The impact of foreign direct investment in emissions reduction targets: evidence from high- and middle-income countries
Rafaela Vital Caetano
Dissertação para obtenção do Grau de Mestre em
Economia
(2º ciclo de estudos)
Orientador: Prof. Doutor António Manuel Cardoso Marques
junho de 2020
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Acknowledgments
A whole year of hard work and spirit of sacrifice has, obviously, times of intensive pressure and discouragement. And it was in the hardest moments that I understood who takes care of me. My family is always my biggest support, but it is normal if sometimes they do not understand why I spent so much time in the library or my room, even on the weekends.
There is an answer: my biggest objective is to make you proud of me, of my work, of my journey. Without you, it would not have been possible. A thank you will not be enough.
My supervisor Professor António Manuel Cardoso Marques had the difficult job of guiding a whirling mind with a lot of fears. Thank you for guiding me, but thank you more for making me like this kind of research and encourage me all time when I needed it.
Next, I have to thank my friends that share all moments of this year with me and who made me laugh even when I did not want to. Thank you for all your support, patience, and understanding. You all made it so much easier.
Last, but not least, my family that is no longer here, wherever you are, thank you for the shared knowledge since I was young: you taught me the meaning of being resilient.
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Resumo
A consciência ambiental tornou-se um tópico de investigação como consequência do aquecimento global. Durante vários anos, os países têm enfrentado um trade-off entre o crescimento económico e os objetivos de redução das emissões de dióxido de carbono. A globalização tem uma elevada relevância na definição do desempenho ambiental dos países.
Um dos processos incluídos na globalização é o fluxo de investimento estrangeiro. Alguns países lutam para equilibrar a sua necessidade de entradas de investimento estrangeiro – que chegam, de modo geral, devido às suas leis ambientais mais relaxadas – com a necessidade de melhorar a sua qualidade ambiental. O modelo Panel Autoregressive Distributed lag foi aplicado para avaliar os impactos que o investimento direto estrangeiro tem as emissões de dióxido de carbono de 21 países divididos por nível de rendimento, entre 2001 e 2017. O nível de eficiência, inovação e regulação dos países recetores foram considerados, de maneira a compreender melhor a complexidade do fenómeno do investimento direto estrangeiro. As medidas regulatórias dos países de alto rendimento demonstram não auxiliar na redução das emissões no curto prazo, algo que merece um debate pormenorizado. O investimento direto estrangeiro diminui as emissões nos países de elevado rendimento, enquanto aumenta nos países de médio rendimento. Contudo, a capacidade absortiva dos países é crucial para fazer com que estes países beneficiem no longo-prazo, tal como os países de alto rendimento beneficiam. A abertura comercial é altamente influenciada pela regulação ambiental nos países de médio rendimento. A Pollution Haven Hypothesis é sustentada, o que significa que as indústrias poluidoras estão a ser transferidas dos países mais desenvolvidos para outras regiões do mundo, impactando o ambiente onde elas se alocam.
Palavras-chave
Investimento direto estrangeiro; Emissões de dióxido de carbono; Pollution Haven Hypothesis; regulação; eficiência energética
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Resumo alargado
A globalização tem um grande impacto no desenvolvimento dos países, assim como o fenómeno associado de investimento direto estrangeiro (FDI) que é considerado uma grande fonte de crescimento económico dos países recetores (e.g. Balsalobre-Lorente et al., 2019). No entanto, é necessário compreender de que modo este FDI pode comprometer o desenvolvimento sustentável dos países. A Pollution Halo Hypothesis sustenta o aspeto mais positivo do FDI. Para além de ser um impulsionador de crescimento económico, os países beneficiam da transferência de novas maquinarias e da difusão de conhecimentos, por exemplo. Numa tentativa de atrair mais FDI, os países menos desenvolvidos criam condições favoráveis para esses investimentos, entre elas o relaxamento das regulações ambientais. Estes países atraem FDI através da vantagem comparativa em indústrias poluidoras. Aqui começam os pontos negativos. As empresas filiadas em países com mais restrições ambientais transferem as suas indústrias poluidoras para países com leis mais relaxadas, de modo a evitar o aumento dos seus custos. Esta transferência é conhecida na literatura como Pollution Haven Hypothesis.
Deste modo, torna-se imprescindível entender de que modo o FDI afeta o ambiente, para que os decisores políticos consigam, caso necessário, reajustar as suas políticas e atingir as metas de desenvolvimento sustentável. Esta investigação pretende responder a questões tais como: (i) qual o impacto do FDI no ambiente nos países recetores?; e (ii) estarão os países de alto rendimento a transferir as suas indústrias poluidoras para países com leis ambientais mais relaxadas? O modelo Autoregressive Distributed Lag (ARDL) foi utilizado, para 21 países divididos por nível de rendimento, com um horizonte temporal entre 2001 e 2017. Como referido por Hao et al., (2020), as decisões de saída do FDI dependem das necessidades dos países emissores: matérias-primas ou recursos naturais, mercado, e tecnologias, por exemplo. Isto também demonstra que os impactos que o FDI tem depende das características dos países recetores. Por isso, a consideração dos níveis de regulação ambiental, eficiência e inovação dos países preenche uma lacuna na literatura.
Por exemplo, países com níveis de regulação ambiental elevados não serão atrativos para indústrias poluidoras, do mesmo modo que países com níveis baixos de eficiência e inovação serão pouco atrativos para indústrias altamente automatizadas, pois irão aumentar os custos de ajustamento (Adom et al., 2019). A análise do efeito do FDI nas emissões de CO2
globais e nas emissões do sector industrial produzem linhas orientadoras úteis e diretas nesta temática, e pode ser considerada uma análise de robustez.
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A variável CO2 foi utilizada como proxy da poluição e a variável CO2 do sector industrial representa a poluição provocada por esse sector, foram ambas utilizadas como variáveis dependentes. Outras variáveis são: stock interno de investimento direto estrangeiro, a formação bruta de capital fixo, o comércio, as patentes e os ganhos relacionados aos impostos ambientais. A eficiência energética dos países foi também considerada e foi calculada através do rácio da produção industrial sobre o consumo de energia. As fontes dos dados são o World Development Indicators do World Bank, International Energy Agency e a Organisation for economic co-operation and development statistics. Foram realizados testes de raízes unitárias de primeira e segunda geração para verificar a ordem de integração das variáveis. A existência de heterocedasticidade, dependência seccional e autocorrelação de primeira ordem, foi testada e a sua existência foi controlada através da utilização do estimador Driscoll & Kraay (1998), uma vez que produz resultados mais robustos na presença destas características.
