Macroeconomic channels of transmission of post-pandemic recovery strategies for African economies

Sodokin, Koffi; Couchoro, Mawuli; Tozo, Kokou Wotodjo Article Macroeconomic channels of transmission of postpandemic recovery strategies for African economies Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Sodokin, Koffi; Couchoro, Mawuli; Tozo, Kokou Wotodjo (2022) : Macroeconomic channels of transmission of post-pandemic recovery strategies for African economies, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 10, Iss. 1, pp. 1-34, https://doi.org/10.1080/23322039.2022.2125656 This Version is available at: https://hdl.handle.net/10419/303804 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Macroeconomic channels of transmission of post-pandemic recovery strategies for African economies Koffi Sodokin, Mawuli K. Couchoro & Kokou Wotodjo Tozo To cite this article: Koffi Sodokin, Mawuli K. Couchoro & Kokou Wotodjo Tozo (2022) Macroeconomic channels of transmission of post-pandemic recovery strategies for African economies, Cogent Economics & Finance, 10:1, 2125656, DOI: 10.1080/23322039.2022.2125656 To link to this article: https://doi.org/10.1080/23322039.2022.2125656 © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 27 Sep 2022. Submit your article to this journal Article views: 1564 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Macroeconomic channels of transmission of post-pandemic recovery strategies for African economies Koffi Sodokin 1 *, Mawuli K. Couchoro 1 and Kokou Wotodjo Tozo 2 Abstract: The Sar-Cov-2 pandemic that began in 2019 has significantly affected the global economy and, in particular, those of African countries. This paper analyzes possible intervention channels by African states to put their economies back on a sustainable growth path once the health crisis is under control. The paper proposes workable macroeconomic channels for these countries’ recovery from postpandemic periods by using historical data to conduct empirical analyses. The paper employs World Bank data and ILOSTAT for 54 African countries within the period 1990–2018. We use a post-Keynesian framework and the difference and system generalized method-of-moments to show that wages drive African economic dynamics in the short run. This is particularly true for Sub-Saharan African countries. In addition, foreign output, proxied by European Union output, has a positive and significant impact on Sub-Saharan African economies in the short and long run. The results highlight strategic policy measures for recovering African economies, including improving wages and deepening international economic relations, particularly with the Eurozone countries. ABOUT THE AUTHOR Mr. Koffi Sodokin is an Associate Professor at the Faculty of Economics and Management of the University of Lome. He has over ten years of experience teaching and supervising MSc students. His research focuses on money, finance, macroeconomics, and microeconomics of development. Mr. Mawuli K. Couchoro is a Full Professor and the Dean of the Faculty of Economics and Management at the University of Lome. He has over 15 years of teaching and research experience and supervision of Ph.D. and MSc students. His research interests include money, finance, macroeconomics, and microeconomics of development. Mr. Kokou W. Tozo is a Postdoctoral Fellow at the Peking University’s Institute of New Structural Economics (INSE) in Beijing, China. He has over five years of experience in field research focused on African and Asian developing countries. His research area includes Development Economics, International Economics, Applied Econometrics and New Structural Economics. PUBLIC INTEREST STATEMENT The search for appropriate strategies by developing countries, specifically African countries, is not new. Political and military unrest, socio-economic inequalities, and lagging development are the aspects on which policymakers focus their attention. Still, beyond that, Africa is a continent in full mutation with very encouraging economic growth prospects. In this context, the Sar-Cov-2 pandemic that started in 2019 constitutes a factor that slows down the efforts made in the past few years. This research analyzes and proposes possible intervention channels for African states to put their economies back on a sustainable growth trajectory once the health crisis is under control. We show that wages drive African economic dynamics in the short run and that foreign production, represented by European Union production, has a positive and significant impact on subSaharan African economies in the short and long run. Our results point to strategic measures for recovering African economies, including improving wages and deepening international economic relations, particularly with eurozone countries. Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 1 of 34 Received: 28 January 2022 Accepted: 14 September 2022 *Corresponding author: Koffi Sodokin, Economy, University of Lome E-mail: [email protected] Reviewing editor: Goodness Aye, AGRICULTURAL ECONOMICS, UNIVERSITY OF AGRICULTURE, MAKURDI BENUE STATE, NIGERIA Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Subjects: International Political Economy; Economics and Development; Economics; Political Economy Keywords: gross domestic product; wage; profit; Covid-19; economic recovery JEL: E02; E23; E24; O55 1. Introduction On Wednesday, 19 March 2020, as the Covid 19 pandemic took a deplorable toll on human life in Asia and began on the European continent, the World Health Organization (WHO) sounded the alarm on the death of the first person from the coronavirus in Sub-Saharan Africa. The WHO then called on authorities to prepare for the worst and most significant economic downturn (Havrlant et al., 2021; Maliszewska et al., 2020). Therefore, it appeared necessary to implement the required measures. On the one hand, Governments implemented sanitary measures to avoid a hecatomb. On the other hand, Governments implemented economic measures (social measures such as offering free treatments to affected citizens and cash transfers) to help the most vulnerable