metadata: authors: 0: author: Kruse,Hagen
1: author: Timmer,Marcel Peter
2: author: De Vries,Gaaitzen Johannes
3: author: Ye,Xianjia
count: World
docna: 0: docna: Export Diversification from an Activity
            Perspective : An Exploration Using Occupation Data
docty: Policy Research Working Paper
abstracts: cdata!: With international production
            fragmentation, countries specialize in activities along the
            production chain rather than particular products. This paper
            therefore analyzes export diversification taking an activity
            perspective. It measures export activities combining new
            data on the export income of workers in industries cross
            classified by occupational classes. Based on the panel data,
            the paper documents that countries initially specialize
            along the extensive margin (shifting activities across
            industries) but later on along the intensive margin
            (shifting activities across occupational classes). New
            activity specialization is found to be strongly related to
            the proximity of this activity to the initial export basket.
            Yet, countries that defy proximity appear to grow faster.
            The results show that an activity perspective delivers novel
            insights into trade development and structural change.
display_title: Export Diversification from an
            Activity Perspective : An Exploration Using Occupation Data
listing_relative_url: /research/2023/05/34072700/export-diversification-activity-perspective-exploration-using-occupation-data
topicv3: Structural Transformation for Growth
            m1326245 1089,Trade m1326924 698
publishtoextweb_dt: 2023-05-31T00:00:00Z
dois: 10.1596/1813-9450-10463
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2257975
1: author_key: 1168139
2: author_key: 1409822
3: author_key: NA
model_extractions: Policy Research Working Paper 10463 Export Diversification from an Activity Perspective An Exploration Using Occupation Data _Hagen Kruse Marcel Timmer Gaaitzen de Vries Xianjia Ye_ Macroeconomics, Trade and Investment Global Practice May 2023
Policy Research Working Paper 10463

# **Abstract**

With international production fragmentation, countries specialize in activities along the production chain rather than particular products. This paper therefore analyzes export diversification taking an activity perspective. It measures export activities combining new data on the export income of workers in industries cross classified by occupational classes. Based on the panel data, the paper documents that countries initially specialize along the extensive margin (shifting activities across industries) but later on along the intensive margin (shifting activities across occupational classes). New activity specialization is found to be strongly related to the proximity of this activity to the initial export basket. Yet, countries that defy proximity appear to grow faster. The results show that an activity perspective delivers novel insights into trade development and structural change.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at g.j.de.vries@rug.nl.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Export Diversification from an Activity Perspective: An Exploration Using Occupation Data**

Hagen Kruse<sup>a</sup> , Marcel Timmer<sup>a</sup> , Gaaitzen de Vries<sup>a</sup> , Xianjia Ye<sup>a</sup> a Groningen Growth and Development Centre, Faculty of Economics and Business, University of Groningen, the Netherlands _Keywords_ : Export diversification; International trade; Structural change; Occupations; _JEL codes_ : F19, O14, O33

## Acknowledgments

This paper is commissioned as part of the project ‘Leveraging trade for more and better job opportunities in developing countries’ by Maryla Maliszewska and Deborah Winkler, a product of the EFI-MTI-TIC-Trade and RI (ETIRI). The project is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at g.j.de.vries@rug.nl.
# **1. Introduction**

Traditionally, trade performance and diversification are studied from a product perspective. This perspective lost focus due to large-scale offshoring trends with countries trading tasks rather than products, carrying out different activities in global value chains (Grossman and Rossi-Hansberg 2008; Baldwin and Robert-Nicoud 2014; Antrás and Chor 2022). The aim of this paper is to explore patterns of export specialization from an activity perspective. To aid intuition, table 1 provides an illustrative example. It shows the distribution of activities in exports of a particular product class, textiles, for four countries at different levels of development. Activities are identified in the data by cross classifying the occupational class of workers and their industry of work. It shows that activities of machine operators in the textile industry make up more than twothirds of domestic value-added exports in Türkiye. Instead, in Italy much more value is added by managers, engineers and other professionals outside the textile industry. In Pakistan agricultural workers provide most value in textile exports as cotton is a major input in domestic textile production, whereas in Vietnam workers in wholesale trade services are major contributors. Activities in textiles exporting thus differ widely across countries, suggesting potential for new insights when using an activity perspective on trade.<sup>1</sup> Exploiting a new panel data set on export activities covering countries at a wide range of income levels, we document four main empirical findings. First, production activities typically account for the majority of export value at low levels of GDP per capita. As countries grow richer, incomes from engineering, managerial, and services support activities grow and eventually account for the majority of export value. Second, poorer countries specialize along the extensive margin, shifting export activity across industries within occupational classes. Advanced countries mostly specialize along the intensive margin, shifting export activity between occupational classes within industries. Third, the probability that a country gains new comparative advantage in a particular activity is positively related to the proximity of this activity to the initial export basket. Proximity appears to be particularly predictive for new specializations along the extensive margin. Fourth, some countries specialize in new activities that are only weakly related to their initial basket. A higher

> 1 The need and potential for an activity perspective has been stressed before, see for example Lederman and Maloney (2012). Wolff (2003) provides an early empirical example of the activity perspective on trade.

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share of these path-defying specializations appears to be correlated with higher growth in GDP per capita.

