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Mexico and Poland's Trade with CUSFTA and the EU*. Maurice Schiff a ... trade bloc partners and reduce the extent and cost of trade diversion. Mexico did ...
Technology Diffusion and Productivity Gains: Mexico and Poland’s Trade with CUSFTA and the EU*

Maurice Schiffa and Yanling Wangb

World Bank

October 2002

Abstract This paper examines the impact of trade with CUSFTA and with the EU (the North) on trade-related technology diffusion and productivity gains for Mexico and Poland (the South). The measures of foreign R&D used to estimate that impact are constructed based on industry-specific R&D in the North, North-South trade patterns, and input-output relations in the South. We find for both Mexico and Poland that trade with CUSFTA has a significantly larger impact on productivity than trade with the EU, and examine implications for trade policy and regional integration. JEL Categories: F02, F15, F43, O39 Keywords: North-South technology diffusion; trading partners; regional integration; Mexico, Poland. * The authors would like to thank Bernard Hoekman and Marcelo Olarreaga for their comments. The opinions expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Directors or the governments they represent. a. Corresponding author. International Trade Unit, DECRG. The World Bank. E-mail: [email protected]. b. Economics Department, Georgetown University, and International Trade Unit, DECRG, the World Bank. E-mail: [email protected].

Technology Diffusion and Productivity Gains: Mexico and Poland’s Trade with CUSFTA and the EU 1. Introduction There are good reasons why a developing country would want to integrate with a large developed neighboring country rather than with a small distant developing one. First, the gains from competitiveness are likely to be larger in a regional integration agreement (RIA) with a large developed country (World Bank, 2000). Second, a RIA will provide greater credibility gains for policy reforms undertaken by the developing member if that member fears retaliation by its partner for breaking RIA rules and if the partner cares sufficiently to retaliate. These are more likely if the partner is large, developed, and a neighbor. Third, if the RIA involves “deep” integration (such as regulatory reform), the developing country is likely to be better off integrating with a developed one. These issues are examined in detail in Schiff and Winters (forthcoming). From these viewpoints, it made sense for Mexico to integrate with Canada and the US (CUSFTA) and for Poland to apply for accession to the EU. For instance, Poland’s accession to the EU requires it to adopt the entire “acquis communautaire” of EU laws and regulations, which should provide it with significant credibility benefits. And Poland’s desire for EU accession also seems to have been motivated by security concerns associated with its historical relations with Russia. There are also good reasons why a developing country should continue to liberalize its trade after joining a RIA. This will lower the RIA-related transfers to its trade bloc partners and reduce the extent and cost of trade diversion. Mexico did

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liberalize prior to joining NAFTA,1 while Poland will have to liberalize and adopt the lower EU common external tariff (CET) following accession. This paper examines another aspect of trade policy and the choice of RIA partner, namely, its effect on trade-related technology diffusion and productivity. As far as we know, this is the first attempt to examine this issue in the literature. It is shown that both Mexico and Poland obtain larger productivity gains from trading with CUSFTA than from trading with the EU. The rest of the paper is organized as follows. Section 2 provides a brief analytical framework. Section 3 presents the empirical results, and Section 4 concludes.

2. Analytical Framework The theoretical basis for the approach used here is the work of Grossman and Helpman (1991) on endogenous growth in the open economy. The basic idea is that goods embody technological know-how and therefore countries can acquire foreign knowledge through imports. A number of papers have explored this idea in an empirical setting. They estimate the impact on total factor productivity (TFP) of “foreign R&D” stocks, where the aggregate stock of foreign R&D is defined as the sum of trading partners’ R&D stocks, weighted by the bilateral trade shares. Using aggregate data, Coe and Helpman (1995) and Lumenga-Neso et al. (2001) find for developed countries and Coe et al. (1997) for developing countries that foreign R&D has a significantly positive impact on TFP. Thus, TFP rises with the degree of a country’s openness and with the trading partners’ R&D 1

Mexico joined the GATT in 1986. Its imports subject to licensing requirements fell from 100 percent in 1983 to 36 percent by 1985 and to 192 tariff lines by 1993, and the average tariff rate fell to 11.4 percent in 1993.

