IRCC Operational Data – Occupations – Shift toward lower skilled

2023/02/28 Leave a comment

Given a number of public discussions and advocacy for a permanent residency stream for lower-skilled workers, given labour market pressures, exploitation of Temporary Foreign Workers and the bias/preference of immigration programs towards the higher skilled (justified IMO), I spent some time looking at IRCC’s operational data: Canada – Admissions of Permanent Residents 15 years of age or older by Province/Territory and Intended Occupation (4-Digit NOC 2011), January 2015 – December 2022 .

Taking out the “other” and “occupation not stated” categories, about two-thirds of all data, the data shows a shift over this period towards lower skilled: the percentage of highest skilled (NOC A – Occupations usually require university education) had fallen from 50.6% to 37.2%.

The other skilled category, NOC B – Occupations usually require college education, specialized training or apprenticeship training, increased from 34.9% to 38.8%, with more dramatic increases in lower skilled. NOC C – Occupations usually require secondary school and/or occupation-specific training, rose from 7.6% to 15.5%, and NOC D – On-the-job training is usually provided for occupations, rose from 6.0% to 8.1%.

The table below provides the annual details.

20152016201720182019202020212022
A (0, 1)31,08528,66531,40541,13544,29024,62039,92542,815
B (2, 321,43519,90524,29027,50532,18519,85580,27044,680
C (4,5)4,6404,0003,9355,6007,6953,03014,92017,905
D (6,7)3,7003,8254,1955,0305,5353,31511,5059,385
Other152,280166,815164,100175,735180,77597,040188,830229,450
Occupation not stated451003520220150240345
Total stated occupations 61,37556,87064,15579,58590,23051,065146,955115,225
Total213,700223,785228,290255,340271,225148,255336,025345,020
A (0, 1)14.5%12.8%13.8%16.1%16.3%16.6%11.9%12.4%
B (2, 310.0%8.9%10.6%10.8%11.9%13.4%23.9%12.9%
C (4,5)2.2%1.8%1.7%2.2%2.8%2.0%4.4%5.2%
D (6,7)1.7%1.7%1.8%2.0%2.0%2.2%3.4%2.7%
Other71.3%74.5%71.9%68.8%66.7%65.5%56.2%66.5%
Occupation not stated0.0%0.0%0.0%0.0%0.1%0.1%0.1%0.1%
Percentage of stated occupations
A (0, 1)50.6%50.4%49.0%51.7%49.1%48.2%27.2%37.2%
B (2, 334.9%35.0%37.9%34.6%35.7%38.9%54.6%38.8%
C (4,5)7.6%7.0%6.1%7.0%8.5%5.9%10.2%15.5%
D (6,7)6.0%6.7%6.5%6.3%6.1%6.5%7.8%8.1%
A, B85.6%85.4%86.8%86.2%84.8%87.1%81.8%75.9%
C, D13.6%13.8%12.7%13.4%14.7%12.4%18.0%23.7%
IRCC Immigration Occupational Codes Summary

8 of the top 10 occupations that increased the most over this period were NOC C and D, all of which increased by 1,000 percent or more:

4412 – Home support workers, housekeepers and related occupations
6541 – Security guards and related security service occupations
6622 – Store shelf stockers, clerks and order fillers
6611 – Cashiers
0601 – Corporate sales managers
3237 – Other technical occupations in therapy and assessment
7247 – Cable television service and maintenance technicians
9461 – Process control and machine operators, food, beverage and associated products processing
7514 – Delivery and courier service drivers
6623 – Other sales related occupations
Top 10 Immigration Occupations

Another interesting aspect of the data is the relative lack of variation between the various occupational codes as shown in the following table with the last column showing the change 2015 to 2022. For all occupations, the share of NOC A decreases by an average of 12.4 percent or more, with the share of NOC C increasing by an average of 8.2 percent:

2015201620172018201920202021202220152016201720182019202020212022Change 22-15
0 – ManagementA (0, 1)26,32524,32025,97534,85538,05021,50036,05037,67566.0%66.4%65.4%67.1%64.7%64.4%42.1%53.1%-12.9%
B (2, 38,3906,9707,9959,60012,1257,21032,56518,61521.0%19.0%20.1%18.5%20.6%21.6%38.0%26.3%5.2%
C (4,5)1,6451,6351,6402,6053,4251,4457,0008,1004.1%4.5%4.1%5.0%5.8%4.3%8.2%11.4%7.3%
D (6,7)3,5003,7254,0854,9005,2053,21510,1006,5008.8%10.2%10.3%9.4%8.9%9.6%11.8%9.2%0.4%
Total39,86036,65039,69551,96058,80533,37085,71570,890100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
1 – Business & AdminA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80063.9%64.4%64.3%66.9%64.7%64.4%41.0%52.2%-11.8%
B (2, 310,0058,65510,27512,19515,0659,28040,77522,74024.9%23.7%26.2%24.0%26.2%28.5%47.5%32.2%7.3%
C (4,5)2,3102,1652,0552,9453,6451,5107,3158,4355.8%5.9%5.2%5.8%6.3%4.6%8.5%12.0%6.2%
D (6,7)2,1602,1651,6501,6651,5958002,5452,5755.4%5.9%4.2%3.3%2.8%2.5%3.0%3.6%-1.7%
Total40,13036,52039,19550,83057,50032,54085,80570,550100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
2 – Sciences A (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80063.1%63.0%63.1%66.6%64.5%63.9%40.6%52.6%-10.5%
B (2, 311,26010,04011,52012,99515,6459,65042,08522,66527.7%26.9%28.8%25.4%27.1%29.4%48.5%32.4%4.7%
C (4,5)1,6051,5901,5602,4403,2451,3756,9057,9503.9%4.3%3.9%4.8%5.6%4.2%8.0%11.4%7.4%
D (6,7)2,1602,1651,6501,6651,5958002,5452,5755.3%5.8%4.1%3.3%2.8%2.4%2.9%3.7%-1.6%
Total40,68037,33039,94551,12557,68032,77586,70569,990100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
3 – HealthA (0, 1)27,46525,47027,90536,65539,04521,69535,99038,15068.3%69.7%71.2%73.5%70.3%70.5%45.3%55.4%-13.0%
B (2, 38,6907,0557,8308,85511,3306,75532,55518,19021.6%19.3%20.0%17.8%20.4%22.0%41.0%26.4%4.8%
C (4,5)1,8701,8701,7802,6953,5451,5058,3109,9754.7%5.1%4.5%5.4%6.4%4.9%10.5%14.5%9.8%
D (6,7)2,1602,1651,6501,6651,5958002,5452,5755.4%5.9%4.2%3.3%2.9%2.6%3.2%3.7%-1.6%
Total40,18536,56039,16549,87055,51530,75579,40068,890100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
4 – Education  & GovtA (0, 1)28,60525,94527,95537,67541,58523,32538,22540,59068.6%68.8%70.0%72.4%68.5%69.3%44.6%54.7%-13.9%
B (2, 38,4457,3107,9609,22512,1357,28034,52019,35520.2%19.4%19.9%17.7%20.0%21.6%40.2%26.1%5.8%
C (4,5)2,5052,2652,3953,5055,4202,25510,50011,7006.0%6.0%6.0%6.7%8.9%6.7%12.2%15.8%9.8%
D (6,7)2,1602,1651,6501,6651,5958002,5452,5755.2%5.7%4.1%3.2%2.6%2.4%3.0%3.5%-1.7%
Total41,71537,68539,96052,07060,73533,66085,79074,220100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
