Inflation and Unemployment in the Republic of Congo: An ARDL Analysis

Inflation and Unemployment in the Republic of Congo: An ARDL Analysis

 

Jurien Dony  BWALGAMBAUD ^{1  (}First and Corresponding author)

School of Economics, Marien Ngouabi University, Brazzaville, Republic of Congo jurienshepherd@gmail.com

Declaration of Competing Interest

The authors declare that has no known competing financial interests or personal relationships that can influence.

Acknowledgements

The authors would like to express their sincere gratitude to the reviewers and editors for their valuable comments and suggestions.

Data availability statement

The data that support this study are available from the corresponding author.

ABSTRACT

This research examines the relationship between inflation and unemployment in the Republic of the Congo over the period 1990–2022. While the traditional Phillips curve suggests an inverse relationship between inflation and unemployment, empirical evidence from developing economies often challenges this assumption. Using the Autoregressive Distributed Lag (ARDL) model, this study investigates both short-run and long-run dynamics between inflation and unemployment, incorporating key macroeconomic control variables including exports, imports, gross fixed capital formation, population growth, and GDP per capita growth.

  The results reveal that inflation has a positive and statistically significant effect on unemployment in both the short and long run, contradicting the conventional Phillips curve hypothesis. Conversely, GDP per capita growth exerts a negative effect on unemployment, highlighting the importance of economic expansion in reducing joblessness. These findings align with alternative theoretical frameworks, particularly those of Tobin (1972) and Fitoussi (1973), which emphasize structural rigidities in the economy.

  The study concludes that inflation control alone is insufficient to reduce unemployment in the Congolese context. Instead, structural reforms, economic diversification, and growth-oriented policies are necessary. The findings contribute to the broader literature on inflation-unemployment dynamics in developing economies and offer policy-relevant insights for monetary authorities.

Keywords: Inflation, Unemployment, ARDL, Republic of the Congo, Phillips Curve, Economic Growth.

INTRODUCTION

Since the acceleration of globalization in the 1980s, the inflation-unemployment relationship has become a major concern in both developed and developing countries, especially in economic and social debates (Bükey and Kalkan, 2024). This concern stems from the question of whether a relationship exists between inflation and unemployment in Congo-Brazzaville. The literature on this issue has generated several theoretical and empirical debates among scholars, notably A. W. Phillips (1958), Richard Lipsey (1960), and Paul Samuelson and Robert Solow (1960). Indeed, Phillips (1958) analyzed the relationship between unemployment and inflation in the United Kingdom and introduced what is known as the Phillips Curve. His findings suggested that when unemployment decreases, workers demand higher wages. Employers transfer the increased production costs to consumers by raising prices, leading to inflationary pressure in the economy.

According to Phillips, policymakers face a trade-off: they can either reduce unemployment at the expense of higher inflation or reduce inflation at the expense of higher unemployment, but not achieve both objectives simultaneously. For Lipsey (1960), the relationship between inflation and unemployment represents a complex dilemma in which governments must choose between tolerating higher inflation to reduce unemployment or accepting higher unemployment to curb inflation. Inspired by Phillips’ work, Samuelson and Solow (1960) explored the inflation-unemployment relationship in the United States. Their findings also revealed a negative Phillips-type relationship between the two variables. According to these authors, this negative relationship offers policymakers several trade-off possibilities depending on the relative importance they assign to price stability and economic activity.

Globally, studies by the International Labour Organization (ILO, 2020, 2024) and the Bank of Central African States (BEAC, 2020, 2022) over the period 2019–2023 showed that the number of unemployed people worldwide increased from 187 million (5.4%) in 2019 to 220 million (6.5%) in 2020. Between 2021 and 2023, unemployment figures stood at 205.2 million in 2021 (6.2%), 207 million in 2022 (5.8%), and 435 million in 2023 (5.1%). In Southern, Eastern, and Central Africa, ILO studies (2020) between 2000 and 2019 showed that youth unemployment in Southern Africa declined slightly from 12% to 11.8%, although it reached 50.3% in 2019. In Eastern Africa, unemployment stood at 6.2%, the lowest rate in the region, while in Central Africa it remained relatively moderate at 10%.

In Congo-Brazzaville, studies by the European Commission (2007) between 2002 and 2004 indicated a worrying unemployment situation, with an average unemployment rate of 19.4%. This rate reached 32.6% in Brazzaville and 31.5% in Pointe-Noire. In rural areas, unemployment was lower at 5.8%, mainly affecting young people aged 15–19. Poverty was also more pronounced in households headed by inactive individuals (46.1%). Nationally, the unemployment rate declined from 19.4% in 2005 to 6.9% in 2011, while the rural unemployment rate is 1.7%. However, a 2014 employment and informal sector survey conducted in Brazzaville, Pointe-Noire, Nkayi, Dolisie, Ouesso, and Mossendjo among individuals aged 15 and above found an unemployment rate of 11.3%. Women were more affected (12.6%) than men (10.3%). Brazzaville and Pointe-Noire recorded the highest unemployment rates, at 11.3% and 9%, respectively. Between 2020 and 2022, unemployment remained significant in Congo-Brazzaville. In 2020, the unemployment rate was 10.3%, while inflation was moderate at 2% in 2021, according to the African Development Bank. In 2022, however, unemployment rose sharply to 21.8% of the working-age population, disproportionately affecting young people (42%), while inflation reached 3.5%.

The analysis of the relationship between inflation and unemployment is a key topic in monetary economics. From a theoretical perspective, it contributes to a better understanding of the competing theories surrounding the inflation-unemployment relationship. Empirically, it verifies whether such a relationship exists in Congo-Brazzaville and formulates policy recommendations aimed at maintaining inflation at a reasonable level while reducing unemployment. The choice of this topic is also justified by the adoption of a multilateral surveillance program by the Congolese government, which included four regional convergence criteria in 2001, notably maintaining annual inflation at or below 3%, with the objective of promoting macroeconomic stability and mitigating unemployment. Due to prudent monetary policy, positive supply-side effects in agriculture, and the stabilization of domestic fuel prices, inflation remained relatively under control, falling to 2.5% in 2011 compared with 5% in 2010.