**Table 1: Top-3 activities in the exports of textiles, four selected countries, 2018**

|**Pakistan**||**Türkiye**||
|---|---|---|---|
|1. Agricultural workers|45.0%|1. Machine operators in textile|67.8%|
|2. Machine operators in textile|20.3%|2. Managers in textile|10.0%|
|3. Sales workers in retail|8.0%|3. Agricultural workers|5.9%|
|All other activities|26.6%|All other activities|16.3%|
|**Vietnam**||**Italy**||
|1. Machine operators in textile|56.4%|1. Machine operators in textile|29.9%|
|2. Sales workers in wholesale|7.2%|2. Other professionals in textile|7.2%|
|3. Other professionals in wholesale|2.9%|3. Managers in textile|6.7%|
|All other activities|33.5%|<br>All other activities|56.2%|

_Notes_ : Entries show for each country the contribution of the top-3 activities in the exports of textiles in 2018 (in percentages of total export value). Activities are classified by industry of work and occupational class of workers. The last line reports the contribution of the other activities outside the top-3. Contributions to the textiles exports are based on the labour income of domestic workers involved in each activity. Own calculations, see section 2 for data sources.

Our main contribution is to the venerable tradition of studying patterns in structural change as incomes rise (Chenery et al., 1986; Syrquin, 1988; Herrendorf et al., 2014). Lack of export development is a traditional concern in this literature as it appears to be linked with slower structural change and productivity growth. Traditionally, countries are assumed to gradually develop manufacturing exports from “light” industries that are intensive in the use of unskilled labor, to “heavy” industries that are intensive in physical and human capital use (Syrquin, 1988; Hanson 2012). Hidalgo et al. (2007) and Hausmann et al. (2014) provided further detail, describing countries’ development paths from exporting simple to increasingly complex goods. The usefulness of the product perspective depends crucially on the assumption that production technologies for a particular good are the same in all exporting countries. Yet, Hummels, Ishii and Yi (2001) documented vertical specialization as countries increasingly make use of imported inputs. With international fragmentation, countries specialize in different stages along the production chain such that they may carry out different activities in the same industry. Koopman, Wang and Wei (2012) showed for example that the domestic value-added content of Chinese 3
exports of goods that are traditionally labelled as complex, such as electronic devices, was particularly low as it relied heavily on imported components. Johnson and Noguera (2012, 2017) quantified the scope of production sharing in the global economy, finding amongst others an increasing share of service activities in manufacturing exports. We continue this line of work breaking down the value-added content of exports and revisiting the relation between export development and income growth from an activity perspective.

To do so, we perform two empirical exercises. First, we redo the analysis of Hausmann and Klinger (2007) and Hidalgo et al. (2007) who identified development paths using the concept of “proximity”. The degree of proximity between a pair of products is empirically inferred from cooccurrence of revealed comparative advantages (RCAs) for these products in a cross-country panel data set. Instead, we perform export analysis based on proximities of activity pairs rather than product pairs. The results show that countries first diversify predominantly through shifting export activities across industries and later in the development process through upgrading of activities within industries. The former diversification process is picked up in traditional industry analyses, see, e.g., Imbs and Wacziarg (2003). The latter process will not show up as export diversification when viewed from a product or industry-based perspective. Moreover, the activity perspective also provides a natural basis to probe further into the role of technology in export specialization as technological change is typically task specific rather than sector or product specific (Acemoglu and Autor, 2011). We find that proximity appears to be particularly predictive for new specializations in activities that are intensive in routine manual tasks, which differs across advanced and developing economies. For developing economies, a 1 standard deviation increase in average proximity relates to a 10.9 percentage point increase in the probability of specializing in a new routine manual activity. In advanced economies the effect is only 2.9 percentage points. In a next step, we also analyze trade specialization patterns of individual countries in the vein of Coniglio et al. (2021), adapting their product-based methodology to the activity perspective. We find that some countries acquire comparative advantages in activities that are not proximate, denoted as path-defying specialization in Coniglio et al. (2021). Parsimonious cross-country regressions suggest that path-defying activity specialization appears to be positively correlated with higher growth in GDP per capita. In contrast, Coniglio et al (2021) found that the effect turns negative for high-income countries when viewed from a product perspective.

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With the new data set we also contribute to a recent wave of studies that use occupational statistics to describe structural change. For example, Newfarmer, Page and Tarp (2018) study various industries in agriculture and services that share important characteristics of manufacturing industries such as tradability, scalability and productivity growth, and name these “industries without smokestacks”. Baccini et al. (2023) stress important variation in activities within the services sector from provision of informal personal services to much more productive formal business services and transport services that are used also for exporting. Duernecker and Herrendorf (2022) show how the share of service occupations within the good sectors increases in later stages of development. Using occupational statistics, Timmer et al. (2019) documented specialization of high-income countries in activities that are knowledge intensive. The _Jobs of the World Project_ is another prominent example of the compilation of a large-scale data set to compare changes in occupational structures across countries and over time. Using this data, Bandiera et al. (2022) show how the nature of jobs vary across countries by stages of development, and within countries by household wealth and gender. In this study we focus specifically on the characteristics of jobs in exports. We show how adding an industry dimension enhances the ability of occupational statistics to capture different types of worker activities. We extend the database of Timmer et al. (2019) increasing the country coverage to 52 countries at all levels of development and adding new data on activities at a higher level of detail. More precisely, activities are identified in the data by cross-classifying 13 occupational classes and 35 industries of work, resulting in 455 distinct activities. This fine-grained dataset allows us to describe for the first time overall development patterns of export specialization from an activity perspective.<sup>2</sup> As a final note we wish to emphasize that the nature of this study is explorative. Our findings are suggestive of important complementarities between various export activities, but we stay agnostic about their precise nature. Co-occurrence of specialization in particular activities might be driven by comparable developments in countries’ endowments such as the buildup of general human capital or the business environment. But it also points to the possibility of spillovers and