A formação bruta de capital fixo e a abertura comercial aumentam a poluição, no curto e no longo-prazo. No entanto, a abertura comercial não se demonstra significante no longo-prazo nos países de médio rendimento. A eficiência contribui para a redução das emissões de CO2 principalmente no sector industrial. As patentes não demonstram ser estatisticamente significantes nos países de alto rendimento, e aumentam as emissões nos países de médio rendimento. A regulação ambiental aumenta a poluição no curto prazo nos países de alto rendimento, o que é inesperado. Nos países de médio rendimento, a regulação diminui as emissões no longo prazo. Os resultados do sector industrial suportam a maioria dos resultados das emissões gerais. No entanto, as patentes e a regulação não demonstram ser estatisticamente significantes para as emissões neste sector. Isto significa que os decisores políticos não estão a prestar especial atenção à inovação e regulação do setor industrial. A Pollution Haven Hypothesis é suportada para os países de médio rendimento, mas apenas no curto prazo. No longo prazo, o FDI diminui a poluição geral, o que realça a importância da capacidade absortiva de tecnologia destes países. O FDI diminui as emissões de CO2 nos países de alto rendimento, o que suporta a Pollution Halo Hypothesis.
Sabendo que o cenário ideal de leis ambientais semelhantes em todo o mundo e a inexistência de corrupção é utópico, a definição de uma estrutura legal estável, a cooperação entre os decisores políticos dos países e a combinação de diferentes instrumentos políticos (impostos e subsídios) poderão atenuar os efeitos negativos do FDI no ambiente e mesmo extingui-los: os países de alto rendimento não vão transferir as suas indústrias poluidoras, uma vez que os riscos não serão compensatórios, e os países de médio rendimento não vão aceitar a entrada de FDI poluidor, uma vez que vai degradar o seu ambiente, trazendo complicações com os acordos internacionais existentes (e que devem continuar presentes em todo o mundo).
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Abstract
Environmental awareness has become a research topic worldwide as a consequence of global warming. For several years, countries have been facing a trade-off between economic growth and targets to reduce carbon dioxide emissions. Globalization has high relevance in defining the environmental performance of countries. One of the processes within globalization is the flow of foreign investments. Some countries struggle to balance the need for inward foreign investments – arriving on the whole due to their laxer environmental policies – with the necessity to improve the quality of their environment. With this impasse in mind, a Panel Autoregressive Distributed Lag was applied to evaluate the impacts of foreign direct investment on the carbon dioxide emissions of 21 countries divided into income levels, for a period ranging from 2001 to 2017. The efficiency, innovation, and regulation characteristics of the host countries are considered to better understand the complexity of the foreign direct investment phenomenon. Regulatory measures appear ineffective in reducing emissions in the short-run in high-income countries, something which deserves a lively debate. Foreign direct investment decreases emissions in high- income countries, while it increases in the short-run in middle-income countries.
Notwithstanding, the technology absorptive capacity of middle-income countries is prominent to make them benefit in the long-run, as high-income countries do.
Furthermore, trade openness is highly influenced by environmental regulation in middle- income countries. The Pollution Haven Hypothesis is supported, meaning that polluting industries are being transferred from more developed countries to other regions of the world, impacting the environment where they are set up.
Keywords
Foreign direct investment; Carbon dioxide emissions; Pollution Haven Hypothesis;
Regulation; Energy-efficiency
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Index
1. Introduction 1
2. Literature Review 6
3. Methodology 12
3.1. Preliminary tests 13
3.2. Method 15
3.3. Diagnostic tests 15
4. Results and discussion 17
4.1. General CO2 emissions 19
4.2. CO2 emissions from the industry sector 22
4.3. Overall comparison and discussion 24
5. Conclusion and policy recommendations 28
References 30
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Figure list
Figure 1. FDI stock in high-income countries Figure 2. FDI stock in middle-income countries
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Tables list
Table 1. Literature review framework Table 2. Variables description
Table 3. Correlation matrices and VIF statistics (high-income countries) Table 4. Correlation matrices and VIF statistics (middle-income countries) Table 5. Panel unit root tests
Table 6. Diagnostic tests
Table 7. Zivot and Andrews unit root test Table 8. Test of overall significance
Table 9. Estimation results with overall CO2 emissions
Table 10. Estimation results with CO2 emissions from the industry sector
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Acronyms List
ARDL Autoregressive Distributed lag
CD Cross-Section Dependence
CIPS Cross-Sectionally Augmented IPS
CO2 Carbon Dioxide
ECM Error Correction Model
EF Energy Efficiency Index
FDI Foreign Direct Investment
IEA International Energy Agency
IPI Industrial Production Index
GHG Greenhouse Gas
MENA Middle East and North Africa
PHH Pollution Haven Hypothesis
RES Renewable Energy Sources
R&D Research and Development
UNCTAD United Nations Conference of Trade and Development UNFCCC United Nations Framework Convention on Climate Change
USD United States Dollar
VIF Variance Inflation Factor
WB World Bank
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1. Introduction
Environmental awareness diverges between countries, something which is reflected in their economic priorities. Countries have different reactions when faced with the trade-off between economic growth and carbon dioxide (CO2) emissions reduction. Indeed, all countries want to increase their income, but environmental issues should not be disconnected from this goal. Foreign direct investment (FDI) could bring countries either benefits or drawbacks. Firstly, FDI is known as a source of economic growth (Balsalobre- Lorente et al., 2019). For that, countries are searching for FDI as an economic booster, mainly the countries with lower levels of development. On the one hand, these countries could enlarge their income and at the same time, benefit through the sharing of knowledge (Shahbaz et al., 2015) and green technologies (Mielnik & Goldemberg, 2002). On the other hand, their FDI could harm the environment through the transference of polluting industries (e.g, Baek, 2016).
