Productivity effects of trade in natural resources: Comparison with mechanisms of technological specialisation

Zarach, Zuzanna; Parteka, Aleksandra Working Paper Productivity effects of trade in natural resources: Comparison with mechanisms of technological specialisation GUT FME Working Paper Series A, No. 2/2022 (68) Provided in Cooperation with: Gdańsk University of Technology, Faculty of Management and Economics Suggested Citation: Zarach, Zuzanna; Parteka, Aleksandra (2022) : Productivity effects of trade in natural resources: Comparison with mechanisms of technological specialisation, GUT FME Working Paper Series A, No. 2/2022 (68), Gdańsk University of Technology, Faculty of Management and Economics, Gdańsk This Version is available at: https://hdl.handle.net/10419/273132 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/deed.pl 1 PRODUCTIVITY EFFECTS OF TRADE IN NATURAL RESOURCES – COMPARISON WITH MECHANISMS OF TECHNOLOGICAL SPECIALISATION Zuzanna Zarach*, Aleksandra Parteka** GUT Faculty of Management and Economics Working Paper Series A (Economics, Management, Statistics) No 2/2022 (68) Revised: September 2022 * Gdańsk University of Technology, Faculty of Management and Economics, Narutowicza 11/12, 80-233 Gdańsk, Poland, zuzanna.h.zar[email protected]du.pl (corresponding author) ** Gdańsk University of Technology, Faculty of Management and Economics, Narutowicza 11/12, 80-233 Gdańsk, Poland, [email protected] 2 PRODUCTIVITY EFFECTS OF TRADE IN NATURAL RESOURCES - COMPARISON WITH MECHANISMS OF TECHNOLOGICAL SPECIALISATION Zuzanna Zarach 1 * & Aleksandra Parteka** * Gdansk University of Technology, Faculty of Management and Economics, Narutowicza 11/12; 80-233 Gdańsk, Poland, [email protected] (corresponding author) ** Gdansk University of Technology, Faculty of Management and Economics, Narutowicza 11/12; 80-233 Gdańsk, Poland, [email protected] This version: September 2022 Abstract This paper compares two alternative growth paths, assessing the effects on productivity of specialisation in natural resources (NR) and in technologically advanced products. The empirical analysis exploits product-level export data for 109 developing and 51 developed economies over the period 1996-2018. We document two distinct types of specialisation, based on exports either of natural resources or of technological products, and compare their role in productivity growth by GMM estimation of a conditional convergence model. In general, reliance on natural resource exports slows growth, but we find that the type of resources exported is important: fuel exports hamper growth while specialisation in metals enhances the catch-up in productivity. Technological specialisation, especially in products typical of the Fourth Industrial Revolution, reinforces productivity growth but does not affect the relationship between resources and productivity growth. JEL: O13, O47, O3, Q32 Keywords: natural resources, technology, specialisation, productivity growth, convergence The research has been conducted within the project financed by the National Science Centre, Poland (2020/37/B/HS4/01302). All errors are the authors’ responsibility. 1 Revised version (September 2022). Previous version of this work was circulated under corresponding author’s maiden name - Bazychowska. 3 1. Introduction This paper assesses the role played by different types of export specialisation 2 in productivity growth. We simultaneously compare the effects of two forces: ‘traditional’ mechanisms relating to natural resource endowment and ‘modern’ specialisation relating to the development of technological capacity. The wealth of empirical literature on economic growth tends to treat these two development paths separately. Commonly, the role played by natural resources (NR) is analysed from the perspective of the developing countries. The main focus is either on the economic and political distortions produced by resource endowments (the ‘resource curse’ debate on the failure of many resource-rich countries to benefit from this abundance and the situation where resource-rich countries' performance is notably worse than others’: see, amongst many, Gylfason, 2001; Sachs and Warner, 2001; Mehlum et al., 2006; Torvik, 2009; Ross, 2015) or, on the risk-accentuating excessive concentration on a narrow basket of primary products in many low-income countries (the export diversification literature: Basile et al., 2018; Cadot et al., 2011; Parteka and Tamberi, 2013a, 2013b). On the other hand, much attention has recently gone to the potential growth effects of the Fourth Industrial Revolution (4IR) and rapid-fire innovations in digital technology (Aghion et al., 2017, 2021; Foster-McGregor et al., 2019; Venturini, 2022). This literature focuses on the developed world and the discrepancy between the official productivity statistics of many countries and the expectations related to the potential of 4IR technologies (the ‘modern productivity paradox’: Brynjolfsson et al., 2019, 2021; Syverson, 2017; Byrne et al., 2016; Crafts, 2018; Gal et al., 2019). However, there are numerous less obvious facts that need to be addressed. First of all, some resource-rich countries have managed to avoid the resource curse, such as Norway and Botswana. Nearly 70% of Norway’s exports consists of NR (mostly crude oil and gas), while Botswana relies heavily on diamonds (around 90% of exports), 3 but the two countries’ per capita income is very high 2 Throughout the paper the term ‘specialisation’ is used to describe a country’s export structure in relation to the other countries in the sample, quantified via export shares and revealed comparative advantage in two specific groups of exported products, natural resources and technology. 3 Export data from CEPII (2021) 4 ($65,000 and $16,000, respectively, in 2018). 