However, despite improvements in inflation control, unemployment persists and continues to disrupt the Congolese economic system. Unemployment is considered one of the most pressing global challenges facing humanity and therefore constitutes a major focus of economic policy. The inflation-unemployment relation has been the subject of numerous theoretical and empirical debates. Keynesian economists support the existence of an inverse relationship between inflation and unemployment, while other authors, such as Phillips, Lipsey, Robert Lucas Jr., Thomas Sargent and Neil Wallace found that regardless of inflation changes, unemployment tends to return to its natural level, implying that economic policy is ineffective in permanently reducing unemployment. Empirical studies provide mixed evidence. In developed countries, Kaletsky (2010) found that inflation positively affects unemployment, while Zaman et al. (2011) identified a long-run causal relationship between the two variables. In developing countries, Sualihu (2016) found a short-run inverse relationship between inflation and unemployment. Similarly, Uche (2019) concluded that the Phillips curve is applicable in Nigeria, where a 1% reduction in unemployment requires sacrificing a 49% increase in inflation. Given these mixed findings, this research answers the following question:

What is the nature of the relationship between inflation and unemployment in Congo-Brazzaville?

The general objective of this study is to empirically examine the relationship between inflation and unemployment in Congo-Brazzaville. In particular, the study aims to determine:

Whether a relationship exists between inflation and unemployment; Whether this relationship is proportional (positive) or inverse (negative).

The remainder of this research is structured as follows: Section 2 Review of theoretical and empirical literature, Section 3 Methodology and model specification, Section 4 Analysis and discussion of results, Section 5 Conclusion and recommendations. 

1.1 Stylized Facts of the Unemployment - Inflation Relationship

  Fig.1: Evolution of unemployment and inflation in the Congo from 1990 to 2022

 

 

 

 

 

 

 

 

 

 

Source: Author from the Eviews 10 software and World Bank data, 2024.

The observation of Fig.1 shows an irregular evolution of the two trends (inflation and unemployment) in the Republic of Congo. This irregularity of unemployment inflation reveals three relatively important phases.

-The first phase period from 1990 to 2008: This phase is characterized by a regression of the inflation trend from 1990 to 1993 and a steady increase in unemployment from 1990 to 2005, before a rebound in the upward inflation trend from 1994 to 1999 and from 2005 to 2007. This increase in inflation and unemployment during this period may be the result of the weak ability of the Congolese economy to withstand the shocks due to the devaluation of the CFA franc. Indeed, this devaluation of the CFA franc has produced negative effects.

-The second phase from 2008 to 2019: The decline of the relationship during this period can be attributed to the good performance of fiscal and monetary policy, good governance, as well as the good use of resources derived from oil exports (World Bank, 2018).

- The third phase from 2019 to 2022: Reveals an increase in unemployment following a depreciation of inflation. This exponential growth in unemployment relative to inflation is the result of the various socio-economic and health policies that the country has gone through with the economic crisis from 2015 to 2016, the Covid-19 health crisis, which hindered the efforts undertaken by the government to reduce unemployment. (Congolese economic outlook, 2022).

1.2 Stylized Facts of Unemployment.

Fig 2: Evolution of unemployment in Congo from 1990 to 2022.                        

Source: Author from the Eviews 10 software and World Bank data, 2024.

The analysis of the Fig above highlights three main phases in the evolution of unemployment in Congo-Brazzaville: 1990-2005; 2005-2019, and 2019-2022.

- The first phase of the evolution of unemployment: 1990-2005

  This phase is characterized by a high rate of unemployment from 1990 to 2005. This increase can be explained by factors, particularly the socio-political unrest experienced by the Congo in the 1993s, the devaluation of the FCFA in 1994, and the expansion of unemployment in the informal sector (Diata, 1994).

- The second phase: 2005-2019

  This second phase is marked by the decrease in unemployment in Congo, attributed to several factors, in particular, the direct consequences of the monetary efforts and budgetary policies with poverty-eradicating policies. Thus, participating in the creation of new jobs helps reduce unemployment and improve the financial situation of the population. In addition, the proper management of international oil prices, the cancellation of the debt during the implementation of the initiative of Highly Indebted Countries, the increase in oil production, the resumption of exports, and the relief of the external debt, which favored the reduction of unemployment to19.9% of GDP in 2011 against 198.7% in 2004 (Economic Outlook in Africa, 2012). This decrease in unemployment can also be explained by the improvement in raw material prices on the world market, the development of all sectors of economic activity, the business climate, the return of economic growth, credibility with credit institutions and the confidence of the business community, the accelerated municipalization of the Departments of Brazzaville, Pointe-Noire, Niari, Likouala and Cuvette as well as labor-intensive work (PNE, 2012).

- The third phase: 2019-2022

This phase is marked by an increase in unemployment in Congo. Compared to the previous period, this increase is the result of three factors: the first is due to the wrong decision in fiscal and monetary policy. This caused a slowdown in oil revenues, plunged the country into a socio-economic crisis, and generated a double deficit in the tax balance of around 5.1% of GDP and the current account balance of 42.8% of GDP in 2017. The second factor results from social inequalities, which remain among the most significant on the continent, thus affecting young people and women aged 15 to 29 in 2016 (AfDB, 2018). The last results from the weaknesses observed in terms of governance and the fight against corruption in the country leaves the country without hope in terms of the creation of new jobs for youth (AfDB, 2015). In addition to the Covid-19 health crisis, which has been a brake on the efforts undertaken by the state to reduce professional inactivity (Congolese economic Outlook, 2022).

1.3 Stylized facts of inflation 

 

 The analysis of the evolution of inflation mainly involves consumer prices in annual percentage (%) in Congo-Brazzaville. We represent this evolution in Fig.3.

Figure 3: Evolution of Inflation in the Republic of the Congo from 1990 to 2022

Source: Author from the Eviews 10 software and World Bank data, 2024.

The observation of the graph above highlights two main periods following the evolution of inflation in the Republic of Congo from 1990 to 2000 and from 2001 to 2022.