> 2 Relatedly, Diodato et al. (2022) enrich export product data with occupational statistics. They assume however that production technologies are the same across countries applying occupation structures found in U.S. production to all countries. We show however that production technologies are not constant around the world such that products will not map one to one into a set of activities.

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complementarities between activities due to for example the need for specific job skills, shared infrastructure or need for specialized inputs and services (Hidalgo et al., 2007; Coniglio et al., 2021). In addition, it may also be related to less tangible spillovers in knowledge and soft technologies associated with the entrepreneurial discovery process (Hausmann and Rodrik, 2003). The remainder of this paper is organized as follows. Section 2 discusses data development. Section 3 explores basic patterns in activity exports as countries develop over time. Section 4 reports on the importance of proximity in determining the development of new specializations. Section 5 explores the degree of path dependence in specialization patterns of countries and relates it to economic growth. Section 6 concludes.

# **2. Data sources for exports of activities**

The analysis of trade in terms of activities rather than in terms of products or industries poses major data challenges. In this section, we discuss how these challenges have been met through combining two main data sets: data on exports of value added and data on the activities of workers. To this end we introduce variable _z_ j , the value added of activity type _j_ in country _c_ ’s exports. It is tracked by the labor income of the workers carrying out the activity _j_ in the production chains of country _c_ ’s exports. The first data set contains information on labor income of workers by occupational group and industry of work. The second data set contains information on the exports of value added by industry. These exports include value added in the exporting industry as well as other domestic industries that contribute more upstream in the production chain through delivery of inputs. The _z_ j ’s are derived for each country and year by joining the two data sets at the countryindustry dimension. A crucial part in data construction is to make sure that occupations and industries are consistently defined and measured across countries and over time. Data is constructed for a set of 52 economies, ranging from low-income to high-income, see Appendix table C1 for a full list.

_Data set on activities._ Activities of workers are identified on the basis of their occupational group as well as the industry in which they work. In total 455 activities are distinguished based on 13 occupational groups in each of 35 industries. The data is taken from the _Occupations Database_ 6
(OD), introduced in Reijnders and de Vries (2018), and extended with a set of lower income Asian economies by Bertulfo et al. (2022), see Appendix Table C2 for the sources. The database provides information on the number of workers by occupation-industry pair as well as their labor earnings. For each country national data has been harmonized using mappings from national occupation classifications to an international classification of thirteen occupational groups based on 2-digit codes in the ISCO88 (International Standard Classification of Occupations), see Appendix table C3. While some countries have adopted the ISCO in their administrative statistics, most follow their own classification and bridge tables needed to be developed to have consistent coding. Furthermore, national industry classifications were mapped to a common set of 35 industries covering the overall economy. These are mostly 2-digit industries in the ISIC (International Standard Industrial Classification) revision 3.1, see Appendix table C4.

Previous studies have compiled large-scale data sets to compare changes in occupational structures across countries and over time. The _Jobs of the World Project_ is a prominent example (Bandiera et al., 2022). We elaborate by adding a cross-classification of occupations by industry. This is an important step to account for the variety in tasks required from workers of the same occupational group in different industries. Compare for example the so-called work activities performed by machine operators in the textile manufacturing industry and similar workers in the electronics manufacturing industry. Both occupations fall under ISCO 1-digit code 8. Data from O*NET indicates that “getting information” is a very important work activity in both industries, ranking first in electronics and second in textiles (out of forty-one work activities that are distinguished). But whereas “evaluating information to determine compliance with standards” and “making decisions and solving problems” are also very important activities in electronics (ranking third and second), they are much less important in textiles (11<sup>th</sup> and 10<sup>th</sup> ). Conversely, “repairing and maintaining mechanical equipment” is barely important in electronics (34<sup>th</sup> ) but above average important in textiles (17<sup>th</sup> ).<sup>3</sup> More generally, one can statistically test for the significance of adding an industry dimension to the occupational class category. To this end, we make use of O*NET’s importance indicators for the full set of industries, occupations and work activities.<sup>4</sup> More

> 3 Based on work activity data from O*NET for job codes 51-2022 - Electrical and Electronic Equipment Assemblers and 51-6062 - Textile Cutting Machine Setters, Operators, and Tenders (assessed 22 July 2022).

