- Email: [email protected]
Multinational investments and product sophistication
Accepted Manuscript Effect of FDI on the quality of nations’ exports Tannista Banerjee, Arnab Nayak
PII: DOI: Reference:
S0165-1765(17)30346-4 http://dx.doi.org/10.1016/j.econlet.2017.08.023 ECOLET 7747
To appear in:
Economics Letters
Received date : 30 June 2017 Revised date : 7 August 2017 Accepted date : 20 August 2017 Please cite this article as: Banerjee T., Nayak A., Effect of FDI on the quality of nations’ exports. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.08.023 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights
FDI is a channel by which developing countries can improve export qualities Cross-country quality variations from Feenstra and Romalis (2014) and Hallak and Schott (2011). Results confirmed using variations in U.S. import prices for large group of countries.
*Manuscript Click here to view linked References
E¤ect of FDI on the Quality of Nations’Exports Arnab Nayaky
Tannista Banerjee August 2017
Abstract In this paper, we show evidence that increasing per capita stocks of foreign direct investment has a signi…cant positive e¤ect on the quality of exports of developing countries. To study the quality variations, we use two recently developed panels of cross-country quality indexes by Feenstra and Romalis (2014) and Hallak and Schott (2011), as well as the traditional method of looking at the variations of U.S. import prices from 135 countries for the period 1989-2004. A positive and signi…cant e¤ect of per capita FDI stocks on host country export quality is noted for the developing nations using all these methods. The study has important policy implications for developing countries, showing that accumulating foreign direct investment has enabled many developing countries in the last two decades to improve their export sophistication, as demonstrated by Hallak and Schott (2011).(JEL F13, F14, F15, F16). Keywords: Quality of Exports, FDI, Panel Data, HS-10
Auburn University, Department of Economics. 0324 Haley Center, Auburn AL 36849, USA. Email: [email protected], phone:334-844-2922 y Mercer University. Department of Economics, Eugene W. Stetson School of Business and Economics. Email: [email protected]
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1
Introduction
The international trade literature has increasingly pointed to the importance of export sophistication as a key determinant of countries’success in international trade (Schott, 2004; Hummels and Klenow, 2005; Hallak, 2006; Schott, 2008; Hallak and Schott, 2011). These studies show that a country’s export composition and quality play an important role in the country’s ability to improve trade balances and achieve export-led growth and development (Hallak and Schott, 2011; Hummels and Klenow, 2005; Hausmann, Hwang, and Rodrik, 2007). Yet, not much is known about how developing countries can improve their product quality given their limited resources and ability to develop all the physical, intellectual, and business know-how required to upgrade product qualities. In this paper, we …ll this gap by showing evidence that increasing per capita stocks of foreign direct investment (FDI) has a signi…cant positive e¤ect on the quality of exports of developing countries. There could be multiple channels through which this e¤ect is generated. For example, technology and knowledge transfers and spillovers from foreign multinationals to local …rms might enable local industries to upgrade quality. Quality upgrades can also happen as domestic industries in the host country get easier access to higher quality intermediates supplied locally. Therefore, this paper provides important policy implications for developing countries by suggesting that they attract more FDI in key export-generating and export-supporting sectors to achieve improved export quality. This, in turn, can increase these countries’export-led growth. This study contributes to two di¤erent strands in the trade literature. First, the e¤ect FDI has on the economic growth and productivity on the local economy has been debated widely in the literature.1 FDI is thought to be an important avenue of technology transfer across nations through spillover e¤ects. According to UNCTAD (2016), nearly 80 percent of technology transfers around the world occur between the headquarters of the multinational enterprises (MNEs) and their subsidiaries. Therefore, this study provides further evidence of the bene…ts of FDI, either directly through knowledge spillovers or through an improved supply of intermediates that, in turn, improves host country export quality.2 Second, this study not only relates to the literature on export quality di¤erences, but also provides a channel— in the form of accumulating FDI— through which many developing countries in the last two decades have been able to improve their export sophistication, as demonstrated by Hallak and Schott (2011).
2
Method and Findings
We take two routes to identify the e¤ect of the levels of per capita FDI stocks on host country export quality. First, we focus on a global analysis of how per capita FDI stocks across 135 countries a¤ected these countries’ export prices to the U.S. from 1989 to 2004. Second, we use recently developed cross-country quality indexes by Hallak and Schott (2011) and Feenstra and Romalis (2014) for 43 and 52 countries, respectively, to see how the panel of per capita FDI stocks of these countries a¤ected the quality indexes of these countries over the periods 1989-2004 and 1 For example, Haddad and Harrison (1993), de Mello (1997), and Harrison (1996) show the failure of multinational corporations in generating positive welfare e¤ects in host countries. Javorcik (2004) and Mullen and Williams (2005) are counter examples. See Lipsey (2004) for a full review. 2 There is also previous theoretical literature that relates to the possible e¤ects on host country product quality due to FDI accumulations. Motta (1993) suggests that in the face of increasing FDI, host country exporters would lower their prices to remain competitive, while the quality of the product is unaltered. Turrini (2000), however, suggests that more FDI into poorer countries would lead to a scarcity of capital, skilled labor, and other scarce resources, forcing poorer host country producers to produce lower quality products.
2
1987-2007, respectively. A positive and signi…cant e¤ect of per capita FDI stocks on host country export quality is noted for the developing nations using all these methods. For the developed countries, however, the results are mixed and not signi…cant. First, we …nd negative and insigni…cant e¤ects of per capita FDI stocks on the export price for the developed countries. Second, the regressions using the cross-country quality indexes show insigni…cant positive results for the richer countries. Therefore, we are inclined to believe in a pro-competitive e¤ect of increasing foreign multinationals in richer countries, as predicted by Motta 1993, where increased competition lowers the host industries’ prices, while keeping the preexisting high quality unaltered.
3
Data and Analysis
3.1 3.1.1
Data Sources Quality Index Data
The data on the …rst panel of country quality indexes is taken from Hallak and Schott (2011), who develop the quality indexes for 43 countries over the period 1989-2003. We also take the aggregate country quality indexes reported by Feenstra and Romalis (2014) for 52 of the largest exporting countries over the period 1987-2007. 3.1.2
Import Price Data
The data on trade ‡ows comes from Feenstra, Romalis, and Schott (2002). It contains information on product customs values (free on board, or f:o:b:, value) of U.S. imports by year, source country, and 10-digit harmonized system (HS) product codes from 1989 to 2004. The unit value or "price" pzct of each good z from a country c in each year t is calculated by dividing the total f:o:b. value vzct by the total quantity qzct of that good supplied by country c in year t. Examples of units of goods considered include: dozens of men’s t-shirts, cotton and knit; stringed musical instruments in numbers; pineapple jams in kilograms; etc. We include only SITC 5-8 manufacturing industries, as is standard in the literature, and we also follow standard practice in removing SITC 68 goods (non-ferrous metals). Missing values on quantity are dropped. Given that measurement error could be a serious problem because of the noisiness of quantity measures, we use two secondary screening methods to screen the data and avoid some of the noise e¤ects as follows: Relevance Constraint: Country-product-year observations must have quantities greater than 25 and values greater than $25,000. Unit-Value Dispersion Constraint: Country-product-year observations are excluded if the country’s unit value is less than one-…fth or more than …ve times the geometric mean of all prices from all countries for the product in that year. These two constraints make the price dispersions tighter and, thus, make the measurement error in quantities smaller. 3.1.3
Other Data
We also used the country-wise gross …xed capital (in current year millions of dollars) and the gross labor force data from the Penn World Table, version 9.0 (Feenstra et al., 2011). The data on stocks of FDI is taken from the UNCTAD database from the World Investment Report 2016. The mean 3
values of all the variables (in 2010 prices) for all countries for the years 1989-2004 used in this study are represented in Table I.
