Abstract
Purpose
This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.
Design/methodology/approach
To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.
Findings
Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.
Practical implications
All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.
Originality/value
Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.
Keywords
Citation
Olaniyi, C.O. and Odhiambo, N.M. (2024), "Inflation-poverty causal nexus in sub-Saharan African countries: an asymmetric panel causality approach", International Trade, Politics and Development, Vol. 8 No. 1, pp. 34-64. https://doi.org/10.1108/ITPD-08-2023-0024
Publisher
:Emerald Publishing Limited
Copyright © 2024, Clement Olalekan Olaniyi and Nicholas M. Odhiambo
License
Published in International Trade, Politics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/ legalcode
1. Introduction
Most developing countries grapple with extreme poverty and double-digit inflation rates. High inflation, particularly in developing countries, makes life very difficult for the poor. Inflation hits the poor worse than the rich (Artuc et al., 2022; Sani and Yahaya, 2021; Easterly and Fischer, 2001). It hurts people living in extreme poverty since it reduces their purchasing power (Sulaeman, 2021; Sehrawat and Giri, 2018). Inflation is treated as a tax since it lowers the poor’s real wages. In this condition, the poor’s nominal pay, which is often fixed, does not rise as quickly as prices in the face of growing inflation rates (Cardoso, 1992). In the economic literature, there are numerous studies on inflation’s impact on poverty. Predominantly, these studies show that inflation affects the poor and the rich differently. Inflation is a significant burden and a harsh tax on those in extreme poverty. Because of this, inflation has been dubbed the “cruelest tax” on the poor (Easterly and Fischer, 2001). As a result, inflation is a primary determinant of poverty (Junaidin and Muniarty, 2020). One of the most explored and saturated issues in economics is the inflation-poverty nexus. As a result, despite all the efforts of international institutions and governments around the world, the two macroeconomic problems continue to pose threats. Poverty and inflation are two phenomena that have varied implications in different parts of the world.
African continent, specifically sub-Saharan Africa (SSA), has remained the most plagued by extreme poverty and hunger (Olaoye et al., 2023; Olaniyi and Ologundudu, 2022; Olaniyi et al., 2023b; Solarin et al., 2021; Folarin and Adeniyi, 2020; Keho, 2017). In SSA, 45–50% of the population is impoverished (Solarin et al., 2021; Osinubi, 2005). More people live in abject poverty on the continent than on any other continent on the planet (Hussen, 2023; Solarin et al., 2021; Ndlovu and Toerien, 2020). Over the last few decades, SSA has remained the only region on the planet to see an increase in extreme poverty (Olaoye and Zerihun, 2023; Olaniyi et al., 2023b; Alpay, 2007). It is also well documented that SSA is the only region in the world that has failed to reach the Millennium Development Goals’ (MDGs’) poverty reduction targets (Mahembe and Odhiambo, 2021). Unlike in other parts of the world, the poor are becoming worse, and more people are falling into abject poverty (Simmons, 2015). In addition, African countries lead the top ten countries with the biggest number of people living in extreme poverty (Yomi, 2018). The total number of people living in extreme poverty in SSA climbed from 278 million in 1990 to 413 million in 2015, according to World Bank Group (2018) statistics. Also, in 2021, 490 million people are said to have been trapped in the web of abject poverty in Africa (Olaoye, 2023; Human, 2021). It was also reported that 27 of the world’s 28 poorest countries, as well as 27 of the world’s 34 lowest-income countries, are found in SSA (Salecker et al., 2020). It has also been predicted that Africa will be home to 70% of the world’s poorest people (Kharas et al., 2018a, b; Coulibaly, 2020; Koomson et al., 2020; Olaniyi et al., 2023b). These statistics suggest that the region has a higher proportion of extremely poor people than the rest of the world combined (Olaniyi et al., 2023b; Asongu et al., 2021; Nwani and Osuji, 2020). In SSA, the prevalence of extreme poverty appears to be higher. According to research, SSA is home to over 50.7% of the world’s poorest (World Bank, 2016; Omar and Inaba, 2020). This complicates and depresses the situation of poverty in SSA countries. It is worrisome that SSA now has the most people living in extreme poverty worldwide, surpassing the Asian region, based on a poverty line of $1.90 per person daily (Asongu et al., 2023; Nwani and Osuji, 2020).
Similarly, countries in SSA have double-digit inflation rates on average. Inflation in SSA is unstable, resulting in unanticipated negative and positive shocks. According to some existing studies (Danlami et al., 2020), high inflation rates could have further harmed and deteriorated the poor’s economic situation in the region. Inflation has been linked to a rise in poverty levels (Cuong, 2011). In most SSA countries, the average inflation rate is in the double digits. According to WDI data, the average values for Nigeria and Kenya between 1981 and 2019 were 19.14% and 11.78%, respectively. In 2021, Nigeria’s inflation rate rose to 21.34%, the highest in 17 years (The Central Bank of Nigeria, 2022; Olaoye et al., 2023). Meanwhile, the country witnessed a phenomenal incidence of 72.84% in 1995 (Olaniyi, 2020). According to a recent study, inflation promotes poverty in South Africa (South African Reserve Bank, 2020). In SSA countries, double-digit inflation rates threaten poor well-being. It could cause an increase in the number of people in extreme poverty, complicating the continent’s macroeconomic challenges. According to Cardoso (1992), inflation exacerbates extreme poverty by reducing the purchasing power of the impoverished. The causal responses of poverty to changes in inflation in SSA remain a subject of empirical investigation. Some studies have looked at the impact of inflation on poverty reduction in SSA, but the results have been inconclusive and mixed.
Few studies have looked at the nonlinear and asymmetric impacts of inflation on poverty (Meo et al., 2018), allowing for testing of poverty responses to negative and positive shock components in inflation. Poverty (inflation) should react in different ways to inflation (poverty) changes, both positive and negative. However, no empirical research has looked into the asymmetric causality between inflation and poverty. All the previously published works on the inflation-poverty causal nexus covertly assumed no asymmetric structure and nonlinearity. This supposition is restrictive, and it is incongruent with socioeconomic realities. Recent advances in empirical and econometric analysis have revealed impracticality of symmetric and linear approach of causality. The asymmetric causality is more flexible, useful and informative. It provides more information than only the asymmetric and differential effects of negative and positive inflation shocks on poverty levels. Existing research has only looked at the asymmetric effect of inflation on poverty, which has little predictive power. Furthermore, it may be more fundamentally and factually true to investigate the sources (causes) before the consequences (Bettinsoli et al., 2020; Trifunov et al., 2019). The consequence is always preceded by the cause. Examining consequences without first addressing the underlying causes may not yield more accurate results. This study explores numerous ways in which negative and positive inflation shocks can produce negative and positive changes in inflation and poverty levels, based on previous research (Olaniyi, 2020, 2022; Olaniyi and Olayeni, 2020; Osabohien et al., 2020). Another reason to investigate the asymmetric causal link between inflation and poverty is that the data for both variables have asymmetric and nonlinear distributions. As a result, reliance on a symmetric and linear approaches may result in inferior outcomes. It has also been stated that allowing for asymmetric structure in the empirical analysis is in line with reality (Olaniyi et al., 2023b; Hatemi-J and El-Khatib, 2020; Olaniyi and Olayeni, 2020; Olaniyi, 2020, 2022; Olayeni et al., 2021; Hatemi-J and Mustafa, 2016). By using a bootstrap simulation approach with leverage changes, this work adds to the previous understanding. The method is essential because it generates more accurate and dependable critical values and compensates for cross-sectional dependence correctly (Olaniyi, 2022, 2023; Olaniyi et al., 2023c; Lopez and Weber, 2017; Hatemi-j, 2012). This technique also accommodates non-normally distributed and volatile variables, which are common in financial data.
