A canonical research design for social scientists is the so-called
“differences-in-differences” (DiD) design.^{1} In the classic 2x2 DiD
case (two units, two periods), a simple interaction effect between two
dummy variables suffices to recover the treatment effect. In base R this
might look something like:

where the resulting coefficient on the
`Dtreated_unitTRUE:Dpost_treatmentTRUE`

interaction term
represents the treatment effect.

Rather than manually specify the interaction term, in practice
researchers often use an equivalent formulation known as *two-way
fixed effects* (TWFE). The core idea of TWFE is that we can subsume
the interaction term from the previous code chunk by adding unit and
time fixed effects. A single treatment dummy can then be used to capture
the effect of treatment directly. A TWFE regression in base R might look
as follows:

where the treatment effect is now captured by the coefficient on the
`Dtreat`

dummy.

The TWFE shortcut is especially nice for more complicated panel data
settings with multiple units and multiple times periods. Speaking of
which, if you prefer to use a dedicated fixed effects / panel data
package like **fixest**, you could also estimate the
previous regression like so:

These TWFE regressions are easy to run and intuitive, and for a long time everyone was happy. But it was too good to last. A cottage industry of clever research now demonstrates that things are not quite so simple. Among other things, the standard TWFE formulation can impose strange (negative) weighting conditions on key parts of the estimation procedure. One implication is that you risk a high probability of estimate bias in the presence of staggered treatment rollouts, which are very common in real-life applications.

Fortunately, just as econometricians were taking away one of our
favourite tools, they were kind enough to replace it with some new ones.
Among these, the proposed approach by Wooldridge (2021,
2022)
is noteworthy. His idea might be paraphrased as stating that the problem
with TWFE is not that we were doing it in the first place. Rather, it’s
that we weren’t doing it enough. Instead of only including a single
treatment × time interaction, Wooldridge recommends that we saturate our
model with all possible interactions between treatment and time
variables, including treatment cohorts, as well as other covariates. He
goes on to show that this approach actually draws an equivalence between
different types of estimators (pooled OLS, twoway Mundlak regression,
etc.) So it’s not entirely clear what to call it. But Wooldridge refers
to the general idea as as *extended* TWFE—or, ETWFE—which I
rather like and is where this package takes its name.

The Wooldridge ETWFE solution is intuitive and elegant. But it is
also rather tedious and error prone to code up manually. You have to
correctly specify all the possible interactions, demean control
variables within groups, and then recover the treatment effects of
interest via an appropriate marginal effect aggregation. The
**etwfe** package aims to simplify the process by providing
convenience functions that do all this work for you.

To demonstrate the core functionality of **etwfe**,
we’ll use the `mpdta`

dataset on US teen employment from the **did** package
(which you’ll need to install separately).

```
# install.packages("did")
data("mpdta", package = "did")
head(mpdta)
#> year countyreal lpop lemp first.treat treat
#> 866 2003 8001 5.896761 8.461469 2007 1
#> 841 2004 8001 5.896761 8.336870 2007 1
#> 842 2005 8001 5.896761 8.340217 2007 1
#> 819 2006 8001 5.896761 8.378161 2007 1
#> 827 2007 8001 5.896761 8.487352 2007 1
#> 937 2003 8019 2.232377 4.997212 2007 1
```

“Treatment” in this dataset refers to an increase in the minimum wage
rate. In the examples that follow, our goal is to estimate the effect of
this minimum wage treatment (`treat`

) on the log of teen
employment (`lemp`

). Notice that the panel ID is at the
county level (`countyreal`

), but treatment was staggered
across cohorts (`first.treat`

) so that a group of counties
were treated at the same time. In addition to these staggered treatment
effects, we also observe log population (`lpop`

) as a
potential control variable.

Let’s load **etwfe** and work through its basic
functionality. As we’ll see, the core workflow of the package involves
two consecutive function calls: 1) `etwfe()`

and 2)
`emfx()`

.

`etwfe`

Given the package name, it won’t surprise you to learn that the key
estimating function is `etwfe()`

. Here’s how it would look
for our example dataset.

```
library(etwfe)
mod =
etwfe(
fml = lemp ~ lpop, # outcome ~ controls
tvar = year, # time variable
gvar = first.treat, # group variable
data = mpdta, # dataset
vcov = ~countyreal # vcov adjustment (here: clustered)
)
```

There are a few things to say about our `etwfe()`

argument
choices and other function options, but we’ll leave those details aside
until a bit later. Right now, just know that all of the above arguments
are required except `vcov`

(though I generally recommend it
too, since we probably want to cluster our standard errors at the
individual unit level).