4 At the same time, the group of countries that depend heavily on primary commodity exports includes such poor economies as Nigeria and Angola (NR export dependency of 91% and 90%, respectively, and 2018 per capita income of $5,000 and $7,000) or Venezuela, where dependence on oil revenue, combined with economic mismanagement and inappropriate government practices, led to one of the most severe crises in modern history (Bull and Rosales, 2020; John, 2019; Weisbrot and Sachs, 2019). Among the fuel exporters of the Middle East, we might compare Iraq (97% of total exports consisting in fuel and per capita income of $10,500) with Kuwait (87%, $50,500). Secondly, the effects on growth of export concentration in natural resources and modern technologies can be intertwined. There are countries that do both (medium and high tech products make up approximately 15% of Norway’s exports), meaning that technological upgrading may be made possible by resource revenues, so the two growth paths need not be mutually exclusive. The question is whether Norway is an isolated case, while most of the developing countries, including resource-rich exporters, are excluded from technology-based growth? We address this question analysing the process of productivity growth in a large sample of 109 developing and 51 developed economies, from 1996 to 2018. We use product-level trade data to compare the degree of specialisation in NR and in high-tech products. Importantly, unlike other studies, in analysing the growth process we consider the role played by different types of resources (forestry products, fuels, metals, minerals) and different types of technology (comparing broadly defined tech exports to ICT exports and 4IR exports). The paper is structured as follows. Section 2 reviews the literature, and Section 3 presents the data and some descriptive evidence on the relationship between the two patterns of specialisation. Estimates of the productivity growth model are described in Section 4, and Section 5 concludes. 2. Natural resources and technology as growth determinants – the literature The early literature on the relationship between natural resources and economic development argued that resource-rich countries, in general, struggle with economic problems and that, almost without exception, they have stagnated since the 1970s (Sachs and Warner, 2001). The phenomenon 4 Per capita income data from WDI (2022). 5 has come to be known as the ‘resource curse’ or ‘paradox of plenty’. Now, however, the debate has grown less one-sided. Havranek et al. (2016) show that of 33 resource-curse studies analysed, about 40 per cent find a negative effect, 40 per cent no effect, and 20 per cent a positive effect of natural resources on long-term economic growth. Certainly, the results of different papers depend on multiple factors: the databases used, the time period analysed, the number of developing and developed countries in the sample, and so on (Van der Ploeg, 2011; Badeeb et al., 2017). Nevertheless, there are several possible explanations why natural resources may have an adverse impact on growth in some countries and beneficial effects in others. Firstly, the quality of institutions and governing bodies is crucial. Torvik (2009) argues that countries of poor institutional quality are more exposed to the risk of a negative impact from NR abundance. An analysis of 40 developing countries by Kim and Lin (2017) finds that natural resources tend to increase per capita income in countries with less government intervention, better protection of property rights and less corruption. Farhadi et al. (2015) contend that it is the quality of institutions that ultimately determines whether the ‘curse’ of natural resources can be turned into a blessing. Countries with bad institutions may actually suffer a double resource curse when worsening institutional setting reinforces the negative effect of resources (Mehlum et al., 2006). Policies that enable resource rents to be used well can spur economic growth, especially in developing countries (Ben-Salha et al., 2018). Growthand welfare-enhancing policies can help counter the adverse effects of specialisation in natural resources (Cavalcanti et al., 2011). Secondly, the relationship between primary commodities and productivity growth may differ with the particular type of resources involved. Cavalcanti et al. (2011) report that oil abundance (in the form of oil rents, production and reserves) doesn’t have to be a curse but in fact has beneficial effects on both the level of output and its growth rate. The combination of institutional quality and type of natural resources also proves to be fundamental. According to Torvik (2009), by comparison with NR in general, oil and minerals have a more pronounced negative impact on growth where the quality of institutions is poor. Minerals (diamonds in particular) have the most detrimental effect possible when combined with poor-quality institutions (Boschini et al., 2007; Olsson, 2006). 6 Another major issue is how resource revenues are spent or saved. Exclusive dependence on these rents obviously carries the risk of price instability. For instance, after the price of oil plunged by almost 50% in 2015, Venezuela was left with the bare minimum of savings during the subsequent economic crisis. It is crucial, that is, for countries to have reserves: according to Torvik (2009), the countries that have escaped the resource curse have higher rates of savings out of resource revenues than those that have not escaped. What is more, the accumulation of physical, human and social capital is inversely correlated with the share of natural resource capital and has a significant effect on the relationship between resources and economic growth (Gyfalson and Zoega, 2006). Subsequent important factor is how resource exploitation combines with other economic activities. A more complex economy, as measured by the Economic Complexity Index 5 (Hidalgo et al., 2007; Hausmann et al., 2007; Hidalgo and Hausmann, 2009; Hausmann et al., 2014) turns out to diminish the importance of NR rents (Canh et al., 2020) and can drive economic development. The quality and profitability of resource extraction industries can also help to avoid the paradox of plenty. Resource abundance can spur economic growth in countries that succeed in developing strong and efficient resource production industries (Gerelmaa and Kotani, 2016). The exploitation of natural resources relies on exogenously given endowments, but countries can also base their growth on a completely different factor – technology. Technology is a key component of the production function (Solow, 1957) and technical progress is a factor in many growth models (Romer, 1986, 1990; Lucas, 1988; Aghion and Howitt, 1992). Since the 1980s, the impact of the ICT revolution on growth has been intensively analysed (Jorgenson et al., 2008; Inklaar et al., 2005; Timmer and van Ark, 2005; Oliner et al., 2007; Acemoglu et al., 2014). Recently, a new kind of technological specialisation, related to the advanced 4IR digital technologies, including artificial intelligence (AI), has gained more and more attention (Baruffaldi et al., 2020; Venturini, 2022; Bassetti et al., 2020) but its role in growth process is umbigous. Aghion et al. (2018), developing the model of economic growth of Zeira (1998), argue that automation (and AI) can increase the economic growth rate either temporarily or permanently, 5 The Economic Complexity Index (ECI), provided by Harvard Growth Lab, ranks countries by the diversity and complexity of their export baskets. 