Existing research on the poverty-inflation nexus has ignored the possibility of cross-sectional dependence in their time series and panel analyses, according to a broad review of the literature. Globalization has made the world’s economies intertwined across borders (Olaniyi, 2022, 2023; Hatemi-J, 2020 a, b; De Hoyos and Sarafidis, 2006). As a result of globalization and financial contagion, cross-sectional interdependence between nations is becoming the norm rather than the rare case (Meo et al., 2020; Olaniyi, 2022; Olaniyi et al., 2023a). Shocks in one country can readily spread to other countries (Olaniyi and Odhiambo, 2023b; Olaniyi, 2022; Olaniyi et al., 2022; Olaniyi et al., 2023c; Olaoye and Aderajo, 2020). Causality findings may be inefficient and biased if cross-sectional dependency is unaccounted for (Olaniyi and Odhiambo, 2023a; Uzar et al., 2023). This calls into question the validity of cross-sectional independence among nations, which is implicitly assumed in studies on the poverty-inflation causal nexus. Similarly, prior time-series data analysis studies have overlooked potential cross-sectional spillover effects that could affect the results. This study covers the gap by accounting for cross-sectional dependence in the panel and time series asymmetric causality between inflation and poverty, which differs from previous research. It follows Hatemi-j’s (2014) approach of decomposing data on inflation and poverty into negative and positive components. Following these processes, this study adopts the Dumitrescu and Hurlin’s (2012) causality approach. This method considers panel causality and generates findings for each cross-sectional unit. Unlike previous studies’ methods, this approach is flexible, and it gives more information on causal inferences by allowing policy to vary across cross-sections (countries) in a panel framework (Olaniyi, 2023; Olaniyi et al., 2023c). Instead of imposing average restrictive policy on all the cross-sectional units, it accommodates heterogeneous features to account for each country’s idiosyncrasies and peculiarities in the causality between inflation and poverty. To account for cross-sectional dependence, Lopez and Weber (2017) used Stata software to develop a practical Morte Carlo bootstrap simulation of Dumitrescu and Hurlin (2012). As a result, Lopez and Weber (2017) executed the Dumitrescu-Hurlin panel Granger causality test using the Stata code from Lopez and Weber (2017).
In essence, this research advances knowledge by giving the first empirical insight into the asymmetric causal relationship between inflation and poverty using a Morte-Carlo bootstrap simulation that accounts for cross-sectional dependency and heterogeneity. The method allows researchers to investigate how inflationary shocks (poverty), both positive and negative, cause negative and positive changes in poverty levels (inflation) in SSA countries. There is little research that has looked at the causality between inflation and poverty (Danlami et al., 2020; Siyan et al., 2016; Gillani et al., 2009). These few studies used symmetric techniques, which provide limited information and can lead to erroneous conclusions and policy suggestions. This differs as it incorporates asymmetric structure into the causality between inflation and poverty within the framework of the Monte Carlo bootstrap simulation. This is unlike earlier panel and cross-sectional studies, which implicitly assume that impose a restrictive average policy to all countries within a panel analysis. This study adds new insights into the extant studies by relaxing this assumption to allow for the potential policy variations across countries in the panel framework to address country-specific issues in the inflation-poverty causal nexus.
Compared to earlier studies, this study has brought new insights and novelties into the poverty-inflation causal relationship. The contributions of this present research effort are as follows: One, all the relevant theories that explain the causal nexus between inflation and poverty are particularly silent on the roles of asymmetric and nonlinear features as well as cross-sectional dependence. Empirical outcomes establish that nonlinearities and asymmetries are more consistent with real-world realities. Both the inflation rate and poverty reduction indicator adopted in the study reveal significant nonlinearity features. Thus, this study has successfully introduced the significance of nonlinearity and asymmetry into the causal nexus between inflation and poverty, which all the previous studies (theoretical and empirical) implicitly assumed nonexistent. Two, regardless of trade across borders, globalization and financial integration and contagion among countries, extant studies are based on the supposition that countries are independent of one another. This assumption has been proved wrong in this study. Robust evidence of interdependence and intertwining is found in this study, and this reveals that shocks from one are transmitted to other countries. This causes actions and reactions among countries in the panel analysis. Three, extant panel studies on the inflation-poverty causal nexus assume homogenous evidence and policies across all the countries. Meanwhile, the research outputs in this study vary across countries, and it has justified the need to account for heterogeneous policies to address country-specific peculiarities. The average policy recommendations from a panel analysis might not work for all countries. The findings vary across countries, and this adds value to the existing body of knowledge. Four, this study innovatively incorporates asymmetries and nonlinearities into the heterogeneous panel Granger non-causality approach proposed by Dumitrescu and Hurlin (2012).
Apart from this introductory aspect in Section 1, the rest of the text is organized as follows. Section 2 examines data description and methodology. Section 3 focuses on the presentation and discussion of empirical findings. Section 4 provides a concise and precise conclusion to the study.
2. Literature review
2.1 Theoretical perspective
This study is rooted in the philosophical foundation of the theories of inflation. From a general perspective, inflation has to do with the persistent increase in the prices of goods and services. Thus, inflation has great implications for the value of money, and it thereby affects the purchasing power of people. High inflation tends to reduce the value of money and its purchasing power, while a decrease in inflation is accompanied by a rise in the value of money and its purchasing power. The poor, on average, has income that is relatively fixed and stagnated. It does not increase as fast as the inflation rate rises. Poverty is associated with the inability to meet the basic needs of life. High inflation deteriorates the purchasing power and worsens the economic condition of the poor. This implies that there is a close link between poverty and inflation. High inflation negatively hits the income and savings of the poor (Easterly and Fischer, 2001), while moderate inflation enables and enriches the poor to afford the basic needs of life (Gyeke-Dako et al., 2022). This explains why a rise in inflation is often tagged as the cruelest tax on the poor and deepens the extremity of poverty (Olaniyi et al., 2023b). It impoverishes and equally widens the real income gap between the poor and the rich. The implication is that inflation influences the poverty level through the mechanism of the price which impacts the real wage of the people in abject poverty. Changes in the inflation rate generate shocks that could trigger changes in poverty indicators. An inflationary spiral (positive shock) is anticipated to reduce the real wage of the poor, which worsens and causes an increase in poverty. On the other hand, declines in inflation (negative shocks) are meant to empower the poor by increasing their real wage and purchasing power.
Aside from the theoretical explanations of causality from inflation to poverty, poverty can also drive inflation (Nwadike et al., 2020; Siyan et al., 2016; Danlami et al., 2020). An increase in the economic power (income) of the impoverished in an economy through exogenous factors without a corresponding increase in productivity and aggregate supply could put extra pressure on aggregate demand for goods and services. The situation could lead to persistent price increases and inflationary pressures. On the contrary, a surge in poverty, given the aggregate supply, could equally dwindle aggregate demand as demand for goods and services falls. The occurrence might cause a persistent decline in price levels. Thus, a shrinkage in poverty could spur a fall in aggregate demand, causing inflation to fall. Inflation and poverty are causally related based on these theoretical propositions from the two strands. It implies that examining the effect of inflation on poverty without considering the likelihood of a feedback effect might bias the inferences and policy options. This study, however, supplements existing theoretical and empirical research by bridging a missing link. This phenomenon could bias the practical relevance of the inflation-poverty nexus. All the theoretical and empirical research assumes the oversimplifying idea that the two-way relationship between inflation and poverty is linear and symmetric. We posit in this study that the assumption might be an oversimplification of realities regarding the fundamental intricacies of the inflation-poverty nexus. There could be several dimensions of hidden information, flexibilities, asymmetric structures, nonlinearities and heterogeneous policy across countries that are not obvious on the surface. Asymmetries and nonlinearities in the inflation-poverty nexus are more appropriate for addressing practical issues and designing more flexible policy options. The two macroeconomic variables generate shocks (positive and negative). These shocks might determine the causal relationship and give more information. Thus, the inflation-poverty nexus without capturing these shocks may result in inaccurate results and wrong policy implications for addressing inflationary spirals and extreme poverty.
