metadata: authors: 0: author: Guerson,Alejandro Daniel
1: author: Parks,James
2: author: Parra Torrado,Monica L.
count: Argentina
docna: 0: docna: Export structure and growth : a detailed
            analysis for Argentina
docty: Policy Research Working Paper
theme: Analysis of economic growth,Export development and competitiveness
majtheme: Economic management,Trade and integration
subtopic: Primary Metals,Oil Refining & Gas Industry,International Trade and Trade Rules,Energy and Mining,Transport Services
teratopic: Rural Development,Macroeconomics and Economic Growth,Law and Development,Water Resources,Transport
abstracts: cdata!: This paper examines recent changes in
            the structure of Argentine exports and the implications for
            future growth. The authors find that the current export
            structure of Argentina is not conducive to future growth
            because it is dominated by low-productivity goods that tend
            to be exported by low-income countries. The productivity
            content of Argentine exports has increased recently
            although, as of 2004, these changes have been relatively
            minor. The authors identify products with characteristics
            similar to those currently exported by Argentina and which
            are more likely to foster growth because they would shift
            the structure of exports more the efficiency frontier. Those
            products include chemicals and primary products with some
            degree of value added, including partly processed meat, fish
            and grains. If economic growth is to be fostered by
            developing new export products and by increasing the value
            added of existing exports, there will be a need for
            sector-specific analysis to address possible market
            failures. The analysis should focus on issues such as the
            provision of public goods needed for production (including
            infrastructure, but also complex intangibles such as
            sector-specific legislation), possible impediments to
            effective coordination, sector-specific and economy wide
            externalities, or barriers to information. This last source
            of potential market failure is critical to a successful
            policy framework for exports and growth.
geo_reg_key: 119228,517191
repnme: repnme: Export structure and growth : a detailed
            analysis for Argentina
display_title: Export structure and growth : a
            detailed analysis for Argentina
listing_relative_url: /research/2007/05/7643170/export-structure-growth-detailed-analysis-argentina
topicv3: Transport m1326661 135,Trade m1326924 698,Iron m1503514 2931,Copper m1503516 2934,Petroleum Industry m1583790 4710
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Rural Development,Macroeconomics and Economic Growth,Law and Development,Water Resources,Transport
no_of_pages: 41
author_keys: 0: author_key: 168308
1: author_key: 93535
2: author_key: 915915
model_extractions: WPS 4237

# **Export Structure and Growth A Detailed Analysis for Argentina**<sup>*****</sup>

## **Alejandro Guerson**

## **James Parks**

## **Mónica Parra Torrado**

## **Abstract**

This paper examines recent changes in the structure of Argentine exports and the implications for future growth. We find that the current export structure of Argentina is not conducive to future growth because it is dominated by “low-productivity” goods that tend to be exported by low-income countries. The productivity content of Argentine exports has increased recently although, as of 2004, these changes have been relatively minor. We identify products with characteristics similar to those currently exported by Argentina and which are more likely to foster growth because they would shift the structure of exports more the “efficiency frontier”. Those products include chemicals and primary products with some degree of value added, including partly processed meat, fish and grains. If economic growth is to be fostered by developing new export products and by increasing the value added of existing exports, there will be a need for sector-specific analysis to address possible market failures. The analysis should focus on issues such as the provision of public goods needed for production (including infrastructure, but also complex intangibles such as sectorspecific legislation), possible impediments to effective coordination, sector-specific and economy wide externalities, or barriers to information. This last source of potential market failure is critical to a successful policy framework for exports and growth.

### JEL No. F1, O4

World Bank Policy Research Working Paper 4237, May 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * We would like to thank Ricardo Hausmann, Roberto Rigobon, Mauricio Abeles and Patricio Narodowsky for useful comments.

1
# **I. Introduction and Summary of Conclusions**

In recent years, the performance of Argentine exports has been particularly strong. Argentina has benefited from positive terms of trade shocks in some sectors but higher export volumes have been the most important source of export growth (Figure 1). In 2005, merchandise exports reached about US$40 billion, which is more than 50 percent above the peak prior to 2002. This trend continued in 2006: as of September exports of goods had grown by 12.3 percent.

<!-- Start of picture text --> Figure 1<br><!-- End of picture text --> <!-- Start of picture text --> Total Exports Total Exports Price and Quantity Real Exchange Rate<br>50 35 300 (Average 1965 - 2005 = 100)<br>200<br>45 30<br>Price<br>40 25 250 180<br>35 20 Quantity 160<br>30 15 200<br>25 10 140<br>20 5 150<br>120<br>15 0<br>10 -5 100 100<br>5 -10 80<br>0 -15 50<br>60<br>0 40<br>Percentage growth, right axis US$ billion, left axis<br>2006 export values are based on January-September annual exports growth.<br>Source: Ministry of Economy and production, Argentina, and International Financial Statistics.<br>199319941995199619971998199920002001200220032004Jan-Sep 2006 2005<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005<br><!-- End of picture text --> The improvement in export performance was accompanied by a drastic change in relative prices following upon the currency devaluation in 2002 which has significantly improved the competitiveness of Argentine exports overall. Aggregate export statistics show that the momentum of export performance has been sustained by manufactured goods which led exports growth in 2006 (Figure 2).

**Figure 2 Evolution of Exports by Sector**

<!-- Start of picture text --> Exports of Primary Products, Fuels and Energy Exports of Industrial and Agricultural Manufactures<br>18 35 35 40<br>16 30<br>30 30<br>14 25<br>20 25<br>12 20<br>15<br>10 20<br>10 10<br>8 5 15<br>6 0<br>0 10<br>4 -5<br>2 -10 5 -10<br>0 -15 0 -20<br>Percentage growth, right axis US$ billion, left axis Percentage growth, right axis US$ billion, left axis<br>2006 export values are based on January-September annual exports growth.<br>Source: Ministry of Economy and production, Argentina.<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Jan-Sep 2006 2005 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Jan-Sep 2006 2005<br><!-- End of picture text --> Looking beyond aggregate trends, this paper examines changes in the structure of Argentine exports, and their potential impact on future growth. To that end, we apply the conceptual 2
framework developed in Hausmann and Rodrik (2003) and Hausmann, Hwang and Rodrik (2006) (HHR). They define the “productivity frontier” of a country as the optimal composite of exports that would maximize prospective future growth. Countries that fall short of the productivity frontier have two basic channels through which they can grow. First, countries can grow by producing more of the same goods they have already been producing. Second, and more interestingly, countries can improve their growth prospects by developing new products that shift them closer to their productivity frontier. The analysis of HHR reveals that, if new products have a high productivity (i.e. they tend to be produced by countries with higher levels of income per capita) then the GDP of the country producing and exporting the new product eventually converges to that of the high income countries. Econometric analysis presented in this paper and in HHR show that productivity of exports is a statistically significant explanatory variable of GDP per capita growth, after controlling for initial GDP per capita, the human capital endowment and period-specific and country-specific dummies<sup>1</sup> .

Market forces alone may not be sufficient to shift the structure of exports towards goods of higher productivity and thereby enhance growth prospects. Here, we mention some of the reasons why market forces alone may be insufficient and cite their main advocates. One reason is that the production of new goods would in many cases require product-specific public goods that are not always available. Even worse, the main provider of those goods, be it the public or the private sector may be unaware of the need for them. Examples of such goods are product or sector specific infrastructure as well as complex non-tangibles such as product-specific legislation. Failures of coordination may also prevent a new high-income product from developing (Morris and Shin 2000). Product-specific services and labor or managerial skills may never develop if there is no demand for them in the economy. At the same time a lack of those same product-specific services, labor and managerial skills may forestall the development of new products that depend upon them. The need for industry-specific learning (Arrow 1962, Bradhan 1970) may make it difficult to become a producer of new goods. Industry externalities (Jaffe, Trajtenberg and Henderson 1993) can make the introduction of new products path-dependent, possibly causing other failures of coordination. In other cases, a country may be “stuck” in a low-value-added equilibrium (Murphy, Schleiffer and Vishny 1989). Some analysts have cited the so-called o-ring effect whereby a small failure in the complex succession of processes needed to produce and export a new good successfully, might cause the whole process to fail (Kremer 1993 and Kremer and Maskin 1994). “Cost discovery” -- the learning process by which an entrepreneur discovers from experience the true cost of producing a new product -- may deter private sector investment in the development of new products (Hausmann and Rodrik 2003). Moreover, static comparative advantages might not necessarily bring about the best results in terms of growth performance. For example, spillovers in learning can make international trade of a less-developed country to be detrimental to growth (Young 1991). Furthermore, low-growth traps can result from static comparative advantages in sectors subject to lower endogenous growth rates (Matsuyama 1991).

In the present paper, we quantify several indicators proposed in HHR to measure the level of income or productivity embedded in Argentine exports and then we compare them with actual GDP per capita. According to the HHR empirical analysis, countries with a high level of export productivity relative to their actual GDP tend to have higher growth rates. In other words, the income content of exports tends to be a good predictor of future growth. If the structure of Argentine exports is changing towards goods of higher productivity (either by exporting more of the

> 1 The results are robust to various estimation techniques and the inclusion of other variables as discussed in the last section of the paper.

3
relatively higher productivity goods already being exported or by exporting new high productivity goods) then, according to HHR, one could expect an increase in future economic growth.

In addition, we compute indicators (“distance” in HHR terminology) which measure the likelihood that new export products emerge. We apply the HHR analysis to Argentina and, for each product in the Argentine export basket, we measure the likelihood that new export products will appear given the density of the product space in which they are located. Thereby, we can identify goods currently exported by Argentina that can potentially develop into new products. Finally, in the last section we include an econometric analysis using cross country data aiming at testing the possible effect of export income on economic growth.

A detailed analysis of the structure of exports (4-digit, 1241 products) points to the following general conclusions:

- Exports in Argentina are highly concentrated. In 2004, the top ten products accounted for 57 percent of total exports and the top twenty accounted for 72 percent. Concentration has been increasing in recent years.

- As of 2004, the income content of Argentine exports was roughly the same as per capita GDP in PPP terms. According to the HHR analysis, this might suggest low expectations about future growth.

- However, the trend in the period 1994-2004 shows an improvement in growth prospects as can be inferred from the relation between the income content of exports and GDP per capita.

- Argentine exports include several high income industrial products, especially in such product groups as motor vehicles, chemicals, metals and pipes. However, high income exports are not limited to industrial products. They also include less-processed products and also primary products such as wheat, malt extract, frozen and filleted fish, and diary products, among others.

- About half of the “new” export products, that is, products that have shown the highest growth in export value in 2004 compared to the average level in 1994-1999, have a high income rating. Most of these high-income “new” products are complex industrial manufactured goods. However, the “new” products still represent a minor proportion of total exports.  The 50 products with the highest growth rate in 2004 relative to the average of 1994-1999 represent only 1.2 percent of total exports.

- On average, the chances of developing new export products (measured by distance) improved slightly from 1994-2004.   A decreasing trend during the first part of the period gave way to an increasing trend since 2000.

- Moreover, the average “distance” has been reduced for both high and low income export products (defined as upscale and downscale products respectively in HHR) through the 1994-2004 period.

- On the other hand, the average product income (ranked by the total value of the product exported) for upscale products has increased while that for the downscale products has decreased.  Hence the range of ranked product income was wider in 2004 compared to 1994. In other words, there are more upscale goods with higher product income but also more downscale goods with lower product income.

4
- With respect to exports by sector, average product income has increased in most sectors while average “distance” has declined through 1994-2004.  According to HHR, that suggests that the prospects for future growth have improved on average for most sectors in the economy.

- Given the current structure of Argentine exports, the products which have the best prospects in terms of their income content and the prospective costs of developing new products with similar characteristics include chemical products and primary products with some degree of value added, including partly processed meat, fish and grains.

- A variety of chemical products, some machinery and tools (printing machinery, auto parts, drawing machines, specialized tools) and optical goods (lenses, micro-optical) appear to have good prospects in terms of generating growth in the future given their high income content. Efforts should focus on removing obstacles that constrain their production. However, it seems less likely that these goods will lead to new products with similar characteristics.

- Primary products and minerals with little or no processing offer less promising growth prospects given their low income content. However, they may lead to new export products that have similar requirements with respect to physical and human capital, sector-specific financial facilities, institutional or regulatory requirements, infrastructure, supply chain characteristics and customer service etc.

# **II. Data**

The dataset used in this paper is drawn from two different sources. The first one, used for most part of the analysis, is drawn from the Harmonized System (HS) of the United Nations Commodity Trade Statistics Database (COMTRADE) obtained through the WITS platform. Under the HS classification there are 1241 products at the 4-digit level and 97 sectors at the 2-digit level. For purposes of consistency the analysis was confined to countries that reported information for every year between 1994 and 2004<sup>2</sup> and for which data on GDP per capita were available from the World Development Indicators. This left us with 73 countries representing around 50 percent of total trade. For Argentina, the export value in the sample accounts for about 99.9 percent of total exports in every year.

The second dataset, used only for the econometric analysis, is drawn from the World Trade Flows dataset (Feenstra et al. 2005). This dataset comprises country-level trade flows under the 4- digit Standard International Trade Classification (SITC rev. 2) for the period 1962-2000. In this case as well, we restricted the sample to 91 countries that had information for every year between 1962 and 2000.

