Chile:
Selected Issues Paper
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International Monetary Fund. Western Hemisphere Dept.
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This study takes stock of the evolution of Chile’s export composition over the past half a century, focusing on key trends in export diversification, sophistication, economic complexity, and revealed comparative advantage. Overall, it finds that Chile’s export basket remains around the median of Latin American peers for diversification and complexity, and below regional peers for export sophistication. Using the proximity of products in Chile’s current export portfolio, the study also tries to provide insights on the possible changes in future export composition, underlining that Chile has a potential to gain comparative advantage in skill-intensive and technology-intensive exports, while lowering the relative importance of commodities.

Abstract

This study takes stock of the evolution of Chile’s export composition over the past half a century, focusing on key trends in export diversification, sophistication, economic complexity, and revealed comparative advantage. Overall, it finds that Chile’s export basket remains around the median of Latin American peers for diversification and complexity, and below regional peers for export sophistication. Using the proximity of products in Chile’s current export portfolio, the study also tries to provide insights on the possible changes in future export composition, underlining that Chile has a potential to gain comparative advantage in skill-intensive and technology-intensive exports, while lowering the relative importance of commodities.

Trends in Chile’s Composition of Exports1

This study takes stock of the evolution of Chile’s export composition over the past half a century, focusing on key trends in export diversification, sophistication, economic complexity, and revealed comparative advantage. Overall, it finds that Chile’s export basket remains around the median of Latin American peers for diversification and complexity, and below regional peers for export sophistication. Using the proximity of products in Chile’s current export portfolio, the study also tries to provide insights on the possible changes in future export composition, underlining that Chile has a potential to gain comparative advantage in skill-intensive and technology-intensive exports, while lowering the relative importance of commodities.

A. Introduction

1. Countries’ economic performance depends not only on how much they trade, but also on what kind of goods they actually trade.2 Some export products contain high technological content and offer opportunities for faster productivity growth, while others require limited set of skills for their production and offer weaker productivity growth prospects. Similarly, some countries export a wide variety of products, while other countries have traditionally exhibited exports concentrated in a narrow group of limited products, either due to natural endowments, such as Chile, or due to policy decisions or historical context. In this context, analyzing the trends in the composition of exports is particularly relevant for countries that aim to diversify their export portfolios into products with potentially higher value added, and hence, better economic prospects in the future (Hausmann et al., 2014).

2. The paper takes stock of the trends in Chile’s composition of exports over the past few decades and provides some considerations about the possible future trends. Section II presents four key dimensions of Chile’s export composition: diversification/concentration, export sophistication, economic complexity, and revealed comparative advantage (RCA). Section III looks at product proximity of the Chilean current export basket and puts forward predictions about possible changes of the export basket in the future. Finally, Section IV provides some concluding remarks. The analysis presented here largely draws on the findings in Ding and Hadzi-Vaskov (2017).

B. Trends in Export Composition

3. The composition of a country’s exports can be captured in different ways. For instance, one perspective will concentrate on the level of diversification across different product categories or the differences in the network of trading partners, while others will look at product characteristics such as quality, technological content, processing stage, or final use. On the basis of Ding and Hadzi-Vaskov (2017), this section provides a brief description of the four dimensions of export composition employed, and presents the evolution of Chile’s composition of exports along these dimensions.

Diversification/Concentration

4. Diversification is a multifaceted concept that can be defined in different ways. In this analysis, product concentration (i.e. limited diversification) is measured by the Herfindahl concentration index given with the following formula:

HIjt=Σs(xsjtΣsxsjt)2

Where xsjt represents exports (in nominal terms) of product category s for country j at time t. This concentration index is calculated across 10 product categories in accordance with the SITC classification for each country in the dataset (see the Annex for product classification). Higher Hljt index values indicate less product diversification across groups of products, on the basis of the products’ economic function.

5. Chile’s improvement in goods export diversification was interrupted by the commodity boom. Figure 1 shows that Chile’s export concentration has been gradually declining from the mid-1970s to the early 2000s. However, with the commodity boom of the 2000s, this trend started to revert, partly owing to a price effect. Despite a mild recovery of the trend decline in export concentration in the 2010s, Chile’s export basket remains less diversified than in the 1990s.3

Figure 1.
Figure 1.

