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So good afternoon. My name is Revoris Moore. I am an assistant professor at New York University, and first thank you all for being here. It's great to see people, and to present in person.

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The title of my paper is Police Shootings, civilian Crime, reporting delays and changing implicit sentiment to police, and I'll just start by noting that this is a 15 min version of a 45 min talk. So a lot has been omitted. And I invite you to ask questions at the end when you have questions.

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Okay?

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So I started this work a few years ago, at a time in the Us. Where

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there were frequently cycles in the news about certain kinds of police violence often focused towards black Americans, and I found this quite unsettling.

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So this led me to wonder. First of all, was it possible for this cycling to continue in the midst of protest and pushback, or at some point would there be a need for

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civilian trust, so that police have the buy back from the community to move forward. And that kind of led me to the research question that I'll be talking about today, which is, do negatively perceived instances of police violence after civilian kind of reporting behavior.

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So there's a lot that we know about how civilians respond to police violence. First, we know that public perceptions of police are heavily influenced by police conduct and tend to respond to instances of perceived use of excessive force.

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Also, we have evidence that extreme examples of police misconduct can lead to a disinformation to a port crime.

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So what's missing? And I'm going to argue for 2 things that I want to address in this presentation.

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So first, we don't have a generalized understanding of how police violence affects crime. Reporting so previous work employs case study designs to study the effects of extreme incidents of police violence on decisions to report crime.

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The issue here is that

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the case study designs focused on a single incident, and those incidents are typically very extreme, and, as you know, studying in extremists may not tell you about the dynamics across the the distribution more broadly. So, therefore we may not know that much about what happens more commonly just from these extreme cases.

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Second, we don't understand the implications of incident, heterogeneity for civilian responses.

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So another issue with having one accident is that

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by definition in one instant has no variance, and thus there's no way to understand what characteristics of incidents are most salient for understanding how people respond to it. And this research has a way of

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approaching that dimension of the question.

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So I'm going to focus on 3 contributions for this short talk. So first.

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I'm going to. I introduce same day, reporting as a useful measure for gauging change in crime, reporting behavior and sentiments to it. Police. And this is a deviation from the literature which focuses on only on

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decisions to report to whether a crime is supported or not.

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Second, I infer from a large sample of incidents to draw generalizable results and uncover several dimensions of effect, heterogeneity.

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and, third, I've seen the the effects of students to by police officers, which is one of the most failure forms of police aggression in the in the United States.

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So I'm going to cover 4 main points moving forward. So you can understand my results. first, the conceptual model, second data and estimation. third, I'm going to focus on 3 main results, and then a few closing thoughts, time permitting.

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So suppose victimization occurs at time, T or a crime occurs

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that presents a choice.

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and

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the person Victor Miles can either choose to report the crime, and that's the Rt. One node, or they can choose not to report. If they choose to report. Then a decision comes of whether to report immediately or to report with delay, and that's the Dt. One note.

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So thus there are 2 dimensions where we could observe the possible effects of beliefs about police.

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So prior beliefs here the Pd. Minus 2 note can affect both. But also, if there was a recent instance of police violence in B sub t minus one that can also affect what what one does at either the reporting node or the timing node.

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The red path in this figure is the path of interest, and that's trying to understand whether recent police violence has any effect on when people or the timing with which comments reported. So the empirical analysis here is centered around trying to understand whether

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a recent incident of police violence has any effect on the timing of crime reporting in subsequent weeks.

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Okay.

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so there's a clear concern here, because

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we only observe the timing decision among those who made the endogenous choice to report.

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so that presents the

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the estimation challenge of how to facilitate causal inference in a setting where you have endogenous selection, and I won't go into great detail about that here. I'm happy to answer questions about it in the Q. And a. But my

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looking at this for several, I guess years

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I've come to conclude that the qualitative findings of this work are not being driven by endogenous selection. So this seems to not be an issue given the way I control, and the descriptive that I've shown is or ran to understand that there aren't major changes between between who reports in the crimes that I study.

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So my dependent, variable data comes from the Los Angeles police department crown data for the years 2,010, through 2,021.

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These data which facilitate the construction of the same day reporting metric.

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The raw data has 2.4 million reported crimes on over 144 crime codes and the spatial units of of analysis is lapd areas. And those are so shown in the

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figure to the right.

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There are 21 areas across Los Angeles, and on average they have a square ballot of about 21 square mounds.

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So these data allowed me to see 2 time points. first T. 0, which is the tower of which crime occurs, and then T. 2, which is the time at which crime is reported.

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Unfortunately, we can't see T. One, which is the time at which the victim becomes aware of the crime.

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And

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what's really of interest here is the the delay between awareness and reporting.

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So this presents a problem because the real quantity of interest is not observed in these data.

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So my solution here was to constrain the sample to what

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Spelman and Brown? 84 call involvement crimes. So these are crimes where there is in direct interaction between the victim and the assailant, and thus it should be fair to assume that the

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point in which the calm occurs is the same point in which the Britain becomes aware.

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imposing that restriction implies that t. 0 equals t. One, or the time of occurrence equals the time of awareness.

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and it allows the empirical observation of the delay of interest, which is t. 2 minus t. 0, so that everything we need becomes observable.

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And then this allows the construction of the binary dependent, variable of interest, which is saying the reporting.

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So

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this binary variable takes a value of one when a crime is reported on the date of occurrence in a value of 0 otherwise, and the regression model will be used will be using this S variable as the outcome.

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Data on police shootings come from 3 different sources. So first, the most topics of source is the Lapd listing of categorical use of forest incidents reviewed by the La Board of Police Commissioners, and this data covers 2,002, 2,020. I use 2,010 through 2,020, because that's where my dependent variable lies.

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So this has the benefit of covering the entire distribution of police shootings by Lapd officers, which gives us an idea or give us a way to argue that

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we can say in general.

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how civilians respond to police shootings because of this set of events.

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Second, I'm using 2 independently gathered forms of so one from a Washington post, the other from Kbcc, which is a radio station in California.

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and these have the

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advantage of having detailed information about each shooting. In particular, they can show whether

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a shooting was fatal, whether the person was unarmed, and whether the person was showing signs of mental illness.

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So one of the aims of this work is to understand the effects of police shootings that are negative negatively perceived. However, it's very difficult to identify

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what it means for an it to be perceived negatively, because people have different views on police shootings.

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For that reason I've identified classes of shootings that have a highlight likelihood of being negative negatively.

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And I'll use these classes to draw inference on how this these sets might differ from the larger set of Lpd incidents.

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Okay.

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so the estimation strategy is very simple. so

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we care about changes in the probability of same the reporting. So the blue dots on this graph are showing the rates of samely reporting for crimes reported 4 weeks before, and 4 weeks after a police shooting that happened at week 0. And we care about this gap of Beta, which is the difference in means between those 2 time periods.

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and

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I use a very simple linear probability model to estimate this difference. It includes controls for a victim 6 age, race, ethnicity, the type of prime, the kind of weapon use, and the month of the crime.

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The model also includes a police shooting incident with a specific fixed effect, and it constrains to only look at

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the 4 weeks before and after incidence. And of course the independent variable of interest is a post-event or a post-ple shooting window, and we would like to know if being after a police shooting has any effect on the timing with which

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that promise reported.

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So now let's move into results. And first question over the entire 10 year span of data. Are we seeing any effects of police shootings on the time you come reporting

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and result one a There is no evidence of delays

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in the Lapd data. So I've ran 3 models for each of these. the first is uncontrolled, the second includes controls for written characteristics, the third controls for

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written characteristics and increment student characteristics. In this case the coefficients are positive and even with full controls, just still a marshal possibility, possibly pitch. So in this case it's about a sixteenth of a percent increase in saying the reporting after police shootings.

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so that offers no evidence of delays on average.

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In the most, you know class of events.

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When we look at the event types that have a higher likelihood of being perceived negatively.

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we still don't see evidence of delays. So for Federal police shootings.

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the first 2 models, marginally negative, marginally significant negative effects for the control model.

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No significance. same thing with pedal police, students of unarmed persons. no significance. So no evidence of delaying effects there as well.

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And then, even with least students of persons showing signs of mental illness, no evidence of delaying effects in the fully controlled model so overall across 10 years of data and

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3 types of events. We're not seeing any consistent evidence that the timing of the reporting depends upon

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a recent police violence incident.

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So question, 2,

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do these null effects, or are these null effects? Time invariant? Which means, is this result consistent over time?

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Okay.

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So

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for Lpd incidents pretty much. Yes, so across Panel A,

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we're seeing nothing but null effects, and a few years where there's a positive effect.

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So

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for the most part, in general, there is no evidence that in the most general class of events that there are any declines

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for the 3 types that have a higher likelihood of being seen negatively, that's not the case. So for fatal shootings we see negative effects in years 2,021 and 2,013, for it also be to put on persons between years 2,012 and 2,015 effects are snip and and and and for signs of mental illness, in year 2,021, and you can see that a bit better in this table. So the previous crabs are based on these estimates.

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So we see a pattern where between 2,012 and 2,015  saying that reporting rates could decline from anywhere from

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about 5% to up to almost 20%, depending upon the incident type. So in the same thing is showing 4 years to 2021, we're seeing 5% declines for fatal shootings and 7% of the clients for students, a person showing signs of mental illness. So this suggests that.

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and for certain kinds of incidents in certain years there can be an effect, even though there seems not to be one more generally

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and final question

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when we do see an effect, who is changing the reporting behavior. Are we seeing it across the sample? Or is it only among certain groups?