- The first period of the evolution of inflation: 1990-2000

The evolution of inflation and consumer costs, as represented in Fig3 indicates a downward trend between 1990 and 1992, before being marked by a very high peak between 1993 and 1994, to decline again in 1998 until 2000. Several factors can explain these variations in inflation. The decrease in inflation is due to economic policy reforms, structural adjustment programs, and the improvement of the oil market, which has generated significant resources for the country (UNDP, 2012). This decrease in inflation observed since the cessation of the civil war, from 1999 to 2000, can also be attributed to the considerable progress that has been made in consolidating economic stability and improving the macroeconomic situation. As a result, the growth of the non-oil sectors of the economy has accelerated, and the budget results have improved (IMF, 2004).

Several factors have been put forward to explain this evolution: the fall in crude oil prices, the devaluation, in 1994, of the African Financial Community Franc (FCFA), which had doubled the prices of domestic products, thus resulting in a record inflation rate of around 42.4%. Also, the political social unrest (civil wars from 1993 to 1997) caused the destruction of a big part of the productive capacities outside oil and the cessation of economic reforms (UNDP, 2002). For the Ministry of the Economy, the surge in prices observed over this period is also largely attributable to the increase in food goods from abroad, in particular rice and corn, on the one hand, and to the increase in the cost of essential construction materials imported by the Congo, on the other hand (CNSEE, 2010).

- The second period: 2001-2022

This second phase is characterized by a moderate evolution of inflation from 2001 to 2005. From 2006 to 2009, the underlying inflation rates were respectively 5.1%, 3.1%, 4.5% and 4.4% (COCI, 2010). This irregularity in the price level could be explained by the transport costs resulting from the poor quality of the infrastructure and the frequent traffic disruptions on the railway or Congo-Ocean Railway (CFCO), between Pointe-Noire and Brazzaville. Also, by pressing internal demand and the increase in energy tariffs and that of the prices of building materials to the tune of 5.8% (BEAC, 2006). From 2010, the Republic of Congo recorded a decrease in inflationary pressures. This is the result of many causes: progress in the supply of electrical energy, a readjustment to the inherent decline in international commodity prices, the contraction of European demand, a prudential monetary policy of the Government within the Community framework, a tax policy, and the control of public spending (PND - Congo, 2012-2016).

 

 

2. Literature Review and hypothesis development

2.1 Related Theories

2.1.1 Keynesian Approach 

  For the Keynesians, particularly Phillips (1958) and Lipsey (1960), the public authorities must choose the fight against "inflation" and the fight against "unemployment". This problem means that as soon as unemployment increases, the country must pursue an expansionary policy to absorb the unoccupied workforce. When inflation is on the rise, the country must pursue a stabilization policy. In this sense, Philips (1958) analyzed the trade-off between inflation and unemployment. For this author, this is based on lower unemployment and higher wages. Thus, when unemployment is on the decline, workers demand wages on the rise. To this end, producers transfer the additional cost of labor to consumers by increasing the prices of goods. The result is an increase in the level of inflation in the economy. According to Philips, the political authorities have an arbitration to either reduce unemployment and increase inflation, or vice versa, but not both at the same time. But also, they can undertake some actions such as promoting full employment, remedying the increase in the general level of prices, or even facing a persistent increase in unemployment.

  Inspired by the work of his predecessor, Lipsey (1960) will explore the inflation-unemployment compromise. He exposes a dilemma according to which the state should make a choice between a lot of inflation to reduce unemployment and a lot of unemployment to reduce inflation. The events that dominated the world, such as the "Glorious Thirties" of the 1970s-1980s, the suspension of the possibility of dollar exchange by the United States in 1971, the first disruption of the oil market following the Yom Kippur war in 1973, and the second oil shock following the Iranian revolution of 1979. All these events made it possible to conclude that the inflation-unemployment dilemma is a false problem, because we could have both variables simultaneously. From the 1970s, a new school called the new Keynesian economists (Lucas, 1972; Sargent and Wallace, 1976) was born. According to this school, in the inflation-unemployment relationship, regardless of the level of inflation, the unemployment rate always returns to its equilibrium level. In this dynamic, these economists confirm that economic policy is ineffective in solving the unemployment problem.

  Thus, Lucas (1972) will also be interested in this question. By dealing with this question, he attests that the unanticipated variations in the money supply are at the origin of the variations in real flows. In other words, it is the surprise effect that brings unemployment back to its natural level. Since expectations are rational and the currency neutral, we note the existence of several factors: the anticipation of the inflationary effects of monetary stimulus policies by agents, the change in the position of the Phillips curve (1958) which becomes a vertical line, the return of the unemployment rate to its equilibrium level regardless of the level of inflation, the ineffectiveness of economic policy to reduce unemployment and the short-term insufficiency of information for producers. For the author, when there is an unexpected inflation, producers will tend to bring their supply up. In this context, prices and economic activities will translate into an increase in the same direction and unemployment in the opposite direction. On the other hand, following the extension of their supply, when producers observe no change in demand over a given period, the latter will, in the long term, bring demand back to the equilibrium level, which will translate into a reduction in unemployment. Relative to Sargent and Wallace (1976), the focal point is on the "principle of invariance" in the long term, any monetary or budgetary policy is ineffective, because the economy always returns to its equilibrium point.

2.1.2 Monetarist Approach 

 

  In this approach, the monetarists, among others, Friedman (1986) and Gbaguidi (2012) have dealt with this problem. For these authors, in the long term, the share of the unemployed active population remains at its equilibrium level, and there is no inverse relationship between the inflation rate and the unemployment rate. In this vein, Friedman (1986), by analyzing the inflation-unemployment dilemma, questions the Phillips curve. According to him, Keynesian policies are ineffective in the long term. Inflation has no influence on unemployment since it does not depend on nominal wages, but rather on real wages. On the other hand, Gbaguidi (2012), by examining this problem, insists on Friedman's ideas. For Gbaguidi (2012), in his work, Friedman (1968) recognizes the existence of Phillips theory in the short term. But, in the long term, this curve becomes vertical, thus showing the existence of a rigid level of unemployment called "natural unemployment" or «NAIRU". Also, he adds that Friedman (1968) notes the absence of arbitration between the two long-term variables. Finally, Friedman (1986) highlighted the role of expectations; in other words, in the event of a salary increase, workers are faced with a monetary illusion.