Export Concentration

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: UN COMTRADE and Fund staff calculations.

6. Overall, the diversification of Chile’s goods export portfolio is in line with several Latin American peers. Figures from the 2010s suggest that Chile’s export concentration in goods is lower than in large energy-commodity exporters such as Colombia, Mexico, and Venezuela, but higher than in Argentina, Brazil, and Peru.

Export Sophistication

7. Sophistication of a product aims to capture the potential income level a product may be associated with, based on the income levels of countries that export that product. For instance, if a country starts exporting (a relatively low-end version of) a new product that is exported by relatively rich countries, this may be an indication that over time this country can increase the prices charged as well as its income. In measuring product sophistication, the analysis follows Hausmann, Hwang, and Rodrik (2007) by computing the productivity level PRODYit associated with product i at time t as the weighted average of per capita GDP levels of countries exporting that product, with the weights corresponding to the revealed comparative advantage of each country in that product4:

PRODYit=Σj[(xijtΣixijt)Σj(xijtΣixijt)GDPpcjt]

Similarly, the sophistication level that is associated with the export portfolio of country j at time t(EXPYjt) is calculated as the weighted average of productivity levels (PRODYit) for all products this country exports, with the weights corresponding to the shares of these products in total exports of country j:

EXPYjt=Σi(xijtΣixijt)PRODYit

While these measures of product and export portfolio sophistication have been widely used in the literature, they necessarily imply an increasing trend of sophistication over time given that GDP per capita for most countries persistently follows an upward trend. Therefore, to account for this time trend, the analysis here presents a “de-trended” product productivity PRODYits by replacing GDP per capita for country; in the original formula with GDP per capita for country; relative to the corresponding value for the world:

PRODYits=Σj[(xijtΣixijt)Σj(xijtΣixijt)GDPpcjtGDPpcjiworld]

and recalculate a standardized measure for sophistication for country j′s export basket accordingly:

EXPYjts=Σi(xijtΣixijt)PRODYits

8. As in many other commodity producers, export sophistication in Chile is low. Chile’s export sophistication is among the lower half in regional peers, suggesting that its export basket may be associated with a relatively low potential income (on the basis of the income level of countries that export similar products). This is largely because its main products, such as copper, are exported by countries with relatively low-income levels. Nonetheless, the level of sophistication of Chilean exports has been on an upward trend over the past few decades, as in several other Latin American countries (Figure 2).

Figure 2.
Figure 2.

Export Sophistication

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source:UN COMTRADE and Fund staff calculations.

Economic Complexity

9. Economic complexity is conceptually related to the amount of productive knowledge that is embedded in a country’s products (Hausmann et al., 2014). Some products require a lot of capabilities and knowledge and can be produced only by a limited group of countries. In this respect, countries that have more capabilities and knowledge are typically able to produce a more diversified set of goods. Moreover, these countries are also able to produce goods that only a selected group of a few other countries can produce as well. In turn, a country’s ability to produce both many goods and a very distinguished set of goods suggests that the country is likely to possess complex knowledge and skills that only a few other countries, if any, enjoy.

10. Hence, higher complexity is linked to lower ubiquity (products that demand large volumes of knowledge that are feasible in a few locations only) and higher diversity (more knowledge can produce a more diverse set of products). A calculation of economic complexity, therefore, needs to correct the information that ubiquity and diversity convey by using each one to correct for the other (Hausmann et al. 2014). There are two measures of complexity used in this study. First, product complexity is calculated by the average diversity of countries that produce/export that product, corrected for the average ubiquity of the products in these countries’ portfolio and so forth. Second, economic complexity of a country is measured by the average ubiquity of products that the country produces (exports), corrected for the average diversity of the products that make those products, etc. These recursive processes of average ubiquity and average diversity provide measures for the Product Complexity Index (PCI) and countries’ Economic Complexity Index (ECI).5

11. Economic complexity in Chile remains close to the average of Latin American peers. 6 Notwithstanding some movements over time, the complexity of Chile’s exports remains close to Brazil’s, Colombia’s, and Uruguay’s, higher than Peru’s, and lower than Mexico’s (the latter’s score primarily reflecting its strong integration into global value chains with North American partners). The large share of products belonging to the quintile with the lowest level of complexity, such as copper, explains a large part of the relatively low overall level of complexity of Chile’s export basket (Figure 4).