This is the primary concern regarding the relationship between FDI and the environment, and as such, countries could be facing a trade-off between FDI and emissions reduction. As stated in the World Investment Report, published by the United Nations Conference of Trade and Development (UNCTAD), in 2018, some countries are introducing policy measures that affect foreign investments, moving in the direction of liberalization, promotion, and facilitation of foreign investments (UNCTAD, 2019). This facilitation attracts more FDI, however, it will attract both clean and dirty FDI, and therein lies the problem. In such a way, it is crucial to know the effect of FDI on the environment to understand how policymakers can act to look for sustainable development targets.
Furthermore, it is of high relevance to fully understand the influence of income level on the transference of polluting industries among countries. Countries with higher environmental stringency could be transferring their polluting industries to countries with milder environmental laws to avoid higher environmental compliance costs, once environmental regulations raise the cost of key inputs to goods with pollution-intensive production. It is also important to discern if environmental regulations are producing the expected result of decreased pollution. Briefly, the central questions of this paper are: (i) What is the impact of FDI on the environment of host countries?; and (ii) Are high-income countries moving their pollution-intensive industries to countries with laxer environmental regulations?
To answer these central questions, the Autoregressive Distributed lag (ARDL) model was used focusing on 21 countries divided into income levels. The consideration of environmental regulation, efficiency, and innovation levels of the countries will help in interpreting the complexity of the FDI phenomenon. Furthermore, this approach divides the impacts into the short- and long-run, which jointly with the disaggregation of countries
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into income levels, and the scrutiny of the effect in the industry sector produces directed and useful guidance in this theme. This study analyses countries with different income levels since it provides better empirical evidence both to the analysis that jointly explores the Pollution Halo and Pollution Haven hypotheses, which is scarce in the literature about this theme (Balsalobre-Lorente et al., 2019).
Figure 1. FDI stock in high-income countries Figure 2. FDI stock in middle-income countries
Graph source: own elaboration; Data: UNCTAD- FDI inward stock in millions of USD
One observes through Figures 1 and 2 that all countries chosen to be under scrutiny in this investigation increased their levels of FDI stock between 2015 and 2017. The Netherlands and the United Kingdom are highlight host countries among high-income countries with higher levels of FDI inflows as presented in Figure 1. The Netherlands has an attractive standard corporate income tax rate of 25%, with predictions to reduce to 21.7% in 2020 regarding the 2019 PWC worldwide tax summaries1, with a lower rate of 19% to taxable profits up to 200.000€, that will further decrease to 16.5% in 2020. Furthermore, double taxation can be usually avoided in the Netherlands. All of this reflects the great treatment for foreign companies; these are the main reasons for the Netherlands to be highly attractive. The United Kingdom has a normal rate of corporation tax about 19% in 2019,
1 https://taxsummaries.pwc.com/netherlands/corporate/taxes-on-corporate-income, 9 June 2020 Austria
Czech Republic Denmark France Germany Greece Hungary Ireland Japan Netherlands Norway Portugal Spain United Kingdom
FDI stock in high-income countries, 2015 and 2017 (millions of USD)
2017 2015
Argentina Bulgaria Croatia Peru Philippines Romania South Africa Turkey
FDI stock in middle-income countries, 2015 and 2017 (millions of USD)
2017 2015
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which is also very attractive to foreign companies. But the main reason for the United Kingdom to be one of the principal recipient countries to FDI could be its high level of development, income, and economic performance also the population density which is highly relevant to the development of new companies. An interesting and current point related to this positive trajectory of FDI growth is that it could be affected by the Brexit, once trade (this is, exports and imports) to the European Union will potentially bring additional tariffs.
Regarding Figure 2, Turkey and South Africa are the countries with a major level of inward FDI stock among the countries under scrutiny. Recently, Turkey had implemented legislative reforms to facilitate foreign investments such as the Investment Support and Promotion Agency of Turkey (ISPAT), according to Santander Trade Markets2. Another example of the attractiveness of Turkey is their lower labor costs that could attract more FDI once they are positively linked as stated by Esiyok (2011). However, Turkey loses attractivity by the slow pace of privatisation process, the high inflation levels, and corruption (Loewendahl & Ertugal-Loewendahl, 2001). According to Santander Trade Markets3, South Africa has strong points to attract foreign investments, as an abundance of natural resources, the great potential on tourism and retail sectors, and a strong mining sector which represents the main relevance of this economy. In turn, it also has some less attractive points, as economic instability due to corruption, lower access to electricity, and the import-export processes that may be difficult. Both Turkey and South Africa are extremely dependent on FDI inflows, which make these countries change their policies to attract as much FDI as possible.
Turkey has been trying to integrate the European Union, which reflects the economic stability of this country lately, reflected in European regulations and trade standards. The geographic location of Turkey is very attractive once it is near Europe, Asia, and the Middle East and North Africa (MENA) economic zone, which is favorable to trade. The same for South Africa that has a geographical locational that ensures access to the Sub-Saharan markets. One states that it is good to boost the economic performance of both Turkey and South Africa; however, the consequences go further the positive effect on economic performance and could affect the environment. According to the Presidency of the Republic of Turkey Investment Office4, 24,1% of the cumulative FDI during the 2003-2018 period is in the manufacturing sector. Moreover, in South Africa, the mining sector corresponds to 21,2% of FDI inflows, and manufacturing with 15,9%, as maintained by Santander Trade Markets in South Africa. The mining belongs to the primary sector of the industry, which
2 https://santandertrade.com/en/portal/establish-overseas/turkey/foreign-investment, 9 June 2020
3 https://santandertrade.com/en/portal/establish-overseas/south-africa/foreign-investment, 9 June 2020
4 https://www.invest.gov.tr/en/whyturkey/pages/fdi-in-turkey.aspx, 9 June 2020
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means that it is extremely pollutant, and an increase in their production level hugely impacts the environment worldwide. Moreover, manufacturing, although be in the secondary sector of the industry is also highly pollutant, considering that is the process of transforming raw materials into goods.
Briefly, the majority of middle-income countries under analysis have greater participation of FDI inflows to the mining and manufacturing sectors. Even if these countries must attract FDI, it is highly required to implement stringent criteria of FDI inflows mainly related to environmental protection, which encourage FDI to transfer new and eco-friendly technologies, that can help these countries in controlling pollution.
