The House of Representatives passed the Farm Workforce Modernization Act (H.R. 5038) last month. The House bill made some improvements to the H‑2A program, which allows farmers to hire foreign guest workers, but it incorporated into the statute the current regulatory requirement in 8 C.F.R. § 214.2(h)(5)(viii)(C)) that an H‑2A worker may not live in the United States continuously for more than 3 years. The Senate should not copy this mistake.

This provision makes no sense from an economic or security perspective. It imposes costly, needless turnover on U.S. farmers, and by forcing out workers, it unnecessarily creates many more opportunities for visa violations. These arguments actually apply even more forcefully under the bill because it expands the H‑2A program to include year-round jobs, unlike the current seasonal jobs that by their nature have a defined end date.

H‑2A Touchbacks Waste Economic Resources

The economic argument against an arbitrary time requirement is simple: losing workers who are creating economic value imposes economic inefficiencies on employers. Turnover has costs associated with lost productivity when the position is unfilled, recruiting and hiring a replacement, training the new hire, and lost productivity from the employee learning the position.

Table 1 reviews the cost of job turnover in a variety of occupations and industries. Unfortunately, I found no study estimating the cost specifically in agriculture, but the consensus across a broad range of industries is that businesses end up paying about a quarter of the person’s salary to replace them.

Table 1: Costs of Turnover in Various Occupations

Sources: See Table Text

The average H‑2A worker’s annualized salary was about $25,000 in 2019. One quarter of that would be $6,500. For seasonal farms that plan to hire new crews every year, this requirement may be less of a burden, particularly since the regulations allow the workers to reenter after a 60-day departure, so seasonal workers can return home and reenter. But for year-round employers, this mandate will be much more expensive. In 2019, the State Department issued 204,000 new H‑2A visas to seasonal workers. If as many nonseasonal workers enter—which is reasonable, should Congress amend the program—simple math would indicate that the three-year restriction would cost them more than $1.3 billion annually after the 3^rd year.

Of course, some might argue that this requirement forces H‑2A employers to retest again the labor market and so create jobs for U.S. workers. For seasonal employers, the employers have to test the job market every year because the jobs are inherently temporary, and the law requires the labor market test before rehiring. In any case, the Department of Labor reports that the H‑2A labor certification rarely produces any U.S. hires, and it’s worse than a neutral move because the turnover costs get passed on to consumers, which harms U.S. job creation and wages in related industries where U.S. workers are more common.

H‑2A Touchbacks Harm Security

The H‑2A touchback has no benefit for U.S. security either. Every time the law requires a legal temporary worker to return home, it creates the risk (and incentive) for a visa violation. The more required departures, the more visa overstays, and the more immigrants living illegally in the country. The touchback requirement imposes additional overstay risk in two ways: first, it forces out an existing worker, and second, it triggers the entry of a new worker who may or may not be as law-abiding as the one who exits.

The best evidence indicates that H‑2A overstays are a very small problem: less than 1 percent of the estimated overstay population had an expired H‑2A visa, according to an estimate by the Government Accountability Office (GAO) in 2012. Nonetheless, forcing legal workers out will create more overstays, and there is no reason to force out a qualified, law-abiding worker: just the opposite, there’s every reason to let them continue to work.

The original justification for the three-year regulatory requirement limit in 1987 was that a worker cannot qualify as a “nonimmigrant” (i.e. non-permanent resident) if the law didn’t require the worker to leave periodically. But this notion is antiquated given the developments in immigration law since then. There are numerous nonimmigrant designations for which there is no time-limit. Most relevantly, H‑1B visas provide for indefinite renewals if an employer sponsors them for a green card. Moreover, the workers’ status as nonimmigrants is grounded in the fact that their residence is conditional—permitted only so long as they perform agricultural labor or services in this country.

The economic, security, and legal arguments for the H‑2A touchback lack merit. The requirement actively undermines the economy and security of the country. It is not required by current law, and if Congress reforms the law, it should not require it, particularly for year-round employers. Indeed, it should remove the requirement entirely.

An important portion of President Trump’s immigration enforcement policy is immigrant detention. Immigrants who are apprehended at the border or in the interior of the United States are detained in Immigration and Customs Enforcement (ICE) facilities until they are removed from the United States. In recent months, many reports have surfaced of immigrants who have died while in detention or shortly after being released to medical facilities for treatment. The rate of death in ICE detention facilities is an important metric of how humane those facilities are.