populations survive the time of the State of Emergency decreed everywhere on the continent (Addison et al., 2020; Dang et al., 2021; Sodokin, 2021). Despite global efforts in the same direction, economic activity still seemed at a standstill worldwide, with much uncertainty about the future (Altig et al., 2020). In this regard, the challenge has been to find the funding necessary to strengthen health care structures and secure the income required for people to survive (K. Liu, 2021). These measures are, in our view, very positive and commendable on the part of African governments to counter the short-term cycle of the pandemic. In practice, pandemic funds have been set up throughout Africa. Beyond the measures favoring vulnerable populations, companies should also benefit from tax cuts, tax exemption for donations, and suspension of tax audits. The States have gradually begun to control the health crisis with the vast vaccination campaigns underway, which involve an increase in public debt and questions related to recovery strategies for sustainable economic resilience for African countries. However, economic growth forecasts remain less optimistic for African countries, especially Sub-Saharan Africa (SSA). The first estimates for April 2021 gave a growth rate of 3.4% in SSA countries against 6% worldwide (International Monetary Fond, 2021a). The International Monetary Fund predicts a recovery for all SSA countries after 2022, and only then could the production level in these countries return to the 2022 level (International Monetary Fond, 2021a). This health crisis raises questions about public strategies for recovering African economies, particularly SSA countries. The question then arises of identifying possible macroeconomic channels for transmitting public plans for economic recovery. The International Monetary Fund proposes such avenues as changes in employment, credit, digital transformation, broad and equitable sharing of gains, and strengthening social security systems (International Monetary Fond, 2021b). In summary, the recovery from such a crisis period, whose economic effects are similar to that of the 2008 crisis, calls for targeted and effective intervention in terms of attractiveness, securing national and international private investment, and promoting employment and wage. These interventions make sense because of their relative importance to the different components of aggregate demand (Lavoie & Stockhammer, 2013; Palley, 2017). Based on the above developments on recent issues (International Monetary Fond, 2021a, 2021b) and previous works (Bhaduri & Marglin, 1990; Lavoie & Stockhammer, 2013; Oyvat et al., 2018), the main question in this paper is what main macroeconomic channels for relaunching growth dynamics in Africa in this period of a health crisis are? Specifically, the study seeks to answer two under-addressed questions in the literature. It first looks at whether African economies are wage-led or profit-led to help to identify which pro-labor or pro-capital growth strategy should be prioritized now and through the post-pandemic period. Secondly, we look at how demand from China, the EU, or the US influences the output level in Africa. To answer these questions, we need Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 2 of 34 a framework in which the overall impact of wage/profit share changes on growth determines whether a regime is profit-led or wage-led. A popular approach to identifying these regimes has been considering the functional income distribution in the literature. This distributional pattern shows how the output is divided between the factors of production, especially capital and labor. Despite the vast existing theoretical literature and some empirical works that have been done (Bhaduri & Marglin, 1990; Dünhaupt, 2013; Oyvat et al., 2018), no specific studies have been carried out on African countries. Our paper tries to fill this gap while proposing workable solutions for these countries to recover from post-pandemic periods by using historical data to conduct empirical analyses. On this note, we have organized the remainder of the paper into four sections: Section 2 provides a brief review of the literature; section 3 introduces a simple income distribution model; section 4 presents the econometric method, the data and discusses the regression results; and finally, section 5 concludes. 2. A brief review of the literature The current health crisis has undoubtedly brought back into the discussion the need for policies to stimulate economic growth after almost all countries recorded very low or even negative growth rates in 2020 (International Monetary Fond, 2021a, 2021b). The question of the response of growth stimulus mechanisms themselves is not new. Harrod (1939), Domar (1946, 1947), and Kaldor (1955) focused attention on the role of income distribution in the economic growth mechanism. These authors explain a link between the decline in wage share and the prevalence of financial crises in developed and developing countries. These authors emphasize the relationship between income distribution between wages and profits and economic growth (Piketty, 2014). Formally the effect of income distribution on demand has been modeled by Dutt (1984), Taylor (1985), Blecker (1989), and extended by Bhaduri and Marglin (1990) and Palley (2017). These authors show whether European governments can generate higher growth while maintaining a more equitable income distribution using wageor profit-based demand regimes. They investigate whether a coordinated wage policy, used at a multi-country level, could be a solution for Europe in keeping wages under control better than the European wage restraint strategy (Bhaduri & Marglin, 1990). The analysis also focuses on the mediumto long-term links between distribution and economic growth. As a result, various interventions can be used to influence income distribution when referring to long-run correlations. These include strengthening unions, improving unemployment benefits, and promoting financial regulation. The authors show that the wage share is not an instrument