Economy-wide effects of reducing illegal immigrants in U.S. employment.
We use an economy-wide model to analyze the effects of three broad programs to reduce illegal immigrants in U.S. employment: tighter border security; taxes on employers; and vigorous prosecution of employers. After looking at macroeconomic industry and occupational effects, we decompose the welfare effect for legal residents into six parts covering changes in: producer surplus and illegal wage rates; skilled employment opportunities for natives; aggregate capital; aggregate legal employment; the terms of trade; and public expenditure. The type of program matters. Our analysis suggests a prima facie case in favor of taxes on employers. (JEL J61, C68)

Article Type:
Migrant labor (Management)
Migrant labor (Laws, regulations and rules)
Illegal immigrants (Management)
Illegal immigrants (Employment)
Employment (Statistics)
Employment (Management)
Dixon, Peter B.
Johnson, Martin
Rimmer, Maureen T.
Pub Date:
Name: Contemporary Economic Policy Publisher: Western Economic Association International Audience: Academic; Trade Format: Magazine/Journal Subject: Business; Economics Copyright: COPYRIGHT 2011 Western Economic Association International ISSN: 1074-3529
Date: Jan, 2011 Source Volume: 29 Source Issue: 1
Event Code: 200 Management dynamics; 930 Government regulation; 940 Government regulation (cont); 980 Legal issues & crime; 530 Labor force information; 680 Labor Distribution by Employer Advertising Code: 94 Legal/Government Regulation Canadian Subject Form: Migrant labour; Migrant labour Computer Subject: Company business management; Government regulation
Product Code: 9108130 Jobs & Employment; E220000 Employment NAICS Code: 92611 Administration of General Economic Programs
Geographic Scope: United States Geographic Code: 1USA United States
Accession Number:
Full Text:

In 2005, there were 7.3 million illegal immigrants working in the United States, 5% of total employment. Public attitudes on illegal immigrants working in the United States, 5% of total employment. Public attitudes on illegal immigrants vary from the view that they are impoverishing low-income legal residents by depriving them of jobs to the view that they are vital to the economy because they perform tasks that legal residents are unwilling to undertake. The illegal-immigrant issue is now a major component of the political debate with policy suggestions ranging from mass deportation to legalization and amnesty.

There is a vast literature on economic aspects of immigration to the United States, dealing with: the causes of immigration flows; (1) the performance of immigrants in the U.S. economy; (2) and the effects of immigrants on the economy. (3) Perhaps reflecting data limitations, the literature on illegal immigrants is small and deals mainly with the causes of illegal flows (4) and the effects on flows of policy interventions such as heightened border security. (5) Apart from estimates of the effects of illegal immigrants on public sector budgets, (6) there is little quantitative analysis of their economy-wide consequences.

The aim of this paper is to contribute to this comparatively underdeveloped aspect of the literature on illegal immigration. Applying USAGE, a multisectoral model, we provide quantitative analysis of the economy-wide effects of three broad approaches to reducing illegal immigrants in U.S. employment: tighter border security; taxes on employers; and vigorous prosecution of employers. In explaining USAGE results, we rely on familiar diagrams and back-of-the-envelope calculations rather than requiring readers to be familiar with USAGE details.

The paper is organized as follows. Section II gives brief background material on USAGE highlighting points that will be useful in interpreting results in later sections. Sections III and IV contain the main results, and Section V provides sensitivity analysis. Concluding remarks are in Section VI.


USAGE is a dynamic CGE model of the United States developed in collaboration with the U.S. International Trade Commission. The theoretical structure of USAGE is similar to that of Australia's MONASH model, Dixon and Rimmer (2002). However, most applications of models such as USAGE and MONASH require theoretical adaptations and database changes. For this study, we created a version of USAGE with 38 industries and 51 occupations. The first 50 occupations are used by the 38 industries. The 51st occupation is employment outside the United States of potential illegal immigrants. The introduction of this 51st occupation facilities the specification of flows of illegal immigrants.

A USAGE simulation of the effects of a policy shock requires two runs of the model: a basecase run and a policy run. The basecase is intended to be a plausible forecast while the policy run generates deviations away from the basecase caused by the policy under consideration. As well as policy changes, the policy run introduces macroeconomic and labor-market assumptions. The main assumptions underlying the results in Sections III-V are as follows:

A. Production Technologies and Household Preferences

In our policy runs, all technology and preference variables are exogenous and kept on their basecase paths. Thus, we assume that these variables are unaffected by changes in immigration policy.

B. Inflation

The price deflator for GDP is exogenous in policy runs and set on its basecase path. Thus, we assume that changes in immigration policy have no effect on inflation.

C. Investment and Rates of Return

In policy runs, USAGE allows for short-run divergences in industry rates of return from their basecase levels. Short-run increases/decreases in rates of return cause increases/decreases in investment and capital stocks. Thereby rate-of-return divergences in early years are gradually eroded.

D. Private and Public Consumption, and the Public-sector Deficit

In policy runs, the average propensities of legal residents and illegal immigrants to consume out of their disposable incomes are exogenous and set on their basecase paths. We assume that illegal immigrants remit their savings to their home countries.

For public consumption, we assume that expenditure per capita on both legal residents and illegal immigrants is proportional to private consumption per capita by legal residents. The factor of proportionality for illegal immigrants is set at 0.49 times the value applying to legal residents. (7) Thus, we assume that as legal residents become richer they demand more services from the public sector; that illegal immigrants cannot be prevented from enjoying improvements in public amenities made available for legal residents; but that not all government services available to legal residents are available to illegal immigrants.

On the income-side of the public sector budget, we assume that income-tax rates adjust to ensure that the public-sector deficit follows its basecase path.

E. The Labor Market

Technical details of the USAGE labor-market specification are in Dixon and Rimmer (2008). Here, we provide an overview, sufficient for understanding the results presented in later sections.

USAGE contains demand and supply equations for jobs classified by occupation (o, the 50 occupations in Table 1); birthplace (b, domestic/foreign); and legal status (s, legal/illegal). The demand equations were developed by assuming that industries minimize their costs per effective unit of labor input. An effective unit is defined as a 3-stage nested CES function of (o, b, s) inputs. At the top level, inputs with different occupational characteristics are poor substitutes (e.g., Cooks are poor substitutes for Carpenters); at the second level, inputs with the same occupational characteristic but different legal statuses are medium substitutes; and at the third level, inputs with the same occupational and legal characteristics but different birthplace characteristics are good substitutes. The three substitution elasticities are 0.35, 5.0, and 7.5. For this paper, the most important of these is the legal/illegal substitution elasticity (5.0), discussed further in Sections III.C and V.B.

