4 ms·

Well yeah, but they have to remove all the other factors like heat, wind and rain, which are correlated to air pollution. There is likely less violent crimes o

by d--b 7y ago

Well yeah, but they have to remove all the other factors like heat, wind and rain, which are correlated to air pollution. There is likely less violent crimes on rainy days, just because more people stay home. To do this properly, they would have to do a controlled experiment with people in rooms with or without air pollution. And measure how sanguine they get or something of that nature.

rockinghigh 7y ago
They obvious control for these factors. They actually report that the effects are stronger at lower temperature.
- mcbits 7y ago
  Statistical controls can remove much of the effect from known confounders, but they don't turn an observational study into a controlled experiment. Sometimes you can use common sense to infer a causal relationship (violent crime linked to emergency room visits), but it seems like a leap here, especially as they're on a bit of a fishing expedition with multiple types of pollution and multiple types of crime.
reroute1 7y ago
And as someone living in Chicago or any similar metro area with strong seasonal weather can tell you, there are far higher crime rates in the summer than in the winter. Young people have more down time in the summer and people are outside and get restless in the heat. People stay inside in the cold winter and keep to themselves more.
PeterStuer 7y ago
From the full paper: "Pollution and crime rates may have common correlations with location and time-varying unobservables. For example, PM2.5 or ozone levels and crime rates may be correlated with county-level covariates such as traffic density, population density, demographics, and industrial activity. Failing to control for such covariates will lead to biased estimates of γPM and γo. Our identification strategy explicitly addresses omitted variable bias in several ways. First, we show that endogeneity with respect to violent crimes and pollution can be addressed by including a series of high-dimensional fixed effects. In our primary specification, we include county-by-year-by-month-by-day of the week fixed effects to control for county level unobservables that are either constant over time, such as state and county-level policies, or that are time-variant, such as changes in population density, demographic composition, seasonal variation in pollution or crime, or changes in state and county-level policies that limit pollution or crime enforcement. These fixed effects also control for cyclical within-week, within-county variation in pollution and crime. Thus, our data allows us to compare for example, the effect of changes in pollution within a series of Mondays within a given county within a given month. We argue that changes in pollution across a series of Mondays within a county-month, conditional on weather controls, is random and thus exogenous to crime. Second, crime has been shown to respond to changes in temperature, and temperature is generally correlated with air pollution (Field 1992; Jacob et al. 2007; Ranson 2014). Thus, failure to adequately control for temperature, and weather more generally, will lead to biased estimates. To address this concern, we include temperature and precipitation splines in our primary specification, we provide robustness checks with alternative functions of temperature, and in Section 5.3 we perform a series of tests to show that our results are not confounded by unaccounted for variation between temperature and air pollution. For example, we show that effect of PM2.5 on violent crime is larger at lower temperatures, opposite of the effect of temperature alone."
- deleted 7y ago
  [deleted]
- jayd16 7y ago
  I'm not sure this fully accounts for the correlation though. Anything that could temporarily increase the number of people going out that day would also increase pollution, no? I'm not sure this methodology corrects for acyclic events.
- powerbroker 7y ago
  I would like to see the predictive value of this study vis-a-vis greatly increasing electric car usage. Holding all variables constant (keeping temperatures level might be tricky), we should see some crime decreases, particularly in Oslo Norway, where electric cars are now > 33% of new sales. This sort of thing has happened before. Lead was removed in the late 70's, and, lo, 20ish years later, crime rates dropped -- perhaps, for the reason there are fewer neurological impacts growing up without airborne lead.
  - PeterStuer 7y ago
    Non exhaust emissions account for 85% of PM 2.5 produced by a car. As EV's are heavier than equivalent combustion engine cars, the end tally is a savings of just 1%-3% by going electric. Don't get me wrong, any savings is an improvement, but EVs are not the 'green' transport revolution the industry wants to picture them as, just an incremental step. https://www.researchgate.net/publication/297889793_Non-exhaust_PM_emissions_from_electric_vehicles https://www.researchgate.net/publication/297889793_Non-exhau...

by d--b 7y ago

rockinghigh 7y ago

They obvious control for these factors. They actually report that the effects are stronger at lower temperature.

mcbits 7y ago
Statistical controls can remove much of the effect from known confounders, but they don't turn an observational study into a controlled experiment. Sometimes you can use common sense to infer a causal relationship (violent crime linked to emergency room visits), but it seems like a leap here, especially as they're on a bit of a fishing expedition with multiple types of pollution and multiple types of crime.

reroute1 7y ago

And as someone living in Chicago or any similar metro area with strong seasonal weather can tell you, there are far higher crime rates in the summer than in the winter. Young people have more down time in the summer and people are outside and get restless in the heat. People stay inside in the cold winter and keep to themselves more.

PeterStuer 7y ago

From the full paper: "Pollution and crime rates may have common correlations with location and time-varying unobservables. For example, PM2.5 or ozone levels and crime rates may be correlated with county-level covariates such as traffic density, population density, demographics, and industrial activity. Failing to control for such covariates will lead to biased estimates of γPM and γo. Our identification strategy explicitly addresses omitted variable bias in several ways. First, we show that endogeneity with respect to violent crimes and pollution can be addressed by including a series of high-dimensional fixed effects. In our primary specification, we include county-by-year-by-month-by-day of the week fixed effects to control for county level unobservables that are either constant over time, such as state and county-level policies, or that are time-variant, such as changes in population density, demographic composition, seasonal variation in pollution or crime, or changes in state and county-level policies that limit pollution or crime enforcement. These fixed effects also control for cyclical within-week, within-county variation in pollution and crime. Thus, our data allows us to compare for example, the effect of changes in pollution within a series of Mondays within a given county within a given month. We argue that changes in pollution across a series of Mondays within a county-month, conditional on weather controls, is random and thus exogenous to crime. Second, crime has been shown to respond to changes in temperature, and temperature is generally correlated with air pollution (Field 1992; Jacob et al. 2007; Ranson 2014). Thus, failure to adequately control for temperature, and weather more generally, will lead to biased estimates. To address this concern, we include temperature and precipitation splines in our primary specification, we provide robustness checks with alternative functions of temperature, and in Section 5.3 we perform a series of tests to show that our results are not confounded by unaccounted for variation between temperature and air pollution. For example, we show that effect of PM2.5 on violent crime is larger at lower temperatures, opposite of the effect of temperature alone."

deleted 7y ago
[deleted]
jayd16 7y ago
I'm not sure this fully accounts for the correlation though. Anything that could temporarily increase the number of people going out that day would also increase pollution, no? I'm not sure this methodology corrects for acyclic events.
powerbroker 7y ago
I would like to see the predictive value of this study vis-a-vis greatly increasing electric car usage. Holding all variables constant (keeping temperatures level might be tricky), we should see some crime decreases, particularly in Oslo Norway, where electric cars are now > 33% of new sales. This sort of thing has happened before. Lead was removed in the late 70's, and, lo, 20ish years later, crime rates dropped -- perhaps, for the reason there are fewer neurological impacts growing up without airborne lead.
- PeterStuer 7y ago
  Non exhaust emissions account for 85% of PM 2.5 produced by a car. As EV's are heavier than equivalent combustion engine cars, the end tally is a savings of just 1%-3% by going electric. Don't get me wrong, any savings is an improvement, but EVs are not the 'green' transport revolution the industry wants to picture them as, just an incremental step. https://www.researchgate.net/publication/297889793_Non-exhaust_PM_emissions_from_electric_vehicles https://www.researchgate.net/publication/297889793_Non-exhau...