Ricardo Hausmann and César A. Hidalgo WEBPUBLICATIONS 74

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1 HOW WILL THE NETHERLANDS EARN ITS INCOME 20 YEARS FROM NOW? A GROWTH VENTURES ANALYSIS FOR THE NETHERLANDS SCIENTIFIC COUNCIL FOR GOVERNMENT POLICY (WRR) Ricardo Hausmann and César A. Hidalgo WEBPUBLICATIONS 74 November 2013

2 2 The Web publications series comprises studies carried out as part of the activities of the WRR. Responsibility for the content and views expressed rests with the authors. A list of all Web publications can be found on the WRR website (

3 3 CONTENTS Executive Summary How has the Netherlands Grown? What is Economic Complexity? How Complex is the Dutch Economy? What is the Product Space? How has the Netherlands Performed in the Product Space? How is the Netherlands Positioned in World Markets? What are the Implications for Public Policy? Academic References Data Appendix... 77

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5 EXECUTIVE SUMMARY The Netherlands is a highly export oriented economy that is venturing into uncharted territory. Europe, which has for centuries been the main destination of Dutch exports, is unlikely to be the engine of future growth. In the next decades, developing countries are expected to drive global growth, generating new opportunities but also new challenges for established Dutch producers. On the one hand, their growth will create new markets for Dutch exports. However, the needs of these markets will differ from the ones traditionally served by the Netherlands. On the other hand, their growth will also increase competition for Dutch exporters. How well is the Netherlands positioned to navigate in these shifting currents? In this report we use global exports data for a period of close to fifty years to uncover what the historical pattern of Dutch exports reveals about the type and the complexity of productive knowledge that resides in the Netherlands. We find that the strongholds of the Dutch economy are its diversified chemicals industry and its somewhat less diversified but nevertheless notable electronics industry. These industries feature the country s most complex productive knowledge. We also note the dominance of two traditional sectors agriculture and food processing which are strong but are primarily dependent on European markets. We also take note of the exceptionally strong global positioning of the Netherlands in a number of tropical products notably flowers and cocoa but argue that these sectors rely on lower-complexity productive knowledge and are subject to more future competition. Finally we uncover a large gap in the productive structure of the Netherlands: its machinery cluster. This gap becomes the primary focus of our policy recommendations for Dutch stakeholders. While the Netherlands has done well in the past by exporting the relatively low complexity goods to its traditional markets in Europe and the U.S. (notably flowers, agricultural goods, processed foods), we believe that the future growth of the Netherlands will require it to reorient its exports more toward the lower- and middle income countries that will increasingly drive global growth. The most competitive exports to these markets will not necessarily be Dutch cheese or flowers, but rather, higher complexity goods, including chemicals, electronics, machinery and the services related to these industries. Dutch exports today already show evidence of this pattern: the products that travel the farthest distances are the most complex products. Our policy recommendations address the dual need for the Netherlands to i) strengthen its machinery cluster, both in terms of goods and services, and integrate it further into its high-

6 6 complexity industries and ii) bridge the distance between Dutch firms and more distant exports markets. In our view, increased efforts in research, development, and innovation, an augmentation and reframing of the way that the Netherlands thinks about its international development efforts, and an improved data infrastructure can be powerful tools that public policy can leverage towards achieving these objectives.

7 1 HOW HAS THE NETHERLANDS GROWN? The principal motor driving economic growth in the Netherlands has been trade. A country with a relatively small domestic market, the Netherlands historically positioned itself as a strong merchant nation, an entry point for goods from around the world making their way to Europe. During the last forty years, a period which saw exponential growth in world trade volumes, the Netherlands experienced steady growth in GDP per capita of roughly two percent on average (figure 1.1). At this rate, it grew faster than most other high income countries and propelled itself to the position of the world s sixth wealthiest nation (PPP, figure 1.2). During the last forty years, the income of the average Dutch citizen effectively doubled from that of his or her predecessors. Meanwhile, the role of trade in the economy continued to expand and as of 2010, total exports of goods and services accounted for 70 percent of GDP, or roughly US$35,000 per capita one of the highest levels in the world (figure 1.3). The majority of Dutch exports are destined for other countries in Europe and indeed, a large share flows to just a handful of trading partners. As of 2009, 77 percent of the Dutch goods exports and 67 percent of services exports went to Europe and more than half went to just five trading partners (figure 1.4). 1 On the other hand, less than sixty percent of its imports were sourced in Europe. Historically the source of strength of the Dutch economy its proximity and strong trade linkages with Europe has also been its principal source of vulnerability. In the forty years since 1970, the Netherlands saw only three years of negative GDP growth, and most recently in Each of these years coincided precisely with periods when European trade registered negative growth. The Netherlands Bureau of Economic Policy Analysis currently predicts that 2012 will see another fall of GDP 2, which again mirrors economic woes that are plaguing a number of countries in Europe. What does its high dependence on trade and its high concentration of export destinations imply for the future of the Dutch economy? Europe is facing a prolonged crisis and full recovery will likely take years to materialize. On the other hand, emerging market economies have rebounded from the global crisis more quickly, and are expected to drive future economic growth. While their growth creates opportunities to supply these regions, it also poses threats to established producers. More emerging countries are entering industries that were traditionally dominated by developed countries and competition is rising. These trends pose a number of important questions for mature economies such as the Netherlands. Will it be able to hold on to its market share in the traditional strongholds of its economy? Will it be able to grow in its traditional export markets? Does it have what it takes to adjust to the changing global patterns of supply and demand? Does it have the capabilities to re-orient its

8 8 economy toward new market opportunities? In other words, how will the Netherlands earn its income 20 years from now? Figure 1.1 Growth in GDP per capita for the Top 15 wealthiest countries NOR FIN JPN AUT BEL DEU GBR USA FRA CAN AUS SWE DNK CHE AUS SWE FIN AUT DEU GBR CAN BEL JPN CHE USA NOR FRA DNK Average GDP per capita growth rate Average GDP per capita growth rate Figure 1.2 Top 15 wealthiest countries in income per capita, 2010 US$, Current US$, PPP NOR CHE DNK SWE USA CAN IRL AUT FIN SGP JPN BEL DEU FRA 55,778 48,754 47,130 47,084 46,060 45,805 44,879 44,531 43,324 43,161 43,019 40,542 39,460 67,236 84,880 NOR SGP USA CHE HKG IRL AUT DNK SWE CAN DEU BEL FIN GBR 47,084 46,424 46,331 42,448 39,996 39,712 39,409 38,885 38,841 37,622 37,491 36,667 35,844 56,921 56, ,000 40,000 60,000 80,000 Average GDP per capita, current US$ 0 20,000 40,000 60,000 Average GDP per capita, PPP

9 9 Figure 1.3 Top 15 countries with highest per-capita exports in the world, 2010 Exports of goods (US$ per capita) Exports of services (US$ per capita) SGP HKG CHE NOR BEL IRL KWT NOC AUT DNK SWE DEU OMN FIN 33,020 29,270 27,162 25,948 24,514 24,472 17,954 17,587 17,338 17,101 15,952 13,154 12,939 55,748 70,614 SGP IRL HKG DNK CYP CHE NOR BEL SWE AUT FIN NOC GBR LBN 10,800 10,427 10,171 8,134 7,905 6,781 6,530 5,743 4,783 4,405 3,837 3,611 15,059 22,122 21, ,000 40,000 60,000 80,000 Export of goods per capita 0 5,000 10,000 15,000 20,000 Export of services per capita Note: WDI and IMF data. Samples restricted to countries with population over one million. Figure 1.4 Destinations of Goods Exports and Sources of Goods Imports, 2009 Where does the Netherlands export to?

10 10 Where does the Netherlands import from? Source: The Observatory of Economic Complexity. To shed light on these questions, this report will draw on a line of academic publications which share the central notion that what a country makes today affects the subsequent development of products and industries, and therefore, future productivity and growth. This idea stands in contrast to traditional trade models, in which countries patterns of specialization and trade have little effect on their future evolution, but rather, reflect underlying comparative advantages, such as the relative abundance of capital and labor. The notion that the specific products a country makes do matter to subsequent growth was first formalized in a paper titled What you export matters by Hausmann, Hwang, and Rodrik (2007). Here the authors argued that what countries make reveals something about what they know and that productive knowledge is key to defining the possibilities for the development of future products and industries. Building on these ideas, Hausmann and Klinger (2007) and Hidalgo, Klinger, Barabasi, and Hausmann (2007) created the product space, a network of product relatedness based on the likelihood that a country jointly exports any pair of products. With the product space, the authors demonstrated how countries move from product to product and found that this evolution occurs in a way that is systematic and to some extent predictable. Thus a country s overall position in the product space today carries important information about what its

11 11 future opportunities for growth and diversification are and how difficult or easy they are to achieve. This characteristic of a country that measures how well it is positioned in the product space was later formalized under the term opportunity value which can be thought of as the value of the option to move into more and more complex products. The Opportunity Value Index (OVI) summarizes the value of this option in one single number. Continuing this line of inquiry, Hidalgo and Hausmann (2009) gave birth to a means of summarizing the information contained in the product space into a single measure of economic complexity, the human and organizational networks that emerge to hold knowledge and combine it into products. They gave birth to the Economic Complexity Index (ECI), a unique number that measures how diversified and complex a country s export basket is i.e. how much productive knowledge it holds. In the Atlas of Economic Complexity, Hausmann, Hidalgo, et al. (2011) showed that economic complexity and opportunity value are powerful concepts which can help to explain the levels of income that countries enjoy. The regression in table 1.1 and figure 1.5 help illustrate this. They show the results of the effects of the ECI and the OVI on growth. All else equal, countries with a higher ECI and countries with higher opportunity value tend to grow faster. This finding is statistically significant, robust to various specifications, and can explain more than 40 percent of total variation in income growth across countries. The estimates of these equations can also be used to forecast the future growth in GDP per capita and rank countries according to their growth potential. We include the forecasts in this report (see figure 1.6) in order to summarize the implications of our approach. The purpose of course is to help shape, not predict the future.

12 12 Table 1.1 Growth Regression Dependent variable: 10 year GDP per capita growth rate VARIABLES (1) (2) (3) (4) Initial GDP per capita, logs *** *** *** (0.125) (0.163) (0.142) (0.145) Increase in real NNRR exports pc 4.034*** 3.794*** 4.062*** 3.905*** (0.830) (0.919) (0.967) (0.979) Initial Economic Complexity Index 1.393*** 0.859*** (0.228) (0.197) Initial Opportunity Value Index 1.235*** 0.832*** (0.226) (0.215) Constant *** 4.894*** 6.776*** (1.097) (1.323) (1.173) (1.177) Observations R-squared Year FE Yes Yes Yes Yes Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 To analyze the impact of the Economic Complexity Index (ECI) and the Opportunity Value Index (OVI) on economic growth, we estimate four equations where the dependent variable is the annualized growth rate of GDP per capita for the periods , and -. In the first of these equations we do not include ECI and use only two control variables: the logarithm of the initial level of GDP per capita in each period and the increase in natural resource exports per capita (in constant dollars) as a share of initial GDP per capita. The first variable captures the idea that, other things equal, poorer countries should grow faster than rich countries and catch up. This is known in the economic literature as convergence. The second control variable captures the effect on growth of increases in income that come from natural resource wealth, which the economic structure of a country and its level of complexity do not explain. In addition, we include a dummy variable for each decade, capturing any common factor affecting all countries during that decade, such as a global boom or a widespread financial crisis. Taken together, these variables account for 26.9 percent of the variance in countries' growth rates. This is shown in the first column of table 1.1. In addition to the above mentioned variables, the second regression includes the effect of economic complexity on growth. We do this by adding the ECI at the beginning of the decade. The second column of table 1.1 shows that economic complexity is positively and strongly associated with future economic growth. The variables contained in

