Two-way Spillover Research Articles

Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.

Características estruturais e fatores de influência da rede espacial do comércio agrícola: evidências de 15 países do RCEP

ABSTRACT: Based on national agricultural trade panel data of Regional Comprehensive Economic Partnership (RCEP) countries from 2002 to 2020, the structural characteristics of the RCEP national agricultural trade spatial network are reconstructed by the social network analysis method in this paper. Specifically, the characteristics consist of overall characteristics, individual characteristics, core-periphery structure analysis, and block models. Moreover, the influence factors on the agricultural trade network are also examined by the QAP regression model. The conclusions are summarized as follows. Firstly, the agricultural trade in RCEP countries has significant spatial correlation, showing favorable stability and accessibility. Secondly, Australia, China, Thailand, and Vietnam are the central actor’s leading position in the correlation network. While Cambodia, Laos, Myanmar, and other countries are in the marginal position. Thirdly, the network can be divided into four blocks, including the net benefit block, the two-way spillover block, the broker block, and the net spillover block. The spillover effect between the blocks is transitive. And finally, geographical distance, economic and social conditions, resource endowment, and language proximity index significantly impact the RCEP agricultural trade network structure.

Spatial correlation network structure of energy-environment efficiency and its driving factors: a case study of the Yangtze River Delta Urban Agglomeration

Improving energy-environment efficiency is not only a requirement for constructing China’s ecological civilization but also inevitable for achieving sustainable economic and social development. Studies on energy-environment efficiency based on relational data and network perspectives are limited, which hinders the development of collaborative regional emission reduction activities. This study uses the SBM-Undesirable model to measure the energy-environment efficiency of the Yangtze River Delta Urban Agglomeration from 2010 to 2020, adopts a modified gravity model and social network analysis to reveal the structural characteristics of its spatial correlation network, and explores its driving factors through the QAP method. The study found (1) an overall upward trend in energy-environment efficiency but with problems of uneven development. (2) The spatial correlation of energy-environment efficiency shows a complex network structure, with increasing network correlation and strong network stability; the network can be divided into four plates: net benefit, net overflow, two-way spillover, and agent. (3) Differences in industrial structure, environmental regulation, economic development, and technological innovation significantly impact the formation of spatial correlation network of energy-environment efficiency. This study provides a reference for the construction of a cross-regional synergistic mechanism to improve energy-environment efficiency.

The Study of Regional Innovation Network Structure: Evidence from the Yangtze River Delta Urban Agglomeration

As a driving force for regional development, innovation holds an increasing position in regional competitiveness, and a reasonable and coordinated innovation network structure can promote high-quality regional development. Utilizing the modified gravity model and social network analysis method, an innovation network composed of 27 cities in the Yangtze River Delta urban agglomeration from 2010 to 2021 was studied. The following conclusions were founded: (1) The innovation development level in the Yangtze River Delta urban agglomeration was constantly improving, and the innovation development level generally showed a spatial pattern of high in the southeast and low in the northwest. (2) The intensity and density of innovation network correlations in urban agglomerations were increasing, and the centrality of network nodes had an obvious hierarchical characteristic. The innovation network had a significant core–periphery spatial structure, with core cities that had higher centrality, such as Shanghai, Nanjing, and Hangzhou, playing the role of “intermediaries” and “bridges”, while cities with lower centrality, such as Anhui and cities in northern Jiangsu, generally played the role of “periphery actors” in the network. (3) The spatial correlation network of innovation of the Yangtze River Delta urban agglomeration could be divided into four blocks, namely, main benefit, broker, two-way spillover, and net spillover, and the spillover effect among them had obvious gradient characteristics of hierarchy.

