Immigration Enforcement, Sanctuary Cities, and Rising Hispanic Suicide Rates

class: title-slide

# Immigration Enforcement, Sanctuary Cities, and Rising Hispanic Suicide Rates

## Immigration Enforcement and Hispanic Suicide

.pull-left[
**Hussain Hadah**<sup>1,2</sup>

<div style="color: white; font-size: 18px; margin-top: 10px;">
October 22, 2025
</div>

<div style="font-size: 0.8em; margin-top: 10px;">
<sup>1</sup>The Murphy Institute
<sup>2</sup>Economics Department, Tulane University<br>
</div>
]

---
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<div style="position: absolute;left:20px;bottom:5px;color:black;font-size: 12px;">Hussain Hadah (Tulane/Murphy Institute) | Immigration Enforcement and Hispanic Suicide | Murphy Institute Health Policy Seminar October 22 2025</div>

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# .text-shadow[.white[Today's Presentation]]

<ol>
    <li><h4 class="white"><a href="#motivation-sec">Motivation</a></h4></li>
    <li><h4 class="white"><a href="#background-sec">Background</a></h4></li>
    <li><h4 class="white"><a href="#data-sec">Data</a></h4></li>
    <li><h4 class="white"><a href="#empirical-strategy-sec">Empirical Strategy</a></h4></li>
    <li><h4 class="white"><a href="#results-sec">Results</a></h4></li>
    <li><h4 class="white"><a href="#hetero-sec">Heterogeneity Analysis and Mechanisms</a></h4></li>
</ol>

---
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name: motivation-sec

# Motivation

---
name: motivation-slide

## .large[Motivation]

<br>

### Immigration enforcement has intensified in recent years

### .brand-medallion[**11-12 million**] undocumented immigrants live in mixed-status families .tiny[(Passel and Krogstad, 2025)]

### Enforcement creates ripple effects beyond targeted individuals

---
name: econ-lit

## .large[Economists studies the effects of immigration enforcement on various outcomes]

### Welfare take-up .tiny[(Alsan and Yang, 2024)]

### Labor market outcomes .tiny[(East, Hines, Luck, Mansour, and Velásquez, 2023)]

### Infant health outcomes .tiny[(Vu, 2024)]

---
name: examples-enforcement

## Examples of recent immigration enforcement

---
name: immigration-lit

## .large[The effects of immigration enforcement on mental health is understudied]

### As immigration enforcement intensifies and becomes more pervasive, understanding its broader impacts is crucial

### These laws, designed to enhance security, they generate significant unintended consequences .tiny[(Vu, 2024; Alsan and Yang, 2024; East, Hines, Luck et al., 2023; Cox and Miles, 2013)]

---
name: research-question

## .large[I use the recent developments in DiD literature to answer the following question] <svg viewBox="0 0 512 512" style="height:1em;display:inline-block;position:fixed;top:10;right:10;" xmlns="http://www.w3.org/2000/svg">  <path d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 448c-110.532 0-200-89.431-200-200 0-110.495 89.472-200 200-200 110.491 0 200 89.471 200 200 0 110.53-89.431 200-200 200zm107.244-255.2c0 67.052-72.421 68.084-72.421 92.863V300c0 6.627-5.373 12-12 12h-45.647c-6.627 0-12-5.373-12-12v-8.659c0-35.745 27.1-50.034 47.579-61.516 17.561-9.845 28.324-16.541 28.324-29.579 0-17.246-21.999-28.693-39.784-28.693-23.189 0-33.894 10.977-48.942 29.969-4.057 5.12-11.46 6.071-16.666 2.124l-27.824-21.098c-5.107-3.872-6.251-11.066-2.644-16.363C184.846 131.491 214.94 112 261.794 112c49.071 0 101.45 38.304 101.45 88.8zM298 368c0 23.159-18.841 42-42 42s-42-18.841-42-42 18.841-42 42-42 42 18.841 42 42z"></path></svg>

### What is the .brand-crawfest[**causal effect**] of immigration enforcement on suicide rates among .brand-blue[**Hispanic**] populations in the United States?