3.2
Empirical Strategies
The …rst empirical strategy of this paper is to look at the e¤ect of the panel of FDI stocks of di¤erent exporters to the U.S. in the variation of the unit values of HS-10 level goods they export over time to the U.S. The underlying assumption is that the countries’exports to the U.S. are representative of their exports to the rest of the world. Also, the U.S. is chosen due to the availability of detailed unit value observations for the time period used. We look at within-product e¤ects of FDI on product prices across countries, and we also run pooled regressions, clustering standard errors by country across all products to see FDI’s e¤ect on product prices across exporters. 3.2.1
With-in Product Variations
The formal model for the within-product variation of exporter “prices”or unit values and exporter FDI is estimated using the following OLS estimation model:
ln(pzct ) =
zt
+
1zt ln
F DIct Lct
+
GDPct Lct
2zt ln
+
3zt ln
Kct
F DIct Lct
+ "zct ,
(1)
where pzct is country c’s unit value for the HS-10 product z at time t, F DIct is the stock of foreign direct investment, GDPct is the real gross domestic product, Lct is the population, and Kct is the gross capital stock for country c in period t. In this estimation, we want to see if per capita FDI a¤ects the unit values of a country’s exports, controlling for factors that have been related to increasing the export product quality of countries in the literature. Therefore, we include per capita income levels and per capita capital stocks along with per capita FDI stocks in the model to see if FDI still has any e¤ect on the product quality of its exports. Table II reports the details and the results of the above regressions. We include only those goods that have more than 40 observations and are sourced from at least …ve countries. Of the 1,786 HS10 products, 26 percent of the goods show positive and signi…cant impacts at a 90 percent level of signi…cance of FDI stock of a country on the country’s unit value of exports. This percentage is slightly lower than the percentage of goods for which Schott (2004) found a positive and signi…cant relationship between unit values of export and per capita GDP of the countries. In looking at this result, we must remember that FDI stocks in an economy may a¤ect only a limited number of industries and, thus, we cannot reject the hypothesis that exporter stock of FDI positively a¤ects the unit values of export of the host counties. Also, note that though the percentage of betas with negative values is lower than the percentage of betas with positive values (signi…cant or otherwise), 26.4 percent of goods show a negative and signi…cant result. 3.2.2
Pooled Model
Next, we conduct pooled panel regressions across all HS-10-level goods’prices on exporter per capita FDI stocks, per capita real GDP, and per capita gross capital stocks net of FDI, with productyear dummy variables.3 and with standard errors clustered for exporter e¤ects. The product-year 3
Following the suggestion of a referee, we also ran the regression using country-product …xed e¤ects and time …xed e¤ects to see whether the results hold for a given country over time. This speci…cation yields results very similar results to the product-year …xed e¤ects we provide in Table IV.
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dummies account for any di¤erences across the price levels of di¤erent goods and also the variation of a good’s price over the 1989-2004 period. The OLS regression is formally stated as: ln(pzct ) =
+
1 ln
F DIct Lct
+
2 ln
GDPct Lct
+
3 ln
Kct
F DIct Lct
+ "zct ,
(2)
8 z; c and t = 1989 2004: Table III reports the results of this OLS regression. The …rst parenthesis under the regression coe¢ cients reports clustered standard errors, and the asterisk on the estimated coe¢ cient reports level of signi…cance (signi…cance levels are denoted as follows: is 99 percent, is 95 percent, and is 90 percent.) As seen in Table III, the coe¢ cient of per capita FDI is positive and signi…cant, even after controlling for GDP per capita and per worker capital stock. Table IV reports the correlation across the regressors and though the three variables have high correlations (especially between GDP per capita and per capital stock), all variables are individually signi…cant, as is the model. Thus, from this analysis we see FDI positively and signi…cantly a¤ects unit values of exports across all exporters to the U.S. If we assume that di¤erences in unit values re‡ect the level of vertical di¤erentiation across exports from di¤erent sources, we infer that FDI positively a¤ects the product quality of exports across all the nations. Theories for the causality of this e¤ect abound, and they align with the …nding that MNEs transfer higher productivity and know-how to the host nation. Though FDI also imposes added burdens on the human capital and physical capital resources of the host country, it seems the e¤ect of technology transfer helps host country …rms improve their export quality due to all the positive externalities and knowledge di¤usion. However, the e¤ects of increased FDI on resource-constrained developing economies might be di¤erent than those on developed nations (which are facing fewer constraints). The analysis next investigates this hypothesis. 3.2.3
E¤ect of FDI within Rich and Poor Nations
In this section, we divide countries into two subgroups: (1) the high-income group of countries (HIC), which have per capita incomes above the 75^{th} percentile of the average per capita GDP range for the given years 1989-2004; and (2) the middle- and upper-low-income countries (MULIC), which are the countries lying between the 25th and 75th percentiles of the per capita GDP. Then we run the OLS regressions in equation (2) to see if the e¤ect of FDI on the unit values varies across the two groups.4 Interestingly, Table V shows that FDI a¤ects the export unit values signi…cantly and positively (at the 95 percent level) only for the middle- and upper-low-income countries. The e¤ect of FDI is slightly negative but insigni…cant for the richer countries. Thus, the analysis of this section refutes theories that relative scarcity of skilled labor and capital in developing economies does not enable them to reap the technological spillovers from FDI. In fact, the higher level of competition among host and foreign …rms is lowering export prices, if not also export quality, for the richer nations. But for the developing countries, foreign investments actually increase the prices of exports, and this might also be indicative of improved quality as long as they have also been able to export higher quantities. 4 Due to the lack of FDI stock and price data for the lowest income group of countries, we could not run this analysis for the poorest economies, where FDI might play a more negative role on the quality of products under increased FDI in‡ows.
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3.3
Analysis Using Country Quality Indexes
The criticism about countries’export prices of HS-10-digit goods re‡ecting the true quality of the good is that prices may not account for varieties within the good (for example the color, cut, or design of a cotton, non-knit men’s shirt). Also, di¤erences in prices might re‡ect a devalued currency, di¤erences in transportation costs, labor costs, e¤ects of pricing-to-market where di¤erent sellers might adjust prices di¤erently to changes in the exchange rate, etc. The analysis in the previous section is not robust to all these e¤ects. We use two di¤erent indexes of exporter quality recently developed by Hallak and Schott (2011) and Feenstra and Romalis (2014), who estimate the export quality from the above export unit values using methods from industrial organization and trade theory to …nd quality measures robust to many of the "impurities" other than quality in the unit values. Hallak and Schott (2011) follow the industrial organization literature in looking at the share of an HS-10-level good in the U.S. imports for that good, accounting for price di¤erences, so that the higher share can be attributed to better quality (be it physical attributes or e¤ects of advertisement). In doing so, they also adjust for di¤erences in varieties across exports for the same HS-10-level good by calculating a price index that accounts for unobserved horizontal di¤erences from a given exporter. Hallak and Schott develop an index of quality for 43 of the world’s largest exporters. Feenstra and Romalis (2014), however, use not only demand-side parameters, but also supply-side components to get their quality measures. Namely, they use the "Washington apples" phenomena (Alchian and Allen (1964); Hummels and Skiba (2004)) as well as the variation of quality of the marginal exporting …rm when demand changes, using a quality Melitz (2003) model, to …lter out the impurities in export unit values to estimate the quality. Feenstra and Romalis (2014) provide the aggregate export quality indexes of 52 of the world’s largest exporters for 19872007. We use both these cross-country quality indexes to check whether FDI a¤ects export quality even when we use these indexes rather than the unit values of exports. Separately, using the quality indexes from Feenstra and Romalis and Hallak and Schott, we run the following panel regression: (QualityIndexct ) =
+
1 ln
F DIct Lct
+
2 ln
GDPct Lct
+
3 ln
Kct
F DIct Lct
+ "ct . (Equation 3)
In this equation, the dependent variable is the quality index of a country c in time t. The right-hand side variables are the same as in the previous models, including time t per capita FDI stocks, per capita real GDP, and per capita gross capital stocks net of FDI. We use both the abovementioned quality indexes to run the regressions. Even though we have 52 and 43 countries’quality indexes from Feenstra and Romalis (2014) and Hallak and Schott (2011), respectively, these are the largest exporters to the U.S. and, in each case, they comprise more than 90 percent of total world exports. We run these regressions for the years for which the country quality indexes are available for each country from Feenstra and Romalis (1987, 1997 , 2007) and from Hallak and Schott (1989, 1993 , 1998, 2003), and we include a year dummy to account for any unobserved time e¤ects. For each of the quality indexes, we also run all countries pooled together and also separate the high-income countries (HIC) and middle- and upper-low-income countries. Table VI reports the results of the three speci…cations each using the Feenstra-Romalis and Hallak-Schott quality indexes. Interestingly, both the Feenstra-Romalis and Hallak-Schott quality indexes show that FDI accumulations positively a¤ect the export quality for all countries pooled together and the HIC and MULIC countries. This reiterates our previous …ndings that increasing FDI stock in a country
6
positively a¤ects the quality of its exports. However, out of the six speci…cations, the positive e¤ect is signi…cant using both the indexes for MULIC countries only, while it is positive and insigni…cant for HIC countries from both indexes. The Feenstra-Romalis indexes also show a positive signi…cant e¤ect when we run the model with all countries pooled together. Therefore, combining the results from the unit value analysis, we are able to con…rm that FDI indeed a¤ects the export quality positively and signi…cantly for the developing countries, who need export sophistication more dearly to be able to compete and gain from global trade. For the developed countries, the mixed results indicate pro-competitive e¤ects in at least lowering prices, while the gains in quality are less prominent.