E-Publication

Reforming the Immigration System: A Brief Outline

With the election of a new president, Congress has the opportunity to correct the two fundamental flaws that plague the current U.S. system. First, the system is too restrictive, and second, the system is too inflexible to adapt to new economic or social conditions, allowing small problems to build into national crises. Here is a compendium of 52 reform proposals, both for enforcing the law, and for a less restrictive and more flexible immigration system.

Read the Reforms

There are two primary pieces of data required to calculate the death rate in immigration detention: The number of people in detention each year and the number of deaths. Immigration and Customs Enforcement (ICE) runs all of the detention facilities and they provide the number of deaths and admissions. The American Immigration Law Association provides some more recent numbers of deaths in detention, but I only include those that ICE also counts. The admissions into ICE detention facilities variable is closest to the number of unique individuals who were present in a detention facility in each year. The numbers for both variables run through the end of Fiscal Year (FY) 2019.

Eight people died in immigration detention in FY2019, down from 10 in FY2018. During the same time, the number of admissions into ICE detention facilities increased from 396,488 to 510,854 (Table 1). The FY2019 death rate in ICE immigration detention was 1.6 per 100,000 detainees, a 38 percent drop from FY2018. That’s good news, but is likely the result of so many younger asylum seeker with fewer health problems being detained along the border rather than improved detention conditions.

Table 1

Deaths in ICE Detention, Death Rates, Detentions

Sources: ICE and Author’s Calculations.

Figure 1 shows the total number of ICE detentions and the total number of deaths in custody. The deaths in ICE detention facilities were highest during the George W. Bush administration at 91 total deaths with an average rate of 6.4 per 100,000 per year. Those death rates fell rapidly after FY2004, the first full year when ICE was operational, from 11.9 per 100,000 detainees to 2.9 per 100,000 detainees in 2008. The death rate rose 26 percent during the first year of the Obama Administration in 2009, then started falling again the next year with an average annual death rate of 2.3 per 100,000 detainees during his entire presidency. We only have data for three years of the Trump administration where the annual death rate is 2.4 per 100,000 detainees, which is well below the 2.7 per 100,000 death rate during the first three years of the Obama administration.

Figure 1

ICE Detentions and Deaths in Detention Facilities

Sources: ICE and Author’s Calculations.

This excellent study of death rates in ICE detention gives three reasons for why death rates fell so much during the Bush years and remained low thereafter. The first is that the length of time that immigrants spent in detention fell, which means there was less opportunity for each individual to die even though more were in detention. The second was that ICE increasingly relied on Secure Communities and local law enforcement to first arrest illegal immigrants and then transfer them to ICE. Local law enforcement agencies typically provided any healthcare that the immigrants needed before being transferred to ICE or, tragically, many of them died in local law enforcement custody. The third is that ICE medical policies and practices improved over time. 

Even though the number of people in ICE detention is increasing, the chance of dying in immigration detention is on a generally downward long-term trend that has been stable for the last several years.

The Trump administration has announced that it will begin deporting Mexican asylum seekers to Guatemala, where they will face homelessness in a country with one of the highest murder rates in the world. Last year, Guatemala signed onto a deal with the United States to accept deportees from other countries, and the administration had sold it as a way to resettle refugees “closer to home.”

But this announcement demonstrates that this justification was simply a farce, as most Mexicans crossing the border live closer to the United States than Guatemala.The administration is apparently justifying the move by citing a major increase in Mexican arrivals. The New York Times states:

about 17,000 Mexicans were caught crossing between ports of entry in October, a 34 percent increase since July , according to Customs and Border Protection (CBP). The Homeland Security Department has been searching for ways to stem the uptick for the past month.

But this excuse is also inaccurate. Border Patrol apprehensions of Mexicans crossing illegally have been roughly stable over the course of the year. They dipped down in July—as a result of the hotter weather—but in December, the most recent month available, Mexican numbers were lower than earlier in the year. Figure 1 shows the Mexican apprehensions by month since April 2019, when CBP started publishing monthly totals by country online.

Nor are Mexican arrivals particularly unusual on an annual basis. Here is the last decade of Mexican apprehensions by year. As it shows, Mexican apprehensions declined dramatically since 2010 from nearly 400,000 to 166,000 in 2019, and while 2019 was up from 2018, it came nowhere close to reversing these gains and was nothing like what the border has seen in terms of changes from Central America in 2019 (9 percent increase compared to a 172 percent increase).