Spatial Network and Driving Factors of Agricultural Green Total Factor Productivity in China

Agricultural green total factor productivity (AGTFP) is an important indicator to reflect the sustainability level of agriculture. The AGTFP network reflects the spatial correlations of the AGTFP among regions; thus, exploring its network structure and influencing factors can provide targeted policy guidance to the coordinated development of the agriculture sector. This study builds an epsilon-based measurement data envelopment analysis (EBM-DEA) model to calculate 31 provincial AGTFPs in China from 2002 to 2020. Then, social network analysis (SNA) was utilized to explore the characteristics of the AGTFP network, and the quadratic assignment procedure (QAP) was applied to find its external influencing factors. We reached four central conclusions: (1) Overall, there was a gradual upward trend of AGTFP in China during 2002~2020, and the average value rose from 0.75 in 2002 to 0.90 in 2020, but there were some differences among regions. (2) There is a complex and stable network characteristic of AGTFP; the average network density is 0.3753, and the average network efficiency is 0.4714. Meanwhile, some eastern and central areas, such as Henan, Anhui, Hubei, Hebei, Jiangsu, etc., have relatively high centrality and are a bridge in the entire network. (3) The AGTFP network can be divided into eight blocks, including two net beneficial blocks (the central-eastern provinces with high centrality); two net spillover blocks, including the provinces located in the developed urban areas (Beijing, Tianjin, Shanghai, and Jilin), the underdeveloped northwest regions (Ningxia, Qinghai, Xinjiang, and Tibet). The other areas are two-way spillover blocks. (4) Transportation development gap, technological progress gap, and the similarities of the agricultural industry structure are critical factors influencing the AGTFP network. Hence, improving the efficiency of the logistics and transportation industry, promoting technology transfer from developed areas to underdeveloped areas, and developing characteristic agriculture are all conducive to promoting the whole region’s AGTFP. Our research provides policy implications for sustainable agricultural development in China and other developing countries.

A Comparative Analysis of Spillover Effects on Surrounding Cities of Beijing And Shanghai

Industrial linkage is the key channel for spillover effect, and improving the spillover effect of central cities is an important task to realize the coordinated development of regional economy. From the perspective of industrial linkages, this paper uses the 2017 China's Inter-city input-output table to build a three regional input-output model to compared with the two-way overflow between Beijing and surrounding cities and the two-way overflow between Shanghai and its surrounding cities. The research findings are as follows: 1. Central cities and cities with higher economic development level have higher two-way spillovers, and the two-way spillover effect between Beijing and surrounding cities is smaller than that between Shanghai and surrounding cities; 2. There is an imbalance between central cities and surrounding cities in value-added gains. Beijing Tianjin Hebei and Yangtze River Delta present different situations. The share of spillover effects in GDP of surrounding cities to Beijing is generally small, and Beijing's value-added spillover to surrounding cities is greater than that of surrounding cities to Beijing. The share of spillover effects in GDP of surrounding cities to Shanghahi is generally high, and most of the surrounding cities' value-added spillovers to Shanghai are greater than Shanghai's value-added spillovers to the surrounding cities. 3. In terms of industry, Beijing and Shanghai have the largest added value spillover in the tertiary sector, followed by the secondary industry, and the value-added spillover of the primary industry is the smallest; From the perspective of 42 industry sectors, Beijing and Shanghai are relatively consistent in industries with large spillover effects on surrounding cities, which are the dominant industries of Beijing and Shanghai, and are concentrated in the secondary and tertiary industries. 4. Compare the value-added spillover effect of Shanghai and Beijing on urban agglomeration as a whole. The per capita spillover effect of Beijing on Beijing Tianjin Hebei is smaller than that of Shanghai on the Yangtze River Delta. Finally, according to the research conclusions, starting from the regional industrial advantages, it provides relevant policy recommendations for regional development and promoting the high-quality development of regional integration.

Analysis of the Spatial Association Network of PM2.5 and Its Influencing Factors in China.

The spatial association network of PM2.5 is constructed using a modified gravity model, with the data of 31 provinces in China from 2009–2020. On this basis, the spatial correlation structure of PM2.5 and its influencing factors were investigated through social network analysis (SNA). The results showed that, first, the PM2.5 has a typical and complex spatial correlation, and the correlation degree tends to decrease with the implementation of collaborative management. Second, they show that there is a clear “core-edge” distribution pattern in the network. Some areas with serious PM2.5 pollution have experienced different degrees of decline in centrality due to policy pressure. Third, the network is divided into “net benefits”, “net spillovers”, “two-way spillovers” and “brokers”. The linkage effect among the four blocks is obvious. Fourth, the government intervention and the industrial structure differentiation promote the formation of the network, but environmental regulation and car ownership differentiation have the opposite effect on the network.