In developing supply equations for year t, we divide the potential U.S. workforce at the start of year t into categories reflecting birthplace, legal status, and labor-market function in t-1. Labor-market functions include: employed in United States occupation o; unemployed in the United States; residing in the United States but not in the workforce; and residing outside the United States. The last two functions are included to cover new U.S. entrants to the workforce and potential legal and illegal immigrants. The people in each category decide their supply to each (o, b, s) activity by solving a utility maximizing problem. Their supplies in year t are sensitive to relative wage rates and via the specification of the utility functions we ensure that supplies from each category are compatible with the category's b and s characteristics and also its skills as reflected in its t-1 function.

In policy runs, wage rates adjust sluggishly from their basecase paths to ensure that the long-run percentage deviation from the basecase in the demand for each (o, b, s) activity matches that for supply.

A key concept in the USAGE treatment of the labor market is vacancies. For every year, the model calculates vacancies in each (o, b, s) activity as the number of jobs in the activity minus the number filled by incumbents (people in [o, b, s] in the previous year). Vacancies are filled by: flows from unemployment; new workforce entrants; and flows of workers changing jobs. Disequilibrium exists in the sense that not everyone is doing what they want to do at the going wage rates. Some new entrants and unemployed people who offer to work in (o, b, s) cannot find a job, and some employed people working in another activity offer unsuccessfully to (o, b, s).

In simulations of the effects of reductions in illegal employment, vacancies open up for legal workers in illegal-immigrant-intensive occupations. At the same time, there are reductions in vacancies for legal workers in other occupations. This is because with fewer illegal immigrants the economy shrinks and total employment (legal plus illegal) in nearly all occupations is reduced. The change in the occupational mix of vacancies biases the flows of legal workers (particularly new entrants and the unemployed) toward the occupations intensively vacated by illegal immigrants.


This section reports the effects on the U.S. of tighter border security (supply restriction, SR) that raises the costs to illegal immigrants of entering the United States.

Chart 1 shows the employment paths for illegal immigrants in the basecase and the SR policy run. In the basecase, illegal employment grows between 2005 and 2019 from 7.3 to 12.4 million, 3.8% a year. Employment of legal residents grows by only 1.0% a year. The share of illegal immigrants in total employment increases from 4.98% to 7.17%. Because illegal immigrants have low-paid jobs, their share in the total wagebill is less than their employment share. In the basecase, their wagebill share goes from 2.69% in 2005 to 3.64% in 2019.


In creating the basecase, we recognized that population growth in the main source country for illegal immigrants, Mexico, is slowing (Hanson and McIntosh 2007) and that, in the absence of fresh U.S. policy initiatives, growth in net inflow of illegal immigrants is likely to be moderate: we assume average annual growth to 2019 of 1%. Nevertheless, the number of illegal immigrants in U.S. employment will grow rapidly (3.8%) because the current net inflow to employment (about 340,000) is high relative to the stock of illegal workers in U.S. employment (7.3 million).

In the policy run, we introduce shocks over 2006 and 2007 that cause potential illegal immigrants (those in the 51st occupation) to behave as if there is a permanent reduction of 25% in the wage they anticipate receiving in the United States. (8) With these shocks, illegal employment grows between 2005 and 2019 at 1.4% a year, from 7.3 to 8.9 million. Thus, the policy reduces illegal employment in 2019 by 3.55 million (= 12.4 - 8.9) or 28.6%.

The focus of our analysis is the effects of the 3.55 million cut in illegal employment, not the size of the shocks that cause it. Nevertheless, it may be helpful to think about the shocks as an increase in the difficulties faced by smugglers in organizing border crossings. This could be expected to increase smugglers' fees and other costs of illegal entry. For a potential illegal immigrant who plans to stay in the United States for 1 year and who anticipates earning $20,000, the policy shocks are equivalent to an increase in illegal entry costs from say $4,000 to about $9,000.

A. Macroeconomic Effects of Reducing the Supply of Illegal Immigrants

Charts 2 and 3 show the effects of the supply-reducing policy on macroeconomic variables, expressed as percentage deviations from the basecase.

Chart 2 shows that the policy causes a long-run (2019) (9) reduction in jobs of 2.2%. This mainly reflects the reduction of 3.55 million in the number of illegal jobs: 3.55 million is 2.1% of the basecase number of U.S. jobs in 2019. (10) Because the lost jobs are mainly for low-paid workers, the reduction in labor input in 2019 is less than 2.2%. Labor input is hours weighted by wage rates to reflect different productivities of workers. We might expect the percentage loss in labor input to be 1.04%: 28.6% (the reduction in illegal employment) of 3.64% (the illegal share in the basecase U.S. wagebill for 2019). However, restriction of illegal employment shifts the occupational mix of remaining employment toward low-paid occupations, expanding the loss in labor input to 1.6%. The implications of this shift in occupational mix for the welfare of legal residents are discussed in Section III.D.


The long-run reduction in United States capital stock approximately matches that of labor input. As mentioned in Section II, we assume that the policy does not change either technologies or long-run rates of return, implying that it has little effect on the K/L ratio. With labor and capital down by about 1.6% and no change in technology, the long-run reduction in GDP is also about 1.6%.

In Chart 3, the long-run percentage effects on C, I, G, X, and M are negative and range around that for GDP. Public consumption (G) falls relative to private consumption (C) because consumption of public goods by illegal immigrants is high relative to their consumption of private goods. (11) Investment (I) falls relative to GDP mainly because even by 2019, the capital stock is not fully adjusted and is still falling slightly relative to the basecase. C plus G rises relative to GDP because the policy improves the U.S. terms of trade (the price of exports divided by the price of imports). This is a benefit from having a smaller economy that demands less imports and supplies less exports. An improvement in the terms of trade allows the United States to increase its consumption relative to its GDP. The increase in C + G relative to GDP generates a deterioration in the real trade balance (X-M), supported by long-run real appreciation.


The short-run results in Chart 3 are dominated by the need for the economy to adjust in the policy run to a lower capital stock than it had in the basecase. In the short run, the policy causes a sharp reduction in investment and a consequent real devaluation. This temporarily stimulates exports and inhibits imports. As the downward adjustment in the capital stock is completed, investment recovers, causing the real exchange rate to rise, exports to fall and imports to rise.

B. Industry Results

Column 4 of Table 2 shows long-run deviation results for industry outputs. Because the SR simulation gives a positive long-run deviation in the real exchange rate (Chart 3), trade-exposed industries show output deviations in 2019 that are more negative than that for GDP. The long-run deviation in Construction is also slightly more negative than that of GDP, in line with the long-run investment deviation (Chart 3). Non-trade-exposed consumption-oriented industries, mainly services, have output deviations that are less negative than that for GDP. This is explained by the long-run increase in the ratio of C + G to GDP. Foreign holiday is an outlier in Table 2. Its strongly positive output result reflects long-run exchange-rate-induced substitution of foreign holidays for domestic holidays.