> 4 This is data for the U.S. only. Caunedo et al. (2021) document some cross-country differences

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specifically, we test for the significance of adding the industry variable to the 1-digit occupation variable in a two-way multivariate analysis of variance (MANOVA) of averaged importance scores. The joint occupation-industry variable is highly significant indicating that it is informative to account for the industry of work beside a worker’s occupational group in characterizing the work activities carried out.<sup>5</sup> _Data set on exports._ In order to determine what activities in an economy are exported, information on the value added of a countries’ exports is needed. The industry composition of value added in exports is determined following the method outlined in Hummels et al. (2001) and Koopman et al. (2012). This is based not only on the value added in the exporting industry, but also on value added further up the production chain in other domestic industries delivering intermediates. Computation requires the use of national input-output tables that contain information on inter-industry deliveries, as outlined in Appendix A1. National input-output tables are taken from the Multiregional Input–Output table (MRIO) Database of the Asian Development Bank available for the year 2000 and annually for the period from 2007 to 2018. The industry classifications used in these tables were harmonized and mapped to a common set of 35 industries akin to the set of industries defined in the activities data.<sup>6</sup> Value added consists of compensation for workers (labor income) and a gross operating surplus (capital income) that accrues as income to the owners of capital assets. Capital assets cannot be straightforwardly allocated to activities, in contrast to workers. For example, a computer can be utilized in many activities and there is no information on its particular use. Data used for the baseline regressions only includes labor incomes. In in the task content of a particular occupation, suggesting that the task content of occupations is not necessarily constant across countries.

5 O*Net provides a list of 41 work activities with importance indicator scores on a scale of 0-100 for each 6-digit SOC code. We weight up the indicators to our activities using data from the US BEA Industry and occupation matrix for 2020. We establish the composition of O*NET 6-digit SOC codes in each of our activities, mapping from 6-digit SOC to our 13 occupational groups and from BEA 2-digit SIC to our industry groups. Next, we test for significant differences in the averaged importance scores across different activity pairs, weighting with the number of workers in each detailed SOC occupation. For ease of computation the 41 indicators are reduced to 10 principal components first, capturing 83% of the variation. Wilks’ test result for the joint occupation-industry variable is (F-value = 4.92 and p-statistic = 0.00), for the occupation dimension (F= 46.98, p=0.00) and for industry dimension (F=0.54 and p=1.00).

6 ADB MRIOTs are not available for the years 2001-2006. The MRIO database vintage used in this study was accessed in October 2021.

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robustness analysis capital income is also taken into account assuming its distribution over activities is equal to the labor income distribution.

More formally, data on exports is used for a particular country-year to derive a vector **_l_** of dimensions ( _i_ x 1) with typical element representing export income by workers in industry _i_ as (1) )<sup>−1</sup> , with **e** a vector of exports (ll= vv� (II−AA _i_ x 1),<sup>DD</sup> ee the domestic input coefficients matrix ( _i_ x _i_ ) and **I** an identity matrix ( _i_ x _i_ ) such that )<sup>−1</sup> is the Leontief inverse matrix, and the diagonal matrix of AA<sup>DD</sup> labor income to gross output ratios ( _i_ x _i_ ). The values of export activities are derived by multiplying (II−AA<sup>DD</sup> vv� export income by industry with the occupation income distribution for each industry from the activity data set. Appendix A1 provides a more detailed technical exposition. Data for all variables vary across countries as well as over time allowing for meaningful cross-country and temporal comparisons.

# **3. Export activities along the development path**

How do export baskets evolve in terms of activities as countries grow richer? Figure 1 shows the development of export activity over GDP per capita. It is estimated using a non-parametric LOWESS smoother on data for 59 countries over a time span of 20 years.<sup>7</sup> For parsimony, panel A aggregates the 455 activities into 5 broad groups: engineering, managerial, production, support, and other activities across all industries. Panel A suggests that at lower levels of economic development, production activities account for a major part of export income. The production share steadily declines when countries grow richer: from more than 50 percent of total exports at GDP per capita levels below 5,000 US$ to about 30 percent at levels above 40,000 US$. Engineering, managerial, and support activities account for the majority of labor income from exporting at higher levels of income. Panel B further details production activities in four broad industry groups. It shows that income from production activities within agriculture, mining, and “light” manufacturing industries (including food and textiles industries) shifts towards production

> 7 For the purpose of Figure 1, data for eight countries has been added to the dataset, in particular data for some low-income countries, see Appendix G. However, the data for these eight countries have a lower level of detail and hence are not used in the rest of the paper.

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activities in “heavy” manufacturing industries (including electrical and transport equipment industries). Remarkable is the increasing relative importance of production activities in services. These activities include, for example, work by drivers in the transport sector or cargo handlers in wholesaling, contributing indirectly to exports (Baccini et al, 2023). Production activities in services industries make up around one-tenth of exports across all income levels, while production activities in other industries rapidly decline during development.

The average cross-country development pattern is broadly reflected in individual country experiences. Figure 2 shows the changing activity content of exports in two rapidly growing countries, China and Vietnam, over the period from 2000 to 2018. Export of production activities decreased rapidly in China after 2007, whereas exports of engineering and support occupations increased. Export of production activities remained relatively high in Vietnam throughout this period. McCaig and Pavcnik (2018) stress the importance of reallocation of activity from informal firms toward formal firms during the export boom following the 2001 United States-Vietnam Bilateral Trade Agreement. Most noticeable is the shift towards production in heavy manufacturing, almost doubling from 7% in 2007 to 13% of export income in 2018.<sup>8</sup> In section 5, we will more formally investigate the extent to which countries follow a common development path in export specialization.

> 8 See Pahl et al. (2022) and Winkler et al. (2023) for a complementary analysis of the job content of exports for a large set of countries.