Given these restrictions on the samples, some of the calculations presented here may differ slightly from others which cover all countries. However, the difference is minor and a consistent sample of countries for every year avoids errors that may result from differences in country reporting from year to year. Most importantly, we avoid biases resulting from the fact that nonreporting countries are more likely to be low income countries. That same fact might possibly lead us to over-estimate the indices we present here, but it would not affect changes over time.

> 2 2004 is the most recent year available for most countries.

5
The sample used in the first part of this paper differs from the one used in Hausmann and Kingler (2006). They use a longer sample drawn from Feenstra et al (2005) for the first part of the period (1975-2000) and from COMTRADE for the last part (2000-2004). The two series are merged using observations from 2000 common to both series. Hence, their sample includes more countries and is scaled to match the levels in the first part of the sample, which explains some differences with our estimates.

# **III. Composition of Argentine Exports**

We start by providing some stylized facts about the structure of Argentina’s exports, both at the sector level and at the more disaggregated product level (4-digit code). Table 1 below shows the value of trade in 2004 for the 17 top 2-digits sectors, which make up 80 percent of total Argentine exports. There is a relatively high level of concentration of exports. The two top sectors alone (mineral fuels and oils and residues, and waste from the food industry) account for 27 percent of the total. When we expand the coverage to the top 5 sectors, exactly half of the value of exports has been accounted for.

**Table 1**

**Argentina: Export Composition 2004, 2 Digit Level**

|**Code**|**Sector Name**|**Trade Value**<br>**$ '000**|**Share**<br>**%**|**Cumm. Share**<br>**%**|
|---|---|---|---|---|
|27|Mineral fuels, oils & product of th|5,536,454|16.1|16.1|
|23|Residues & waste from the food indu|3,835,098|11.2|27.3|
|15|Animal/veg fats & oils & their clea|3,172,732|9.2|36.6|
|10|Cereals|2,688,226|7.8|44.4|
|87|Vehicles o/t railw/tramw roll-stock|2,047,796|6.0|50.4|
|12|Oil seed, oleagi fruits; miscell gr|1,833,068|5.3|55.7|
|02|Meat and edible meat offal|1,023,305|3.0|58.7|
|39|Plastics and articles thereof.|932,664|2.7|61.4|
|26|Ores, slag and ash.|914,096|2.7|64.1|
|41|Raw hides and skins (other than fu|817,447|2.4|66.4|
|03|Fish & crustacean, mollusc & other|787,196|2.3|68.7|
|84|Nuclear reactors, boilers, mchy & m|767,530|2.2|71.0|
|04|Dairy prod; birds' eggs; natural ho|651,486|1.9|72.9|
|73|Articles of iron or steel.|639,865|1.9|74.7|
|08|Edible fruit and nuts; peel of citr|598,931|1.7|76.5|
|72|Iron and steel.|571,391|1.7|78.2|
|29|Organic chemicals.|498,351|1.5|79.6|
|20|Prep of vegetable, fruit, nuts or o|443,953|1.3|80.9|

Source: WITS Database-Comtrade What is interesting about this breakdown is that, for the largest sector, mineral fuels and oils, Argentina is not a particularly large player in the world market according to 2004 data. Indeed, it only accounts for 0.8 percent of the total global exports, compared with Russia, Canada, Norway and Venezuela, each of which have on average more than 8 percent of the market. In contrast, Argentina is the world leader in some other sectors, and in many of them it constitutes the largest 6
exporter. For instance, Argentine exports account for more than 13 percent of total world exports in residues from the food industry, for 15 percent in animal fats and for 6 percent in cereals.

The majority of these top sectors comprise commodities and primary products, although there may be a manufacturing component in such sectors as 27, 23, 15, 10, 12, and 02 in Table 1, which together account for more than 50 percent of total exports. In fact, a significant portion of exports are derived from petroleum products, soy bean and its derivatives, cereals and bovine meat. The largest industrial manufacturing sector is vehicles, which accounts for around 6 percent of total exports, followed by plastics, with almost 3 percent of exports, and nuclear reactors and their components, with 2.2 percent of total exports.

From the more disaggregated data of Table 2, we can see that oil cake and other solid residues were the largest export of Argentina in 2004, accounting for more than 10 percent of exports with a total value of US$3.6 billion. However, it is interesting to note that this product is the only one of the top 50 export products belonging to this sector. This product by itself accounts for 93 percent of the exports within its own 2-digit category. The second and fifth products by rank are obviously closely related, although belonging to different groups. Again, the majority of export products are agricultural and commodity based, although automobiles, trucks and tubes and pipes account for 5 percent of total exports. We return to the analysis of these industrial products in a subsequent section below.

**Table 2 Argentina: Export Composition 2004, 4 Digit Level**

|**Code**|**Product Name**|**Trade Value**<br>**$ '000**|**Share**<br>**%**|**Cumm. Share**<br>**%**|
|---|---|---|---|---|
|2304|Oil-cake and other solid residues,|3,597,953|10.5|10.5|
|1507|Soya-bean oil and its fractions|2,335,749|6.8|17.3|
|2709|Petroleum oils and oils obtained fr|2,255,856|6.6|23.9|
|2710|Petroleum oils, etc, (excl. crude);|1,940,005|5.7|29.5|
|1201|Soya beans|1,740,114|5.1|34.6|
|1001|Wheat and meslin|1,365,480|4.0|38.6|
|1005|Maize (corn)|1,193,805|3.5|42.0|
|2711|Petroleum gases and other gaseous h|1,106,768|3.2|45.3|
|2603|Copper ores and concentrates|904,184|2.6|47.9|
|4104|Leather of bovine or equine animals|804,059|2.3|50.3|
|8703|Motor cars and other motor vehicles|676,554|2.0|52.2|
|8708|Parts and accessories of the motor|658,787|1.9|54.1|
|1512|Sunflower-seed, safflower or cotton|570,612|1.7|55.8|
|8704|Motor vehicles for the transport of|545,764|1.6|57.4|
|7304|Tubes,pipes and hollow profiles,sea|485,482|1.4|58.8|
|0202|Meat of bovine animals, frozen|421,734|1.2|60.0|

Source: WITS Database-Comtrade

# **IV. Concentration Analysis**

7
Figure 3 below presents a simple measure of concentration -- the total share of exports for the top 10 and top 20 products over a 10 year period. It can be observed that the level of concentration has been high through the period, and has increased over the last five years. In 1994 the top 10 products accounted for 39 percent of total exports while the top 20 accounted for 54 percent of total exports. In 2004, the proportions increased to 57 and 72 percent respectively.

**Figure 3**

<!-- Start of picture text --> Share of Top 10 and Top 20 Export Products for Argentina, 1994-2004<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Top 10 in 2004 Next 10<br>Percentage<br><!-- End of picture text --> In order to measure more accurately the degree of concentration of the Argentine exports, Figure 4 presents the Herfindahl-Hirschman Index (HHI) of concentration for each year between 1993 and 2004. The HHI is simply the sum of the squares of the shares of each product, and it ranges from 0 to 10,000 (if the shares are expressed as a percent) with higher values representing a higher concentration. Figure 4 presents the index for export groups at a 4 digit level. Since it covers 1241 product groups the absolute numerical scale of the index is very low.

**Figure 4**

<!-- Start of picture text --> Herfindahl-Hirschman Index for Argentine Exports at 4-digit<br>level, 1994-2004<br>0.040<br>0.035<br>0.030<br>0.025<br>0.020<br>0.015<br>0.010<br>0.005<br>0.000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>HH Index<br><!-- End of picture text --> 8
Both graphs above show essentially the same pattern. Between 1994 and 1995 there was a reduction in the level of concentration, but it increased in 1996 and again in 2000. After that there was an upward trend which peaked in 2003 at the highest level in the last 11 years. Although there was a slight reduction in 2004, the HH Index was still higher than in previous years.

One reason for the increased the level of concentration during the last couple of years could be an increase in the relative importance of sectors that are themselves highly concentrated. Alternatively, the internal concentration within all categories may have increased, while the relative weights of the sectors have remained roughly constant. In order to test these alternative hypotheses, we calculate the within-sector HHI for each of the most important sectors in each of the years 1999, 2003 and 2004. The results are presented in Table 3.

**Table 3**

|**Argenti**<br>**Product**|**na: Export Concentration by Sector**<br>**Description**||||
|---|---|---|---|---|
|**Code**||**HHI 1999**|**HHI 2003**|**HHI 2004**|
|27|Mineral fuels, oils & product of their distill|0.66|0.34|0.30|
|23|Residues & waste from the food indust; prepr a|0.78|0.87|0.88|
|15|Animal/veg fats & oils & their cleavage produc|0.38|0.53|0.52|
|10|Cereals|0.38|0.44|0.45|
|87|Vehicles o/t railw/tramw roll-stock, pts & acc|0.15|0.16|0.16|
|12|Oil seed, oleagi fruits; miscell grain, seed,|0.42|0.86|0.90|
|02|Meat and edible meat offal|0.32|0.48|0.48|
|39|Plastics and articles thereof.|0.04|0.06|0.07|
|26|Ores, slag and ash.|0.98|0.99|0.98|
|41|Raw hides and skins (other than furskins) and|0.48|0.42|0.42|
|03|Fish & crustacean, mollusc & other aquatic inv|0.19|0.26|0.21|
|84|Nuclear reactors, boilers, mchy & mech applian|0.03|0.03|0.03|
|04|Dairy prod; birds' eggs; natural honey; edible|0.33|0.32|0.36|
|73|Articles of iron or steel.|0.19|0.23|0.21|
|08|Edible fruit and nuts; peel of citrus fruit or|0.20|0.18|0.15|
|72|Iron and steel.|0.07|0.07|0.08|
|29|Organic chemicals.|0.05|0.06|0.05|

The results show that the degree of concentration increased in the vast majority of sectors during 2003 and 2004 compared with 1999, when overall concentration was relatively low. The only major exception is mineral fuels, where concentration decreased significantly during the last few years. The results confirm that increased concentration within sectors account for the rise in overall concentration rather than changes in the relative shares of exports.

# **V. The productivity level of Argentina’s Exports**

Following the methodology developed in HHR, the “productivity” of each product group is defined as a weighted average of the per capita GDPs of countries that export the product. The formula is as follows: where EXP is the value of exports in US dollars at the 4 digit level, and GDP per capita is the PPP adjusted GDP per capita expressed in 2000 constant terms for each country. Hence, the index 9
reflects a weighted average of per capita GDP where the weights reflect the “revealed comparative advantage of each country” in that particular product. Using “revealed comparative advantage” as a weight ensures that the ranking of products is not distorted by country size. The index is intended as a proxy for the level of productivity of each particular export product.

Based on these estimates, the export income for each country is calculated as a weighted average of the productivity of exports. Weights are defined as the share of each product in total exports of an individual country, as follows: Figure 5 below shows GDP per capita and export income in 2004 for all countries in the sample. As can be observed in the graph, there is a positive and strong correlation between export income and GDP per capita. The relationship does not seem to be linear but it has a shape of half a U. Furthermore, there are some interesting and important “outliers” which support the argument that high-growth countries tend to have export incomes that are larger than their GDP per capita which, in turn, suggests that these values tend to converge over time. For example, the export income for China in 2004 was $16,967 compared to a PPP-adjusted GDP per capita of only $5,419. Similarly, in 2004 India had an export income equivalent to 4.7 times its GDP per capita ($13,550 and $2,907 respectively).

# **Figure 5**

<!-- Start of picture text --> Export Income and GDP per capita for All Countries, 2004<br>40,000<br>USA<br>35,000<br>30,000 CHE<br>25,000<br>45 o line<br>20,000<br>ARG<br>15,000<br>10,000<br>5,000 CHN<br>MWI IND<br>0<br>0 5,000 10,000 15,000 20,000 25,000<br>Export Income, 2000 PPP $<br>GDP per capita, 2000 PPP $<br><!-- End of picture text --> The export income for Argentina in 2004 was US$12,440.40 compared to a GDP per capita of US$ 12,225.00. Hence Argentina is in the group of countries which have an export income almost equal to their GDP per capita. This would indicate, following the argument of HHR, that the prospects for future growth in Argentina are not as good as some other countries. Furthermore, Argentina has an export income that is lower than that of countries with a similar level of GDP, such as Lithuania and Poland (see Figure 6 below). On the other hand, some countries that have a level of export income similar to that of Argentina have lower levels of GDP per capita. This applies to Tunisia, Colombia, Venezuela and El Salvador, among others. The conclusion would appear to be that Argentine exports are not particularly productive which, according to HHR, might imply lower growth prospects.

10
<!-- Start of picture text --> Figure 6<br><!-- End of picture text --> <!-- Start of picture text --> Export Income and GDP per capita in 2004, Selected Countries<br>20,000 5<br>16,000 4<br>12,000 3<br>8,000 2<br>4,000 1<br>0 0<br>ExpY GDP pc EXPY/GDPpc<br><!-- End of picture text -->

# **VI. Recent Changes in the Productivity of Argentine Exports**

In this section we examine trends in Argentine export income during the last few years and analyze in more detail the productivity of the top Argentine exports. Figure 7 below plots Argentine export income, GDP per capita and their ratio for the period 1994-2004. During the 1990s the ratio was below one (i.e. export income was lower than GDP per capita) but for the last five years of the sample it was above one. Nevertheless, the main fact revealed by the graph is that Argentine export income has been very close to GDP per capita in the last 11 years. That may be undermining prospects for economic growth<sup>3</sup> .