13 13 Column 2 jointly account for 39 percent of the variance in GDP per capita growth rates across countries. The difference between these two regressions indicates that the ECI increases the regression's R-squared by 12 percentage points, a relatively large increase compared to other variables used in the economic growth literature. The third column of table 1.1 introduces into the regression the Opportunity Value Index (OVI). The coefficient tells us that OVI has a positive and high correlated with economic growth. The variables used in the regression shown in column 3 account for 39.9 percent of the variance in economic growth rates. Finally, the fourth column of table 1.1 uses both the Economic Complexity and Opportunity Value indexes and the control variables used in the previous columns. This regression shows us that the ECI and OVI contain different information useful to understanding economic growth. Compared to the previous regressions, both coefficients are smaller but jointly improve our understanding of growth by explaining 43.1 percent of the variance in growth rates across countries. Figure 1.5 Predicted versus Actual Growth of countries, DEU GAB ARE KHM BLR LVA TJK RUS TTO LTUEST GEO UKR ALB BGR CUB VNM ROU ETH LAO MDA KOR MNG UZB BIH SVK MOZ DOM PAN HKG BGD IRN LKA JOR HRV SVN POL GRC IRL CZE HUN KGZ MUS SGP UGATZATUN PER CRI GHA MAR EGY MYS THA OMN IDN NGA CHL FIN SLV MKD NICHND AUS ECU LBN AUTDZA MLI NZL ZMB PAK PHL SWE ARG GBR BEL COL CAN ESP CMR GIN ISR BOL BRA ZAF TUR JAM GTM SEN USA NOR KEN FRA URYMRT CHE DNK MEX ITA COGJPN LBR SYR VEN YEM MDGPRT SAU PRY PNG CIV ZWE CHN KAZ AGO Expected GDP growth pc - IND TKM Source: Own calculations using COMTRADE and WDI data. Figure 1.5 (above) shows the expected and actual per capita GDP growth rates for a number of countries in the period -. For the Netherlands our regression predicted (an in sample) growth rate of 3.3% while the effective growth was 1.9%. The expectation was high mainly due to the high opportunity value we estimate for the Netherlands, which will be discussed in more detail later in the report.

14 14 Figure 1.6 Predicted Growth versus Current Income, IND CHN EGY IDN TZA VNM BGR KEN UKR THA UGA BIH BLR SRB KGZ MDA MDG PAK LBNBRA ROU TUR ZWE SEN LTU GTM JOR TUN MEX HUN ALB COL ZAF PAN MYS HRV EST KOR LBR ETH LKA ARG POL LVASVK MWI PHL PRT CRI GRC ESPGBRFIN BGD HND ISR JPN MOZ NIC SYR SLV SGP MAR SVN DOM BEL CAN CHE GIN KHM UZB MKD LAO CIV GEO URY HKG MLI GHA PRY NAM RUS CZE SWE TJK DNK ZMB PER NZL BOL IRL YEM MUS CMR NGA ECU JAM ITA USA MNG CUB KAZCHL FRA PNG AUT MRTSDN TKM BWA NOR IRN DZA SAU AUS COG AZE TTO AGO ARE GAB VEN OMN LBY QAT DEU KWT GDP per capita 2010, logs Source: Own calculations using COMTRADE and WDI data. Just as they can be used to calibrate past growth, the results of table 1.1 can be used to provide an estimate of future growth. However, to predict average annualized growth between 2010 and 2020 we need to make two additional assumptions. First, we assume a worldwide common growth term for the decade, which we take to be the same as that observed in the - period. Changing this assumption would affect the growth rate of all countries by a similar amount but would not change the rankings. Second, we assume that there will be no change in the real value of natural resource exports as a share of initial GDP. This implies that we assume that natural resource exports in real terms in the next decade will remain at the record-high levels achieved in. This assumption may underestimate the effect on countries whose volumes of natural resource extraction will increase significantly and over-estimate the growth in countries that will see their natural-resource export volumes decline. A higher (lower) constant dollar price of natural resource exports would improve (reduce) the projected growth performance of countries by an amount proportional to their natural resource intensity. The estimate of future economic growth for the Netherlands is 2.3 percent. This is relatively high among other high-income countries, which are expected to growth from less than half a percent per year on average (Austria) to more than 3 percent per year on average (Turkey). However, more useful than the single estimate of a growth rate is understanding the

15 15 different components that we think matter for growth ECI and opportunity value and how the Netherlands fares on each of these measures and why. This will be the subject of the next several chapters of this report. Textbox 1: A Note on Data To analyze what countries make, ideally we would like to use data on production. However, such data is not available for a large number of products, countries, and years. Instead, we use international data on trade compiled by the United Nations. The UN COMTRADE database is the only dataset currently available that has detailed crosscountry information linking countries to the products that they make using a comparable standardized classification across time. The advantages of the data are that, following the Standard International Trade Classification Revision 2 at the 4 digit level (SITC4), it provides detailed information of the export baskets of countries using 1,006 different product categories for a significant period of time, 1962 until. While the COMTRADE data offers great advantages, it also has limitations. First, it is data on exports, not production. Countries may be able to make things that they do not export. The fact that they do not export them, however, suggests that they may not be very good at them. Countries may also export things they do not make. COMTRADE processes the data to clean it from re-exports, however, to more fully circumvent this issue in our analysis we require a country to have a significant presence in a product to assume that it makes it. We say a country has a significant presence in a product if its revealed comparative advantage (RCA) is greater than 1. RCA is defined as the ratio between the share of a product in the exports of a country and the share of the product in the exports of the world, per Balassa (1964). Second, the COMTRADE data is not always complete. Specifically, as some countries now have transitioned to more granular systems of classification (such as the Harmonized System at 6-digits of classification), once the data is converted into the 4-digit system, information is lost. As a result, a number of countries have large chunks of Unclassified transactions in the COMTRADE data. Finally, because the data is collected by customs offices, it includes only goods and not services. This is an important drawback, as services are becoming a rising share of international trade. Unfortunately, the statistical efforts of most countries of the world have not kept up with this reality. To compensate for these shortcomings, in this report we make use of several other datasets. Once is the BACI dataset from the Centre d'etudes Prospectives et d'informations Internationales (CEPII) which uses COMTRADE data but at a higher level of product disaggregation. It is able to correct for the large share of Unclassified

16 16 transactions in some countries, such as the Netherlands. However, this dataset is only available for the years 1995 to Therefore we make use of the SITC4 COMTADE data as the main source for the calculations of complexity and the product space and use the BACI data to cross-check the findings of the COMTRADE data and ensure that both lead to the same conclusions. However, none of the data that we have available includes information on non-tradable activities. These are an important part of the economic eco-system that allows products and services to be made. In order to study local economies using both traded and nontraded goods would require data on all establishments and their production, such as may be available through value-added records provided by companies to governments, broad-based business directories, etc. Capturing non-traded goods is an area of current and ongoing research.

17 17 Notes 1 By comparison, the share of goods exports to Europe for Germany was 63 percent and for the UK 56 percent. 2 CPB Policy Brief 2011/13 "Decemberraming 2011 Economische vooruitzichten 2012."

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19 2 WHAT IS ECONOMIC COMPLEXITY? 1 Modern societies have demonstrated increasing sophistication over time. We have evolved from hunter-gatherers to producers of computers and airplanes. What has underpinned this progress? More than the pure acquisitions of raw materials, capital, and labor, societies have been able to make ever-more sophisticated products by increasing the amount of productive knowledge that they hold. This does not mean that we have given birth to ever-wiser individuals, but rather that modern societies have learned to distribute bits and pieces of knowledge more effectively among its many members. Ultimately these pieces of knowledge are put back together and made into products through organizations and markets. Thus, individual specialization has begot diversity at national and global levels. This social accumulation of productive knowledge has not been a universal phenomenon. It has taken place in some parts of the world but not in others. Why? Accumulating productive knowledge is difficult because, for the most part, it is not the type of information available in books or on the web. Rather, it is the knowledge embedded in brains and human networks. It comes from years of experience rather than from years of schooling. This is why expanding the amount of productive knowledge available in a country involves enlarging the set of activities that the country is able to do. Productive knowledge needs to be active in order to survive. However, the process by which productive knowledge is accumulated is tricky. Industries cannot exist if the requisite productive knowledge is absent, yet accumulating bits of productive knowledge will make little sense in places where the industries that require it are not present. This chicken and egg problem slows down the accumulation of productive knowledge and also creates important path dependencies in the economic development process. It is easier for countries to move into industries that mostly reuse what they already know, since these industries require adding modest amounts of productive knowledge. Indeed, we find empirically that countries move from the products that they already make to others that are close by in terms of the productive knowledge that they require. Key to this insight is recognizing that, just as nations differ in the amount and types of productive knowledge they hold, products differ in the amount of productive knowledge that is required to make them. Most modern products require more knowledge than what a single person can hold and therefore need to be produced by organizations. However some products, like medical imaging devices, require vast amounts of knowledge. Others, like harvesting sesame seeds, require little. Thus only those countries that have the productive knowledge required will be able to produce the most sophisticated products. Economic complexity is the social structure that emerges to hold and combine productive knowledge. Complex economies are those that can weave vast quantities of productive knowledge

20 20 together, across large networks of people, to generate a diverse mix of knowledge-intensive products. Simpler economies, in contrast, have a narrow base of productive knowledge and produce fewer and simpler products, which require smaller webs of interactions. But how can we measure economic complexity? Because we cannot measure the knowledge embedded in social structures directly, we use of the insight that economic complexity will be expressed in the composition of the products that a country makes. If making a product requires a particular type and mix of knowledge, then the countries that make the product reveal having the requisite knowledge. Said differently, countries do not make the products and services they want, but those that they can. Thus they reveal their economic complexity by demonstrating which product they are able to make. To introduce a few technical concepts, let s use a simple analogy where a product is equivalent to a Lego model and where each Lego piece represents a chunk of productive knowledge. In this analogy, products differ in the variety of Lego pieces that they require. Countries, on the other hand, are buckets of Legos and can make all the products for which they have all of the necessary Lego pieces. Countries differ in the variety of Lego pieces they have, and hence only those with a larger diversity of pieces will be able to make more and more unique products. On the other hand, products that require more Lego pieces will be made only in the countries that have all the requisite pieces. We define as the diversification of a country the number of different products that it can make and the ubiquity of a product as the number of countries that can make that product. Since countries with a larger variety of Lego pieces will be able to make more and more unique products they will be more diversified. By contrast, products that require more Lego pieces will be made only in the countries that have all the requisite pieces they will be less ubiquitous. This implies that there should be a negative relationship between the diversification of a country and the average ubiquity of its products. Using world trade data, figure 2.1 shows that the average ubiquity of products is negatively related to the diversity of a country. From the figure we see that relatively diverse countries (those that export a large number of products) make products that have, on average, relatively low ubiquity (they are made by few other countries). Netherlands is such a country it has a relatively high level of diversity and its products have a relatively low average ubiquity.

21 21 Figure 2.1 Diversification of Countries and Average Ubiquity of Products, KHM AGO LAO MWI SLV HND MDGTM NICBGD QAT MUS JAM MNG SYR MKD DOM GAB GIN CMR CRI ALB MAR MRT SDN TKM UGA BIH KWT SEN KEN LVA YEM MOZ PAK LBY COG TJK TZA MDA LTU NGA ETH GHA LKA CUB BLREGY EST OMN TTO AZE ECU BOL CIV LBN TUNVNM PNG VENZWE HRV ZMB BGR DZA IRN UZB AREKGZ PHL PANPER COL ROU TUR JOR GEO GRC IDN SAU PRY MLI CHL URY PRT LBR NZL UKR IND THA ARG ZAF HUN SVK SVN KAZ MEX NOR AUS RUS HKG BRACAN DNK ISR FIN KOR MYS SWE IRL CHE SGP JPN GBR POL CHN ESP AUT CZE BEL FRA Diversity of a country (% of products with RCA over total number of products) USA ITA DEU Source: Own calculations using COMTRADE data. Diversity is the number of products in which the country has Revealed Comparative Advantage (RCA) equal or greater than one. Average ubiquity is the number of countries that export with the products of a country with RCA equal or greater than one. Diversity and ubiquity are, respectively, crude approximations of the variety of productive knowledge available in a country, or required by a product. Both mappings are affected by the existence of rare Lego pieces. To address this problem, diversity can be used to correct the information carried by ubiquity and ubiquity can be used to correct the information carried by diversity (See Textbox 2). We can repeat this correction an infinite number of times. The process converges after a few iterations and represents our quantitative measures of complexity. For countries, we refer to this as the Economic Complexity Index (ECI) and the corresponding measure for products gives us the Product Complexity Index (PCI). The PCI is a number unique to each product that captures how much productive knowledge the product requires. This measure is very much related to the concept of ubiquity. The ECI is a number unique to each country that measures the amount of productive knowledge contained in the country. Countries with a high ECI are well-diversified countries exporting, on average, high- PCI products. Figure 2.2 shows the world map colored according to countries ECI ranking, from most diverse (Japan) to least diverse (Mauritania).