5 – Arts culture & SportA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80067.5%67.8%68.3%71.1%68.4%68.3%44.9%55.7%-11.8%
B (2, 38,5957,4308,5159,75512,3257,53033,75018,73522.6%21.4%23.1%20.4%22.7%24.6%43.1%28.4%5.8%
C (4,5)1,6051,5901,5602,4403,2451,3756,9057,9504.2%4.6%4.2%5.1%6.0%4.5%8.8%12.0%7.8%
D (6,7)2,1602,1651,6501,6651,5958002,5452,5755.7%6.2%4.5%3.5%2.9%2.6%3.2%3.9%-1.8%
Total38,01534,72036,94047,88554,36030,65578,37066,060100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
6 – Sales and serviceA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80065.1%66.1%65.4%67.6%64.7%64.6%40.2%50.3%-14.8%
B (2, 39,3057,9759,76511,67514,7709,05040,97022,18523.6%22.4%25.3%23.2%25.7%27.9%46.8%30.3%6.7%
C (4,5)2,1151,8801,8652,8703,7051,5407,7459,1655.4%5.3%4.8%5.7%6.4%4.8%8.8%12.5%7.2%
D (6,7)2,3102,2151,7351,7701,8608753,6904,9655.9%6.2%4.5%3.5%3.2%2.7%4.2%6.8%0.9%
Total39,38535,60538,58050,34057,53032,41587,57573,115100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
7 – Trades, transport and equipment operatorsA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80064.3%65.8%67.2%70.8%67.9%68.7%42.3%53.4%-10.9%
B (2, 310,3658,3459,0059,86512,6457,31537,91020,41026.0%23.3%24.0%20.5%23.1%24.0%45.6%29.6%3.6%
C (4,5)1,7051,7051,6002,4853,2901,3907,4408,9004.3%4.8%4.3%5.2%6.0%4.6%8.9%12.9%8.6%
D (6,7)2,1852,2001,6751,6851,6358202,6752,8205.5%6.1%4.5%3.5%3.0%2.7%3.2%4.1%-1.4%
Total39,91035,78537,49548,06054,76530,47583,19568,930100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
8 – Natural resources, agricultureA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80068.2%68.8%70.2%72.7%69.7%70.2%46.0%56.3%-11.9%
B (2, 38,0806,8657,4208,54011,1756,67531,67517,62521.5%20.1%20.6%18.2%20.9%22.4%41.4%27.0%5.5%
C (4,5)1,7401,6651,6552,6003,4001,4107,0708,2854.6%4.9%4.6%5.6%6.4%4.7%9.2%12.7%8.1%
D (6,7)2,1602,1651,6501,6651,6008002,5852,6455.7%6.3%4.6%3.6%3.0%2.7%3.4%4.0%-1.7%
Total37,63534,23035,94046,83053,37029,83576,50065,355100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
9 – Manufacturing and utilitiesA (0, 1)25,65523,53525,21534,02537,19520,95035,17036,80068.1%69.0%70.0%72.3%69.2%70.0%45.3%55.7%-12.4%
B (2, 37,8256,4557,2758,37510,9556,57532,01517,47520.8%18.9%20.2%17.8%20.4%22.0%41.2%26.5%5.7%
C (4,5)1,9851,9451,8652,9753,9801,6007,8758,9955.3%5.7%5.2%6.3%7.4%5.3%10.1%13.6%8.4%
D (6,7)2,1852,1801,6501,6701,6158052,6352,7555.8%6.4%4.6%3.5%3.0%2.7%3.4%4.2%-1.6%
Total37,65034,11536,00547,04553,74529,93077,69566,025100.0%100.0%100.0%100.0%100.0%100.0%100.0%100.0%
Immigration NOC Codes breakdown by skill level and occupation.

