Tag: fair

FAIR’s Confused Criticism of Our Immigration Crime Research

Spencer Raley at the Federation for American Immigration Reform (FAIR) recently wrote a criticism of a recent Cato brief that estimates illegal immigrant incarceration rates in the United States.  Much of Raley’s critique is perplexing as following his methodology advice would not only lead to an erroneous result but it would reduce illegal immigrant incarceration rates – which is the opposite result that he and his organization desire.  Raley’s points are quoted below, my responses follow.

The authors rely on faulty, voluntary data from the Census Bureau’s American Community Survey (ACS).  Even mainstream organizations like Pew Research acknowledge that many illegal aliens are slow to volunteer information about themselves to the federal government.  That’s why reputable research organizations assume a certain undercount when relying on ACS data.  Hesitation to self-report personal information is only increased when surveys include questions about criminal history. So, from the start, the primary source used in this study will yield an undercount of incarcerated illegal aliens because it relies on self-reported data.

The responses of prisoners are recorded by Census officials who interview a sample under the supervision of prison officials who also supply information like immigration status and country of birth.  Although it’s easy for people outside of prison to avoid a Census official, it’s quite difficult for a prisoner to do so if he or she has been selected for an interview by the ACS.  Since the ACS doesn’t ask about the respondent’s criminal histories, Raley’s criticism here is perplexing.  If anything, using the ACS would yield an undercount of the illegal immigrant population – which would increase the illegal immigrant incarceration rate in our brief. 

They also misstate illegal alien crime data from Texas. The authors sliced and diced data from Texas’ Department of Public Safety, claiming that the original data offered by the state was far too high, and that illegal aliens in Texas are half as likely to be incarcerated as U.S. citizens. The real numbers, however, tell a different story. Based on data compiled between June 2011 and February 2019, 25,000 illegal aliens are booked into Texas state and local jails annually, on average.

Raley makes several errors in summarizing my Texas crime research.  First, I didn’t “slice and dice” any data from Texas.  I took the numbers released by the Texas Department of Public Safety, divided them by the relevant subpopulation of Texas in 2015, and then multiplied the result by 100,000 to get a criminal conviction rate.  Second, I didn’t compare illegal immigrants to U.S. citizens.  I compared illegal immigrants to native-born Americans and legal immigrants separately.  Third, my Texas study did not analyze incarceration rates.  My Texas study looked at criminal conviction rates.  Incarceration rates and criminal conviction rates are different. 

Raley’s other criticisms are answered by reading the methodology section of our brief.  This is the most relevant section here which explains why we looked at the 18-54 population:

Another limitation of the ACS data is that not all inmates in group quarters are in correctional facilities. Although most inmates in the public-use microdata version of the ACS are in correctional facilities, the data also include those in mental health and elderly care institutions, as well as those in institutions for people with disabilities. These inclusions add ambiguity to our findings about the illegal immigrant population but not about the immigrant population as a whole, because the ACS releases macrodemographic snapshots of inmates in correctional facilities, which allows us to check our work.

The ambiguity in illegal immigrant incarceration rates mentioned above prompted us to narrow the age range to those who are ages 18-54. This age range excludes most inmates in mental health and retirement facilities. Few prisoners are under age 18, many in mental health facilities are juveniles, and many of those over age 54 are in elderly care institutions. Additionally, few illegal immigrants are elderly, whereas those in elderly care institutions are typically over age 54. As a result, narrowing the age range does not exclude many individuals from our analysis. We are more confident that our methods do not cut out many prisoners because winnowing the 18-54 age range reduces their numbers to about 4.5 percent above that of the ACS snapshot. Natives in our results include both those born in the United States and those born abroad to American parents.

 

FAIR SCAAP Crime Report Has Many Serious Problems

The Federation for American Immigration Reform (FAIR) recently released a report on illegal immigrant incarceration rates that is poorly contrived and terribly executed.  FAIR uses the number of illegal immigrants counted under the State Criminal Alien Assistance Program (SCAAP), which compensates local and state governments for incarcerating some illegal immigrants, to calculate the illegal immigrant incarceration rate.  FAIR finds that illegal immigrants, as measured by the number of SCAAP aliens, are incarcerated at much higher rates than natives and legal immigrants according to their faulty methods. 

There are many problems with FAIR’s report. 

The first problem is that it doesn’t state what year or period of years that it is analyzing.  I can’t even work backward because their citations only indicate when they accessed the relevant data, not which annual data they accessed.  This means that it would take many dozens of hours of guesswork to recreate their work.

