COMESA Migration Flows: Evidence from Top-5 Inflows and Outflows

COMESA Migration Flows: Evidence from Top-5 Inflows and Outflows

Jonathan Ang’ani Omuchesi

The Institute for Regional Integration and Development, The Catholic University of Eastern Africa, Nairobi, Kenya.

DOI: https://dx.doi.org/10.47772/IJRISS.2024.801148

Received: 29 December 2023; Revised: 11 January 2024; Accepted: 15 January 2024; Published: 15 February 2024

ABSTRACT

While migration out of the African continent seems more visible given the attention it receives based on recent debates mostly shaped by the media, intra-African migration and inter-state migration in Africa is large owing to the numbers involved in absolute terms. This study considers the Top-5 inflows and the reciprocal top five outflows by each COMESA country. Utilizing the gravity model of migration, the study sought to predict the degree of migration interaction. Focusing on the COMESA region integration block (21 countries), this study examined its Top-5  inflows and outflows migrations. Using national data migration flows statistics available from migrationdataportal.org for the year 2010. The statistics defines an international migrant as an individual who has a different usual country of residence in 2010 compared with 2005. These statistics were estimated by combining country-to-country international flows with estimates of 5-year internal migration flows. The study estimated an empirical model as a measure for both Inflows and Outflows to better understand the COMESA migration flows as a part of intra-African migration and consider the factors may explain changes in the volumes and the direction of these changes in migrations. The study finds that; there is significant reciprocity between COMESA inflows and outflows, the common border and the common 2^nd/ethnic language significantly influences both COMESA inflows and outflows, and the Political Stability negatively influences COMESA inflows and outflows, notably, however, the distance between countries neither influences COMESA Inflows nor Outflows.

Keywords: Inflows, Outflows, Top-5 migration flows, COMESA. 

INTRODUCTION

In the year 2000, the World Bank estimated that 51% of worldwide migration was to the South, and such as, this global population were living in a developing country. Additionally, 69% of the Sub-Saharan Africa (SSA) migration movement were considered as South-South migration. An examination of the South-South migration suggests substantial intra-regional migration within the African continent (Adebusoye, 2006; Mcube et al., 2010).  Almost 2 decades later, based on Population and Vital statistics report 2017(United Nations, 2017), Africa was the Top 5 largest hosts global migrants with at least 25 million migrants moving within and outside the continent. The report also indicated that most of African migration was intra-Africa with up-to 19 million people involved. Potts (2015) considers that this phenomenon is partly due to the efforts of individual states and resident international institutions on the continent to enhance the quality and quantity of regional integration.

Several key frameworks of the African Union including frameworks including the Agenda 2063 and the Boosting Intra-African Trade (BIAT) has established a link between mobility and Economic development in Africa. AU (2022) in a report re-affirms the positive link between trade and movement of persons (mobility) on the continent. This reinforces the drive towards continental and regional initiatives to enhance free movement of persons. This is seen continentally through the African Continental Free Trade Area (AfCFTA) and the African Union Free Movement of Persons (AUFMP) being the two initiatives essential in realizing successful continental integration (Bisong and Mayer, 2021).

The African continent is home to 8 African Union recognized Regional Economic Communities (RECs) and a myriad of integration schemes within and besides these RECs. Since these RECs are considered building blocks to continental integration, continental initiatives towards free movement of persons and free trade have been replicated at regional levels. Continentally, the protocol on the free movement of persons is yet to be operationalized as it has not attained the requisite ratifications. Regionally, majority of the RECs have protocols or agreements activating free movement of persons them. However, due to lack of reliable data and gaps therein, there has been little or no scholarly review of migration within Africa and/or its RECs/regions (Ruyssen & Rayp, 2014).  Existing data and studies on intra-African and/or intra-regional migration in Africa has largely focused on the West and Central African countries considering a selection of few countries. This study considers migration in COMESA countries by considering the Top 5 origin and destination countries of the inflows and outflows. COMESA as a REC is suitable for this study because its composition includes countries from the Horn of Africa, Northern Africa, the Indian Ocean, the Great Lakes, Southern Africa and Central Africa. Specifically, the COMESA membership as the name suggests, is made of Eastern and Southern Africa countries. The COMESA region therefore forms part of the largest migration route in and out of Africa especially in the Horn of Africa.

For a long time, African migration has been desperately perceived to be towards Europe. The surge in interviews and surveys on existing African migration trends and volumes have gone a long way to dispel the notion in the media. Actually, migrations out of Africa have been to the Arabian Penisula and beyond, this is captured in DTM-Data Stories (2019a, b). Earlier, Sander & Maimbo (2003) and later Schoumaker et al. (2015) show that most African migrations were directed towards other African countries. In addition, the diversity of African migration has been highlighted in Lessault & Flahaux (2013) among other studies.  This is reinforced by the Population and Vital statistics report 2017.

