Implementing a Change in the Poverty Data Brian Reeder, Assistant Superintendent Michael Elliott, State School Fund Coordinator April 24, 2014

Introduction What was implemented? Why it was implemented? What happened? Why this happened? What was learned?

What was implemented? HB 2098 passed in 2013 New Source of Poverty data Moved from 2000 Census data to new federal Small Area Income Poverty Estimate data Moved from two poverty calculations to one poverty calculation

Why was this implemented? Districts over 2,500 ADMw were using out-of-date data Smaller districts were using problematic data Recent poverty severity changes being overlooked

What happened New source of data resulted in a net increase in the number of students considered to be in poverty Some districts saw increases in their poverty counts, and some saw decreases Winners and Losers: 80 districts saw relative increase in funding 117 saw relative decrease in funding 54.5% of state-wide ADMw saw funding increase 45.5% of state-wide ADMw saw funding decrease

Why did this happen? Need to review the mechanics of the State School Fund to fully understand how changing the poverty formula caused winners and losers.

State School Fund Mechanics State School Fund Formula at equilibrium: Total Weights Total Funding = Funding Per Weight

State School Fund Mechanics Higher Total Funding = Higher Funding Per Weight Additional funding no additional weights: Same Total Weights

State School Fund Mechanics More Total Weights = Lower Funding Per Weight Additional weights, but no additional funding: Same Total Funding

Poverty Funding Mechanics No additional funding provided for poverty Result: Less funding per weight Estimated funding for per weight before change: $6,818 Estimated funding for per weight after change: $6,787 Loss per weight: ~$30 The data change does not mean more poverty…it means a more accurate count of the number of students in poverty NOTE: Current estimated funding per weight for : $6,866

Poverty Funding Mechanics Districts gained relative funding if they had an above-average change in their ADMw Districts lost relative funding if they had a below-average change in ADMw The largest percent increase in funding was Joseph SD at 6.5% The largest percent decrease in funding was Juntura at -2.1%

Implementation Lessons Data changes that affect districts differently will create winners and losers Need universally understood reason to change the formula or to change data sources Need to involve stakeholders early Need to outreach Change needs to be impartial Changes that improve the equity of the funding formula for the state as a whole can create winners and losers at the district level

Questions?