The moderating role of institutions between FDI and GDP: evidence from China and India

Abstract

Several research efforts were dedicated to analysing the effects of foreign direct investment (FDI) inflows on gross domestic product (GDP) and employment in the host economies. The variations in the conclusions signal that other variables influence and moderate the effects of FDI on GDP and employment. Institutional elements receive limited research attention, despite their influence on the host economies. This paper investigates the moderating role of institutional elements in the FDI-host economies on FDI’s effects on GDP and employment with China and India as case studies. The approach utilises three principal methodologies. The first methodology presents an in-depth analysis of China and India, highlighting selected institutional elements with the potential to influence FDI’s effects. The second methodology confirms the presence of a positive correlation between FDI and GDP and a negative correlation between FDI and employment-to-population ratio (EPR) in both China and India. The FDI, GDP purchasing power parity per capita, and EPR datasets are extracted from the World Bank – DataBank World Development Indicators to ensure the consistency of the data. The results of the quantitative analysis validate the qualitative analysis. The qualitative analysis confirms the moderating role of the selected institutional elements with variations in direction and strength. Significant variations in FDI’s effects on GDP and employment are strongly related to variations in the institutions of governance. The institutions of governance include the functionality of the state organs, the efficiency of the legal system and enforcement of the rule of law, and the quality of implementation of FDI-supportive policies. The findings aim to increase the absorption of the positive effects of FDI on GDP and employment in the respective countries. The research is a cornerstone of in-depth future research into the following areas: the role of selective FDI and constructive conditional FDI policies, the functionality of judicial authority controls, and FDI favourable exchange rates and interest rates policies. The novelty and contribution of the paper lie in its comprehensive exploration of the moderating role of institutional elements on the effects of FDI on GDP and employment in host economies, with a specific focus on China and India. The paper contributes significantly to the academic literature on FDI and economic development by emphasising the importance of institutional factors and providing actionable insights for policymakers and researchers.

Keywords:

• Institutions
• FDI
• GDP
• employment-to-population ratio (EPR)
• China
• India

JEL CODES:

• B22
• F6
• F3
• P48
• P37

1. Introduction

Foreign direct investment (FDI) is the acquisition of an interest in a company by another company or an investor outside the country’s borders (Scott, 2021). This paper revolves around the effect of FDI inflows on the gross domestic product (GDP) and employment in China and India. The paper studies the role of institutions as moderating variables in the relationship between FDI and GDP and between FDI and employment-to-population ratio (EPR). The investigated period is from 1991 to 2020. The paper examines whether institutions hold an influence on the relationship, its direction (positive or negative), and its strength.

GDP and employment are macro-economic indicators displaying the level of development and prosperity. GDP reflects the economy’s overall output, whereas the employment level illustrates the conditions of the labour force.

The GDP is the principal measurement of the value-added of goods and services produced in a country in a specific period (OECD, 2022). The GDP is frequently examined and compared among economies to assess their performance and living standards. Generally, GDP growth is associated with the growth of income levels and increased demand for employment. GDP level is an indicator of the economy’s health and a reference for policymakers and central banks on the required interventions (Callen, 2020; Fegan, 2022; Picardo & Boyle, 2021; Stobierski, 2021). Several researchers have considered the GDP level the primary variable of economic performance and growth (Bertoletti et al., 2022; Hunjra et al., 2022; Misini & Badivuku-Pantina, 2017; Raju et al., 2018; Soytas & Sari, 2003). FDI inflows have the potential to influence the host economy’s GDP. Thus, the paper investigates the relationship between FDI and GDP, and how institutions impact this relationship.

The World Bank defines the EPR as the ‘proportion of a country’s population that is employed’ (The World Bank, 2022b). The working-age population is the population aged 15 years and above. Employment features the supply of human resources (a factor of production) into the economy and the population’s income. Income is a source of domestic demand for goods and services. Hence, employment levels contribute to economic activities and their growth. Socially, employment endorses living standards and facilitates the social welfare of the population (United States Institute of Peace, 2022). Simpson and Walters (2022) inferred that unemployment causes high economic costs, such as rising government payments and unemployment benefits. Accordingly, unemployment leads to decreased GDP levels and inefficient allocation of economic resources. A wide range of research investigated the behaviour of employment levels and their management methods (Falk et al., 2021; Hjazeen et al., 2021; Korkmaz, 2020; Nepram et al., 2021; Piton & Rycx, 2018; Saraireh, 2020). Like GDP, employment may be influenced by FDI inflows into the economy. This paper explores the impact of institutions on FDI’s effect on employment.

North (1991) defines institutions as man-made limits constraining social, political, and economic interactions. Institutions could be seen as systems and frameworks of rules. Institutions create order and reduce uncertainty in exchange. Institutions support defining the choice set in the economy and determining the transaction and production costs. Consequently, they influence the profitability and feasibility of economic activities. Institutions are the blueprints of an economy that construct engagement incentives. Institutions can lead the economy toward growth or stagnation (North, 1991, p. 97). When FDI is injected into an economy, it operates and performs under the structures of the economy, which are managed by rules and constraints. This paper studies how these rules and constraints in China and India impact FDI’s effects on GDP and employment.

The choice of India and China as case studies to explore the moderating role of institutions between FDI and GDP is based on several factors:

1. Significant FDI and GDP impact: India and China are primary recipients of FDI and have experienced substantial economic growth over the past few decades. FDI has dramatically influenced their economies, making them suitable candidates for studying the interplay between FDI, institutions, and GDP.

2. Diverse institutional contexts: India and China have different institutional setups, legal systems, regulatory environments, and governance structures. This diversity allows for a comparative analysis of how institutions in each country influence the relationship between FDI and GDP.

3. Economic size and growth: India and China are the world’s most populous countries with rapidly growing economies. Their size and growth trajectory provide ample data points to analyse the dynamics of FDI, institutions, and GDP.

4. Policy reforms and opening up: Both countries have undergone significant policy changes and reforms to attract FDI and promote economic growth. Studying their experiences can show how these policy changes interact with institutional factors to impact GDP growth.

5. Variation in institutional quality: China and India have varying levels of institutional quality, ranging from property rights protection and contract enforcement to bureaucratic efficiency and rule of law. This variation provides a basis for examining.

6. Policy implications: Understanding how institutions moderate the FDI–GDP relationship in India and China can offer insights into policy decisions to attract FDI, enhance economic growth, and improve institutional quality.

7. Global significance: As significant players in the global economy, the experiences of India and China have implications beyond their borders. Findings from the case studies could contribute to discussions on FDI’s impact on GDP and the role of institutions in other developing and emerging economies.

8. Overall, choosing India and China as case studies provides an opportunity to explore how varying institutional contexts influence the relationship between FDI and GDP, contributing to a deeper understanding of the complex dynamics at play in these economies.

China and India are two consequential emerging economies. They are part of the _BRIC countries, believed to hold global dominance in supplying goods, services, and raw materials by 2050 (Majaski, 2020). China and India attract FDI based on their abundant economic resources and growing product markets. This paper investigates China and India simultaneously to provide a reference country in the analysis. Every country has its own unique sets of institutions and their corresponding constraints. Comparing the results of the analysis between China and India increases the quality of the research, provides a better understanding of each country, and establishes the basis for constructing concrete conclusions on the influence of institutions on FDI’s effects.

In the 1990s, the GDP levels of the two countries were similar. A significant divergence between the two countries throughout the 2000s and 2010s could be noticed, leading to a multiple of around two and a half times the GDP purchasing power parity (PPP) per capita in China (17,210.76 International$) of that in India (6,503.95 International$) in 2020. When compared with other BRIC countries in 2020, Brazil recorded a GDP PPP per capita of (14,835.40 International$), and Russia recorded (29,812.20 International$) (The World Bank, 2022c). India has the lowest GDP PPP per capita, and China has a higher level than Brazil but is lower than Russia. Higher GDP PPP per capita is a goal aimed at every country.

A negative trend in the EPR (%) is detected in both countries. An average yearly percentage change of −0.59% is recorded in China and −0.96% in India between 1991 and 2020. In 2020, the EPR was 66.84% in China and 41.33% in India (The World Bank, 2022a). The negative trend observed in both countries signifies a critical challenge for both economies. Countries seek to increase the EPRs to increase the income and living standards of the population.

The increases in GDP level and EPR in China and India are central macro-economic goals. Overcoming the challenges and achieving a higher level of these indicators would positively impact the population. FDI inflows, a source of capital input, hold the potential to support GDP and employment in China and India.

A fluctuating positive trend of both countries’ FDI net inflows per capita is observed. The maximum value of FDI net inflows per capita of US$213.41 in China was recorded in 2013, whereas in India, the maximum value of US$46.64 was recorded in 2020. In 2020, FDI net inflows per capita recorded US$150.59 in China (The World Bank, 2022a).

The percentage of FDI inflow (per capita) to the GDP (per capita) reflects the relative volume of the FDI to the size of the economy. FDI percentages in both countries could be described as erratic. China recorded the highest percentage of 2.02% in 2011, and India recorded the highest percentage of 0.99% in 2008 (The World Bank, 2022a).

Throughout most of the period (1991 to 2020), the FDI in China was significantly greater than in India (to the extent of multiples). In relevant terms, FDI in China and India could be proximate by considering the size of the economy.

In 2020, the FDI-to-GDP percentage recorded a value of 0.87% in China and 0.72% in India. Based on UNCTAD’s (2021) data for 2020, developing economies recorded an average FDI-to-GDP percentage of 2.0%, whereas developed economies recorded an average value of 0.7% (UNCTAD, 2021). In 2020, the relative values of FDI to the size of the market (GDP) in India and China will be lower than the average of developing economies. This echoes the potential of attracting higher levels of FDI in both countries and raises the question of the reasons behind such below-average values of FDI-to-GDP percentages in China and India.