2.1.3 Approach of the New Classical School 

Samuelson and Solow (1960), in their research, found that the inflation-unemployment dilemma is in the approach of the New Classical School. Thus, they insisted on the analysis of the Phillips curve theory while describing it as having had no echoes and proposed to compensate the rate of change in wages with the rate of inflation and propose a Phillips curve (1958) like the American economy of the 1958s. Also, they based their relationship on the so-called "Mark-up pricing" approach, which stipulates that the margin added to costs to determine prices is fixed when wages are considered as the main component of costs. In this dynamic, Samuelson and Solow (1960) confirmed that an unemployment rate of between 5 and 6% represents the cost to be paid for maintaining stable prices in the coming years, and to have an unemployment rate of about 3%, prices must increase by 4 to 5%, which represents the cost to be paid to obtain a level of unemployment at this rate. The state has the choice between "low inflation" associated with a "high unemployment" rate and "high inflation associated with a low unemployment rate"; this will depend on the place it gives to price stability.

2.2 Empirical Review

2.2.1 Related research in Developed Countries 

  St-Amant and Tessier (1998) in their research analyzed the relationship between public spending, inflation, and the unemployment rate in Canada and the United States from 1965 to 1995. The authors investigated the VAR model, and the results showed that the evolution of the unemployment rate in these countries is a function of the choice of their fiscal policy. These results also attest that monetary policy influences the unemployment rate. Matthew (2000), in his research, which falls within the framework of this research orientation, studies the relation between inflation and unemployment in three Latin American countries, in particular, Argentina, Brazil, and Chile, over the periods 1966-1997, 1979-1997, and 1966-1998, respectively. The author used the method of ordinary least squares (OLS), and the results indicate an absence of a relationship between inflation and unemployment in Argentina and Brazil.

  Dumont (2000) carries out a study on the verification of the validity of the long-term Phillips curve as well as the wage rigidities in Canada over the period 1956-1997. Used variables (direct taxation, minimum wage, delayed wage growth), tests (exclusion, adequacy), and non-nested models. His results showed the validity of the nonlinear Tobin-type model. On the other hand, the linear model of the standard type is rejected. These results also showed that in the long term, the minimum sustainable unemployment rate is approximately 6.6%, with a standard deviation of 0.9%. According to this author, to achieve an equilibrium unemployment rate above 7%, it would be necessary to accept an inflation rate between 4% and 6%. Kitov et al. (2008) studied the link between inflation, unemployment, and the rate of change of the active population in French territory over the period 1971-2004. Using the VAR model and the vector correction model (VECM) and various tests, they support the validity of the delayed linear relationship between inflation, unemployment, and the evolution of the active population. By empirically testing the relationship between inflation and unemployment in the United Kingdom over the period 1971-2009, Kaletsky (2010) concluded that inflation exerts a positive and significant influence on the unemployment rate.

  The analysis of the relationship between inflation and unemployment, conducted by Zaman et al. (2011) on the data of Greece from 1980 to 2010 via the vector error correction model (VECM), shows the existence of a causal relationship between inflation and unemployment in the long term. According to these authors, for the next 10 years, the inflation shocks will translate into a reduction in the number of jobless for the first few years, followed by an increase for the remaining years of the study. In the same direction, Caporale and Škare (2011) examined the link between employment growth, inflation, and output growth in the Phillips tradition for 119 countries of the Organization for Economic Cooperation and Development (OECD) over the period from 1970 to 2010. Using the dynamic fixed-effect econometric models (DFE) and the error-correcting vector model (VECM), they confirm the existence of a unidirectional cointegration relationship between employment growth, inflation, and production growth, with a bidirectional causality between employment growth and inflation, as well as production growth. They confirm the existence of the theory of the "Golden Triangle of Phillips". Rajarshi (2013) found that the Phillips curve for the case of the American economy over the period 1977-2012, using the unit root, Johnson cointegration, and Granger causality tests. His results revealed the absence of evidence of a causal effect between the two variables. These results also revealed the existence of an inverse relationship between the two variables.

  This problem has also been investigated by Ho, Sin-Yu, and Njindan Iyke (2018). Indeed, these authors explore the thresholds from which the unemployment inflation relationship changes from the negative effect to the positive effect in 11 European countries for the period from 1999 to 2017 by the fixed-effects threshold regression proposed by Hansen (1999). These authors also estimate the Phillips curve for these different countries. Their results indicate, in the short and long term, that under the assumption of linearity, there is a Phillips curve. By estimating this curve using the selected thresholds, the authors note that the relationship between inflation and unemployment is negative only when unemployment is below 5%. The negative relationship becomes positive when unemployment is between 5% and 6.54%. They conclude that inflation and unemployment are not linked once the threshold of 6.54% unemployment rate is exceeded.

   Similarly, Alev et al. (2022) have worked on the relationship between inflation and unemployment in the G7 countries in Germany, the United States, the United Kingdom, Canada, France, Japan, and Italy. They used the panel causality test and the period 1991-2021. Their results indicated the existence of a two-way causal relationship between inflation and unemployment, that is, a causality of the inflation rate towards the unemployment rate and the unemployment rate towards the inflation rate. This result thus confirms the existence of a Phillips curve in developed countries. In their study published in 2024, Bükey and Kalkan examined the link between unemployment and inflation in Germany. They used time series, Toda Yamamoto’s causality test, Johansen's cointegration test, and the period 1992- 2023. Their results showed, in the short and long term, no causal relationship between unemployment and inflation.

