Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** the special supplemental…

Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** the special supplemental nutrition program for women, infants, and children (WIC) is the nation's third largest food and nutrition assistance program. In 2019, WIC served 6.4 million low-income pregnant and postpartum women, infants and children up to age five at a cost of $5.2 billion. The economic literature is sparse concerning the effects of WIC on food purchases and prices. Efforts to expand the literature are hampered by underreporting of WIC in household surveys. Our objective is to bridge these gaps with the following specific aims:1. Apply machine learning (ML) to improve the precision of WIC participation status in scanner data.2. Use the ML-based WIC participation variable from aim 1 to quantify the causal effects of the 2009 WIC food package revision on participants' food purchases and nutrition.3. Use ML to quantify the effects of the 2009 revision on retail prices, and, by extension, nonparticipants' food purchases and nutrition. We will train ML models using the restricted-use foodaps, where WIC participation is more accurately reported, and state-level variation in WIC-eligible food brands and timing of benefit issuance. The trained ML models will then predict WIC participation for households in the IRI consumer network household scanner panel. The 2009 WIC food package revision serves as a policy experiment to identify the effects of WIC on participants' food purchases and their overall nutritional quality. The policy-driven changes in WIC-eligible food types, brands and amounts will be leveraged to identify the effects of the revision on retail price and nonparticipants.