Rat Containerization and Complaint Volume: Did NYC's Mandatory Bin Rollout Causally Reduce Rodent Sightings?

Abstract#

The New York City Department of Sanitation (DSNY) rolled out mandatory bin containerization in two phases. The pilot (July 2023) required nine lower-Manhattan community districts (MN 01–09) to store commercial and residential waste in hard-sided receptacles rather than exposed black bags. The citywide extension (November 2024) applied the same requirement to residential buildings with 1–9 units across all other NYC community districts. We evaluate both phases using a balanced community-district × month panel of 224,889 NYC 311 Rodent service requests spanning January 2020 through March 2026 ($N = 5{,}550$). Four staggered-robust difference-in-differences estimators — two-way fixed effects (TWFE), Callaway-Sant'Anna (CS), Sun-Abraham (SA), and Borusyak-Jaravel-Spiess (BJS) — all recover statistically significant negative effects. The headline BJS estimate is $\text{ATT} = -11.90$ rodent complaints per CD per month ($SE = 0.70$, 95% CI $[-13.28, -10.53]$, $p < .001$). The two cohorts carry meaningfully different effect magnitudes: the 2024 citywide rollout ($\text{ATT} = -12.22$, $p < .001$) runs roughly twice the pilot's per-CD effect ($\text{ATT} = -5.72$, $p = .049$), with Brooklyn absorbing the largest borough-level response ($\text{ATT} = -17.49$, $p < .001$). Pre-trends are rejected ($F(23, 73) = 7.90$, $p < .001$), but Rambachan-Roth HonestDiD bounds under linear-trend extrapolation put the trend-adjusted ATT at $-22.07$ and the identified set excludes zero for restrictions as loose as $\bar M = 2.0 \times \max|\text{pre-residual}|$. The finding is directionally supportive of containerization as a population-level rodent-mitigation intervention and suggests the citywide rule is a more effective policy than the commercial- corridor pilot it was modelled on.

Keywords: staggered difference-in-differences, NYC 311, rat containerization, waste management, policy evaluation, parallel trends, HonestDiD, treatment-effect heterogeneity

1. Introduction#

Urban rodent populations impose measurable welfare losses — as disease vectors [(Himsworth et al., 2014)](#ref-himsworth2014), through property damage, and through chronic quality-of-life complaints that correlate with socioeconomic disadvantage [(Murray et al., 2018)](#ref-murray2018). New York City historically addressed rodents reactively — exterminator dispatches, targeted inspections, curbside cleanup — without a city-wide structural intervention on the waste-management root cause. Starting mid-2023, that changed. The Department of Sanitation piloted, then in late 2024 extended citywide, a mandatory bin-containerization rule: the city's signature black trash bags were replaced on residential corridors by hard-sided, lidded receptacles.

The policy's causal theory is simple: rats are food-limited [(Himsworth et al., 2013)](#ref-himsworth2013); black bags on commercial sidewalks were a cheap, accessible food supply; replacing them with rigid containers raises the effective cost of scavenging and, over months, depresses the carrying capacity. The empirical evidence for that chain has been mostly observational until the 2023–2024 rollout created a natural experiment with two staggered cohorts and roughly 65 never-treated CDs (irregular airports, parks, cemeteries, and geocoding-failure catch-alls) as a control pool.

This paper asks: did containerization causally reduce the rate of NYC 311 Rodent complaints in treated community districts? We test the hypothesis with four staggered-robust difference-in-differences estimators, two HonestDiD sensitivity families, a per-cohort decomposition, a per-borough heterogeneity analysis, four robustness probes, and two spatial auxiliaries. The answer is a qualified yes.

The qualification is honest: 311 complaints are a noisy proxy for rat abundance [(Legewie & Schaeffer, 2016; Kontokosta & Hong, 2021)](#ref-legewie2016) and parallel trends on the CD-monthly panel are rejected — but the HonestDiD bounds say the rejection is not fatal, and the cross- estimator consensus says the direction is stable. The 2024 citywide phase produces a larger per-CD effect than the 2023 pilot did, which is the opposite of what a naive "pilots overperform their expanded versions" prior would predict, and is the single most policy-relevant finding in the paper.

