The Economic Complexity of Namibia: A Roadmap for Productive Diversification

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Abstract

After a large growth acceleration within the context of the commodity super cycle (2000–2015), Namibia has been grappling with three interrelated challenges: economic growth, fiscal sustainability, and inclusion. Accelerating technological progress and enhancing Namibia’s knowhow agglomeration is crucial to the process of fostering new engines of growth that will deliver progress across the three targets. Using net exports data at the four-digit level, we estimate the economic complexity of Namibia – a measure of knowhow agglomeration – vis-à-vis its peers. Our results suggest that Namibia’s economy is relatively less complex and attractive opportunities to diversify tend to be more distant. Based on economic complexity metrics, we define a place-specific path for productive diversification, identifying industries with high potential and providing inputs – related to their feasibility and attractiveness in Namibia – for further prioritization. Namibia’s path to structural transformation will likely be steeper than for most peers, calling for a more active policy stance geared towards progressive accumulation of productive capacities, well-targeted “long jumps”, and strengthening state capacity to sort out market failures associated with the process of self-discovery.

Authors: Ricardo Hausmann, Miguel Angel Santos, Douglas Barrios, Nikita Taniparti, Jorge Tudela Pye, and Jessie Lu

CID Faculty Working Paper No. 410 · March 2022
Growth Lab, Center for International Development, Harvard University

1. Introduction

Thirty years after independence, Namibia finds itself grappling with three interrelated challenges: reigniting economic growth, restoring fiscal sustainability, and promoting a more inclusive economy. After a prolonged growth acceleration driven by the investments and exports associated to the super cycle of commodity prices, the economy has stagnated, fiscal accounts deteriorated, and endemic inequality has become more prominent. Diversifying the Namibian economy will likely deliver progress along these three targets but has proven elusive to the resources and policy attention devoted by successive governments. We argue that productive diversification is constrained by the lack of productive knowhow out of the resource sector. Using net exports data from UNCOMTRADE we estimate the Economic Complexity – a measure of knowhow agglomeration – for Namibia and a group of African and international peers. Our results suggest that Namibia is relatively less complex and attractive opportunities to diversify tend to be more distant. We identify a set of products with high potential to be exported from Namibia, as they rely on existing productive capacities and knowhow. That approach differs from the aim towards beneficiation that has characterized Namibia’s industrial policy efforts and calls for a more active policy stance geared towards progressive accumulation of productive capacities, well-targeted “long jumps,” and strengthening state capacity to sort out market failures associated with the process of self-discovery.

Technological progress and knowhow agglomeration are fundamental to the process of structural transformation that characterizes economic development. Previous authors (Hidalgo, Klinger, Barabasi, and Hausmann, 2007) have documented that a consistent feature of development is that richer countries tend to produce a larger variety of goods, that on average very few countries are able to make. Alternatively, relatively poorer countries tend to produce fewer goods, that on average many places can make. This counters conventional wisdom, which states that societies should specialize in a narrow set of activities in which they have competitive advantages.

The progressive accumulation of productive capacities and knowhow, which allows places to produce a larger variety of goods competitively, does provide an account of structural transformation that is more consistent with the dynamics observed in the evolution of the productive structures of countries. The premise behind this theory, originally presented by Hausmann and Hidalgo (2009), is based on the idea that capabilities and knowhow are not observable but are signaled by the number and nature of the products and services that a place is able to produce and render competitively. Countries lacking many capacities will only be able to assemble a relatively modest number of products (little variety), which will also be feasible in many other places (higher ubiquity). Countries that accumulate many capacities will be able to produce a relatively large number of goods (large variety), which on average only a few places will be able to produce (lower ubiquity).

In this context, the process of diversification poses a chicken-and-egg dilemma: nobody wants to acquire skills for an industry that does not exist; if those skills remain absent, it is unlikely the industry will develop. Hidalgo and Hausmann (2009) have provided insights on how societies have come around this dilemma: Countries do not diversify randomly; they rather spread towards activities that demand productive capabilities that are similar to those they already possess. Current capacities and knowhow can be recombined and redeployed into new, “adjacent,” economic activities.

This paper is aimed at quantifying the depth of the knowhow agglomeration in Namibia – as signaled by the products the country is capable to manufacture and export competitively – and identifying opportunities for productive diversification based on their technological proximity to the existing set of capacities. We define proximity between a pair of products by estimating the conditional probabilities for a country to have a revealed comparative advantage in one product, given that it already exhibits revealed comparative advantage in another product. Following that process, our proximity matrix between pairs of products is estimated by their tendency to co-locate, the same criteria used by Hausmann et al (2014). The idea is that if two sectors require a similar set of capabilities, the fact that one of them already exists in a place suggests a high likelihood for competitiveness on the other.

In the case of Namibia, we have defined revealed comparative advantage (RCA) by applying the definition of Balassa (1964) to net exports at the four-digit level. We relied on net exports to correct for potential re-exports that we have detected within the course of our research, based on differences between UNCOMTRADE and domestic databases.¹ Correcting for re-exports allows for a more precise characterization of Namibia’s productive capacities and identification of sectors that can be potentially developed by redeploying existing skills. This is also why our results differ from the visualizations of the Atlas of Economic Complexity for Namibia that are publicly available online.²

Our results highlight three important and interrelated lessons. First, Namibia’s Economic Complexity Index (ECI) ranks amongst the lowest of regional and international peers for the previous two decades, only surpassing Angola. This is consistent with the relatively low diversity and high ubiquity of its existing exports. Second, Namibia has been able to diversify differentially more than the average country and most of its peers, given its current set of productive capabilities. Third, and related to the previous one, the problem is not so much that Namibia has not diversified into adjacent products, but rather that given the relatively low depth of its knowhow agglomeration these opportunities have limited strategic value.³ The Complexity Outlook Index (COI), which captures the number of absent complex products that demand knowhow and productive capabilities that are similar to those already in place, shows that Namibia has few complex products within a relatively short Distance.

These three lessons suggest that the path to productive diversification and ultimately structural transformation in Namibia might be steeper than for most peers, calling for a more active policy stance geared towards progressive capability accumulation, well targeted “long jumps”, and strengthened state capacity to sort out market failures associated with the process of self-discovery.

Using economic complexity metrics we identify a place-specific path for productive diversification, highlighting industries with high potential and providing inputs – related to their feasibility and attractiveness in Namibia – for further prioritization. The different feasibility and attractiveness dimensions have been informed by policy priorities as highlighted in several interviews with government officials. These are meant to be illustrative prioritization criteria that helps in better targeting government efforts and may vary in response to changes in data availability, conditions on the ground, or in policy priorities. To facilitate policy efforts, we have organized the resulting set of products with high potential to lead productive diversification in Namibia into five diversification themes: (i) Chemicals & Basic materials, (ii) Food industry, (iii) Machinery and electronics, (iv) Metals, mining & adjacent industries, and (v) Transportation and logistics. This exercise is meant to serve as a roadmap to inform broader diversification process and should be refined and improved through iterations with relevant stakeholders and the authorities responsible for leading the efforts on productive diversification.

Namibia’s previous industrial policy efforts have not followed an approach based on productive capabilities and knowhow but have rather focused on the idea of adding value to raw materials. As stated in the Growth at Home Strategy 2015–2020 of the Ministry of Industrialization and Trade (2015), “value addition is perhaps the most important feature of Growth at Home. Namibia is well endowed with numerous raw materials, and this presents a tremendous opportunity for value addition”. We identify industries with strong forward linkages from Namibia’s raw materials and demonstrate that in terms of productive capacities the strategy is clearly suboptimal. The institutional effort needed to supply the missing skills required by industries downstream Namibia’s raw materials are larger than those required by other industries of similar Economic Complexity. Alternatively, with the same effort required to fill the capability gaps needed to materialize an industry that adds value to Namibia’s raw materials, the country could develop industries of higher Economic Complexity.

The report is organized in five sections. Section 2 outlines the conceptual framework of the report, including a description of the theory behind the Economic Complexity methodology and relevant considerations for its application in the Namibian context. Section 3 is devoted to analyzing the depth of existing knowhow agglomeration in Namibia vis-à-vis a group of regional and international peers. In section 4 we identify opportunities for productive diversification based on Economic Complexity metrics, and in section 5 we introduce additional filters based on feasibility and attractiveness considerations that may complement the Economic Complexity methodology. Section 6 concludes the report by summarizing our most significant insights and their policy implications, as well as exploring potential avenues for future research.

¹ The most prominent cases are related to the re-exports of machinery associated with mining that was previously imported, as well as vessels of deep sea exploration which are often re-exported after use.

² https://atlas.cid.harvard.edu

³ In terms of enabling further diversification across new economic sectors of higher economic complexity.

2. Conceptual Framework

2.1 Theory of Economic Complexity

The theory of economic complexity, introduced by Hausmann, Hidalgo et al. (2011), is based on the realization that the development of products and services not only requires raw materials, labor, and machinery, but also tacit knowledge (or “knowhow”) of how to put inputs together to produce things and run business operations. This tacit knowledge tends to be the limiting factor for diversifying economic activities because it is the component most difficult to procure. Knowhow can only be acquired through experience and tends to be spread across many individuals who need to coordinate across teams and organizations.

Some products and services incorporate large amounts of knowhow and types of knowhow that are valuable for multiple uses. In contrast, other products and services incorporate much less knowhow or knowhow that is not transferable for other valuable uses. As an analogy, different products and services can be understood as “words” whose production requires “letters” (knowhow-based capabilities), like in a game of Scrabble. The production of long and sophisticated words requires many letters, including some high-value letters, while few are needed to generate short and simple words.

The knowhow embedded in places varies in terms of type and quantity. That is, some places have many diverse letters, which they can use in many combinations to make many different and valuable words, while others have few letters and letters with limited uses, which limits the possibility of creating new words. The differences in productive capacities brought by uneven “endowments” of letters are further amplified by the fact that the number of words that can be constructed increases exponentially as new letters are added.⁴

Ultimately, places develop the products and services (words) that their knowhow-based capabilities (letters) can support. Tools of economic complexity aim to measure and utilize the patterns that result. By observing patterns of production across places and time, we can infer and mathematically construct quantitative measures that capture the diversity of knowhow embedded in a place (Economic Complexity Index, ECI) and how much knowhow specific goods and services require (Product Complexity Index, PCI). Places with a high ECI are able to support a diverse set of economic activities, including activities that are not common across places, while places with low ECI support a less diverse set of activities, and those activities tend to be ubiquitous across places.

Given that economic complexity reflects the amount of knowhow that is embedded in the productive structure of an economy, it is not surprising to find a strong correlation between measures of complexity and income. Figure 1 shows the relationship between per capita income and economic complexity across all countries of the world.

Hausmann, Hidalgo et al. (2014) also found that the prediction errors in Figure 1 – i.e., the difference between a country’s actual income levels and those predicted by its complexity – tend to be predictive of future growth dynamics. Countries with an economic complexity greater than expected given their level of income tend to grow faster than countries that display a level of income that is higher than expected for their current level of economic complexity. In other words, countries positioned below the regression line are often poised to enter long periods of sustained growth, because removing key constraints (such as infrastructure, access to financial capital, or institutional gaps) will enable them to capitalize their existing stock of knowhow into higher output. Meanwhile, places above the regression line may be in a more precarious position (in terms of long-term growth) as they may be benefitting from a temporary positive shock. If this boom is not leveraged to increase the complexity of the economy to a level consistent with the current level of income, they run the risk of having their income fall toward the regression line once the boom comes to an end.