Notwithstanding, some high-income countries also have a wide percentage of FDI inflows to these sectors. For example, in 2017, Spain has 31,8% of FDI inflows to the manufacturing sector, and Norway has 27% of FDI inflows to the mining sector and 14,1% to the manufacturing sector. This upsurges some questions. For example, considering countries that receive FDI directed to the manufacturing sector, have FDI inflows the same impacts in high- and middle-income countries? Or high-income countries are taking advantage of middle-income countries to transfer these polluting industries? In fact, even if the FDI inflow occurs in the same sector of the industry, each country has different levels of efficiency, innovation, eco-friendly technologies, and environmental regulations, which will make countries deal differently with the same industries, mostly due to the different environmental standards and regulations. These questions will also be further investigated in this paper.
This paper fills a gap in the literature as it considers the innovation, regulation, and efficiency levels of the host countries in the relationship between FDI and emissions. It not only debates the impact of these variables on pollution but further assesses complementary information about the capacity that these variables have to change impacts between each other. A country that wants to transfer green technologies and knowledge through FDI will evaluate some capabilities of the recipient countries, such as their innovation, and efficiency skills. Adversely, a country that wants to relocate its polluting industries to another country to avoid environmental taxes, will search for a country with lower environmental regulations. This paper contributes to the enlargement of knowledge in this area, accompanied by empirical evidence that reveals that applied regulatory measures seem not to be efficient in helping to reduce emissions in high-income countries.
This paper exposes that the regulatory structure of these countries must be discussed, as is in this paper, and that it should be further investigated. Trade openness is highly influenced by regulation in middle-income countries. These countries seem to be focused only on developing new patents with the aim of increasing their income. The dichotomous impacts found between the short- and long-run, further emphasizes the use of
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the ARDL model; otherwise, if that separate analysis was not carried out, misleading results could be achieved. Indeed, FDI is shown to decrease overall pollution and from the industry sector in high-income countries, while increases it in middle-income countries in the short- run. This seems to support the transference of polluting industries from high-income countries to middle-income countries, that is, supporting the Pollution Haven Hypothesis (PHH). However, middle-income countries could be receiving clean FDI as well, but as will be discussed in this paper, their technology-absorptive-capacity influences the impact of FDI on the environment. The ARDL model also provides better evidence of the technology- absorptive-capacity of the countries as it analyses the impacts through time.
The rest of the paper is organized as follows: Section 2 presents a theoretical background; Section 3 discloses the data and methodology; Section 4 reveals the results and discussion about overall CO2 emissions and those from industry, with a brief comparison between models; and Section 5 concludes.
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2. Literature Review
The industrial structure of countries has been changing since the industrial revolution. As industrial output has increased, so have CO2 emissions, and with that, environmental degradation has emerged. FDI has an important role in economic growth (Omri et al., 2014).
However, besides the effect on economic growth, FDI also impacts the environment of host countries. The relationship between FDI and the environment was initially analysed through the impact of international trade, and posteriorly considering the effect of FDI on the environmental quality of host countries (Shahbaz et al., 2015). International trade (reflected in trade openness) is still of great value and its effect is commonly evaluated in this theme (e.g., Essandoh et al., 2020; Sbia et al., 2014). Besides the high correlation and Granger causality with FDI, trade openness has an effect on pollution. Essandoh et al., (2020) find that trade openness is environmental favourable for developed countries, with no evidence of impact in developing countries. However, as noted by Ren et al., (2014), trade openness could harm the environment if countries have a comparative advantage in dirty production under weak environmental regulations. The unwise behaviour of some countries that need to increase their income impacts the whole environment, increasing pollution, and ultimately leading to climate change. However, unlike the recessions faced by world economies, climate changes may be irreversible (Doytch & Uctum, 2016).
The characteristics of the countries matter when to evaluating the impact of FDI on the environment, because FDI, per se, does not have an impact as stated by Pazienza (2019).
There is diverse empirical evidence in the literature about the effect of FDI on the environment. For some countries, FDI reveals a positive and relevant role in reducing emissions due to the transference and adoption of greener technology. This technology boosts efficiency gains (Pao & Tsai, 2011) and improves environmental quality. However, in other countries, FDI increases CO2 emissions (Ren et al., 2014) contributing to environmental degradation. Beyond the different characteristics of the countries, also the adoption of different empirical methodologies or different periods could impact the conclusions made (Zhang & Zhou, 2016), as it is possible to conclude regarding Table 1.
In several reports, the effect of FDI on the environment has been divided into three categories: technique, structural, and scale effects (see Liang, 2009; He, 2008; Cole &
Elliott, 2003). The technique effect is based on the diffusion of new and more efficient machines, for example, though FDI (Pazienza, 2019), which decreases the emissions per unit of a good produced. This effect also supports that the introduction of environmental regulations can improve the environment decreasing emissions (Shahbaz et al., 2018). The structural effect is related to the characteristics of an economy. For instance, an economy that produces energy-intensive goods consumes more energy than an economy specialized in the services sector (Pazienza, 2019). Succinctly, the effect of FDI depends on the
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comparative advantage and the specialization sectors of that economy (Shahbaz et al., 2018). Lastly, the scale effect states that FDI increases the industrial output in the host country, then increasing energy consumption and CO2 emissions (Pao & Tsai, 2011). As stated by Hao et al., (2020), the FDI outflow decisions could be divided into three types:
resources-seeking, market-seeking, and technology-seeking. Countries that are resources- seeking, will invest in countries with an abundance of resources, while market-seeking countries will search and invest in a country with market potential. Moreover, technology- seeking FDI will invest in a country with higher levels of technology and knowledge and will further participate in the management activities of this country, to gain “know-how”, for example. This type of FDI will benefit the domestic environment, increasing the human capital level of the host countries, and raising the competitiveness of the source firm (Pradhan & Signh, 2008).
As such, the countries’ characteristics could amplify the impacts of FDI. It can harm the environment in countries with lower environmental consciousness (Xing & Kolstad, 2002), meanwhile, it can improve the environmental quality in countries with higher environmental awareness (Demena & Afesorgbor, 2019). These characteristics are more exploited in the three main hypotheses associated with the FDI-environment nexus: Porter, Pollution Halo, and Pollution Haven hypotheses. The Porter hypothesis states that FDI can improve the environmental quality of the host countries due to the introduction of new technologies that consume less energy; the so-called eco-friendly technologies (C. Zhang &
Zhou, 2016; Mielnik & Goldemberg, 2002). The main reason for this is the environmental regulations that encourage firms to invest in green innovation (C. Shen et al., 2020) to improve their efficiency. However, both regulation and the development of new technologies leads to an increase in costs. This reveals that it is important to bear in mind the cost-benefit analysis because regulations can only promote innovation when the benefits exceed the costs. In other words, regulation should offset the environmental compliance costs to benefit the environment and improve the firms’ competitiveness (C.