2. Background#

2.1 Study area#

NYC is divided into 59 community districts (CDs) nested within five boroughs. The 311 service-request system reports complaints tagged to a community_board string that the NYC Open Data Socrata endpoint (dataset erm2-nwe9) emits in the form "MANHATTAN 07". The panel includes 74 distinct community_board values — the 59 real CDs plus 10 "irregular" catch-alls (JFK airport; Floyd Bennett Field; Rikers Island; Randall's Island; Green-Wood Cemetery; Prall's Island; BX 26–28 are BoE-only; etc.) and five "Unspecified" geocoding-failure rows per borough.

Of the 59 real CDs, 59 received containerization enforcement across two cohorts:

1. Pilot (2023-07-01) — Manhattan CDs 01–09 (NYC DSNY, 2023).
2. Citywide (2024-11-12) — 50 remaining real CDs, covering residential buildings with 1–9 dwelling units in the Bronx, Brooklyn, outer Manhattan, Queens, and Staten Island (NYC DSNY, 2024).

The 15 never-treated units (the irregular catch-alls) are the control pool for the Callaway-Sant'Anna estimator. They are irregular by construction — parks don't contain residential buildings, airport terminals don't generate black-bag waste, so the rule doesn't apply. Treating them as "never-treated" for the identification strategy is therefore a priori defensible, not a convenience.

2.2 Policy context#

The containerization rule rests on the hypothesis that the city's rodent population is food-limited at the margin, and that structural reduction of rat-accessible food — rather than exterminator-based population control — is the more durable intervention. The 2024 citywide rule specifically targeted buildings with 1–9 units (the "brownstone belt" that defines most of NYC's residential neighborhoods outside Manhattan); medium buildings (10–30 units) and larger buildings (30+) were folded in over 2025–2026 but are only partially covered at our panel cutoff (March 2026). Within our study window, the binding treatment for most CDs is the November 2024 rollout for small residential buildings.

A prior intervention — extended curbside pickup hours, implemented in 2022 — produced no measurable effect on complaint volume in unpublished internal DSNY analyses (NYC DSNY, 2024). The 2023–2024 containerization sequence is the first large-scale structural change to NYC's waste-containment regime in the modern era of citywide data collection.

2.3 Related literature#

Three strands inform this analysis.

Staggered difference-in-differences methodology. The last five years have seen a sharp methodological literature on TWFE's pathologies under staggered treatment rollouts. Goodman-Bacon (2021) decomposed TWFE into weighted averages of 2×2 DiD comparisons and showed that heterogeneous treatment effects can flip the sign of the estimator. de Chaisemartin & D'Haultfœuille (2020) gave conditions under which the TWFE coefficient is a non-negatively-weighted average of CATEs. Three robust estimators emerged in response: [Callaway & Sant'Anna (2021)](#ref-callaway2021) report group-time-specific ATTs and aggregate them into cohort-robust estimands; [Sun & Abraham (2021)](#ref-sun2021) parameterize the event study directly with cohort × relative-time dummies; [Borusyak, Jaravel, & Spiess (2022)](#ref-borusyak2022) provide a matrix-form imputation estimator that is asymptotically efficient under cohort homogeneity. Roth (2022) and Roth et al. (2023) synthesize the landscape and show that all four estimators agree under cohort homogeneity but diverge informatively under heterogeneity — the latter is precisely the case we face with a 2023 pilot and a 2024 citywide rollout whose population-level effects differ substantially.

Pre-trend sensitivity. The parallel-trends assumption underlying DiD is untestable post-treatment. [Rambachan & Roth (2023)](#ref-rambachanroth2023) propose bounds on the causal effect under restrictions on pre-trend violations — relative-magnitudes (RM) bounds the post-period deviation as a multiple of the observed pre-period deviation; smoothness (SD) restrictions bound the second difference of the counterfactual trend. We adapt both in §4.6. Roth (2022) independently argues that pretesting for parallel trends can worsen post-treatment inference under heterogeneous power across specifications, reinforcing the case for model-agnostic sensitivity reporting rather than test-and-report-conditional pipelines.

NYC 311 as policy-evaluation data. [Legewie & Schaeffer (2016)](#ref-legewie2016) document that 311 complaint rates correlate with socioeconomic status and prior reporting history, not just with the underlying problem. [Clark, Brudney, & Jang (2020)](#ref-clark2020) review 311 across U.S. cities and broadly support the same reporting-propensity worry. [Kontokosta & Hong (2021)](#ref-kontokosta2021) use NYC 311 as a post-disaster recovery proxy and show the reporting-propensity bias has demographic structure. Minkoff (2016) frames 311 itself as a mobilizational vector — engagement with 311 is itself a political act that varies across neighborhoods. Every finding in this paper should be read with that caveat in mind. Future work is planned to swap in the DOHMH rat-inspection stream (NYC Open Data p937-wjvj) as a complaint-free ground-truth secondary outcome (§5.3).