7 depending on how they are implemented. The effects of AI and automation depend also on institutions and policies. AI can foster growth but it may also inhibit it if combined with improper competition policy (Aghion et al., 2020). There exists a rich literature on the problem of the recent slowdown in productivity growth in many developed countries, partly dashing the high hopes for the use of digital technologies – a phenomenon dubbed the ‘modern productivity paradox’ (Brynjolfsson et al., 2019). Inklaar et al. (2020) show that the productivity slowdown of the past decade began well before the 2007-2008 crisis and consequently cannot be considered a simple business cycle effect. Bloom et al. (2020) document that in many technological fields research productivity has been falling, and Nordhaus (2015) finds that the hypothesis of an acceleration of technology-driven growth fails a variety of tests. Are resource-rich countries excluded from the technology race? The empirical analysis of Fagerberg and Verspagen (2020) shows the wide gap between the countries that specialise in high-tech production and those, lagging behind in terms of technology and income, that specialise in resource-based products. The results of Foster-McGregor et al. (2019) suggest that only the inner circle of the most highly developed countries display a high degree of specialisation in 4IR technologies. However, to the best of our knowledge, none of the studies evaluates simultaneously the role of resources and technology (especially the newest digital solutions) in the growth process. The next section describes the data we use to address this issue. 3. Data and descriptive evidence 3.1 Dataset The analysis covers a total of 160 countries – 109 developing and 51 developed economies (listed in Table A.1. in Appendix A), from 1996 to 2018. The final choice of countries depend on data availability and representativeness: microstates (with population under 100,000) and countries with limited data on GDP and productivity are excluded. The disaggregated export data (HS96 6-digit) 6 used to compute 6 The number of HS96 product codes in BACI CEPII diminishes over time, so to hold it constant we delete the product codes that “disappear” between 1996 and 2018 and those that are no longer present in subsequent revisions. The final product-level export database used here contains 4895 product codes. 8 indices of natural resource (NR) and tech specialisation comes from the BACI CEPII database 7 (CEPII, 2021; Gaulier and Zignago, 2010). To gauge the importance of NR in countries’ exports we use the taxonomy of mining and forestry products based on the WTO International Trade Statistics classification (Bacchetta et al., 2010). 8 We divide products into four groups: forestry products, fuels, metals, and minerals (Appendix A - Table A.4.). To measure technological specialisation, we employ three alternative classifications, denoting technologically advanced products broadly defined (TECH) based on Lall (2000), ICT exports (ICT) from UNCTAD (2021), and 4IR-related products including robots, 3D printers and CAD/CAM machines (4IR) from Parteka et al. (2022) 9 – see Table A.5. (Appendix A). The 4IR and ICT classifications use 6-digit HS96 product level detail. To match the SITC-based taxonomy of Lall (2000) with the HS96 schema in BACI data, we use the SITC (Rev.3) – HS96 correspondence tables from UN Trade Statistics. 3.2 Trends in resources’ trade and technological trade Figure 1 illustrates the growth in the value of NR and TECH exports between 1996 and 2018. Developed countries contribute the greater part of TECH exports, whose total value tripled. The value of developing countries’ TECH exports also tripled over these years, but their share of total exports is smaller than in the developed countries. For almost 15 years (1996-2010) the value of NR exports was practically equal in developed and developing countries. Afterward it soared in the developed countries while declining steadily in the developing countries. As of 2018, values of natural resources exports were on an uptrend again. Figure 2 shows the relative importance of specific NR types in overall NR exports of all the countries in our sample. Unsurprisingly, fuels account for some 70% of all NR exports, and this proportion has basically increased over the years (from 63% in 1996 to a peak of 78% in 2013 before 7 The BACI database has yearly product-level data on bilateral trade flows; only strictly positive exports are recorded, and trade flows below 1,000 USD do not appear. We aggregate bilateral trade data to the reporter-world dimension. 8 The WTO classification also comprises fish, raw materials and other semi-manufactures as product groups. 9 The taxonomy builds upon Domini et al. (2021) and Foster-McGregor et al. (2019). 