4
Conclusion
In this paper, we show evidence that increasing per capita stocks of FDI has a signi…cant positive e¤ect on the quality of exports of developing countries. This result holds when using either unit values of exports or the recently developed cross-country quality indexes as the measure for quality. The e¤ect is also positive, but not signi…cant, for the developed countries when using the quality indexes to measure quality, and it is negative and not signi…cant for the developed countries when using the unit values of exports as a proxy for quality. Though this points toward some important di¤erences in how FDI a¤ects export quality and prices across developed vs. developing nations (and should be of interest for further investigations), we believe this has more important policy implications for developing nations. This is because they attract more FDI in important exportingand export supporting sectors to improve their export quality. This study also …lls a gap in the quality literature by showing that attracting and accumulating FDI explains, at least partially, how developing countries have been able to narrow the quality di¤erences of their exports from that of the developed nations’exportsover the last few decades.
5 Tables
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Hong Kong (HKG) 403 6.3 27841.5 43217.3 Ireland (IRL) 73 3.8 35890 24777.1 Singapore (SGP) 198 3.6 36998.8 22088.6 Netherlands (NLD) 195 15.6 36370.7 12512.5 Belgium (BEL) 211 10.1 30499.7 11944.5 Switzerland (CHE) 240 7 50712.9 10878.6 Denmark (DNK) 68 5.3 38962.8 8497.1 Sweden (SWE) 181 8.8 32705.6 7620.8 Macau (MAC) 89 0.4 45223.4 7235.6 Canada (CAN) 1427 29.9 35303.2 7228.8 Saint itts and Nevis (KNA) 4 0 14881.2 7054.3 Australia (AUS) 79 18.6 36659.7 7038 Bahrain (BHR) 12 0.6 39769.6 6889.1 Bahamas (BHS) 3 0.3 23210 6797.7 New Zealand (NZL) 24 3.8 26717 6774.6 Malta (MLT) 7 0.4 17939.5 6698.4 Norway (NOR) 44 4.4 69613.2 6505.4 United Kingdom (GBR) 703 58.3 29114 5931.5 Bermuda (BMU) 1 0.1 51681.1 4828.7 Germany (DEU) 1188 81.3 35594.4 4755.6 Equatorial Guinea (GNQ) 1 0.6 19822.9 4589.2 France (FRA) 536 60.4 31800 4401.2 Trinidad and Tobago (TTO) 8 1.3 15195.7 4138.1 Spain (ESP) 176 40.6 27238.2 3985.1 Austria (AUT) 102 8 34119.2 3806.6 Finland (FIN) 75 5.1 29677.9 3600.4 Portugal (PRT) 61 10.2 22150.9 2949.1 Qatar (QAT) 14 0.6 116941.7 2708.7 Czech Republic (CZE) 46 10.3 19803.6 2660.3 Lebanon (LBN) 1 3.2 12268.2 2631 Hungary (HUN) 37 10.3 16144.5 2528 Chile (CHL) 25 14.6 12679.7 2305.8 Israel (ISR) 154 5.7 23191.8 2232.9 Iceland (ISL) 2 0.3 29977.5 2156.8 Estonia (EST) 3 1.4 13134 2038.4 Italy (ITA) 688 57.3 33303.7 1845.2 Slovakia (SVK) 16 5.4 13682.2 1753.4 Slovenia (SVN) 22 2 19143.9 1543.2 Malaysia (MYS) 291 21.9 13018.2 1494.8 Tunisia (TUN) 3 9.4 7102.6 1258.3 Liberia (LBR) 1 2.1 627.5 1256.2 Panama (PAN) 9 2.6 8760.4 1205.3 Greece (GRC) 17 10.6 21548.1 1203.7 Cyprus (CYP) 1 0.7 22184.7 1162.4 Jordan (JOR) 40 5 9549.1 1100.1 Oman (OMN) 16 2.2 42877.4 1081.7 United Arab Emirates (ARE) 41 2.8 116060.3 1044.3 Argentina (ARG) 53 35.6 13020.3 1023.8 Barbados (BRB) 1 0.3 10480 968.7 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Belize (BLZ) 2 0.2 5915.4 922.3 Venezuela (VEN) 47 23.1 15262.2 915.7 Taiwan, Province of China (TWN) 839 21.5 21393.1 911.3 Mexico (MEX) 1027 98.9 13226.4 909.7 Saudi Arabia (SAU) 12 20.2 43188.5 853.9 South Africa (ZAF) 96 43.8 9483.1 768.2 Latvia (LVA) 3 2.4 10007.5 728.1 Jamaica (JAM) 31 2.5 7091 720.4 Poland (POL) 46 38.5 12692.4 717 Lithuania (LTU) 3 3.5 11086.9 682.8 Korea, Republic of (KOR) 716 45.1 18224.7 671.2 Kazakhstan (KAZ) 9 15.3 10143.3 631 Fiji (FJI) 6 0.8 6172.8 608.5 Costa Rica (CRI) 67 3.6 8198.2 559 Brazil (BRA) 261 167.5 11029.9 476.2 Ecuador (ECU) 4 12.3 7671.7 459.3 Bolivia (BOL) 4 8.2 4122.4 456.4 Congo (COG) 1 2.9 5024.8 448.2 Bulgaria (BGR) 20 8.1 9517.8 438.7 Angola (AGO) 1 13.7 5148.5 426.4 Thailand (THA) 390 61.1 8835.4 424 Croatia (HRV) 6 4.6 13265.5 417.3 Uruguay (URY) 11 3.2 11103.4 351.5 Mauritius (MUS) 23 1.1 9895.8 344 Turkmenistan (TKM) 7 4.6 8138.4 331.9 Turkey (TUR) 145 61.9 12496.1 329.3 Peru (PER) 22 24.9 6084.2 312.9 Dominican Republic (DOM) 131 8.4 7843.5 302.6 Morocco (MAR) 12 28 4785 299 Colombia (COL) 73 38.6 8493.1 298.8 Japan (JPN) 1488 124.6 32039.3 292.7 El Salvador (SLV) 92 5.7 6098 288.9 Nicaragua (NIC) 33 5 3382.8 275.5 Russian Federation (RUS) 84 146.6 13718.7 272 Guatemala (GTM) 83 11.1 5679.8 267.8 Egypt (EGY) 46 66.4 7487 266.3 Viet Nam (VNM) 152 82.3 3050.2 222.3 Georgia (GEO) 1 4.8 4212.3 189.6 Honduras (HND) 88 6 3462.4 184.4 Gambia (GMB) 1 1.1 1593.1 172.5 Kuwait (KWT) 4 1.9 77859.6 167.7 Armenia (ARM) 1 3.1 3807.3 148.1 Paraguay (PRY) 4 4.7 5485.7 147 Nigeria (NGA) 1 108.6 3009.7 142.2 Cambodia (KHM) 81 12.5 1406.6 139.4 China (CHN) 1387 1248.5 4013.1 122.2 Mongolia (MNG) 12 2.4 5271.2 121.6 Cote D'Ivoire (CIV) 2 14.2 2787.7 121.1 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Moldova, Republic Of (MDA) 5 4.2 3008.7 116.8 Philippines (PHL) 285 73.3 4198.5 116.1 Algeria (DZA) 1 30.3 10337.1 114.3 Albania (ALB) 1 3.1 4270.1 110.6 Bosnia And Herzegowina (BIH) 1 3.8 5906 108.2 Belarus (BLR) 7 10 7762.7 106.5 Gabon (GAB) 1 1.3 18910.7 105.3 Sri Lanka (LKA) 127 18.5 4798.2 104.8 Cameroon (CMR) 1 15.5 2260.6 96.6 Iran (Islamic Republic of) (IRN) 3 65.8 12506.9 94.9 Indonesia (IDN) 330 204.4 5808.4 92.2 Ukraine (UKR) 26 49.3 6696.8 76.2 Lao People's Democratic Republic (LAO) 3 5 2114.3 74.8 Myanmar (MMR) 22 47.1 1258.9 69.1 Syrian Arab Republic (SYR) 1 16.2 5088.4 68.6 Ghana (GHA) 3 18.2 2142.2 67.2 Sierra Leone (SLE) 1 4 1460.1 63.1 Zimbabwe (ZWE) 4 11.7 1326.8 61.5 Uganda (UGA) 1 27.1 1367.3 60.7 Pakistan (PAK) 110 132 3550 45 Central African Republic (CAF) 1 4 837.9 41.6 Malawi (MWI) 1 11.1 887.6 35.5 Tajikistan (TJK) 1 6.4 2087.3 29.9 Uzbekistan (UZB) 3 24.7 4096.8 29.4 Kenya (KEN) 12 31.4 2253.3 29.4 Togo (TGO) 1 5 1223.8 28.2 Mali (MLI) 1 8.3 1034.8 26.9 Guinea (GIN) 1 7.9 1271.2 24.3 Tanzania, United Republic of (TZA) 1 27.6 1380.1 23.3 Kyrgyzstan (KGZ) 1 4.6 2488.3 21 India (IND) 317 1021.7 2414.7 15.4 Haiti (HTI) 25 8 1942.9 13.5 Bangladesh (BGD) 125 125.8 1654.2 12.6 Madagascar (MDG) 19 16.6 1375.4 12.4 Niger (NER) 1 11 759.1 3.8 Nepal (NPL) 16 22.7 1552.9 2.8 Mozambique (MOZ) 1 13.4 364.3 1.9 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
10
Table II: Exporter FDI, by Product and Year: Percent of Manufacturing Products with Signi…cant (10%) Relationship Between Unit Value and Exporter FDI (Minimum source countries=40) P ercentage of Goods N umber of Goods Signif icant (90%) P ositive Betas 24:6 439 Signif icant (90%) N egative Betas 26:0 465 P ositive (N ot Signif icant) 23:0 410 N egative (N ot Signif icant) 26:4 472 Total 100% 1; 786 Total number of HS10 Products 1; 786 Max Observations Within a Product 694 Min Observations Within a Product 40 Mean Observations Within a Product 127 Total number of Countries Included 133 Max Countries Supplying a Product 66 Min Countries Supplying a Product 5 Mean Countries Supplying a Product 18 Note: Note: Table II presents the percentage of coe¢ cients that are positive, positive-signi…cant at the 90 percent level, negative, negative-signi…cant at the 90 percent level for the 1,786 withinproducts regressions from Equation (1).
