From Policy to Practice: The Influence of Institutional Quality on The Management of Migration in Africa

From Policy to Practice: The Influence of Institutional Quality on the Management of Migration in Africa

Elangwe Cyntia Malike¹, Ongo Nkoa Bruno Emmanuel², Minkoua Nzie Jules Rene²

¹University of Buea – Cameroon

²University of Yaoundé II–Cameroon

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110120

Received: 30 October 2024; Accepted: 04 November 2024; Published: 07 December 2024

ABSTRACT

This study assesses the extent to which institutional quality affects international migration in Africa, using a panel data of 51 countries within the time period of 2000 to 2020. The new economics of labour migration theory and the new institutional economics theory were mobilized. Through the adoption of the principal component analysis, the institutional quality index variable was obtained by the joint combination of the six institutional quality measures including; voice and accountability, political stability, rule of law, control of corruption, government effectiveness and regulatory quality. The study employed the use of the generalized methods of moments estimation technique to investigate the relationship between institutional quality and international migration in Africa. Findings show that government effectiveness, political stability, regulatory quality and the institutional quality index have positive significant correlation with international migration in Africa; the control variables including carbon-dioxide emissions, infrastructure, unemployment, inflation, population growth, total natural resource rents, trade openness and gross fixed capital formation all record positive and negative significant correlations international migration and these results were robust to those analysed by use of the simultaneous quantile regression and the Driscoll and Kraay standard errors estimation techniques. The study therefore, recommends that the governing organs of the nations in the African Continent should endeavour to stimulate cooperation, develop concrete political policies regarding immigration and co-development through effectively ensuring the adoption of strong natured institutions that embark on sustainably adhering to equitable reduction in corruption practices, political instabilities and full government effectiveness in every sector of the nations.

Keywords: Institutional quality, International Migration, Generalized Method of Moments, Quantile Regression, Driscoll and Kraay Standard Errors.

INTRODUCTION

The growing body of evidence on migration and mobility shows that migration is in large part related to the broader global economic, social, political, institutional, technological and environmental transformations that are affecting a wide range of high-priority policy issues in the World today (IOM, 2020). As far back in historical times, African migrations are being divided into two broad sub-fields looking at respectively: people moving because they were forced to and people choosing to move on their own free will and Africa has been the source of the largest forced migrations in history (Zetter, 2018). The African trans-Atlantic slave trade is being recorded as the largest long-distance forced migration of people, even though it happened over a shorter period than the trans-Saharan and Indian Ocean slave trades. The rise of development as a rationale for the government of African societies influenced migrations in multiple ways through national and international policies aimed at channeling people’s mobility (Curtin, 2018). In the last two centuries, African migrants have been unfolding projects of self-development by traveling to places where they hoped to find better opportunities. Yet contemporary trafficking and displacements caused by wars, intolerance, and natural catastrophes attest to the continuing relevance of violence as a key aspect of the experience of African migrants.

The patterns of these migrations has been rhizomic rather than unidirectional, and occur gradually through a variety of localized processes including agrarian expansion, trade and economic integration, political conquest, technological innovation, environmental change and religious conversion (Flahaux et al., 2016). In the past century, trade was labour intensive and involved circular migrations of relatively large groups of people and some of the people moving along these trajectories may have been enslaved, but for regions beyond the coast and periods preceding the Muslim conquest, the evidence for early trans-regional trade in slaves is scanty (Bakewell and De Hass, 2007). The trans-Atlantic trade was the largest long-distance forced migration of people in history which lasted about 400 years, during which European and American vessels developed specifically for carrying human cargo transported over 12 million enslaved Africans to the Americas (Zetter, 2018). After the abolition of the slave trade and post-colonial period, international migration became increasingly popular and since the beginning of the 1990s data for it has been made available by international migration organizations all over the world.

International migration is a central phenomenon in human international development discussion, because it implies the benefits on an individual, household level, the countries of origin, as well as the reception of those migrating and also the potential costs to the home countries through brain drain (De Hass, 2010). In 1990, the African continent registered a total of 20,621,464 international migrant stocks which slowly increased to 22,341,394 in 1995 and it experienced a slight fall in the year 2000 as it stood at 22,080,003 (United Nations Department of Economic and Social Affairs – UN DESA, 2020). The statistics have been increasing as from 2005 when it stood at 25,211,406; 29,193,852 in 2010; 35,111,959 in 2015 and 40,567,163 in 2020, partly due to the presence of armed conflict in countries such as Burkina Faso, Cameroon, Nigeria, Central Africa Republic, Democratic Republic of Congo, Ethiopia, Mali, Mozambique, Senegal, Somalia, South Sudan and Sudan (UN DESA, 2020). Despite the increase in absolute numbers, the share of international migrants in proportion to the world’s population has remained relatively stable between 1990 and 2020 at around 2.8 to3.6 % (UN DESA, 2020) and while most migration in Africa is occurring within the continent and attributed to the type of present environmental, economic, political and social institutions in the various African regions, the estimates of international mobility also vary depending on the nature of institutions in the region (IOM, 2020).