15 Figure 4. Share of NR and TECH products in total exports (%) over time – selected countries Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 16 4. The role of NR and tech exports in productivity growth – empirical analysis 4.1 The models Given these two distinct types of specialisation – either natural resources or technological exports – we now compare their roles in productivity growth. We are particularly interested in countries’ relative positions and accordingly apply the empirical model of conditional convergence (based on catching-up theory 13 ). The first step is to assess the role of natural resources in the productivity growth process: 𝑔(𝑦)𝑖𝑡 = 𝛼 + 𝛽1𝑦𝑖,𝑡−1 + 𝛽2𝑁𝑅𝑖,𝑡−1 + 𝛽3𝑋𝑖,𝑡−1 + 𝛿𝑡+ 𝜀𝑖𝑡 (1) where i denotes country (i=1, …,160), t time (t=1996, …,2018), and g(y) the annual rate of growth in labour productivity (in %). Productivity (y) is measured as output per worker (real GDP in constant 2017 USD divided by the number of workers from PWT 10.0, Feenstra et al., 2015). NR is the share of NR in total exports. X refers to a set of control variables that could potentially affect productivity growth. Specifically, the extension of model (1) adds the share of technological exports (T), to check whether technological specialisation affects the relationship between NR and productivity growth: 𝑔(𝑦)𝑖𝑡 = 𝛼 + 𝛽1𝑦𝑖,𝑡−1 + 𝛽2𝑁𝑅𝑖,𝑡−1 + 𝛽3𝑇𝑖,𝑡−1 + 𝛿𝑡+ 𝜀𝑖𝑡 (2) NR and T are computed using 6-digit HS96 product-level trade data from BACI CEPII (Gaulier and Zignago, 2010), matched with product-level taxonomies (NR in Table A.3. and T in Table A.4.). We consider different types of NR and tech products: NR={FORESTRY, FUEL, METAL, MINERAL} and T={TECH, ICT, 4IR} which measures technological exports broadly defined (TECH) or ICT and 4IR products. The effects of activities in these fields are not immediate, so NR and T are lagged. Other control variables (investment ratio, INV, 14 i.e. the share of gross fixed capital formation in GDP, the human capital index, HCI, 15 and R&D expenditures as percentage of GDP, RD 16 ) come from 13 Convergence theory posits the catch-up effect, whereby poor countries tend to grow faster than rich (Solow, 1957; Barro and Sala-i-Martin, 1992). 14 Following the World Bank’s definition of gross fixed capital formation, this consists in land improvements, plant, machinery, and equipment purchases, construction of roads, railways, schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings (World Bank, 2022). Straub (2008) argues that investment in public infrastructure can enhance productivity; good infrastructure allows time and capital to be invested in more efficient activities, improving productivity. 15 HCI (range 0–1) proxies for the productivity of future generations of workers and assesses the amount of capital lost due to poor education and health. It is measured by reference to the quality and quantity of education, state of health and 17 the World Bank’s World Development Indicators (WDI) database. Since the productivity effect of the control variables may not be immediate, these too are lagged. As to primary commodities, there may be problems of simultaneity: the relationship between NR and productivity growth is potentially open to reverse causality and endogeneity (Farhadi et al., 2015), so we use a two-step GMM estimator with a one-year lag of the potentially endogenous variable as instrument. The same applies to the technological variables. Moreover, NR exports tend to be highly persistent 17 (Table A.6. in the Appendix A), so we include time fixed effects to account for the business cycle but not country fixed effects to avoid wiping out all cross-country variability. 4.2 The results Table 3 reports the basic estimation results of model 1. Separate columns refer to estimates obtained with NR export share measured as a total (column 1) or by type (columns 2-5). In keeping with the convergence theory, the correlation between productivity growth and past productivity level is negative and significant. Natural resources tend to inhibit, weakly, the process of catching up. Ceteris paribus, a 1-percentage-point increase in the NR export share is related to a 0.007-p.p. decrease in the productivity growth rate. As the NR share in reality holds relatively fixed, this implies weak but constant negative pressure on productivity growth. Importantly, the subdivision of NR into types reveals that not all natural resource endowments act in the same way. While the correlation between fuel exports and productivity growth is negative and significant (column 2), metal exports instead are a positive factor in growth (column 5). This result holds also after adding control variables (INV, RD, HCI) – Table 4. children’s survival. The knowledge and skills of the population are crucial to generating new technologies, hence to productivity gains (Kim & Loayza, 2019). 16 RD gauges spending on basic and applied research and experimental development. This expenditure is divided into four main sectors: business enterprise, government, higher education and private non-profit. Innovation spending has an enormous impact on productivity and leads to the development of more sophisticated activities, products and processes (Kim & Loayza, 2019). 17 Countries with proven reserves of natural resources usually maintain a constant level of extraction, which tends to result in a relatively constant share of NR in total export value. Situations that can alter such conjunctures are rare and may involve new resource discoveries (increasing the share of NR in total exports), resource depletion (decreasing the NR share) or efforts at export diversification. 18 Table 3. The relationship between NR exports and productivity growth (estimates of eq. 1) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.007* -0.011** 0.046 -0.011 0.022*** [0.0038] [0.0045] [0.0287] [0.0096] [0.0061] No.of obs. 3358 3264 3257 3333 3240 No. of countries 160 160 160 160 160 R2 0.064 0.068 0.062 0.064 0.073 K-P rk Wald F 76986.240 48571.050 1008.039 3568.880 5792.576 K-P rk LM 1219.286 730.347 86.066 178.083 101.453 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5%, and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). Table 4. The relationship between NR exports and productivity growth (estimates of eq. 1, with control variables) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.005 -0.008* 0.049* -0.011 0.022*** [0.0042] [0.0049] [0.0287] [0.0096] [0.0061] INV 0.039*** 0.040*** 0.042*** 0.035*** 0.047*** [0.0119] [0.0126] [0.0121] [0.0117] [0.0118] RD 0.237** 0.194* 0.306*** 0.298*** 0.333*** [0.0977] [0.0993] [0.0860] [0.0859] [0.0845] HCI 0.632 0.605 0.814 0.687 0.787 [0.7370] [0.7380] [0.7703] [0.7480] [0.7440] No. of obs. 3358 3264 3257 3333 3240 No. of countries 160 160 160 160 160 R2 0.073 0.077 0.073 0.073 0.088 K-P rk Wald F 63238.359 40326.759 1012.407 3504.070 5781.898 K-P rk LM 1172.695 695.607 86.409 177.908 101.204 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: as under Table 3. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). Turning to the importance of technological specialisation (model 2), all three types of tech exports are positively related to productivity growth. The results reported in column 1 in Table 5, Table 6 and Table 7 indicate that, other things being equal, a 1-p.p. increase in the export share of 4IR, ICT, and TECH products generates an increment of 0.4, 0.02 and 0.01 point in the productivity growth rate, respectively. That is, the most economically proficuous technological activity relates to exports of 4IR 19 products. The magnitude and the significance of the T coefficients vary with the type of NR. Most importantly, the inclusion of T variables does not alter the benchmark result, namely the adverse effect of fuel exports and the beneficial effect of metal exports (reported in Table 3). This means that even if a country increases the importance of tech exports, the focus on fuels may still slow productivity catchup. Table 5. The relationship between NR exports, 4IR exports and productivity growth (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.010*** -0.014*** 0.021 -0.009 0.022*** [0.0035] [0.0042] [0.0262] [0.0100] [0.0060] Ti,t-1 0.036 -0.021 0.374** 0.331* 0.422** [0.1959] [0.1954] [0.1693] [0.1704] [0.1686] No.of obs. 3239 3184 3159 3227 3173 No. of countries 160 160 160 160 160 R2 0.078 0.080 0.074 0.076 0.081 K-P rk Wald F 306.191 307.495 367.245 368.833 368.811 K-P rk LM 163.662 164.382 164.283 164.736 164.428 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5%, and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). Table 6. The relationship between NR exports, ICT exports and productivity growth (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.005 -0.010** 0.050* -0.008 0.024*** [0.0040] [0.0047] [0.0288] [0.0098] [0.0062] Ti,t-1 0.018** 0.015** 0.027*** 0.023*** 0.029*** [0.0072] [0.0069] [0.0068] [0.0070] [0.0068] No.of obs. 3358 3264 3257 3333 3240 No. of countries 160 160 160 160 160 R2 0.065 0.069 0.064 0.066 0.076 K-P rk Wald F 9267.795 22237.431 502.887 1836.138 7407.393 K-P rk LM 415.314 858.664 86.677 190.025 291.670 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: as under Table 5 Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 20 Table 7. The relationship between NR exports, TECH exports and productivity growth (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.003 -0.008 0.052* -0.006 0.027*** [0.0049] [0.0053] [0.0289] [0.0101] [0.0065] Ti,t-1 0.011* 0.008 0.014*** 0.013*** 0.016*** [0.0059] [0.0053] [0.0044] [0.0047] [0.0045] No.of obs. 3358 3264 3257 3333 3240 No. of countries 160 160 160 160 160 R2 0.065 0.069 0.064 0.066 0.077 K-P rk Wald F 10541.141 13605.858 507.626 2590.774 15868.519 K-P rk LM 1117.358 1206.426 85.712 298.207 1082.118 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5%, and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 4.3 Extensions and robustness checks As a first robustness check we run a regression with alternative measures of natural resource endowment (Table B.1. and Table B.2. in Appendix B), replacing the NR export share with the share of NR rents in GDP. This variable comes from the World Development Indicators database and can be described as the difference between the value (at world prices) of the natural resources extracted and their total production cost. The types of commodities (coal, forestry products, oil, gas and minerals) and their total value are largely in line with the NR taxonomy presented in Table A.4. The only discrepancy is the lack of metal rents and the division of fuels into three separate groups (coal, gas and oil). The results show that total NR rents have a negative – but not significant – effect on catching up. There is a positive and statistically significant correlation between coal, gas and mineral rents and productivity growth. Other thins being equal, a 1-p.p. increase in the GDP share of coal/gas/mineral rents results in an increase of 0.573/0.067/0.12 points respectively in the productivity growth rate. The addition of control variables (Table B.2.) confirms the previous results. To adjust for the possible heterogeneity between developing and developed countries, we split the sample of 160 countries into two groups: 51 developed and 109 developing countries (see Table A.1.). The estimation results are reported in Tables B.3-B.12. In the developed countries (with and without 21 control variables) all types of NR have a statistically significant impact on productivity growth, but it is positive only for forestry products. Ceteris paribus, a 1-p.p. increase in the share of forestry exports corresponds to a 0.14-p.p. increase in the productivity growth rate. For the developed countries, the addition of the technological export shares did not change either the magnitude or the significance of the NR-productivity correlation. Estimations for the group of developing countries (results in Tables B.5-6 and B.10-12) indicate that the only type of NR exports that can enhance productivity growth in a statistically significant way is metals. Other things being equal, a 1-p.p. increase in the share of metal exports raises the productivity growth rate by 0.029 points. And while the effect is positive and statistically significant, it is still very small. Turning to technological factors, ICT and TECH exports have a positive effect on productivity growth in the developing countries. Additionally, given that some resource abundant countries report very high shares of NR in exports (nearly 100%), we have also checked the robustness of the results once outliers (defied as observations below 1st and above 99th percentiles of the dependent variable) are excluded. The results are reported in Tables B.13-16 and they stay in compliance with main regression outcomes, both in terms of statistical significance and the magnitude of the relationship. 