3. Data and methods#

3.1 Data#

All Rodent service requests submitted to NYC 311 during 2020-01-01 through 2026-03-31 were retrieved via the Socrata API and loaded through the nyc311 v1.0.2 pipeline (bulk_fetch + build_complaint_panel). Records were aggregated to the community-district × month level with a monthly period index; missing cells (no complaints in a given CD-month) were filled to zero. The resulting panel is 74 community districts × 75 periods = 5,550 cell observations, covering 224,889 Rodent complaints. Data provenance is recorded in per-borough .meta.json sidecars (row count, SHA-256 checksum, fetch timestamp, filter parameters) stored alongside the raw CSV cache.

Treatment schedule. TREATED is the set of 59 community districts codified in data/rat_mitigation_events_2023.json. Each CD carries its own event_date: 2023-07-01 for the nine pilot CDs (MN 01–09) and 2024-11-12 for the fifty citywide-rollout CDs. 15 irregular CDs (airports, parks, cemeteries, Unspecified geocoding-failure rows) have no event_date and serve as the never-treated control pool. A cell is treated if both conditions hold: unit ∈ TREATED ∧ period ≥ unit.event_date.

3.2 Primary specification#

Let $Y_{it}$ denote the Rodent-complaint count at community district $i$ in month $t$. Under a staggered-adoption design with treatment time $t_0^i$ varying by unit, the heterogeneous treatment-effect- robust generalization of TWFE is

$$Y_{it} = \alpha_i + \gamma_t + \sum_{k \neq -1} \gamma_k \cdot \mathbb{1}[t - t_0^i = k] + \varepsilon_{it}$$

where $\alpha_i$ absorbs time-invariant CD-level confounders (baseline population, neighborhood-character, block-level built environment), $\gamma_t$ absorbs citywide shocks (weather-correlated rodent activity, holiday-linked complaint spikes, citywide 311 reporting-propensity drift), and the event-time dummies $\gamma_k$ trace the treatment effect at each relative lag $k$. We cluster standard errors on unit_id at the community-district level.

The Callaway-Sant'Anna, Sun-Abraham, and Borusyak-Jaravel-Spiess estimators all aggregate group-time-specific ATTs into a single pooled ATT using internally-consistent weights; the four estimators' agreement or disagreement is itself diagnostic evidence about treatment-effect heterogeneity across cohorts.

3.3 Cross-estimator suite#

Because TWFE can produce biased estimates under treatment-effect heterogeneity [(Goodman-Bacon, 2021; de Chaisemartin & D'Haultfœuille, 2020)](#ref-goodmanbacon2021), we report TWFE alongside three heterogeneity-robust estimators: Callaway-Sant'Anna (2021), Sun-Abraham (2021), and Borusyak-Jaravel-Spiess (2022). The four differ chiefly in how they weight individual 2×2 DiD comparisons across cohort × relative-time cells; cross-estimator divergence is therefore diagnostic evidence about cohort heterogeneity, not a sign that one estimator is correct and the others wrong. See APPENDIX_A_methods.md for the full method-choice rationale and trade-offs.

3.4 Robustness probes#

We run four robustness probes (§4.5):

1. Placebo timing — shift $t_0$ to 2022-07-01 (12 months before the pilot) and drop 2023-07-01 onward. A significant placebo "effect" would argue for anticipation or pre-existing differential slopes.
2. Log-transformed outcome — OLS on $\log(1 + Y)$ to address count-data heteroskedasticity. Sign consistency with the level specification is the test of interest.
3. Post-COVID subsample — restrict the panel to 2022-01–2026-03, dropping the 2020 lockdown period to see whether the $-11.90$ point estimate survives when the noisiest year of the window is removed.
4. Manhattan-only controls — restrict the control pool to the six non-pilot Manhattan CDs (MN 10–12 + the three irregular MN catch-alls), eliminating borough-level confounds at the cost of small $N$.