Figure 1. Economic Complexity Index (ECI) and Income per Capita
Source: Own calculations based on World Bank WDI and the Atlas of Economic Complexity

A final critical theoretical foundation of economic complexity was introduced by Hausmann and Klinger (2006). They showed that the probability that a place develops a new product is contingent on the set of products that it already produces. This allowed for the measurement of the similarity between products based on their shared capabilities. Based on this pattern, they proposed a measure of similarity or proximity between products. In essence, they measure the “proximity” between any pair of products based on the probability that countries are intensively engaged in both. The collection of all the resulting proximities can be visualized as a network connecting pairs of products based on their tendency to be co-exported by countries. They refer to this network as the Product Space and use it to study the productive structure of countries.

The structure of the product space is crucial because it determines the ability of countries to move into new products. A highly connected position in the Product Space reflects relatively easier paths to diversification than a sparse position. Hausmann and Klinger (2006) find that the product space is highly heterogeneous: some sections are composed of densely connected groups of products whereas others are more loosely connected. This heterogeneity has significant implications for the speed and patterns of structural transformation: the ability of countries to diversify and to move into products that are more complex is crucially dependent on their initial location in the product space. The complete product space and Namibia’s position in the space are shown in Figure 2 and Figure 3.

The location of a country’s production in the product space captures information regarding both the productive knowledge that it possesses and the capacity to expand that knowledge by moving into other nearby products. The strategic positioning of a place in the product space can be leveraged as an insightful tool for formulating economic diversification strategies.

Figure 2. Product Space Clusters
Source: Atlas of Economic Complexity and own calculations

Figure 3. Namibia’s Position in the Product Space (2018, based on net exports)
Source: Atlas of Economic Complexity and own calculations

⁴ For example, according to the Official Scrabble Dictionary online (https://scrabble.hasbro.com/en-us/tools), in the English language with 1 letter, “a”, one word can be formed of up to 1 letter; with 3 letters, “a”, “c” and “t”, you can form up to 4 words of up to 3 letters (“a”, “at”, “cat” and “act”); with 4 letters, “a”, “c”, “t” and “r”, you can form 9 words of up to 4 letters (“a”, “at”, “cat”, “act”, “rat”, “car”, “art”, “tar” and “cart”); and with 10 letters, “a”, “c”, “t”, “r”, “o”, “l”, “g”, “s”, “n” and “i”, you can form 595 words of up to 10 letters.

2.2 Methodological Adjustments on Complexity Metrics for the Case of Namibia

The seminal contributions of economic complexity and most of the applied research that followed has been based on UNCOMTRADE export data at the country level. The reason being that it offers the most complete, granular, and lengthy internationally comparable database, and that algorithms to clean and process this information, and to calculate economic complexity metrics leveraging this data, have been adequately tried and tested. In the case of Namibia, however, even after applying algorithms specially designed to tease out errors in data gathering and reporting the information available in UNCOMTRADE was not consistent with local databases. Namely, there are UNCOMTRADE reported exports associated to industries that are not prevalent or existing in Namibia. These may occur for various reasons. First, because UNCOMTRADE may include re-exports, or exports originated in neighboring countries that leverage Namibia’s logistical infrastructure and are accounted as Namibia’s exports. Second, because Namibia may import machinery to be deployed in activities of exploration or exploitation of its mineral wealth, which may potentially be re-exported as secondhand after being used.

If we do not adjust for this possibility, we could be overestimating the real latent productive capabilities of Namibia and distort the identification of sectors with potential to be developed by redeploying existing skills. Products with little real basis to be considered as a diversification opportunity may be prioritized, and legitimate diversification opportunities may end up being discarded. To address that, we used net exports at an industrial aggregation of 4-digits⁵ (rather than the more granular 6-digits) for all relevant economic complexity metrics. This approximation should correct for most misclassified exports and does a better job at identifying latent productive capabilities.

⁵ This corrects for situations in which a product was temporarily imported under one category and re-exported under a similar but different category.

Box 1: Relevant Concepts in Economic Complexity

A description of several of the main variables in economic complexity methodology follows. It is important to bear in mind that apart from Revealed Comparative Advantage (RCA) and Diversity, all these measures are normalized indices that carry ordinal but not necessarily cardinal meaning. That is, the order of values may matter, but it may be meaningless to interpret the precise numerical value of an index.

• Revealed Comparative Advantage (RCA): A place-specific measure that captures the relative prevalence of a product in a place. Following the methodology of Balassa (1964), it is usually calculated as the ratio between the proportion of the product in the export basket of a place and the proportion of the product in world trade. If this relationship is greater than one, the place has a “revealed comparative advantage” in that product, which is equivalent to saying that the place produces the good with higher relative intensity than the rest of the world.
• Product Complexity Index (PCI): A product-specific measure that ranks the Diversity and Ubiquity of the productive knowledge required for its production. It is determined by an iteration between the average Diversity of countries that make the product, and the average Ubiquity of the other products that these countries make.
• Economic Complexity Index (ECI): A place-specific measure that captures how complex a place’s export basket is. It is calculated as the average PCI of those products in which the place shows an RCA equal or greater than one.
• Distance: A place-product measure that corresponds to the sum of the proximities connecting a new good to all the products that country is not currently exporting. This value is normalized by dividing it by the sum of proximities between the new product and all other products. In turn, proximity is a product-to-product measure that is calculated as the minimum conditional probability that a country intensively exports one product given that it already intensively exports the other.
• Complexity Outlook Gain (COG): A place-product measure that quantifies the extent to which adding a new product to the current export basket can open links to more, and more complex, new products. A high COG implies that a product is in the vicinity of more new products and/or of new products that are more complex, while a low COG means that a product is near many existing products and/or new products that are less complex.
• Complexity Outlook Index (COI): A place-specific measure that evaluates the overall position of a place in the Product Space by calculating how far it is to alternative products and how complex these products are. A high COI implies that the place has an easier path towards greater levels of complexity, while a low COI means that achieving them will be more difficult as it implies moving into products that are further away.

3. The Economic Complexity of Namibia

Deploying the framework outlined above based on net-exports data from UNCOMTRADE at the four-digit level, we constructed economic complexity metrics for Namibia to infer collective knowhow. The results indicate that Namibia has a very low agglomeration of knowhow and low connectedness. The export acceleration – driven by higher prices and market shares – recorded over the large 2000–2015 expansion was restricted to a few natural resources with very low shares of employment. That feature characterizes the growth patterns observed and is at the core of the challenges the country has faced to promote inclusive growth and increase the living standards of Namibians.

Namibia’s ECI is amongst the lowest of its regional and international peers (Figure 4), with an export basket composed mostly of primary products (Figure 5). Low ECI has been a constant for the previous two decades, surpassing only Angola among the group of regional and international peers. That feature is consistent with the low diversity and high ubiquity of its existing export products.

Figure 4. Economic Complexity Index: Namibia vs. Peers (2018)
Source: Atlas of Economic Complexity

Figure 5. Namibia’s Net Export Basket (2018)
Source: Atlas of Economic Complexity

Namibia’s low ECI is explained in part because no product in its current export basket displays an average PCI above the global median, and the products that concentrate most of the country’s diversity – agriculture and mineral products – tend to be of low complexity. Only one sector – chemicals and plastics – has an average weighted PCI higher than zero, which contributes positively to Namibia’s economic complexity (Figure 6).

Figure 6. Namibia’s ECI by Sector (2018, %)
Source: Atlas of Economic Complexity

To assess the capacity of the Namibian economy to diversify into nearby products – from a technological proximity standpoint – we estimated the probability of developing one product with RCA greater or equal than one for the period 2010–2018. We performed that calculation for Namibia and its peers, controlling for their position in the product space, following specification:

jump_ij = f(density_ij, rca_ij, country_i, country_density_ij),

where jump is a dichotomic variable that takes the value of 1 if in a period of 8 years the RCA of industry j in country i went from 0.25 or lower to 1 or greater than 1. The parameter of interest is country_density_ij, which captures the relationship between the density of the country’s product to its diversification process over time vis-à-vis the average country. Thus, a statistically significant and positive coefficient indicates that the country has been able to jump differentially more than the average country.

Our results suggest that over the previous decades Namibia has been able to diversify into products which are adjacent to its existing capabilities. As a matter of fact, the country has been able to diversify differentially more than the average country and more than most of its peers, given its current set of productive capabilities (Figure 7). Put in a different way, within the context of low ECI, the country has been able to materialize diversification opportunities by conquering adjacent products.

Figure 7. Differential Effect of Density over the Probability of Jumping by Location (2010–2018)
Source: Atlas of Economic Complexity

The problem is not so much the capacity of Namibia to diversify into adjacent products, but rather that – given its positioning in the product space – the country has very limited diversification opportunities, and these opportunities tend to be of limited strategic value. Most Namibian export products lie at the periphery of the product space and distant from each other, which leaves very few potential nearby jumps (as depicted in Figure 3). Out of the products added since 2003, 95% of their value added corresponds to products with an average PCI lower than the global mean, essentially transport, metals, and stones.⁶ This trend has been reinforced from 2013 onwards, by a relative increase in the number of products that many other places are also likely to make (high ubiquity).

The country’s Complexity Outlook Index (COI), which captures the number of absent complex products that demand knowhow and productive capabilities that are similar to those in place, shows that Namibia has few complex products within a short distance (Figure 8 and Figure 9). That feature is mirrored at a more granular level by the average density by export category, which is lower for Namibia – for all export categories – than for the average of its regional and international peers. All of these indicators suggest that productive diversification in Namibia might follow a steeper – longer, riskier – process than in peers, calling for a policy strategy geared towards progressive accumulation of capabilities, targeted long jumps, and strengthening the state capacity needed to sort out market failures associated with the process of self-discovery.

Figure 8. Complexity Outlook Index: Namibia vs. Peers (2018)
Source: Atlas of Economic Complexity

Figure 9. Evolution of Complexity Outlook Index: Namibia vs. Peers (2000–2018)
Source: Atlas of Economic Complexity

⁶ Products that appeared once in the country’s export basket with an RCA greater than 1 for 3 years.

4. Identification of Diversification Opportunities

4.1 Scope of the Exercise

The objective of this exercise is to leverage the information associated with Namibia’s latent productive capabilities to develop a list of potential diversification opportunities. This exercise should not be interpreted as a final product, but rather as an initial contribution for an iterative process – involving a variety of stakeholders (policy makers, academia, industry experts, civil society, etc.) – to prioritize efforts around productive diversification and investment promotion. Furthermore, this effort is largely anchored around economic complexity, which is one of several possible approaches to approximate diversification paths. The fact that some industries or sectors are not accounted for in this approach does not imply they must be excluded from a broader national diversification strategy, as there may be other valid evidence to substantiate their feasibility or attractiveness.

4.2 Process of Sector Identification

As was highlighted in Section 3, an assessment of Namibia’s Economic Complexity suggests that the country might benefit from a more active policy stance geared towards progressive capability accumulation, well targeted “long jumps”, and strengthening state capacity to sort out market failures associated with the process of self-discovery. An initial step in this direction is the identification of industries that may partially leverage existing productive capabilities and enable transitions towards more sophisticated economic activities.