The administration has absolutely no business deporting Mexicans to Guatemala using the extraordinary and already inhumane policies that it has implemented for Central Americans. The administration needs to channel these immigrants into legal avenues for entry, not forcibly remove them to a country that they do not know and do not want to go to.

Update 1/9/2020: This blog was updated to reflect apprehensions through December 2019.

Two dozen states are raising their minimum wages in 2020. While the federal government’s minimum for all workers remains the same, the feds have hiked one minimum wage in 2020: the Adverse Effect Wage Rate (AEWR) paid to H‑2A foreign seasonal farm workers. Despite the state changes, this rate will still far exceed the federal or state minimum wage in every state in 2020 by an average difference of 57 percent.

The AEWR is the Department of Labor (DOL)-mandated wage for H‑2A seasonal farm workers. Farmers who hire even a single H‑2A worker must also pay the AEWR to every American worker as well if it is higher than what they would otherwise receive. Except for Alaska, each state has its own AEWR, which run from $11.71 to $15.83 per hour in 2020 (based on 15 regions). State minimum wages start at the federal minimum of $7.25 hourly and go to $13.50 in the state of Washington.

In every state, the AEWR far outstrips every state minimum. The average difference between each state’s AEWR and its minimum wage is 57 percent in 2020. In Iowa, Indiana, Kansas, New Hampshire, North Dakota, Utah, and Wisconsin, the AEWR is fully double the state minimum wage. In 2020, DOL is raising the straight average by 5.5 percent from $12.96 to $13.68 hourly. Every state will see the AEWR increase with Ohio seeing the largest increase of 9.5 percent.

The 2020 rate increases continue a 2‑decade AEWR march higher. Since 2000, the AEWR has increased from a nominal wage of $7.21 (straight average) to $12.96. This 80 percent growth in the AEWR has doubled the pace of general price inflation as calculated by the Personal Consumption Expenditures (PCE) Index.

The AEWR is unfair discrimination against farmers who are trying to follow the rules and hire foreign workers legally. It is supposed to reflect the actual hourly wage that farmers are already paying workers. But in practice, this is not the case.

The DOL uses the Department of Agriculture’s Farm Labor Survey of U.S. farmers to calculate the AEWR. To determine the wage, DOL annually divides the total monetary compensation to all farm workers in a region in the prior year by total hours worked. Of course, this wage overlooks differences between localities, detailed job types, skills, and experience, leading to a misleading average.

More importantly, by treating all monetary compensation as “wages,” the AEWR scoops in overtime, hazard pay, bonuses, performance incentives, and all other additional payments. This means that the AEWR artificially inflates the base hourly wage in the following before including these types of extra compensation.

In addition, by pricing out workers with below-average wages—typically less experienced workers who received less bonus pay—the mandate further inflates wages in each successive year. The AEWR is obviously designed to suppress hiring of foreign farm workers—which is why labor unions and left advocacy organizations argue that the AEWR should increase even more. This is ironic given the common claims that the minimum wages don’t suppress hiring.

This analysis actually understates the difference between the AEWR and the state minimums because farmers who hire H‑2A workers must also provide their workers many other benefits that—unlike most state minimum wages—they cannot deduct even partially from the workers’ wages. DOL requires, for example, that farmers provide housing at no cost to the worker, cover their transportation to and from their home country (or state, for U.S. workers), and daily transit to and from work.

At the end of the day, there is no evidence that hiring foreign workers harms U.S. workers and substantial evidence that unemployed U.S. workers refuse to take H‑2A jobs even though they all have a shot at them first.

The AEWR is a political wage that the administration or Congress should terminate. I have previously written about the Farm Workforce Modernization Act, a bill that now has passed the House that would help rein in the AEWR. If we want to end illegal immigration, we should start by making it easier to hire needed workers legally.

Of all the concerns about immigration, perhaps none is more important to politicians than how immigration affects political control. In particular, many Republicans believe that immigration has clearly boosted the Democratic Party and that higher immigration will obviously doom the GOP. But historically (and recently), congressional Republicans have performed much better during periods when the immigrant share of the population is high. By contrast, Democrats dominated the low immigration periods.

