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Effects of Export Product Diversification on Quality Upgrading:

An Empirical Study

Muhlis Can, Ph.D.

Hakkari University, Department of Economics and Finance

Hakkari, Turkey

E–mail: [email protected]

Giray Gozgor, Ph.D.

Istanbul Medeniyet University, Faculty of Political Sciences

Istanbul, Turkey

E–mail: [email protected] &[email protected]

Abstract

This paper empirically examines the effects of export product diversification on overall export

quality in a panel data set of 115 countries from 1970 to 2010. It uses the data sets of the overall

export quality and three export diversification measures of the International Monetary Fund:

the extensive margin (variation in number of new products exported), the intensive margin

(variation in export values among existing exports), and the overall (Theil) index. It finds that

export quality has only been increasing with a higher variation in export values among existing

exports in low- and lower-middle-income countries. It also observes that export quality has

been increasing with both a higher variation in export values among existing exports and new

products exported in upper-middle- and high-income countries. The results are robust to the

changing measures of controls in the benchmark model, the inclusion of many other controls

(various measures of globalization, country size, factor endowments, macroeconomic stance,

etc.), and the exclusion of outliers.

Key Words: Export Performance; Quality Upgrading; Product Diversification; Export Prices;

Export Volume

JEL Classification Codes: F14; O11; L15; L25

Acknowledgements

We would like to thank Chris Papageorgiou for his valuable comments and suggestions on the

paper.

mailto:[email protected]

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1. Introduction

It is well-known that a significant number of studies have empirically tested the effects of the

export volume on economic growth. However, the literature has recently shifted the focal point

to the quality of exports rather than volume, but a limited number of studies focus on the

importance of upgrading export quality,1 rather than the volume of exports, for economic

growth. Scholars have recently revealed that upgrading the quality of goods has positively

influenced key indicators of countries’ economic performance, boosting gross domestic product

(GDP) growth, employment, trade volume, and wages (Crino and Ogliari 2015). Indeed, export

quality upgrading yields not only an increase in export revenues, but also foster economic

growth. (International Monetary Fund, 2014a).

As a matter of fact, measuring export quality is quite difficult, and first-generation

studies simply define export quality as the unit price of export products (e.g., Hallak, 2006;

Hummels and Klenow, 2005; Schott, 2004).2 Second-generation studies evaluate export quality

in the context of adding trade costs to the demand-or-supply model (determining unit price)

within microeconomic foundations (e.g., Feenstra and Romalis, 2014; Hallak and Schott, 2011;

Khandelwal, 2010; Martin and Mejean, 2014). Although these studies have novel contributions

to the literature, their data sets are unfortunately limited in terms of number of countries and

time coverage. The recent studies conducted by Henn et al. (Henceforth HPS) (2013 and 2015)

differentiate from the previous ones, and their time and country coverages are more

comprehensive than those other seminal papers. HPS (2015) calculate the quality of the

products exported by different countries using a gravity model based on three factors: the

unobservable quality of the product, income level of the exporting country, and distance

between importing and exporting countries. The purpose of our paper is to further investigate

the determinants of the recent export quality data set by HPS (2013 and 2015) and specifically

examine the effects of the diversification of the export basket on upgrading quality.

It is also essential to explain that our measure for diversification of export products is

composed of the intensive margin (within a sector) and the extensive margin (across sectors).

The sum of the intensive and extensive margins is expressed as overall export diversification.

1 In the paper, export quality upgrading is defined as the increase in export product quality. Nevertheless, first-

generation studies synonymously use the concepts of sophistication and quality upgrading. However, according to

HPS (2015), while rich countries also tend to produce higher-quality varieties, the concepts of quality and

sophistication are quite different. Quality refers to the relative price of a country’s varieties within their respective

product lines. Product sophistication, as in Hausmann et al. (2007), is the composition of the aggregate export

basket (PRODY). Referring to HPS (2015), we use export quality upgrading in the context of export product

quality growth, while sophistication is the shift toward more value-added and complex products. 2 Export product quality is also evaluated in the context of unit price in some studies in the 2010s. (e.g., Amighini

ve Sanfilippo, 2014; Shi, 2011; Xu, 2010).

2

According to Cadot et al. (2011: 590), the intensive margin reflects variation in export values

among existing exports, whereas the extensive margin reflects variation in the number of new

products exported or in the number of new markets for existing exports". On the other hand,

Hummels and Klenow (2005) define the intensive margin as exporting the larger quantities of

each good and the extensive margin as a wider set of goods. Considering the close relationship

between a country’s product diversification and its knowledge, capabilities, infrastructure, and

human capital, it should be expected that the contributions of intensive and extensive margins

to export quality upgrading can depend on the level of development. The existence of the

aforementioned factors could help countries in their efforts to utilize from both intensive and

extensive margins with regard to the export quality. However, it is more likely that the intensive

margin significantly contributes to export quality in poor countries because manufactured

products readily more accessible as product quality increases. The impact of the extensive

margin (new products) on export quality also depends on the capabilities of the country. If such

capabilities are sufficient (as in rich economies), the new products are much easier to emerge.

Otherwise, if the country has limited know-how, the emergence of new products is then more

difficult (Hausmann and Hidalgo 2011), and this means that export quality upgrades will also

remain at a limited level.

To examine whether export basket diversification is a catalyst for quality upgrading, we

focus on 115 countries over the period 1970–2010. The main variable of interest in our paper

is the export quality index, which is recently proposed by the IMF (HPS 2015). Indeed, there is

only one paper in the literature that examines the determinants of the new export quality index,

and that is the original paper that introduced a data set for the quality of exports (HPS 2015).