22 22 Figure 2.2 Map of the World colored according to ECI Ranking (Red= High, Green = Low) Source: The Atlas of Economic Complexity. Textbox 2: Developing Measures of Economic Complexity The PCI and ECI Diversity and ubiquity are approximations of the complexity of a country and the complexity of a product, respectively. However, these basic concepts are affected by the existence of rare inputs. For instance, diamonds are non-ubiquitous (i.e. exported by very few countries) not because they are complex to produce, but rather because the raw materials are rare. A simple measure of product ubiquity would over-estimate the complexity of diamonds. To correct for such instances, the Product Complexity Index (PCI) takes into account the ubiquity and diversity of all countries and products. In the case of diamonds for example, it does not only look to the ubiquity of diamonds, but also to the diversity of countries that produce diamonds, the ubiquity of the products exported by diamondproducers, the diversity of the countries who exports those products, etc. Thus, we seek to understand whether the low ubiquity of diamonds originates in their scarcity or in their complexity. If the countries that export diamonds make few other products, then it is likely that rarity rather than complexity explains the low ubiquity of diamonds. However, if the countries that can make these rare products are, in general, able to put together many other products, then it is likely that the low ubiquity of the product reflects the fact that it requires a large number of capabilities, and not just a few rare ones. Diversity can therefore be used to correct the information carried by ubiquity and

23 23 ubiquity can be used to correct the information carried by diversity. Repeating this process ad infinitum through mathematics, we find that it converges after a few iterations. The results are the quantitative measures of product complexity, the PCI. Table below shows the products that rank highest and lowest in the product complexity scale, respectively. The PCI is a normalized measure with an average of zero and a standard deviation of 1 within the observations of each year. The differences between the world s most and least complex products are stark. The most complex products are sophisticated machinery and chemicals which tend to emerge from organizations where a large number of highly skilled individuals participate. The world s least complex products, on the other hand, are raw materials or simple agricultural products. Table The World s Most and Least Complex Products Source: The Atlas of Economic Complexity. The Economic Complexity Index (ECI) of a country is connected intimately to the PCI of the products that it exports. Ultimately, countries can only increase their ECI score by becoming competitive in an increasing number of complex products. Textbox 3:What the ECI is not about It is important to note what the Economic Complexity Index is not about: It is not another measure of trade openness or export diversification. Although the ECI is calculated using export data (see Textbox 1), the channel through which it contributes to growth is not limited to its impact on the growth of exports. Clearly, countries whose exports grow faster, all other things equal, will necessarily experience higher GDP

24 24 growth. This is simply because exports are a component of GDP. However the ECI is not a new measure of openness to trade. The impact of the ECI on growth is essentially unaffected once the ratio of exports to GDP is included the growth regression shown in table Also, the ECI is not a new measure of export diversification: When including a standard measure of export concentration in the growth regression, such as the Herfindahl-Hirschman (HH) Index, the impact of the ECI on economic growth remains essentially unaffected. On the contrary, neither openness (as measured by the ratio of exports to GDP) nor export concentration (as measured by the HH index of exports) are statistically significant determinants of economic growth after controlling for the ECI. Table Growth Regression with Additional Controls Dependent variable: 10 year GDP per capita growth rate VARIABLES (1) (2) (3) (4) (5) Initial GDP per capita, logs *** *** *** *** *** (0.148) (0.149) (0.145) (0.143) (0.147) Increase in real NNRR exports pc 3.889*** 3.611*** 3.899*** 3.915*** 3.960*** (0.974) (0.810) (0.974) (0.980) (1.025) Initial Economic Complexity Index 0.900*** 0.902*** 0.798*** 0.873*** 0.845*** (0.198) (0.190) (0.207) (0.199) (0.197) Initial Opportunity value Index 0.758*** 0.788*** 0.714*** 0.728*** 0.755*** (0.216) (0.217) (0.232) (0.193) (0.216) Exports to GDP (0.190) Exports Herfindahl-Hirschman (0.835) Population, logs (0.124) Population density (1.070) Observations R-squared Year FE Yes Yes Yes Yes Yes Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Control variables are normalized to have a mean of zero and a standard deviation of 1 for ease of comparison. Hong Kong and Singapore, two extreme outliers in terms of the level of Exports to GDP, were removed from the sample for all regressions. The ECI is also not about a country s size. Some very small countries have high revealed comparative advantage in many and very complex products and as such, are ranked highly on the ECI. Switzerland, for example, with a population of only 7.6 million and a GDP of US$ 502 billion ranks as the country with the 3rd highest ECI in the world. On the other hand, some very large countries make few products with RCA and their products are relatively unsophisticated. For example the Russian Federation, with a population of

25 million and a total GDP of US$ 1.7 trillion, ranked 46th on the ECI. Figures and below illustrate this graphically, showing that there is a very weak association between the ECI and population size. Somewhat stronger is the association between the ECI and population density, which appears consistent with our theory in which productive knowledge is accumulated and shares in human networks. Human proximity as measured by population density could plausibly be further the existence and strength of our networks of productive knowledge. In regressions, the ability of the ECI to predict growth is unaffected after taking into account a country s size, as measured by its population, or its population density. Figure ECI and Population, Figure ECI and Population density, JPN JPN CHE DEU AUT SWE SGP FIN CZE SVN GBR HUN KOR FRA SVK USA IRLDNK ITA ISRBLR BEL MEX HRV HKG POL ESP EST PANNOR MYS THA LTU LVA BIH SRBPRT BGR CANUKR LBN TUR MDA NZL CRI JOR TUN RUS GRC COL BRA ALB URY ARG ZAF MKD SLV ARE DOM EGY PHL IDN GTM GEO KGZ SAU VNM MUS TTO NAM JAM SEN ZWE CHL SYR LKA AUS KEN QAT CUB MAR PAK PRY HND UGA BWA OMN NIC ECU KAZ MDGPER KHM UZB TZA LAO BOL ZMB CIVGHA ETH BGD MWI KWTLBR TJK MLI MOZ YEM VEN MNG TKM AZE DZA LBY IRN GAB GIN CMR NGA PNG COG AGO SDN MRT CHN IND CHE DEU SWE AUT FIN CZE GBR USA SVN HUN FRA KOR SVK IRL DNK ITA BLR MEX ISR BEL HRV POL ESPCHN NOR ESTPAN MYS THA LTU CAN LVA BGR BIH SRBPRT UKR RUS LBN NZLBRA JOR TUN TUR CRI COL GRC MDA IND ARG URY ZAF ALB EGY MKD IDN SLV PHL ARE GTMDOM SAU KGZ VNM NAM AUS CHL ZWE GEO SEN KEN SYR LKA MAR JAM TTO MUS CUB PAK PRY QAT HND UGA BWA KAZOMN PERMDGTZA NIC ECU UZB KHM BOL ZMBLAO CIV ETH GHA BGD MLI MOZ VEN YEM MWI MNG DZA LBR TJK IRN AZE KWT LBY TKM GAB CMR GIN NGA PNG COG AGO SDN MRT SGP HKG Population, logs Population density, logs Source: Own calculations using COMTRADE, WDI. Hausmann, Hidalgo et al. (2011) find that in regressions with income per capita as the dependent variable and years of schooling, cognitive ability, and the ECI as explanatory variables, the ECI explains 17.2 percent of the variance in income while years of schooling and cognitive ability, combined, account only for 3.6 percent. Conceptually, while education indicators measure how much standardized knowledge individuals have, the ECI tries to capture the diversity of productive knowledge embedded in the society as a whole, often acquired by practice rather than schooling. Finally, the ECI is not simply a proxy for education. Hausmann, Hidalgo et al. (2011) find that the ECI is correlated with typical measures of human capital, such as average years of formal schooling, school enrollment, and test scores from standardized international exams (e.g. PISA, TIMMS) which are themselves found to be correlated with economic growth. However, the information captured by the ECI and by other measures of human capital

26 26 such as education is not just two sides of the same coin. The figures below show that, in contrast to its ECI, Netherlands levels of educational achievement correspond with predictions, given its level of income. Moreover, the population of the Netherlands has been steadily achieving more education on average and greater rates of tertiary school enrolment. Figure GDP per Capita and Average Years of Schooling of Adults (aged 25+), 2010 Figure GDP per Capita and Tertiary Completion Rate (aged 25+), LBR COD BDI NOR CZE CAN EST DEU RUS ISR HUNSVKKOR SVN IRL UKR JPN LKARM LTU SWE ALB ROU KAZLVA GRC ESPFRA ISL BEL FINDNK CHE BGR HKG LUX TJK POL FJI JAM CHL AUT KGZ BOL TON BLZ PAN MYS ARGBRB TTO ITA GBR BWA HRV PHL GUY JOR PER SGP MNG CRI MEX ZAF URY PRYSLVECU CHN DZA PRT COL GAB ZWE GHA MUS BRA KEN SWZ ZMB DOM HNDEGY TUN THA TUR NAM VEN KHMLSO CMR NIC IDN COG VNM IRQ TGO TZA UGA BGD HTI PAK SYR LAO MWI SEN CIV IND MAR MDV GTM CAF MRT PNG SLE NPL RWABEN SDN GMB NER MOZ MLI USA BDI COD LBR PHL UKR GRC JPN CAN BEL IRL ISL EST KOR LTU ESP GBR PAN SWE PER HUN KAZLVA DNK MEX NOR THA DEU ARM CHE BGR CRI CHL SGP SVN FIN FRA LKA POL LUX JAM NIC MNG SLVECU COL ROU AUT KGZ BOL EGY FJITUN HKG ITA TON SVK DZA CHN JOR GAB HRV IRQ MAR TURURY CZE BLZ MYS ALB BRA PAK HND DOM VEN PRY LAO VNMIND ARGBRB TTO PRT BGD BEN TJKSEN CIV KEN CMR GHA SDN SWZ BWA MUS CAF GMB NPL GTM IDN SLE TGO UGA SYR MLI HTI KHM LSO MRT MOZ GUY COG MWI NER RWA PNG MDV NAM TZA ZWE ZMB ZAF RUS ISR USA Log of GDP per capita, current US$ Log of GDP per capita, current US$ Source: Own calculations using WDI and Barro and Lee (2010).

27 27 Note 1 This section draws on the ideas presented in full in The Atlas of Economic Complexity.