**Figure 7**

<!-- Start of picture text --> Export Income and GDP per capita for Argentina, 1994-2004<br>13,000<br>1.10<br>12,500<br>1.05<br>12,000<br>11,500 1.00<br>11,000<br>0.95<br>10,500<br>10,000 0.90<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Export Income GDP per capita Ratio<br>Ratio<br>PPP 2000 $<br><!-- End of picture text --> 3 As mentioned in the data section, the results presented here differ to some extent from those presented in Hausmann and Klinger (2006) due to the different samples used. However, observe that the differences are only in terms of levels and not in terms of the evolution. Moreover, the difference in levels does not affect the relative comparisons among countries since the differences are not country specific.

11
Next, we focus on a more detailed study of productivity by identifying the top 50 Argentine exports (Table 4) and the products that have experienced rapid growth in 2004 (Table 5). The assumption behind this analysis is that a country should be able to achieve higher export incomes either by exporting more of the high productivity products it is already exporting (and in which it is likely to have a comparative advantage), or by exporting new products in sectors that have higher productivity.

Columns 3 to 5 of Table 4 present the top 50 export products in 2004 for Argentina, their trade export value deflated at 2000 prices using the export price index, their share of total exports and their product income<sup>4</sup> . A number of product groups are highlighted. They represent products with a product income above the mean value for Argentina in 2004. The mean (US$16,548,) roughly corresponds to China’s export income for that year and is well above the value of Argentina’s export income. There are 15 products among the top 50 in this category which together account for 16 percent of total exports. The export products with the highest product income value for Argentina are unwrought aluminum, medicaments, malt extract, motor cars, parts and accessories of motor vehicles, cyclic hydrocarbons and polyethers. Together they account for 6.8 percent of total exports. Not all of the 15 high income products are from the same group; on the contrary there is one from each sector, with the exception of the products from group 87, related to the automobile industry, that together account for 5.5 percent of total exports.

> 4 Distance and revealed comparative advantage (RCA), columns 6 and 7, are explained in detail in a subsequent section.

12
**Table 4**

**Top 50 Exports for Argentina, 2004**

|**Product**<br>**code**|**Description**|**Export**<br>**value**<br>Thousands of<br>2000 US$|**Exports**<br>**share**<br>%|**Product**<br>**income**<br>2000 PPP $|**Distance**<br>**to export**<br>**basket**<br>log (1/density)|**RCA**|
|---|---|---|---|---|---|---|
|2304|Oil-cake and other solid residues,|3,493,158|10.60%|6,808|1.17|73.3|
|1507|Soya-bean oil and its fractions|2,267,717|6.88%|7,241|1.27|94.9|
|2709|Petroleum oils and oils obtained fr|2,190,152|6.64%|11,728|1.38|2.6|
|2710|Petroleum oils, etc, (excl. crude);|1,883,500|5.71%|11,940|1.51|2.5|
|1201|<br>Soya beans|1,689,431|5.12%|6,378|1.04|24.4|
|1001|<br>Wheat and meslin|1,325,709|4.02%|17,800|1.43|16.9|
|1005|Maize (corn)|1,159,034|3.52%|9,500|1.32|23.3|
|2711|Petroleum gases and other gaseous h|1,074,532|3.26%|12,728|1.27|2.8|
|2603|Copper ores and concentrates|877,849|2.66%|9,522|1.19|17.6|
|4104|Leather of bovine or equine animals|780,639|2.37%|10,992|1.42|11.9|
|8703|Motor cars and other motor vehicles|656,848|1.99%|20,342|1.87|0.3|
|8708|Parts and accessories of the motor|639,599|1.94%|19,283|1.86|0.7|
|1512|Sunflower-seed, safflower or cotton|553,993|1.68%|5,902|1.40|73.6|
|8704<br>|<br>Motor vehicles for the transport of<br>|529,868<br>|1.61%<br>|18,342<br>|1.63<br>|1.7<br>|
|7304|Tubes,pipes and hollow profiles,sea|471,342|1.43%|16,588|1.75|9.1|
|0202|Meat of bovine animals, frozen|409,451|1.24%|10,366|1.09|13.0|
|0201|Meat of bovine animals, fresh or ch|390,197|1.18%|11,002|1.40|8.0|
|0402|Milk and cream, concentrated or swe|383,984|1.16%|16,192|1.42|9.7|
|3004|Medicaments of mixed or unmixed pro|286,259|0.87%|22,396|1.78|0.4|
|3901|Polymers of ethylene, in primary fo|283,937|0.86%|16,372|1.52|2.9|
|3808|Insecticides, rodenticides... and s|283,841|0.86%|12,792|1.52|4.6|
|0304|Fih fillt d th fih t f|283346|086%|17852|138|75|
|7601|s es an oer s mea,<br>Unwrought aluminium|,<br>278,566|.<br>0.85%|,<br>22,980|.<br>1.41|.<br>2.6|
|0808|<br>Apples, pears and quinces, fresh|236,939|0.72%|11,810|1.36|11.4|
|2204|<br>Wine of fresh grapes, (incl. fortif|220,211|0.67%|8,722|1.39|2.5|
|0306<br>|Crustaceans,fresh,chilled or frozen<br>|211,458<br>|0.64%<br>|5,561<br>|1.34<br>|5.2<br>|
|0805|Citrus fruit, fresh or dried|206,112|0.63%|13,357|1.31|7.2|
|1602|<br>Other prepared or preserved meat, m|194,182|0.59%|16,472|1.39|7.8|
|2401|<br>Unmanufactured tobacco; tobacco ref|178,762|0.54%|2,349|1.36|7.4|
|2009|Fruit juices (incl. grape must) and|176,590|0.54%|10,646|1.36|6.0|
|7210|Flat-rolled products of iron/non-al|166,921|0.51%|10,876|1.54|1.7|
|9401|Seats whether or not convertible in|138,063|0.42%|15,616|1.78|0.9|
|7108|Gold(platinum plated)unwrought,semi|135,417|0.41%|5,102|1.23|1.1|
|4703|<br>Chemical wood pulp, soda or sulphat|133,279|0.40%|17,321|1.39|1.9|
|0303|Fish, frozen, (excl. those of 03.04|129,919|0.39%|17,851|1.43|3.6|
|2902|Cyclic hydrocarbons|126,708|0.38%|19,388|1.57|1.3|
|3907|<br>Polyethers and epoxide resins; poly|123,855|0.38%|19,970|1.71|1.1|
|7209|Flat-rolled products of iron/non-al|123,172|0.37%|11,360|1.53|2.5|
|4011|New pneumatic tyres, of rubber|122,535|0.37%|15,163|1.68|0.8|
|0409|Natural honey|117,026|0.35%|9,870|1.47|33.8|
|1901|<br>Malt extract; food preparations of|115,984|0.35%|20,808|1.54|4.3|
|2008|<br>Fruit, nuts and other parts of plan|111,473|0.34%|12,471|1.44|5.2|
|1517|Margarine; edible preparations of a|111,155|0.34%|6,706|1.49|12.5|
|5105|<br>Wool and fine or coarse animal hair|107,983|0.33%|10,292|1.40|14.8|
|0307|Molluscs & aquatic invertebrates,ne|105,602|0.32%|10,625|1.42|5.2|
|8409|<br>Accessory parts suitable for engine|105,164|0.32%|18,364|1.89|0.6|
|0703|<br>Onions, shallots, garlic, leeks...e|98,575|0.30%|10,652|1.36|10.0|
|2716|Electrical energy|94,784|0.29%|16,424|1.58|1.3|
|2905|Acyclic alcohols and their halogena|91,919|0.28%|13,157|1.48|1.8|
|3307|Shaving preparations, personal deod|84,953|0.26%|17,709|1.58|3.1|

Source: Author's calculations based on WITS-Comtrade database Figures 8 and 9 below show real exports and product income for the top 10 exports. The general trend is upwards especially after the year 2000. Product income seems to be increasing for some products and is somewhat flat for others. Notice that, as shown in Figure 9, there is only one product with product income higher than the 2004 mean throughout the period; all others have product income smaller than the Argentine export income.

13
<!-- Start of picture text --> Figure 8<br><!-- End of picture text --> <!-- Start of picture text --> Export Value, 2000 US$<br>Top 10 of Argentine Exports 2004<br>3,500,000<br>3,000,000<br>2,500,000<br>2,000,000<br>1,500,000<br>1,000,000<br>500,000<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text -->

**Figure 9**

<!-- Start of picture text --> Product Income, PPP 2000 $<br>Top 10 of Argentine Exports 2004<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text --> In the following figures we show the performance of high-productivity goods among the top 125 Argentine exports, which account for 20.88 percent of total exports, in terms of their trade value, their export share, their product income and “distance”. The first graphs in figures 10 to 12, show high-productivity products from the top 25 Argentine exports. The second graphs show highproductivity products from the top 50 that were not already included in the previous graph.

14
Subsequent graphs follow a similar sequence. In Figure 10 one can observe that the real export value of high productivity goods grows steadily over the period with few exceptions. Also, looking at Figure 11, one observes that the shares of the products have tended to converge in each graph except for the top 25 products. That indicates that, none of the most high-productivity products, are increasing as a share of total exports. In terms of product income, depicted in Figure 12, there has been an increasing trend with very few exceptions.

# **Figure 10 Top Argentine Exports: High Productivity Products, Export Value 2000 US$**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>1,800,000 Wheat and meslinParts and accessories of the motorTubes,pipes and hollow profiles,seaFish fillets and other fish meat, f Motor cars and other motor vehiclesMotor vehicles for the transport ofMedicaments of mixed or unmixed proUnwrought aluminium 200,000180,000 Chemical wood pulp, soda or sulphatFish, frozen, (excl. those of 03.04Cyclic hydrocarbonsPolyethers and epoxide resins; polyMalt extract; food preparations of<br>1,600,000 Accessory parts suitable for engine<br>160,000 Shaving preparations, personal deod<br>1,400,000<br>140,000<br>1,200,000 120,000<br>1,000,000 100,000<br>800,000 80,000<br>600,000 60,000<br>400,000 40,000<br>200,000 20,000<br>0 0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Next in the Top 100<br>Cheese and curd Other plates..., of plastics, not r 140,000<br>140,000 Fibreboard of wood or other ligneou Chocolate and other food preparatio Wool, not carded or combed<br>120,000 Compression-ignition,combustion pisUncoated paper..., for writing... iTapes,valves,for pipes pressure red Polymers of propylene or of other oUncoated paper..., for writing... i 120,000100,000 Other vegetables preserved other thPumps for liquids,with or without mAutomatic regulating,controlling inTractors(other than tractors of heaFood preparations not elsewhere spe<br>100,000 Edible offal of bovine animals...,<br>Hormones; derivatives thereof used<br>80,000 Paper, paperboard, etc, coated...,<br>80,000 Halogenated derivatives of hydrocar<br>60,000<br>60,000<br>40,000<br>40,000<br>20,000 20,000<br>0 0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>100,000 Synthetic rubber derived from oils, Polyamides in primary forms<br>All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>80,000 Refrigerators,freezers electric or Mixtures of odoriferous substances<br>60,000<br>40,000<br>20,000<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> 15
# **Figure 11 Top Argentine Exports: High Productivity Products, Share of Total Exports %**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>8%7% Wheat and meslinMotor cars and other motor vehiclesParts and accessories of the motorMotor vehicles for the transport ofTubes,pipes and hollow profiles,sea 1.0%0.9% Chemical wood pulp, soda or sulphatFish, frozen, (excl. those of 03.04Cyclic hydrocarbons<br>6% Medicaments of mixed or unmixed pro 0.8% Polyethers and epoxide resins; poly<br>Fish fillets and other fish meat, f Malt extract; food preparations of<br>5% Unwrought aluminium 0.7% Accessory parts suitable for engineShaving preparations, personal deod<br>0.6%<br>4% 0.5%<br>3% 0.4%<br>0.3%<br>2%<br>0.2%<br>1%<br>0.1%<br>0% 0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Next in the Top 100<br>0.8%<br>Cheese and curd Other plates..., of plastics, not r<br>Fibreboard of wood or other ligneou Chocolate and other food preparatio Wool, not carded or combed<br>0.5% Compression-ignition,combustion pis Polymers of propylene or of other o 0.7% Other vegetables preserved other thPumps for liquids,with or without m<br>Uncoated paper..., for writing... i Uncoated paper..., for writing... i Automatic regulating,controlling in<br>Tapes,valves,for pipes pressure red 0.6% Tractors(other than tractors of hea<br>0.4% 0.5% Food preparations not elsewhere speEdible offal of bovine animals...,Hormones; derivatives thereof used<br>Paper, paperboard, etc, coated...,<br>0.3% 0.4% Halogenated derivatives of hydrocar<br>0.3%<br>0.2%<br>0.2%<br>0.1%<br>0.1%<br>0.0% 0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>0.4%<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>Synthetic rubber derived from oils, Polyamides in primary forms<br>All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>0.3% Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>Refrigerators,freezers electric or Mixtures of odoriferous substances<br>0.2%<br>0.1%<br>0.0%<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> 16
# **Figure 12 Top Argentine Exports: High Productivity Products, Product Income PPP 2000 $**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>25,000 Wheat and meslinMotor cars and other motor vehicles 23,000 Chemical wood pulp, soda or sulphat<br>Parts and accessories of the motor Fish, frozen, (excl. those of 03.04<br>23,000 Motor vehicles for the transport of Cyclic hydrocarbons<br>Tubes,pipes and hollow profiles,seaMedicaments of mixed or unmixed pro 21,000 Polyethers and epoxide resins; polyMalt extract; food preparations of<br>21,000 Fish fillets and other fish meat, fUnwrought aluminium Accessory parts suitable for engineShaving preparations, personal deod<br>19,000<br>19,000<br>17,000<br>17,000<br>15,000 15,000<br>13,000 13,000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Wool, not carded or combed Next in the Top 100<br>Other vegetables preserved other th<br>23,000 Cheese and curdFibreboard of wood or other ligneou Other plates..., of plastics, not rChocolate and other food preparatio 29,000 Pumps for liquids,with or without mAutomatic regulating,controlling inTractors(other than tractors of hea<br>Compression-ignition,combustion pisUncoated paper..., for writing... i Polymers of propylene or of other oUncoated paper..., for writing... i 27,000 Food preparations not elsewhere speEdible offal of bovine animals...,<br>21,000 Tapes,valves,for pipes pressure red Hormones; derivatives thereof used<br>25,000 Paper, paperboard, etc, coated...,<br>Halogenated derivatives of hydrocar<br>19,000 23,000<br>21,000<br>17,000 19,000<br>17,000<br>15,000<br>15,000<br>13,000 13,000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>Synthetic rubber derived from oils, Polyamides in primary forms<br>32,000 All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>29,000 Refrigerators,freezers electric or Mixtures of odoriferous substances<br>26,000<br>23,000<br>20,000<br>17,000<br>14,000<br>11,000<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Finally, we explore the extent to which products that experienced high growth in 2004 have high levels of productivity. In order to identify “new” exports, we rank products according to the growth of their export value in 2004 in relation to the average value between 1994 and 1999. We chose this period of reference to exclude the effects of the recent Argentine crisis. Some “new” export products had not even been exported before 2000 resulting in high growth rates. Most of the “new” export products constitute a very low share of total exports. Altogether they sum to only 1.2 percent of total exports in 2004. The ranking of the top 50 high-growth products is presented in Table 4. The products belong to a variety of sectors, although there is some small degree of concentration in groups such as edible fruits and nuts, ores, natural or cultured pearls, precious stones, iron and steel, lead, inorganic chemicals, compounds of precious metals, wood and articles of wood, and nuclear reactors, boilers, and related machinery. Also, it is important to point out that 17
half of the products in the list have high productivity levels as it is shown by the groups highlighted in Table 5. Not surprisingly, most of these products reflect a higher level of industrial manufacturing.