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Changes in the activity basket of exports may be driven by changes along the extensive margin, that is shift of activity across industries within occupational classes, and changes along the intensive margin, that is shift of activity across occupational classes within industries. We quantify the relative importance of these shifts conducting a standard shift-share decomposition. The overall change in , the share of occupation _o_ in overall export income of a country in a particular period can be expressed as oo SS (2)<sup>�</sup> i , oo ii iioo ii ioo ii where denotes the share of workers with ∆SS = ∑SS ∆SS + ∑SS���� ∆SS <mark>occupation</mark> _<mark>o</mark>_ <mark>in industry</mark> _<mark>i</mark>_ <mark>in overall export income, and S</mark> denotes the share of industry iioo _i_ in overall export income in the country. Δ denotes the change SS during the period and a bar over a variable indicates the period average of this variable. The first ii term on the right-hand side represents the change in the overall occupation share that is attributable to changes in the intensive (occupation) margin, while the second term reflects changes in the extensive (industry) margin _._ Table 2 shows the decomposition for each of five broad occupational groups between 2000 and 2018. The set of economies is split into a group of advanced and a group of developing economies according to their income level in 2000 (listed in Appendix Table C1). Results are reported as simple averages across all economies in a group. The decline in export of production activities in developing economies is mostly accounted for by shifts along the extensive margin as workers move towards less production-intensive industries. As countries grow richer, shifts along the intensive margin start to dominate, in line with the production outsourcing hypothesis, also known as “servicification” of goods industries highlighted in Duernecker and Herrendorf (2022). Export shares of engineering activities continue to grow at all levels of development, mainly accounted for by shifts along the extensive margin. In contrast, the increase in export share of managerial activities is exclusively through the increasing shares within industries, both in developing and advanced economies. The relative importance of shifts along the intensive and extensive margins is further analyzed in the next section.

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**Table 2: Decomposition of changes in activity export shares, 2000-2018.**

||Adv<br>|anced econom<br>|ies<br>|Deve<br>|loping econo<br>|mies<br>|
|---|---|---|---|---|---|---|
|Occupational group|Within<br>industry|Between<br>industry|Total|Within<br>industry|Between<br>industry|Total|
|Managerial|104.4|-4.4|100|138.2|-38.2|100|
|Support services|84.0|16.0|100|47.3|52.7|100|
|Production|57.0|43.0|100|22.0|78.0|100|
|Other occupations|41.8|58.2|100|-10.3|110.3|100|
|Engineering|32.6|67.4|100|39.4|60.6|100|

_Notes_ : The change in the share of a broad occupation group in export incomes is decomposed into between-industry and within-industry effects according to equation (2) for five broad aggregations of occupational groupings. Results are standardized by the total change within each broad aggregation and ordered by the within-industry component for advanced economies. Simple country averages are given for advanced and developing economies (listed in Appendix Table C1).

# **4. Development patterns of activity specializations**

We make use of the full detail in our data set to probe particular patterns in the development of new export activities. To this end we make use of the concept of ‘proximity’ introduced by Hidalgo et al. (2007) and adapt this from a product to an activity perspective.

# _4.1 Measuring activity specialization_

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The numerator measures the share of activity _j_ in overall activity income in country _c_ ’s exports. The denominator calculates the same share across all countries. If the index is above one for a particular activity _j_ , country _c_ is said to be specialized in exporting that activity.

The AS indices can be used to track activity specialization in a country over time. Figure 3 provides heat maps for the change in the AS indices during the period 2000-2018 for four countries at different levels of development. Changes in the AS indices for Cambodia are relatively minor compared to the changes in the other countries. The AS for various occupations in hotels and restaurants (industry number 22) and in inland transport (23) industries went up. But in many manufacturing industries, AS indices barely changed for any occupational class. In contrast, AS indices in Vietnam changed frequently with major declines in mining (2), food (3) and leather manufacturing (5) industries, while in many other manufacturing industries and in utilities (17) and water transport (24) AS indices increased. In particular, AS for machine operators (occupation class 10) increased, but also for clerical support workers (7) and personal service workers (8). In Mexico AS developments diverged across occupations: AS for various occupations went up, for example in food manufacturing (3), petroleum refining (8) and transport equipment (15), but at the same time there were strong declines in AS for legislators (1) and managers (2) in the same industries. In China, a clear shift in specialization away from activities in goods producing industries (1-16) towards activities in services production (17-35) is visible. The strong increase in the AS of sales workers (9) is remarkable, also in most goods producing industries. Overall, it is noteworthy that in all countries AS indices within the same industry do not necessarily move in the same direction for all occupations. Both increases and declines in AS for particular classes are frequently found within the same industry illustrating that shifts along the extensive and intensive margins both play a role in determining export activity specializations.

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=min{P(A > 1|AS > 1),P(AS > 1|AS > 1)}

P(AS > 1)AS = > 1) = <u>X {14 1}</u> {ajAs > 1}
where is binary being 1 when > 1 and 0 otherwise, the average proximity of j (see equation 6), jj,jj,tt+TT _t_ and _t+T_ indicate begin and end year of the period and jj,jj,tt+1 **Z** is a vector of control xx AASS ωω variables (discussed below).<sup>10</sup> The regression only considers activities where the country initially does not have a comparative advantage in (i.e. < 1). The _β2_ coefficient is expected to be positive as a higher initial AS level for an activity is likely to increase the possibility that it AASSjj,jj,tt develops into a new specialization, that is, that it passes the threshold of AS=1. The main coefficient of interest is _β1._ If it is positive, it indicates that a new activity which is more proximate to the current specialization basket of a country has a higher likelihood of developing into a new specialization.