2.2 Empirical evidence
Humanity continues to face a major challenge due to poverty, which is the lack of capacity and resources to meet the necessities of life. Poverty has multidimensional causes. One of the most prominent factors is inflation. Debates continue to trail the inflation-poverty nexus. Some scholars see inflation as the cruelest tax that drains the poor’s economic power (purchasing power) and deepens poverty extremity in an economy (Loewald and Makrelov, 2020; Cardoso, 1992). Some others follow the notion that rising inflation is a stimulant that spurs investment prospects, resulting in more job opportunities and income for the poor (Olaniyi et al., 2023b; Easterly and Fischer, 2001; Romer and Romer, 1998). Due to the macroeconomic implications of inflation on poverty, a plethora of studies have focused on the impact of inflation on poverty, with scanty or no attention paid to the likelihood of a reactionary effect from poverty to inflation (Danlami et al., 2020). Meanwhile, the research outcomes of the existing research are mixed and inconclusive (Olaniyi et al., 2023b; Vinayagathasan and Ramesh, 2022; Inegbedion and Obadiaru, 2022; Rizki and Solihati, 2022; Gyeke-Dako et al., 2022; Sari and Rofiuddin, 2022; Sani and Yahaya, 2021; Sehrawat and Giri, 2018; Yolanda, 2017; Hassan et al., 2016; Shrestha and Chaudhary, 2012). These studies are restricted to the effect of inflation on poverty. The research outcomes of these studies vary. Most of these studies establish that inflation weakens the real purchasing power of the poor’s real wage and makes the poor worse off. Few others find the poverty-reducing effect of inflation The other group of studies establishes the insignificant role of inflation in driving poverty.
Aside from the inconclusiveness of the existing research, empirical studies on the causality between inflation and poverty are very scanty and still growing. The few studies on the two-way relationship produce divergent findings. In analysing Nigerian data covering 1980–2014, Siyan et al. (2016) used a vector autoregressive (VAR) estimator and found bidirectional causality between inflation and poverty. Danlami et al. (2020) also obtained similar findings on Nigeria from 1980 to 2016, using the Toda-Yamamoto causality approach. These two studies affirm the two-way perspective of modeling the inflation-poverty nexus. On the one hand, it implies that inflation drives poverty, as argued earlier. It suggests that high inflation could reduce the poor’s purchasing power and drain their welfare. Low inflation, on the other hand, raises real wages and improves the poor welfare. Moreover, poverty reduction could cause inflation through its effects on aggregate demand and the price mechanism. Poverty reduction enables the needy to meet their fundamental needs, which may increase aggregate demand while aggregate supply remains unchanged. This situation might increase the economy’s price level.
The findings, however, are inconsistent with the study by Gillani et al. (2009). This study establishes a unidirectional causality from inflation to poverty in Pakistan from 1975 to 2007 via the Toda-Yamamoto causality method. It also aligns with Cardoso’s (1992) findings, which reveal that inflation causes poverty. The study posits that inflation passes through the real wage to cause poverty. The study’s highlights suggest that prices rise faster than wages. Economic implication is that a higher inflation rate tends to spur a surge in poverty (Yusoff et al., 2023; Keynes et al., 2023). This situation weakens impoverished people’s purchasing power to meet their basic needs. Meanwhile, a related study by Nwadike et al. (2020) on Nigeria, covering 2000–2018, utilized pairwise Granger causality to find a unidirectional causal flow from poverty to inflation. Different from studies that establish a bidirectional or unidirectional causality between inflation and poverty, there are few other research efforts that find no causal relationship. Vinayagathasan and Ramesh (2022) report evidence of no causality between inflation and poverty in a panel analysis of eight South Asian countries, covering the 1996–2019 period. Similar results are obtained in a study by Sehrawat and Giri (2018) in India over the period of 1970–2015. All the studies reviewed give exciting dimensions of causality between inflation and poverty.
Existing research has the following key characteristics. One, all the existing studies assume a linear relationship between inflation and poverty. Recent advances in empirical analysis and econometrics have invalidated the linearity assumption as impractical and unfit to unravel the realities and dynamics of modern socioeconomic and macroeconomic complexities in the real world. Two, previous research assumed no asymmetric structures in inflation and poverty dynamics. Thus, all the studies utilize symmetric causal approaches, which neglect asymmetric structure and nonlinear trends in the data analysis of the inflation rate and poverty reduction indicators. Three, despite the rich evidence of interdependence and intertwining among countries due to international trade and alliances, economic integration and financial contagion, existing research on the inflation-poverty causal nexus neglects the necessity of accounting for cross-sectional dependence. There is a possibility that neglect may result in biased causal inferences that lead to faulty policy implications. Four, all panel studies on the inflation-poverty causal nexus ignore the importance of accommodating heterogeneous policy perspectives across countries in the panel analysis. The underlying assumptions produce restrictive policy dimensions that offer no opportunity to address country-specific matters on inflation-poverty causal relationships. Thus, this study uses a causality methodology that accounts for both the average panel dimension and the country-specific dimension of causality in order for the inferred policy to differ across countries. This present study differs from all previous research by bridging the identified gaps and correcting the observed deficiencies. It adds new insights to existing research on the inflation-poverty causal link, enhancing policy options and relevance to explain real-world practicalities.
3. Methodology and data source
3.1 Data source
Annual data on the twelve selected sub-Saharan countries (Botswana, Burkina Faso, Cameroon, Congo Republic, Gabon, Kenya, Madagascar, Mauritania, Nigeria, Senegal, South Africa and Togo) are utilized for the period 1981–2019. The population of the study is the entire SSA countries, but the data availability restricts us to the selection of twelve countries. This study would have embraced an unbalanced panel dataset but the estimation technique of Dumitrescu and Hurlin Granger non-causality approach requires a balanced panel. The countries with missing data are dropped from the analysis. Following the position of extant studies, the sample size in this study (country-year observations, 468) is adequate to give reliable and unbiased estimates in panel analysis (Olaniyi, 2022; Ahmad et al., 2021; Olaniyi and Oladeji, 2021; Nathaniel et al., 2021; Meo et al., 2020). The data on poverty measured by per capita consumption expenditure and the consumer price index, a measure of inflation, are sourced from World Development Indicator (WDI). To maintain fair representation, all the sub-regions of SSA are well represented.
3.2 Definitions of variables
3.2.1 Poverty
Consistent with the definition of poverty by the World Bank which describes poverty as “the inability to attain a minimal standard of living,” this study makes use of per capita consumption as a proxy for poverty. Poverty is defined in terms of basic consumer needs in this definition. Consumption expenditure data among the poor are generally more recorded and available, and it is also steadier than income metrics such as per capita income, according to existing studies (Olaniyi et al., 2023b; Olaniyi and Ologundudu, 2022; Odhiambo, 2009; Woolard and Leibbrandt, 1999; Ravallion, 1992). Consumption-based metrics of poverty, according to academics, represent welfare and material well-being better than income-based measures (Koomson et al., 2020; Norris and Pendakur, 2013; World Bank, 2001). Because it reveals people’s ability to satisfy and meet basic and minimum consumption needs, both food and non-food components, consumption expenditure has been widely used as a reliable indicator of welfare and a preferred measure of household living standards (Chen et al., 2021; Norris and Pendakur, 2013; Beegle et al., 2012). Also, the consumption distribution, rather than the income distribution, maybe a better indicator of utility distribution or lifetime wealth (Norris and Pendakur, 2013). Furthermore, there are a plethora of previous studies which have adopted this measure of poverty, and examples include Olaniyi et al. (2023), Olaniyi and Ologundudu (2022), Akinlo and Dada (2021), Solarin et al. (2021), Musakwa and Odhiambo (2021), Das et al. (2021), Appiah et al. (2020), Adeleye et al. (2020), Danlami et al. (2020), Garza-Rodriguez (2018), Ho and Iyke (2018), Sehrawat and Giri (2016a, b), Dhrifi (2015), Uddin et al. (2014), Odhiambo (2010, 2009) and Quartey (2005). In addition, despite being the world’s second-most populous continent, household consumption expenditures in Africa have been estimated to be among the lowest (Solarin et al., 2021).