2.2.2 Related research in Developing Countries 

The studies based on the relationship between inflation and unemployment, although numerous, compared to those of the previous category, are not as rare as we might think. In the Philippines, for example, Furuoka (2008) analyzed the relationship between the inflation rate and the unemployment rate using data from the period 1980-2006. The compilation via the cointegration method generated the results indicating the absence of a direct relationship between the inflation rate and the unemployment rate. For Besso (2010), by evaluating the fundamental assumptions of the Phillips curve, in Cameroon over the period 1993-2003, using the maximum likelihood method, attests that in the short term, inflation reduces unemployment, thus validating the Phillips curve hypothesis. The work of Kogid et al. (2011), carried out using data collected over the period 1975-2007 for Malaysia, uses the ARDL model. They validated in the long term the existence of a cointegration relationship between inflation and unemployment. But also, the existence of a unidirectional causal relationship between inflation and unemployment.

  The research of the authors, in particular, Umoru and Anyiwe (2013), Mutiu (2017), Balewa and Hassan (2018), Gwandzang et al. (2018), Eje (2018), Edeme (2018), Uche (2019), Nurudeen (2019), as well as Efayena et al. (2020) on this theme, provides diverse results. Indeed, by exploiting the data collected in Nigeria over the periods (1977-2009 ; 19902012 ; 1986-2016 ; 1980-2016 ; 1980-2014 ; 1972-2015 ; 1981-2017 ; 1980-2016; 1985-2020), based on the models of autoregressive conditional heteroscedasticity (ARCH), generalized Autoregressive conditional heteroscedasticity (GARCH), fully modified ordinary least squares (FMOLS) and static ordinary least squares (MCO), these authors revealed the existence of a negative relationship between inflation and unemployment, thus confirming the hypothesis of Philips (1958). Still on the case of Nigeria, Jelilov et al. (2016), Idenyi et al. (2017), as well as Okoebor et al. (2023) have, in their work on the Phillips curve in Nigeria, exploited data from the periods ranging from 2001-2013 and 1999-2021. The modeling carried out using the ARDL, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and static ordinary least squares (MCO) models allowed them to find a positive relationship between the two variables, thus validating the Tobin (1972) and Foutissi (1973) type hypothesis.

 Based on data from China collected over the period 1978-2011, Qianyi (2013) analyzed the causal link between unemployment and the inflation rate. He observed the ineffectiveness of the Phillips curve. According to Qianyi (2013), this inefficiency is explained by the complexity of the country's economy. Focusing on the case of Malaysia, Furuoka and Munir (2014) tried to analyze the relationship between the inflation rate and the level of unemployment. The error-corrected model, applied to the data for the period 1975-2004, proves the existence of an equilibrium relationship between the inflation rate and the level of unemployment, thus confirming the hypothesis of the Phillips curve.

 

 

 

  2.2.3 Hypothesis

 

Hypothesis	Variable Relationship	Expected Effect
H1	Inflation → Unemployment	Positive
H2	GDP per capita growth → Unemployment	Negative
H3 a	Exports → Unemployment	Negative
H3b	Imports → Unemployment	Positive
H5	Investment → Unemployment	Negative
H6	Population growth → Unemployment	Positive

 

3-Data and Methodology

 

This section outlines the data sources, variable definitions, and econometric methodology used to analyze the relationship between inflation and unemployment in the Republic of Congo. Given the dynamic nature of macroeconomic relationships and the structural characteristics of the Congolese economy, a robust empirical strategy is required to capture both short-run and long-run interactions among variables. The study adopts a time-series econometric approach, using annual data over the period 1990–2022. The choice of methodology is guided by both theoretical considerations and the statistical properties of the data. In particular, the Autoregressive Distributed Lag (ARDL) model is employed due to its flexibility in handling variables with different orders of integration and its suitability for small sample sizes.

3.1 Data Sources and Sample Period

 

The empirical analysis is based on annual time-series data from 1990 to 2022, which provides a sufficiently long horizon to capture structural changes, economic cycles, and external shocks affecting the Congolese economy. The data are collected from recognized and reliable sources.

Category	Detail	Purpose / Justification
Data source 1	World Bank – World Development Indicators (WDI)	Ensure consistency, comparability, and reliability of the data
Data source 2	National statistical agencies and central bank reports (where applicable)	Ensure consistency, comparability, and reliability of the data
Sample period rationale 1	Captures key economic events	Includes the 1994 CFA franc devaluation, oil price boom periods, and the COVID-19 pandemic
Sample period rationale 2	Sufficient observations for econometric analysis	Provides an adequate number of data points to ensure statistical robustness
Sample period rationale 3	Covers pre- and post-reform conditions	Reflects both pre- and post-reform economic conditions in the Republic of Congo

3.2 Model Specification

 

To examine the determinants of unemployment, the study specifies the following functional relationship: This research methodology is based on the empirical approach of Okoebor et al. (2023), who studied the relationship between unemployment and price level in Nigeria. These authors used an autoregressive model (ARDL) whose equation is as follows:

UER_t = f (INF_t, GDPG_t, InEXR_t)

Avec t : the temporal index; UR: the unemployment rate; INF: the inflation rate; GDPG: the gross domestic product; InEXR : the natural logarithm of the exchange rate

t: the temporal index; UR: the unemployment rate; INF: the inflation rate; GDPG: the gross domestic product; InEXR : the natural logarithm of the exchange rate.

The choice of this model in the context of the Republic of Congo is dictated by practical considerations. This model seems more willing to capture the relationship between inflation and unemployment. Based on Okoebor et al. (2023) research, our econometric model is built by admitting that there is no relationship between inflation and Phillips-type unemployment in the Republic of Congo. Thus, we have extended our model by integrating the following control variables: exports, imports, gross fixed capital formation, population growth or population growth rate, and the per capita GDP growth rate. These data have been retained because of their theoretical and practical consideration. We have selected two models in this study, namely: theoretical model and the empirical model.