Shen et al., 2020).
The Pollution Halo Hypothesis is built on the positive effect that FDI has on the environment. The transfer of new technologies that can decrease energy consumption (Mielnik & Goldemberg, 2002), and the transfer of business knowledge- so-called “know- how”- (Shahbaz et al., 2015) are some examples of benefits that FDI can bring to countries if multinationals are less pollution-intensive (Cole et al., 2011). The countries’
characteristics have relevance to defining this impact because countries with higher levels of environmental consciousness would not allow the entry of polluting FDI. Finally, the PHH states that FDI harms the environment in host countries (e.g., Baek, 2016; Al-mulali, 2012). Countries with strict environmental regulations transfer their polluting industries to
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countries with more relaxed environmental laws, to run away from further additional environmental costs and taxes (Shahbaz et al., 2015). However, PHH only occurs when the industries transference is relatively easy and does not involve high costs, which divides industries into two groups as Dou & Han, (2019) states: strongly, and weakly mobile pollution industries. Strongly mobile industries will be relocated when an increase in the stringency of the environmental regulations happens, and weakly mobile industries will invest in R&D to gain efficiency, obtaining an “Innovation Compensation” effect (Dou &
Han, 2019).
As such, environmental regulation is the main focus of two previous hypotheses which state that the effect of FDI on pollution depends on the environmental regulation level of host countries. Some literature linked the environmental regulation and the environmental performance of the countries, mainly reflected in the pollution level. On the one hand, environmental regulation could improve environmental quality, once increase the productivity and efficiency of the firms in that country mainly due to energy savings (N.
Zhang & Choi, 2013), which means that regulation can decrease pollution (Hashmi & Alam, 2019). On the other hand, environmental regulation could produce an inhibitory effect on green innovation for some firms and industries, by imposing additional costs (Gray &
Shadbegian, 2003). This negative effect could make industries to be transferred to countries with lower environmental standards (Z. Dong et al., 2020), as stated in PHH.
Energy-efficiency, and innovation upsurges in the debate about the effects of environmental regulation on pollution. The large literature about energy consumption states that it is detrimental to the environment, which means that advances in energy- efficiency could be helpful to reduce pollution (Balsalobre-Lorente et al., 2019). Both inflows of clean FDI and investment in R&D with the aim of innovation could provide energy-efficiency gains. As such, energy innovation policies are required, since economic growth cannot improve pollution by itself, and the continued promotion of energy innovation can reduce emissions (Balsalobre-Lorente et al., 2017). Furthermore, innovation can help countries to improve their environmental performance, reducing environmental compliance costs, but also could help countries to boost their sustainable economic growth (Balsalobre-Lorente et al., 2019).
9 Table 1. Literature review framework
Author(s) Period Study area Method Conclusions
Pao & Tsai
(2011) 1980-2007 and 1992- 2007 (Russia)
BRIC (Brazil, Russian Federation, India, and China)
Panel cointegration technique; Multivariate Granger causality
Strong bidirectional causality between emissions and FDI.
Omri et al.,
(2014) 1990-2011 3 regional sub-panels:
Europe and Central Asia, Latin America and the Caribbean; the Middle East and North Africa;
and sub-Saharan Africa.
Dynamic simultaneous-
equation model Bidirectional causality between FDI inflows and CO2 emissions in all panels excluding Europe and North Asia.
Ren et al.,
(2014) 2000-2010 China’s industrial sectors IOA; Two-step GMM FDI inflows deteriorate China’s CO2 emissions.
Sbia et al., (2014)
1975Q1- 2011Q4
United Arab Emirates (UAE)
ARDL bounds testing approach; VECM Granger causality
FDI saves energy consumption; FDI Granger causes green energy.
Seker et al.,
(2015) 1974-2010 Turkey ARDL; VECM based Granger
causality FDI Granger causes CO2 emissions in the long- run; FDI increases pollution but with a small impact.
Shahbaz et
al., (2015) 1975-2012 99 economies worldwide (high-, middle-, and low- income)
FMOLS Inverted-U shaped relationship between FDI and CO2 emissions in the global and middle-income panels; FDI reduces CO2 emissions in high- income countries; FDI increases environmental degradation, confirming PHH, in low-income countries.
Zhu et al., (2016)
1981-2011 Association of South East Asian Nations (ASEAN- 5): Indonesia, Malaysia, the Philippines,
Singapore, and Thailand
Panel quantile regression FDI decreases CO2 emissions in the middle- and high-emissions countries, supporting the pollution halo hypothesis.
Sapkota &
Bastola (2017)
1980-2010 14 Latin American countries (high and low- income countries)
Panel fixed and random effects models
FDI increases CO2 emissions, which validates PHH, for both high- and low-income countries.
Shahbaz et al., (2018)
1955-2016 France Bootstrapping ARDL bounds test
FDI degrades the environment Rafindadi et
al., (2018) 1990-2014 GCC (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates)
Panel ARDL estimation: PM);
MG; and Dynamic fixed effects
While FDI inflows reduce environmental degradation, the energy consumption associated with the FDI inflows can lead to pollution, whereas energy consumption increases it.
Adom et al.,
(2019) 2000-2014 27 African countries GMM Concave effect of FDI on energy consumption;
Benefits from FDI happen faster for countries with higher technology absorptive capacities;
Albulescu et al., (2019)
1980-2010 14 Latin American countries
Panel Quantile regression analysis
FDI has no clear impact on pollution Demena &
Afesorgbor (2019)
Not
applicable 65 primary studies Meta-analysis An inverse relationship between FDI and emissions: an increase in FDI reduces pollution, supporting pollution halo hypothesis for
developed countries; The quality of FDI inflow to developing countries is lower compared to FDI that goes to developed countries, supporting PHH.
Dong et al.,
(2019) 2002-2015 China regions FGLS FDI conserves energy in high-income regions, but there is no evidence supporting that FDI inflows increase energy consumption in low- and middle-income regions.
Dou & Han,
(2019) 2000-2015 30 provinces, municipalities, and autonomous regions (except Tibet) of China.