**Table 5**

**High Growing Argentine Exports 2004**

|**Growth**<br>**Ranking**|<br><br>**Code**|**Description**|**Growth***<br>**%**|**Export**<br>**value**<br>**Thousands**<br>|**Exports**<br>**share**<br>**%**|**Product**<br>**income**<br>**2000 PPP $**|**Distance**<br>**to export**<br>**basket**|<br><br>**RCA**|**Exports**<br>**ranking**|**Product**<br>**income**<br>**ranking**<br>|**Distance**<br>**ranking**|**RCA**<br>**ranking**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**2000 US$**|||||||||
|1|1514|Rape colza or mustard oil and frac|-|3903.5|0.012%|18610.3|1.70|0.52|370|433|355|286|
|1<br>|2609<br>|,<br>Tin ores and concentrates<br>|-|,<br>0.3<br>|0.000%<br>|,<br>2,152.0<br>|1.83<br>|0.00<br>|1109<br>|1132<br>|580<br>|1066<br>|
|1<br>|2705<br>|Coal gas, water gas, producer gas a<br>|-|0.2<br>|0.000%<br>|11,700.8<br>|2.03<br>|0.00<br>|1117<br>|903<br>|921<br>|1053<br>|
|1|7101|Pearls natural,cultured,graded/ungr|-|0.1|0.000%|25,758.7|2.26|0.00|1122|42|1089|1133|
|1|6906|Ceramic pipes,conduits,guttering an|-|0.0|0.000%|24,232.9|1.97|0.00|1136|88|820|1129|
|2|2616|Precious metal ores and concentrate|37,021,669|9,233.8|0.028%|3,808.7|1.25|4.78|248|1118|14|66|
|3<br>|5802<br>|Terry towelling and similar woven t<br>|987,501<br>|8,141.6<br>|0.025%<br>|9,557.8<br>|1.57<br>|15.44<br>|260<br>|1013<br>|198<br>|21<br>|
|4<br>5|2619<br>4405|Slag, dross, etc, from the manufact<br>Wood wool; wood flour|312,733<br>115,375|104.5<br>6.8|0.000%<br>0.000%|17,404.9<br>19,097.5|1.72<br>1.85|0.23<br>0.03|851<br>1034|545<br>397|395<br>611|429<br>801|
|6|8108|<br>Titanium and articles thereof,inclu|32,833|1,824.6|0.006%|26,442.1|2.13|0.25|474|29|1027|408|
|7|2814|Ammonia, anhydrous or in aqueous so|31,916|17,853.1|0.054%|11,256.5|1.35|1.75|171|935|30|144|
|8|7106|Silver(plated with gold,platinum)un|22,786|3,276.0|0.010%|10,584.2|1.77|0.15|398|967|464|514|
|9|3102|Mineral or chemical fertilizers, ni<br>|16,798|83,049.2|0.252%|11,811.9|1.49|4.01|53|892|113|79|
|10|5305|Coconut, abaca, ramie, etc, not spu|15,891|3.2|0.000%|6,035.9|1.98|0.02|1062|1092|841|855|
|11|0812|Fruit and nuts,provisionally preser|15,782|116.9|0.000%|1,091.1|1.60|0.21|838|1138|230|447|
|12|0814|Peel of citrus fruit or melons, fre|11,792|847.3|0.003%|6,402.5|1.40|5.46|576|1084|57|59|
|13|7801|Unwrought lead|10,968|21,266.6|0.065%|12,885.9|1.44|2.78|154|837|80|104|
|14|6113|Garments made up of knitted or croc|10,246|39.4|0.000%|16,027.0|2.06|0.03|930|642|961|838|
|15|1518|Animal or vegetable fats and oils..|8,221|34,804.1|0.106%|17,059.2|1.60|16.35|108|574|221|18|
|16<br>|9204<br>|Accordions and similar instruments;<br>|5,078<br>|1.4<br>|0.000%<br>|13,019.8<br>|2.16<br>|0.00<br>|1088<br>|831<br>|1044<br>|1007<br>|
|17|3815|Reaction initiators, accelerators a|5,036|23,015.1|0.070%|24,007.6|2.03|0.98|145|100|928|205|
|18|7118|Coin|3,768|358.6|0.001%|19,459.4|1.80|0.38|700|371|529|338|
|19|7804|Lead plates,sheets,strip and foil;l|3,700|559.9|0.002%|25,015.9|1.97|0.76|625|63|821|229|
|20|7609|<br>Aluminium tube or pipe fittings(cou|3,229|5,117.0|0.016%|17,120.5|1.62|3.64|324|567|250|87|
|21|0104|Live sheep and goats|3,205|1,358.7|0.004%|11,657.3|1.55|0.57|518|907|163|270|
|22|2707|Products of the distillation of coa|2,962|38,239.2|0.116%|9,834.8|1.42|1.39|98|1003|72|165|
|23|8602|Other rail locomotives;locomotive t|2,448|68.8|0.000%|13,904.1|1.91|0.01|891|776|722|920|
|24|0810|Other fruit, fresh, nes|2,131|16,523.9|0.050%|15,656.3|1.42|1.21|184|669|71|184|
|25|0811|Fruit and nuts, frozen|1,909|8,744.5|0.027%|10,855.1|1.49|1.46|253|955|115|159|
|26|0210|Meat and offal, salted... or smoked|1,899|2,477.4|0.008%|25,177.6|1.67|0.22|437|56|326|439|
|27|7004|Drawn glass and blown glass,etc.|1,877|37.4|0.000%|12,282.9|2.01|0.03|932|872|899|800|
|28|7225|Flat-rolled products of other alloy|1,790|662.9|0.002%|21,528.7|2.08|0.02|603|229|975|902|
|29|9706|Antiques of an age exceeding one hu|1,737|305.3|0.001%|28,434.8|2.18|0.03|716|10|1057|803|
|30|2817|<br>Zinc oxide; zinc peroxide|1,662|4,431.6|0.013%|10,561.2|1.49|2.23|349|969|109|123|
|31|5609|Articles of yarn, strip, etc, twine|1,609|215.1|0.001%|17,413.4|1.88|0.32|765|544|679|359|
|32|1105|Flour, meal, flakes, granules and p|1,500|2,716.1|0.008%|22,264.0|1.50|2.17|422|176|124|128|
|33|7001|<br>Cullet and other waste and scrap of|1,466|85.8|0.000%|16,047.8|1.88|0.08|866|641|685|635|
|34|8906|Other vessels including warships,li|1,413|76.0|0.000%|17,889.0|1.71|0.02|882|501|364|846|
|35|2517|Pebbles, gravel, etc; macadam of sl|1,405|650.9|0.002%|12,891.7|1.82|0.12|607|836|568|558|
|36|0404|Whey and other natural milk constit|1,385|20,699.1|0.063%|21,298.1|1.51|2.79|157|243|132|102|
|37|2102|Yeasts; other single-cell micro-org|1,364|11,836.9|0.036%|10,530.8|1.55|3.31|213|972|167|92|
|38|7224|Other alloy steel in ingots etc.sem|1,350|16,090.4|0.049%|17,720.7|1.71|2.60|188|515|375|112|
|39|2818|Artificial corundum; aluminium oxid|1,338|656.2|0.002%|22,188.8|1.65|0.02|605|182|289|864|
|40|6803|Worked slate and articles of slare|1,301|740.8|0.002%|19,070.2|1.64|0.28|590|401|276|389|
|41|4409|Wood, continuously shaped along any|1,244|36,245.1|0.110%|10,282.8|1.49|2.18|102|989|111|127|
|42|8004|<br>Tin plates,sheets and strip of a th|1,231|0.9|0.000%|15,868.5|1.76|0.01|1094|648|446|956|
|43|7806|Other articles of lead|1,165|177.9|0.001%|15,610.4|1.71|0.29|791|673|368|382|
|44|8444|Machines for extruding,drawing,text|1,125|1,532.6|0.005%|24,070.5|2.44|0.49|498|96|1131|289|
|45|6809|<br>Articles of plaster or of compositi|1,061|9,166.7|0.028%|10,780.8|1.57|1.99|251|958|183|133|
|46|8404|Auxiliary plant for use with boiler|972|1,077.2|0.003%|20,423.2|1.77|0.35|549|300|470|347|
|47|7206|<br>Iron,non-alloy steel in ingots/othe|968|79.1|0.000%|9,498.8|1.77|0.09|878|1018|468|620|
|48|5206|Cotton yarn, with <85% cotton, not|955|1,546.5|0.005%|16,736.2|1.75|0.52|494|599|437|285|
|49<br>|4503<br>|Articles of natural cork<br>|953<br>|1,942.5<br>|0.006%<br>|16,740.3<br>|1.80<br>|0.52<br>|467<br>|598<br>|534<br>|284<br>|
|50|2939|Vegetable alkaloids and their salts|912|1,088.6|0.003%|19,114.9|2.07|0.17|547|396|969|485|

* Growth was calculated between 2004 and the average value of the 1994-99 period Source: Author's calculations based on WITS-Comtrade database

Figures 13 and 14 summarize the findings of this section. They show the export value and product income in 2004 for Argentina weighted by export shares. Products are ranked according to high and low productivity for the top 50 larger exports and for the top 50 “new” exports. In Figure 13 we observe that the export value of the low productivity goods is, on average, double the value of the high productivity goods. Specifically, it is 2.5 times higher for the top 50 and 2 times higher for 18
the “new” exports respectively. Also, notice that the value of the top 50 exports is 40 and 32 times higher than the value of “new” exports for both the low and high productivity categories respectively.

# **Figure 13**

<!-- Start of picture text --> Weighted Average of Exports Value by Productivity Level, 2004<br>Top 50 and "New" Exports<br>2,000,000<br>1,636,803.45<br>1,600,000<br>1,200,000<br>800,000 649,839.31<br>400,000<br>20,568.62 41,265.96<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br>ppp 2000 $<br><!-- End of picture text --> In terms of product income, in Figure 14 shows that the low productivity goods have a product income 49 smaller than the high productivity goods in the case of the top 50 products. For the “new” exports, low productivity goods have a product income 44 smaller than the high productivity goods. Interestingly, when comparing these two rankings, there is not much of a difference within the productivity groups. That is, the ratio of the top 50 exports to the “new” exports is 0.87 for the low productivity goods and 0.95 for the high productivity goods.

# **Figure 14**

<!-- Start of picture text --> Weighted Average of Product Income by Productivity Level, 2004<br>Top 50 and "New" Exports<br>25,000<br>19,925.59<br>20,000 18,918.81<br>15,000<br>11,098.95<br>9,602.61<br>10,000<br>5,000<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br>ppp 2000 $<br><!-- End of picture text --> 19
# **VII. Proximity of goods to the Argentine Export Basket**

In this section we construct a measure of “distance” of each Argentine product with respect to the world export basket. This measure is important because, as the HHR model argues, firms are more likely to move to new products if the “distance” is low. Knowing what goods are close to the export basket and which of those ones have high productivity might be important in terms of assessing growth prospects. Following Hausmann and Klinger (2006), “distance” is estimated as shown below.