This process – based on the tenets of Economic Complexity methodology – is summarized in Figure 10 and further detailed below. Given the relatively small population of Namibia – and hence limited long-run scope of local demand – and its exposure to sector-specific exogenous shocks, it makes sense to focus diversification efforts on tradable industries with export growth potential. Furthermore, it is possible to consider export growth along two dimensions: the intensive margin, where existing products can be scaled up; and the extensive margin, where new or nascent products can be successfully developed. Industries to be identified on the intensive margin are taken from the pool of products where RCA is greater than one (products that have a relatively larger presence in Namibia than in the rest of the World), while products to be identified on the extensive margin are taken from the pool of products with an RCA less than one (industries that have a relatively larger presence in Namibia than in the rest of the World).

Figure 10. Process for Sector Identification
Source: Own construction

Diversification opportunities are then selected based on economic complexity metrics – Distance, Product Complexity Index (PCI), and Complexity Outlook Gain (COG) – in different ways for the intensive and extensive margins. Distance indicates how “nearby” a product is to products where Namibia already exhibits RCA>1, and it serves as a proxy of the likelihood that the country further specializes in the prospective industry; PCI measures how complex a certain product is, and it serves as a proxy of whether the industry would help improve the country’s overall economic complexity; and COG quantifies how much developing a new product would enable access to additional new products of higher complexity, and serves as proxy of whether the industry would help improve the country’s overall strategic positioning.

While PCI and COG may be positively correlated, in most countries⁷ there tends to be a negative correlation between each of these variables and distance. This reflects an important trade-off: the most complex products and those with the best strategic positioning tend to be further away from existing capabilities, while less complex products tend to be closer. This negative relationship can be thought of as a risk-return curve. That is, the country may have less chance of success when trying to promote the development of more sophisticated products, because they require capabilities that are further away from its initial stock. However, if the place’s efforts are successful, rewards are greater as it will have gained greater complexity and improved its long-term strategic positioning. This trade-off can be visualized in Figure 11, which plots PCI and distance for all products in Namibia’s extensive margin.

Figure 11. Namibia’s Extensive Margin: Product Complexity Index and Distance (2018)
Source: UNCOMTRADE, Atlas of Economic Complexity, own calculations based on net-exports

The process for identifying diversification opportunities aims to balance these three dimensions. On the extensive margin, two approaches are put forth. One – parsimonious industrial policy – prioritizes likelihood of success (distance) and the other – strategic bets – prioritizes strategic value (PCI & COG). Both approximations give positive weights to all three complexity variables.⁸ For the Parsimonious Industrial Policy (PIP) approximation, a weight of 60% is applied on distance, while the remaining 40% is applied on PCI (15%) and COG (25%). For the Strategic Bets (SB) approximation, a weight of 45% is applied on distance, while the remaining 55% is applied on PCI (20%) and COG (35%). On the intensive margin, only the PCI variable is used because distance and COG are effectively zero for products where Namibia already has a revealed comparative advantage. For all products considered in both the intensive and extensive margin, a minimum threshold of PCI>-0.93 (Namibia’s Economic Complexity Index by 2018) is set to safeguard that identified products would favorably contribute to Namibia’s economic complexity.

The process aims to identify the top 50 products from the intensive margin⁹ and the top 100 products from the extensive margin (Top 50 under each of the PIP and SB approximations).¹⁰ Figure 12 and Figure 13 show how the different extensive margin approximations end up prioritizing different types of products given the differential weights allocated to the Economic Complexity metrics. At this point, we consolidate findings across the different approximations and classify identified products into groups of related economic activities or diversification themes.¹¹ This yields a final list of 97 products, which are drawn from the intensive and extensive margins, and are organized into 5 cohesive themes.

Figure 12. Top 50 Products Identified Based on Parsimonious Industrial Policy Approach
Source: UNCOMTRADE, Atlas of Economic Complexity, own calculations based on net-exports

Figure 13. Top 50 Products Based on Strategic Bets Approach
Source: UNCOMTRADE, Atlas of Economic Complexity, own calculations based on net-exports

⁷ Particularly in countries with relatively low Economic Complexity but tends to be true for most countries that are not at the edge of the innovation frontier.

⁸ These weights are preliminary in nature and are informed from previous Growth Lab experience. However, these may be adjusted in further iterations of this work.

⁹ Given the minimum threshold of PCI mentioned earlier, Namibia only is intensive in 34 products that can be considered for this set of products.

¹⁰ Given that there is an overlap of products identified under the PIP and SB approximation, the final list of industries in the intensive margin falls below 100.

¹¹ To optimize eventual policy design and efforts to favor productive diversification, only industries that neatly fall into one of these diversification themes are considered further. The logic of this step is that resources would be most effectively used if targeted toward collections of industries as opposed to very specific industries.

4.3 Potential Themes of Diversification Opportunities

The five diversification themes that encompass the preliminary identified diversification opportunities for Namibia include:¹² (i) Chemicals & basic materials, (ii) Food industry, (iii) Machinery and electronics, (iv) Metals, mining & adjacent industries, and (v) Transportation and logistics. Figure 14 highlights the relative prevalence of each of these sub-themes and how they could be divided into narrower sub-themes. Figure 15 highlights the relative prevalence of industries in the intensive margin and the extensive margin within each of these themes.¹³

Figure 14. Treemap of Diversification Themes and Sub-Themes
Source: Own construction

Figure 15. Treemap of Diversification Themes by Approximation to Industries’ Identification
Source: Own construction

¹² These diversification themes are preliminary in nature and could be adjusted based on feedback from stakeholders, Namibia’s strategic priorities and other relevant considerations.

¹³ The industries identified in each of the diversification themes can be reviewed in Annex 1.

5. Complementary Perspectives on Diversification Opportunities

5.1 Scope of the Exercise

Having identified potential diversification opportunities, it may be beneficial to tease out further both the strategic opportunity they offer and the challenges inherent to their development. In particular, it can be useful to evaluate, through complementary metrics, how feasible and attractive each diversification opportunity can be, given the challenges faced by Namibia, its comparative advantages, strategic priorities and features of its labor-market and geography.

In this section, we offer a preliminary set of feasibility and attractiveness factors to foster a broader discussion around this complementary approach. These factors have been defined based on the Growth Lab’s experience, data availability, and our interactions with stakeholders.

This analysis could be useful not only to assess in a more tangible manner the challenges and upside associated to diversification opportunities, but it could also be leveraged as an input for further prioritization efforts. Namely, even within the narrower set of opportunities identified through the Economic Complexity methodology efforts may be focused further on the sub-set of industries which offer more tangible upside and imply less explicit challenges to its development.

5.2 Potential Complementary Feasibility and Attractiveness Factors

Below we briefly describe the feasibility and attractiveness factors leveraged to assess, evaluate, and refine the previously identified list of 97 products. Feasibility factors aim to measure whether a given industry or product is more likely to thrive in Namibia, whereas attractiveness factors aim to measure how desirable a given industry or product is based on various policy-relevant criteria.

Proposed Feasibility Factors

• Existing presence. A prospective product is more likely to thrive in Namibia if it is already produced with some intensity. We can use two metrics to assess whether a product is already present drawing from the Atlas of Economic Complexity. First, we measure product existence by using an RCA value. Second, we can use export values to assess whether Namibia currently exports a good with a positive value. To smooth out variation, RCA and export values were calculated by averaging years 2016, 2017 and 2018, our three most recent years of data.
• Intensive use of scarce resources. Namibia faces a unique challenge given its aridity and vast desert land. Because of this, products that are intensive in scarce resources – most notably, water – are less likely to thrive in the country.¹⁴ To calculate water use intensity, input-output matrices from the United States of America (USA) were used to estimate their intensity in the use of water.¹⁵
• Implied availability of inputs. Products will be more likely to thrive in Namibia if they share inputs with industries that already exist in the country. This includes availability of physical resource inputs as well as availability of human capital. To measure the extent to which certain products share inputs with others that already exist in Namibia, we calculated the share of inputs that are intensively demanded by prospective industries that are either part of Namibia’s productive matrix or that are intensively demanded by products in Namibia’s productive matrix. A similar calculation was conducted to measure shared occupations by finding the share of occupations intensively demanded by prospective industries that are also intensively demanded by products in Namibia’s productive matrix.¹⁶ A combination of Atlas data, USA input-output data,¹⁷ and USA Bureau of Labor Statistics employment data¹⁸ was used in this calculation. More information about this calculation can be found in Annex 5 and Annex 6.
• Intensive use of strategic resources. While some resources are scarce in Namibia, others are relatively more abundant and represent a key comparative advantage. An important strategic resource is its newly expanded port and favorable logistical infrastructure. Because of this, it is possible that products that have a higher propensity of being imported by sea are more likely to thrive in the country. To calculate port export/import propensity, we assume that the European Union is the main prospective importer by sea of products that tend to ship from Namibia and sub-Saharan Africa. We used Eurostat data to calculate a sea import RCA by taking the ratio of the share of a given product imported by sea out of total imports of that product to the share of all products imported by sea out of total imports.
• Likelihood to thrive in locations with limited population agglomeration. Because of Namibia’s low population density, prospective industries should be able to thrive even in areas with low agglomeration. To assess this factor, two parallel measurements were made using Dun & Bradstreet data. First, we assessed whether a given product is more likely to thrive in sparsely populated places by taking the coefficients from the correlation between county population size in the USA¹⁹ and the RCA of the given product. Second, we assessed whether a given product is likely to thrive in isolated places by taking the coefficients from the correlation between geographic proximity to populated areas in the USA²⁰ and the RCA of the product.

¹⁴ See Hausmann, R., Santos, M.A., Barrios, D., Muci, F., Taniparti, N. Tudela, J. (2021). The report does not identify water as a binding constraint, mainly because despite significant scarcity demand did not seem to outweigh supply. Having said that, water availability was highlighted as a potential constraint for certain water-intensive industries in certain parts of the country. Hence, it may be worthwhile to deprioritize diversification opportunities that may face the same type of challenges given their high water intensiveness.

¹⁵ The implicit assumption here is that these are industry characteristics that should, when fully developed, have external validity across borders. The USA is used frequently as a reference point both because of its ample diversity of industries in which it is specialized, and for the relative ease in building concordances across industry classifications that cover exports, use of inputs, FDI attraction, employee characteristics, etc.

¹⁶ For both calculations a threshold was established to estimate if the input is implicitly available. Namely, it should be intensively demanded by at least 4 industries in Namibia’s productive matrix. This relatively low threshold is somewhat arbitrary, but it seeks to balance the fact that the exercise is only considering goods exports, and hence likely underestimating the latent availability of inputs in the country, and that a minimum scale should be required to imply the implicit availability of inputs.

¹⁷ Idem.

¹⁸ Idem.

¹⁹ Idem.

²⁰ Idem.