#### I use the .brand-crawfest[**staggered rollout**] of .brand-blue[**Secure Communities**] (2008-2013)---a deportation expansion program---to estimate .brand-crawfest[**triple difference-in-differences (DDD)**]

---
name: results-preview

## .brand-blue[**Preview of Results**]

### Key Findings:

- **.brand-mardigras[Hispanic children (5-14)]**: .brand-blue[**35-100% decrease**] in suicide rates relative to Whites
- **.brand-crawfest[Hispanic adults (34+)]**: .brand-red[**18-60% increase**] in suicide rates relative to Whites
- **.brand-blue[Sanctuary cities]**: Protective effects across most age groups
- **.brand-blue[Low unemployment]**: Key mechanism moderating effects
- **.brand-blue[Political affiliation]**: Democratic counties show larger increases in adult suicide rates

### Implications:

- Immigration enforcement creates .brand-crawfest[**substantial spillover effects**] beyond direct targets.
- Local policies can .brand-blue[**mitigate harm**].

---
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background-image: url("assets/TulaneLogo.svg")
background-size: 20%
background-position: 95% 95%
name: background-sec

# Background

---
name: secure-communities

# .brand-blue[**Secure Communities**] (2008-2014)

<br>

### Immigration enforcement program run by the .brand-blue[**Immigration and Customs Enforcement (ICE)**]

### Operated from 2008 to 2014, counties opted into the program voluntarily until the federal mandates the participation in 2013

### Fingerprints of all arrestees automatically shared with .brand-blue[**ICE**] for immigration status checks

### .brand-blue[**ICE**] issued "detainer" requests to hold individuals for up to 48 hours for potential deportation

---
name: secure-communities-rollout

## Map: .brand-blue[**Secure Communities**] staggered rollout (2008-2013)

---
name: secure-communities-rollout

## The .brand-crawfest[**staggered rollout**] of .brand-blue[**Secure Communities**]

---
name: sanctuary-cities

## Local and state governments that resisted .brand-blue[**Secure Communities**] implementation were designated as .brand-blue[**sanctuary cities**]

<br>

### Local jurisdictions that limited cooperation with federal immigration enforcement and refused .brand-blue[**ICE**]' detainer requests

### Three main types of .brand-blue[**sanctuary policies**]:

#### • "Don't ask" - prohibit inquiring about immigration status
#### • "Don't enforce" - limit local enforcement of immigration law  
#### • "Don't tell" - restrict information sharing with federal authorities

---
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background-image: url("assets/TulaneLogo.svg")
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name: data-sec

# Data

---
name: data-sources-one

## I use multiple data sources for my analysis

<br>

<h3>Mortality data from the National Vital Statistics System (NVSS) .tiny[(National vital statistics system, 2007)]</h3>

<div style="margin-left: 20px;">
<h4>• Annual count of suicides by county, race, and age group</h4>
<h4>• Sample period: 1999-2013 to align with SC implementation</h4>
</div>

<h3>Secure Communities (SC) data .tiny[(Alsan and Yang, 2024)]</h3>

<div style="margin-left: 20px;">
<h4>• They use data from Freedom of Information Act (FOIA) requests</h4>
<h4>• Determined when counties opted into the program</h4>
</div>

---
count: false

## I use multiple data sources for my analysis (cont.)

<br>

<h3>.brand-blue[**Sanctuary city**] data from .brand-blue[**ICE**]'s reports</h3>

<div style="margin-left: 20px;">
<h4>• Listed all local and state jurisdictions that adopted .brand-blue[**sanctuary policies**]</h4>

--
<h4> For heterogeneity analysis, I also use:</h4>

<div style="margin-left: 20px;">
<h4>• County-level demographic and socioeconomic data from the Census Bureau</h4>
<h4>• Prejudice measures from the Implicit Association Test (IAT), American National Election Studies (ANES), and Uniform Crime Reports (UCR) to calculate the number of hate crimes against Hispanics .tiny[(Greenwald, McGhee, and Schwartz, 1998; American National Election Studies, 2021; Bureau of Justice Statistics, 2023a)]</h4>
<h4>• County-level unemployment data using Current Population Survey (CPS) and Local Area Unemployment Statistics (LAUS) .tiny[(U.S. Bureau of Labor Statistics, 2025)]</h4>
<h4>• County-level election results .tiny[(Amlani and Algara, 2021)]</h4>
</div>