3.2.2 Inflation
Inflation, according to existing research, is a protracted rise in the price level. The common measure of inflation in the extant studies is the consumer price index (Amin et al., 2020; Meo et al., 2018). This proxy is more reliable because it captures and measures consumer purchasing power and welfare, as well as the economy’s overall price level. It is also “an index that measures the rate at which the prices of consumption goods and services are changing from month to month (or from quarter to quarter)” (ILO, 2004). It is commonly defined as the average change in price over time that consumers pay for a basket of goods and services. Thus, it measures changes in the prices of goods and services that households consume (ILO, 2004). The inflation rates are expressed in percentages.
3.3 Estimation procedural steps
Before proceeding with the modeling for the causal nexus between inflationary shocks and poverty, the models for all the preliminary tests are concisely discussed in this section. These tests are necessary to reveal the true characteristics of the data, which will inform the appropriate estimation techniques.
3.3.1 Cross-sectional dependence tests
In modern econometrics, evaluating for cross-sectional dependence (CD) in panel analysis has become the rule rather than the rare case (Olaniyi, 2022; Olaniyi et al., 2022; Olaniyi et al., 2023c; Olaniyi et al., 2023a; De Hoyos and Sarafidis, 2006; Meo et al., 2020). Also, the increasing level of globalization and financial liberalization have spurred the intertwining and integration of countries across the globe. Countries rely on one another to transact and trade. Thus, shocks to a country are easily transmitted to other countries through the contagion effect within an economic bloc. In many regards, African countries are highly integrated in policies and macroeconomic decisions, and shocks to one could transmit to other countries (Aluko et al., 2021). Given this assertion, this study follows the CD modeling of Pesaran (2004) under the null hypothesis that there is no CD in panel data as thus:
3.3.2 Slope homogeneity test
Following the work of Pesaran and Yamagata (2008), this study examines whether there exists slope homogeneity across the cross-sectional units in the panel analysis or not. Pesaran and Yamagata’s (2008) test is adopted, as other prevalent slope homogeneity tests do not account for CD. The model specifications for the delta tilde and adjusted delta tilde are expressed as follows:
3.3.3 Panel unit root tests
Adoption of the DH panel causality approach requires the stationarity of the variables. Due to the prevalence of cross-sectional dependence in panel analysis, second-generation panel unit root tests are chosen in this study. Following this assertion, cross-sectionally augmented Dicky-Fuller (CADF) and cross-sectionally augmented IPS (CIPS) tests developed by Pesaran (2007) are utilized. The panel data set’s cross-sectional dependence and heterogeneity are not a problem for these unit root tests. Although it has been proven that CIPS performs better when CD and heterogeneity occur, the two outcomes are provided to guarantee robustness. The model for CIPS is specified thus:
The statistical test of CIPS is specified as follows:
CADF denotes cross-sectionally augmented Dicky-Fuller test.
3.4 Data decomposition and model specification
Modeling the causality process starts with the decomposition of data into negative and positive shock components. Consistent with the pioneer thoughts of Granger and Yoon (2002), data on inflation (consumer price index) and poverty (per capita consumption) are transformed into negative and positive shock components. This study pioneers incorporating asymmetric structures into the causal relationship between poverty and inflation. Since negative and positive shocks are expected to have different causal effects, asymmetric causation is thereby inferred (Olaniyi, 2020; Olaniyi and Olayeni, 2020; Hatemi-J, 2020a). This important aspect has been neglected in extant studies. The baseline assumption of Hatemi-J (2012) is followed that the variables [poverty
Following the processes stated earlier, the positive and negative shocks in both poverty
The constructed negative and positive shocks components of poverty and inflation are described as follows:
It should be noted that the generated variables such as negative components and positive ones are expected to have causal effects on the underlying variable. After that, we examine the causality between constructed variables. In an attempt to unravel asymmetric causality between poverty and inflation, the pairs between positive cumulative shocks
The fact that
The alternative hypothesis-heterogeneous non-causality (HENC) hypothesis of the presence of causality for at least one cross-section is expressed thus:
Under the null hypothesis of homogeneous non-causality (HNC), the test statistic is said to be tending sequentially to a typical normal distribution with k degrees of freedom. Individual Wald statistics from Granger non-causality tests are averaged to get the Wald test statistic (Aluko et al., 2021; Lopez and Weber, 2017; Dumitrescu and Hurlin, 2012). The individual Wald statistic is presented as follows:
This study applies a bivariate approach to analyze causality between inflation and poverty reduction. This method does not undermine the importance of other variables in causality. The unique nature of asymmetric causality reveals hidden information through pairing the positive and negative shock components in the variables. This process necessitates bivariate causality in the following ways. One, this study uses the panel causality technique of Dumitrescu and Hurlin (2012). This method accounts for cross-sectional dependence and policy variations across countries in the panel framework. The approach is distinctive, and it is developed within bivariate causal analysis (Dumitrescu and Hurlin, 2012; Lopez and Weber, 2017; Saba and Ngepah, 2019; Aluko et al., 2021; Uzar et al., 2023; Olaniyi et al., 2023c; Yıldırım et al., 2023; Olaniyi, 2023). Two, asymmetric causality, as developed by Hatemi-j (2012) within bivariate causality, reveals hidden causal inferences among all possible pairs of positive and negative shocks. Asymmetric causality is innovatively developed within the context of pairwise Granger causality to pair negative and positive shock components in variables in turn. Various extant studies have followed this approach (Ikhsan et al., 2022; Hatemi-J and El-Khatib, 2020; Hatemi-J, 2020a; Olaniyi, 2020; Olaniyi and Olayeni, 2020; Hatemi-J et al., 2017, 2019; Hatemi-J and El-Khatib, 2016), but none have verified it in the case of the inflation-poverty causal nexus, which is examined in this study. Three, Hacker and Hatemi-J (2012) developed the bootstrap causality test within bivariate causality. Other studies adopted this approach (Hatemi-J and Shamsuddin, 2016; Hatemi-J and Uddin, 2012; Gunduz and Hatemi-J, 2005; Hatemi-J, 2002).
Four, the Dumitrescu and Hurlin (2012) causality technique used in this work was designed to pair variables to determine underlying causality (see Lopez and Weber, 2017; Olaniyi, 2023; Uzar et al., 2023; Olaniyi et al., 2023c; Olaniyi and Odhiambo, 2023a). This approach’s causal inference is valid since it compensates for potential simultaneity bias, omitted variable bias and endogeneity concerns. Five, aside from the Dumitrescu-Hurlin approach, there are other variants of bootstrap causality that account for policy variations across cross-sectional units in panel causality that are also designed within bivariate analysis (Yıldırım et al., 2023; Hatemi-J, 2022; Gorus et al., 2023; Usman and Bashir, 2022; Aytun and Akin, 2022; Gezer, 2022; Abar, 2022; Juodis et al., 2021; Dumitrescu and Hurlin, 2012; Kar et al., 2011; Emirmahmutoglu and Kose, 2011; Kónya, 2006). Six, Hatemi-j (2012) introduced the novel idea of asymmetric causality based on bivariate causality principles. All other known studies on asymmetric causality follow Hatemi-j’s (2012) guidelines within bivariate causality by pairing all probable positive and negative shock components in turn (Yilanci et al., 2021; Wang et al., 2021; Olaniyi, 2020; Olaniyi and Olayeni, 2020; Olaniyi and Ologundudu, 2022; Hatemi-J, 2014; Hatemi-J et al., 2016, 2017, 2018, 2019; Hatemi-J and El-Khatib, 2016, 2020; Hatemi-J and Uddin, 2012; Destek, 2016).