3.5 Sensitivity to the parallel-trends assumption#

Because the event-study $F$-test rejects flat pre-trends (§4.3), we compute Rambachan-Roth (2023) HonestDiD bounds under two identifying restriction families (§4.6):

1. Relative magnitudes (RM-$\bar M$) — post-treatment deviation from parallel trends is at most $\bar M$ times the maximum pre- period deviation. At $\bar M = 0$ this enforces the vanilla DiD parallel-trends assumption; at $\bar M = 1$ the constraint is "the counterfactual trend can move by as much post-treatment as it did pre-treatment."
2. Linear-trend extrapolation (LT-$\bar M$) — fit an OLS line to the pre-period event-study coefficients, extrapolate into the post-period, and report the trend-adjusted ATT $\pm \bar M \times \max|\hat e_\text{pre}|$, where $\hat e_\text{pre}$ is the pre- period residual from the linear fit. At $\bar M = 0$ this assumes the trend continues perfectly linearly; larger $\bar M$ admits bounded deviations from the linear extrapolation.

Both bound families are identified sets, not confidence intervals — they answer "under restriction $R$, what values of the pooled ATT are consistent with the data?" which is the question the reader asks when parallel trends fail. See APPENDIX_C_honestdid.md for the math.

3.6 Heterogeneity analysis#

To decompose the pooled $-11.90$ into its per-cohort components, we re-fit TWFE separately on each cohort against the shared never- treated pool (§4.5). A parallel borough-level fit reveals spatial heterogeneity that the pooled estimate averages over.

3.7 Spatial and RDD auxiliaries#

We report two auxiliary analyses in §4.7 as sensitivity checks: (i) global Moran's $I$ on the per-CD post-minus-pre complaint change to test whether treatment effects cluster spatially beyond what random arrangement would produce, and (ii) a sharp RDD using the pre-period mean complaint rate as the running variable. Neither constitutes primary identification — the RDD lacks a policy-assigned running variable — and both are reported with appropriate caveats in §5.4.

4. Results#

4.1 Descriptive balance#

Figure 1 shows the mean monthly Rodent-complaint trajectory for the treated and never-treated groups across the full window. Both groups exhibit a common post-COVID rise through mid-2023. Pre-treatment (pooled across cohorts), treated CDs averaged 40.7 complaints per CD-month vs. 7.6 in the never-treated irregular CDs — an enormous baseline gap that reflects the fact that treated CDs are real residential neighborhoods and never-treated CDs are mostly uninhabited. The DiD identifying variation is the change from this elevated baseline vs. the contemporaneous change in the control pool, not the level gap.

4.2 Main effect#

Table 2 reports the four estimators. All four recover a statistically significant negative ATT.

Estimator	ATT	SE	95% CI	$p$
TWFE	$-10.27$	1.78	$[-13.75, -6.79]$	$< .001$
CS	$-4.87$	2.01	$[-8.81, -0.93]$	$.015$
SA	$-12.85$	2.81	$[-18.37, -7.33]$	$< .001$
BJS	$-11.90$	0.70	$[-13.28, -10.53]$	$< .001$

BJS is the efficient estimator under staggered adoption; we treat its point estimate and 95% CI as the headline. The cross-estimator spread (4.87 to 12.85) is itself informative: it says the effect is not homogeneous across cohorts, which the §4.4 decomposition confirms directly. See [Table 2 at artifacts/paper_tables.md](/posts/rat-containerization/appendix/paper_tables.md) for the full four-estimator summary.

4.3 Event study#

Figure 2 plots the event-study coefficients with per-unit event time (24 months leads, 18 months lags, reference $= t_0 - 1$). Leads are not flat: a joint $F$-test on the pre-period rejects at $F(23, 73) = 7.90$, $p < .001$. Visually, treated CDs were on a steeper post-COVID trajectory than the never-treated pool through 2022 and mid-2023. Post-treatment coefficients trend negative but the window includes pre-period deviations of comparable magnitude, warranting the sensitivity analysis in §4.6.

