4. Interpret a differences-in-differences (DiD) quasi-experiment. Suppose that in 2018 the Ministry of Labour in India announced a program to reduce unemployment amongst youth from low-income, urban households, involving soft-skills training and youth employment centres. They have a 5-year strategy to roll out the program across 120 tier 1 and tier 2 cities, selecting the initial 60 cities based on a complex formula that includes youth unemployment statistics and other job market characteristics. After some bureaucratic and construction delays, they started rolling out the program at the beginning of 2020, just before the COVID-19 pandemic, which caused unemployment to increase overall. Suppose that in 2022 you are asked to use government data to evaluate the initial impacts of the program on urban youth unemployment, using a DiD design. You compare the 60 initial treatment cities (T) to the 60 control cities (C). a. Interpret the results of the DiD from the following figure, including providing the DiD estimate of the impact of the program. How would you test if the effect is statistically significant? Rate of youth unemployment 0.15 0.105 0.11 0.12 0.12 0.08 2018 2020 2022 b. Explain one potential threat to the validity of this DiD design, and what you would do to test for it