5. Conclusions The conventional wisdom, with much of the ‘resource curse’ literature, holds that the growth of developing countries is hampered by overspecialisation in natural resources, while in the developed world technological advance drives growth. But today’s world is considerably more complicated than this simple schema would suggest. Some low-income countries produce advanced technologies, some countries totally escape the resource curse and increase the technological content of their exports, and some economies, finally, have comparative advantages in both commodity-based and technologyintensive goods. In short, the relationship between natural resources and growth is not so obvious or straightforward. We analyse this issue for a large sample of 160 countries (109 developing and 51 developed economies) from 1996 to 2018. Detailed product level trade data allows us to distinguish various types 22 of resources and of technologies embodied in exports. Specifically, natural resources may consist in forestry products, fuels, metals, or minerals, and tech exports generically defined are distinguished from newer generation technologies embodying ICT and 4IR solutions. The GMM estimates of a conditional productivity convergence model confirm that greater total NR exports slow productivity growth and impede, weakly, the catch-up process. However, we find that the type of resources exported matters: fuels hamper productivity growth while metals enhance it (this result applies to the whole sample and is also sustained for the subsample of developing countries). For technological exports too, type matters: the products typical of the Fourth Industrial Revolution, in particular, accelerate productivity growth. However, the magnitude of this effect is small, and in any case it does not affect the relationship between natural resources and productivity growth. Knowing that oil exporting countries are the ones who were able to initiate the diversification process towards the technological production, possible extension for our work should include division of fuel resources into subsequent three groups - coal, natural gas and oil. This will help in verifying whether all fossil fuels actually hamper productivity growth. Further additions to the empirical model could consist of interaction terms between the share of NR and technological exports and the incorporation of GVC and FDI as control variables. References Acemoglu, D., Johnson, S., and Robinson, J. A. (2002). Reversal of fortune: Geography and institutions in the making of the modern world income distribution. The Quarterly Journal of Economics, 117(4), 1231-1294. Acemoglu, D., Dorn, D., Hanson, G. H., and Price, B. (2014). Return of the Solow paradox? IT, productivity, and employment in US manufacturing. American Economic Review, 104(5), 394-99. Aghion, P, and Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–51. Aghion, P., Jones, B. F., and Jones, C. I. (2018). Artificial intelligence and economic growth. In The economics of artificial intelligence: An agenda (pp. 237-282). University of Chicago Press. Aghion, P., Antonin, C., and Bunel, S. (2020). On the Effects of Artificial Intelligence on Growth and Employment. OpenMind BBVA. https://www.bbvaopenmind.com/en/articles/on-the-effects-ofartificial-intelligence-on-growth-and-employment/ Bacchetta, M., Beverelli, C., Hancock, J., Keck, A., Nayyar, G., and Nee, C. (2010). World Trade Report 2010, Trade in Natural Resources. World Trade Organization. Badeeb, R. A., Lean, H. H., & Clark, J. (2017). The evolution of the natural resource curse thesis: A critical literature survey. Resources Policy, 51, 123-134. 23 Barro, R. J., and Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223-251. Baruffaldi, S., van Beuzekom, B., Dernis, H., Harhoff, D., Rao, N., Rosenfeld, D., and Squicciarini, M.. (2020). Identifying and measuring developments in artificial intelligence: Making the impossible possible, OECD Science, Technology and Industry Working Papers, No. 2020/05, OECD Publishing, Paris, https://doi.org/10.1787/5f65ff7e-en Bassetti, T., Borbon Galvez, Y., Del Sorbo, M., Pavesi, F. (2020). Artificial Intelligence – impact on total factor productivity, e-commerce and fintech, EUR 30428 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-24693-0, doi:10.2760/333292, JRC122268. Ben-Salha, O., Dachraoui, H., and Sebri, M. (2018). Natural resource rents and economic growth in the top resource-abundant countries: a PMG estimation. Resources Policy, 101229. Bloom, N., Jones, C. I., Van Reenen, J., and Webb, M. (2020). Are ideas getting harder to find?. American Economic Review, 110(4), 1104-44. Boschini, A. D., Pettersson, J., and Roine, J. (2007). Resource curse or not: A question of appropriability. Scandinavian Journal of Economics, 109(3), 593-617. Brynjolfsson, E., Rock, D., and Syverson, C. (2019). Artificial intelligence and the modern productivity paradox. The economics of artificial intelligence: An agenda, 23. Brynjolfsson, E., Rock, D., and Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333-372. Bull, B., and Rosales, A. (2020). The crisis in Venezuela. European Review of Latin American and Caribbean Studies, (109), 1-20. Byrne, D. M., Fernald, J. G., and Reinsdorf, M. B. (2016). Does the United States have a productivity slowdown or a measurement problem?. Brookings Papers on Economic Activity, 2016(1), 109-182 Cadot, O., Carrère, C., and Strauss-Kahn, V. (2011). Export diversification: what's behind the hump?. Review of Economics and Statistics, 93(2), 590-605. Canh, N. P., Schinckus, C., and Thanh, S. D. (2020). The natural resources rents: Is economic complexity a solution for resource curse?. Resources Policy, 69, 101800. Cavalcanti, T. V. D. V., Mohaddes, K., and Raissi, M. (2011). Growth, development and natural resources: New evidence using a heterogeneous panel analysis. The Quarterly Review of Economics and Finance, 51(4), 305-318. CEPII (2021). BACI database (2021 version). http://www.cepii.fr/CEPII/en/welcome.asp [assessed on 02 Febr 2022] Crafts, N. (2018). The productivity slowdown: is it the ‘new normal’?. Oxford Review of Economic Policy, 34(3), 443-460. Domini, G., Grazzi, M., Moschella, D., & Treibich, T. (2021). Threats and opportunities in the digital era: automation spikes and employment dynamics. Research Policy, 50(7), 104137. Fagerberg, J., and Verspagen, B. (2021). Technological revolutions, structural change and catchingup. New perspectives on structural change: causes and consequences of structural change in the global economy, 131. Farhadi, M., Islam, M. R., and Moslehi, S. (2015). Economic freedom and productivity growth in resource-rich economies. World Development, 72, 109-126. Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), The Next Generation of the Penn World Table, American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt Foster-McGregor, N., Nomaler, Ö., and Verspagen, B. (2019). Measuring the creation and adoption of new technologies using trade and patent data. Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT). 24 Gal, P., Nicoletti, G., von Rüden, C., Sorbe, S., and Renault, T. (2019). Digitalization and Productivity: In Search of the Holy Grail-Firm-level Empirical Evidence from European Countries. International Productivity Monitor, (37), 39-71. Gaulier G., Zignago S. (2010). BACI: International Trade Database at the Product-Level. The 19942007 Version. CEPII Working Paper, N°2010-23, Octobre 2010 Gerelmaa, L., and Kotani, K. (2016). Further investigation of natural resources and economic growth: do natural resources depress economic growth?. Resources Policy, 50, 312-321. Gylfason, T. (2001). Natural resources, education, and economic development. European economic review, 45(4-6), 847-859. Gylfason, T., and Zoega, G. (2006). Natural resources and economic growth: The role of investment. World Economy, 29(8), 1091-1115. Hausmann, R. (2001). Prisoners of geography. Foreign Policy, 45-53. Hausmann, R., Hwang, J., and Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12(1), 1-25. Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Chung, S., Jimenez, J., ... and Yıldırım, M. A. (2014). The Atlas of Economic Complexity. Cambridge, MA: Center for International Development. Havranek, T., Horvath, R., and Zeynalov, A. (2016). Natural resources and economic growth: A metaanalysis. World Development, 88, 134-151. Hidalgo, C. A., Klinger, B., Barabási, A. L., and Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487. Hidalgo, C. A., and Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the national academy of sciences, 106(26), 10570-10575. Inklaar, R., O'Mahony, M., and Timmer, M. (2005). ICT and Europe's productivity performance: Industry‐level growth account comparisons with the United States. Review of Income and Wealth, 51(4), 505-536. Inklaar, R., Jäger, K., O'Mahony, M., and van Ark, B. (2020). European productivity in the digital age: evidence from EU KLEMS. In Measuring economic growth and productivity (pp. 75-94). Academic Press. John, M. (2019). Venezuelan economic crisis: crossing Latin American and Caribbean borders. Migration and Development, 8(3), 437-44 Jorgenson, D. W., Ho, M. S., and Stiroh, K. J. (2008). A retrospective look at the US productivity growth resurgence. Journal of Economic Perspectives, 22(1), 3-24. Kim, D. H., and Lin, S. C. (2017). Natural resources and economic development: new panel evidence. Environmental and resource economics, 66(2), 363-391. Kim, Y. E., and Loayza, N. (2019). Productivity growth: Patterns and determinants across the world. World Bank Policy Research Working Paper, (8852). Lall, S. (2000). The technological structure and performance of developing country manufactured exports, 1985‐98. Oxford Development Studies, 28(3), 337-369. Lucas Jr, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3-42. Mehlum, H., Moene, K., and Torvik, R. (2006). Cursed by resources or institutions?. World Economy, 29(8), 1117-1131. Nordhaus, W. D. (2015). Are we approaching an economic singularity? Information technology and the future of economic growth. NBER working Paper No. w21547. National Bureau of Economic Research. 31 Table B.2. The relationship between NR rents and productivity growth (estimates of eq. 1, with control variables) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 6 TOTAL NR COAL FORESTRY OIL GAS MINERAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 0.000 0.500*** -0.013 -0.013 0.076** 0.118*** [0.0134] [0.1756] [0.0291] [0.0161] [0.0322] [0.0441] INV 0.039*** 0.034*** 0.038*** 0.039*** 0.038*** 0.037*** [0.0117] [0.0121] [0.0117] [0.0117] [0.0117] [0.0118] RD 0.304*** 0.299*** 0.303*** 0.247** 0.341*** 0.328*** [0.1016] [0.0844] [0.0850] [0.1016] [0.0869] [0.0861] HCI 0.671 0.700 0.667 0.656 0.712 0.778 [0.7461] [0.7235] [0.7464] [0.7413] [0.7451] [0.7404] No.of obs. 3360 3360 3360 3360 3360 3360 No. of countries 160 160 160 160 160 160 R2 0.073 0.079 0.073 0.074 0.074 0.076 K-P rk Wald F 3802.505 20.643 2209.574 3508.037 135.784 767.702 K-P rk LM 429.314 7.789 207.626 293.566 18.794 93.096 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. NRi,t-1 is the natural resources rents share (total, coal, forestry, oil, gas and mineral) in GDP (%). Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 32 Table B.3. The relationship between NR exports and productivity growth – developed countries (estimates of eq. 1) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.022*** -0.024*** 0.140*** -0.030* -0.026** [0.0053] [0.0063] [0.0275] [0.0156] [0.0121] No.of obs. 1071 1071 1069 1071 1071 No. of countries 51 51 51 51 51 R2 0.226 0.228 0.214 0.204 0.206 K-P rk Wald F 17613.910 11560.278 2329.853 2046.408 3550.803 K-P rk LM 394.947 282.428 67.006 39.775 62.721 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. Sample: 51 developed countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 33 Table B.4. The relationship between NR exports and productivity growth – developed countries (estimates of eq. 1, with control variables) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.024*** -0.027*** 0.143*** -0.024 -0.025** [0.0074] [0.0088] [0.0276] [0.0156] [0.0121] INV -0.017 -0.023 -0.004 0.001 0.000 [0.0156] [0.0161] [0.0156] [0.0156] [0.0155] RD -0.043 -0.031 0.207*** 0.165** 0.169** [0.1164] [0.1156] [0.0796] [0.0815] [0.0806] HCI 1.664 1.402 1.888 2.181 2.217 [1.3544] [1.3277] [1.3598] [1.4172] [1.4193] No.of obs. 1071 1071 1069 1071 1071 No. of countries 51 51 51 51 51 R2 0.228 0.231 0.221 0.210 0.212 K-P rk Wald F 14496.107 9232.430 2355.729 2026.068 3541.062 K-P rk LM 322.816 231.006 68.147 40.643 62.709 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. Sample: 51 developed countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 34 Table B.5. The relationship between NR exports and productivity growth – developing countries (estimates of eq. 1) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.002 -0.007 0.029 -0.009 0.029*** [0.0048] [0.0058] [0.0330] [0.0102] [0.0068] No.of obs. 2287 2193 2188 2262 2169 No. of countries 109 109 109 109 109 R2 0.039 0.043 0.038 0.040 0.054 K-P rk