University Education: The Way to Fight Poverty in Africa

Steve Bertrand Mboko Ibara

doi:10.12688/f1000research.158375.1

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Research Article

University Education: The Way to Fight Poverty in Africa

[version 1; peer review: awaiting peer review]

Steve Bertrand Mboko Ibara

PUBLISHED 03 Dec 2025

Author details Author details

Economic Sciences, Marien Ngouabi University, Brazzaville, Brazzaville, Congo

Steve Bertrand Mboko Ibara
Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

This study, entitled “University education: the way to fight poverty in Africa,” aimed to analyze the effect of university education on poverty. The research highlights the need for Congo, in particular, and sub-Saharan Africa, in general, to invest massively in university education to hope for the economic and social development so proclaimed. Indeed, the university education of individuals in a country is a critical factor in poverty reduction (Samo, 2022, Li et al., 2024). However, in sub-Saharan Africa, there needs to be more space for university education, focusing rather on primary education policies (UNESCO, 1997).

Methods

Data used are those from the 2011 Congolese household survey. The paper uses the decomposition of Foster Greer and Thorbecke’s (1984) poverty intensity index to show the differences in poverty by level of education attained among individuals with a higher level of education. The Full information maximum likelihood method using the Conditional mixed process (CMP) was preferred because it considers the variables’ causality by avoiding the endogeneity bias (Roodman, 2011).

Results

A Lorenz curve analysis shows that more education does not mean less income inequality. A multivariate explanatory analysis also shows the net effects of university education on poverty. The results obtained showed that university education helps reduce poverty.

Conclusions

The final analysis allows us to realize the gap between the political discourse regarding the desire to escape underdevelopment and the actions undertaken to do so, at least in terms of education. The results of this research, which are quite clear and relevant to the meaning and direction of this poverty-university education relationship, give rise to avenues of questioning on public policy in financing higher education practiced today by these sub-Saharan African countries.

Keywords

Poverty, Lorenz curve, University education, Conditional Mixed Process, Congo, Africa

Corresponding author: Steve Bertrand Mboko Ibara

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Mboko Ibara SB. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Mboko Ibara SB. University Education: The Way to Fight Poverty in Africa [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1353 (https://doi.org/10.12688/f1000research.158375.1) First published: 03 Dec 2025, 14:1353 (https://doi.org/10.12688/f1000research.158375.1) Latest published: 03 Dec 2025, 14:1353 (https://doi.org/10.12688/f1000research.158375.1)

Introduction

Education entered the economy with the pioneering work of Adam Smith (1776), for whom learning a trade could be compared to acquiring an “expensive machine.” Since then, the importance given to the acquisition of knowledge stems, according to (Becker, 1964), from the correlation between the levels of economic growth of States and the positive evolution of the levels of education and health of populations over time. These facts have fueled economic thought since the 1980s when many theoretical works have given rise to a new perspective on economic growth processes (Lucas, 1988). However, Hanushek (1979) points out that the automatic link between the level of human capital and the level of development through economic growth showed weakness when growth rates and productivity gains began to decline in a constant increase in education. It is this two-faced trend of human capital that has fueled the interest of our research on the poverty-university education relationship in Africa.

According to Samo (2022), the university education of individuals in a country is a critical factor in poverty reduction. Li et al. (2024) illustrate this by indicating that university graduates with the necessary general and technical skills are essential for development and poverty reduction. However, in sub-Saharan Africa, little space is given to university education. Indeed, meetings on education in Africa following independence focused on primary education: Abidjan (1964), Nairobi (1968), Lagos (1976), Harare (1982), Cairo (1994), and Dakar (1998) (UNESCO, 1997). This focus of public policies of African States on primary education continued until the 1990s and 2000s, following the international conferences on education in Jomtien in 1990 and that of New York in 2000, which set Education for All (EFA) as the Millennium Development Goal (MDG). 2015, the United Nations adopted the Sustainable Development Goals to replace the Millennium Development Goals. Here again, the section concerning higher education appears only very laconically at the level of the sustainable development goal. In this sense, indicator 4.3.1, relating to target 4.3 of sustainable development goal n°4 in the sustainable development program 2030, does not mention higher education (United Nations, 2017).

Although the progress made in primary education cannot be denied, it is difficult to justify the low importance of higher education as a priority target for educational policies. It is believed that university education plays a minor role in improving the living standards of populations in Africa. However, several authors have shown the role of higher education in promoting economic growth and development (Kalamova, 2020; Liu, 2017). Thus, university education has repercussions on economic growth through science and technology (Li et al., 2024; OECD, 2022).

To illustrate the concern of university education in the fight against poverty in Africa, we considered the Republic of Congo as a case study due to the availability of data and our mastery of the context. In this sense, our study is based on data from the 2011 Congolese household survey conducted by the National Institute of Statistics (INS). Congo is a Central African country with 6,142,180 inhabitants (INS, 2023), with nearly 47 percent living below the poverty line (World Bank, 2024). The country has 12 regions (departments), including Brazzaville and Pointe-Noire, which account for nearly 60 percent of the total population. Like other African countries, Congo is part of a dynamic fight against poverty (Ouadika, 2018). The Congolese government has made the fight against poverty one of the principal axes of its development strategy. This primary concern has been manifested by adopting more than three decades of development plans focused on the fight against poverty.

Regarding higher education in Congo, we note that in 2024, the country will have two public universities, namely Marien Ngouabi University (Brazzaville) and Denis Sassou University. Nguesso (Kintélé) and private institutes, mainly in Brazzaville (political capital) and Pointe-Noire (economic capital). The higher education system remains characterized by insufficient physical infrastructure despite the many efforts undertaken by the various governments. Budgetary expenditure on higher education in Congo constitutes approximately 26 percent of total expenditure on education. Public expenditure on education as a percentage of GDP declined significantly between 2010 and 2015, from 26 percent to 11 percent (World Bank, 2024).

