Tracking Temporary Migration Patterns in Senegal

Understanding temporary migration is important for tackling different social, economic, and environmental issues in developing countries. Traditional surveys often miss these movements, leading to a lack of reliable data, especially in sub-Saharan Africa. This article introduces a clear dataset that uses mobile phone data to track temporary migration in Senegal. This dataset shows migration patterns in detail over space and time.

The dataset covers 151 locations in Senegal and tracks migration events lasting between 20 and 180 days from 2013 to 2015. It offers tools for researchers to detect temporary migration events and tackle problems in gathering migration statistics. These tools can be useful for creating migration statistics from mobile phone data in other regions as well.

#Background

Movements of people are closely linked to economic activity and development. Previous studies mainly focused on permanent migration and its role in economic growth. They examined how people move from less productive rural areas to urban areas for better jobs.

However, recent research shows that short-term moves, or temporary migration, are also significant in developing countries. These internal movements are very common and often outpace permanent migration. Initially seen as a sign of rural poverty, temporary migration is now recognized as a key part of household strategies.

Despite its importance, temporary migration is seldom included in national statistics. Short-term movements are hard to measure and usually require costly surveys. Additionally, surveys often define temporary migration in ways that overlook shorter trips that happen frequently. As a result, temporary migration patterns are poorly documented at the national level, especially in sub-Saharan Africa.

#New Dataset Overview

This article presents a new open-access dataset that captures temporary migration estimates from mobile phone data in Senegal. It includes migration flow and stock estimates across 151 locations in the country, covering rural areas and cities. Data is available for every half-month from 2013 to 2015, focusing on movements lasting from 20 to 180 days.

The aim of this dataset is to provide various researchers, including economists, demographers, and environmental sociologists, with solid information to improve knowledge about temporary migration. This information is crucial for development practitioners and policymakers, helping to design effective interventions when facing challenges like environmental shocks and climate change.

The dataset benefits from the rise of digital data from mobile phone usage, which has proven effective in measuring human movement on broader scales. Some studies have already used mobile phone data to track migration movements but have not made their datasets publicly available.

#Data Collection and Methodology

#Call Detail Records

The main data source for this research is Call Detail Records (CDR) from Sonatel, the main telecommunications company in Senegal. These records show when users made or received calls or texts and include important details like user phone number, date and time of the call, and the cell tower identifier.

During the study period, Sonatel had over 2,000 phone towers. Each tower's location was used to create contiguous areas called Voronoi cells, which represent the coverage area of each tower. These cells were organized to show the density of phone usage across Senegal.

#Data Filtering

A filtering process was used to ensure high-quality data. Users needed to meet certain criteria to be included in the dataset. For example, they had to have a minimum length of observation (at least 330 days), be observed on at least 80% of those days, and not have long periods without observation. These criteria help provide a solid base for identifying users' home locations and detecting temporary migration events.

Higher filtering standards can lead to a smaller sample size, but they ensure better accuracy. A secondary, less strict subset was also created to allow comparisons and checks on the main findings. This included users observed for at least 250 days and observed more than half the time.

#Migration Event Detection

To identify temporary migration events, the researchers developed a four-step process.

#Step 1: Determine Locations

First, each user's location was recorded based on the frequency of their calls and texts over time. Hourly, daily, and monthly locations were established to show where users were at different times of the day.

#Step 2: Identify Home Locations

This step involved figuring out where each user lived based on the most frequently observed locations. This information was used to group months where the user was at the same location as their home.

#Step 3: Determine Temporary Migration Events

In this step, shorter migrations, or meso-segments, were identified based on daily locations. These movements had to show that the user was away from their home location for a certain period.

#Step 4: Establish Migration Events

Finally, migration events were identified as periods where users stayed in a location different from their home for at least 20 days. If a user moved to a different location for this duration, it was marked as a temporary migration event.

#Migration Statistics

#Creating Migration Estimates

The dataset provides migration estimates at the level of origin, destination, and time. This means it shows how many individuals moved from one location to another during specific periods. The estimates are produced based on various user characteristics, including their home location and the timing of their migration.

To calculate these migration statistics, a weighting scheme was applied. Since not all users are representative of the general population, this scheme corrects for biases in the sample to better reflect the true state of migration in Senegal.

#Addressing Sampling Bias

The researchers recognized that phone ownership varies by socio-economic factors like age and gender. To create more accurate population-level estimates, they categorized users into rural and urban groups based on their home location. Each group received a specific weight according to its size in the overall population.

#Limitations and Challenges

While this dataset is valuable, it also comes with limitations. The focus on phone users might not capture the full range of temporary migration in Senegal. Additionally, the methods used might miss shorter migration events that fall outside the defined durations.

Using mobile phone data also introduces challenges, including privacy concerns and the potential presence of users who are not representative of the general population. Future research could further enhance this dataset by incorporating socio-demographic information or exploring other digital data sources.

#Conclusion

This dataset represents a significant advancement in understanding temporary migration in Senegal by leveraging mobile phone data. It can assist researchers and policymakers in making informed decisions regarding migration patterns, particularly in the context of socio-economic development and environmental changes.

By providing a detailed view of temporary migration flows and stocks, this dataset opens avenues for further research and a better understanding of the dynamics of human mobility in Senegal and beyond. The dataset and methodology laid out here can serve as a model for similar studies in other regions, underscoring the potential of mobile phone data in migration research.