USED) HS 8703240010(AMB HERCE&VANS,SPK IOGN INT COMB PST ENG EX) 3000CC HS 8703240030(MOTOR HOMES W SPARK IGN INTERNAL COMBUST PIST ENG) HS 8703240032(PASS MTR VEH, SPARK IGN ENG, OV 3000CC, 4 CYL & UN HS 8703240052(PASS VEH,SPARK IGN, OV 4 BT NT OV 6 CYL,OV 3000 CC HS 8703240062(PASS VEH,SPARK IGN, OV 6 CYL, OV 3000 CC HS 8703240090(PASS MTR VEH, SPARK IGN, OVER 3000 CC, USED HS 8703310000(PASS VEH, DIESEL ENG, NOT OVER 1500 CC, NEW HS 8703320010(PASS VEH, DIESEL ENG, OV 1500 BT NT OV 2500 CC,NEW) HS 8703320050(PASS VEH,DIESEL ENG, OV 1500 BT NT OV 2500 CC,USED) HS 8703330010(AMBULANCE,HEARSE,PRISON VANS,DIESEL,OV 2500CC, NEW) HS 8703330030(MOTOR HOMES WITH DIESEL ENGINE, OV 2500 CC) HS 8703330045(PASS MTR VEH, DIESEL ENG, OVER 2500 CC, NEW) HS 8703330085(PASS MTR VEH, DIESEL ENG, OVER 2500 CC, USED) HS 8703900000(PASSENGER MOTOR VEHICLES, NESOI) |
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Brito, R.D., Correia de Souza, E. & Moita, R. Separating Vertical from Horizontal Differentiation. Rev Ind Organ 64, 183–218 (2024). https://doi.org/10.1007/s11151-023-09924-y
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DOI: https://doi.org/10.1007/s11151-023-09924-y