Unfortunately, WordPress tables do not allow table formatting so if interested, will send the spreadsheets on request. Pdf below:

immigration-noc-codes-en_odp-pr-provnoc4Download

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Mahboubi, Skuterud – The Unintended Consequences of Category-Based Immigrant Selection

2023/02/10 Leave a comment

Valid critique:

From: Parisa Mahboubi and Mikal Skuterud

To: Sean Fraser, Minister of Immigration, Refugees and Citizenship Canada

Date:  February 6, 2023

Re: The Unintended Consequences of Category-Based Immigrant Selection

Immigration, Refugees, and Citizenship Canada (IRCC) recently held consultations on plans aimed at giving the department more flexibility in how it prioritizes economic-class applicants for permanent residency.

The new rules will, in effect, free the immigration minister to bypass the existing system for selecting candidates, known as the Comprehensive Ranking System (CRS), to target applicants with particular “attributes” such as work experience in a particular occupation.

This may alleviate some labour shortages, but we see significant unintended consequences.

Leveraging immigration to boost average living standards in the population requires selecting immigrants whose Canadian earnings exceed average earnings in the pre-existing population, thereby pulling up average incomes and per capita GDP.

The CRS aims to achieve this by ranking and cream-skimming economic class candidates who have the highest expected Canadian earnings. This is estimated using data on the earnings of previous cohorts of immigrants who arrived with similar human capital characteristics. Of particular importance in the CRS calculation are education, age, language abilities, and Canadian work experience.    

Recent analysis using Statistics Canada survey and census data, as well as our own examination of immigrants’ income tax records (see Figure below,) provides encouraging evidence that the CRS has contributed to rising earnings for newcomers since its launch in January 2015.  

By prioritizing applicants’ occupations, IRCC hopes it can be more responsive to employer needs, as well as address Canada’s chronic labour shortages.

But accurately identifying labour market requirements and being sufficiently responsive is difficult, if not impossible. Tight labour markets can quickly become slack. By the time targeted immigrants arrive, their skills may no longer align with employer needs, thereby exacerbating long-standing mismatch issues between immigrant skills and job openings. For this reason, the CRS does not use specific occupational information in its calculation.

The raison d’être of the Temporary Foreign Worker Program, which allows Canadian businesses to employ guest workers on limited-term contracts, is to meet temporary labour-market shortages. The objective of our permanent immigration system, on the other hand, should be to drive new employment growth in high-productivity sectors that are intensive in their use of skills and new technologies.

Unfortunately, we increasingly have a system where our temporary and permanent immigration systems are focused on the same objective – satisfying employers’ current labour needs. The risk is that the overall immigration system fails to do anything well.

An important advantage of the CRS is its transparency. Candidates can determine their own scores using a simple online tool and IRCC reports cutoff scores in their bi-weekly draws allowing unsuccessful candidates to identify what’s needed to be selected. The category-based selection system that IRCC is proposing compromises this transparency by leaving screening criteria to the whims of the minister of the day. This risks increasing applicant confusion and frustration and increases the need for immigration consultants and lawyers to help applicants navigate the system. At worst, it drives applicants with the best outside options to other countries.

Allowing the ministers to determine which candidate attributes are prioritized also risks politicizing the process. Research shows that while temporary worker inflows in Canada are responsive to the intensity of corporate lobbying, the same has not been true for permanent immigration. One explanation is that ‘point systems’ like the CRS remove immigrant selection decision making from the political realm in the same way that the Bank of Canada’s inflation mandate keeps its interest rate decisions from being politicized. Look for that to change.  

In our view, prioritizing candidates’ occupational work experience in immigrant selection makes most sense in sectors where the competitive market mechanism to address labour shortages does not exist, such where wages are set by collective agreements or government regulation.

In these settings, labour shortages are less likely to induce the wage adjustments necessary to encourage job switching and training and education investments within the existing population. Chronic shortages of nurses and other healthcare workers are an important example.

Nonetheless, we question if it makes sense to prioritize applicants for permanent residency whose foreign work experience is in an occupation where credential recognition in Canada is problematic. It doesn’t really matter if credential recognition problems reflect genuine skill and competence issues, or the self-interested behaviour of professional associations. Either way, we are prioritizing applicants who will contribute relatively little to Canadian economic growth, thereby compromising the key objective of our economic immigration system.

In our view, IRCC’s planned reform of how it selects economic-class immigrants is just one step in a series of pandemic-era policies compromising the prioritization of skilled immigrants. The CRS has come to be seen by IRCC as a constraint rather than an effective quality-control mechanism. In prioritizing employers’ short-term labour needs, IRCC is being forced to lower the average CRS score of selected immigrants and, in turn, average expected earnings. The hard reality is that Canada’s newcomers continue to experience labour market challenges that are longstanding and exceptional. The risk is that the last decade’s significant gains will be undone.

Parisa Mahboubi is a senior policy analyst at the C.D. Howe Institute and Mikal Skuterud is professor of economics at the University of Waterloo. 

Source: Mahboubi, Skuterud – The Unintended Consequences of Category-Based Immigrant Selection

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Electoral candidates shouldn’t need white-collar backgrounds

2019/10/23 Leave a comment

Good piece by Mike Morden of Samara:

After the votes are counted tonight, 338 candidates will be headed to Ottawa to claim their seats as members of Parliament. The other 1500-plus candidates will be headed home. For some of them, that will mean coming to terms with a rough financial picture.