---
name: suicides-by-age

## Suicides by age groups

---
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name: empirical-strategy-sec

# Empirical Strategy
---
name: triple-diff-spec-intro

## Triple Difference-in-Differences (DDD) event study specification

### Outcome variable transformation

- Suicide counts often zero or small integers at county level

- Apply .brand-blue[**inverse hyperbolic sine (IHS)**] transformation: `$IHS(y) = \ln(y + \sqrt{y^2 + 1})$`

- Key advantages: defined for zero values, approximates log for large values, coefficients interpretable as percentage changes .tiny[(Burbidge, Magee, and Robb, 1988; MacKinnon and Magee, 1990)]

$$
`\begin{align*}
\text{IHS}(y_{crst}) = \sum_{l = -K}^{L} \beta_l \mathbf{1}\{ t - E_c = l \} \times Hispanic_{r} + \theta_{cr} + \lambda_{rt} + \gamma_{ct} + \varepsilon_{crst}
\end{align*}`
$$

### I use Borusyak, Jaravel, and Spiess (2024) to estimate a .brand-crawfest[**causal impact**] of .brand-blue[**SC**] on .brand-blue[**Hispanic**] suicide rates

---
name: triple-diff-spec-one
count: false

## Triple Difference-in-Differences (DDD) event study specification (cont.)

$$
`\begin{align*}
\color{red}{\text{IHS}(y_{crst})} = \sum_{l = -K}^{L} \beta_l \mathbf{1}\{ t - E_c = l \} \times Hispanic_{r} + \theta_{cr} + \lambda_{rt} + \gamma_{ct} + \varepsilon_{crst}
\end{align*}`
$$

- **Outcome**: `$\color{red}{\text{IHS}(y_{crst})}$` IHS transformation of suicide counts by race *r*, county *c*, time *t*

---
name: triple-diff-spec-two
count: false

## Triple Difference-in-Differences (DDD) event study specification (cont.)

$$
`\begin{align*}
\text{IHS}(y_{crst}) = \sum_{l = -K}^{L} \beta_l \color{green}{\mathbf{1}\{ t - E_c = l \}} \times \color{blue}{Hispanic_{r}} + \theta_{cr} + \lambda_{rt} + \gamma_{ct} + \varepsilon_{crst}
\end{align*}`
$$

- **Treatment interaction**: 
  - `$\color{green}{\mathbf{1}\{ t - E_c = l \}}$` is an indicator variable equal to 1 when time `$\textit{t}$` is `$\textit{l}$` years away from the adoption of waiting years in county `$\textit{c}$`
  - `$\color{blue}{\text{Hispanic indicator}}$` (1 for Hispanic, 0 for White)

---
name: triple-diff-spec-three
count: false

## Triple Difference-in-Differences (DDD) event study specification (cont.)

$$
`\begin{align*}
\text{IHS}(y_{crst}) = \sum_{l = -K}^{L} \beta_l \mathbf{1}\{ t - E_c = l \} \times Hispanic_{r} + \color{green}{\theta_{cr}} + \color{orange}{\lambda_{rt}} + \color{purple}{\gamma_{ct}} + \varepsilon_{crst}
\end{align*}`
$$

### Fixed effects control for:

- **County-race** (`$\color{green}{\theta_{cr}}$`): Time-invariant differences between Hispanic and White rates within counties

- **Race-year** (`$\color{orange}{\lambda_{rt}}$`): National trends affecting Hispanic and White populations differently

- **County-year** (`$\color{purple}{\gamma_{ct}}$`): County-specific time-varying shocks affecting both groups equally

---
name: triple-diff-spec-four
count: false

## Triple Difference-in-Differences (DDD) event study specification (cont.)

$$
`\begin{align*}
\text{IHS}(y_{crst}) = \sum_{l = -K}^{L} \color{red}{\beta_l} \mathbf{1}\{ t - E_c = l \} \times Hispanic_{r} + \theta_{cr} + lambda_{rt} + \gamma_{ct} + \varepsilon_{crst}
\end{align*}`
$$