Figure 3.
Figure 3.

Economic Complexity

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: Observatory of Economic Complexity.
(Share of exports in each quintile of product complexity)
Figure 4.

Chile’s Product Complexity

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Note: 1st quintile contains least complex and 5th quintile most complex products.Source: Observatory of Economic Complexity and IMF staff

Revealed Comparative Advantage

12. Revealed comparative advantage (RCA) shows the relative advantage or disadvantage that a country has in exporting a certain good or group of products. It is measured according to the RCA index introduced by Balassa (1965) that compares the share of a certain good in a country’s total exports with the share of that product’s world exports in total world exports of all goods. In this way, an RCA larger than one indicates that a country exports more than its “fair” share of a certain product, and therefore, enjoys revealed comparative advantage in that product. Similarly, an RCA below unity means that a country exports less than its “fair” share in world trade, and therefore, has a revealed comparative disadvantage for that certain product.

The RCA index for country j in good i is calculated with the following formula:

RCAij=xijΣixijΣjxijΣiΣjxij

Where xij stands for gross exports of product i for country j, so the numerator xij/Σixij refers to the share of product i in the overall exports of country j, and the denominator Σjxij/ΣiΣjxij captures the global share of product i exports in total world exports. Hence, if exports of product i for country j represents 10 percent of its total exports, while global exports of product i represent only 5 percent of total world exports, then country j is said to enjoy RCA of 2 in product i.

13. Chile has a very strong revealed comparative advantage in non-fuel primary commodities, but not in other categories of goods exports. The share of metallic commodities in Chile’s exports is about 5 times larger than the share of these commodities in the overall world exports of goods. However, Chile does not show comparative advantage in any other major category, such as fuels, resource-intensive manufactures or skill- and technology-intensive manufactures, despite some incipient signs of improvement in the last two categories in recent years.

Figure 5.
Figure 5.

Chile’s Revealed Comparative Advantage

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: UN COMTRADE and IMF staff calculations.

C. Product Proximity and Predicting Future Trends in Export Composition

14. Does Chile’s current portfolio of export products reveal some information about the possible future changes in its patterns of trade? Product proximity as a concept aims to capture the intuition that it is easier for countries to move into industries/products that mostly reuse what countries already know, or into industries that require adding little new productive knowledge.7 Hence, the capability of a country to produce a certain product is expected to depend on how similar or close that product is to the country’s current production set, because similar products are expected to share more characteristics used in production. In turn, such products are more likely to be co-produced, and therefore co-exported, and can provide insights about the products that may be easier for a country to add to its export portfolio in the future.

Product Proximity

15. Product proximity between two products can be formally measured as the conditional probability of exporting one good given that the other good is also exported. Moreover, the literature typically refers to the co-exporting of goods with a revealed comparative advantage above one, given that this is a stronger concept that better captures the ability to export a certain product. Hence, the product proximity between two goods A and B can be defined as:

Proximity(A,B)=min{P(RCAAt1|RCABt1),P(RCABt1|RCAAt1)}

where, given that conditional probabilities are not symmetric, taking the minimum of the two probabilities reflects a more conservative stance to calculating proximity.8

16. The calculation of product proximity can be illustrated with a concrete example. The proximity between grapes and wine can be deduced on the basis of the set of countries that export them. For instance, if 16 countries export wine with RCA ≥ 1, 24 countries export grapes with RCA ≥ 1, and 8 countries export both with RCA ≥ 1, then the proximity between wine and grapes will be calculated as:9

Proximity(wine,grapes)=P(winegrapes)max[P(wine),P(grapes)]=824=0.33

Hereby, using the classification of product groups according to the degree of skill- and technology-intensity (Box 1), product proximities are calculated for the period 1962–2013.

Predicting Composition of Trade

17. The current export portfolio and the proximity between its components can provide some guidance about the likely future changes in the composition of trade.10 Following the methodology in Ding and Hadzi-Vaskov (2017), such predictions are formed on the basis of two elements that are known at present11 first, the distribution of RCAs for the different product groups; and second, all pairwise proximities between these product groups calculated from historical data.12 Using these two pieces of information, the groups of products in which Chile is more likely to gain RCA as follows:

E(RCAitg)=Σh=17RCAithProximity(g,h)

where g and h are product groups, and E(RCAitg) is the expected future RCA value for country i in product group g.