Spatial correlation network characteristics of embodied carbon transfer in global agricultural trade.

Agricultural carbon emission is an important cause of climate change, and the carbon transfer caused by agricultural trade is a key area related to carbon emissions of all countries. Based on the Eora database, this paper aims to constructs a multi-region input–output database of 185 countries or regions, analyzes a spatial correlation network of embodied net carbon transfer in global agricultural trade by using UCINET, selects multi-dimensional network measurement indicators, and comprehensively studies the global evolution characteristics and functional features of network plate role of embodied carbon transfer in the global agricultural trade. The result shows that the embodied net carbon transfer network of global agricultural trade is densely connected, the spatial correlation spillover effect is significant, and the edge of the network core structure is clear. On the one hand, the top four countries or regions in terms of embodied carbon outflow in agricultural trade are the USA, Australia, Vietnam, and China. On the other hand, the top four countries or regions of embodied carbon inflow are Malaysia, Central Africa, Singapore, and Serbia. From the perspective of outdegree, indegree, proximity centrality, and intermediary centrality, Cambodia, the Netherlands, Vietnam, Ghana, and South Africa, with the high frequency of the shortest path of the globally embodied net carbon transfer network, have a strong influence and linking facility in spatial correlation and have a strong control ability to the spatial correlation of other countries or regions. The embodied carbon emission network of global agricultural trade can be divided into four sectors: main spillover, two-way spillover, broker, and main benefit. The main spillover segment, constituted by the USA, India, Germany, and China, has significant embodied carbon spillover effects on the internal segment and other segments. It is the main embodied carbon spillover sector of embodied net carbon transfer of global agricultural trade. Countries should reasonably allocate the responsibility of carbon reduction according to the trading embodied carbon transfer and made efforts to optimize the export structure of agricultural products.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-22337-w.

Research on the Structure of Carbon Emission Efficiency and Influencing Factors in the Yangtze River Delta Urban Agglomeration

Climate change caused by CO2 emissions has become one of the most serious environmental problems facing the world today, and it has a strong relevance to sustainability. This paper measures the carbon emission efficiency of the Yangtze River Delta urban agglomeration from 2001 to 2019 using the U-S SBM model. The modified gravity model and social network analysis methods are used to explore its spatially correlated network structure, and QAP regression is used to explore the influencing factors. The results show the following: (1) The spatial correlation of the carbon emission efficiency in the Yangtze River Delta urban agglomeration increased during the study period, showing a complex network structure with multiple threads and directions, and a strong mobility of the network. (2) The spatial network of the carbon emission efficiency in the Yangtze River Delta urban agglomeration gradually formed a core−edge structure with southern Jiangsu as the core area, northern Zhejiang and central Jiangsu as the secondary core area, and central Anhui and southern Zhejiang as the edge area during the study period. (3) The spatial correlation network of carbon emission efficiency in the Yangtze River Delta urban agglomeration is divided into “net benefit”, “net spillover”, “two-way spillover”, and “broker”. (4) Differences in energy intensity, government environmental regulations, technology research and development, and economic export orientation are the main factors affecting the spatial correlation of carbon emission efficiency in the Yangtze River Delta urban agglomeration.

Spatial Correlation Network and Driving Factors of Trade between China and RECP Countries: Empirical Investigation Based on the Social Network Analysis Method

This study adopts the revised gravity model to construct the spatial association network of trade in RECP countries and reveals its characteristics through a social network analysis method. The results are as follows: the spatial correlation of trade among RECP countries presents a complex, multithreaded network structure; the spatial correlation network of trade among RECP countries appears to fluctuate, indicating that their correlations, although influenced by the national environment, are still moving in the direction of regional integration; the degree centrality of China, Australia, and Korea is higher in terms of intermediary centrality and proximity centrality. This indicates that these countries are not only at the core of the network and have many associated relationships with other countries but also all are located at the center of the trade spatial association network; and the analysis results of the block model show that the trade spatial association network of RECP countries can be divided into four sections: net spillover, net benefit, broker, and two‐way spillover. The spillover effect between the two sections has obvious gradient transmission characteristics.