The increasing focus and inquiry into trends and cause of African and intra-African migration is noteworthy. However, missing or incomplete data has mostly allowed general and/or aggregate analysis (Flahaux & De Haas, 2016). Given the spotlight on migration and movement of persons under the framework of regional integration and associated initiatives, what has been lacking is an evaluation of intra-regional migration patterns, a documentation of the level of intra-regional migration and associated determinants.

In an attempt to seek better understanding of the COMESA migration flows, this study analyses the interaction between COMESA countries with their respective TOP 5 destinations and origins of migration flows between 2005 and 2010 (based on 2010 migration flow data) and considers what factors may explain changes in the volumes and the direction of these changes in migrations. This will be done using Official National data migration statistics from the migration data portal.

Intra-regional/intra-African Migration Trends

Flahaux and De Haas (2016) have noted that the Southern African countries, West African countries and several small states (mostly in the South and Horn of Africa region) account for the most of the Intra-African continental migration. Furthermore, countries such as South Africa, Egypt, Nigeria, and the North African Maghreb countries which are most densely populated, have low intra-African emigration rates as most of its nationals tend to move to Europe.

Based on UNDESA statistics and DTM enumerators, in one-year (2017-2018), there were more than 7.5 million migrants moving and living in West and Central Africa, most of which were from within the region. Additionally, the data showed that over 95% of them were intra-regional or in-country. For instance, around 50% of migrant movements monitored in Senegal reported continuing movements within the country. Niger, Mali, Senegal and Nigeria were reported to be the main destinations for migrants interviewed at the Flow Monitoring Points (DTM-Data Stories, 2019a).

Historically, migrant movements in the Horn of Africa have primarily been: (i) through the East via Yemen towards the Middle East and beyond; (ii) through the North via Sudan/Libya/Egypt towards Europe/Israel; and (iii) through the South via Kenya/Tanzania onwards to South Africa. Besides these routes, a very significant portion of the movements takes place within the Region (DTM-Data Stories, 2019b).

Based on IOM-DTM stats, 54% of the observed movements at the Flow Monitoring Points were within the Horn of Africa and neighbouring countries such as Sudan and Kenya. On the other hand, most of the outflows out of the HoA region were to the Arabian Peninsula (36%) with Saudi Arabia being the preferred destination. Comparably, Inflows into HoA from the Arabian Peninsula were modest. Notable observable movements from the Arabian Peninsula originated from Yemen and were destined to Somalia. Conversely, most of the outflows to the Arabian Peninsula were of Ethiopian nationality that travelled through Djibouti to Saudi Arabia and other Gulf countries (DTM-Data Stories, 2019b). 

The Displacement Tracking Matrix (DTM) and UN Migration-Data Stories on migration in the Horn of Africa (HoA) collected and examined data across 40 flow monitoring points (FMPs).  It showed that, movements along the Eastern route mainly Ethiopian nationals represented a significant portion (37.76 %) of the total. Movements within the HoA (Ethiopia, Eritrea, Somalia and Djibouti) represented 52.41 per cent of the total movements. Migrants traveling along the Northern Route (3.93%) and Southern Route (5.70%) contributed about 10% of the total movements (DTM-Data Stories, 2019b).

What is Driving intra-regional Migration Trends in Africa?

Existing studies show mixed results on the influence of sharing a common language on migration flows: Grogger and Hanson (2011), Beine te al. (2011), find that sharing a common language increases migration flows whereas   Mayda (2010), Ortega and Peri (2010) studies do not. Adserà (2015) argue that an explanation for this inconsistency could be the smaller size and restricted samples in studies that do not find a positive relationship.

Economic reasons have accounted for the bulk of international migration in Africa. This has been driven by cross-border migration in search of better jobs. The need of Education and joining family across the borders have also been documented among the most common reasons for African migration. Most of the trans-border migration of largely unskilled workers ends up being “permanent” though initially intended to be temporary (McAuliffe & Kitimbo, 2018; FAO, 2001). The issue of classification of migrants as temporary or permanent in most literature regardless of their legal status has been a challenge in review of migration studies in Sub-Saharan Africa (SSA) (Agadjanian, 2008). It is also true to say that a majority of intra-Africa migrants could be due to cumbersome and highly restrictive visa requirements (Agadjanian, 2008; McAuliffe & Kitimbo, 2018). De Haas (2007) observes that the common perceived root causes of migration were poverty and income gaps between rich and poor countries.