2015 Congo developed a sectoral education strategy for 2015-2025 (UNICEF, 2015). The need for such a strategy for higher education was based on the observation that the country did not have the infrastructure, which caused the saturation of existing infrastructure. This infrastructure needed to be of better quality and distributed across the territory. Student supervision was diagnosed as insufficient. Research activities were mentioned as needing to be more developed. Materials and equipment were deemed insufficient. This diagnostic table of higher education in Congo is from UNICEF (2015). The program for higher education within the national strategy for education in Congo aimed to “meet the training needs of qualified, creative and technologically-aware executives who will contribute to the social and economic development of the country tomorrow.” However, one year before the end of the implementation period of this strategy, the situation of higher education has mostly stayed the same, and the same weaknesses are still present today. Indeed, the vast investment and reorganization program for higher education with the creation of departmental centers, planned by the 2015-2025 education sector strategy, has yet to see the light of day.

Given these facts, the limited place of higher education in educational policies in sub-Saharan Africa raises several questions. Is it because a higher level of education does not help reduce poverty in Africa?

This study’s general objective is to analyze Africa’s poverty-university education relationship. More specifically, it is about:

- analyze the differences in poverty according to the level of university education attained;
- analyze the effect of university education level on poverty.

We hypothesize that African university education could be more robust and that its graduates do not impact poverty reduction. This hypothesis is justified because educational policies in Africa in general, particularly in Congo, give very little importance to higher education. We will verify this hypothesis using the Congo data from the 2011 INS poverty survey. The rest of the work is structured in 4 sections. The first is a brief literature review; section 2 presents the data and methodology; section 3 is devoted to presenting and interpreting the results; and the last is devoted to the discussion.

Literature reviews

From a theoretical point of view, since the work of Schultz (1961) and Becker (1964), supported by the endogenous growth theories (Lucas, 1988), human capital is now part of the factors of economic growth in the same way as physical capital. Therefore, if the workforce is efficient and qualified in an economy, its capacity to contribute to growth will be very high (World Bank, 2018). On the other hand, the productivity of illiterate people or people suffering from diseases is generally low, and these people offer no hope of development for a country (Mingat et al. 2013). When human capital remains unused, or if labor management is defective, the same people who could have contributed positively to growth activity burden the economy. Therefore, the debate surrounding the role of education in the fight against poverty and development remains wide open.

From an empirical perspective, very few studies have addressed the link between university education, economic growth, and poverty in Africa and the Congo. That fact leads us to focus on international studies on the issue. Thus, it emerges that the population with higher education is considered an essential contributor to economic growth, generating scientific knowledge and providing labor, increasing productivity and human capital (Jungblut, 2017; Kalamova, 2020; Liu, 2017). In Brazil, Lamichhane et al., 2021, using a sample of 35 households, examined the role of expanding education in reducing income inequality and poverty. Their findings suggest that reducing poverty and inequality would take several decades and that expanding higher education would be critical to that success. However, this outcome would only be achieved if optimistic growth assumptions materialize. Using a panel conducted in 125 countries from 1999 to 2014, Ledger et al. (2019) showed that tertiary education impacted labor productivity in the economies studied. Yassine and Bakass (2022), in their analysis of poverty, employment, and education among young people in Morocco, used two indicators of poverty: a first binary indicator based on household income with a threshold of 60 percent of median annual expenditure and a second indicator based on the socioeconomic characteristics of the household. These authors found that being more educated was a bulwark against poverty for a young person, regardless of the poverty indicator used. However, more than access to employment was needed to guarantee a decent level of well-being (Yassine and Bakass, 2022).

The benefits of education extend beyond economic aspects to contribute to sustainable development. Indeed, pro-environmental behaviors are essential for sustainable development and the fight against poverty and insecurity (Hassan and Umar, 2024).

In short, this brief overview of the context and the empirical review shows how the economic development objective chosen by African states could only be achieved with a highly qualified workforce. However, public policies in terms of higher education still need to take this into account.

Methods

Description of data and variables used

Data sources

In this study, we use data from the second Congolese household survey to evaluate poverty in 2011 (CNSEE, 2011) driven by the Congolese National Institute of Statistics (INS). The sampling for this survey was a two-stage stratified survey, with a draw proportional to the size of the enumeration areas, in the first stage, and in the second stage, a simple random sample of households enumerated in each enumeration area. The choice of these is due to the opportunity offered by this survey to analyze poverty. From this base, we selected 929 households whose heads had a university education level. Our target population consists of male and female heads of households with a higher level of education.

Study variables

Several variables will be used in this research. The main (endogenous) variable is poverty, estimated from household expenditure per adult equivalent. The primary variable of interest is the level of university education.

The endogenous variable of our study is household poverty captured by the relative approach, following Haughton and Khandker (2009). This study’s “poverty” variable is obtained from the household’s expenditure per adult equivalent. Thus, we define the monetary poverty threshold as a fixed proportion of the level of expenditure per adult equivalent, i.e., 60 percent of the average. This setting allows us to split our population into two modalities. Thus, households below this line will be considered poor (modality 1). Those above will be non-poor (modality 0).

The primary exogenous variable in our study is household heads’ university education level. Indeed, we seek to know whether, in the context of Congo, higher education helps reduce poverty. This variable is coded as follows: First/second year of university = 31; Third/fourth year of university = 32; Fifth year or more = 33. The sign of the expected coefficients is negative (-). Poverty is expected to decrease each time university education increases.