This emerges from the fact that previous papers face a key challenge: the quality of exports

cannot be directly observed and needs to be estimated. In addition, the methodologies of

previous papers do not allow for calculation of a set of export quality estimates with large

country and time coverage, because of data limitations for unit export prices and trade costs

(HPS 2013 and 2015). However, HPS (2015) propose the directly observed data for the quality

of exports in 178 countries and employ 10-year averaged estimations. They find that income

level, human capital, institutional quality, foreign direct investment (FDI) inflows, and trade

openness (liberalization) enhance the quality of the export basket. The benchmark models in

our paper are somehow close to the empirical models in HPS (2015), and they test the

hypothesis of whether export product diversification has a significant impact on export quality.

For this purpose, we separately control for three measures of export diversification (the overall

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Theil index, extensive margin, and intensive margin). In other words, the main hypothesis of

our paper is that at least one diversification measure boosts the quality of exports.

The main contribution of the paper is as follows. To the best of our knowledge, our

paper will be the first to examine the effects of the diversification of the export basket on quality

upgrading for countries at different stages of development. Our paper also improves the findings

of HPS (2015) on determinants of the quality of the export basket by providing various

robustness exercises. The paper finds that export quality has only been increasing with a higher

volume of existing exports (intensive margin) in low- and lower-middle-income countries. It

also observes that export quality has been increasing with a higher volume of both existing

exports (intensive margin) and new products exported (extensive margin) in upper-middle- and

high-income countries. The results are robust to the changing measures of controls in the

benchmark model, the inclusion of many other controls (various measures of globalization,

country size, factor endowments, macroeconomic stance, etc.), and the exclusion of outliers.

The paper also provides the implications of export performance for countries at different stages

of economic development.

The rest of the paper is organized as follows. Section 2 discusses the motivation behind

the research questions and reviews the previous literature. Section 3 explains the data and the

econometric methodology and sets the empirical model. Section 4 reports and discusses the

empirical results. Section 5 provides various robustness checks on the benchmark findings.

Section 6 concludes.

2. Motivation and Literature Review

2.1. Why Export Product Diversification Matters for Quality Upgrading

Export quality upgrading can be defined as the increase in the quality of existing export

products through structural transformation (HPS 2013). Quality upgrading is particularly rapid

during the early stages of development, with the process largely completed as a country reaches

the upper-middle-income status (real per capita GDP is $10,000). It should be expected that

export quality increases with income, particularly sharply during the early stages of

development (HPS 2015). However, the influence of export product quality on economic

growth is also closely related to the level of human capital, the quality of physical investment,

and knowledge and skills of workers (IMF, 2014a). For instance, electronic elements and

devices3 require more capital and qualified (engineering) knowledge and skills, when compared

3 These products are regarded as sophisticated or complex products in literature. Sophisticated (complex) products

meant to use knowledge- and skills-intense products (Hausmann et al. 2011). In other words, sophisticated does

4

in terms of natural resources (Lederman and Maloney 2012:70). From this point of view, many

developing countries try hard to increase the quality of their export basket.

Improving export basket quality is also closely associated with a country’s

diversification of its export basket, and product discovery is required in the first place.

Discovery means here is the start of production of manufacturing products, which have not been

produced beforehand. This stage does not depend on the country’s income level. Therefore, no

matter what income level they are, countries initially discover a product and begin exporting it.

In other words, discovery is closely related to the purpose of exporting “new” product (Klinger

and Lederman 2004). Moreover, innovations in existing products or imitations of products

manufactured in other countries can also be defined as discovery (Klinger and Lederman 2006).

However, discovery takes place predominantly in nontraditional and dynamic industries

(Klinger and Lederman 2004). In the second stage (diversification), the product is added to the

export basket. At this point, it is important for countries to diversify their product baskets as

much as possible. The low levels of diversification lead to low technological spillovers.

Additionally, the limited diversification also limits the improvement of the quality of export

products (Papageorgiou and Spatafora 2012). The third stage is export quality upgrading. At

this stage, it is necessary for countries to add new products to their export baskets when they

reach the world frontier in the export quality of existing products (HPS 2013). In other words,

diversification and export quality upgrading processes complement each other (IMF 2014a).

Increasing export basket diversification is necessary when exported goods reach the world

frontier. Therefore, forming the export basket out of different products will reveal the

possibility of an increase in the quality of new products. Sutton and Trefler (2016) emphasize

that as low-income countries produce low-quality and low-value-added (primary) products,

their export quality does not significantly contribute to economic growth, and the most

important reason for this is that export product diversification in these countries has not reached

the desired level. In short, the success of export quality upgrading is strongly tied to the

diversification process. A summary of the process for these stages is also illustrated in Figure

1.

[Insert Figure 1 around here]

2.2. Previous Literature on Determinants of Quality

not mean specialization. Therefore, adding sophisticated products to the export basket can be regarded as an

increase of the quality of export products in the basket. For economic complexity, see Hausmann et al. (2011), and

Simoes and Hidalgo (2011).

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Poor countries are at the low level of physical and human capital and also lack the incentives

for rapid accumulation of production factors, therefore perpetuating their low income levels.

Low growth rates of production factor accumulation are mainly explained by a lack of important

economic and political institutions such as a solid law system assuring property rights,

mechanisms for the efficient enforcement of contracts, and so on (Lederman and Maloney,

2012: 70). For these reasons, several scholars have underlined the different determinants of

export quality. For instance, Hallak and Schott (2011) emphasize the importance of the external

trade balance to identify export quality. Schott (2004) points out the roles of the capital-labor

ratio and skill per worker to determine the export quality. According to Sutton and Trefler

(2016), export quality is closely related to the capabilities of a country. In their recent paper,

HPS (2015) identified the determinants of export quality upgrading as the per capita GDP,

institutional quality, human capital level, trade openness (liberalization), and FDI inflows.

Additionally, Hausmann et al. (2007) point out the significance of country size and the terms-

of-trade shocks in the increase of knowledge- and skills-intense export products

(sophistication). Within this context, our paper controls for the effects of all these variables on

the new export quality data set of HPS (2013 and 2015), and the previous findings are improved

by using export diversification measures.

