The objective of this paper is to examine the effects of mayors’ human capital on the investments of northern Cameroonian municipalities. The human capital of mayors is operationalized in terms of the level of education (diploma), training, and age. The data in the study are from 46 councils in northern Cameroon, including 21 in the Adamawa region, 12 in the north, and 13 in the far north. After carrying out the preliminary statistical tests (multicollinearity and homoscedasticity) for the cross-sectional data, the result of OLS shows that the human capital of mayors has an effect on the investments of northern Cameroonian municipalities. Particularly, the level of education of mayor (as measured by the diploma) has positive effects on municipal investments. However, the coefficients of educational attainment and age of mayors are not significant. The result also shows that the coefficients of some control variables are statistically significant.


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A crisis of confidence in the government has encouraged the search for solutions that can both win back the support of citizens and improve the performance of the state. To achieve these goals, development efforts have shifted from central to local governments (World Bank, 1997). The 1980s saw the experimentation of Structural Adjustment Programs (SAPs) presented by the Bretton Woods institutions (IMF and WB) as a panacea for the development failures observed since 1960. Once again, the failure will be resounding. Faced with these repeated failures in economic development, several developed and developing countries embarked on a process of decentralization in the early 1990s.

Thus, Cameroon, like other countries around the world, has embraced decentralization in the hope of improving public performance and the living conditions of citizens. With the new legal framework of the Constitution of Cameroon of 1996, Laws Nos. 2004/017, 2004/018, and 2004/019 of 22 July 2004, respectively on the orientation of decentralization, setting the rules applicable to municipalities and rules applicable to the regions, the State of Cameroon has entrusted the municipalities with the mission of promoting local development and improving the environment and living conditions of their inhabitants. Municipalities now play a leading role in the process of socio-economic development of the populations of their communities.

Decentralization now gives local elected representatives (mayors) substantial decision-making power to mobilize their own financial resources and determine their spending policy and area of competence (budgetary decentralization). One of the major objectives of decentralization is to promote development at the grassroots level, particularly through local investment (Lemieux, 2001). Various studies converge on the idea that investment takes on considerable importance with the process of decentralization (Besson, 2002).

However, in many developing countries, while local administrative units have the legal authority to invest (World Bank, 1997), there is a permanent increase in inequalities between different jurisdictions in terms of investment (even within homogeneous municipalities and within the same country). For example, West and Wong (1995) point to an increase in inequalities in access to education and health between rural China provinces. The 2017 performance window report of the PNDP1 1PNDP: National Participatory Development Program, created with the aim of supporting Cameroonian municipalities in the local development process. , for example, shows that municipal investment varies from one municipality to another. Indeed, according to the report, only 17% (59/354) of Cameroonian municipalities have an investment budget execution rate of more than 70% while more than 69% (246/354) of municipalities do not manage to consume 50% of their investment budget. Moreover, this situation is more worrying in the east of the country, where no municipality has consumed more than 70% of its investment budget. The widening investment gap between municipalities can jeopardize the economic and social cohesion of the nation (Caldeira & Rota-Graziosi, 2014). It would be of great importance to identify the factors accounting for the differences in investment between different municipalities.

Among the usual economic variables (growth rate, productivity…), fiscal institutions appear to be particularly important in explaining differences in fiscal performance between countries (Alesinaet al., 1999; Dabla-Norriset al., 2010). The electoral competitiveness hypothesis, which holds that more competition is associated with better performance (Holbrook & Van, 1993) and political support of the leader (O’Toole & Meier, 2004), also explains differences in performance. In addition, party alternation can create instability and excessive spending, as shown by Calcagno and Escaleras (2007). For international institutions, African countries should consider improving human capital as a normative policy to reduce high levels of inequality (Murtin & Morrisson, 2016). Although it is very difficult to define human capital, most researchers agree that human capital represents the knowledge, skills, and experiences of the individual that generate economic value for the organization (Hittet al., 2001).