Theoretical Model

  This relationship is attached to an idea related to the existence of an adjustment mechanism on the labor market; an increased demand for work reduces the unemployment rate. This increases the bargaining power of employees and stimulates an increase in wages. To simplify, we generally present this relationship as a linear relationship between the unemployment rate and the rate of wage growth:

                                                Δw_𝑡 = 𝑎 − 𝑐𝑈_𝑡 + εt                                             (1) 

Δw_𝑡 is the rate of wage growth (approximated as a function of the variation in the logarithm w of the salary per person), corresponds to unemployment rates, and the random shock, in which the relationship is presumed to be decreasing, which is expressed by a "minus" sign in front of the parameter c which is considered positive. Formally, this entails work after the introduction of inflation expectations in the Phillips curve. This curve in its augmented form is represented by the following relation:

                                    Δw_𝑡 = 𝑎 − 𝑏Δp^𝑒_𝑡 − 𝑐𝑈_𝑡 + εt                                              (2) 

 Δp^𝑒_𝑡 illustrates the expected inflation rate. The Phillips curve increased by a unit indexation manifests itself in the hypothesis of absence of nominal illusion, which is represented in relation (2) by the condition b=1. In this context, it seems that a continuous increase in inflation, with an equivalent increase in the rate of wage growth, cannot lead to a lasting decrease in unemployment.  In other words, it is a model that has the particularity of estimating the short- and long-term dynamics for series that are cointegrated or that are not integrated into the same orders, thus allowing the estimation of an error correction/MCE model. The equation of this model can be represented as follows:

             (3)

Empirical model

 The model chosen in this work for estimation purposes is the stepped delay autoregressive model (ARDL). In the sense of Kuma (2018), it is a dynamic model, which considers in a particular way the temporal movement (adjustment delay, expectations, etc.) in the explanation of a variable (time series), with the aim of improving the forecasts and the effectiveness of policies, decisions, actions, etc. In opposition to the simple or non-dynamic model, whose instantaneous explanation restores only part of the variation of the variable to be explained.

  In other words, it is a model that has the particularity of estimating the short- and long-term dynamics for series that are cointegrated or that are not integrated into the same orders, thus allowing the estimation of an error correction/MCE model. The equation of this model can be represented as follows:

p              q t         

                                                                 i                                i

 y_t the variable to be explained; x_t−1 the vector of the explanatory variables; α_1i et   α_2i   the short-term effects; β₁ et β₂ the long-term effects; ∆ the primary difference; and the error term; ε_t the error term. Thus, our model (1) can be rewritten in the following form:

TCHO_t = f(TINF_t , X_t , M_t, FBCF_t, TPOP_t, TPIBH_t)                                 (2)                             

By applying the general form of the ARDL model to the variables selected in this research, the specified model translates as follows:   

        ∆TCHOt p TCHOt   i q           q   q

           q                                                     q                                                 q

FBCFti 6iTPOPti TPIBHt−i + β1 TCHOt−1 + β2TINFt−1 +

 β₃X_t−1 + β₄ M_t−1+β₅ FBCF_t−1 + β₆ TPOP_t−1 + β₇ TPIBH_t−1 + μ_t                              (3)                        

 ∆: the first difference operator; α₀: the constant; α₁…α₈ : the short-term effects; β₁…β₇: the long-term dynamics of the model; ε~ (0, ) : the error term (white noise); (p, q) : the optimum shifts ; i : the index of the country of origin; t : the temporal index; α et β :  the unknown parameters to be estimated.

4. Results

  For our empirical study, we selected annual data for the Republic of Congo. Thus, all the data used in this study except for the GDP per capita growth rate (GDPPCGR), which is obtained from the Perspective Monde database namely the unemployment rate (UR), inflation rate (INF), exports (EXP), imports (IMP), gross fixed capital formation (GFCF), and population growth rate (POPGR), are sourced from statistics provided by the World Bank (2023).

Table 1: Definition of Variables and Expected Signs

 

Abréviation	Définition	Sources	Signe attendu
UR	Unemployment Rate	World Bank (WDI) (WDI)
INF	Inflation Rate	World Bank (WDI) (WDI)	+
EXP	Exports	World Bank (WDI) (WDI)	−
IMP	Imports	World Bank (WDI) (WDI)	+
GFCF	Gross Fixed Capital Formation	World Bank (WDI) (WDI)	−
POPGR	Population Growth Rate	World Bank (WDI) (WDI)	+
GDPPCGR	GDP per Capita Growth Rate	Perspective Monde	−

 Source: Author, based on the documentary analysis.

Table 2: Results of Descriptive Statistics

 

Statistic	UNEMP	INF	EXP	IMP	GFCF	POPGR	GDPPCGR
Mean	16.67806	4.371887	65.19989	54.07496	33.95776	2.865471	-1.471463
Median	19.72000	3.043443	69.08843	53.16100	26.58525	2.745972	-0.610669
Maximum	22.37300	42.43968	81.51643	84.81484	79.46179	4.155897	8.031166
Minimum	9.600000	-3.935468	40.69210	16.44861	17.23097	2.053684	-12.44246
Standard Deviation	4.652736	7.587596	12.68158	16.34700	16.25600	0.598178	5.183724
Skewness	-0.634413	3.950273	-0.655015	-0.283999	1.474499	0.713981	-0.065941
Kurtosis	1.627110	20.53743	2.222509	3.036625	4.468270	2.249310	2.220365
Jarque–Bera	4.805279	508.7225	3.190919	0.445451	14.92205	3.578586	0.859683
Probability	0.090479	0.000000	0.202815	0.800335	0.000575	0.167078	0.650612
Sum	550.3760	144.2723	2151.596	1784.474	1120.606	94.56056	-48.55827
Sum of Squared Deviations	692.7344	1842.292	5146.320	8551.181	8456.237	11.45013	859.8717
Observations	33	33	33	33	33	33	33

Source: Author’s calculations based on data from Perspective Monde and the World Bank (2024), using EViews 10.

4.1 Model Estimation, Results and Interpretation

 

Any estimation requires the examination of the various econometric tests. Thus, we examined the tests of stationarity of the time series, correlation, causality between the variables, and cointegration at the terminals. The choice of these tests depends on the nature of the data.

4.1.1 Stationarity test

To evaluate the order of data integration, the documentation on unit roots offers several tests. We used two-unit root tests, namely: the augmented Dickey Fuller test (ADF) and the Phillips-Perron test (1988). These tests are usually used to verify the existence of a unit root in a series.