The effects of export diversification mainly focus on economic growth. Scholars have

empirically studied the effects of export product diversification on economic growth in different

countries/country group samples (e.g., Aditya and Acharyya, 2013; Al-Marhubi, 2000; De

Pineres and Ferrantino, 1997; Funke and Ruhwedel, 2005; Gozgor and Can, 2016; Herzer and

Nowak-Lehmann, 2006; Hesse, 2008; Mau, 2015). Most of these papers find that export

diversification positively affects economic growth. The empirical studies on export quality also

observe that upgrading the quality of the export basket enhances economic growth. (e.g., Anand

et al., 2015; Papageorgiou et al., 2015; Poncet and de Waldemar, 2013).

There are also different types of studies conducted on the various determinants of export

quality upgrading. 4 Scholars have tried to obtain more comprehensive evidence on the

determinants of export quality upgrading in different countries/country group samples. Through

different variables, Amighini and Sanfilippo (2014), Bas and Strauss-Kahn (2015), Chen and

Juvenal (2016), Crino and Ogliari (2015), Fan et al. (2015), Faruq (2011), Gnangnon and

Roberts (2015), Harding and Javorcik (2012), Oladi et al. (2008), Wang and Wei (2010), and

4 Effects of export sophistication (as PRODY in Hausmann et al., 2007) are explained in Lederman and Maloney

(2012), Schott (2008), and Sutton and Trefler (2016). See for instance, Anand et al. (2012), Hausmann et al. (2007),

and Mishra et al. (2011) for the positive effects of export sophistication on economic growth.

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Zhu and Fu (2013) have identified the determinants of export quality upgrading. However,

despite providing important contributions to the literature, these studies have limitations on

both the number of countries and time coverage. In addition, these studies have totally neglected

the role of export product diversification, which can be considered as the requirement to achieve

export quality upgrades.5 In addition to the explanatory variables used in the empirical models

of HPS (2015), our paper uses additional controls and aims to contribute to the empirical

findings of previous papers.

3. Empirical Model, Data, and Econometric Methodology

Following HPS (2015), we consider the following empirical model to analyze the effects of

export product diversification on the growth of overall export quality:

0 1 2 3_ ln _ _

it it it it i t itGR QUA INT QUA INT EXPDIV CONTROL (1)

where _it

GR QUA is the annualized growth rate of (overall) export quality in country i at time

t, and it is calculated as the difference between the logarithms of quality levels in the initial and

the final years of the five-year non-overlapping period. 6 ln _it

INT QUA is the logarithm of the

initial product quality level in country i at time t, _it

INT EXPDIV is the initial level of product

diversification (the Theil index, intensive margin, and extensive margin) in country i at time t,

and it

CONTROL is the control variables in the benchmark model in country i at time t (initial

per capita GDP, initial human capital level, and initial institutional quality). All the explanatory

variables are defined as initial conditions and are observed in the first year of any five-year non-

overlapping period. The benchmark specifications also include the “fixed effects for time” (t

), and the “fixed effects for country” (i

), and the “error term” is represented as,i t . See Table

1 for the sources of the data and a summary of the descriptive statistics.

[Insert Table 1 around here]

The paper uses three measures for export diversification, which is the product

diversification indexes of the export basket: the extensive margin (variation in the number of

new products exported), the intensive margin (variation in export values among existing

5 HPS (2013, 2015) have not used the export product diversification model in their empirical models as well. They

just mention the significance of the export product diversification process of export quality upgrading. 6 The panel data set covers the period from 1970 to 2010. The 5-year non-overlapping periods are 1970–1975,

1976–1980, 1981–1985, 1986–1990, 1991–1995, 1996–2000, 2001–2005, and 2006–2010.

7

exports), and the Theil (overall) index (sum of intensive margin and extensive margin).7 The

relevant data are obtained from the IMF (2014b). It is important to note that higher values of

the diversification indexes indicate a lower product diversification of exports (Papageorgiou

and Spatafora 2012), and therefore, the diversification of export indexes is expected to be

negatively associated with the growth rate of the export quality indexes.

Following HPS (2015), three control variables are used in the benchmark model

estimations: i) logarithm of per capita GDP (Purchasing Power Parities (PPPs), constant

2005US$ prices) as a benchmark indicator of the income level, and the data are obtained from

the Penn World Table (PWT) version 8.1 of Feenstra et al. (2015), ii) total lower secondary

completion rate (percent of relevant age group) as a benchmark indicator of the human capital

level, and the data are obtained from the World Development Indicators (WDI) of the World

Bank, iii) "executive constraints concept" (index from 1 to 7) as a benchmark indicator of the

institutional quality, and the data are obtained from the Polity IV annual time series data set. It

is also expected that the income level, human capital level, and institutional quality will be

positively related to the growth rate of the export quality. The expected signs are also observed

in the correlation matrix for the variables used in the benchmark regressions (see Table 2).


The panel data set covers the period from 1970 to 2010 in 115 countries. The empirical

analysis also considers two subgroups of countries in terms of income levels, i.e., low and

lower-middle income (54 countries) as well as the upper-middle- and high-income countries

(61 countries). The income definition of countries is based on the classifications of the “country

and lending groups” data set by the World Bank. The details of countries in the data set are also

provided in Appendix I.

We run the feasible generalized least squares (FGLS) regressions, and this approach is

standard in the literature (e.g., Meinhard and Portrafke 2012). We also consider the

heteroskedastic and autocorrelation (HAC)–consistent Newey-West-type standard errors and

control for the country (cross section) fixed effects and the year (time) fixed effects in the

regressions.