Let’s take a look at our model object.

```
mod
#> OLS estimation, Dep. Var.: lemp
#> Observations: 2,500
#> Fixed-effects: first.treat: 4, year: 5
#> Varying slopes: lpop (first.treat: 4), lpop (year: 5)
#> Standard-errors: Clustered (countyreal)
#> Estimate Std. Error t value
#> .Dtreat:first.treat::2004:year::2004 -0.021248 0.021728 -0.977890
#> .Dtreat:first.treat::2004:year::2005 -0.081850 0.027375 -2.989963
#> .Dtreat:first.treat::2004:year::2006 -0.137870 0.030795 -4.477097
#> .Dtreat:first.treat::2004:year::2007 -0.109539 0.032322 -3.389024
#> .Dtreat:first.treat::2006:year::2006 0.002537 0.018883 0.134344
#> .Dtreat:first.treat::2006:year::2007 -0.045093 0.021987 -2.050907
#> .Dtreat:first.treat::2007:year::2007 -0.045955 0.017975 -2.556568
#> .Dtreat:first.treat::2004:year::2004:lpop_dm 0.004628 0.017584 0.263184
#> .Dtreat:first.treat::2004:year::2005:lpop_dm 0.025113 0.017904 1.402661
#> .Dtreat:first.treat::2004:year::2006:lpop_dm 0.050735 0.021070 2.407884
#> .Dtreat:first.treat::2004:year::2007:lpop_dm 0.011250 0.026617 0.422648
#> .Dtreat:first.treat::2006:year::2006:lpop_dm 0.038935 0.016472 2.363731
#> .Dtreat:first.treat::2006:year::2007:lpop_dm 0.038060 0.022477 1.693276
#> .Dtreat:first.treat::2007:year::2007:lpop_dm -0.019835 0.016198 -1.224528
#> Pr(>|t|)
#> .Dtreat:first.treat::2004:year::2004 3.2860e-01
#> .Dtreat:first.treat::2004:year::2005 2.9279e-03 **
#> .Dtreat:first.treat::2004:year::2006 9.3851e-06 ***
#> .Dtreat:first.treat::2004:year::2007 7.5694e-04 ***
#> .Dtreat:first.treat::2006:year::2006 8.9318e-01
#> .Dtreat:first.treat::2006:year::2007 4.0798e-02 *
#> .Dtreat:first.treat::2007:year::2007 1.0866e-02 *
#> .Dtreat:first.treat::2004:year::2004:lpop_dm 7.9252e-01
#> .Dtreat:first.treat::2004:year::2005:lpop_dm 1.6134e-01
#> .Dtreat:first.treat::2004:year::2006:lpop_dm 1.6407e-02 *
#> .Dtreat:first.treat::2004:year::2007:lpop_dm 6.7273e-01
#> .Dtreat:first.treat::2006:year::2006:lpop_dm 1.8474e-02 *
#> .Dtreat:first.treat::2006:year::2007:lpop_dm 9.1027e-02 .
#> .Dtreat:first.treat::2007:year::2007:lpop_dm 2.2133e-01
#> ... 10 variables were removed because of collinearity (.Dtreat:first.treat::2006:year::2004, .Dtreat:first.treat::2006:year::2005 and 8 others [full set in $collin.var])
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.537131 Adj. R2: 0.87167
#> Within R2: 8.449e-4
```

What `etwfe()`

has done underneath the hood is construct a
treatment dummy variable `.Dtreat`

and saturated it together
with the other variables of interest as a set of multiway interaction
terms.^{2}

You may have noticed that our `etwfe()`

call returns a
standard **fixest**
object, since this is what it uses to perform the underlying estimation.
All of the associated methods and functions from the
**fixest** package are thus compatible with our model
object. For example, we could plot the raw regression coefficients with
`fixest::coefplot()`

, or print them to a nice regression
table with `fixest::etable()`

. However, the raw coefficients
from an `etwfe()`

estimation are not particularly meaningful
in of themselves. Recall that these are complex, multiway interaction
terms that are probably hard to to interpret on their own. This insight
leads us to our next key function…

`emfx`

If the raw `etwfe`

coefficients aren’t particularly useful
by themselves, what can we do with them instead? Well, we probably want
to aggregate them along some dimension of interest (e.g., by groups or
time, or as an event study). A natural way to perform these aggregations
is by recovering the appropriate marginal effects. The
**etwfe** package provides another convenience function for
doing so: `emfx()`

, which is a thin(ish) wrapper around `marginaleffects::slopes()`

.

For example, we can recover the average treatment effect on the treated (ATT) as follows.

```
emfx(mod)
#>
#> Term Contrast .Dtreat Estimate Std. Error z Pr(>|z|)
#> .Dtreat mean(TRUE) - mean(FALSE) TRUE -0.0506 0.0125 -4.05 <0.001
#> S 2.5 % 97.5 %
#> 14.3 -0.0751 -0.0261
#>
#> Columns: term, contrast, .Dtreat, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
```

In other words, our model is telling us that an increase in the minimum wage leads to an approximate 5 percent decrease in teen employment.