Results for output and labor input in Table 2 are not closely linked to the industry's use of illegal labor. Column (2) shows the illegal labor share of industry costs in 2005. When we regress the output and labor input results in columns (4) and (5) against the share data in column (2), we obtain [R.sup.2]s of 0.04 and 0.06. Industries that rely heavily on illegal immigrants incur cost increases. In the long run, these industries suffer adverse substitution effects as the prices of their products rise relative to those of other industries. These effects are included in USAGE simulations, but are weak. Illegal-labor cost shares are small, the largest being 4.51% for Construction. As explained in Section III.C, wage rates for illegal workers increase on average by about 9.5% while those of competing legal workers increase by considerably less. These wage increases are not sufficient to generate significant substitution effects because they do not induce significant changes in relative prices. In column (3) of Table 2, the maximum percentage point difference between the deviation results for any pair of industry prices is only 2.02.

When we regress the jobs results in column (6) of Table 2 against the share data in column (2), we obtain an [R.sup.2] of 0.27. This higher [R.sup.2] reflects replacement of illegal labor with legal labor in industries that currently rely heavily on illegal labor. Illegal workers in any occupation receive lower wages than legal workers in the same occupation. (12) We assume that this means that illegal workers have lower productivity than legal workers in the same occupation. Consequently, when we simulate the effects of restricting the supply of illegal workers, we find that job numbers fall sharply in those industries in which there is a significant replacement of low-productivity illegal workers with higher-productivity legal workers.

C. Occupation and Wage Effects

Column (1) of Table 1 shows the share of illegal residents in the wagebill of each U.S. occupation. The occupational classification was chosen to give maximum detail on employment of illegal immigrants. About 90% of their employment is in the first 49 occupations. The last occupation, Services other, accounts for about 60% of U.S. employment but only 10% of illegal employment. Columns (2) and (3) show the long-run effects of the supply-restriction policy on employment and real wage rates of legal U.S. residents by occupation.

In broad terms, the employment results in Table 1 show a long-run transfer of U.S. legal employment from Services other to the occupations that currently employ large numbers of illegal immigrants. The regression of legal-job deviations in column (2) against the illegal shares in column (1) gives an [R.sup.2] of 0.998. Consistent with the demand-supply theory of wage determination (Section II.E), the wage results for legal residents in column (3) reflect the employment results in column (2). Regressing column (3) against column (2) gives an [R.sup.2] of 0.999.

At the aggregate level, the long-run deviation in legal employment is -0.16% (column [2], Table 1): in the absence of supply shocks to the legal workforce, restricting illegal immigration can have no more than a minor effect on aggregate long-run employment of legal residents. With illegal employment falling by 28.6%, giving a 28.4% reduction in the illegal/legal employment ratio, USAGE implies that the average illegal/legal wage rate ratio must increase by 9.2%. This is brought about by a 9.5% increase in the real wage (consumer-price-deflated) of illegal workers combined with a 0.3% increase for legal workers. The most important determinant of the size of illegal/legal wage movement is the assumed value for the elasticity of substitution between legal and illegal workers. In our main simulations, we use the value 5 for all occupations. Sensitivity analysis on this value is discussed in Section V.B. In choosing 5, we were guided by Ottaviano and Peri (2006) who found that the elasticity of substitution between native-born and foreign workers in the United States is about 7.5. (13) Our view is that the substitution elasticity between legal and illegal workers is somewhat lower. It is likely that legality is an important characteristic to employers. Many employers who do not currently use illegal immigrants may require considerable reductions in illegal/legal wage ratios to tempt them to switch to illegal workers.

There are two further aspects of Table 1 worth considering: the long-run increase of 0.3% in the average (14) real wage rate of legal workers and the long-run reduction of 0.16% in their employment. The wage increase arises from the improvement in the terms of trade, which raises the value of the marginal product of labor in terms of consumer goods. The decrease in legal employment arises from the occupational shift in its composition toward low-skilled occupations in which there are high equilibrium rates of unemployment. In popular discussions, it is often asserted that cuts in employment of illegal immigrants would reduce unemployment rates of low-skilled domestic workers. Although our modeling suggests that there would be increases in the number of jobs for legal workers in low-skilled occupations, this does not mean that unemployment rates in these occupations would fall. In fact, with cuts in illegal immigration, low-skilled domestic workers might find themselves under increased pressure from higher-skilled workers who can no longer find vacancies in higher-skilled occupations.

D. Effects on Aggregate Welfare of Legal U.S. Residents

The last row in column (1) of Table 3 shows the long-run deviation in real consumption (private plus public) by legal residents in the SR simulation. This is an indicator of the effect on the overall economic welfare of legal residents. Why does USAGE imply that the SR policy leads to a long-run reduction in the welfare of legal residents of 0.52%? As summarized in Table 3 and quantified via back-of-the-envelope calculations in Table 4, there are six underlying factors.

Factor 1: Direct Effect. The first factor is the change in GDP directly attributable to the reduction in employment of illegal immigrants compared with the change in the after-tax cost of employing them. Figure 1 helps us to measure this factor. DD is the demand curve for illegal immigrants in 2019, drawn as a function of their pre-income-tax wage rate. SS and S'S' are the supply curves for illegal immigrants in the basecase and policy runs. Both relate supply to pre-income-tax wage rates and are drawn on the assumption that the income-tax rate applying to illegal immigrants is held constant. The numbers in Figure 1 refer to simulation results for 2019. In the basecase for 2019, the average pre-tax wage rate for illegal workers is $1, rising to $1.092 in the policy run. (15) The 2019 basecase U.S. wagebill is $17,941,636 million. As mentioned earlier, the share of illegal immigrants in the total wagebill is 3.64%. Thus, with the wage rate at $1, basecase employment of illegal immigrants in 2019 is 653076 million units (= 0.0364*17,941,636 million). In the policy run, the quantity of illegal-immigrant employment is 28.6% lower. 466468 million units.


A back-of-the-envelope specification of the demand curve DD is

(1) Wage(illegal) = [P.sub.g]/(1 + [T.sub.1])MPL(illegal)


Wage(illegai) is the wage rate (as a cost to employers) for illegal immigrants;

[P.sub.g] is the price deflator for GDP, that is the price of U.S. products;

MPL(illegal) is the marginal product of illegal workers; and

[T.sub.1] is the average rate of indirect tax applying to U.S. output.