The exogenous control variables for the poverty-university education relationship are household size, age, gender, marital status, and economic activity status:

➢ Household size: This variable measures the number of people in the household. The expected coefficient sign is positive (+). The larger the household size, the poorer the household.
➢ Age: This variable measures the age of the individual in completed years. The sign of the expected coefficient is positive (+). Beyond a certain age, the probability of remaining poor is expected to increase.
➢ Gender: In this research, gender is a binary variable that takes one if male and two if female. The expected sign is positive (+). It is assumed that the probability of being poor increases when the head of household is a woman rather than a man.
➢ Marital status: To better capture the impact of marital status on poverty, the variable is recoded into three (3) categories: 1 Single; 2 In a union; 3 Divorced/Separated. Higher risks of being poor are expected among single people than among people in a union and those who are separated/divorced.
➢ Economic activity status: This variable is recorded in two modalities: Active = 0 and Unemployed = 1. The expected sign of the coefficient is positive (-) under the hypothesis that unemployment status increases the risk of being poor.

Ethics statement

The household consumption survey (ECOM 2) was conducted in 2011 by the National Center for Statistics and Economic Studies (currently the National Institute of Statistics-INS) of the Republic of Congo to assess poverty in the country. The INS is the public body responsible for collecting, processing, analyzing, and publishing official statistics.

Law No. 8-2009 of October 28, 2009, in its chapter 2, and article 8 stipulates that: “Natural and legal persons must respond, accurately and within the set deadlines, to censuses and statistical surveys carried out using questionnaires or other forms developed by public administrations of the national statistics system.” The INS regularly collects official data from administrations and households using that disposal of low. In the case of the data from this survey, they were approved by the Minister responsible for statistics on November 11, 2011.

Methods

Our research methodology is structured in two points: the presentation of poverty indicators and the method of analyzing the results. This research uses the Lorenz curve, the relative poverty index, and the Gini index, based on the Duclos and Araar (2006) equally distributed equivalent incomes concept. That concept is then applied to distributions whose incomes have been censored at the poverty line.

Presentation of poverty and inequality indicators

- The incidence of poverty

The incidence of poverty apprehended in this study is part of the Foster-Greer-Thorbecke (1984) indices for measuring poverty. These authors developed a family of indices considering the size of the total population N, the poverty level or line z, the income of the individual yi, a parameter α, which is an indicator of a version to poverty and I(yi ⩽ z) a function taking 1 if yi ⩽ z and 0 if not.

(1)

{FGT}_{α} = \frac{1}{N} \sum_{i = 1}^{N} {(\frac{z - y_{i}}{z})}^{α} I (y_{i} \leq z)

Modifying the parameter α allows one to move from one index to another. The larger this parameter, the greater the weight given to the poorest individuals in calculating the poverty rate. Where α equals zero, the FGT index divides the number of poor people (people below the poverty line) by a country’s total population.

(2)

{FGT}_{0} = \frac{1}{N} \sum_{i = 1}^{N} I (y_{i} \leq z)

Thus, when α = 0, the FGT index gives the most straightforward and commonly used poverty index (Duclos and Araar, 2006). It is called the poverty rate and is simply the proportion of a population that lives in poverty. The shorter term “poverty rate” is sometimes intended to indicate the absolute (as opposed to relative) number of poor people in the population.

- The Lorenz Curve

The Lorenz curve is the most popular tool for visualizing inequality and comparing it across given distributions. For a given percentile pi, the Lorenz curve shows the share of total population income held by the poorest people. Formally, the Lorenz curve in percentile pi is given by:

(3)

L_{(pi)} = \frac{\sum_{j = 1}^{i} Q_{(pj)}}{\sum_{j = 1}^{n} Q_{(pj)}} = \frac{\sum_{j = 1}^{i} y_{j}}{\sum_{j = 1}^{n} y_{j}}

For several decades, this curve has been the most popular graphical tool for visualizing and comparing income inequality (Duclos and Araar, 2006). It provides complete information about the entire income distribution relative to the mean. It, therefore, provides a complete description of relative incomes than any of the traditional summary dispersion statistics, and it also provides a better starting point for studying income inequality than calculating the many inequality indices that have been proposed.

- The Gini coefficient

The Gini index or coefficient is a statistical measure used to account for the distribution of a variable (income, wealth) within a population. It measures the degree of inequality in a country’s income distribution. Developed by Corrado Gini in 1912, this index varies between 0 (perfect equality) and 1 (perfect inequality) (Duclos and Araar, 2006). Alternatively, the Gini index can be defined as half of the average relative Gini difference of the income series:

(4)

G = \frac{E}{2 M}

- where M is the average of incomes and E, the Gini’s average difference of incomes. That is, if (x_i) _{1 ⩽ i ⩽ n} were the n individual incomes:
$G = \frac{1}{n^{2}} \sum_{i = 1}^{n} \sum_{j = 1}^{n} | x_{i} - x_{j} |$

M = \frac{1}{n} \sum_{k = 1}^{n - 1} x_{k}

The method of analyzing the results:

To estimate the effect of university education on poverty, we use a model based on nonlinear binary regression, given the binary nature of our poverty variable (1 for poor and 0 otherwise), such variables can be analyzed using a probit model (Greene, 2019). In such a model, the expression of the probability of being poor can be written:

(5)

P_{r} (y = 1 / x) = Φ (β^{'} x)

• Pr is the probability of being poor;
• $β$ is a vector of coefficients;
• x represents a set of exogenous variables.

However, when there are measurement errors and omitted simultaneity in econometric models, the explanatory variables can be correlated with random components, and standard estimation methods no longer provide consistent estimates of the model parameters (Kim & Frees, 2006). Three sources of correlation errors can be encountered (Kim & Frees, 2006): the omission of certain exogenous variables, measurement errors of the variables, and simultaneity bias. Several studies addressing endogeneity have focused on resolving level 2 endogeneity (Rabe-Hesketh and Skrondal, 2008; Mboko and Ikiemi, 2021).

