Neighborhood Intervention, Crime and School Achievement

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.title[
# Neighborhood Intervention, Crime and School Achievement
]
.author[
### Marcelino Guerra
]
.author[
### University of Illinois at Urbana-Champaign
]
.date[
### October, 2022
]

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# Motivation and Background

* Cities continue to seek meaningful, evidence-based policies to improve residents' quality of life
  
  * Interventions that involve the local community are more likely to strengthen neighborhood bonds, which might impact violence levels (see Sampson, Raudenbush, and Earls (*Science* 1997)) and also help the conservation of the public good  
  
* In that sense, there is also an increasing interest in crime prevention through environmental design

* For instance, Chalfin et al. (*JQC* 2021) found that increased levels of street lighting led to a substantial reduction in serious crimes that took place outdoors at night in New York City

* If changes in the physical environment can both reduce victimization and increase the level of amenities enjoyed by residents, chances are the public policy passes the cost-benefit analysis 
  
  * Even better if the intervention has other positive impacts on the affected population (such as human capital accumulation)

* **This study** wants to evaluate an ongoing urban renewal project in a large Brazilian city. Specifically, the project aims to investigate the effects of local amenities on crime and student achievement

---

# The Areninhas Project

.pull-left[

* In the middle of 2014, The City of Fortaleza began an ongoing urban renewal project called "Areninhas." The intervention consists of synthetic football turf, sometimes with a playground and an outdoor gym. Besides, there is a substantial increase in street lighting

* The project targets highly vulnerable communities, and the City Hall aims to offer the population an urban amenity that enhances the well-being of residents, promoting physical activity and a sense of community

* The local government works with residents to supervise and conserve the public equipment. Regularly, free football lessons are offered to the young locals, and amateur football championships are held on weekends

]

.pull-right[

As of now, there are 102 areninhas in Fortaleza and more than 150 in the rest of Ceara State. **The project aims to investigate the effects of this local amenity on crime and student achievement**.

![](figs/final2.png) 
.small[**Source: Fortaleza City Hall website and *O Povo* newspaper.**]

]
---
# Murders in Fortaleza

.pull-left[

* The data consists of 13,370 georreferenced homicide occurrences recorded between **January 1st, 2011, and January 30th, 2020**.

* Most occurrences have information about gender, age, schooling, criminal record, etc.

* The statistical group of the Secretariat of Public Security of Ceará (SSPDS-CE) is the primary data source. I got coordinates of occurrences with precise addresses using the Google Maps API

* To avoid too many zeros, homicides are aggregated to quarters and at the census tract level (this spatial aggregation might change). In some regressions, the outcome is homicide per 1,000 people.

]

.pull-right[

]

---

# SPAECE Exam and School Census

### SPAECE

* Since 1992, the State Government of Ceara has been consistently evaluating the performance of students in the public system. Every year, middle and high school students take the SPAECE exam by late November. 
  
* The SPAECE microdata contains standardized Math and Portuguese test scores and detailed information about these students.

### Schools Census

* The school census is the central database that covers primary education. It provides annual information about all schools and enrollment, such as school characteristics (this includes faculty characteristics), and approval/failure/dropout rates, among others.

---
# Potential Mechanisms

.pull-left[

### Urban renewal and crime

* To establish the relationship between urban redevelopment and crime, multiple mechanisms are hypothesized

* **More eyes on the streets** - McMillen, Sarmiento-Barbieri and Singh (*JUE* 2019), Sanfelice (*JEBO* 2019)
  
  * **Improvement in street lighting** - Chalfin et al. (*Journal of Quantitative Criminology* 2021)
  
  * **Increase in social controls, cohesion, and trust** - Sampson, Raudenbush, and Earls (*Science* 1997)

]

.pull-right[

### Urban renewal, violence and school achievement 
  
* The connection between urban renewal and school achievement relies on the potential reduction (or displacement) of violent crimes in the schools' surroundings. Recent studies established a relationship between violent events and school performance - for instance, Sharkey (*PNAS* 2010), Monteiro and Rocha (*Restat* 2017), and Koppensteiner and Menezes (*JOLE* 2021)
  
  * Direct victimization and/or violence exposure may cause psychological distress, disrupt school routine, decrease attendance and grades, increase student dropout rates, etc.