7 According to the International Monetary Fund (IMF) data set, explanation of the export diversification toolkit

set as follows: "… the overall, intensive (within), and extensive (between) Theil indices following the definitions

and methods used in Cadot et al. (2011). We first create dummy variables to define each product as "traditional",

"new", or "non-traded". Traditional products are goods that were exported at the beginning of the sample, and

non-traded goods have zero exports for the entire sample. Thus, for each country and product, the dummy values

for traditional and non-traded remain constant across all years of our sample. For each country/year/product

group, products classified as "new" must have been non-traded in at least the two previous years and then exported

in the two following years. Thus, the dummy values for new products may change over time." (IMF, 2014b).

8

4. Empirical Results

4.1. Results from Benchmark Regressions

Table 3 provides the results of the benchmark regressions with the growth rate of the overall

export quality index as the dependent variable. The result of the product diversification of

exports (Theil index) is reported in Column 1, while the results for intensive margin and

extensive margin are in Columns 2 and 3, respectively.


All measures of export diversification lead to a higher growth rate of export quality, but

the coefficient of extensive margin is not statistically significant. We also observe that an

increase of one standard deviation in the export product diversification (Theil index) implies a

0.71-point decrease (1.92 standard deviation) in the logarithm growth rate of the overall export

quality index. The effects of extensive margin and intensive margin are also in a similar order

of magnitude.

Among the controls, the per capita income, the human capital level, and institutional

quality are positively related to export quality in the benchmark regressions. The results on the

control variables are consistent with those of HPS (2015). Moreover, the growth rate of product

quality depends negatively on the initial quality level. This result implies that “new, low-quality

entrants” into a sector experience their “quality rise” over time, relative to other economies, and

therefore, there is a significant unconditional convergence toward the world quality frontier

(HPS 2015), which is also consistent with the results of HPS (2015). Nevertheless, the novel

finding of our paper is that export product diversification enhances the growth rate of export

quality in 115 countries.

4.2. Results from Countries at Different Levels of Economic Development

At this stage, we aim to see whether our findings differ according to the countries’ level of

development. For this purpose, following previous empirical papers (e.g., Bergh and Nilsson,

2010; Gozgor and Ranjan, 2015), we divide our sample into two groups: (i) the low- and lower-

middle-income economies and (ii) the upper-middle- and high-income economies. We also

consider the World Bank classification of the country and lending groups according to which

the sample of the low and lower-middle economies includes countries with per capita income

smaller than $4,125 in 2015 (54 countries), while the upper-middle- and high-income

economies are those with per capita income above this level (61 countries). Table 4 reports

results for the low- and lower-middle-income countries, and Table 5 reports results for the

upper-middle- and high-income countries.


9


We observe that the negative relationship between the Theil index for export

diversification and the growth rate of export quality is primarily driven by the upper-middle-

and high-income countries. Even though the Theil index of export diversification is negatively

related to export quality in the low- and lower-middle-income countries, its coefficient is not

found as statistically significant. We also find that export quality has only been increasing with

a higher intensive margin (variation in export values among existing exports) in the low- and

lower-middle-income countries, while it has been increasing with both a higher intensive

margin and extensive margin (new products exported) in the upper-middle- and high-income

countries. We also provide various robustness exercises for our findings below.

5. Robustness Checks

5.1. Robustness to the Changing Measures of Controls

Table 6 provides the results of the robustness of the changing measures of controls with the

growth rate of the overall export quality index as the dependent variable. The results for all

countries are reported in Column 1, while the results for the low- and lower-middle-income and

the upper-middle- and high-income countries are in Columns 2 and 3, respectively. All the

variables in the robustness checks are again defined as initial conditions and are observed in the

first year of any five-year non-overlapping period. The regressions in Table 6 also include the

log initial export quality, the country fixed effects, and the year fixed effects.


First, when per capita GDP squared is added, the nonlinear effect of real income on the

growth rate of export quality is also included in the benchmark model.

Second, the benchmark indicator of human capital level (total lower secondary

completion rate) is replaced with (i) index of the human capital per person in the PWT 8.1,

which is based on the data set of Barro and Lee (2013), and (ii) index of the economic

complexity of Hausmann et al. (2011).

Third, the benchmark indicator of institutional quality (executive constraints concept)

is replaced with (i) the civil liberties ratings of Freedom House (index from 1 to 7) and (ii) the

level of institutionalized democracy (index from 1 to 10) in Polity IV annual time series. We

observe that the results for the diversification indexes of export products are similar to those

for the benchmark regressions.

5.2. Robustness to the Inclusion of Globalization Measures

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Table 7 provides the results of the robustness to the inclusion of controls for various

globalization measures with the growth rate of the overall export quality index as the dependent

variable. For example, HPS (2015) observe that both FDI inflows and trade openness

(liberalization) enhance the quality of the export basket. According to Bas and Strauss-Khan

(2015) and Fan et al. (2015), trade openness (liberalization) is positively associated with export

quality. In addition, Amighini and Sanfilippo (2014), Gnangnon and Roberts (2015), Harding

and Javorcik (2012), HPS (2015), and Iacovone et al. (2013) find that FDI inflows promote

export quality. Following these previous findings, we therefore also control for the effects of

globalization measures on the growth rate of export quality. The basic controls (log initial

export quality, log per capita GDP, total lower secondary completion rate, and executive

constraints concept) are also included in the regressions.


First, we include the real trade openness measure in PWT 8.1 (share of the merchandise

exports plus imports relative to the PPP GDP that is defined by the constant (real) prices, due

to the significance of Harrod-Balassa-Samuelson (HBS) effects) (Alcala and Ciccone 2004).

Second, we add FDI net inflows (percent of GDP) to the benchmark regressions.

Third, we include both real trade openness and FDI net inflows in the benchmark

regressions.

Fourth, we control for the levels of the KOF index of globalization (economic, social,

political aspects of globalization, as well as their sum as overall globalization). The KOF index

of globalization in 2015 is used in the regressions, and the relevant data are obtained from the

database of the Swiss Federal Institute of Technology (ETH) in Zurich. Dreher (2006) first

introduces the KOF index of globalization, which is updated annually (Dreher et al. 2008).8 The

KOF index of globalization considers the economic, social, and political dimensions of

globalization (Potrafke, 2015).9 We again observe that the results for the diversification indexes

of export products are similar to those for the benchmark regressions.