Beyond simple ATTs, `emfx()`

also supports other types of
aggregations via the `type`

argument. For example, we can use
`type = "calendar"`

to get ATTs by period, or
`type = "group"`

to get ATTs by cohort groups. But the option
that will probably be useful to most people is
`type = "event"`

, which will recover dynamic treatment
effects *a la* an event study. Let’s try this out and then save
the resulting object, since I plan to reuse it in a moment.

```
mod_es = emfx(mod, type = "event")
mod_es
#>
#> Term Contrast event Estimate Std. Error z Pr(>|z|) S
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -0.0332 0.0134 -2.48 0.013 6.3
#> .Dtreat mean(TRUE) - mean(FALSE) 1 -0.0573 0.0172 -3.34 <0.001 10.2
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -0.1379 0.0308 -4.48 <0.001 17.0
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -0.1095 0.0323 -3.39 <0.001 10.5
#> 2.5 % 97.5 %
#> -0.0594 -0.00701
#> -0.0910 -0.02373
#> -0.1982 -0.07751
#> -0.1729 -0.04619
#>
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
```

Our event study suggests that the teen disemployment effect of a minimum wage hike is fairly modest at first (3%), but increases over the next few years (>10%). In the next section, we’ll look at ways to communicate this kind of finding to your audience.

Since `emfx()`

produces a standard
`marginaleffects`

object, we can pass it on to other
supported methods and packages. For example, we can pass it on to **modelsummary**
to get a nice regression table of the event study coefficients. Note the
use of the `shape`

and `coef_rename`

arguments
below; these are optional but help to make the output look a bit
nicer.

```
library(modelsummary)
# Quick renaming function to replace ".Dtreat" with something more meaningful
rename_fn = function(old_names) {
new_names = gsub(".Dtreat", "Years post treatment =", old_names)
setNames(new_names, old_names)
}
modelsummary(
mod_es,
shape = term:event:statistic ~ model,
coef_rename = rename_fn,
gof_omit = "Adj|Within|IC|RMSE",
title = "Event study",
notes = "Std. errors are clustered at the county level"
)
```

(1) | |
---|---|

Years post treatment = 0 | -0.033 |

(0.013) | |

Years post treatment = 1 | -0.057 |

(0.017) | |

Years post treatment = 2 | -0.138 |

(0.031) | |

Years post treatment = 3 | -0.110 |

(0.032) | |

Num.Obs. | 2500 |

R2 | 0.873 |

FE..first.treat | X |

FE..year | X |

Std. errors are clustered at the county level |

For visualization, you can pass it on to your preferred plotting method. For example:

```
library(ggplot2)
theme_set(
theme_minimal() + theme(panel.grid.minor = element_blank())
)
ggplot(mod_es, aes(x = event, y = estimate, ymin = conf.low, ymax = conf.high)) +
geom_hline(yintercept = 0) +
geom_pointrange(col = "darkcyan") +
labs(x = "Years post treatment", y = "Effect on log teen employment")
```

Note that `emfx`

only reports post-treatment effects. All
pre-treatment effects are swept out of the estimation because of the way
that ETWFE is set up. In fact, all pre-treatment effects are
mechanistically set to zero. This means that ETWFE cannot be used for
interrogating pre-treatment fit (say, a visual inspection for parallel
pre-trends). Still, you can get these zero pre-treatment effects by
changing the `post_only`

argument. I emphasize that doing so
is strictly performative—again, pre-treatment effects are zero by
estimation design—but it might make your event study plot more
aesthetically pleasing.

```
# Use post_only = FALSE to get the "zero" pre-treatment effects
mod_es2 = emfx(mod, type = "event", post_only = FALSE)
ggplot(mod_es2, aes(x = event, y = estimate, ymin = conf.low, ymax = conf.high)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = -1, lty = 2) +
geom_pointrange(col = "darkcyan") +
labs(
x = "Years post treatment", y = "Effect on log teen employment",
caption = "Note: Zero pre-treatment effects for illustrative purposes only."
)
#> Warning: Removed 4 rows containing missing values (`geom_segment()`).
```

So far we’ve limited ourselves to homogeneous treatment effects, where the impact of treatment (i.e., minimum wage hike) is averaged across all US counties in our dataset. However, many research problems require us to estimate treatment effects separately across groups and then, potentially, test for differences between them. For example, we might want to test whether the efficacy of a new vaccine differs across age groups, or whether a marketing campaign was equally successful across genders. The ETWFE framework naturally lends itself to these kinds of heterogeneous treatment effects.