In this research, we use the simultaneous equations approach proposed by Steele, Vignoles, and Jenkins (2007) to consider the supposed endogeneity of employment on poverty. Indeed, seeking to estimate the equations individually assumes that the error terms of the different equations are independent of each other (a strong assumption for our estimates). That is why the Full information maximum likelihood method was preferred. The technique used to carry out this method is the so-called “conditional mixed process (CMP)” developed by Roodman (2011). This model allows us to consider the variables’ causality by avoiding the endogeneity bias (Roodman, 2011) and to study the impact of university education on poverty in sub-Saharan Africa. Thus, the probability for an individual to have a paid job and to be poor is respectively given by the following latent variables:

(6)

y_{1}^{*} = β_{1}^{'} x_{1} + ε_{1}, y_{1} = 1 si y_{1}^{*} > 0, y_{1} = 0, otherwise,

(7)

y_{2}^{*} = β_{2}^{'} x_{2} + δ y_{1} + ε_{1}, y_{2} = 1 si y_{2}^{*} > 0, y_{2} = 0, otherwise,

where:

E [ε_{1} | x_{1}, x_{2}] = E [ε_{2} | x_{1}, x_{2}] = 0,

Var [ε_{1} | x_{1}, x_{2}] = Var [ε_{2} | x_{1}, x_{2}] = 1,

Cov [ε_{1}, ε_{2} | x_{1}, x_{2}] = ρ

[ε_{1}, ε_{2}] \sim Φ_{2} [0, 0, a, 1, ρ], ρ ϵ [- 1, 1]

Φ₂ denotes the distribution function for a bivariate standard normal distribution, $ρ$ being the dependence parameter measuring the endogeneity of $y_{1}$ in the equation of $y_{2}^{*}$ . $δ$ is the coefficient on the outcome variable of the first-step analysis appearing in the second-step equation.

The variables in our model are:

$y_{1}$ = Employment

$y_{2}$ = Poverty

$x_{1}$ = Independent variables in the employment equation

$x_{2}$ = Independent variables in the poverty equation

Such a recursive model of simultaneous equations can be estimated using the full information maximum likelihood approach.

Given the probabilities in (6) and (7), the conditional mean of being poor can then be written as:

(8)

\begin{matrix} E [y_{2} | y_{1} = 1, x_{1}, x_{2}] = \frac{Pr [y_{2} = 1, y_{1} = 1 | x_{1}, x_{2}]}{Pr [y_{1} = 1 | x_{1}, x_{2}]} \\ = \frac{Φ_{2} (β_{1}^{'} x_{1}, β_{2}^{'} x_{2} + δ, ρ)}{Φ (β_{1}^{'} x_{1})} \end{matrix}

On the other hand, the unconditional mean function can then be written as

(9)

\begin{matrix} E [y_{2} | x_{1}, x_{2}] = Pr [y_{1} = 1 | x_{1}, x_{2}] E [y_{2} | y_{1} = 1, x_{1}, x_{2}] \\ + Pr [y_{1} = 0 | x_{1}, x_{2}] E [y_{2} | y_{1} = 0, x_{1}, x_{2}] \\ \begin{matrix} = Pr [y_{2} = 1, y_{1} = 1 | x_{1}, x_{2}] + Pr [y_{2} = 1, y_{1} = 0 | x_{1}, x_{2}] \\ = Φ_{2} (β_{1}^{'} x_{1}, β_{2}^{'} x_{2} + δ, ρ) + Φ_{2} (- β_{1}^{'} x_{1}, β_{2}^{'} x_{2}, - ρ) \end{matrix} \end{matrix}

This technique not only has the advantage of taking into account a greater diversity like endogenous variables (discrete, censored, continuous) but is also adapted and appropriate mainly for recursive models insofar as it allows to take into account the relationships that may exist between the different equations (endogeneity problem) and manage selection and simultaneity biases ( Mboko and Ikiemi, 2021). Hence, the systemic estimation of our system will lead to more efficient estimates than the individual estimation of each equation (Roodman, 2011). This is what characterizes the advantage of this method compared to others, in particular, the two-step Heckman approach, which does not consider simultaneously several equations with dependent variables of different natures (Mboko and Ikiemi, 2021).

Presentation and interpretation of results

This section is devoted to presenting and interpreting the results of the descriptive and explanatory analyses on poverty. We used the variable of expenditure per adult equivalent to measure household poverty in the population of heads of households with a higher level of education. Poverty was approached from the poverty incidence indicator developed by Foster, Greer, and Thorbecke (FGT) in 1984. For this reason, the poverty threshold retained was set as 60 percent of the average expenditure per adult equivalent of the household. This threshold corresponds to 405109 CFA francs.

The results of the descriptive analyses of poverty show that in 2011 Congo ( Table 1), 33.6 percent of heads of households with a higher level of education were poor. The effects of higher education on poverty are very noticeable. Indeed, the poverty rate is 40.4 percent among heads of households who have not reached the third year of university and only 27.8 percent among those with a master’s degree or higher. Hence, a university education mechanically reduces poverty and improves the population’s well-being.

Table 1. Incidence of poverty of heads of household by level of university education.

	Estimate¹	Std. Err.	t	P > t	[95% Conf. Interval]		P.Line (Threshold)
CM education level
1st/2nd year	0.4041	0.0269	15,03477	0	0.3513	0.4568	405109.21
3rd/4th year	0.2715	0.0301	9,014927	0	0.2124	0.3306	405109.21
5th year and above	0.2783	0.0425	6,541796	0	0.1948	0.3618	405109.21
Population	0.3357	0.0184	18,24672	0	0.2996	0.3718	405109.21

1 Unweighted estimate.

The analysis of the Gini index ( Table 2) shows a non-negligible level of inequality at 0.37. It is in the class of the most educated heads of household (Master’s degree or more) that we find the most inequality in expenditure (0.41). These results are visible on the Lorenz curve, where we observe a clear demarcation of the curve of the fifth year or more, far from the 45-degree line than the other curves. This distance means that inequalities increase with the level of education in higher education.