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Okay, so an answer to that question. I interacted

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race with the independent, variable, and for the most part we're seeing a fairly ubiquitous change across groups. So, with the exception of null effects for the Asian subgroup. There is evidence that

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almost everyone is reporting or is modifying their behavior. So this suggests that all groups are somewhat internalizing these events in the same way.

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Okay?

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So a few closing thoughts, I set out to try to find a generalizable result. And it's kind of muddy. So

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police violence sometimes affects civilian crime reporting behavior. This seems to be a tendency toward null effects, but in certain years, and under certain conditions for the incident there can be effects that are quite noticeable and detectable.

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Why were delaying affects present in certain years, but not others.

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The analysis doesn't say directly.

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However.

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we noticed effects between years 2012 and 2015, and then again in 2021. And in the Us. Those were years were issues of police violence and

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and racial justice. We're at the forefront of national consciousness. So I hypothesized that there may be something about those conversations that led to more thought, and when people call the the police which might have led to some of these results that I find.

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and last, we'll negatively perceive. Police violence lead to permanent shifts, independence upon police services. I argue likely not, because the results or the effects we find here seem to be temporary, and there is not real evidence of lasting effects.

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and I will stop there.

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Now, look forward to your questions and comments.

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Okay, thank you very much.

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Just preparing for the second one.

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René Iwo: I can share my screen with.

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René Iwo: are you seeing the presentation?

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Yes.

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René Iwo: okay, thanks.

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Is this

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your your Renee? Oh, yes.

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René Iwo: Okay, yeah. That's me.

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Okay, so

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please, you can take over and then start your online presentation.

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René Iwo: thank you so much. for the opportunity. just to reintroduce myself.

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René Iwo: my name is Renee. I I am a graduate student in sociology at the University of North Carolina, at Chapel Hill. As you can see here and today I'm presenting work with my advisors, Elizabeth, Frank and Berke and Duncan Thomas about the educational aspirations and attainment of postsunami youth in Indonesia.

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René Iwo: So a little bit of a kind of overview this is our main question, how does disaster exposure affect youth, educational aspirations, and attainment?

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René Iwo: So this is important because traumatic events are becoming increasingly common and more intense over the life course of children, as you can see here. especially in terms of natural disasters. It is becoming way more common over time. and studies on use outcomes. Post exposure to these traumatic events is pretty limited.

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René Iwo: mostly focusing on short-term outcomes for health outcomes. But what about the longer term social education. So here we're looking at one of those educational aspirations and attainment.

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René Iwo: So studies have shown that educational aspirations do indeed map onto eventual education and labor market outcomes.

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René Iwo:  and what about children who are exposed to disasters? So disasters represent a significant disruption to education and also life trajectories over the long run.

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René Iwo: and previous studies about the effect of disasters on children's education usually draw a very good picture. So we are here to kind of investigate that in this case the

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René Iwo: Indian Ocean tsunami in 2,004, and the province of adding particularly so. On December 26 and 2,004, the first boxing day there was an undersea earthquake that triggered a very strong to

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René Iwo: with the shore within 15 min of the quick, with no warning, and it was early morning out of many countries in the Indian Ocean. Indonesia was the hardest hit, particularly these 2 provinces of actually in most Sumatra and the northern tip of this island right here.

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René Iwo: And

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René Iwo: as you can imagine, there was wide ranging so and health impacts, including mortality, post-traumatic stress

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René Iwo: economic stress and so on. But, interestingly,

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René Iwo: Following the tsunami, we also saw a large influx of aid and rebuilding effort which was largely successful in building that better

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René Iwo: so our just administration here. But the devastation. This was a satellite image prior to the tsunami. This was a few days after the and that's the we building? coming in 5 years after.

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René Iwo: Now, focusing on the data that we use for this study, we use data for our study of the sun on the aftermath and recovery, which is a long it to the middle Household Survey. Collecting information on a library of topics and tsunami, effective districts of the 2 provinces I mentioned earlier.

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René Iwo: It is representative of the pretty tsunami population. and it has so far a total of 7 waves, the first one starting 5 months posts, not me on the ground. So you can imagine how intense that was.

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René Iwo: And then there were annual follow ups in years, 2 to 5, and then again follow up 10 years after the cinema, and 15 years after the so the 15 year follow up was the latest in starting in 2,020

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René Iwo: and there was no attrition overall because of concert and effort from the serving team to follow everyone, including

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René Iwo: so our sample focuses on a cohort of children is 5 to 14 years old at the first person on the interview, one year after the.

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René Iwo: and we look at 3 time points 1, 5, and 15 years post. So we use 3 waves of the survey. and we have about 5,000 children in the sample, only about 5 to 8% last to attrition even 50 years.

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René Iwo: and all of our analysis are stratified into 2 age group cohorts. the younger cohort here starts out one year after this, not me, aged 5 to 9, and then 5 years after this money, so 4 years later, they have age to 10 to 14 years old.

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René Iwo: and as for the older cohort. they started out, each 10 to 14. And then, 5 years later, they've aged to 52 and as for 15 years

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René Iwo: the younger cohort has age to 2,024. All your cohort has to 25 to 30. So you can see these people. 15 years after this they have all age past the schooling age.

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René Iwo: As for our outcome, variable the first one is about aspirations for super higher education. One and 5 years goes to Melanie.

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René Iwo: So this comes from the question that was asked in both ways. what is the highest school in you have to attend. There are 7 response categories due to the way that the categories end up plotting in terms of observations. We dichotomy that to whether or not the respondent wants to pursue higher education.

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René Iwo: So just the descriptive here overall aspirations are quite high. Even given the tsunami right after the tsunami they were, at least 60 of the children aspired to pursue higher education. You can imagine this was quite

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René Iwo: an intense time in their lives.

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René Iwo: And this is also high. Given that in the province of that that year only 17% of the population is 19 to 40 as or secondary education.

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René Iwo: So 17 compared to my 6 60, that's quite a lot.

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René Iwo: And This is the one year person only 5 years after the tsunami. The aspirations have increased 76 to 81

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René Iwo:  hope students on university or higher education. And if you look here per cohort, the increase over time is much higher for the younger to work from 60 to 81%. That's 17%

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René Iwo: compared to 13%.

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René Iwo: And our second outcome variable is about educational attainment. 15 years posting on me. So this is the 15 year. Follow up interview. There's this question, what is the high level of schooling you attended, or I think it's a new.

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René Iwo: and we made it into 3 categories, incomplete high school, and in high school it only but stop at that point and enroll in some kind of higher education.

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René Iwo: So about one fifth of left school before graduating high school

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René Iwo: and about 41 to 46% attended a higher education. So this was spot on because national surveys found that the post secondary participation rate in the a tech province at that year wasn't that 44%?

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René Iwo: And if you kind of map on people's aspirations from 5 years after the tsunami 10 years later. At this time point. only about 50 of the respondents who set may aspire to its own university, actually met their goal.

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René Iwo: So 50 of the younger cohort actually, who aspire to it's on university 5 years since we actually did. And 60%.

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René Iwo: And there's a significant difference for sex here. emails are always higher in terms of education

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René Iwo: our main independent, variable of interest is about too much damage. we measured it at 2 levels at the housework level. whether or not their house is damaged from this grid and 68% experiences at the community level. We try and do it. Several sources of information, including respondent reports from the interviews, interview or observations, or not sensing.

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René Iwo: And as you can see here, and we found that 53% in communities that were at least moderately or heavily damaged compared to

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René Iwo: so our analysis plan. Since all the outcomes are binary, we estimated logistic regression models stratified by age 4. As I as stated earlier.

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René Iwo: and we for aspirations, we, the outcome variable, was not aspiring for higher education.

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René Iwo: both at one year and 5 years. So this is whether or not there's no no aspirations. and for attainment. We did 2 things, we as we.

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René Iwo: to address whether or not the respondent and chief, at least a high school diploma.

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René Iwo: and among those who did, whether or not they enroll in some kind of higher education.

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René Iwo: and we also adjusted for demographics, and from this to economics, namely, parental education, separately for mothers and sellers, and also it's been a cheaper calendar. Now, this lateral variable has been shown to be a better measure. So it's kind of well being compared to income, especially in low middle income settings, and we will send it. This is why

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René Iwo: standard error. And just for the screen, just to kind of give you that disclaim it. Now I know this is a table. So bear with me This table shows us ratios

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René Iwo: from A, with the outcome of having a low asking, so not aspiring to higher education

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René Iwo: at one year person on me, and 5.

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René Iwo: So for the younger cohorts here, first of all one year posts money, damage at both the household and community levels

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René Iwo: increases the odds of expressing lower. Ask emissions. So, in other words, damage, exposure leads to the press the aspirations. But for years later this effect has actually faded

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René Iwo: and the results for the older cohort right here on the right side of the table. We're practically identical. Now. Sex patterns here show that males express lower aspirations compared to females

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René Iwo: and in terms of socioeconomic status and resources at the household level.

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René Iwo: we see that the effects are quite constant. Across those coord overall. There is a positive correlation between status and aspirations, and higher parental education

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René Iwo: or

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René Iwo: expenditure actually needs to respondents less likely to express no explanations

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René Iwo: so interestingly. Also, pretty soon on your resources here does not matter for aspirations.

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René Iwo: Now, this is a second table, like I said, the second outcome of interest is educational attainment. At 15 years

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René Iwo: this table shows odds, ratios from separate logic models about education maintainments on the left here.

