IFRS adoption and foreign direct investment in Sub-Saharan African countries: Does the levels of Adoption Matter?

Abstract

This study makes an effort to determine how international financial reporting standards (IFRS) adoption levels affect net FDI inflows to sub-Saharan African (SSA) countries using a panel data spans from 2005 to 2020. The results of the two-step system’s generalized methods of moments (GMM) estimation reveal that while both partial and full adoption is found to be insignificant, the sign is negative for full IFRS adoption. However, a statistically significant and positive effect of the interaction between institutional attributes and full IFRS adoption has been discovered. Among other factors controlled, the most significant influencing FDI flows to Africa are found to be infrastructure, trade openness, and human capital. The empirical result is used to derive some policy implications.

Keywords:

• IFRS adoption
• institutional quality
• net FDI inflows
• system GMM
• Africa

JEL CODE:

• M21
• M41
• M42

1. Introduction

Accounting standards and financial data quality are seen as essential components of a country’s institutional framework. Accounting information prepared in accordance with international financial reporting standards (IFRS) is thought to ensure accountability and transparency, appropriate resource and capital allocation, efficient financial market functioning, financial system stability, and corporate and organizational governance (Akpomi & Nnadi, 2017; Lungu et al., 2017; Nnadi & Soobaroyen, 2015). IFRS adoption is considered to reduce information costs among countries and is, therefore, an important way to encourage international trade flows and investments (Márquez-Ramos, 2008). As a result, the significance of accounting standards in creating quality accounting information and attracting foreign direct investment (FDI) in a country has received significant research attention (Müller, 2014; Păşcan, 2015).

Two theoretical paradigms are in the literature as to the motives behind FDI. The first is the OLI framework first proposed by Dunning (1980), which considers FDI as determined by ownership (O), location (L), and internalization (I) advantages. The second paradigm divides FDI into three different models: a horizontal model, a vertical model, and a knowledge capital model (Dunning, 1980; Dunning & Lundan, 2008). The concept of the OLI paradigm is that the costs of doing business in a foreign country must be compensated by these three benefits in order to get a higher return on investment than in one’s own country (Chen et al., 2014; L. Gordon et al., 2012a). The vertical FDI aims to exploit international factor-cost differences, and horizontal FDI aims to gain access to host-country markets, and the knowledge-capital model allows for both vertical and horizontal firms to arise in equilibrium as a function of technology and country characteristics.

Concerning the evolvement of IFRS adoption, the trend is on the rising. IFRS adoption begins since 1987 by Bangladesh with the adoption of modified international accounting standards (IAS), followed by Kuwait (IAS, 1990), Bahamas and Mongolia (IAS, 1993), Malta (IAS, 1995), Zimbabwe (IAS), Uzbekistan (IAS-modified), Cyprus (stock market), Maldives (IAS-SMEs), and Lebanon (IAS-except Bank) all in 1996. In a paper that provides a cross-reference of IFRS adoption dates and types around the world, more than 195 countries have adopted IFRS at different levels (Song & Trimble, 2022). The adoption of IFRS by well over 100 countries since 2004, with more countries moving towards adoption, has been a major development in accounting regulation throughout the world (L. Gordon et al., 2012a). This shows that the adoption of IFRS is increasing from time to time especially in the time framework covered in this study (2005 to 2020). The question of whether the adoption of IFRS results in economic benefits is of particular interest, especially in light of the region’s recent adoption of IFRS. Indeed, there is a growing consensus that harmonization of accounting standards carries with it the potential to increase transparency, comparability, reducing information processing costs and asymmetric information. Bushman and Smith (2001) suggested three channels through which financial accounting information might influence economic performance: 1) Improved project identification by managers and investors; 2) Discipline in project selection and expropriation by managers; and 3) Lessening of investor information asymmetries. Moreover, in the long term, the harmonization of the accounting standards would improve trust and familiarity, improving investor confidence, enhancing market liquidity and reducing the cost of capital and hence increasing flows of foreign investment between countries (Márquez-Ramos, 2008). Countries where the transparency of financial reports is poor, relative to the transparency provided under IFRS, have the greatest potential for increasing transparency through IFRS adoption. In general, countries with developing economies have weaker domestic financial reporting regulations, with less transparency in their financial reports, than countries with developed economies (Ding et al., 2007).

Despite being implemented in Zimbabwe as early as 1996, the first research paper on IFRS in Africa was first released in 2005 (Ezenwoke & Tion, 2020). Only 21 Authors and 18 institutions out of the more than 600 institutions on the continent contribute to more than one publication of IFRS research. Only six African nations are represented by the institutions and authors in this category (South Africa, Nigeria, Tunisia, Egypt, Uganda, and Ghana). On top of that the focus of prior studies was on the effect of IFRS adoption on accounting information quality and relevance, and some others take samples of adopters and non-adopters, and few others on the role of stock market development in attracting FDI. More academic research on IFRS is required especially on the comparability effect of IFSR adoption in attracting FDI to developing nations. Thus, this study therefore focuses on examining the effect of IFRS adoption levels (full/partial) on net FDI flows to 31 sub-Saharan African (SSA) countries over the period of 2005–2020, and makes several contributions to the body of literature: First, it provides new empirical evidence on the comparability effect of full and partial IFRS adoption on the net FDI flows to SSA countries. Second, it accounts the mediating role of country’s institutional quality on the effect of IFRS adoption to net FDI flows to SSA. Thirdly, it uses a panel dynamic model such as the two-step system generalized methods of moments (GMM) estimation technique to account for the persistent nature of variables and endogeneity problems in the model. Fourth, it conducts a thorough literature review to determine whether these nations’ adoption of IFRS is motivated by their desire to comply with the directives and policies of various international organizations like the World Bank (WB) and the International Monetary Fund (IMF) without having a sound domestic and global legal framework.

