Florida state Senator Joe Gruters (R-Sarasota) introduced a bill (SB 168) earlier this year to ban so-called sanctuary jurisdictions in Florida and require local governments to cooperate fully with Immigration and Customs Enforcement (ICE). A sanctuary jurisdiction is any state or local government that has a policy to comply with fewer than 100 percent of ICE detainers, which are ICE requests for the local government to release an arrested or imprisoned person into ICE custody for deportation. Local and state governments still prosecute illegal immigrants for crimes in sanctuary jurisdictions, but they only turn some illegal immigrants over to ICE and uniformly if they are charged with or convicted of serious crimes.
The complaint over sanctuary jurisdictions is that they result in increased crime, but the limited research on the topic finds no increase in crime in sanctuary jurisdictions relative to non-sanctuary jurisdictions. Regardless, the methods employed in that paper, the potential for sample selection bias, and the poor quality of national crime data have impeded research into how sanctuary jurisdictions impact crime.
Regardless, we decided to do our best in looking at how sanctuary jurisdiction policies affect crime in Florida. According to the Center for Immigration Studies, Clay and Alachua counties in Florida will not honor ICE detainers without a judicial order or a criminal warrant and their policies were enacted in December 2014 and September 2015, respectively.
To compare whether the adoption of anti-detainer sanctuary policies had an impact on crime in Alachua and Clay Counties, we draw on crime data from the FBI’s Uniform Crime Reports (UCR) Return A file. The Return A is the gold standard in crime data in the economics and criminal justice literature. Since these data are provided at the reporting agency level, we aggregate the crime counts up to the county-year level to reflect the extent of geographic coverage for each anti-detainer policy. As a basis for comparison, we identify counties neighboring Alachua and Clay as counterfactual counties using the county adjacency file from the National Bureau for Economic Research. We then compute county crime rates per 100,000 to compare crime rates across counties. For illustrative purposes, we compute an “adjacent counties” counterfactual crime rate as the sum of all crimes in surrounding counties normalized by their combined population.
Figure 1 shows that the crime rates in Clay and Alachua counties have fallen just like in their neighboring counties, except for Baker County, from 2010 through 2017. If sanctuary policies in Clay and Alachua counties affected crime rates, there is no obvious indication of that in Figure 1.
Change in Crime Rates in Sanctuary Counties and Neighboring Counties
Sources: FBI, Census Bureau TIGER/Line, and Center for Immigration Studies.
Figure 2 displays the crime rates in Alachua County relative to its neighboring counties before after the sanctuary policy was enacted. The crime rates were roughly parallel before the enactment of the sanctuary policy and stayed parallel afterward, meaning that the change in policy likely had no effect on crime rates. The results look nearly identical if trends in property or violent crime rates are compared separately.
All the elements for swiftly legalizing marijuana in New Jersey seemed to be in place: A proposed bill was enthusiastically backed by Gov. Philip D. Murphy and had been endorsed by leaders of the Democratic‐controlled State Legislature. Also, statewide polls showed support for the issue.
Then the plans unraveled.
Some lawmakers were unsure about how to tax marijuana sales. Others feared legalization would flood the state’s congested streets and highways with impaired drivers. Some would not be deterred from believing that marijuana was a dangerous menace to public health.
A disagreement existed among lawmakers about … whether it was necessary to expunge criminal records for marijuana‐related offenses for those found with as much as five pounds of the drug.
For states like California and Massachusetts, legalizing marijuana has led to some negative results: underwhelming tax revenue; a host of public health and safety concerns, such as keeping the drug out of teenagers’ hands; and a burgeoning industry dominated by white corporate interests even as advocates in Hispanic and black communities say their neighborhoods have been most negatively affected by the drug.
1. The claim that NJ could not figure out how to tax marijuana makes no sense. NJ taxes thousands of products, and ten states plus DC already tax MJ sales.
2. The concerns about impaired drivers, public health, and teens are no doubt real, but grossly overstated and based on misleading anecdotes or faulty statistics; the evidence from existing state legalizations finds little evidence of adverse effects.
3. Expungement of past marijuana offenses should be a separate issue from whether to legalize going forward.
4. Antipathy to “corporate” provision of legalized marijuana is mainly protectionism for existing marijuana sellers, whether underground or medical. Legalization will likely drive out small, high cost suppliers; that is how capitalism works.