28

29 3 HOW COMPLEX IS THE DUTCH ECONOMY? How does the Netherlands rank on the index of economic complexity? Table 3.1 shows that out of 128 countries, the Netherlands was the 23 rd most sophisticated country in the world in. 1 Given its income level, this economic complexity ranking is somewhat surprising: the Netherlands is ranked lower than all of its major trading partners and it is lower than a number of much less wealthy countries, including Slovenia, Korea, Hungary, the Slovak Republic, Israel, Mexico and Belarus. 2 Moreover, the Netherlands ranked 13 th among all countries in 1964, falling to 23 rd by. Figure 3.1 on the following page shows this in a different way. It illustrates the partial correlation between GDP per capita and ECI, controlling for each country s natural resource exports per capita. It shows that a tight relationship that exists between the ECI and per capita income of countries, which is consistent with the finding that the ECI is a good predictor of economic growth discussed in Chapter 1. Given its level of complexity as of, the Netherlands appeared as an outlier its level of income was higher than what would be expected given its economic complexity. Table 3.1 Economic Complexity Ranking, top 30 countries in Country Name Economic Complexit y Index* Ran k ECI GDP per capita Country Name Economic Complexi ty Index* Ran k ECI GDP per capita Japan ,212 Italy ,382 Germany ,264 Denmark ,157 Switzerland ,699 Ireland ,573 Sweden ,731 Israel ,652 Austria ,739 Mexico ,909 Finland ,905 Belarus ,328 Singapore ,136 Belgium ,187 Czech Republic ,729 Netherlands ,106 United Kingdom ,286 Hong Kong ,865 Slovenia ,033 Poland ,886 France ,117 Croatia ,767 Korea ,162 Romania ,300 United States ,971 Spain ,988 Hungary ,485 China ,414 Slovak Republic ,212 Panama ,752 Source: Own calculations using COMTRADE data. *Note: The ECI index is a normalized measure (with average zero and standard deviation of one) and has no absolute interpretation. It is specific to a country-year pair. What explains the relatively low ECI of the Netherlands? As discussed in Chapter 2, the ECI reflects the average complexity of products that a country exports with revealed comparative advantage (RCA) equal or greater than one. To get a better sense of what is included among the products in which the Netherlands has comparative advantage, table 3.2 lists the top 20 products for which the Dutch RCA is highest. The list contains a

30 30 large number of agricultural and horticultural products, many of which have a PCI of less than zero indicating low product complexity (since the PCI is a normalized measure with a mean of zero and a standard deviation of 1). Notably, sophisticated machinery, electronics, and chemical products are absent from this list. Figure 3.1 Economic Complexity Index and GDP per capita (controlling for resource exports), SDN CMR FRA CHE DOM GRC QAT ESPIRL ITA USA DEU AUT KWT LBN GBR NOR BEL COG MWI LBY PRT CRI DNK SAU FIN SWE AREOMN AGO SGP BGD HRV KOR JOR HUN AUS NZL KHM TUR MUS CAN HKG CZE GAB EST SRB SVK URY LVALTU ROU PRYTTO POL ISR LKA KAZ MDA SLV PAN THA BLR VEN ARG CHL DZA BRA CUB BWA RUS MEX CHN TKM IRN MKDALB BGR MYS MAR COL BIH JAM PAK SEN PER GEO ECU GTM KEN ZAF NAM UKR CIV HND MNG PNG TUN MRT NGA EGY PHL BOL SYR NIC IDN IND YEMDG GHA ETH ZMB UGA KGZ GIN TJK LAO UZB VNM MLI MOZTZA ZWE LBR E(Economic Complexity Index X) SVN AZE JPN Note: Both axes are normalized with respect to the sample mean. Units represent standard deviations from the mean. Table 3.2 The Top 20 Products of the Netherlands ranked by RCA, Product RCA () Exports (US$mm) Mkt Share PCI () PCI (1988- ) Top 3 Countries (by RCA) Bulbs, tubers & foliage , % Uganda, Ethiopia, Kenya Cut flowers and foliage , % Zimbabwe, Ethiopia, Kenya Cocoa powder, unsweetened % Ivory Coast, Malaysia, Swine, live 8.9 1, % Denmark, Lithuania, Eggs not in shell % , Bulgaria, Argentina Cocoa butter and cocoa paste 8.7 1, % Ivory Coast, Ghana, Cameroon Flours or meals, oil seeds % Bolivia, Ethiopia, Malawi Tomatoes, fresh or chilled 6.7 1, % Jordan, Morocco, Macedonia Seeds, fruit & spores 6.6 1, % Tanzania, New Zealand, Denmark

31 31 Eggs in shell % Syria, Jordan, Albania Ethers, alcohol 6.0 3, % , Qatar, Belgium peroxides Potatoes % Egypt, Lebanon, Syria Poultry, live % Slovenia,, Malaysia Iron pyrites, unroasted % Greece, Finland, Zimbabwe Tobacco, manufactured % Senegal, Ivory Coast, Sri Lanka Shavers and hair clippers % , Panama, Hong Kong Beer made from malt 5.4 2, % Namibia, Uganda, Jamaica Flours, meals of fruits & veget % Georgia, Lebanon, Ghana Cigarettes 5.2 3, % Kenya, Zimbabwe, Senegal Bacon, ham and other pig meat % Denmark, Italy, Spain Source: Own calculations using COMTRADE, SITC4 Revision 2. In red are shown products with a PCI below the world average for. What is more, the column in table 3.2 that shows how the PCI of each product has changed in the last two decades shows that the majority of the products have seen their PCI fall over time, which suggests that the production of these goods has been moving to less-complex destinations. Let s take the example of flowers. With technological diffusion and the falling cost of global shipping, more countries have been able to establish themselves as flower exporters. According to our measure, the PCI value of Cut flowers and foliage fell from 1.7 in 1968 to in. During this time, a number of producers in South America and Africa increased their share in this market while a number of wealthy countries, including France, Italy, Denmark, the U.S. and Canada all but exited the market. The Netherlands, the dominant producer in the 1960, maintained its leading position and still holds a close to 50 percent global market share in flower exports. This pattern of specialization and comparative advantage is surprising a priori. The list of top-rca products for most other wealthy countries has become dominated by sophisticated electronics, machinery and chemicals exports products which have a high PCI and are almost exclusively exported by other developed countries. What explains this difference between the Netherlands and other countries of its income group? Why does the Netherlands maintain strong comparative advantage in non-complex products? Figure 3.2 shows the global market share that the Netherlands held in world exports by product community, or industry, in. 3 The size of each bubble is proportional to the share of that community in the export basket of the Netherlands and the vertical distance on

32 32 the y-axis denotes the average PCI of each community calculated at the product level. This graph points to the fact that there are roughly three dominant sectors in the Dutch economy. One includes the sophisticated and high-value machinery, electronics, and chemicals communities. They represent a relatively large share of the value of the Dutch exports; however, the market share of the Netherlands among other countries, and thus its RCA, in these communities is relatively low. Figure 3.2 Global Market Share of the Netherlands, by community and complexity, Machinery Chemicals and health related products Electronics Pulp and paper Other Chemicals Aircraft Boilers Home and office products Petrochemicals Milk & cheese Construction materials Ships Not classified Metal and equipment products Meat and eggs Agrochemicals Textile Coal & Fabrics Precious Stones Beer, Spirits and cigarettes Food Processing Inorganic salts and acids Garments Cereals and vegetable oils Mining Fruit Animal Fibers Leather Misc Agriculture Fish & Seafood Tobacco Oil Tropical tree-crops and flowers Cotton, rice, soy beans and others share in world exports Source: Own calculations using COMTRADE, SITC4 Revision 2. Bubble size is proportional to the value of product community exports in total Dutch exports The second sector includes the domestic agricultural and animal-products. The Netherlands benefitted from an early agricultural revolution. These early capabilities for agriculture have over time been complemented by more modern developments: Supportive governmental policy, state-of-the-art agricultural research and education institutions, strong related industries, low cost greenhouse horticulture and floriculture, and cheap fertilizers supported by a strong local chemicals industry. Today, the Dutch agricultural sector is highly mechanized and productive. It is also a very visible part of the economy and as of 2007, accounted for more than half of the total land utilization in the Netherlands (3 percent of employment and 1.8 percent of value added in GDP). 4 The third prominent sector features low-complexity exports such as cocoa, tobacco, and iron and also flowers. 5 In the case of flowers, the Netherlands became the nation of tulips as early as the 16th century when the flower was first introduced in the Netherlands. Production of flowers in the Netherlands leverages many of the agricultural capabilities described above. For the other products in this third sector, the raw inputs are primarily sourced abroad,

33 33 provided with a degree of processing in the Netherlands, and exported to Europe. The strong comparative advantage of the Netherlands in this sector likely mirrors the strength and reach of its historical commercial links and its well-established networks of commerce and trade. The Netherlands became one of the strongest merchant and trading nations of the world already in the seventeenth century. With its ports and the many inland waterways, the Netherlands became a hub for imports of goods for distribution to Europe. Indeed, the Port of Rotterdam, dating back to the 14th century, held the title of the world s busiest port from 1962 until Its status as historical commercial center could explain why among the products where the Netherlands has very high revealed comparative advantage there are a number of products that are exported mainly by tropical countries. 6 These features, the historical dominance of a high-productivity agricultural sector and its history as a nation of merchants have rendered the economy of the Netherlands somewhat different from other developed countries, such as Germany or Japan which earn their income primarily by exporting machinery and electronics. What does this spell for the future of the Netherlands? As discussed in Chapter 1, economic complexity is one of the predictors of future growth. Countries whose income is less than expected given their economic complexity tend to grow faster, and those that are too rich for their level of economic complexity tend to grow slower. For the Netherlands, its positive gap between income and complexity suggests slower future grow. Moreover, the reduction in PCI experienced by many of the Netherlands dominant exports suggests that, going forward, Dutch exporters will experience increased competition from countries with an important wage advantage. The competitive landscape in these sectors, therefore, will be tougher in the years to come. A second predictor of growth, however, is opportunity value how close a country is in the product space to new products and industries that are complex and how well it takes advantage of these opportunities for growth. The next chapters will expand on these concepts and evaluate the Netherlands performance on these measures. Textbox 4: Product Communities Analyzing in detail the exports of countries at the product level becomes quite unwieldy, since the COMTRADE data consists of more than 800 different products. To make the analysis more tractable we group products that are seen to form natural groups in the product space into communities. These represent the families within which products are more likely to be co-exported. We interpret this to be an indication that these products share much of the requisite productive knowledge required to produce them. The nearly 800 products in the SITC4 classification were grouped into 34 communities. The names,

34 34 average complexity, market size and other characteristics of the communities appear in table Overall, products tend to have similar levels of complexity within the community. Products in the Machinery, Electronics and Chemical communities tend to be much more complex than those in the garments cluster or in peripheral communities such as Oil. It is important to highlight that the concept of communities is different from the idea of clusters, since the later emphasize input-output connections in the value chain. By contrast, products belong to a community because their production in a given location requires similar knowledge. For example, we find that textiles and garments are quite distinct communities, because the capabilities that go into textiles are different from those that go into garments, even through textiles are used in garments, and thus belong to the same value chain. Table Product Communities, Community Name Average PCI Number of Products World Trade Share of World Trade (%) Top 3 Countries by Export Volume Machinery T DEU, USA, JPN Electronics T CHN, HKG, USA Oil T SAU, RUS, NOR Chemicals & Health T 7.47 USA, DEU, BEL Other Chemicals T 5.49 DEU, USA, FRA Const. Materials & Equip T 5.23 CHN, DEU, ITA Mining T 5.01 AUS, USA, CHL Garments T 4.63 CHN, HKG, ITA Food Processing B 2.74 DEU, ITA, USA Metal Products B 2.26 JPN, DEU, KOR Aircraft B 2.00 FRA, DEU, GBR Not Classified B 1.94 USA, CHN, DEU Cereals & Vegetable Oils B 1.34 USA, BRA, ARG Home & Office B 1.14 CHN, CHE, USA Meat & Eggs B 1.10 USA, BRA, DEU Ships B 1.05 KOR, CHN, JPN Petrochemicals B 1.00 DEU, USA, BEL Boilers B 0.88 CHN, DEU, JPN Fish & Seafood B 0.87 CHN, NOR, THA Textile & Fabrics B 0.86 CHN, ITA, HKG Tropical Agriculture B 0.86 IDN,, MYS Coal B 0.83 AUS, IDN, RUS Misc Agriculture B 0.78 BRA, DEU, FRA Precious Stones B 0.77 IND, ISR, BEL Pulp & Paper B 0.67 USA, CAN, SWE Agrochemicals B 0.64 DEU, USA, CAN Milk & Cheese B 0.61 DEU, FRA, Beer, Spirits & Cigarettes B 0.57 GBR,, DEU Inorganic Salts & Acids B 0.53 USA, CHN, DEU Cotton, Rice, Soy & others B 0.44 USA, IND, THA Tobacco B 0.29 DEU,, BRA Leather B 0.24 ITA, USA, HKG Fruit B 0.21 ESP, USA, CHL Animal Fibers B 0.06 AUS, CHN, ITA

