Fiscal policy-growth nexus in CFA countries: assessing the role of institutional quality and debt

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Fiscal policy-growth nexus in CFA countries: assessing the role of institutional quality and debt

Christelle Meniago^a,band Joel Hinaunye Eita^b

aSchool of Economics and Management Sciences, Sol Plaatje University, Kimberley, South Africa;

bSchool of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa

ABSTRACT

The importance of government debt and institutional quality for economic growth has become fundamental, predominantly in a context where policy makers must face snowballingfiscal imbalan- ces. This study investigates the relationship betweenfiscal policy and economic growth in CFA countries, while also examining the role of institutions and debt in the relationship. Using panel data of thirteen countries over the period 1995–2017, the system GMM estimates have clearly established that contrary to the Keynesian view which postulates a positive relationship betweenfiscal policy and economic growth, there is strong evidence of a negative relationship betweenfiscal policy and economic growth. The economic reason behind this result could be because most developing countries (CFA countries included) do not spend on productive sectors of the economy. This could adversely affect growth, despite the fact that government spending increases every year. Thefindings of the interaction terms show mixed results.

ARTICLE HISTORY Received 21 August 2021 Revised 13 April 2022 Accepted 26 May 2022 KEYWORDS Fiscal policy; economic growth; institutional quality;

government debt JEL CLASSIFICATIONS F30; F41; H10

1. Introduction

Is there any successfulfiscal policy design and implementation without an in-depth or thorough understanding of the determinants of fiscal performance? Probably not. It is shared knowledge thatfiscal policy alongside monetary policy is one of the main tools accessible to public authorities to intervene in the economy. In fact, the 2008financial crisis has renewed interest in the importance of government intervention to stabilise the economy. Thefinancial crisis has thus placed fiscal policy at the forefront of economic debate, predominantly on its use in mitigating the negative effects of thefinancial crisis on output. Practical examples of this scenario were seen when the International Monetary Fund (IMF,2008) called for fiscal stimulus (alongside monetary policy) in response to this global downturn. Of more recent interest is the observance of many countries adoptingfiscal stimulus packages to combat the negative impact of the economic crisis caused by the COVID-19.

Even though from a theoretical standpoint, the effects of ﬁscal policy in boosting economic growth are well known, the empirical literature has clearly demonstrated 12

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CONTACTChristelle Meniago [email protected] School of Economics and Management Sciences, Sol Plaatje University, Kimberley 8301, South Africa

ß2022 Stellenbosch University

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inconclusive results. More importantly, in this extant context of achieving the Sustainable Development Goals (SDGs), ﬁscal policy plays an essential role. However, ﬁscal challenges are at the core of development challenges across developing countries, and CFA (Communaute Financiere Africaine (African Financial Community)) countries are not exempted. While some studies (see Medee & Nenbee, 2011; Ocran, 2011;

Ugwuanyi & Ugwunta, 2017) have suggested some factors that contribute to the effectiveness of fiscal policy on growth, notably the composition of government expenditures, and the size of the government’s fiscus, there is still limited comprehensive empirical research dealing with institutions and debt in explaining thefiscal-policy growth effect in the context of developing economies. Abdullah, Habibullah, and Baharumshah (2008) emphasised the role of quality institutions in sustaining long- term economic growth in Asian economies, which was also affirmed by Bhattacharjee (2017). On the other hand, Combes, Minea and Sow (2017) established that public debt significantly affects the cyclicality of fiscal policy in a panel of 56 developed, emerging and developing economies and recommended that a more thoughtful supervision of the public debt pathway is desired. This research is particularly timely because in the inclusive context of Africa, and most especially CFA countries, studies on the contributions of institutions and debt in explaining the fiscal policy-growth nexus are lacking in a comprehensive work, despite its relative importance.

Why the focus on CFA countries? The main reason is that fiscal policy in CFA countries remains more fragile when compared to other developing countries in Sub- Saharan Africa (SSA). CFA countries are mostly characterised by underdevelopedfinan- cial markets, small capital inflows and narrow tax bases, which make them highly dependent on grants and foreign loans for financing their national budgets. In addition to the above, this study has chosen to direct its attention to CFA because these countries operate under afixed exchange rate regime (the Franc CFA franc is fixed to the Euro), and fiscal policy seems to be the main macroeconomic policy option they can use to achieve their development outcomes. That is because monetary policy has been shown to be less effective in fixed exchange rate regimes. As highlighted by Meniago and Eita (2017), the Franc CFA franc is used by two distinct monetary unions.

These are Central African Economic and Monetary Community (CEMAC) and the West African Economic and Monetary Union (WAEMU). The CEMAC comprises of six countries (Cameroon, Gabon, Central African Republic, Chad, Congo and Equatorial Guinea), while the WAEMU is made up of eight countries (Benin, Burkina Faso, Ivory Coast, Guinea Bissau, Mali, Niger, Senegal and Togo), where each member of these monetary blocs issues its own Franc CFA franc. The Franc CFA franc was devalued in 1994, and since then; the currency has been pegged to the euro at aﬁxed rate of FCFA655.957/

Euro. The terms and conditions governing this arrangement are governed by four vital codes. These are unlimited convertibility of the currency,ﬁxed parity, free transferabil- ity, and centralisation of foreign exchange reserves. In exchange, each country belong- ing to the CFA community is mandated to preserve at least 50% of its foreign reserves at its current account held at the Bank of France. The countries in the CFA zone present some similarities in terms of their economic, trade and institutional features, and have been broadly exposed to analogous shocks in recent years owing to their heavy dependence on agricultural commodities.