The rest of the paper is organized as follows: Section two presents the review of related literature; section three specifies research methodology and data; section four presents results and discussions; and section five conclude the study.

2. Literature reviews

Theoretically, there are two main reasons for firms to go multinational; to serve a foreign market and to get lower cost inputs. This distinction is used to differentiate between two main types of FDI: horizontal and vertical (forward and backward; Protsenko, 2003). The former refers to the foreign manufacturing of products and services roughly similar to those the firm produces in its home market, duplicates the same activities in different countries. Horizontal FDI arises because it is too costly to serve the foreign market by exports due to transportation costs or trade barriers. The latter refers to those multinationals that fragment production process geographically, separates the production chain vertically by outsourcing some production stages abroad. The basic idea behind vertical FDI is that a production process consists of multiple stages with different input requirements. If input prices vary across countries, it becomes profitable for the firm to split the production chain.

In his consecutive studies, Dunning (1980) developed the eclectic paradigm, Ownership (O), Location (L), and Internalization (I), to explain FDI activities, and the drivers have categorized into three types: market-seeking, resource-seeking, and efficiency-seeking. In fact, to the eclectic paradigm, economic efficiency is regarded as the ultimate determinant of location choice. However, the primary criterion of choosing a location is the crucial difference between the eclectic paradigm and the institutional approach to the issue of FDI location choice. From this perspective, multinational companies adopt the ability of institutions to lower the transaction costs associated with FDI that result from an uncertain environment (Treviño & F. G, 2004).

Thus, multinational companies are motivated to become isomorphic with their environment in order to improve their legitimacy (Yiu & Makino). Due to the eclectic paradigm’s lack of institutional content, Ajayi (2006) suggested that institutional elements should be heavily considered while extending the model. Dunning and Lundan (2008) suggested that institutions have strong impact on all the three components of the paradigm. Therefore, integrating an institution-based view into FDI theory is virtually essential for the case of developing countries as the FDI theory has been developed on the experience of multinational companies from Western countries, where fully developed market-based institutions enable background conditions for business activities, although these institutions are almost invisible. Empirically, Im et al. (2003) suggested that FDI inflows to advanced countries are usually horizontal investments driven by market seeking strategies while to developing is input prices variation.

In the case of Africa, the role of FDI as a source of capital has grown significant due to its potential both to close the savings-investment gap and help the continent meet its Millennium Development Goal (MDG) commitments (Ajayi, 2006). Given the region’s low income, low level of local savings, resource needs, and limited capacity to collect funds domestically, the majority of its financial support will need to come from outside the country, primarily in the form of FDI. A number of studies examine the determinants of capital flows to developing countries (Ajayi, 2006; Asiedu, 2006; Bayraktar, 2013; Bekana, 2016; Dupasquier & Osakwe, 2006; Naudé & Krugell, 2007; Onyeiwu & Shrestha, 2004; Zhang & Daly, 2011). The pull factors identified include macroeconomic policy and performance, current and capital account openness, tax rates, and the presence of incentives to encourage capital inflows, the calibre of judicial and other institutions, conflict resolution techniques, political regime, and the scope of domestic markets and the base of available natural resources (Bekana, 2016). Similarly, Dupasquier and Osakwe (2006) and Zhang and Daly (2011) found that openness to FDI, lower inflation, political stability, infrastructure, human capital, low corruption, and a reliable legal system, all have a positive effect on FDI. Whereas, weak infrastructure, macroeconomic instability, poor governance, low growth, hostile regulatory environment, and poor investment promotion policies negatively affects FDI.

However, developing countries are more likely to benefit from cross-boarder investments of countries in a similar category of growth than from developed economies due to the very reason that emerging and developing countries are less attractive to foreign investors due to their poor infrastructural developments, and poor legal and political systems (Groh & Wich, 2012). Government consumption, inflation rate, investment, governance (political stability, accountability, regulatory burden, rule of law) and initial literacy are the main determinants of FDI in Africa. It is concluded that geography does not seem to have a direct influence on FDI flows to Africa (Naudé & Krugell, 2007). Neither market-seeking nor re-exporting motives of FDI seem to dominate, with different policy instruments being significant in the different specifications. Institutions, in the form of political stability showed up as a significant determinant of FDI. The initial results show that countries, which have better records of doing business tend to attract more FDI (Bayraktar, 2013).

There are reasons to believe that the IFRS could be another key driver of FDI. However, there are only a few looked at IFRS as a factor of FDI, and the results are equivocal (L. Gordon et al., 2012a). With limited concern in Africa as to the contribution of IFRS adoption to Africa’s FDI, some pioneering empirical works supplement the argument that IFRS attracts FDI to developed and developing countries (Golubeva, 2020; Gordon et al., 2012a; Gu & Prah, 2020; Jinadu et al., 2016; Márquez-Ramos, 2008; Pricope, 2017). These empirical studies generated mixed results due to behavioural factors such as unfamiliarity aversion, institutional qualities and some argue that the desire to get financial aid from the World Bank could be a major motivation for developing economies to embrace IFRS (L. Gordon et al., 2012a).