5.3. Robustness to the Inclusion of Other Controls

Table 8 provides the results of the robustness to the inclusion of several additional controls for

country size, structure of economy, and macroeconomic stance with the growth rate of the

overall export quality index as the dependent variable. For example, according to Hausmann et

al. (2007), IMF (2014a), and HPS (2015), countries with a small population have insufficient

capabilities, human capital, and fundamental factors of production (capital, labor, and land) for

8 The beginning date of the KOF index of globalization is also 1970. 9 See the website of KOF (http://globalization.kof.ethz.ch), for details on the KOF index of globalization.

11

producing high-quality (or sophisticated) export products. In other words, it should be expected

that smaller countries would have a lower growth rate of export quality.

Therefore, we first control for the effects of country size in the benchmark regressions.

Basic controls (log initial export quality, log per capita GDP, total lower secondary completion

rate, and the executive constraints concept) are also included in the regressions. For this

purpose, three benchmark measures of country size are used: (i) logarithm of the population

(the total population in millions), (ii) logarithm of the population density (people per square

kilometers of land area), and (iii) logarithm of the land area (square kilometers). When the

country size measures are included, the results for the diversification indexes of export products

are still similar to those for the benchmark regressions.


Second, we control for the effects of terms-of-trade shocks on the growth rate of the

export quality and the terms-of-trade measure in PWT 8.1 (the price level of exports relative to

the price level of imports) is considered. Hausmann et al. (2007) point out the significance of

the terms-of-trade shocks on the measure of export sophistication (PRODY). Similarly, terms-

of-trade shocks can be the significant determinant of cross-country export quality differences,

and therefore, it is expected that there would be a positive relationship between the terms-of-

trade level and the growth rate of export quality.

Third, we control for the extremal (trade) balance on goods and services (percent of

GDP) in the benchmark regressions. According to Hallak and Schott (2011), export basket

quality is closely related to external trade balance, which can be seen as a strong indicator of

competitiveness in global markets. Therefore, a negative relationship between external trade

imbalances and the growth rate of the export quality should be expected.

Fourth, we control for the age-dependency ratio (percent of the working-age population)

in the benchmark regressions to capture the potential capabilities and skills of a country. For

instance, Sutton and Trefler (2016) emphasize that export quality is associated with the

capabilities of a country, and therefore, it is expected that the age-dependency ratio would be

negatively associated with the growth rate of export quality.

Fifth, we control for the capital-labor ratio, which is calculated from the PWT 8.1 data

set in the benchmark regressions. According to Schott (2004), the capital-labor ratio, as a

benchmark measure of productivity (physical capital per worker), is one of the significant

determinants of export quality. Therefore, it is expected that the capital-labor ratio is positively

related to the growth rate of export quality. After implementing this set of robustness exercises,

12

we once again observe that the results for the diversification indexes of export products are

quite similar to those for the benchmark regressions.

5.4. Robustness to Outliers and the Different Cases

Our next set of robustness exercises involves excluding the extreme observations for the growth

rate of export quality and the measures of export product diversification. Following previous

empirical papers (e.g., Bergh and Nilsson, 2010; Gozgor and Ranjan, 2015), we define extreme

observations as those which are more than two standard deviations away from the average. As

reported in Table 9, the results are robust, i.e., they are similar to those for the benchmark

regressions.


We also analyze that the effect of export diversification on export quality can be region-

specific, and therefore, we separately exclude the observations for Latin America and the

Caribbean, the developing East Asia, and Sub-Saharan African countries to check the

robustness of their findings. Following many empirical papers (e.g., Bergh and Nilsson, 2010;

Gozgor and Ranjan, 2015), we re-estimate our benchmark regressions by excluding

observations from Latin America and the Caribbean, the developing East Asia, and Sub-

Saharan Africa, one region of time. The results are robust to the exclusion of these regions, i.e.,

the results in the benchmark regressions are not specifically driven by the presence of

observations from any one of these regions (see Table 9).

Finally, we separately exclude the observations for the oil-exporting countries.

Following the IMF (2014a) and HPS (2015), we define oil-exporting countries as those

countries with more than 50 percent of their total exports consisting of fuels, and the relevant

data are obtained from the WDI data set by the World Bank. Excluding the oil-exporting

countries can be noteworthy because of previous evidence known as the natural resource curse

(e.g., Sachs and Warner 2001). The natural resource curse leads to the Dutch disease and a lack

of strong institutions in the economy and therefore suppresses export quality upgrading. The

results are also robust to the exclusion of the oil-exporting countries (see Table 9).

Shortly, the various robustness exercises indicate that the indexes of export product

diversification are negatively related to the growth rate of export quality as is the case in the

benchmark results in Tables 3, 4, and 5.

6. Conclusion

In this paper, we empirically examined the effects of export product diversification on the

growth rate of export quality in a panel data set of 115 countries for the period 1970 to 2010.

13

We used the data sets of overall export quality and three export diversification measures of the

International Monetary Fund: the extensive margin (variation in number of new products

exported), the intensive margin (variation in export values among existing exports), and the

overall (Theil) index.

We observed that export quality has been increasing with a higher variation in export

values among existing exports in low- and lower-middle-income countries. We also found that

export quality has been increasing with both a higher variation in export values among the

existing exports and new products exported in the upper-middle- and high-income countries.

The results are robust to the changing measures of controls in the benchmark model, the

inclusion of many other controls (various measures of globalization, country size, factor

endowments, macroeconomic stance, etc.), and the exclusion of outliers.

