Data is essential for defining and measuring poverty. It provides the foundation that is necessary to understand the extent and nature of poverty in a given region or community. It also provides a basis for informed decision-making, effective policies, targeted interventions, and ongoing evaluation. Without good data, it would be challenging to establish poverty thresholds or determine who is living in poverty. By extension, it will be challenging to make meaningful progress in combatting poverty.
Various types of data can be useful in getting an accurate understanding of economic well-being and living conditions. In fact, different approaches to understanding poverty and different measurement methodologies may rely on specific subsets of data types, hence the choice of data can vary depending on the context and the goals of the analysis. Data types include consumption and expenditure data, asset and wealth data, employment and labor market data, education and skills related data, health and nutrition data, geospatial and demographic data, and administrative data among others. These data types provide us with a multifaceted perspective on poverty from multiple angles. Income and expenditure data shed light on the daily choices and trade-offs individuals and families make as they navigate limited resources. Employment and education data offer insights on opportunity and barriers. Housing and living conditions related data uncover (oftentimes harsh) realities of inadequate shelter and basic amenities. Together, these create a complex mosaic of narratives on what it means to live in a state of poverty.
However, poverty measurements constructed using the above data types can only be as good as the quality of data sources that they come from. The quality, accuracy, and reliability of data sources can significantly impact the validity and effectiveness of poverty assessments and policies that address it. High-quality data sources minimize errors, inconsistencies, and potential biases in the poverty measurement process. Timely data helps make real-time decisions based on updated data, responding to newly emerging poverty-related issues (such as loss of income during a pandemic), and adjusting policies as needed. It can also lessen the chance of outdated data being used for decision making and prevent missed opportunities for intervention. Apart from these, there are other considerations when obtaining data from various sources – from coverage issues to barriers to accessibility to ethical concerns.
The LIRNEasia working paper on ‘Data for poverty measurement’ discusses a number of data sources, how they feed into different types of poverty and their relative merits and limitations. It matters because data, and where it comes from, underpins the accuracy and relevance of poverty assessment and policy formulation. Recognizing the diversity of data sources allows us to construct a more comprehensive and ‘multi-dimensional’ view of poverty. While enabling the tailoring of poverty reduction interventions to specific needs and circumstances, it helps develop a more nuanced, evidence-based approach to poverty reduction that addresses the diverse challenges or deprivations faced by individuals and communities. This ultimately leads to more effective and targeted poverty alleviation strategies.