]

---
# The Staggered Rollout of Areninhas

.pull-left[

<table class="table table" style="width: auto !important; margin-left: auto; margin-right: auto; margin-left: auto; margin-right: auto;">
 <thead>
  <tr>
   <th style="text-align:left;"> Year </th>
   <th style="text-align:right;"> Areninhas </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> 2014 </td>
   <td style="text-align:right;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2015 </td>
   <td style="text-align:right;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2016 </td>
   <td style="text-align:right;"> 18 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2017 </td>
   <td style="text-align:right;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2018 </td>
   <td style="text-align:right;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2019 </td>
   <td style="text-align:right;"> 17 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2020 </td>
   <td style="text-align:right;"> 31 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2021 </td>
   <td style="text-align:right;"> 15 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 2022 </td>
   <td style="text-align:right;"> 14 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Total </td>
   <td style="text-align:right;"> 102 </td>
  </tr>
</tbody>
<tfoot>
<tr>
<td style = 'padding: 0; border:0;' colspan='100%'><sup></sup> Source: Fortaleza's City Hall website. Note: There are two areninhas inside the same census tract, so the total number of city blocks is 101.</td>
</tr>
</tfoot>
</table>

]

.pull-right[

]

---
# Identification I: Treated vs Never Treated

.pull-left2[

* To construct the counterfactual scenario - i.e., what would have happened to the treated areas without the urban renewal - the choice of the control group is crucial

* The Local Government did not choose the areas under intervention randomly: neighborhoods with very-low/low HDI were targeted

* The table shows that the treated areas and the rest of the city are fundamentally different in observables

]

.pull-right2[

<table>
 <thead>
   <tr>
   <td style="text-align:center;" colspan="5"> <b> t-test mean equality: Treated vs Never Treated </b> </td>
   </tr>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> Treated </th>
   <th style="text-align:center;"> Never treated </th>
   <th style="text-align:center;"> Difference </th>

</tr>
 </thead>
<tbody>

<tr>
   <td style="text-align:left;"> Average Income (R$) </td>
   <td style="text-align:center;"> 1,907.982  </td>
   <td style="text-align:center;"> 2,489.519  </td>
   <td style="text-align:center;"> -581.53***  </td>
  
  </tr>
  <tr>
   <td style="text-align:left;"> More than 10 Minimum Wages   </td>
   <td style="text-align:center;"> 0.92% </td>
   <td style="text-align:center;"> 2.19% </td>
   <td style="text-align:center;"> -1.27%*** </td>
 
  </tr>
  
  <tr>
   <td style="text-align:left;"> Up to 1 Minimum Wage  </td>
   <td style="text-align:center;"> 69.05% </td>
   <td style="text-align:center;"> 62.46%  </td>
   <td style="text-align:center;"> 6.58%*** </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> Population density </td>
   <td style="text-align:center;"> 11,097.98  </td>
   <td style="text-align:center;"> 18,796.28 </td>
   <td style="text-align:center;"> -7,699*** </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Young 12-21 </td>
   <td style="text-align:center;"> 19.10% </td>
   <td style="text-align:center;"> 17.88% </td>
   <td style="text-align:center;"> 1.21%*** </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Black and Brown 15-29 </td>
   <td style="text-align:center;"> 9.41% </td>
   <td style="text-align:center;"> 8.77% </td>
   <td style="text-align:center;"> 0.64%** </td>
  </tr>
  <tr>
   <td style="text-align:left;"> No pavement </td>
   <td style="text-align:center;"> 13.47% </td>
   <td style="text-align:center;"> 9.02% </td>
   <td style="text-align:center;"> 4.45%** </td>

</tr>
  
  <tr>
   <td style="text-align:left;"> No Street Lighting </td>
   <td style="text-align:center;"> 4.09% </td>
   <td style="text-align:center;"> 1.98% </td>
   <td style="text-align:center;"> 2.11%** </td>

</tr>

<tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;"> 101 </td>
   <td style="text-align:center;"> 2,919 </td>
   <td style="text-align:left;">  </td>

</tr>
    <tfoot><tr><td colspan="5"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>

]

---

# Identification II

---

# Identification III: Treated before vs Treated after 2019

.pull-left2[

* The table shows that future treated regions (census tracts) are very similar to the ones treated earlier (until December 2018) on observables

* I conjecture that census tracts treated before 2019 and after 2019 are similar in observables and unobservables, and this choice of control group produces an apples-to-apples comparison

* The main identifying assumption is that, in the absence of the urban policy, crime in treated and control groups would have evolved in a parallel fashion

]