The issue of conductingﬁscal policy in a monetary union has been ongoing. There is considerable literature, both theoretical and empirical, onﬁscal discipline in developed countries, with huge attention focussed on the European Union. Examples of these studies include Beetsma and Jensen (2005); Hallerberg, Strauch, and Von Hagen (2007);

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and Barbier-Gauchard and Betti (2021). However, a scrutiny of the literature suggests that this research has been largely concentrated in the developed world and remain limited (albeit growing) in the milieu of African monetary unions, especially the Franc Zone, which is of special interest to this study. The few studies that have attempted to analyse this relationship in the African region include Mbemba (2011), Chigbu and Njoku (2013), and Comlan (2017).

The 2008 ﬁnancial crisis has proven that even a monetary union could be unsuc- cessful in absorbing the adverse effects stemming from large macroeconomic shocks.

This is particularly the case because, though distinct countries belong to the same monetary union, they have diverse macroeconomic fundamentals and experience vari- ous levels of economic development. Thus, ﬁscal policy can prove to be rather deﬁ- cient in responding to these shocks, since its disparities constitute an important stumbling block to the ability of policymakers to restore macroeconomic stability.

From a theoretical standpoint, the famous Keynesian theory postulates that reducing government expenditures will negatively affect private demand and hence output. At the other end of the spectrum, the neo-classical theories hypothesise that ﬁscal con- tractions give room for an expansion of the private sector, which lead to an expansion of the economy.

The 2014 oil price rout positioned a majority of the CEMAC countries in a difficult situation since most of them are oil producers. For instance, countries such as Gabon, the fifth largest oil producer in Africa (according to the World Bank), which had long recorded surpluses in its fiscus due to favourable oil prices, plunged into a deficit for the first time in 2015 since 1998. At the aggregate level, revenues generated by the oil sector accounted for approximately 80% of exports and more than half of fiscal resources on average over the past years (World Bank, 2021). In light of the current situation, several ambitious public investment programmes were also underway at the time when oil prices collapsed. This aggravated the macro-financial shocks. Albeit most countries in the CEMAC are now starting to diversify their economies, revenues from the hydrocarbon sector remain a primary source of government revenue, and this remains a major downside risk to economic activity. In some countries of the WAEMU, fiscal discipline seems to be stronger. With an urgent goal to become an emerging country by 2020 as stated in its National Development Plan (NDP), Ivory Coast fiscal stance has remained satisfactory and under control. In response to the recent decline in cocoa prices on the international market, authorities were proactive and decided to reduce the taxes on the product rather than allowing these fluctuations toflow through increased prices for cocoa beans.

According to the IMF (2008),fiscal policy plays an important role in ensuring macroeconomic stability, which is a prerequisite for achieving and maintaining economic growth. Adding to that, Buti and Franco (2005) suggest that fiscal policy should endeavour to flatten business cycle fluctuations by reinforcing the automatic adjust- ment of taxes and by altering government expenditures. In other words, government spending should decrease during growth accelerations and increase in economic downturns. That said, during periods of favourable economic conditions, governments should ensure to create a goodfiscal space that will be required to manoeuvre during downturns. This is especially important in CFA countries, where the level offiscal defi- cits is often affected by developments (especially infrastructural) beyond the control of policy makers.

Considering the current era of rising debt levels in many SSA countries, this study thus attempts to investigate the factors that affects ﬁscal performance in CFA 99100

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countries. More particularly, we are interested in understanding the nexus betweenfiscal policy and economic growth. This research question has dominated both theoretical and empirical literature in previous years, with one view believing that government policies spur growth while its opponents consider otherwise. In the same inconclusive vein of theories, the existing empiricalfindings are indecisive, with some studies finding that the nexus between fiscal policy and growth is either positive, negative, or inconclusive.

Our main aim is therefore not to provide a deﬁnite solution to the prevalent debate on the ﬁscal policy – growth nexus, but rather contribute to the academic literature by examining the issue in a sample of homogenous developing countries, who appear not to entice a lot of scholarly interest. In this light, the contributions of this paper are twofold:

i. To the best of our knowledge, majority of studies have concentrated mostly on developed countries, with very few directed on developing countries, and more particularly on CFA countries. The literature on the ﬁscal policy–growth relationship in CFA countries is still limited and therefore warrants more investigation.

This is especially true when considering the fact that ﬁscal policy remains the main macroeconomic policy tool that can help these countries to achieve their development outcomes.

ii. Majority of studies, and more particularly those concentrated on CFA countries, have disregarded the role of public debt and institutions in analysing the ﬁscal policy-growth nexus. Unlike previous studies, this paper thus contributes to the existing literature and incorporates this important element into the relationship. In this extant era of rising debt levels, where most CFA countries record high debt levels, this study attempts to demonstrate that the effects of the countries’ ﬁscal position on growth can be very limited. This limit may be triggered by the high debt to GDP ratio and governance issues which are prevalent among CFA countries.

The rest of the paper is organised as follows. Section 2 gives a brief overview of the literature, whilesection 3describes the dataset and the methodology employed in the study. Section 4 discusses the results, while section 5 gives the conclusion and recommendations.