but an aggregation of all these interventions (Lavoie & Stockhammer, 2013). From an econometric point of view, several studies have analyzed the relationship between demand components and economic growth. Barbosa-Filho and Taylor (2006) and Carvalho and Rezai (2016), using VAR and TVAR (threshold vector autoregressive) models, respectively, find that profit is the engine of the United States of America economy. Bowles and Boyer (1995) tested the impact of wages on each component of Gross Domestic Product at the level of five (developed) countries. They found that profit is the engine of economic growth. Baccaro and Pontusson (2016) develop an analytical approach of comparative political economy to show from data from the United Kingdom, Sweden, Germany, and Italy that economic growth is driven by the different components of aggregate demand: exports and household consumption. In the same dynamic, D. Liu (2020) has shown in a non-linear model with regional disparities that economic growth in Eastern China is slightly driven by profits, while wages drive the Chinese hinterland. Several other authors have tried to show that the effect of domestic demand through wage dynamics could be transitory (Blanchard & Quah, 1989; Fazzari et al., 2020; Yılmaz, 2015). Even in this hypothetical case where multiple equilibrium points can be found at lower or higher levels, Nikiforos and Foley (2012) show that the United States of America’s economy depends on the level of the wage share, and wages drive capacity utilization. Yılmaz (2015) shows from Turkish data that the economic growth regime can be caused by wages or profits depending on whether the Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 3 of 34 economic dynamics are sensitive to the combined effect of consumption and investment or export and import. Gurara and Ncube (2013) have shown in a global vector autoregression model (GVAR) that African economies experience a significant spillover effect from the growth of the Eurozone and BRIC economies. Finally, according to the local and international component of demand and their impact on economic growth, these issues are still to be addressed for African economies in a robust analysis and according to the new macroeconomic environment with the health crisis. For instance, a few studies on South Africa found that historically South African economic growth is profit-driven and argue that an increase in wages could adversely affect economic growth (Malikane & Chitambara, 2017; Onaran & Galanis, 2012). The main goal of this paper is to overcome this challenge in a simple wage, profit, and foreign output-driven growth model. 3. A simple model of wage/profit share and foreign output as drivers for economic growth We follow the previous works (Bhaduri & Marglin, 1990; Naastepad, 2006; Oyvat et al., 2018) to establish the relationship between profit share πð Þ, wage share, foreign output, and a country’s domestic total output Yð Þ . Bhaduri and Marglin’s (1990) modeling framework provides practical tools based on distributive scrap and the principle of effective demand deriving different demand and growth regimes. Bhaduri and Marglin’s (1990) model includes several approaches to the analysis of economic growth, including the profit-oriented methods of the new growth theory (Aghion & Howitt, 2009; Barro & Sala-i-Martin, 2004; Lucas, 1988; Romer, 1986), and from the classical Marxian model to the neo-Kaleckian model of wage-driven demand and growth (Kaldor, 1957, 1961; Kalecki, 1939, 1969; Robinson, 1956, 1962), with an intermediate regime of wagedriven demand and profit-driven growth (Hein, 2017). The difference between the Bhaduri and Marglin (1990) model and growth theories based on supply-side neoclassical assumptions is the conception of the economy in terms of dynamics based on technical progress and factor growth (Hartwig, 2014). The Bhaduri and Marglin (1990) model is, therefore, a post-Keynesian model that allows us to study and discusses the conditions that would drive the African post-pandemic economic recovery. We begin with the expression of the production in an open economy: Y¼CþIþGþXM(1) Where: C is the private consumption, I the private investment, G the government expenditures on goods and services, X the export, and M the import. We follow the literature to outline and describe plausible determinants that control the right-hand side (see López-Gallardo and ReyesOrtiz, 2011; Oyvat et al., 2018). Below, we will discuss the impact of changes in π on each demand component in detail. To begin with, let us rewrite equation (1) in the following way: Y¼C W;Pð ÞþI Y;π;v;Cr;Rð ÞþXπ;YF;eð Þ Mπ;Y;eð Þ (2) For simplicity, government expenditures are omitted since π is part of Y as we will soon elaborate, and G is exogenous to Y, i.e., π do not influence G. From (2), we expect π to have adverse effects and positive effects on C and M. Workers and capitalists earn wages and profits in the economy, respectively. Both factors share the total income Yð Þ . Suppose we define total wage as the compensation of workers Wð Þ, a fraction of total income, and total profit as the residual. In that case, the total wage equals total income minus total profit. In other words, total income is the sum of total profits �ð Þ and total wage payments Wð Þ. Based on these details, we can express them as: Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 4 of 34 �¼πYand W¼1πð ÞY(3) Following (3), the Keynesian consumption function can be written as: C¼c0þc�πYþcW1πð ÞY;c0>0;c�>0;cW>0 (4) Where: c0 is the autonomous consumption and c�and cW the marginal propensities to consume by capitalists and workers, respectively. The impact of π on C is negative in practice since a higher profit share diminishes the wage share, and a lower wage share implies less desire for consumption. This is verified in the literature (Alarco, 2016; Obst et al., 2017) and is consistent with Keynes (1936), who considers that workers tend to spend more on consumption than capitalists on investment. Investment and other functions are defined following Blecker, (1989) and Naastepad (2006). The private