The direct effect on GDP of reducing illegal employment by one unit is [P.sub.g] * MPL(illegal). Thus, a 28.6% cut in illegal employment directly reduces GDP by (1 +[T.sub.1]) times area(abcd) in Figure 1. Pre-tax payments to illegal workers are reduced by the area(bcde), reflecting the reduction in their employment, but are increased by the area(aefg), reflecting the increase in their wage rates. In after-tax terms, the change in payments to illegal workers is (1 - [T.sub.2]) * [area(aefg)--area(bcde) ] where [T.sub.2] is the rate of income tax applying to the wages of illegal workers. In total, the Direct effect is:


Direct effect = - (1 + [T.sub.1]) * area(abcd) - (1 - [T.sub.2]) * [area(aefg) - area(bcde)].

In the absence of taxes, the Direct effect would be a loss of income for legal residents of area(abfg). As indicated in Figure 1, this is $51,672 million. The USAGE database indicates that the average indirect tax rate ([T.sub.1]) is 4.385% and we assume that the rate of tax ([T.sub.2]) on the wages of illegal immigrants is 11.95%.16 Taking these taxes into account increases the Direct effect to $77,305 million. This is 0.29% of the basecase value of the income of legal residents in 2019 (i.e, GNP for legal residents of $26,210,266 million, L16 in Table 4).

Factor 2: Occupation-mix Effect. As explained in Section III.C, restricting the supply of illegal immigrants changes the occupational mix of employment of legal residents toward low-paid (low marginal product) occupations. Using basecase occupational wage rates for 2019, we calculate that the shift in the occupational mix of legal employment in the SR simulation reduces the average hourly wage rate of legal workers by 0.4647%. This costs legal residents in 2019 $80,343 million, calculated as the wage effect times the basecase wagebill of legal residents [0.04647*(1 -0.364)*17941636, Table 4].

The Occupation-mix effect does not imply that existing legal workers change their occupations. For each occupation, restricting the supply of illegal workers presents legal workers with opportunities to replace illegal workers. However, the economy is smaller, generating a negative effect on employment opportunities for legal workers. The positive replacement effect dominates in the low-paid occupations that currently employ large numbers of illegal immigrants. The negative effect of a smaller economy dominates in high-paid occupations that currently employ few illegal immigrants. Thus, there is an increase in vacancies in low-paid occupations relative to high-paid occupations, allowing low-paid occupations to absorb an increased proportion of new entrants to the workforce and unemployed workers.

Another way of understanding the Occupation-mix effect is to recognize that our modeling of the labor market involves job shortages. At any time, not everyone looking for a job in a given occupation can find a job in that occupation. So people settle for second best. The college graduate who wants to be an economist settles for a job as an administrative officer. The unemployed person who wants to be a chef settles for a job as a short-order cook, and so on. It is this shuffling process that is captured in USAGE.

Factor 3: Capital Effect. The SR simulation shows a -1.7% long-run deviation in capital (Chart 2). This causes a reduction in GDP of 0.44% (= 1.7*0.26; the capital share of GDP is 0.26). Investment at the margin is financed predominantly by foreigners. As a first approximation, we might assume that all of the reduction in capital is a reduction in foreign-owned capital and that none of the 0.44% reduction in GDP affects the income of legal residents. However, USAGE recognizes two complications.

First, the Direct effect and the other negative effects identified in Table 3 leave legal residents with less income and less savings throughout the simulation period in the policy run than in the basecase. With less accumulated savings, legal residents own less capital in 2019. The deviation in capital in 2019 of -1.7% is composed of a 0.9428% reduction in U.S.-owned capital and a 4.8202% reduction in foreign-owned capital (P15 and P16, Table 4). The reduction in U.S.-owned capital cuts the income of legal residents in 2019 by $49,378 million. This is 0.009428 times the U.S. share (80.13%, L13) of U.S. capital income ($6,536,265 million, L7).

The second complication is taxes. The 4.8202% reduction in foreign-owned capital causes a loss of capital-income-tax revenue to the United States of $9,238 million, calculated as 4.8202% of foreign-owned capital income [= (1 - 0.8013)*6536265] times the capital tax rate of 14.756%. There is also an indirect tax effect. The value of the marginal product of capital is 1 + [T.sub.1] times the pre-income-tax return to a unit of capital. Consequently, a reduction in capital stock imposes a GDP loss beyond the effect on the returns to capital. In the present case, this additional GDP loss, which is borne by legal residents, is $4,911 million (the indirect tax rate [0.04385] times 1.7% of the total returns to capital).

In aggregate, the capital effect imposes a loss on legal residents of $63,524 million (= 49378 + 9238 + 4911).

Factor 4: Legal-employment Effect. As explained in Section III.C, the deviation in 2019 in legal employment is -0.1553% (P17). This imposes a loss on legal residents of $26,849 million (0.1553% of 1 + [T.sub.1] times the basecase labor income of legal residents).

Factor 5: Public-expenditure Effect. We assume that public expenditure per illegal immigrant is 0.49 times public expenditure per legal resident (Section II.D). In the basecase for 2019, the share of illegal immigrants and their dependents in the U.S. population grows to 7.24%. Thus in the basecase for 2019, the share of illegal immigrants in public expenditure is 3.70% [0.49*0.0724/(0.49*0.0724+ 1 - 0.0724)]. With a 28.6% reduction in the number of illegal immigrants, the public sector reduces its expenditure in 2019 by $44,617 million, calculated as 28.6% of 3.70% of public consumption. As shown in Tables 3 and 4, this is a benefit to legal residents.

Factor 6: Macro-price Effects. Table 4 shows a reduction in the price deflator for private and public consumption relative to that for GDP of -0.2305 (P12 and P6). This increases the consuming power of the income of legal residents by 0.2305%, which is equivalent to an increase in income of $60,405 million (0.2305% of the GNP of legal residents, L16). The main reason for the relative decline in the price of consumption is the improvement in the terms of trade, discussed in Section III.A (0.7955, P14). A terms-of-trade improvement generally reduces the price deflators for expenditure aggregates relative to that for GDP because expenditure deflators include import prices but not export prices while the GDP deflator includes export prices but not import prices. In SR, the reduction in the price of consumption relative to the price of GDP is accentuated by movements in the component price deflators of GNE. Table 4 shows a decline in the price of consumption relative to the price of GNE. In the SR policy run, the price of investment is elevated relative to the price of consumption because of heavy representation of illegal immigrants in the construction industry.