         - Increased Dickey-Fuller Test

 This test proceeds with the verification of the null hypothesisH₀ : ρ = 1 against the alternative hypothesis.

𝐻1 ∶ / 𝑝 /< 1 ∶ It is based on the least squares estimation of the following three models: ∆𝑋_𝑡 = (𝑝 − 1) 𝑋𝑡₋₁ + ∑^𝐾_𝐽=2 ∅𝐽 ∆_{𝑡−𝑗+1} + 𝜀_𝑡 Process without trend or constant; ∆𝑋_𝑡 = (𝑝 − 1) 𝑋_𝑡−1 + ∑^𝐾_𝐽=2 ∅𝐽 ∆_{𝑡−𝑗+1} + 𝛼 + 𝜀_𝑡 Process without trend or constant;

∆𝑋_𝑡 = (𝑝 − 1) 𝑋_𝑡−1 + ⋯ Dynamic without trend and with constant;

 The hypotheses of the Augmented Dickey Fuller Test (ADF) are:  

▪ Ho: P= (ɷ -1) (1 -θ1 -……-θ P–1) ↔0     Φ= 1 (the series is non-stationary)

▪ H1: ǀ ø ǀ < 1 the series is stationary.

 

-If the absolute value of the ADF statistics is higher than the critical value (or if the probability is less than 5%), the hypothesis H1 is accepted: the X series is stationary. 

-If the absolute value of the Dickey Fuller Augmented statistic (ADF) is less than the critical value (or if the probability is higher than or equal to 5%), then we accept the Ho hypothesis: the X series is non-stationary. The tests are carried out at the significant level of 5%.

- Phillips-Perron test

 

Phillips-Perron (1988), on the other hand, submits a non-parametric method to correct the presence of autocorrelation, based on the verification of the hypothesis posed by Dickey-Fuller, without having to add delayed endogenous variables as in the ADF method. The procedure aims to examine the hypothesis of unit root Ho: ρ = 0 in the models below:         

          ΔYt= ρYt−1 + α + βt + εt 

          ΔYt= ρYt−1 + α + ε_t 

       ΔYt= ρYt−1 + ε_t

Table 3: Descriptive Statistics Results.

Variables	Level of Tests	Test Type	Without Constant & Trend	With Constant & No Trend	With Constant & Trend	Critical Value (5%)	Test Statistic	Decision
UR	Level	ADF	No	No	Yes	-1.951687	-0.317435	I(1)
		PP	No	No	No	-1.951687	-0.317435
	First Difference	ADF	Yes	Yes	No	-1.956406	-2.873184
		PP	Yes	Yes	Yes	-1.952066	-4.818248
INF	Level	ADF	Yes	Yes	Yes	-1.951687	-3.511878	I(0)
		PP	Yes	Yes	Yes	-1.951687	-3.481616
	First Difference	ADF	Yes	Yes	Yes	-1.955020	-4.250057
		PP	Yes	Yes	Yes	-1.952066	-14.84195
EXP	Level	ADF	No	No	No	-1.951687	0.090829	I(1)
		PP	No	No	No	-1.951687	0.065458
	First Difference	ADF	Yes	Yes	Yes	-1.952066	-5.235351
		PP	Yes	Yes	Yes	-1.952066	-5.236623
IMP	Level	ADF	No	Yes	Yes	-1.951687	-0.548684	I(1)
		PP	No	Yes	Yes	-1.951687	-0.323587
	First Difference	ADF	Yes	Yes	Yes	-1.952910	-4.919088
		PP	Yes	Yes	Yes	-1.952066	-8.588552
GFCF	Level	ADF	No	No	No	-1.951687	-0.986947	I(1)
		PP	No	No	No	-1.951687	-0.770747
	First Difference	ADF	Yes	Yes	Yes	-1.952066	-5.950639
		PP	Yes	Yes	Yes	-1.952066	-6.533489
POPGR	Level	ADF	No	No	No	-1.952473	-0.502816	I(1)
		PP	No	No	No	-1.951687	-0.569217
	First Difference	ADF	Yes	Yes	Yes	-1.952473	-6.033904
		PP	Yes	Yes	Yes	-1.952066	-6.305269
GDPPCGR	Level	ADF	Yes	Yes	Yes	-1.951687	-3.692763	I(0)
		PP	Yes	Yes	Yes	-1.951687	-3.684022
	First Difference	ADF	Yes	Yes	Yes	-1.952066	-8.986080
		PP	Yes	Yes	Yes	-1.952066	-18.37463

 

4.1.2. Correlation test 

The purpose of this test is to verify whether the series of explanatory variables is linked together (phenomenon of multicollinearity). This test not only encourages the instability of the estimated coefficients, but it also leads to an increase in the estimated variance of certain values (Erkel, 1995). These results are explained by the degree of association, which exceeds 50% for certain variables (positive or negative) on almost all the rows and columns of the table. This makes it possible to note the existence of a probable multicollinearity between the variables studied. The results of this test are recorded in Table 4.

Table 4: Correlation Matrix of the Study Variables

 

	UNEMP	INF	EXP	IMP	GFCF	POPGR	GDPPCGR
UNEMP	1	0.1733	0.3187	-0.1394	-0.5919	0.0660	0.0683
INF	0.1733	1	0.0089	0.3416	0.2168	-0.0970	-0.1611
EXP	0.3187	0.0089	1	0.2710	-0.4460	0.2732	0.2511
IMP	-0.1394	0.3416	0.2710	1	0.6042	-0.0335	-0.2987
GFCF	-0.5919	0.2168	-0.4460	0.6042	1	0.0180	-0.2470
POPGR	0.0660	-0.0970	0.2732	-0.0335	0.0180	1	0.5438
GDPPCGR	0.0683	-0.1611	0.2511	-0.2987	-0.2470	0.5438	1

Source: Author’s calculations based on data from the World Bank (2024), processed using EViews 10 software.

These are the inflation rate (TINF) and the GDP per capita growth rate (TPIBH), while the other five series are stationary in prime difference I (1). Therefore, we can say that not all the series selected in this work are integrated in the same order. This leads us to confirm the hypothesis of a cointegration relationship and, therefore, the use of the ARDL model.