Golubeva (2020) investigated whether the introduction of the IFRS affects FDI and profitability of multinational enterprises (MNEs) by looking at Swedish companies’ FDI in 73 countries from 2007 to 2014. The findings demonstrated that the implementation of IFRS has a considerable impact on FDI and earnings generated by MNEs, depending on the extent of IFRS adoption and convergence. Jinadu et al. (2016) evaluated the impact of IFRS on FDI in Nigerian public companies and discovered that IFRS has a positive and significant impact on FDI. Márquez-Ramos (2008) investigated the impact of IFRS implementation on trade and FDI in developed and developing nations. The results of panel data estimate revealed that IFRS adoption has a positive and significant impact on FDI, with the impact being stronger in transition economies in Europe.

Gordon et al. (2012a) used a panel data set of over 1300 observations from 124 nations from wealthy and developing economies from 1996 to 2009 to test the core assumption that a country’s adoption of IFRSs resulted in greater FDI inflows. The findings back up the theory that IFRS adoption leads to more FDI inflows. The total rise in FDI inflows from IFRS adoption is mainly to an increase in FDI inflows by developing, rather than developed, nations and a key potential motivator for IFRS adoption by developing economies is the desire to receive financial aid from the World Bank. Another study on the matter covering 116 developing countries over 17 years concluded that adopting IFRS alone may not be enough for developing countries to attract the much-needed FDI, implying that more research is needed to determine the conditions under which developing countries can reap the economic benefits of adopting the IFRS (Owusu, Saat, Suppiah, Law et al., 2017).

Gu and Prah (2020) investigated the impact of IFRS implementation on the relationship between FDI and economic growth in 12 African nations regarded to be the largest FDI recipients from 1996 to 2018. As a result, IFRS is found to be highly favorable, with non-fully IFRS adopting nations experiencing larger FDI inflows than fully IFRS adopting countries. Overall, the implementation of IFRS encourages FDI inflows, which boosts economic growth. Musah et al. (2020) studied the impact of IFRS adoption on FDI in 20 IFRS-adopted African nations from 1980 to 2015 and came to the conclusion that IFRS adoption has a positive and significant impact on FDI in Africa. Between 2008 and 2014, Pricope (2017) investigated the association between IFRS implementation and FDI in 38 poor countries. According to the propensity score matching analysis approach, the adoption of the IFRS has a positive and significant impact on FDI flows in developing nations. The aforementioned empirical works support the argument that IFRS adoption is considered to reduce information costs among countries and is, therefore, an important way to encourage international trade flows and investments (Márquez-Ramos, 2008).

Furthermore, Nnadi and Soobaroyen (2015) studied the comparability effect of full, partial, and modified IFRS adoption in 34 African nations. The empirical conclusion was that full IFRS adoption has a detrimental influence on net FDI flows to Africa. As a result, authors have proposed two conclusions: first, foreign investors appear to be concerned about the costs of operating in an IFRS-regulated environment, and second, fundamental institutional structures such as the rule of law, the legal system, and the level of corruption, than IFRS adoption alone, appear to be more important in maintaining or increasing the level of FDI in Africa.

In sum, results are inconclusive as to the effect of IFRS adoption on FDI to Africa. Some studies suggest the benefits of IFRS in attracting FDI and some other recommend the merits of institutional qualities than the mere IFRS adoption. Only a single study, Nnadi and Soobaroyen (2015), has tried to see the comparability effect of IFRS adoption on FDI to Africa even that is methodologically questionable since results are based on OLS and a two-stage instrumental variable (IV) model taking regulatory quality index as instrumental variable. However, using a simple regulatory quality index as instrumental variable may not be sufficient to resolve endogeneity problem in such dynamic feature of financial and economic variables. Besides, estimated results may not be reliable and efficient since inferences based on OLS estimates are typically thought to be a poor guide, and OLS presupposes a lot of linearity, which may or may not be right when discarding a potentially non-significant yet endogenous variable (Roodman, 2009). On top of that, coefficients estimated using OLS are found biased in the presence of endogeneity and serial correlation due to the persistent nature of financial and economic data in the model. Thus, the contribution of this study to the body of literature can be justified by its new insights into the comparability effects of IFRS adoption on net FDI flows to developing nations like SSA, its investigation of the moderating effect of country-level institutional quality variables on the relationship between full or partial IFRS adoption and FDI inflows, and its use of the dynamic generalized methods of moments (GMM) estimation technique which is more efficient and reliable in terms of power and type-I error with the best small sample bias and precision features. Therefore, this study stands to close this gap.

3. Data and empirical model

3.1. The data

The study used a panel data set of 31 SSA nations with annual data from 2005 to 2020 to investigate the effect of full and partial IFRS adoption, and institutional quality on FDI. The availability of data for all of the variables in the model determines the number of countries considered in this study. The year 2005 was chosen as a cut-off date because it was the year that the IFRS were officially implemented by European Union governments. The year 2020 is selected to limit the time range, from 2005 to 2020, due to the same reason that determines the number of countries accounted in the study. Study variables are organized as macroeconomic, institutional quality, and adoption levels of IFRS. Data for all macroeconomic variables and institutional quality are taken from the recently updated World Development Indicators (WDI), IMF international financial statistics (IFS), and world governance index (WGI). The Deloitte (www.iasplus.com), Price Waterhouse Coopers (PWC) (www.pwc.com), and international accounting standards board (IASB) (www.ifrs.org) websites were the main sources of data for the period and level of IFRS adoption.