To sum up, our paper shows that export diversification promotes the upgrading of export

baskets, but it mainly affects countries which are already exporting higher-value products. The

main implication of our findings is that in the absence of initial product diversification, the

achievement of a higher growth rate on the quality of the export basket, thus converging the

world frontier, will be difficult. This issue may lead to a possible middle-income trap in upper-

middle-income countries and a possible secular stagnation in high-income countries. However,

we need more studies on the drivers of export quality upgrading and their effect on the growth

rate of real income to infer such a conclusion.

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18

Figure 1

Dynamic Relationship between Product Diversification and Quality Upgrading of the Export Basket

19

Table 1

Descriptive Summary Statistics (1970–2010)

Variables Definition Data Source Mean Standard Deviation Minimum Maximum Observations

Export Quality Growth Rate Growth of Index IMF (2014b) & Henn et al. (2013, 2015) –0.005 0.104 –0.784 0.597 1241

Diversification of Exports Level of Index IMF (2014b) & Papageorgiou and Spatafora (2012) 3.624 1.269 1.019 6.436 1480

Extensive Margin Level of Index IMF (2014b) & Papageorgiou and Spatafora (2012 0.650 0.728 –0.064 5.064 1472

Intensive Margin Level of Index IMF (2014b) & Papageorgiou and Spatafora (2012 2.983 1.075 0.960 6.113 1475

Per Capita GDP (PPPs, Constant 2005US$) Logarithmic form PWT 8.1: Feenstra et al. (2015) 8.399 1.195 5.163 13.92 1365

Total Lower Secondary Completion Rate % of Relevant Age Group World Bank, World Development Indicators 53.92 32.75 0.650 113.8 558

Executive Constraints Concept Index from 1 to 7 Polity IV Annual Time Series 4.045 2.355 1.000 7.000 1266

Trade Openness (Merchandise Exports plus Imports / PPP GDP) Level, Ratio PWT 8.1: Feenstra et al. (2015) 51.02 56.78 0.110 1068 1365

Foreign Direct Investment, Net Inflows % of GDP World Bank, World Development Indicators 3.363 14.50 –19.77 466.5 1195

Economic Globalization Level of Index KOF: Dreher (2006), Dreher et al. (2008) 47.56 18.96 8.490 97.44 1252

Social Globalization Level of Index KOF: Dreher (2006), Dreher et al. (2008) 39.66 20.59 3.570 93.09 1609

Political Globalization Level of Index KOF: Dreher (2006), Dreher et al. (2008) 43.93 26.09 1.000 97.49 1668

Overall Globalization Level of Index KOF: Dreher (2006), Dreher et al. (2008) 42.97 17.18 11.77 91.69 1532

Population, Total Logarithmic form PWT 8.1: Feenstra et al. (2015) 1.620 1.990 –3.195 7.164 1365

Population Density (People per Square Km of Land Area) Logarithmic form World Bank, World Development Indicators 3.910 1.622 –1.996 9.916 1810

Land Area (Square Km) Logarithmic form World Bank, World Development Indicators 11.04 2.885 0.693 16.61 1820

Terms-of-Trade Level of Index PWT 8.1: Feenstra et al. (2015) 104.8 31.91 13.95 787.5 1365

External Balance on Goods and Services % of GDP World Bank, World Development Indicators –6.643 22.02 –310.5 93.25 1365

Age Dependency Ratio (% of Working–age Population) Level, Ratio World Bank, World Development Indicators 73.99 19.76 22.98 120.7 1709

Capital-Labor Ratio Level, Ratio PWT 8.1: Feenstra et al. (2015) 1.028 0.793 0.090 6.424 1025

Index of Human Capital per Person Level of Index PWT 8.1: Feenstra et al. (2015) 2.123 0.610 1.040 3.583 1108

Economic Complexity Index Level of Index Atlas of Economic Complexity: Hausmann et al. (2011) –0.009 1.059 –2.829 2.695 987

Civil Liberties Ratings (1 Representing the Most Free; 7 the Least Free) Index from 1 to 7 Freedom House, Freedom in the World Database 3.790 1.921 1.000 7.000 1192

Level of Institutionalized Democracy Index from 0 to 10 Polity IV Annual Time Series 4.062 4.186 0.000 10.00 1266

20

Table 2

Correlation Matrix

Regressors Export Quality

Growth

Export

Diversification

Extensive

Margin

Intensive

Margin

Log Per

Capita GDP

Lower Secondary

Completion Rate

Executive

Constraints Concept

Export Quality Growth 1.000 – – – – – –

Export Diversification –0.075 1.000 – – – – –

Extensive Margin –0.074 0.582 1.000 – – – –

Intensive Margin –0.044 0.848 0.063 1.000 – – –

Log Per Capita GDP 0.105 –0.475 –0.193 –0.457 1.000 – –

Lower Secondary Completion Rate 0.156 –0.553 –0.401 –0.417 0.721 1.000 –

Executive Constraints Concept 0.168 –0.529 –0.387 –0.396 0.446 0.516 1.000

21

Table 3

Benchmark Regressions: Determinants of the Export Quality Growth (1970–2010)

Regressors (I) (II) (III)

Log Initial Export Quality –0.131 (0.016)*** –0.136 (0.015)*** –0.118 (0.016)***

Log per Capita Real GDP 0.358 (0.203)* 0.408 (0.157)*** 0.493 (0.192)**

Human Capital (Lower Secondary Completion Rate) 0.020 (0.005)*** 0.022 (0.006)*** 0.020 (0.007)***

Institutional Quality (Executive Constraints Concept) 0.469 (0.082)*** 0.469 (0.083)*** 0.490 (0.092)***

Product Diversification of Exports –0.559 (0.129)*** – –

Product Intensive Margin – –0.787 (0.134)*** –

Product Extensive Margin – – –0.409 (0.328)

Constant Term 0.202 (0.192) 0.100 (0.184) 0.496 (0.190)***

Year Fixed Effects Yes Yes Yes

Country Fixed Effects Yes Yes Yes

Observations 403 403 403

Number of Countries 115 115 115

R–Squared 0.270 0.271 0.277 Notes: Dependent variable is the growth rate of the overall export quality index. Regressions include the country and the year fixed effects. The Feasible Generalized Least

Squares (FGLS) with the Heteroskedastic and Autocorrelation Consistent (HAC) Newey–West type standard errors are in parentheses. ***, ** and * indicate statistical

significance at the 1%, 5%, and 10% levels, respectively.