Although previous studies have sought to determine which variables influence budget differences between countries (Alesinaet al., 1999; Dabla-Norriset al., 2010, for example), we did not find research that analyzed the effects of mayoral characteristics on budget differences between Cameroonian municipalities. Moreover, unlike the few studies that have focused on the subject (Avellaneda, 2009, for example), this paper relies on new statistical information that allows us to understand human capital from a different angle to explain the differences in municipal investment.

Unlike previous studies, which use the number of years of study or the cycle of study (primary, secondary, university, with somewhat mixed results) as an indicator of the human capital of individuals to explain a country’s economic growth or local performance, the present work uses the diploma, age and training of mayor, as indicators of human capital, to explain local investments. In this context, this study aims to answer the following question: What are the effects of mayors’ human capital on the investments of northern Cameroonian municipalities? To answer this question, the rest of the article will focus respectively on the literature review (2), the methodology (3), the result, and discussion (4). We conclude in section 5.

Literature Review

Theoretical Framework: Cognitive Resource and Human Capital Theory

Cognitive resource theory assumes that intelligent and competent leaders plan, decide, and act more effectively than less intelligent and less competent leaders; working group leaders communicate their strategic plans, decisions, and actions in the form of guidelines (Gallinaet al., 2019; Fiedler & Garcia, 1987). However, theory predicts that intelligence imposes a higher weight in low-stress situations, whereas experience contributes more effectively when the group is coping with high levels of stress (Fiedler & Gibson, 2001). According to these mechanisms, education, and experience help mayors to anticipate technical and administrative obstacles by enabling them to dictate strategies to overcome them.

According to Becker (1994), education and training are the most important investments in human capital. The salaries of the most educated are almost always well above average, especially in less developed countries. With regard to public policy, Tingjin (2012) demonstrated that human capital theory is a powerful tool to explain the rate of rise of public services in Chinese municipalities.

Previous Empirical Studies

Empirical studies cover a large set of methods used to assess the positive effects of high level of education and training on various economic variables. Besleyet al. (2011) used data on more than 1,000 political leaders between 1875 and 2004 to determine whether having a more educated leader affects the rate of economic growth. They have provided evidence that growth is higher with more educated leaders. Congleton and Zhang (2013) conclude that highly skilled political leaders promote higher economic growth. For Gohlmann and Vaubel (2007), in addition to central banker qualifications (education and vocational training), the gender of monetary policymakers is important for inflation. Dreheret al. (2009) find evidence that the career path of the head of government influences the adoption of market liberalization reforms. Rochaet al. (2018) assessed the role of mayor characteristics (education, experience, and gender) on the fiscal indicators of Brazilian municipalities. They used the discontinuity of the regression and the results of close elections to identify causal effects for 2000, 2004, and 2008. The result shows that experienced and educated mayors choose to spend a smaller fraction of the budget on current and personnel expenditures, and so they appear to be concerned about the quality of public finances. In addition, educated mayors are better able to negotiate discretionary transfers. Leiteet al. (2015) investigated the relationship between the cognitive aspects of financial performance of Chilean and Brazilian firms. The results showed that firms’ financing decisions have a positive impact on their bottom line in firms run by people with cognitive biases.



This study seeks to analyze the effects of mayors’ human capital on the investments of northern Cameroonian municipalities. There are 46 municipalities with available and complete data from three regions, including 21 for Adamawa, 12 for the North, and 13 for the Far North. The data collected concerns are: mayors’ diplomas (CEPE, BEPC, Probatoire, Baccalaureat, Bachelor’s, Master’s, and Doctorate); training of mayors (on procurement, decentralization, setting up the local development plan, etc.); ages of mayors; the political affiliation of the mayor; the number of advisors; municipal populations; communal properties, communal budgets; and municipal investments. This information is based on the municipal diagnostic reports and the administrative accounts of various municipalities. The structure of this information results in cross-sectional data. Certain data, such as the local population, the population growth rate, and the number of mayoral mandates, are collected from the Internet.


Based on the above information and in the light of the literature, the variables are operationalized as variables of interest and control variables. The variables of interest are Human capital of mayors (INSTRUC, FORP, AG) and municipal investment (INVMHT).