Proposed Attractiveness Factors

• Export propensity. Given the limited scope for local demand, a given product may be more attractive if it allows Namibia to tap international demand. To assess whether a prospective product is likely to be exported, we calculate an export propensity score using Dun & Bradstreet data. We take the percentage of establishments in each product that self-report exports in the dataset and use this to estimate the likelihood that establishments engaged with the prospective product will export.
• Propensity to attract FDI. Given that investment attraction is an important priority for Namibia, potential products may be more desirable if there is evidence that they are likely to mobilize FDI. Because different regions and countries may attract different levels of FDI, an FDI attractiveness score by product was calculated looking at three recipient groups of interest using FDI Markets data: FDI flows to all countries, FDI flows to all international peers,²¹ and FDI flows to regional and Southern African Customs Union (SACU) peers.²²
• Likelihood to employ groups of interest. Namibia faces high levels of unemployment and low levels of labor force participation, particularly among women, youth, and low-skill workers. Products that are more likely to employ these excluded groups may be more attractive to the country. We use USA²³ census and Integrated Public Use Microdata Series (IPUMS) survey data to find three shares: the share of employees in each good or activity that are female, the share of employees that are between the ages of 15 and 24, and the share that have lower than a tertiary level of education as an imperfect proxy for low-skill employment. To smooth volatility, the averaged shares for years 2017, 2018, and 2019.
• Resilience to terms of trade volatility. Namibia’s exports and economy are sensitive to price fluctuations for specific commodities. Products that face a demand pattern largely uncorrelated with that of these commodities may help smooth terms of trade volatility or at least increase economic resilience. We estimated the sensitivity of exports of all products to fluctuations in the price of commodities in Namibia’s current export basket. The resulting index captures the strength of this association, and therefore how much each product might be independent of exogenous shocks faced by Namibia’s main commodities.²⁴
• Extent of demand in the country and in the region. Products are likely to be attractive to Namibia if they are demanded by nearby markets. That may enable nascent activities to achieve sufficient scale. For these products, Namibia has the potential to displace or add to what is currently being imported. To proxy regional demand, we examine the products that are imported by Namibia as well as the products that are imported by nearby countries (the SACU countries and regional peers Angola and Zambia) using data from the Atlas of Economic Complexity. Again, to smooth volatility, we averaged numbers from 2016, 2017, and 2018.

²¹ Angola, Australia, Botswana, Chile, New Zealand, South Africa, Peru, Zambia.

²² Angola, Zambia, and SACU countries.

²³ The implicit assumption here is that these are industry characteristics that should, when fully developed, have external validity across borders. The United States of America is used frequently as a reference point both because of its ample diversity of industries in which it is specialized, and for the relative ease in building concordances across industry classifications that cover exports, use of inputs, FDI attraction, employee characteristics, etc.

²⁴ More information about this calculation can be found in Annex 7.

5.3 Normalization and Visualization of Complementary Factors

To facilitate aggregation and comparison across indicators and products, we normalized the calculations for each of the factors described above into a scale of 0 to 10. Given the various distributions of the values that emerged from the calculations for each factor, slightly different normalization techniques were employed. First, some factors had values that were distributed normally or within bounds, while other factors had values that were clustered with long tails. To ensure that these factors with skewed distributions had scores that could be adequately distributed in the 0 to 10 range, the raw values from these factors were transformed using logs.

Second, some factors should have higher scores if the factor value is high, while other factors should have higher scores if the factor value is low (i.e. intensity in the use of scarce resources). For factors for which a higher value was more desirable, normalization of value i for factor f was calculated using the formula:

score_i,f = (value_i − min_f) / (max_f − min_f)

For factors for which a lower value was more desirable, the inverse equation was used:

score_i,f = (max_f − value_i) / (max_f − min_f)

Some of the factors had multiple sub-pillars that contributed to the score. For these factors, a simple average was taken across sub-factors. The table below provides a summary of how each factor was calculated based on the two considerations described above. Full details on the resulting scores of each factor for each product can be found in Annex 2 and Annex 3.

Table 1. Summary of Normalization Techniques for Feasibility and Attractiveness Factors

	Positively Calculated	Negatively Calculated
Raw Values	(F) Implied availability of inputs (average of shared intermediate inputs and shared occupations) (F) Likelihood to thrive in places with low population agglomeration (average of propensity to thrive in sparsely populated places and propensity to thrive in geographically isolated places) (F) Intensive use of strategic resources (A) Export propensity (A) Resiliency to exogenous shocks to current basket of commodities (average of correlation values and beta coefficients) (A) Likelihood to employ groups of interest (average share of female, youth, low-skill workers)
Log-Corrected Values	(F) Existing presence (average of total exports and RCA) (A) Propensity to attract FDI (average of global, international, regional peers, and SACU values) (A) Demand in nearby markets (average of Namibian, SACU, and regional peer demand)	(F) Intensive use of scarce resources

Notes: F accounts for feasibility and A for attractiveness. Source: Own construction.

5.4 Product Example

The normalization process facilitates the visualization of how each specific factor influences the feasibility and attractiveness of each product. For example, Figure 16 below depicts the normalized score for each feasibility and attractiveness factor for code HS8433: Harvesting or threshing machinery. Focusing first on feasibility, this specific type of farming machinery performs well in terms of using more of Namibia’s strategic resources, while relying less on the country’s scarce resources. It also performs well in terms of sharing many of the current intermediate inputs and occupations that exist in Namibia. However, it does not currently have a strong presence in Namibia, nor does it perform especially well in places with low population agglomerations.

Turning our attention to the attractiveness scores, HS8433: Harvesting or threshing machinery performance seems to be relatively close to the average. However, it has a particularly low score in employing groups of interest. Overall, the product’s relative performance on these feasibility and attractiveness factors might inform the decision to prioritize or not efforts around its development.

Figure 16. Feasibility and Attractiveness Scores for HS8433: Harvesting or Threshing Machinery
Source: Atlas of Economic Complexity, D&B, EUROSTAT, IPUMS, FDI Markets, US Census, US Input/Output, and own construction

5.4 Input for Potential Prioritization

For each of the 97 products, the feasibility factors and the attractiveness factors were averaged into a single score. The summary scores for each product can be found in Annex 4.

The scatterplot below (Figure 17) locates each product in the attractiveness-feasibility space based on its final, aggregated scores. The red products are the products with the highest feasibility and attractiveness, relative to the median²⁵ of all products. These should be the set of products that the country may want to prioritize. The orange and blue products have less compelling attractiveness-feasibility tradeoffs, they perform below the median in one of these categories. These products may be less of a priority for immediate action.

Lastly, the gray products represent products that fall below the median for both criteria, and hence the country may not want to prioritize them soon – yet continue to consider. For illustrative purposes we call the products in red as part of a potential Phase I (25 products), the products in orange and blue as part of a potential Phase II (47 products), and the ones in gray as part of a potential Phase III (25 products). Figure 18 and Figure 19 highlight the relative presence of each diversification theme and sub-theme for potential Phases I and II.

To make our findings more readily accessible and actionable for policymakers, we created an online tool with viability and attractiveness scores and their corresponding prioritization phase; not only for the 97 selected products but for all the product codes that exist. The tool also contains other relevant information at the product level, including sources of demand in the region; occupations demanded by product and an indicator of relative availability of the occupation in Namibia; wage distribution on the industry manufacturing the product versus the average; and the ten products that are more proximate to each product from a technological standpoint and an indicator of whether Namibia already has RCA>1 on them or not.²⁶

Figure 17. Potential Prioritization Matrix of Identified Products (Illustrative)
Source: Own construction

Figure 18. Treemap of Diversification Themes and Sub-Themes in Preliminary Phase I
Source: Own construction

Figure 19. Treemap of Diversification Themes and Sub-Themes in Preliminary Phase II
Source: Own construction

²⁵ Median performance in each criterion is represented by the gray lines.

²⁶ https://growthlab.app/namibia-tool

5.5 Contrast Between Economic Complexity and a Beneficiation Approach to Diversification

Figure 20. Top 50 Industries by Strength of Forward Linkage: A Beneficiation Strategy
Source: Own construction

5.5 Contraste entre la Complejidad Económica y el enfoque de beneficiación para la diversificación

Podemos contrastar sistemáticamente los resultados, desde una perspectiva de diversificación, de una estrategia basada en el redespliegue del knowhow existente —Complejidad Económica— y la estrategia de beneficiación consistente en agregar valor a las materias primas, que ha sido destacada en los planes de política industrial de Namibia.²⁷

La Figura 20 identifica los 50 sectores más cercanos “aguas abajo” a las exportaciones actuales de Namibia y los grafica dentro del esquema de Complejidad del Producto/Distancia que utilizamos en la Figura 12 (enfoque de Política Industrial Parsimoniosa) y la Figura 13 (enfoque de Apuestas Estratégicas). Comparar las oportunidades identificadas siguiendo un enfoque de Complejidad Económica con las derivadas de una estrategia de beneficiación revela algunos patrones interesantes. En primer lugar, la mayoría de las actividades aguas abajo se sitúan a una mayor distancia, lo que significa que requieren el desarrollo simultáneo de un conjunto más amplio de nuevas capacidades para materializarse. Dado que en términos relativos estos sectores tienen más capacidades ausentes y que el proceso de adquirirlas es más desafiante, probablemente tardará más y estará sujeto a mayores riesgos (mayor tasa de fracaso). Además, a juzgar por la experiencia internacional en diversificación productiva, es menos probable que tenga éxito. En segundo lugar, estos sectores aguas abajo tienden a ubicarse por debajo de la frontera óptima destacada en las Figuras 12 y 13, lo que implica que ofrecen menores rendimientos esperados en términos de valor agregado y complejidad que otras industrias que se encuentran a la misma distancia.

Figura 20. Las 50 industrias principales según la fortaleza del vínculo hacia adelante: una estrategia de beneficiación

Fuente: UNCOMTRADE, Atlas de Complejidad Económica, cálculos propios basados en exportaciones netas.

²⁷ Véase el Marco de Política Industrial de Namibia (2012), el Marco Estratégico y de Implementación de la Política Industrial de Namibia 2014–2017 (noviembre de 2013) y la Estrategia de Crecimiento en Casa 2015–2020 (2015).

Esto no significa que las actividades atractivas de agregación de valor deban ignorarse por completo. Por ejemplo, existen algunos productos en el grupo de plásticos y cauchos que son directamente aguas abajo y que surgen en la estrategia parsimoniosa anterior (ofrecen la mayor complejidad de producto por unidad de distancia). La diferencia radica en que estas oportunidades deberían surgir naturalmente dentro de un marco más amplio para acelerar la transformación estructural que considere todos los sectores potenciales. Mirar hacia abajo en las cadenas de valor impediría la identificación y el desarrollo de oportunidades de diversificación que requieren menos esfuerzos, agregan más valor y pueden conectarse potencialmente con otros sectores complejos. También distraería de los esfuerzos de política que de otro modo podrían lograr una transformación estructural más amplia, y desviaría los recursos y la atención de las políticas de donde más se necesitan.

Recuadro: Un riguroso marco de costo-beneficio para analizar la beneficiación

Incluso si la beneficiación no conduce a la transformación estructural, podría tener el potencial de crear algunos empleos. En ese caso, ¿no es mejor tener algunos empleos que ninguno?

La respuesta a esta pregunta requiere un análisis detallado de costo-beneficio. Por ejemplo, en el caso del procesamiento de diamantes, el beneficio es fácil de medir: ¿cuántos empleos se crean a partir de estas políticas en el procesamiento de diamantes y actividades relacionadas? Esto debe compararse con el costo de generar esos empleos, y aquí es fundamental tener en cuenta que la beneficiación no es gratuita. En primer lugar, y quizás de manera más significativa, desvía la atención de lo que son vías más prometedoras de transformación estructural que, a diferencia de agregar más valor, podrían entregar la transformación que el país busca. En segundo lugar, existe un costo directo. Gravar en exceso o prohibir la exportación de materias primas no procesadas, como los diamantes, para promover el procesamiento local tiene un impacto negativo en los precios y en la producción del sector minero de diamantes, y por tanto reduce los ingresos del gobierno provenientes del mismo. Ese impacto en los precios y la consiguiente disminución en la producción y en los ingresos fiscales y regalías del gobierno deben calcularse para estimar el costo por empleo creado. Es probable que las políticas sean una forma económica de generar un número pequeño pero significativo de empleos; o que el costo por empleo creado sea enorme y que los recursos públicos estarían mejor empleados en otro lugar.