18. Product proximity correctly predicted the direction of change for most categories of Chilean goods exports over the last quarter century. Figure 6 shows that the product proximity among different export categories over the period 1962–89 correctly predicted the direction of the changes in the relative importance of these export categories (measured by the RCA) over the period 1990–2013. As predicted, Chile increased its RCA in most categories and lowered its RCA in non-fuel primary commodities (category that includes copper). Nevertheless, one category where these predictions underperformed somewhat is high skill-intensive and technology-intensive manufactures: Chile’s RCA barely changed, while product proximity suggested an increase.

Figure 6.
Figure 6.

Prediction of Past Export Trends Based on Product Proximity

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: UN COMTRADE and IMF staff caluclations.Note: Units of measurement are “standardized” RCA that sum up to 1 across the seven product categories.

19. Going forward, proximity among product categories in Chile’s current export basket suggests that the relative importance of skill-intensive products is likely to increase and the importance of commodities to decrease. The same setup that helped correctly predict the direction of change in RCA of different export groups over 1990–2013 now implies that Chile is likely to continue advancing its relative share in all product categories with the exception of non-fuel (metallic) commodities (Figure 7). While the approach presented here reveals a “potential” to move into new export groups, based solely on proximities of the current export portfolio, the future outcome is likely to reflect a complex set of factors that facilitate or inhibit the transition towards new exports. In this context, the Article IV Staff Report and Hadzi-Vaskov (2018) discuss growth-enhancing structural reforms that could also favor diversification into new products.

Figure 7.
Figure 7.

Prediction of Future Export Trends

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: UN COMTRADE and IMF staff caluclations.Note: Units of measurement are “standardized” RCA that sum up to 1 across the seven product categories.

D. Determinants of Export Composition

20. Policy decisions can help shape the prospects for diversification into new products and determine trends in export composition. Table 1 presents results about the role different factors play in shaping export composition. The results suggest that policies that facilitate investment in infrastructure and improve infrastructure quality are likely to result in more complex and sophisticated exports, and raise the relative importance of high-skill and technology-intensive manufactures in overall exports. Similarly, better education quality is likely to support diversification efforts into such high-skill and technology-intensive exports that are potentially associated with higher value added. On the other hand, more protective trade policies, measured by average trade tariffs, can negatively affect economic complexity, sophistication, and diversification.

Table 1.

Factors that Affect Composition of Exports

article image
pval in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Ding and Hadzi-Vaskov (2017). Note: Estimation results from instrumental variables (IV) panel regressions that include time fixed effects. Infrastructure, tariffs, education, and Gini index are instrumented by their first two lags. Infrastructure is measured by the density of the railway network from the WDI, tariffs refer to average applied tariffs retrieved from the WITS database, education refers to secondary school enrol Iment rate and to share of population with tertiary education in regressions for RCAand share of high-skill products, and income inequality is measured by the net Gini index from the SWIID.

E. Concluding Remarks

21. Overall, the diversification, sophistication, and complexity of Chile’s export basket have remained below or around the median of Latin American peers. Following some important improvements in the earlier period, Chile’s trend of increasing export diversification was interrupted during the commodity boom in the 2000s (to the extent that such change is driven by a price effect, it would be less of a reflection of a structural change). Given the dominance of copper exports, Chile continues to enjoy a very strong revealed comparative advantage in non-fuel commodity exports. The composition of the current export portfolio suggests that Chile has the potential to gain comparative advantage in skill-intensive and technology-intensive exports, while lowering the relative importance of commodities. Nonetheless, the future outcome is likely to reflect a complex set of factors, including policy measures, that facilitate or inhibit the transition towards new exports. In particular, policies that support infrastructure investment, education quality, and trade integration are likely to support export diversification, and result in a higher share of skill and technology-intensive exports with potentially higher value-added. Growth-enhancing structural reforms, such as those discussed in the 2018 AIV Staff Report and Hadzi-Vaskov (2018), could also support changes in Chile’s export composition.

