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Difference-in-differences for a treated vs control institution set

Source: R/did.R
sm_did.Rd

Fits a difference-in-differences (DiD) model comparing the yearly outcome series of a treated institution set against a control set, before and after intervention_year. Group membership is resolved via the institution-attribution columns from sm_affiliation_match() / sm_attribute_institution().

Usage

sm_did(
  corpus,
  treated,
  control,
  intervention_year,
  outcome = c("count", "share_q1", "cnci", "leadership"),
  family = NULL,
  call = rlang::caller_env()
)

# S3 method for class 'sm_did'
print(x, ...)

# S3 method for class 'sm_did'
summary(object, ...)

# S3 method for class 'sm_did'
autoplot(object, ...)

Arguments

corpus

An sm_corpus with institution-attribution columns.

treated, control

Character vectors of institution labels (matched against institution_name / institution_match / institution_id).

intervention_year

Integer year the intervention takes effect.

outcome

One of "count", "share_q1", "cnci", "leadership" (see sm_its()).

family

Optional GLM family; defaults to Gaussian (DiD is an additive contrast). Override for, e.g., a Poisson count model.

call

Caller environment for error reporting.

x

An sm_did object.

...

Ignored.

object

An sm_did object.

Value

An sm_did S3 object with components: model, did_estimate (a one-row tibble: estimate, std.error, conf.low, conf.high, p.value), series (group-by-year tibble: year, group, value, n_works), and metadata (outcome, intervention_year, family).

print returns x invisibly.

summary returns the DiD estimate tibble.

autoplot returns a ggplot (parallel trends + divergence).

See also

sm_its(), sm_synth()

Other causal: sm_its(), sm_synth()

Examples

corpus <- sm_example_corpus(n_works = 120, seed = 1)
# Tag two institution groups for illustration
corpus$authorships$institution_name <- rep(
  c("Inst A", "Inst B"), length.out = nrow(corpus$authorships))
did <- sm_did(corpus, treated = "Inst A", control = "Inst B",
              intervention_year = 2020, outcome = "count")
did
#> 
#> ── <sm_did> ────────────────────────────────────────────────────────────────────
#> Outcome: count Family: gaussian
#> Intervention year: 2020
#> 
#> DiD estimate: 0.6 [-5.1075, 6.3075] (p = 0.84)