3.3. Empirical model

To empirically examine the effect of climate change on financial stability, the dynamic panel data model is used, which is specified as follows:

(1) $FD{I}_{it}=\alpha +\theta FD{I}_{i,t-1}+\sum _{e=1}^E{\beta }_{e}X\begin{array}{c}E\\ it\end{array}+\sum _{i=1}^I{\beta }_{i}X\begin{array}{c}I\\ it\end{array}+{\sum }_{a=1}^A{\beta }_{a}X\begin{array}{c}A\\ it\end{array}+{\sum }_{ep=1}^{ep}{\beta }_{ep}X\begin{array}{c}ep\\ it\end{array}+u_{it},u_{it}={\mu }_{it}+v_i$(1)

where $FD{I}_{it}$ is FDI of country i contemporaneous with the time t, with i = 1, …,N, t = 1, …,T, $\alpha$ is a constant term, where the $X{B}_{its}$ with superscripts E, A, l, and ep denote economic, institutional, IFRS adoption, and endogenous (e) and predetermined (p) variables, respectively. With the purpose of arriving at a comprehensive and efficient model, this study has specified a dynamic model via incorporating a lagged response variable ($\delta FD{I}_{i,t-1}$) among the regressors and $\theta ,$ the speed of adjustment to equilibrium. A value of $\theta$ that fall in between 0 and 1 signifies FDI persistence, but they will ultimately come back to their normal level. A value of $\theta$ close to 0 shows a market that is fairly competitive, while a value of $\theta$ close to 1 signifies a less competitive market structure (Dietrich & Wanzenried, 2011), and finally $u_{it}$ is the error term, with $v_i$ the unobserved country-specific effect and ${\mu }_i$ the idiosyncratic error term. Thus, the final model specified is a one-way error component regression model, where $v_i$∼ (IIN (0, ²_v)) and independent of ${\mu }_{it}$∼ (IIN (0, ²_u)). In addition, the variables specified in the dynamic model, the interaction effect between IFRS adoption and institutional quality has been incorporated in the regression.

The dynamic generalized method of moments (GMM) is used in this study for different reasons: first, it works to eliminate serial correlation, heteroscedasticity, and endogeneity. Second, it is capable to correct for unobserved country heterogeneity omitted variable biases, measurement error, and endogeneity problems. Third, it is efficient while having fewer periods and more cross-sections. Fourth, it is more advantageous than instrumental models such as two-stage least square (2sls) if heteroscedasticity is present and addresses potential bias stemming from the use of country-specific fixed effects and lagged dependent variables as a regressors (Roodman, 2009). In the presence of high persistency among variables, the system GMM model does perform well as compared to the difference GMM (Wang et al., 2022; Dietrich & Wanzenried, 2011; Yalta, 2010). Besides, the model is reliable in terms of power and type-I error and has the best small sample bias and precision features, and generally considered better acceptable for the estimation of growth models employing country-level panel data with small T and large N (Dalgaard et al., 2004). In more operational terms, equation number (1) can further be specified as follows:

(2) $FD{I}_{it}=\alpha +\theta FD{I}_{it-1}+{\beta }_{1}lnGC{F}_{it}+{\beta }_{2}lntrad{e}_{it}+{\beta }_{3}infr{a}_{it}+{\beta }_{4}lnhca{p}_{it}+{\beta }_{5}IFR{S}_{it}+{\beta }_{6}INS{T}_{it}+v_i+u_{it}$(2)

where $FD{I}_{it}$ is the FDI for country i at period t; $FD{I}_{it-1}$ is the one-year lag of FDI; $lnGC{F}_{it}$ is the natural log of gross capital formation (%GDP), $lntrad{e}_{it}$ is trade openness (%GDP) to capture trade liberalization, $infr{a}_{it}$ is fixed telephone subscriptions per 100 persons to measure infrastructure development, $lnhca{p}_{it}$ is the natural log of human capital index to capture the contributions of health and education to worker productivity, $IFR{S}_{it}$ is a dummy variable assigned 1 if a country has fully adopted IFRS and 0 for partial adopters, $INS{T}_{it}$ is institutional qualities measured by the world governance index ranges from approximately −2.5 (weak) to 2.5 (strong) governance performance), $v_i$ is the individual-specific effects, and $u_{it}$ is the error term.

The dependent variable, net FDI inflows (%GDP), is measured by the sum of equity capital, reinvestment of earnings, and other long- and short-term capital reflected in a country’s balance of payments. The benefit of utilizing this net FDI inflow is that it captures new investment inflows while reducing disinvestment by foreign investors in the economy of the reporting country (Nnadi & Soobaroyen, 2015).

Infrastructure development is proxied by fixed telephone subscriptions per 100 persons, with the expectation that infrastructural improvement would attract FDI to African countries. An additional variable included to proxy infrastructure development is the gross capital formation (%GDP) for each sampled countries. This variable is included since fixed telephone subscriptions per 100 alone cannot capture the whole system of infrastructure development, and it is expected to have positive and significant impact (Bekana, 2016).