Level of education (INSTRUC) is a categorical variable with values ranging from 1 to 4, depending on whether the mayor has the next degree, which is considered to be his or her highest degree (Ludwig, 2002; Besleyet al., 2011), CEPE = 1, BEPC = 2, PROB = 3, BAC+ = 4 (BAC+, a university degree or at least a baccalaureat).

  • The training received by the mayor (FORP): This is a binary variable that takes the value 0 or 1. 1 for at least one of the above subjects, 0 otherwise.
  • Past age of mayors (AG): This is a continuous variable measured as the year a person becomes mayor minus their year of birth, Congleton and Zhang (2013).
  • Municipal investment (INVMHT): The average values of the investments of the first three years of each mayor’s mandate are calculated using Microsoft Excel software. In order to standardize these averages, we have divided them by the size of the population of each municipality. So we have the average communal investments per capita (INVMHT). The formula is written: INVMHT=INV1+INV2+INV3POPU

INV1, INV2, and INV3 represent the first-year, second-year, and third-year investments by each municipality, respectively. INVMHT is the average municipal investment per capita for three financial years.

The following describes the control variables. Mayor Political Affiliation (APP) is a binary variable; we define the indicator of this variable as follows: 1—the mayor is from the ruling party, 0—otherwise or if the mayor is from any other political party; the wealth of the commune (RICHC) is the value of many of the assets of the commune; the size of the municipality (TAIC) is assessed by the size of the population, the size of the budget (BUD) and the proportion of women among councilors (GEN) must also be taken into account in this work. Descriptive statistics for different variables are summarized in Table I.

Variable Obs Mean Std. dev. Minimum Maximum
INVESTHT 46 329.8554 131.8581 101.0862 626.4161
INSTRUC 46 2.891304 1.268915 1 4
FORP 46 0.4130435 0.4978213 0 1
APP 46 0.7173913 0.4552432 0 1
AG 46 45.32609 9.314396 26 63
GEN 46 0.1826087 0.1194698 0 0.52
TAIC 46 74169.11 57499.04 10228 275609
RICHC 46 3.22e + 08 2.65e + 08 3.00e + 07 1.26e + 09
BUD 46 4.50e + 08 8.85e + 08 1.55e + 07 6.04e + 09
Table I. Descriptive Statistics

The model specification is as follows: LogINVMHT=β0+β1INSTRUTCi+β2FORPi+β3AGi+β4logTAICi+β5APPi+β6GENi+β7logBUDi+β8logRICHCi+εi

Results and Discussion

Given the cross-sectional nature of our data, we used the OLS for regression.

Statistical Test Results: Multicollinearity and Homoscedasticity

In a regression, multicollinearity occurs when some of the model’s prediction variables measure the same phenomenon. Pronounced multicollinearity is problematic because it can increase the variance of the regression coefficients and make them unstable and difficult to interpret. The variance inflation factor gives an index that measures how much the variance of a coefficient is increased because of collinearity. If the factor is closer to 1, then the model is much more robust, as the factors are not influenced by correlation with other factors.

Based on our data, Stata16 gives us the result of Table II.

Variable VIF 1/VIF
LogTAIC 2.11 0.473061
logBUD 2.06 0.485774
INSTRUC 1.52 0.658990
FORP 1.43 0.698282
GEN 1.27 0.788490
logRICHC 1.18 0.844044
APP 1.08 0.922222
AG 1.07 0.936482
Mean VIF 1.47
Table II. Inflation Variance Factor (VIF)

The test result of Table II shows that there is no multicollinearity problem among the different explanatory variables because all VIF values are less than 5. In addition to the multicollinearity test, the homoscedasticity test of error terms is also important for the use of OLS.

If the error term distribution is not the same for all observations, there is heteroscedasticity. However, the OLS remains unbiased and convergent, even in the presence of heteroscedasticity. The quality of the estimation measured by R² or Adjusted-R² remains valid. But, the variance-covariance matrix of the estimated coefficients is biased in the presence of heteroscedasticity, so the standard errors are invalid. Among the most important homoscedasticity tests, we can cite the Breusch-Pagan and White tests from Stata 16 software, we have the result in Table III for both approaches: Breusch-Pagan and White tests.