Table 2. Gini inequality index by level of education of household heads.

Population groups	Estimate	Std. Err.	t	P>t	[95% Conf. Interval]
CM Education Level
1st/2nd year	0.3663	0.0138	26,53271	0	0.339166	0.3933472
3rd/4th year	0.3589	0.02 28	15,75671	0	0.314229	0.4036402
5th year and above	0.4087	0.0299	13,65607	0	0.349949	0.4674124
Population	0.3755	0.0122	30,73771	0	0.35154	0.3994919

Table 3 presents the recursive simultaneous equations model estimates from the conditional mixed model of Roodman (2011), considering the endogeneity of employment on university education. It presents both the coefficients and the marginal effects. The values in parentheses are standard errors.

Table 3. Estimation of the net effects of university education on poverty.

		Marginal effects
	Coef./(Std. Err)	dy/dx/(Std. Err)
EMPLOYMENT EQUATION
Constant	3.546 (0.773)***
Marital status
Bachelor	(R)	(R)
In union	-0.536 (0.203)***	-0.114 (0.049)**
Divorced/separated	-0.530 (0.199)***	-0.113 (0.047)**
Sex
Man	(R)	(R)
Women	0.141 (0.181)	0.026 (0.035)
Age	-0.226 (0.0361)***	-0.0395 (0.006)***
Age squared	0.0027 (0.0003)***	0.00047 (0.00)***
Household size	0.017 (0.0607)	0.003 (0.010)
Poverty status	-0.281 (0.823)	-0.045 (0.121)
Not poor	(R)	(R)
Poor	-0.281 (0.823)
POVERTY EQUATION
Constant	-2.61 (0.847)***
Marital status
Bachelor	(R)	(R)
In union	-0.271 (0.212)	-0.067 (0.055)
Divorced/separated	-0.029 (0.215)	0.0077 (0.057)
Sex
Man	(R)	(R)
Women	-0.193 (0.188)	-0.044 (0.041)
Age	0.042 (0.038)	0.0102 (0.009)
Age squared	-0.0004 (0.0004)	-0.000 (0.0001)
Household size	0.234 (0.023)***	0.057 (0.005)***
Level Education
1st/2nd year	(R)	(R)
3rd/4th year	-0.499 (0.125)***	-0.1209 (0.029)***
5th year and above	-0.435 (0.149)***	-0.108 (0.034)***
MODEL FIT STATISTICS
Wald Chi-Square	256.22***
atanhrho	- 0.1523 (0.3803)
rho	- 0.1508 (0.3716)
Number of observations	914

* Significant at 10%;

** Significant at 5%; and

*** Significant at 1%.

The Wald test statistics for the overall model fit are significant at the 1 percent level. The atanhrho statistic is non-significant with a positive correlation between the error terms of the two equations of the order of 7 percent. These last two pieces of information on the model fit suggest the absence of endogeneity of the exercise of economic activity on poverty in Congo.

The employment equation analysis shows a non-significant relationship between employment and poverty, all else equal. This result is inconsistent with the work of Siddique (2023), who showed that good-quality jobs helped households escape poverty while poor-quality jobs did not. This author suggested that poverty eradication requires the government to direct labor market policies to focus more on distributing jobs from individuals to households and transforming bad jobs into good jobs rather than simply creating new jobs in the economy. Among the control variables, only household size is significantly related to poverty. Thus, all else being equal, household size increases with the poverty level. In other words, the poorest households are those with many people.

The poverty equation shows that university education has a very significant negative impact on household poverty in Congo. Indeed, even after controlling for economic activity and other characteristics, the coefficients associated with households whose head has a bachelor’s/master’s degree or a master’s/doctorate are significant at the 1 percent level. Hence, the probability of remaining poor for a household whose head is of a higher level decreases significantly with the improvement in the level of university education.

Table 3 also presents the marginal effects calculated from the sample mean. These are the marginal effects on the probabilities of realization (success) of the two simultaneous equations (Work and poverty) retained in the model. The interpretation will be limited to the main variable of interest (Level of university education). University education acts directly on poverty (poverty equation). Compared to heads of households with 1st/2nd year of university education, heads with 3rd/4th year have 12 percent less risk of finding themselves in poverty. Those with 5th year of university education or more have 10 percent less risk of being poor than their counterparts in the reference modality.

Discussion

Ultimately, our descriptive and explanatory results suggest a strong correlation between university education level and poverty reduction. At the descriptive level, the decomposition of the FGT poverty incidence index by higher education level showed that the least educated are the most affected by poverty. However, the analysis of the Gini index shows that education does not reduce inequalities. The Lorenz curve for heads of households with a master’s degree or higher is further from the 45-degree axis than all the other curves. At the explanatory level, all other things being equal, the most educated people are the least affected by poverty.

Our results invalidate our working hypothesis that higher education has no impact on poverty reduction and contradict, in part, those of Janjua and Kamal (2011). Indeed, dealing with the relationship between education and poverty in underdeveloped countries, these two authors first divided the countries into three categories: low-income, lower-middle-income, and upper-middle-income. They found that the relationship between poverty and education was significant only for countries in the lower part of their classification (low-income and lower-middle-income). As for the country in the upper middle-income bracket, the relationship was no longer significant. Since Congo is classified among the upper-middle-income countries, one would have expected an absence of a relationship between poverty and education. Several reasons can explain this difference in results. Indeed, Janjua and Kamal (2011) worked on school attendance rather than on the level of education achieved. Their field of intervention was the secondary level of education rather than the higher level. These two reasons may explain the differences observed.