The second problem with using SCAAP data is that they are the oddest crime data.  The SCAAP aliens are a measure of the stock and flow of illegal immigrants into state and local correctional facilities over a year.  SCAAP is used to compensate local communities and states for the costs of incarcerating some illegal immigrants, it is not designed to be used to estimate illegal immigrant incarceration rates.  As a result, the normal operation of dividing the number of prisoners who are in SCAAP by the stock of all prisoners to calculate a rate is not useful here because the proper denominator is the stock of the prison population at the beginning of a year plus the number of people admitted throughout the rest of that year.  However, FAIR uses just the stock of the population in state prisons at the end of the year plus estimates from a private group for the number of people incarcerated in local facilities.  FAIR does not add the flow to the stock to estimate a proper denominator. 

As a result, there is no correct way to use SCAAP data to estimate incarceration rates because there is not a similar number published by the government that can serve as a denominator.  Using the Bureau of Justice Statistics (BJS), one can estimate a flow plus stock denominator for some years without eliminating the significant problem of overcounting.  Although I caution against drawing a lesson from this back of the envelope calculation, Stephen Dinan of the Washington Times piqued my curiosity and I could not resist.  This is the only way to use SCAAP data to estimate illegal immigrant incarceration rates in state prisons.  

My back of the envelope calculation uses only the state-level incarceration numbers and excludes the number of local SCAAP prisoners and people jailed on the local level.  This is because the BJS does not collect the local numbers and they are not available for many years.  The number of illegal immigrants nationwide comes from the Department of Homeland Security (DHS) while the number of non-illegal immigrants (legal immigrants and native-born Americans) comes from the American Community Survey.  I merely divided the number of SCAAP aliens nationwide who were incarcerated and who entered state correctional facilities by the number of illegal immigrants nationwide in each year.  I then compared that rate to the same rate for non-illegal immigrants who were in correctional institutions that year or that entered them.  Lastly, I presented the figure per 100,000 for each subpopulation because that is common in the crime literature.

Figure 1 shows the nationwide results: Illegal immigrants in the SCAAP program are incarcerated at a lower rate than non-illegal immigrants.  Just to reiterate, this is back-of-the-envelope calculation with significant problems.  It excludes all prisoners incarcerated on the federal and local levels.  There is a lot of double counting.  SCAAP does not count all illegal immigrants.  The non-illegal immigrant number is a combination of legal immigrants and native-born Americans, which makes the latter look more peaceful and the former more dangerous than they really are.  These figures underestimate the incarceration rates for illegal immigrants and non-illegal immigrants

 

Figure 1
Illegal Immigrants and Non-Illegal Immigrant SCAAP-Adjusted Incarceration Rates, Per 100,000

 

Sources: Bureau of Justice Statistics, American Community Survey, and the Department of Homeland Security.

Note: The per 100,000 is for each subpopulation. 

 

The SCAAP figures are not refined enough to use for estimating an illegal immigrant incarceration rate because there is not a good enough denominator available.  However, using the best data and methods available still shows that the nation-wide incarceration rate for SCAAP aliens is below that of non-illegal immigrants – which includes native-born Americans and legal immigrants.  FAIR’s report is a prime example of poor scholarship, but the authors are only partly to blame.  The lack of adequate crime and incarceration data make such desperate attempts tempting.  

FAIR’s “Fiscal Burden of Illegal Immigration” Study Is Fatally Flawed

The Federation for American Immigration Reform (FAIR) is devoted to reducing legal and illegal immigration. Its recent report, “The Fiscal Burden of Illegal Immigration on United States Taxpayers (2017)” by Matthew O’Brien, Spencer Raley, and Jack Martin, estimates that the net fiscal costs of illegal immigration to U.S. taxpayers is $116 billion. FAIR’s report reaches that conclusion by vastly overstating the costs of illegal immigration, undercounting the tax revenue they generate, inflating the number of illegal immigrants, counting millions of U.S. citizens as illegal immigrants, and by concocting a method of estimating the fiscal costs that is rejected by all economists who work on this subject. 

FAIR’s Errors

Merely using the correct numbers when it comes to the actual size of the illegal immigrant population, the correct tax rates, and the effect of immigrants on property values lowers the net fiscal cost by 87 percent to 97 percent, down to $15.6 billion or $3.3 billion, respectively.  Below is a list of FAIR’s errors and how the correct numbers affect the results:

  1. FAIR assumes that there are 12.5 million illegal immigrants, over a million more than other organizations estimate (FAIR is inconsistent here as the number of illegal immigrants they report on page 34 is 12.6 million).  Pew estimates there are 11.3 million illegal immigrants, the Center for Migration Studies (CMS) estimates that there are 11 million illegal immigrants, and the Center for Immigration Studies (CIS) estimates there are 11.43 million illegal immigrants.  FAIR’s estimate of the number of illegal immigrants is more than a million more than that of their sister organization, the Center for Immigration Studies, that also shares their goal of reducing immigration.  Using the average number of illegal immigrants as estimated by Pew, CMS, and CIS instead of FAIR’s number lowers their report’s estimated cost by $11.6 billion.