Breusch-Pagan test White test
Variables ui2 ui2
INSTRUC −0.000569
FORP −0.00431*
AG 0.000118
APP 0.00244
GEN −0.0145
LogTAIC 0.00295
logBUD −0.00142
logRICHC −0.00204
yhat −0.106
yhat2 0.0214
Constant 0.0184 0.134
(0.0328) (0.227)
Observations 46 46
F 1.37 0.24
Prob > F 0.243 0.789
R2u2 0.228 0.011
R2u2ad 0.061 −0.035
Table III. The Breusch Pagan and White Alternative Tests for Homoscedasticity

All the statistical probability of F-statistics in Table III are greater than 5% threshold (0.243, 0.789), with both techniques, we can conclude that the residual variances of the model are constant. In addition, the determination coefficients R2 of different residuals are very low, which also explains homoscedasticity.


From Stata16 software, we have the estimation result in Table IV.

OLS OLS huber white SE
Variables logINVMHT logINVMHT
INSTRUC 0.155*** 0.155***
(0.011) (0.011)
FORP 0.001 0.001
(0.027) (0.024)
AG −0.001 −0.001
(0.001) (0.001)
APP −0.047* −0.047*
(0.025) (0.024)
GEN 0.301*** 0.301***
(0.104) (0.104)
LogTAIC −0.090* −0.090*
(0.050) (0.052)
logBUD −0.015 −0.015
(0.037) (0.038)
logRICHC −0.060* −0.060**
(0.031) (0.027)
Constant 3.106*** 3.106***
(0.388) (0.352)
Observations 46 46
F 38.42 37.76
Prob > F 0.000 0.000
R2 0.893 0.893
R2adjusted 0.869
Table IV. Model Estimation Results

The result shows that our model is globally significant at the 1% threshold (Prob > F = 0.000). In addition, the values of the determination coefficient R2 are high to explain the variability of municipal investment by the explanatory variables of the model. In accordance with our expectation, our hypothesis that the human capital of mayors has effects on municipal investments is verified. The increase in the human capital of mayors leads to an increase in municipal investment. Indeed, despite the non-significance of certain human capital indicators (FORP, AG), the positive effects of human capital, in terms of education level, on municipal investment are significant. Thus, the result of Table IV shows that municipalities led by mayors with a BAC+ degree make more investments than those led by mayors with a CEPE level. In other words, mayors with a CEPE level make less local investment than those with a BAC+.

More precisely, keeping other variables constant, mayors with a BAC+ degree invest, on average, about 454% more in communal investment than mayors with a level lower than or equal to CEPE. In other words, mayors with a BAC or BAC+ degree direct more than 454% of total municipal expenditures towards investment compared to those with less than a BAC level. This result is consistent with some previous studies, notably Rochaet al. (2018), Persson and Zhuravskaya (2011), Modes (2012), Besleyet al. (2011), and Etogo (2019).

However, this result opposes that of Freier and Thomasius (2016), who verified the importance of the qualification of politicians, in terms of education and experience, for fiscal results and their electoral success. The results showed that mayors with previous experience tend to reduce the level of local public debt, reduce total municipal expenses, and reduce taxes. On the other hand, the mayor’s level of education has no significant effects on the overall fiscal performance of the municipality, but education and experience show importance in the electoral success of city candidates.

The result of Table IV also shows that some control variables are statistically significant at the 5% and 1% threshold. These include the size of the municipality (logTAIC, negative), communal wealth (logRICHC, negative), communal budget (logBUD, negative), political affiliation (APP, negative), and gender (GEN, positive).


This article has analyzed the effects of the human capital of mayors on the investments of northern Cameroonian municipalities. We measured the human capital of mayors in terms of education level (degree), training, and age. The data are from 46 municipalities in northern Cameroon, including 21 in the Adamawa region, 12 in the North, and 13 in the Far North. After conducting preliminary statistical tests (multicollinearity and homoscedasticity) for cross-sectional data, the OLS results showed that the human capital of mayors does affect local investments. Specifically, the level of education of the mayor (measured by the degree) positively affects municipal investments. However, the coefficients of the level of training and age of mayors are not significant. The result also showed that the coefficients of certain control variables are statistically significant.


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