Dividing industries into strongly and weakly mobile;
Mediation model
Strongly mobile pollution industries tended to be transferred to areas with more relaxed
regulations, which supports PHH.
10 Haug & Ucal
(2019)
1974-2014 Turkey ARDL; Non-linear ARDL FDI increases CO2 emissions in the long-run.
Shahbaz et
al., (2019) 1990-2015 The Middle East and
North African (MENA) GMM N-shaped association is validated between FDI and carbon emissions.
Shen et al.,
(2019) 2001-2014 Guangdong’s 21 administered cities: 9 cities in the Pearl River Delta (PRD), and 12 cities in the Peripheral Non- Pearl River Delta (NPRD) area.
DEA; PMG/ARDL Pollution transfer by the migration of pollution- intensive industries from the PRD to the NPRD region that supports PHH.
Xu et al.,
(2019) 2006-2016 China: east, central, and
west regions STIRPAT model FDI reduces air pollutants.
Bildirici &
Gokmenoglu (2020)
1975-2017 9 countries: Afghanistan, Iraq, Nigeria, Pakistan, Philippines, Syria, Somalia, Thailand and Yemen
Panel cointegration tests;
ANOVA tests, long-run estimators, and panel trivariate Causality tests
FDI causes CO2 emissions in the short-run; In the long-run, there is bidirectional causality between FDI and CO2 emissions; FDI concentration on high-emissions industries.
Essandoh et
al., (2020) 1991-2014 52 countries PMG-ARDL FDI transfer high emission-intensive production units from developed to developing countries, decreasing pollution in the developed countries thus increasing in the developing countries.
Xie et al.,
(2020) 2005-2014 11 emerging countries (Argentina, Brazil, China, India, Russia, South Korea, Mexico, Turkey, Indonesia, South Africa, and Saudi Arabia)
Extended (PSTR) with nonlinear and dynamic features
FDI ascend CO2 emissions concentrations; the Spillover effect through economic growth suggests that FDI can decrease CO2 emissions.
FDI has a “W+V-shaped” temporal effect in carbon emissions.
Zhou et al.,
(2020) 2005-2015 47 cities in the Bohai Rim OLS; GMM; Panel quantile
regression FDI has a positive influence on eco-efficiency (with carbon emissions as the undesired output).
Notes: ARDL- Autoregressive Distributed Lag; FGLS- Feasible Generalized Least Squares; FMOLS- Fully Modified Ordinary Least Squares; GMM- Generalized Method of Moments; IOA- Input-output analysis; MG- Mean Group; OLS- Ordinary Least Squares;
PMG- Pooled Mean Group; PSTR- Panel Smooth transition regression; STIRPAT- Stochastic Impacts by Regression on Population, Affluence and Technology; VECM- Vector error correction model.
To analyze the whole impact of FDI on the environment, it is crucial to consider variables that represent specific characteristics of the host countries, complemented with a transverse consideration of impacts in time. The ARDL is used to divide the impacts into the short- and long-run. To better understand the entry of FDI into host countries, this paper empirically considers the level of innovation, efficiency, and regulation levels in countries, thus filling an important gap in the literature. The source country evaluates specific features of a country before providing investment there; these are considered in this paper.
Countries that want to transfer polluting industries will check if the environmental stringency is lower in the host country. However, countries that want to diffuse global technology access, transferring their green technologies to another country, will check the efficiency, and innovation skills of host countries.
Once the transference of polluting industries happens among countries with different development and income levels, countries are divided into their income levels to improve the inquiry of this transference. Furthermore, the analysis of the industry sector produces better evidence of PHH and could provide a robustness check. Policymakers ought
11
to realize that policies directly related to FDI should be debated considering that they do not have only effects of FDI, but also on the environment.
12
3. Data and methodology
In this work, a panel of 21 countries was studied, namely: Argentina, Austria, Bulgaria, Croatia, the Czech Republic, France, Germany, Greece, Hungary, Ireland, Japan, the Netherlands, Norway, Peru, Philippines, Portugal, Romania, South Africa, Spain, Turkey, and the United Kingdom. The period ranges from 2001 to 2017. The sample was selected according to the availability of data to handle a larger panel as possible. The environmentally related tax revenue variable has a lower availability of low-middle and upper-middle countries, as the industrial production does. These variables reduced the number of countries under scrutiny, mainly related to middle-income countries. The countries were divided into their income levels, high and middle, according to the World Bank’s classification (see more in Table Appendix 1 (A1)). Given the restricted availability of variables to lower-middle-income and upper-middle-income countries, both the upper- and lower-middle countries were combined in the same group.
Table 2. Variables description
Variables Definition Sources
CO2 CO2 emissions in tonnes per capita IEA
CO2I CO2 emissions from industry in million tonnes IEA
FDI FDI inward stock in millions of USD UNCTAD
GFCF Gross fixed capital formation in constant 2010 USD WB
TO Trade as a share of Gross Domestic Product WB
EF Energy efficiency index – Calculated using equation 1 IPI (WB) Energy (IEA)
PAT Patent applications for residents WB
REG Environmentally related tax revenue as a share of Gross Domestic Product OECD stat
The efficiency level of the countries has relevance to evaluate the impacts of FDI, as previously stated. In this work, the energy efficiency index represents the industrial efficiency of the countries and was calculated through equation (1). This concept was developed by Patterson (1996) and reveals how many units of input are necessary to produce an output unit (Marques et al., 2019).
𝐸𝐹 = 𝑂𝑢𝑡𝑝𝑢𝑡 𝐸𝑛𝑒𝑟𝑔𝑦
(1)
Industrial production is used as a proxy of the Industrial Production Index (IPI) to represent the output. Energy consumption from industry in kWh is also used. Recent studies stated that the incorporation of energy consumption in CO2 emissions regressions
13
could produce biased results (Jaforullah & King, 2017), and for that reason, the use of energy efficiency seems to be more suitable.