Institutions are often considered to be efficient responses to transaction costs and it is supposed that agents operate exclusively driven by rational optimization criteria, that social dynamics get rid of inefficient institutions and that the existing ones improve social welfare (Rodrik, 2003). As a consequence, there is no problem whatsoever in defining ideal institutions the ones of the successful countries and in transplanting them to other nations. These premises have inspired a good part of international donors’ institutional reform programs. The institutional structure defines incentives and penalties, shapes social behaviour and articulates collective action, thus conditioning development. In last years, a myriad of empirical studies has supported this relationship between institutional quality and development; and, though less conclusively, the one between institutional quality and growth (Aron, 2000). The positive impact of institutional quality on development has been pointed out by crossed section analyses (Hall & Jones, 1999; Acemoglu et al. 2002; Rodrik et al. 2002).

The literature relating to the evolution of institutional quality has continually been renewed as far back as the late 20th century and these renewed literatures focus on the role and the quality of domestic institutions as an important point for the explanation of cross-country differences in both growth rates and income per capita (Acemoglu et al., 2005). These renewed studies advocate for the efficient protection of civil and property and by so doing extending economic and political freedom as well as inducing lower levels of corruption; a case which has been reviewed to be primarily associated with higher prosperity. North (1990) defines institutions as the humanly devised constraints that shape human interaction and emphasized the key implications of institutions in an economy.

The debate on the relevance of institutions on international migration, Acemoglu et al. (2005) linked colonial migration to the shaping of institutions themselves within the environment and in turn to subsequent economic development. With increasing restrictive policies in traditional destinations such as Europe and new opportunities in emerging economies and trade routes, Africans are increasingly migrating to countries such as Turkey, China and South Africa for business, trade, study and employment (Taddele, 2022). The rule of law which is a fundamental governing institution on which democracy and democratic practices needed for political stability and economic growth are founded (Manga, 2018) has also appeared to have seen a steady weakening after the wave of democratic and constitutional reforms in the 1990s (International IDEA’s Global State of Democracy report, 2022).

Democracy in Africa has faced setbacks of at least three kinds with the most obvious one being the continuation of the wave of coups d’états, and political coups in Burkina Faso, Mali, Senegal, Gabon and Guinea-Bissau; Political conflicts in Ivory Coast, Democratic Republic of Congo (DRC), South Sudan, Sudan, Cameroon, Nigeria; turmoil in Burundi, the DRC, Somalia and South Sudan; labour disputes in South Africa and monetary tightening in Kenya and Uganda (Gadon, 2019).The crisis around the rule of law is at the heart of Africa’s present developmental predicaments such as political instability, economic decline, poverty and unemployment; as such it is for this reason that refugee numbers records pretty high stocks from the African continent (UN Refugee Agency, 2021). Africans tend to risk their lives in the perilous crossing of deserts and the Atlantic (over 10,000 migrants have been reported as dead or missing in the 2014–2020 period (UN Refugee Agency, 2021). It is therefore due to this nature of institutions that fail to coordinate and regulate developmental policies geared towards nation building fuel forced international migration interests in the African Region.

A BRIEF LITERATURE REVIEW

A vast nature of theories exist which attempt to explain why people migrate and they range from Lee’s Push and Pull theory of international migration (Lee, 1965) to the classical and neoclassical theories of migration. However, Stark and Bloom (1985) adopted the new economics theory of labour migration which instituted that migration can be an economic strategy of households to overcome market restraints; NELM situates migration decision making in households. The NELM is one of the first migration theories that have made a connection between the cause of migration and the phenomenon of economic remittances (remittances are sums of money sent as payment or gift from migrants to relatives in their country of origin) (Solimano, 2003). Individuals move to support the larger family/economic unit, either voluntarily or at the behest of the household head or community members (Stark and Bloom, 1985). People act collectively not only to maximize expected income, but also maximize status within an embedded hierarchy, to overcome barriers to capital and credit, and to minimize risk and diversify the incomes (Massey, 2001). Empirical studies carried out by Khan (2021), Tran et al. (2017, 2019) and Bertocchi and Strozzi (2008), Arif (2020), Gignarta et al. (2020), Ariu et al. (2016) all indicate that institutional quality is major determinant to international migration in the African Continent. Their results indicate that weak institutions have an overall negative and economically significant effect on international migration and also that some institutional aspects especially the unpredictability of laws, regulations and policies, excessive regulatory burden, government instability and the lack of commitment play a vital role in increasing forced international migration interests.

The notion that the volume of natural resource availability determines the level of citizen welfare has been proven otherwise with respect to the findings of this study. In contrast to the results obtained by Venard (2013) found that a certain level of corruption is beneficial for economic development since it helps to “facilitate” economic activities, which is known as the “greasing-the-wheel” hypothesis. But the results obtained from this study indicate the presence of weak nature of institutions does significantly influence forced international migration interests in Africa. In the case of developing countries, governance fails to reduce international migration due to the exogeneity issue because of poor control of corruption and rule of law instability. It is instead the results for developed countries regarding the impact of governance on international migration that indicate strong relationship which implies that forced international migration inflows can be decreased by improving the institutional framework and providing a good macroeconomic environment. Other empirical works including Bove et al. (2023) provided the first empirical evidence that government ideology affects the choice of migration decisions and the findings indicate that ideology-induced effect is larger when receiving countries have a relatively lower value of traditional morality, which served as a proxy for the extent of social tolerance towards out-groups. This result is similar with those obtained by Angin et al. (2023) who focused on the international migration fund (IMF) to explain why some countries secure better deals from international organizations on migration interests by analyzing all IMF programs from 1978 to 2014 with findings indicating that when migration pressures on the G5 countries increase, borrowing countries receive larger loan disbursements and fewer conditions from international organizations.