This section applies USAGE to compute the effects of policies to reduce the demand for illegal immigrants by increasing their cost to employers. These policies raise costs by increasing the risks to employers of incurring taxes, fines, or criminal prosecutions. We consider two extreme cases: simulation DR1 in which the increased cost is a tax or fine collected by the government; and DR2 in which the increased cost is in the form of resource wastage. In DR2, employers use professional services as a complementary input with labor from illegal immigrants. These services are provided by lawyers and other professionals to reduce the likelihood of successful prosecutions.

The DR1 and DR2 basecase runs are the same as for SR. As in SR, the policy shocks in the DR policy runs are introduced in the early years and scaled so that the reduction in employment of illegal immigrants in 2019 is 28.6%.

The deviation results in the DR simulations for nearly all variables are similar to those in the SR simulation. However, for consumption by legal residents there are interesting differences. As shown in Table 3, the long-run reduction in legal-resident consumption is considerably smaller when illegal employment is curtailed by a demand-reducing tax (DR1) rather than by supply-side restrictions (SR) or by demand-reducing prosecutions that generate resource-absorbing avoidance activities (DR2).

Table 3 shows that most of the difference in long-run consumption by legal residents as we go from SR to DR1 is because of the Direct effect which switches from -0.29 to +0.12%. This can be understood by comparing Figure 1 with Figure 2 in which the imposition of a tax on illegal employment moves the demand curve to D'D' and generates tax revenue of area(ahjg). Whereas in SR, there was a transfer of income from U.S. employers to illegal immigrants via higher wages, in DR1, there is a transfer from illegal immigrants (and employers) to the United States Treasury via taxes and fines. We assume that extra revenue for the Treasury is used on behalf of legal residents.


The Capital effect is a second significant source of difference between the SR and DR1 results for legal-resident consumption. In the DR1 policy run the income, and therefore savings, of legal residents is higher throughout the simulation period than in the SR policy run. This reflects the more favorable Direct effect in DR1. With higher saving, the Capital effect is less negative because legal residents own a higher share of the 2019 capital stock.

The DR2 deviation result for long-run consumption by legal residents is similar to that for SR (-0.47% compared with -0.52%). However, Table 3 shows differences in the contributing factors. The Direct effect is noticeably more unfavorable in DR2 than in SR. This is offset by more favorable DR2 results for the Occupation-mix and Macro-price effects.

To work out the Direct effect for DR2, we can draw a diagram similar to Figure 2. For DR2, area(ahjg) is the deadweight loss of resources absorbed in prosecution avoidance. In SR, this area was part of the pre-tax income of illegal workers. However, in SR, part of area(ahjg), the income-tax component, contributes to the welfare of legal residents. In DR2, none of area(ahjg) contributes to the welfare of legal residents, leaving the Direct effect for DR2 more negative than for SR.

The assumption in the DR2 policy run that employment of illegal immigrants requires complementary employment of domestic professionals means that DR2 generates favorable employment deviations for highly paid domestic workers. This explains the less unfavorable Occupation-mix effect in DR2 compared with SR.

The more favorable Macro-price effect in DR2 relative to SR is explained by a lower level of exports in DR2. As mentioned in Section III, less exports allows higher foreign-currency export prices. The need to employ domestic professionals as a complement to illegal workers is equivalent to a technological deterioration: more inputs are required to produce a given amount of output. A technological deterioration reduces GDP directly and also indirectly by making the capital stock smaller than it otherwise would have been. With a smaller GDP in DR2 than in SR, the U.S. has less imports and consequently less exports.


In this section, we look at how the results from Sections III and IV are affected by varying critical assumptions and parameter values.

A. Varying the Provision of Public Services to Illegal Immigrants

In Sections III and IV, we assumed that the ratio of public consumption per capita devoted to illegal people to that for legal people is 0.49. In this subsection, we conduct an alternative SR simulation with this ratio set at 0.71. (17) The alternative simulation produces no surprises. As can be seen in Table 5, the difference in the long-run consumption result for legal residents is associated almost entirely with the Public expenditure effect (F5, which increases in the proportion 0.71/0.49, from 0.17 to 0.24.

Perhaps the most interesting aspect of Table 5 is the relative unimportance of variations in the public expenditure assumption. A 45% increase in the assumed level of public expenditure on illegal immigrants (an increase in the public consumption ratio from 0.49 to 0.71) reduces the simulated negative effect on long-run consumption by legal residents by only 0.09 percentage points (from -0.52% to -0.43%).

B. Varying the Key Demand and Supply Parameters

Next, we vary the USAGE parameters that determine the elasticity of demand by employers for illegal labor and the elasticity of supply of that labor. On the demand side, we look at how the results are affected when the elasticity of substitution between legal and illegal workers is set at 7.5 rather than 5. On the supply side, it can be seen from Figure 2 that our original parameter settings imply a long-run elasticity of supply of illegal labor to the United States of 1.64 (a reduction in real wage rates of 17.4% is consistent with a reduction in illegal employment of 28.6%). For our sensitivity analysis, we reset the parameters in the utility functions of potential and actual illegal workers so that their long-run supply elasticity is about 1.28.

We continue to scale the shocks so that the long-run reduction in illegal employment is 28.6%. With the reduction in illegal employment unchanged, there is no prior reason to expect factors F2 to F6 to be sensitive to a change in either the demand or supply elasticity. This is borne out in Tables 6 and 7.

The effects of changes in the elasticities on the Direct effect (F1) can be understood by redrawing Figures 1 and 2 with hatter demand curves and steeper supply curves. In Figure 1, the negative Direct effect, represented by area (abfg) is reduced when the demand curve is flatter but unchanged when the supply curve is steeper. Consequently, in the SR results in Tables 6 and 7, F1 is noticeably less negative with an increase in demand elasticity but shows almost no sensitivity to a change in supply elasticity. In Figure 2, the positive Direct effect, represented by area(fehj) minus area(abe), is increased when DD is flattened: the negative triangle becomes smaller without affecting the positive rectangle. When SS is steepened, the positive rectangle becomes larger without affecting the negative triangle. Consistent with these geometric explanations, Tables 6 and 7 show that F1 in the DR1 simulation increases as we increase the demand elasticity and reduce the supply elasticity.

Reassuringly, Tables 6 and 7 show that our main conclusions are robust with respect to quite large changes in demand and supply elasticities.


Our main conclusion is that policies to limit employment of illegal immigrants should have a significant focus on taxing and fining employers. Any limitation policy will raise costs to the employers of those illegal immigrants who remain in the United States. However, policies differ with respect to the nature of these extra costs. In the case of taxes and fines, the extra costs are a transfer to the U.S. Treasury which is then able to improve the welfare of legal residents through tax cuts or increased public spending. In the case of discouragement of entry via tighter security and deportations, the extra costs are generated by an increase in the wage rates of the remaining illegal immigrants, with little benefit to the legal residents. In the case of prosecutions, the extra costs are likely to be a dissipation of real resources through the use of lawyers and other professionals involved in defending charges and mitigating their effects.