According to literature forced migration often attributable political instability, coups, electoral related violence and chaos have also led to mass movement of persons in Africa (Agadjanian, 2008). Flahaux and De Haas (2016) notes that contrary to popular belief, forced migration only accounts for 14% of international migration in Africa. They also find high geographical mobility for those with higher educational and occupational specializations in search for more opportunities.

The Displacement Tracking Matrix (DTM) and UN Migration-Data Stories on migration in the West and Central Africa (WCA) examined over 24,000 sampled respondents taken between January and September 2018. It was determined that employment-seeking migration accounted for the biggest share of intra-regional mobility. Economic conditions were the key drivers of migration in West and Central Africa (WCA) among other reasons. For movement within the WCA the identified driver/reasons as flows: 62% economic, 27% rejoin family, 4% other issues, 4% access to services, 2% war/conflict. For movement out of the WCA: 95% were economic, 2% rejoin family, 1% other issues, 1% access to services, 0% war/conflict (DTM-Data Stories, 2019a).

Equally, based on the Displacement Tracking Matrix (DTM) and UN Migration-Data Stories, with over 15,000 sampled respondents, the drivers/reasons for migration in the region (HoA) were significantly different from those observed on the other routes. While drivers of migration inside the HoA are mixed, movements along the Eastern, Southern and Northern routes were mainly driven by economic reasons (90%). However, it notable that migrants moving inside the HoA reported notably more difficulties than those moving along the other routes (DTM-Data Stories, 2019b).

The existence of regional organisations such as ECOWAS, SADC, EAC and COMESA and agreements have for free movement of persons between the respective partner states has gone a long way in spurring migration. However, poor implementation or contradictions of the agreements by the restrictive and retrogressive policies and practices of partner states has been found to undermine this intention (Adepoju, 2001). This as noted by Castles, De Haas, & Miller (2014) has led to infringement of migrant rights and just like elsewhere in the world resulting in mass deportations especially in times of economic crisis

Role States and regional/subregional Institutions

The value of institutions and organizations at national and regional levels cannot be over emphasized. It’s notable that in recent times, African regional institutions through reduction of immigration, barriers have enhanced continental integration (Agadjanian, 2008). To this end, ECOWAS and EAC have been on the forefront in recognizing that mobility is key towards their greater regional integration (McAuliffe & Kitimbo, 2018).

Migration patterns and routes have been existing since colonial occupation and slave trade in Africa. The end of the Cold War re-ignited the fights for second political liberation in some cases even before healing from the colonial liberation fights. These conditions aided by globalization have created an environment of constant turmoil and uncertainty have been an incentive for migration. States have moved in most cases to curtail emigration and immigration due looking inward policies through processes such as nationalism, protectionism, acts of xenophobia (Flahaux & De Haas, 2016). Analyzing international migrant stock data, De Haas (2010) finds a strong positive association between political freedom and emigration levels, a result that supports existing phenomenon in Africa. The existence of regional and sub-regional organization has delivers hope to help reverse these state inward looking processes by advocating for regionalism. The African continent has a plethora of regional integration schemes. However, we have 8 AU recognized Regional Organizations which are key towards an integrated continent. Free movement of persons is a recognizable pillar among the five pillars of regional integration in Africa (ECA, 2023). This is due to the associated trade gains as the realization of continental free movement of people promises a significant boost in economic growth and skill development of citizens as they move for business, education and other endeavors, to mean, with open borders, there are massive benefits (ECA, AfDB and AUC, 2018).  De Haas (2007) noted that the common perceived root causes of migration were poverty and income gaps between rich and poor countries. He also notes that given this situation and the ineffectiveness of inward-looking state level policies, the preferable long-term solution was to spur development in migration originating countries through trade, aid and/or remittances.

For avoidance of doubt and clarity, the Abuja Treaty captures the aspiration in realization of the continental integration project as: free movement of people and the rights of residence and establishment. Mobility of professionals and associated services have been hampered by restrictions in immigration laws and regulations resulting in shortages despite existing surpluses on the continent.

To this end, states should reduce their high desire to control and restrict emigration by managing characteristics of state-formation process that are retrogressive and inward looking such as nationalism and protectionism. Considerations such as the single African Passport, possibility of Visa on arrival, free Visas have all been options largely to improve intra-Africa trade. These aspects have also been considered in the African Agenda 2063 (AfDB, 2013; AfDB, 2016 and AU, 2014).

Through the RECs, countries and regions are promoting reciprocity and thus restrictions towards free movement are being eliminated through establishment of Common Markets protocols. However, there has been a feeling of lack of commitment at the continental level with the lack of ratification of the AU Free Movement of Persons Protocol. Notably, this Protocol existed before the African Continental Free Trade Area (AfCFTA) Protocol. Bisong (2022) points this out and advises that the two documents are essential towards an economically integrated continent.