investment function is in the form: I¼θ0Yθ1πθ2vθ3Cθ4 rRθ5;θ1>0;θ2>0;θ3>0;θ4>0;θ5<0 (5) Investment has a positive response to all the variables except interest rate. Greater total demand would increase capacity utilization and stimulate investment. Similarly, higher profit shares would have a direct positive effect on investments. The improving business confidence, as well as credit to the private sector, would also encourage investment. By contrast, investment is inversely related to interest rates for two main reasons. Firstly, if interest rates rise, the opportunity cost of investment rises. This means that an increase in interest rates increases the return on funds deposited in an interest-bearing account or makes a loan, reducing investment attractiveness relative to lending. Hence, investment decisions may be postponed until interest rates return to lower levels. Secondly, if interest rates rise, firms may anticipate that consumers will reduce their spending, and the benefit of investing will be lost. Funding to expand requires that consumers at least maintain their current spending. Therefore, a predicted fall will likely discourage firms from investing and force them to postpone their investment decisions. In the next step, we express export as a function of foreign demand, profit share, and exchange rate: X¼ϕ0Yϕ1 Fπϕ2eϕ3;ϕ0>0;ϕ1>0;ϕ2>0;ϕ3>0 (6) An increase in e is equivalent to domestic currency depreciation, therefore ϕ3>0. A higher wage share resulting from a rise in wage reduces the international competitiveness of domestic firms by increasing the unit cost of labor relative to the unit cost of labor in the trading partner countries. Conversely, profit share would favor domestic export, i.e. ϕ2>0 (Hein, 2017; Onaran & Obst, 2016). Lastly, we similarly define import as export, except that import depends on domestic output rather than foreign output, in which case: M¼ζ0Yζ1πζ2eζ3;ζ0>0;ζ1>0;ζ2<0;ζ3<0 (7) Imports price increases following currency depreciation which means demand for foreign goods decreases. A higher profit share implies a lower wage share, and this also causes a decline in demand for foreign products From the equations above, we can derive the impact of profit share on the percentage change in output as: Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 5 of 34 βπ¼ @y @π Y¼cWc� ð Þþθ2I Pþϕ2X Pζ2M P 1Ψ;Ψ¼@c @yþθ1I Yζ1 M Y(8) Let’s assume the term Ψ (the change in aggregate demand concerning total output) is less than unity, and the Keynesian stability condition holds, i.e., 1 Ψð Þ>0. This means that the sign of βπ will only depends on the numerator. The growth regime in the economy will be wage-driven if βπ<0. To estimate the value of βπ, there are usually two possible ways. The first method is the structural approach used to estimate the individual components of Y using separate econometric equations for C, I, X, and M. In this approach, the sign of βπ is then obtained by summing the various partial derivatives for consumption, investment, export, and import. Wage-divers hold if: βπ<0;cWc� ð Þ <0 and cWcP j j>θ2 I Pþϕ2 X Pζ2 M P ��������(9) In other words, wage-driven growth implies that the rising profit share’s negative impact through the gap in marginal propensities to consume cWc�is larger than its positive effect through investment, exports, and imports. The regime will be profit-driven otherwise. The second method, or the aggregative approach, estimates the following reduced form of the output equation: Y¼Yπ;1π¼ωð Þ;Cr;YF;e;Rð Þ (10) In this case, the derivative @Y=@π or @Y=@ω is calculated directly by regressing output Y on the profit or wage share and other control variables, including various lags (Blecker, 2016). Wagedriver holds if: βπ<0;@Y @π<0 or @Y @ω>0 (11) It is worth noting that both structural and aggregative approaches only aim to estimate the slope of the demand relationship in (2) without considering possible simultaneity bias. Wage share is often endogenous to growth to obtain demand from a simultaneous equation (AD = AS). To address the endogeneity bias, equation (10) is usually estimated using a systems approach such as the cycle model developed by Barbosa-Filho and Taylor (2006) or the well-known dynamic panel GMM-SYS (see Arellano & Bond, 1991; Arellano & Bover, 1995; Holtz-Eakin et al., 1988; Roodman, 2009). Using one of these methods, we can prove that African economies are wage-led. According to Keynes (1936), poor populations spend a higher share of their income on consumption, a proposition supported empirically in recent literature (Alarco, 2016; Obst et al., 2017; Onaran & Obst, 2016). We also know that the necessary condition in (9) for wage-ledness is: cWc� ð Þ <0;cWc�>0;cW>c�(12) Condition (12) explicitly shows that the propensity to consume is more significant for workers than capitalists, suggesting that African economic growth is more likely to be wage-led. This is because, on the one hand, Africa is a worker-abundant continent. On the other hand, it is a region where one in three people still live below the global poverty line, i.e., around 70% of the world’s poorest people (Hamel et al., 2019; Kharas et al., 2018). 4. Method, data variables, and descriptive analysis (1) Econometrics Models and estimation methods Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 6 of 34 We use the difference and system generalized method-of-moments (GMM-SYS) estimators to test our hypothesis. The GMM-SYS can be attributed to many scholars (Arellano & Bond, 1991; Arellano & Bover, 1995; Blundell & Bond, 1998; Holtz-Eakin et al., 1988). The GMM-SYS is designed for panel data analyses with “small T, large N,” i.e., a few periods and many individuals (T < N). It is suitable to analyze independent variables that are not strictly exogenous or, for instance, independent variables correlated with past and current realizations of the error. We begin with a data-generating process without strictly exogenous variables; an autoregressive specification of the form: yit ¼αyi t1ð Þ þx0 itβþεit;εit ¼μiþ#it ð12:1Þ Following Roodman (2009), we can also express the y increased level equation as: Δyit ¼α1ð Þyi t1ð Þ þx0 itβþεit ð12:2Þ Based on Arellano and Bond (1991), we assume the disturbance terms εit have finite moments so that Eεit ð Þ ¼ Eεitεis ð Þ ¼ 0;t�s. We assume an absence of serial correlation but not necessarily independence over time. With these assumptions, values of y lagged two periods or more will be valid instruments in the equations in the first differences, in which case at least T�3. The first difference transformation removes the individual effect, therefore: Δyit ¼αΔyi t1ð Þ þΔx0 itβþΔ#it (13) We aim to find the optimal estimator for α first. Arellano and Bond suggest that the condition ^αj j<1 should be satisfied. All ^αj j>1 may imply an unstable dynamic, accelerating divergence away from equilibrium values (Arellano & Bond, 1991; Blundell & Bond, 1998). These hints are crucial conditions in addressing potential biases in our estimations. Following Roodman’s (2009) approach, We begin with the classical OLS estimator applied to equation (12.1) and then modify it step by step to address the abovementioned concerns, ending with our estimators of interest. Notably, two different estimators will help create a range for a credible ^α. The first is the pooled OLS estimator ^αOLS, and the second is the within-group fixed effects (FE) estimator ^αOLS. Thus, we are looking for an estimator that satisfies previous conditions ^α�0;^αj j<1 and additionally ^α�0;^αj j<1 where: ^αOLS ¼∑N i¼1yi t1ð Þ yi t1ð Þ  �yit yit ð Þ ∑N iyi t1ð Þ yi t1ð Þ  �2(14) and ^αiFE ¼∑N i¼1∑T t¼1yi t1ð Þ yi t1ð Þ  �yit yit ð Þ ∑N i¼1∑T t¼1yi t1ð Þ yi t1ð Þ  �2(15) For different reasons, OLS and FE estimators cannot be used a priori for inference. The problem with equation (12.1) after running regression using OLS is that yi t1ð Þ is correlated with the fixed effects in the error term, which, according to Nickell (1981), will give rise to the “dynamic panel bias” issue. So the next attempt is to deal with the fixed effects by drawing them out of the error term. This is done by entering dummies for each unit and running the regression. But again, the within-group fixed effects estimator will not eliminate the dynamic panel bias (Bond & Windmeijer, 2002; Nickell, 1981). Furthermore, the least squares dummy variables (LSDV) suggested by Kiviet (1995) do not solve the problem because the approach fails to work for unbalanced data and addresses the potential endogeneity of other regressors. Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 7 of 34 performed, including the Arellano-Bond test for AR (1) and AR (2) in first differences; the Sargan and Hansen tests of over-identification restrictions, and the number of instruments that is less than the number of groups, implying that the models do not suffer from misspecifications. Thirdly, the estimator values α indicate that the accurate coefficients appear in the last two columns. We have both one-step 0:955 20:888;0:997ð Þ and two-step 0:944 20:888;0:997ð Þ. These observations, especially the positive and highly significant sign on ω, suggest that wages drive the dynamics of African economies. The result seems to be valid only when the pairing involves the EU. Assuming households, firms, and governments are indebted, the results suggest that higher wages could translate into dynamic demand and growth. According to Keynes (1936), poor populations spend a higher share of their income on consumption, a proposition supported empirically in recent literature (Alarco, 2016; Obst et al., 2017; Onaran & Obst, 2016). By estimating the impact of wage share on different countries, the authors found that a higher wage share increases consumption. The results are not significant when matched with the Gross Domestic Production of the United States (Table 9) and China (Table 10). These two countries have been trying to accelerate their economic relationship in recent years with African countries before the beginning of the Sars-Cov-2 pandemic. These results suggest, in our view, that there are still opportunities for African countries to seek productivity gains in the US and China through aggressive and pragmatic economic diplomacy for now and in the post-pandemic period. 4.0.3. Result 3. Robustness check: GMM-SYS models with restrictions 4.0.3.1. Considering the regional context of SSA. Despite the restrictions, signs of the estimates remain unchanged (Table 11). Specification tests and the number of reported IVs indicate that the models are well-specified. As before, it can be noted that the good range of estimators α is in the last two columns. The coefficient on ω is still positive and significant in the credible range. The Table 8. GMM-SYS, overall (Y F = Y EU ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.970*** 0.964*** 0.955*** 0.944*** (0.0219) (0.0268) (0.0229) (0.0372) ω 0.0898** 0.101** 0.0853** 0.100*** (0.0380) (0.0438) (0.0357) (0.0362) Log(Y EU ) 0.640 0.548 0.549 0.507 (0.546) (0.339) (0.462) (0.313) FD Arellano-Bond AR (1), P zð Þ 0.033 0.037 0.034 0.039 FD Arellano-Bond AR (2), P zð Þ 0.488 0.494 0.559 0.613 Sargan overid. restr, Pχ2 ð Þ 0.605 0.605 0.452 0.452 Hansen overid. restr, Pχ2 ð Þ 0.108 0.108 0.118 0.118 Number of instruments 34 34 34 34 Observations 1,458 1,458 864 540 Number of group 54 54 54 54 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 14 of 34 Table 9. GMM-SYS, overall (Y F = Y CHN ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.939*** 0.949*** 0.905*** 0.901*** (0.0377) (0.0245) (0.0365) (0.0400) ω 0.125* 0.123** 0.110* 0.127* (0.0721) (0.0585) (0.0623) (0.0702) Log(Y CHN ) 1.189 0.811 1.467 0.781 (0.748) (0.597) (0.908) (0.773) FD Arellano-Bond AR (1), P zð Þ 0.031 0.034 0.030 0.036 FD Arellano-Bond AR (2), P zð Þ 0.639 0.329 0.766 0.990 Sargan overid. restr, Pχ2 ð Þ 0.538 0.532 0.494 0.494 Hansen overid. restr, Pχ2 ð Þ 0.080 0.158 0.231 0.231 Number of instruments 35 34 34 34 Observations 1,458 1,458 1404 1404 Number of group 54 54 54 54 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Table 10. GMM-SYS, overall (Y F = Y US ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.965*** 0.959*** 0.961*** 0.950*** (0.0247) (0.0382) (0.0332) (0.0481) ω 