In generating our results we used a large-scale, economy-wide model. This has both advantages and disadvantages. On the advantage side, a well-designed, large-scale model can reveal and quantify effects that were previously unanticipated. Examples in our analysis of reducing illegal employment include: the dominance in the determination of industry outcomes of macro effects rather than dependence on illegal employment; and the importance of the occupation-mix effect. On the disadvantage side, the creation and application of large-scale, economy-wide models require detailed theoretical specifications, many pages of computer code and years of work on industry, trade and occupational data. It is not practical for consumers of analyses from large-scale models to be familiar with all their aspects. Fortunately, this is not necessary. As illustrated in this paper, even complex economy-wide analyses can be elucidated by back-of-the-envelope calculations. These calculations highlight the assumptions, data items and parameter values that are important in determining the principal results. In this paper, we used back-of-the-envelope calculations to quantify the six factors that explain USAGE results for the effect on the welfare of legal residents of policies to limit illegal employment.

Dixon: Sir John Monash Distinguished Professor, Centre of Policy Studies, Monash University, Clayton, Victoria 3800, Australia. Phone 61 3 9905 5464, Fax 61 3 9905 2426, E-mail

Johnson: U.S. Department of Commerce, 1402 Constitution Avenue, Washington DC, 20230. Phone 1 202 482 2000, Fax 1 202 482 1790, Email:

Rimmer: Centre of Policy Studies, Monash University, Clayton, Victoria 3800, Australia. Phone 61 3 9905 54640, Fax 61 3 9905 2426, Email:

(1.) See, for example, Hanson and McIntosh (2007).

(2.) See, for example, the survey of Borjas (1994).

(3.) See, for example, Borjas (2003), Card (2005), Ottaviano and Peri (2006), and Borjas et al. (2008).

(4.) In surveying research on illegal immigration to the United States, Hanson (2006, p. 872) devotes most space to determinants of flows not consequences because: " ... there is little research on specific aspects of these consequences that are attributable to illegal immigration."

(5.) See for example Kossoudji (1992) and Hanson and Spilimbergo (1999).

(6.) See Rector and Kim (2007).


GDP: Gross Domestic Product

SR: Supply Restriction

(7.) The source for this estimate was Rector and Kim (2007). They provide detailed estimates by function (education, health, etc.) of government expenditures on households headed by low-skilled immigrants. We used these estimates as a starting point for calculating government expenditures on households headed by illegal immigrants. In doing this, we recognized that not all government services available to legal immigrants are available to illegal immigrants.

(8.) The shocks were implemented as changes in the coefficients of the utility functions of people in the 51st occupation.

(9.) Our model has no formal steady state but as can be seen from the charts the policy-induced deviations in most variables have settled down reasonably well by 2019, 12 years out from the policy shocks. We interpret these 12-year results as long-run or sustainable effects.

(10.) There is also a small loss of legal jobs (Section III.C).

(11.) In the basecase for 2019, illegal immigrants account for 3.7% of public consumption but only 2.4% of private consumption.

(12.) In the database for the initial year, 2005, we set wage rates for legal and illegal immigrants in any occupation at 0.9 and 0.8 times those of native workers. Support for the 0.9 is provided by Rector and Kim (2007, Table 2, p. 11). The 0.8 is an assumption.

(13.) Borjas et al. (2008) argue that Ottaviano and Peri have underestimated the native/immigrant substitution elasticity. In sensitivity analysis (Section V.C.), we investigate in the direction of higher substitution elasticities. Further discussion of the substitution elasticities is in Dixon and Rimmer (2008).

(14.) This is a weighted average of percentage deviations in real occupational wage rates. The weights are occupational shares in the 2019 basecase. The real wage rate per job for legal workers falls because of an unfavorable shift in the occupational composition of legal employment (Section III.D).

(15.) As mentioned in Section III.C. the illegal wage deflated by consumer prices rises by 9.5%. The wage referred to in Figure 1 is deflated by the price deflator for GDP.

(16.) We used Rector and Kim (2007) to calculate that the tax rate on the income of low-skilled on-the-books immigrants is 21.72%. Rector and Kim estimate that 45% of illegal immigrants are off the books. On this basis, the average tax rate applying to the income of illegal immigrants is 11.95% (=21.72 * 0.55).

(17.) As indicated in footnote 7, we made judgments concerning the availability of public services to illegal immigrants. The 0.71 corresponds to the most generous possible interpretation of this availability.


Borjas, G. J. "The Economics of Immigration." Journal of Economic Literature, 32, 1994, 1667-717.

--"The Labor Demand Curve Is Downward Sloping: Re-examining the Impact of Immigration on the Labor Market." Quarterly Journal of Economics, 118(4), 2003, 1335-74.

Borjas, G. J., J. Grogger, and G. Hanson. "Imperfect Substitution between Immigrants and Natives: A Reappraisal." NBER Working Paper 13887, Cambridge, MA, 2008, 40. Accessed 12 April 2010. 13887.

Card, D. "Is the New Immigration Really So Bad?" Economic Journal, 115(507), 2005, F300-F323.

Dixon, P. B., and M. T. Rimmer. Dynamic General Equilibrium Modelling for Forecasting and Policy. Amsterdam: North-Holland, 2002.

--"The USAGE Labor-market Extension for the Study of Illegal Immigration." Centre of Policy Studies, Monash University, 2008, 17. Accessed 12 April 2010.

Hanson, G. H. "Illegal Migration from Mexico to the United States." Journal of Economic Literature, 44, 2006, 869-924.

Hanson, G. H., and C. McIntosh. "The Great Mexican Emigration." NBER Working Paper 13675, Cambridge MA, 2007, 55. Accessed 12 April 2010.

Hanson, G. H., and A. Spilimbergo. "Illegal Immigration, Border Enforcement and Relative Wages: Evidence from Apprehensions at the US-Mexico Border." American Economic Review, 89(5), 1999, 1337-57.

Kossoudji, S. "Playing Cat and Mouse at the Mexican-American Border." Demography, 29(2), 1992, 159-80.

Ottaviano, G. I. P., and G. Peri. "Rethinking the Effects of Immigration on Wages." NBER Working Paper 12497, Cambridge, MA, 2006, 53. Accessed 12 April 2010. 12497.

