## 本次作业是来自美国的关于一个因果推断的**R语言代写**限时测试

**Problem 1 (20 points)**

This problem will have you replicate and analyze the results from Moser and Voena’s 2012 AER paper on the impact of the World War I “Trading with the Enemy Act” on U.S. domestic invention. The full citation is below Moser, P., & Voena, A. (2012). Compulsory licensing: Evidence from the trading with the enemy

act. American Economic Review, 102(1), 396-427.

The premise of the study is to evaluate the effect that “compulsory licensing” policy – that is, policies that permit domestic firms to violate foreign patents and produce foreign inventions without needing to obtain a license from the owner of the foreign patent – have on domestic invention. Does access to foreign inventions make domestic firms more innovative? The authors leverage an exogenous event in U.S. licensing policy that arose from World War I – the 1917 “Trading with the Enemy Act” (TWEA) which permitted U.S. firms to violate patents owned by enemy-country firms. This had the consequence of effectively licensing all patents from German-owned firms to U.S. firms after 1918 (that is, from 1919 onward), allowing them to produce these inventions without paying for a license from the German-owned company.

The authors look specifically at domestic innovation and patent activity in the organic chemicals sector. They note that only some of the sub-classes of organic chemicals (as defined by the US Patent Office) received any compulsory licenses under the Trading with the Enemy Act while others did not. They leverage this variation in exposure to the “treatment” of compulsory licensing to implement a differences-in-differences design looking at domestic firm patent activity in each of these sub-classes (comparing sub-classes that were exposed to compulsory licensing to those that were unexposed).

The unit of the dataset is the sub-class/year (471,120 observations) of 7248 US Patent and Trademark Office (USPTO) patent sub-classes over 65 years.

The dataset is patents.csv and the relevant variables are:

• uspto_class – USPTO Patent Sub-Class (unit)

• grntyr – Year of observation (year)

• count_usa – Count of patents granted to US-owned firms in the year

• count_for – Count of patents granted to foreign-owned (non-US) firms in the year

• treat – Treatment indicator – Whether the patent sub-class received any German patents under TWEA (after 1918 when the policy went into effect) (Note that this is not an indicator for the overall treatment group (whether the unit ever received treatment) – it is only 1 after 1918 for units that receive treatment but is still 0 for those “treated” units prior to the initiation of treatment)

**Question A (5 points)**

If you try to use a two-way fixed effects estimator on the dataset as it is, it will likely freeze up your computer as this is a very large dataset. We’ll instead first aggregate the data in a way that will let you use a simple difference-in-differences estimator to estimate the treatment effect.

Generate a point estimate for the average treatment effect of receiving treatment on the average annual count of US patents using a difference-in-differences estimator (using all post-treatment (1919-1939) and pre treatment (1875-1918) time periods. You should aggregate your data such that the outcome is the post-/pre difference in the outcome (preferably using tidyverse functions like group_by and summarize) and each row is a USPTO patent sub-class (rather than a sub-class/year observation) and use a difference-in-means estimator with the differenced outcome. Again, if you use lm_robust or even lm with two-way fixed effects,your computer will likely freeze up as there are many FE parameters to estimate.

Provide a 95% robust confidence interval and interpret your point estimate. Do we reject the null of no treatment effect at the α = .05 level?

**Question B (5 points)**

A colleague suggests that you should instead just compare the average differences in the count of US patents in the post-1918 period between exposed and unexposed sub-classes to estimate the treatment effect. Based on what we observe in the pre-1919 period, is ignorability of the treatment likely to hold under this strategy?

Discuss why or why not – what do you observe in the patent counts in the pre-treatment period between exposed and unexposed subclasses?

**Question C (5 points)**

We might be concerned that there are differential trends in pre-treatment patenting between those sub-classes exposed to the treatment and those exposed to control. Estimate the difference in the trend in US patents between exposed and unexposed sub-classes from 1918 to 1917, 1916, 1915, and 1914 (four estimates in total:

1918-1917, 1918-1916, 1918-1915, 1918-1914). Provide a 95% robust confidence interval for each of these estimates and interpret your results. Do we reject the null that any of these differ from 0 (at α = .05)? If the outcome trends were evolving in parallel, what would we expect these estimates to be? What do your results suggest for the validity of the parallel trends assumption?

**Question D (5 points)**

The authors adjust for covariates in addition to their out of concern for possible parallel trends violations.

One possible confounder that might be driving a parallel trends violation is the overall amount of foreign patenting in the sub-class and its change over time – reflecting general technological differences that might differ between the patent sub-classes. Since the treatment does not affect the amount of foreign patenting,this is a valid control.

Create a variable for the change between the post- and pre-treatment count of foreign patents in the USPTO subclass. Bin this variable into six (6) roughly-equally sized strata and estimate the effect of the treatment on US patenting (again using the differenced outcome) using a stratified difference-in-means estimator. Provide a robust 95% confidence interval and interpret your results. Do we reject the null of no treatment effect at the α = .05 level? Compare your results to your estimate from Question A and discuss why they might differ.

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