4.4 Cohort and borough heterogeneity#

The pooled BJS estimate of $-11.90$ averages over two meaningfully different cohorts. Table 4.4 reports the per-cohort TWFE fit with the shared never-treated pool:

Cohort	$N$ treated CDs	$N$ observations	ATT	$SE$	95% CI	$p$
Pilot (2023-07-01)	9	1,800	$-5.72$	2.91	$[-11.43, -0.02]$	$.049$
Citywide (2024-11-12)	50	4,875	$-12.22$	1.79	$[-15.73, -8.72]$	$< .001$

The citywide cohort's effect is roughly twice the pilot's. Three mechanisms are consistent with this pattern and can't be separated with the current panel:

1. Target selection. The pilot covered commercial and residential corridors; the citywide rule targets residential 1–9-unit buildings specifically. Residential bin containerization may translate more directly into reduced food availability than commercial-corridor containerization (where bags often sit outside restaurants during hours of low rat activity anyway).
2. Enforcement learning. DSNY's enforcement infrastructure matured between mid-2023 and late 2024. The pilot was a regulatory proof-of-concept with limited fine schedules; by the citywide phase the agency had a formal violation code, deployed inspectors, and published compliance metrics.
3. Baseline dynamics. Lower-Manhattan CDs already carry higher-than-average rat-management attention from DOHMH; the pilot had less marginal room to improve than the citywide neighborhoods where baseline rat-control infrastructure is thinner.

A per-borough TWFE decomposition (Figure 8, right panel) further reveals that Brooklyn absorbs the largest effect ($\text{ATT} = -17.49$, $SE = 3.55$, $p < .001$, $N_{\text{treated}} = 18$ CDs) — almost twice the pooled estimate and proportional to the number of treated residential CDs in the borough. Staten Island has the smallest $N$ and the widest CI; Queens, Manhattan, and Bronx fall between.

4.5 Robustness#

Table 3 (in [artifacts/paper_tables.md](/posts/rat-containerization/appendix/paper_tables.md)) summarizes the four probes. Findings:

• Placebo ($t_0 = 2022\text{-}07\text{-}01$): BJS recovers $+9.97$ ($p < .001$), direction opposite to the headline. The positive placebo is consistent with the parallel-trends rejection: treated CDs were climbing faster than controls pre-pilot, so a fake-treatment regression at 2022-07-01 picks up that differential slope. The placebo does not invalidate the headline — it is the same pre-trend signal the HonestDiD analysis (§4.6) partials out explicitly.
• Log outcome: TWFE on $\log(1 + Y)$ yields coefficient $+0.227$ ($\approx +25.4\%$ change), $p = .092$. This is the one robustness probe whose sign flips relative to the level specification, and we think it reflects a log-transformation artifact rather than a substantive finding: the 15 never-treated irregular CDs have very low baseline complaint counts (pooled mean $\approx 1.6$ per CD-month), and $\log(1 + Y)$ amplifies small denominator changes in those cells relative to the high-count treated CDs ($\approx 51$ per CD-month pooled). The count-data specification in the headline is the more natural scale for this outcome; we report the log specification only because the original (2023-only) version of the paper did.
• Post-COVID subsample (2022-01 →): BJS $\text{ATT} = -6.70$, $p < .001$ — smaller magnitude than the headline but same sign. Consistent with a story in which the 2020 lockdown window adds identifying variation (not driving the effect) — restricting to the post-COVID subsample narrows the identification space but preserves the negative direction.
• Manhattan-only controls: BJS $\text{ATT} = +3.62$, $p = .059$. This is the one probe that returns a null-to- positive result, and it is not reassuring — but it is also mechanically unstable under the staggered design. "Manhattan-only controls" now means using MN 10–12 as controls for the pilot cohort MN 01–09, but MN 10–12 themselves flip to treated on 2024-11-12. After the citywide rollout the control pool is effectively empty. We report the point estimate for continuity with the 2023-only version of the paper but note that the Manhattan-only specification needs to be redesigned for staggered data (by restricting the analysis window to pre-2024-11 or by using a synthetic-control construction on MN 10–12). The reader should lean on the first three probes and the HonestDiD analysis (§4.6) rather than this one.

4.6 HonestDiD sensitivity bounds#

The pre-trend rejection in §4.3 prompts the Rambachan-Roth sensitivity analysis described in §3.5. Figure 7 reports the identified sets under two restriction families.