Rector, R., and C. Kim. "The Fiscal Cost of Low-Skill Immigrants to the U.S. Taxpayer." Heritage Special Report SR-14, Heritage Foundation, Washington, DC, 2007, 70. Accessed 12 April 2010. hlip://

doi: 10.1111/j.1465-7287.2010.00208.x

[c] 2010 Western Economic Association International
Occupational Data for 2005 and SR Deviation Results for 29

                         Illegal           % Deviation in 2019
                         % of Labor Costs  Legal Jobs (2)  Legal Wage
                         in 2005                           (3)

1. Cook                              15.6            4.20        1.89

2. Grounds maintenance               24.8            7.45        3.19

3. House keeping and                 22.0            6.56        2.82

4. Janitor and building              10.4            2.31        1.19

5. Misc. agriculture                 34.3           10.70         455

6. Construction laborer              23.9            7.10        3.16

7. Transport packer                  24.6            7.37        3.19

8. Carpenter                         15.1            3.90        1.92

9. Transport laborer                  7.2            1.09        0.71

10. Cashier                           4.7            0.31        0.43

11. Food serving                      6.4            0.88        0.62

12. Transport driver                  4.0           -0.09        0.25

13. Waiter                            5.7            0.64        0.53

14. Production, misc.                 8.3            1.07        0.72

15. Food prep. worker                13.3            3.42        1.61

16. Painter                          24.9            7.46        3.31

17. Dishwasher                       22.7            6.83        2.86

18. Construction,                    24.8            7.42        3.30

19. Retail sales                      2.4           -0.50        0.11

20. Production, helper               20.4            5.54        2.52

21. Packing machine                  23.6            6.88        3.01

22. Butcher                          21.0            6.20        2.74

23. Stock clerk                       4.6            0.26        0.40

24. Child care                        5.2            0.56        0.51

25. Misc. food prep.                 14.5            3.80        1.74

26. Dry wall installer               35.8           11.43        4.87

27. Nursing                           2.8           -0.01        0.29

28. Industrial truck                  8.5            1.47        0.87

29. Transport, cleaner               15.8            4.24        1.93

30. Automotive repairs                6.3            0.88        0.64

31. Sew. Machine oper.               18.8            4.95        2.39

32. Concrete mason                   22.6            6.61        3.00

33. Roofer                           28.2            8.64        3.78

34. Plumber                           7.1            1.07        0.80

35. Personal care                     5.7            0.91        0.66

36. Shipping clerk                    5.2            0.35        0.43

37. Brick mason                      22.5            6.56        2.97

38. Carpet installer                 21.4            6.21        2.82

39. Laundry                          15.5            4.22        1.93

40. Other production                  9.1            1.57        0.91

41. Maintenance and                   2.2           -0.71       -0.01

42. Repair, helper                   16.8            4.56        2.09

43. Welder                            6.2            0.31        0.41

44. Supervisor, food                  3.4           -0.20        0.22

45. Construction                      3.4           -0.27        0.27

46. Farm-food -clean,                 6.1            0.61         053

47. Construction, other               5.5            0.38        0.49

48. Production, other                 4.6          -0. 11        0.21

49. Transport, other                  3.2           -0.40        0.13

50. Services, other                   0.4           -1.27       -0.13

Total                                 2.6           -0.16         0.3

Data for 2005 on Labor Costs as Percentages of Industry Costs and
Percentage Deviation Results for 2019 in Simulation SR

Percentage Shares in Costs, 2005
SR Simulation: Percent Deviations in 2019

Industry           Legal  Illegal  Price  Output  Labor   Jobs (6)
                   (1)    (2)      (3)    (4)     Input

1. Agriculture     14.18     0.59   0.09   -2.10   -2.17     -3.26

2. Ground          50.44     4.08   0.85   -1.71   -2.19     -3.70

3. Mining          23.51     0.55  -0.10   -2.18   -2.23     -2.61

4. Construction    45.99     4.51   1.20   -2.14   -2.12     -3.30

5. Dairy and       10.88     0.76   0.22   -1.57   -1.60     -2.62
sugar manu.

6. Other food      15.43     1.39   0.30   -1.81   -1.94     -3.18

7. Tobacco         16.20     0.89   0.14   -1.54   -1.61     -2.58

8. Apparel         14.12     1.34   0.25   -1.95   -2.00     -3.32

9. Textiles        21.90     1.50   0.08   -2.38   -2.69     -3.67

10. Wood and       28.26     1.82   0.30   -2.71   -2.72     -3.57

11. Paper and      32.07     0.76   0.07   -2.10   -2.11     -2.58

12. Chemicals      20.71     0.53  -0.33   -2.02   -3.71     -4.22

13. Petroleum       4.38     0.10  -0.60   -1.53   -1.04     -1.43

14. Footwear       20.05     1.45  -0.12   -2.14   -2.18     -3.17

15. Metal          26.87     1.00   0.03   -2.87   -2.94     -3.52

16. Machinery      31.61     0.76  -0.01   -2.97   -3.01     -3.43

17. Computers       7.33     0.07   0.05   -1.69   -1.62     -1.84

18. Electrical     21.40     0.49  -0.05   -2.63   -2.71     -3.15

19. Motor          16.59     0.71  -0.09   -2.60   -2.61     -3.25

20. Transport      27.79     0.60   0.00   -2.25   -2.23     -2.64

21.                30.82     1.08   0.09   -2.54   -2.61     -3.21

22. Communication  17.77     0.15   0.20   -1.52   -1.50     -1.63

23. Utilities       9.87     0.27   0.07   -1.19   -0.94     -1.38

24. Wholesale      45.76     1.17   0.27   -1.54   -1.51     -2.01
and retail

25. Housing        10.26     0.15   0.94   -1.68   -1.54     -1.92

26. Business       52.78     0.29   0.09   -1.65   -1.62     -1.73
and fin. serv.

27. Medical        60.52     0.45   0.19   -1.05   -0.90     -1.15

28. Education      51.15     0.24   0.18   -1.58   -1.50     -1.64

29. Social         57.24     0.79   0.22   -1.17   -1.16     -1.57

30. Govt.          32.92     0.32   0.23   -1.24   -1.26     -1.49

31. Other          53.11     1.98   0.37   -1.63  - 1.68     -2.48

32. Govt.          53.33     0.36   0.25   -1.54   -1.52     -1.72

33. Holiday            0        0   0.23   -1.48      --        --

34. Foreign            0        0  -0.82    1.68      --        --
holiday (a)

35. Export             0        0  -0.11   -2.64      --        --
tourism (a)

36. Other              0        0   0.18   -1.46      --        --

37. Transport      32.00     0.89   0.09   -1.93   -1.95     -2.43

38. Auto           24.12     0.84   0.04   -1.70   -1.69     -2.28

Total              37.31     1.00   0.00           -1.66     -2.19

(a) In USAGE, the Holiday industry is a collection of inputs such
as hotels and airline travel that are used by U.S. residents when
they lake a holiday in the United States. Foreign holiday is a
collection of inputs such as airline travel and shopping in
foreign countries that are used by U.S. residents when they take
a holiday outside the United States. Export tourism is a collection
of inputs used by foreign tourists when they take a holiday in the
United States. Other nonresident is a collection of inputs purchased
in the United Stales by diplomats, World-Bank officials, and so forth.
None of these artificial industries employ people directly.