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René Iwo: about whether you respond to graduate high school. And then among those who graduated high school, this

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René Iwo: and this table focuses on image exposures. First of all, on educational.

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René Iwo: So damage to house actually injured educational attainment for both 4 months. This is marginal, significant. lower ox is graduating from high school overall or lower odds of and higher education.

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René Iwo: and this shows the lasting impact of disaster on

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René Iwo: human capital accumulation

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René Iwo: and contrarily for community damage. This was quite interesting. Those living in communities with heavy damage were actually more likely to graduate from high school or involving a higher education. In other words, damage actually in uses a higher education.

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René Iwo: this is compared to communities that were not

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René Iwo: Now, this may be because zoom because of investments in educational infrastructure and heavily damaged communities, as you can imagine, aid was more concentrated in those heavily damaged communities, perhaps making recovery smoother there, and faster also, compared to moderately and lightly damaged communities.

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René Iwo: And, in fact, something is actually upgraded. Their educational infrastructure

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René Iwo: now, same as the aspirations. there is a sex effect in which males are less likely to, I'd say in higher education.

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René Iwo: And now the same table. But we focus on aspiration impacts.

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René Iwo: now, we have a variable for aspirations with 4 categories here, basically combining the responses from the survey one year and 5 years for.

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René Iwo: And here we are comparing everyone for those who helped that fast, high aspirations at both time.

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René Iwo: so generally, those with consistently no aspirations

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René Iwo: here, or those who transition in and out of high aspirations. As you can see here, those 2 categories. So those who basically favored

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René Iwo: are less likely to graduate in high school or enroll in higher education, as you can see here, the

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René Iwo: so aspirations that are high and consistent are strongly correlated with education.

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René Iwo: and we also see what aspirations do matter for all ages, both cohorts. But it does place from the role for all their children compared to your children.

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René Iwo: This is probably because aspirations, for he's group. For this group of children are expressed. Much goes to the position for learning and higher education. As for sex, you see a similar thing. answer his advantage

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René Iwo: and parental education. Here we went on to socioeconomic status, and so education are positively associated with educational attainment, as previous research has also shown.

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René Iwo: and interestingly, higher economic resources are associated positively only with enrolling in higher education. So you can see here. but not with completing high school. So this perhaps points to an economic barrier to higher education because it is paid compared to high school education. That is

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René Iwo: so. Some takeaways quickly. Aspirations are high, especially given the stats that I showed you before

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René Iwo: rebuilding actually help maintain or elevate this as well, and these aspirations do matter because they not

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René Iwo: damage exposures? the presence, the process aspirations in the short run. But this effect wains over time. However, there is still a long term effect of causing damage in terms of education.

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René Iwo: As I said, aspirations from like containment and socioeconomic status of my family hosts, you know I mean protect aspirations.

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René Iwo: Now, these 2 things show that basically, the dynamics of educational aspirations and attainment would similarly in disaster and non disaster settings.

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René Iwo: so the the disaster permanently disrupt aspirations for attainment.

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René Iwo: Our simplified answer is that not as much as previously thought

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René Iwo: in previous research, with sense to show kind of a gloomy picture of children. So, that is it. Thank you very much. to answer your questions today.

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Okay.

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okay, thank you very much.

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Then we move on to the third paper.

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Which will be presented by Carmel Blank. Is that right?

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She's great. wonderful. It's all yours.

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carmel blank: Okay? So we we are moving from tsunami and police shooting to death in the family. We have a very cheerful session here.

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carmel blank: and what we are going to talk about

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carmel blank: is the same subject as the the previous title. But we have a different to focus because we are at the Rc. 28. We would like to focus on the a family income and the relation between family income and a parental education.

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carmel blank: parental death and education and achievement.

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carmel blank: Okay.

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carmel blank: So what we would like to demonstrate here is as whether that in the family is indeed a stratifying mechanism.

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carmel blank: and what we will show you is that first there is a some answer that it does. We will show you that the risk of parental loss is higher among children from from lower socioeconomic status homes, and that parental laws, indeed, as expected, negatively relate to educational achievement.

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carmel blank: But what surprises us is that the negative correlation is stronger among children of higher socioeconomic families.

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carmel blank: and the theoretical context is, first, that the parental death in the family is not as uncommon as we would like to think, and it happens to 3 to 5 of the families it depending on. The country in Israel is about the 4 of the families.

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carmel blank: and the literature reveals a what we would expect that there is a negative correlation between socioeconomic background and parental risk of death. It happens more in lower Scs families

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carmel blank: and there. But there are really few studies that

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carmel blank: and tested the relations between parental death and educational achievement.

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carmel blank: Now, when we look at the role of socioeconomic status, we can make it to contradiction assumptions, what would be the relations between the effect of parental death and educational achievement, and with relations to socioeconomic status of the family. First is there is the a compensatory a advantage, a theory, or approach.

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carmel blank: It says that the advantage groups have more resources to recover

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carmel blank: from setbacks, or from a destructive Events such as death in the family, and therefore we would expect that disadvantage groups will suffer more from death in the family.

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carmel blank: But another speculation is, maybe, that when we have a strong background. A

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carmel blank: children benefit from the capital of their parents. But if one of the parent dies, then the reproduction of this capital is interrupted, so the the child can't they get what he was supposed to get from his parents, and therefore a. We will see a great loss in the advantaged families, and more a damage to their educational achievement.

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carmel blank: And so we have 3 research question. First, does the risk of parental loss in childhood differ by socioeconomic strata. Whether this is the case in Israel, and what is the correlation between the parental loss and educational achievement? And the third question is regards the interaction between socioeconomic, Strata and education and achievement.

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carmel blank: Now we rely on administrative that data in Israel where we have the the population of the Israeli born children, we have only a children of parents who worked in a salary workers.

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carmel blank: We don't have families, that where where the parents are a independent or they have no salary because of the is some problems with the reports of their income. So we have only a children from an accelerate workers, and we have all Israeli born children from 1985 to 1,998. It's about 1 million children.

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carmel blank: the dependent, variable. Where how we measure educational achievement is the eligibility for a circulation diploma which happens in Israel at age a 18,

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carmel blank: the twelfth grade and the independent variables. Our parental death by the age of 15, because we didn't want. This will happen at the same year as the Matriculation exams. So we separated the By 3 years.

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carmel blank: and we'll also measure parental income from salary work before birth, because we assume that once one of the parent, Is dead, then the family income will, and about to to change. So we test the the income prior to the respondents birth. We have this in their sales.

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carmel blank: and whether one of the parents or both of them a have a academic degree. We also controlled for parental age mother and father, number of older and younger children, ethnicity, a birth, weight, and a birth year.

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carmel blank: So as for the first research question.

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carmel blank: we can see a in the upper bar, the pink bar that, having an a, a academic, a father or mother, and negatively a related to the odd of losing one of them.

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carmel blank: So we can see the familiar a finding that Advantage family have lower risk of parental death, whether the parents are a educated or have higher income.

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carmel blank: A. Here is the second, the research question regarding the correlation between parental lost in childhood and matriculation eligibility. We have here a logistic regression, and we can see in the pink bar the closing one of the parents by the age of 15 is negatively related to the odds of eligibility from a circulation diploma.

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carmel blank: even when we control for having an academic parent or parental income, that by themselves, of course, increase the art of having a matriculation, a diploma.

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carmel blank: And now we did another check to see whether the correlation of parental death and educational achievement

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carmel blank: can a. Whether we can test a for some what of the causal relations?

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carmel blank: And what we did we did was checking the eighth grade. Science test scores. This. This is here a a over less regression, and we tested the change in scores in science scores between fifth and eighth grade.

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carmel blank:  Then we see in the lower a bar that we control for fifth grade science scores, and we can see in the upper pink bar that even within the same a child. Once we control for prior achievement that parental, a death, a reduces the A test scores.

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carmel blank: and of course, we control for academic education of the parents and parental income and ethnicity, and having lower birth rate. So we can see that the reduction in educational achievement we can say that is somewhat a a real or a causal effect.

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carmel blank: And now, for the final analysis, we wanted to see the interaction

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carmel blank: between a

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carmel blank: socioeconomic background and parental death.

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carmel blank: and we can see here. The second and third models are the same as the one I showed you a before. We just added an interaction effect. We can see.

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carmel blank: hey? Here. okay.

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carmel blank: we can see here in the

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carmel blank: the A all 93, that the interaction between having an academic parent and parental death is negative.

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carmel blank: meaning that the effect of parental death on the on the odds of having a matriculation diploma is negative.

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carmel blank: and if the parent who died was an academic parent, then the negative negative effect is even higher meaning. The children of academic parent lose more in terms of eligibility from a circulation diploma in comparison to children with non academic parent who died

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carmel blank: a

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carmel blank: in the lower a row. We can see the interaction between parental death and income. And it's the same

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carmel blank: is with having an academic parent, we see that the higher the in the family income is

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carmel blank: the lower, the odds of a eligibility form a circulation diploma, meaning that children from a higher income. Families lose more, or have a lower and odds of attending a metriculation diploma when we compare them to children who lost? Who who lose the parents without a higher income in the family

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carmel blank: is so to summer up our findings we see, as I show, as I a revealed before that the risk of parental loss is indeed higher among children of lower and socioeconomic background, and the current cost negatively related to educational outcomes.

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carmel blank: And this implies that a death in the family is some. What of a stratified mechanism, where a disadvantage groups suffer more because they have higher risk of losing a parent, and this effects, and negatively on educational outcomes. But

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carmel blank: once we have a family which one of the parents died, the negative correlation with educational and achievement is stronger among the stronger groups.