Some empirical works have focused on bilateral migration including that of Khan (2021) who investigated the role of institutional quality on bilateral migration for developing countries and employed the used of the gravity model to estimate 60 home and host countries (OECD and non-OECD) for the time period of 2010 – 2017. The findings reveal that institutional quality acts as a push factor in migration from home to host countries and suggests that institutional quality is a good proxy for the factors that trigger migration and migration decision depends on the expectations about future income levels for which institutions serve as meaningful proxies. Arif (2020) on its part studied the determinants of international migration by use of global bilateral migration flows from 103 countries over the period 1990 -2000. The study explored whether emigrants are self-selected based on the economic or political or social institutions and employed the pseudo-maximum likelihood (PPML) estimator. The study findings indicate that economic, political and social institutions are significant pull factors of migration; economic freedom has the substantial pull effect followed by the political institutions relating to the degree of stability within the countries, while social institutions have the weakest pull effect on migration. On the other hand, the study findings indicate that economic and social institutions are significant push factors for migration, while political institutions do not show any push effect. The study further indicates that educated migrants are more sensitive to the destination economic, political and social institution and less-educated migrants are more sensitive to the social institutions of the origin.

Furthermore, Tran et al. (2019) examined the link between local institutional quality in the home country and locational choices of international return migrants. The study scrutinized the locational choices of Vietnamese return migrants to the south central and south regions of Vietnam in 2014. Binary and multinomial regression models were fitted to identify the influence of migrants’ individual attributes and characteristics of regional destinations within Vietnam. The study results indicate that both individual specific and region specific variables are significantly related to Vietnamese return migrants’ choices when registering for permanent residency back in their home country. The findings further indicated the positive role of institutional quality at the local level in these migration decisions; as the effect of institutional quality differs with the characteristics of migrants. Hence, the study indicates that regions with better institutional quality are more attractive both to younger return migrants and to those who returned from host countries with better institutional quality.

Ariu et al. (2016) also explored governance quality and net migration flows of high skilled migrants by employing the use of a random utility model from which the net balance of migrants was derived and an exclusion restriction to control for selection of migrants. The study indicates that bilateral differences in governance quality impact the size and educational structure of bilateral net migration flows, backed by the study findings which show that the quality of institution can affect human capital stocks through migration. The study findings show that countries with better governance quality experience positive flows of college graduate emigrants, while countries with worse institutions tend to experience negative net flows. The study indicates that college graduates are more reactive to government quality and take into consideration both home and destination governance quality when choosing to emigrate, while low-skilled seem to focus instead only on local conditions in their choices. Therefore, the study holds that bilateral differences in institutional quality are more important for high-skilled than for low-skilled net migration flows

METHODOLOGY STRATEGY, ESTIMATION TECHNIQUE AND DATA

The Theoretical Model

The theoretical model of this thesis is drawn from the NELM theory, which argues that migration is a mutual decision by household members to maximize household utility and to diversify risks to household income and wellbeing (Stark and Bloom 1985; Porumbescu, 2015; Manan and Fedricks, 2015). Within the NELM framework, individual, household, and community characteristics including household composition, gender, institutions, educational attainment, climatic environment, employment, and social networks are important determinants of migration decisions in response to shocks. To estimate the effect of man-made environmental factors of migration decisions within the NELM framework, the following simple model is followed:

\[
\ln\left(\frac{P_{it}}{1 – P_{it}}\right) = \mu_t + P_{it} + \beta X P_{it} + \gamma Z_i + \varepsilon_i \tag{1}
\]

where \( P_{it} \) is the migration probability for household i at time t; \( \mu_t \) is an intercept that may be different for each period t; \( X_{it} \) is a vector of time-varying predictors (such as household size, the number of employed adults, and environmental factors); Z_i is a vector of time invariant predictors (such as the completed education of household adults, the gender of the household head, and the location of the main household); ε_i indicates the combined effects of all time-invariant unobserved variables; and β and γ are the coefficient vectors.

3.2 The Empirical Model

Docquier et al. (2016) suggested that openness to emigration has a positive impact on democracy in origin countries. Similarly, Docquier et al. (2014) found that openness to emigration (as measured by the general emigration rate) has a positive effect on home-country institutional development (as measured by standard democracy indices). Tran et al. (2017) indicated a significant positive impact of Diasporas living in OECD countries on institutional quality in home countries. Valette (2018) finds that technological transfers are more likely to occur when the intensity of emigration is high and when technology levels in destination countries are high, thereby suggesting that productive knowledge is indeed transferred by migrants from receiving to sending countries.

This generalized methods of moments estimation technique (GMM) is being used to estimate the model for this study. The GMM is a centrepiece of semi-parametric estimation frameworks. The GMM is a statistical method that combines observed economic data with the information in population moment conditions to produce estimates of the unknown parameters of this economic model and under some sufficient conditions the GMM estimator is consistent and asymptotically normally distributed. The GMM estimation technique is the best suggested estimation technique (Ongo and Song, 2018; Arellano and Bond, 1991) and is used in this analysis since it is more efficient in estimating a dynamic panel data model; it provides consistent estimates and efficiently deals with the issue of endogeneity. It is important to emphasize that the efficiency result is valid only for a given set of moment conditions. That is, GMM is asymptotically efficient in the class of consistent and asymptotically normal estimators that do not use any additional information on top of that is contained in the moment conditions.