As shown in the formula above, the density measure for country _c_ is calculated as the summation of the pairwise proximity ( φ _ij_ ) of each product that has revealed comparative advantage ( _RCA_ ) in that country relative to the summation of the pairwise proximity of all products in the world market. This pairwise proximity is estimated as the minimum of the conditional probability of a product _i_ having revealed comparative advantage given that product _j_ has too, as can be seen in the formulae below.

The intuition behind the density measure is that it reflects the degree in which a good exported by Argentina is in close proximity to the world’s export basket, making it easier for firms to adopt new products and export them. Hence, under this framework, the probability of exporting a good in the future depends on how close is a good to the current country’s export basket.

In Figure 15 we plot the average distance for Argentine export products where the dotted lines indicate the standard deviation. As can be observed, the average shows a decreasing trend over the period, although during 2000-03 presented a notorious increase that was reverted in 2004.

20
**Figure 15**

<!-- Start of picture text --> Average Distance Measure for Argentina<br>2.35<br>2.25<br>2.15<br>2.05<br>1.95<br>1.85<br>1.75<br>1.65<br>1.55<br>1.45<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Log of Inverse Density<br><!-- End of picture text --> Figure 16 shows how “distance” has changed for the top 10 export products. As can be observed, it is generally flat although with a slight downward trend over the period. Also, notice that the mean distance for these 10 products is around 1.4, except for soy beans which is around 1.1. Finally, notice that the distance of petroleum gases shows a sharp decrease in the late nineties, reflecting a possible “catching up”.

**Figure 16**

<!-- Start of picture text --> Distance to the Argentine Export Basket<br>Top 10 of Argentine Exports 2004<br>1.9<br>1.8<br>1.7<br>1.6<br>1.5<br>1.4<br>1.3<br>1.2<br>1.1<br>1.0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Oil-cake and other solid residues, Soya-bean oil and its fractions<br>Petroleum oils and oils obtained fr Petroleum oils, etc, (excl. crude);<br>Soya beans Wheat and meslin<br>M aize (corn) Petroleum gases and other gaseous h<br>Copper ores and concentrates Leather of bovine or equine animals<br><!-- End of picture text --> In Figure 17, we present “distance” for the high-productivity goods analyzed in the previous section. As can be observed in the graphs, the distance to the Argentine export basket seems to have been stable during the entire period for most products. However, when looking more carefully 21
it seems that, for many products, distance was decreasing until year 2000 and then increasing again for some, (although not reaching the high levels observed in the early nineties) and remaining flat for others for the rest of the period. In general, a decreasing trend means that relatively higher productivity goods are getting slightly closer to the Argentine export basket.

# **Figure 17 Top Argentine Exports: High ProductivityProducts, Distance (log inverse density)**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>2.4 Wheat and meslin<br>Motor cars and other motor vehicles 2.3 Chemical wood pulp, soda or sulphat<br>Parts and accessories of the motor Fish, frozen, (excl. those of 03.04<br>2.22.0 Motor vehicles for the transport ofTubes,pipes and hollow profiles,seaMedicaments of mixed or unmixed proFish fillets and other fish meat, fUnwrought aluminium 2.1 Cyclic hydrocarbonsPolyethers and epoxide resins; polyMalt extract; food preparations ofAccessory parts suitable for engineShaving preparations, personal deod<br>1.9<br>1.8<br>1.7<br>1.6<br>1.4 1.5<br>1.2 1.3<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Next in the Top 100<br>Cheese and curd Wool, not carded or combed Other vegetables preserved other th<br>Other plates..., of plastics, not r Pumps for liquids,with or without m Automatic regulating,controlling in<br>2.4 Fibreboard of wood or other ligneouChocolate and other food preparatio 2.4 Tractors(other than tractors of heaEdible offal of bovine animals..., Food preparations not elsewhere speHormones; derivatives thereof used<br>Compression-ignition,combustion pis Paper, paperboard, etc, coated..., Halogenated derivatives of hydrocar<br>Polymers of propylene or of other o 2.2<br>Uncoated paper..., for writing... i<br>2.2 Uncoated paper..., for writing... i<br>Tapes,valves,for pipes pressure red 2.0<br>2.0 1.8<br>1.6<br>1.8<br>1.4<br>1.6<br>1.2<br>1.4 1.0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>Synthetic rubber derived from oils, Polyamides in primary forms<br>All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>2.5 Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>Refrigerators,freezers electric or Mixtures of odoriferous substances<br>2.2<br>1.9<br>1.6<br>1.3<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> When comparing the distance of high-productivity and low-productivity Argentine exports it is clear that those with low productivity are closer to the world export basket. Specifically, low productivity goods have a distance that is 19 and 15 percent smaller than the high productivity goods for both the top 50 exports and the “new” exports, as shown in Figure 18. On the other hand, when comparing the two rankings, one finds that the top 50 products are, as expected, closer to the Argentine exports basket than the “new” products, although not by much. The ratios are 0.89 and 0.94 for the low and high productivity goods respectively.

22
**Figure 18**

<!-- Start of picture text --> Weighted Average of Distance by Productivity Level, 2004<br>Top 50 and "New" Exports<br>2.0<br>1.72<br>1.62<br>1.6 1.47<br>1.31<br>1.2<br>0.8<br>0.4<br>0.0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br><!-- End of picture text --> Regarding the relationship between RCA and product income, observe in Table 4 that Argentina does not have revealed comparative advantage for many of the high-productivity goods. For products where Argentina has a RCA the product income is generally low. However, notice that it has a considerable RCA for wheat, aluminum, fish, tubes and pipes all of which are of high productivity.

Furthermore, in Figure 19, we present the weighted average of RCA separately for the top ranked 50 and for the top ranked “new” exports, both grouped according to high and low productivity. As can be observed, Argentina has on average five times more revealed comparative advantage for the low productivity goods within its top 50 exports. It is important to notice though, that for the top 50 high productivity exports the weighted average RCA is greater than unity which means that Argentina is not without some comparative advantage in these products too.

In contrast, when looking at the “new” exports, the high productivity goods have a level of RCA twice that of the low productivity products. Actually, the level is very close to that of the top 50 high productivity goods. Conversely, the RCA level of the low productivity “new” exports is one-tenth of that of the low productivity top 50 exports.

**Figure 19**

<!-- Start of picture text --> Weighted Average of RCA by Productivity Level, 2004<br>Top 50 and "New" Exports<br>35 31.59<br>30<br>25<br>20<br>15<br>10<br>5.95 6.25<br>5 3.06<br>0<br>High Low High Low<br>Productivity Productivity Productivity Productivity<br>"NEW" EXPORTS TOP 50 EXPORTS<br><!-- End of picture text --> 23
# **VIII. Productivity-Proximity Tradeoff and the Efficient Frontier**

As discussed in HHR, there is a tradeoff between product income and distance. Countries are more likely to produce and export goods that are close to what they already produce but those products might not be of high value. There is a choice between producing the goods that are close to the production frontier but with lower product value, and producing high product value goods that are further away from the production frontier. Hence, there is a tradeoff between exporting goods that are relatively “easy” for the country to export even though they have less impact on economic growth, or producing and exporting goods that will induce greater economic growth but are more “difficult” to produce. However, HHR also argue that there is an efficiency frontier where some goods are close to the export basket but still show high product income. In the subsequent analysis we apply these concepts to the Argentine case and look at the particular goods that could reflect the tradeoff as well as those close to the efficiency frontier.

In order to illustrate this tradeoff, in Figure 20 we plot the product income of each product exported by Argentina minus the corresponding Argentine export income against their distance to the world export basket for 2004. The idea behind subtracting export income from the product income of each product is to give a sense of the attractiveness of each good with respect to the trade-weighted average product currently exported. For example, the greater this difference the more attractive is the product and the smaller the difference the less attractive it is. Using HHR terminology, we will apply the term “upscale products” those whose difference is positive and “downscale products” those whose difference is negative.

**Figure 20**

<!-- Start of picture text --> Price-Proximity Tradeoff and the Efficient Frontier, 2004<br>20<br>18<br>15 U<br>13 p<br>s<br>10 c<br>a<br>8<br>l<br>5 e<br>3<br>0<br>D<br>-3 o<br>w<br>-5<br>n<br>-8 s<br>c<br>-10<br>a<br>-13 l<br>e<br>-15<br>0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75<br>Distance (Log of Inverse Density)<br>2000 PPP Internat. $<br>Product Income - Argentinean Export Income,<br><!-- End of picture text --> 24
In Figure 20 above, there is a clear positive relationship between the two variables reflecting the tradeoff between product income and distance. That is, there are many products very close to the world export basket but whose product income is very low, and there are many others away from the world export basket but whose product income is very high. However, notice that there are some products, (inside the oval), that are close to the export basket and whose product income is quite high. These are the products at the efficiency frontier. Examples of these three categories are presented in the table below.

**Table 6**

**Productivity-Proximity Tradeoff and the Efficient Frontier: Argentine examples**

|**Upscale and far away**|**Dow**|**nscale and close**|**Efficient frontier**|
|---|---|---|---|
|2843 Colloidal precious metals; their co<br>2930 Organo-sulphur compounds|0201<br>0202|Meat of bovine animals, fresh or ch<br>Meat of bovine animals, frozen|0101 Live horses, asses, mules and hinni<br>0203 Meat of swine, fresh, chilled or fr|
|2934 Other heterocyclic compounds<br>2935 Sulphonamides|0205<br>0306|Meat of horses, asses, mules or hin<br>Crustaceans, fresh, chilled or frozen|0204 Meat of sheep or goats, fresh, chilled<br>0206 Edible offal of bovine animals...,|
|3707 Chemical preparations for photograp<br>3911 Petroleum resins..., polysulphides.|0503<br>0506|Horsehair and waste<br>Bones and horn-cores|0304 Fish fillets and other fish meat, f<br>0305 Fish, salted, dried...;smoked fish; fi|
|4812 Filter blocks, slabs and plates, of|0510|Ambergris, castoreum, civet and mus|1003 Barley|
|6702 Artificial flowers, foliage and fru|0703|Onions, shallots, garlic, leeks...e|1004 Oats|
|7101 Pearls natural, cultured, graded/ungr|0713|Dried leguminous vegetables, shelle|1007 Grain sorghum|
|<br>7410 Copper foil(whether or not printed<br>8113 Cermets and article thereof, includi|<br>0808<br>0813|<br>Apples, pears and quinces, fresh<br>Fruit, dried, nes|<br>1105 Flour, meal, flakes, granules and p<br>1204 Linseed|
|8209 Plates, sticks, tips unmounted, of sin|0903|Mate|1214 Swedes, mangolds...and similar fora|
|8443 Printing machinery; machines for use|1005|Maize (corn)|1502 Fats of bovine animals, sheep or go|
|<br>8444 Machines for extruding, drawing, text|<br>1006|<br>Rice|<br>1503 Lard stearin, lardoil, oleostearin,|
|8456 Machines-tools for working any mate|1201|Soya beans|1605 Crustaceans... and other aquatic in|
|8457 Machining centres, unti construction<br>8458 Lathes fr removing metal<br>8461 Machine-tools for planing, shaping,g|1202<br>1507<br>1515|Ground-nuts, not roasted or otherwise<br>Soya-bean oil and its fractions<br>Other fixed vegetable fats and oils|2004 Other vegetables preserved other th<br>2301 Flours, etc, of meat, fish, etc, un<br>2829 Chlorates, perchlorates; bromates,|
|8464 Machine-tools for working stons, cer|1521|Vegetable waxes (excl. triglyceride|2840 Borates; peroxoborates (perborates)|
|8515 Electrical brazing, welding machines<br>9002 Lenses, prisms, mirrors, other such el<br>9011 Micro optical compounds(microscopes<br>9101 All types of portable watches with|2009<br>2204<br>2207<br>2304|Fruit juices (incl. grape must) and<br>Wine of fresh grapes, (incl. fortif<br>Ethyl alcohol, undenatured of >=80%<br>Oil-cake and other solid residues,|2937 Hormones; derivatives thereof used<br>3001 Glands, etc, dried; extracts of gla<br>3502 Albumins, albuminates and other alb<br>3913 Natural polymers and modified natur|
|9102 All types of portable watches other<br>9104 Instrument panel clocks, similar typ<br>9109 Clock movements, complete and assemb<br>9111 Watch cases and parts thereof|2305<br>2308<br>2401<br>2528<br>2603<br>2616<br>2709<br>2810<br>2814<br>5105<br>7108|Oil-cake and other solid residues,<br>Vegetable materials, waste, residue<br>Unmanufactured tobacco; tobacco ref<br>Natural borates and concentrates; n<br>Copper ores and concentrates<br>Precious metal ores and concentrate<br>Petroleum oils and oils obtained fr<br>Oxides of boron; boric acids<br>Ammonia, anhydrous or in aqueous so<br>Wool and fine or coarse animal hair<br>Gold(platinumplated)unwrought,semi|4102 Raw skins of sheep or lambs, but no<br>4703 Chemical wood pulp, soda or sulphat<br>5101 Wool, not carded or combed<br>7601 Unwrought aluminium|

The upscale products which are far away from the world export basket are mainly chemical products, metals and related products, machinery and other high-tech products, so these are either valuable in their own right or they are industrial goods. On the other hand, the downscale products that are very close to the world export basket are mainly animal and vegetable products, prepared food, textiles and minerals. Finally, observe that products at the efficiency frontier are chemical and plastic products but also animal and vegetable products, prepared food, textiles and minerals. Therefore, interestingly, there is an important similarity between these two categories that might be exploited to enhance economic growth.