### Coefficients of interest (`$\color{red}{\beta_l}$`):

- For `$l < 0$`, `$\color{red}{\beta_l}$` captures the pre-treatment trends and parallel trends assumption
- For `$l \geq 0$`, `$\color{red}{\beta_l}$` captures the differential effect of Secure Communities adoption on Hispanic suicide rates relative to Whites  <div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Causal Identification Assumptions]](#causal-identification-assumptions)]
</div>

#### All regressions are weighted by county population and standard errors are clustered at the county level

---
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name: results-sec

# Results

---
name: results-5to14-all

## I find significant .brand-olive[decreases] in suicide rates among .brand-medallion[**ages 5-14**] year old Hispanic children

---
name: results-5to14-by-gender

## The .brand-olive[decreases] in suicide is more pronounced among .brand-medallion[**ages 5-14**] year old Hispanic boys

.pull-left[
<img src="assets/figures/ddd_ddd_fem_5to14_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Girls**]
]

.pull-right[
<img src="assets/figures/ddd_ddd_male_5to14_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Boys**]
]

---
name: results-15to24-all

## I find a small .brand-olive[decreases] in suicide rates among .brand-medallion[**ages 15-24**] year old Hispanic youth

---
name: results-15to24-by-gender

## I find that SC .brand-neutralgrounds[did not] affect the suicides among .brand-medallion[**ages 15-24**] year old youth except for the fifth year post SC for female youth
  
.pull-left[
<img src="assets/figures/ddd_ddd_fem_15to24_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/ddd_ddd_male_15to24_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

---
name: results-34plus-all

## I find a large and .brand-crawfest[significant increase] in suicide rates among .brand-medallion[**ages 34+**] Hispanics

<img src="assets/figures/ddd_ddd_34plus_event.png" width="70%" style="display: block; margin: auto;" />
---
name: results-34plus-by-gender

## The large .brand-crawfest[increase] in suicide rates is driven by .brand-medallion[**34+ males**]
  
.pull-left[
<img src="assets/figures/ddd_ddd_fem_34plus_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/ddd_ddd_male_34plus_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

---
name: results-all-ages-all

## I find a .brand-crawfest[large and significant increase] in suicide rates among all Hispanics but parallel trends and non-anticipation likely do not hold

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#results-app-all-ages-bygender)]
</div>

---
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background-image: url("assets/TulaneLogo.svg")
background-size: 20%
background-position: 95% 95%
name: hetero-sec

# Sanctuary City, Heterogeneity, and Mechanisms

---
name: sanctuary-hetero-5to14

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among .brand-medallion[**ages 5-14**] Hispanic children

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#sanctuary-app-5to14-bygender)]
</div>

---
name: sanctuary-hetero-15to24

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among .brand-medallion[**ages 15-24**] Hispanic youth

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#sanctuary-app-15to24-bygender)]
</div>

---
name: sanctuary-hetero-34plus

## Suicide rate increase among .brand-crawfest[**ages 34+**] were higher in .brand-blue[**Sanctuary cities**]

<div style="position: absolute; bottom: 60px; right: 20px; background: rgba(255,255,255,0.85); padding: 2px 8px; border-radius: 6px; z-index: 10;" class="small">
  <a href="#sanctuary-app-34plus-bygender">[By gender</a> | <a href="#sanctuary-hetero-all">All ages]</a>
</div>

---
name: bias-hetero-main

## .brand-blue[**Anti-Hispanic bias**] does not seem to be driving the effects of .brand-blue[**Secure Communities**] on Hispanic suicide rates

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#bias-app-bygender)]
</div>

---
name: political-hetero-main

## .brand-blue[**Political affiliation**] moderates the effects of .brand-blue[**Secure Communities**] on Hispanic suicide rates

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#political-app-bygender)]
</div>

---
name: unemployment-hetero-main

## .brand-blue[**Unemployment (CPS)**] moderates the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#unemployment-app-bygender)]
</div>

---
name: unemployment-laus-hetero-main

## .brand-blue[**Unemployment (LAUS)**] moderates the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#unemployment-laus-app-bygender)]
</div>

---
name: density-hetero-main

## .brand-blue[**Population density**] moderates the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[By gender]](#density-app-bygender)]
</div>