Several research efforts were dedicated to the analysis of the effects of FDI inflows on GDP (Agrawal & Khan, 2011; Falk et al., 2021; Su & Zhiqiang, 2016) and employment (Mishra & Palit, 2020; Tsaurai, 2018; Zdravkovic et al., 2017) in the host economies. Significant variations in the conclusions of econometric research are detected regarding GDP (Haydaroglu, 2016; Herzer, 2010; Iamsiraroj & Ulubaşoğlu, 2015; Nadar, 2021) and regarding employment (Liu, 2011; Mucuk & Demirsel, 2013; Rizvi & Nishat, 2009; Wei, 2013). The variations in the conclusions signal that other variables influence and moderate the effects of FDI on GDP and employment. Consequently, this posits the need for a systematic study around complementary thinking and methodology to fill the gap and empower the analysis. Institutional elements receive limited research attention, despite their influence on the host economies.

Thus, the paper aims to create an analysis framework of the leading institutional elements moderating the effects of FDI on GDP and employment in China and India. Additionally, this paper seeks to generate a list of findings to increase the positive effects of FDI in host economies.

Based on the research gap and the objectives, two main research questions are constructed:

• How do selected institutional elements and their conditions moderate FDI’s effects on GDP and employment in China?

• How do selected institutional elements and their conditions moderate FDI’s effects on GDP and employment in India?

To answer the research questions, the authors adopted three main research methods:

• analysis of institutions in China and India based on institutional theories;

• quantitative analysis of correlation measurements between the FDI per capita, GDP PPP per capita, and EPR;

• qualitative analysis based on guided semi-structured experts’ interviews about selected institutional elements in China and India, and their moderating role on FDI’s effects on GDP and employment.

This research aims to create an analysis framework of the leading institutional elements moderating the effects of FDI on GDP and employment in China and India. Additionally, this paper seeks to generate a list of findings to increase the positive impact of FDI in host economies. The paper contributes to the literature in several ways. Theoretically, it merges the econometric research of correlation analysis (FDI, GDP, and EPR) and the institutional theories-based analysis. The paper tries to fill the gap of unspecific or vivid inclusion and presentation of FDI in institutional-based theories and research and the limited elaborated impact of institutions on FDI’s effects in the host economies.

The novelty and contribution of the paper lie in its comprehensive exploration of the moderating role of institutional elements on the effects of FDI on GDP and employment in host economies, with a specific focus on China and India. Here is a breakdown of its novelty and contribution:

1. Addressing a gap in existing literature: The paper identifies a gap in the existing research, which often overlooks the influence of institutional elements on the relationship between FDI, GDP, and employment. By bringing attention to this aspect, the paper contributes to a more holistic understanding of the dynamics involved in FDI-led economic growth.

2. Utilisation of multiple methodologies: The paper employs a mixed-method approach, utilising both qualitative and quantitative methodologies. This multi-faceted approach enhances the robustness of the findings and allows for a deeper exploration of the subject matter.

3. Empirical analysis with real-world data: The study utilises real-world data from reputable sources such as the World Bank – DataBank World Development Indicators. This empirical analysis adds credibility to the findings and provides tangible evidence supporting conclusions.

4. Insights into the moderating role of institutional elements: By examining specific institutional elements such as state functionality, legal system efficiency, and policy-implementation quality, the paper sheds light on how these factors moderate the impact of FDI on GDP and employment. This nuanced understanding can inform policymakers and practitioners on strategies to enhance the positive effects of FDI.

5. Implications for Policy and Future Research: The paper’s findings directly affect policy formulation aimed at maximising the benefits of FDI on economic growth and employment generation. Furthermore, by identifying areas for future research, such as the role of selective FDI policies and judicial controls, the paper paves the way for continued scholarly inquiry into this critical field.

Overall, the paper contributes significantly to the academic literature on FDI and economic development by emphasising the importance of institutional factors and providing actionable insights for policymakers and researchers.

2. Literature review

This chapter sets the grounds for the empirical research by presenting a literature review of recent publications on the effects of FDI inflows on GDP (section 2.1) and employment (section 2.2). Likewise, section 2.3 introduces institutional theories that expand on institutions’ role in economic performance.

2.1. FDI and GDP

This section presents the conclusions of published research on the relationship between FDI inflows and the GDP of the host economies. A wide variation in the conclusions is detected.

A study concluded that FDI has an average negative effect on economic growth. Herzer’s (2010) research on 44 developing countries from 1970 to 2005 concluded an average negative effect of FDI on GDP growth. Herzer (2010) indicated that FDI’s positive effects are related to high levels of freedom from government interventions and business regulations. At the same time, FDI’s adverse effects are related to high FDI volatility (political and economic instability) and high host economic dependence on natural resources (Herzer, 2010, pp. 27–28).

On the other hand, a study by Chaudhury et al. (2020) concluded that FDI significantly positively impacts economic growth (GDP growth). Chaudhury et al.’s (2020) research investigated eight South Asian economies (India, Bangladesh, Sri Lanka, Afghanistan, Bhutan, Nepal, Pakistan, and the Maldives) from 1990 to 2014. The authors stress the importance of governmental policies in directing and channelling the FDI to obtain maximum benefits for economic growth (Chaudhury et al., 2020, p. 66).

Other explorations inferred a positive impact of FDI on economic growth. Haydaroglu’s (2016) study detected a positive impact of FDI in BRIC countries (1995–2013). The author noted that governments should develop policies that ignite the positive development impact of FDI and should not be limited to policies of attracting FDI.

Although the Iamsiraroj and Ulubaşoğlu’s (2015) research on 140 countries from 1970 to 2009 suggested that FDI generates economic growth, the research detected variations in the effect of FDI within regions (group of countries) and variations within countries. The authors note that the positive effects of FDI on economic growth depend significantly on the economy’s absorptive capacity. Moreover, financial development, trade openness, and contemporaneous (not lagged) FDI contribute strongly to economic growth.

Regarding the Chinese economy, Su and Zhiqiang (2016) highlighted the positive effects of FDI on the GDP per capita growth rate (1991 and 2010). Su and Zhiqiang (2016) elaborated that the effects of FDI are intensified by the human capital endowment and stressed the importance of policies toward developing human capital to promote higher absorption of FDI’s advantages.

Divergence in FDI’s effects on GDP is observed between the long-term and the short-term. Nadar’s (2021) inspection of the FDI’s impact on GDP per capita in India between 1970 and 2019 indicated a long-run causality from FDI to GDP per capita. It denied a short-term causality between FDI and GDP per capita. The inspection emphasised the long-term effect of FDI on GDP growth in India.

Agrawal and Khan’s (2011) comparative study on FDI’s effect on economic growth in China and India (between 1993 and 2009) has found a higher FDI effect in China than in India. The authors provided several reasons for the differences in FDI’s effect, such as the Chinese developed infrastructure, cost-effectiveness elements, and the supportive macro-economic climate. On the other hand, India has a capable management system, better conditions of the rule of law, and a transparent work system (Agrawal & Khan, 2011, p. 76).

Previous research and papers have emphasised the significant variability of FDI’s effects on GDP. FDI’s effects on GDP can be adverse (Herzer, 2010), can be positive (Chaudhury et al., 2020; Haydaroglu, 2016), can vary among countries (Agrawal & Khan, 2011), can vary within regions and countries (Iamsiraroj & Ulubaşoğlu, 2015), and can diverge among different investigated periods (long-term vs short-term) (Nadar, 2021). Furthermore, several authors indicated that the variations in FDI’s effects on GDP are connected to differences in the governance systems, policies management, and business ecosystems environment of the host economies (Agrawal & Khan, 2011; Chaudhury et al., 2020; Haydaroglu, 2016; Herzer, 2010; Iamsiraroj & Ulubaşoğlu, 2015; Su & Zhiqiang, 2016).

We also need to mention how the latest health, economic, and geopolitical crises can influence FDI:

1. Health crises (e.g., pandemics) can have significant effects on FDI owing to disruptions in global supply chains, reduced economic activity, and uncertainty:

   • Supply chain disruptions: Restrictions on movement, lockdowns, and quarantines can disrupt supply chains, affecting the production and distribution of goods and services. This can lead to reduced profitability and uncertainty for foreign investors.

   • Economic downturn: Health crises often lead to economic downturns as consumer spending decreases and businesses struggle. These conditions may deter foreign investors from committing capital to a country with uncertain economic prospects.

   • Uncertainty: The uncertainty surrounding the duration and impact of a health crisis can make long-term investment decisions more challenging. Investors may delay or reconsider their investment plans until the situation stabilises.

2. Economic crises: Economic crises, such as recessions or financial meltdowns, can impact FDI in several ways:

   • Market volatility: Economic crises often increase market volatility, making investors more cautious and risk-averse. This can lead to a decrease in FDI as investors seek safe options.

   • Capital availability: During economic downturns, access to capital becomes limited. Foreign investors need help obtaining project financing, leading to reduced FDI.

   • Asset valuations: Economic crises can lead to reduced asset valuations. While this might present investment opportunities, it can raise concerns about investing in assets that could further depreciate.

3. Geopolitical crises: Geopolitical crises, such as conflicts, trade disputes, or sanctions, can have complex effects on FDI:

   • Country risk profile: Geopolitical instability can change a country’s risk profile. Investors might perceive higher political and regulatory risks, leading to decreased FDI.

   • Trade disruptions: Trade disputes and sanctions can disrupt cross-border trade, impacting the operations of multinational companies and making long-term investments less appealing.

   • Policy uncertainty: Geopolitical tensions can change government policies and regulations. Investors might hesitate to commit funds if uncertain about the regulatory environment’s stability.

In response to these crises, governments often implement measures to attract or retain FDI:

• Incentives: Governments might offer incentives such as tax breaks, subsidies, or regulatory easing to encourage foreign investment during crises.

• Infrastructure investment: Governments may prioritise infrastructure projects to stimulate economic activity, making the country more attractive to foreign investors.

• Reforms: Crisis periods could prompt governments to enact structural reforms to improve their business environment, which can positively impact FDI.