2. Theoretical and empirical review of the literature 2.1. Theoretical literature

The ﬁscal policy-growth nexus cannot be studied accurately without a formal theoretical framework, combined with suitable econometric methods. Research on the effects of ﬁscal policy on growth took off since the development of the endogenous growth models.

Research on the effectiveness of fiscal policy on economic growth increased especially after the global economic andfinancial crisis of 2008. The Covid-19 pandemic is expected to lead to many studies on the impact offiscal policy on economic growth.

It is also important to mention that there are many economic theories in the literature that attempt to provide answers on the impact of ﬁscal policy on economic growth.

This is due to the fact that the ﬁscal process is complex and not captured fully.

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There are several arguments that are provided on the appropriateness ofﬁscal policy and the impact of government expenditure on economic growth.

To investigate the effectiveness of fiscal policy, it is generally important to start with Keynesian theory. In the Keynesian theory, it is assumed that prices are sticky and that there is excess capacity. This means that aggregate output is determined by aggregate demand. This suggests that government expenditure is expected to have a multiplier effect on both aggregate demand and output. According to Canh (2018), the main question in thefiscal policy effectiveness literature is whether it (fiscal policy) causes crowding out or crowding in effects in the economy. Crowding in effect is where the government is encouraged to increase its expenditure during periods of low economic activity or recession in order to take care of the lack of consumption and investment. This argument is also supported by Jahan, Mahmud, and Papageorgiou (2014). The crowding out effect on the other hand happens when the impact of fiscal policy on output is negative. This usually occurs through changes in variables such as interest rate and exchange rate if the economy is open. The Keynesian theory also postulates that an increase in interest rate will have a negative impact on private investment. This happens mainly in a situation wherefiscal policy is backed by borrowing (Canh,2018).

The other theory of fiscal policy effectiveness is the neoclassical view. The focus of this view is on the determination of output, goods and distribution of income that takes place through the forces of demand and supply. In this view, it is assumed that firms and individuals are maximising utility from their limited income. This happens under the limits of the factors of production and available information. According to Davis (2006), the neoclassical view brings in rational expectations in comparison to adaptive expectations that happen under the Keynesian theory. Bringing in rational expectations means that there will be forward adjustments in economic variables, which happen more progressively. This suggests that fiscal policy will be important in both short term and long term periods. It is argued that if there is a permanent change in fiscal policy, it will lead to crowding out effects. That is because economic agents in the private sector will expect permanent change in interest rate and exchange rate.

In addition to the neoclassical view, there is another theory ofﬁscal policy effectiveness called the Ricardian equivalence. According to the Ricardian equivalence, individuals in the economy are forward looking in the present economic activities. This is contrary to the Keynesian theory where individuals and economic agents rely on current income (Barro, 1989; Mosikari & Eita, 2017; Ogba, 2014). According to Mosikari and Eita (2017), the Ricardian equivalence postulates that if there is a cut in tax in the present period, individuals will expect higher taxes in the future. That is because a tax cut in the current period implies more borrowing and higher taxes in the future.

There will be no change in permanent income. The Ricardian equivalence assumes that there are no liquidity constraints and that the markets that are perfect will not cause changes in the general private consumption. This argument is supported by Barro (1974). The Ricardian equivalence argues that there will be no crowding out or crowding in effects of fiscal policy. However, Hemming, Kell, and Mahfouz (2002) argue that if the government changefiscal policy lump taxes, it will have an effect on the permanent income, aggregate demand and ultimately output. This suggests that the productivity of government expenditure and how it is distributed throughout the economy determine the effectiveness offiscal policy.

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2.2. Empirical literature

There is an extensive empirical literature on the effectiveness of fiscal policy on economic growth. The interest in empirical investigation of fiscal policy on economic growth dates back to the period of the great depression in the 1930s. It was then extensively revisited after the global economic andfinancial crisis of 2008 and the current Covid-19 pandemic. Although the debate on the sign and size of thefiscal multiplier is far from being settled, empirical studies indicate that there is general consensus that there are specific country variables which make economic growth to respond significantly to changes in fiscal policy. Among others, whether the country is in an upward trend or economic recession, degree of government spending during the recessions, whether the country has flexible or constrained monetary policy and levels of public debt (Auerbach & Gorodnichenko, 2012; 2017; Bachmann & Sims, 2012; Corsetti, Meier, & M€uller,2012; Miyamoto, Nguyen, & Sergeyev,2018). Other variables include openness of the economy, exchange rate regime (such as flexible or fixed exchange rate regimes), employment and composition of government expenditure (Kasselaki & Tagkalakis,2016).

The empirical literature has showed that there has been interest amongst research- ers in understanding how effective isfiscal policy in boosting growth, although most of the analysis has been directed mostly towards advanced economies. We also note that for the few research that attempted to analyse the relationship within the African context, most of the studies were done mostly in a single-case country analysis, particularly South Africa. Ugwuanyi and Ugwunta (2017) examined the effectiveness offiscal policy on growth in SSA using panel data econometric techniques. The results of the analysis revealed that government productive and unproductive expenditures, as well as distortionary tax and non-distortionary taxes have significant effects on the economic growth of sub-Saharan African countries. In a similar vein, Praise and Jacob (2018) analysed the effects of fiscal and monetary policies on economic growth in a panel of 47 SSA economies using the Generalised Methods of Moments. Thefindings indicated thatfiscal and monetary policies affected economic growth positively in the sub-region, albeit fiscal policy was found to have a greater scale-effect in increasing economic growth in SSA than monetary policy.