4. Discussion of empirical findings
4.1 Preliminary analysis
4.1.1 Descriptive statistics
The synopses of descriptive statistics of the variables in the study are presented in Table 1. The coefficients of variation, computed by dividing the standard deviation by the mean, suggest the two variables exhibit relatively large variations from their respective mean values across cross countries in the panel data set. It is, however, more revealing that the log of per capita consumption as a proxy for poverty
4.1.2 Cross-sectional dependence and slope homogeneity tests
To confirm whether cross-sectional dependence (henceforth CD) exists or not in the series, four cross-sectional dependence tests, namely Pesaran (2021), Pesaran et al. (2008), Breusch and Pagan (1980) and Baltagi et al. (2012), are employed. The four different tests are examined to ensure the robustness of the findings (Olaniyi et al., 2022; Olaniyi, 2023). The results of the tests are presented in Table 2. The null hypothesis of the independence of countries is strongly rejected. All the CD tests adopted confirm clear evidence of CD among the countries. This reaffirms the argument in the literature that countries are not independent but highly interlinked via globalization and other means (Meo et al., 2020; Hatemi-J and El-Khatib (2020); De Hoyos and Sarafidis, 2006). Thus, the assumption of extant studies touching on the independence of cross-sectional units appears to be unrealistic. This backs up the decision to use an estimator that takes care of cross-sectional interdependence in the evaluation of the asymmetric causal link between inflation and poverty. It implies that the data series experience CD over the study period.
The results indicate clear evidence of common shocks, spatial dependence and degree of integration among countries in SSA due to financial integration and contagion which emanate from shocks that are transmitted from one country to others (Aluko et al., 2021). This result has faulted the first-generation panel unit root tests and supports the adoption of second-generation unit root tests which produce robust and appropriate inferences in the presence of CD. Furthermore, as a sequel to the presence of CD, we follow the study of Pesaran and Yamagata (2008) to examine the slope homogeneity test across cross-sectional units. The test is a standardized form of Swamy’s (1970) test. The slope homogeneity test assumes that error terms are independently distributed, but it accommodates heterogeneous variance (Bersvendsen and Ditzen, 2021). The results, as presented in Table 3, show that heterogeneity of slopes exists across the countries in the panel model. Thus, the null hypothesis of homogenous slopes is rejected. The slopes tend to vary across countries in the panel analysis. The result of CD and slope homogeneity tests have validated and justified the adoption of DH panel causality, which is robust to take care of CD and heterogeneity in the panel model.
4.1.3 Panel unit root tests
The second-generation panel unit root tests that account for CD are carefully used in this work to follow the condition of the DH panel Granger non-causality approach that the variables must be stationary. The presence of CD in the series makes it a matter of necessity to adopt second-generation panel unit root tests that produce valid inferences when the presence of CD is confirmed (Zoaka et al., 2022). Unlike the prevalent first-generation panel unit root approaches which fail in the occurrence of CD, following the work of Pesaran (2007), this study adopts more robust cross-sectionally augmented IPS (CIPS) and cross-sectionally augmented DF (CADF) to explore the stationarity properties of per capita consumption expenditure and inflation rate. This is done to prevent misleading inferences about the affirmation of CD in the series. The results of CIPS and CADF are presented in Table 4. The results reveal that both variables are integrated of order zero. It implies that the variables attain stationarity at a level without passing through the process of difference. This further justifies the adoption of the DH causality procedure, which necessitates the stationarity of variables. Meanwhile, the result of the cointegration test is not reported because the DH panel causality test does not require it.
4.2 Presentation and discussion of empirical findings
This aspect addresses the main discourse of the paper, which is to examine asymmetric structures in the causal nexus between poverty and inflation in selected SSA countries. To ensure the robustness of the results, we begin the empirical analyses with symmetric and linear causality between inflation and poverty. Subsequently, asymmetric causality is rigorously explored through econometric analysis. The implications of the findings are well expatiated. Consistent with the explanation of Lopez and Weber (2017), optimal lag length is determined endogenously in each case of both symmetric and asymmetric causality using Bayesian information criteria (BIC). Also, to align with the explanations of Lopez and Weber (2017), 1,000 bootstrapped iterations are conducted to properly account for cross-sectional dependence in the dataset.
4.2.1 Linear and non-homogenous causality
The synopses of the result of bootstrapped symmetric DH panel causality are presented in Table 5. The research outputs of panel analysis indicate that there is a symmetric bidirectional causality between inflation (inf) to poverty level (pov). The economic implication suggests that the inflation rate in SSA can Granger-cause either an increase or a decline in poverty reduction. This implies that inflation in SSA is a casual and determining factor that could trigger a change in the level of poverty in SSA. This result of symmetric causality may have supported the argument in the previous studies that inflation is the “cruelest tax” on the poor (Easterly and Fischer, 2001; Cardoso, 1992). Stakeholders and policymakers must keep closely monitoring the inflationary trends to curb their adverse causal effect on the level of poverty in SSA countries. The causality from poverty reduction to inflation suggests that a reduction in poverty could trigger an increase in inflation in Africa. The implication of the findings indicates that poverty reduction can spur a rise in aggregate demand that does not go along with a corresponding increase in aggregate supply and productivity. The gap between aggregate demand and supply created by poverty reduction has potency of causing inflationary spiral which triggers continuous rise in prices. These research outcomes indicate the need for African countries to design poverty-mitigating approaches, policies and initiatives that have the potency to spur an increase in productivity and aggregate supply in African countries. These findings are consistent with the research outcomes of earlier works such as Danlami et al. (2020) and Siyan et al. (2016) which established a bidirectional causality between inflation and poverty. Interestingly, there is no evidence of bidirectional symmetric causality between inflation and poverty in the cases of country-specific analysis. This validates the earlier argument that the imposition of average panel causality findings on all the countries in the panel analysis might be too restrictive and less informative, as it cannot address the country-specific idiosyncrasies and peculiarities in the inflation-poverty nexus. Thus, the inferred policy dimensions and implications from the panel causality findings might be inappropriate for specific countries in the panel data set.
Similarly, the results of country-specific symmetric causal flow from inflation to poverty show that it is only established in the cases of the Congo Republic and Madagascar. This finding aligns with that of Gillani et al. (2009) and Cardoso (1992), which establish that inflation is causal driver of poverty. This study finds a one-way causality from poverty to inflation in the country-specific cases of Botswana, Burkina Faso, Congo Republic, Mauritania and Togo. This result implies that changes (increases or decreases) in the poverty level in these countries could spur an upward or downward trend in the inflation rate through aggregate demand and price level channels if aggregate supply and productivity levels do not respond appropriately. This research outcome is consistent with the study of Nwadike et al. (2020). The third dimension of country-specific causal analysis between inflation and poverty reveals no symmetric causal explanation in the case of Cameroon, Gabon, Kenya, Nigeria, Senegal and South Africa. The findings indicate that these six countries should strategically plan, design and execute policies to maintain macroeconomic stability and reduce poverty without causally linking inflation and poverty to each other. The findings on symmetric causality between inflation and poverty vary across the countries in the panel analysis. This position reinforces the preceding assertion that a restrictive and homogeneous policy may be unsuitable for explaining a country’s specific characteristics and issues in the inflation-poverty causal nexus.
Meanwhile, all these extant studies assumed symmetric causal nexus between inflation and poverty, which did not consider how shocks in each variable cause shocks in the other. This symmetric causal inference in the inflation-poverty nexus does not give a decisive nature of causality either negative or positive. Thus, this study maintains a step ahead of previous studies by incorporating asymmetric and nonlinear into the causality between poverty and inflation.