• Stability measures: Countries demonstrating political stability and effective crisis management may be more resilient to FDI declines.

2.2. FDI and employment

Similarly, research on FDI’s effects on employment delivers various conclusions and results. Some studies elaborated that FDI has an insignificant effect on employment. Zdravkovic et al. (2017) tested the impact of FDI per capita on the unemployment rate in 17 transition countries from 2000 to 2014. The paper concluded that there is a weak or no linkage between FDI and unemployment in the long run.

Other studies have concluded a significant relationship between FDI and employment in the long run. Mucuk and Demirsel’s (2013) research on the FDI’s effects on unemployment in seven developing countries (Argentina, Chile, Colombia, Philippines, Thailand, Turkey, and Uruguay) from 1981 to 2009 expressed that unemployment rates and FDI are cointegrated in the long run. FDI significantly increases unemployment in Turkey and Argentina, and reduces it in Thailand. The authors highlighted that a causal relationship between FDI and unemployment is detected in the long run but not in the short run.

FDI’s effects on employment further vary among economic sectors. Wei’s (2013) paper on China (from 1985 to 2011) identified a significant positive relationship between FDI and employment in the overall economy. However, regarding sectoral employment, a significant positive relationship was found in the primary sector, a significant negative relationship was detected in the tertiary sector, and no significant relationship was detected in the secondary sector. Additionally, variations based on the investigated period were discovered. Liu (2011) noted that in China, on a long-run basis, the growth of FDI promotes employment in the secondary and tertiary industries. On the other hand, in the short term, FDI has a constraining and even negative effect on total employment.

Mishra and Palit (2020) deduced that FDI inflows in India have not been considered the main factor in employment growth throughout the past two decades. Likewise, Rizvi and Nishat (2009) revealed that FDI has no statistically significant impact on employment creation in Pakistan, India, and China (1985 to 2008). Rizvi and Nishat (2009) stressed that it should not be expected from FDI to create employment opportunities. However, FDI-related policies should complement other policies that stimulate employment growth. Furthermore, Tsaurai (2018) found that high economic growth, human capital, and financial development support FDI’s positive effects on employment generation in BRIC countries.

Based on the literature review, equivalently, FDI’s effects on employment could be significant (Mucuk & Demirsel, 2013), could be insignificant (Rizvi & Nishat, 2009; Zdravkovic et al., 2017), could vary among economic sectors (Wei, 2013), and could vary among different investigated periods (Liu, 2011). In addition, several publications brought attention to the role of government policies, business infrastructure, and human resource and labour force management in influencing the FDI’s effects on employment (Rizvi & Nishat, 2009; Tsaurai, 2018; Wei, 2013).

2.3. Theories of institutions

An OECD overview report (2002) stated that ‘the net benefits from FDI do not accrue automatically, and their magnitude differs according to the host country and context’ (OECD, 2002, p. 21). As indicated, variations in the FDI benefits are based on the economic environment of the host country and its context (structures). Institutions reflect the systems under which FDI operates in the host country. Consequently, this proposes the investigation of the institutions present in the host economies and their role in moderating FDI’s effects on GDP and employment.

First, a tool is required to identify the primary levels of institutions in China and India to investigate the role of institutions in moderating FDI’s effects on GDP and employment. The ‘four levels of social analysis’ framework introduced by Williamson (2000) is utilised for this aim. After that, three institutional theories will be introduced and elaborated to analyse and investigate the different institutions. The three institutional theories are as follows:

• North (1991) investigates the extent of informal institutions’ influence on the economic system;

• Acemoglu et al. (2005) emphasise the influence of the political institutions on the economic institutions;

• Acemoglu and Robinson (2012) distinguish between inclusive and extractive political and economic institutions.

The ‘four levels of social analysis’ framework and the three intuitional theories are utilised to analyse the institutions in China and India. Furthermore, the analysis is employed to construct a list of selected institutional elements. Experts’ interviews will explore the moderating role of the selected institutional elements on FDI’s effects on GDP and employment. The experts’ interviews are a core part of the qualitative empirical analysis of this paper.

2.3.1. Four levels of social analysis

Williamson introduces the ‘four levels of social analysis’ framework (Table 1). The framework identifies societal institutions, categorises them into four levels of distinctive characteristics, and elaborates on their interrelations (Williamson, 2000, pp. 596–600).

Table 1. Four levels of social analysis framework.

Level	Characteristics
Level 1: Social embeddedness (informal rules)	The first level of institutions is the informal constraints influencing the social exchanges. Informal constraints in society include customs and tradition, norms and morals, religion, and other cultural dimensions. Social embeddedness elements constitute how societies conduct themselves. Institutions of the first level are often seen as a given, owing to their spontaneous origin and significantly slow-changing pace.
Level 2: Institutional environment	The second level of institutions is the institutional environment. The institutional environment manifests the formal rules in society: the constitution, the laws and regulations, and the rights and duties. The second level’s elements are constrained by the elements of the first level (social embeddedness). The institutional environment resembles the formal rules of the game. The institutional environment constitutes the executive, legislative, judicial, and government bureaucratic functions and their associated power. Core features of the institutional environment are the definition and enforcement of contract laws and private property rights. A private enterprise system can only function with the presence and protection of property rights. Acemoglu and Robinson (2012) point out that secure property rights provide incentives for investment. Property rights should be provided to the board base of the society (Acemoglu & Robinson, 2012, p. 75). Changes in the second level of institutions require a defining moment to occur. Such moments include economic crises, wars, coups, etc.
Level 3: Institutions of governance	The third level of institutions is the ‘institutions of governance’. Such institutions are dedicated to aligning the governance structures with the transactions in society. The management, functionality, and costs of the legal system, which defines contract laws and enforces contracts, are the main elements of the institutions of governance. Institutions of governance resemble the play of the game (enforcement). Contractual relations (ex-ante and ex-post contract conditions), dispute settlement, and non-compliance management are core aspects of the institutions of governance (play of the game). The conditions of the court system may act as incentives for contracting or repulsion from it. An inefficient court system would decrease the value of the whole contracting system.
Level 4: Resource allocation and employment	The fourth level of institutions is resource allocation and employment. Williamson (2000) relates this level to the ‘neoclassical analysis’. Companies (business entities) at this level perform, under the constraints of the first, second, and third levels of institutions, as production functions (input to output). At the fourth level of institutions, prices and quantities adjust at a high frequency (continuously).

Source: Williamson (2000).

The ‘four levels of social analysis’ framework will dissect the institutions in China and India.

2.3.2. Two institutional frameworks

A core dimension of analysing the moderating role of institutions on FDI’s effect is investigating the extent of informal institutions’ influence on the economic system and transactions.

North (1991) distinguishes between two institutional frameworks, one primitive and the other complex. The first framework holds the primitive ‘personalistic’ relationships as cornerstones of economic and political exchanges. The second framework allows for complex ‘impersonal’ exchanges that capture the potential economic benefits of modern technology and back up political stability. The first framework may produce economic growth but would need to be more consistent and short-term. Moreover, the first framework would not result in economic or political stability or the realisation of the potential of modern technology (North, 1991, p. 111).

The establishment of a second framework requires three main cornerstones. The three cornerstones enable the complex, impersonal exchange and limit the primitive personalistic (relationships) exchanges:

1. development of institutional innovations;

2. setting constraints on the state’s arbitrary actions;

3. institutionalisation process of the state’s debt circulation.

To sum up, constituting a complex, impersonal exchange based on formal laws reduces the influence of informal rules and supports economic growth and prosperity. Institutional innovations, constraints on the state’s arbitrary actions, and the institutionalisation of government debt catalyse economic efficiency in resource management. ‘Evolution is a consistent story of incremental change induced by the private gains to be realised by productivity-raising organisational and institutional changes’ (North, 1991, p. 108).

2.3.3. A theory of political institutions

The institutional framework considers ‘the political institutions’ and ‘the distribution of resources’ as its core variables, as they are society’s sources of political power. Acemoglu et al.’s (2005) institutional framework is illustrated in Figure 1.

Figure 1. Institutional framework.

Source: Adapted by the authors from Acemoglu et al. (2005, p. 392).

Acemoglu et al. (2005) stress that economic development and growth, based on higher efficiency of economic activities, are attained when:

• political institutions allocate power to a broad society interested in board-based property rights and investment opportunities;

• the political institutions create practical constraints on political power holders (a functional system of checks and balances against misuse of power and private gains);

• there are relatively low rents to be captured by powerholders (which ensures the security of property rights).

Additionally, a fundamental dimension of analysing the moderating role of institutions on FDI’s effects is the distinction between the two forms of institutions, the inclusive and extractive. The distinction is laid out by Acemoglu and Robinson (2012). Inclusive and extractive institutional forms are found in political and economic institutions.

Acemoglu and Robinson (2012) indicate that economic growth and development are possible under extractive political institutions in two cases:

1. Economic growth occurs when resources are allocated to high-productive economic activities owned by the political power elites.

2. Economic growth arises when the extractive political institutions permit the development of semi-inclusive economic institutions. These limited inclusive economic institutions are permitted only when there are sufficient securities that the power of the extractive political institutions will not be affected by the growth.

However, these two cases do not lead to sustainable long-term growth or creative destruction.

Inclusive political and economic institutions collectively lead to a better allocation of economic resources, higher economic efficiency and productivity, and higher incentives for innovation. All these elements increase the probability of creative destruction (the utmost potential of the economy). An overview of the employed institutional theories is presented in Figure 2.

Figure 2. Institutional theories overview.

Source: own illustration, based on Acemoglu et al. (2005), Acemoglu and Robinson (2012), North (1991) and Williamson (2000).

2.3.4. FDI and institutions

Few researchers have examined the influence of institutions on the effects of FDI. Wang et al.’s (2013) research on the Chinese cities’ economies (1999–2005) has assessed the positive and negative impacts of FDI and the role of local institutional development in moderating FDI’s impacts. The paper underlined the presence of both positive and negative impacts of FDI. The research concluded on the positive impacts of FDI on economic growth, labour productivity, and innovation. At the same time, FDI’s negative impacts on employment levels and pollution were revealed.