Desarrollar un marco de costo-beneficio para evaluar las políticas de beneficiación actuales y planificadas es importante. Dicho marco permitirá a los formuladores de política en Namibia decidir si y cuándo desplegar políticas de beneficiación. Pero lo que queda claro a partir de la evidencia internacional es que la beneficiación es, a lo sumo, una política secundaria dentro de la estrategia de recursos naturales del país. No puede ser el núcleo de la estrategia del país para la diversificación productiva y la creación de empleo: 50 años de experiencia en 200 países muestran que no es un vehículo que llevará a Namibia a donde quiere ir.

6. Observaciones finales e implicaciones de política

Hemos explorado la estructura productiva de la economía de Namibia e identificado una lista inicial de oportunidades prometedoras para la diversificación económica. La base del análisis presentado en este informe surge de datos sobre exportaciones netas a nivel de cuatro dígitos provenientes de UNCOMTRADE. Esto permite una comprensión descriptiva de la posición de Namibia en el Espacio de Productos, así como de las oportunidades de ganancias en complejidad económica.

Las exportaciones de Namibia durante las dos décadas anteriores muestran una diversidad y ubicuidad relativas bajas, lo que es coherente con un Índice de Complejidad Económica relativamente bajo. Esto es consistente con una canasta exportadora dominada por bienes agrícolas y minerales, es decir, bienes con un Índice de Complejidad del Producto relativamente bajo. El éxito diferencial de Namibia en el desarrollo de productos adyacentes ilustra el potencial para diversificarse con éxito hacia oportunidades que aprovechan sus capacidades existentes. En conjunto, no es tanto que Namibia no pueda diversificarse, sino que las oportunidades disponibles para Namibia tienen baja complejidad y bajo valor estratégico. Teniendo en cuenta que el camino hacia la diversificación productiva y, en última instancia, la transformación estructural en Namibia podría ser más empinado que para la mayoría de sus pares, esto necesariamente exige una postura política activa que equilibre el objetivo de acumulación progresiva de capacidades, “saltos largos” coordinados y una mayor capacidad estatal para apoyar e internalizar las externalidades del autodescubrimiento.

Siguiendo un proceso de identificación sectorial que considera tanto las oportunidades en el margen intensivo como en el extensivo, se identificaron en total 97 productos potenciales. Estos fueron a su vez agrupados en cinco temas de diversificación preliminares que incluyen: (i) Químicos y materiales básicos, (ii) Industria alimentaria, (iii) Maquinaria y electrónica, (iv) Metales, minería e industrias adyacentes, y (v) Transporte y logística. Las industrias identificadas en cada uno de estos amplios temas de diversificación se enumeran además y son indicativas de las capacidades inherentes que Namibia posee actualmente; no constituyen necesariamente una lista exhaustiva de recomendaciones precisas a seguir de manera dogmática y estrecha.

El informe también introduce datos sobre varios factores relevantes de viabilidad (presencia existente, acceso implícito a insumos, intensidad en el uso de factores escasos, intensidad en el uso de factores estratégicos, propensión a prosperar en lugares con baja aglomeración poblacional) y de atractivo (propensión exportadora, propensión a atraer IED, probabilidad de emplear a grupos de interés, independencia respecto a los shocks de demanda que enfrentan los productos básicos relevantes, alcance de los factores de demanda regional) para cada una de las oportunidades industriales prometedoras. Con base en el desempeño relativo en cada métrica, pueden desglosarse los desafíos y oportunidades específicos asociados a cada oportunidad de diversificación. Adicionalmente, esta información puede aprovecharse para priorizar los esfuerzos de diversificación. El informe destaca un ejercicio de esta naturaleza, asignando productos a las Fases I, II o III potenciales.

Hemos contrastado los resultados de una estrategia de diversificación basada en el knowhow y en los principios de la Complejidad Económica con el enfoque de beneficiación que ha predominado en los esfuerzos de política industrial de Namibia. Nuestros resultados sugieren que un enfoque de beneficiación es probablemente subóptimo, ya que obligará al gobierno a centrarse en industrias para las cuales falta un mayor número de insumos, y que al mismo tiempo tienen un menor dividendo en términos de Complejidad Económica y valor estratégico.

Nuestro objetivo es proporcionar información complementaria que los funcionarios gubernamentales y otros actores puedan utilizar para ayudar a estrategizar cómo catalizar mejor la diversificación en el país. La información está destinada a utilizarse en combinación con otros análisis cuantitativos de las oportunidades de diversificación y el conocimiento específico del contexto sobre las instituciones y las restricciones locales.

Los esfuerzos convencionales para formular políticas “verticales” —es decir, políticas que apuntan a sectores específicos— han estado, por un lado, detrás de las transformaciones estructurales más exitosas y, por otro lado, también son la causa de decepcionantes fracasos de política. En las experiencias globales, la variación significativa observada en el impacto de las políticas parece estar impulsada esencialmente por dos conjuntos de factores. En primer lugar, algunos países han utilizado políticas verticales para responder a presiones políticas de ciertos sectores y grupos de interés, en lugar de fomentar aquellos que tienen más probabilidades de desarrollarse de manera orgánica y competitiva. En segundo lugar, incluso con buenas intenciones, seleccionar los sectores correctos para apoyar es técnicamente difícil, ya que implica procesar grandes cantidades de información y recopilar insumos de múltiples actores con perspectivas diferentes.²⁸ El análisis presentado aquí abarca un cierto conjunto de supuestos y una comprensión de los datos de comercio internacional, y las entrevistas cualitativas complementarias con entidades de diversos sectores enriquecieron el proceso de selección sectorial. En última instancia, el trabajo comprendido en este documento sigue dos de los principios esenciales de un proceso de selección sólido —análisis objetivo de los datos disponibles y evaluación independiente paralela—, pero debe considerarse no obstante como una hoja de ruta, y no como una lista definitiva.

²⁸ Véase Crespi, Fernandez-Arias y Stein (2014).

Siguiendo un proceso iterativo y colaborativo de validación y actualización de los sectores identificados, los esfuerzos para promover los sectores de alto potencial deben centrarse en identificar los factores que impiden que estas oportunidades se materialicen de forma espontánea. A partir de ahí, diseñar intervenciones de política que apunten a resolverlos o aliviarlos es esencial para desbloquear los obstáculos que impiden el despegue de nuevos sectores. Los dispositivos institucionales necesarios para identificar las restricciones específicas de cada sector y luego movilizar a los actores relevantes del sector privado en torno a una solución varían con la intensidad relativa o la presencia de estos sectores en Namibia. En algunos casos, hay empresas bien establecidas con actores pertinentes en el país, mientras que en otros casos, donde las industrias están ausentes, se requiere un esfuerzo para llegar a los actores internacionales. Los objetivos de política de promoción de inversiones y desarrollo de exportaciones deben funcionar en conjunto con los actores existentes y nuevos dentro de cada sector objetivo.

A medida que los actores incorporen los resultados de este documento en sus decisiones de estrategia, política e inversión pública, será fundamental centrarse menos en qué industrias exactas se identifican y dónde, y más en cómo catalizar el surgimiento de estas oportunidades en toda Namibia. El proceso de diversificación ocurre a través de las empresas que exploran cómo pueden expandir los productos que fabrican y los servicios que prestan en un lugar y, con frecuencia, a través de empresas en un lugar que determinan que pueden hacer lo que actualmente hacen en un nuevo lugar. En ambos casos, el proceso implica que las empresas y los emprendedores descubran oportunidades y asuman riesgos. Este documento busca mejorar los roles que el país puede desempeñar para apoyar el descubrimiento, reducir los riesgos y proporcionar bienes públicos que el sector privado necesita para tener éxito en nuevas actividades empresariales.

Como se señala en varias secciones de este informe, el objetivo de este ejercicio fue aprovechar la información asociada a las capacidades productivas latentes de Namibia como país. En este sentido, nuestro esfuerzo de investigación tiene dos limitaciones que podrían potencialmente abordarse en investigaciones futuras: se ha realizado a nivel nacional (y no asocia las industrias con potencial a regiones específicas dentro de Namibia) y solo se ha realizado a nivel de bienes (no incluye servicios).

Una estrategia a nivel nacional presenta oportunidades de escala y coherencia de política para impulsar la inversión y desbloquear las oportunidades de diversificación; sin embargo, un enfoque regional podría permitir acceder más fácilmente a ciertos tipos de insumos y puede ser necesario para perseguir ciertos objetivos de política en torno al crecimiento y la inclusión. La evidencia de otros contextos y en la literatura respalda la prevalencia de la relación entre crecimiento y complejidad a nivel subnacional: las tendencias se mantienen a nivel de estado, ciudad y municipio.

Dada la limitada disponibilidad de datos representativos estandarizados internacionalmente sobre servicios y el consecuente enfoque de este informe en la identificación de oportunidades de diversificación basadas en las exportaciones de bienes, las iteraciones futuras de este trabajo podrían aprovechar nuevos conjuntos de datos y enfoques metodológicos que puedan incluir las industrias de servicios en el análisis y arrojar una lista preliminar de oportunidades de diversificación en servicios transables. Los servicios tienden a ser actividades altamente especializadas y requieren que se combinen diferentes tipos de knowhow. Por lo tanto, el alcance más prometedor de este esfuerzo podría ser centrarse en las oportunidades de diversificación en el sector de servicios para las mayores aglomeraciones urbanas del país.

Referencias

Hausmann, R. (2016). Economic Development and the Accumulation of Knowhow. Welsh Economic Review, N.° 24, pp. 13–16.

Hausmann, R., et al. The atlas of economic complexity: Mapping paths to prosperity. MIT Press, 2014.

Hausmann, R., O’Brien, T., Santos, M.A., Grisanti, A., Kasoolu, S., Taniparti, N., Tapia, J. y Ricardo Villasmil (2019). Jordan: The elements of a growth strategy. Harvard CID Faculty Working Paper Series No. 346.

Hausmann, R., Santos, M.A., Barrios, D., Muhaj, D., Noor, S., Pan, C., y Jorge Tapia (2020). Emerging Cities as Independent Engines of Growth: The Case of Buenos Aires. Harvard Center for International Development Faculty Working Paper No. 385.

Hausmann, R., Santos, M.A., Tudela, J., Li, Y., y Ana Grisanti (2020). The hidden wealth of Loreto: Economic complexity analysis and productive diversification opportunities. Harvard Center for International Development Faculty Working Paper No. 386.

Hausmann, R., et al. Economic Complexity Report for Western Australia. No. 394. Center for International Development at Harvard University, 2021.

Hausmann, R., Santos, M.A., Barrios, D., Muci, F., Taniparti, N., Tudela, J. (2021). A Growth Diagnostic of Namibia. Harvard CID Faculty Working Paper Series No. 405.

Hausmann, R. y Klinger, B. (2006). Structural Transformation and Patterns of Comparative Advantage in the Product Space.