Furthermore, it is important to mention that some of the products at the efficiency frontier are not industrial goods but primary goods, contradicting the so-called "primary products curse".

25
What is important for economic growth is the product income of the goods in the export basket regardless of the sector or industry to which they belong.

In Figure 21 below, we present the same graph as in Figure 20 for every year from 1994 to 2004. As can be observed, the average distance in 1994 for the upscale products is 2.05 while for 2004 it is 1.87. For the downscale products the average distance is relatively lower being 1.79 for 1994 and 1.65 for 2004. So, it seems that on average, both there are both upscale and downscale products close to the Argentine export basket.

On the other hand, the average product income (weighted by export income) for the upscale products was $4.948 in 1994 and $6.524 in 2004 while for the downscale products these values are $- 2.862 and $-3.486 respectively. These numbers mean that the range of weighted product income widened in 2004 compared to 1994. That is, there are more upscale goods with higher product income but also downscale goods with lower product income.

**Figure 21**

**Visual Open Forest for Argentina**

<!-- Start of picture text --> 1994 1995 1996<br>25 25 25<br>20 20 20<br>15 15 15<br>10 10 10<br>5 5 5<br>0 0 0<br>-5 R 2 = 0.3494 -5 R 2 = 0.418 -5 R 2 = 0.3456<br>-10 -10 -10<br>-15 -15 -15<br>0 1 2 3 4 0 1 2 3 4 0 0.5 1 1.5 2 2.5 3 3.5 4<br>Distance Distance Distance<br>1997 1998 1999<br>25 25 25<br>20 20 20<br>15 15 15<br>10 10 10<br>5 5 5<br>0 0 0<br>-5 R 2 = 0.2844 -5 R 2 = 0.274 -5 R 2 = 0.2097<br>-10 -10 -10<br>-15 -15 -15<br>0 1 2 3 4 0 1 2 3 4 0 1 2 3 4<br>Distance Distance Distance<br>2000 2001 2002<br>25 25 25<br>20 20 20<br>15 15 15<br>10 10 10<br>5 5 5<br>0 0 0<br>-5 R 2 = 0.2263 -5 R 2 = 0.2627 -5 R 2 = 0.2663<br>-10 -10 -10<br>-15 -15 -15<br>0 1 2 3 4 0 1 2 3 4 0 1 2 3 4<br>Distance Distance Distance<br>2003 2004<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 R 2 = 0.2597 -5 R 2 = 0.2535<br>-10 -10<br>-15 -15<br>0 1 2 3 4 0 1 2 3 4<br>Distance Distance<br>Prody-Expy (thousands) Prody-Expy (thousands) Prody-Expy (thousands)<br>Prody-Expy (thousands) Prody-Expy (thousands) Prody-Expy (thousands)<br>Prody-Expy (thousands) Prody-Expy (thousands) Prody-Expy (thousands)<br>Prody-Expy (thousands) Prody-Expy (thousands)<br><!-- End of picture text --> 26
Also, it can be observed from Figure 22 that there a general trend across sectors whereby the distance has been reduced and the product income has increased. In this graph, the rightmost point for all series corresponds to year 1994, the middle one to year 1998, and the leftmost point to year 2004.

# **Figure 22**

<!-- Start of picture text --> Product Income and Distance for Argentina, by sectors<br>13,000<br>11,000<br>9,000<br>7,000<br>5,000<br>3,000<br>1,000<br>-1,000<br>-3,000<br>1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3<br>Distance (Mean of Log of Inverse Density)<br>Ch. 01-05, Animals and Animal Products Ch. 06-14, Vegetable Products<br>Ch. 15, Animal or Vegetable Fats Ch. 16-24, Prepared Food Stuffs<br>Ch. 26-27, Mineral Products Ch. 28-38 Chemical Products<br>Ch. 39-40, Plastics and Rubbers Ch. 41-43, Hides and Skins<br>Ch. 44-46, Wood and Wood Products Ch.47-49, Wood Pulp Products<br>Ch. 50-63, Textikes and Textiles Products Ch. 64-67, Footw ear and headgear<br>Ch. 68-70, Articles of Stone, Plaster, Cement, Asbestos Ch. 71, Pearls, Precious or Semi-precious Stones, Metals<br>Ch. 72-83, Base Metals and Articles Thereof Ch. 84-85, Machinary and Mechanical Appliances<br>Ch. 86-89, Transportation Equipment Chpater 90-92, Instruments - Measuring, Musical<br>Ch. 93, Arms and Ammunition Ch. 94-96, Miscelaneous<br>Ch. 97, Works of Art<br>PPP, constant 2000 international $<br>Mean of (Product Income-Arg. Export Income),<br><!-- End of picture text -->

# **IX. Effect of Export Income on Growth: Econometric Analysis**

In this section we examine in more detail the relationship between economic growth and export income. Specifically, we estimate the effect of EXPY on growth of GDP per capita applying different econometric techniques at the cross-section and panel level. For the former we use the measures calculated using the COMTRADE dataset and for the latter we calculate PRODY and EXPY using the World Trade Flows dataset (Feenstra et al. 2005) described in Section II.

# **Cross-national Analysis**

All specifications include the initial level of GDP per capita and EXPY in logs as covariates. Additionally, we include in some specifications the log of average number of years of secondary school attained as proxy for human capital<sup>5</sup> , the Rule of Law Index as proxy for institutional quality,

> 5 Similar results are obtained including years of primary school attained instead.

27
the log of domestic credit as percentage of GDP as proxy for financial depth, and an index of export diversification<sup>6.</sup> Table 7 shows OLS and Instrumental Variables estimates. As can be observed from the OLS estimates presented in columns (1) to (5), EXPY has a significantly positive effect on growth that is robust to the inclusion of other covariates. However, notice that the magnitude of the effect varies between 0.035 and 0.043, being invariant only to the inclusion of the Rule of Law Index. Regarding the effect of other covariates on growth, the initial level of GDP per capita is always negative and significant. The human capital variable and the Rule of Law Index have a robust positive and significant effect on growth as expected. Domestic credit and export diversification are insignificant in all specifications<sup>7</sup> .

The last five columns of Table 7 show IV estimates where log of population and log of land area were used as instruments<sup>8</sup> . We choose these instruments following HHR (2006) who argue that specializations patterns depend on idiosyncratic elements and fundamentals like human capital and labor force size. HHR base their excludability restriction in the fact that there has not been found yet any empirical evidence of scale effects in growth models<sup>9</sup> , then arguing that population size and land area, as proxies of country size, are plausible instruments for EXPY<sup>10</sup> .

By looking at the IV estimations in Table 7 one can observe that, as is generally the case, the standard errors are much bigger (almost double) compared to the OLS estimations, although the EXPY point estimates are relatively close to the previous estimates. Notice, however, that the effects of the other covariates continue to be robust (except for GDP per capita in some specifications). The specification tests presented at the bottom of the table are useful to shed light on understanding these results. The overidentification test fails in practically all cases. Moreover, the identification tests are rejected at only 5 and 10 percent of significance level. Suspecting weak instruments problem we run the Stock-Wright S-statistic to do weak-instrument-robust inference and find that EXPY is significant at 5 and 10 percent level in most cases<sup>11</sup> .

Given that the EXPY might affect growth differently depending on the level of income of the economies we run two sets of experiments. First, as shown in Table 8, we run the same specifications as before but excluding from the sample high-income economies according to The World Bank classification. The OLS estimates on the restricted sample are very similar to the ones encountered in Table 7. Yet, the coefficients of EXPY are smaller in some specifications. Also, the estimates are in general less precise, possibly due to the smaller sample size. On the other hand,

> 6 The diversification index is defined as 1-Herfindahl Index following Hwang (2006).  The source for GDP per capita and domestic credit as percentage of GDP is the Penn World Tables.  For the average years of schooling we used the Barro-Lee dataset.  See Barro and Lee (1994,2000)

> 7 Credit is significant only when schooling is not included. Other specifications and test are available upon request from the authors.

> 8 These variables were extracted from the Penn World Tables.

> 9 For this purpose HRR cite Rose, Andrew K. “Size Really Doesn’t Matter: In Search for a National Scale Effect”, NBER Working Paper No. 12191, April 2006.

> 10 HHR show a regression showing the correlation between EXPY and the instrument set and find land area and population to be significant.  In our estimations, though, land is only slightly significant in the first stage of the specification presented in column (6).  We run a test for the redundancy of this variable and fail to reject it in all specifications.  Also, we run the IV regressions excluding land from the instrument set and got very similar results, except that the point estimates of both EXPY and GDP per capita were very small and the latter was no longer significant.

> 11 We obtained the same results when running the Anderson-Rubin test with the exception of column (6) in Table 10, which rejects the null at 1% of significance level.

28
observe that the IV estimates of EXPY are now significant and higher than those of the OLS estimates. Looking at the bottom of Table 8, the under and weak identification tests are rejected. But, just as in the full sample case, the overidentification tests are rejected which again casts doubts on the instrument set.

Second, to overcome the small sample problem and have a better insight on the effect of EXPY on growth for different income-level economies we include dummies indicating the type of economy and these interacted with EXPY. The coefficients of the interactions tell us the specific effect of EXPY on growth given the economy’s level of income relative to high-income economies. The results of these estimations are shown in Table 9. In the OLS estimations, the biggest and most significant positive effect of EXPY is on growth in lower-middle-income economies, followed by the effect in lower-income economies. The point estimates for the lower-income economies are actually half the ones of the lower-middle-income economies, after controlling for institutional quality, financial depth and export diversification. The effect of EXPY on growth in upper-middleincome economies appears to be insignificant.

In columns (6) to (10) of Table 9 we present the IV estimates for this exercise. The estimates are far more imprecise and in most cases they have increase dramatically. Looking at the specification test, in one hand, we cannot reject the null hypothesis of weak identification and, in the other hand, we reject the overidentification test. Finally, the weak-instrument-robust inference tells us that EXPY is significant at 5 and 10 percent level.

# **Panel Level Analysis**

In view of the fact that the relationship between EXPY and growth is more likely to be one of long term and aiming at obtaining more efficient and consistent estimates, we present in what follows estimation results using panel data. We construct 5-year, 8-year and 10-year panels spanning from 1962 to 2000 using Feenstra et al. (2005). In the next set of tables we present results of OLS, IV, OLS-Fixed Effects and GMM estimations for the full sample as well as for the sample excluding high-income countries. All estimations include human capital and initial GDP per capita as additional covariates. The OLS estimates although informative, are likely to be biased since they do not take into account the potential endogeneity of EXPY nor the unobserved time-invariant country characteristics that might affect growth. We can correct the first problem by making use of IV estimators (2SLS) where the same variables as in the cross-section analysis would serve to instrument EXPY. But by only using this technique, however, we do not solve the problem of unobserved time-invariant characteristics. On the other hand, we can estimate a Fixed Effects model to correct for the second problem but we still need to correct for the potential endogeneity of EXPY. Also, we cannot estimate an IV model with fixed effects using the variables employed earlier as instruments since these are practically time invariant and therefore they get differenced out under this estimation approach. Given that finding more suitable instruments represents an extremely difficult task, we apply the System-GMM technique suggested originally in Arellano and Bover (1995) and fully developed in Blundell and Bond (1998). The advantage of this technique, designed to estimate dynamic panel data models, is that addresses both problems. It consists of setting a system of equations in differences and in levels and using lagged levels and the first lag of the first-differences of the endogenous variables as instruments in these equations respectively in addition to allowing for fixed effects. This approach has become very popular in growth-related empirical work, examples are Caselli (1996), Levine et al (2000), Loayza et al (2004), HHR(2006), among others.

29
Table 10 presents the results of the different estimation methods. As can be observed, the effect of EXPY on growth is positive and significant in the OLS estimation and very similar to the cross-section estimates. The IV estimate is significant in this case although twice the size of the OLS estimate. Here once again, the Hansen J-statistic test for overidentification is rejected which casts doubts on the specification of the model. The FE estimate of EXPY is considerably smaller and insignificant.

In the next columns of Table 10 we present five different specifications using System-GMM techniques which treat all covariates as potentially endogenous and include their lags and firstdifferences as well as the log of population and land area as instruments<sup>12</sup> . Column (4) includes lags of second and higher order of the variables in levels in the instrument set, additional to the firstdifference and other mentioned instruments. The resulting point estimates of EXPY are within the range of the cross-section analysis, 0.041. Nonetheless, the crucial assumption of non-second-order serial correlation of the residuals is violated invalidating the use of second period lags in the instrument set<sup>13</sup> . Moreover, by looking at other test not reported in the tables, the model seems to be saturated by the large number of instruments. In columns (5) and (6) we present results using lags of third and higher order and fourth and higher order, respectively. In both cases, the models fail to reject the test of lack of second-order serial correlation of the residuals in the first-difference residuals validating the use of these lags as instruments. The point estimates of EXPY in these specifications are higher than in the previous case: 0.046 and 0.051 respectively.