# _4.3 Empirical results_

The results of the baseline regression are reported in Table 3, with initial specialization and average proximity lagged with five years.<sup>11</sup> Country-year and industry-year dummies were added to control for any time-varying country or industry characteristics. In the baseline regression, a positive and significant coefficient on average proximity is found (column 1). A one standard deviation increase in average proximity of a new activity increases the probability of specializing in this activity by 6.1 percentage points in the full sample of economies. This positive and significant relation is found for both advanced and developing economies, with a stronger relation for the latter group (3.3 and 6.3 percentage points respectively, see columns 2 and 3).

> 10 See Hausmann and Klinger (2007) for a comparable regression setup.

> 11 Note that the period is 7 years for the initial period 2000-2007 and 5 years for the periods after using overlapping periods 2007-2012, 2008-2013, ..., 2013-2018. This is because ADB MRIOTs are not available for the years 2001-2006. Robustness of the results to alternative lags and non-overlapping periods is considered below.

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**Table 3: Probability regressions for new activity specializations, baseline.**

||Dependent<br>comparativ|variable: whether<br>e advantage in a<br>_t+5_(xj,j,t+5=|country_c_has a<br>ctivity_j_at time<br>1)|
|---|---|---|---|
||(1)|(2)|(3)|
|ASj,j,t<br>|0.075***|0.190***|0.055**|
||(0.025)|(0.006)|(0.022)|
|ωj,j,t|0.061***|0.033***|0.063***|
||(0.006)|(0.003)|(0.005)|
|Country-Year FE|X|X|X|
|Industry-Year FE|X|X|X|
|Sample|Total|Advanced|Developing|
|N|137,723|56,919|80,804|
|AdjustedR<sup>2</sup><br>|0.069<br>|0.087<br>|0.065<br>|

_Notes_ : Results from linear probability regression using equation (4). The independent variables are activity specialization and average proximity , defined in equations (3) and (6). is normalized by subtracting the mean and dividing by the standard deviation to ease interpretation. Robust standard errors jj,jj,tt AASS ωω ωω (clustered by country) are reported in parentheses. * p<0.1, ** p<0.05 and *** p<0.01. Own calculations based on annual data for 2000-2018, see section 3.

_Extensive and intensive margins_ We next investigate the predictive power of proximity for specializations along the extensive and intensive margins. Columns (1-3) in Table 4 report on activity specializations along the intensive margin by adding country-industry fixed effects. Columns (4-6) report on specializations along the extensive margin, adding a country-occupation dummy instead. The role of average proximity is highly significant for both types of specializations. For developing countries proximity appears slightly more relevant for specialization along the extensive margin than the intensive margin, with one standard deviation impact size of 5.2 and 5.0 percentage points respectively. At later stages of development, proximity for specialization along the extensive margin becomes much less important, with impact of only 1.4 percentage points for the extensive margin, and 3.3 percentage points for the intensive margin for the group of advanced economies.

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# **Table 4: Probability regressions for new activity specializations at the intensive and extensive margins**

||Dependent<br>|variable: wheth<br>|er country_c_has a<br>_t+5_(xj,j<br>|comparative a<br>,t+5= 1)<br>|dvantage in act<br>|ivity_j_at time<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ASj,j,t<br>|0.068***|0.171***|0.049***|0.083***|0.255***|0.058**|
||(0.023)|(0.006)|(0.019)|(0.029)|(0.006)|(0.023)|
|ωj,j,t|0.050***|0.033***|0.050***|0.045***|0.014***|0.052***|
||(0.005)|(0.003)|(0.004)|(0.005)|(0.003)|(0.004)|
|Year FE|X|X|X|X|X|X|
|Country-Industry FE|X|X|X||||
|Country-Occupation FE||||X|X|X|
|Sample|Total|Advanced|Developing|Total|Advanced|Developing|
|N|137,723|56,919|80,804|137,723|56,919|84,804|
|Adjusted R<sup>2</sup>|0.145|0.150|0.148|0.124|0.165|0.105|

_Notes_ : see Table 3.

_Routine-intense activities_ Advances in information technology have changed the way in which certain tasks are performed. In particular, Autor et al. (2003) argue that computers and robots tend to displace labor in the performance of routine and non-cognitive tasks. This is nowadays typically referred to as routine-biased technological change. Lewandowski et al. (2020) and Caunedo et al. (2022) find that all countries experienced a shift away from routine to non-routine jobs, but its pace was slower in developing countries than in developed countries. Reijnders and de Vries (2018) show that relocation of routine occupations from advanced countries accounted for a major part of this difference, moderating the effect of the technology bias for developing countries. At the same time, innovation generates new tasks and activities and allows for the development of new specializations (Acemoglu and Restreppo 2019).