11
Table III: Pooled Regression of Product Unit Values on Countries’FDI stocks Stocks per capita Capita over the period Period 1989-2004 Dependent V ariable Ln(U nitV alue) Estimated Coef f icients F DIct log( Lct ) 0:0158 (0:0060) ln
GDPct Lct
0:2489
(0:0232) -0:1582 (0:0017) Product Year Dummies Y es Product-Country-Year Observations 148; 182 Number of Countries 135 Adj R2 0:04 Notes: Table III reports OLS regression results of Equation (2) in text of exporter unit value on exporter capital per worker and exporter stock of FDI per worker. Robust standard errors adjusted for exporter are noted in the second parenthesis below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. log( (Ki LFctDIct ) )
Table IV: Correlation coe¢ cient between explanatory variables P er-Capita F DIct P er-Capita GDPct P er-Capita GrossKct P er-Capita F DIct 1:0000 P er-Capita GDPct 0:7824 1:0000 P er-Capita GrossKct 0:6691 0:9029 1:0000 Notes: Table IV presents the correlation coe¢ cients between the explanatory variables; they are computed by taking the average of the correlation values for the variables over 1989-2004. Table V: E¤ect of FDI on Export Quality across High-Income and Middle- and Upper-LowIncome Countries Dependent V ariable Estimated HIC Estimated M U LIC Ln(U nit V alue) Coef f iciients Coef f iciients F DIct log( Lct ) 0:0200 0:1691 (0:0299) (0:0809) ln
GDPct Lct
1:010
0:8077
(0:2898) (0:2621) 0:1104 0:0489 (0:2220) (0:1336) Product Year Dummies Y es Y es Product-Country-Year Observations 68; 279 29; 290 Number of Countries 39 65 Adj R2 0:10 0:09 Number of goods 1; 041 352 Notes: Table V reports OLS regression results of Equation (2) of exporter unit value on exporter capital per worker and exporter stock of FDI per worker for the two subgroups of countries, HIC and MULIC. Robust standard errors adjusted for exporter clustering are noted in the second parenthesis log( (Ki LFctDIct ) )
12
below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. TABLE VI: Results of the Panel Data Model of the E¤ect of a Country’s FDI Stock on its Feenstra-Romalis and Hallak-Schott Quality Indexes Estimated Coef f icients using Estimated Coef f icients using Ef f ect On Country Romalis-F eenstra Qual-Indexes Hallak Schott Qual-Indexes Quality Index All Countries HIC MULIC All Countries HIC MULIC F DIct 0:0264 0:0304 0:0316 0:0082 0:0184 0:0580 log( Lct ) (0:0123) 0:0200 (0:0138) (0:0356) (0:0360) (0:0381) ln
GDPct Lct
0:0461
0:0239
0:0034
0:5176
0:4127
0:1128
(0:0487) (0:0638) (0:0484) (0:1351) (0:1910) (0:0917) 0:0170 0:0860 0:0351 0:1274 0:3960 0:0162 (0:0332) (0:0337) (0:0267) (0:1325) (0:2186) (0:0621) Y ear Dummies Yes Yes Yes Yes Yes Yes Observations 144 81 51 152 90 58 N umber of Countries 51 28 19 39 23 15 AdjR2 0.3430 0.1481 0.2836 0.5223 0.1353 0.3236 Notes: Table VI reports OLS regression results of Equation (3) of exporter quality indexes on exporter capital per worker and exporter stock of FDI per worker for all the countries pooled together and for the two subgroups of countries, HIC and MULIC. Standard errors are noted in the second parenthesis below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. log( (Ki LFctDIct ) )
13
References [1] Alchian, A. & Allen, W. R. (1964). University economics. Belmont CA: Wadsworth Publishing. [2] de Mello, L. R., Jr. (1997). Foreign direct investment in developing countries and growth: A selective survey. Journal of Development Studies, 34(1), 1-34 [3] Feenstra, R. C., Inklaar , I., &Timmer, M. P. (2015). The next generation of the Penn World Table. American Economic Review 105(10), 3150-3182 [4] Feenstra, R. C., & Romalis, J. (2014). International prices and endogenous quality. Quarterly Journal of Economics, 129(2), 477-527 . [5] Feenstra, R. C., Romalis, J., & Schott, P. (2002). U.S. imports, exports, and tari¤ data, 1989-2001. Working paper 9387, National Bureau of Economic Research, Cambridge, MA. [6] Haddad, M., & Harrison, A. (1993). Are there positive spillovers from direct foreign investment? Journal of Development Economics, 42, 51-74. [7] Hallak, J. C. (2006). Product quality and the direction of trade. Journal of international Economics 68(1), 238-265. [8] Hallak, J. C., & Schott, P. K. (2011). Estimating cross-country di¤erences in product quality. Quarterly Journal of Economics, 126(1), 417-474. [9] Harrison, A. (1996). Determinants and e¤ects of direct foreign investment in Côte d’Ivoire, Morocco, and Venezuela. In M. J. Roberts & J. R. Tybout (Eds.), Industrial evolution in developing countries (pp. 163-186). New York: Oxford University Press for the World Bank. [10] Hausmann, R., Hwang, J. & Rodrik, D. (2007). What you export matters. Journal of Economic Growth 12(1), 1-25. [11] Hummels, D., & Klenow, P. J. (2005). The variety and quality of a nation’s trade. American Economic Review, 95(3), 704-723. [12] Hummels, D., & Skiba, A. (2004). Shipping the good apples out? An empirical con…rmation of the Alchian-Allen conjecture. Journal of Political Economy, 112, 1384-1402. [13] Javorcik, B. S. (2004). Does foreign direct investment increase the productivity of domestic …rms? In search of spillovers through backward linkages. American Economic Review, 93, 605-627. [14] Lipsey, R. E. (2004). Home- and host-country e¤ects of foreign direct investment. In R. E. Baldwin & L. A. Winters, Challenges to globalization: Analyzing the economics (pp. 333382). Chicago, IL: University of Chicago Press. [15] Melitz, M. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica, 71, 1695-1725. [16] Motta, M. (1993). Endogenous quality choice: Price vs. quantity competition. Journal of Industrial Economics, 41(2), 113-131. [17] Mullen, J., & Williams, M. (2005). Foreign direct investment and regional economic performance. Kyklos, 58(2), 265-282. 14
[18] Schott, P. K. (2004). Across-product versus within-product specialization in international trade, Quarterly Journal of Economics, 129(2), 477-527. [19] Schott, P. K. (2008). The relative sophistication of Chinese exports. Economic Policy, 53, 5-49. [20] Turrini, A. (2000). High-quality bias in vertically di¤erentiated oligopolies: A note on skills, trade, and welfare. Journal of Economics, 71(2), 133-147. [21] UNCTAD. (2016). World Investment Report. Retrieved from http://unctad.org/en/Pages/DIAE/World%20Investment%20Report/Annex-Tables.aspx
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PII: DOI: Reference:
S0165-1765(17)30346-4 http://dx.doi.org/10.1016/j.econlet.2017.08.023 ECOLET 7747
To appear in:
Economics Letters
Received date : 30 June 2017 Revised date : 7 August 2017 Accepted date : 20 August 2017 Please cite this article as: Banerjee T., Nayak A., Effect of FDI on the quality of nations’ exports. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.08.023 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Highlights (for review)
Highlights
FDI is a channel by which developing countries can improve export qualities Cross-country quality variations from Feenstra and Romalis (2014) and Hallak and Schott (2011). Results confirmed using variations in U.S. import prices for large group of countries.