4.2. Causality test between variables

  

When the non-stationary variables are not cointegrated or are integrated in different orders, the traditional Granger causality test becomes ineffective. In this case, we found the causality test in the sense of Toda-Yamamoto (1995); this study is based on the Wald "W" statistic. It is distributed according to a chi-square. The null hypothesis, therefore, implies the absence of a causal link between the variables (probability greater than 5%). The results of this test are given in the following table:

Table 5: Results of the causality test of Toda and Yamamoto

 

Dependent Variables	UR	INF	EXP	IMP	GFCF	POPGR	GDPPCGR
UR	–	0.4012	1.1463	4.1137	0.5889	1.4065	0.4116
INF	0.5321	–	0.8840	0.5076	0.1615	0.3151	1.8479
EXP	0.4181	0.3274	–	10.6677**	5.6761*	0.0466	0.2868
IMP	0.5935	0.0809	0.4973	–	6.2973**	0.5863	1.4908
GFCF	2.8965	0.0025	2.4681	8.6992**	–	0.3568	0.1609
POPGR	0.1472	0.0412	3.1106	6.1057**	4.0563	–	1.9590
GDPPCGR	3.4692	0.0365	3.9502	1.5370	2.1842	0.6311	–

NB: *** significant at the 1% threshold; ** significant at the 5% threshold; * significant at the 10% threshold%

Source: Author on the World Bank 2024 database, from the Eviews 10 software.

The table above shows a one-way causality between certain variables of the model, namely:

- A unidirectional and statistically significant causality between imports and variables such as exports, gross fixed capital formation, and population growth at the 5% threshold. This result implies that imports positively and significantly influence exports, gross fixed capital formation, and population growth. In the Republic of Congo, he reveals that imports are an effective way to promote public investment, exports, and population growth.

 

-A statistically significant unidirectional causality between gross fixed capital formation and the variables "exports and imports", respectively, at the limits of 10% and 5%. This means that gross fixed capital formation has a significant effect on the export and import variables. All these results reveal that in the Republic of Congo, an improvement in public investments leads to an increase in exports and imports.   

4.3 Terminal cointegration test

The boundary cointegration test or Pesaran et al. test (2001) is an important test in the exclusive verification of cointegrated variables in an ARDL model. Finds its foundations on the comparison between the calculated Fisher statistics (or Fisher F-statistic) and the critical values that form limits, to possibly detect a cointegration relationship as stated by the following hypotheses:

- If Fisher > upper bound: cointegration exists;

- If Fisher < lower bound: cointegration does not exist;

- If lower bound < Fisher < upper bound: no conclusion.

 

Table 6: Results of the Pesaran et al. (2001) Bounds Test

Significance Level	Lower Bound (I(0))	Upper Bound (I(1))
10%	1.99	2.94
5%	2.27	3.28
2.5%	2.55	3.61
1%	2.88	3.99

Source: Author on the World Bank 2024 database, from the Eviews 10 software.

The results of this test confirm the existence of a cointegration relationship between the variables of the model, because the value of the Fisher statistic (F-Stat = 4.53) is higher than that of the upper bound and lower than the thresholds of 1%, 2.5%, 5% and 10%.

5- Discussion

  This research showed that over 96% of unemployment variation in Congo (R² = 0.969), with a valid error correction mechanism confirming a long-run relationship among variables. In the short term, inflation, exports, GDP per capita, and gross fixed capital formation (GFCF) all reduce unemployment, with past monetary policy by BEAC cutting unemployment by up to 0.44%. Local export-oriented firms created jobs, public investment through special economic zones absorbed labor, and rising GDP per capita improved living standards. Conversely, imports and population growth increased unemployment in the short run, as imported goods reduced domestic labor needs while job-creation policies failed to keep pace with demographic growth. Imports and GDP per capita continue to reduce unemployment, while inflation and exports paradoxically increase it, reflecting Congo's heavy dependence on natural resource exports, which generate little employment, and the failure of public policies to contain inflation's damaging effects. Overall, the inflation-unemployment relationship in Congo does not follow the classical Phillips curve trade-off. Instead, it aligns with the Tobin-Fitoussi framework, where inflation and unemployment rise simultaneously because of deep structural economic weaknesses and recurring political instability, which prevent the country from implementing effective macroeconomic stabilization policies.

6- Conclusion and Recommendations

6.1 Conclusion

 Using an ARDL model over the period 1990–2022, the study examined the relationship between inflation and unemployment in the Republic of Congo, incorporating variables such as exports, imports, gross fixed capital formation, population growth, and GDP per capita. In the short term, all variables significantly affect unemployment. Inflation, imports, and population growth increase unemployment, while exports, GFCF, and GDP per capita reduce unemployment. In the long term, inflation, exports, and GFCF positively drive unemployment, while only GDP per capita has a negative influence, and population growth becomes insignificant. Overall, the inflation-unemployment relationship in Congo does not conform to the Phillips curve but rather reflects structural economic weaknesses. The authors acknowledge that results could be strengthened through further methodological refinement in model specification and variable selection.

6.2 Policy Recommendations

1. Inflation and Employment Policy: Monetary authorities should adopt an effective inflation-targeting policy in line with CEMAC recommendations, keeping inflation at or below 3% annually. This would stabilize the macroeconomic environment, incentivize investment financing, support economic diversification, and ultimately promote job creation.

2. Export-Oriented Trade Policy. Since rising exports paradoxically increase unemployment in the long run, the government should redesign its trade policy to make exports explicitly employment-generating. This includes training the workforce in skills aligned with import and export sector needs, stabilizing the nominal exchange rate to avoid imported inflation, and coordinating actions across relevant ministries.

3. Population Growth and Youth Employment Policy Given the positive relationship between population growth and unemployment, authorities should implement youth-focused support policies, including subsidies for entrepreneurial initiatives, public-private partnerships, vocational and qualifying training programs, and tax exemptions for private firms that commit to hiring from the growing population.

 

 

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