Arshad’s (2019) analysis of 104 countries from 1996 to 2015 concluded that better institutional quality in low- and middle-income countries enhances FDI-led economic growth. However, despite the country’s institutional quality, FDI slows down the economic growth in high-income economies (Arshad, 2019). Similarly, Roy and Roy’s (2016) paper on the Middle East and North Africa Region from 2006 to 2012 disclosed that supportive institutional elements positively impact economic growth in conjunction with FDI.

Elkomy et al. (2015) researched 61 emerging and developing countries from 1989 to 2013 to examine the effect of political development.¹ Scores on FDI-led growth varied significantly under three political regimes² of democracies, hybrid democracies, and authoritarianism. The paper stated that political development in conjunction with FDI under authoritarian regimes tends to suppress FDI-led growth. An opposite tendency was observed in hybrid democracies, as political development supports FDI-led growth. In more democratic countries, the effect of FDI-led growth appears insignificant (Elkomy et al., 2015).

Conclusions on the influence of institutions on FDI’s effect emphasised several aspects. Host economic institutions’ development moderates FDI’s impact (Wang et al., 2013). Supportive institutional elements impact economic growth in conjunction with FDI (Roy & Roy, 2016). Adequate policies and regulations strengthen the positive effect of FDI and weaken the adverse effects (Wang et al., 2013). FDI inflows and institutional quality endorse economic growth (Arshad, 2019). Variations in FDI-led growth are based on political development scores among different political regimes (Elkomy et al., 2015).

3. Empirical research

A two-tier empirical research structure is constructed on quantitative and qualitative analysis to answer the main research questions on how selected institutional elements and their conditions moderate FDI’s effects on GDP and employment in China and India. The two-tier research structure is employed owing to the nature of the investigated relationships. The first set of the investigated relationships is between FDI and GDP, and between FDI and employment. These relationships allow for quantitative analysis as quantitative variables represent them. The variables are measured, and the World Bank publishes their respective values. The quantitative analysis tests the relationship’s presence, direction, and strength between FDI and GDP, and between FDI and employment. The second set of investigated relationships is the moderation role of instructional elements on the FDI’s effects on GDP and employment. A qualitative analysis is suitable for investigating such relationships. Institutional elements are rules, constraints, and structures in society. The conditions of institutional elements and their impact on FDI’s effects are better suited for qualitative rather than for quantitative analysis. The qualitative analysis seeks to explore the presence, direction, and strength of the moderation role of institutional elements on FDI’s effects. The results of the qualitative analysis could be modelled.

The first research structure tier consists of correlation measurements between FDI net inflows per capita as an independent variable, GDP PPP per capita, and EPR as the dependent variables. The second research structure tier investigates how selected institutional elements and their conditions in China and India moderate FDI’s effects on GDP and employment. The selection of the institutional elements is based on the analysis of institutions in China and India based on the institutional theories. The second research tier consists of the qualitative analysis of guided and semi-structured interviews with related experts.

The empirical research combines two principal research methodologies. The quantitative and qualitative methods complement each other and ensure a broad scope of investigation. The econometrics analysis previews the numeral relationships of FDI on GDP and EPR. At the same time, the institutional analysis elaborates on the moderating role of institutional elements in relationships. The two research methods increase the reliability and validity of the research.

3.1. Quantitative analysis

The conceptual framework of the research is based on the conceptual framework presented by Wang et al. (2013) and depicted in Figure 3. A customised conceptual framework for the investigated variables is constructed. It presents the core investigation of the relationship between the independent variable of FDI net inflows per capita and the dependent variables of GDP PPP per capita and EPR. The moderating variables are the institutional elements derived from the institutional theories. The controlled variables are the economic conditions reflected by emerging markets. The selected timeframe of the research is 1991 to 2020. The year 1991 resembles the year of India’s economic reform initiation and the adoption of the Liberalisation, Privatisation, and Globalisation policy. India’s 1991 economic reform resulted in igniting significant inflows of FDI. China has been attracting FDI more significantly since a previous period (Chinese economic reform of 1978).

Figure 3. Conceptual framework.

Source: own illustration.

The datasets of FDI net inflow per capita, GDP PPP per capita, and EPR are extracted from the World Bank – DataBank World Development Indicators (The World Bank, 2022a) to ensure the consistency of the data.

Table 2 presents the operationalisation of variables. All the variables are extracted as yearly values from the databases, except the FDI net inflows (owing to the availability of total FDI). The FDI net inflow per capita values were calculated by dividing the total value of FDI net inflows by the total population of the corresponding year. Performing the calculations on yearly values ensures the calculations’ equivalence and validity.

Table 2. Operationalisation of variables.

Variable and units	Characteristics
FDI per capita (current US$)	Yearly net inflows in current US$ (1991–2020) Per capita values are calculated by dividing FDI net inflows by the total population of the corresponding year.
GDP PPP per capita (current International$)	Yearly values of current International$ (1991–2020)
EPR (%)	Yearly total employment-to-population ratio (%) (1991–2020) (age: 15+) (modelled International Labour Organisation (ILO) estimate)
FDI per capita percentage change (%)	Percentage change in FDI per capita yearly net inflows in current US$ (1992–2020)
GDP PPP per capita percentage change (%)	Percentage change of GDP PPP per capita yearly values of current International$ (1992–2020)
EPR percentage change (%)	Percentage change of total yearly employment-to-population ratio (%) (1992–2020)

Source: own illustration.

Accordingly, correlation measurements are constructed between FDI net inflows per capita as an independent variable, GDP PPP per capita, and EPR as dependent variables. Correspondingly, hypotheses for the correlation measurements are constructed. Based on previous research and consolidated practice (Crescenzi et al., 2022; Wang et al., 2013), the correlation relationships are also explored while considering a ‘1-year lag’ of the effect of FDI per capita on GDP per capita and EPR. Accordingly, hypothesis groups 3 (China) and 4 (India) were constructed, investigating the same correlations but considering a 1-year lag of the FDI’s effect. Table 3 displays two hypothesis groups.

Table 3. Hypothesis groups 1 and 2.

Hypothesis group 1: China (without 1-year lag)

H1a: There is a correlation between the FDI per capita and GDP PPP per capita in China.

H1b: There is a correlation between the FDI per capita percentage change and GDP PPP per capita percentage change in China.

H2a: There is a correlation between the FDI per capita and the EPR in China.

H2b: There is a correlation between the FDI per capita and the EPR percentage change in China.

Hypothesis group 2: India (without 1-year lag)

H3a: There is a correlation between the FDI per capita and GDP PPP per capita in India.

H3b: There is a correlation between the FDI per capita percentage change and GDP PPP per capita percentage change in India.

H4a: There is a correlation between the FDI per capita and the EPR in India.

H4b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in India.

Hypothesis group 3: China (1-year lag)

H5a: There is a correlation between the FDI per capita and GDP PPP per capita in China, considering a 1-year lag of the FDI per capita effect.

H5b: There is a correlation between the FDI per capita percentage change and GDP PPP per capita percentage change in China, considering a 1-year lag of the FDI per capita effect.

H6a: There is a correlation between the FDI per capita and EPR in China, considering a 1-year lag of the FDI per capita effect.

H6b: There is a correlation between the FDI per capita and the EPR percentage change in China, considering a 1-year lag of the FDI per capita effect.

Hypothesis group 4: India (1-year lag)

H7a: There is a correlation between the FDI per capita and GDP PPP per capita in India, considering a 1-year lag of the FDI per capita effect.

H7b: There is a correlation between the FDI per capita percentage change and GDP PPP per capita percentage change in India, considering a 1-year lag of the FDI per capita effect.

H8a: There is a correlation between the FDI per capita and the EPR in India, considering a 1-year lag of the FDI per capita effect.

H8b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in India, considering a 1-year lag of the FDI per capita effect.

Source: own illustration.

The correlation matrix presents the eight pairs of variables (A and B) to which the correlation tests would be applied. Sixteen correlation tests will be performed. The first eight correlation tests are dedicated to hypothesis groups 1 and 2 (without considering the 1-year lag of the FDI effect), and the second eight correlation tests are dedicated to hypothesis groups 3 and 4 (considering the 1-year lag of the FDI effect). Shapiro–Wilk tests are conducted to test for the normality of the variables. Most variables’ distributions deviate (do not follow a normal distribution). Accordingly, Spearman’s rank correlation rho test is applied to the investigated variables to test the correlation relationship.

Tables 4 and 5 display the results; Table 4 presents the interpretation table for Spearman rank correlation coefficients (r) adapted from Leclezio et al. (2015), and Table 5 presents the key to the colour-coding of the correlation test conclusions.

Table 4. Interpretation table for Spearman rank-order correlation coefficients.

r	≥0.70	0.40–0.69	0.30–0.39	0.20–0.29	0.01–0.19
Correlation strength	Very strong	Strong	Moderate	Weak	No/negligible

Source: Based on Leclezio et al. (2015, p. 18).

Table 5. Essential colour-coding for the correlation test conclusion.

Color
Correlation conclusion	Very strong positive	Strong positive	Weak positive	No/ negligible	Weak negative	Strong positive	Very strong negative

Source: own illustration.

Table 6 shows the correlation test between the variables (A) and (B), the correlation coefficient, the p-value, the significance level (considering a 5% significance level), and the correlation conclusion. When analysing the significance, direction, and strength of the correlation relationships in China, highly significant (p < 0.01) positive correlations are observed between (FDI and GDP: very strong) and (FDI % change and GDP % change). A highly significant (p < 0.01) negative correlation is observed between (FDI and EPR: very strong). Moreover, an insignificant negative correlation is observed between FDI % change & EPR % change (weak).

Table 6. Spearman’s rank correlation rho.