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carmel blank: So if we want to conclude, we can say, the death of a parent interferes with the intergenerational reproduction of capital.  we see. The defining support, the resource, loss, assumption, the damage of educational achievement.

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carmel blank: if one of the parented died is greater among children who lost a at least part of the advantage they were supposed to get from their parents. Once the parent died, the capital that was supposed to transfer to his or her children

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carmel blank: is a is not going through, and the children doesn't get the advantage they were supposed to get. So the damage is a stronger.

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carmel blank: and we see the trends are the same, whether we test a parental education or parental income.

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carmel blank: So the picture is overall a stable, and children from higher ses families. They have lower chance to lose a parent, but once they lose it, they, the the damage for them is greater.

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carmel blank: Thank you very much.

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Thank you very much.

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We move to the fourth paper, which is a pre-recorded presentation.

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So it will be recorded right now.

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today, I'm going to talk about new work called stepping in and stepping away. Variation on how children navigate responsibilities stemming from paternal incarceration.

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I want to acknowledge my co-authors. Amy Gong Liu and Stephanie Marin who are both graduate students at the University of California, Irvine.

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So, first and foremost, the rapid growth of incarceration over the past half century in the United States means that a historically unprecedented number of children experienced parental incarceration over their lives.

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More than 2.6 million children have a parent most commonly a father currently incarcerated, and millions more children have fathers recently released from jail or prism.

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More than 16% of Us. Adults which corresponds to about 40 million individuals report that their father has been confined in jail or prison.

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and among urban children, about one-third experienced paternal incarceration by age 9. The concentration of paternal incarceration among children of color and economically vulnerable children.

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combined with the negative intergenerational repercussions of paternal incarceration, can exacerbate already existing inequalities in children's volume

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we know that paternal incarceration is stressor. It strains familiar resources, creates challenges for caregivers as they navigate the demands of parenting and fractures, relationships between parents.

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all of which can contribute to deleterious intergenerational outcomes from childhood through adulthood. We know from prior to research that paternal incarceration impairs children's well-being.

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It impairs their mental and their physical health. it reduces educational achievement and attainment, and it increases hardship and deprivation. All of this net of characteristics associated with selection into experiencing paternal incarceration.

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This research also highlights children's heterogeneous responses to paternal incarceration. that is, there is considerable variation in how children respond to the stressor of paternal incarceration in terms of the magnitude of the differences between children with and without incarcerated fathers.

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differences across different outcomes and subgroup variation and associations. So, despite an accumulating body of research that suggests deleterious intergenerational consequences of paternal incarceration.

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There are a lot of opportunities to advance our understanding of this population. So perhaps most importantly, existing research nearly exclusively relies on survey data, including a lot of my own work, to look at differences in outcomes between children who do and do not have incarcerated parents or fathers.

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And this difference is focus on differences between groups means that research generally hasn't been able to systematically sort of consider the processes underlying children's responses to paternal incarceration. So a more sort of complete understanding of children's responses could help reconcile inconsistent or heterogeneous findings across data sources

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identify potential places of intervention to reduce inequalities between children who do and do not experience paternal incarceration and inform a survey of all of them.

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So without sort of background in mind. This paper answers one sort of this broad but yet very specific question, how in the chart, how do children respond to paternal jail incarceration?

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We use data from the jail and family life study, which is a study of 123 families who experience paternal incarceration in Southern California. So for the broader study. We interviewed fathers in jail, and then we asked fathers to provide us with contact, information and permission to interview their family members on the outside.

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So we also interviewed the children of these fathers, children's mothers and caregivers, and the mothers of the fathers themselves. So we have a really nice intergenerational story here, or portrait here. We also re interview fathers and family members after he was released from jail. So, about 2 months after release from jail, and for fathers who were not. Some fathers, weren't released during the course of the study. So we interviewed them again in jail or in prison. If they had been transferred to prison, which some of them had

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for the purposes of this paper we draw on interviews with the young children, so minor children, children, ages 8 to 17. We use 68 interviews, including 38 baseline interviews. So those first round interviews and 30 follow up interviews. we. The interviews come across our sort of across 26 families, because, whenever possible, we interviewed siblings

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just to give you a sort of sort of quick description of the analytic sample, so that half of the children in the sample are sort of in middle childhood to their ages, 8 to 12, and the other half is a little bit older. So they're between the ages of 13 and 17,

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about two-thirds of the sample. Identify as girls about two-thirds of the sample, identify as Latinx. about you know, 71, most children. So 71% of children in the analytic sample.

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we're living with their biological mother at Baselines. Their biological mother is their primary caregiver. very few children. only 5% were living with their father prior to his incarceration. That becomes important later on, and most fathers. So most children's fathers had been incarcerated previously. So 95% of

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children's fathers had been incarcerated at least one other time prior to the time when we interviewed them. So, to give you a sense of the results, there's sort of 3 key findings here. So first, we find that children experienced changes to their emotional and instrumental responsibilities resulting from their father's incarceration.

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Second, we find that children express considerable variation in their responses to their father's incarceration. fathers to children's responses were heterogeneous with many children reporting increased emotional and instrumental responsibilities.

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and other children reporting conscious decisions to step away from these responsibilities sometimes, while simultaneously describing increased responsibilities as well.

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And then, third, these heterogeneous responses stand partially from variation in children's age, so particularly differences between those in middle childhood. So those children, ages 8 to 12, and those in adolescence. So children, ages 13 to 17, which intersects their children's age intersects with, a variation in their father's incarceration history. So, for example, the duration and the frequency of incarceration.

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so, first as expected, we find that most children with incarcerated fathers in our sample reported increased emotional and instrumental responsibilities that arose directly from their father's incarceration.

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These children incurred emotional responsibilities that included concealing their own emotions from their family members, so, including their mothers, their siblings, and after their fathers as well.

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and providing consolation to these family members. So children engage in these types of emotional responsibilities, so concealing their own emotions and consoling their family members to protect both themselves and their family members.

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So consider Shawn a 16 year old boy, who describes how he hides his emotions about his father's incarceration. So Sean tells us that his dad's incarceration, as well as the behaviors that led to his dad's incarceration has taken a toll on his family, especially his 3 siblings.

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So Sean describes sadness about his father's year-long absence and worry about the uncertainty surrounding his father's release. but tells us that he does not share these emotions with his siblings.

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I don't really say much, and I try not to say anything, because I know that it will, that it will affect them on the outside, but I know inside I will not affect them on the outside. But I know inside it's going to hurt them.

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Children also incurred instrumental responsibilities in addition to their emotional responsibilities. and these commonly included, maintaining relationships with their father through visitation and other forms of contact, managing aspects of their father's incarceration, and taking on caregiving responsibilities for their father and other family members.

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So, for example, 16 year old, Renee tells us about the instrumental responsibilities she and her 3 siblings took on to maintain contact with her father during his time in jail.

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So, Renee, like Many of the children we spoke to, talked about. Have she visited her father? Have she accepted his frequent collect calls. How she wrote him letters, so she thought I was always there for him. I thought if we were there for him, and giving support. It would help him through it

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if he needed any money or food, if he needed money on his books. I put money on it. He knew that we were there for him and supported him.

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And now many children describe, taking on these increased emotional and instrumental responsibilities during their father's incarceration, notably by concealing their emotions, consoling their family members, engaging in sort of maintaining contact with their father. It's important to note that adapting these sort of additional responsibilities was not universal.

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which is perhaps expected, given prior research that demonstrates children's heterogeneous responses to paternal incarceration. So some children talked about just consciously stepping away from these responsibilities to protect themselves or to protect others. So many of these children describes making decisions to minimize contact with their father

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or minimize the support that they provided to other family members. Like their mothers or their siblings. they sometimes talked about scaling back their duties over time, or being selective or choosy about the types of responsibilities they took on.

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So Violet, 12 years old, is an example of a child who reports stepping away from responsibilities. During her father's most recent incarceration. Her father had been in and out of jail for most of her life, and when he requested that she visit him by writing and asking her in a Christmas card that he sent her, she decided that she didn't want to see him. She says he doesn't exist.

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I try to forget about him. I try to not focus on the past and focus on the presence. I don't want to know about my dad anymore, because it's too much information.

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Therefore a violet, like many of the children we interviewed, describes, not wanting to incur additional responsibilities stemming from her father's incarceration. So we see that many children step into responsibilities, and other children step away from responsibilities, sometimes importantly, sometimes goes to step in, also step away. And so we wanted to understand why some children step into these responsibilities, and others are more tepid or more sort of mixed, and they're stepping in and stepping away.

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So we systematically examine variation by child's gender, by the father child relationship and by child children's age. As there is some evidence from survey research that these 2

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either, like demographic characteristics or familial processes, may explain variation in children's outcome. So we find, briefly, we find little evidence in iteration by gender, we find little evidence of variation by the bother child relationship measured by both

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father's residential status, which we know that many of the dads were not living with their children prior to incarceration, but also measured by the quality of the father-child relationship, where we actually see quite a lot of variability in those pre- incarceration relationships.

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however, so we don't find evidence on a variation by gender or father child relationship. But we do find that children's age is a key factor in understanding how children respond to paternal incarceration. So children become better positioned to take on these responsibilities as they age. So we find that younger children

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often played a supportive role to other family members, but as children get older they particularly either step into these instrumental responsibilities sort of independently of their mother or their caregiver, or they sort of take a more active role in stepping away.