.pull-right2[

<table>
 <thead>
   <tr>
   <td style="text-align:center;" colspan="5"> <b> t-test mean equality: Treated (2014-2018) vs Future Treated </b> </td>
   </tr>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> Treated before 2019 </th>
   <th style="text-align:center;"> Treated after 2019 </th>
   <th style="text-align:center;"> Difference </th>

</tr>
 </thead>
<tbody>

<tr>
   <td style="text-align:left;"> Average Income (R$) </td>
   <td style="text-align:center;"> 1,764.08  </td>
   <td style="text-align:center;"> 1,821.59  </td>
   <td style="text-align:center;"> -57.51 </td>
  
  </tr>
  <tr>
   <td style="text-align:left;"> More than 10 Minimum Wages   </td>
   <td style="text-align:center;"> 0.62% </td>
   <td style="text-align:center;"> 0.84% </td>
   <td style="text-align:center;"> -0.22% </td>
 
  </tr>
  
  <tr>
   <td style="text-align:left;"> Up to 1 Minimum Wage  </td>
   <td style="text-align:center;"> 70.23% </td>
   <td style="text-align:center;"> 70.80%  </td>
   <td style="text-align:center;"> -0.57% </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> Population density </td>
   <td style="text-align:center;"> 13,875.05  </td>
   <td style="text-align:center;"> 9,702.87 </td>
   <td style="text-align:center;"> 4,172.88 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Young 12-21 </td>
   <td style="text-align:center;"> 18.79% </td>
   <td style="text-align:center;"> 19.60% </td>
   <td style="text-align:center;"> -0.81% </td>
  </tr>
    <tr>
   <td style="text-align:left;"> Black and Brown 15-29 </td>
   <td style="text-align:center;"> 9.70% </td>
   <td style="text-align:center;"> 9.72% </td>
   <td style="text-align:center;"> 0.02% </td>
  </tr>
  <tr>
   <td style="text-align:left;"> No pavement </td>
   <td style="text-align:center;"> 10.71% </td>
   <td style="text-align:center;"> 18.72% </td>
   <td style="text-align:center;"> -8.01%* </td>

</tr>
  
  <tr>
   <td style="text-align:left;"> No Street Lighting </td>
   <td style="text-align:center;"> 3.76% </td>
   <td style="text-align:center;"> 4.49% </td>
   <td style="text-align:center;"> -0.73% </td>

</tr>

<tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;"> 25 </td>
   <td style="text-align:center;"> 54 </td>
   <td style="text-align:left;">  </td>

</tr>
    <tfoot><tr><td colspan="5"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>

]

---
# Estimation Method (Crime)

**TWFE**

`$$\text{Murder Rate}_{iq}=\lambda_{q}+\gamma_{i}+\beta Open_{iq}+\varepsilon_{iq}$$`

where `$\lambda_{q}$` is the time fixed effects (quarter-year), and `$\gamma_{i}$` refers to census tract fixed effects. `$\beta$` is the difference-in-differences estimate that captures the causal effect of interest: the extent to which the treated and non-treated city blocks differ in their homicide rates after the urban renewal policy

**TWFE with time-of-day heterogeneity**
  
To check whether this urban intervention have different effects within hours of day, I consider the following:

`$$\text{Murder Rate}_{iqt}=\lambda_{qt}+\gamma_{i}+\beta_{1}Open:Morning_{iqt}+\beta_{2}Open:Afternoon_{iqt}+\beta_{3}Open:Night_{iqt}+\varepsilon_{iqt}$$`

where `$\lambda_{qt}$` represents quarter-year-time-of-day fixed effects. The morning covers the hours from 8 to 11:59 am, afternoon 12:00 pm until 5:59 pm, and night 6:00 pm to 8:59 pm. The omitted category is 'closed,' covering the hours from 9:00 pm to 7:59 am.

---

# Direct Effect I

.pull-left2[

* The first column shows a naive comparison of murder rates between all treated census tracts and the rest of the city. The second column still uses full sample, but exploits the staggered rollout of the urban policy. This difference-in-differences estimate is negative but not statistically significant

* The other three columns use the restricted sample (treated before 2019 *vs* treated after 2019). Results are more precise and statistically significant: the urban renewal policy caused a decrease in murder rates around 47-61%

]