35 Source: The Atlas of Economic Complexity 35

36 36 Textbox 5: What is the complexity of Services? Here we address a hypothesis that the ECI of the Netherlands could be biased downwards because the COMTRADE export data does not include services, which could potentially be a source of additional sophistication for the country. We already saw in Chapter 1 that the Netherlands has a high level of service exports. However, figure below shows that across countries, volumes of service exports per capita are highly collinear with volumes of service exports per capita. Thus relying on goods exports data as we do using COMTRADE is not a bad proxy for most countries. For the Netherlands specifically, its level of service exports is actually somewhat less intensive than its level of goods exports would predict. Figure Goods and Service Exports per capita, SDN GIN BGD PAK MOZTZA SGP HKG CHE KWT BEL NOR IRL DEU AUT SWEDNK OMNSVK CAN CZESVNFIN SAU KOR HUN AUSEST ITA NZL FRAISR GBR JPN LTU ESP CHL POL KAZ LVA USA PRT AZE PAN MEX RUSBLR THA BGR HRV VEN URY CRI GRC ZAF ARG MKD TUR MUS CHN PER PRYBIHSRB BRA JOR LBN MNG UKR VNM COL IDN SLV ZMB NIC PHL GTM GEO MAR NGA ALB LKA MDA GHA KGZ CMR TJK IND UGA Service exports pc, logs Source: Own calculations using WDI. To address the possibility that a highly complex service export basket is supporting the high income level of the Netherlands we make use of data on service exports compiled by the World Bank and applied in a research paper titled Service Export Sophistication and Economic Growth. It contains data for ten different categories of services (the Appendix lists the service categories) for a sample of about 100 countries and is available from 1990 to We complement this dataset with similar data from the Netherlands Central Bureau of Statistics (Statistics Netherlands) which includes, in addition to the value of service exports, their destinations. Figure shows the decomposition of Dutch service exports into ten categories given

37 37 in the data and the Dutch global market share in each (the Appendix has a more detailed description of what is contained in each category). It shows that the Netherlands is best positioned in communication services, government services, other business services, and transportation. Somewhat surprisingly, the Netherlands has a much less competitive market share in financial and insurance services. While our ability to replicate the precise calculations of PCI for services exports is limited due to the lack of sufficient granularity in the service exports data, we explore a related concept first introduced by Hausmann, Hwang, Rodrik (2007). They defined PRODY as the weighted average income level of all countries that export a given product. Income being a proxy for productivity, PRODY was the first crude measure of the measure of the sophistication of products. Textbox 5: What is the complexity of Services, cont d.? Figure Dutch share in world exports of services by category, average Figure Service weighted average income (PRODY), 2009 Royalties & license fees 2.4 Royalties & license fees 41,866 Financial services 0.6 Financial services 39,844 Insurance services 0.9 Insurance services 38,351 Other business services 4.0 Other business services 34,470 Government services 4.5 Government services 33,222 Communication services 5.0 Communication services 32,513 Transportation 3.8 Transportation 31,141 Personal, cultural & recreation 3.1 Personal, cultural & recreation 29,887 Computer and info. services 3.3 Computer and info. services 29,274 Construction services 3.0 Construction services 28,411 Travel 1.5 Travel 28, Share of world service exports 0 10,000 20,000 30,000 40,000 Service weighted average income Source: Own calculations using IMF data. Figure calculates PRODY for all the service categories available in the IMF database using data on 100 countries and ranks them according to most sophisticated (Royalties and License fees) to least sophisticated (Travel). Comparing the two figures, we see that the service sectors where the Netherlands is most intensively present are of a medium level of complexity. Exports of royalties and license fees, as well as of financial and insurance services, which are the most sophisticated categories, are ones where the Netherlands has a weak presence. Thus, a rough analysis of service exports provides no evidence in support of a hypothesis that the ECI systematically undervalues the overall complexity of the Netherlands by excluding services.

38 38 Notes 1 Year is the last for which reliable trade data is available. 2 As a robustness check, the complexity rankings were also performed with CEPII HS4 data and are reported in the Appendix. The rankings of countries do not change substantially from the results using SITC4 rev 2 data. For the Netherlands the ranking changes marginally; it moves three positions to 20th place. 3 Textbox 4 provides a description of product communities. 4 Landbouw-Economisch Instituut (LEI), Land- en tuinbouwcijfers Flowers are included in the Tropical treecrops and flowers community grouping. The Netherlands both produces flowers domestically and also sources them from various global locations for re-export to Europe. 6 Note that products that use any port of the Netherlands as a transit hub are not accounted for as exports in COMTRADE data unless there is some degree of processing or value added that take place in the Netherlands. See Textbox 1.

39 4 WHAT IS THE PRODUCT SPACE? The prior chapter focused on economic complexity the productive knowledge that is expressed through the products that a country makes and summarized it in one number, the Economic Complexity Index. But how does economic complexity evolve? How do societies increase the amount of productive knowledge embedded in them? The answer lies in moving into making more products and products that are more complex than the ones currently produced. However, expanding the set of products requires new productive knowledge, which is costly to acquire and to transfer. This gives rise to a chicken and egg problem where there are scant incentives to accumulate new productive knowledge in places where the industries that demand it do not exist. To solve this problem, countries tend to move from products that they are making to new products that are nearby in terms of the productive knowledge they require. For example, the broad set of knowledge required to produce artichokes is rather similar to that required for asparagus production, and therefore it will be easier for firms in the country to enter the artichoke industry if the asparagus industry already exists. Artichokes and asparagus are close to one another. On the other hand, the knowledge required to produce an aircraft engine is rather far from that required for asparagus production. A testable implication of this hypothesis is that countries will diversify by moving from the products they already produce to others that require a similar set of productive knowledge. Hausmann and Klinger (2006 and 2007) investigated this hypothesis and the process by which countries moved to new export activities, and motivated the concept of a product space in which some products are close to one another and others are far. Rather than attempting to define and measure the similarity of the knowledge that products require directly (an exercise that would involve collecting a mindboggling volume of information), Hidalgo et al. (2007) defined how close two products are empirically. If artichokes require knowledge that is similar to that required by asparagus, but different from that required by engines, then the probability that a country exporting asparagus will also export artichokes will be higher than the probability that it will also export engines. So the probabilities that pairs of products are co-exported by countries carry information about how similar these products are. 1 If two goods require roughly the same knowledge, this should show up in a higher probability of a country having comparative advantage in both products vis-à-vis other countries.

40 40 The probability of being co-exported defined the distance between every pair of products and created the network connecting all products known as the product space. It is important to keep in mind that the product space is a technological feature of products, not of countries. There is therefore one product space in which countries move, rather than a product space for each country. Figure 4.1, shows the visual representation of the product space using international trade data for the years (see Textbox 1). Each node is a product and its size is determined by its share of world trade. The color of each product node corresponds to its community, which as we see naturally group themselves in the product space. Two products are connected by links based on their probability of being co-exported by countries. The higher this probability, the thicker and darker is the link between the products. In order for the network representation to remain legible, only the links above a certain probability threshold are shown.

41 41 Figure 4.1 The Product Space, Source: The Atlas of Economic Complexity The figure of the product space reveals that is heterogeneous, with a core-periphery structure. There are products in the periphery of the product space that are only weakly connected to other products. There are some strong groupings among these peripheral goods such as the garments cluster (the very dense light green cluster at the right of the network). At the center of the network is a core of closely connected products, consisting mainly of machinery (cerulean blue). This is no coincidence but rather points to the central role that knowledge of designing and producing mechanized products plays in acquiring knowledge required for many other types of production. To the left of machinery are two other wellinterconnected clusters of chemicals (purple grouping in the bottom left) and electronics (light blue at the top-right of the space).

42 42 This heterogeneous structure of the product space has important implications for export diversification. If a country is producing several goods in a dense part of the product space, for example the central machinery cluster, then the process of export diversification is much easier because the set of capabilities available in the current productive structure can be more easily used to produce other nearby products. However, if a country is specialized in peripheral products, for example petroleum production, then this redeployment is more challenging because the distance to new products is more substantial. Thus the ability of countries to diversify and to move into more complex products is crucially dependent on their initial location in the product space. Ceteris paribus, countries want to move to products that are at short distances, are more sophisticated, and are in more densely connected parts of the product space, making future diversification easier and more attractive. We can evaluate a country s overall position in the product space by calculating how far it is to other products and how complex these products are. We call this measure opportunity value and it can be thought of as the value of the option to move into more and more complex products. This value is higher for countries that are closer to more products and to products that are more complex. Figure 4.2 compares opportunity value with the Economic Complexity Index and income. It shows that countries with low levels of complexity tend to have few opportunities available. This is because the products they make tend to be peripheral in the product space. At the same time, countries with high levels of complexity also tend to have few remaining opportunities because they already occupy a large fraction of the better part of the product space. Finally, countries with an intermediate level of complexity differ largely in their opportunity value. In the right half of the image, the figure also shows that opportunity value does not measure income but rather than countries with similar incomes can face dramatically different opportunities.

43 43 Figure 4.2 Opportunity Value as a function of the Economic Complexity Index and GDP per capita GRC BGR IDN TUR LTU EGY BRA SRB LBN CAN LVA PRT EST UKR CHN VNM ESP ARG THA ROU ZAF BEL BIH HRV TZA ALB BLR KENGTM COL TUN NZL MEX ISR HUN KOR GBRFIN DNK CRI JOR MYS POL SGP PANHKG SVK UGA PAK URY MDA CHE AUS KGZ IRL MAR SYR SEN LKADOM MKD SLV SVN HND RUS PER MDG NOR NIC MUS KAZ CHL GEO NAM PHL SWE CIV PRY ARE ETH GHA ITA AGO BGD BOL ECU DZA COG CMR GAB BWACUB JAM FRA CZE GIN NGA LBY IRN KWT LBR LAO USA MNG MOZ UZB ZWE QAT TTO SAU MLI MWI OMN MRT SDNPNG AZE TKMTJK VEN YEM ZMBKHM AUT IND JPN IND BGR GRC IDN TUR LTU EGY SRBRA CHN LBN LVA ESTPRT UKR VNM ESPCAN THA ARG ROU BEL BIH ZAF HRV TZA ALB BLR KEN GTMTUNCOL MEX HUN KOR ISR GBRFIN JOR MYS POL NZLSGP PAN SVK DNK CRI HKG UGA CHE KGZ PAK MDA URY SEN DOM AUSIRL LKASYR MAR SLV SVN MKD HND RUS PER MDG JPN NOR NIC PHL GEONAM MUS SWE KAZ CHL CIV PRY ARE ETH ZWE LAOGHA ITA USA UZB BGD BOL ECUJAM SAU TTO LBRMWI MOZ CMR COG AGO DZA CUB BWA GAB CZE QAT GIN MLI NGA KHM MNG IRN LBY OMN TJK YEM ZMB FRAKWT MRT PNG TKM VEN SDN AZE AUT Economic Complexity Index GDP per capita, logs Source: The Atlas of Economic Complexity. Note: Germany, an outlier, is excluded from the figure. We now have two ways in which countries grow in our model: One, if they are underperforming in income given their level of complexity and two, if they are close to a number of complex products that they are currently not exporting with high revealed comparative advantage, suggesting that they may be able to develop greater RCA in these product in the future. We saw that the relatively low level of ECI and the gap between its complexity and income serves to reduce the expected future growth rate of the Netherlands. How the Netherlands is positioned in the product space and how high is its opportunity value? These questions will be addressed in the next chapter.