0.0935** 0.123* 0.0903* 0.124 (0.0446) (0.0657) (0.0528) (0.0759) Log(Y US ) −1.355 −0.370 −2.134 −0.662 (4.401) (2.333) (4.659) (2.273) FD Arellano-Bond AR (1), P zð Þ 0.027 0.032 0.026 0.035 FD Arellano-Bond AR (2), P zð Þ 0.997 0.897 0.635 0.992 Sargan overid. restr, Pχ2 ð Þ 0.552 0.552 0.483 0.483 Hansen overid. restr, Pχ2 ð Þ 0.047 0.047 0.109 0.109 Number of instruments 34 34 34 34 Observations 1,458 1,458 1404 1404 Number of group 54 54 54 54 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 15 of 34 meaningful positive sign of the wage share suggests that African countries have had labordependent growth dynamics historically. Some authors argue (Lavoie & Stockhammer, 2013; D. Liu, 2020) that if the labor share increases in an economy where labor is a competitive advantage, the economy’s growth will gain due to solid demand. The last but crucial remark is that the positive impact of the variable Log(Y EU ) is now significant. This result suggests that in addition to positive wage dynamics, the other source of available demand, exports of goods and services, would help accelerate the recovery process for African countries. In the same perspective, Awokuse (2008) finds that real GDP growth is determined by international trade and labor for Latin American countries. Bekaert et al. (2020) also suggest that aggregate demand may be, among other factors, one of the most critical drivers for economic recovery in the United States of America context. In Tables 12 and 13, Log(Y CHN ) and Log(Y US ) are still positive and negative but non-significant. This restriction could not apply to non-SSA (see appendix for non-SSA countries) countries as the criteria of small T Large N (T < N) would have been violated. 4.0.3.2. Considering time dimension. The other possible restrictive approach we considered was to look at the time dimension taking as reference the year 2008, where the most notable financial shock occurred throughout the studied period. But, compared with the previous results, these models have performed very poorly when paired with the EU (Table 14). The first observation is that the estimates remain positive despite a change in magnitude before and after the crisis. Second, the effects of wage share have decreased while EU GDPs have risen in the post-crisis period. The other remark is that only the estimate of EU GDP is statistically significant in the pre-crisis period for a two-step regression. But how valid are these results? The specification tests show that the estimates may be biased. The first two column outputs suggest that the estimator α belongs to the good range. However, we failed to reject the Arellano-Bond AR Table 11. GMM-SYS, Sub-Saharan Africa (Y F = Y EU ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.978*** 0.974*** 0.961*** 0.953*** (0.0179) (0.0207) (0.0177) (0.0263) ω 0.169** 0.138 0.158** 0.120* (0.0780) (0.0859) (0.0711) (0.0700) Log(Y EU ) 0.934* 0.825* 0.877* 0.769** (0.546) (0.466) (0.449) (0.339) FD Arellano-Bond AR (1), P zð Þ 0.039 0.040 0.039 0.040 FD Arellano-Bond AR (2), P zð Þ 0.408 0.341 0.364 0.359 Sargan overid. restr, Pχ2 ð Þ 0.747 0.747 0.611 0.611 Hansen overid. restr, Pχ2 ð Þ 0.088 0.088 0.110 0.110 Number of instruments 34 34 34 34 Observations 1,296 1,296 1,248 1,248 Number of group 48 48 48 48 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 16 of 34 Table 12. GMM-SYS, Sub-Saharan Africa (YF = YCHN) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.946*** 0.959*** 0.913*** 0.909*** (0.0364) (0.0162) (0.0315) (0.0303) ω 0.232 0.180* 0.178* 0.141 (0.148) (0.104) (0.101) (0.0952) Log(Y CHN ) 1.231 0.830 1.527 0.771 (0.938) (0.849) (1.105) (0.876) FD Arellano-Bond AR (1), P zð Þ 0.037 0.037 0.036 0.040 FD Arellano-Bond AR (2), P zð Þ 0.699 0.376 0.432 0.945 Sargan overid. restr, Pχ2 ð Þ 0.530 0.526 0.445 0.445 Hansen overid. restr, Pχ2 ð Þ 0.125 0.109 0.130 0.130 Number of instruments 35 34 34 34 Observations 1,296 1,296 1,248 1,248 Number of group 48 48 48 48 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Table 13. GMM-SYS, Sub-Saharan Africa (YF = YUS) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.968*** 0.957*** 0.963*** 0.949*** (0.0218) (0.0291) (0.0315) (0.0371) ω 0.161* 0.166 0.163 0.136 (0.0872) (0.0998) (0.106) (0.0836) Log(Y US ) −0.663 −0.156 −1.441 −0.349 (4.005) (3.328) (4.436) (3.672) FD Arellano-Bond AR (1), P zð Þ 0.036 0.040 0.034 0.044 FD Arellano-Bond AR (2), P zð Þ 0.999 0.361 0.607 0.569 Sargan overid. restr, Pχ2 ð Þ 0.566 0.566 0.480 0.480 Hansen overid. restr, Pχ2 ð Þ 0.102 0.102 0.209 0.209 Number of instruments 34 34 34 34 Observations 1,296 1,296 1,248 1,248 Number of group 48 48 48 48 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 17 of 34 (1)’s no autocorrelation null hypothesis and to accept the IVs’ Hansen validity assumption. In the last two columns, the estimator α is not only out of range but also greater than one. These findings suggest that the 2008 financial shock has negatively impacted African economies despite their strong trade connection with the EU countries, as shown in previous results. There are several plausible reasons why the crisis does not seem to directly explain the level of output in Africa. First, the financial networks in most African economies are weakly webbed domestically and with global partners. This situation makes African countries less vulnerable to international financial shocks. Second, Africa’s bank account penetration rate is relatively low compared to Europe. The effect is also similar since banking is a component of the financial system. Only a tiny part of the population depends on it to carry out their economic activities. Furthermore, the informal sector is predominant in Africa. The International Labor Organization (ILO) report estimates that around 80% to 94% of Africa’s active population is in the informal sector (Kiaga and Leung, 2020). Although the informal contribution is computed in national accounts, it is often poorly reported. The fraction of the informally employed is less dependent on or has no connection with banks or the financial sector. All these factors explain the apparent