E-Verify Does Not Lower Unemployment

The Federation for American Immigration Reform (FAIR) released a report claiming that E-Verify lowered unemployment rates in states that implemented it.  FAIR’s report is deeply flawed.  The first section of this blog will catalog FAIR’s errors and show that states with mandatory universal E-Verify typically had higher unemployment.  The second portion of this blog will use the synthetic control method to look at E-Verify’s effect on unemployment in Arizona after the E-Verify mandate.  The flaws in FAIR’s report are important to highlight as more states are considering a universal E-Verify mandate.  There is little evidence that E-Verify mandates lower unemployment but much evidence that they raise it.   

Criticisms of FAIR’s Report

E-Verify is a taxpayer funded federal government run system that is supposed to exclude illegal immigrants from the workforce.  The system would be used at the point of hire to verify that any new worker is actually authorized to work in the United States.  FAIR attempted to show that states with E-Verify have higher employment growth relative to other states.  This is likely an attempt to overcome one of the stronger criticisms of E-Verify: It is an expensive labor market regulation that will increase unemployment by raising the cost of hiring new workers among other problems.  However, FAIR excluded the first state to mandate E-Verify and made numerous other silly methodological choices that make their results unreliable. 

First, the FAIR authors excluded Arizona from their report.  Arizona was the first state to mandate E-Verify for all new hires.  Unemployment rates as measured by U3 were lower in Arizona than in the rest of the United States prior to the implementation of E-Verify and they shot up afterward, remaining consistently above the rest of the United States (Figure 1).  The result is even more extreme for the U6 unemployment rate that the FAIR report insisted on using (Figure 2).  Narrowing the comparison to the southwestern states of California, Colorado, Nevada, New Mexico, Oklahoma, Texas, and Utah shows similar results whereby Arizona had relatively lower unemployment prior to mandating E-Verify and higher unemployment afterward (Figures 3-4).  Utah mandated E-Verify for some employers during this time but excluding that state does not affect the results.  Mandatory E-Verify did not appear to improve employment in Arizona. 

Figure 1

Arizona Unemployment Rate (U3) vs. United States Unemployment Rate (U3)

Source: Bureau of Labor Statistics.

FAIR’s Anti–Legal Immigration Principles

Jack Martin at the Federation for American Immigration Reform (FAIR) argues that I inaccurately characterized FAIR’s pledge as anti–legal immigration. On FAIR’s pledge, it states that its purpose is this:

It is therefore essential that we know whether you will support TRUE immigration reform policies.

What are FAIR’s “TRUE immigration reform policies” that the pledge references and emphasizes with blue underlined font? Here they are, in a document with the same title.  One of FAIR’s points of “TRUE immigration reform” reads as follows:

End family chain migration. Family-based immigration must be limited to spouses and unmarried minor children. Entitlements for extended family migration lead to an immigration system that is not based on merit, runs on autopilot and fosters exponential growth in immigration.

Depending on what FAIR means exactly, such a policy change would decrease annual lawful immigration to the United States by at least 138,066 or as many as 340,000 annually if we use 2011 as a benchmark. To put that in perspective, FAIR’s TRUE immigration reform policies advocate for a decrease in legal immigration of between 13 percent and 32 percent.  Sound anti–legal immigration to me. If Mr. Martin is so concerned about inaccurate characterizations of FAIR’s pledge, perhaps he should be more troubled by FAIR President Dan Stein’s reference to it as the “No New Amnesty Pledge” since most of the pledge’s points concern opposition to legal immigration and not amnesty.

Fair and Balanced, Think Tank Edition

The website CapitolWords.org allows you to track the use of words uttered by members of Congress. Our intern wrangler, Michael Hamilton, decided to compare uses of the term “Cato Institute” to the names of other think tanks around town. Here’s what he found:

Cato is mentioned roughly equally by both Republicans and Democrats in Congress. It’s hard to draw conclusions based solely on members’ use of the names of think tanks, but it seems clear that Democrats and Republicans make roughly equal use of Cato research in making appeals to their colleagues and the public.

Note: The Brookings Institution is sometimes misstated as “Brookings Institute,” so both are included.