To investigate possible differences in the development of activity specializations, we constructed for each of our 455 activities a measure of routine task intensity. We closely follow the approach by Acemoglu and Autor (2011) and make a distinction between two types of routine tasks: manual and cognitive tasks based on O*NET measures.<sup>12</sup> Routine manual tasks are more prevalent in

> 12 In this approach, O*NET work activities and work context importance scales are combined and standardized. However, whereas Acemoglu and Autor (2011) construct task measures by broad occupation classes, we match to detailed occupation-industry data from the U.S. Bureau of Labor Statistics and then collapse using labor supply weights to create task measures by activity. See Appendix F for details.

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production and operative occupations, and routine cognitive tasks are most intensively used in clerical and sales occupations. We split the activity data accordingly into two samples and redo the baseline regressions for each sample, see Table 5. For activities with a high routine manual intensity, we indeed observe a major difference across advanced and developing economies. New specializations in routine manual activities strongly relate to proximity of the activities to current specialization. For developing economies, a 1 standard deviation increase in average proximity relates to a 10.9 percentage point increase in the probability of specializing in a new routine manual activity. In advanced economies the effect is only 2.9 percentage points. The impact of proximity on specialization in routine cognitive activities however is comparable to the impact for all activities found in the baseline (columns 4-6).

The qualitative results presented in Tables 3, 4 and 5 appear robust in a battery of additional tests, as shown in Appendix D. Taken together, the regression results show a strong commonality in the way activity export baskets develop over time, especially in earlier stages of development. Proximity to the initial product basket plays a strong role in the development of new product specializations. Low-income countries initially specialize across the extensive margin, exporting new activities carried out by workers of the same occupational grouping but in different industries. Proximity to the initial specialization basket is particularly important for the development of new routine manual activities in this phase. As income levels rise, specialization along the intensive margins becomes more prevalent, and workers shift towards different activities in the same industries. Proximity to the initial basket becomes less important for new specializations in this later phase of development.

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**Table 5: Probability regressions for new specializations in routine task intense activities**

||Dependent<br>|variable: wheth<br>|er country_c_has<br>_t+5_(xj,j<br>|a comparative a<br>,t+5= 1)<br>|dvantage in act<br>|ivity_j_at time<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ASj,j,t<br>|0.071**|0.236***|0.051**|0.248***|0.218***|0.267***|
|ωj,j,t|(0.030)<br>0.089***|(0.009)<br>0.029***|(0.024)<br>0.109***|(0.009)<br>0.053***|(0.012)<br>0.055***|(0.014)<br>0.055***|
||(0.008)|(0.005)|(0.007)|(0.004)|(0.005)|(0.005)|
|Routine manual|0.001|0.009***|-0.001||||
||(0.002)|(0.003)|(0.003)||||
|Routine cognitive||||-0.016***|-0.030***|-0.008**|
|||||(0.003)|(0.006)|(0.004)|
|Country-Year FE|X|X|X|X|X|X|
|Industry-Year FE|X|X|X|X|X|X|
|Sample|Total|Advanced|Developing|Total|Advanced|Developing|
|N|56,697|23,982|32,715|40,227|15,901|24,326|
|Adjusted R<sup>2</sup>|0.074|0.098|0.078|0.112|0.093|0.132|

_Notes_ : see Table 3. Results in columns 1-3 only include observations where the routine manual task intensity of the activity is above the mean, and results in columns 4-6 where the routine cognitive task intensity is above the mean. Accordingly, the regressions control for manual (columns 1-3) and cognitive (columns 4-6) routine task intensity of the activity. Routine task intensity constructed following Acemoglu and Autor (2011), see Appendix F.

# **5. Path-dependence in activity specialization and growth**

The cross-country average patterns found in the previous section are used to benchmark trade specialization patterns of individual countries in the vein of Coniglio et al. (2021), adapting their product-based methodology to the activity perspective. Coniglio et al. (2021) found that economic growth is weaker in countries with a higher degree of path dependence in their export specializations. We revisit this relationship using our activity export data. Following Coniglio et al. (2021) a country is denoted as “path-defying” when its new specializations are weakly related to its initial basket. For a particular country, the cumulative distribution function (CDF) of proximity for new activity specializations between time _t_ and time _t + T_ is compared with a hypothetical random CDF of proximity for all possible new specializations, that is, activities in which the country did not have a comparative advantage yet.<sup>13</sup> Subsequently, stochastic

> 13 Specialization is also shaped by the exit of activities in exports, i.e., de-specialization. This paper focuses on the entry of activities in which countries gain specialization.

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={f=1..,M|AS >1}

={f=1,..,M|AS <1}

= {j = 1,...,M| AS

<1} ={g } = x{D }
<u>{1| {As</u>

x} ~~—~~ ~~—~~ 1{d <a} 1{d <d}
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mainly serves to investigate to what extent results from an activity perspective will differ from results based on a product perspective.