Variable A	Variable B	r	p-value	Significance (5% significance level)	Correlation conclusion
China (without 1-year lag)
FDI per capita	GDP PPP per capita	0.90	<.01	Highly significant	Very strong positive
FDI per capita % change	GDP PPP per capita % change	0.68	<.01	Highly significant	Strong positive
FDI per capita	EPR	−0.90	<.01	Highly significant	Very strong negative
FDI per capita % change	EPR % change	−0.23	.22	Insignificant	Weak negative
India (without 1-year lag)
FDI per capita	GDP PPP per capita	0.94	<.01	Highly significant	Very strong positive
FDI per capita % change	GDP PPP per capita % change	−0.14	.56	Insignificant	No/negligible
FDI per capita	EPR	−0.90	<.01	Highly significant	Very strong negative
FDI per capita % change	EPR % change	0.23	.22	Insignificant	Weak positive

Source: own illustration.

When analysing the significance, direction, and strength of the correlation relationships in India, a highly significant (p < 0.01) positive correlation is observed between FDI and GDP (very strong). A highly significant (p < 0.01) negative correlation is observed between FDI and EPR (very strong). In addition, an insignificant positive correlation is observed between FDI % change and EPR% change (weak). At the same time, an insignificant no/negligible correlation is observed between FDI % change and GDP % change.

Table 7 shows the correlation test while considering the 1-year lag of the FDI effect between the variables (A) and (B), the correlation coefficient, the p-value, the significance (considering a 5% significance level), and the correlation conclusion. When analysing the significance, direction, and strength of the correlation relationships in China, highly significant (p < 0.01) positive correlations are observed between FDI and GDP (very strong) and FDI % change and GDP % change. A highly significant (p < 0.01) negative correlation is observed between FDI and EPR (very strong). Moreover, an insignificant negative correlation is observed between FDI % change and EPR % change (weak).

Table 7. Spearman’s rank correlation rho: 1-year lag.

Variable A	Variable B	r	p-value	Significance (5% significance level)	Correlation conclusion
China (1-year lag)
FDI per capita	GDP PPP per capita	0.91	<.01	Highly significant	Very strong positive
FDI per capita % change	GDP PPP per capita % change	0.56	<.01	Highly significant	Strong positive
FDI per capita	EPR	−0.91	<.01	Highly significant	Very strong negative
FDI per capita % change	EPR % change	−0.077	.70	Insignificant	No/negligible
India (1-year lag)
FDI per capita	GDP PPP per capita	0.93	<.01	Highly significant	Very strong positive
FDI per capita % change	GDP PPP per capita % change	0.068	.73	Insignificant	No/negligible
FDI per capita	EPR	−0.90	<.01	Highly significant	Very strong negative
FDI per capita % change	EPR % change	0.088	.65	Insignificant	No/negligible

Source: own illustration.

When analysing the significance, direction, and strength of the correlation relationships in India, a highly significant (p < 0.01) positive correlation is observed between FDI and GDP (very strong). A highly significant (p < 0.01) negative correlation is observed between FDI and EPR (very strong). In addition, insignificant no/negligible correlations are observed between (FDI % change and GDP % change) and (FDI % change and EPR% change).

3.1.1. Results

Based on the calculations and data analysis, the outcome of hypotheses testing for groups 1 and 2 is presented in Table 8.

Table 8. Outcome of hypothesis testing for groups 1 and 2.

Hypothesis group 1: China (without 1-year lag)	Relationship’s description	Judgement
H1a: There is a correlation between the FDI per capita and GDP per capita in China	Very strong positive	Accepted
H1b: There is a correlation between the FDI per capita percentage change and GDP per capita percentage change in China	Strong positive	Accepted
H2a: There is a correlation between the FDI per capita and the EPR in China	Very strong negative	Accepted
H2b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in China	Weak negative	Rejected
Hypothesis group 2: India (without 1-year lag)	Relationship’s description	Judgement
H3a: There is a correlation between the FDI per capita and GDP per capita in India	Very strong positive	Accepted
H3b: There is a correlation between the FDI per capita percentage change and GDP per capita percentage change in India	No/negligible	Rejected
H4a: There is a correlation between the FDI per capita and EPR in India	Very strong negative	Accepted
H4b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in India	Weak positive	Rejected

Source: own illustration.

Moreover, the outcomes of hypotheses’ testing for groups 2 and 3 considering the 1-year lag of the FDI’s effect) are presented in Table 9.

Table 9. Outcome of hypothesis testing for groups 2 and 3.

Hypothesis group 3: China (1-year lag)	Relationship’s description	Judgement
H5a: There is a correlation between the FDI per capita and GDP per capita in China, considering a 1-year lag of the FDI per capita effect	Very strong positive	Accepted
H5b: There is a correlation between the FDI per capita percentage change and GDP per capita percentage change in China, considering a 1-year lag of the FDI per capita effect	Strong positive	Accepted
H6a: There is a correlation between the FDI per capita and the EPR in China, considering a 1-year lag of the FDI per capita effect	Very strong negative	Accepted
H6b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in China, considering a 1-year lag of the FDI per capita effect	No/negligible	Rejected
Hypothesis group 4: India (1-year lag)	Relationship’s description	Judgement
H7a: There is a correlation between the FDI per capita and GDP per capita in India, considering a 1-year lag of the FDI per capita effect	Very strong positive	Accepted
H7b: There is a correlation between the FDI per capita percentage change and GDP per capita percentage change in India, considering a 1-year lag of the FDI per capita effect	No/negligible	Rejected
H8a: There is a correlation between the FDI per capita and the EPR in India, considering a 1-year lag of the FDI per capita effect	Very strong negative	Accepted
H8b: There is a correlation between the FDI per capita percentage change and the EPR percentage change in India, considering a 1-year lag of the FDI per capita effect	No/negligible	Rejected

Source: own illustration.

3.2. Qualitative analysis

3.2.1. Methodology

The qualitative analysis aims to gather data and investigate the role of selected institutional elements in moderating FDI’s effects on GDP and employment in China and India. Accordingly, guided semi-structured interviews are constructed to investigate ‘what’ the influence of institutions is and ‘how’ they moderate the effects of FDI on GDP and employment.

The interviewees used purposive sampling. Interviewees were deliberately and precisely selected. The interviewees’ main selection criteria were education, experience, and knowledge. The interviewees are professors and researchers in international trade, macroeconomics, and institutional economics (or a related subject) with knowledge of the Chinese and Indian economies. During the interviewing phase, a challenge/limitation was encountered. The response rate of the purposely selected interviewees was low. This limitation was overcome by employing a parallel sampling technique, snowball sampling. The snowball sampling aims to provide access to potential interviewees and benefit from ‘gatekeepers’ networks’ to increase the low response rate and time. Altogether, seven experts from China and seven experts from India were interviewed.

The selected institutional elements reflected the core reference in developing the interview questions. The questions embody two main types of questions: knowledge questions and opinion questions. The knowledge questions aim to gather facts and factual information on the investigated elements. The opinion questions intend to understand the cognitive and interpretive process of the interviewees on the explored element/relationship. The nature of the questions is specific and detail-oriented. The purpose of the questions is to inspect the elements rather than collecting general knowledge precisely.

Table 10 lists the interview questions and the institutional elements that are investigated. The interview includes seven questions, and the average duration is 1 hour. The selection of investigated institutional elements was based on the literature review’s analysis, particularly the institutions’ theories.

Table 10. Interview questions and related institutional elements.

Investigated institutional elements	Constructed questions
Williamson (2000): social embeddedness (level 1) North (1991): two institutional frameworks: Personalistic relationships and social network Complex, impersonal relationships and formal laws	In your opinion, how do informal rules in China and India moderate FDI’s effects on GDP? In your opinion, how do informal rules in China and India moderate FDI’s effects on employment?
Williamson (2000): institutional environment and institutions of governance (level 2 + 3) Acemoglu et al. (2005): impact of political institutions on economic productivity and efficiency: Form of state Pluralistic identity of state Distribution of political power	How do the distribution of power and the pluralistic identity in China and India moderate FDI’s effects on GDP? How do the distribution of power and the pluralistic identity in China and India moderate FDI’s effects on employment?
Williamson (2000): institutional environment and institutions of governance (level 2 + 3) Acemoglu et al. (2005): impact of political institutions on economic productivity and efficiency: state organs’ functionality and independence	In your opinion, how do the state organs’ functionality and independence in China and India moderate FDI’s effects on GDP? In your opinion, how do the state organs’ functionality and independence in China and India moderate FDI’s effects on employment?
North (1991): cornerstones of the second institutional framework: setting constraints on state’s arbitrary actions Acemoglu et al. (2005): impact of political institutions on economic productivity and efficiency: Political power constraints (system of balances and checks) Political power rents and the use of public office for private gains	In your opinion, how do the political power constraints conditions in China and India moderate FDI’s effects on GDP? In your opinion, how do the political power constraints conditions in China and India moderate FDI’s effects on employment?
Acemoglu and Robinson (2012). Inclusive vs. extractive institutions. Main features of inclusive institutions: Property rights (property registration and protection) North (1991): cornerstones of the second institutional framework: securing property rights	In your opinion, how do property rights conditions in China and India moderate FDI’s effects on GDP? In your opinion, how do property rights conditions in China and India moderate FDI’s effects on employment?
Williamson (2000): institutions of Governance (level 3): the play of the game North (1991): cornerstones of the second institutional framework: the rule of law Acemoglu and Robinson (2012): inclusive vs. extractive institutions. Main features of inclusive institutions: the rule of law (judicial process) contract laws and their enforcement	How do rule of law (contract laws and their enforcement) conditions in China and India moderate FDI’s effects on GDP? How do rule of law (contract laws and their enforcement) conditions in China and India moderate FDI’s effects on employment?
North (1991): cornerstones of the second institutional framework: institutional innovations Acemoglu and Robinson (2012): inclusive vs. extractive institutions. Main features of inclusive institutions: Government policies supporting economic freedom and inclusivity: entry barriers, trade and business freedoms, public services, and income distribution	In your opinion, how do ‘governmental policies supporting economic freedom and inclusivity’ conditions in China and India moderate FDI’s effects on GDP? In your opinion, how do ‘governmental policies supporting economic freedom and inclusivity’ conditions in China and India moderate FDI’s effects on employment?