4.2.2 Asymmetric and nonlinear causality tests
The results of all the possible pairs of the asymmetric causality tests are presented in Tables 6–9. Table 6 provides the concise results of asymmetric causal links between the pair of positive shock components in the poverty indicator and inflation in selected SSA countries. The findings reveal several dimensions of the inflation-poverty causal nexus to be robustly asymmetric. The heterogeneous panel causality affirms evidence of a unidirectional asymmetric causality from positive shock in inflation
In contrast, an increase in inflation rate does not spur a poverty reduction in the case of Botswana, Cameroon, Kenya, Mauritania and South Africa. This indicates that average policy descriptions and recommendations from the panel analysis could not apply to all countries as some countries reported appreciable levels of exceptions. Thus, an inflationary spiral does have not the potency to spur poverty reduction in the same manner in these five countries. This appears to have invalidated the outcomes of the extant studies which implicitly ignore the importance of cross-sectional dependence in the inflation-poverty nexus. The reserve causality from positive shock in poverty indicator
Increases in inflation is nonexistent in the heterogeneous panel causality. The results are equally valid for countries such as Burkina Faso, the Congo Republic, Kenya, Madagascar, Nigeria and Senegal. This means that a poverty reduction does not cause an increase in aggregate demand that could spur an inflationary spiral. Meanwhile, the results appear different in the country-specific cases of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo, as an increase in consumption per capita causes a rise in inflation. The implication is that poverty reduction empowers the poor to demand more goods and services, which culminates in a persistent rise in price levels. This might have been attributed to a situation where poverty reduction does not translate to an increase in productivity and aggregate supply. This puts extra pressure on aggregate demand in the economy. It should be noted that bidirectional causality between positive shock components of inflation and poverty reduction is reported in Gabon and Togo. Thus, macroeconomic policy dimensions that explain and address the two-way relationship between an increase in inflation and a rise in consumption per capita – poverty reduction – should be prioritized in the two countries. A rising inflation rate could catalyze a reduction in poverty by stimulating investment prospects, generating employment opportunities and providing more income for poor people. On the other hand, poverty reduction could also fuel inflation by increasing aggregate demand. This is if it does not spur productivity and aggregate supply. Hence, the two-way relationship is sensitive.
Similarly, following the result in Table 7, the panel causality and that of Cameroon’s country-specific case reveal evidence of bidirectional causality between a fall in the inflation rate
On the other side, there is a causality from an increase in poverty
Furthermore, as reported in Table 8, the research outputs indicate a unidirectional asymmetric causality from the negative shock of inflation
Thus, stakeholders and policymakers in these countries are advised to introduce macroeconomic policies that will keep inflation at a moderate level to dwindle poverty. A continuous decline in the general price level has a favorable effect on the poor. Meanwhile, no causal flow is reported from the negative shocks’ component of inflation to the positive shocks’ component of poverty in panel analysis of selected SSA countries and country-specific cases of Botswana, Burkina Faso, Cameroon, Gabon, Kenya, Mauritania and South Africa. The causality from poverty reduction, an increase in per capita consumption, to a fall in inflation equally produces some interesting findings. The panel causality estimates and country-specific cases of Burkina Faso, the Congo Republic, Gabon, Kenya, Madagascar, Nigeria, Senegal, South Africa and Togo show no causality from a reduction in poverty to a decline in inflation. These findings imply that poverty reduction that culminates in the poor’s economic empowerment does not spur inflation decline. Meanwhile, this causal inference is established in the country-specific cases of Botswana, Cameroon and Mauritania. A reduction in poverty triggers a fall in inflation. The economic implication is that the poor’s economic empowerment that ensues from poverty reduction is not a burden on aggregate demand. This indicates that a rise in the poor’s purchasing power, which is a by-product of poverty reduction, might have been accompanied by an increase in productivity and aggregate supply in the economy.
The results in Table 9 reveal that asymmetric causal inference is not detected from the positive component of inflation
As reported in Table 9, the increase in poverty (cumulative falls in poverty) causes an increase in the inflation rate (cumulative upward trends in inflation) in the panel and specific cases of Botswana, Cameroon, The Congo Republic, Gabon, Madagascar, Mauritania and Togo. These findings suggest there are exogenous forces that seem to help the poor increase aggregate demand without contributing meaningfully to increasing aggregate supply and productivity. It might also reveal some complexities in the inflation-poverty nexus. It could indicate that there were financial provisions and packages designed to improve the welfare of the poor but did not get to them due to corruption, opportunism and rent-seeking, which are prevalent in African countries. Since these financial resources were released into the economy without ameliorating poverty’s severity, there is a high likelihood that these situations will culminate in higher inflation. Welfare packages and provisions for the poor need to be properly monitored and scrutinized to prune them of exploitative tendencies and sharp practices. The inability to do this will divert the resources and thwart the poverty reduction process in SSA. Meanwhile, causality does not exist in the cases of Burkina Faso, Kenya, Nigeria, Senegal and South Africa. Consistent with the explanations provided earlier, this confirms the case of bidirectional causality between cumulative increases in poverty and inflation rates in Botswana and the Congo Republic.
5. Conclusion
The inflation-poverty nexus remains one of the most saturated areas in the economics literature. However, the discourse on it is ever relevant as inflation has continued to be described as the cruelest tax on the poor because it weakens and erodes the purchasing power of the poor. The debates on the nexus have intensely continued to emerge as poverty remains a socioeconomic problem while double-digit inflation rates in developing countries have worsened. Although there have been several studies on the inflation-poverty nexus, most of them have implicitly assumed that there is no asymmetric structure in the inflation-poverty causal nexus. This does not accord well with realities, and it has been faulted in the recent development in econometrics as the symmetric approaches provide limited information on the causal links between various pairs of shocks generated by inflation and poverty indicator. Premised on this obvious gap in the extant literature, this study pioneer testing asymmetric causality between inflation and poverty within the framework of a panel causality test developed by Dumitrescu and Hurlin (2012) which accounts for cross-sectional dependence and heterogeneity following the path of Monte Carlo bootstrap simulation that generates robust critical values. Data on selected SSA countries from 1981 to 2019 are chosen as the case study because the region remains the most impoverished in the world, and most of the countries in SSA have continued to cope with the severe problem of double-digit inflation rates.
Unlike existing research, we confirm strong evidence in support of cross-sectional dependence (CD) and heterogeneity in SSA. This finding validates the adoption of a causality approach that takes care of CD and policy variations across countries in the panel dataset. A bidirectional symmetric causal nexus is detected between inflation and poverty in the panel analysis. Interestingly, there is no evidence of symmetric bidirectional causality in the country-specific analysis. A unidirectional symmetric causality from inflation to poverty is seen in the Congo Republic and Madagascar. It means that a rise in inflation could trigger an increase in investment and employment opportunities. This will benefit the poor and subsequently lead to poverty reduction. Still on the symmetric causality front, the findings reveal a reverse one-way linear causal inference from poverty to inflation in the country-specific analysis of Botswana, Burkina Faso, Mauritania and Togo. It suggests that poverty reduction increases aggregate demand, while aggregate supply and productivity remain unchanged. Hence, there is an increase in inflation. On asymmetric causality, the research outputs reveal different levels of asymmetries in the causality between inflation and poverty in the selected SSA countries. The findings detect asymmetric causality from a positive inflationary shock to a positive shock in the poverty indicator in Burkina Faso, the Congo Republic, Gabon, Madagascar, Nigeria, Senegal and Togo. This suggests that a rise in inflation could reduce poverty through an increase in investment and employment opportunities. This would improve life for the poor in these seven countries. Evidence of reverse asymmetric causality is equally found from poverty reduction to an increase in inflation in the cases of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Several dimensions of asymmetric causality are evident in all probable pairings of negative and positive components of inflation and poverty. Diverse asymmetric causal structures are robust and persistent in all results. Asymmetric causality findings also vary across countries in the panel framework. These research outcomes demonstrate that existing studies that assumed no asymmetric structures in the causality between inflation and poverty might have overestimated their models with limited or restricted information and policy implications.