Hausmann, R. y Rodrik, D. (2003). Economic Development As Self-Discovery. Journal of Development Economics, v72 (dic.), pp. 603–633.

Hidalgo, C. y Hausmann, R. (2009). The Building Blocks of Economic Complexity. Proceedings of the National Academy of Sciences of the United States of America. 106, pp. 10570–10575.

Anexo 1: Temas de diversificación, subtemas e industrias preliminarmente identificadas

[Tablas incluidas en las páginas 36–39 del documento original.]

Anexo 2: Desempeño en factores de viabilidad

Tema	Subtema	HS4	Presencia existente del producto	Uso intensivo de recursos escasos	Disponibilidad implícita de insumos	Uso intensivo de recursos estratégicos	Probabilidad de prosperar en lugares con baja aglomeración poblacional
Industria alimentaria	Ganadería y agricultura	102	9,30	3,43	3,82	0,00	10,00
Industria alimentaria	Carne y productos lácteos	201	7,92	6,01	1,79	8,79	4,15
Industria alimentaria	Carne y productos lácteos	206	6,01	6,01	1,79	6,91	4,15
Industria alimentaria	Carne y productos lácteos	210	6,52	6,01	1,79	8,84	4,15
Industria alimentaria	Carne y productos lácteos	401	2,34	3,35	1,21	2,34	2,33
Industria alimentaria	Carne y productos lácteos	402	5,70	3,89	1,94	6,14	2,95
Industria alimentaria	Carne y productos lácteos	403	3,20	3,35	1,21	2,40	2,33
Industria alimentaria	Carne y productos lácteos	406	3,30	5,84	1,18	0,12	3,22
Industria alimentaria	Ganadería y agricultura	409	.	3,66	4,39	7,98	2,95
Industria alimentaria	Carne y productos lácteos	506	5,96	6,01	1,79	6,29	4,15
Industria alimentaria	Ganadería y agricultura	712	6,89	3,40	2,24	8,56	3,75
Industria alimentaria	Ganadería y agricultura	1104	3,32	5,18	4,40	5,95	3,34
Industria alimentaria	Carne y productos lácteos	1502	6,38	6,01	1,79	9,93	4,15
Industria alimentaria	Carne y productos lácteos	1517	4,12	5,29	2,89	5,33	2,88
Industria alimentaria	Carne y productos lácteos	1602	7,27	6,01	1,79	9,99	4,15
Industria alimentaria	Bebidas y otros	1901	3,90	3,89	1,94	4,62	2,95
Industria alimentaria	Manufactura de alimentos	1902	7,43	2,90	2,45	7,68	2,37
Industria alimentaria	Bebidas y otros	2009	4,14	3,40	2,24	8,81	2,50
Industria alimentaria	Manufactura de alimentos	2106	5,18	2,90	2,45	4,96	2,37
Industria alimentaria	Bebidas y otros	2202	5,66	2,91	3,28	2,46	1,73
Industria alimentaria	Bebidas y otros	2203	8,66	2,18	10,00	8,84	2,48
Industria alimentaria	Bebidas y otros	2206	6,77	4,18	4,37	8,12	2,32
Industria alimentaria	Bebidas y otros	2208	6,57	5,04	3,81	8,80	2,46
Industria alimentaria	Manufactura de alimentos	2309	5,96	5,47	3,13	7,63	3,11
Industria alimentaria	Bebidas y otros	2402	6,38	4,33	3,66	5,17	3,12
Químicos y materiales básicos	Químicos	2832	8,10	2,31	10,00	8,79	2,19
Químicos y materiales básicos	Químicos	2834	6,46	2,31	10,00	10,00	2,19
Químicos y materiales básicos	Químicos	2844	9,50	2,31	10,00	6,81	2,19
Químicos y materiales básicos	Químicos	2912	.	0,00	10,00	6,50	2,24
Químicos y materiales básicos	Químicos	2914	1,09	0,00	10,00	7,11	2,24
Químicos y materiales básicos	Químicos	2920	.	0,00	10,00	8,14	2,24
Químicos y materiales básicos	Químicos	3101	6,57	6,03	10,00	8,05	3,38
Químicos y materiales básicos	Químicos	3403	4,03	3,51	3,11	4,60	3,05
Químicos y materiales básicos	Químicos	3810	2,81	2,38	5,51	3,51	1,82
Químicos y materiales básicos	Químicos	3815	2,36	2,38	5,51	3,61	1,82
Químicos y materiales básicos	Químicos	3821	1,14	4,30	1,19	3,10	1,98
Químicos y materiales básicos	Plásticos y caucho	3906	7,40	3,73	10,00	7,73	1,71
Químicos y materiales básicos	Plásticos y caucho	3908	0,00	3,73	10,00	4,51	1,71
Químicos y materiales básicos	Plásticos y caucho	3909	2,23	3,73	10,00	6,26	1,71
Químicos y materiales básicos	Químicos	3912	6,29	0,00	10,00	9,25	2,24
Químicos y materiales básicos	Plásticos y caucho	3914	1,09	3,73	10,00	5,97	1,71
Químicos y materiales básicos	Plásticos y caucho	3915	5,48	3,73	10,00	4,95	1,71
Químicos y materiales básicos	Plásticos y caucho	3920	3,95	4,82	4,80	6,70	2,08
Químicos y materiales básicos	Plásticos y caucho	3921	3,89	4,66	2,98	4,33	0,16
Químicos y materiales básicos	Plásticos y caucho	4005	1,52	5,36	5,11	7,08	1,56
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6804	3,28	3,37	6,13	3,91	2,73
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6805	1,21	3,37	6,13	5,59	2,73
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6815	3,54	4,32	5,23	6,56	2,77
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7204	7,32	9,66	7,25	6,50	3,79
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7214	4,36	4,28	5,28	5,53	1,85
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7220	1,23	4,28	5,28	8,15	1,85
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7225	3,60	4,28	5,28	9,24	1,85
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7303	6,75	3,73	10,00	8,01	2,59
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7307	4,69	4,66	9,26	6,37	2,57
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7308	5,83	7,04	7,13	6,77	0,85
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7309	3,83	5,81	6,69	4,98	3,03
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7315	5,02	5,68	8,94	8,41	1,57
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7318	4,49	5,49	4,51	6,36	2,50
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7320	2,61	5,68	8,94	5,19	2,49
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7401	7,07	6,22	10,00	9,88	2,69
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7409	8,88	6,22	10,00	4,64	2,69
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7901	9,78	5,17	10,00	9,43	3,08
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7902	6,08	.	10,00	5,22	4,06
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7904	8,70	5,54	10,00	7,92	2,48
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7907	5,67	5,25	10,00	6,37	1,56
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8207	5,25	5,66	4,26	3,56	1,47
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8208	3,17	4,16	6,95	3,30	2,04
Maquinaria y electrónica	Bombas, motores y aparatos similares	8408	5,86	7,20	2,97	6,88	2,81
Maquinaria y electrónica	Bombas, motores y aparatos similares	8412	5,62	6,26	7,85	4,85	3,17
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8419	4,80	5,35	5,13	4,14	1,20
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8421	4,66	5,35	5,13	5,39	1,20
Transporte y logística	Maquinaria pesada para logística y transporte	8427	5,46	6,77	7,00	9,03	2,47
Transporte y logística	Maquinaria pesada para logística y transporte	8428	5,11	6,77	7,00	7,48	1,94
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8433	4,04	7,59	6,76	8,61	5,16
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8434	1,16	7,59	6,76	3,13	5,16
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8436	5,03	7,59	6,76	7,49	5,16
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8458	2,70	6,54	4,30	7,77	1,34
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8459	5,59	6,54	4,30	7,37	1,34
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8462	4,25	6,54	4,30	5,22	1,34
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8466	4,83	5,66	4,26	2,51	1,47
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8475	2,91	5,35	5,13	1,80	1,20
Químicos y materiales básicos	Plásticos y caucho	8477	3,31	5,23	5,19	4,58	1,27
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8479	5,34	7,83	4,24	4,07	2,85
Maquinaria y electrónica	Bombas, motores y aparatos similares	8481	5,60	4,66	9,26	5,20	2,57
Maquinaria y electrónica	Bombas, motores y aparatos similares	8483	5,27	6,32	4,99	5,61	2,51
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8485	5,23	5,35	5,13	.	1,20
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8514	5,34	5,47	8,17	4,30	2,22
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8515	4,25	5,35	5,13	2,31	3,04
Maquinaria y electrónica	Electrónica	8526	4,47	5,63	2,04	2,64	2,52
Transporte y logística	Locomotoras de ferrocarril, vías férreas y partes	8602	7,76	8,07	10,00	5,36	2,66
Transporte y logística	Locomotoras de ferrocarril, vías férreas y partes	8608	.	.	4,36	2,32	2,15
Transporte y logística	Vehículos y partes de vehículos	8703	6,10	10,00	0,69	9,44	2,91
Transporte y logística	Vehículos y partes de vehículos	8704	6,47	8,63	1,44	9,59	3,07
Transporte y logística	Vehículos y partes de vehículos	8707	3,22	7,50	3,56	9,64	2,56
Transporte y logística	Vehículos y partes de vehículos	8708	5,04	7,56	3,24	6,00	1,27
Maquinaria y electrónica	Electrónica	9024	4,56	8,02	3,90	1,86	2,08
Maquinaria y electrónica	Electrónica	9026	4,02	7,54	4,15	1,73	1,88