The foregoing cements the notion that regional integration institutions and organs through initiated projects and programs have a central role in positively intra-regional and continental movement of migrations. Since migrants on various routes in the West and Central Africa and the Horn of Africa have expressed security and protection among the challenges experienced, these will promote more legal/formal migration and assure migrants security and protection (DTM-Data Stories, 2019a and DTM-Data Stories, 2019b). These assurances will enhance migrations thus harnessing and delivering the elusive benefits of economic integration to the various stakeholders, the majority of whom are the citizens.

The Protocol on the Gradual Relaxation and Eventual Elimination of Visa Requirements, and the Protocol on Free Movement of Persons, Labour, Services, the Right of Establishment and Residence are the two primary legal instruments govern the free movement of persons in COMESA. There is mixed signing and ratification of the Free Movement Protocol since its adoption in June 1998. This is seen by some countries waiving visas to all COMESA citizens for those on official business (COMESA, 2014). In recognition of the national implementation challenges, COMESA set up the COMESA Model Law on Immigration to harmonize national laws and practices of member States. In spite of this, domestication is still slow (UNECA, Online).

DATA AND METHODOLOGY

Empirical Model Specification

In specifying the model, we argue that migration flows or the decision to move is influenced by many factors. At the individual level, it involves a consideration of the expected benefits and costs of the migration, to mean, the Expect Returns of Migration (ERM) from the origin to the destination country at a given point in time (Sjaastad’s, 1962). Borrowing from several aspects of this model, our model seeks to understand what influenced migration flows between COMESA countries and their Top 5 destinations and origins.  Based on this understanding and literature, several factors have been suggested to influence intra-African and international migration. The gravity model has been utilized to understand geographically locations and importance using proxy measures. As noted by Rodrigue et al. (2009), the gravity model of migration can help to ascertain the interaction of migration between two locations. In order to alleviate the impact of outliers, robust estimation has been used. Given the flexibility of the gravity model, the following two gravity equation are specified to estimate migration flows: that is, (i) COMESA Inflows (Model 1), and (ii) COMESA Outflows (Model 2);

lnCOMIF_ot= β0 + β1PolStab_ot + β2PolStab_dt + β3lnGDPPC_ot + β4lnGDPPC_dt + β5lnDist_od + β6Comcol_od + β7Comborder_od + β8Comlangoff_od + β9Comlang2_od + β10Regint_od + ε_odt           (1)

lnCOMOF_dt= β0 + β1PolStab_ot + β2PolStab_dt + β3lnGDPPC_ot + β4lnGDPPC_dt + β5lnDist_od + β6Comcol_odt + β7Combord_od + β8Comlangoff_od + β9Comlang2_od + β10Regint_od + ε_odt          (2)

Where COMIF_ot  and COMOF_dt is the Top 5 COMESA inflows and outflows respectively in year t (o and d denote origin and destination countries respectively, t denotes time); Dist_od is the geographical distance between the cities of the two respective countries; Combord_od is a dummy variable that takes the value 1 if the two countries share a land border, otherwise, they take value 0; Comlangoff_od is a dummy variable that takes the value 1 if the two respective countries share a common official language, otherwise, they take value 0; Comlang2_od is a dummy variable that takes the value 1 if the two respective countries share a common mother tongue or 2^nd language, otherwise, they take value 0; GDPPC_ot and GDPPC_dt is the per capita gross domestic product of the origin and destination countries respectively; PolStab_ot and PolStab_dt is the Political stability and absence of violence, it measures perceptions of the likelihood that the origin or destination government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism (World Bank-WDI, 2023); Comcol_od is a dummy variable that takes the value 1 if the two respective countries had the same common colonizer, otherwise, they take value 0; Regint_od is a dummy variable measure for regional integration that takes the value 1 if the two respective countries belong to the COMESA regional community, otherwise, they take value 0. Finally, ε_sdt is the error term, β0 is the constant and β1, β2, β3, β4, β5, β6, β7, β8, β9 and β10 are regression coefficients.

The bilateral distance between countries and the presence/absence of a common border between respective countries have been considered as suitable proxies for financial expenses and non-financial opportunity costs incurred migrant (Pedersen et al., 2008 and Mayda, 2010). The closer the distance/ the presence of a common border between the two countries, the lesser the expense and/or cost of movement and the more the expected movement between them and vice versa. Equally, the presence of a common official language and/or common 2^nd language/mother tongue, and a common colonial past are expected to reduce the costs of movement. These are identities for cultural similarities for the home and destination country.  They make migrants to adapt to the new environment easier; this prospectively induces migration between the respective countries (Tayyab, 1997). As already considered, regional integration has been found to favor movement of persons, in view of the various efforts towards free movement of persons, in this case therefore, the influence of regional integration on migration examined. A positive sign would indicate that belonging to the same regional block successfully stimulates the movement of people, to mean, a complimentary relationship between trade and labour and vice versa in the case of a negative sign (Mayer & Zignago, 2011).