Trade openness (%GDP) is used to account government policy of trade openness of a country as used by multiple studies at the level of countries. The degree of a country’s openness to international trade is an important consideration for investment projects that are focused on the tradable sector. Jordaan (2004) argued that the effect of openness on FDI depends on the nature of the investment. Trade restrictions (and hence less openness) can have a positive effect on FDI when investments are market-driven because foreign businesses that want to serve local customers may choose to establish subsidiaries in the host nation if it is challenging to import their goods there. The increased defects that come with trade protection typically indicate greater transaction costs associated with exporting. In contrast, multinational enterprises engaged in export-oriented investments may prefer to invest in a more open economy.

The benefits of health and education on worker productivity are referred to as human capital. Human capital can still influence any sort of FDI to the extent that it improves factors such as political stability, health, crime/corruption reduction, and civil liberties—all of which are regarded as important factors for any type of FDI (Tariq & Eatzaz, 2008). Noorbakhsh et al. (2001) suggested that human capital is a statistically significant determinant of FDI inflows, is one of the most important determinants and its importance has become increasingly greater through time.

IFRS is a dummy variable used to capture the effect of its full as well as partial adoption on FDI flows to Africa. A dummy variable assigned 1 if a country has fully adopted IFRS as its national accounting standard and it is mandatory for listed firms and 0 otherwise (includes countries adopt IFRS for specific sectors such as financial and lending institutions, and multinationals, and countries adopted and modified to suit the local use). The question of whether the adoption of IFRS results in economic benefits is of particular interest, especially in light of the region’s recent adoption of IFRS. Indeed, there is a growing consensus that harmonization of accounting standards carries with it the potential to increase transparency, comparability, reducing information processing costs and asymmetric information. Bushman and Smith (2001) suggested alternative channels through which financial accounting information might influence economic performance: (1) Improved project identification by managers and investors; (2) Discipline in project selection and expropriation by managers; and (3) Lessening of investor information asymmetries.

In this paper, institutions refer to any public organization and entity, law and norm that influence the political, legal, and economic environment in a particular country. Institutions are proxied by the quality of governance and are measured by means of six indicators developed by Kaufmann et al. (2011). Government effectiveness (GE), rule of law (RL), Political Stability and Absence of Violence/Terrorism (POS), control of corruption (COR), Voice and Accountability (VA), and Regulatory Quality (RQ) are the six governance indexes assumed in this study to capture the role of institutional quality in the process of adopting IFRS in Africa (Bon, 2015).

Government effectiveness is used to proxy the quality of public services, the capacity of the civil service and its independence from political pressures; and the quality of policy formulation. Rule of law is used to proxy the extent to which agents has confidence in and abides by the rules of society, and in particular the quality of contract enforcement, the police, and the courts, as well as the likelihood of crime and violence. Political stability is used to proxy the likelihood that the government will be destabilized by unconstitutional or violent means, including terrorism. Control of corruption is used to proxy the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. Regulatory quality is used to proxy the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Voice and accountability is used to proxy the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media (Kaufmann et al., 2011, 1999).

According to some studies, good institutions in Africa may deter large foreign investors from engaging in monopolistic or oligopolistic behaviour, permit companies to organize and protect themselves from foreign capital, and make it harder for host governments to provide benevolent fiscal conditions (Li & Resnick, 2003). Others contend that the quality of institutions, which is proxied by the level of corruption and the rule of law, is also a significant element in explaining why FDI chooses some countries in the region over others in Africa. Natural resources or the existence of reasonably large markets are not absolute criteria for success. A nation’s FDI stock is more likely to rise in one where corruption is low and the rule of law is upheld (Asiedu, 2006). Nevertheless, despite their abundant natural resources and significant market potential, most African countries have reported having trouble attracting foreign direct investment due to historically inadequate infrastructure and weak institutions (Shan et al., 2018). In any case, the most astonishing gap in the literature relates to institutions and good governance.

3.4. Diagnostic tests

This study employed the two-step system GMM estimation technique since it is more robust and efficient to one-step GMM in the presence of Heteroscedasticity and autocorrelation. The two important post-estimation tests of the model are autocorrelation and instrument validity tests (Roodman, 2009). For the former, the Arellano–Bond test for first-order AR (1) and second-order AR (2) autocorrelation of the differenced residuals is reported. For the latter, the Hansen J-statistic is reported. For AR (1), the null hypothesis of no autocorrelation has been rejected with Prob <0.05, while AR (2) accepts the null hypothesis of no autocorrelation when Prob.>0.05. In the case of the instrument validity test, the null hypothesis that instruments are valid for both Hansen and Sargan tests, are accepted (i.e., Prob. >0.05). The rule of thumb for avoiding over-identification of instruments is that the number of instruments is less than or equal to the number of groups in the regression.