---
name: mechanism-mental-health

## .brand-blue[**Mental health mechanism**]: Secure Communities does not significantly impact poor mental health days

---
name: conclusion-slide

## .brand-blue[**Secure Communities**] has heterogeneous effects on Hispanic suicide rates across age groups

- **.brand-mardigras[Hispanic children (5-14)]**: .brand-mardigras[**35-100% decrease**] vs. **.brand-crawfest[Hispanic adults (34+)]**: .brand-crawfest[**18-60% increase**] in suicide rates relative to Whites

- **.brand-blue[Sanctuary cities]** provide protective effects among the younger age groups by limiting local cooperation with federal enforcement

- **.brand-olive[Low unemployment]** serves as a key mechanism moderating the effects of immigration enforcement on Hispanic suicide rates

- Effects vary significantly by **.blue[political affiliation]**: Democratic counties show larger increases in adult suicide rates

- Next step: test for the magnitude of deportation as a mechanism (waiting for a funding source to purchase the data)
  
---
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background-image: url(assets/TulaneLogo-white.svg)
background-size: 260px
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# Thank you!

.pull-left[

]  
.pull-right[.pull-down[

<a href="mailto:hhadah@tulane.edu">
.white[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M440 6.5L24 246.4c-34.4 19.9-31.1 70.8 5.7 85.9L144 379.6V464c0 46.4 59.2 65.5 86.6 28.6l43.8-59.1 111.9 46.2c5.9 2.4 12.1 3.6 18.3 3.6 8.2 0 16.3-2.1 23.6-6.2 12.8-7.2 21.6-20 23.9-34.5l59.4-387.2c6.1-40.1-36.9-68.8-71.5-48.9zM192 464v-64.6l36.6 15.1L192 464zm212.6-28.7l-153.8-63.5L391 169.5c10.7-15.5-9.5-33.5-23.7-21.2L155.8 332.6 48 288 464 48l-59.4 387.3z"></path></svg> hhadah@tulane.edu]
</a>

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</a>

<a href="https://github.com/hhadah">
.white[<svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg">  <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> @hhadah]
</a>

]]

---
class: segue-yellow
background-image: url("assets/TulaneLogo.svg")
background-size: 20%
background-position: 95% 95%
count:false

# Appendix

---
count:false
name: causal-identification-assumptions

## Key Causal Identification Assumptions

### 1. Stable Unit Treatment Value Assumption (SUTVA)
- No spillovers between counties
- Treatment of one county does not affect outcomes in other counties

### 2. Parallel Trends Assumption
- Absent SC implementation, Hispanic and White suicide rates would have followed parallel differential trends across counties
- Testable: If coefficients `$\beta_l$` for `$l < 0$` are statistically insignificant, the assumption holds

### 3. Limited Anticipation Assumption
- Hispanic youth do not alter behavior affecting suicide risk prior to SC's actual implementation
- Testable: If coefficient `$\beta_{-1}$` is statistically insignificant, the assumption holds .small[[[Back]](#triple-diff-spec-four)]

---
count:false
name: causal-identification-assumptions-two

## Testing Parallel Trends Assumption using Borusyak, Jaravel, and Spiess (2024)

`$$Y_{it} = \alpha_i + \beta_t + X_{it}'\delta + \sum_{h=1}^{K} \gamma_h \cdot \mathbb{1}\{K_{it} = -h\} + \varepsilon_{it}$$`

.pull-left[
### Sample Construction
- Uses **only untreated observations**
- All unit-time pairs where treatment has not yet occurred
- All observations from never-treated units
- `$K_{it} = t - E_i$` measures periods relative to treatment date
]

.pull-right[

### Testing Pre-Trends

- Indicators capture pre-treatment outcome differences

- Testing `$H_0: \gamma_1 = \cdots = \gamma_K = 0$` assesses parallel trends

- Significant pre-treatment coefficients would indicate violations

.small[[[Back]](#triple-diff-spec-four)]]

---
count:false
name: causal-identification-assumptions-three

## Advantages of This Approach

.pull-left[
### Methodological Strengths
- Direct test of parallel trends validity
- No restrictions on treatment-effect heterogeneity
- More efficient test than placebo-based strategies

]

.pull-right[
### Statistical Improvements
- Maintains valid inference without conditioning on passing pre-test
- Avoids inflated variances and overly conservative inference
- Provides rigorous diagnostic of potential violations .small[[[Back]](#triple-diff-spec-four)]
]