Data on Top 5 immigration(inflows) and emigration (outflows) was obtained from the Migration Data Portal (https://www.migrationdataportal.org/dashboard/national-data?i). The National data are available on immigration and emigration, labour migration, asylum seekers, etc. from theory, logic and literature analysis, migration determinants are Development and the state. Such that, the volume of emigration (outflow) is influenced by the direction of development, to mean, with increasing development, there would be an increase in proportion of migrant populations to other continents. Autocratic and nationalist leaning governments, have the capacity and willingness to reduce levels of outflows and inflows. However, De Haas (2010) and Clemens (2014) find that in poor countries(specifically), development does not decrease levels of migration which challenges the conventional push-pull migration models. On the other hand, African migration has been found to ignore the role of the African state in influencing migration. Existing relationships colonizers enhances migration towards former colonizers. However, it diminishes with time.

The data for GDP per capita in USD for each country was extracted from the World Development Indicators (World Bank, 2023). The data for bilateral distance, common border and common language, common colony, common 2^nd language/mother tongue were obtained from the database of Centre d’Etudes Prospectives et d’Informations Internationales (CEPII- Centre for Prospective Studies and International Information). The Political stability and absence of violence estimates (PolStab) were obtained from Worldwide Governance Indicators (World Bank, 2023.

The GDP per capita and the Political stability and absence of violence estimates (PolStab) used 5-year average figures, to mean the GDPPC and PolStab were an average for the years 2005, 2006, 2007, 2008 and 2009, the assumption was that the decision to move is based on a trend and could not be wholly pegged on the figures in the year of migration. The COMESA regional bloc has a membership of 21 countries (see Appendix 1). However, due to data availability specifically on inflows and outflows to Top 5 destinations and origins, only 19 countries were considered, with the exclusion of Seychelles and Eritrea.

RESULTS AND DISCUSSION

Before we considered the empirical estimation, we considered the volumes and direction of the migration flows. This is presented with the help of figures and charts.

Volumes and Direction of Migration to and from COMESA

Fig. 1 Top 5 origin and destination of Migration flows to/from respective COMESA countries

A look at Figure 1 reveals that out of the listed 19 COMESA countries in the study, only 6 countries (Egypt, Sudan, Tunisia, Madagascar, Mauritius and Burundi) had more inflows than outflows of migrants. It’s important to note that of all the Top 5 inflows to COMESA, Sudan accounted for nearly a third of all the inflows (28%)!

All the other countries had more outflows than inflows and Figure 2 gives us the mix of the inflows and outflows with an indication of the origin or destination of the migration flows by each COMESA country. Figure 3 provides a consolidated explicit view of the total inflows and outflows and we can see that COMESA Top 5 outflows were more than the inflows. Additionally, the inflows and outflows by COMESA countries (intra-COMESA) migration flows were more than extra-COMESA migration flows. This is further supported by the high positive correlation ® between the inflows and outflows. We find an r=0.86 and r=0.70 for all inflows and outflows and COMESA only(intra-COMESA) inflows and outflows respectively. This was an indication of strong level of positive reciprocity between the Top 5 destination and origin of migration flows for all and COMESA only countries. This confirms the emerging and observed trend of African migrations, to mean, they are Intra-Africana and Intra-regional. A further analysis of Figure 3 indicates that of all the Top 5 inflows to COMESA countries, 72.3% originated from COMESA countries; and that of all the Top outflows, only 58.5% were destined to COMESA countries and 41.5% were destined to non-COMESA countries. However, a further examination revealed that the out of COMESA destined flows, 74% were destined to other African countries. This further re-affirms that African migrations are majorly Intra-African and Intra-regional.

Fig. 2 Proportions of the Migration Flows in Respective COMESA Countries

An analysis of the migration flows reveals that, out of the total migration flows (inflow and outflows) to and from COMESA countries by the Top 5 destinations and origins, 69% originated or were destined to COMESA countries while 31% originated or were destined to non-COMESA countries. A further investigation indicated that out of the total non-COMESA destined/originated migration flows, 62% were out flows while 38% were inflows; and that, out of the total COMESA destined/originated migration flows, 45% were inflows while 55% were outflows.

