Kirill Borusyak Profile picture
Jun 23, 2021 13 tweets 5 min read Read on X
📢 I’d like to share with #TradeTwitter a 🧵 on what @XJaravel and I have learned about the unequal effects of international trade through both cost-of-living and wages in the U.S.

For those of you who have seen my JMP, this is a much-revised draft

dropbox.com/s/eiygfth61vp4… Image
@XJaravel Let’s start with the effects on costs-of-living, which are understood less well

Who benefits more from lower prices of imports in the US?

A typical guess & prior estimates: poor consumers who buy more tradable goods, esp. from China

So trade could REDUCE (real) inequality
@XJaravel But has it actually been documented who buys imported products and benefit when they become cheaper?

Not much, and that’s what we do as accurately as we can!

We measure import shares of spending across income and education groups using several newly linked datasets
Why focus on import shares of spending?

They are sufficient statistics for this “expenditure channel”. Consumer group that buys more imports benefits more when import prices fall

(Terms and conditions [of our Prop. 1] apply: perfect competition, partial equilibrium, and more)
But how can we measure who buys imports?

👉 Consumption data don’t say which products are imported, or contain imported inputs (and how much)
👉 Trade data don’t say who the consumers are

We need both at once. For all products. With detailed product definitions. Ugh.
We build not 1 but 3 new datasets:

👉 Industry-level for all goods & services: CEX spending data matched to US Input-Output table
👉 Firm-level for supermarket products: Nielsen spending data + confidential Economic Censuses
👉 Brand-level for cars: CEX + stats on car imports Image
Key finding: All income groups have similar spending shares on imports!

True, the poor buy more Chinese supermarket products and the rich buy European cars

But, the poor also buy cars assembled in Mexico, the rich buy Chinese electronics, and all these differences are small Image
This contrasts with findings from parametric approaches, e.g. by Fajgelbaum-@akhandelwal8. But we reconcile the results!

Turns out the AIDS demand system mechanically implies higher import shares for the poor

Non-homothetic CES does better, but direct measurement is our choice Image
Okay, so there is no heterogeneity by income in consumer exposure to trade

But what about the labor market: aren’t trade wars class wars these days, with trade hurting the poor through 10 different channels?

We develop a new exposure-based approach to shed light on that, too
Idea: if you work in an industry that exports (directly or indirectly), demand for your labor goes⬆️ with more trade. In import competing industries, ⬇️. Etc.

We derive 5 sufficient statistics of worker labor market to exposure to trade in a quantitative model with both channels Image
We find that >99% of exposure variation is within income groups

After a trade liberalization, wage changes can be unequal if industry mobility is limited, generating winners & losers

But effects are not correlated w/initial wage!
✔️ Unequal effects
❌ No effects on inequality Image
Recap:
👉Consumer & worker exposure patterns are informative about the distributional effects of trade shocks
👉In the US, income groups have similar % of imports in spending
👉And similar avg labor market exposure
👉Trade can generate winners/losers without affecting inequality
Many more theoretical and empirical extensions in the paper, and comments are welcome!

dropbox.com/s/eiygfth61vp4…

Bonus plot: find the brand of your car if you have one! Image

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More from @borusyak

Jul 25, 2023
🧵 Do shift-share IV regressions suffer from negative weight problems?

@CdeChaisemartin and Lei recently posted a WP (Worrying Paper, one could say) arguing that way

@instrumenthull and I are more optimistic and decided to share our view in a brief note
https://t.co/J6dUwHyMWkdropbox.com/scl/fi/vi0jkwo…
Image
We make 4 points about shift-share IVs under heterogeneous effects:

1/4) They identify a convexly weighted average of causal effects when the shocks are as-good-as-random

It's like a LATE result, and our ReStud paper w/@instrumenthull @XJaravel proves it in more generality
@instrumenthull @XJaravel 2/4) Negative "ex-post" weights in shift-share IVs are inevitable, as @CdeChaisemartin and Lei show - but they are not a problem with as-good-as-random shocks.

Indeed, they also arise in a RCT with multiple dosages levels but we (correctly) don't worry
Read 6 tweets
Jul 20, 2023
Here's my version of this very important point about negative weights:

You should consider the nature of treatment assignment and reasonable restrictions on treatment effects before worrying about negative weights, especially when you can’t avoid them by imputation

➡️🧵
There are two types of weights in regressions: "ex post" (conditionally on treatment realizations) and "ex ante" (before treatments are realized)

Ex post weights are negative for some treated observations for almost every regression: some obs in the comparison group are treated
Imputation methods as in our work w/@XJaravel @jannspiess are an important exception

But they require truly untreated observations which are not always available. Think minimum wage or tax changes where levels are never zero
Read 6 tweets
May 28, 2021
On the 1st anniversary of my twitter account, I'm happy to share w/the #EconTwitter community our #stata commands for event studies:

did_imputation: robust and efficient imputation estimator

event_plot: event study plots after various estimation methods

github.com/borusyak/did_i…
I’ve discussed did_imputation in the 🧵 on our paper w/@XJaravel and @jannspiess

Key points: the estimator is
- robust to heterogeneous effects
- efficient under homoskedasticity
- transparent
- robust to pre-testing
- works w/controls, triple-diffs, etc

Now on the plotting command:

With event_plot, you can make event study plots for at least 5 methods:
- our did_imputation
- robust estimators of @CdeChaisemartin-D’Haultfoeuille, Callaway-@pedrohcgs, Sun-Abraham
- traditional OLS

And combine them with each other if you wish! 5 event study estimators us...
Read 6 tweets
May 18, 2021
🥁 I’m thrilled to announce our paper w/@XJaravel and @jannspiess, “Revisiting Event Studies: Robust and Efficient Estimation” 🥁

It’s a fully revised version of our 2017 draft that the diff-in-diff loving audience may have seen

dropbox.com/s/y92mmyndlbku… Image
@XJaravel @jannspiess Event studies are diff-in-diff research designs with staggered adoption of treatment:

- Units in a panel get treated at different times (and stay treated forever)
- Some units may be never treated

We are interested in heterogenous or dynamic treatment effects
What do we do?

1) Clarify the assumptions behind event studies
2) Discuss problems with conventional OLS estimation, arising because the assumptions get conflated
3) Derive a robust & efficient "imputation" estimator, inference for it & a new pre-trend test (Stata code included)
Read 18 tweets
Sep 16, 2020
New WP: “Non-Random Exposure to Exogenous Shocks” (w/ @autoregress). Summary 🧵:

Papers often estimate causal effects by leveraging exogenous shocks that affect many observations jointly, to different extents

We show problems w/this & offer new solutions
dropbox.com/s/brhuxe1b1k8x… Image
Examples:

- New railroad construction affects all regions via increased market access

- State-level policies affect different people depending on demographics

- Trade shocks affect different regions

- Shocks propagate through networks.

(More in the paper!) Image
We discuss a likely bias in such settings due to non-random exposure to exogenous shocks

Imagine randomly connecting regions by roads, as in an RCT. These random shocks increase market access. But a regression on MA growth may suffer from omitted varables bias (OVB). Why? Image
Read 15 tweets

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