Anexo 3: Desempeño en factores de atractivo

Tema	Subtema	HS4	Propensión exportadora	Propensión a atraer IED	Probabilidad de emplear a grupos de interés	Resiliencia a shocks exógenos a la canasta actual de productos básicos	Demandado en el país y la región
Industria alimentaria	Ganadería y agricultura	102	0,00	3,14	6,66	5,28	5,73
Industria alimentaria	Carne y productos lácteos	201	4,56	5,25	7,28	5,86	5,15
Industria alimentaria	Carne y productos lácteos	206	4,56	5,25	7,28	7,68	5,37
Industria alimentaria	Carne y productos lácteos	210	4,56	5,25	7,28	5,74	4,06
Industria alimentaria	Carne y productos lácteos	401	4,75	5,45	5,45	5,45	6,28
Industria alimentaria	Carne y productos lácteos	402	6,04	5,09	5,45	4,63	6,79
Industria alimentaria	Carne y productos lácteos	403	4,75	5,45	5,45	6,13	5,94
Industria alimentaria	Carne y productos lácteos	406	4,67	5,05	5,45	6,08	6,65
Industria alimentaria	Ganadería y agricultura	409	1,32	0,00	6,66	10,00	4,00
Industria alimentaria	Carne y productos lácteos	506	4,56	5,25	7,28	7,82	0,47
Industria alimentaria	Ganadería y agricultura	712	6,97	4,76	6,32	6,01	4,15
Industria alimentaria	Ganadería y agricultura	1104	2,26	4,71	4,79	4,42	4,47
Industria alimentaria	Carne y productos lácteos	1502	4,56	5,25	7,28	5,08	2,84
Industria alimentaria	Carne y productos lácteos	1517	4,45	4,86	4,79	5,68	6,36
Industria alimentaria	Carne y productos lácteos	1602	4,56	5,25	7,28	5,18	6,34
Industria alimentaria	Bebidas y otros	1901	6,04	5,09	5,45	6,60	6,11
Industria alimentaria	Manufactura de alimentos	1902	4,13	5,01	5,47	7,00	5,89
Industria alimentaria	Bebidas y otros	2009	9,63	5,31	6,32	3,77	7,43
Industria alimentaria	Manufactura de alimentos	2106	4,13	5,01	5,47	5,91	7,28
Industria alimentaria	Bebidas y otros	2202	2,69	6,88	3,83	5,87	7,28
Industria alimentaria	Bebidas y otros	2203	2,76	.	3,83	6,17	6,61
Industria alimentaria	Bebidas y otros	2206	2,31	4,58	3,83	4,13	6,40
Industria alimentaria	Bebidas y otros	2208	2,14	7,52	3,83	4,05	7,90
Industria alimentaria	Manufactura de alimentos	2309	5,39	4,29	4,79	5,62	7,52
Industria alimentaria	Bebidas y otros	2402	2,38	5,01	5,78	5,72	7,46
Químicos y materiales básicos	Químicos	2832	5,67	7,68	6,75	4,23	5,27
Químicos y materiales básicos	Químicos	2834	5,67	7,68	6,75	6,45	5,23
Químicos y materiales básicos	Químicos	2844	5,67	7,68	6,75	3,28	3,72
Químicos y materiales básicos	Químicos	2912	8,30	7,26	2,32	4,53	2,68
Químicos y materiales básicos	Químicos	2914	8,30	7,26	2,32	4,91	2,97
Químicos y materiales básicos	Químicos	2920	8,30	7,26	2,32	6,78	2,05
Químicos y materiales básicos	Químicos	3101	3,92	6,51	3,19	6,97	4,33
Químicos y materiales básicos	Químicos	3403	10,00	2,38	5,42	3,83	5,97
Químicos y materiales básicos	Químicos	3810	4,64	6,17	2,32	4,56	3,41
Químicos y materiales básicos	Químicos	3815	4,64	6,17	2,32	3,89	5,08
Químicos y materiales básicos	Químicos	3821	6,35	7,40	2,76	6,73	3,15
Químicos y materiales básicos	Plásticos y caucho	3906	5,37	2,75	5,07	4,72	6,67
Químicos y materiales básicos	Plásticos y caucho	3908	5,37	2,75	5,07	3,39	2,92
Químicos y materiales básicos	Plásticos y caucho	3909	5,37	2,75	5,07	4,03	5,27
Químicos y materiales básicos	Químicos	3912	8,30	7,26	2,32	5,54	5,12
Químicos y materiales básicos	Plásticos y caucho	3914	5,37	2,75	5,07	3,76	4,14
Químicos y materiales básicos	Plásticos y caucho	3915	5,37	2,75	5,07	4,49	3,05
Químicos y materiales básicos	Plásticos y caucho	3920	6,90	5,57	5,18	4,07	7,03
Químicos y materiales básicos	Plásticos y caucho	3921	6,77	3,40	5,18	4,01	6,45
Químicos y materiales básicos	Plásticos y caucho	4005	6,53	3,64	5,24	2,52	4,21
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6804	7,17	4,56	4,54	4,24	5,18
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6805	7,17	4,56	4,54	4,75	4,62
Metales, minería e industrias adyacentes	Arena, hormigón y material de construcción	6815	2,61	5,37	4,54	3,97	3,85
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7204	1,39	8,30	3,04	2,48	3,88
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7214	6,52	7,52	4,02	2,90	5,90
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7220	6,52	7,52	4,02	2,40	4,03
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7225	6,52	7,52	4,02	3,98	6,27
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7303	4,04	4,91	.	7,67	4,85
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7307	8,46	3,15	4,36	3,32	6,85
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7308	4,54	2,89	4,43	4,49	7,83
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7309	7,16	2,36	4,43	4,05	5,12
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7315	6,85	4,68	4,36	3,94	5,87
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7318	5,34	3,70	4,10	3,80	7,19
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	7320	6,87	4,28	4,02	4,53	5,04
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7401	5,09	7,74	4,02	1,70	0,95
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7409	5,09	7,74	4,02	2,21	3,86
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7901	4,98	6,78	4,02	0,54	3,76
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7902	6,43	9,50	1,68	0,63	0,00
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7904	5,25	6,48	3,64	6,46	2,16
Metales, minería e industrias adyacentes	Metales y productos metálicos básicos	7907	4,49	5,74	4,36	2,47	2,99
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8207	4,62	2,13	3,48	4,32	6,72
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8208	4,25	1,94	4,02	5,14	4,56
Maquinaria y electrónica	Bombas, motores y aparatos similares	8408	8,48	5,05	2,95	3,81	7,58
Maquinaria y electrónica	Bombas, motores y aparatos similares	8412	3,60	4,56	.	4,44	6,65
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8419	5,56	5,94	.	4,67	6,75
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8421	5,56	5,94	.	3,97	7,93
Transporte y logística	Maquinaria pesada para logística y transporte	8427	8,96	3,84	.	3,14	6,71
Transporte y logística	Maquinaria pesada para logística y transporte	8428	5,43	4,24	.	4,20	6,77
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8433	6,30	5,61	4,00	5,91	6,24
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8434	6,30	5,61	4,00	5,48	3,46
Industria alimentaria	Fabricación de maquinaria para la industria alimentaria	8436	6,30	5,61	4,00	3,37	5,56
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8458	4,49	2,83	3,48	3,01	4,12
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8459	4,49	2,83	3,48	3,36	4,60
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8462	4,49	2,83	3,48	3,37	5,58
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8466	4,62	2,13	3,48	4,59	5,44
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8475	5,56	5,94	.	4,80	3,61
Químicos y materiales básicos	Plásticos y caucho	8477	3,67	5,06	.	4,33	6,10
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8479	4,47	6,25	3,31	3,24	7,63
Maquinaria y electrónica	Bombas, motores y aparatos similares	8481	5,76	4,42	4,36	3,62	7,99
Maquinaria y electrónica	Bombas, motores y aparatos similares	8483	6,53	6,02	2,95	4,14	7,54
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8485	5,56	5,94	.	5,22	6,01
Maquinaria y electrónica	Máquinas herramienta, otras máquinas y partes	8514	5,68	3,80	.	5,46	4,93
Metales, minería e industrias adyacentes	Manufactura de metales, procesamiento de metales, maquinaria y herramientas de soldadura	8515	4,58	4,28	.	4,30	5,57
Maquinaria y electrónica	Electrónica	8526	4,04	3,68	2,28	3,03	6,41
Transporte y logística	Locomotoras de ferrocarril, vías férreas y partes	8602	4,21	6,53	2,69	5,00	6,73
Transporte y logística	Locomotoras de ferrocarril, vías férreas y partes	8608	5,72	6,70	2,20	9,17	3,04
Transporte y logística	Vehículos y partes de vehículos	8703	8,01	9,28	4,53	4,74	9,82
Transporte y logística	Vehículos y partes de vehículos	8704	8,93	8,05	4,53	3,60	9,53
Transporte y logística	Vehículos y partes de vehículos	8707	6,69	5,89	4,53	4,39	5,01
Transporte y logística	Vehículos y partes de vehículos	8708	6,35	6,86	.	4,34	9,42
Maquinaria y electrónica	Electrónica	9024	5,68	4,24	2,28	4,21	3,68
Maquinaria y electrónica	Electrónica	9026	4,99	2,86	2,28	4,20	6,49

Anexo 4: Insumos para la priorización potencial: desempeño promedio en viabilidad/atractivo