As a proxy of environmental pollution, CO2 emissions are used as is commonly found in the literature. CO2 emissions from the industry sector represent the environmental pollution derived from the industry sector. Gross fixed capital formation (GFCF) was used as a proxy of a countries’ economic performance. GFCF could be related to capital intensive industries, as an increase in the capital in a production process generally consumes more energy which could, in turn, increase pollution (Sapkota & Bastola, 2017). Trade (TO) performs trade openness as is usual in the literature (e.g., Essandoh et al., 2020; Sbia et al., 2014), and has a higher correlation with FDI, and also Granger Causality, which means that trade could be related to FDI, without a doubt. Environmentally related tax revenue (REG) is used as a proxy for environmental regulation (Hashmi & Alam, 2019). Environmental regulations could stimulate innovation as stated by the literature (Kneller & Manderson, 2012; Lee et al., 2011; Johnstone et al., 2010), which could mean that countries with a high level of innovation do not allow the entry of polluting and inefficient industries. For that, patents (PAT) are used to measure the innovation level of the countries (Burhan et al., 2017).
All numerical variables are converted into per capita, and then into natural logarithms. The variables TO and REG, once they are in a share of Gross Domestic Product, are directly converted into natural logarithms. The descriptive statistics of the variables are shown in Table A2.
3.1. Preliminary tests
Preliminary tests were carried out to assess the presence of multicollinearity, collinearity, and the cross-sectional dependence of variables. To do that, correlation matrices, VIF, and cross-sectional dependence tests were used.
Table 3. Correlation matrices and VIF statistics (high-income countries)
LCO2 LFDI LY LE LTO LPAT LREG DLCO2 DLFDI DLY DLE DLTO DLPAT DLREG
LCO2 1.000 DLCO2 1.000
LFDI 0.044 1.000 DLFDI 0.073 1.000
LY 0.423 0.378 1.000 DLY 0.353 0.229 1.000
LE 0.156 0.472 0.690 1.000 DLE -0.033 0.185 0.154 1.000
LTO 0.069 0.688 -0.111 0.059 1.000 DLTO 0.187 -0.088 0.171 0.192 1.000
LPAT 0.384 -0.245 0.503 0.477 -0.458 1.000 DLPAT 0.080 0.033 -0.143 -0.019 -0.071 1.000
LREG -0.015 0.322 -0.285 -0.402 0.552 -0.482 1.000 DLREG -0.038 -0.083 -0.351 -0.122 -0.054 -0.025 1.000
VIF 4.71 2.68 3.77 3.04 2.48 2.21 1.12 1.28 1.10 1.09 1.03 1.15
VIFMEAN 3.15 1.13
Cross-sectional dependence
CD-test 25.49*** 29.06*** 10.21*** 17.02*** 25.91*** 4.10*** 5.49*** 9.86*** 16.82*** 14.55*** 0.49 22.29*** 0.76 3.79***
14 Table 4. Correlation matrices and VIF statistics (middle-income countries)
LCO2 LFDI LY LE LTO LPAT LREG DLCO2 DLFDI DLY DLE DLTO DLPAT DLREG
LCO2 1.000 DLCO2 1.000
LFDI 0.648 1.000 DLFDI 0.205 1.000
LY 0.648 0.793 1.000 DLY 0.447 0.306 1.000
LE -0.725 -0.214 -0.076 1.000 DLE 0.109 -0.102 0.004 1.000
LTO 0.086 0.197 0.022 0.016 1.000 DLTO 0.157 -0.222 0.058 0.335 1.000
LPAT 0.573 0.192 0.503 -0.271 -0.167 1.000 DLPAT 0.111 -0.011 0.073 -0.016 0.049 1.000
LREG 0.768 0.558 0.725 -0.387 0.183 0.429 1.000 DLREG -0.057 0.080 -0.138 0.031 -0.127 -0.109 1.000
VIF 4.54 8.54 1.90 1.28 1.95 3.37 1.19 1.16 1.14 1.21 1.02 1.06
VIFMEAN 3.60 1.13
Cross-sectional dependence
CD-test 2.37** 17.55*** 15.86*** 4.56*** 2.41** -2.44** 2.54** 2.90*** 5.21*** 6.72*** -0.26 8.94*** -1.13 2.99***
Cross-sectional dependence (CD) must be checked in panel data studies since, if it is present, this means that countries are no longer independent observations, but affect each other’s outcomes; this must be corrected once can produce misleading results as stated by De Hoyos & Sarafidis (2006). The null hypothesis of the cross-sectional dependence test proposed by Pesaran (2004) is cross-sectional independence. Tables 3 and 4 reveal that none of these phenomena are a concern. First-generation unit root tests could be inefficient to assess the order of integration of the variables in the presence of individual CD as announced by Pesaran (2007).
Table 5. Panel Unit Root tests
High income Middle income
CIPS (Zt-bar) Maddala-WU Levin-
Lin-Chu CIPS (Zt-bar) Maddala-WU Levin-Lin- without Chu
trend with
trend without
trend with trend without
trend with
trend without
trend with trend
LCO2 -1.49* 0.29 10.20 37.09* 0.73 2.44 1.41 30.71** 18.18 -1.66**
LCO2I -4.73*** -4.94*** 19.12 103.60*** -1.88** -0.42 0.88 18.19 20.97 -0.93 LFDI 0.35 -1.14 54.96*** 20.04 -4.64*** 0.26 -0.21 51.69*** 15.41 -3.41***
LY 1.44 -1.38* 36.12* 38.29* -3.48*** 1.91 2.19 38.16*** 12.56 -2.76***
LTO 0.28 1.09 20.52 53.63*** -3.03*** 1.16 2.34 15.74 20.86 -1.44*
LEF -2.38*** -1.70** 28.17 78.52*** -2.60*** 0.42 2.23 22.74 20.77 -0.65 LPAT 1.52 0.47 27.34 26.62 -0.36 -0.92 -1.16 13.12 21.61 -1.51*
LREG 2.05 3.09 20.71 19.84 -0.17 -0.27 -1.70** 39.29*** 41.99*** -2.89***
DLCO2 -4.83*** -3.98*** 98.32*** 83.95*** -5.66*** -2.09** -2.53*** 74.83*** 77.95*** -5.62***
DLCO2I -5.94*** -3.70*** 183.33*** 137.26*** -10.10*** -1.69** -3.18*** 60.51*** 44.91*** -5.42***
DLFDI -3.17*** -1.73** 75.21*** 93.35*** -5.47*** -2.51*** -1.26 32.95*** 31.56** -1.76**
DLY -3.18*** -1.35* 73.10*** 44.85** -5.44*** -1.75** -2.18** 29.68** 32.52*** -3.09***
DLTO -1.61* -1.29* 118.27*** 75.00*** -7.93*** -0.53 0.59 64.61*** 45.17*** -3.97***
DLEF -5.41*** -4.44*** 166.31*** 126.11*** -8.62*** -1.33* -0.57 62.10*** 44.19*** -4.11***
DLPAT -3.77*** -3.20*** 105.95*** 91.93*** -5.11*** -3.82*** -2.85*** 69.44*** 51.54*** -5.03***
DLREG 0.11 0.11 53.94*** 41.68** -3.76*** -4.21*** -2.67*** 105.81*** 85.55*** -7.68***
Notes: ***,**, * denote statistical significance at 1%, 5%, 10% level, respectively.