resilience of the continent in times of financial crises. On the other hand, it is unlikely that African countries will face the same fate with the COVID-19 shock, a combination of health and economic crises. Contrary to the 2008 financial shock, the impacts on the real economy are felt. Economies on the continent have been harshly hit by unprecedented surging commodities and consumables prices caused by the disruption in the global supply chains. Restrictive measures such as border closure and lockdowns have also halted daily activities and prevented people from carrying out production or doing business. Such situations have prompted many governments to resort to welfare policies such as cash transfers to the people, the release of emergency food stocks, and rethink their trade strategies and development Table 14. GMM-SYS, pre/post-2008 crisis periods VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Pre-2008 crisis period Post-2008 crisis period Log(Y_1) 0.887*** 0.806*** 1.011*** 1.010*** (0.0941) (0.212) (0.0440) (0.0425) ω0.184* 0.213 0.0642 0.0528 (0.108) (0.157) (0.231) (0.353) Log(Y EU ) 0.312 0.424** 1.856* 1.169 (0.304) (0.169) (0.993) (1.346) FD Arellano-Bond AR (1), P zð Þ 0.119 0.156 0.037 0.067 FD Arellano-Bond AR (2), P zð Þ 0.791 0.917 0.798 0.907 Sargan overid. restr, Pχ2 ð Þ 0.946 0.946 0.000 0.000 Hansen overid. restr, Pχ2 ð Þ 0.025 0.025 0.110 0.687 Number of instruments 23 23 34 34 Observations 864 864 540 540 Number of group 54 54 54 54 Note: Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 18 of 34 plans for an effective recovery. In the trade sector, recall that African countries enjoy export tariff cuts and free access to some significant economies around the world, such as the African Growth and Opportunity Act (AGOA) in the US and Everything But Arms (EBA) in Europe, among others). The previous results show that prolonged travel and trade bans in Europe can cause further losses for African economies. 4.0.4. Result 4: Long-run effects The long-run coefficients are only estimated for the significant short-run parameters. For the α parameter, the LRE is given by: LRE ¼βk 1α(22) The α coefficient recalls the persistence of the lag effect. Since αj j is the portion of the short-run adjustment translated to the following year, the closer αj j to 1, the higher the persistence in the dependent variable (Bruno et al., 2016). Estimation results for ω and Log(Y EU ) are reported in Table 15 below. We checked whether the relationship holds in the long run. The coefficients are obtained for the factors that significantly impact the output in different contexts. Only ω has the expected sign in the overall model, while both ω and Y EU ’s estimates have the predicted sign when the sample is restricted to SSA only. The results in Table 15 suggest that the EU’s GDP level has a long-term positive and significant impact on SSA economies. Following these findings, we can further shed light on circumstances that can make a growth regime more viable based on some preconditions (Figure 3). A wage-led regime will be more viable for countries seeing a consistent increase in wage share, and policymakers in such countries should pursue pro-labor strategies. Those strategies may include increasing the minimum wage or government support for collective bargaining. These interventions can result in rising wages, spur consumption and contribute to expansionary wageled growth. Benin is a recent case where the government and labor union representatives agreed in late April 2022 to raise the minimum wage by 30%. The steady rise in wage share, as shown above, and the move by the Benin government to increase the minimum wage is the sustainable approach for a wage-led regime, as in Lavoie and Stockhammer (2013), which is also consistent with our findings. Similar interventions should be encouraged in many other African countries in this recovery period from the pandemic to boost demand for consumption, which can incentivize domestic firms to scale up their activities and increase production capacity. Countries that proceed otherwise, i.e., by weakening wages or suppressing collective bargaining, can stagnate or have unstable growth (Figure 4). Note that wage weakening is currently observed in many countries due to inflation, but policymakers can take appropriate decisions to reverse the situation. Table 15. Long-run coefficients Variables Coefficients P>|z| Overall SSA Overall SSA ω 1.795 2.552 0.163 0.222 Log(Y EU ) N/A 16.358* N/A 0.093 *** p < 0.01, ** p < 0.05, * p < 0.1 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 19 of 34 Figure 3. Changes in income distribution and State policy tools for regimes viability. Source: Author’s chart based on the description in Lavoie and Stockhammer (2013) 8 9 10 11 1990 2000 2010 2020 Wage and salaried workers, total (% of total employment) Figure 4. Trend in wage share, Benin (%) Source: authors. Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 20 of 34 5. Conclusion and Policy Implications This paper examined the possible macroeconomic channels of transmission of post-pandemic recovery strategies for African economies as governments began to handle this health crisis. Our findings indicate that employment favors higher aggregate demand, suggesting that African countries would be better off pursuing pro-labor distributional policies and improving wages. In other words, the results show that enhanced wages have a significant and positive effect on increasing gross domestic production, pointing to the way forward for policy strategies for recovering African economies. Our results show that to boost consumption and build resilience to future crises, it is still crucial for these countries to create effective social protection systems around wage distribution and social transfers. Most importantly, these results suggest that macroeconomic performance can be improved through negotiations and social agreements to protect wages, profits, and jobs, even if these effects on output are likely transitory. The results also suggest that African economies, particularly Sub-Saharan Africa, have a dynamic determined by foreign trade relations with Eurozone countries. Therefore, it is reasonable to conclude that for African countries, public policies should be based on strategies to strengthen external trade relations with Eurozone countries in terms of economic recovery policy strategies in this time and the post-pandemic period. Author details Koffi Sodokin 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-1763-4363 Mawuli K. Couchoro 1 ORCID ID: http://orcid.org/0000-0002-9573-0684 Kokou Wotodjo Tozo 2 ORCID ID: http://orcid.org/0000-0003-3421-6507 1 Research and Training Center in Economics and Management (CERFEG), FaSEG, University of Lome, Lome – Togo. 2 Institute of New Structural Economics (INSE), Peking University, 100871 – Beijing (China). Disclosure statement No potential conflict of interest was reported by the author(s). 