On the contrary, our results confirm, in every respect, those of Li et al. (2024), according to which higher education helps reduce poverty but not inequalities. These results lead us to question, ultimately, the education strategies encouraged by UNESCO for sub-Saharan African countries where all focus on primary education, knowing that only a highly qualified workforce, through the effect of “technological spillovers,” can boost economic growth and development (Lucas, 1988; Barro and Sala-i-Martin, 1992). Another element of misunderstanding is the behavior of African countries. They behave almost exclusively as consumers of the orientations of United Nations agencies, in this case, UNESCO, which places more emphasis on primary education. African countries need to be more capable of thinking for themselves and understanding that only an aggressive and massive policy on high-tech higher education can provide a highly qualified workforce that will allow them, in the long term, to achieve the much-proclaimed development.

The case of Congo is just as challenging to understand. Indeed, Congo began developing development programs in the 1980s with the policy of five-year plans, like the five-year plan 1982-1986 (Congo, 2022). However, this policy continued in the 2000s, when it was transformed into a poverty reduction strategy. In the early 2010s, these policies were transformed into a national development plan, and three plans have been developed since then (2012-2016, 2018-2022, and 2022-2026). Despite their variations in sectoral strategies, these plans could be faster to fulfill their primary roles, namely growth, development, and poverty reduction. The various sectoral education strategies have yet to succeed in improving the quantity and quality of higher education in Congo after more than 40 years of implementation.

Conclusion

In short, our study aimed to analyze the effect of university education on poverty. It used Congolese household survey data to assess Congo’s poverty in 2011. The sample included 929 heads of households with a higher level of education at the time of the survey. We used three indicators to measure poverty and inequality: the incidence of poverty FGT, the Gini index, and the Lorenz curve. To analyze the effect of education on poverty, we used the conditional mixed model of Roodman (2011). The results showed that the higher the level of university education, the less poor one is. Our results invalidate our hypothesis and show that improving access to quality higher education will substantially reduce poverty and contribute to economic growth.

To overcome the weaknesses observed in African higher education, we suggest that African countries review their strategies for university education. In the Congo, for university research to be more effective in reducing poverty, a national innovation system should be established, consisting of a complex set of institutions and practices to produce and disseminate scientific and technological knowledge. Such a system should be accompanied by an appropriate macroeconomic and regulatory framework and knowledge-producing organizations (research centers, laboratories, and innovative business networks).

Our study has a significant limitation that should be mentioned. This limitation is related to the obsolescence of the data. Indeed, the data used in this study are more than 10 years old, which suggests that the reality of poverty in the country must have changed a lot since then. However, due to the need for more current data, all research on the issue of poverty is forced to use them.

We suggest that future research extend the study to a broader sample of developed and developing countries and from all world regions. Such research will, on the one hand, show how university education has contributed to lifting currently developed countries out of underdevelopment and, on the other hand, analyze the university education systems of underdeveloped countries and the role that these countries play in higher education in the fight against poverty and the achievement of development.

Ethics and consent

The household consumption survey (ECOM 2) was conducted in 2011 by the National Center for Statistics and Economic Studies (currently the National Institute of Statistics-INS) of the Republic of Congo to assess poverty in the country. The INS is the public body responsible for collecting, processing, analyzing, and publishing official statistics.

Law No. 8-2009 of October 28, 2009, in its chapter 2, and article 8 stipulates that: “Natural and legal persons must respond, accurately and within the set deadlines, to censuses and statistical surveys carried out using questionnaires or other forms developed by public administrations of the national statistics system.” The INS regularly collects official data from administrations and households using that disposal of low. In the case of the data from this survey, they were approved by the Minister responsible for statistics on November 11, 2011. The data is collected by the public body responsible for collecting it for the state, so there is no recourse to the ethics committee.

Data availability statement

This is a poverty survey database conducted by the National Institute of Statistics (INS) of Congo, and the entire database is accessible upon request from the INS.