5. The failure of revenues to match expectations, in the more recent legalizations, was completely predictable. On the one hand, many revenue forecasts have been wildly optimistic. On the other hand, the early legalizers collected substantial revenues because of limited competition from other states; as more states have legalized, the remaining demand is inevitably smaller.
Bottom line: New Jersey should just legalize.
Economic nationalism and pandering to farmers are two classic parts of presidential campaigning. In this post by Senator Elizabeth Warren, she does both at the same time:
Advancing the Interests of American Farmers
Washington has also bowed to powerful foreign interests instead of standing up for American farmers. Congress repealed mandatory country‐of‐origin labeling for beef and pork in 2015 after a series of World Trade Organization challenges from Canada and Mexico, and it hasn’t established a new rule to protect American farmers. The result is that beef and pork can be given a US origin label if it is processed in the United States — even if the animals are not born and raised here. This misleads consumers looking for American‐grown meat and undermines American beef and pork producers.
That’s why I will push hard for new country‐of‐origin rules for beef and pork — and use the trade tools available to me as President to push Canada and Mexico to accept them. These new rules will not only be good for consumers because they promote transparency, but good for independent American farmers, who are otherwise undercut by global agribusinesses passing off foreign beef and pork as American.
We also must stop foreign governments and companies from buying up American farmland. Foreign companies and countries like China and Saudi Arabia already own 25 million acres of American farmland. That’s about the size of Virginia. And one in four American hogs has a Chinese owner. That jeopardizes our food security, which threatens our national security too.
Iowa has the right idea. It passed a law prohibiting foreign individuals or entities from purchasing farmland for the purpose of farming. I support a national version of that law, and as President, will use all available tools to restrict foreign ownership of American agriculture companies and farmland. And I’m committed to stronger beneficial ownership laws so that foreign purchasers can’t set up fake American buyers to get around these restrictions.
Her argument about country of origin labelling and the World Trade Organization channels Donald Trump (“Washington has … bowed to powerful foreign interests”), but misunderstands the nature of the legislation/regulation at issue. Country of origin labelling requirements are not per se prohibited under WTO rules, but the rules do say that you can’t use them as a disguised means of protectionism, as was the case with the U.S. legislation/regulation at issue. My colleague Inu Manak wrote about the COOL legislation/regulation here, and she and I did a case study of the issue for this book. We explained that the trade problem was not the labelling requirement itself, but rather the structure of the particular legislation/regulation at issue, which created an incentive for meat processors to use domestic rather than foreign beef and pork. We also found a good deal of evidence in the legislative history indicating that protectionism, rather than consumer information, was the real purpose.
As for Warren’s reference to “countries like China and Saudi Arabia” buying up 25 millions acres of American farmland, that is a very creative use of the actual data. If you follow her links, you get to this explanation by the USDA:
Canadian investors own the largest amount of reported foreign held agricultural and non‐agricultural land, with 28 percent, or 7,250,834 acres (report 1B). Foreign persons from an additional four countries, the Netherlands with 19 percent, Germany with 7 percent, the United Kingdom with 6 percent, and Portugal with 5 percent collectively hold 9,511,437 acres or 36 percent of the foreign held acres in the United States. The remaining 9,577,982 acres, or 36 percent of all reported foreign held agricultural and non‐agricultural land, is held by various other countries.
But wait, where is Saudi Arabia in all of this? Her inclusion of a reference to that country seems designed to get people thinking about terrorism or human rights abuses, but here’s what is actually going on and it’s not all that scary: “Saudi Arabia and the UAE alone have acquired more than 15,000 acres in Arizona and Southern California to grow fodder for dairy cattle.”
Warren’s attempt to appeal to economic nationalism is not surprising. But given recent polling showing that Democrats are more supportive of free trade these days, it might be smart if other Democrats took a different approach.
John Maynard Keynes once marveled at “how, starting with a mistake, a remorseless logician can end up in Bedlam.” (Bedlam was the nickname of a London madhouse.) Keynesian or New Keynesian macroeconomists who start with the mistaken premise that a central bank cannot fight recession except by lowering nominal interest rates have been remorseless logicians in Keynes’s sense. In the hope of further empowering central banks to fight recessions, presumably for the benefit of the public, they have ended up like mad social scientists with schemes that would deliberately punish the public for holding currency.