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So we find that this combination of father of age which it intersects with fathers. Incarceration. History, of course, becomes especially pronounced when children age, and they're more likely as the age they're more likely to report, choosing to step away from these responsibilities. both because they have agency to step away, and because they often report being just worn down by their father. Cyclical incarceration.

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so, just briefly, I want to conclude. So. Children consistently identified emotional and instrumental responsibilities related to the stressor of their father's incarceration. these responsibilities took a toll potentially shedding light on processes underlying the consequences of paternal incarceration. I want to say that I think moving forward.

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it is important to take into account these increased instrumental responsibilities, these increase emotional responsibilities as both potential outcomes to better understand the relationship between paternal incarceration and these outcomes with survey data, and also to think through how these increased. Or it's sort of in instrumental. And you can. I'm sorry. Emotional responsibilities

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and potentially mediate the relationship between paternal incarceration and other outcomes for children. so with that, I just want to conclude by thinking first and foremost, the respondents, the children, and all of the family members, and and family members who shared their time and stories and lives with us.

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Second I'd like to, you know, just acknowledge that this project has been funded by the National Science Foundation, the William T. Grant Foundation, and also on the School Social sciences at Uc. Irvine, and wanted to acknowledge the The large number of undergraduate and graduate students who have worked on this project

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over the years. if you have any questions or comments, I would be really happy to take them and connect over email, or virtually so. Thank you so much for your time today. I appreciate it.

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Okay?

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Oh.

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we go to the final presentation. and it will be presented by Matthew Kerley, the in-person presentation.

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Hi, everyone. Thanks for having me. My name is Matthew Curry. I'm a research fellow at the Institute for Social Science Research at the University of Queensland, up in Brisbane.

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I'll start with a a brief acknowledgment of country. So just like on behalf of the University of Queensland, to pay my respects to the traditional owners of the land on which we're meeting today. So for us in person in Melbourne, I believe that's the we're Injury people. and I'd like to pay my respects to their ancestors and descendants who continue their cultural and spiritual connections to country.

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 So I'm going to be talking about the effects of parental joblessness on how children! Develop personality traits so I guess, compared to the other ones. This is all this will will kind of end on a light note only. only talking about unemployment, not not parental death or imprisonment or police shootings.

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 and so the the basic question is whether parental joblessness, negatively, is negatively associated with the development of personality traits that tend to be rewarded in the labor market. So this kind of you may have noted for some

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the kind of clunky wording. So I don't want to make any sort of normative statements about these personality traits, right? But they're interesting to study insofar as they are rewarded or penalized in the labour market.

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 and so this this specific piece comes out of a larger a larger project with my co-author, Irma. We Racy, who's from the University of Melbourne about the effects of printed joblessness on children's sort of long term outcomes and some of the work we've done there has looked at children's educational attainment or their employment outcomes in in early adulthood. And so now we're we've

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kind of started looking a little bit

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earlier in time and trying to look at some of the mechanisms that might link these things, and so kind of personality traits or soft skills or non-cognitive skills is one area that we're kind of focusing on with this paper.

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and so we see this as contributing to the literature on intergenerational mobility, by linking the the adverse event of of a parent's loss of employment or non-employment to to long-term outcomes kind of through this channel.

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 and so we have just I'll just kind of start again with some brief takeaway findings. So what we find are small yet significant negatives, associations between the experience of parental joblessness during kind of childhood and adolescence, and 4 of what are called the Big 5 personality traits.

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There are some evidence of heterogeneity by gender of the child, but not by income. But even there, like there's not a he like a stand out coherent story that we can really tell

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And so so we kind of conclude by saying that the development of personality traits during adolescence may partially mediate the intergenerational effects of kind of parental employment history on these long-term outcomes.

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So just a kind of very brief background on the person I traits we're studying. So here are the Big 5 personality traits and a lot of studies. so agreeableness, conscientiousness, openness to experience, neuroticism, and extraversion. So there's been a lot of work in psychology and social social psychology that have found that these traits correlate with a wide variety of things. But for our purposes.

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what's kind of important is that they they correlate with socioeconomic outcomes like employment and income

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and some previous work has shown previous work in Australia, in in fact, has shown that personality traits are relatively stable in adulthood. So they're not completely stable, though they do change in response to dramatic life events such as the unemployment events they can but also the family context and environment.

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And they're particularly flexible during childhood adolescence and early adulthood.

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and so a large genetic component is thought to exist. But of course, that's not the only thing that affects people's scores on these personality traits.

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and

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kind of again, they tend to calcify or solidify in adulthood.

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And so.

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thinking about the effects of parental employment on children. Of course we know that there are. That parental labour, market experiences and histories are associated with children's subsequent labor market outcomes. This is kind of the foundation of much of intergenerational mobility work?

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 and so how could parental employment affect children's outcomes? So there's a resource argument where resources allow parents to material resources that come with employment allow parents to invest in their children's cognitive and non-cognitive skill formation. It allows them to be situated in neighborhoods that are higher, resourced, and healthier and more conducive to the development

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of these things. These traits which go on to help children as they progress through education in the labor market

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employment is also associated. Or maybe I should say that like loss of employment or unemployment is associated with higher parental stress, so higher? intra-family, instability so that can lead to marital dissolution. Changes in household composition, or just

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can change the way that parents discipline their children. The the can affect the quality of relationships within the family.

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 employment also can affect social capital and cultural capital. So the networks that people are embedded in can be more or less resource-rich, depending on employment of the parents and the types of

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cultural capital that can be important for children as they age Through school and into the labor market that can be a resource that they can rely on. It may also depend on the employment experiences of their parents.

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And so these factors in turn affect the educational attainment of children and their early labor market outcomes.

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And so a bit about the Australian context. So

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in Australia, about 12% of children live in a household where no parent works for pay. and this is a

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like the statistics on, different from different Oecd countries. they tend to use slightly different definitions. But Australia is kind of in the top quartile of Oecd nations about the 70 75 percentile So you know, I again, like the definitions are always slightly different, but I think it's fair to say, Australia's probably in the top half of Oecd countries in terms of children who who are living in a household where no parent works for pay at a given time.

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And just for context. So like the Us was about 9%. And again, they use slightly different definitions. I think they had a stricter definition on around employment.

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But just to provide kind of a bit of context, the other thing that's maybe a different about Australia than a lot of first world countries or wealthy countries is that it does not have a unemployment insurance system per se. So the main low the main kind of State assistance offered to people who lose a job is called the Job Seeker payment used to be called the new start allowance and it's a relatively low level of cash assistance

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that's given to people who are unemployed. And it's a flat rate. Well, the maximum is a flat rate. It does not depend on your prior earnings. it's means tested by household income and assets, and it requires usually active job search or some other type of activity.

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It's not time limited. So again, this differs from a lot of uninsurance

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unemployment insurance systems. so you can theoretically be on it forever as long as you meet the the other requirements, and there are slightly higher payments which fall under. They're not job seeker payments. It's a slightly different program, but single mothers whose children are under 8 and those who can't work because of the disability.

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 some more kind of background about the Australian welfare seat. So there's universal health care.

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we?

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And there's additional rent assistance that goes on top of job-seeker payment if you qualify their child and kind of relatively modest child tax credits that

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certainly anyone on job Seeker would qualify for there are large childcare subsidies. that are dependent on some activity. But If you meet the activity requirements, they can be quite generous depending on your income. So. that's to say that you know Australia for for people that lose a job, but particularly for those with low income and low assets. I guess it's probably relatively generous, certainly, compared to the Us. Where I have

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where I come from. But at the same time it's a targeted welfare system. So these things aren't universal. And and if you lose a high paying job, the hit might actually be a lot more for you, because it's we don't have that uninsurance or unemployment insurance system.

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the data I'm using come from the Hilda survey. So it's a large longitudinal household panel survey. So it's sort of.

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you know, kind of like the Psid. If you're familiar with that from the Us. So panel members have been surveyed annually since 2,001,

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and the outcomes that we're interested in are the personality trait measures which were completed in a self-complete questionnaire, and those were only asked every 4 years, starting actually in 2,005 for our sample. We're looking at children as they as they age. We're using the 2,009, 13, and 17 waves.

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And these questions are asked to or offered to people who are 15 years or older at the time of the survey. So those are kind of considered adults for the purposes of the Hilda survey, and so they get a much wider range of questions. So

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people in our sample will have had there the first time they will have had their

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personality. Traits measured would be the youngest. At age 15. Some could have been 18. The first time that they were measured, assuming, you know, not accounting for attrition and all that stuff or people that leave the sample, but then come back in.

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The nice thing about the Hilda data is that for for our purposes that it has detailed event history calendars for employment. And so using this, this calendar data where

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adults

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01:14:13.270 --> 01:14:18.289
basically put down their labor force status in every actually, 3 periods per month

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01:14:18.340 --> 01:14:38.169
for the entire kind of history that that they're in the sample. we're able to construct employment histories for parents, and then link that to their to their children. And so we looked at the parental employment calendars from 2,001 through 2,008. So this is before we start looking at the outcome measurements of children's personality.

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traits!

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And then we get the proportion of time that children spent in a household where no parent was working for pay. and then we include some demographic and socio-economic co-grates there at the bottom.

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we end up using a multi-level, a mix effects panel regression model due to the nested nature of the observation. So we have a person here, observations nested within individuals who are nested within households. So there are some siblings, basically

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and

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the kind of limitations with that methodology are that one. It's difficult to establish causality.