---
name: results-app-all-ages-bygender
count: false

## SC does not affect the suicide rates among all Hispanic females and increases the rates among all Hispanic males but the parallel trends likely does not hold
  
.pull-left[
<img src="assets/figures/ddd_ddd_fem_all_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/ddd_ddd_male_all_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

.right[.small[[[Back]](#results-all-ages-all)]]

---
name: sanctuary-app-5to14-bygender
count: false

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among .brand-mardigras[**ages 5-14**] .brand-blue[**Hispanic children**] by gender
  
.pull-left[
<img src="assets/figures/fem_5to14_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/male_5to14_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

.right[.small[[[Back]](#sanctuary-hetero-5to14)]]

---
name: sanctuary-app-15to24-bygender
count: false

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among .brand-medallion[**ages 15-24**] .brand-blue[**Hispanic youth**] by gender
  
.pull-left[
<img src="assets/figures/fem_15to24_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/male_15to24_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

.right[.small[[[Back]](#sanctuary-hetero-15to24)]]

---
count:false
name: sanctuary-hetero-all

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among all .brand-blue[**Hispanics**]

---
name: sanctuary-app-34plus-bygender
count: false

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among .brand-crawfest[**ages 34+**] .brand-blue[**Hispanic adults**] by gender
  
.pull-left[
<img src="assets/figures/fem_34plus_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/male_34plus_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

.right[.small[[[Back]](#sanctuary-hetero-34plus)]]

---
name: sanctuary-app-all-bygender
count: false

## .brand-blue[**Sanctuary cities**] buffer the effects of .brand-blue[**Secure Communities**] on suicide rates among all .brand-blue[**Hispanics**] by gender
  
.pull-left[
<img src="assets/figures/fem_all_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Females**]
]

.pull-right[
<img src="assets/figures/male_all_event.png" width="100%" style="display: block; margin: auto;" />

.center[**Males**]
]

.right[.small[[[Back]](#sanctuary-hetero-all)]]

---
name: bias-app-bygender
count: false

## .brand-blue[**Anti-Hispanic bias**] does not seem to be driving the effects of .brand-blue[**Secure Communities**] on Hispanic suicide rates: Female Analysis

.right[.small[[[Back]](#bias-hetero-main)]]

---
name: bias-app-males
count: false

## .brand-blue[**Anti-Hispanic bias**] does not seem to be driving the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates: Male Analysis

.right[.small[[[Back]](#bias-hetero-main)]]

---
name: political-app-bygender
count: false

## .brand-blue[**Political affiliation**] moderates the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates: Female Analysis

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#political-hetero-main)]
</div>

---
name: political-app-males
count: false

## .brand-blue[**Political affiliation**] moderates the effects of .brand-blue[**Secure Communities**] on .brand-blue[**Hispanic**] suicide rates: Male Analysis

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#political-hetero-main)]
</div>

---
name: unemployment-app-all-gender
count: false

## .brand-blue[**Unemployment rates**] moderate the effects: All Ages and Gender-Specific Analysis

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#unemployment-hetero-main)]
</div>

---
name: unemployment-app-age-gender
count: false

## .brand-blue[**Unemployment rates**] moderate the effects: Gender-Specific Analysis by Age Group

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#unemployment-hetero-main)]
</div>

---
name: unemployment-laus-app-all-gender
count: false

## .brand-blue[**Unemployment rates (LAUS)**] moderate the effects: All Ages and Gender-Specific Analysis

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#unemployment-laus-hetero-main)]
</div>

---
name: unemployment-laus-app-age-gender
count: false

## .brand-blue[**Unemployment rates (LAUS)**] moderate the effects: Gender-Specific Analysis by Age Group

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#unemployment-laus-hetero-main)]
</div>

---
name: density-app-all-gender
count: false

## .brand-blue[**Population density**] moderates the effects: All Ages and Gender-Specific Analysis

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#density-hetero-main)]
</div>

---
name: density-app-age-gender
count: false

## .brand-blue[**Population density**] moderates the effects: Gender-Specific Analysis by Age Group

<div style="position: absolute; bottom: 60px; right: 20px;">
.small[[[Back]](#density-hetero-main)]
</div>