Annex I. Dataset and Data Description

Data used in this analysis comes from several sources. The core part of the dataset employed in the calculation of dimensions of trade composition consists of series on gross exports of goods that come from the United Nations Commodity Trade Statistics (COMTRADE) database at annual frequency over the period 1962–2013. Data on economic complexity comes from the Observatory of Economic Complexity (atlas.media.mit.edu). The analysis makes use of two data series: the product complexity index (PCI) and the country-level economic complexity index (ECI).

The analysis of trade composition follows two product classifications. First, it uses the skill- and technology-intensity product classification from UNCTAD for most of our analysis (Box 1). Besides distinguishing between technology-intensive and other products, it also allows differentiation within technology-intensive products according to the level of technology required for their production. When looking at export product diversification (concentration), the analysis classifies products on the basis of their economic function and processing stage according to the main sections of the SITC classification (Box 2).

Skill- and Technology-Intensity Product Classification from UNCTAD

This classification distinguished products according to their level of skill- and technology-intensity. It has been developed by Basu and Das (2011) and Basu (forthcoming) on the basis of UNCTAD (1996, 2002) and Lall (2000). According to this classification, the products are organized into the following seven categories:

  • High skill- and technology-intensive manufactures

  • Medium skill- and technology-intensive manufactures

  • Low skill- and technology-intensive manufactures

  • Resource-intensive manufactures

  • Non-fuel primary commodities

  • Mineral fuels

  • Unclassified products

Standard International Trade Classification (SITC)

Developed by the United Nations with the purpose to classify trade products not only on the basis of their material/physical properties, but also according to their economic function and the processing stage, the SITC into the following ten broad sections:

  • Food and live animals

  • Beverages and tobacco

  • Crude materials, inedible, except fuels

  • Mineral fuels, lubricants and related materials

  • Animal and vegetable oils, fats, and waxes

  • Chemicals and related products

  • Manufactured goods (classified by material)

  • Machinery and transport equipment

  • Miscellaneous manufactured articles

  • Commodities not classified elsewhere

Annex II. Trends in Services Exports

(In percent of GDP)

Composition of exports

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: Central Bank of Chile and IMF staff calculations.Note: Non-commodities include agriculture, forestry, fishing, and all industry.
(In percent of GDP)

Composition of services exports

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: Central Bank of Chile and IMF staff calculations.
(Share of total exports)

Services exports

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A004

Source: Central Bank of Chile and IMF staff calculations.

References

  • Balassa, B., 1965, “Trade Liberalization and “Revealed” Comparative Advantage”, The Manchester School, Vol. 33, pp. 99123.

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1

Prepared by Metodij Hadzi-Vaskov.

3

Due to data constraints, the analysis only considers goods and not services. Nonetheless, despite the substantial diversification of the Chilean economy into services in recent years, preliminary analysis suggests that services export diversification is not likely to lead to different conclusions as the share of services in total exports/GDP has been declining (see Annex II).

4

Hausmann, Hwang, and Rodrik (2007) argue that the rationale for using revealed comparative advantage as a weight in the formula is to ensure that country size does not distort the ranking of products. The use of RCA allows higher weights for those countries that export more than their fair share in certain product.

5

For a formal derivation of Product Complexity Index (PCI) and the Economic Complexity Index (ECI), see Hausmann et al. (2014) and the Observatory of Economic Complexity resources.

6

See Hausmann et al. (2014) for detailed exposition of the concepts of economic complexity and product complexity.

7

For a detailed discussion about product proximity and its measurement see Hausmann et al. (2014).

8

Taking the minimum between the two probabilities in this asymmetric case is particularly relevant for minimizing the likelihood of a false relationship when one of the countries is the sole exporter of a certain good with RCA>1.

9

Note that the proximity is calculated as 8/24=0.33, not as 8/16=0.5 in line with the conservative stance to minimize the chances of false relationship.

10

See Ding and Hadzi-Vaskov (2017) for more details on the methodology for predicting future patterns of trade based on the product proximities in the current export portfolio.

11

The present moment here refers to 2013, the latest year with historical data.

12

Proximities between product groups are calculated from data series for the period 1962–2013. The results are quite robust to alternative sub-periods used in the calculation of product proximities.

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Chile: Selected Issues Paper
Author:
International Monetary Fund. Western Hemisphere Dept.