Consider the following example, where we first create a logical dummy variable for all US counties in the eight Great Lake states (GLS).

```
gls_fips = c("IL" = 17, "IN" = 18, "MI" = 26, "MN" = 27,
"NY" = 36, "OH" = 39, "PA" = 42, "WI" = 55)
mpdta$gls = substr(mpdta$countyreal, 1, 2) %in% gls_fips
```

Now imagine that we are interested in estimating separate treatment
effects for GLS versus non-GLS counties. We do this simply by invoking
the optional `xvar`

argument as part of our
`etwfe()`

call.^{3} Any subsequent `emfx()`

call on
this object will automatically recognize that we want to recover
treatment effects by these two distinct groups.

```
hmod = etwfe(
lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta,
vcov = ~countyreal,
xvar = gls ## <= het. TEs by gls
)
# Heterogeneous ATTs (could also specify `type = "event"`, etc.)
emfx(hmod)
#>
#> Term Contrast .Dtreat gls Estimate Std. Error z
#> .Dtreat mean(TRUE) - mean(FALSE) TRUE FALSE -0.0637 0.0376 -1.69
#> .Dtreat mean(TRUE) - mean(FALSE) TRUE TRUE -0.0472 0.0271 -1.74
#> Pr(>|z|) S 2.5 % 97.5 %
#> 0.0906 3.5 -0.137 0.01007
#> 0.0817 3.6 -0.100 0.00594
#>
#> Columns: term, contrast, .Dtreat, gls, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
```

The above point estimates might tempt us to think that minimum wage
hikes caused less teen disemployment in GLS counties than in the rest of
the US on average. However, to test this formally we can invoke the
powerful `hypothesis`

infrastructure of the underlying **marginaleffects**
package. Probably the easiest way to do this is by using
`b[i]`

-style positional arguments, where “`[i]`

”
denotes the row of the `emfx()`

return object. Thus, by
specifying `hypothesis = "b1 = b2"`

, we can test whether the
ATTs from row 1 (non-GLS) and row 2 (GLS) are different from one
another.

```
emfx(hmod, hypothesis = "b1 = b2")
#>
#> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> b1=b2 -0.0164 0.0559 -0.294 0.769 0.4 -0.126 0.093
#>
#> Columns: term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high
```

Here we see that there is actually no statistical difference in the average disemployment effect between GLS and non-GLS counties.

One final aside is that you can easily display the results of
heterogeneous treatment effects in plot or table form. Here’s an example
of the latter, where we again make use of the
`modelsummary(..., shape = ...)`

argument.

```
modelsummary(
models = list("GLS county" = emfx(hmod)),
shape = term + statistic ~ model + gls, # add xvar variable (here: gls)
coef_map = c(".Dtreat" = "ATT"),
gof_map = NA,
title = "Comparing the ATT on GLS and non-GLS counties"
)
```

GLS county | ||
---|---|---|

FALSE | TRUE | |

ATT | -0.064 | -0.047 |

(0.038) | (0.027) |

While the simple example here has been limited to a binary comparison
of group

ATTs, note the same logic carries over to richer settings. We can use
the exact same workflow to estimate heterogeneous treatment effects by
different aggregations (e.g., event studies) and across groups with many
levels.

Another key feature of the ETWFE approach—one that sets it apart from
other advanced DiD implementations and extensions—is that it supports
nonlinear model (distribution / link) families. Users need simply invoke
the `family`

argument. Here’s a brief example, where we
recast our earlier event-study as a Poisson regression.

```
mpdta$emp = exp(mpdta$lemp)
etwfe(
emp ~ lpop, tvar = year, gvar = first.treat, data = mpdta, vcov = ~countyreal,
family = "poisson"
) |>
emfx("event")
#>
#> Term Contrast event Estimate Std. Error z Pr(>|z|)
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -25.35 15.9 -1.5942 0.111
#> .Dtreat mean(TRUE) - mean(FALSE) 1 1.09 41.8 0.0261 0.979
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -75.12 22.3 -3.3696 <0.001
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -101.82 28.1 -3.6234 <0.001
#> S 2.5 % 97.5 %
#> 3.2 -56.5 5.82
#> 0.0 -80.9 83.09
#> 10.4 -118.8 -31.43
#> 11.7 -156.9 -46.75
#>
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
```

Thinking of the **etwfe** workflow again as a pair of
consecutive functional calls, the first `etwfe()`

stage tends
to be very fast. We’re leveraging the incredible performance of
**fixest** and also taking some shortcuts to avoid wasting
time on nuisance parameters. See the Regarding fixed effects section
below for more details about this.

For its part, the second `emfx()`

stage also tends to be
pretty performant. If your data has less than 100k rows, it’s unlikely
that you’ll have to wait more than a few seconds to obtain results.
However, `emfx`

’s computation time does tend to scale
non-linearly with the size of the original data, as well as the number
of interactions from the underlying `etwfe`

model object.
Without getting too deep into the weeds, we are relying on a numerical
delta method of the (excellent) **marginaleffects** package
underneath the hood to recover the ATTs of interest. This method
requires estimating two prediction models for *each* coefficient
in the model and then computing their standard errors. So it’s a
potentially expensive operation that can push the computation time for
large datasets (> 1m rows) up to several minutes or longer.