Tema	Subtema	HS4	Nombre HS4	Fase de priorización potencial	Viabilidad agregada	Atractivo agregado
Industria alimentaria	Ganadería y agricultura	102	Animales bovinos; vivos	2	5,31	4,16
Industria alimentaria	Carne y productos lácteos	201	Carne de animales bovinos; fresca o refrigerada	1	5,73	5,62
Industria alimentaria	Carne y productos lácteos	206	Despojos comestibles de animales bovinos, porcinos, ovinos, caprinos, caballar, asnal o mular; frescos, refrigerados o congelados	1	4,97	6,03
Industria alimentaria	Carne y productos lácteos	210	Carne y despojos comestibles de carne; salados, en salmuera, secos o ahumados; harinas y sémolas comestibles de carne o de despojos	1	5,46	5,38
Industria alimentaria	Carne y productos lácteos	401	Leche y nata; sin concentrar, sin adición de azúcar u otro edulcorante	2	2,32	5,48
Industria alimentaria	Carne y productos lácteos	402	Leche y nata; concentradas o con adición de azúcar u otro edulcorante	2	4,12	5,60
Industria alimentaria	Carne y productos lácteos	403	Suero de mantequilla, leche y nata cuajadas, yogur, kéfir y demás leches y natas fermentadas o acidificadas, incluso concentradas, con adición de azúcar, edulcorante, aromatizadas o con adición de frutas o cacao	2	2,50	5,54
Industria alimentaria	Carne y productos lácteos	406	Quesos y requesón	2	2,73	5,58
Industria alimentaria	Ganadería y agricultura	409	Miel natural	3	4,74	4,40
Industria alimentaria	Carne y productos lácteos	506	Huesos y núcleos córneos, en bruto, desgrasados, simplemente preparados (pero sin cortar a forma), tratados con ácido o desgelatinizados; polvos y desperdicios de estas materias	2	4,84	5,08
Industria alimentaria	Ganadería y agricultura	712	Hortalizas secas; enteras, cortadas, en rodajas, quebrantadas o en polvo, pero sin otra preparación	1	4,97	5,64
Industria alimentaria	Ganadería y agricultura	1104	Granos de cereales trabajados de otro modo (p.ej., mondados, aplastados, en copos, perlados, troceados o quebrantados), excepto el arroz de la partida 1006; germen de cereales entero, aplastado, en copos o molido	3	4,44	4,13
Industria alimentaria	Carne y productos lácteos	1502	Grasas de animales bovinos, ovinos o caprinos, excepto las de la partida 1503	2	5,65	5,00
Industria alimentaria	Carne y productos lácteos	1517	Margarina; mezclas o preparaciones alimenticias de grasas o aceites animales o vegetales o de fracciones de diferentes grasas o aceites de este capítulo, excepto las grasas y aceites alimenticios de la partida 1516	2	4,10	5,23
Industria alimentaria	Carne y productos lácteos	1602	Preparaciones y conservas de carne, de despojos o de sangre	1	5,84	5,72
Industria alimentaria	Bebidas y otros	1901	Extracto de malta; preparaciones alimenticias de harina/grañones/sémola/almidón/fécula/extracto de malta, sin cacao o con menos del 40% en peso, y preparaciones alimenticias de productos de las partidas 04.01 a 04.04, sin cacao o con menos del 5% en peso, calculado sobre una base totalmente desgrasada, N.C.O.P.	2	3,46	5,86
Industria alimentaria	Manufactura de alimentos	1902	Pastas alimenticias; incluso cocidas o rellenas de carne u otras sustancias o preparadas de otra forma, como espaguetis, macarrones, fideos, lasaña, ñoquis, ravioles, canelones; cuscús, incluso preparado	2	4,56	5,50
Industria alimentaria	Bebidas y otros	2009	Jugos de frutas (incluido el mosto de uva) y jugos de hortalizas, sin fermentar, sin adición de alcohol; con o sin adición de azúcar u otro edulcorante	2	4,22	6,49
Industria alimentaria	Manufactura de alimentos	2106	Preparaciones alimenticias no expresadas ni comprendidas en otra parte	2	3,57	5,56
Industria alimentaria	Bebidas y otros	2202	Aguas, incluidas las aguas minerales y gaseosas, con adición de azúcar u otro edulcorante o aromatizadas; demás bebidas no alcohólicas, excepto los jugos de frutas u hortalizas de la partida 2009	2	3,21	5,31
Industria alimentaria	Bebidas y otros	2203	Cerveza de malta	2	6,43	4,84
Industria alimentaria	Bebidas y otros	2206	Sidra, perada, aguamiel y demás bebidas fermentadas N.C.O.P. en el capítulo 22	2	5,15	4,25
Industria alimentaria	Bebidas y otros	2208	Alcohol etílico sin desnaturalizar con graduación alcohólica volumétrica inferior al 80%; aguardientes, licores y demás bebidas espirituosas	1	5,33	5,09
Industria alimentaria	Manufactura de alimentos	2309	Preparaciones del tipo de las utilizadas para la alimentación animal	1	5,06	5,52
Industria alimentaria	Bebidas y otros	2402	Cigarros puros, cigarritos y cigarrillos de tabaco o de sucedáneos del tabaco	2	4,53	5,27
Químicos y materiales básicos	Químicos	2832	Sulfitos; tiosulfatos	1	6,28	5,92
Químicos y materiales básicos	Químicos	2834	Nitritos; nitratos	1	6,19	6,36
Químicos y materiales básicos	Químicos	2844	Elementos químicos radioactivos e isótopos radioactivos (incluidos los elementos químicos e isótopos fisionables o fértiles) y sus compuestos; mezclas y residuos que contengan estos productos	1	6,16	5,42
Químicos y materiales básicos	Químicos	2912	Aldehídos, aunque tengan otras funciones oxigenadas; polímeros cíclicos de los aldehídos; paraformaldehído	2	4,69	5,02
Químicos y materiales básicos	Químicos	2914	Cetonas y quinonas, aunque tengan otras funciones oxigenadas, y sus derivados halogenados, sulfonados, nitrados o nitrosados	2	4,09	5,15
Químicos y materiales básicos	Químicos	2920	Ésteres de otros ácidos inorgánicos de no metales (excepto los ésteres de haluros de hidrógeno) y sus sales, sus derivados halogenados, sulfonados, nitrados o nitrosados	1	5,10	5,34
Químicos y materiales básicos	Químicos	3101	Abonos de origen animal o vegetal, incluso mezclados entre sí o tratados químicamente; abonos procedentes de la mezcla o del tratamiento químico de productos de origen animal o vegetal	1	6,81	4,98
Químicos y materiales básicos	Químicos	3403	Preparaciones lubricantes y las utilizadas para el aceitado o engrase de materiales textiles y similares, excluyendo las que contengan el 70% o más en peso de aceites de petróleo o de minerales bituminosos	2	3,66	5,52
Químicos y materiales básicos	Químicos	3810	Preparaciones para el decapado de metales; fundentes y demás preparaciones auxiliares para soldar, con plata u otras aleaciones; pastas y polvos para soldar, de metal y demás materiales; preparaciones utilizadas como núcleos o revestimientos para electrodos o varillas de soldadura	3	3,21	4,22
Químicos y materiales básicos	Químicos	3815	Iniciadores y aceleradores de reacción y preparaciones catalíticas N.C.O.P. o incluidas	3	3,13	4,42
Químicos y materiales básicos	Químicos	3821	Medios de cultivo preparados para el desarrollo o mantenimiento de microorganismos (incluidos los virus y organismos similares) o de células vegetales, humanas o animales	2	2,34	5,28
Químicos y materiales básicos	Plásticos y caucho	3906	Polímeros acrílicos en formas primarias	2	6,12	4,92
Químicos y materiales básicos	Plásticos y caucho	3908	Poliamidas en formas primarias	3	3,99	3,90
Químicos y materiales básicos	Plásticos y caucho	3909	Resinas amínicas, resinas fenólicas y poliuretanos, en formas primarias	3	4,79	4,50
Químicos y materiales básicos	Químicos	3912	Celulosa y sus derivados químicos N.C.O.P. o incluidos, en formas primarias	1	5,56	5,71
Químicos y materiales básicos	Plásticos y caucho	3914	Intercambiadores de iones a base de polímeros de las partidas 3901 a 3913, en formas primarias	3	4,50	4,22
Químicos y materiales básicos	Plásticos y caucho	3915	Desechos, recortes y desperdicios, de plástico	2	5,17	4,15
Químicos y materiales básicos	Plásticos y caucho	3920	Plásticos: placas, láminas, hojas, películas, cintas y tiras (no autoadhesivas); no celulares y sin reforzar, laminar, combinar con soporte o asociar de forma similar a otras materias, N.C.O.P. en el capít Anexo 6: Metodología para evaluar el acceso a los insumos requeridos Un elemento importante para el desarrollo de cualquier actividad productiva es la capacidad de las empresas para acceder a los insumos intermedios requeridos en el proceso de producción, que generalmente son provistos por terceros, ya sean domésticos o importados. La capacidad de acceder a insumos intermedios en una ubicación determinada es crítica para determinar la viabilidad de una industria. Es importante señalar que para que un insumo intermedio esté disponible en una ubicación particular, no es necesario que las industrias que ofrecen dicho insumo existan en la misma ubicación, ya que es suficiente con que el insumo sea accesible a través de importaciones (en la medida en que el insumo sea transable). En vista de su relevancia, el Growth Lab desarrolló una metodología para medir implícitamente el desempeño de un país en este factor, con base en información de las tablas Input-Output de EE.UU. Como se señala en el informe, utilizar datos de la economía estadounidense es útil no solo porque el país cuenta con bases de datos accesibles y confiables, sino también porque exhibe una estructura productiva avanzada y una amplia colección de industrias, lo que puede ofrecer una buena aproximación de cómo las industrias individuales interactuarían entre sí en caso de que estuvieran plenamente desarrolladas en Namibia. La metodología identifica en primer lugar qué bienes y servicios son intensivamente requeridos por las industrias de interés. Con este fin, se calcula una VCR en el uso de los diferentes insumos (VCRI) para cada industria. Este indicador es análogo al utilizado para medir la intensidad con la que una industria está desarrollada en el país. En el caso de la VCRI, el cálculo es el siguiente: el porcentaje de la demanda total de insumos de la industria específica que corresponde a un insumo particular se divide por el porcentaje de la demanda total de insumos en la economía que corresponde a ese mismo insumo. Si la VCRI es igual o mayor que uno, el insumo es demandado intensivamente por la industria en cuestión, en relación con el resto de la economía. A continuación, para evaluar si los insumos intensivamente requeridos por las oportunidades de diversificación identificadas están disponibles en Namibia, se aplica una combinación de dos pruebas. La primera evalúa si el insumo —una industria en sí misma— está presente en el país. Para ello se utiliza la medida tradicional de VCR. Si la industria muestra una VCR igual o mayor que uno, entonces el insumo que ofrece se considera disponible. Si no es el caso, la segunda prueba evalúa si otras industrias que demandan intensivamente el mismo insumo están presentes en el país (usando VCR). Si un número suficientemente grande (4 o más30) de industrias cumple este criterio, el insumo también se considera disponible. En síntesis, la metodología presupone que un insumo está disponible en Namibia si proviene de una industria que está intensivamente presente en Namibia, o si un número suficientemente grande de industrias que lo demandan intensivamente están intensivamente presentes en Namibia. El resultado de este ejercicio es una lista de los insumos intermedios que son intensivamente demandados por cada oportunidad de diversificación, los cuales pueden clasificarse como disponibles o faltantes. El desempeño en este factor se mide por la proporción de insumos que son intensivamente requeridos por la industria en cuestión y que se consideran accesibles en Namibia. 30 Este umbral relativamente bajo es algo arbitrario, pero busca equilibrar el hecho de que el ejercicio solo considera exportaciones de bienes y, por tanto, probablemente subestima la disponibilidad latente de insumos en el país, y que se requiere una escala mínima para implicar la disponibilidad implícita de insumos. Anexo 7: Metodología para evaluar la exposición a choques exógenos sobre las exportaciones namibias Los minerales representan entre el 50% y el 60% de las exportaciones de Namibia, alrededor del 10% del PIB y el 6% de los ingresos fiscales, lo que implica que la economía namibia enfrenta riesgos significativos a la baja ante choques exógenos adversos sobre la demanda de sus principales materias primas. En ese sentido, puede resultar beneficioso que las potenciales oportunidades de diversificación exhiban una dinámica de demanda que sea en cierta medida independiente de la de sus principales materias primas, con el fin de introducir una mayor resiliencia en su actividad económica. Para evaluar de qué manera la demanda de productos HS4 se relaciona (o no) con la demanda de las principales materias primas de Namibia, se estimó en qué medida las exportaciones mundiales brutas de productos HS4 están vinculadas con un índice de precios de materias primas relevantes. Esto implicó cuatro pasos. Primero, se construyó un índice de precios de metales y minería para Namibia utilizando precios de materias primas del FMI y otras fuentes, ponderados por las participaciones actuales en la canasta exportadora. Segundo, se calcularon las exportaciones mundiales brutas a nivel HS4 con datos del Atlas de Complejidad Económica. Tercero, se estimó la correlación entre el cambio porcentual del índice y el cambio porcentual en las exportaciones mundiales brutas a nivel HS4. Por último, se regresó el cambio porcentual del índice sobre el cambio porcentual en las exportaciones mundiales brutas a nivel HS4. La regresión viene dada por: %chg indext = βi × %chg exportst,i + αi + εt,i donde t indica el período de tiempo, i indica el producto HS4, βi son los coeficientes de interés y αi son los términos constantes. Como verificación de los resultados del ejercicio, los 257 coeficientes HS4 en las categorías HS2 clasificadas como vinculadas a metales y minería31 tuvieron un beta promedio de 1,40, lo que significa que cada punto porcentual de aumento en el índice de precios de materias primas de Namibia está asociado con un aumento de 1,40 puntos porcentuales en esas exportaciones. Entre tanto, los restantes 963 productos HS4 no asociados con metales y minería tienen un beta promedio de 0,69, lo que indica que un punto porcentual de aumento en el índice de materias primas de Namibia está asociado con un aumento de 0,69 en las exportaciones mundiales brutas de esa categoría HS4. Una minoría de códigos HS4 tuvo coeficientes negativos, lo que significa que las exportaciones brutas de esos productos tienden a aumentar cuando los precios de las materias primas de Namibia caen. A los códigos HS4 que mostraron un beta no significativo se les asignó un beta de 0 (dado que no fue posible rechazar la hipótesis nula de que el beta es 0). Para tratar un pequeño número de valores atípicos, los betas superiores a 3 e inferiores a -3 fueron reemplazados por 3 y -3, respectivamente. Con el fin de informar el factor de atractivo, tanto los betas como las correlaciones calculadas en el paso 3 fueron normalizados y promediados según lo descrito previamente en el informe. 31 Los códigos HS2 para metales y minería incluyen: 25, 26 y 27 para minerales, piedras y sal; 28 para uranio; 71 para metales y piedras preciosas; 72 y 73 para hierro y acero; 74 y 75 para cobre y níquel; 76 y 78 para aluminio y plomo; 79, 80 y 81 para zinc, estaño y otros metales.