4. Empirical results and discussion

4.1. Panel unit root test

The first result of this study is the examination of stationarity of time series as presented in Table 2. In doing so, all series were analysed with the inclusion of intercept and line. The different panel unit root tests such as (Levin et al., 2002; Breitung & Das, 2005; Im et al., 2003; Hadri, 2000, and the Lagrange multiplier-LM) tests require that either the ratio of the number of panels to periods tend to zero asymptotically or either T or N or the square of each tends to infinity, they are not well suited to datasets with a large number of panels and few periods. Harris and Tzavalis (1999) and Fisher-type (Choi, 2001) are the two other potential tests that fit big panels with small and moderate periods. However, because the latter requires an unbalanced panel, the Harris–Tzavalis test, which assumes that the number of panels increases to infinity while the number of periods remains constant, was used to determine whether variables in the full dataset of 31 countries include a unit root. Their simulation results suggest that the test has favorable size and power properties for N greater than 25. The Harris and Tzavalis (1999) test results (Table 2) suggest that only institutional variables are stationary at their level form while other variables are not, and that all variables are stationary at their first difference. Thus, the variables became stationary after the first difference and hence, the unit root does not exist. Definition of variables and data sources are indicated in Table 1 bellow.

Table 2. Unit-Root Test

Variables	Level		First difference		Order of integration
Foreign direct invt. (FDI as %GDP)	0.571***	0.536	−0.205***	−0.204***	I(1)
Log of gross capital formation (lnGCF)	0.675***	0.594	−0.011***	0.015***	I(1)
Log of trade openness (lntrade)	0.665***	0.456***	−0.139***	−0.115***	I(0)
Log of Infra	0.820	0.620	−0.271***	−0.217***	I(1)
Log of human cap. (lnhcap)	0.983	0.512*	−0.017***	−0.0175***	I(1)
INST*	0.809	0.528	−0.019***	0.067***	I(0)

Note: Harris-Tzavalis (1999) unit-root test for each variable is performed. Ho: Panels contain unit roots; Ha: Panels are stationary. INST*, represents the six governance indicators.

Table 1. Definition of Variables and Sources of Data

Variable	Code	Description	Source
Foreign direct investment	FDI	FDI (%GDP)	WB’s WDI
Gross capital formation	GCF	Gross capital formation (%GDP)	IMF’s IFS
Infrastructure	Infra	fixed telephone subscriptions per 100 persons	WB’s WDI
Trade openness	Trade	[Exports + imports]/ GDP as a measure of trade liberalization and globalization	WB’s WDI
Human capital index	HCI	the contributions of health and education to worker productivity(ranges from 0–1)	WB’s WDI
Institutional quality	Insqua	World governance index	WGI
IFRS adoption	IFRS	A dummy variable assigned 1 if a country has fully adopted IFRS and 0 otherwise.	Deloitte, PWC, IASB

Source: Author’s own compilation

4.2. Panel Co-Integration Test Results

The second result obtained by the study is the Cointegration analysis to check for the Cointegration of the time series (Table 3).The application of this test is very important because it allows the study to examine the relationships between the variables. The existence of Cointegration indicates that there is a balance between the variables of the model in the long-run (Hdom & Fuinhas, 2020).

Table 3. Panel test of Cointegration

Kao test statistics(Ho: No Cointegration; Ha: all panels are cointegrated)
Modified Dickey-Fuller t-statistics	0.424(0.335)
Dickey-Fuller t—statistics	−2.928***(0.001)
Augmented Dickey-Fuller t—statistics	0.836(0.201)
Unadjusted modified Dickey-Fuller t	−13.280***(0.000)
Unadjusted Dickey-Fuller t	−10.934***(0.000)
Pedroni test statistics (Ho: No Cointegration; Ha: all panels are cointegrated)
Modified variance ratio	−10.500***(0.000)
Modified Phillips-Perron t-statistics	7.177***(0.000)
Phillips-Perron t-statistics	−21.368***(0.000)
Augmented Dickey-Fuller t-statistics	−12.601***(0.000)
Westerlund test statistics(Ho: No Cointegration; Ha: some panels are cointegrated)
Variance ratio statistics	−1.373(0.084)

Note: * significant at 10%, ** significant at 5%, *** significant at 1%. Critical probabilities are in parentheses

In order to examine whether a long-run relationship exists among the interested variables, the researcher performs the Kao (1999), Pedroni (2004), and Westerlund (2005) tests of cointegration on the dataset. Majority of the tests show the existence of a long-term relationship between the dependent variable and the independent variables in the proposed research model.

Table 4 shows the two-step system GMM estimation results. Across all models estimated independently to avoid multicollinearity problem for most of the institutional variables, the lagged FDI has a highly significant and positive impact on FDI inflows, implies that FDI persists over time. These findings are in line with what has already been published (Bekana, 2016; Dupasquier & Osakwe, 2006; Emeni, 2014; Zhang & Daly, 2011).