Source: own illustration.

3.2.2. Data analysis

The qualitative data analysis was initiated by establishing the interview transcripts to support the meaningful communication and presentation of the results (Mayring, 2000, p. 8).

Based on the ‘evaluation coding’ and ‘descriptive coding’ of data gathered from the interviews, a ‘matrix display’ is utilised to preview the results. The matrix display allows the comparison of the data analysed among the interviewees (experts). The two coding methods would consider the answers to each interviewee’s question (experts) as the ‘chunk’ of material (unit of data). Table 11 displays the example of the analysis on question 1 regarding China based on descriptive coding and the evaluation-coding analysis methods.

Table 11. Question 1: analysis of China.

Expert no.	Question 1: Social embeddedness (informal rules)
1	Descriptive coding: weak positive moderation Evaluation coding: (+): current limited positive impact on FDI (information network), (+): Guanxi networks positively impact (GDP and employment), (+): resolve challenges (relationships with officials).
2	Descriptive coding: positive moderation Evaluation coding: (+): social connections and informal rules (attract socially connected FDI), (+): judicial systems’ favouritism (socially connected FDI), (+): socially connected FDI results in higher effects on GDP and employment.
3	Descriptive coding: no negative moderation Evaluation coding: (not −): no significant impeding role on FDI’s attraction or effects
4	Descriptive coding: positive moderation Evaluation coding: (+): positive moderation on FDI’s effects on GDP and employment creation. (+/−): Guanxi significantly influences business efficiency and relationships.
5	Descriptive coding: positive moderation Evaluation coding: (+): informal institutions positively impact China and India by gathering capital from FDI and injecting it into their Guanxi network and caste system. This might have a very positive effect on GDP and employment. (+): understanding and following the informal rules would positively impact the effects of FDI. (+): the Guanxi network and caste system positively impact FDI’s effects on GDP and employment.
6	Descriptive coding: negative moderation Evaluation coding: (−): Guanxi would have a negative impact on the ability of FDI to generate GDP and employment in China. (−): Guanxi relationships include costs associated with navigating through the network.
7	Descriptive coding: negative moderation Evaluation coding: (−): Guanxi would have a negative impact on the ability of FDI to generate GDP and employment in China. (−): Guanxi relationships include costs associated with navigating through the network.

Source: own illustration.

The same procedure has been adopted for all questions.

3.2.3. Results

Based on the qualitative analysis of the moderation role of selected intuitional elements in China on FDI’s effects on GDP and employment, the following results are concluded:

• Social embeddedness (level 1 institutions) and the informal rules of the society hold a positive moderation role in FDI effects on GDP and employment. Two experts concluded the positive moderation role. One expert elaborated on the positive but limited influence of the informal rule on FDI’s effects based on the high development of ‘institutional innovations such as the symmetric information networks in China.’ Another expert concluded that social embeddedness elements (such as the Guanxi network) have no impeding/negative role on FDI’s attraction or effects. On the other hand, Guanxi relationships may result in extra costs to navigate through the network, negatively impacting FDI’s effects.

• The form state in China tends to moderate FDI’s effects positively (expressed by three out of four experts). One expert highlighted that the concentrated power structure in China could increase FDI’s productivity and profits. Another expert elaborated that the authoritarian regime has dictated policies and successfully managed and developed the FDI ecosystem in China. Another perspective stressed the critical role of the state’s official policies in constituting the effects of FDI; FDI’s positive/negative effects are strongly conditional (dependent) on the state’s official policies. In contrast, another expert elaborated on the limited role (neither positive nor negative) of the form of state in FDI’s effects.

• State organs in China are perceived as functional but lack dependency (work in tandem). The significant control of the Communist Party of China (CPC) translated into the state official policy strongly influences FDI’s effects; FDI’s effect increases when the state is interested in developing a specific sector. The possibility of manipulating the state organs supports FDI’s effects on GDP and employment. On the other hand, when the state previews FDI activities as unfavourable, FDI would be forced to scale back. Hence, its effects on GDP and employment would be limited.

• The low political power constraints of officials and the lack of transparency created an environment where bribery is utilised to increase FDI’s effects. On the other hand, an expert noted that low constraints on political power, enabling political power rents (corruption), leads to a negative moderation of FDI effects. In contrast, a percentage of the FDI inflows is allocated for private gains rather than the economy. Another perspective emphasises that the developed Chinese legal system and the political and economic stability present increased positive FDI effects.

• Property rights condition’s role in moderating FDI’s effects received various evaluations. A conditional moderation is noted, as the distinctive ‘collective resource ownership system’ could be considered a risk/uncertainty based on the perspective of FDI and their avoidance of such risks. A negative moderation is embodied by the risk of property rights violations and the concerns about justice towards FDI. On the other hand, an expert stated that although property rights are limited in China, there has been no significant invasion of property rights, negatively influencing FDI’s effect on GDP and employment.

• The rule of law is concluded to have a positive moderation role in FDI’s effects. China’s well-established contract laws and effective judicial system positively influence FDI’s effects. The efficient implantation and enforcement of contract laws underpins FDI’s confidence in China. Considering China’s growth in world trade, the conditions of the rule of law and contract laws in China are accepted by its trade partners (other countries), and no significant limitations are recognised. On the other hand, an expert emphasised challenges in the judicial system (limitations in the functional administration and the implementation of contract laws).

• China’s government policies significantly impact FDI’s effects on GDP and employment. The intelligent, efficiently implemented educational system, FDI’s favourable exchange policy, developed infrastructure, low trade barriers, and integration of digital services in public services are elements of the established functioning of FDI’s ecosystem. These elements raise the absorption rate of positive FDI effects on GDP and employment. On the other hand, ‘selective FDI policies’ and concrete FDI entry barriers are present in specific sectors. On the other side of the coin, there are FDI-supportive policies; the government in China periodically identifies specific sectors and sets initiatives for supporting specific FDI sectors. Such policies support FDI’s effects on GDP and employment.

Based on the qualitative analysis of the moderation role of selected intuitional elements in India on FDI’s effects on GDP and employment, the following results are concluded:

• Contrary to all experts, expert two highlighted that FDI holds a negative/negligible effect on employment (as unemployment is increasing in India). Expert 2 elaborated that FDI is accompanied by higher levels of technology, which leads to an increase in unemployment rather than a decrease. Expert 2 expressed that employment rates in India have remained relatively the same over the last 40 years. FDI affects GDP more than employment, but the FDI’s impact on GDP is limited.

• In India, informal rules (institutions of social embeddedness) are observed to have a mix of moderating roles on FDI’s effects. Owing to the variations among the castes, variations in the labour force quality are observed. This creates limitations on absorbing the FDI’s positive effects on employment. Although the impact of the caste system in rural areas is constrained, the asymmetric information network in India reinforces its presence and influence. On the other hand, the social context of doing business in India increases FDI inflows and supports their effects on the local markets (GDP and employment). Understanding and following society’s informal rules and norms could contribute positively to the effects of FDI. In the case of a lack of knowledge and understanding of the caste system mechanisms, FDI must allocate additional resources to navigate the system. In such cases, a negative impact on FDI’s effects is expected. Another viewpoint considers the negligible/no impact of the informal rules, moderating the traditional sector in India, on FDI’s effects on GDP and employment. FDI does not interact with the traditional sector and seeks more complex business forms with specific skills.

• India’s democratic system (the form of state) incorporates periodical government changes and policies. This reflects uncertainty about FDI and its effects. On the other hand, the democratic system in India prohibits discrimination and ensures proper laws and justice. Despite the openness and accessibility of the systems in India, a high level of bureaucracy is present. These reflect a negative moderation role in FDI’s effects. The pluralistic political identity resulted in an active system supporting FDI and advancing its positive effects on GDP and employment. Parallel to that, it was communicated that the form of state and the pluralism identified by the state in India hold a neglected moderation role in FDI’s effects.

• The clear distinction and independence of the state organs in India support FDI’s effects on GDP. However, concluding a direct positive effect on employment based on the governance structure (functionality and independence of the state organs) cannot be formulated. Current conditions of the state organs act as enablers of employment but not direct moderators. The fairness of the judicial system is a significant attraction of FDI inflows. On the other hand, inefficiencies in the judicial system are observed (insufficient numbers of judges). The political institutions in India are generally receptive to and supportive of FDI and its effects. Parallel to that, it was expressed that even if FDI does not support GDP, it will still be supported, and there is low emphasis from state organs regarding FDI’s effects on employment.

• Indian efficient decision-making (based on the pluralistic political identity) and an active system of checks and balances support attracting FDI and increasing its positive effect on GDP and employment. On the other hand, it was pointed out that although the Indian judicial system (reflecting the system of checks and balances) attracts FDI, it is not the only factor in attracting FDI or defining its effects. The solid legal system, the abundance of regulatory laws, and the current empowered and efficient government in ratifying laws and imposing them enhance the rate of response of GDP and employment to FDI. A contrary opinion stated that the Indian political institutions are, to some extent, corrupt. Limitations of FDI’s effects result from low political power constraints, which enable private gains. On another dimension, an opinion declared that FDI’s effect on GDP does not improve in balances and checks, and political constraints do not impact FDI’s effect on employment.

• Property rights laws exist and are honoured in India. These conditions increase FDI’s confidence and help FDI’s effects on GDP and employment. FDI has the legal right to utilise the court (in case of violation of property rights). Property rights conditions in India may positively affect economic productivity. On the other hand, limitations of property rights are detected in exceptional cases of compulsory licensing. Compulsory licensing may hurt FDI’s effects. An expert elaborated that there is no significant impact of property rights constraining the FDI effect on GDP in India. In contrast, when it comes to property rights and its impact on FDI’s effects on employment, FDI’s impact on employment is negligible.