Research on Time-Varying Two-Way Spillover Effects Between Carbon and Energy Markets: Empirical Evidence From China

With the improvement of China’s carbon emission trading system, the spillover effect between carbon and energy markets is becoming more and more prominent. This paper selects four representative pilot carbon markets, including Beijing (BEA), Guangdong (GDEA), Hubei (HBEA) and Shanghai (SHEA). And three representative energy markets, including Crude Oil Futures (SC), power index (L11655) and China Securities new energy index (NEI). Combining the rolling window technology with DY spillover index, set a 50-weeks rolling window to measure the spillover index, and deeply analyze the time-varying two-way spillover effect between China’s carbon and energy markets. The results show that the spillover effect between China’s carbon and energy markets has significant time variability and two-way asymmetry. The time-varying spillover effect of different carbon pilot markets on the energy market has regional heterogeneity. The volatility spillover effect of Beijing and Shanghai carbon markets mainly comes from the crude oil futures market, Guangdong carbon market mainly comes from the new energy market, and Hubei carbon market mainly comes from crude oil and electricity market. The above research results contribute to the prevention of potential risk spillover between carbon and energy markets, which can promote the establishment of China’s unified carbon market and the prevention of systemic financial risks in energy market.

Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration.

Based on the panel data of 41 cities in the Yangtze River Delta from 2008 to 2017, this paper constructs an evaluation indicators system for urban tourism eco-efficiency. By measuring the tourism eco-efficiency in the Yangtze River Delta urban agglomeration, we analyze its spatial-temporal evolution characteristics. Furthermore, the modified gravity model and social network analysis are introduced to explore the spatial network structure of tourism eco-efficiency and its evolution trend.The results show that:(1) The overall eco-efficiency of tourism in the Yangtze River Delta region presents a fluctuating downward trend, among which Jiangsu and Zhejiang have high eco-efficiency, Shanghai and Anhui are relatively low. The gap within the region first increased and then decreased. (2) During this decade, the spatial network structure of tourism eco-efficiency in the Yangtze River Delta has become increasingly loose. The weakening of the network connection strength has led to a decrease in the regional tourism eco-efficiency to a great extent. (3) The network centrality of cities such as Zhoushan, Huzhou, and Huangshan has always maintained a high level, and these cities have firmly occupied the core position of network. (4) The spatial association network of tourism eco-efficiency can be divided into four blocks: “two-way spillover”, “net spillover”, “net benefit” and “agent”. The synergy and spillover effect between various blocks are significant, and there is a spatial polarization trend centered on a few cities. Based on this, this paper puts forward optimization suggestions for the spatial network structure of the Yangtze River Delta urban agglomeration, in anticipation of promoting the improvement of regional tourism eco-efficiency.

A Dreadful Loop: Can Reverse Zoonosis of COVID-19 Seed Unrestrained Spread and Mutations in Wild Species and Transmission of Novel Strains to Humans?