GOP Almost Always Controls a House of Congress During High Immigration Periods, Rarely Controls Either House During Low Immigration Periods

The Republican Party came into existence in 1854, and while it quickly dominated, the Civil War and Reconstruction make its early history anomalous. Looking solely at the period since Reconstruction, Republicans have controlled at least one House of Congress 85 percent of years when the immigrant share of the population was greater than 10 percent, while not controlling either House 83 percent of all other years (Fig. 1). Moreover, they have controlled both houses 59 percent of the high immigration years, compared to just 7 percent of the low immigration years.

Starting this analysis earlier or later hardly changes this relationship between high immigrant shares and GOP success. From 1936 to 1994, when the immigrant population share fell below 10 percent, the GOP won a majority in House elections just twice. It had slightly better success in the Senate. In 1995, the immigrant population would again exceed 10 percent of the population, and the GOP has controlled the House or Senate 92 percent of the years since. Figure 2 shows that regardless of the period considered, Republicans outperformed Democrats when the immigrant share of the population was high.

This relationship is less dramatic for presidential elections, where the parties have basically split White House control regardless of the immigrant population. Republicans have held the White House 61 percent of years with immigrant populations greater than 10 percent, while they held it 47 percent of other years (Figure 3). In other words, the outcomes of presidential elections appear to have little relationship with immigration.

Why Republicans Succeed Despite Immigration

Explaining results in 72 elections with any simple narrative is obviously impossible. But some theories make more sense than others. To begin with, it is true that Democrats have generally benefited from the immigrant vote, so Republicans have had to win even more votes from the native-born population. 

It is likely that backlash against immigrants can explain at least some of the GOP’s success. The Republican “revolution,” when the GOP took over the House in 1994 for the first time in decades, corresponded with the peak in support for immigration restriction since polling began in the 1950s, and they quickly tried to limit illegal immigration. Though support for restriction quickly dissipated and, by 2019, the public had never been more supportive of immigration, it is possible that the backlash caused a sustained political realignment (particularly in the South).

A related explanation is that high immigration periods have undermined support for key Democratic priorities (at least starting with FDR). Even if they aren’t opposed to foreigners being in the country, people appear less willing to support more expansive welfare benefits if they might benefit foreigners. For this reason, economists Paul Krugman and Vernon Briggs have contended that the New Deal and Great Society entitlement programs of FDR and LBJ might never have happened had immigration remained open. Consistent with this view, one of the first actions that the GOP took when it took over in 1995–96 was to restrict welfare, particularly to immigrants.

Perhaps the best explanation for the GOP’s continued success focuses on the fact that high immigration occurred during periods when Democrats have lacked a key Democratic constituency: unions. Like naturalized citizens, a majority of union members have backed Democrats throughout U.S. history. Beyond members’ votes, unions also fundraise, donate, and campaign for Democrats. Since 1895, Republicans have controlled at least one house of Congress 85 percent of years in which the unionization rate was below 13 percent, while they haven’t controlled either house 83 percent of all high unionization rate years (Figure 4).

Unionization spiked thanks to the Wagner Act of 1935, but while Congress never repealed it, economic factors have produced a steady decline in unionization since the 1950s. Unionization rates fell as the foreign-born share of the population rose, and vice versa. But the lost union members made up a much larger portion of voters than the increase in naturalized citizens, leaving Democrats worse off.

Conclusion

This brings up another important consideration in this analysis: the fact that native-born citizens are still 92.2 percent of U.S. voters, meaning that natives will always play a much more important role in the elections than naturalized citizens. The fact that immigrants tend to clusters in certain cities and states further dilutes their effect. Helping Democrats win California, for example, by an even larger margin than they would otherwise does little to influence the overall outcomes.

Not only are immigrant voters a small part of the electoral picture, but immigration policy is only one part of the debates in each campaign. Party success depends on many other factors, policy choices, and strategies, and it would be impossible to consider every change. History need not be destiny, but it is nonetheless evidence for the proposition that Republicans can continue to win elections even during times with many more immigrants in the country.

Last week, we published a short blog post looking at whether immigrants litter more than native-born Americans. That post was motivated by a segment on Tucker Carlson Tonight where he and Seth Barron, associate editor of City Journal, talked about how a neighborhood in New York City was filthy and full of trash because immigrants who live there dump trash on the ground. Besides showing scary-looking photos and videos of trash on the ground, Barron and Carlson offered no evidence that immigrants litter more. They weren’t the first to make such claims without evidence.