In order to avoid saturation of the model, several authors have proposed the use of a collapsed version of the original instrument set<sup>14</sup> . The estimations in column (7) and (8) of Table 10 incorporate this modification. The EXPY estimates are very close to the ones obtained before, but in both cases the overidentification test is rejected. Moreover, in column (7) the AR(2) is rejected, just as expected given the results from column (4).

Table 11 presents the same estimations as Table 10 but excluding from the sample the highincome economies. In general the results are similar to those of the full sample analysis, although somewhat mixed in terms of the magnitude. In the case of the FE estimation, the coefficient of EXPY increases to 0.026 and becomes significant at 10 percent. In Table 12 we present the estimations for the 8-year and 10-year panel for the full sample and in Table 13 for the restricted sample. By and large, the results are very similar to those of the 5-year panel. In the GMM estimations, there is no evidence of second-order autocorrelation of the residuals in first difference validating the use of lags of second order as instruments.

> 12 The results presented here are two-step estimates.  The one-step estimates are very similar and we do not include them to preserve space.

> 13 For the lags of the endogenous variables to be valid instruments they need to be uncorrelated with the residuals in first differences.  In order for these ortogonality conditions to hold, it is assumed that the residuals are serially uncorrelated and the initial conditions are predetermined.  In cases where the former condition is not satisfied but rather the residuals follow a MA( _q_ ) process, with _q_ ≥1, only the lags of order 2+ _q_ and further of the endogenous variables are valid instruments.

> 14 In a collapsed instrument set the moment conditions are applied such that each of them correspond to all available periods instead of each moment condition corresponding to a particular time period as is originally the case. Hence, by collapsing the instrument matrix we make the number of moment conditions independent of the number of time periods whereas in the original case the number of moment conditions increases more than proportionally with the number of time periods.  Loayza et al (2002), Beck and Levine (2004), Carkovic and Levine (2005) and Roodman (2006) use this approach.

30
# **Discussion and comparison with HHR’s results:**

In the econometric analysis presented above we follow closely HHR (2006). In this subsection we compare our results to those in HHR and mention some differences in the assumptions made. In the cross-section analysis, HHR construct EXPY by using the average of PRODY for 1999-2001 while we use the current PRODY for every year. Additionally, HHR uses the COMTRADE database for the periods 1992-2003 and 1994-2003 whereas we use the same data source from the period 1994-2004. In terms of the specifications our estimates include additional covariates such as domestic credit as percentage of GDP, an export diversification index, and in some of them we also include type-of-economy dummies. HHR discuss briefly, though, the inclusion of some variables as robustness checks and mention not finding major changes in their results. Also, they present separate panel estimates for different subgroups of countries according to their income level.

Our OLS estimations are similar to those of HHR however somewhat stronger in terms of both magnitude and significance. In the IV estimations we do not find any significant effect and given the specification tests we find the model behaves rather poorly. HHR, although having very similar specification tests, find EXPY to have a significant and bigger effect than in their OLS estimates. Finally, in contrast with HHR we find that human capital and the Rule of Law index are significantly positive and robust to all specifications. It is most likely that these small differences in results are due to the different approaches employed in constructing EXPY.

For the panel regressions both papers use the World Trade Flows database and we find that the estimations results are very similar. In the GMM estimations for example, in the 5-yr panel we find that point estimates are between 0.046 and 0.051 compared to 0.0446 in HHR and in the 10year panel the estimates are the same 0.044, although, in both panels our estimates are more precise. In regards to the different effects of EXPY on growth according to the economies’ income level, we find the effects to be important for both the lower-income and lower-middle economies, although much stronger for the latter. We do not find any effect for the upper-middle economies. HHR in their corresponding exercise get very similar results although finding some effect for the uppermiddle income economies when using IV techniques.

To summarize, the econometric analysis presented here and in the HHR (2006) paper constitute evidence of EXPY having a robust positive effect on growth. The magnitude of the effect, however, depends very much on the specifications and estimation methods employed. As discussed above, in the cross-section analysis the OLS point estimates go from 0.035 in our estimates to 0.047 in HHR estimates. While in the panel estimations the EXPY coefficient goes from 0.034 using OLS to 0.046 using System-GMM. The corresponding numbers in HHR are 0.029 and 0.045 respectively. Furthermore, the effect of EXPY seems to be stronger for lower-middleincome economies and somewhat important for the lower-income economies. These results however, should be taken with caution due to the fact that our sample is restricted to countries reporting information in every year which decreases the representation of lower-income economies in the sample<sup>15</sup> .

> 15 In our cross-section sample the lower-income economies account for only 7% of the sample, while the lower-middle and upper middle account for 25% each, and the high-income economies for the remaining 44%.  In the panel regressions the distribution is rather different, the lower-income economies account for 20%, the lower-middle for 30%, the upper-middle for 18% and the high-income economies for 32%.  The HHR panel sample seems to have a similar distribution, although with similar participation of the upper-income and high-income economies (around 25%).

31
**Table 7**

# **Cross-national Growth Regressions 1994-2004**

||(1)<br>**OLS**|(2)<br>**OLS**|(3)<br>**OLS**|(4)<br>**OLS**|(5)<br>**OLS**|(6)<br>**IV**|(7)<br>**IV**|(8)<br>**IV**|(9)<br>**IV**|(10)<br>**IV**|
|---|---|---|---|---|---|---|---|---|---|---|
|Log of GDP pc 1994|-0.012**|-0.017***|-0.022***|-0.022***|-0.022***|-0.008|-0.014|-0.019**|-0.020**|-0.022**|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.011)|(0.008)|(0.009)|(0.009)|(0.010)|
|Log of EXPY 1994|0.043***|0.035***|0.035***|0.038***|0.040***|0.032|0.024|0.024|0.030|0.039|
||(0.015)|(0.012)|(0.012)|(0.012)|(0.014)|(0.030)|(0.024)|(0.025)|(0.024)|(0.033)|
|||0.008**|0.006*|0.006*|0.006*||0.009**|0.007*|0.007*|0.006*|
|Log of % secund. school att. 1985||(0.003)|(0.003)|(0.003)|(0.004)||(0.004)|(0.004)|(0.004)|(0.004)|
||||0.007**|0.008**|0.008**|||0.007**|0.007**|0.007**|
|Rl f L Id 1996|||||||||||
|ue o aw nex|||(0.003)|(0.003)|(0.003)|||(0.003)|(0.003)|(0.003)|
|Domestic credit provided by banking||||-0.000|-0.000||||-0.000|-0.000|
|sector, % of GDP||||(0.000)|(0.000)||||(0.000)|(0.000)|
|Diversification (1 - Herfindahl Index)|||||-0.011<br>(0.023)|||||-0.011<br>(0.032)|
||-0.275***|-0.183**|-0.134*|-0.152*|-0.166*|-0.204|-0.111|-0.062|-0.106|-0.159|
|Constant|(0.092)|(0.078)|(0.079)|(0.082)|(0.087)|(0.188)|(0.152)|(0.158)|(0.153)|(0.202)|
|Observations|73|59|59|59|59|71|58|58|58|58|
|R-squared|0.21|0.25|0.32|0.34|0.34|0.21|0.24|0.30|0.33|0.34|
|First stage F-statistic of excluded<br>instrument p-value (H0:<br>underidentified/weakly identified)||||||0.019|0.018|0.019|0.021|0.089|
|<br>Hansen J statistic p-value (H0:<br>instruments are valid and correctly<br>excluded -overidentification test)||||||0.130|0.042|0.025|0.009|0.012|
|<br>Stock-Wright S-statistic p-value (H0: all|||||||||||
|endogenous regressors==0 and overid.<br>rest valid, weak-instrument-robust<br>inference)||||||0.149|0.098|0.054|0.014|0.023|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** sig<br>Instruments for IV regressions: log of populatio|nificant at 1%<br>n 1994 and log of lan|d area|||||||||

32
**Table 8**

**Cross-national Growth Regressions 1994-2004 - Restricted Sample: Non-high-income Economies**

||(1)<br>**OLS**|(2)<br>**OLS**|(3)<br>**OLS**|(4)<br>**OLS**|(5)<br>**OLS**|(6)<br>**IV**|(7)<br>**IV**|(8)<br>**IV**|(9)<br>**IV**|(10)<br>**IV**|
|---|---|---|---|---|---|---|---|---|---|---|
|Log of GDP pc 1994|-0.010|-0.020***|-0.023***|-0.022***|-0.022***|-0.015*|-0.021**|-0.025***|-0.025***|-0.025***|
||(0.008)|(0.007)|(0.007)|(0.007)|(0.007)|(0.009)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log of EXPY 1994|0.044**|0.036**|0.032**|0.030**|0.035**|0.058***|0.041**|0.040*|0.040*|0.049**|
||(0.017)|(0.014)|(0.015)|(0.014)|(0.016)|(0.021)|(0.019)|(0.020)|(0.020)|(0.022)|
|Log of % secund. school att. 1985||0.011**|0.008|0.008|0.009||0.011**|0.007|0.007|0.008|
|||(0.004)|(0.005)|(0.005)|(0.005)||(0.005)|(0.005)|(0.005)|(0.005)|
|Rule of Law Index 1996|||0.012**|0.012**|0.012*|||0.012**|0.012**|0.012*|
||||(0.005)|(0.006)|(0.006)|||(0.006)|(0.006)|(0.006)|
|Domestic credit provided by banking<br>sector, % of GDP||||0.000|0.000||||0.000|0.000|
|||||(0.000)|(0.000)||||(0.000)|(0.000)|
|Diversification (1 - Herfindahl Index)|||||-0.021|||||-0.031|
||||||(0.035)|||||(0.036)|
|Constant|-0.295***|-0.169**|-0.104|-0.090|-0.117|-0.380***|-0.203*|-0.154|-0.153|-0.203|
||(0.103)|(0.082)|(0.101)|(0.095)|(0.105)|(0.130)|(0.110)|(0.128)|(0.128)|(0.143)|
|Observations|45|33|33|33|33|45|33|33|33|33|
|R-squared|0.26|0.35|0.46|0.46|0.47|0.24|0.35|0.45|0.45|0.46|
|First stage F-statistic of excluded<br>instrument p-value (H0:<br>underidentified/weakly identified)||||||0.000|0.000|0.000|0.000|0.000|
|Hansen J statistic p-value (H0:<br>overidentified)||||||0.037|0.011|0.007|0.007|0.003|
|Stock-Wright S-statistic p-value (H0: all|||||||||||
|endogenous regressors==0 and overid.<br>rest valid, weak-instrument-robust<br>inference)||||||0.027|0.011|0.011|0.008|0.008|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** sig<br>Instruments for IV regressions: log of populatio|nificant at 1%<br>n 1994 and log of la|nd area|||||||||