Single-case studies like Zungu, Makhoba, and Greyling (2022) analysed the impact offiscal policy on growth within the context of the South African economy. Using the Bayesian Vector Autoregression (BVAR) model, the authors found that while an unanticipated shock in government expenditure and public debt led to a substantial negative and persistent impact on economic growth, a shock in investment had a significant positive effect on economic growth. Notwithstanding, using quarterly data and non-linear auto-regressive distributive lag models, Nuru and Gereziher (2022) investigated the short and long-run asymmetric effects offiscal policy (proxied by government expenditures) on economic growth in South Africa. The results of the analysis exhibited that the negative change effect of government spending was found to be greater than the positive change effect of government spending on economic growth.

M’Amanja and Morrissey (2005) also investigated the relationship in Kenya and found that productive expenditures had a strong negative effect on growth whilst there was no evidence of distortionary effects on growth of distortionary taxes. The results also revealed that government investment was advantageous to growth in the long run.

Notwithstanding, Yusuf and Mohd (2021) used non-linear ARDL econometric techniques to investigate the asymmetric impact of ﬁscal policy on economic growth in Nigeria. The ﬁndings of the analysis demonstrated that growth responds 249250

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disproportionately to changes in recurrent expenditure in both the long and short run, while it responded symmetrically to changes in customs and excise levies.

Remarkably, the study also found that disparities in domestic and external debt had an uneven impact on growth in the long-run.

It is interesting to note at this point that most of the empirical studies did not explore the role of institutions nor debt in contributing to the effectiveness of fiscal policy despite its importance. It is expected that institutions have a potential to play a role in ensuring that fiscal policy is effective in contributing to economic growth. According to Avellan, Galindo, and Leon-Diaz (2020), the quality of institutions can provide an important economic framework that can enhance the effectiveness of fiscal policy on economic activity. It is expected that countries, which have better quality institutions, will be able to efficiently use their human and physical capital in order to achieve a higher level of income. This suggests that countries with better institutions are expected to have higher responsiveness of economic growth to fiscal policy. This argument is based on Acemoglu, Johnson, and Robinson (2001). It is also in line with Rodrik (2008) who postulated that quality institutions are very important for the achievement and maintenance of macroeconomic stability. Studies such as Tanzi and Davoodi (1997) included the governance variable of corruption in the analysis. The results indicate that a high level of corruption is associated with lower productivity and lower economic growth. These results were supported by Shankha and Era (2011) who stated that corruption negatively influences the quality and efficiency of public capital. This will ultimately result in low economic growth. Mauro (1995) as well as Haque and Kneller (2012) also came to the same conclusion. Avellan et al. (2020) provide evidence in support of the importance of the institutional quality on the effectiveness of fiscal policy in contributing to economic growth. A panel of 113 countries was used to test the effect of institutions on economic growth. The results show that in countries that have better institutions, economic growth responds positively and significantly to expansionary fiscal policy. Countries with poor institutions have volatile economic activity. Economic activity did not respond significantly to fiscal stimulus in these countries.

On the other hand, using a mixed sample of developed and developing economies, Teles and Mussolini (2014) empirically established that a country’s level of the public debt-to-gross domestic product (GDP) ratio negatively impact the effect ofﬁscal policy on growth. Interestingly and contrary to previous literature, the study further demonstrated that the size of the debt might also lead to higher economic growth especially if the said debt is associated with increases in productive expenditures. In a panel of 20 emerging markets, Canh (2018) equally established that ﬁscal policy is effective in promoting economic growth, even though it was also found to lose its effectiveness in the case of highly indebted countries.

Although we acknowledge that there are studies who investigated the effect offiscal policy on economic growth within the African context, this study will be thefirst of its kind (to the best of our knowledge) to investigate the fiscal policy – growth nexus while taking into account the role of institutions and debt in the empirical analysis. Besides the fact that CFA countries have not aroused a lot of interest in the literature, it is worth investigating this relationship within this monetary union, since fiscal policy remains the main effective macroeconomic policy option available to these countries.

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3. Data and methodology

3.1. Data

The sample consists of a panel of 13 CFA countries¹(Equatorial Guinea, though a CFA country, was excluded from the analysis because of lack of data on most of the variables under consideration), with annual data covering the period 1995 to 2017. The choice of the countries as well as the selected time frame is largely inﬂuenced by the availability of data.

The data for Gross Domestic Product growth rate (GDP), Government Expenditures (GEXP), Gross Capital Formation (GCF) growth rate, Population growth rate (POP), Trade as a percentage of GDP (Trade) and Government Debt as a percentage of GDP (Debt) is from the World Development Indicators (WDI) of the World Bank; Control of Corruption (Corr) and Government effectiveness (GovE) which are proxies for the institutional quality variables are from the World Governance Indicators (WGI) of the World Bank.

For robustness check, we will use two economic growth variables, namely the GDP growth rate, and the GDP per capita growth rate. The choice of our control variables is in accordance with latest literature on the determinants of economic growth (Barro, 2003; Ssozi & Asongu, 2016).

The deﬁnitions of variables and corresponding sources, the summary statistics and correlation matrix are presented inAppendix 1, 2 and 3respectively.