Then, the first-generation unit root tests (Levin et al., 2002; Maddala & Wu, 1999) and the second-generation unit root test cross-sectional augmented IPS (CIPS) (Pesaran, 2007) were carried out as Shahbaz et al., (2015) do (see Table 5), suggesting that all – this means, I(0) - and on their first differences – I(1) –, which reinforce the use of the ARDL model. It is crucial to verify if none of these variables are I(2).
15
3.2. Method
The ARDL model was proposed by Pesaran et al., (2001). The main motivation for the use of this methodology is that it allows an analysis of the dynamic effects of the variables, by analysing effects in the short- and long-run. The specification of the ARDL model is the following:
𝐿𝐶𝑂2𝑖𝑡 = 𝛼1𝑖+ 𝛿1𝑖𝑇𝑅𝐸𝑁𝐷 + 𝛽1𝑖1𝐿𝐶𝑂2𝑖𝑡−1+ 𝛽1𝑖2𝐿𝐹𝐷𝐼𝑖𝑡+ 𝛽1𝑖3𝐿𝐹𝐷𝐼𝑖𝑡−1+ 𝛽1𝑖4𝐿𝐺𝐹𝐶𝐹𝑖𝑡+ 𝛽1𝑖5𝐿𝐺𝐹𝐶𝐹𝑖𝑡−1+ 𝛽1𝑖6𝐿𝑇𝑂𝑖𝑡+ 𝛽1𝑖7𝐿𝑇𝑂𝑖𝑡−1+ 𝛽1𝑖8𝐿𝐸𝐹𝑖𝑡+ 𝛽1𝑖9𝐿𝐸𝐹𝑖𝑡−1+ 𝛽1𝑖10𝐿𝑃𝐴𝑇𝑖𝑡+ 𝛽1𝑖11𝐿𝑃𝐴𝑇𝑖𝑡−1+ 𝛽1𝑖12𝐿𝑅𝐸𝐺𝑖𝑡+ 𝛽1𝑖13𝐿𝑅𝐸𝐺𝑖𝑡−1+ 𝜇1𝑖𝑡
(2)
To capture the dynamic relationships between variables, the equation (2) was reparametrized to the following equation:
𝐷𝐿𝐶𝑂2𝑖𝑡= 𝛼2𝑖+ 𝛿2𝑖𝑇𝑅𝐸𝑁𝐷 + 𝛽2𝑖1𝐷𝐿𝐹𝐷𝐼𝑖𝑡+ 𝛽2𝑖2𝐷𝐿𝐺𝐹𝐶𝐹𝑖𝑡+ 𝛽2𝑖3𝐷𝐿𝑇𝑂𝑖𝑡+ 𝛽2𝑖4𝐷𝐿𝐸𝐹𝑖𝑡+ 𝛽2𝑖5𝐷𝐿𝑃𝐴𝑇𝑖𝑡+ 𝛽2𝑖6𝐷𝐿𝑅𝐸𝐺𝑖𝑡+ 𝛾2𝑖1𝐿𝐶𝑂2𝑖𝑡−1+ 𝛾2𝑖2𝐿𝐹𝐷𝐼𝑖𝑡−1+ 𝛾2𝑖3𝐿𝐺𝐹𝐶𝐹𝑖𝑡−1+
𝛾2𝑖4𝐿𝑇𝑂𝑖𝑡−1+ 𝛾2𝑖5𝐿𝐸𝐹𝑖𝑡−1+ 𝛾2𝑖6𝐿𝑃𝐴𝑇𝑖𝑡−1+ 𝛾2𝑖7𝐿𝑅𝐸𝐺𝑖𝑡−1+ 𝜇2𝑖𝑡
(3)
The prefix “D” represents the first differences of variables, and “L” the natural logarithm. 𝛼 is an intercept. 𝛽 are the short-run coefficients of the explanatory variables, 𝛾 are the long- run outputs, t refers to the analysing period in years, i represents the cross (countries), and 𝜇 is the error term. The LCO2it-1 represents the Error Correction Mechanism (ECM), that is, the long-run coefficient of the lagged dependent variable.
3.3. Diagnostic tests
To avoid biased results, the Robust Hausman test was carried out (see, e.g., Neves et al., 2017) with 20 bootstrap repetitions to check the presence of the individual effects of countries. This test was carried out instead of the traditional Hausman test since it is more appropriate in the presence of heteroscedasticity and/or serial correlation (Neves et al., 2017). The result shows that the fixed effects estimator is suitable to use, and it also highlights the existence of individual effects. Moreover, three diagnostic tests were carried out, upon the residuals, and revealed in this section to go further into the analysis of the data’s characteristics, namely: (i) the Modified Wald test to verify the existence heteroskedasticity with the null hypothesis of homoscedasticity; (ii) the Breusch Pagan LM test to analyse the cross-sectional correlation with the null hypothesis of cross-sectional independence; and (iii) the Wooldridge to test the existence of first-order serial autocorrelation with the null hypothesis of no first-order serial autocorrelation.
16 Table 6. Diagnostic tests
High-income Middle-income
Robust Hausman test 60.88*** 77.80***
Wooldridge test 58.935*** 40.230***
Breusch Pagan LM 176.260*** 32.149
Modified Wald test 47.33*** 33.70***
Notes: *** denotes significance at 1% level. The null hypothesis for the Robust Hausman test is “difference in coefficients is not systematic”.
The existence of cross-section dependence, first-order serial correlation, and heteroscedasticity in the high-income countries model allows the use of the Driscoll & Kraay (1998) estimator (DK), as this estimator produces robust standard errors with these characteristics and allows the utilization of fixed effects (Neves et al., 2017). The DK was also used in the middle-income countries model.