22

Table 4

Benchmark Regressions: Low- and Lower Middle Income Economies (1970–2010)



Log per Capita Real GDP –1.947 (0.609)*** –2.065 (0.581)*** –1.799 (0.636)***

Human Capital (Total Lower Secondary Completion Rate) 0.048 (0.016)*** 0.050 (0.015)*** 0.049 (0.016)***

Institutional Quality (Executive Constraints Concept) 0.286 (0.188) 0.245 (0.166) 0.442 (0.194)**

Product Diversification of Exports –0.113 (0.409) – –

Product Intensive Margin – –0.984 (0.388)** –

Product Extensive Margin – – 0.887 (0.610)





R–Squared 0.158 0.164 0.165 Notes: Dependent variable is the growth rate of the overall export quality. The constant term is estimated, but not reported. Regressions include the country and the year fixed

effects. The FGLS with the HAC Newey–West type standard errors are in parentheses. *** and ** indicate statistical significance at the 1% and 5% levels, respectively.

23

Table 5

Benchmark Regressions: Upper Middle- and High Income Economies (1970–2010)



Log per Capita Real GDP 1.258 (0.413)* 1.160 (0.346)*** 1.627 (0.378)***

Human Capital (Total Lower Secondary Completion Rate) 0.048 (0.011)*** 0.042 (0.010)*** 0.019 (0.011)*

Institutional Quality (Executive Constraints Concept) 0.743 (0.139)*** 0.697 (0.133)*** 0.546 (0.139)***

Product Diversification of Exports –0.946 (0.288)*** – –

Product Intensive Margin – –0.699 (0.250)*** –

Product Extensive Margin – – –2.612 (0.812)***





R–Squared 0.558 0.564 0.561 Notes: Dependent variable is the growth rate of the overall export quality. The constant term is estimated, but not reported. Regressions include the country and the year fixed

effects. The FGLS with the HAC Newey–West type standard errors are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

24

Table 6

Robustness Checks: Changing Measures of Controls Type of

Analysis Variables

All

Countries

Low- and Lower

Middle Income

Upper Middle- and

High Income

Results of the Benchmark Regressions

Diversification of Exports –0.559 (0.129)*** –0.113 (0.409) –0.946 (0.288)***

Intensive Margin –0.787 (0.134)*** –0.984 (0.388)** –0.699 (0.250)***

Extensive Margin –0.409 (0.328) 0.887 (0.610) –2.612 (0.812)***


Including per Capita GDP Squared Intensive Margin –0.845 (0.248)*** –0.418 (0.234)* –0.671 (0.252)***

Extensive Margin –0.299 (0.424) 0.982 (0.586)* –2.831 (0.813)***

Replacing Total Lower Secondary Completion Rate

with Human Capital Index in PWT 8.1


Intensive Margin –0.614 (0.186)*** –0.433 (0.266)* –0.768 (0.221)***

Extensive Margin –1.029 (0.332)*** 0.112 (0.623) –2.535 (0.534)***

Replacing Total Lower Secondary Completion Rate

with Economic Complexity Index

Diversification of Exports –0.379 (0.158)** –0.075 (0.522) –0.507 (0.178)***

Intensive Margin –0.430 (0.195)** –0.275 (0.151)* –0.390 (0.212)*

Extensive Margin –0.377 (0.337) 0.552 (0.680) –0.991 (0.458)**

Replacing Executive Constraints Concept

with Civil Liberties Ratings

Diversification of Exports –0.628 (0.128)*** –0.755 (0.414)* –0.948 (0.250)***

Intensive Margin –0.751 (0.157)*** –1.329 (0.393)*** –0.550 (0.259)**



Replacing Executive Constraints Concept with Democracy Index Intensive Margin –0.876 (0.154)*** –0.929 (0.356)*** –0.778 (0.247)***


Notes: Dependent variable is the growth rate of the overall export quality. The constant term and the log initial export quality are also estimated, but their coefficients are not

reported. The regressions include the country fixed effects and the year fixed effects. The FGLS with the HAC Newey–West type standard errors are in parentheses. ***, **

and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

25

Table 7

Robustness Checks: The Inclusion of Globalization Measures Type of

Analysis Variables

All

Countries

Low- and Lower

Middle Income

Upper Middle- and

High Income





Controlling Globalization: Including Trade Openness




Controlling Globalization: Including Foreign Direct Investment (FDI)

Diversification of Exports –0.728 (0.165)*** –0.824 (0.306)*** –0.580 (0.282)**

Intensive Margin –1.102 (0.156)*** –1.133 (0.318)*** –0.196 (0.105)*

Extensive Margin –0.394 (0.345) 0.853 (0.578) –1.162 (0.579)**

Controlling Globalization: Including Both Trade Openness and FDI

Diversification of Exports –0.599 (0.180)*** –0.809 (0.289)*** –0.683 (0.295)***

Intensive Margin –0.913 (0.183)*** –1.080 (0.333)** –0.014 (0.006)**

Extensive Margin –0.275 (0.385) 0.733 (0.573) –1.368 (0.745)*

Controlling Globalization: Including Economic Globalization Index



Extensive Margin 0.019 (0.383) 0.633 (0.556) –2.353 (0.537)***

Controlling Globalization: Including Social Globalization Index

Diversification of Exports –0.360 (0.141)** –0.014 (0.467) –1.014 (0.282)***

Intensive Margin –0.476 (0.172)*** –0.857 (0.432)** –0.487 (0.274)*



Controlling Globalization: Including Political Globalization Index Intensive Margin –0.866 (0.179)*** –0.820 (0.356)** –0.682 (0.253)***