3.2. Empirical model and estimation 3.2.1. Specification

The purpose of this paper is to quantify the effects of ﬁscal policy on economic growth in CFA countries. Considering that the government constitutes a relatively large share in CFA economies, the currency hard peg to the Euro, and the level of development, changes in tax and public expenditure policies may play an important role in output dynamics. Contrariwise, other important factors, which have often been overlooked in the literature such as the high level of public indebtedness, and the weak level of institutions may weaken the effectiveness of theﬁscal policy in stimulat- ing economic activity in the region.

In this paper, we use the conventional determinants of economic growth, and following the work of Canh (2018), the basic empirical model is thus speciﬁed as follows:

GDPit¼ b₀GDPi,t1þc₁GEXPitþaXtþeit

where i stands for country, and t stands for time. GDP here stands for GDP growth rate, which in this case accounts for economic growth. GEXP stands for government spending which proxies forfiscal policy variable. In this study, we decided to use only government spending, excluding government revenue, on the rationale that the for- mer is a more useful representation of the changes in fiscal policy, while the latter, which also comprises of tax revenue has a rather strong correlation with government expenditures (government spends whatever it receives). Thus, using both proxies in the same specifications may cause biased estimates and the model suffering from ser- ial correlation.

Xt here stands for the vector of control variables which include gross capital formation growth rate (CAF) which accounts here for private investment, population growth rate (POP), trade, and inﬂation.

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Because we are also interested in understanding the role of government debt and institutions in the relationship between ﬁscal policy and economic growth, we also incorporate these variables into the model. We will incorporate government debt as a

% of GDP (Debt) and control of Corruption (Corr) and government effectiveness (GovE) to proxy for institutions.

The final choice of the model employed in this study is based on a modified ver- sion of the endogenous growth theory which assumes thatfiscal policy has an effect on economic growth. In light of the above,Equation (1)and(2) respectively represent the level andfirst difference GMM specification for estimating the effect of the inde- pendent variables on the outcome variable.

Gi,t¼r0þr1Gi,tsþr2GEXPi,tþr3Ci,tþr4Interi,tþX³

h¼1

dhWh,i,tsþg_iþn_tþei,t (1)

Gi,tGi,ts ¼r1ðGi,tsG_i,t2sÞ þr2ðGEXPi,tGEXPi,tsÞ þr3ðCi,tCi,tsÞ

þr4ðInteri,tInteri,tsÞ þX³

h¼1

dhðWh,i,tsWh,i,t2sÞ þ ðn_tn_tsÞ þ ðei,tei,tsÞ (2) G_i,t is an economic growth (GDP growth rate or GDP per capita growth rate) variable for country i at period t; GEXP is government expenditures; C is a policy channel (i.e. debt & governance); Inter is the interaction between GEXP and a policy channel;

r₀ is a constant; s is the degree of auto-regression which is one because a lag of seven years is enough to capture past information;Wis the vector of control variables (Gross capital formation, population growth, inflation, and trade openness), g_i is the country-specific effect, n_t is the time-specific constant and e_i,_t is the error term.

Equations (1) and (2) are replicated for all combinations of outcomes, GEXP and policy variables.

The expected signs for the control variables are as follows. First, there is a broad consensus in the literature that population growth is positively linked to economic activity and output (Becker, Laeser, & Murphy, 1999; Headey & Hodge,2009). Second, trade openness has been documented to be a source of economic development in developing countries (Sakyi, Commodore, & Osei Opoku, 2015; Bresnahan, Coxhead, Foltz, & Mogues, 2016) and therefore, a positive nexus is expected between trade openness and the outcome variables. On the other hand, it is expected a positive relationship between gross capital formation and the outcomes variables, while inﬂation is expected to bear a negative sign.

Examining the effects ofﬁscal policy on economic growth is not without hurdles. In actual fact, the estimations ofEquations (1)and(2)may be biased due to the endoge- neity problem that may arise if some of the explanatory variables are correlated with the error term. Nonetheless, the use of the Generalised Method of Moments (GMM) estimation technique developed by Arellano and Bond (1991) and Blundell and Bond (1998) permits us to deal with this issue appropriately. As suggested by Arellano and Bover (1995) and Blundell and Bond (1998), two tests are indispensable to make sure that estimates are reliable. These are identifying restrictions tests of Sargan and Hansen, with the latter being more robust, but more sensitive to the number of instruments. The null hypothesis in both cases is that instruments are valid. In addition, we apply the extension of Arellano and Bover (1995) in Roodman (2009) which limits the proliferation of instruments.²Therefore, the estimation technique utilised in this study is the GMM. It is also worth noting that the GMM is well designed for a dataset for 399400

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which the number of countries should be higher than the number of periods in each country. In this regard, our dataset is decomposed into averages of seven-year non- overlapping intervals. Accordingly, data points corresponding to the following intervals are engaged: 1995–1999; 2000–2004; 2005–2009; 2010–2014; 2015–2017. As cited in Meniago and Lartey (2021), the use of non-overlapping period averages also has the advantage of mitigating business cycle disturbances that may loom considerably (Islam,1995).

To the best of our knowledge, this study will be the ﬁrst of its kind to conduct such empirical estimations in CFA countries. A special feature of our empirical analysis is also to attempt to estimate the role of public debt and institutions in the relationship betweenﬁscal policy and economic growth.

4. Empirical results

Tables 1 to 4 report results of the regressions based on speciﬁcations without interaction terms, whereasTables 5–7feature results based on speciﬁcations that incorporate interaction terms. Different set of controls are also used in the distinct model estimation.