Table 4. Regression results for IFRS and FDI

Dependent variable (FDI)	IFRS	IFRS+INST	IFRS2*INST	IFRS2+ INST	Partial
FDI(−1)	0.303*** (0.014)	0.313*** (0.017)	0.255*** (0.12)	0.243*** (0.023)	0.303*** (0.13)
lnGCF	0.171*** (0.012)	0.164*** (0.013)	0.181*** (0.026)	0.263*** (0.033)	0.171*** (0.011)
Lntrade	0.105*** (0.005)	0.105*** (0.005)	0.097*** (0.008)	0.078*** (0.011)	0.104** (0.005)
InINFRA	1.082*** (0.151)	1.054*** (0.162)	1.603*** (0.125)	1.542*** (0.174)	1.082*** (0.150)
HCI	0.301*** (0.099)	0.276** (0.103)	0.395*** (0.084)	0.451*** (0.122)	0.301*** (0.099)
IFRS (full)	−0.634 (4.573)	1.816 (5.279)
IFRS (partial)					0.634 (4.573)
IFRS2			21.866*** (3.429)	25.775*** (6.250)
INST		−2.474 (2.466)		3.649 (4.386)
Constant	−12.216*** (2.225)	−14.680*** (3.396)	−12.867*** (1.656)	−12.669*** (3.250)	−12.850*** (2.847)
Observations	465	465	465	465	465
Number of groups	31	31	31	31	31
Instruments	30	30	30	30	30
F/Wald test	227.85***	210.41	1734.98***	3755.43	227.85***
AR(1): p-value	0.211	0.204	0.230	0.221	0.211
AR(2): p-value	0.242	0.231	0.245	0.217	0.242
Hansen/Sargan Test	0.172	0.160	0.160	0.197	0.172

Note: ***, ** and * are significant levels at 1%, 5%, and 10%, respectively. Standard errors are in parenthesis, Windmeijer (2005) finite sample standard errors for GMM estimate. IFRS2 is the interaction between IFRS and the institutional variables (INST) of the six aggregate indicators. IFRS is a dummy variable assigned 1 if a country has fully adopted IFRS as its national accounting standard and it is mandatory for listed firms and 0 otherwise. Partial is for the sake of reconciliation of results by assigning 1 for partial adopters and 0 for full.

Gross capital formation, which includes all types of infrastructure, and fixed telephone subscriptions per 100 persons are found positive and significant. This indicates that countries with better infrastructure will attract more FDI. Therefore, infrastructure improvement is one of the priorities of SSA countries in attracting FDI inflows. Both hard and soft infrastructures are important in attracting FDI inflows to SSA. Soft infrastructure implies market-oriented institutions, governance structures and such, and hard means physical infrastructure (such as roads, telephone connections, airports, roads, fast distribution networks, electricity transmissions, and railroads; Jaiblai & Shenai, 2019).The well established and quality infrastructure is an important determinant of FDI flows, consistent with (Bon, 2015) and contracting with (Onyeiwu & Shrestha, 2004).

The effect of trade openness is found to be positive and statistically significant. This indicates that the degree of a country’s openness to international trade is an important determinate of FDI flows to SSA that is focused on the tradable sector. Trade openness can have a positive effect on FDI when investments are not market-driven, rather input driven (Jordaan, 2004). Therefore, this result is in line with the fundamental argument that multinational companies modality towards developing countries is the vertical FDI which aims to exploit international factor-cost differences (Dunning & Lundan, 2008). The effect of human capital index on FDI to SSA is also positive and significant, suggesting that human capital can influence any sort of FDI to the extent that it improves political stability, health, crime, corruption reduction, and civil liberties, among others (Tariq & Eatzaz, 2008).

The effect of full IFRS adoption is determined to be negative but insignificant. While the coefficient for the aggregate measures of institutional quality is likewise negative and insignificant, the sign of full IFRS adoption changed to positive when the aggregate measures of instructional quality were taken into consideration. Overall, it is discovered that the interaction term between institutional quality and IFRS adoption is positive and profoundly significant. This shows that the increase in foreign investment flow is also restricted to target countries with a strong government capability to apply sound rules because the economic effects of IFRS are likely to be dependent on the quality of local institutions and regulatory execution. Institutions that foster effective governance are not exogenously bestowed upon nations; rather, they are determined endogenously, based on the nature of the country’s legal system, its historical evolution, and its level of economic growth (Asiedu, 2006; Peres, Ameer, Xu et al., 2018a). Therefore, SSA’s nations with strong institutions will draw more FDI than other nations.

This study further examined the independent impact of each element of the institutional quality indicators on FDI, as shown in Table 5, in order to ensure the reliability of the findings. The outcome demonstrates that political stability and corruption have a negative and profoundly significant effect on FDI. The impact of regulatory quality was negative and only marginally significant at 10%. However, the effect of rule of law and government effectiveness is found to be positive and significant, whereas the coefficient of voice and accountability is found to be positive but insignificant. The result suggests that corruption, political stability, rule of law, and government effectiveness are paramount important factors affecting FDI flows to SSA countries accounted in this study. The first two are the most important institutional factors that deter FDI inflows, and the last two are the critical institutional qualities attracting FDI to SSA.