To understand the nature of relationships between the considered variables, we did a Pearsons pairwise correlation matrix of the variables. * Indicates statistical significance at 5% level of error. As can be seen from Table 1, there is a strong positive significant correlation between the inflows (lnComif_ot ) and Outflows (lnComof_dt). due to this, inflows (lnComif_ot ) and Outflows (lnComof_dt) will only be considered as Dependent variables in each of the specified models to avoid challenges of multi-collinearity.

Fig. 3 Total Top-5 COMESA Migration Flows 

Table 1: Pearsons Pairwise Correlations

Source: Authors’ Computation (2023).

The study considered two models to help explain the changes in the volume and the direction of the COMESA migrations. In considering the inflows to the COMESA countries, the indicated Model 3 was used. The summary statistical results are captured in Table 2.

When; lnCOMIF_ot= β0 + β1PolStab_ot + β2PolStab_dt + β3lnGDPPC_ot + β4lnGDPPC_dt + β5lnDist_od + β6Comcol_od + β7Comborder_od + β8Comlangoff_od + β9Comlang2_od + β10Regint_od + ε_odt       (3)

Table 2. Robust Linear Regression Results (with all variables except lnComof_dt as an independent variable)

Source: Authors’ Computation (2023).          

The dependent variable is lnComif_ot; ***, ** indicates statistical significance at 1% and 5% error level respectively.

With F=0.0000, it is evident that all the regressors are not simultaneously zero.  With R² =.5841; about 58% of the total variability of the dependent variable is explained by the explanatory variables considered in the model, an indication that the model has a moderate explanatory power.

Table 2 presents Robust Linear regression results when we consider all variables except lnComof_dt as an independent variable. The results as reported in Table 2 indicate that, the common language (Comlang2_od), the common border (Combord_od), Political stability and absence of violence estimates for the home country (Polstab_ot), and Political stability and absence of violence estimates for the destination country (Polstab_dt) had significant influence (positive/negative) on COMESA inflows. Therefore, with a coefficient of 0.951, this is an indication that the fact the presence of a 2^nd common/ethnic language between the origin and destination countries stimulated COMESA inflows by 0.95%; with a coefficient of 1.73, indicated that COMESA countries had 462% higher inflows from countries it had with common borders compared to those it did have common borders; with a coefficient of -0.81, if there is a 1 point change in Political stability for the home country, then inflows decreases by 81%; and lastly, with a coefficient of -0.62, if there is a 1 point change in Political stability for the destination country, then inflows decreases by 62%.  These results are in agreement with Grogger  and Hanson (2011), Beine te al. (2011) who find that sharing a common language increases migration inflows. As argued by Adserà (2015), there exists multiple channels through which the linguistic “distance” between a migrant’s own language and that of the destination country affects the migration experience such as greater return to human capital in the labour market of the destination country, better job matches, and faster career progression among others. It’s expected as is captured in most literature, that there is a negative relationship between changes in political stability/instability with migration  inflows, the significant negative influences on the migration inflows is confirmed by the significant negative relationships captured by the correlation matrix in Table 1.

In considering the outflows from the COMESA countries, the indicated Model 4 was used. The summary statistical results are captured in Tables 3.

When; lnCOMOF_dt= β0 + β1PolStab_ot + β2PolStab_dt + β3lnGDPPC_ot + β4lnGDPPC_dt + β5lnDist_od + β6Comcol_odt + β7Combord_od + β8Comlangoff_od + β9Comlang2_od + β10Regint_od + ε_odt   (4)

Table 7. Robust Linear Regression Results with all variables except lnComif_ot as an independent variable

Source: Authors’ Computation (2023).

The dependent variable is lnComof_dt ; ***, ** indicates statistical significance at 1% and 5% error level respectively.

With F=0.0000, it is evident that all the regressors are not simultaneously zero.  With R² =.5522; about 55% of the total variability of the dependent variable is explained by the explanatory variables considered in the model, an indication that the model has a moderate explanatory power.

Table 3 presents Robust Linear regression results when we consider all variables except lnComif_ot as an independent variable. The results as reported in Table 3 indicate that, the common language (Comlang2_od), the common border (Combord_od), Political stability for the home country (Polstab_ot), and Political stability for the destination country (Polstab_dt) had significant influence (positive/negative) on COMESA outflows. Therefore, with a coefficient of 1.62, indicated that if the countries have a 2^nd common/ethnic language then the outflows will 159% higher than those without; with a coefficient of 1.81, indicated that COMESA countries had 511% higher outflows to countries it had common borders compared with countries it did not have common borders; with a coefficient of -0.69, a if there is a 1 point change in Political stability for the home country, then outflows decreases by 69%; and lastly, with a coefficient of -0.85, if there is a 1 point change in Political stability for the home country, then outflows decreases by 85%. These results are in agreement with Grogger and Hanson (2011), Beine te al. (2011) who find that sharing a common language increases migration outflows. As argued by Adserà (2015), there exists multiple channels through which the linguistic “distance” between a migrant’s own language and that of the destination country affects the migration experience such as greater return to human capital in the labour market of the destination country, better job matches, and faster career progression among others. It’s expected as is captured in most literature, that, there exists a negative relationship between changes in political stability with migration outflows, the significant negative influences on the migration inflows is confirmed by the significant negative relationships captured by the correlation matrix in Table 1.