Table 1reports the results of the basic estimated model that examine the effects ofﬁs- cal policy (proxied by government expenditures) on economic growth. While Models 1–3 use real GDP growth as the dependent variable, Models 4–6 use GDP per capita as the dependent variable to account for robustness check. In column 1 ofTable 1, the estimation reveal that when all the control variables (Gross capital formation, inﬂation, population, trade) are incorporated into the model, there is a strong negative relationship between government expenditures and economic growth. Similarly, when controlling for host 449450

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Table 1. Effects of Government Expenditures on Growth.

(1) (2) (3) (4) (5) (6)

Variables GDP growth GDP growth GDP growth GDP per capita GDP per capita GDP per capita GDP Growth (–1) –0.276 –0.224 –0.289

(0.069) (0.047) (0.044)

Gov Exp –0.454 –0.606 –0.361 –0.581 –0.581 –0.504

(0.004) (0.099) (0.045) (0.048) (0.038) (0.029)

GCF 0.255 0.253 0.153 0.153

(0.065) (0.044) (0.076) (0.076)

INFL –0.316 –0.311 –0.262 –0.262

(0.097) (0.051) (0.088) (0.088)

POP 1.508 2.725 –0.392

(1.522) (0.347) (1.616)

Trade 0.031 0.060 0.023

(0.031) (0.015) (0.054)

GDP per Capita (–1) –0.182 –0.182 –0.084

(0.132) (0.132) (0.148)

Constant 4.922 8.650 –1.616 6.532 6.532 –4.789

(5.134) (2.109) (3.267) (4.044) (4.044) (5.893)

Observations 51 51 52 51 51 52

Countries 13 13 13 13 13 13

AR(2) 0.203 0.649 0.440 0.509 0.509 0.380

Hansen OIR 0.796 0.855 0.617 0.475 0.775 0.130

Sargan OIR 0.740 0.657 0.660 0.141 0.135 0.356

Instruments 9 9 9 9 9 9

,, : Signiﬁcance levels at 1%, 5% and 10%, respectively. DHT: Difference in Hansen Test for Exogeneity of Instruments Subsets. Dif: Difference. OIR: Over-identifying Restrictions Test. The signiﬁcance of bold values is twofold.

(1) The signiﬁcance of estimated coefﬁcients and the Wald statistics. (2) The failure to reject the null hypotheses of (a) no autocorrelation in the AR (2) test and; (b) the validity of the instruments in the Sargan and Hansen OIR tests.

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499500 501502 503504 505506 507508 509510 511512 513514 515516 517518 519520 521522 523524 525526 527528 529530 531532 533534 535536 537538 539540 541542 543544 545546 547548

Table 2. Effects of Government Expenditures on Growth: The Role of Institutions.

(1) (2) (3) (4) (5) (6)

Variables GDP growth GDP growth GDP growth GDP growth GDP growth GDP growth

GDP Growth (–1) –5.638 –0.059 0.032 –0.431 –0.211 –0.076

(0.173) (0.071) (0.204) (0.025) (0.045) (0.084)

Gov Exp –3.937 –0.483 –0.727 –1.643 –0.406 –0.675

(0.087) (0.064) (0.078) (0.086) (0.041) (0.837)

GCF 3.435 0.121 –0.006 0.259

(0.331) (0.186) (0.658) (0.012)

INFL 9.439 –0.438 0.656 –0.111

(3.162) (0.421) (1.402) (0.307)

POP –0.611 –1.177 –3.374 –0.843

(0.070) (0.054) (0.073) (0.044)

Trade 0.598 –0.024 0.451 0.100

(0.169) (0.096) (0.508) (0.177)

Gov Eff 1.758 1.952 2.635

(0.095) (0.048) (0.050)

Corr 3.583 3.325 0.187

(1.048) (1.125) (0.065)

Countries 13 13 13 13 13 13

AR (2) 0.905 0.267 0.561 0.298 0.428 0.893

Hansen OIR 0.986 0.237 0.361 0.669 0.127 0.698

Sargan OIR 0.980 0.0625 0.363 0.493 0.0903 0.742

(1) The signiﬁcance of estimated coefﬁcients and the Wald statistics. (2) The failure to reject the null hypotheses of (a) no autocorrelation in the AR (2) test and; (b) the validity of the instruments in the Sargan and Hansen OIR tests.

Table 3. Effects of Government Expenditures on GDP per Capita: The Role of Institutions.

(1) (2) (3) (4) (5) (6)

Variables GDP per capita GDP per capita GDP per capita GDP per capita GDP per capita GDP per capita GDP per Capita (–1) –1.937 –0.170 –0.112 –0.483 –0.309 –0.230

(0.087) (0.096) (0.052) (0.012) (0.051) (0.021)

Gov Exp –1.775 –0.393 –0.583 –1.478 –0.337 –0.534

(8.445) (0.053) (0.049) (0.047) (0.085) (0.032)

GCF 0.399 0.145 0.003 0.237

(0.084) (0.039) (0.084) (0.084)

INFL –6.081 –0.428 –0.572 –0.102

(0.041) (0.062) (0.063) (0.048)

POP –0.472 –0.633 –0.002 –0.536

(0.172) (0.048) (0.061) (0.063)

Trade 0.593 0.041 0.409 0.098

(0.013) (0.064) (0.029) (0.027)

Gov Eff 2.942 2.463 2.255

(0.089) (0.049) (0.080)

Corr 0.159 4.124 11.017

(0.893) (4.217) (8.061)

Countries 13 13 13 13 13 13

AR (2) 0.852 0.296 0.517 0.260 0.147 0.987

Hansen OIR 0.865 0.227 0.120 0.610 0.151 0.607

Sargan OIR 0.838 0.0539 0.215 0.443 0.100 0.709

1) The signiﬁcance of estimated coefﬁcients and the Wald statistics. (2) The failure to reject the null hypotheses of (a) no autocorrelation in the AR (2) test and; (b) the validity of the instruments in the Sargan and Hansen OIR tests.

