Background/Context: To stimulate national and regional economic growth, policymakers and businesses have expressed great interest in growing the supply of workers with skills in science, technology, engineering, and math (STEM). A key link in this pipeline is high school, where learning experience plays a critical role in shaping student's interest in STEM careers (e.g., Bottia et al., 2015). This is also the period when females begin to lose interest in STEM (Sadler, et al., 2012). To improve STEM education, one of the national initiatives during the early 2010's focused on integrated applied STEM curricula (Gottfried & Bozick, 2016). By connecting the traditional STEM disciplines to the real-world/applied problems, the goal was to increase student participation in more meaningful and engaging STEM learning and generate greater interest in STEM or related careers. Project Lead the Way (PLTW) is one such program. PLTW began over two decades ago in a handful of high schools as career and technical education (CTE). Since then, the program has grown rapidly and is currently adopted by over 12,000 high schools nationally. In Missouri, the site of this study, the program expanded from 13 schools in 2005 to 174 schools by 2020 -- roughly 35% of all public high schools. Purpose/Objective/Research Question: Using the data on the population of Missouri public high school students, we investigate the impact of PLTW program expansion (ITT) and the impact of program participation on program participants (TOT). Our key outcomes are STEM major declaration in college, five-year STEM degree attainment, and completing a STEM degree or still enrolled in a STEM major in the fifth-year of college. Setting: Missouri has 537 traditional and charter public high schools, and 26% of them are in urban and suburban areas, and 18% and 57% are in town and rural areas, respectively. Population/Participants/Subjects: This study uses four cohorts of first-time 9th-grade students in Missouri who began public high school between 2010 and 2014. Each cohort has approximately 68,000 students attending 500 high schools. Intervention/Program/Practice: PLTW is designed to provide active, project-based learning in the largest three applied STEM fields--Engineering, Computer Science, and Biomedical Science ("Pathways"). Each pathway begins with foundational courses. They provide an overview and introduce major ideas of the field with a goal of developing an enthusiasm for further study. Advanced courses are designed to expand students' understanding of the field through deeper and specialized content. The Engineering and Biomedical Science pathways end with a capstone course which requires students to take their own idea from design through development of a product or plans to produce one. Research Design: This study exploits within-school variation in course availability across cohorts to estimate the ITT impact of PLTW expansion. We then use 2SLS to identify ToT. Our identification strategies are similar to Altonji (1995), Levine and Zimmerman (1995), Rose and Betts (2004), and Darolia et al. (2020). Data Collection and Analysis: Student data are linked to National Student Clearinghouse for post-secondary outcomes. School course-offering data contains all courses and course sections offered each semester by high school as well as class size for each course section. Following Darolia et al. (2020), we construct cohort-specific PLTW course availability, defined as the total number of PLTW course sections offered per 100 students during high school. Of all high schools, 62 schools first introduced PLTW in 2010 or earlier, 28 high schools introduced the program between 2011 and 2015, and 102 schools introduced it in or after 2016. These schools are considered "treatment" schools. Our data shows that PLTW course availability expanded over time in the treatment schools. The remaining 308 schools never offered PLTW, constituting "control schools". The following model is used to estimate the impact of program expansion (ITT impact) for student i, cohort c, and school j: Yicj = B0 + B1(cohort_c) + B2(Z_c) + B3(X_i) + B4(SFE_j) + e_icj, where cohort_c is cohort fixed effects, Zcj is course availability, Xicj is student covariates (8th-grade math and science scores, gender, race, and FRL), and SFEj is school fixed effects. The coefficient B1 captures cohort-to-cohort outcome fluctuations unrelated to Z, assumed to be common to all schools, and B2 is the effect associated with changes in PLTW availability (i.e., ITT). We use the 2-stage least squares to identify the impact on the treated. The first stage model estimates program participation using Z as an instrument. The predicted probability of program participation is entered as a predictor in the second stage model. Preliminary Results: Our ITT results show that, comparing across cohorts in the same school, PLTW participation rose when school expanded course availability. Also, within the same high school, we see improvements in STEM major declaration associated with increases in PLTW course availability. Five-year STEM degree completion is unaffected by PLTW expansion. However, when we include students who are still enrolled in STEM in their fifth-year of college as part of this outcome (i.e., this outcome equals one if students completed or are still enrolled as a STEM major), we find positive impacts of program expansion. The TOT results show that participating in PLTW increases the likelihood of STEM major declaration by nearly 10 percentage points (SE=0.02), as compared to the OLS estimate of 0.139 percentage points, SE=0.007. The impact on completing or still enrolled in STEM in the fifth-year of college is 5 percentage points (SE=0.016), vs. the OLS estimate of 0.095, SE=0.007. We find no impact on five-year STEM completion (the OLS estimate of 0.055, SE=0.005). Our next step is to investigate whether ITT and TOT impacts depend on students' academic readiness upon high school entry. We hypothesize that impacts are greater for students with higher academic readiness. We will also conduct subgroup analysis by gender. We then conduct sensitivity analysis for violations of identification assumptions (common trends assumption, exclusion restrictions, and exogeneity of instruments).