Critical decisions, from strategic investments to the allocation of humanitarian aid, rely on accurate assessments of the distribution of wealth and poverty. Yet, most poverty maps are out of date, often subject to respondent bias from household surveys, and only exist at very coarse levels of granularity. This lack of high quality insights into the distribution of wealth and population makes it difficult for decision makers to determine how to allocate resources in the most successful and impactful ways.
Having visibility into current and past socioeconomic trends and conditions across emerging markets is critical to informing how capital should be deployed to drive forward economic and societal progress.
At Alas AI, we developed economic insights on consumer spending power per capita across Africa from 2003 to 2020 for every 2 km across the continent in order to show how much households spend on the goods and services they consume.
This data is helping organizations assess the size and maturity of a given market, prioritize markets for expansion, identify new site locations, find the right customer segments, price products and services, and manage field operations.
What does “Spending Power” mean?
A common question we get asked is “Why did you focus on spending as the metric and not income?”. A big part of generating empirical outputs, especially when a big part of the input comes from subjectively reported survey data, is the need to be reliable and trustworthy. There may be cultural, literacy or personal reasons but putting aside the nuance of proper survey frames, it’s a lot more reliable to ask someone how much they spend for themselves or their household than it is to ask someone how much they earn for a living.
What is “Spending Power”?
The Spending Power concept is one good way to measure economic well-being. It reflects what people need to spend money on for basic needs and services, as well as what they choose to spend money on for discretionary activities and goods. To make comparisons and calculations easier, we normalized this as household spending per person, per day.
Our unit of measure is the 2011 International Dollar. This was chosen because it currently has the most adoption in the world of development economics and can be a good reference for a variety of applications. The conversion to another unit of measure is quite easy and as we receive better feedback from our customers and stakeholders, we’ll ensure that we can be as interoperable and usable to suit their needs.
To learn more about the Spending Power data layer, check out our documentation!
Why is “Spending Power” useful?
Whether you’re an energy provider, a retailer, healthcare provider, telecom operator, or a financial services institution - having the latest data on economic livelihood can help you:
- Estimate addressable markets: an energy service provider can use spending data along with other market insights in Aperture™, such as population dynamics and electricity access to calculate the specific number of people that have the need and ability to pay for electricity or cooking fuel.
- Prioritize locations for expansion of services: a financial service provider can use the Spending Power dataset to better understand how to pick the next areas to market lending programs or banking services with a more granular lens into local market dynamics
- Target the highest priority population for an anti-poverty programs: a direct cash transfer program can better identify underserved regions and understand where ground teams can focus their efforts to get cash to the communities that need it most
The power of our market insights is the ability to highlight underserved regions as well as fast growing well-known locations across emerging markets. For example, the Spending Power dataset highlights that Kenyans living just 8 km outside of Eldoret have a spending power of $3.35 per day as of 2020, compared to almost $10 per day in the center of Eldoret.
Organizations across the private and public sectors can use our datasets to identify periods of growth, stagnation and decline in population dynamics, economic activity, electricity usage and agricultural productivity to gain a deeper understanding of local market trends at the village level and apply a nuanced and granular approach to define and validate market assumptions, strategies and success rates of new projects.