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Journal of Economic Issues, 49(3), 814–834. https://doi.org/10.1080/00213624.2015.1072429 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 23 of 34 Appendix 6: GMM-SYS, Sub-Saharan Africa (Y F = Y CHN ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.946*** 0.959*** 0.913*** 0.909*** (0.0364) (0.0162) (0.0315) (0.0303) Log(Y_2) −0.0276 −0.0127 −0.00856 −0.00395 (0.0198) (0.0180) (0.00618) (0.00528) Log(Y_3) −0.0324 −0.0241 (0.0357) (0.0280) Log(Y CHN ) 1.231 0.830 1.527 0.771 (0.938) (0.849) (1.105) (0.876) Log(Y CHN _1) −1.267 −0.792 −1.442 −0.684 (1.030) (0.840) (1.075) (0.842) C r 0.0273 0.0109 0.0428 0.0188 (0.0248) (0.0170) (0.0272) (0.0134) C r _1 −0.0341* −0.0173 −0.0373 −0.0158 (0.0200) (0.0114) (0.0260) (0.0119) ω 0.232 0.180* 0.178* 0.141 (0.148) (0.104) (0.101) (0.0952) ω_1 −0.232 −0.179* −0.178* −0.142 (0.149) (0.105) (0.101) (0.0962) R 0.0154 0.00211 0.0138 0.00233 (0.0173) (0.00825) (0.0155) (0.00972) R_1 0.0200 0.00478 0.00731 −0.00351 (0.0182) (0.00709) (0.0156) (0.00658) Log(H) −0.0867 −0.0594 −0.0744 −0.0126 (0.0915) (0.0627) (0.0780) (0.0421) Log(H_1) 0.0680 0.0452 0.0238 0.0244 (0.0670) (0.0805) (0.0855) (0.0628) Log(O) 0.111 0.0864 0.0652 0.0111 (0.119) (0.111) (0.0865) (0.0671) Log(O_1) −0.0369 −0.0520 0.0123 −0.0319 (0.0676) (0.118) (0.0821) (0.0915) FD Arellano-Bond AR (1), P zð Þ 0.037 0.037 0.036 0.040 FD Arellano-Bond AR (2), P zð Þ 0.699 0.376 0.432 0.945 Sargan overid. restr, Pχ2 ð Þ 0.530 0.526 0.445 0.445 Hansen overid. restr, Pχ2 ð Þ 0.125 0.109 0.130 0.130 Number of instruments 35 34 34 34 Observations 1,296 1,296 1,248 1,248 Number of group 48 48 48 48 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 30 of 34 Appendix 7: GMM-SYS, Sub-Saharan Africa (Y F = Y US ) VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS One-step Two-step One-step Two-step Log(Y_1) 0.968*** 0.957*** 0.963*** 0.949*** (0.0218) (0.0291) (0.0315) (0.0371) Log(Y_2) −0.0124 −0.00787 −0.00543 −0.00343 (0.0144) (0.0119) (0.0139) (0.0105) Log(Y_3) −0.00858 −0.00510 (0.0119) (0.00849) Log(Y US ) −0.663 −0.156 −1.441 −0.349 (4.005) (3.328) (4.436) (3.672) Log(Y US _1) 0.695 0.193 1.477 0.392 (4.019) (3.342) (4.453) (3.692) C r 0.0302 0.0195 0.0404 0.0198 (0.0198) (0.0196) (0.0264) (0.0165) C r _1 −0.0326 −0.0211 −0.0426 −0.0207 (0.0202) (0.0177) (0.0270) (0.0164) ω 0.161* 0.166 0.163 0.136 (0.0872) (0.0998) (0.106) (0.0836) ω_1 −0.160* −0.165 −0.162 −0.134 (0.0875) (0.102) (0.107) (0.0851) R 0.000804 0.00182 0.00370 0.000589 (0.0114) (0.00992) (0.0134) (0.00631) R_1 0.0143 0.00288 0.0154 −0.00330 (0.0307) (0.00952) (0.0369) (0.0115) Log(H) −0.0730 −0.0406 −0.0752 −0.00866 (0.142) (0.0709) (0.150) (0.0669) Log(H_1) 0.0833 0.0679 0.0834 0.0404 (0.0854) (0.0602) (0.0922) (0.0551) Log(O) 0.0532 0.0389 0.0429 0.00356 (0.145) (0.132) (0.147) (0.119) Log(O_1) −0.0674 −0.0792 −0.0520 −0.0471 (0.0553) (0.0957) (0.0651) (0.0915) FD Arellano-Bond AR (1), P zð Þ 0.036 0.040 0.034 0.044 FD Arellano-Bond AR (2), P zð Þ 0.999 0.361 0.607 0.569 Sargan overid. restr, Pχ2 ð Þ 0.566 0.566 0.480 0.480 Hansen overid. restr, Pχ2 ð Þ 0.102 0.102 0.209 0.209 Number of instruments 34 34 34 34 Observations 1,296 1,296 1,248 1,248 Number of group 48 48 48 48 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 31 of 34 Appendix 8: GMM-SYS, pre/post-2008 crisis periods VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS Onestep Twostep Onestep Twostep Pre-2008 crisis period Post-2008 crisis period Log(Y_1) 0.887*** 0.806*** 1.011*** 1.010*** (0.0941) (0.212) (0.0440) (0.0425) Log(Y_2) −0.0245 −0.00837 −0.00104 −0.00113 (0.0253) (0.0200) (0.00476) (0.00542) Log(Y_3) 0.00374 0.00512 (0.0358) (0.0335) Log(Y EU ) 0.312 0.424** 1.856* 1.169 (0.304) (0.169) (0.993) (1.346) Log(Y EU _1) −0.223 −0.290 −1.873* −1.188 (0.324) (0.226) (1.005) (1.339) C r 0.0134 0.0103 0.0606 0.0288 (0.0107) (0.0179) (0.0767) (0.0424) C r _1 −0.00602 −0.00144 −0.0502 −0.0255 (0.00616) (0.0123) (0.0843) (0.0544) ω0.184* 0.213 0.0642 0.0528 (0.108) (0.157) (0.231) (0.353) ω_1 −0.182* −0.213 −0.0618 −0.0478 (0.108) (0.159) (0.233) (0.354) R 0.00792 0.0104 0.0692** 0.0693** (0.00718) (0.0102) (0.0275) (0.0265) R_1 0.00719 0.00239 0.0772*** 0.0769*** (0.00567) (0.00694) (0.0259) (0.0254) Log(H) 0.0239 0.0476 −0.150 −0.131 (0.0350) (0.0356) (0.191) (0.144) Log(H_1) 0.0154 −0.0223 0.0740 0.0624 (0.0427) (0.0464) (0.138) (0.107) Log(O) −0.0422 −0.0451 0.224 0.214 (0.0440) (0.0396) (0.344) (0.259) Log(O_1) −0.00528 0.0400 −0.222 −0.185 (Continued) Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 32 of 34 VARIABLES Dependent variable: Log(Y) Level GMM-SYS Increased y level GMM-SYS Onestep Twostep Onestep Twostep Pre-2008 crisis period Post-2008 crisis period (0.0622) (0.0662) (0.226) (0.192) FD Arellano-Bond AR (1), P zð Þ 0.119 0.156 0.037 0.067 FD Arellano-Bond AR (2), P zð Þ 0.791 0.917 0.798 0.907 Sargan overid. restr, Pχ2 ð Þ 0.946 0.946 0.000 0.000 Hansen overid. restr, Pχ2 ð Þ 0.025 0.025 0.110 0.687 Number of instruments 23 23 34 34 Observations 864 864 540 540 Number of group 54 54 54 54 Sodokin et al., Cogent Economics & Finance (2022), 10: 2125656 https://doi.org/10.1080/23322039.2022.2125656 Page 33 of 34 © 2022 The Author(s). 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