.pull-right2[
<font size="3.5" face="Helvetica" >
<table style="table-layout: fixed;">
 <thead>
   <tr>
   <td style="text-align:center;" colspan="6"> <b> Dependent variable: Murder Rate (per 1,000)/Murder counts (Poisson) </b> </td>
   </tr>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;" colspan="2"> Full Sample  </th>
   <th style="text-align:center;"> Restricted Sample  </th>
   <th style="text-align:center;"> Restricted Sample (Poisson)  </th>
   <th style="text-align:center;"> Restricted Sample (w/o outlier)  </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> <b>Open </b> </td>
   <td style="text-align:center;">  </td>
   <td style="text-align:center;"> -0.204   </td>
   <td style="text-align:center;"> -0.166** </td>
   <td style="text-align:center;"> -0.513**  </td>
   <td style="text-align:center;"> -0.115**  </td>
  </tr>
  
  <tr>
   <td style="text-align:left;">           </td>
   <td style="text-align:center;"> </td>
   <td style="text-align:center;"> (0.224)</td>
   <td style="text-align:center;">(0.067) </td>
   <td style="text-align:center;"> (0.257) </td>
   <td style="text-align:center;"> (0.044) </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b>Areninha </b> </td>
   <td style="text-align:center;"> 0.039538 </td>
   <td style="text-align:center;">   </td>
   <td style="text-align:center;">   </td>
   <td style="text-align:center;">  </td>
   <td style="text-align:center;">  </td>

</tr>
  <tr>
   <td style="text-align:left;">  </td>
   <td style="text-align:center;"> (0.0346)</td>
   <td style="text-align:center;"> </td>
   <td style="text-align:center;">  </td>
   <td style="text-align:center;">  </td>
   <td style="text-align:center;">  </td>
  </tr>
  
  <tr>
   <td style="text-align:left;" > <b>Fixed-Effects:</b>  </td>
   <td style="text-align:left;" > None </td>
   <td style="text-align:left;" > Census tract and Quarter-Year </td>
   <td style="text-align:left;" > Census tract and Quarter-Year </td>
   <td style="text-align:left;" > Census tract and Quarter-Year </td>
   <td style="text-align:left;" > Census tract and Quarter-Year </td>
  </tr>
    <tr>
   <td style="text-align:center;" colspan="6">  </td>
  </tr>
  
  <tr>
  
   <td style="text-align:left;"> <b>Mean of Outcome</b> </td>
   <td style="text-align:center;" colspan="2"> 0.1736  </td>
   <td style="text-align:center;" > 0.2719 </td>
    <td style="text-align:center;" >  </td>
     <td style="text-align:center;" > 0.2427 </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;" colspan="2"> 92,938   </td>
   <td style="text-align:center;"> 2,449 </td>
   <td style="text-align:center;"> 2,108  </td>
   <td style="text-align:center;"> 2,418   </td>

</tr>
   <tfoot><tr><td colspan="6">Standard-errors in parentheses and clustered at the Neighborhood level. The sample considers homicides occurrences between 8 am to 9 pm. The outlier is a treated census tract with only 38 people, hence the really high murder rate.  </td></tr></tfoot>
    <tfoot><tr><td colspan="6"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>
]

---
# Direct Effect II

---

# Time-of-day Heterogeneity I

.pull-left2[

* Time-of-day heterogeneity analysis shows that the decrease in murder in treated census tracts is concentrated between 12 pm and 6 pm, supporting the "more eyes on the streets" thesis

* Pupils that study during the afternoon and commute from home to school around 1:00 pm and from school to home between 5:00-5:30 pm would benefit the most

* Although negative, estimates related to crime occurrences in the morning and night are not statistically significant.

]

.pull-right2[
<font size="3.5" face="Helvetica" >
<table style="table-layout: fixed;">
 <thead>
   <tr>
   <td style="text-align:center;" colspan="4"> <b> Dependent variable: Murder Rate (per 1,000)/Murder counts (Poisson) </b> </td>
   </tr>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> Restricted Sample  </th>
   <th style="text-align:center;"> Restricted Sample (Poisson)  </th>
   <th style="text-align:center;"> Restricted Sample (w/o outlier)  </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> <b>Open:Morning </b> </td>
   <td style="text-align:center;"> -0.103   </td>
   <td style="text-align:center;"> -1.242**  </td>
   <td style="text-align:center;"> -0.034  </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b> </b> </td>
   <td style="text-align:center;"> (0.074)   </td>
   <td style="text-align:center;"> (0.611) </td>
   <td style="text-align:center;"> (0.028) </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b> Open:Afternoon </b>  </td>
   <td style="text-align:center;"> -0.150*</td>
   <td style="text-align:center;"> -0.952*** </td>
   <td style="text-align:center;"> -0.080** </td>