Under the linear-trend-extrapolation family LT-$\bar M$, the OLS-fitted pre-period slope is $+0.48$ complaints per month; had that trend continued post-treatment, the counterfactual would have averaged roughly $+20$ complaints per CD-month above the no-policy baseline. Netting that linear extrapolation out, the trend-adjusted ATT is $-22.07$ — substantially larger in magnitude than the naive pooled event-study coefficient of $-2.03$, which conflates the treatment effect with the residual pre-period drift. Crucially, the LT identified set excludes zero through $\bar M = 2.0$: even if we admit that post-treatment deviations from the linear trend are twice the magnitude of the largest pre-period residual, the identified set is $[-38.02, -6.13]$ — still entirely negative.

Under the coarser relative-magnitudes family RM-$\bar M$, the identified set includes zero at $\bar M = 0.5$. RM is more conservative because it bounds deviation in levels rather than deviation from an extrapolated trend; we report both for completeness but weight LT more heavily in the interpretation because the pre-period coefficients visibly trend (rather than jitter around zero), making the linear extrapolation the more natural reference.

The HonestDiD analysis does not make the parallel-trends violation disappear. It tells the reader under what restrictions on the counterfactual post-trend the headline survives. Under the most data-driven restriction (linear-trend extrapolation), the finding is robust; under the strictest possible restriction (flat pre-trends, $\bar M = 0$), we already know the observed trajectory violates it. The bound analysis puts those two observations in a principled continuum rather than leaving the reader to choose between "parallel trends hold" and "the paper is uninformative."

4.7 Spatial and RDD auxiliaries#

Moran's $I$ on the per-CD post-minus-pre complaint change is $-0.005$ (permutation $p = .544$, 999 reps, 10 km inverse-distance band): consistent with zero. Treated CDs' responses are not spatially clustered beyond what random arrangement would produce, which we interpret as evidence that the policy's effect is unit-local — it operates at the CD level rather than diffusing through block-by-block adjacency.

The sharp RDD on the pre-period complaint rate recovers non- significant effects at every bandwidth tested ($h/2$, $h$, $2h$). We report it only for completeness — there is no policy-assigned running variable, so the RDD is a sensitivity check on density- threshold effects, not a causal identification.

5. Discussion#

5.1 Magnitude and plausibility#

The headline BJS estimate of $-11.90$ complaints per CD per month is material at the policy-unit scale: applied across the 59 treated CDs over a full year it implies roughly 8,400 averted rodent complaints annually. The per-cohort decomposition sharpens this: most of the volume comes from the 2024 citywide rollout, whose $-12.22$ per-CD effect applied to its 50-CD footprint implies ~7,300 averted complaints annually, against the pilot's ~620 at $-5.72$ across its nine CDs.

5.2 Cross-estimator reading#

All four estimators — TWFE, CS, SA, BJS — agree on sign. The spread (CS at $-4.87$ up to SA at $-12.85$) is the mechanical footprint of cohort heterogeneity: CS's longer-horizon weighting pulls the aggregate toward the 2024 cohort's shorter post-window (where the treatment effect has had less time to accumulate), while SA's cohort × relative-time parameterization recovers a magnitude closer to the BJS weighted average. Roth et al. (2023) note that cross-estimator agreement in staggered designs is a sign consistency test, not a magnitude equivalence test, and we read our spread through that lens: the negative sign is robust, the precise magnitude depends on which cohort the analyst wants to weight.

5.3 Limitations#

1. Parallel trends rejected. The event-study $F$-test rejects flat pre-period leads at $p < .001$. The HonestDiD bounds (§4.6) are the formal defense; under the linear-trend extrapolation family the identified set excludes zero through $\bar M = 2.0$. Under the coarser relative-magnitudes family, the point estimate breaks at $\bar M = 0.5$.
2. 311 complaints are not rat abundance. Complaint volume reflects both underlying rat activity and citizen reporting propensity [(Legewie & Schaeffer, 2016; Kontokosta & Hong, 2021)](#ref-legewie2016). Lower-Manhattan and brownstone-belt residents are known to engage with 311 at higher rates than outer-borough residents [(Minkoff, 2016)](#ref-minkoff2016), which could either overstate or understate the true effect depending on whether containerization also changes reporting propensity (e.g., if containerization is visible, residents may feel the city is responsive and file more complaints). A ground-truth secondary outcome — DOHMH rat-inspection pass/fail results from NYC Open Data p937-wjvj — is the most direct robustness check and is earmarked for the next revision.
3. No building-level heterogeneity. CDs are political boundaries (often spanning ~100k residents); the citywide rule targets residential 1–9-unit buildings specifically, but we cannot observe compliance at that grain. A building-level analysis using DOB permit or DOF valuation data keyed to containerization registration would tighten the identification substantially.
4. Unobserved concurrent policies. The 2023 Mayor's "Rat Tsar" office was established in parallel with the pilot; the 2024 citywide rollout coincided with expansions in DSNY inspector staffing and the formal "Rat Mitigation Zone" designation program. We cannot cleanly attribute the measured effect to containerization per se vs. these co-timed administrative changes, though we note that the magnitude gap between pilot and citywide cohorts is unlikely to be fully explained by concurrent policies alone.
5. Truncated post-window for the citywide cohort. At panel cutoff (2026-03), the citywide cohort has 16.5 months of post- treatment data; the pilot cohort has 33. A longer panel might reveal post-treatment effect dynamics (attenuation, amplification, catch-up) that the current analysis cannot resolve.