References

Barro R, Sala-i-Martin X: Convergence. J. Polit. Econ. 1992; 100: 223–251. Publisher Full Text
Becker G: Human Capital: A Theoretical Empirical Analysis with Special Reference to Education. New York: Columbia University Press; 1964.
CNSEE (National Center for Statistics and Economic Studies): Congolese Household Survey for the Assessment of Poverty. Poverty Profile in Congo. QUIBB Analysis Report. Brazzaville: CNSEE; 2011.
Congo: The main document is the National Development Plan 2022-2026, A robust, diversified, and resilient economy for inclusive growth and irreversible sustainable development.2022; 1–129.
Duclos J-Y, Araar A: Poverty and equity: measurement, policy, and estimation with DAD. New York: Springer; 2006.
ECOM 2: Data sets used and Supplementary Table 1.2011. Publisher Full Text Reference Source
Foster J, Greer J, Thorbecke E: A Class of Decomposable Poverty Measures. Econometrica. 1984; 52: 761–776. Publisher Full Text
Greene WH: Econometric Analysis. Munich, Pearson: Global edition; 8th ed. 2019.
Hanushek EA: Conceptual and empirical issues in the estimation of educational production functions. J. Hum. Resour. 1979; 14(3): 351–388. Publisher Full Text
Hassan SJ, Umar OM: Actualizing the Sustainable Development Goals 2030: The Role of Environmental Education and Tackling Insecurity in Nigeria. Scholar J. 2024; 2(1). Reference Source
Haughton J, Khandker SR: Handbook on Poverty and Inequality. World Bank; 2009.
INS: Preliminary report of the results of the fifth general census of the population and housing of Congo. National Institute of Statistics; 2023; 51.
Janjua PZ, Kamal UA: The Role of Education and Income in Poverty Alleviation: A Cross-Country Analysis. The Lahore Journal of Economics. 2011; 16(1): 143–172. Publisher Full Text
Jungblut J: The political economy of higher education finance. The politics of tuition fees and subsidies in OECD countries, 1945–2015. Eur. J. High. Educ. 2017; 7(1): 96–99. Publisher Full Text
Kalamova M: Resourcing higher education: challenges, choices, and consequences. Higher Education Policy Team. Paris: OECD Publishing; 2020.
Kim J-S, Frees EW: Omitted Variables in Multilevel Models. Psychometrika. 2006; 71: 659–690. Publisher Full Text
Lamichhane S, Egilmez G, Gedik R, et al.: Benchmarking OECD countries’ sustainable development performance: a goal-specific principal component analysis approach. J. Clean. Prod. 2021; 287. Publisher Full Text
Ledger S, Thier M, Bailey L, et al.: OECD’s approach to measuring global competency: powerful voices shaping education. Teach. Coll. Rec. 2019; 121(8): 1–40. Publisher Full Text
Li J, Xue E, Wei Y, et al.: How popularizing higher education affects economic growth and poverty alleviation: empirical evidence from 38 countries. Humanities and social sciences communications. 2024; 1–11. Publisher Full Text
Liu L: Exploring the relationship between education economy and individual civic attitudes: a cross-national analysis in England, Spain, Sweden, Poland, and Chinese Taipei. Southampton: University of Southampton; 2017. Thesis for the degree of Doctor of Philosophy.
Lucas RE: On the Mechanics of Economic Development. J. Monet. Econ. 1988; 22: 3–42. Publisher Full Text
Mboko ISB, Ikiemi CS: Effect of functional illiteracy on living conditions of households in Congo. Mod. Econ. 2021; 2021(12): 469–676.
Mingat A, Ndem F, Seurat A: The measurement of illiteracy in question: the case of sub-Saharan Africa. Cahiers de la recherche sur l’éducation et les savoirs, 2013. 2013; 12: 25–47. ffhalshs -00832742.
United Nations: Work of the Statistical Commission on the 2030 Agenda for Sustainable Development.2017. A/RES/71/313. The General Assembly adopted a Resolution on July 6, 2017. Reference Source
OECD: Education at a Glance 2022. OECD Indicators. Paris: OECD Publishing; 2022. Publisher Full Text
Ouadika S: Analysis of poverty dynamics in Congo. Marien Ngouabi University of Brazzaville; 2018. Doctoral thesis.
Rabe-Hesketh S, Skrondal A: Multilevel and Longitudinal Modeling Using Stata. Texas: Stata Press; 2008.
Roodman D: Fitting Fully Observed Recursive Mixed-Process Models with CMP. Stata J. 2011; 11: 159–206. Publisher Full Text
Samo V: A snapshot of equity and inclusion in OECD education systems: findings from the Strength through Diversity Policy Survey. OECD Education Working Papers. 2022. Publisher Full Text
Schultz TW: Investment in Human Capital. Am. Econ. Rev. 1961; 51: 1–17.
Siddique AB: Job market polarization and American poverty. Journal for Labor Market Research. 2023; 57(30): 1–24. Publisher Full Text
Smith A: An Inquiry into the Nature and Causes of the Wealth of Nations. Cannan E, editors. London: Methuen and Co., Ltd.; Fifth edition. 1776; 1904.
Steele F, Vignoles A, Jenkins A: The Effect of School Resources on Pupil Attainment: A Multilevel Simultaneous Equation Modeling Approach. J. R. Stat. Soc. 2007; 170: 801–824. Publisher Full Text
UNESCO: Report on the State of Education in Africa in 1997: Innovations and Rebuilding. Dakar: UNESCO/BREDA; 1997; 311.
UNICEF: Congo Education Sector Strategy 2015-2025.2015. Accessed April 15, 2024. Reference Source Reference Source
World Bank: The Human Capital Gap: Getting Governments to Invest in People. World Bank; 2018. Accessed May 10, 2024. Reference Source
World Bank: Republic of Congo - Overview. World Bank; 2024. Accessed May 10, 2024. Reference Source
Yassine A, Bakass F: Do Education and Employment Play a Role in Youth’s Poverty Alleviation? Evidence from Morocco. Sustainability. 2022; 14: 11750. Publisher Full Text

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Economic Sciences, Marien Ngouabi University, Brazzaville, Brazzaville, Congo

Steve Bertrand Mboko Ibara
Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation

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No competing interests were disclosed.

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The author(s) declared that no grants were involved in supporting this work.

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[1] Barro R, Sala-i-Martin X: Convergence. J. Polit. Econ. 1992; 100: 223–251. Publisher Full Text

[2] Becker G: Human Capital: A Theoretical Empirical Analysis with Special Reference to Education. New York: Columbia University Press; 1964.

[3] CNSEE (National Center for Statistics and Economic Studies): Congolese Household Survey for the Assessment of Poverty. Poverty Profile in Congo. QUIBB Analysis Report. Brazzaville: CNSEE; 2011.

[4] Congo: The main document is the National Development Plan 2022-2026, A robust, diversified, and resilient economy for inclusive growth and irreversible sustainable development.2022; 1–129.

[5] Duclos J-Y, Araar A: Poverty and equity: measurement, policy, and estimation with DAD. New York: Springer; 2006.

[6] ECOM 2: Data sets used and Supplementary Table 1.2011. Publisher Full Text Reference Source

[7] Foster J, Greer J, Thorbecke E: A Class of Decomposable Poverty Measures. Econometrica. 1984; 52: 761–776. Publisher Full Text

[8] Greene WH: Econometric Analysis. Munich, Pearson: Global edition; 8th ed. 2019.

