📢 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
@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
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
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
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
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
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!
🧵 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…
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
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
Key points: the estimator is
- robust to heterogeneous effects
- efficient under homoskedasticity
- transparent
- robust to pre-testing
- works w/controls, triple-diffs, etc
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
@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)
- 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!)
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?