CONCLUSIONS

This paper considers migration in COMESA countries by considering the Top-5 origin (inflows) and destination(outflow) countries using Official National data migration statistics from the migration data portal. It particularly explores trends and patterns of migration and whether some known determinants influences migration in the COMESA region. The analysis confirms that there exists reciprocity in Top-5 country inflow and outflows within the COMESA region, suggesting that bilateral relations contribute largely. Secondly, the presence of a common 2^nd language (Mother Tongue) and shared Border significantly and highly COMESA Top-5 migration flows, this suggests that countries within the region need to invest more in other languages beyond the official national language to help spur migration/mobility within the COMESA region. However, findings also reveal that changes in Political stability negatively influences COMESA Top-5 migration flows. An understanding of what influences existing or past Intra-African migration flows will help improve current continental and regional efforts to bolster mobility in the future.

REFERENCES

1. Adebusoye, P. (2006). Geographic labour mobility in sub-Saharan Africa (Technical report, IDRC Canada Working Papers on Globalization, Growth and Poverty).
2. Adepoju, A. (1995). Emigration dynamics in sub-Saharan Africa. International Migration, 33, 315–390.
3. Adserà, A. (2015). Language and culture as drivers of migration. IZA World of Labor: 164 doi: 10.15185/izawol.164
4. African Development Bank (2013). Visa restrictions and economic consequences in Africa (2013). Available at: http://www.afdb.org/en/blogs/afdb-championing-inclusive-growth-across-africa.
5. African Development Bank (2016). Africa visa openness report 2016. Available at: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Generic-Documents/Africa_Visa_Openness_Report_2016.pdf
6. African Union (2014). Agenda 2063: The Africa we want. Available at: http://archive.au.int/assets/images/agenda2063.pdf
7. Agadjanian, V. (2008). Research on International Migration within Sub-Saharan Africa: Foci, Approaches, and Challenges. The Sociological Quarterly. 49 (3): 402–421. doi:10.1111/j.1533-8525.2008.00121.x.
8. (2019). AU Member States urged to ratify the protocol on free movement to achieve free trade. Addis Ababa: African Union.
9. (2022). Second Continental REPORT ON THE IMPLEMENTATION OF AGENDA 2063. February 2022. Addis Ababa: African Union.
10. AUC (African Union Commission). (2018). African Regional Integration Report 2016. Available at: https://www.integrate-africa.org/reports-data/download-the-2019-reports/africa-regional-integration-index-report-2016-76/
11. Bakewell, O. & Jόnsson, G. (2011). Migration, mobility and the African city. Oxford: IMI: International Migration Institute.
12. Beine, M., Docquier, F., & Ozden, C. (2011).  Diasporas. Journal of Development Economics 95:1: 30–41.
13. Berriane, M. & De Haas, H. (2012). African migrations research: innovative methods and methodologies. Trenton: Africa World Press.
14. Bisong, A. & Mayer, N. (2021). Connecting people and markets in Africa in 2021. ECDPM Commentary. Maastricht: ECDPM.
15. Bisong, A. (2022). Labour mobility as a key element of the AfCFTA: What role for the AU’s free movement protocol? Available at: https://ecdpm.org/application/files/5216/7119/6114/Labour-Mobility-Key-Element-AfCFTA-Role-AU-Free-Movement-Protocol-ECDPM-Briefing-Note-153-2022.pdf
16. COMESA (2014). Three Member States drop visa requirements. Available from http://www.comesa.int/attachments/article/1433/e-comesa_newsletter_440.pdf
17. De Haas, H. (2007). Turning the tide? Why development will not stop migration. Development and Change, 38(5), 819–841.
18. De Haas, H. (2010). Migration transitions: a theoretical and empirical inquiry into the developmental drivers of international migration, DEMIG, Working Paper No. 24, International Migration Institute, University of Oxford.
19. DTM-Data Stories. (2019a). Migration Flows in West and Central Africa.  https://dtm.iom.int/data-stories/migration-flows-west-central-africa
20. DTM-Data Stories. (2019b). Migration Flows in the Horn of Africa and Yemen. https://dtm.iom.int/data-stories/migration-flows-horn-africa-and-yemen