**Figure 5: Economic growth and degree of path-defying specialization**

<!-- Start of picture text --> 4 IRL<br>MNG CHN<br>LTU<br>TUR LKA TWN MLT<br>KOR<br>POL<br>ROULVA<br>2 EST INDSVK VNM<br>IDNLUX<br>RUS BGR SWD E U THA USA<br>CZE KHM AUS<br>NOR HUNJPN GBRAUT BELDNKCANNLDBGDPHL<br>FRA<br>FIN SVN FJI<br>0 ESP<br>NPL KGZPRT MEXBRA<br>ITA<br>CYP<br>PAK<br>-2<br>GRC<br>.2 .3 .4 .5 .6<br>Average Path Defying Share<br>Orthogonal Component of Real GDP per Capita Growth<br><!-- End of picture text --> _Notes_ : Figure plots the orthogonal component of average real GDP per capita growth rate against the average share of path defying entries (as defined in equation 11) across the eight overlapping periods for each country. Regression control for initial (log) GDP per capita. Slope (standard error) of the linear fit is 3.73 (2.18). Size of circles represent country size measured as average real GDP in 2017 US$. _Sources_ : Authors’ calculations based on Penn World Table version 10.0, ADB MRIOTs and Occupations Database.

Additional data sources and regression results are given in Appendix B. The dependent variable is the average growth rate of GDP per capita in the period [t, t + 5]. The key variable of interest is the share of path-defying entries which appears to be strongly significant and positively related to growth. Interestingly, the positive impact remains even after controlling for factor endowments in terms of human capital levels and financial development. This suggests that the capabilities needed to undertake new path-defying activities go beyond the mere expansion of educational attainment levels and general development of financial markets in a country. It is also found that the impact of path defiance on growth is moderated by the initial level of GDP per capita: it appears to be 26
stronger for poorer countries than for richer countries. The average marginal effect (AME) is positive at all levels of development albeit only significantly different from zero (at 95% confidence) for GDP per capita levels between about 9,000 and 30,000 US$ (Figure 6). This qualifies the finding of Coniglio et al (2021, Figure 5) who also find that the marginal effect is positive for the poorest countries and declining with higher incomes, yet in contrast establish that the effect is small and turns negative for middle-income countries. Confidence intervals are too large to draw firm conclusions, but the different findings do illustrate the potential for analyses based on the activity perspective to generate new insights beyond those based on the product perspective.

**Figure 6: Average marginal effects of path-defying entries on economic growth**

<!-- Start of picture text --> 2<br>1.5<br>1<br>.5<br>0<br>-.5<br>2,000 4,500 10,000 23,000 53,000<br>Real GDP per capita, log scale<br>AME of ln path def share on GDP per capita growth<br><!-- End of picture text --> Notes: Figure shows average marginal effects (AME) of path-defiance on GDP per capita growth at various levels of economic development. Effects calculated based on regression estimates reported in column 2 of Appendix Table B1. Point estimates (in blue line) and 95 confidence intervals (in grey) are shown over levels of (log) GDP per capita.

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# **6. Concluding remarks**

This study documented for the first-time patterns in export specialization from an activity perspective and showed how it can generate additional insights beyond those based on the product perspective. First, export incomes from production activities decline and engineering, managerial, and services support activities grow as countries develop. Second, countries initially specialize along the extensive margin (shifting activities across industries) but later along the intensive margin (shifting activities across occupational classes). Third, new activity specialization is strongly related to the proximity of this activity to the initial export basket, in particular for specializations along the extensive margin and in routine intensive occupations. Fourth, countries that defy proximity and diversify quicker appear to grow faster in GDP per capita. This is correlation however and no claim for causation is made.

We see at least two promising avenues for further research. One avenue is in the modeling of structural change and the role of international trade. The canonical macro-structural change framework focuses on the sectoral composition of the economy in terms of employment and valueadded. Trade can shape the sectoral composition in various ways (Alessandria, Yi and Johnson 2021). Lower trade barriers facilitate specialization, for example through shifting comparative advantage and promoting economies of scale. Sectoral specialization will consequently affect the sectoral composition of the economy. And given a set of trade barriers, policy changes or technology shocks to the economy may also affect specialization patterns and consequently sector composition. Trade barriers are typically related to products whereas technological change such as automation affects particular activities rather than products or sectors. Modeling the composition of the economy in terms of activities in addition to sectors appears therefore to be a promising way forward as for example in Bárány and Siegel (2018) and Duernecker and Herrendorf (2022).

A second avenue for research is in further developing an integrated product and activity data set. Our data tracks exports at a rather aggregate industry level compared to the detailed product level export data that is available. Suppose that production technologies for a particular detailed product are the same around the world. In that case, cross-country differences in the occupational composition of the exporting industry (as documented in this study) are due to a different set of products being exported. In that case, activity specialization across the extensive margin can also 28
be studied from a product perspective as in Diodato et al. (2022). If production technologies are not constant around the world, however, detailed products will no longer map one to one into a set of activities. Caunedo et al. (2021) document some cross-country differences in the task content of a particular occupation, suggesting that a description of production technologies in terms of occupational structures is only a first step towards better understanding of activity specializations.

These avenues are worthwhile to pursue also for the purpose of policy. Our findings re-open the debate on the appropriate target of development and trade policies. The product space paradigm by Hidalgo et al. (2007) has been used to guide countries in the design of industrial policies. According to this paradigm, policy makers should follow a gradualist approach and focus on introducing new products that are close to their current product mix to avoid failure, as introduction of radically new products purportedly requires capabilities that are scarce and difficult to create (Coniglio et al., 2021). Our results highlight that products that might seem far apart (close) in product space might actually be rather close (far apart) in terms of activities. As such, an activitybased analysis provides a clearer link with the export capabilities that are required. This study and the associated database hopefully provide a fruitful steppingstone for deriving well-founded policy implications for trade development and structural change.

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