This study contributes to the existing debates on the poverty-inflation causal nexus literature by introducing asymmetric structure, cross-sectional dependence, bootstrap simulation and heterogeneous policy across countries, which have been neglected in previous studies. Based on the outcomes of this study, general and specific recommendations are made. SSA countries should take advantage of investment and employment opportunities that follow any rise in inflation as it tends to benefit the poor in terms of poverty reduction. Also, it should be noted that countries in SSA tend to enjoy a reduction in poverty levels if the rates of inflation are reduced to moderate levels. Stakeholders, policymakers and entrepreneurs are encouraged to consider asymmetric structure in the trends of inflation rate before embarking on policy formulations and implementations to reduce the severity of poverty in SSA countries. This is advised as the failure to consider asymmetric structure might undermine the effectiveness of the policies. It is obvious from the findings that there are policy variations in the inflation-poverty nexus across the selected SSA countries. As it is evident from the findings, country-specific peculiarities and matters should be cautiously considered in the choice of policy and initiatives on the inflation-poverty nexus. The prevalent homogeneous policies across countries suggested in the existing literature to address issues surrounding inflation-poverty nexus are inadequate. Welfare packages and financial provisions designed to empower the poor in Africa should be properly monitored and scrutinized. This will ensure that they are expended on things and schemes that will enable the poor to contribute to the nations’ productivity and aggregate supply without putting extra burden on aggregate demand that could create demand deficit and fuel inflationary spiral.
6. Research limitations and future research recommendations
There are, however, obvious areas to improve on in the subsequent research efforts by other scholars. Efforts should be made to consider as many countries as possible in SSA in the subsequent studies. Although all the sub-regions of SSA are well represented in this study, the policy relevance is restricted to SSA countries. Thus, other scholars are enjoined to conduct similar studies for other continents to enrich and further validate the content of the empirical findings. Other scholars should endeavor to enrich these findings by undertaking similar works for other regions. Future research endeavors should consider other measures of poverty. Meanwhile, in this analysis, we are limited to using consumption per capita due to insufficient data availability and a lack of apparent asymmetric structures in the data of some variables. The identified limitations do not diminish the importance of the originality, novelty, study’s scientific findings and policy implications. We raise them to enrich and supplement study research contents and innovative ideas.
Descriptive statistics
| Poverty (pov) | Inflation (inf) | |
|---|---|---|
| Mean | 6.838 | 7.856 |
| Median | 6.732 | 5.715 |
| Maximum | 8.431 | 72.836 |
| Minimum | 5.545 | 0.016 |
| Standard deviation | 0.734 | 9.002 |
| Coefficient of variation (%) | 10.731 | 114.591 |
| Skewness | 0.577 | 3.193 |
| Kurtosis | 2.386 | 16.813 |
| Jarque-Bera | 33.347 | 4515.959 |
| Probability | 0.000 | 0.000 |
| Observations | 468 | 468 |
Source(s): Authors' computations
Cross sectional dependence result test
| Variables | CD-tests | p-value |
|---|---|---|
| Inflation (inf) | 21.840*** | 0.000 |
| Poverty (pov) | 22.480*** | 0.000 |
Note(s): The symbol *** refers to the rejection of null hypothesis of CD at 1% level of significance
Source(s): Authors' computations
Pesaran and Yamagata’s (2008) slope homogeneity test
| Poverty = f(inflation) | 1.980** (0.048) | 2.100** (0.036) |
Note(s): The symbol ** represents rejection of null hypothesis at 1% level of significance
Probability values are in brackets ( )
Source(s): Authors' computations
Second-generation panel unit root tests
| Variables | Level | |
|---|---|---|
| Constant and trend | ||
| CIPS | CADF | |
| Inflation (inf) | −3.949*** (0.000) | −3.079*** (0.002) |
| Poverty (pov) | −3.476*** (0.000) | −3.257*** (0.000) |
| constant | ||
| Inflation (inf) | −4.083*** (0.000) | −2.804*** (0.000) |
| Poverty (pov) | −2.819*** (0.000) | −2.803*** (0.000) |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% levels of significance, respectively
Probability values are in bracket ( )
Source(s): Authors' computations
Panel symmetric and heterogeneous causality between inflation and poverty (Infl and pov)
| Cross sections | Null hypothesis | Null hypothesis | ||||||
|---|---|---|---|---|---|---|---|---|
| Inflation (inf) does not cause poverty (pov) | Poverty (pov) does not cause inflation (inf) | |||||||
| Wald stat | p-value | lag | Decision | Wald stat | p-value | lag | Decision | |
| Panel | 31.992** | 0.048 | 11 | Reject | 3.463*** | 0.004 | 1 | Reject |
| Botswana | 20.561 | 0.254 | 11 | Accept | 4.893** | 0.034 | 1 | Reject |
| Burkina Faso | 26.856 | 0.167 | 11 | Accept | 3.137* | 0.085 | 1 | Reject |
| Cameroon | 6.095 | 0.808 | 11 | Accept | 0.705 | 0.407 | 1 | Accept |
| Congo, Rep | 67.274** | 0.029 | 11 | Reject | 2.452 | 0.126 | 1 | Accept |
| Gabon | 29.133 | 0.146 | 11 | Accept | 2.216 | 0.146 | 1 | Accept |
| Kenya | 34.260 | 0.110 | 11 | Accept | 0.231 | 0.634 | 1 | Accept |
| Madagascar | 93.951** | 0.014 | 11 | Reject | 1.365 | 0.250 | 1 | Accept |
| Mauritania | 18.138 | 0.303 | 11 | Accept | 8.026*** | 0.008 | 1 | Reject |
| Nigeria | 31.83 | 0.125 | 11 | Accept | 1.288 | 0.264 | 1 | Accept |
| Senegal | 8.471 | 0.668 | 11 | Accept | 1.606 | 0.213 | 1 | Accept |
| South Africa | 20.142 | 0.262 | 11 | Accept | 0.047 | 0.830 | 1 | Accept |
| Togo | 27.194 | 0.164 | 11 | Accept | 15.585*** | 0.000 | 1 | Reject |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% level of significance, respectively. Also, computation of p-values is based on 1,000 bootstrap replications. The lag length criteria is endogenously determined based on Bayesian information criteria (BIC)
Source(s): Authors' computations
Panel asymmetric causality (Inf+ and pov+)
| Cross sections | Null hypothesis | |||||||
|---|---|---|---|---|---|---|---|---|
| Inf+ does not cause pov+ | Pov+ does not cause inf+ | |||||||
| Wald stat | p-value | lag | Decision | Wald stat | p-value | lag | Decision | |
| Panel | 55.695*** | 0.006 | 11 | Reject | 3.512 | 0.220 | 1 | Accept |
| Botswana | 28.506 | 0.152 | 11 | Accept | 3.446* | 0.072 | 1 | Reject |
| Burkina Faso | 82.095** | 0.019 | 11 | Reject | 2.423 | 0.129 | 1 | Accept |
| Cameroon | 9.905 | 0.591 | 11 | Accept | 6.320** | 0.017 | 1 | Reject |