*Manuscript Click here to view linked References
E¤ect of FDI on the Quality of Nations’Exports Arnab Nayaky
Tannista Banerjee August 2017
Abstract In this paper, we show evidence that increasing per capita stocks of foreign direct investment has a signi…cant positive e¤ect on the quality of exports of developing countries. To study the quality variations, we use two recently developed panels of cross-country quality indexes by Feenstra and Romalis (2014) and Hallak and Schott (2011), as well as the traditional method of looking at the variations of U.S. import prices from 135 countries for the period 1989-2004. A positive and signi…cant e¤ect of per capita FDI stocks on host country export quality is noted for the developing nations using all these methods. The study has important policy implications for developing countries, showing that accumulating foreign direct investment has enabled many developing countries in the last two decades to improve their export sophistication, as demonstrated by Hallak and Schott (2011).(JEL F13, F14, F15, F16). Keywords: Quality of Exports, FDI, Panel Data, HS-10
Auburn University, Department of Economics. 0324 Haley Center, Auburn AL 36849, USA. Email: [email protected], phone:334-844-2922 y Mercer University. Department of Economics, Eugene W. Stetson School of Business and Economics. Email: [email protected]
1
1
Introduction
The international trade literature has increasingly pointed to the importance of export sophistication as a key determinant of countries’success in international trade (Schott, 2004; Hummels and Klenow, 2005; Hallak, 2006; Schott, 2008; Hallak and Schott, 2011). These studies show that a country’s export composition and quality play an important role in the country’s ability to improve trade balances and achieve export-led growth and development (Hallak and Schott, 2011; Hummels and Klenow, 2005; Hausmann, Hwang, and Rodrik, 2007). Yet, not much is known about how developing countries can improve their product quality given their limited resources and ability to develop all the physical, intellectual, and business know-how required to upgrade product qualities. In this paper, we …ll this gap by showing evidence that increasing per capita stocks of foreign direct investment (FDI) has a signi…cant positive e¤ect on the quality of exports of developing countries. There could be multiple channels through which this e¤ect is generated. For example, technology and knowledge transfers and spillovers from foreign multinationals to local …rms might enable local industries to upgrade quality. Quality upgrades can also happen as domestic industries in the host country get easier access to higher quality intermediates supplied locally. Therefore, this paper provides important policy implications for developing countries by suggesting that they attract more FDI in key export-generating and export-supporting sectors to achieve improved export quality. This, in turn, can increase these countries’export-led growth. This study contributes to two di¤erent strands in the trade literature. First, the e¤ect FDI has on the economic growth and productivity on the local economy has been debated widely in the literature.1 FDI is thought to be an important avenue of technology transfer across nations through spillover e¤ects. According to UNCTAD (2016), nearly 80 percent of technology transfers around the world occur between the headquarters of the multinational enterprises (MNEs) and their subsidiaries. Therefore, this study provides further evidence of the bene…ts of FDI, either directly through knowledge spillovers or through an improved supply of intermediates that, in turn, improves host country export quality.2 Second, this study not only relates to the literature on export quality di¤erences, but also provides a channel— in the form of accumulating FDI— through which many developing countries in the last two decades have been able to improve their export sophistication, as demonstrated by Hallak and Schott (2011).
2
Method and Findings
We take two routes to identify the e¤ect of the levels of per capita FDI stocks on host country export quality. First, we focus on a global analysis of how per capita FDI stocks across 135 countries a¤ected these countries’ export prices to the U.S. from 1989 to 2004. Second, we use recently developed cross-country quality indexes by Hallak and Schott (2011) and Feenstra and Romalis (2014) for 43 and 52 countries, respectively, to see how the panel of per capita FDI stocks of these countries a¤ected the quality indexes of these countries over the periods 1989-2004 and 1 For example, Haddad and Harrison (1993), de Mello (1997), and Harrison (1996) show the failure of multinational corporations in generating positive welfare e¤ects in host countries. Javorcik (2004) and Mullen and Williams (2005) are counter examples. See Lipsey (2004) for a full review. 2 There is also previous theoretical literature that relates to the possible e¤ects on host country product quality due to FDI accumulations. Motta (1993) suggests that in the face of increasing FDI, host country exporters would lower their prices to remain competitive, while the quality of the product is unaltered. Turrini (2000), however, suggests that more FDI into poorer countries would lead to a scarcity of capital, skilled labor, and other scarce resources, forcing poorer host country producers to produce lower quality products.
2
1987-2007, respectively. A positive and signi…cant e¤ect of per capita FDI stocks on host country export quality is noted for the developing nations using all these methods. For the developed countries, however, the results are mixed and not signi…cant. First, we …nd negative and insigni…cant e¤ects of per capita FDI stocks on the export price for the developed countries. Second, the regressions using the cross-country quality indexes show insigni…cant positive results for the richer countries. Therefore, we are inclined to believe in a pro-competitive e¤ect of increasing foreign multinationals in richer countries, as predicted by Motta 1993, where increased competition lowers the host industries’ prices, while keeping the preexisting high quality unaltered.
3
Data and Analysis
3.1 3.1.1
Data Sources Quality Index Data
The data on the …rst panel of country quality indexes is taken from Hallak and Schott (2011), who develop the quality indexes for 43 countries over the period 1989-2003. We also take the aggregate country quality indexes reported by Feenstra and Romalis (2014) for 52 of the largest exporting countries over the period 1987-2007. 3.1.2
Import Price Data
The data on trade ‡ows comes from Feenstra, Romalis, and Schott (2002). It contains information on product customs values (free on board, or f:o:b:, value) of U.S. imports by year, source country, and 10-digit harmonized system (HS) product codes from 1989 to 2004. The unit value or "price" pzct of each good z from a country c in each year t is calculated by dividing the total f:o:b. value vzct by the total quantity qzct of that good supplied by country c in year t. Examples of units of goods considered include: dozens of men’s t-shirts, cotton and knit; stringed musical instruments in numbers; pineapple jams in kilograms; etc. We include only SITC 5-8 manufacturing industries, as is standard in the literature, and we also follow standard practice in removing SITC 68 goods (non-ferrous metals). Missing values on quantity are dropped. Given that measurement error could be a serious problem because of the noisiness of quantity measures, we use two secondary screening methods to screen the data and avoid some of the noise e¤ects as follows: Relevance Constraint: Country-product-year observations must have quantities greater than 25 and values greater than $25,000. Unit-Value Dispersion Constraint: Country-product-year observations are excluded if the country’s unit value is less than one-…fth or more than …ve times the geometric mean of all prices from all countries for the product in that year. These two constraints make the price dispersions tighter and, thus, make the measurement error in quantities smaller. 3.1.3
Other Data
We also used the country-wise gross …xed capital (in current year millions of dollars) and the gross labor force data from the Penn World Table, version 9.0 (Feenstra et al., 2011). The data on stocks of FDI is taken from the UNCTAD database from the World Investment Report 2016. The mean 3
values of all the variables (in 2010 prices) for all countries for the years 1989-2004 used in this study are represented in Table I.