The responses to our post, which was a simple regression between the share of a city’s population that was foreign-born and complaints about trash in the American Housing Survey (AHS), ranged from confusion to thoughtful parsing of the available evidence to links to a 2001 survey that didn’t ask about immigrant littering behavior. Many commentators weren’t happy and some had good reason because our simple regression didn’t test whether immigrants had a higher tolerance for trash, the quality of the data and the survey question weren’t ideal, there are vast endogeneity concerns, the sample size was small, and the size of the geographic unit was vast. All fair points.

The burden of providing evidence for anti-immigration claims (or any claim) rests on those who make them, but they rarely do so. As a result, we looked around for additional ways to measure littering and trash patterns in American cities. We found geocoded records of nearly 4 million 311 calls that request city cleanup services in San Francisco during 2008–2019. We compared the density of 311 complaints with local Census tract demographics. In most specifications, we find no statistically significant relationship between the share of the population that is foreign-born and reports of litter. However, some specifications show a negative relationship, meaning that some neighborhoods with more foreign-born, particularly noncitizen, residents produce fewer 311 trash complaints. Focusing on the Hispanic foreign-born resident population as the independent variable reveals no statistically significant relationship with 311 trash complaints.

This blog post explains our methodology, findings, brief discusses them, and then concludes in that order. As always, our dataset is available upon email request to one or both of us.

Data and Methodology

Our first data source is the 311 call record for the City of San Francisco for 2008–2019. The 311 phone line can be used by people in San Francisco to request city services, information, file complaints, or report various issues anywhere in the city such as littering, property damage, graffiti, homeless encampments, and other issues. Through its open data initiative, the San Francisco city government makes data on these calls available with information regarding the call’s date, type of request, details on the request, location by latitude and longitude, the outcome of the complaint, and other data that are all compiled into a case.

The initial 311 calls dataset contained 4 million cases and required a bit of data cleaning. We narrowed down the dataset to 311 call requests for street cleaning and litter removal, property damage, homeless issues, and homeless encampment removal. We then filtered out duplicate cases and those that did not contain valid latitude and longitude coordinates. With these selected cases, we match each case to a Census tract using the provided latitude and longitude coordinates and the 2017 Tiger/​LINE shapefiles from the Census Bureau. We could not use this method to assign a geographic location to the 311 calls for 16 cases. Fortunately, each case has an address assigned to it and we were able to use a geocoding tool developed by the Federal Financial Institutions Examination Council (FFIEC) to match these cases to their proper Census tract. This step revealed 212 cases that were not reported in San Francisco county that we then removed. After applying those filters and cleaning the data as we described, we were left with 1,701,499 cases.

Our second data source is the American Community Survey (ACS) 2013–2017 5‑year estimate. The ACS is our source for the demographic and economic data for each Census tract in San Francisco that we match up with the geocoded 311 calls. A Census tract is a small geographic region with an average population of about 4,000. San Francisco has a total of 196 Census Tracts, but six of them are located in areas that are either underwater or in park areas. Thus, we only have 190 populated tracts in our main analyses. Due to data disclosure policies and the small population of Census tracts, the Census Bureau only releases data for areas with fewer than 65,000 residents in its 5‑year estimates. From these data, we compute population shares by nativity in each Census tract, including the share that is native, foreign-born, naturalized, and noncitizen. We further collect data on population shares for Hispanic foreign-born residents.

We then map Census tracts into official San Francisco neighborhoods using a crosswalk available from the city’s open data platform.

To test whether immigrant neighborhoods receive more litter complaints, we look at a cross-section of San Francisco Census tracts. We run a set of basic regressions to test the correlation between 311 cases and Census tracts’ share of foreign-born residents. In each regression, we regress the cumulative number of 311 cases over 2008–2019, expressed as a log rate per 1,000 residents, on the share of native and foreign-born residents from the ACS, using a neighborhood fixed effect. Neighborhood fixed effects are important because they allow us to control for common, unobserved factors that affect Census tracts within the same neighborhood, such as neighborhood attitudes towards cleanliness and their propensity to report trash or litter.