From the premise that nominal interest rates must be cut, together with the fact that nominal interest rates are currently low by historical fiat-currency standards, one readily finds that the “Zero Lower Bound” on nominal interest rates is a looming obstacle to anti-recession policy. At the ZLB the central bank supposedly “runs out of ammunition.” Economist Lawrence H. Summers, thinking of the US Federal Reserve’s policy-making under the nominal Fed Funds Rate targeting approach that it used in previous recessions, has warned that “typically interest rates come down 500 basis points to contain recessions” but “there isn’t going to be 500 basis points of room any time in the foreseeable future.” Thus central bankers “don’t really have the fuel in the tank to respond” to a new recession.
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.
The 2017 Tax Cuts and Jobs Act included numerous pro-growth provisions, but it did little to simplify the tax code. Business taxation is particularly complex, partly because of the many tax credits that politicians have embedded to incentivize favored activities.
The research and development (R&D) tax credit is a good example. You may think that it is straightforward for businesses to add up the total they spend on R&D and multiple by a factor to find their credit amount. But the credit is so complicated that the largest and most sophisticated corporations hire specialists at the major accounting firms to calculate their claims. This guide to R&D tax credits is more than 700 pages long.
I received the following advertisement in my inbox today from a public relations company. Apparently, corporations contract with KPMG to calculate their R&D tax credits, then KPMG subcontracts with artificial intelligence experts at IBM Watson to help them out.
I wanted to get in touch today to share a recent success story from KPMG Research Credit Services. This particular division of KPMG works with IBM Watson to infuse the specialized knowledge of KPMG tax professionals with artificial intelligence capabilities with IBM’s artificial intelligence technology. This approach automates much of the qualitative analysis required to support an R&D tax credit claim by reviewing a variety of structured and unstructured data (i.e., R&D documentation) and then comparing the documents to relevant tax rules. The end result is more thorough and higher-quality documentation for a company’s IRS filings. And it saves time on research so that a company’s R&D professionals minimize their time on tax compliance activities.
KPMG’s tax professionals are feeding Watson with information so that it can learn to correctly and consistently pinpoint the right data that can help them assess a company’s qualifying R&D – often saving them cash along the way. Using intelligent automation in this way is a dramatically new approach to problem-solving for tax departments, which in the past have had to muscle their way through such challenges by adding staff. It’s also just one example of the way KPMG is taking advantage of intelligent automation, both internally to improve our own processes and externally to help their clients sharpen theirs.
Secretary Carson’s Department of Housing and Urban Development (HUD) is in the process of revising Affirmatively Furthering Fair Housing (AFFH), an Obama‐era regulation. The idea is to reform the regulation to simplify and streamline it, and encourage local beneficiaries to liberalize zoning regulations in order to qualify for funding. Peter Van Doren and I outline one way of doing this in a public comment, here.
Under the reform scenario, CDBG funding would act as a federal carrot to induce communities to rethink counterproductive local zoning policies that reduce housing affordability. CDBG funds are supposed to improve housing affordability, but they can’t do that when local government policies actively and effectively undermine affordability goals.
In order for AFFH reform to work, CDBG must be a politically popular program with local politicians and policymakers. By all accounts, it is highly popular with politicians and policymakers (for evidence, witness the political reaction to the White House’s proposal to cut CDBG funding the last three years). If politicians and policymakers value CDBG funding as much as they say they do, withholding CDBG or other HUD funding in the absence of local reform may act as a powerful incentive for change.
However, the idea has been challenged in some places, including this Brookings article from last year. The article suggests that 1) many of the HUD jurisdictions that recieve funding are counties, rather than cities, and counties don’t have control over zoning and 2) the most exclusionary jurisdictions don’t receive much CDBG money, so the reform might have minimal impact.
These arguments warrant a second look. First, it is accurate that HUD awards some CDBG money to counties (and states, too). However, this probably constitutes an advantage, rather than a disadvantage under the reform. Indeed, effectively liberalizing zoning regulations likely benefits from aligning pro‐growth city incentives with higher levels of government, including county and state governments.