Long-run (2019) Percentage Effects on Consumption of Legal Residents

                           SR Simulation  DR1          DR2
                           (1)            Simulation   Simulation
                                          (2)          (3)

F1  Direct effect                  -0.29         0.12        -0.35

F2  Occupation-mix effect          -0.31        -0.31        -0.22

F3  Capital effect                 -0.24        -0.18        -0.26

F4  Legal-employment               -0.11        -0.10        -0.08

F5  Public-expenditure              0.17         0.17         0.17

F6  Macro-price effect              0.23         0.25         0.29

    Back-of-me-envelope      -0.55 -0.52  -0.05 -0.08  -0.45 -0.47
    totals USAGE result

SR Simulation: Why Does Consumption of Legal Residents Decline by
0.52% in the Long Run?

     Basecase Data     $Million or                        % Deviation
     for 2019          Fraction                           in 2019

L1   GDP                25.600,320  P1   Illegal             -28.5737

L2   Private            17,881,766  P2   Illegal wage          9.2310
     consumption                         rate, cost to

L3   Public              4,222,785  P3   Illegal wage          9.2310
     consumption                         rate,

L4   Investment          5,437,971  P4   Occupation-mix       -0.4647
                                         effect on
                                         average hourly
                                         wage rate of
                                         legal workers

L5   Aggregate           3,901,019  P5   Capital stock        -1.7134

L6   Aggregate           5,843,221  P6   Price deflator             0
     imports                             for GDP

L7   Returns to          6,536,265  P7   Price deflator       -0.2808
     capital                             for private

L8   Aggregate          17,941,636  P8   Price deflator       -0.0173
     wagebill                            for public

L9   Indirect taxes      1,122,420  P9   Price deflator        0.1482
                                         for investment

L10  Indirect tax           0.0438  P10  Price deflator       -0.3501
     rate on U.S.                        for exports
     output (T1)

L11  Tax rate on            0.1195  P11  Price deflator       -1.1456
     illegal income                      for imports

L12  Tax rate on            0.1476  P12  Price deflator       -0.2305
     capital income                      for consumption
                                         (priv. & pub.)

L13  Share of U.S.          0.8013  P13  Price deflator       -0.1557
     capital                             lor GNE

L14  Share of illegal       0.0364  P14  Terms of trade        0.7955
     workers in

L15  Share of illegal       0.0370  P15  U.S. capital         -0.9428
     people in public                    domestically
     consumption                         owned

L16  GNP for legal      26,210,266  P16  U.S. capital         -4.8202
     residents                           foreign owned

                                    P17  Employment of        -0.1553
                                         legal workers

    Six Factors Explaining the Long-run        $Million  Percent of
    Reduction in Consumption by Legal                    GNP for
    Residents                                            Legal

F1  Direct effect: -(P2/100) * L14 * L8 * (2    -77,305       -0.29
    + Pl/100)/2 + L10 * L14 * L8 * (P1/100) *
    (2 + P2/100)/2 + L11 * {L8 * L14 *
    (P1/I00) + (P2/100) * L14 * L8 * (1 +

F2  Occupation-mix effect: (P4/100) * L8 *      -80,343       -0.31

F3  Capital effect: L7 * L13 * (P15/100) + L7   -63,524       -0.24
    * (1-L13) * (P16/100) * L12 + L7 * L10 *

F4  Legal-employment effect: (PI7/100) * L8 *   -28,027       -0.11
    (1- LI4) * (1 + L10)

F5  Public-expenditure effect: - L15 * L3 *      44,617        0.17

F6  Macro-price effect: L16 * (P6 - PI2)/100     60,405        0.23

    Back-of-the-envelope totals                 -144178       -0.55

    USAGE result                                              -0.52

Varying the Public Expenditure Assumption: SR Percentage Deviation
Results for Consumption by Legal Households in 2019

                                        Public Consumption Ratio

                                               0.49         0.71
F1  Direct effect                             -0.29        -0.29
F2  Occupation-mix effect                     -0.31        -0.31
F3  Capital effect                            -0.24        -0.24
F4  Legal-employment effect                   -0.11        -0.10
F5  Public-expenditure effect                  0.17         0.24
F6  Macro-price effect                         0.23         0.23
    Back-of-the-envelope totals               -0.55        -0.48
    USAGE result                              -0.52        -0.43

Note: In the first column of results, we adopt our standard assumption
that public sector consumption undertaken on behalf of" people in
illegal-immigrant households is 0.49 times as much per person as
public sector consumption undertaken on behalf of people in legal
households. In the second column, we reset this ratio to 0.71.


Varying the Legal/Illegal Substitution Elasticity: Percentage
Deviation Results for Consumption by Legal Households in 2019

                                     SR Simulation     DR1 Simulation

    Legal/Illegal Substitution         5.0    7.5         5.0    7.5

F1  Direct effect                    -0.29  -0.25        0.12   0.14

F2  Occupation-mix effect            -0.31  -0.32       -0.31  -0.32

F3  Capital effect                   -0.24  -0.24       -0.18  -0.18

F4  Legal-employment effect          -0.11  -0.11       -0.10  -0.10

F5  Public-expenditure effect         0.17   0.17        0.17   0.17

F6  Macro-price effect                0.23   0.23        0.25   0.25

    Back-of-the-envelope             -0.55  -0.52       -0.05  -0.05

    USAGE result                     -0.52  -0.50       -0.08  -0.08


Varying the Supply Elasticity for Illegal Immigrants: Percentage
Deviation Results for Consumption by Legal Households in 2019

                                     SR Simulation     DR1 Simulation

    Elasticity of Supply              1.64   1.28        1.64   1.28
    Illegal Immigrants to U.S.

F1  Direct effect                    -0.29  -0.30        0.12   0.20

F2  Occupation-mix effect            -0.31  -0.30       -0.31  -0.30

F3  Capital effect                   -0.24  -0.24       -0.18  -0.17

F4  Legal-employment effect          -0.11  -0.10       -0.10  -0.09

F5  Public-expenditure effect         0.17   0.17        0.17   0.17

F6  Macro-price effect                0.23   0.24        0.25   0.26

    Bark-of-the-envelope             -0.55  -0.54       -0.05   0.07

    USAGE result                     -0.52  -0.51       -0.08   0.02
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