The dependent variable in this model is international migration explained by institutional quality and other control variables including: Carbon-dioxide emissions infrastructure unemployment, inflation population growth, school enrolment in tertiary sector, trade openness, gross fixed capital formation and total natural resource rents. Given the suitability of the method (the methodological argument) as well as the equivalence between the estimate approach and the data behaviour, the model is estimated following the system generalized method of moments (GMM) estimation technique.

Specifically, the system GMM is used because it augments difference GMM by estimating two equations simultaneously where the first equation is the original equation or equation at levels and the second is the transformed equation or first difference and the equations are being distinctly instrumented. The adoption of the GMM estimator for objective one is done following the empirical literatures including; Dimant et al., 2015; Bergh et al., 2016; Li et al., 2018; Docquier et al., 2014; which demonstrated the effectiveness of system GMM in capturing the nuanced and dynamic effects of governance on migration patterns.

\[
\text{LogIM}_{i,t} = \alpha_0 + \beta_1 \text{LogIM}_{i,t-1} + \beta_2 \text{CC}_{i,t} + \beta_3 \text{CC}_{i,t}^2 + \beta_4 \text{RL}_{i,t}
+ \beta_5 \text{RL}_{i,t}^2 + \beta_6 \text{VA}_{i,t} + \beta_7 \text{VA}_{i,t}^2 + \beta_8 \text{PS}_{i,t} + \beta_9 \text{PS}_{i,t}^2
+ \beta_{10} \text{RQ}_{i,t} + \beta_{11} \text{RQ}_{i,t}^2 + \beta_{12} \text{GE}_{i,t} + \beta_{13} \text{GE}_{i,t}^2
+ \beta_{14} \text{IQ Index}_{i,t} + \beta_{15} \text{IQ Index}_{i,t}^2 + \beta_{16} \text{XP} \sum_{k=1}^9 Z_{k,i,t-1}
+ u_i + v_t + \epsilon_{i,t} \tag{2}
\]

The first difference of the above model is given by:

\[
\text{LogIM}_{i,t} – \text{LogIM}_{i,t-1} = \alpha_0 + \beta_1 (\text{LogIM}_{i,t-1} – \text{LogIM}_{i,t-1})
+ \beta_2 (\text{CC}_{i,t} – \text{CC}_{i,t}) + \beta_3 (\text{CC}_{i,t}^2 – \text{CC}_{i,t}^2)
+ \beta_4 (\text{RL}_{i,t} – \text{RL}_{i,t}) + \beta_5 (\text{RL}_{i,t}^2 – \text{RL}_{i,t}^2)
+ \beta_6 (\text{VA}_{i,t} – \text{VA}_{i,t}) + \beta_7 (\text{VA}_{i,t}^2 – \text{VA}_{i,t}^2)
+ \beta_8 (\text{PS}_{i,t} – \text{PS}_{i,t}) + \beta_9 (\text{PS}_{i,t}^2 – \text{PS}_{i,t}^2)
+ \beta_{10} (\text{RQ}_{i,t} – \text{RQ}_{i,t}) + \beta_{11} (\text{RQ}_{i,t}^2 – \text{RQ}_{i,t}^2)
+ \beta_{12} (\text{GE}_{i,t} – \text{GE}_{i,t}) + \beta_{13} (\text{GE}_{i,t}^2 – \text{GE}_{i,t}^2)
+ \beta_{14} (\text{IQ Index}_{i,t} – \text{IQ Index}_{i,t})
+ \beta_{15} (\text{IQ Index}_{i,t}^2 – \text{IQ Index}_{i,t}^2)
+ \beta_{16} \text{XP} \sum_{k=1}^9 (Z_{k,i,t-1} – Z_{k,i,t-1}) + (u_i – u_i) + (v_t – v) + (\epsilon_{i,t} – \epsilon_{i,t}) \tag{3}
\]

Where:

IM_i,t is the international migration stocks for country i at time t;

IM_i,t-1 is the lagged international migration stocks;

CC_i,t is the control of corruption for country i at time t;

GE_i,t is government effectiveness for country i at time t;

RQ_i,t is regulatory quality for country i at time t;

RL_i,t is rule of law for country i at time t;

VA_i,t is voice and accountability for country i at time t;

PS_i,t is political stability for country i at time t;

IQ Index_i,t is the institutional quality index for country i at time t;

Z_k,i,t-1 is the vector of control variables identified above;

u_i is the country specific effect;

v_t is the time specific effect;

ε_i,t is the error term.

A priori expectation

PS>0; RL>0, VA>0, GE>0; RQ>0; INFRAS>0; Pop Growth>0; SET>0; TNRR>0; OPEN>0; GFKF>0: implying that they have a positive significant effect on international migration.

CC<0; CO2<0; Une>0; Inflation<0: implying that they have a negative significant effect on international migration.