Fortunately, there are two complementary strategies that you can use
to speed things up. The first is to turn off the most expensive part of
the whole procedure—standard error calculation—by calling
`emfx(..., vcov = FALSE)`

. Doing so should bring the
estimation time back down to a few seconds or less, even for datasets in
excess of a million rows. Of course, the loss of standard errors might
not be an acceptable trade-off for projects where statistical inference
is critical. But the good news is this first strategy can still be
combined our second strategy: it turns out that collapsing the data by
groups prior to estimating the marginal effects can yield substantial
speed gains on its own. Users can do this by invoking the
`emfx(..., collapse = TRUE)`

argument. While the effect here
is not as dramatic as the first strategy, collapsing the data does have
the virtue of retaining information about the standard errors. The
trade-off this time, however, is that collapsing our data does lead to a
loss in accuracy for our estimated parameters. On the other hand,
testing suggests that this loss in accuracy tends to be relatively
minor, with results equivalent up to the 1st or 2nd significant decimal
place (or even better).

Summarizing, here is a quick plan of attack for you to try if you are worried about the estimation time for large datasets and models:

- Estimate
`mod = etwfe(...)`

as per usual. - Run
`emfx(mod, vcov = FALSE, ...)`

. - Run
`emfx(mod, vcov = FALSE, collapse = TRUE, ...)`

. - Compare the point estimates from steps 1 and 2. If they are are
similar enough to your satisfaction, get the approximate standard errors
by running
`emfx(mod, collapse = TRUE, ...)`

.

It’s a bit of performance art, since all of the examples in this vignette complete very quickly anyway. But here is a reworking of our earlier event study example to demonstrate this performance-conscious workflow.

```
# Step 0 already complete: using the same `mod` object from earlier...
# Step 1
emfx(mod, type = "event", vcov = FALSE)
#>
#> Term Contrast event Estimate
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -0.0332
#> .Dtreat mean(TRUE) - mean(FALSE) 1 -0.0573
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -0.1379
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -0.1095
#>
#> Columns: term, contrast, event, estimate, predicted, predicted_hi, predicted_lo
# Step 2
emfx(mod, type = "event", vcov = FALSE, collapse = TRUE)
#>
#> Term Contrast event Estimate
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -0.0216
#> .Dtreat mean(TRUE) - mean(FALSE) 1 -0.0635
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -0.1379
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -0.1095
#>
#> Columns: term, contrast, event, estimate, predicted, predicted_hi, predicted_lo
# Step 3: Results from 1 and 2 are similar enough, so get approx. SEs
mod_es2 = emfx(mod, type = "event", collapse = TRUE)
```

To put a fine point on it, we can can compare our original event study with the collapsed estimates and see that the results are indeed very similar.

```
modelsummary(
list("Original" = mod_es, "Collapsed" = mod_es2),
shape = term:event:statistic ~ model,
coef_rename = rename_fn,
gof_omit = "Adj|Within|IC|RMSE",
title = "Event study",
notes = "Std. errors are clustered at the county level"
)
```

Original | Collapsed | |
---|---|---|

Years post treatment = 0 | -0.033 | -0.022 |

(0.013) | (0.011) | |

Years post treatment = 1 | -0.057 | -0.063 |

(0.017) | (0.017) | |

Years post treatment = 2 | -0.138 | -0.138 |

(0.031) | (0.031) | |

Years post treatment = 3 | -0.110 | -0.110 |

(0.032) | (0.032) | |

Num.Obs. | 2500 | 2500 |

R2 | 0.873 | 0.873 |

FE..first.treat | X | X |

FE..year | X | X |

Std. errors are clustered at the county level |

Now that you’ve seen **etwfe** in action, let’s circle
back to what the package is doing under the hood. This section isn’t
necessary for you to use the package; feel free to skip it. But a review
of the internal details should help you to optimize for different
scenarios and also give you a better understanding of
**etwfe’s** default choices.

As I keep reiterating, the ETWFE approach basically involves saturating the regression with interaction effects. You can easily grab the formula of an estimated model to see for yourself.

```
mod$fml_all
#> $linear
#> lemp ~ .Dtreat:i(first.treat, i.year, ref = 0, ref2 = 2003)/lpop_dm
#> <environment: 0x5555680c11d0>
#>
#> $fixef
#> ~first.treat + first.treat[[lpop]] + year + year[[lpop]]
```