21. ECA (United Nations Economic Commission for Africa), AfDB (African Development Bank) and
22. ECA (United Nations Economic Commission for Africa). (2023). Key Pillars of Africa’s Regional Integration. Available at: https://archive.uneca.org/oria/pages/key-pillars-africa%E2%80%99s-regional-integration
23. ECA (United Nations Economic Commission for Africa). 2023. Regional Economic Communities. Available at: https://archive.uneca.org/oria/pages/regional-economic-communities
24. Flahaux, M-L., & De Haas, H. (2016). African migration: trends, patterns, drivers. Comparative Migration Studies 4, 1, https://doi.org/10.1186/s40878-015-0015-6
25. Food and Agriculture Organization of the United Nations (FAO). (2001).Yearbook of the United Nations 2001. Yearbook of the United Nations. doi:10.18356/319bd501-en. ISBN 9789211558968.
26. Grogger, J., & Hanson, G. H. (2011). Income maximization and the selection and sorting of international migrants. Journal of Development Economics 95:1: 42–57.
27. Lessault, D. & Flahaux, ML. (2013). Regards statistiques sur l’histoire de l’émigration internationale au Sénégal [Statistical Views on the History of International Migration from Senegal]. Revue Européenne des Migrations Internationales, 29(4), 59–88.
28. Mayda, A. M. (2010). International migration: A panel data analysis of the determinants of bilateral flows. Journal of Population Economics 23:4: 1249–1274.
29. Mayer, T. & Zignago, S. (2011). Notes on CEPII’s distances measures : the GeoDist Database, CEPII Working Paper 2011-25
30. McAuliffe, M., & Kitimbo, A. (2018). African Migration: What the Numbers Really Tell Us. World Economic Forum. Available at: https://www.weforum.org/agenda/2018/06/heres-the-truth-about-african-migration/
31. Mcube, M., Leyeka L, C., & Ndikumana, L. (2010). Africans abroad: Migration trends, patterns and determinants. AFDB Economic Brief, 1(6), 23 September 2010.
32.  Migration Data Portal (online). National Data: National data are available on immigration and emigration, labour migration, asylum seekers, etc. https://www.migrationdataportal.org/dashboard/national-data?i
33. Ortega, F., & Peri, G. (2009). The Causes and Effects of International Migrations: Evidence from OECD Countries 1980–2005 NBER Working Paper No.14833.
34. Potts, D. (2015). Debates about African urbanisation, migration and economic growth: what can we learn from Zimbabwe and Zambia?. The Geographical Journal. 182 (3): 251–264. doi:10.1111/geoj.12139
35. Rodrigue, J.-P., Comtois, C., Slack, B. (2009). The Geography of Transport Systems. London, New York: Routledge.
36. Ruyssen, I., & Rayp, G. (2014). Determinants of Intraregional Migration in Sub-Saharan Africa 1980-2000. The Journal of Development Studies, 50:3, 426-443, DOI: 10.1080/00220388.2013.866218
37. Sander, C. & Maimbo, SM. (2003). Migrant Labor Remittances in Africa: Reducing Obstacles to Developmental Contributions. In Africa Region Working Paper Series. Washington DC: World Bank.
38. Schoumaker, B., Flahaux, ML., Schans, D., Beauchemin, C., Mazzucato, V. & Sakho, P. (2015). Changing patterns of African Migration: A Comparative Analysis. In C Beauchemin (Ed.), Migration between Africa and Europe: Trends, Factors and Effects. New-York: Springer-Verlag & INED Population Studies series.
39. Tayyab M. (1997). Migration, Identity & the Colonial Encounter, 76 OR. L. REV. 633 . https://digitalcommons.law.seattleu.edu/faculty/292
40. Udelsmann Rodrigues, C & Bjarnesen, J. (2020). Intra-African Migration. Study of the Policy Department for External Relations, Directorate General for External Policies of the Union. October 2020.
41. UNECA. (ONLINE). COMESA – Free Movement of Persons. Available at:  https://archive.uneca.org/pages/comesa-free-movement-persons#:~:text=There%20are%20two%20primary%20legal,Right%20of%20Establishment%20and%20Residence.
42. United Nations (2017).  Population and Vital Statistics Report, January 2017.  doi:10.18356/92630ac3-en
43. World Bank. (online). Worldwide Governance Indicators: Political Stability and Absence of Violence/Terrorism https://databank.worldbank.org/source/worldwide-governance-indicators/Series/PV.EST

 Appendix 1: The COMESA Countries