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549550 551552 553554 555556 557558 559560 561562 563564 565566 567568 569570 571572 573574 575576 577578 579580 581582 583584 585586 587588 589590 591592 593594 595596 597598

Table 4. Effects of Government Expenditures on GDP Growth: The Role of Government Debt.

(1) (2) (3) (4) (5) (6)

(0.596) (0.144) (0.255)

Gov Exp –0.120 –0.486 0.141 –0.191 –0.358 0.272

(0.093) (0.033) (0.014) (0.032) (0.044) (0.049)

GCF 0.402 0.060 0.229 0.060

(0.009) (0.087) (0.037) (0.000)

INFL –0.024 –0.419 –0.092 –0.415

(0.646) (0.084) (0.866) (0.036)

POP 6.222 4.334 2.254 3.480

(0.212) (0.990) (0.525) (0.300)

Trade –0.072 –0.056 –0.032 –0.076

(0.109) (0.073) (0.097) (0.065)

Gov Debt –0.002 –0.016 –0.029 –0.014 –0.016 –0.024

(0.040) (0.025) (0.026) (0.049) (0.026) (0.015)

GDP per Capita (–1) –0.260 –0.033 –0.091

(0.545) (0.079) (0.059)

Constant –12.988 11.439 –3.481 –4.028 6.487 –5.053

(7.290) (5.525) (20.077) (4.885) (4.646) (12.744)

Countries 13 13 13 13 13 13

AR (2) 0.0787 0.334 0.383 0.980 0.256 0.263

Hansen OIR 0.141 0.431 0.255 0.136 0.406 0.0725

Sargan OIR 0.741 0.356 0.294 0.638 0.106 0.201

,, : signiﬁcance levels at 1%, 5% and 10%, respectively. DHT: Difference in Hansen Test for Exogeneity of Instruments Subsets. Dif: Difference. OIR: Over-identifying Restrictions Test. The signiﬁcance of bold values is twofold.

(1) The signiﬁcance of estimated coefﬁcients and the Wald statistics. (2) The failure to reject the null hypotheses of (a) no autocorrelation in the AR (2) test and (b) the validity of the instruments in the Sargan and Hansen OIR tests.

Table 5. Effects of Government Expenditures on GDP Growth: The Role of Institutions–with Interaction Terms.

(1) (2) (3) (4) (5) (6)

VARIABLES GDP Growth GDP Growth GDP Growth GDP per Capita GDP per Capita GDP per Capita

GDP Growth (–1) 0.177 0.114 0.274

(0.216) (0.324) (0.267)

Gov Exp –4.916 –5.357 –5.815 –3.904 –5.450 –6.343

(0.098) (0.030) (0.042) (2.863) (2.980) (3.904)

GCF 0.188 0.288

(0.047) (0.056)

INFL –0.236 –0.058

(0.087) (0.058)

Gov Eff 8.444 4.839 2.706 9.063 2.735 6.889

(0.055) (0.052) (0.077) (0.018) (0.083) (0.060)

GovExpGovEff 3.846 0.177 4.100 3.029 4.320 4.497

(0.022) (0.042) (0.058) (0.056) (0.008) (0.096)

POP –5.262 –6.846

(0.314) (4.703)

Trade 0.140 0.11

(0.076) (0.041)

GDP per Capita (–1) 0.042 –0.042 0.200

(0.255) (0.330) (0.301)

Constant 7.717 5.074 4.199 3.189 3.906 4.709

(5.988) (5.191) (8.996) (4.771) (6.179) (0.682)

Countries 13 13 13 13 13 13

AR (2) 0.891 0.639 0.164 0.804 0.278 0.164

Hansen OIR 0.314 0.986 0.405 0.131 0.973 0.258

Sargan OIR 0.576 0.969 0.366 0.368 0.959 0.224

, ,: signiﬁcance levels at 1%, 5% and 10%, respectively. DHT: Difference in Hansen Test for Exogeneity of Instruments Subsets. Dif: Difference. OIR: Over-identifying Restrictions Test. The signiﬁcance of bold values is twofold.

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countries’characteristics in column 2 and 3, there is evidence of a negative and statistically significant relationship across the distinct specifications with values ranging between –0.361 and –0.606. More specifically in column 1, the results show that a percentage increase in government expenditure will lead a decrease in economic growth by about 0.45%. These results are interestingly contrary to the Keynesian view, which postulates a positive relationship betweenfiscal policy and economic growth. Albeit astounding, the negativefiscal policy-nexus found in this study is consistent with Ndjokou (2013). The economic reason behind this result could be because the majority of developing countries (CFA countries included) do not spend in productive sectors of the economy, which could thus adversely affect growth, despite government spending increasing every.