At this point, however, you may notice a few things. The first is
that our formula references several variables that aren’t in the
original dataset. An obvious one is the `.Dtreat`

treatment
dummy. A more subtle one is `lpop_dm`

, which is the
*demeaned* (i.e., group-centered) version of our
`lpop`

control variable. All control variables have to be
demeaned before they are interacted in the ETWFE setting. Here’s how you
could have constructed the dataset ahead of time and estimated the ETWFE
regression manually:

```
# First construct the dataset
mpdta2 = mpdta |>
transform(
.Dtreat = year >= first.treat & first.treat != 0,
lpop_dm = ave(lpop, first.treat, year, FUN = \(x) x - mean(x, na.rm = TRUE))
)
# Then estimate the manual version of etwfe
mod2 = fixest::feols(
lemp ~ .Dtreat:i(first.treat, i.year, ref = 0, ref2 = 2003) / lpop_dm |
first.treat[lpop] + year[lpop],
data = mpdta2,
vcov = ~countyreal
)
```

We can confirm that the manual approach yields the same output as our
original etwfe regression. I’ll use **modelsummary** to do
that here, since I’ve already loaded it above.^{4}.

```
modelsummary(
list("etwfe" = mod, "manual" = mod2),
gof_map = NA # drop all goodness-of-fit info for brevity
)
```

etwfe | manual | |
---|---|---|

.Dtreat × first.treat = 2004 × year = 2004 | -0.021 | -0.021 |

(0.022) | (0.022) | |

.Dtreat × first.treat = 2004 × year = 2005 | -0.082 | -0.082 |

(0.027) | (0.027) | |

.Dtreat × first.treat = 2004 × year = 2006 | -0.138 | -0.138 |

(0.031) | (0.031) | |

.Dtreat × first.treat = 2004 × year = 2007 | -0.110 | -0.110 |

(0.032) | (0.032) | |

.Dtreat × first.treat = 2006 × year = 2006 | 0.003 | 0.003 |

(0.019) | (0.019) | |

.Dtreat × first.treat = 2006 × year = 2007 | -0.045 | -0.045 |

(0.022) | (0.022) | |

.Dtreat × first.treat = 2007 × year = 2007 | -0.046 | -0.046 |

(0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2004 × lpop_dm | 0.005 | 0.005 |

(0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2005 × lpop_dm | 0.025 | 0.025 |

(0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2006 × lpop_dm | 0.051 | 0.051 |

(0.021) | (0.021) | |

.Dtreat × first.treat = 2004 × year = 2007 × lpop_dm | 0.011 | 0.011 |

(0.027) | (0.027) | |

.Dtreat × first.treat = 2006 × year = 2006 × lpop_dm | 0.039 | 0.039 |

(0.016) | (0.016) | |

.Dtreat × first.treat = 2006 × year = 2007 × lpop_dm | 0.038 | 0.038 |

(0.022) | (0.022) | |

.Dtreat × first.treat = 2007 × year = 2007 × lpop_dm | -0.020 | -0.020 |

(0.016) | (0.016) |

To transform these raw coefficients into their more meaningful ATT
counterparts, we just need to perform the appropriate marginal effects
operation. For example, here’s how we can get both the simple ATTs and
event-study ATTs from earlier. This is what `emfx()`

is doing
behind the scenes.

```
library(marginaleffects)
# Simple ATT
slopes(
mod2,
newdata = subset(mpdta2, .Dtreat), # we only want rows where .Dtreat is TRUE
variables = ".Dtreat",
by = ".Dtreat"
)
#>
#> Term Contrast .Dtreat Estimate Std. Error z Pr(>|z|)
#> .Dtreat mean(TRUE) - mean(FALSE) TRUE -0.0506 0.0125 -4.05 <0.001
#> S 2.5 % 97.5 %
#> 14.3 -0.0751 -0.0261
#>
#> Columns: term, contrast, .Dtreat, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
# Event study
slopes(
mod2,
newdata = transform(subset(mpdta2, .Dtreat), event = year - first.treat),
variables = ".Dtreat",
by = "event"
)
#>
#> Term Contrast event Estimate Std. Error z Pr(>|z|) S
#> .Dtreat mean(TRUE) - mean(FALSE) 0 -0.0332 0.0134 -2.48 0.013 6.3
#> .Dtreat mean(TRUE) - mean(FALSE) 1 -0.0573 0.0172 -3.34 <0.001 10.2
#> .Dtreat mean(TRUE) - mean(FALSE) 2 -0.1379 0.0308 -4.48 <0.001 17.0
#> .Dtreat mean(TRUE) - mean(FALSE) 3 -0.1095 0.0323 -3.39 <0.001 10.5
#> 2.5 % 97.5 %
#> -0.0594 -0.00701
#> -0.0910 -0.02373
#> -0.1982 -0.07751
#> -0.1729 -0.04619
#>
#> Columns: term, contrast, event, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo
```

Let’s switch gears and talk about fixed effects quickly. If you are a
regular **fixest** user, you may have noticed that we’ve
been invoking its varying
slopes syntax in the fixed effect slot (i.e.,
`first.treat[lpop]`

and `year[lpop]`

). The reason
for this is part practical, part philosophical. From a practical
perspective, `factor_var[numeric_var]`

is equivalent to base
R’s `factor_var/numeric_var`

“nesting” syntax but is much
faster for high-dimensional factors.^{5} From a philosophical perspective,
**etwfe** tries to limit the amount of extraneous
information that it reports to users. Most of the interaction effects in
the ETWFE framework are just acting as controls. By relegating them to
the fixed effects slot, we can avoid polluting the user’s console with a
host of extra coefficients. Nonetheless, we can control this behaviour
with the `fe`