• Despite the well-established contract laws in India, inefficiencies in their supporting systems are detected. On the other hand, an expert stated that reducing corruption activates the implementation of laws in India. Causality between the rule of law and the ‘opportunity’ of FDI could be established, whereas quantifying the extent of the moderation of FDI’s effect is challenging. The expert expressed that qualitatively, India’s safe, legal environment ignites FDI’s contribution to GDP and employment. Uncertainty is voiced by expert 2 on whether ‘the independence of the judicial system and the tendency in ruling in favour of FDI’ serves FDI’s positive impact on GDP. Additionally, expert 2 stated that this has no moderating role on FDI’s effects on employment.

Current governmental policies in India are capitalistic and support FDI’s effects on GDP and employment. Several governmental policies are devoted to strengthening the FDI ecosystem in India (opening economic sectors for FDI, establishing special economic zones, improving infrastructure, etc.). Conversely, challenges in the educational and health sectors act as barriers against the absorption of FDI’s full potential and benefits. Additionally, doubts about the impact of governmental policies, supporting economic freedom, and FDI inflows on FDI’s effect on GDP are projected. In contrast, their influence on FDI’s effect on employment is uncertain.

4. Discussion

This paper investigates the role of institutions in moderating FDI’s effect on GDP and employment in China and India. This paper explores FDI’s effects in different institutional frameworks of two major emerging economies. This paper contributes to the international business literature in several ways. Theoretically, the paper merges the econometric research of correlation analysis (FDI, GDP, and EPR) and the institutional theories-based analysis. The paper tries to fill the gap of unspecific or vivid inclusion and presentation of FDI in institutional-based theories and research and the limited elaborated impact of institutions on FDI’s effects in the host economies. Methodologically, three research methods are used to answer the research questions in-depth. The first is the analysis of institutions in China and India based on prominent institutional theories (Acemoglu et al., 2005; Acemoglu & Robinson, 2012; North, 1991; Williamson, 2000); the analysis concludes with a constructed list of selected institutional elements. The second is the quantitative analysis of correlation measurements; the quantitative analysis examines the presence, direction, and strength of relationships between the variables of FDI per capita, GDP PPP per capita, and EPR. The third is the qualitative analysis of guided semi-structured experts’ interviews; the qualitative analysis explores the moderating role of the selected institutional elements on FDI’s effects on GDP and employment in China and India (presence, direction, and strength of the moderation role).

4.1. Quantitative analysis

The quantitative results can be divided into two main groups: the correlations among the absolute values (raw change) and the correlations between the percentage changes value (relative change, dynamics). In absolute values, in China and India, significant strong positive correlations are detected between FDI inflows and GDP, whereas significant robust negative correlations are detected between FDI inflows and EPR.

In relative values (percentage changes), only a significant strong positive correlation between FDI and GDP in China is found. The positive correlation between the FDI and GDP percentage changes reflects the high relationship dynamics between the two variables in China. Although China’s economic reform of 1978³ ocurred more than 40 years ago, the dynamic and active relationship between FDI and GDP was still present and detected. India’s latest defining moment occurred in 1991, later than the Chinese defining moment. In contrast, such a dynamic relationship between FDI and GDP is fragile and statistically insignificant.

Additionally, when considering the 1-year lag of FDI’s effects, similar conclusions (correlation results) are detected in absolute and relative values (percentage changes). Accordingly, the 1-year lag of FDI’s effects creates no significant differences in the relationship between FDI and GDP or between FDI and EPR.

The results of the quantitative analysis, concluding the presence of relationships between the dependent variable of FDI and the independent variables of GDP and EPR, validate the qualitative analysis for investigating the moderating role of institutions on the effects of FDI on GDP and EPR in China and India (host economies).

Throughout the qualitative analysis, all experts highlighted that FDI positively affects GDP and employment in China and India, except expert 2. Expert 2 elaborated that FDI negatively affects the employment levels in the host economy. Additionally, expert 2 noted that FDI might hold a moderate positive influence on GDP.

4.2. Qualitative analysis

The qualitative analysis concludes the presence of the moderation role of the selected institutional elements on the effects of FDI on GDP and employment in both China and India. The qualitative analysis results correspond to the findings of Wang et al. (2013), which conclude that the development of the host economy’s institutions (particularly formal institutions) moderates FDI’s impacts.

Three main themes arose regarding social embeddedness (level 1 institutions). First, social networks in China and India support attracting FDI inflows and endorse the effects of FDI on GDP and employment. Second, efficient management of the social context of doing business in the host economy supports FDI’s business operation and relationships, thus positively influencing FDI’s effect. Third, the increase in formalisation and rationalisation of the economy and the increase in the symmetry of information networks reduce the impacts of informal rules and their influence on business. The third theme aligns with North’s (1991) theories of institutional innovations. Institutional innovations reinforce complex, impersonal exchanges based on formal rules and laws.

Although the significant variations in the elements of the institutional environment (level 1 institutions) and governance structure in China and India, such as the form of state (pluralism and distribution of power), the political power constraints, and the respective state organs’ independence, the moderating role of institutions on FDI’s effects is more significant based on variations among other institutional dimensions.

The significant variations in the moderating role are closely tied to how governance institutions influence the dynamics of the host economy’s “game”. Core elements of the ‘play of the game’ are the state organ’s functionality, the efficiency of the legal system and enforcement of the rule of law, and the presence of FDI’s supportive governmental policies and the ability for their implementation.

The deductions of the qualitative analysis correspond to the conclusions of published research. Arshad (2019) indicated that institutional quality and FDI endorse economic growth. Moreover, Arshad (2019) concluded that the institutional quality in low- and middle-income countries (such as China and India) enhances FDI-led economic growth. Roy and Roy (2016) concluded that supportive institutional elements (government transparency, lower risk in operating business, independent judicial authority) positively impact economic growth in conjunction with FDI.

This paper achieves diverging conclusions from other research. Elkomy et al.’s (2015) findings demonstrate that political development in conjunction with authoritarian regimes, such as the Chinese, suppresses FDI-led growth. The qualitative analysis of this research conveys that the nature of the authoritarian regime in China and the highly concentrated power of political actors reflect a governance structure in which supportive FDI policies are efficiently imposed. On the other hand, it was noted by several experts’ interviews that the general official policy of the CPC, labelled as ‘FDI selective policies’, constitutes the sectors in which FDI is allowed to operate and prosper, hence leading to higher effects on GDP and employment. Elkomy et al. (2015) also conclude that in higher degrees of democracies.⁴ Political development supports the effect of FDI on growth. Similarly, the interviews emphasised the presence of supportive conditions and inefficiencies in India’s democratic system, which play a diverse moderation role in FDI’s effects.

The qualitative analysis revealed that a minimum degree of the rule of law, political and economic stability, protection of property rights, and enforcement of business contracts are prerequisites to ensure the functionality of FDI and its positive effects on the host economy. Furthermore, having a democratic structure (highly pluralistic identity), a broad base distribution of power, and high political power constraints without functional supporting structures of laws, policies, and contract enforcement impedes the positive effects of FDI.

Protecting property rights is a cornerstone of the private enterprise system. However, when property rights and a concentrated state-owned sector are limited (such as in China), FDI can still perform and develop. On the other hand, in India, where property rights are relatively more protected, a higher confidence level in FDI inflows is observed. Thus, this leads to an increase in FDI’s effects on GDP and employment.

Governmental policies towards creating a highly skilled labour force (efficient educational system), well-functioning public services, developed infrastructure, supportive financial services, and FDI’s favourable exchange rate and interest rate policies increase the absorption rate of FDI’s positive effects on GDP and employment.

FDI is profit-oriented (lowering the costs and increasing the revenues). One of the main reasons for FDI inflows into emerging economies is the low costs of production (labour force, natural resources, etc.). Job creation and increasing the GDP levels are not the main aims of FDI. Management of GDP and employment are central targets of the government and policymakers in a country. Governmental policies (rules of the games, level 2 instructions) must implement policies that ensure both the convenience and attraction of FDI inflows and the positive effects of FDI on the economy (GDP) and the population (employment).

According to Acemoglu and Robinson (2012), the governance structure in China can be described as an extractive political institution that catalyses the creation and persistence of extractive economic institutions. Acemoglu and Robinson’s (2012) theory expresses that economic growth might occur (possibly) under extractive political institutions in two prominent cases. The first case is when resources are allocated to highly productive economic activities owned by the political power elites. This can be applied to the case of the growing state-owned concentrated Chinese economy. The second case is when semi-inclusive economic institutions can exist and perform. These intuitions are only allowed in cases where there are sufficient securities that the power of the extractive political institutions will not be negatively affected by the semi-inclusive economic institutions’ growth. However, Acemoglu and Robinson (2012) express that the two cases will not accomplish sustainable long-term growth and creative destruction. Based on the results of the analysis, in the case of China, economic growth under extractive political institutions has been recorded throughout the past decades. While inclusive political institutions are present in India, the full potential of economic growth is hindered owing to constraints and challenges in the functionality of inclusive institutions.

4.3. Policy implications

Here are some policy implications that can be drawn from the study’s findings:

1. Strengthening informal networks and relationships: The positive moderation role of social embeddedness and informal rules (such as the Guanxi network in China) suggests that policymakers should consider strategies that promote and facilitate these networks. Encouraging informal relationships can lead to improved FDI outcomes. However, policymakers should also know the potential costs of navigating such networks.

2. Supporting state involvement: The study suggests that the state’s form, state organs’ functionality, and political power constraints play a role in moderating FDI effects. Policymakers could focus on strengthening and maintaining effective state institutions to facilitate positive FDI outcomes. A well-functioning state with transparent policies can attract more FDI and enhance its impact on GDP and employment.

3. Promoting transparency and rule of law: The positive moderation role of property rights and the rule of law implies that enhancing property rights protection and ensuring an adequate legal framework are crucial for attracting and retaining FDI. Policymakers should work to create an environment where property rights are secure, contract laws are enforceable, and a fair judicial process exists.