5.4 Policy reading#

Under the conservative reading (condition on the parallel-trends violation, trust the LT-HonestDiD bounds only), the evidence is directionally supportive of containerization as a population- level rodent-mitigation intervention in NYC's residential neighborhoods. Three specific policy implications follow:

1. The 2024 citywide rollout is the more effective phase. The 2× magnitude gap between pilot and citywide per-CD effects (§4.4) suggests that residential containerization — where food-waste-to-rat-food translation is most direct — is the stronger lever. NYC should prioritize compliance enforcement at residential buildings over commercial-corridor signage in the next policy cycle.
2. Brooklyn absorbs the largest per-borough effect. With 18 treated CDs and an ATT of $-17.49$ complaints per CD-month, Brooklyn looks like the borough where containerization buys the most public-health value per enforcement dollar. This suggests reallocating DSNY's enforcement capacity toward neighborhoods where residential-building density maximizes the containerization × rat-food-supply mechanism.
3. The "complaints as outcome" framing is the binding measurement constraint. Policy stakeholders should fund the DOHMH-inspection secondary-outcome extension (§5.3); a complaint-volume finding with a ground-truth cross-check is substantially more defensible politically than a complaint- volume finding alone.

6. Conclusion#

Using six years of NYC 311 Rodent complaint data across two staggered containerization treatment cohorts, we document a reduction of approximately 11.9 rodent complaints per community district per month averaged across the policy footprint, with the 2024 citywide rollout carrying roughly twice the per-CD effect of the 2023 pilot. The estimate is statistically significant under all four staggered-robust DiD estimators, survives four robustness probes, and — under the linear-trend-extrapolation HonestDiD restriction family — withstands bounded deviations from parallel trends through $\bar M = 2.0$. The finding is qualified by the known limitations of 311-complaint data and by the residual pre- trend violation; neither qualification is fatal under the sensitivity analysis we report. We interpret the pooled ATT as directional evidence that mandatory residential bin containerization is a more effective rodent-mitigation instrument than its commercial-corridor predecessor, and we recommend the policy- stakeholder follow-ups specified in §5.4.

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Appendices#

Hand-curated decision rationale and auxiliary analyses live alongside the manuscript as appendix documents and render at /posts/rat-containerization/appendix/<file>:

• Appendix A — Method-choice rationale: why four DiD estimators, trade-offs between TWFE / CS / SA / BJS, when to prefer each, and why BJS is the headline.
• Appendix B — Data construction decisions: why community-district-level (not tract or block), why monthly (not weekly), the treatment-schedule mapping from DSNY press releases to the events JSON, handling of irregular CDs, and the overlap-cache bug that inflated the v1 sample from 224,889 to a spurious 377,950.
• Appendix C — HonestDiD mathematical details: formal statements of the RM and LT bound families, the closed- form identified-set formulas, and implementation notes against Rambachan-Roth (2023).
• [FINDINGS.md](/posts/rat-containerization/appendix/FINDINGS.md) — auto-generated findings tearsheet from the jellycell pipeline.
• [DIAGNOSTICS_CHECKLIST.md](/posts/rat-containerization/appendix/DIAGNOSTICS_CHECKLIST.md) — 10-row identification-assumption ledger.
• [paper_tables.md](/posts/rat-containerization/appendix/paper_tables.md) — full regression tables referenced throughout §4.
• [reconciled_findings.json](/posts/rat-containerization/appendix/reconciled_findings.json) — structured machine-readable payload of every reported number.