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01:15:18.460 --> 01:15:39.350
I think maybe ideally, we would have. So we have a much larger sample, and we'd have like a sibling, fixed effects model, and we would maybe have a time varying measure of parental employment. But we can't really do that. So so we have. So it's again kind of difficult to establish any causal effects. And and we don't necessarily claim to be doing that

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But of course we are able to estimate coefficients for time, invariant variables. And so for for our purposes. Again, that's going to be the parental. employment. variable and we're able to include multiple observations per person where they exist. But then also to include cases that only contribute one observation which for us is the majority of cases actually.

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So I I back up to and say just that the

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again, because of the kind of time or the the timing of the waves of Hilda. We are looking at people who are kind of aging into late adolescents and early adulthood, so the

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the birth covers are now.

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01:16:31.920 --> 01:16:51.240
evading me. But it's something like in kind of being born between, I think, is 84 and 96, something like that. So that's kind of the age range we're dealing with. So here are the headline results. So we have negative, statistically significant coefficients for 4 of the 5

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big 5 personality traits. And so this sorry. This is the effect of parental joblessness on these, on these outcomes. So we run separate models for each, and we see these relatively small but significant effects. And this is, you know, net of our our demographic controls.

402
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 there are some other significant coefficients. So the boys had

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basically negative or being a boy, was negatively correlated with all of these

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big 5 correspondent. So it's a positive sign for neuroticism. But that's that's bad. Right so so kind of negative effects. for education. Those with less than a high school Degree, had lower agreeableness and conscientiousness than either high school graduates or university graduates.

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Those born in Australia had lower openness to experience and lower agreeableness, but higher neuroticism.

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and then aboriginal Australians and Torres strait islanders. So, so, so indigenous. Australians, who are low proportion of the sample, about 3 or 4% had lower openness to experience.

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so we were also interested in whether we could kind of detect any patterns of heterogeneity. So we looked at interactions with parental income, thinking that the resources might again kind of help to buoy the that shock of of having a parent who's experiencing joblessness.

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We also looked at the timing of preferences. So here we're thinking about a particular effects of given the child's age at which it's occurring. And then we also looked at gender and birth cord. So again, we did find a lot of

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overwhelming trends. But

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kind of one thing we did find was that

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for boys presental joblessness had a significant interactive interaction effect for openness to experience in agreeableness. So for a boy's parental javascript was more harmful on these on these 2 of the 5 traits than it was for girls.

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and kind of, you know by by contrast, that gender gap that we see is a lot less for Families that did not experience that. that disruption to parental employment.

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for income. We see that there's a There's a main effect like we would, we would have predicted from prior research, but we don't see an interaction effect on any of the. So this is worded. This slide is worded a bit incorrectly, so we don't see an interactive effect for any of them. There is a negative main effect on 3 of the 5

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outcomes.

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So again, our kind of basic conclusions.

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So we observe negative correlations between parental joblessness during childhood and adolescence, and personality, trait development in late adolescence in early adulthood, and these are a net of demographic controls and and kind of crucially net of

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the parent's own score on that personality trade, so that has a huge effect. But but does not meaningfully cut into the the coefficients that we reported earlier.

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effect. Sizes, though, are relatively small in practice. So in in looking at standardized coefficients, a a standard deviation change in parental joblessness. correlates to, you know, a roughly 5% change in the standard deviation of the personality trait outcomes where there was a significant, significant effect.

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And this is.

420
01:20:38.030 --> 01:20:45.980
 somewhat moderated by household income. So there, I'm not talking about an interaction, but just that

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01:20:46.630 --> 01:20:52.960
the the addition of that term to the to the model kind of reduces the size of the effect.

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But there's no strong of of the direct effect. I should say a Colonel Johnson, but it's not a significant interaction.

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And then for again, for agreeableness and openness to experience. With boys we do seem to see some evidence of of heterogeneity by gender. But again, I think overall, we don't see a lot of

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 a lot of or I should say we don't see a real clear pattern where we can say, you know, with a lot of confidence, what's going on in terms of how parental jobs might affect different subgroups.

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Yes, I'll just talk there. Thanks.

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Ok, thank you very much. So that's the end of the presentation. And we still have about 20 min for questions and answers.

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01:21:50.990 --> 01:22:08.720
So Those people who are in the room! Please raise your hand, or you can go to the microphone to ask questions. And then those who are online. I guess if you can use a reaction bottom to raise your hand, then I'll try to

428
01:22:08.730 --> 01:22:12.279
see. Who is? Who has questions.

429
01:22:12.880 --> 01:22:13.690
Oh.

430
01:22:13.820 --> 01:22:14.910
yes, please.

431
01:22:15.390 --> 01:22:21.619
yeah, please. Then people online will hear what you say.

432
01:22:21.800 --> 01:22:29.469
Question for regardless. Actually, first, I want to thank you for this presentation. I think it was really fantastic. And it's really important, for

433
01:22:29.580 --> 01:22:35.720
my question is kind of with regards to whether my user to think about the actual mechanisms.

434
01:22:35.870 --> 01:22:39.469
we find these kind of functional or temporary decreases in

435
01:22:40.140 --> 01:22:40.940
right.

436
01:22:41.110 --> 01:22:46.789
The reason I ask this question, I guess, is because I was a bit surprised when each of the interaction terms between them

437
01:22:46.860 --> 01:22:49.770
raise and like in the 3 fourth, that

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01:22:49.920 --> 01:22:52.280
really wasn't

439
01:22:52.430 --> 01:22:54.980
is, except for helps. People be in backgrounds.

440
01:22:55.120 --> 01:22:57.409
And I've seen that in some of the love was actually.

441
01:22:57.580 --> 01:23:00.820
But it's increasing like the white correspondence

442
01:23:01.500 --> 01:23:03.850
of the magnitude.

443
01:23:04.100 --> 01:23:08.520
I guess I was surprised by this because there was this

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01:23:08.790 --> 01:23:09.860
where I was kind of

445
01:23:09.890 --> 01:23:12.650
going on this idea that it was perhaps

446
01:23:13.950 --> 01:23:16.030
 the top, that

447
01:23:16.780 --> 01:23:20.959
there's a kind of personal key I'm less likely to

448
01:23:21.460 --> 01:23:26.280
that is this one that's involved.

449
01:23:26.780 --> 01:23:29.450
and I guess I know the sense that that

450
01:23:30.450 --> 01:23:32.460
to to

451
01:23:33.850 --> 01:23:34.510
yes.

452
01:23:34.690 --> 01:23:39.479
So then, in conclusion, we were talking briefly about this idea that it was obviously in a broad media environment.

453
01:23:40.360 --> 01:23:44.160
perhaps you're able to. So, after everybody

454
01:23:51.620 --> 01:23:56.190
I know, I just have one small therapy by your outcome, variable

455
01:23:56.510 --> 01:23:59.010
or not, this is kind of a

456
01:23:59.370 --> 01:24:06.189
within 24 h. Your sponsor is within the same deed. So in the 19 to 9 I asked this, because I

457
01:24:07.620 --> 01:24:08.300
yeah

458
01:24:09.770 --> 01:24:10.600
in your.

459
01:24:14.610 --> 01:24:17.199
you can go to the mic. And then, probably.

460
01:24:17.320 --> 01:24:21.559
or even better, is to come here. And then, yeah.

461
01:24:23.150 --> 01:24:24.760
for so many places.

462
01:24:27.710 --> 01:24:32.980
So first thank you for all those questions. They are all great questions. I think I'll try to go

463
01:24:33.010 --> 01:24:35.300
backwards. So

464
01:24:35.970 --> 01:24:41.870
on the dates it is been been night to midnight. So if there was some reason that

465
01:24:42.940 --> 01:24:53.560
police shootings changed the timing of events, of of crimes in the day, that might be an issue. But I've seen no evidence that it's a problem. So I don't think that there's anything systematic there.

466
01:24:53.580 --> 01:24:56.070
concerning

467
01:25:04.690 --> 01:25:10.490
all right. Thank you. So on the question of whether or not I could control for

468
01:25:10.520 --> 01:25:15.550
the hypothesis that differences in

469
01:25:17.550 --> 01:25:28.069
consumption, consumption of information about police shootings or differences in media coverage might be a mediating factor. That's

470
01:25:28.360 --> 01:25:47.590
I. I'll speculatory at this point. So I haven't introduced anything into the model that can control for that based on the years in which we see the effects it seems to line up. So that's why I have that hypothesis. But thus far I haven't been able to introduce any kind of measures into the model that would show that in a clean way

471
01:25:48.020 --> 01:25:49.959
there are.

472
01:25:50.960 --> 01:25:54.609
So the surprise about larger effects. For

473
01:25:54.700 --> 01:25:57.980
actually White-stand Hispanics had larger effects, and

474
01:25:58.250 --> 01:26:07.180
at first I was surprised by that as well. But I ended up approaching this work from a kind of Beijing standpoint, from the idea that.

475
01:26:07.510 --> 01:26:11.710
given that someone observed police violence.

476
01:26:11.920 --> 01:26:16.989
the larger effect should be on the person who had the

477
01:26:17.380 --> 01:26:22.849
prior that was most effective or most changed. Does that make sense? So? If

478
01:26:25.600 --> 01:26:27.240
black individuals

479
01:26:28.070 --> 01:26:45.599
had already been exposed to that kind of behavior, and might have already expected it in a certain way, they might learn less from that experience which might admit more room for behavior change from groups who had seen less of that material of that kind of incident. So

480
01:26:45.840 --> 01:26:49.410
that's how I'm rationalizing it at present.