44 44 Note 1 The distance between any two products is the minimum of the pair wise conditional probabilities of having revealed comparative advantage.

45 5 HOW HAS THE NETHERLANDS PERFORMED IN THE PRODUCT SPACE? In this Chapter, we look at the product basket of the Netherlands and its evolution in the product space over time. The product treemap below shows what the Netherlands exported with Revealed Comparative Advantage (RCA) equal or larger than one in. Each rectangle is proportional to the product s share in the basket of products of RCA larger than one. Because the share of Unclassified transactions in the SITC4 data is large for the Netherlands, the treemap uses HS4 level data from CEPII (see Textbox 1). Figure 5.1 The Product Treemap of the Netherlands, HS4 data as of Source: Comparing with SITC4 data, the Unclassified products are mainly classified as Petroleum in HS4 data. Their large share in Dutch exports may be due to local refining and re-export activities as, according to data from British Petroleum, the Netherlands has no oil production, consumes 1.3 percent of world output, and has 1.4 percent of world refining capacity. However, this has no effect on the calculations of ECI or OVI as Unclassified products are excluded for the purpose of calculating them.

46 46 The treemaps shows that that the Netherlands is relatively diversified with significant exports in chemicals (purple), electronics and machinery (shades of blue), tropical tree crops and flowers (orange), cereals and oils, fruit, tobacco and miscellaneous agriculture (shades of green and yellow), metals (shades of brown), and smaller clusters in construction materials and equipment (red) and garments and textiles (shades of green). 1 The treemap shows a point in time a snapshot of the Dutch economy. By what process did the Netherlands come to have high revealed comparative advantage in these products? In figures 5.2, we can see the gradual evolution of the revealed comparative advantage of the Netherlands over the past four decades in the product space of exports. Black squares are superimposed on the product space to indicate those products which the Netherlands exported with RCA equal or greater than one in each of the years shown. They reveal that there are a diverse number of goods that the Netherlands has produced competitively over the years but upon closer inspection that is has decreased its RCA in certain products categories and increased it in others. The advances in new products have tended to occur close to the products in which the Netherlands had high RCA. For example, the Netherlands developed revealed comparative advantage greater than one in a number of products in the electronics cluster (turquoise, top left). In 1978, the Netherlands had high RCA in only ten products in the cluster; by, it had it in seventeen. The country lost RCA in photo cameras and typewriters, but gained it in telephone lines, data processing equipment, and office machines. It also gained RCA in video and sound recorders, record players, TVs, and electric toys. The chemicals and health cluster (purple, bottom left), already strong in 1978, also saw a number of products gain comparative advantage during the same period. By, the Netherlands had gained RCA in almost every product of the tightly-connected chemicals cluster and has also expanded its presence in petrochemicals, as well as inorganic chemical products. The strong food processing sector of the Netherlands (brown, top right) also added RCA in products that were previously missing. The Netherlands made a meaningful advance in fruits (yellow, top right), becoming competitive in fresh and dried oranges and citrus fruits, apples, and grapes. However, the Netherlands also lost RCA in certain parts of the product space. It had a significant presence in the construction materials and equipment sector (red, center right) in 1978, but has gradually lost comparative advantage, exporting only four products competitively in. It also lost comparative advantage in a number of textile and garment products (green).

47 47 Figure 5.2 The Evolution of the Netherlands in the Product Space of Exports

48 48 Source: The Observatory of Economic Complexity,

49 49 However, still today there remains a visible gap in the center of the product space of the Netherlands the machinery sector (cerulean blue, center). Most countries transit through the product space from the peripheral communities such as garments and electronics towards the central communities of chemicals and machinery. The Netherlands however, has RCA in a relatively low number of products in the machinery sector in this respect, it is a significant outlier compared to other wealthy countries (figures 5.3 and 5.4). This lack of high RCA in the Netherlands in the machinery community does not reflect a complete absence of the relevant productive knowledge, as this industry does exist, but at low levels of output, meaning that they do not reach an RCA of one. It may be that the low level of machinery production in the Netherlands is an equilibrium outcome of an inadequate knowledge base, the wage structure, land constraints, or other exogenous factors. It may also be that Dutch firms have factories that produce machinery abroad (this hypothesis is explored in Textbox 6). However, without other evidence to the contrary, our theory predicts that it should be relatively easy for the Netherlands to develop high RCA in machinery because the Netherlands is present in a lot of products that are nearby. In particular figure 5.4 suggests that, among those countries with a low presence in machinery, the Netherlands is the best positioned to increase its presence. Figure 5.3 Presence in Machinery OECD countries, Figure 5.4 Presence and distance to Machinery OECD countries, BEL GBR DNK ESP CHE FRA SWE AUT ITA DEU DEU ITA AUT CZE FRA SWE JPN USA CHE ESP SVN POL DNK FIN SVK HUN GBR BEL PRT EST TUR GRC CANKOR NZL AUS IRL NOR Presence in the Machinery Community Distance to products without presence Source: Own calculations using COMTRADE data. Indeed, in recent years the country has successfully capitalized on some of these opportunities. Comparing the machinery cluster in and in the product space, we can see that the Netherlands has achieved RCA greater than one in a number of products that were nearby (figure 5.5). It expanded its small presence in the bottom left corner of the cluster, which included printing machines, food processing equipment, and agricultural machinery (dairy machinery, trucks, tractors) to nearby products, which now include

50 50 machine tools, construction and mining machinery, navigating devices, gas generators, centrifuges, and printing machines (printing presses, bookbinding machines) as well as agricultural machinery. Interestingly, the country appears to be leveraging on the knowledge of communities in which it is strong (agriculture, food processing, oil & gas etc.) to specialize in developing machinery that is applied in these industries an observation that is consistent with our theory of capabilities (see table A.3.3. in the Appendix). Figure 5.5 Netherlands in the Machinery Community Note: The machinery cluster includes those products that are shaded in blue. The squares represent products that the Netherlands exports with RCA. Source: The Observatory of Economic Complexity, What other opportunities are nearby in the product space of the Netherlands? Figure 5.6 shows the distance to products by community for the Netherlands.2 The size of the circles is proportional to the communities share of world trade. The white slice of each circle represents the fraction of products in that community that the Netherlands exports with RCA greater than one thus the filled share represents the products to be conquered. The horizontal line indicates the ECI of the Netherlands. The left half of the picture suggests that the easiest opportunities for the Netherlands to grow into new export products, while not decreasing its average level of complexity, lie in expanding its dominance in the chemicals industry. These products are closest to its current productive knowledge. However, the largest number of opportunities is in machinery and electronics. The Netherlands has substantial room to grow in these large communities, in which it has achieved RCA greater than one in just over a quarter of all products. However, those investments are more distant companies in the Netherlands will face greater risks or will need more investment upfront in order to create all the capabilities needed for production.

51 51 The left half of the picture shows, on average, how a move into a community will affect the opportunity value of the Netherlands. 3 It shows that all moves will reduce the opportunity value of the Netherlands (a common feature of well-developed countries); however, any move above the horizontal line will also increase its complexity. Overall, given the positioning of the Netherlands in the product space, it has a very large opportunity value: it is ranked as the country in the world with the 18th highest value (table 5.1). This high opportunity value is quite unusual for a country as wealthy as the Netherlands, because most developed countries have already exploited all nearby opportunities to develop comparative advantage in more complex products. This is also be seen quite clearly in figure 4.2 (page 27), where the Netherlands is the country with the highest OVI for any country at its level of income or complexity. Germany, Austria, and France are at the bottom of the opportunity value rankings. Thus opportunity value has good news in store for the Netherlands. According to our model, the gap between the current income of the Netherlands and its complexity acts to drag to down estimates of future growth, however, its high opportunity value has the opposite effect it creates room for future growth if the Netherlands is able to develop higher comparative advantage in these products. Figure 5.6 Distance and Opportunity Gain in the Product Space of the Netherlands, Source: The Atlas of Economic Complexity.

52 52 Table 5.1 Opportunity Value Ranking, Top 20 and Bottom 20 countries in Country Name Opportunity Value Rank Country Name Opportunity Value Rank India France Greece Gabon Bulgaria Botswana Turkey Venezuela Indonesia Zambia Lithuania Yemen Brazil Mongolia Egypt Guinea Serbia Mali Portugal Turkmenistan Lebanon Cambodia Estonia Cameroon Ukraine Congo, Rep Latvia Tajikistan China Azerbaijan Canada Papua New Guinea Spain Mauritania Netherlands Sudan Vietnam Austria Romania Germany Source: Own calculations using COMTRADE data. * Note: The Opportunity value index is a normalized measure (with average zero and standard deviation of one) and has no absolute interpretation. It is specific to a country-year pair. Textbox 6: How much do Dutch firms earn from productive knowledge deployed abroad? We have shown that according to exports data, the Netherlands lack comparative advantage in Machinery and a few other sophisticated industries. Could it be that Dutch firms have the knowledge to produce machinery, electronics, and other sophisticated products but have decided to produce them abroad? Recently, value chains have been globalized: Products invented and designed in country A are assembled in country B, with components from countries C and D. In such cases, the trade of these products would count as exports of country B, but a share of the profits would be accounted for as an investment income of country A. If Dutch firms were using their productive knowledge to produce abroad in a meaningful way, they should be earning a high net return on their investments (because productive knowledge earns a return in excess of the return on pure capital). * Therefore, to look for evidence of the use of productive knowledge in production located aboard, we focus on the excess returns earned by assets invested abroad. To illustrate how productive knowledge can increase the returns on capital invested abroad we can use the example of the United States. Since 1980, the US has accumulated a current account deficit of 8 trillion dollars. Standard considerations would have

53 53 expected to see a deterioration of the net financial income to reflect the increased net external debt of the country. However, net financial income actually improved in spite the cumulative deficit. To understand why, it is important to note that the US actually borrowed some US$ 20 trillion from the rest of the world; it used US$ 8 trillion to cover its external deficit, and invested the remaining US$ 12 trillion abroad, mainly in the form of foreign direct investment. Most of the US$ 20 trillion borrowed is a financial debt that comes with no productive knowledge. But the US$ 12 trillion that the U.S. invested abroad come with productive knowledge and hence earn a higher return abroad. Figure shows the cumulative current account and the implied interest rate for the US. The graph shows that as the cumulative deficit turned strongly negative, the implied interest rate became also negative: instead of paying money for its net debt, the U.S. was earning money because the returns to its investments abroad more than compensated for the cost of its liabilities. Figure United States Figure The Netherlands year Cumulative Current Account, Billions Implied interest rate Implied interest rate year Cumulative Current Account, Billions Implied interest rate Implied interest rate Note: Data from IMF Net Investment Income is calculated using the income credit (IFS: code 78AGDZF) minus the income debit (IFS: code 78AHDZF). The implied interest rate is defined as the ratio of Net Investment Income over the cumulative current account. The calculation of the cumulative current account starts in Unlike the U.S., the Netherlands is a net creditor with an accumulated positive current account of us $600 billion dollars. And unlike the U.S., the Netherlands is not earning a high return on its net foreign assets. The average implied interest rate since year 2000 for the Netherlands is just 1%. This is a low return compared to what they could have earned even from investing in bonds. We conclude that the Dutch economy is not a meaningful exporter of productive knowledge otherwise the returns on their savings would have been higher. *Hausmann, R. and Sturzenegger, F. (2007). Dark matter and international imbalances. Economic Policy.

54 54 Notes 1 Using HS4 data, the colors do not correspond to product space communities, but rather the Harmonized System Codes (HS Code) industry classification. 2 Tables A.3.1 and A.3.2 in the Appendix present the most nearby opportunities for the Netherlands at the product level. 3 Opportunity gain is the increase / decrease in opportunity value that results when a country develops RCA greater than one in a new product.