4. Enhancing government policies: The study indicates that supportive government policies significantly impact FDI effects. Policymakers should continue implementing policies that facilitate FDI entry, provide business-friendly conditions, and support sectors with growth potential. FDI-friendly policies can lead to higher FDI inflows and positive effects on GDP and employment.

5. Addressing institutional challenges: The study also identifies potential challenges in the institutional environment that could negatively impact FDI effects, such as corruption, political power rent, and selective policies. Policymakers should address these challenges through anti-corruption measures, promoting accountability, and formulating transparent policy.

6. Balancing state intervention and market mechanisms: While state involvement can have positive effects, policymakers should also be cautious about overregulation. Balancing state intervention with market mechanisms is essential to create an environment that fosters FDI while allowing market forces to drive growth and competition.

7. Improving infrastructure and education: The positive influence of government policies on FDI effects highlights the importance of improving infrastructure and education systems. These factors create an attractive environment for FDI and support its positive impact on economic growth and employment.

8. Continuous monitoring and adaptation: Since the study is based on data up to 2020, policymakers should continue to monitor the evolving institutional landscape and adapt their strategies accordingly. Economic and political changes can affect the moderation role of institutional elements, so policymakers need to remain agile in responding to new challenges and opportunities.

9. International collaboration: Since FDI involves interactions with foreign investors and trade partners, policymakers should collaborate with international organisations and trade partners to ensure consistent and favourable conditions for FDI. This can include harmonising regulations, improving cross-border trade facilitation, and promoting investor-friendly policies.

10. Promoting research and expertise: The study’s use of expert interviews highlights the value of academic and expert insights in understanding the moderation role of institutional elements. Policymakers should continue to engage with researchers, economists, and subject-matter experts to inform their decisions and policies.

In conclusion, the study’s findings suggest that policymakers in China and India should focus on leveraging institutional elements to enhance the positive effects of FDI on GDP and employment. By strengthening informal networks, promoting transparency, supporting effective state institutions, and implementing FDI-friendly policies, these countries can create an environment that attracts and maximises the benefits of foreign investment. Continuous monitoring, adaptation, and international collaboration will ensure long-term success.

5. Conclusion

In conclusion, the research investigated the moderating effects of selected institutional elements on the relationship between FDI and GDP and employment in China and India. A two-tier research structure combining quantitative and qualitative methods was employed.

The quantitative analysis revealed several significant correlations between FDI and GDP and FDI and employment, both with and without a 1-year lag in China and India. Specifically, there were strong positive correlations between FDI and GDP, indicating that higher FDI was associated with higher GDP. Conversely, there were strong negative correlations between FDI and employment, suggesting that a higher FDI was linked to lower employment rates.

The qualitative analysis, conducted through guided semi-structured interviews with experts, provided insights into how selected institutional elements moderated the effects of FDI on GDP and employment in China. The findings highlighted the importance of social embeddedness, informal rules, the form of the state, state organs’ functionality and independence, political power constraints, property rights conditions, the rule of law, and government policies in shaping the impact of FDI.

The results of the qualitative analysis indicated that social embeddedness and informal rules had a mixed impact, with both positive and negative effects on FDI’s influence on GDP and employment. The form of the state was generally seen as having a positive role in moderating FDI’s effects, with China’s concentrated power structure contributing to effective FDI policies. State organs’ functionality and independence were considered to enhance or limit FDI’s effects by aligning state interests with FDI activities.

The presence of low political power constraints and concerns about corruption were identified as potential negative factors that could hinder FDI’s positive effects on GDP and employment. Property rights conditions were found to have mixed effects, with uncertainties related to collective resource ownership potentially deterring FDI. The rule of law was generally viewed positively, with efficient contract enforcement contributing to FDI’s positive effects.

Lastly, government policies in China were recognised as critical in fostering a supportive environment for FDI, leading to increased positive effects on GDP and employment. However, there were also instances of selective FDI policies and entry barriers that could influence FDI’s impact.

In conclusion, the research integrated quantitative and qualitative analyses to explore how selected institutional elements moderated the relationship between FDI, GDP, and employment in China. The findings shed light on the complex interplay between institutional factors and economic outcomes, providing valuable insights for policymakers, researchers, and practitioners seeking to understand the dynamics of FDI’s effects in these economies.

List of main findings for China:

1. The introduction of constructive conditional FDI policies that encourage employment creation and do not reduce economic attractiveness (FDI packages and incentive programmes such as tax reductions).

2. Reducing selective FDI policies, protectionism, and entry barriers increases FDI’s attractiveness and creates a level playing field.

3. Separating state organs (independence) and empowering the democratic institutions (higher political pluralism and broader political power distributions).

4. Empowerment of the judicial system to hold better controls (increase power constraints and reduce corruption activities and bribery) over the political power holders (state officials).

5. Increasing the controls over the judicial system to limit the actions of favouritism and biased ruling (based on the ethnic identity of FDI) and increase the rule of law.

6. Decreasing property rights-violating activities (reduce governmental unlawful property expropriation and induce adequate compensation).

List of main findings for India:

1. Introduction to FDI favourable exchange rates and interest-rate policies (encourage exporting from India rather than importing to India).

2. Introduction of constructive conditional FDI policies that encourage employment creation and do not reduce the attractiveness of the economy (tax reduction and incentives programmes).

3. Development of the educational sector, including the quality and number of highly developed educational institutions and services.

4. Develop policies to support the functionality of the judicial system and solve the challenge of an insufficient number of judges (incentives and partnerships with educational institutions).

5. Reinforce the democratic structures of the country and reduce corruption activities (including the manipulation of the poor during the voting processes, women’s treatment, and equal opportunity for education).

6. Improvements in the registering property processes.

7. Introduction of policies supporting the formalisation and rationalisation of the informal, traditional business sector (increase complexity and skills which FDI seeks).

8. Increase the awareness of FDI’s benefits on employment and GDP by the state organs and increase the attention towards policies regulating FDI’s effects to increase the absorption of their benefits.

9. Development of infrastructure⁵Distribution channels and the integration of digital public services.

10. Development of an employment supporting system (unemployment and compensation programmes)

11. Acceptance and welcoming of opinions of resistance against FDI as means to improve the decision-making processes and introduce the required ratification and modifications of laws and policies.

12. Developing the symmetric information network (balance access to data and technology).

13. Development of governmental policies to act against income inequality.

This research has encountered a few limitations. In response, several techniques and initiatives are implemented to reduce the limitations. The core limitation of this research is the investigation of one primary moderating variable (i.e., institutions) on the relationship between FDI, the independent variable, and the dependent variables of GDP and employment-to-population ratio. Moreover, the effects of FDI on the host economy are further than the two precisely selected dependent variables. The qualitative analysis is based on secondary data, including estimated and rounded figures. Accordingly, the data source was limited to one trusted official source, the World Bank Database.

Nevertheless, using one data source ensures the consistency and reliability of the data. The investigated data are available yearly. A more in-depth analysis can be applied when monthly data are available. Additionally, acquiring high-quality and accurate data on the two investigated economies is challenging. This challenge reflects limitations of institutional innovations (cost of access to information), which hinder the exploration, analysis, and decision-making process in the economies themselves. The low response rate of potential candidates from China and Southeast Asia is another limitation encountered. It was mitigated by implementing the snowball sampling technique.

Moreover, the nature of the investigated topic requires establishing specific in-depth questions, lowering the range of suitable candidates. Lastly, the interviews are conducted online, using video calls. This reflects some limitations in data projection and the need for face-to-face interactions.

This research paper acts as a cornerstone of in-depth future research into the following areas, for example, not limited to the role of selective FDI and constructive conditional FDI policies, the functionality of judicial authority controls, and FDI favourable exchange rates and interest rates policies.

This research paper makes significant contributions on both policy and practical fronts, as well as enriching theoretical understanding:

1. Policy contribution:

2. The paper offers specific policy recommendations tailored for both China and India. These recommendations include introducing constructive conditional FDI policies, reducing selective FDI policies and entry barriers, empowering democratic institutions, strengthening judicial systems, and developing infrastructure and educational sectors.

3. By delineating such policy measures, the paper provides actionable insights for policymakers aiming to enhance FDI’s positive impacts on GDP and employment in these economies.

4. Additionally, the paper emphasises the importance of governmental awareness and attention towards regulating FDI effects to maximise its benefits.

5. Practical contribution:

6. Through its mixed-method approach, combining quantitative analysis with qualitative insights from expert interviews, the paper offers a nuanced understanding of the moderating role of institutional elements on the FDI–GDP–employment relationship.

7. The recommendations outlined in the conclusion offer practical strategies for policymakers and practitioners to address the identified institutional gaps and capitalise on FDI for economic growth and employment generation.

8. Theoretical contribution:

9. The paper advances theoretical understanding by highlighting the complex interplay between institutional factors and economic outcomes, particularly concerning FDI’s effects on GDP and employment.

10. By identifying and analysing specific institutional elements and their varying impacts, the paper enriches theoretical discussions on the role of institutions in shaping economic development trajectories.

11. Moreover, the research sets the stage for further theoretical inquiries into the mechanisms through which institutional factors moderate FDI’s effects, paving the way for more nuanced theoretical frameworks in international economics and development studies.

This research contributes significantly to the academic discourse by offering practical policy recommendations, enriching theoretical understanding, and providing valuable insights for policymakers and practitioners seeking to navigate the complexities of FDI-led economic development in emerging economies like China and India.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Political development categories: electoral process and pluralism, civil liberties, the functioning of government, political participation, and political culture.

2 Classifications are based on the Economist Intelligence Unit Democracy Index Scores.

3 A defining moment of the institutional environment that initiated significant FDI inflows.

4 India holds a higher democracy ranking than China but is considered a flawed democracy (Elkomy, 2015).

5 Expert 5 noted that FDI projects in infrastructure significantly affect employment creation positively in India.