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stocks. Similar findings are obtained at the industry level by Keller (2002) for developed countries and by Schiff et al. (2002) for developing countries. Trade-related foreign R&D was defined by Coe and Helpman (1995) at the aggregate level. Following their approach, we define trade-related foreign R&D for industry i, NRDi, as:   M jk NRDi ≡ ∑ aij RD j = ∑ a ij ∑  j j  k  VA j

  RD jk    

(1)

where k indexes OECD countries in the sample, j indexes industries, M (VA) (RD) denotes imports (value added) (R&D stock), and a ij is the import input-output coefficient (which measures the share of imports of industry j that is sold to industry i). The first part of equation (1) says that foreign R&D in industry i, NRDi , is the sum, over all industries j, of RD j , the industry-j R&D obtained through imports from OECD countries, multiplied by a ij , the share of imports of industry j that is sold to industry i. The second part of equation (1) says that RD j is the sum, over OECD countries k, of M jk VA j , the imports of industry-j products from OECD country k per unit of industry-j value added (i.e., the bilateral openness share), multiplied by RD jk , the stock of industry-j R&D in OECD country k. We split NRD into two parts, the NRD obtained through trade with CUSFTA (NRDCUS), and the NRD obtained through trade with the EU (NRDEU). The estimated equation is: ln TFPit = β 0 + β CUS ln NRDitCUS + β EU ln NRDitEU + ∑ β t Dt + ∑ β i Di + ε it t

where Dt (Di) represents time (industry) dummies. 3

i

(2)

The TFP index is calculated as the difference between the logs of output and factor income, with inputs (labor and capital) weighted by their income shares, i.e., lnTFP=lnY-αlnL-(1-α)lnK, with α equal to labor’s income share. Data sources and methods used to derive imports, value added, labor and capital stock in Mexico and Poland, and R&D stock in the US, Canada and the EU for the 16 manufacturing industries, are described in detail in Schiff et al. (2002).2

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The sample period is 1981-

1999.

3. Estimation Results Before turning to the empirical results, we need to consider the issue that variables may contain unit roots, making the regression results spurious (unless the variables are co-integrated). We use the Levin and Lin (1993) procedure to test for panel data unit roots for lnTFP, lnNRDCUS and lnNRDEU. Test results are shown in Table 1 with both one and two lags. These indicate that the hypothesis of panel unit roots is rejected for all three variables.

3.1. Mexico Columns (i), (ii) and (iii) of Table 2 present the estimation results for Mexico. Coefficients of time and industry dummies are not shown. As shown in column (i), the elasticity of TFP with respect to foreign R&D from CUSFTA, NRDCUS, is .319 and is 2

The 16 manufacturing industries are: 31-Food, Beverage & Tobacco; 32-Textiles, Apparel & Leather; 33Wood Products & Furniture; 34-Paper, Paper Products & Printing; 351/2-Chemicals, Drugs & Medicines; 353/4-Petroleum Refineries & Products; 355/6-Rubber & Plastic Products; 36-Non-Metallic Mineral Products; 371-Iron & Steel; 372-Non-Ferrous Metals; 381-Metal Products; 382-Non-Electrical Machinery, Office & Computing Machinery; 383-Electrical Machinery and Communication Equipment; 384Transportation Equipment; 385-Professional Goods; and 39-Other Manufacturing. 3 EU countries in our sample are: Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Norway, Spain, Sweden and United Kingdom.

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significant at the 5% level (t = 1.98). The elasticity of TFP with respect to foreign R&D from the EU, NRDEU, is .168, but is not significant. Given the .92 degree of correlation between lnNRDCUS and lnNRDEU, we also estimate equation (2) with each regressor separately.4 We find (column ii) that the elasticity of TFP with respect to NRDCUS is .346 (significant at the 5% level), which is very close to the value obtained when both measures are used (.319), the reason being that NRDEU is highly non-significant in that regression. When only NRDEU is used (column iii), the elasticity remains non-significant.

3.2. Poland Columns (iv), (v) and (vi) in Table 2 present the estimation results for Poland. The results in column (iv) are similar to those with respect to Mexico in column (i), i.e., the elasticity of TFP with respect to NRDCUS is large and significant, while that with respect to NRDEU is small and not significant. However, the former is much larger for Poland than for Mexico (3.24 versus .319), and we return to this in Section 3.3. Given the .94 degree of correlation between lnNRDCUS and lnNRDEU, we also estimate equation (2) with each regressor separately. The elasticity of TFP with respect to NRDCUS in column (v) is very similar to that in column (iv). The elasticity of TFP with respect to NRDEU in column (vi) is larger than in column (iv) and is significant. This result is probably due to the fact that NRDEU is capturing some of the effect of the omitted NRDCUS variable.