Controlling Globalization: Including Overall Globalization Index Intensive Margin –0.545 (0.127)*** –0.773 (0.420)* –0.557 (0.335)*


Notes: Dependent variable is the growth rate of the overall export quality. The constant term, the log initial export quality, the log per capita real GDP, the total lower secondary

completion rate, the executive constraints concept are also estimated, but their coefficients are not reported. The regressions include the country fixed effects and the year fixed


26

Table 8

Robustness Checks: The Inclusion of Other Controls Type of

Analysis Variables

All

Countries

Low- and Lower

Middle Income

Upper Middle- and

High Income





Controlling Country Size: Including Log Population




Controlling Country Size: Including Log Population Density




Controlling Country Size: Including Log Land Area




Other Controls: Including Terms-of-Trade





Other Controls: Including External Balance on Goods and Services Intensive Margin –0.791 (0.149)*** –0.928 (0.387)** –0.885 (0.283)***


Other Controls: Including Age Dependency Ratio


Intensive Margin –0.764 (0.193)*** –0.987 (0.426)** –0.400 (0.207)*

Extensive Margin 0.248 (0.452) 0.892 (0.612) –2.198 (0.861)**

Other Controls: Including Capital-Labor Ratio

Diversification of Exports –0.328 (0.185)* –0.345 (0.485) –0.386 (0.197)*

Intensive Margin –0.559 (0.236)** –1.288 (0.446)*** –0.011 (0.006)*

Extensive Margin –0.049 (0.452) 1.430 (0.631)** –1.231 (0.691)*

Notes: Dependent variable is the growth rate of the overall export quality. The constant term, the log initial export quality, the log per capita real GDP, the total lower secondary

completion rate, the executive constraints concept are also estimated, but their coefficients are not reported. The regressions include the country fixed effects and the year fixed


27

Table 9

Robustness Checks: Outliers and Different Cases Type of

Analysis Variables

All

Countries

Low- and Lower

Middle Income

Upper Middle- and

High Income





Excluding Extreme Units of Growth Rate of Export Quality

Diversification of Exports –0.246 (0.133)* –0.500 (0.367) –0.587 (0.181)***

Intensive Margin –0.335 (0.170)** –0.665 (0.382)* –0.394 (0.196)**


Excluding Extreme Units of Export Diversification Measures


Intensive Margin –0.733 (0.143)*** –1.069 (0.376)*** –0.735 (0.255)***

Extensive Margin –0.708 (0.302)*** 0.845 (0.816) –1.911 (0.830)**

Excluding Latin American and Caribbean Countries


Intensive Margin –0.792 (0.147)*** –0.386 (0.199)* –0.684 (0.297)**


Excluding Developing East Asian Countries


Intensive Margin –0.862 (0.189)*** –1.043 (0.594)* –0.672 (0.284)**

Extensive Margin –0.103 (0.359) 1.332 (0.637)** –2.717 (0.820)***

Excluding Sub-Saharan Africa Countries


Intensive Margin –0.618 (0.150)*** –0.223 (0.136)* –0.507 (0.279)*

Extensive Margin –1.782 (0.497)*** 0.832 (1.252) –2.267 (0.795)***

Excluding Oil-exporting Countries

Diversification of Exports –0.359 (0.172)*** –0.246 (0.404) –0.539 (0.263)**

Intensive Margin –0.575 (0.177)*** –0.998 (0.357)*** –0.587 (0.286)**

Extensive Margin 0.498 (0.429) 1.038 (0.527) –1.191 (0.482)**

Notes: Dependent variable is the growth rate of the overall export quality. The constant term, the log initial export quality, the log per capita real GDP, the total secondary

school completion rate, the executive constraints concept are also estimated, but their coefficients are not reported. The regressions include the country fixed effects and the

year fixed effects. The FGLS with the HAC Newey–West type standard errors are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, and 10% levels,

respectively.

28

Data Appendix I

List of Countries in the Panel Data Set (Country and Lending Groups Classification of the World Bank for Fiscal Year 2016)

54 Low- and Lower Middle Income Countries (GNI of $4,125 or less)

Benin, Burkina Faso, Burundi, Cambodia, the Central African Republic, Chad, Comoros, Congo Democratic Republic, the Gambia, Guinea,

Guinea–Bissau, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Rwanda, Sierra Leone, Togo, Uganda.

Armenia, Bangladesh, Bolivia, Cameroon, Congo Republic, Cote D'Ivoire, Djibouti, Egypt, El Salvador, Georgia, Ghana, Guatemala, Honduras,

India, Indonesia, Kenya, Kyrgyz Republic, Laos, Mauritania, Moldova, Mongolia, Morocco, Paraguay, the Philippines, Senegal, Sri Lanka, Syria,

Tajikistan, Ukraine, Uzbekistan, Vietnam, Yemen, Zambia.

61 Upper Middle- and High Income Countries (GNI of $4,126 or more)

Albania, Argentina, Azerbaijan, Belarus, Bulgaria, China, Colombia, Costa Rica, the Dominican Republic, Ecuador, Equatorial Guinea, Fiji,

Gabon, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Lebanon, Macedonia FYR, Malaysia, Mauritius, Mexico, Panama, Peru, Romania, Russia, South

Africa, Suriname, Tunisia, Turkey.

Austria, Bahrain, Belgium, Chile, Cyprus, the Czech Republic, Estonia, Finland, France, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea

Republic, Kuwait, Lithuania, the Netherlands, Norway, Oman, Poland, Portugal, Qatar, Slovakia, Slovenia, Spain, Trinidad and Tobago, Uruguay,

Venezuela.

effects of export product diversification on quality …0 effects of export product diversification...

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