</tr>
  
  <tr>
   <td style="text-align:left;"> <b> </b> </td>
   <td style="text-align:center;"> (0.080)   </td>
   <td style="text-align:center;"> (0.337) </td>
   <td style="text-align:center;"> (0.039) </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b>Open:Night </b> </td>
   <td style="text-align:center;"> -0.097 </td>
   <td style="text-align:center;"> -0.536  </td>
   <td style="text-align:center;"> -0.028  </td>

</tr>

<tr>
   <td style="text-align:left;" > <b>Fixed-Effects:</b>  </td>
   <td style="text-align:left;" > Census tract and Quarter-Year-Time-of-day </td>
   <td style="text-align:left;" > Census tract and Quarter-Year-Time-of-day </td>
   <td style="text-align:left;" > Census tract and Quarter-Year-Time-of-day</td>
  </tr>

<tr>
  
   <td style="text-align:left;"> <b>Mean of Outcome</b> </td>
   <td style="text-align:center;" > 0.066  </td>
   <td style="text-align:center;" >        </td>
    <td style="text-align:center;" > 0.061 </td>

</tr>
  
  <tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;"> 12,524 </td>
   <td style="text-align:center;"> 11,232  </td>
   <td style="text-align:center;"> 12,400   </td>

</tr>
   <tfoot><tr><td colspan="4">Standard-errors in parentheses and clustered at the Neighborhood level. The sample considers all homicides occurrences. The omitted category for time-of-day is when the equipment is closed: between 9 pm and 8 am. </td></tr></tfoot>
    <tfoot><tr><td colspan="4"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>

]

---
# Time-of-day Heterogeneity II

---
# Spatial Distribution of Public Schools

.pull-left2[

* As mentioned before, the research design identifies 25 treated and 55 control areas and excludes 22 regions due to either opening dates or very short distances to treated
  
* Among the 458 public schools in Fortaleza, **92 (29 T/63 C) are within 350 meters** and **163 (62 T/101 C) within 500 meters** of quasi-experimental areas

* Not all schools have fifth or ninth grade, and some schools are not evaluated in every year from 2012-2019 (hence the smaller final sample)

]

.pull-right2[

]

---
# Estimation Method (School Achievement)

**TWFE**

`$$\text{y}_{st}=\lambda_{t}+\gamma_{s}+\beta Open_{st}+\varepsilon_{st}$$`

where `$\lambda_{t}$` is the year fixed effect, and `$\gamma_{s}$` refers to School fixed effects. `$\beta$` is the difference-in-differences estimate that captures the causal effect of interest: the extent to which the treated and non-treated Schools differ in their Portuguese and Math average scores/Approval and Dropout rates after the urban renewal policy

---

# Direct Effects - Middle School

.pull-left2[

* The first three columns display the causal effects of the urban policy on Portuguese scores of students in `$5^{th}$` grade. Results point to an increase between 0.45-1.49%, but are not statistically significant

* Using the preferred ring size, Math scores increase by 3% on average

* As the radius increases, point estimates get smaller

]

.pull-right2[
<font size="3.5" face="Helvetica" >
<table style="table-layout: fixed;">
 <thead>
   <tr>
   <td style="text-align:center;" colspan="7"> <b> Dependent variable: Average Score </b> </td>
   </tr>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;" colspan="3"> Portuguese </th>
   <th style="text-align:center;"  colspan="3"> Math  </th>
  </tr>
  
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> 350m </th>
   <th style="text-align:center;"> 500m </th>
   <th style="text-align:center;"> 650m </th>
   <th style="text-align:center;"> 350m </th>
   <th style="text-align:center;"> 500m </th>
   <th style="text-align:center;"> 650m </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> <b>Open </b> </td>
   <td style="text-align:center;">    3.156    </td>
   <td style="text-align:center;">  2.502  </td>
   <td style="text-align:center;"> 0.963 </td>
   <td style="text-align:center;"> 7.198**  </td>
   <td style="text-align:center;"> 6.469***  </td>
   <td style="text-align:center;"> 3.23  </td>
  </tr>
  
  <tr>
   <td style="text-align:left;">           </td>
   <td style="text-align:center;"> (2.17) </td>
   <td style="text-align:center;"> (1.72)</td>
   <td style="text-align:center;"> (1.80)</td>
   <td style="text-align:center;"> (3.06) </td>
   <td style="text-align:center;"> (2.29) </td>
   <td style="text-align:center;"> (2.51) </td>
  </tr>