481
01:26:49.620 --> 01:26:57.379
But I yeah, I did go into it with the same part that you did. But from a Bayesian standpoint. It kind of makes more sense that

482
01:26:57.610 --> 01:27:05.980
those who would have more room for a difference between their prior beliefs and their updated beliefs might also have more change in behavior.

483
01:27:07.070 --> 01:27:08.649
Thank you for those questions.

484
01:27:15.680 --> 01:27:18.689
Okay. any other questions.

485
01:27:19.720 --> 01:27:21.930
Yeah, okay.

486
01:27:26.440 --> 01:27:28.810
And I also have a question for you. Right? So.

487
01:27:29.560 --> 01:27:38.759
my question is about the duration of the window that you actually are looking at. People reporting

488
01:27:39.520 --> 01:27:46.589
crime is being committed to them, which is, if I remember, about 4 weeks. And I recently.

489
01:27:46.760 --> 01:27:57.210
 what's your presentation also about police misconduct, and how that affected also the likelihood of people reporting and what they found is that the

490
01:27:57.260 --> 01:28:02.539
effect of that misconduct is very shortly. It's more like 4 days instead of 4 weeks.

491
01:28:02.580 --> 01:28:17.530
So I wondered if the no effects or the very few effects that you find from these shocking events is actually because of the very large window that you are now modeling. In a moment. If you have robustness checks to corroborate

492
01:28:17.570 --> 01:28:23.010
whether or not shorter windows actually can be

493
01:28:24.320 --> 01:28:25.880
to have a

494
01:28:26.430 --> 01:28:32.249
that when you use shorter windows, you are more likely to find these effects

495
01:28:37.910 --> 01:28:38.610
so

496
01:28:39.880 --> 01:28:44.620
great question and thanks for that question, and honestly.

497
01:28:44.900 --> 01:28:58.419
I always thought the folk window was short. So this work is coming beyond, behind Desmond, 2,016 and 2,020, and also Zoo go up 2,020, and they look over.

498
01:28:59.160 --> 01:29:00.209
I think.

499
01:29:00.580 --> 01:29:08.480
50 or 60 weeks in their estimation. So I feel that constraining the floor is just pretty short. I I chose the 4 week period because.

500
01:29:09.000 --> 01:29:16.240
unlike previous work. as I said, I use a sequence of different events, and it came out to maybe

501
01:29:16.470 --> 01:29:31.529
I want to say over 300 police shootings across the entire sample and the longer the wind, but you wanted a shorter window, so my first concern was with with using long windows would be the pre event window for another. So

502
01:29:31.580 --> 01:29:37.020
my thinking was that with a four-week window it allows time for an event to happen.

503
01:29:37.120 --> 01:29:43.770
It allows time for people to find out about it, and then allows time for some kind of documentation to occur where

504
01:29:43.780 --> 01:29:45.440
the information might matter.

505
01:29:45.450 --> 01:29:47.480
But to your point

506
01:29:47.490 --> 01:29:55.439
I did not try shorter events and or shorter windows, and I really should.

507
01:29:55.710 --> 01:29:59.990
I'm not quite sure which way I would expect that

508
01:30:01.110 --> 01:30:05.500
direction to go, but it is something that makes sense to run. So thank you for that.

509
01:30:11.450 --> 01:30:13.429
Yes, go ahead.

510
01:30:19.380 --> 01:30:38.099
Have a little bit of a provocative comment for all of the presenters is a very original session, with quite innovative topics that really bring to light the variety of disruptive events that can occur and can affect

511
01:30:38.250 --> 01:30:52.930
individual lines. But in 27 years of attendance to our C. 28 sessions. I think I've never been to to a session in which such economic status and social class

512
01:30:53.060 --> 01:30:55.409
have features so

513
01:30:55.550 --> 01:31:07.000
bully. I mean, most people don't treat such a class of such economic status, with some exceptions or heterogeneity by social class, is not taken into account.

514
01:31:07.240 --> 01:31:12.899
Ethnicity plays a larger part. I'm thinking of of your paper, for instance.

515
01:31:13.040 --> 01:31:26.340
So my provocative question is, is this driven by the nature of the data? Or there is some theoretical consideration behind this sort of paradigm shift in A. C. 28.

516
01:31:26.520 --> 01:31:27.430
Thank you.

517
01:31:28.390 --> 01:31:31.309
The question is for all presenters, of course.

518
01:31:33.510 --> 01:31:36.069
Okay. it's a

519
01:31:36.170 --> 01:31:49.750
provocative question. Let me ask the the people online to to address this question. I I see. Camille Blank, can can can you address that question first? And

520
01:31:51.150 --> 01:32:02.379
carmel blank: yes. thank you, We have a control for ethnicity, whether if the a child was a Jewish or Arab.

521
01:32:02.420 --> 01:32:05.469
carmel blank: and we saw some

522
01:32:05.510 --> 01:32:24.850
carmel blank: complexity, and we want to explore the ethnic issue in a different paper. But then I'm not sure that I agree that the education level or an income of the family have somehow somewhat lose their charm

523
01:32:24.940 --> 01:32:36.020
carmel blank: in a stratification and research. And I think they are when we talk about, for example, for if I'm talking about our research

524
01:32:36.020 --> 01:32:59.640
carmel blank: for a losing a parent. The economic crisis in the family is very prominent. That's why we first looked only at income. And then when we present the paper. And people offer us to a test also for a education level. And we did. And we saw that there they they work the same.

525
01:32:59.790 --> 01:33:01.230
carmel blank: And

526
01:33:02.560 --> 01:33:12.070
carmel blank: you. I'm just asking a yeah, and my calling is in near me. I want to ask her. She wants to add something

527
01:33:12.160 --> 01:33:26.909
carmel blank: now. Just want to say that. Our first question asks about the old ratio of a some of of one of the parents dying, and in this question you can see, I think, all the

528
01:33:27.060 --> 01:33:34.240
carmel blank: the immediate, the immediate suspects, at least in Israel.

529
01:33:34.660 --> 01:33:38.919
carmel blank: oh, so I say, come on, economic, our demographic, a background.

530
01:33:38.980 --> 01:33:41.559
carmel blank: And and you can see it works

531
01:33:42.540 --> 01:34:12.330
carmel blank: as expected, it works as it's expected. We see that they are up the parents in the greater of the dying. But what I take from your remark, which is not so provocative is that maybe we should write also a different paper about ethnicity. If you think that is more interesting. We have a a few, a demographic characteristics, because we rely on administrative data.

532
01:34:12.340 --> 01:34:27.310
carmel blank: but we do have a immigrant versus not immigrant. We have also a Jose versus Arab. And we really believe that is a an interesting analysis which we are working on currently.

533
01:34:30.650 --> 01:34:33.210
Okay, thank you. Oh.

534
01:34:33.370 --> 01:34:36.359
Renee, would you like to address a question.

535
01:34:36.560 --> 01:34:43.529
René Iwo: Sure, yeah. this is my first time ever at Isa, and also my first time ever being involved in our C. 28.

536
01:34:43.630 --> 01:34:49.010
René Iwo: So I can't really respond to your comments about how

537
01:34:49.100 --> 01:34:56.159
René Iwo: You know the trends over time about how social stratification takes into account. so it's my status, but

538
01:34:56.260 --> 01:35:08.840
René Iwo: in terms of how our research sees it. I think a little bit of a flaming as an order here. the way that we kind of. See, this research is one of the ways that we see. This research is also thinking about how

539
01:35:09.000 --> 01:35:13.049
René Iwo: clearly these outcomes of educational attainment

540
01:35:13.070 --> 01:35:15.829
René Iwo: is related to social stratification.

541
01:35:16.180 --> 01:35:20.779
René Iwo: and we want to see how these outcomes are shaped.

542
01:35:20.870 --> 01:35:49.719
René Iwo: whether or not they're shape differently or the same way, or similarly, in disaster or not disaster concepts. And of course we did have some variables regarding socioeconomic status, but we did kind of see it as not peripheral, but interacting in a way, with the damage very damage. Expl your variables, which were, of course, the focus given. How disruptive that those events were! So I think we did

543
01:35:49.820 --> 01:35:59.680
René Iwo: give a little bit more weight to the disruption, but overall this research, I think, sits very well within the stratification literature, because it tests

544
01:35:59.880 --> 01:36:18.019
René Iwo: these outcomes that have very close relations to ratification within a new context, and introduces kind of a new variable. So I hope that I answer your question in terms of kind of clarifying how we see this research. But it's just to the next status.

545
01:36:19.560 --> 01:36:20.710
Thank you.

546
01:36:21.070 --> 01:36:26.840
How about giving Matthew Curly first

547
01:36:28.750 --> 01:36:31.420
yourself that. Yeah, please come.

548
01:36:33.900 --> 01:36:49.140
yeah. So just in response to that question, I would say, you know, for us it's probably something we should. We should maybe look at occupation of, or, you know, previous occupation as as a thing you know, we have parental income. We have parental education.

549
01:36:49.270 --> 01:36:52.720
 but I think

550
01:36:52.740 --> 01:37:04.780
I think there's probably room for for occupation in there, or last occupation, or kind of highest occupation, something like that. And so I think that that's something that will take on board. Yeah. So thanks for that.

551
01:37:06.020 --> 01:37:07.579
Thank you. So.

552
01:37:07.960 --> 01:37:09.020
okay.

553
01:37:10.410 --> 01:37:11.640
going to ask the question.

554
01:37:12.530 --> 01:37:18.289
okay, we want to respond to it only weekly. I would just say that.