| Congo, Rep | 104.645** | 0.011 | 11 | Reject | 0.823 | 0.370 | 1 | Accept |
| Gabon | 40.183* | 0.082 | 11 | Reject | 2.954* | 0.094 | 1 | Reject |
| Kenya | 21.315 | 0.241 | 11 | Accept | 1.563 | 0.220 | 1 | Accept |
| Madagascar | 148.935*** | 0.005 | 11 | Reject | 1.736 | 0.196 | 1 | Accept |
| Mauritania | 19.285 | 0.278 | 11 | Accept | 4.931** | 0.033 | 1 | Reject |
| Nigeria | 94.206** | 0.014 | 11 | Reject | 0.006 | 0.938 | 1 | Accept |
| Senegal | 43.404* | 0.071 | 11 | Reject | 0.833 | 0.368 | 1 | Accept |
| South Africa | 22.435 | 0.223 | 11 | Accept | 12.755*** | 0.001 | 1 | Reject |
| Togo | 53.429** | 0.047 | 11 | Reject | 4.354** | 0.044 | 1 | Reject |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% level of significance, respectively. Also, computation of p-values are based on 1,000 bootstrap replications. The lag length criteria is endogenously determined based on Bayesian information criteria (BIC)
Source(s): Authors' computations
Panel asymmetric causality (Inf− and pov−)
| Cross sections | Null hypothesis | |||||||
|---|---|---|---|---|---|---|---|---|
| Inf− does not cause pov− | Pov− does not cause inf− | |||||||
| Wald stat | p-value | lag | Decision | Wald stat | p-value | lag | Decision | |
| Panel | 63.161*** | 0.008 | 11 | Reject | 11.669** | 0.018 | 2 | Reject |
| Botswana | 16.081 | 0.355 | 11 | Accept | 6.191* | 0.059 | 2 | Reject |
| Burkina Faso | 14.832 | 0.392 | 11 | Accept | 25.526*** | 0.000 | 2 | Reject |
| Cameroon | 117.357*** | 0.009 | 11 | Reject | 7.211** | 0.039 | 2 | Reject |
| Congo, Rep | 375.937*** | 0.000 | 11 | Reject | 3.299 | 0.208 | 2 | Accept |
| Gabon | 54.711** | 0.045 | 11 | Reject | 4.791 | 0.107 | 2 | Accept |
| Kenya | 3.575 | 0.944 | 11 | Accept | 10.356*** | 0.011 | 2 | Reject |
| Madagascar | 64.885** | 0.031 | 11 | Reject | 2.861 | 0.254 | 2 | Accept |
| Mauritania | 18.145 | 0.303 | 11 | Accept | 6.385** | 0.054 | 2 | Reject |
| Nigeria | 8.988 | 0.639 | 11 | Accept | 2.548 | 0.294 | 2 | Accept |
| Senegal | 15.811 | 0.362 | 11 | Accept | 45.688*** | 0.000 | 2 | Reject |
| South Africa | 54.350** | 0.045 | 11 | Reject | 3.972 | 0.154 | 2 | Accept |
| Togo | 13.263 | 0.445 | 11 | Accept | 21.204*** | 0.000 | 2 | Reject |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% level of significance, respectively. Also, computation of p-values are based on 1,000 bootstrap replications. The lag length criteria is endogenously determined based on Bayesian information criteria (BIC)
Source(s): Authors' computations
Panel asymmetric causality (Inf− and pov+)
| Cross sections | Null hypothesis | |||||||
|---|---|---|---|---|---|---|---|---|
| Inf− does not cause pov+ | Pov+ does not cause inf− | |||||||
| Wald stat | p-value | lag | Decision | Wald stat | p-value | lag | Decision | |
| Panel | 40.251** | 0.046 | 11 | Reject | 2.194 | 0.683 | 1 | Accept |
| Botswana | 8.088 | 0.689 | 11 | Accept | 5.554** | 0.024 | 1 | Reject |
| Burkina faso | 16.022 | 0.356 | 11 | Accept | 0.881 | 0.354 | 1 | Accept |
| Cameroon | 15.945 | 0.359 | 11 | Accept | 5.626** | 0.023 | 1 | Reject |
| Congo, Rep. | 114.648*** | 0.009 | 11 | Reject | 2.407 | 0.130 | 1 | Accept |
| Gabon | 16.131 | 0.353 | 11 | Accept | 0.952 | 0.336 | 1 | Accept |
| Kenya | 23.898 | 0.202 | 11 | Accept | 0.007 | 0.932 | 1 | Accept |
| Madagascar | 62.120** | 0.034 | 11 | Reject | 0.036 | 0.850 | 1 | Accept |
| Mauritania | 29.088 | 0.147 | 11 | Accept | 9.755*** | 0.004 | 1 | Reject |
| Nigeria | 36.150* | 0.099 | 11 | Reject | 0.003 | 0.956 | 1 | Accept |
| Senegal | 61.639** | 0.035 | 11 | Reject | 0.163 | 0.689 | 1 | Accept |
| South Africa | 32.833 | 0.119 | 11 | Accept | 0.001 | 0.975 | 1 | Accept |
| Togo | 66.455** | 0.030 | 11 | Reject | 0.948 | 0.337 | 1 | Accept |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% level of significance, respectively. Also, computation of p-values are based on 1,000 bootstrap replications. The lag length criteria is endogenously determined based on Bayesian information criteria (BIC)
Source(s): Authors’ computations
Panel asymmetric causality (Inf+ and pov−)
| Cross sections | Null hypothesis | |||||||
|---|---|---|---|---|---|---|---|---|
| Inf+ does not cause pov− | Pov− does not cause inf+ | |||||||
| Wald stat | p-value | lag | Decision | Wald stat | p-value | lag | Decision | |
| Panel | 1.501 | 0.789 | 1 | Accept | 5.387** | 0.030 | 1 | Reject |
| Botswana | 5.290** | 0.028 | 1 | Reject | 2.951* | 0.095 | 1 | Reject |
| Burkina Faso | 0.727 | 0.400 | 1 | Accept | 1.459 | 0.235 | 1 | Accept |
| Cameroon | 2.773 | 0.105 | 1 | Accept | 7.272** | 0.011 | 1 | Reject |
| Congo, Rep. | 3.136* | 0.085 | 1 | Reject | 2.974* | 0.093 | 1 | Reject |
| Gabon | 1.254 | 0.270 | 1 | Accept | 4.021** | 0.053 | 1 | Reject |
| Kenya | 0.653 | 0.425 | 1 | Accept | 1.672 | 0.204 | 1 | Accept |
| Madagascar | 0.428 | 0.517 | 1 | Accept | 4.894** | 0.034 | 1 | Reject |
| Mauritania | 0.095 | 0.760 | 1 | Accept | 7.761*** | 0.009 | 1 | Reject |
| Nigeria | 0.409 | 0.527 | 1 | Accept | 0.466 | 0.499 | 1 | Accept |
| Senegal | 0.065 | 0.800 | 1 | Accept | 2.222 | 0.145 | 1 | Accept |
| South Africa | 2.735 | 0.107 | 1 | Accept | 0.761 | 0.389 | 1 | Accept |
| Togo | 0.444 | 0.510 | 1 | Accept | 28.185*** | 0.000 | 1 | Reject |
Note(s): The symbols ***, ** and * represent 1%, 5 and 10% level of significance, respectively. Also, computation of p-values are based on 1,000 bootstrap replications. The lag length criteria is endogenously determined based on Bayesian information criteria (BIC)
Source(s): Authors' computations
Authors’ contribution: The work was equally contributed by all authors.
Availability of data and materials: On reasonable request, data are readily available.
Competing interests: There are no competing interests declared by the authors.
Consent for publication: The permission is expressly given.
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Further reading
Garza-Rodriguez, J. (2019), “Tourism and poverty reduction in Mexico: an ARDL cointegration approach”, Sustainability, Vol. 11 No. 3, p. 845, doi: 10.3390/su11030845.
Nathaniel, S. and Khan, S.A.R. (2020), “The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries”, Journal of Cleaner Production, Vol. 272, 122709, doi: 10.1016/j.jclepro.2020.122709.
Olaniyi, C.O. (2019), “Asymmetric information phenomenon in the link between CEO pay and firm performance: an innovative approach”, Journal of Economic Studies, Vol. 46 No. 2, pp. 306-323, doi: 10.1108/jes-11-2017-0319.
Acknowledgements
The authors appreciate the editorial team’s professionalism as well as the anonymous referees’ impartial suggestions and comments, which have increased the paper’s quality. There is a normal disclaimer in place.