By Trenton Manson
Liliʻiuokalani Trust’s strategic plan is based on the vision of E Nā Kamalei Lupalupa – Thriving Hawaiian Children.
Over the past few years, we have increased the use of data in our decision-making and planning. Data on key indicators allow us to deepen our understanding of the conditons of kamaliʻi across the pae ʻāina.
In this article, we are looking at the “Concentration of Native Hawaiian Children in Households with Less than Liveable Income” mapped to our different kīpuka, or regional service areas.*
This geospatial approach enables us to use information about where kamaliʻi in need of support are most likely to reside as we make decisions about how we plan to care for current and future generations.
We use liveable income (which we estimate as three times the poverty guideline for Hawaiʻi) as one of our key measures. High-quality research has shown that higher family incomes improve childrens’ educational, behavioral, and health outcomes. We also know that these benefits are passed on to future generations. In the figure we see that the estimated percentage of NH kamaliʻi who live in households without livable incomes ranges from 38% for Kīpuka Kauaʻi to 86% for Kīpuka Hilo.
Exploring indicators about Native Hawaiian wellbeing as part of planning and programming helps us use the resources left to us by Queen Liliʻuokalani more effectively to achieve the vision of Nā Kamalei Lupalupa. Learn more about our strategic plan and vision at our website www.onipaa.org or contact our research and data specialists by emailing email@example.com.
*Note: The estimated percentages of children living in households with less than a livable income are based on the 2015-2019 American Community Survey dataset as summarized for LT by SCIMA, with further rough estimates for Maui and Kauaʻi counties prepared by LT researchers using information on poverty levels in those areas published by Census Reporter.
Trenton Manson is the manager for Data Science at Liliʻuokalani Trust. He was raised in Kailua, Oʻahu, and has an M.S. in Data Science from Southern Methodist University.