3.3 Data

In order to investigate the effect of institutional quality on international migration in Africa, the combination of time-series and cross-sectional data is used, in which the data of five explanatory variables and one dependent variable are collected repeatedly within Africa economic grouping (unit analysis) over a time period of twenty-one years (2000-2021; time horizon). This study therefore makes use of a dynamic panel data. In this study, the data for n = 54 entities (countries), where each entity is expected to be observed in T = 21 years of time period, that is from 2000 to 2020 providing an overall 1,134 observations with each variable recording different observations over the research time period (missing data for some variables) (Asteriou and Hall, 2007) and as such the dataset in this study demonstrates an unbalanced panel data. Note should be taken that the number of observations may increase or reduce when dealing with the degree of freedom.

RESULT S AND DISCUSSIONS

We focus on the effect of institutional quality in general on international migrant stocks under the economic, political, social institutions and the identified control variables above.

4.1 Descriptive Statistics

The descriptive statistics is an analytical tool that explains the nature of the data of the variables including describing their mean, the standard deviations, the minimum and maximum values and the total number of observations.

Table 4.1: Descriptive Statistics

Source: Computed by Author with the use of STATA 17;

The average log of international migration is 11.911, with a standard deviation of 1.615. The minimum value is 7.455, while the maximum is 16.44. This indicates that migration levels vary significantly across countries, with some experiencing low migration and others having substantially higher levels.

The mean score for government effectiveness is -0.727, with a standard deviation of 0.659. The minimum value is -2.665, while the maximum is 1.211. This negative mean indicates widespread challenges in governance across the continent, highlighting significant variability in governance quality among nations. The average score for political stability is -0.707, with a standard deviation of 0.650. The minimum value is -2.669, and the maximum is 1.23. Similar to government effectiveness, the negative mean reflects considerable instability in many African nations, with some countries experiencing greater political turmoil than others. The mean for regulatory quality is -0.764, with a standard deviation of 0.621. The minimum observed value is -2.48, while the maximum is 1.131. The negative average indicates weaknesses in regulatory frameworks, with significant variability pointing to differing levels of regulatory effectiveness across the continent. The mean score for the rule of law is -0.616, with a standard deviation of 0.816. The minimum is -2.673, and the maximum is 1.111. The negative mean value indicates challenges in upholding the rule of law, with a wide range suggesting disparities in legal systems and enforcement across African nations.

The average score for voice and accountability is -0.643, with a standard deviation of 0.861. The minimum value is -3.324, while the maximum is 1.192. The negative mean implies limited public participation and accountability in governance, with significant variability indicating that some countries perform better in terms of civic freedoms and political rights. The mean for control of corruption is 4.132, with a very high standard deviation of 7.82. The minimum value is -6, while the maximum is 40.821. The positive mean suggests some countries have effective mechanisms to control corruption, but the wide standard deviation indicates significant outliers where corruption remains pervasive. The mean score for the institutional quality index is 0.516, with a standard deviation of 0.183. The minimum value is 0, and the maximum is 1. This average indicates a moderate level of institutional quality across the continent, with substantial room for improvement in many areas.

The average carbon dioxide emissions are 0.331, with a standard deviation of 0.191. The minimum value is 0.062, while the maximum is 1.18. This suggests a low to moderate level of emissions across African nations, with variability reflecting differing industrial activities and environmental policies. The mean level of infrastructural development is 34.513, with a standard deviation of 34.837. The minimum value is 0, while the maximum is 214.75. This indicates that while some nations have made significant investments in infrastructure, others lag behind, as highlighted by the high standard deviation. The average unemployment rate is 9.194, with a standard deviation of 7.149. The minimum value is 0.32, and the maximum is 28.678. This average suggests a moderate level of unemployment, but the high standard deviation points to significant variation, with some countries facing severe unemployment challenges.

The mean inflation rate is 8.243, with a very high standard deviation of 24.538. The minimum value is -9.798, while the maximum is 513.907. The average suggests moderate inflation, but the high standard deviation and maximum value indicate that some countries are experiencing hyperinflation, while others may face deflation. The average population growth rate is 2.365, with a standard deviation of 0.943. The minimum value is -2.629, and the maximum is 5.605. This average indicates steady growth in the population, but the wide range suggests varying demographic trends across nations, with some experiencing negative growth. The average enrolment rate in tertiary education is 0.755, with a standard deviation of 0.107. The minimum value is 0.624, and the maximum is 0.958. This high average reflects a commitment to education, but the narrow range indicates that most countries are performing similarly in this area.

The average total natural resource rents are 11.14, with a standard deviation of 11.82. The minimum value is 0.001, and the maximum is 67.918. The average indicates that some countries benefit significantly from their natural resources, while others derive minimal benefit, as demonstrated by the broad standard deviation. The mean level of trade openness is 15.488, with a standard deviation of 1.347. The minimum value is 11.757, while the maximum is 18.278. This average indicates a relatively high degree of openness to international trade, with low variability suggesting that most countries are similarly engaged in trade. The average log of gross fixed capital formation is 21.445, with a standard deviation of 1.678. The minimum value is 15.758, while the maximum is 25.542. This average points to significant investment in physical assets, with low variability indicating a general trend of investment across the continent.

4.2 The System GMM Results

The research study focuses on the effect of institutional quality on international migration is analysed by the system GMM estimator. The dependent variable international migration is regressed with the interest independent variable institutional quality and other control independent variables including the gross fixed capital formation, carbon-dioxide emissions, school enrollment in the tertiary sector, population growth, unemployment, infrastructure, total natural resource rents, trade openness and inflation.