(“fixed effects”) argument. Consider the
following options and their manual equivalents.

```
# fe = "feo" (fixed effects only)
mod_feo = etwfe(
lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta, vcov = ~countyreal,
fe = "feo"
)
# ... which is equivalent to the manual regression
mod_feo2 = fixest::feols(
lemp ~ .Dtreat:i(first.treat, i.year, ref = 0, ref2 = 2003) / lpop_dm +
lpop + i(first.treat, lpop, ref = 0) + i(year, lpop, ref = 2003) |
first.treat + year,
data = mpdta2, vcov = ~countyreal
)
# fe = "none"
mod_none = etwfe(
lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta, vcov = ~countyreal,
fe = "none"
)
# ... which is equivalent to the manual regression
mod_none2 = fixest::feols(
lemp ~ .Dtreat:i(first.treat, i.year, ref = 0, ref2 = 2003) / lpop_dm +
lpop + i(first.treat, lpop, ref = 0) + i(year, lpop, ref = 2003) +
i(first.treat, ref = 0) + i(year, ref = 2003),
data = mpdta2, vcov = ~countyreal
)
```

I’ll leave it up to you to pass any of these models to
`emfx`

to confirm that they give correct aggregated treatment
effects. But we can quickly demonstrate in a regression table that they
all return the same raw coefficients.

```
mods = list(
"etwfe" = mod,
"manual" = mod2,
"etwfe (feo)" = mod_feo,
"manual (feo)" = mod_feo2,
"etwfe (none)" = mod_none,
"manual (none)" = mod_none2
)
modelsummary(mods, gof_map = NA)
```

etwfe | manual | etwfe (feo) | manual (feo) | etwfe (none) | manual (none) | |
---|---|---|---|---|---|---|

.Dtreat × first.treat = 2004 × year = 2004 | -0.021 | -0.021 | -0.021 | -0.021 | -0.021 | -0.021 |

(0.022) | (0.022) | (0.022) | (0.022) | (0.022) | (0.022) | |

.Dtreat × first.treat = 2004 × year = 2005 | -0.082 | -0.082 | -0.082 | -0.082 | -0.082 | -0.082 |

(0.027) | (0.027) | (0.027) | (0.027) | (0.027) | (0.027) | |

.Dtreat × first.treat = 2004 × year = 2006 | -0.138 | -0.138 | -0.138 | -0.138 | -0.138 | -0.138 |

(0.031) | (0.031) | (0.031) | (0.031) | (0.031) | (0.031) | |

.Dtreat × first.treat = 2004 × year = 2007 | -0.110 | -0.110 | -0.110 | -0.110 | -0.110 | -0.110 |

(0.032) | (0.032) | (0.032) | (0.032) | (0.032) | (0.032) | |

.Dtreat × first.treat = 2006 × year = 2006 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 |

(0.019) | (0.019) | (0.019) | (0.019) | (0.019) | (0.019) | |

.Dtreat × first.treat = 2006 × year = 2007 | -0.045 | -0.045 | -0.045 | -0.045 | -0.045 | -0.045 |

(0.022) | (0.022) | (0.022) | (0.022) | (0.022) | (0.022) | |

.Dtreat × first.treat = 2007 × year = 2007 | -0.046 | -0.046 | -0.046 | -0.046 | -0.046 | -0.046 |

(0.018) | (0.018) | (0.018) | (0.018) | (0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2004 × lpop_dm | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 | 0.005 |

(0.018) | (0.018) | (0.018) | (0.018) | (0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2005 × lpop_dm | 0.025 | 0.025 | 0.025 | 0.025 | 0.025 | 0.025 |

(0.018) | (0.018) | (0.018) | (0.018) | (0.018) | (0.018) | |

.Dtreat × first.treat = 2004 × year = 2006 × lpop_dm | 0.051 | 0.051 | 0.051 | 0.051 | 0.051 | 0.051 |

(0.021) | (0.021) | (0.021) | (0.021) | (0.021) | (0.021) | |

.Dtreat × first.treat = 2004 × year = 2007 × lpop_dm | 0.011 | 0.011 | 0.011 | 0.011 | 0.011 | 0.011 |

(0.027) | (0.027) | (0.027) | (0.027) | (0.027) | (0.027) | |

.Dtreat × first.treat = 2006 × year = 2006 × lpop_dm | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 | 0.039 |

(0.016) | (0.016) | (0.016) | (0.016) | (0.016) | (0.016) | |

.Dtreat × first.treat = 2006 × year = 2007 × lpop_dm | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 |