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The sampling variance of the coefficient estimates increases with the degree of collinearity and their covariance is negative when, as in this case, collinearity is positive. The method of principal components cannot be used here to solve the collinearity problem because we only have two explanatory variables. We did attempt to estimate ridge regressions, a method that provides biased estimates but with lower variance, but without success (the estimated coefficients did not converge). Thus, we decided to examine the robustness of our results by estimating the regression with each variable separately.

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3.3. Explaining the Results This section examines the paper’s two main results. First, why is the elasticity of TFP larger for NRDCUS than for NRDEU? Endogenous growth theory is based on the assumption of increasing returns to knowledge: the greater the stock of knowledge, the lower the cost of obtaining additional knowledge. Thus, the larger the stock of R&D, the greater the marginal productivity effect. Given that the average R&D stock for CUSFTA is about six times larger than for the EU (US$6.8 trillion versus US$1.16 trillion) one would expect a stronger productivity effect with the former than with the latter. Second, why is the elasticity of TFP with respect to NRDCUS larger for Poland than for Mexico? A larger trade share implies a larger quantity of imported inputs.5 The import share is subject to diminishing knowledge returns in the sense that the marginal effect on knowledge of importing a given input diminishes as the number of imported units of that input rises. The share of CUSFTA in Poland’s imports from the US, Canada and the EU averaged 6% over the 1981-98 period, while the share in Mexico’s imports averaged 82.5%, almost fourteen times more. Consequently, one would expect a larger impact of NRDCUS on TFP in Poland than in Mexico.6 Not only is the elasticity of TFP with respect to NRDCUS larger for Poland than for Mexico, but the former is greater than one and the latter is smaller than one. The elasticity of TFP with respect to NRDCUS can be expressed as the marginal effect of NRDCUS divided by its average effect. An elasticity that is larger (smaller) than one means 5

A larger trade share could also imply a greater diversity of inputs. We examined, for Mexico and Poland, the share of tariff lines for which imports from CUSFTA were non-zero, and found no significant difference between them. 6 Another possible explanation is that the level of education is higher in Poland than in Mexico. Education cannot be used as an explanatory variable in the single-country regressions because it is collinear with the time dummies. From the multi-country estimation, we find that education has a positive effect on TFP and interacts positively with NRD in high-R&D industries in Latin America (including Mexico) but not elsewhere. Thus, education cannot explain why the elasticity is greater in Poland than in Mexico.

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that the marginal effect is greater (smaller) than the average one, i.e. it implies increasing (decreasing) returns to NRDCUS. As is clear from equation (1), NRDCUS has two basic components, R&D from CUSFTA, and import shares for either Mexico or Poland. As discussed above, R&D is likely to be subject to increasing returns and import shares are likely to be subject to decreasing ones. With a small import share from CUSFTA, the increasing returns to R&D seem to dominate in the case of Poland, which would explain that the elasticity is larger than one. On the other hand, the import share is very large in the case of Mexico and the decreasing returns to imports are likely to dominate, which would explain an elasticity smaller than one.

4. Conclusion This paper examined the impact of trading with CUSFTA and the EU on traderelated technology diffusion and productivity in Mexico and Poland. We find that the productivity gains from trade with CUSFTA are significantly larger than those from trade with the EU in both countries. Mexico and Poland did well to choose their large developed neighbors as trade bloc partners for the reasons listed in Section 1. Moreover, Mexico did well to join CUSFTA from the viewpoint of technology diffusion and productivity gains. Whether Poland’s accession to the EU will also be beneficial from that viewpoint is unclear. With an average tariff on manufacturing in Poland in the year 2000 of 10.281% (Table 3), accession to the EU will provide a significant advantage to imports from the EU in the Polish market. Thus, by eliminating tariffs on imports from the EU, Polish

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imports from the EU will rise while those from CUSFTA will fall.7 On the other hand, accession to the EU implies that Poland will have to lower its tariffs to the level of the EU’s CET. Data for 2000 (Table 3) show that Poland’s average MFN tariff rate will have to fall from 10.281% to the EU’s CET of 3.395%, or by close to 7 percentage points, and this will result in an increase in imports from CUSFTA.8 Thus, the net impact on Poland’s trade with CUSFTA is ambiguous and so is the impact on productivity.