4.2.1 The Effect of institutional quality on international migration

The research objective one which focuses on the effect of institutional quality on international migration is analysed by the system GMM estimator. The following figure 4.1.6.1 indicates the scatter plot analysis between the six institutional quality variables and international migration.

Figure 4.2.1 Fitted Scatter Plots

Source: Author, 2024

The scatter plots illustrate the relationship between the log of international migration and several governance-related variables, including Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law, Voice and Accountability, Control of Corruption, and the Institutional Quality Index. Each plot reveals a consistent negative trend, indicating that as these governance variables improve, international migration levels tend to decrease. Specifically, higher government effectiveness, political stability, regulatory quality, adherence to the rule of law, and enhanced voice and accountability correlate with lower international migration rates. This suggests that effective governance and strong institutional frameworks may reduce the factors driving individuals to migrate, highlighting the important role of governance in influencing migration patterns.

Table 4.2.2: The Effect of institutional quality on international migration (System GMM Estimates)

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 4.2.2 presents the System Generalized Method of Moments (GMM) estimates examining the effect of institutional quality on international migration, as indicated by the log of international migration. The diagnostic tests presented in the regression results—Hansen test, Sargan test, AR(1) test, and AR(2) test—provide important insights into the validity of the model estimates. The Hansen test assesses the validity of the instrument set used in the GMM estimation. A high p-value (typically above 0.05) suggests that the instruments are valid and not correlated with the error term. In this table, the Hansen p-values range from 0.297 to 0.691 across models, indicating that the instruments used in the estimation are appropriate and meet the required conditions. The AR(2) test assesses second-order autocorrelation in the residuals. A high p-value indicates the absence of second-order autocorrelation, which is desirable for valid model estimates. The AR(2) p-values range from 0.771 to 0.871, indicating no evidence of second-order autocorrelation, thus supporting the robustness of the model. The lagged log of international migration consistently exhibits a strong positive and statistically significant relationship across all models, with coefficients ranging from 0.736 to 0.800, suggesting that past levels of migration significantly influence current migration trends.

Among the governance variables, Government Effectiveness contributes positively and significantly (coefficient of 0.127 with p<0.05), indicating that improvements in government effectiveness correlate with increased international migration. Political Stability also shows a positive and significant effect (coefficient of 0.0759 with p<0.05), further supporting the notion that stable political environments may facilitate migration. Regulatory Quality has a substantial positive impact (coefficient of 0.301 with p<0.01), suggesting that better regulatory frameworks encourage international migration.

In contrast, the Rule of Law, Voice and Accountability, and Control of Corruption do not show significant effects, with coefficients close to zero. The Institutional Quality Index exhibits a marginally significant positive effect (coefficient of 0.406 with p<0.1), indicating that higher institutional quality may be associated with increased international migration, although the significance level is weaker.

Regarding control variables, carbon dioxide emissions have a negative and significant effect on international migration in several models, with coefficients ranging from -0.232 to -0.542, suggesting that higher emissions may deter migration, potentially due to environmental degradation. Infrastructural development shows mixed results, with one model indicating a significant positive relationship (coefficient of 0.00122 with p<0.01), while others are not significant.

The unemployment rate has a positive and significant effect in some models, indicating that higher unemployment may drive migration, particularly in models where it is significant at p<0.05. Inflation consistently shows a negative and significant relationship, implying that higher inflation rates may discourage migration.
Population growth positively correlates with international migration across all models, with coefficients ranging from 0.408 to 0.515, suggesting that growing populations may seek migration opportunities. Tertiary school enrollment rate and total natural resource rents negatively impact migration, indicating that higher education levels and resource rents may reduce the need for migration.

The log of trade openness consistently shows a strong positive effect, with coefficients between 0.157 and 0.286, reinforcing the idea that increased trade openness facilitates migration. The log of gross fixed capital formation presents mixed results, with some negative coefficients, indicating that higher investment may not necessarily correlate with increased migration.

Table 4.2.3. Robustness Check Through Simultaneous Quantile Regression

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The results from the simultaneous quantile regression analysis provide a robust check on the findings derived from the System GMM estimates regarding the effects of various variables on international migration across different quantiles (25th, 50th, 75th, and 90th). The estimates suggest a negative impact on international migration at the lower quantiles (q25 and q50), but the effect becomes positive at higher quantiles (q75 and q90). This indicates that the influence of institutional quality varies across migration levels, aligning with the System GMM findings that showed a positive impact at higher levels of institutional quality.
The quantile regression results show a significant positive effect at the 25th quantile (0.760, p<0.05) and a strong negative impact at the 90th quantile (-1.635, p<0.01). This pattern suggests a complex relationship where lower levels of emissions may encourage migration, while higher emissions are associated with decreased migration, supporting the negative relationship found in the System GMM. The positive and significant effect at the 25th quantile (0.00467, p<0.05) indicates that better infrastructure may facilitate migration, aligning with the GMM results. However, the effect diminishes at higher quantiles, suggesting that the impact of infrastructure on migration may be more pronounced at lower levels of migration.