(0.022) | (0.022) | (0.022) | (0.022) | (0.022) | (0.022) | |

.Dtreat × first.treat = 2007 × year = 2007 × lpop_dm | -0.020 | -0.020 | -0.020 | -0.020 | -0.020 | -0.020 |

(0.016) | (0.016) | (0.016) | (0.016) | (0.016) | (0.016) | |

lpop | 1.065 | 1.065 | 1.065 | 1.065 | ||

(0.022) | (0.022) | (0.022) | (0.022) | |||

first.treat = 2004 × lpop | 0.051 | 0.051 | 0.051 | 0.051 | ||

(0.038) | (0.038) | (0.038) | (0.038) | |||

first.treat = 2006 × lpop | -0.041 | -0.041 | -0.041 | -0.041 | ||

(0.047) | (0.047) | (0.047) | (0.047) | |||

first.treat = 2007 × lpop | 0.056 | 0.056 | 0.056 | 0.056 | ||

(0.039) | (0.039) | (0.039) | (0.039) | |||

year = 2004 × lpop | 0.011 | 0.011 | 0.011 | 0.011 | ||

(0.008) | (0.008) | (0.008) | (0.008) | |||

year = 2005 × lpop | 0.021 | 0.021 | 0.021 | 0.021 | ||

(0.008) | (0.008) | (0.008) | (0.008) | |||

year = 2006 × lpop | 0.011 | 0.011 | 0.011 | 0.011 | ||

(0.011) | (0.011) | (0.011) | (0.011) | |||

year = 2007 × lpop | 0.021 | 0.021 | 0.021 | 0.021 | ||

(0.012) | (0.012) | (0.012) | (0.012) | |||

(Intercept) | 2.260 | 2.260 | ||||

(0.088) | (0.088) | |||||

first.treat = 2004 | 0.038 | 0.038 | ||||

(0.154) | (0.154) | |||||

first.treat = 2006 | 0.461 | 0.461 | ||||

(0.213) | (0.213) | |||||

first.treat = 2007 | -0.286 | -0.286 | ||||

(0.167) | (0.167) | |||||

year = 2004 | -0.090 | -0.090 | ||||

(0.031) | (0.031) | |||||

year = 2005 | -0.110 | -0.110 | ||||

(0.033) | (0.033) | |||||

year = 2006 | -0.052 | -0.052 | ||||

(0.040) | (0.040) | |||||

year = 2007 | -0.057 | -0.057 | ||||

(0.045) | (0.045) |

A final point to note about fixed effects is that
**etwfe** defaults to using group-level (i.e.,
cohort-level) fixed effects like `first.treat`

, rather than
unit-level fixed effects like `countyreal`

. This design
decision reflects a neat ancillary result in Wooldridge (2021), which
proves the equivalence between the two types of fixed effects for linear
cases. Group-level effects have the virtue of being faster to estimate,
since there are fewer factor levels. Moreover, they are
*required* for nonlinear model families like Poisson per the
underlying ETWFE theory. Still, you can specify unit-level fixed effects
for the linear case through the `ivar`

argument. Again, we
can easily confirm that this yields the same estimated treatment effects
as the group-level default (although the standard errors will be
slightly different).

```
mod_es_i = etwfe(
lemp ~ lpop, tvar = year, gvar = first.treat, data = mpdta,
ivar = countyreal # NEW: Use unit-level (county) FEs
) |>
emfx("event")
modelsummary(
list("Group-level FEs (default)" = mod_es, "Unit-level FEs" = mod_es_i),
shape = term:event:statistic ~ model,
coef_rename = rename_fn,
gof_omit = "Adj|Within|IC|RMSE"
)
```

Group-level FEs (default) | Unit-level FEs | |
---|---|---|

Years post treatment = 0 | -0.033 | -0.033 |

(0.013) | (0.015) | |

Years post treatment = 1 | -0.057 | -0.057 |

(0.017) | (0.019) | |

Years post treatment = 2 | -0.138 | -0.138 |

(0.031) | (0.034) | |

Years post treatment = 3 | -0.110 | -0.110 |

(0.032) | (0.036) | |

Num.Obs. | 2500 | 2500 |

R2 | 0.873 | 0.993 |

FE..year | X | X |

FE..first.treat | X | |

FE..countyreal | X |

Good textbook introductions to DiD are available here and here, among many other places.↩︎

It has also demeaned the

`lpop`

control variable, but that again is something we’ll get back too later. It’s not particularly important for interpreting the final results.↩︎Note that the “x” prefix in “xvar” represents a covariate that is

*interacted*(×) with treatment, as opposed to a regular control variable (which could obviously be included as part of the`fml`

RHS.↩︎Another option would be to use

`fixest::etable()`

↩︎We won’t see a speed-up for this small dataset, but it can make a significant difference for large datasets.↩︎