3.2
Empirical Strategies
The …rst empirical strategy of this paper is to look at the e¤ect of the panel of FDI stocks of di¤erent exporters to the U.S. in the variation of the unit values of HS-10 level goods they export over time to the U.S. The underlying assumption is that the countries’exports to the U.S. are representative of their exports to the rest of the world. Also, the U.S. is chosen due to the availability of detailed unit value observations for the time period used. We look at within-product e¤ects of FDI on product prices across countries, and we also run pooled regressions, clustering standard errors by country across all products to see FDI’s e¤ect on product prices across exporters. 3.2.1
With-in Product Variations
The formal model for the within-product variation of exporter “prices”or unit values and exporter FDI is estimated using the following OLS estimation model:
ln(pzct ) =
zt
+
1zt ln
F DIct Lct
+
GDPct Lct
2zt ln
+
3zt ln
Kct
F DIct Lct
+ "zct ,
(1)
where pzct is country c’s unit value for the HS-10 product z at time t, F DIct is the stock of foreign direct investment, GDPct is the real gross domestic product, Lct is the population, and Kct is the gross capital stock for country c in period t. In this estimation, we want to see if per capita FDI a¤ects the unit values of a country’s exports, controlling for factors that have been related to increasing the export product quality of countries in the literature. Therefore, we include per capita income levels and per capita capital stocks along with per capita FDI stocks in the model to see if FDI still has any e¤ect on the product quality of its exports. Table II reports the details and the results of the above regressions. We include only those goods that have more than 40 observations and are sourced from at least …ve countries. Of the 1,786 HS10 products, 26 percent of the goods show positive and signi…cant impacts at a 90 percent level of signi…cance of FDI stock of a country on the country’s unit value of exports. This percentage is slightly lower than the percentage of goods for which Schott (2004) found a positive and signi…cant relationship between unit values of export and per capita GDP of the countries. In looking at this result, we must remember that FDI stocks in an economy may a¤ect only a limited number of industries and, thus, we cannot reject the hypothesis that exporter stock of FDI positively a¤ects the unit values of export of the host counties. Also, note that though the percentage of betas with negative values is lower than the percentage of betas with positive values (signi…cant or otherwise), 26.4 percent of goods show a negative and signi…cant result. 3.2.2
Pooled Model
Next, we conduct pooled panel regressions across all HS-10-level goods’prices on exporter per capita FDI stocks, per capita real GDP, and per capita gross capital stocks net of FDI, with productyear dummy variables.3 and with standard errors clustered for exporter e¤ects. The product-year 3
Following the suggestion of a referee, we also ran the regression using country-product …xed e¤ects and time …xed e¤ects to see whether the results hold for a given country over time. This speci…cation yields results very similar results to the product-year …xed e¤ects we provide in Table IV.
4
dummies account for any di¤erences across the price levels of di¤erent goods and also the variation of a good’s price over the 1989-2004 period. The OLS regression is formally stated as: ln(pzct ) =
+
1 ln
F DIct Lct
+
2 ln
GDPct Lct
+
3 ln
Kct
F DIct Lct
+ "zct ,
(2)
8 z; c and t = 1989 2004: Table III reports the results of this OLS regression. The …rst parenthesis under the regression coe¢ cients reports clustered standard errors, and the asterisk on the estimated coe¢ cient reports level of signi…cance (signi…cance levels are denoted as follows: is 99 percent, is 95 percent, and is 90 percent.) As seen in Table III, the coe¢ cient of per capita FDI is positive and signi…cant, even after controlling for GDP per capita and per worker capital stock. Table IV reports the correlation across the regressors and though the three variables have high correlations (especially between GDP per capita and per capital stock), all variables are individually signi…cant, as is the model. Thus, from this analysis we see FDI positively and signi…cantly a¤ects unit values of exports across all exporters to the U.S. If we assume that di¤erences in unit values re‡ect the level of vertical di¤erentiation across exports from di¤erent sources, we infer that FDI positively a¤ects the product quality of exports across all the nations. Theories for the causality of this e¤ect abound, and they align with the …nding that MNEs transfer higher productivity and know-how to the host nation. Though FDI also imposes added burdens on the human capital and physical capital resources of the host country, it seems the e¤ect of technology transfer helps host country …rms improve their export quality due to all the positive externalities and knowledge di¤usion. However, the e¤ects of increased FDI on resource-constrained developing economies might be di¤erent than those on developed nations (which are facing fewer constraints). The analysis next investigates this hypothesis. 3.2.3
E¤ect of FDI within Rich and Poor Nations
In this section, we divide countries into two subgroups: (1) the high-income group of countries (HIC), which have per capita incomes above the 75^{th} percentile of the average per capita GDP range for the given years 1989-2004; and (2) the middle- and upper-low-income countries (MULIC), which are the countries lying between the 25th and 75th percentiles of the per capita GDP. Then we run the OLS regressions in equation (2) to see if the e¤ect of FDI on the unit values varies across the two groups.4 Interestingly, Table V shows that FDI a¤ects the export unit values signi…cantly and positively (at the 95 percent level) only for the middle- and upper-low-income countries. The e¤ect of FDI is slightly negative but insigni…cant for the richer countries. Thus, the analysis of this section refutes theories that relative scarcity of skilled labor and capital in developing economies does not enable them to reap the technological spillovers from FDI. In fact, the higher level of competition among host and foreign …rms is lowering export prices, if not also export quality, for the richer nations. But for the developing countries, foreign investments actually increase the prices of exports, and this might also be indicative of improved quality as long as they have also been able to export higher quantities. 4 Due to the lack of FDI stock and price data for the lowest income group of countries, we could not run this analysis for the poorest economies, where FDI might play a more negative role on the quality of products under increased FDI in‡ows.