Table 5. The independent effect of institutional variables on FDI

DV (FDI)	COR	RL	RQ	POS	VA	GOE
FDI(−1)	0.305*** (0.016)	0.208*** (0.014)	0.289*** (0.015)	0.323*** (0.019)	0.289*** (0.015)	0.283*** (0.018)
lnGCF	0.090*** (0.024)	0.201*** (0.027)	0.157*** (0.014)	0.078*** (0.027)	0.211*** (0.023)	0.267*** (0.034)
Lntrade	0.082*** (0.008)	0.106*** (0.010)	0.109*** (0.005)	0.139*** (0.007)	0.092*** (0.007)	0.058*** (0.008)
InINFRA	1.065*** (0.157)	1.506*** (0.194)	1.276*** (0.180)	1.559*** (0.250)	1.225*** (0.326)	1.083*** (0.172)
HCI	0.175* (0.097)	0.312*** (0.084)	0.316*** (0.099)	0.259*** (0.086)	0.224*** (0.091)	0.296*** (0.102)
IFRS	−3.638 (4.884)	1.138 (9.819)	1.877 (4.769)	3.846 (5.098)	−3.793 (3.827)	−8.981 (9.849)
COR/RL/RQ/POS/VA/GOE	−7.655*** (1.374)	10.847*** (2.367)	−3.833* (1.958)	−3.782*** (0.970)	1.802 (3.099)	8.459*** (1.198)
Constant	−11.486*** (2.751)	−9.775** (4.554)	−16.133*** (2.974)	−18.310*** (2.926)	−10.210*** (2.148)	−2.635 (4.914)
Observations	465	465	465	465	465	465
Number of groups	31	31	31	31	31	31
Instruments	30	30	30	30	30	30
F/Wald test	241.97***	757.06***	197.04***	1548.69***	201.46***	1742.23***
AR(1): p-value	0.206	0.215	0.211	0.218	0.218	0.210
AR(2): p-value	0.249	0.218	0.239	0.280	0.241	0.215
Hansen/Sargan Test	0.279	0.244	0.276	0.237	0.194	0.372

Note: ***, ** and * are significant levels at 1%, 5% and 10%, respectively. Standard errors are in parenthesis, Windmeijer (2005) finite sample standard errors for GMM estimate.

Overall, the most significant institutional factors influencing FDI flows to SSA are government effectiveness, rule of law, political stability, and corruption. The first two are the crucial institutional characteristics that draw FDI to SSA, where as the last two are the most significant institutional elements that discourage FDI inflows to these countries. Thus, countries experiencing good government effectiveness in terms of quality of public services, the capacity of the civil service and its independence from political pressures, among other, will attract more FDI inflows. The same is true for those experiencing better rule of law which could be seen in terms of the quality of contract enforcement, the police, and the courts, among others.

On the contrary, countries suffering from political unrest and petty and grand forms of corruption discourage foreign firms partnering local firms, thereby reducing inward FDI. To the extent, corruption makes local bureaucracy less transparent and hence adds to the cost of doing business (Bokpin et al., 2017; Borojo & Yushi, 2020). This finding is consistent with (Bon, 2015; Peres, Ameer, Xu et al., 2018a; Shan et al., 2018). Political stability, which can be affected by government stability, internal conflict, external conflict, and ethnic tensions, can make the host country less attractive to investors because it is generally associated with higher levels of expropriation, which may worry potential investors, and impede their commitment to investment.

5. Conclusion

The study looked at how the adoption of IFRS, whether fully or partially, affected net FDI flows to Africa between 2005 and 2020. The outcome of the two-step system GMM estimation indicated that the impact of full IFRS adoption is negative but insignificant. However, it has been discovered that the influence of the interaction term between institutional quality characteristics and IFRS adoption is found to be positive and profoundly significant in drawing FDI to SSA. This implies that adopting IFRS is more beneficial economically when quality institutions are in place, as opposed to doing so just to comply with the IMF and World Bank’s international regulations in order to qualify for financial assistance. Not all nations are equally attracted to FDI as a result of the implementation of IFRS. In other words, countries with strong institutions would gain more from the adoption of IFRS in luring FDI than nations with weak institutions. As a result, institutional factors that are crucial in determining FDI flows to SSA include government effectiveness, the rule of law, political stability, and corruption. The first two enters positive and significant into the model, whereas the second two are shown to be negative and significant. Additionally, consistent with the eclectic paradigm, it is found that the effects of the variables controlled in this study—infrastructure, trade openness, and human capita—are positive and significant.

In order to encourage FDI flow to Africa, which significantly depends on FDI inflows and foreign capital accumulation, policies aiming at improving government effectiveness, the rule of law, political stability, and corruption control are essential. It won’t be enough for these countries to just embrace IFRS to receive recognition from various international bodies and appear to have a socially acceptable and respectable business climate. It is obvious in many African countries that the implications of locational advantages in terms of institutional infrastructure such as government performance, the rule of law, political stability, corruption, and accounting standards are yet emerging, albeit with little benefit. Besides, severe lack of resources in these nations is making costly and time-consuming initiatives towards enhancing institutional infrastructure. Therefore, prioritizing and investing on a specific piece of infrastructure, such as enhancing the application of the rule of law and sustaining political stability, may result in fewer resources and chances for developing other elements of a country’s institutional framework. Because of this, policy makers can prioritize expensive reforms that have the potential to considerably increase FDI by having a better understanding of the implications of the institutional infrastructure in general and the adoption of IFRS in particular. To draw FDI to African nations, it is equally crucial to increase infrastructure spending, human capital development, and trade openness.

Disclosure statement

The author has no relevant financial or non-financial interests to disclose.

Additional information

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Notes on contributors

Fentaw Leykun Fisseha

Fentaw Leykun Fisseha (PhD) is currently an Assistant Professor in the Department of Accounting and Finance, College of Business & Economics, Bahir Dar University, Ethiopia. His research interests include international business finance, public and corporate finance, Accounting and auditing, financial institutions and markets, Taxation and project management. He has published several articles in different scholarly journals such as the Ethiopian journal of economics (EEA), cogent economics and finance (Taylor & Francis), and African Multidisciplinary Tax Journal. He is currently serving as a reviewer for several academic journals such as Public Finance Review.