55 6 HOW IS THE NETHERLANDS POSITIONED IN WORLD MARKETS? For a country like the Netherlands, with a ratio of exports to GDP equal to 70 percent, trends in global trade matter a lot. We already saw in Chapter 1 that the only two growth downturns that the Netherlands felt in the last four decades coincided with falls in intra-european trade. Over the past decade, two major trends in world trade have taken place: One, global trade volumes have continued their exponential growth and two, developed countries have lost market share, both as importers and as exporters of traded goods, to developing countries and most notably the BRICs. More importantly, both of these trends are likely to continue as we look to the future. Figure 6.1 illustrates the IMF s World Economic Outlook projections of global imports separately for the BRICs and European countries. The expectation is that the growth engine of global trade is now going to be the developing countries rather than Europe. Figure 6.1 Imports volume index, simple average ( , projected) year BRICS imports index Europe imports index Note: Own calculations using IMF s WEO data (September 2011). BRICs countries are Brazil, Russia, India and China. How has the position of the Netherlands as a global exporter been affected by the evolution of global trade in recent years? To answer this we look to tables 6.1 and 6.2 which show the export and import dynamics for the dominant product communities of the Netherlands. For each community, the ten largest suppliers and demanders are shown. 1 In both tables the horizontal axis shows the change in the countries world market share of exports and imports in that community from to. In table 6.1 the vertical axis shows the change in the number of products that the countries exported with RCA larger than one. These graphs point

56 56 to a striking dynamic that has played out over the past decades: Across the various product communities, developed economies lost significant market share, primarily to China but also to other developing economies. For example, in Tropical treecrops and Flowers, the Netherlands lost its position as the number one supplier. Its global market share fell by about three percentage points and it reduced the number of different products that it exports with RCA>1 in that community. On the other hand, Indonesia gained more than ten percentage points of global market share and increased the number of products it exported with RCA>1. What gave rise to these trends? While the Netherlands managed to hold its roughly 50 percent overall market share in the exports of flowers and its roughly 30 percent share in the exports of processed cocoa, Indonesia and Malaysia took over as the largest exporters in the community largely on the strong growth of their exports of palm oil, an export market that is significantly bigger than the flower or cocoa markets. On the other hand, the participation of the Netherlands as an exporter of palm nuts and palm kernels dropped from a high of 32 percent in 1988 to zero two decades later. On the demand side, China emerged as a dominant importer, climbing to 10 percent of the community s imports, reducing the dominance of the United States, Germany and Great Britain as the primary sources of demand in this community. In the export markets of the Milk and Cheese community, the Netherlands saw its largest market share loss together with the other large producers, Germany and France. In this market there was no one big winner. Rather, New Zealand, the U.S., and Italy gained some market share but also a host of newcomers in Asia who are too small to appear on the list of the ten largest exporters, but cumulatively took sizable market share in the community from the dominant exporters. They are demonstrating that an increasing number of countries have the capabilities needed to export milk, often in preserved and concentrated form. The Netherlands also lost significant market share in the Meat and Eggs community. Here the largest winner was Brazil, which gained market share mainly through growth of exports of beef and poultry meat. In the diverse Food Processing community, China gained market share but Germany and Belgium also managed to increase their presence at the expense of the U.S., France, Italy, the Netherlands and Spain. Among the more sophisticated product communities, the losses were less drastic in chemicals and machinery and larger in electronics. In chemicals, the U.S., Netherlands, Great Britain and France lost market share predominantly to China, but also to Germany, Belgium and Switzerland who not only held on to their market share but also grew it slightly. China also became a more prominent importer of chemicals. In machinery, the U.S., Japan,

57 57 Great Britain and France lost market share mainly to China. However, Germany, Italy, Belgium, Spain and the Netherlands managed to hold their market share. Netherlands, while small, was able to also increase the number of products that it exports with RCA in this community. The electronics community saw a drastic re-alignment of market position by the main players with the U.S. and Japan losing their leading positions to China and Hong Kong, who became the number one and number two exporters in the community. They also became the top importers in the community, suggesting that a lot of the activity that we are seeing is due to the globalization of the value chain and that a lot of what takes place in China and Hong Kong is component manufacturing and assembly of electronics products designed in the U.S., Japan and elsewhere (see Textbox 6). Amid this, the Netherlands managed to roughly maintain its market share in electronics while increasing the number of products it exports with RCA. Table 6.1 Competitive dynamics in global markets by community, - Tropical tree-crops and flowers Supply-Side (Top 10 exporters) Beers, spirits and cigarretes BEL DEU ECU COL BRA CIV THA IDN MYS BRA SGP BEL MEX SAU USA DEU FRA GBR Share in world exports of product community Share in world exports of product community Milk and cheese Meat and eggs ITA AUS DNK IRL BEL USA NZL FRA DEU ESP BRA BEL DNK AUS DEU CAN FRA USA Share in world exports of product community Share in world exports of product community

58 58 Food Processing Chemicals and health related products MEX CAN BEL CHN ESP DEU ITA FRA USA CHN CHE BEL IRL GBR FRA JPN DEU USA Share in world exports of product community Share in world exports of product community Machinery Electronics ESP BEL KOR CHN GBR ITA FRA JPN USA DEU HKG SGP MYS KOR MEX DEU CHN JPN USA Share in world exports of product community Share in world exports of product community Source: Own calculations using COMTRADE data. Countries shown are the top ten countries in terms market share in (plus the Netherlands in Machinery). Table 6.2 Competitive dynamics in global markets by community, - cont d. Demand-Side (Top 10 importers) Tropical tree-crops and flowers Beers, spirits and cigarretes MYS RUS BEL FRA GBR JPN DEU CHN USA Share in world imports of product community ITA JPN BEL GBR ESP FRA DEU CHN USA Share in world imports of product community

59 59 Milk and cheese Meat and eggs JPN CHN ESP USA BEL FRA GBR ITA DEU Share in world imports of product community Food Processing MEX HKG FRA RUS USA ITA GBR JPN DEU Share in world imports of product community Chemicals and health related products ESP ITA JPN BEL CAN FRA GBR DEU USA Share in world imports of product community Machinery KOR JPN GBR ITA FRA BEL CHN DEU USA Share in world imports of product community Electronics ESP BEL RUS ITA CAN GBR FRA CHN DEU USA Share in world imports of product community KOR MEX GBR SGP JPN DEU HKG USA CHN Share in world imports of product community Source: Own calculations using COMTRADE data. Top ten importer countries are sorted from smallest to largest importer in. What emerges from this broad-brushed picture is that competition is heating up in global markets. The recent rise of developing countries, especially the BRIC economies, has increased their presence at the expense of developed economies. As part of these trends, the Netherlands is feeling a lot of heat in some of its dominant but lower complexity communities such as Tropical tree-crops and Milk and Cheese. How can we explain these trends? In Chapter 2 we discussed how as countries develop, they move from products of lesser to greater complexity. The world s least complex products are produced by many countries and the most complex products are produced only by those few who have the productive knowledge required to make them. Therefore, in most cases, the barriers to entry are lower in low-complexity products compared to high-complexity products. However,

60 60 developed countries also appear to be feeling the heat in the most sophisticated communities, such as electronics, predominantly from China and other Asian countries. We believe that this phenomenon is largely related to the strategic choices of companies regarding where to locate their production rather than a pushing-out of players like the U.S. or Japan who are becoming more specialized in the research, development, and design phase. Which of the Dutch product communities are likely to drive future growth? There are two main concerns regarding the ability of the Netherlands to maintain its leading position and/or achieve growth in its traditional strong-holds such as flowers, tropical treecrops, cocoa, and tobacco. The fact that these products have a low PCI implies that the capabilities needed to make them have become relatively abundant in the world and that production has increasingly moved to lower-complexity countries. Secondly, not only is competition in lowcomplexity goods more rapidly increasing, but the global export markets of these goods are relatively small. On average, the size of the global export market of the Dutch top-20 products by RCA (table 3.2) is roughly US$5 billion. Given the already high market share of the Netherlands in these communities, it is unlikely that growth will be driven by further market penetration. Are the Dutch mid-complexity products, such as fresh agricultural goods (dairy, eggs, fruit, vegetables, meat) and processed foodstuffs likely to drive growth? Traditionally, the destination of the Dutch exports of these goods has been the European market (albeit beer exports are sold mainly in the United States). Compared to the lowest-pci communities, the Netherlands currently faces somewhat less of a threat in these communities because logistics and transportation are more complex. However, as technology advances, more and more countries will be able to export these products. For example, milk is now largely being exported in concentrated and powdered form meaning that it can more easily be shipped across the globe. Indeed, with total exports of more than US$ 20 billion, this market is now almost twice the size of the export market for fresh milk and cream. Also, strict quality standards and sophisticated tastes have provided a barrier that newcomers have had to overcome in order to service the European consumer. But Europe is not expected to be the growth engine of the future: its share in world imports has been declining, a dynamic that is expected to continue (figure 4.4). Due to an explosion of income and population growth, emerging countries in Asia, Africa, and South America are likely to be the future drivers of demand. Figure 6.4 shows that the Dutch trade to select countries in these regions, albeit low at roughly 5 percent, has been

61 61 increasing at a fast pace. For the mid-complexity communities to drive significant future growth, they will need to overcome barriers to transportation and differences in tastes among the traditional European consumer and the new target groups. Nevertheless, if the Netherlands is able to penetrate these emerging markets, this could help drive future growth in these sectors. Figure 6.3 Share in World Imports Figure 6.4 Share in Dutch Exports Share in export basket year year EU CIVETS BRIC EU CIVETS (right axis) BRIC (right axis) Note: Own calculations using COMTRADE. BRIC countries are Brazil, Russia, India and China. CIVETS countries are Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa. Can the Netherlands continue to grow by expanding its presence in its high-complexity communities and products? We believe that the answer is yes, for three reasons. One, a theme that has emerged in much of this report is that for its level of income, the Dutch economy is still very concentrated in certain low complexity areas but is very close in the product space to higher complexity goods. The Dutch economy appears to have the knowledge needed to increase their comparative advantage in more complex products. Secondly, there is evidence almost all of the products that grew the most over the last decade (in terms of exports volume) were more complex than the Netherland s average product. 2 Thirdly, figure 6.5 shows a remarkably neat relationship between geographic distance and the complexity of the products and services that the Netherlands exports. It shows that the Netherlands exports its least sophisticated products to its nearby European neighbors. The most sophisticated products go to Korea, Hong Kong, Japan, China, Russia, Turkey, etc. Since a number of these countries are likely to be the drivers of future demand growth, they will create a market in which the Netherlands can expand its presence as a supplier of high complexity goods and related services.

62 62 Figure 6.5 Dutch exports by Geographical distance and PCI Goods exports HUN CZE AUT IRLNOR SWE POL DNK CHE GBR ITA ESP PRT FIN GRC DEU FRA BEL Distance, logs TUR ISR RUS CHN USA KOR HKG JPN BEL DEU DNK GBR CHE Service exports FRA IRL ITA NOR SWE ESP RUS CHNJPN BRA HKG USA SGP Distance, logs Note: Own calculations using COMTRADE. Average Showing countries with more than one percent of service export basket. The size of the bubble corresponds to the share of the country in The Dutch export basket. To illustrate these potential opportunities more specifically, we make use of a tool that we have developed to locate the natural trade partners to which the Netherlands is currently underperforming the destinations that have an import structure that is complementary to the export basket of the Netherlands but so not receive a proportionally high share of their imports from the Netherlands. The horizontal axis of figure 6.6 shows the degree of complementarity between the Dutch exports and the imports of each country listed. A high value, like that found for Belgium, France and Canada, indicates countries that tend to import what the Netherlands exports. The vertical axis shows the logarithm of the fraction of each country s imports represented by the Netherlands (-1 represents 10%, whereas -2 represent 1%). Thus, the countries inside the red square represent countries that tend to import a mix of products that is compatible with what the Netherlands exports, yet where the Netherlands has a surprisingly low market share. Most of these are countries located in Asia and the Americas; countries like China, Peru, Japan, Argentina and Canada. They represent opportunities for the Dutch export economy, since they are markets that are compatible with the Netherlands current export structure and where there could be room for an increase in Dutch exports. In the Appendix we provide a list of the products with most potential to reach each of these destinations. These are products that the Netherlands exports intensively but not to these countries and which these countries import in great quantities but not form the Netherlands. We limit the analysis only to products with a PCI more than one standard deviation above the mean in order to identify those opportunities that are likely to add to rather than subtract from the economic complexity of the Netherlands.