<tr>
   <td style="text-align:left;" > <b>Fixed-Effects:</b>  </td>
   <td style="text-align:left;" > School and Year</td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
  </tr>
    <tr>
   <td style="text-align:center;" colspan="7">  </td>
  </tr>
  
  <tr>
  
   <td style="text-align:left;"> <b>Mean of Outcome</b> </td>
   <td style="text-align:center;"> 211.70 </td>
   <td style="text-align:center;" > 209.90 </td>
    <td style="text-align:center;" > 211.35 </td>
    <td style="text-align:center;" > 215.89 </td>
    <td style="text-align:center;" > 214.45 </td>
    <td style="text-align:center;" > 217.06 </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;"> 256   </td>
   <td style="text-align:center;"> 368 </td>
   <td style="text-align:center;"> 552  </td>
   <td style="text-align:center;"> 256   </td>
   <td style="text-align:center;"> 368  </td>
   <td style="text-align:center;"> 552   </td>

</tr>
   <tfoot><tr><td colspan="7">Regressions are weighted by the number of students taking the exam in each school-year. Standard-errors in parentheses and clustered at the Areninha level.  </td></tr></tfoot>
    <tfoot><tr><td colspan="7"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>
]

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# Direct Effects - Middle School II

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# Direct Effects - High School

.pull-left2[

* Using the preferred ring size (500m), results point to an increase, on average, around 4.8% in approval rates for High School Students (15-17 years old)

* Again, as the radius increases, point estimates get smaller 
]

.pull-right2[
<font size="3.5" face="Helvetica" >
<table style="table-layout: fixed;">
 <thead>

<tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;" colspan="3"> Dependent Variable: Approval Rates </th>
   <th style="text-align:center;"  colspan="3"> Dependent Variable: Dropout Rates  </th>
  </tr>
  
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:center;"> 350m </th>
   <th style="text-align:center;"> 500m </th>
   <th style="text-align:center;"> 650m </th>
   <th style="text-align:center;"> 350m </th>
   <th style="text-align:center;"> 500m </th>
   <th style="text-align:center;"> 650m </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> <b>Open </b> </td>
   <td style="text-align:center;">    5.968*    </td>
   <td style="text-align:center;">  3.783**  </td>
   <td style="text-align:center;"> 3.029** </td>
   <td style="text-align:center;"> -3.447  </td>
   <td style="text-align:center;"> -0.572  </td>
   <td style="text-align:center;"> -0.799  </td>
  </tr>
  
  <tr>
   <td style="text-align:left;">           </td>
   <td style="text-align:center;"> (3.265) </td>
   <td style="text-align:center;"> (1.636)</td>
   <td style="text-align:center;"> (1.353)</td>
   <td style="text-align:center;"> (2.350) </td>
   <td style="text-align:center;"> (1.512) </td>
   <td style="text-align:center;"> (1.209) </td>
  </tr>

<tr>
   <td style="text-align:left;" > <b>Fixed-Effects:</b>  </td>
   <td style="text-align:left;" > School and Year</td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
   <td style="text-align:left;" > School and Year </td>
  </tr>
    <tr>
   <td style="text-align:center;" colspan="7">  </td>
  </tr>
  
  <tr>
  
   <td style="text-align:left;"> <b>Mean of Outcome</b> </td>
   <td style="text-align:center;"> 79.88 </td>
   <td style="text-align:center;" > 79.05 </td>
    <td style="text-align:center;" > 79.52 </td>
    <td style="text-align:center;" > 10.43 </td>
    <td style="text-align:center;" > 12.32 </td>
    <td style="text-align:center;" > 11.81 </td>
  </tr>
  
  <tr>
   <td style="text-align:left;"> <b>Observations</b> </td>
   <td style="text-align:center;"> 144   </td>
   <td style="text-align:center;"> 296 </td>
   <td style="text-align:center;"> 464  </td>
   <td style="text-align:center;"> 144   </td>
   <td style="text-align:center;"> 296  </td>
   <td style="text-align:center;"> 464   </td>

</tr>
   <tfoot><tr><td colspan="7">Regressions are weighted by the number of students enrolled in each school-year. Standard-errors in parentheses and clustered at the Areninha level.  </td></tr></tfoot>
    <tfoot><tr><td colspan="7"> <b>Note:</b> ***: 0.01, **: 0.05, *: 0.1 </td></tr></tfoot>
</tbody>
</table>
]

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# Direct Effects - High School II