The negative effect at the 50th quantile (-0.0479, p<0.05) and higher quantiles suggests that rising unemployment may deter migration, consistent with the System GMM findings indicating a potential discouragement of migration due to high unemployment. The quantile regression results highlight a negative effect at the median (q50, -0.0274, p<0.05) and other higher quantiles, reinforcing the GMM results that indicated inflation may discourage migration. The quantile regression results show weak and inconsistent effects across quantiles, suggesting that population growth does not have a strong or clear impact on migration, which aligns with the mixed significance observed in the GMM estimates.

The consistently negative effect across all quantiles supports the GMM results, indicating that higher natural resource rents are associated with reduced migration. Strong positive effects across all quantiles (ranging from 1.056 to 1.455, all significant) affirm the GMM findings that increased trade openness facilitates migration. The effects are mostly insignificant across quantiles, suggesting that investment levels do not have a consistent impact on migration, which aligns with the mixed results from the GMM analysis.

Table 4.2.4: Sensitivity Across Assessment Regions (Drisc/Kraay Estimates)

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 4.2.4 presents the Driscoll-Kraay estimates of the impact of various variables on international migration, represented as the logarithm of international migration, across five regions in Africa: Central Africa, East Africa, North Africa, South Africa, and West Africa. The results indicate significant regional variations in how different factors influence migration patterns. The Institutional Quality Index shows a highly significant positive effect in Central Africa (5.630, p<0.01), suggesting that improvements in institutional quality are strongly associated with increased migration. In contrast, North Africa (-2.156) and South Africa (-7.715, p<0.1) exhibit negative effects, indicating that higher institutional quality may correlate with reduced migration in these regions. The lack of significance in East Africa and West Africa suggests varied impacts depending on local conditions.

Carbon Dioxide Emissions have a negative and significant effect in Central Africa (-2.681, p<0.1), implying that higher emissions may deter migration. Other regions display insignificant effects, indicating that the influence of emissions on migration varies significantly by region. Similarly, the effects of infrastructure are mixed across regions; while South Africa (0.00344) and West Africa (0.00360, p<0.05) show positive impacts, Central Africa and East Africa exhibit negligible effects. This suggests that infrastructure improvements may have a more pronounced effect on migration in certain regions.

The unemployment rate also demonstrates significant variability. In Central Africa, there is a positive relationship (0.126, p<0.01), indicating that higher unemployment may encourage migration. Conversely, West Africa shows a negative and significant effect (-0.0348, p<0.05), suggesting different responses to unemployment across regions. Inflation impacts migration inconsistently, with East Africa showing a negative and significant effect (-0.0490, p<0.05), while other regions have negligible effects, indicating that inflation may deter migration primarily in East Africa.

Population growth reveals wide-ranging effects. Central Africa (0.318, p<0.1) and North Africa (0.947, p<0.05) indicate positive impacts, whereas West Africa exhibits a negative relationship (-0.0683). This suggests that population growth influences migration differently across regions. The effects of tertiary natural resource rents also vary, with South Africa showing a strong negative effect (-0.231, p<0.01) and West Africa displaying a significant negative impact (-0.0436, p<0.01), while other regions show negligible or positive effects.

Trade openness demonstrates significant positive effects in Central Africa (0.945, p<0.01), East Africa (0.844, p<0.01), and West Africa (1.193, p<0.01), suggesting that increased trade openness consistently facilitates migration in these regions. However, North Africa shows no significant effect, indicating regional differences in how trade policies impact migration. The effects of gross fixed capital formation are mixed, with South Africa showing a positive effect (1.050, p<0.05), while other regions exhibit negligible or negative impacts, suggesting that investment levels may influence migration differently across territories.

DISCUSSION OF RESULTS

Government Effectiveness positively correlates with increased migration, indicating that when governments provide better public services and create conducive economic environments, individuals are more inclined to migrate. Effective governance enhances living conditions, economic opportunities, and social stability, making migration a more attractive option for those seeking improved quality of life. Recent studies highlight that effective governance can lead to greater economic resilience, which is particularly important in the context of global challenges such as climate change and economic downturns (Schneider et al., 2021; D’Odorico et al., 2018). For instance, countries with efficient public institutions tend to experience lower levels of corruption, which fosters trust in government and encourages migration as a viable pathway for individuals seeking better opportunities (Hainmueller & Hopkins, 2015).

Political Stability further supports this notion, as stable political environments create a sense of security that facilitates migration. In politically stable contexts, potential migrants feel more empowered to make relocation decisions without the fear of instability or violence at home. Research indicates that political uncertainty, conflict, and repression can drive individuals to seek safer environments, reinforcing the significance of stability in migration patterns (Vogel & Wichmann, 2022; Czaika & de Haas, 2014). For example, regions experiencing political turmoil often see spikes in emigration, as individuals flee from unsafe conditions to seek refuge in more stable nations (Harris, 2020). Thus, maintaining political stability is crucial for both retaining populations and managing migration flows effectively.