63 63 Figure 6.6 Export Complementarities and Share of Imports Represented by the Netherlands Source: Own calculations using COMTRADE. In the final chapter of this report we explore what the government of the Netherlands can do to facilitate future adjustments, whether those involved in strengthening the links to new markets for Dutch products or those related to re-aligning the domestic workforce and industrial landscape to focus on the most unique and complex strengths of the Netherlands. This final section will provide a set of policy recommendation aimed to help improve the ability of the Netherlands to navigate its economic future.

64 64 Notes 1 In the Machinery community, where the Netherlands is not among the top ten exporters or importers, it is nevertheless included for comparison. 2 See Table A.3.4 in Appendix

65 7 WHAT ARE THE IMPLICATIONS FOR PUBLIC POLICY? This report used public sources of data to paint a comprehensive picture of historical export patterns of the Netherlands. It described the productive knowledge of the Dutch economy which has helped it grow into the world s 6 th most prosperous nation. We learned that the key comparative advantages and areas of highest complexity of the Netherlands are its chemicals, electronics, and food processing industries, which are supported by a strong agricultural base and commercial ties for the sourcing of raw materials. We also uncovered a relatively weak presence in the machinery sector. However, the machinery sector is the repository of productive knowledge. It is not an autonomous sector per se, but rather, exists in deep relation to the producing sectors of the economy. In the modern age, all production, be it in agriculture, horticulture, chemicals or electronics, is supported by automated processes driven by machines. Innovations in the design and processes used to make products are embedded in the machines that make them. The importance of machinery is clearly revealed in the product space, which positions this industry at the core of the network of products (see figure 4.1). Moreover, because knowledge of the production process is key to automating and improving that process, countries tend to develop comparative advantage in machinery that caters to the industries that they have. We see this phenomenon clearly in the Netherlands, whose machinery production prominently features tools used in agriculture, food processing, and chemicals. While the Netherlands does not produce many cars, it is the country with the third-largest revealed comparative advantage in the world in the export of tractors and trailers (see Appendix table A.3.3). Successfully developing new products in machinery requires continuous knowledge feedback from the other producing sectors to makers of machinery. It also requires continuous experimentation and innovation, both producer- and user-driven. Hence, a successful machinery sector relies on research and development. For example, Chile has US$40 billion of copper exports compared to Australia s US$8 billion; however Australia is a major exporter of machinery and services for the mining industry while Chile imports these goods and services. Chile has knowledge about mining, but the research and development infrastructure needed to support a capital goods industry for mining is absent. Thus, the ability of the Netherlands to compete in the future will not only depend on how much milk its farmers produce and sell, but also on the strength of its R&D sector and its ability to embed knowledge about milk production into mechanized processes and machinery. Importantly the development of capabilities relevant to machinery design and manufacturing will not only further a country s comparative advantage in machinery but will

66 66 also generate an expansion of the related services. High-end machinery exports stimulate high-value service exports, such as plant design, technological support services, and other business services. These capabilities can also advance foreign direct investment and generate returns to knowledge exports, such as those that we see in the United States (and not yet in the Netherlands) (see Textbox 6). Hence, a strategy that focuses on furthering machinery, improving production processes, and exporting these improvements together with related services is not solely a strategy for manufacturing but for the whole value chain. In this report, we have described how the nature of global economic growth is changing. In the years to come, lower- and middle-income countries around the world will be the new engines of growth. According to the United Nations, the world population is expected to grow from 7.0 billion in 2011 to 9.3 billion in 2050, and most of the growth is expected to happen in lower and middle-income countries. Many of them are expected to exhibit high growth in income per capita levels. This growth will generate consumers with different tastes and needs but leaner budgets. From the perspective of the Netherlands as a supplier of goods and services, these new potential consumers are more distant, geographically, economically, and culturally, than traditional European customers. This presents a business opportunity for Dutch businesses but also an innovation challenge. How can Dutch firms become good at understanding the needs of consumers and firms in settings as different as Brazil, India and China? Firms in middle-income countries will also need to grow and become more productive. In agriculture, for example, the Food and Agriculture Organization (FAO) estimates that global food production needs to increase roughly 50 percent from current levels in order to meet future demand for food by As the world s middle class expands, the demand for animal proteins is bound to increase more than proportionally. Dutch firms can leverage on their strength in the agriculture and food processing industries and become the exporters of technology and productive knowhow to these growing regions. With its experience and its base of productive knowledge, the Netherlands can become a leading provider of solutions in the agricultural and food processing sectors. However, Dutch firms will need to innovate in these new markets, pushing their own knowledge frontier while bringing knowledge, technology, and solutions to countries very different from the Netherlands. What role can public policy play to bridge the realities today with this vision of the future? Below we spell out a number of recommendations for stakeholders in the Netherlands to consider:

67 67 Our first set of recommendations are to increase efforts at R&D and collaboration which leverages the productive know-how in the strongholds of the Dutch economy (chemicals, electronics, food processing, and agriculture) to strengthen production of related machinery. As we explained, cross-sector collaboration and knowledge sharing among the producing sectors and machinery producers are key for the success of a machinery industry. Currently, the R&D results of the Netherlands rank very low compared to other countries at its income level (figure 7.1). As of, the Netherlands invested 1.63 percent of GDP in Research and Development, which is below the OECD average of close to 2 percent. The Netherlands is also lagging in measures of R&D-related human capital with less than 3,100 researchers per million people, near the bottom of the ranking of countries with an annual income per capita of more than $20,000. Figure 7.1 Research and Development and Researchers in OECD countries, TUR POL MEX CZE EST HUN SVK ISR SWE JPN FIN KOR USA ISL DEU DNK AUT BEL FRA CAN GBR SVN PRT ESP ITA IRL NZL GRC NOR LUX TUR MEX POL EST CZE SVK HUN FIN ISL JPN DNK SWE KOR NZL GBR DEU AUT FRA SVN BEL PRT ESP IRL GRC ITA NOR LUX Log of GDP per capita current US Log of GDP per capita current US Source: Own calculations using WDI. These outcomes can be the result of poor public R&D funding, low private returns to R&D, or both. In the Netherlands, a recent report by the Dutch government suggests that the main source of underperformance is low private sector R&D spending. 1 It shows that the Dutch government funds a fair amount of the country s total R&D. However, Dutch firms rank among the lowest in Europe in terms of their average R&D intensity and they continue to fall behind. Surprisingly, these results do not appear to be driven by low returns to R&D investments, at least at the aggregate level. Indeed, returns to private R&D as measured by patent output per R&D expenditures are high in the Netherlands. On this measure, Dutch companies are among the top performers. 2 However, these results are driven principally by a few companies such as Phillips, who together with eight other large multinationals, accounted for more than half of the Dutch private sector R&D expenditures. 3 Overall, Dutch

68 68 performance, while high per unit of investment, are low in terms of patents per capita and has declined significantly since the 1960s (figures 7.2 and 7.3). Figure 7.2 Patents per capita in OECD countries 2007 Figure 7.3 Patents per capita in the Netherlands JPN KOR USA DEU NZL FIN DNK GBR SWE AUT NOR FRA CHE IRL ISL SVN CAN ITA ISR AUS ESP HUN CZE POL LUX BEL GRC SVK EST CHL PRT TUR MEX Source: Own calculations using WDI. Rather, the results appear to be driven by the structure of the Dutch economy which over time has shifted away from manufacturing and more specifically high-tech manufacturing and toward lower-tech production and services. In this report we have sought to show that such an industrial positioning is less prone to growth and more vulnerable to future competition. However, it is also important to remember that innovation does not occur solely in private firms or research universities. In fact, a large number of innovations tend to be driven by users, rather than producers. Here, we use the von Hippel 4 definition of a user, in which a company like Boeing is considered a user when it comes to machines that mold metals, but a producer when it comes to planes. In most cases, however, user driven innovations are performed by individuals rather than firms 5. Studies of consumer innovation based upon national representatives samples in the UK, US and Japan have shown user innovation to represent a large share of a country's total innovation, but to be driven by different motivations than producer driven innovations. Producers profit from their innovation by selling it on the market. Hence producer are unlikely to be early innovators, since early on the life cycle of products, markets tend to be small. Policies targeted at producer driven innovation need to incentivize creativity and innovation among producers, rather than diffusion of innovation. This is because diffusion is not an issue for producers with a natural incentive to market their products. User driven innovations, on the other hand, tend to be driven by the need of the user to use that

69 69 innovation, rather than to commercialize it. Hienerth and Von Hippel (2011) report that 54.82% of user innovators are motivated by the personal need to use the innovation themselves, while only 2.07% are motivated by the potential sales of their innovation. This implies that different policies are needed for user driven and producer driven innovations. When it comes to user innovations policies need to focus on the diffusion of innovations, rather than on the creation of them. This is because for most user-innovators the diffusion of the innovation is seen as an externality instead of a primary goal. We understand the Dutch government is aware of the challenges of R&D and innovation in the Netherlands and already has initiatives ongoing to fund and stimulate R&D and increase public-private collaboration in strategic areas and key sectors of the economy. In such efforts, we would recommend that the effort focus on incentivizing and facilitating research and innovation which translates productive knowledge from existing industries into machinery and production engineering for those industries. In these efforts, suggestions on how to improve results include: Consider creating and supporting industry-based R&D networks that would increase the interaction between corporations, universities and research labs focused on improving the technology used by each industry and to find ways of incorporating new technologies into the machinery and production processes used in the industry. This approach should be designed so as to stimulate the effectiveness of private-sector R&D and to diversity it from a dominance of the biggest firms to smaller innovative companies and SMEs. This would not only lead to higher productivity in the industries using the new technology, but should create new world class sectors in the machinery, technology, and capital goods that support this and other industries. Create urban spaces in which industrial actors (including SMEs, start-ups), researchers, academics and policymakers can collaborate, share information and network. Increase funding, information about opportunities, and other incentives to increase the output of R&D researchers by encouraging Dutch university students to choose careers in, science, and engineering and related fields, especially those that leverage Dutch current comparative advantage in food science, chemistry, bio-tech, electronics and material science. Consider incentives to increase formal cooperation between companies and universities. Consider stimulating an R&D-intensive venture capital industry, looking, in particular at the experience in Israel. Create mechanisms to harvest and scale user generated innovations.

70 70 Our second recommendation is to complement the massive international development effort of the Netherlands with an additional focus aimed at connecting the needs of fast-growing middle-income developing countries to solutions that can be developed through extensions of the productive knowledge base of the Netherlands. The Netherlands, through its government, non-profit organizations, and private donors has an impressive commitment to global poverty reduction. We commend the Netherlands for the leadership it has shown in this field. However, the current effort in the international development arena is focused on the poorest countries. For example, the map below shows that SNV is active in 36 of the poorest countries worldwide. 6 It is, however, not present in any of the BRIC countries or a number of other countries that are bound to play a key role in future growth. Figure 7.4 Where SNV is active Source: The government and development institutions of the Netherlands may wish to consider augmenting international development efforts targeted at the poorest countries with increased efforts targeted at finding technological solutions for the needs of lower- and middle-income countries. The strategy would be one which re-aligns the Netherlands as a contributor to the engines of growth of the new growth leaders. Thus the topic of development becomes more than a moral responsibility to reduce poverty but also a practical and business opportunity to engage the productive needs and provide technological solutions to the range of middle and lower-income countries world-wide. Thus, for example, the Netherlands could complement its Centre for the Promotion for Imports from developing countries 7 with a Centre for the Promotion of Technological Solutions for Developing countries in the areas of industrial strengths of the Netherlands. Dutch firms could be incentivized by the government to increase their efforts to use their knowledge to develop technological solutions for the lower- and middle-income countries of the word. The public