Results

Our first regression has the various measures of the immigrant populations as the independent variables and litter reports as the dependent variables, controlling for neighborhood fixed effects (Table 1). We find no significant correlation between average litter reports and the native, foreign-born, and naturalized shares of the population. However, we find that a percentage point increase in a Census tract’s noncitizen share is correlated with an 8 percent decline in the litter reporting rate that is significant at the 5 percent level (Table 1, column 4).

Next, we regress street cleaning requests and the foreign-born population (Table 2). We find no evidence that either higher native or foreign-born shares correlate with more street cleaning requests in San Francisco Census tracts. Again, we find a negative correlation between the rate of street cleaning requests and a tract’s noncitizen share. We estimate that a percentage increase in a tract’s noncitizen share is correlated with 8 percent fewer street cleaning requests per 1,000 residents, on average.

We next test the correlation between immigrant shares and homeless concern reports (Table 3). According to the San Francisco 311 website, “homeless concern” reports include aggressive behavior or panhandling, leaving hazardous waste, trespassing on private property, and public intoxication. Similarly, we find a negative, significant association between homeless concern reporting rates and a Census tracts’ noncitizen population share of around 10 percent, although it is only significant at the 10 percent level.

Next, we look specifically at foreign-born Hispanic shares by Census tract to see whether higher proportions of foreign-born Hispanics correlate with 311 reporting rates. Perhaps Hispanic immigrants litter more, according to many comments we received on twitter, so we thought to test those concerns. Each of these regressions is shown in Tables 4, 5, and 6 and fail to find a statistically significant relationship between the foreign-born Hispanic share of the population and type of 311 case.

The geographic locations of homeless people, as measured by 311 calls reporting homeless concerns, are significantly correlated with complaints for litter, street cleaning requests, and all incidents combined at the 10 percent level. Most readers won’t be surprised that the location of the homeless population in San Francisco is correlated with litter and street cleaning complaints, but controlling for the foreign-born population has virtually no effect on the regression results in Table 7. Like our previous results, the coefficients on the foreign-born share remain insignificant.

Discussion

There are numerous reasons why the foreign-born share of a Census tract is not related to the number of 311 cases. The first possibility is that immigrant Census tracts have less litter and fewer homeless concerns. The second possibility is that immigrants have lower standards of cleanliness, so they are less likely than native-born Americans to call 311 ceteris paribus.

Our second concern is greater for our first blog post, which analyzed AHS response data about the person’s neighborhood of residence. Since anybody can make a 311 call from anywhere in San Francisco about trash in any other section of the city, native-born Americans could call 311 if they are traveling through San Francisco city Census tracts where immigrants are more likely to live. Although this doesn’t resolve the concern about differing cleanliness standards, it should increase confidence in our results.

The third possibility is that endogeneity issues are extreme here. We cannot make causal statements about immigrants littering less, we can merely show that there are fewer 311 calls about trash in San Francisco Census tracts where they live. There are other variables that influence whether a neighborhood is dirty enough to receive 311 complaints. We included controls for a few of them like housing value and median family income, but the regression results were nearly identical to the tables we reported above. Neighborhood fixed effects will have to suffice.

The fourth possibility is that trash pickup in San Francisco is excellent and cleans up neighborhoods before people have a chance to call 311. Thus, immigrants might be greater litterers but the super-efficient city government (don’t laugh, we’re trying to be serious) cleans up their mess before other city residents report the trouble.

Conclusion

This second blog post in the Great Litter Debate of 2019 is an attempt to provide better evidence in this difficult to resolve disagreement. In San Francisco, a city fiercely criticized by immigration restrictionists and conservatives for its very real sanitation problems, there is no association between immigrant resident populations by Census tract and 311 complaints about garbage. Proponents of the “immigrants litter” theory have supplied zero evidence to support their claims. If they don’t like these analyses, we invite them to conduct their own. After all, numerous cities make these data readily available, such as New York, Los Angeles, Chicago, and the District of Columbia to name a few.

The Department of Homeland Security (DHS) recently released a report detailing deportations (henceforth “removals”) conducted by Immigration and Customs Enforcement (ICE) during the fiscal year 2019. The DHS report divides removals into two categories based on the arresting agency: those removed from the interior of the United States and those removed from the border. Interior removals are those who are initially arrested by ICE and then subsequently removed. Border removals are individuals initially apprehended by a Customs and Border Protection (CBP) officer while they attempted to illegally enter the United States. Those apprehended by CBP are still called removals because they are turned over to ICE who subsequently remove them. To a large degree, border apprehensions are independent of a president’s immigration enforcement policies because factors outside of the United States also influence whether people decide to come in large numbers. However, interior immigration enforcement is much more under the president’s control. Thus, it is important to separate border removals and interior removals when gauging the extent and effectiveness of interior immigration enforcement.