33
**Table 9**

# **Cross-national Growth Regressions 1994-2004, Including Type of Economy Dummies**

||(1)<br>**OLS**|(2)<br>**OLS**|(3)<br>**OLS**|(4)<br>**OLS**|(5)<br>**OLS**|(6)<br>**IV**|(7)<br>**IV**|(8)<br>**IV**|(9)<br>**IV**|(10)<br>**IV**|
|---|---|---|---|---|---|---|---|---|---|---|
|Lo of GDP c 1994|-0.016*|-0.023***|-0.026***|-0.026***|-0.026***|-0.008|-0.013|-0.024***|-0.025***|-0.023***|
|g p|(0.008)|(0.006)|(0.007)|(0.006)|(0.006)|(0.015)|(0.018)|(0.007)|(0.007)|(0.008)|
|L f EXPY 1994|-0.001|0.003|-0.010|-0.006|-0.006|-0.101|-0.127|-0.016|0.015|-0.052|
|og o|(0.022)|(0.022)|(0.024)|(0.024)|(0.025)|(0.192)|(0.251)|(0.092)|(0.082)|(0.112)|
|Log of EXPY 1994 * Dummy Low- Income|0.051**|0.035|0.045*|0.042*|0.045**|0.151|0.156|0.049|0.021|0.086|
|Economy|(0.021)|(0.021)|(0.022)|(0.022)|(0.022)|(0.183)|(0.234)|(0.086)|(0.078)|(0.105)|
|Log of EXPY 1994 * Dummy Lower-Middle-|0.061**|0.074***|0.083***|0.081***|0.082***|0.205|0.197|0.083|0.055|0.119|
|Income Economy|(0.030)|(0.026)|(0.027)|(0.028)|(0.028)|(0.208)|(0.250)|(0.094)|(0.085)|(0.114)|
|Log of EXPY 1994 * Dummy Upper-Middle-|0.038|-0.003|0.015|0.015|0.017|0.093|0.110|0.009|-0.026|0.057|
|Income Economy|(0.027)|(0.029)|(0.033)|(0.032)|(0.030)|(0.188)|(0.264)|(0.114)|(0.099)|(0.135)|
|Dumm Lo- Income Econom|-0.494**|-0.348*|-0.439**|-0.417**|-0.442**|-1.429|-1.478|-0.477|-0.220|-0.823|
|y w y|(0.193)|(0.194)|(0.206)|(0.203)|(0.199)|(1.732)|(2.205)|(0.819)|(0.739)|(0.986)|
|D LMiddlI E|-0.584**|-0.705***|-0.784***|-0.766***|-0.772***|-1.933|-1.859|-0.785|-0.516|-1.124|
|ummy ower-e-ncome conomy|(0.278)|(0.241)|(0.251)|(0.256)|(0.257)|(1.961)|(2.365)|(0.882)|(0.802)|(1.068)|
|D UMiddlI E|-0.367|0.007|-0.151|-0.156|-0.177|-0.896|-1.066|-0.098|0.234|-0.551|
|ummy pper-e-ncome conomy|(0.249)|(0.270)|(0.304)|(0.300)|(0.275)|(1.791)|(2.506)|(1.077)|(0.938)|(1.272)|
|f d hl 1||0.009***|0.007**|0.008**|0.008**||0.013|0.008*|0.008**|0.009*|
|Log o % secun. scoo att. 985||(0.003)|(0.003)|(0.003)|(0.003)||(0.009)|(0.004)|(0.004)|(0.005)|
|Rl f L Id 1996|||0.007*|0.007*|0.007**|||0.007|0.005|0.009|
|ue o aw nex|||(0.004)|(0.004)|(0.003)|||(0.005)|(0.005)|(0.007)|
|Domestic credit provided by banking sector, % of||||-0.000|-0.007||||-0.000|-0.007|
|GDP||||(0.000)|(0.027)||||(0.000)|(0.031)|
|Diversification (1 - Herfindahl Index)|||||-0.000<br>(0.000)|||||-0.000<br>(0.000)|
|Ctt|0.190|0.191|0.329|0.299|0.309|1.068|1.319|0.376|0.098|0.715|
|onsan|(0.175)|(0.184)|(0.205)|(0.207)|(0.204)|(1.727)|(2.215)|(0.853)|(0.763)|(1.023)|
|Observations|73|59|59|59|59|71|58|58|58|58|
|R-squared|0.31|0.42|0.46|0.47|0.47|0.05|0.07|0.45|0.45|0.43|
|First stage F-statistic of excluded inst. p-value<br>(H0: weakly identified)||||||0.000|0.625|0.715|0.628|0.736|
|<br>Hansen J statistic p-value (H0: overidentified)||||||0.517|0.053|0.029|0.037|0.037|
|Stock-Wright S-statistic p-value (H0: all|||||||||||
|<br>endogenous regressors==0 and overid. rest. valid,<br>weak-instrument-robust inference)||||||0.336|0.062|0.040|0.047|0.045|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** significant at 1%<br>Instruments for IV regressions: log of population 1994 and log of lan|d area||||||||||

34
# **Table 10**

# **5-year Panel Growth Regressions**

||(1)<br>**OLS**|(2)<br>**IV**|(3)<br>**FE**|(4)<br>**GMM**|(5)<br>**GMM**|(6)<br>**GMM**|(7)<br>**GMM**|(8)<br>**GMM**|
|---|---|---|---|---|---|---|---|---|
|Log of GDP pc, beg. period|-0.014***|-0.027***|-0.039***|-0.017***|-0.015**|-0.019***|-0.012|-0.007|
||(0.003)|(0.005)|(0.007)|(0.006)|(0.007)|(0.005)|(0.008)|(0.007)|
|Log of EXPY, beg. period|0.034***|0.071***|0.017|0.041***|0.046***|0.051***|0.038**|0.035**|
||(0.006)|(0.017)|(0.012)|(0.013)|(0.016)|(0.015)|(0.015)|(0.017)|
|Log of % secund. school att., beg. period|0.005***|0.001|-0.003|0.006|0.004|0.007|0.008*|0.009|
||(0.002)|(0.003)|(0.003)|(0.004)|(0.005)|(0.006)|(0.005)|(0.007)|
|Constant|-0.170***|-0.380***|0.221**|-0.206**|-0.268***|-0.291***|-0.223**|-0.247**|
||(0.034)|(0.101)|(0.091)|(0.086)|(0.099)|(0.107)|(0.095)|(0.115)|
|Observations|620|604|620|604|604|604|604|604|
|Number of group(ecode)|||79|77|77|77|77|77|
|Instruments||||91|70|52|31|28|
|R-squared|0.45|0.40|0.52||||||
|First stage F-statistic of excluded instrument p-value<br>(H0: underidentified/weakly identified)||0.000|||||||
|Hansen J statistic p-value (H0:overidentified)||0.009||0.750|0.320|0.213|0.019|0.022|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-<br>robust inference)||0.000|||||||
|AB test for AR(2) in first diff. p-value (H0: No<br>autocorrelation)||||0.066|0.179|0.785|0.063|0.192|

Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries. GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

35
**Table 11**

# **5-year Panel Growth Regressions, Restricted Sample: Non-high-income Economies**

||(1)<br>**OLS**|(2)<br>**IV**|(3)<br>**FE**|(4)<br>**GMM**|(5)<br>**GMM**|(6)<br>**GMM**|(7)<br>**GMM**|(8)<br>**GMM**|
|---|---|---|---|---|---|---|---|---|
|Log of GDP pc, beg. period|-0.015***|-0.029***|-0.043***|-0.029***|-0.016**|-0.024***|-0.031***|-0.019***|
||(0.004)|(0.006)|(0.009)|(0.005)|(0.007)|(0.006)|(0.011)|(0.007)|
|Log of EXPY, beg. period|0.034***|0.079***|0.026*|0.037***|0.033**|0.047***|0.053***|0.041**|
||(0.006)|(0.020)|(0.014)|(0.012)|(0.016)|(0.014)|(0.015)|(0.017)|
|Log of % secund. school att., beg. period|0.006***|0.002|0.001|0.016***|0.009|0.008*|0.018**|0.016|
||(0.002)|(0.003)|(0.004)|(0.004)|(0.006)|(0.005)|(0.009)|(0.012)|
|Constant|-0.168***|-0.442***|0.144|-0.108|-0.161|-0.219*|-0.238|-0.220|
||(0.043)|(0.135)|(0.105)|(0.096)|(0.103)|(0.129)|(0.142)|(0.172)|
|Observations|420|412|420|412|412|412|412|412|
|Number of group(ecode)|||53|53|53|53|53|53|
|Instruments||||91|70|52|31|28|
|R-squared|0.43|0.35|0.49||||||
|First stage F-statistic of excluded instrument p-value<br>(H0: underidentified/weakly identified)||0.000|||||||
|Hansen J statistic p-value (H0:overidentified)||0.086||1.000|0.988|0.814|0.131|0.299|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-<br>robust inference)||0.000|||||||
|AB test for AR(2) in first diff. (p-value)||||0.112|0.159|0.358|0.091|0.155|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** significant at 1%|||||||||

All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries. GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

36
# **Table 12**

# **8-year and 10-year Panel Growth Regressions**

|||**8-ye **|**arpanel**|||**10-ye **|**arpanel**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
||**OLS**|**IV**|**FE**|**GMM**|**OLS**|**IV**|**FE**|**GMM**|
|Log of GDP pc, beg. period|-0.015***|-0.028***|-0.037***|-0.017***|-0.017***|-0.028***|-0.043***|-0.016***|
||(0.003)|(0.006)|(0.006)|(0.006)|(0.003)|(0.006)|(0.006)|(0.005)|
|Log of EXPY, beg. period|0.033***|0.068***|0.017|0.048***|0.036***|0.068***|0.015|0.044***|
||(0.005)|(0.014)|(0.011)|(0.012)|(0.006)|(0.016)|(0.010)|(0.016)|
|Log of % secund. school years attained., beg. period|0.006***|0.003|-0.002|0.007|0.005***|0.002|-0.004|0.003|
||(0.002)|(0.002)|(0.003)|(0.005)|(0.002)|(0.003)|(0.004)|(0.008)|
|Constant|-0.142***|-0.338***|0.204**|-0.269***|-0.156***|-0.334***|0.288***|-0.229*|
||(0.030)|(0.083)|(0.085)|(0.082)|(0.034)|(0.096)|(0.092)|(0.115)|
|Observations|387|377|387|377|308|300|308|301|
|Number of group(ecode)|||79|77|||78|77|
|Instruments||||34||||26|
|R-squared|0.50|0.42|0.62||0.56|0.50|0.70||
|First stage F-statistic of excluded instrument p-value (H0:<br>underidentified/weakly identified)||0.000||||0.000|||
|Hansen J statistic p-value (H0:overidentified)||0.004||0.192||0.005||0.269|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-robust<br>inference)||0.000||||0.000|||
|AB test for AR(2) in first diff. (p-value)||||0.829||||0.454|

Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries. GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

37
# **Table 13**

**8-year and 10-year Panel Growth Regressions, Restricted Sample: Non-high-income Economies**

|||**8-ye **|**arpanel**|||**10-ye **|**arpanel**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
||**OLS**|**IV**|**FE**|**GMM**|**OLS**|**IV**|**FE**|**GMM**|
|Log of GDP pc, beg. period|-0.016***|-0.029***|-0.040***|-0.026***|-0.018***|-0.030***|-0.050***|-0.028***|
||(0.003)|(0.006)|(0.008)|(0.009)|(0.004)|(0.006)|(0.009)|(0.010)|
|Log of EXPY, beg. period|0.032***|0.075***|0.025**|0.050***|0.035***|0.075***|0.024*|0.043***|
||(0.006)|(0.018)|(0.012)|(0.012)|(0.006)|(0.020)|(0.013)|(0.013)|
|Log of % secund. school years attained., beg. period|0.007***|0.004|0.003|0.013***|0.006***|0.003|-0.001|0.013|
||(0.002)|(0.003)|(0.004)|(0.005)|(0.002)|(0.003)|(0.004)|(0.009)|
|Constant|-0.133***|-0.398***|0.135|-0.231**|-0.143***|-0.380***|0.228**|-0.145|
||(0.041)|(0.115)|(0.096)|(0.114)|(0.045)|(0.130)|(0.108)|(0.118)|
|Observations|262|257|262|257|208|204|208|204|
|Number of group(ecode)|||53|53|||53|52|
|Instruments||||34||||21|
|R-squared|0.48|0.34|0.59||0.54|0.46|0.69||
|First stage F-statistic of excluded instrument p-value (H0:<br>underidentified/weakly identified)||0.000|||||||
|Hansen J statistic p-value (H0:overidentified)||0.042||0.584||0.046||0.856|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-robust<br>inference)||0.000|||||||
|AB test for AR(2) in first diff. (p-value)||||0.640||||0.913|

Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1% All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries.  GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

38
# **X. Conclusions**

The indicators computed in this paper provide guidance for policies to expand cost efficient exports and foster growth. Three important groups of products are identified which are important in terms of potential government action to improve export performance and future growth. First, there are goods which can be called “high return-low cost products”, and which are close to the efficiency frontier, using HHR terminology. This group is composed of goods currently exported by Argentina that show both high product income (and therefore can yield high returns in terms of GDP growth) and low distance (which means that are positioned in a dense region of the product space and are therefore more likely to result in the development of new export products of high product income). In general, goods at the efficiency frontier include chemical products and primary products with some degree of value added, including moderately processed meat, fish and grains. Among this group wheat, fish fillets and unwrought aluminum figure prominently in total exports.

Second, there is a set of exported products that can be called high return-high cost products. This set is composed of goods showing high product income but also high distance. They can potentially foster growth given their intrinsic high income content, but they are located in a low density region of the product space, which makes it relatively less likely that new products with similar characteristics will be brought into the Argentine export basket. In other words, developing new export products with similar characteristics to the goods in this second group might prove more costly in terms of the investment required. The most important exports in this group are a variety of chemical products, several types of machinery and tools (printing machinery, auto parts, drawing machines, specialized tools) and optical goods (lenses, micro-optical).

Third, there is a set of goods than can be called low cost-low return products. This group comprises goods with low product income as well as low distance. Typical exports in this category are primary products and minerals with little or no processing. The advantage of these goods in terms of potentially fostering economic growth lies in their low distance. They belong to a region of the product space that would make it relatively easy to develop new export products with relatively similar input needs (physical and human capital, sector-specific financial requirements, institutional or regulatory requirements, infrastructure, supply chain characteristics, customer characteristics, etc.).

It is important to note that the analysis in this paper does not identify specific policies needed for a successful export performance. The value of the analysis is to identify promising export sectors and to focus on sector policies that might limit further export expansion, or possibly impede the development of new export products.

A second set of conclusions is that public policies to improve export performance as a means to foster higher growth should not be confined to industrial products alone. Industrial goods do indeed show higher product-income values, as would be expected. However, several of the goods that appear to be most efficient at the efficiency frontier are related to agriculture. Within the HHR framework for growth through exports, the key element is to identify goods showing the best tradeoff in terms of having high product income and low distance, regardless of the sector they belong to.

<mark>This analysis should be seen as the first step towards a well-focused policy framework for exports and growth. The value of this analysis is to focus efforts on setting up mechanisms to identify shortages in public goods that might constrain the development of higher income exports. A next step could be, for example, to undertake international case studies which illustrate success</mark> 39
<mark>stories. Specific policy measures can only be identified by in-depth sector-specific analysis. An initial minimum list of core ingredients would include elements such as (a) setting up channels for privatepublic sector communication and partnerships, (b) facilitating mechanisms for private sector selforganization if necessary in industries or sectors in which producers are located, (c) developing institutions within the public sector to improve the provision of public goods.</mark>

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