The substantial positive impact of Regulatory Quality highlights how effective regulatory frameworks can facilitate migration. High regulatory quality typically reduces bureaucratic obstacles, ensures individuals’ rights, and provides protection for migrants, making the migration process smoother and more accessible. Evidence suggests that countries with robust regulatory environments attract more migrants, as these conditions enhance economic prospects and provide a sense of security (Mastrorillo et al., 2021; Rojas et al., 2019). Regulatory frameworks that promote labor rights, streamline visa processes, and protect against discrimination contribute significantly to the willingness of individuals to migrate, as they perceive less risk associated with relocating (Chaloff & Lemaitre, 2009).
Conversely, the lack of significant effects from Rule of Law, Voice and Accountability, and Control of Corruption may indicate that while these factors are essential for good governance, their direct impact on migration decisions may not be as pronounced. For instance, a strong rule of law is critical for migration, yet it may not suffice if practical barriers, such as restrictive immigration policies or economic insecurity, hinder individuals from moving (Bettin et al., 2020; De haas, 2011). Additionally, while voice and accountability empower citizens to express their needs and grievances, these factors do not necessarily translate into migration incentives if economic conditions remain unfavorable or if individuals perceive limited opportunities in their home countries (Kerr & Lincoln, 2021; Adams & Page, 2005).

Finally, the marginally significant positive effect of the Institutional Quality Index suggests that overall institutional quality does relate to migration, albeit less directly. This indicates that while institutional quality is essential in shaping migration patterns, its influence may be mediated by factors such as economic opportunities, social networks, and cultural ties that facilitate movement (Kerr & Lincoln, 2021; Dustmann & Frattini, 2014). Strong institutions that foster integration and provide support for migrants can significantly influence their decisions to migrate and their subsequent adaptation in host countries (Piekut et al., 2020). Hence, enhancing institutional quality is not only important for governance but also for addressing the complex dynamics of migration in an increasingly interconnected world.

The findings regarding the Institutional Quality Index and its varying effects on migration across different regions of Africa highlight the complex relationship between governance and migration dynamics. In Central Africa, the highly significant positive effect of the Institutional Quality Index suggests that improvements in governance and institutional frameworks strongly correlate with increased migration. This may be attributed to several factors. Central Africa has faced significant governance challenges, including corruption and political instability, which have historically hindered economic development and individual mobility. As institutional quality improves, individuals may perceive better opportunities for employment, education, and quality of life in other regions or countries, prompting increased migration (Schneider et al., 2021; Mazzucato, 2020). Improved governance can also enhance the safety and rights of migrants, making relocation more attractive.

In contrast, the negative effects observed in North Africa and South Africa indicate a different dynamic. In these regions, higher institutional quality may correlate with reduced migration due to several reasons. For North Africa, where migration routes to Europe are well established, increased institutional quality may lead to stricter border controls and immigration policies that deter out-migration (Czaika & de Haas, 2014). As governments improve their capabilities, they may also strengthen enforcement mechanisms to manage migration flows, resulting in a paradox where better governance leads to lower migration rates.

Similarly, in South Africa, which has historically attracted migrants from other African nations, improved institutional quality may have led to tighter immigration policies aimed at protecting local job markets and social services. The negative coefficient observed suggests that as South Africa enhances its governance structures, it may inadvertently create barriers for migrants, leading to a decline in migration (Harris, 2020). This reflects a broader trend where higher quality institutions can sometimes prioritize national interests over the facilitation of migration.

The lack of significant effects in East Africa and West Africa indicates that local conditions greatly influence the relationship between institutional quality and migration. In these regions, the variability in governance quality may lead to mixed outcomes. For instance, in some East African countries, institutional weaknesses may coexist with high levels of out-migration due to conflict or economic instability, overshadowing the potential positive impacts of institutional improvements (Betts et al., 2021). Similarly, in West Africa, socio-economic factors such as regional instability, poverty, and existing migration networks may play a more critical role than institutional quality alone, leading to ambiguous effects on migration patterns (Mastrorillo et al., 2021).

CONCLUSION

The research aimed at studying the extent to which institutional quality affects international migration in Africa. The institutional quality index variable was obtained through the joint combination of the six governance indicators; control of corruption, political stability, voice and accountability, rule of law, quality of regulation through the use of the principal component analysis. The other control variables adopted in the study included; carbon-dioxide emissions, infrastructure, unemployment, inflation, population growth, total natural resource rents, trade openness and gross fixed capital formation.The system of generalized method of moment, simultaneous quantile regression and the Driscoll and Kraay standard errors estimation techniques were adopted in estimating the results of the study.

Looking at the institutional quality-international migration relationship, the research findings indicate that there exists a positive significant relationship between the two variables within the African Continent for the research time period from the year 2000 to 2020. Hence, the present nature of institutional quality fosters forced international migration interests there by implying that the joint effect of the nature of government effectiveness, regulatory quality and political stability increase people’s desire to internally migrate out of the continent for better opportunities. And as such since the data for the institutional quality variables indicate that the present institutions are weak in nature indicated by the negative values, then it is important that the government of the nations in the African Continent develop and implement policies geared towards monitoring and evaluating weak institutions to ensure effective functioning and reduce forced international migration interests in the Continent.

The study therefore recommends that the governing organs of the nations in the African Continent should endeavour to stimulate cooperation, develop concrete political policies regarding immigration and co-development through effectively ensuring the adoption of strong natured institutions that embark on sustainably adhering to equitable reduction in corruption practices, political instabilities and full government effectiveness in every sector of the nations, so as to grow an appreciation of and integrate the fundamentals for which immigration can lead to a source of development, economic and social progress in the host country and country of origin with the greater focus on the latter: the contribution of migrants in both countries. It is necessary to drive alternative development policies, which lead to structural and political changes to improve living and working conditions of the population in home countries that make migrant partakers through projects of social transformation.

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