In 2019, ICE deported 85,958 illegal immigrants from the interior of the United States, down about 10 percent from 95,360 in 2018. Annual removals from the interior of the United States peaked at 237,941 in 2011 during the Obama administration (Figure 1). The Trump administration would have to increase the pace of interior removals dramatically to reach Obama’s previous peak. That is unlikely to occur because local law enforcement agencies are much less likely to cooperate with President Trump’s ICE than they were with President Obama’s ICE. Sanctuary policies have an unclear (probably null) effect on crime, but they do reduce deportations. Beginning in 2012, border removals have outnumbered those from the interior of the United States. 

Figure 1 includes numbers for interior and border removals going back to 2003. The government didn’t start separating them in official statistics until 2008, so it has always been difficult to compare immigration enforcement in earlier eras to 2008 and after. Since the difference between a border removal and an interior removal is the arresting agency, I used TRAC data to back out the number of border and interior removals by year going back to 2003 when CBP and ICE were created. For years where ICE data and TRAC data overlap, the difference is usually less than 1 percent. Interior removals and border removals began to increase in 2007 and continued to remain high through 2011, falling thereafter.

The Obama administration removed 1,242,486 from the interior of the United States during its full eight years, averaging 155,311 removals per year. George W. Bush’s administration removed 819,964 illegal immigrants from the interior of the United States during the last 6 years of his administration, equal to 136,661 per year. If the percent of the illegal immigrant population deported annually during 2003–2006, before the big increase in 2007, held in 2001 and 2002, George W. Bush’s administration would have deported 1,000,653 from the interior of the United States with an annual average of 125,082. In comparison, the Trump administration has only managed to remove 262,921 total people from the interior of the United States during his first three years in office – only about 30,000 more than President Obama did in 2009 or President Bush did in 2008. On average, the Trump administration has only removed an average of 87,640 people per year.

The percentage of all illegal immigrants removed from the United States is a better measure of the intensity of interior enforcement than the total numbers removed. Based on estimates of the total size of the illegal immigrant population from Pew, ICE removed about 0.82 percent of the illegal immigrant resident population from the interior of the United States in 2019, down from 0.91 percent in 2018. The total immigrant population numbers are estimated to be the same in 2018 and 2019 as in 2017, so the percent deported in the later years is likely an overestimation. Interior removals as a percent of the illegal immigrant population peaked at 2.11 percent in 2009.

ICE under President Obama’s administration removed an average of 1.38 percent of the interior illegal immigrant population per year of his presidency. The Obama administration’s interior removal statistics show a downward trend beginning in 2011 and continued until the end of his administration. So far, ICE under President Trump has only managed to deport an average of 0.83 percent of the illegal immigrant population each year. The annual average for the George W. Bush administration was 1.21 percent deported annually, which drops to 1.15 percent if the 2003–2006 average is applied to 2001 and 2002 as an estimate.

The Obama administration also targeted immigration enforcement on criminal offenders (not all illegal immigrants are criminals and many of them committed immigration crimes). During the Obama administration, 52.6 percent of all illegal immigrants removed were convicted criminals, including those convicted of immigration crimes. During the George W. Bush administration for 2003–2008, the years for which we have data, about 41.3 percent of all illegal immigrants removed were criminals. About 56.5 percent of those deported during the Trump are convicted criminals so far (Figure 3). Compared to the last year of the Obama administration, criminal removals are up 11,472 or about 8.3 percent while non-criminal removals are up 15,531 or about 15.3 percent.

Using TRAC data, I can also estimate the number of interior and border removals who were criminals during the 2003–2019 period (Figure 4). During the Obama administration, about 71 percent of all interior removals were criminals. The comparable figure for the Bush administration for 2003–2008 was 51 percent. A full 78 percent of those deported from the interior of the United States during the Trump administration have been criminals so far. Figure 5 estimates border removals by criminality using TRAC data for earlier years. In every year, fewer than half of them are criminals.

The Trump administration is floundering in its efforts to increase removals. President Trump is not enforcing immigration laws nearly as harshly as his two recent predecessors did.