[9] Hanushek EA: Conceptual and empirical issues in the estimation of educational production functions. J. Hum. Resour. 1979; 14(3): 351–388. Publisher Full Text

[10] Hassan SJ, Umar OM: Actualizing the Sustainable Development Goals 2030: The Role of Environmental Education and Tackling Insecurity in Nigeria. Scholar J. 2024; 2(1). Reference Source

[11] Haughton J, Khandker SR: Handbook on Poverty and Inequality. World Bank; 2009.

[12] INS: Preliminary report of the results of the fifth general census of the population and housing of Congo. National Institute of Statistics; 2023; 51.

[13] Janjua PZ, Kamal UA: The Role of Education and Income in Poverty Alleviation: A Cross-Country Analysis. The Lahore Journal of Economics. 2011; 16(1): 143–172. Publisher Full Text

[14] Jungblut J: The political economy of higher education finance. The politics of tuition fees and subsidies in OECD countries, 1945–2015. Eur. J. High. Educ. 2017; 7(1): 96–99. Publisher Full Text

[15] Kalamova M: Resourcing higher education: challenges, choices, and consequences. Higher Education Policy Team. Paris: OECD Publishing; 2020.

[16] Kim J-S, Frees EW: Omitted Variables in Multilevel Models. Psychometrika. 2006; 71: 659–690. Publisher Full Text

[17] Lamichhane S, Egilmez G, Gedik R, et al.: Benchmarking OECD countries’ sustainable development performance: a goal-specific principal component analysis approach. J. Clean. Prod. 2021; 287. Publisher Full Text

[18] Ledger S, Thier M, Bailey L, et al.: OECD’s approach to measuring global competency: powerful voices shaping education. Teach. Coll. Rec. 2019; 121(8): 1–40. Publisher Full Text

[19] Li J, Xue E, Wei Y, et al.: How popularizing higher education affects economic growth and poverty alleviation: empirical evidence from 38 countries. Humanities and social sciences communications. 2024; 1–11. Publisher Full Text

[20] Liu L: Exploring the relationship between education economy and individual civic attitudes: a cross-national analysis in England, Spain, Sweden, Poland, and Chinese Taipei. Southampton: University of Southampton; 2017. Thesis for the degree of Doctor of Philosophy.

[21] Lucas RE: On the Mechanics of Economic Development. J. Monet. Econ. 1988; 22: 3–42. Publisher Full Text

[22] Mboko ISB, Ikiemi CS: Effect of functional illiteracy on living conditions of households in Congo. Mod. Econ. 2021; 2021(12): 469–676.

[23] Mingat A, Ndem F, Seurat A: The measurement of illiteracy in question: the case of sub-Saharan Africa. Cahiers de la recherche sur l’éducation et les savoirs, 2013. 2013; 12: 25–47. ffhalshs -00832742.

[24] United Nations: Work of the Statistical Commission on the 2030 Agenda for Sustainable Development.2017. A/RES/71/313. The General Assembly adopted a Resolution on July 6, 2017. Reference Source

[25] OECD: Education at a Glance 2022. OECD Indicators. Paris: OECD Publishing; 2022. Publisher Full Text

[26] Ouadika S: Analysis of poverty dynamics in Congo. Marien Ngouabi University of Brazzaville; 2018. Doctoral thesis.

[27] Rabe-Hesketh S, Skrondal A: Multilevel and Longitudinal Modeling Using Stata. Texas: Stata Press; 2008.

[28] Roodman D: Fitting Fully Observed Recursive Mixed-Process Models with CMP. Stata J. 2011; 11: 159–206. Publisher Full Text

[29] Samo V: A snapshot of equity and inclusion in OECD education systems: findings from the Strength through Diversity Policy Survey. OECD Education Working Papers. 2022. Publisher Full Text

[30] Schultz TW: Investment in Human Capital. Am. Econ. Rev. 1961; 51: 1–17.

[31] Siddique AB: Job market polarization and American poverty. Journal for Labor Market Research. 2023; 57(30): 1–24. Publisher Full Text

[32] Smith A: An Inquiry into the Nature and Causes of the Wealth of Nations. Cannan E, editors. London: Methuen and Co., Ltd.; Fifth edition. 1776; 1904.

[33] Steele F, Vignoles A, Jenkins A: The Effect of School Resources on Pupil Attainment: A Multilevel Simultaneous Equation Modeling Approach. J. R. Stat. Soc. 2007; 170: 801–824. Publisher Full Text

[34] UNESCO: Report on the State of Education in Africa in 1997: Innovations and Rebuilding. Dakar: UNESCO/BREDA; 1997; 311.

[35] UNICEF: Congo Education Sector Strategy 2015-2025.2015. Accessed April 15, 2024. Reference Source Reference Source

[36] World Bank: The Human Capital Gap: Getting Governments to Invest in People. World Bank; 2018. Accessed May 10, 2024. Reference Source

[37] World Bank: Republic of Congo - Overview. World Bank; 2024. Accessed May 10, 2024. Reference Source

[38] Yassine A, Bakass F: Do Education and Employment Play a Role in Youth’s Poverty Alleviation? Evidence from Morocco. Sustainability. 2022; 14: 11750. Publisher Full Text

University Education: The Way to Fight Poverty in Africa

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Literature reviews

Methods

Description of data and variables used

Methods

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Table 1. Incidence of poverty of heads of household by level of university education.

Table 2. Gini inequality index by level of education of household heads.

Table 3. Estimation of the net effects of university education on poverty.

Discussion

Conclusion

Ethics and consent

Data availability statement

References

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