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This is especially likely given the high degree of substitutability between CUSFTA and EU exports. Table 3 shows that EU’s average CET fell more rapidly than Poland’s average MFN tariff between 1991 and 2000, with an increase in the tariff reduction Poland will have to undertake when acceding to the EU.

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References Coe, D.T., and E. Helpman. 1995. “International R&D Spillovers,” European Economic Review 39 (5), 859-887. ______, _____ and A. W. Hoffmaister. 1997. “North-South R&D Spillovers,” Economic Journal 107, 134-149. Grossman, M.G., and E. Helpman. 1991. Innovation and Growth in the Global Economy, The MIT Press, Cambridge, MA. Levin, A. and C. Lin. 1993. “Unit Root Test in Panel Data: New Results,” Discussion Paper 93-56, Department of Economics, University of California, San Diego. Keller, W. 2002. “Trade and the Transmission of Technology,” Journal of Economic Growth 7, 5-24. Lumenga-Neso, O., M. Olarreaga and M. Schiff. 2001. “On ‘Indirect’ Trade-Related Research and Development Spillovers,” World Bank Policy Research Working Paper No. 2580. http://www.worldbank.org/research/trade Schiff, M., Y. Wang and M. Olarreaga. 2002. “North-South and South-South TradeRelated Technology Diffusion: An Industry-Level Analysis,” World Bank Policy Research Working Paper No. 2861. http://www.worldbank.org/research/trade. ______ and L. Alan Winters. Forthcoming. Regional Integration and Development. Oxford: Oxford University Press. World Bank. 2000. Trade Blocs. A Policy Research Report. Washington, D.C.

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Table 1. Panel Unit Root Test Results under Levin and Lin (1993)

Variables

Test Statistics for Mexico

Test Statistics for Poland

P=1

P=2

P=1

P=2

LnTFP

-9.42

-6.72

-14.69

-5.08

lnNRDCUS

-25.95

-7.49

-4.00

-2.73

lnNRDEU

-3.54

-1.97

-2.36

-2.37

Note: The model under the Levin and Lin (1993) test is specified as: Pi

∆y i ,t = α 0,i + α 1,i t + δ i y i ,t −1 + ∑θ iL ∆y i ,t − L + ε i ,t . where L=1,…, Pi, L =1

t=1,…, T, and i=1,…, N. Pi is the number of lags included in each panel. The null hypothesis is that δ i = 0 for all i and the alternative hypothesis is that δ i < 0 for all i. The critical values at 1%, 5% and 10% confidence levels are –2.94, -2.23 and –1.84 respectively.

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Table 2. Elasticity of TFP with respect to Trade-Related Foreign R&D Mexico

Variable

lnNRDCUS

lnNRDEU

(i)

(ii)

0.319 (1.98) **

Poland (iii)

(iv)

(v)

0.346

3.236

3.273

(2.19) **

(12.54) ***

(13.90) ***

(vi)

0.168

0.249

0.106

1.60

(0.82)

(1.24)

(0.36)

(4.74) ***

Adj. R2

0.79

0.79

0.79

0.60

0.60

0.36

No. of Obs.

282

282

282

304

304

304

Note: Figures in parenthesis are t-statistics. The *** (**) means that the coefficient is significant at the 1% (5%) significance level. NRDCUS is the trade-related R&D from CUSFTA and NRDEU is the trade-related R&D from the EU.

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Table 3. MFN Tariff of EU and Poland (%) 1991

2000

Industry

EU

Poland

EU

Poland

31

13.724

12.523

8.436

21.884

32

11.049

9.143

9.843

12.459

33

4.516

17.003

1.533

10.027

34

4.333

9.970

1.638

7.759

351

7.868

10.779

3.768

8.576

352

6.252

13.709

1.325

6.631

353

5.966

12.072

2.701

17.380

354

4.320

9.872

2.317

8.691

355

5.018

21.108

3.576

9.000

356

9.206

14.865

7.650

10.409

36

6.694

14.790

4.111

9.036

371

5.202

8.497

2.208

12.954

372

1.817

7.293

2.072

8.428

381

5.575

13.783

3.064

10.753

382

4.181

8.670

0.938

6.548

383

8.067

9.373

1.967

6.969

384

5.905

11.658

4.497

10.614

385

5.438

9.787

1.447

6.839

39

4.097

16.964

1.423

10.375

Average

6.275

12.203

3.395

10.281

Note: The ISIC Rev. 2 industries are described in footnote 2 in the main text.

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