5
3.3
Analysis Using Country Quality Indexes
The criticism about countries’export prices of HS-10-digit goods re‡ecting the true quality of the good is that prices may not account for varieties within the good (for example the color, cut, or design of a cotton, non-knit men’s shirt). Also, di¤erences in prices might re‡ect a devalued currency, di¤erences in transportation costs, labor costs, e¤ects of pricing-to-market where di¤erent sellers might adjust prices di¤erently to changes in the exchange rate, etc. The analysis in the previous section is not robust to all these e¤ects. We use two di¤erent indexes of exporter quality recently developed by Hallak and Schott (2011) and Feenstra and Romalis (2014), who estimate the export quality from the above export unit values using methods from industrial organization and trade theory to …nd quality measures robust to many of the "impurities" other than quality in the unit values. Hallak and Schott (2011) follow the industrial organization literature in looking at the share of an HS-10-level good in the U.S. imports for that good, accounting for price di¤erences, so that the higher share can be attributed to better quality (be it physical attributes or e¤ects of advertisement). In doing so, they also adjust for di¤erences in varieties across exports for the same HS-10-level good by calculating a price index that accounts for unobserved horizontal di¤erences from a given exporter. Hallak and Schott develop an index of quality for 43 of the world’s largest exporters. Feenstra and Romalis (2014), however, use not only demand-side parameters, but also supply-side components to get their quality measures. Namely, they use the "Washington apples" phenomena (Alchian and Allen (1964); Hummels and Skiba (2004)) as well as the variation of quality of the marginal exporting …rm when demand changes, using a quality Melitz (2003) model, to …lter out the impurities in export unit values to estimate the quality. Feenstra and Romalis (2014) provide the aggregate export quality indexes of 52 of the world’s largest exporters for 19872007. We use both these cross-country quality indexes to check whether FDI a¤ects export quality even when we use these indexes rather than the unit values of exports. Separately, using the quality indexes from Feenstra and Romalis and Hallak and Schott, we run the following panel regression: (QualityIndexct ) =
+
1 ln
F DIct Lct
+
2 ln
GDPct Lct
+
3 ln
Kct
F DIct Lct
+ "ct . (Equation 3)
In this equation, the dependent variable is the quality index of a country c in time t. The right-hand side variables are the same as in the previous models, including time t per capita FDI stocks, per capita real GDP, and per capita gross capital stocks net of FDI. We use both the abovementioned quality indexes to run the regressions. Even though we have 52 and 43 countries’quality indexes from Feenstra and Romalis (2014) and Hallak and Schott (2011), respectively, these are the largest exporters to the U.S. and, in each case, they comprise more than 90 percent of total world exports. We run these regressions for the years for which the country quality indexes are available for each country from Feenstra and Romalis (1987, 1997 , 2007) and from Hallak and Schott (1989, 1993 , 1998, 2003), and we include a year dummy to account for any unobserved time e¤ects. For each of the quality indexes, we also run all countries pooled together and also separate the high-income countries (HIC) and middle- and upper-low-income countries. Table VI reports the results of the three speci…cations each using the Feenstra-Romalis and Hallak-Schott quality indexes. Interestingly, both the Feenstra-Romalis and Hallak-Schott quality indexes show that FDI accumulations positively a¤ect the export quality for all countries pooled together and the HIC and MULIC countries. This reiterates our previous …ndings that increasing FDI stock in a country
6
positively a¤ects the quality of its exports. However, out of the six speci…cations, the positive e¤ect is signi…cant using both the indexes for MULIC countries only, while it is positive and insigni…cant for HIC countries from both indexes. The Feenstra-Romalis indexes also show a positive signi…cant e¤ect when we run the model with all countries pooled together. Therefore, combining the results from the unit value analysis, we are able to con…rm that FDI indeed a¤ects the export quality positively and signi…cantly for the developing countries, who need export sophistication more dearly to be able to compete and gain from global trade. For the developed countries, the mixed results indicate pro-competitive e¤ects in at least lowering prices, while the gains in quality are less prominent.
4
Conclusion
In this paper, we show evidence that increasing per capita stocks of FDI has a signi…cant positive e¤ect on the quality of exports of developing countries. This result holds when using either unit values of exports or the recently developed cross-country quality indexes as the measure for quality. The e¤ect is also positive, but not signi…cant, for the developed countries when using the quality indexes to measure quality, and it is negative and not signi…cant for the developed countries when using the unit values of exports as a proxy for quality. Though this points toward some important di¤erences in how FDI a¤ects export quality and prices across developed vs. developing nations (and should be of interest for further investigations), we believe this has more important policy implications for developing nations. This is because they attract more FDI in important exportingand export supporting sectors to improve their export quality. This study also …lls a gap in the quality literature by showing that attracting and accumulating FDI explains, at least partially, how developing countries have been able to narrow the quality di¤erences of their exports from that of the developed nations’exportsover the last few decades.
5 Tables
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Hong Kong (HKG) 403 6.3 27841.5 43217.3 Ireland (IRL) 73 3.8 35890 24777.1 Singapore (SGP) 198 3.6 36998.8 22088.6 Netherlands (NLD) 195 15.6 36370.7 12512.5 Belgium (BEL) 211 10.1 30499.7 11944.5 Switzerland (CHE) 240 7 50712.9 10878.6 Denmark (DNK) 68 5.3 38962.8 8497.1 Sweden (SWE) 181 8.8 32705.6 7620.8 Macau (MAC) 89 0.4 45223.4 7235.6 Canada (CAN) 1427 29.9 35303.2 7228.8 Saint itts and Nevis (KNA) 4 0 14881.2 7054.3 Australia (AUS) 79 18.6 36659.7 7038 Bahrain (BHR) 12 0.6 39769.6 6889.1 Bahamas (BHS) 3 0.3 23210 6797.7 New Zealand (NZL) 24 3.8 26717 6774.6 Malta (MLT) 7 0.4 17939.5 6698.4 Norway (NOR) 44 4.4 69613.2 6505.4 United Kingdom (GBR) 703 58.3 29114 5931.5 Bermuda (BMU) 1 0.1 51681.1 4828.7 Germany (DEU) 1188 81.3 35594.4 4755.6 Equatorial Guinea (GNQ) 1 0.6 19822.9 4589.2 France (FRA) 536 60.4 31800 4401.2 Trinidad and Tobago (TTO) 8 1.3 15195.7 4138.1 Spain (ESP) 176 40.6 27238.2 3985.1 Austria (AUT) 102 8 34119.2 3806.6 Finland (FIN) 75 5.1 29677.9 3600.4 Portugal (PRT) 61 10.2 22150.9 2949.1 Qatar (QAT) 14 0.6 116941.7 2708.7 Czech Republic (CZE) 46 10.3 19803.6 2660.3 Lebanon (LBN) 1 3.2 12268.2 2631 Hungary (HUN) 37 10.3 16144.5 2528 Chile (CHL) 25 14.6 12679.7 2305.8 Israel (ISR) 154 5.7 23191.8 2232.9 Iceland (ISL) 2 0.3 29977.5 2156.8 Estonia (EST) 3 1.4 13134 2038.4 Italy (ITA) 688 57.3 33303.7 1845.2 Slovakia (SVK) 16 5.4 13682.2 1753.4 Slovenia (SVN) 22 2 19143.9 1543.2 Malaysia (MYS) 291 21.9 13018.2 1494.8 Tunisia (TUN) 3 9.4 7102.6 1258.3 Liberia (LBR) 1 2.1 627.5 1256.2 Panama (PAN) 9 2.6 8760.4 1205.3 Greece (GRC) 17 10.6 21548.1 1203.7 Cyprus (CYP) 1 0.7 22184.7 1162.4 Jordan (JOR) 40 5 9549.1 1100.1 Oman (OMN) 16 2.2 42877.4 1081.7 United Arab Emirates (ARE) 41 2.8 116060.3 1044.3 Argentina (ARG) 53 35.6 13020.3 1023.8 Barbados (BRB) 1 0.3 10480 968.7 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Belize (BLZ) 2 0.2 5915.4 922.3 Venezuela (VEN) 47 23.1 15262.2 915.7 Taiwan, Province of China (TWN) 839 21.5 21393.1 911.3 Mexico (MEX) 1027 98.9 13226.4 909.7 Saudi Arabia (SAU) 12 20.2 43188.5 853.9 South Africa (ZAF) 96 43.8 9483.1 768.2 Latvia (LVA) 3 2.4 10007.5 728.1 Jamaica (JAM) 31 2.5 7091 720.4 Poland (POL) 46 38.5 12692.4 717 Lithuania (LTU) 3 3.5 11086.9 682.8 Korea, Republic of (KOR) 716 45.1 18224.7 671.2 Kazakhstan (KAZ) 9 15.3 10143.3 631 Fiji (FJI) 6 0.8 6172.8 608.5 Costa Rica (CRI) 67 3.6 8198.2 559 Brazil (BRA) 261 167.5 11029.9 476.2 Ecuador (ECU) 4 12.3 7671.7 459.3 Bolivia (BOL) 4 8.2 4122.4 456.4 Congo (COG) 1 2.9 5024.8 448.2 Bulgaria (BGR) 20 8.1 9517.8 438.7 Angola (AGO) 1 13.7 5148.5 426.4 Thailand (THA) 390 61.1 8835.4 424 Croatia (HRV) 6 4.6 13265.5 417.3 Uruguay (URY) 11 3.2 11103.4 351.5 Mauritius (MUS) 23 1.1 9895.8 344 Turkmenistan (TKM) 7 4.6 8138.4 331.9 Turkey (TUR) 145 61.9 12496.1 329.3 Peru (PER) 22 24.9 6084.2 312.9 Dominican Republic (DOM) 131 8.4 7843.5 302.6 Morocco (MAR) 12 28 4785 299 Colombia (COL) 73 38.6 8493.1 298.8 Japan (JPN) 1488 124.6 32039.3 292.7 El Salvador (SLV) 92 5.7 6098 288.9 Nicaragua (NIC) 33 5 3382.8 275.5 Russian Federation (RUS) 84 146.6 13718.7 272 Guatemala (GTM) 83 11.1 5679.8 267.8 Egypt (EGY) 46 66.4 7487 266.3 Viet Nam (VNM) 152 82.3 3050.2 222.3 Georgia (GEO) 1 4.8 4212.3 189.6 Honduras (HND) 88 6 3462.4 184.4 Gambia (GMB) 1 1.1 1593.1 172.5 Kuwait (KWT) 4 1.9 77859.6 167.7 Armenia (ARM) 1 3.1 3807.3 148.1 Paraguay (PRY) 4 4.7 5485.7 147 Nigeria (NGA) 1 108.6 3009.7 142.2 Cambodia (KHM) 81 12.5 1406.6 139.4 China (CHN) 1387 1248.5 4013.1 122.2 Mongolia (MNG) 12 2.4 5271.2 121.6 Cote D'Ivoire (CIV) 2 14.2 2787.7 121.1 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
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Table I : Average (1989-2004) Statistics of Different Countries included in the Study (After all Filters) Average Average Per Yearly Average Per Capita Capita FDI Number of Population GDP (1989Country Stock (1989HS-10 (1989-2004) 2004) 2004) Products (Millions) ($Millions) ($Millions) (1989-2004) Moldova, Republic Of (MDA) 5 4.2 3008.7 116.8 Philippines (PHL) 285 73.3 4198.5 116.1 Algeria (DZA) 1 30.3 10337.1 114.3 Albania (ALB) 1 3.1 4270.1 110.6 Bosnia And Herzegowina (BIH) 1 3.8 5906 108.2 Belarus (BLR) 7 10 7762.7 106.5 Gabon (GAB) 1 1.3 18910.7 105.3 Sri Lanka (LKA) 127 18.5 4798.2 104.8 Cameroon (CMR) 1 15.5 2260.6 96.6 Iran (Islamic Republic of) (IRN) 3 65.8 12506.9 94.9 Indonesia (IDN) 330 204.4 5808.4 92.2 Ukraine (UKR) 26 49.3 6696.8 76.2 Lao People's Democratic Republic (LAO) 3 5 2114.3 74.8 Myanmar (MMR) 22 47.1 1258.9 69.1 Syrian Arab Republic (SYR) 1 16.2 5088.4 68.6 Ghana (GHA) 3 18.2 2142.2 67.2 Sierra Leone (SLE) 1 4 1460.1 63.1 Zimbabwe (ZWE) 4 11.7 1326.8 61.5 Uganda (UGA) 1 27.1 1367.3 60.7 Pakistan (PAK) 110 132 3550 45 Central African Republic (CAF) 1 4 837.9 41.6 Malawi (MWI) 1 11.1 887.6 35.5 Tajikistan (TJK) 1 6.4 2087.3 29.9 Uzbekistan (UZB) 3 24.7 4096.8 29.4 Kenya (KEN) 12 31.4 2253.3 29.4 Togo (TGO) 1 5 1223.8 28.2 Mali (MLI) 1 8.3 1034.8 26.9 Guinea (GIN) 1 7.9 1271.2 24.3 Tanzania, United Republic of (TZA) 1 27.6 1380.1 23.3 Kyrgyzstan (KGZ) 1 4.6 2488.3 21 India (IND) 317 1021.7 2414.7 15.4 Haiti (HTI) 25 8 1942.9 13.5 Bangladesh (BGD) 125 125.8 1654.2 12.6 Madagascar (MDG) 19 16.6 1375.4 12.4 Niger (NER) 1 11 759.1 3.8 Nepal (NPL) 16 22.7 1552.9 2.8 Mozambique (MOZ) 1 13.4 364.3 1.9 Note: This table represents the mean values of all the variables for all countries for the years 1989-2004
10
Table II: Exporter FDI, by Product and Year: Percent of Manufacturing Products with Signi…cant (10%) Relationship Between Unit Value and Exporter FDI (Minimum source countries=40) P ercentage of Goods N umber of Goods Signif icant (90%) P ositive Betas 24:6 439 Signif icant (90%) N egative Betas 26:0 465 P ositive (N ot Signif icant) 23:0 410 N egative (N ot Signif icant) 26:4 472 Total 100% 1; 786 Total number of HS10 Products 1; 786 Max Observations Within a Product 694 Min Observations Within a Product 40 Mean Observations Within a Product 127 Total number of Countries Included 133 Max Countries Supplying a Product 66 Min Countries Supplying a Product 5 Mean Countries Supplying a Product 18 Note: Note: Table II presents the percentage of coe¢ cients that are positive, positive-signi…cant at the 90 percent level, negative, negative-signi…cant at the 90 percent level for the 1,786 withinproducts regressions from Equation (1).
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Table III: Pooled Regression of Product Unit Values on Countries’FDI stocks Stocks per capita Capita over the period Period 1989-2004 Dependent V ariable Ln(U nitV alue) Estimated Coef f icients F DIct log( Lct ) 0:0158 (0:0060) ln
GDPct Lct
0:2489
(0:0232) -0:1582 (0:0017) Product Year Dummies Y es Product-Country-Year Observations 148; 182 Number of Countries 135 Adj R2 0:04 Notes: Table III reports OLS regression results of Equation (2) in text of exporter unit value on exporter capital per worker and exporter stock of FDI per worker. Robust standard errors adjusted for exporter are noted in the second parenthesis below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. log( (Ki LFctDIct ) )
Table IV: Correlation coe¢ cient between explanatory variables P er-Capita F DIct P er-Capita GDPct P er-Capita GrossKct P er-Capita F DIct 1:0000 P er-Capita GDPct 0:7824 1:0000 P er-Capita GrossKct 0:6691 0:9029 1:0000 Notes: Table IV presents the correlation coe¢ cients between the explanatory variables; they are computed by taking the average of the correlation values for the variables over 1989-2004. Table V: E¤ect of FDI on Export Quality across High-Income and Middle- and Upper-LowIncome Countries Dependent V ariable Estimated HIC Estimated M U LIC Ln(U nit V alue) Coef f iciients Coef f iciients F DIct log( Lct ) 0:0200 0:1691 (0:0299) (0:0809) ln
GDPct Lct
1:010
0:8077
(0:2898) (0:2621) 0:1104 0:0489 (0:2220) (0:1336) Product Year Dummies Y es Y es Product-Country-Year Observations 68; 279 29; 290 Number of Countries 39 65 Adj R2 0:10 0:09 Number of goods 1; 041 352 Notes: Table V reports OLS regression results of Equation (2) of exporter unit value on exporter capital per worker and exporter stock of FDI per worker for the two subgroups of countries, HIC and MULIC. Robust standard errors adjusted for exporter clustering are noted in the second parenthesis log( (Ki LFctDIct ) )
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below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. TABLE VI: Results of the Panel Data Model of the E¤ect of a Country’s FDI Stock on its Feenstra-Romalis and Hallak-Schott Quality Indexes Estimated Coef f icients using Estimated Coef f icients using Ef f ect On Country Romalis-F eenstra Qual-Indexes Hallak Schott Qual-Indexes Quality Index All Countries HIC MULIC All Countries HIC MULIC F DIct 0:0264 0:0304 0:0316 0:0082 0:0184 0:0580 log( Lct ) (0:0123) 0:0200 (0:0138) (0:0356) (0:0360) (0:0381) ln
GDPct Lct
0:0461
0:0239
0:0034
0:5176
0:4127
0:1128
(0:0487) (0:0638) (0:0484) (0:1351) (0:1910) (0:0917) 0:0170 0:0860 0:0351 0:1274 0:3960 0:0162 (0:0332) (0:0337) (0:0267) (0:1325) (0:2186) (0:0621) Y ear Dummies Yes Yes Yes Yes Yes Yes Observations 144 81 51 152 90 58 N umber of Countries 51 28 19 39 23 15 AdjR2 0.3430 0.1481 0.2836 0.5223 0.1353 0.3236 Notes: Table VI reports OLS regression results of Equation (3) of exporter quality indexes on exporter capital per worker and exporter stock of FDI per worker for all the countries pooled together and for the two subgroups of countries, HIC and MULIC. Standard errors are noted in the second parenthesis below coe¢ cients. ***, **, and * refer to statistical signi…cance at the 1 percent, 5 percent, and 10 percent levels, respectively. log( (Ki LFctDIct ) )
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