To further assess the robustness of these estimates, we introduce GDP per capita growth rate as the dependent variable. The results of which are given in column 4–6 ofTable 1. As a robustness check, the same models are estimated, but with GDP per capita as the dependent variables (see column 4–6). The results are in line with the previous estimations, which further conﬁrms that there is a negative relationship between government expenditures and economic growth. In addition, majority of the control variables bear the appropriate signs in most of the speciﬁcations.

To further access the extent to which institutional quality and government debt matter in thefiscal policy growth–nexus, interaction terms involving the variables are included in the different model specifications. Similar toTable 1, there are distinct set of specifications, where we estimate the models with and without control variables. To separately 599600

601602 603604 605606 607608 609610 611612 613614 615616 617618 619620 621622 623624 625626 627628 629630 631632 633634 635636 637638 639640 641642 643644 645646 647648

Table 6. Effects of Government Expenditures on GDP per Capita: The Role of Institutions–with Interaction Terms.

(1) (2) (3) (4) (5) (6)

(0.259) (0.346) (0.387)

Gov Exp –1.040 –0.828 0.139 –0.618 –0.721 0.834

(0.052) (0.867) (3.294) (1.479) (1.348) (2.622)

GCF 0.275 0.268

(0.149) (0.092)

INFL 0.028 0.004

(0.247) (0.187)

Corr 9.167 12.521 0.604 11.034 12.135 –6.266

(5.393) (3.371) (8.277) (8.637) (6.349) (3.583)

GovExpCorr 0.338 0.331 0.894 0.364 0.357 1.501

(0.194) (0.316) (0.225) (0.425) (0.362) (0.324)

POP –1.127 –2.182

(6.086) (5.499)

Trade 0.110 0.104

(0.091) (0.012)

GDP per Capita (–1) –0.140 –0.447 –0.323

(0.233) (0.323) (0.317)

Constant 2.323 7.310 9.649 4.799 1.678 1.082

(5.528) (1.335) (7.584) (8.865) (6.311) (9.171)

Countries 13 13 13 13 13 13

AR (2) 0.672 0.0822 0.963 0.258 0.110 0.908

Hansen OIR 0.105 0.134 0.499 0.683 0.117 0.485

Sargan OIR 0.144 0.923 0.526 0.147 0.817 0.540

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access the extent to which institutional quality variables and government debt matter in the relationship betweenfiscal policy and economic growth, wefirst re-estimate the basic model as inTable 1. We then incorporate the institutional quality variables (proxied by government effectiveness and control of corruption). The institutional quality variables are firstly incorporated without the interaction terms (as shown in Table 2and3). Secondly, they are incorporated with interaction terms (as shown in Table 5 and 6). Government debt is also separately incorporated in the basic modelfirst without interaction terms, as shown inTable 4and secondly with interaction terms as shown inTable 7.

The results from Table 2 and 3 which presents estimates from specifications that incorporate the institutional quality variables reveal that only government effectiveness is positive and statistically significant, whereas the control of corruption estimate is not statistically significant. Interestingly, our variable of interest, government expenditures in both specifications still show a negative statistical relationship with economic growth (GDP growth and GDP per capita alike).

At the other end of the spectrum, Tables 5 and 6 present estimates for specifications that incorporate an interaction term for government expenditures and government effectiveness in one scenario (Table 5); and an interaction term of government expenditures and control of corruption (Table 6) respectively. The results show that the standalone effect of government expenditures on economic growth is statically significant. The interaction term between government expenditures and government effectiveness (GovExp GovEff) is positive and statistically significant in all specifications (1–6). On the other hand, the interaction term between government 649650

651652 653654 655656 657658 659660 661662 663664 665666 667668 669670 671672 673674 675676 677678 679680 681682 683684 685686 687688 689690 691692 693694 695696 697698

Table 7. Effects of Government Expenditures on Economic growth: The Role of Government Debt – with Interaction Terms.

(1) (2) (3) (4) (5) (6)

Variables GDP growth GDP growth GDP growth GDP Per capita GDP Per capita GDP per capita GDP Growth (–1) –0.039 0.105 –0.027

(0.185) (0.194) (0.632)

Gov Exp –0.031 0.470 4.367 0.799 1.055 4.736

(0.929) (1.213) (2.341) (1.015) (2.332) (1.708)

GCF –0.052 –0.198

(0.208) (0.517)

INFL –0.604 –0.735

(0.333) (0.817)

Gov Debt –0.064 –0.149 –0.548 –0.163 –0.164 –0.609

(0.050) (0.031) (0.019) (0.095) (0.002) (0.050)

GovExpGovDebt –0.004 –0.012 –0.043 –0.012 –0.014 –0.046

(0.011) (0.017) (0.042) (0.014) (0.031) (0.031)

POP 10.911 7.408

(8.683) (7.693)

Trade –0.286 –0.321

(0.225) (0.148)

GDP per Capita (–1) –0.047 0.175 0.143

(0.220) (0.468) (0.379)

Constant –4.528 –0.721 –2.338 –9.295 –6.039 –9.127

(2.615) (3.350) (3.628) (3.555) (3.446) (5.289)

Countries 13 13 13 13 13 13

AR (2) 0.294 0.141 0.491 0.249 0.209 0.726

Hansen OIR 0.601 0.472 0.206 0.627 0.593 0.176

Sargan OIR 0.167 0.357 0.466 0.301 0.470 0.484