Wald F 45627.288 27200.363 767.734 3179.164 4280.456 K-P rk LM 900.073 500.182 65.964 159.669 75.887 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. Sample: 109 developing countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 35 Table B.6. The relationship between NR exports and productivity growth – developing countries (estimates of eq. 1, with control variables) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.002 -0.006 0.043 -0.009 0.027*** [0.0049] [0.0058] [0.0333] [0.0102] [0.0068] INV 0.043*** 0.048*** 0.047*** 0.039*** 0.055*** [0.0145] [0.0155] [0.0146] [0.0139] [0.0141] RD 1.811*** 1.745*** 1.950*** 1.828*** 2.051*** [0.3127] [0.3121] [0.3216] [0.3232] [0.3096] HCI -0.274 -0.335 0.069 -0.239 -0.005 [1.1453] [1.1515] [1.2008] [1.1574] [1.1544] No.of obs. 2287 2193 2188 2262 2169 No. of countries 109 109 109 109 109 R2 0.058 0.062 0.059 0.056 0.082 K-P rk Wald F 43178.128 26178.260 765.601 3143.829 4278.375 K-P rk LM 887.355 506.781 66.312 161.480 75.621 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variable: NR. Constant included – not reported. Sample: 109 developing countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 36 Table B.7. The relationship between NR exports, 4IR exports and productivity growth – developed countries (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.028*** -0.028*** 0.144*** -0.028* -0.025** [0.0071] [0.0079] [0.0277] [0.0162] [0.0126] Ti,t-1 -0.466** -0.383** 0.169 0.077 0.070 [0.1939] [0.1830] [0.1219] [0.1258] [0.1265] No.of obs. 1071 1071 1069 1071 1071 No. of countries 51 51 51 51 51 R2 0.231 0.232 0.215 0.204 0.206 K-P rk Wald F 860.655 939.223 1185.964 1416.255 1167.878 K-P rk LM 154.313 161.907 152.432 178.249 147.575 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Sample: 51 developed countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 37 Table B.8. The relationship between NR exports, ICT exports and productivity growth – developed countries (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.020*** -0.022*** 0.165*** -0.019 -0.018 [0.0058] [0.0066] [0.0287] [0.0164] [0.0127] Ti,t-1 0.009 0.014 0.039*** 0.029*** 0.027*** [0.0103] [0.0097] [0.0103] [0.0102] [0.0102] No.of obs. 1071 1071 1069 1071 1071 No. of countries 51 51 51 51 51 R2 0.227 0.230 0.228 0.212 0.213 K-P rk Wald F 1931.382 2110.511 2386.246 2502.047 2055.986 K-P rk LM 109.359 114.124 147.874 139.429 114.604 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Sample: 51 developed countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 38 Table B.9. The relationship between NR exports, TECH exports and productivity growth – developed countries (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.037*** -0.029*** 0.180*** -0.015 -0.017 [0.0103] [0.0086] [0.0318] [0.0199] [0.0145] Ti,t-1 -0.023** -0.009 0.018*** 0.010* 0.009 [0.0101] [0.0067] [0.0052] [0.0055] [0.0054] No.of obs. 1071 1071 1069 1071 1071 No. of countries 51 51 51 51 51 R2 0.232 0.229 0.225 0.208 0.209 K-P rk Wald F 10021.675 14506.162 4329.181 2132.410 9839.606 K-P rk LM 229.126 590.045 308.884 123.879 480.170 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Sample: 51 developed countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 39 Table B.10. The relationship between NR exports, 4IR exports and productivity growth – developing countries (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.005 -0.009 -0.003 -0.008 0.029*** [0.0046] [0.0055] [0.0299] [0.0106] [0.0069] Ti,t-1 0.973 0.852 1.868 1.549 2.076 [1.8498] [1.8514] [1.6871] [1.7009] [1.7233] No.of obs. 2168 2113 2090 2156 2102 No. of countries 109 109 109 109 109 R2 0.054 0.056 0.052 0.054 0.061 K-P rk Wald F 6.879 6.619 7.407 7.581 7.466 K-P rk LM 26.432 25.912 30.837 30.696 30.028 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Sample: 109 developing countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 40 Table B.11. The relationship between NR exports, ICT exports and productivity growth – developing countries (estimates of eq. 2) Dependent variable: 𝑔(𝑦)𝑖𝑡 1 2 3 4 5 TOTAL NR FUEL FORESTRY MINERAL METAL yi,t-1 -0.000*** -0.000*** -0.000*** -0.000*** -0.000*** [0.0000] [0.0000] [0.0000] [0.0000] [0.0000] NRi,t-1 -0.001 -0.006 0.031 -0.008 0.030*** [0.0049] [0.0058] [0.0330] [0.0104] [0.0069] Ti,t-1 0.016* 0.013 0.020** 0.016* 0.025*** [0.0091] [0.0089] [0.0090] [0.0092] [0.0087] No.of obs. 2287 2193 2188 2262 2169 No. of countries 109 109 109 109 109 R2 0.040 0.043 0.039 0.040 0.055 K-P rk Wald F 6375.823 13878.672 382.555 1636.339 4610.466 K-P rk LM 245.349 551.901 66.244 169.619 151.716 K-P rk LM (p-val) 0.000 0.000 0.000 0.000 0.000 Notes: *,**,*** denote significance at the 1%, 5% and 10% levels respectively; robust standard errors in parentheses; all specifications contain time fixed effects; K-P refers to Kleibergen-Paap test statistics. Instrumented variables: NR, T. Constant included – not reported. Sample: 109 developing countries; in compliance with 2018 World Bank income classification. Source: Based on 6-digit HS export data from BACI CEPII (CEPII, 2021; Gaulier and Zignago, 2010). 47 Original citation: Zarach Z., Parteka A. (2022). Productivity effects of trade in natural resources – comparison with mechanisms of technological specialisation. GUT FME Working Papers Series A, No 2/2022(68). Gdansk (Poland): Gdansk University of Technology, Faculty of Management and Economics. Previous version of this work was circulated as: Bazychowska Z., Parteka A. (2022). Productivity effects of trade in natural resources – comparison with mechanisms of technological specialisation. GUT FME Working Papers Series A, No 2/2022(68). Gdansk (Poland): Gdansk University of Technology, Faculty of Management and Economics. All GUT Working Papers are downloadable at: http://zie.pg.edu.pl/working-papers GUT Working Papers are listed in Repec/Ideas https://ideas.repec.org/s/gdk/wpaper.html GUT FME Working Paper Series A jest objęty licencją Creative Commons Uznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Unported. GUT FME Working Paper Series A is licensed under a Creative Commons AttributionNonCommercial-NoDerivs 3.0 Unported License. Gdańsk University of Technology, Faculty of Management and Economics Narutowicza 11/12, (premises at ul. Traugutta 79) 80-233 Gdańsk, phone: 58 347-18-99 Fax 58 347-18-61 www.zie.pg.edu.pl