The disucssion deals with a hypothetical but an emerging scenario: reverse zoonosis and consequences. The infection of SARS-CoV-2 is effectuated by interactions between the SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 (ACE2) receptor proteins. An immense diversity of mammals can be infected by SARS-CoV-2 via their ACE2 proteins, which has been proven on some 30 species. In a pandemic with numerous locations of community-wide spread of the zoonotic agent, human-animal contacts or anthropogenic wastes carrying SARS-CoV-2 allows reverse zoonosis, namely the transmission of the virus from infected persons to wildlife species. Reverse zoonosis of SARS-CoV-2 may seed unrestrained spread and mutations in wildlife and, at some stage, transmission of novel strains back to humans (“reverse anthroponosis”). With no human intervention, spontaneous behaviors of wildlife such as foraging, predation, mating, and defecating enable both in-species and cross-species transmission, facilitating genotype changes and evolution of SARS-CoV-2 in various wildlife reservoirs. Multiple studies showed the complex evolutionary origin of SARS-CoV-2, which is susceptible to further mutations with its transmission between different hosts and species. As of week 21, 2020, a total of 11,627 genomes (34%) of SARS-CoV-2 isolated from humans showed single mutations, and there have been at least six published cases of SARS-CoV-2 reinfections around the globe since the first case appeared in Hong Kong on Aug 24, 2020. In all six cases, viral strains isolated from reinfected individuals showed substantial genetic differences from those causing their first infection. Although there has been no report on mutated SARS-CoV-2 strains from infected animals, COVID-19 is an evolving situation and recent accounts of clustered infections in farmed minks and reports of human-to-animal transmission in a New York zoo and in a Dutch mink farm highlighted these risks. Should reverse zoonosis occur on a considerable scale, mutated viruses may then spillover to humans again through different natural or intermediate hosts, forming a dangerous loop of zoonosis, anthroponosis, and reverse anthroponosis of the evolving virus between humans and other hosts. Once transmitted to humans, those novels strains will pose new challenges for future infection control by causing reinfections, re-emergent outbreaks, and rendering current vaccines less effective. In this article, we present this hypothesis based on emerging evidence on two-way spillover of SARS-CoV-2 between humans and animals, and call for early intervention with strategies proposed to prevent the risks down the road.

Dynamic correlation and volatility spillover between the stock markets of Shenzhen and Hong Kong

Considering the two-way spillovers of market information, this paper establishes multivariate GARCH models to study the impact of Shenzhen-Hong Kong Stock Connect (SHSC) on the complex co-movements relation between the stock markets of Shenzhen and Hong Kong from the aspects of dynamic correlation and volatility spillover. On the one hand, a t-Copula DCC-GARCH model which combines the Copula function with the DCC-GARCH model is established to model the return rate series of stock index in different stages, and the characteristic that the dynamic correlation coefficient changes with time is analyzed emphatically. On the other hand, a BEKK-GARCH model is established to measure the changes of the volatility spillover effect between the stock markets in Shenzhen and Hong Kong. The results show that the opening of SHSC has gradually increased the dynamic correlation coefficient of the two stock markets, and the openness degree of the two stock markets has increased. At the same time, the volatility spillovers of stock markets in Shenzhen and Hong Kong have shifted from one-way spillover to two-way spillovers, which indicates that the SHSC mechanism has strengthened the correlation degree and has improved the ability of risk spillover in the two stock markets.

The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective.

The association effect between provincial transportation carbon emissions has become an important issue in regional carbon emission management. This study explored the relationship and development trends associated with regional transportation carbon emissions. A social network method was used to analyze the structural characteristics of the spatial association of transportation carbon emissions. Indicators for each of the structural characteristics were selected from three dimensions: The integral network, node network, and spatial clustering. Then, this study established an association network for transportation carbon emissions (ANTCE) using a gravity model with China’s provincial data during the period of 2007 to 2016. Further, a block model (a method of partitioning provinces based on the information of transportation carbon emission) was used to group the ANTCE network of inter-provincial transportation carbon emissions to examine the overall association structure. There were three key findings. First, the tightness of China’s ANTCE network is growing, and its complexity and robustness are gradually increasing. Second, China’s ANTCE network shows a structural characteristic of “dense east and thin west.” That is, the transportation carbon emissions of eastern provinces in China are highly correlated, while those of central and western provinces are less correlated. Third, the eastern provinces belong to the two-way spillover or net benefit block, the central regions belong to the broker block, and the western provinces belong to the net spillover block. This indicates that the transportation carbon emissions in the western regions are flowing to the eastern and central regions. Finally, a regression analysis using a quadratic assignment procedure (QAP) was used to explore the spatial association between provinces. We found that per capita gross domestic product (GDP) and fixed transportation investments significantly influence the association and spillover effects of the ANTCE network. The research findings provide a theoretical foundation for the development of policies that may better coordinate carbon emission mitigation in regional transportation.