555
01:37:22.930 --> 01:37:23.840
Think that

556
01:37:24.430 --> 01:37:31.220
at least my work has been somewhat more pushed by what's going on currently, and that might affect the

557
01:37:32.930 --> 01:37:34.349
what we are using now.

558
01:37:37.480 --> 01:37:44.299
 I had a question of what they're going.

559
01:37:44.330 --> 01:37:45.469
Question.

560
01:37:46.260 --> 01:37:49.870
call me. I was, I had 3 questions.

561
01:37:50.110 --> 01:37:55.330
First, I was one of these low percentage of kids have Sally parents. So out of all kids in Israel.

562
01:37:55.360 --> 01:37:57.229
what we're seeing on Ucat training and data.

563
01:37:57.299 --> 01:38:02.850
second, I want to do it as possible to cut your data by the age at which

564
01:38:03.129 --> 01:38:08.650
kids experienced criminal loss. And third, do you know if

565
01:38:09.400 --> 01:38:17.880
their missions for college where I, Israel. If there is any preferential treatment, or if that has nothing to do with it.

566
01:38:19.190 --> 01:38:20.459
those are my questions.

567
01:38:22.559 --> 01:38:23.740
Come in.

568
01:38:26.389 --> 01:38:32.720
carmel blank: Yeah, we had the some travels, the hearing the question, can we repeat it, please?

569
01:38:34.010 --> 01:38:36.559
So why don't you come here?

570
01:38:36.570 --> 01:38:38.990
carmel blank: Thank you.

571
01:38:44.150 --> 01:38:53.710
So I had 3 questions. First, what percent of kids have salary parents. So I was wondering what proportion of kids in Israel you were capturing it in your data.

572
01:38:53.770 --> 01:39:02.369
second, is it possible to cut your data by the age at which kids experience for print on job law. I'm sorry parental loss.

573
01:39:02.480 --> 01:39:04.439
And third.

574
01:39:04.780 --> 01:39:10.800
do kids of academics get preferential admissions in Israel? Or if is that not a part of your picture?

575
01:39:14.559 --> 01:39:35.420
carmel blank: And so I will first answer your third question. We have a larger aid. If you, if you lost the parents in the military or terror attack and some assistance if you lost if you lost the parents in a car accident. But if you lose a parent from a disease

576
01:39:35.420 --> 01:39:57.120
carmel blank: that you don't. You don't have any assistance from the State. They do have some insurance that that are depended in the Family income, of course, and the salary of the parent. But the of Israel doesn't help the families with the loss of a parent.

577
01:39:57.379 --> 01:40:19.079
carmel blank: regarding the sample. We have about a million out of 1 million and a half children, which some of them we we excluded because they were born outside of Israel, and we don't know exactly which time the parent died, and but the children with the

578
01:40:19.100 --> 01:40:32.369
carmel blank: a salary. They of children of salary workers are about 70% of the population. If one of the parents have the salary, and the other one not, we also included them in in the sample.

579
01:40:33.020 --> 01:40:39.850
carmel blank: and with regards to the a age at the

580
01:40:39.910 --> 01:40:48.370
carmel blank: the controlled for it. Yeah. we have it as a control variable in the in the regressions.

581
01:40:48.560 --> 01:40:50.369
I was wondering if you can

582
01:40:50.410 --> 01:40:56.400
look, for example, that kids who lost parents at a younger age versus an older age to see if there's a difference. In effect.

583
01:40:59.100 --> 01:41:10.549
carmel blank: we did We actually did in in another wolf. We we done It's not that different. That's that's what I can say. But it

584
01:41:10.630 --> 01:41:13.730
carmel blank: throughout childhood. Okay.

585
01:41:15.150 --> 01:41:23.389
carmel blank: it makes same effects on the or or same correlation with them, with educational outcome. At age 18,

586
01:41:24.200 --> 01:41:44.020
carmel blank: we thought similar. It's not that. It's not saying that it's similar. We thought it first, that maybe when you lose a parent, when you're very young, at the critical age of mainly a birth to 2 years, where changes affect children at the most, because of the environment, the effect would be larger. And we didn't find it.

587
01:41:44.020 --> 01:41:58.540
carmel blank: And we decided because we have a lot of variables, and we also in different work, we compared between losing a parent and losing a one of the siblings. And we compared between ages. So we've decided to limit this analysis

588
01:41:58.540 --> 01:42:17.420
carmel blank: only for a losing a parent and looking at the A a interaction with socioeconomic status. But we did test for it effect at different ages, and we didn't find any effect, and we also compared the effect of losing a parent and losing a sibling

589
01:42:17.420 --> 01:42:30.729
carmel blank: which the literature may suggest that losing a parent would be more devastating or affecting more negatively on educational outcomes. But we actually found it losing a parent and losing it, stabling 150.

590
01:42:30.730 --> 01:42:36.369
carmel blank: It's pretty much have the same negative, the correlation with the attainment.

591
01:42:37.500 --> 01:42:38.440
Thank you.

592
01:42:39.460 --> 01:42:40.910
carmel blank: Thank you for the question.

593
01:42:42.080 --> 01:42:46.760
Okay, the final question is from Matthew to runet.

594
01:42:48.100 --> 01:42:56.889
Yeah. Hi, I've said a question, Rene, and or 2 questions, really. So one is whether you're able to.

595
01:42:57.060 --> 01:42:58.090
If you

596
01:42:58.100 --> 01:43:17.529
know anything about the people that moved out of those that area after the tsunami and then the other is whether there are any pre-existing differences between the areas that had heavier light damage. So were those like coastal verse inland areas, high versus low-lying areas. And yeah.

597
01:43:19.490 --> 01:43:26.570
René Iwo: thank you for those questions. yeah. The second question I'll answer first. the pre-existing differences.

598
01:43:26.720 --> 01:43:55.640
René Iwo: I think one of the most interesting aspects of this latest that is, that it is kind of a natural experiment. the tsunami hit. What is it? It wasn't predicted, and the nature of the tsunami is that the topography and kind of like the slope of the coast and stuff like that. They determine the impact of the tsunami. So one house could be sitting next to each other while one and house A would be completely destroyed, how we would be completely untouched.

599
01:43:55.700 --> 01:44:00.990
René Iwo: Same with like different villages that are like next to each other. So it is.

600
01:44:01.090 --> 01:44:14.150
René Iwo: It is kind of a natural experiment. That sense that, like the distribution of damages, is quite random, and we have tested it. And it is, There are no significant differences between the communities that are damaged. But it's not.

601
01:44:14.240 --> 01:44:23.540
René Iwo: however, It is true that the inland communities are less likely to be the more in when you go the less likely it is to be

602
01:44:23.740 --> 01:44:34.900
René Iwo: a suffering, heavy damage. And that's just the nature of the enumeration areas that we got from the baseline survey, which was this the Census Bureau survey, I mean.

603
01:44:34.990 --> 01:44:46.270
René Iwo: So the shape of this immuneration areas might be extending far from the coast might, you know, like those we do, some of we can't really control, for, unfortunately.

604
01:44:46.520 --> 01:44:57.250
René Iwo: and for the moderate damage areas. Interestingly, too, some of it happened to be from flash floods. when rivers kind of

605
01:44:57.290 --> 01:45:06.750
René Iwo: swell up that are more inlet. So it's not really saying that inland areas are not vanished at all. So that's kind of just adding to the randomness of it all.

606
01:45:07.160 --> 01:45:11.739
René Iwo: and whether or not about their your first question about knowing about

607
01:45:11.750 --> 01:45:14.890
René Iwo: information about people who use that from the area.

608
01:45:15.090 --> 01:45:35.899
René Iwo:  it is a very complex picture. because, yes, there are a lot of population that's especially out of the heavily damaged areas. But at the same time those heavily damaged areas attracted a lot of workers from other areas in order to help with the rebuilding efforts.

609
01:45:35.910 --> 01:45:45.150
René Iwo: And those communities like, I said, might actually recover faster than those communities that have less. They concentrated in those areas.

610
01:45:45.170 --> 01:45:51.870
René Iwo: So it is kind of a complex picture. but in terms of educational provision

611
01:45:51.890 --> 01:46:00.109
René Iwo: actually, in our paper, we have more details about this. And we're trying to. We're trying to submit it to journalists right now. but

612
01:46:00.670 --> 01:46:06.399
René Iwo: the education profession for 96% of children. Within 2 weeks

613
01:46:06.730 --> 01:46:13.580
René Iwo: they had been back to some sort of education. So these are kind of like school in a box.

614
01:46:13.610 --> 01:46:19.110
René Iwo: facilities so kind of tents that were set up by different charity organizations by the government.

615
01:46:19.480 --> 01:46:21.969
René Iwo: you miss that especially as well.

616
01:46:22.010 --> 01:46:28.339
René Iwo: so yes, there are people who miss out of the area, but that doesn't necessarily say much about

617
01:46:28.390 --> 01:46:35.130
René Iwo: their educational trajectories per safe. So we were kind of focusing on the traumatic exposure. That's why we didn't really

618
01:46:36.070 --> 01:46:41.680
René Iwo: talk too much about it, because we thought it was kind of complicated to address in a 15 min presentation.

619
01:46:44.210 --> 01:46:46.280
Okay, thank you very much.

620
01:46:46.450 --> 01:47:05.059
I think we just run out of time. So I think we have to end the session. I learned a great deal about the destructive events in life affecting ours, and I want to thank everyone in the room and also online to stay until the the end of the session, okay, thanks very much.

621
01:47:05.690 --> 01:47:07.060
carmel blank: Thank you.