Hierarchical growth: Basic and applied research

We develop a model that incorporates salient features of growth in modern economies. We combine the expanding-variety growth model through horizontal innovations with a hierarchy of basic and applied research. The former extends the knowledge base, while the latter commercializes it. Two-way spillovers reinforce the productivity of research in each sector. We establish the existence of balanced growth paths. Along such paths the stock of ideas and the stock of commercialized blueprints for intermediate goods grow with the same rate. Basic research is a necessary and sufficient condition for economic growth. We show that there can be two different facets of growth in the economy. First, growth may be entirely shaped by investments in basic research if applied research operates at the knowledge frontier. Second, long-run growth may be shaped by both basic and applied research and growth can be further stimulated by research subsidies. We illustrate different types of growth processes by examples and polar cases when only upward or downward spillovers between basic and applied research are present.

The hidden soul of financial innovation: An agent-based modelling of home mortgage securitization and the finance-growth nexus

This paper investigates the interaction between financial innovation and securitization. To this end, it introduces the rate of financial innovation (RoFIN) as an endogenous variable in an Agent-Based Model (ABM) set up and studies its interaction with the non-fixed fraction of securitized mortgage loans. RoFIN is able to capture financial agents’ business decisions on using financial innovation tools, processes and services, such as the home mortgage securitization process. In the aftermath of the 2007–2009 financial and economic crisis it has been argued that financial innovation and securitization have increased macro/finance systemic instability via, for example, non-linear two-way spillovers between the financial system and the macroeconomy. The ABM model proposed enables the capture of these dynamics. High values of RoFIN (i.e. exceeding the threshold of 50%) make financial innovation become harmful for the economic system, leading to a switch from a virtuous to an unvirtuous business cycle. When RoFIN reaches 90%, the numerical simulations come close to the macro/finance dynamics observed before and during the financial crisis. Given its potential role in triggering financial and economic instability, RoFIN is of interest for financial regulation and supervision. How this endogenous variable may be influenced by means of operational variables under the control of policymakers remains a subject for future research.

Global Bonding: Do U.S. Bond and Equity Spillovers Dominate Global Financial Markets?

This paper uses a novel variant of identification through hetroscedacity to estimate spillovers across U.S., Euro area, Japanese, and UK government bond and equity markets in a vector autoregression. The results suggest that U.S. financial shocks reverberate around the world much more strongly than shocks from other regions, including the Euro area, while inward spillovers to the U.S. from elsewhere are minimal. There is also evidence of two-way spillovers between the UK and Euro area financial markets and spillovers from Europe to Japan. The results also suggest that the uncertainty about the direction of causality of contemporaneous correlations — an issue that other techniques cannot tackle — is the dominant source of uncertainty in the estimated impulse response functions.

Global Bonding: Do U.S. Bond and Equity Spillovers Dominate Global Financial Markets?

This paper uses a novel variant of identification through hetroscedacity to estimate spillovers across U.S., Euro area, Japanese, and UK government bond and equity markets in a vector autoregression. The results suggest that U.S. financial shocks reverberate around the world much more strongly than shocks from other regions, including the Euro area, while inward spillovers to the U.S. from elsewhere are minimal. There is also evidence of two-way spillovers between the UK and Euro area financial markets and spillovers from Europe to Japan. The results also suggest that the uncertainty about the direction of causality of contemporaneous correlations—an issue that other techniques cannot tackle—is the dominant source of uncertainty in the estimated impulse response functions.

Hierarchical Growth: Basic and Applied Research

We develop a model that incorporates salient features of growth in modern economies. We combine the expanding-variety growth model through horizontal innovations with a hierarchy of basic and applied research. The former extends the knowledge base, while the latter commercializes it. Two-way spillovers reinforce the productivity of research in each sector. We establish the existence of balanced growth paths. Along such paths the stock of ideas and the stock of commercialized blueprints for intermediate goods grow with the same rate. Basic research is a necessary and sufficient condition for economic growth. We show that there can be two different facets of growth in the economy. First, growth may be entirely shaped by investments in basic research if applied research operates at the knowledge frontier. Second, long-run growth may be shaped by both basic and applied research and growth can be further stimulated by research subsidies. We illustrate different types of growth processes by examples and polar cases when only upward or downward spillovers between basic and applied research are present.