Running for office in a competitive campaign is very expensive. Serious candidates have to leave or quit their jobs, forgoing income for weeks or months. Some won’t have jobs to return to, if they weren’t fortunate in having flexible employers. The self-employed will have to make up for lost time and lost clients.

Drumming up sympathy for politicians is a difficult business. But it’s important to see the costs of standing for election, because those costs mean that few of us will ever be in a financial position to run — or to do so seriously. Our political class is drawn from those who have the means. The result is a form of underrepresentation in our national politics that often goes unnoticed or unchallenged. We need to find ways to make running for office more accessible.

The Samara Centre has been working with research partners and a team of volunteers to compile demographic profiles of all 2019 federal candidates in the major parties, based on information made public in candidates’ biographies. This data, which is not yet published, reveals the predicted underrepresentations — of women, Indigenous people and people of colour. But it also reflects class- and occupation-based underrepresentations. We can’t identify the income levels of candidates, of course, but we can make some inferences based on the information available to us.

For example, on the basis of publicly available information alone, it becomes clear that most candidates hold one or more university degrees; by comparison,  fewer than 30 percent of working-age Canadians have those credentials. Lawyers, entrepreneurs and private sector executives are well represented among candidates. So are office holders from other levels of government, and some middle-class professionals like teachers. But what about service workers in retail or hospitality? What about child care workers, or tradespeople? They’re largely absent from Canada’s political class.

None of this is remotely surprising. But it should bother us more than it does.

Education and income are strong predictors of Canadians’ attitudes toward political issues and of their general views of Canadian democracy. They are stronger predictors, in many cases, than the other identities we carry. There’s evidence that working-class politicians behave differently in office, that their life experiences inform different priorities. Our white-collar parties and Parliament make substantively different decisions than they would with a more economically diverse membership. And working-class Canadians don’t see themselves reflected in their leaders, strengthening the existing tendency toward greater political dissatisfaction and distrust.

These demographic absences are reflected in how politics is done, and for whom. Indeed, the lack of a lived experience of the working class is apparent in the political discourse today, which has become peculiarly conscious of just a single class: the middle class (whoever that is). It’s also reflected in the woolly notions held by political elites about what a working-class Canadian is in 2019 (it almost always involves a hard hat).

Much of the responsibility for recruiting a more diverse candidate slate falls to the parties. But fixing economic underrepresentation, deliberately and through policy, is not easy. It involves wrestling with social and economic structures that are pervasive and deeply entrenched — beyond the reach of most available political reforms.

Nevertheless, we can think creatively about policy avenues to make political candidacy more affordable and more accessible. We can start by replacing some of the income that is lost when someone seeks office. Employment insurance provides income support for people who are unexpectedly unemployed. But it is also a tool to replace income for people who have to step away from work temporarily, to do something that is personally costly but beneficial to society — like raising a baby or caring for a sick family member. This logic can be applied to political candidacy.

The federal government should consider a new carve-out in the Employment Insurance Act, to allow registered (non-incumbent) candidates for federal, provincial and municipal elections, if they are otherwise eligible for EI, to collect it for a limited period (say, for a maximum of 50 days, which is also the maximum length of a federal campaign). Right now, candidates aren’t formally disqualified from collecting EI. But they have to be available for work and job-searching in the usual ways while collecting the benefit. Anyone who is truly campaigning full-time, with the goal of actually winning and holding office, is essentially ruled out.

This should be changed. There would be some potential for abuse, but that’s no different from the conventional uses of EI. In fact, when it becomes necessary, distinguishing between real and fake candidates would be, relatively speaking, easier to adjudicate.

It’s really important that good people put their hands up to run in our elections. It’s really important that those people aren’t only the relatively wealthy. Replacing candidates’ income is a small change. Obviously, it wouldn’t be enough to overcome the huge structural obstacles facing working-class Canadians: precarious employment, lack of time and a want of political resources like personal access and fundraising networks, to name a few. The take-up would likely be small. And it may prove that more targeted measures are needed to move the needle on working-class representation.

But it’s a simple policy step to help relieve the immediate financial costs of candidacy. It would also send a message to some of the people who most need to hear it: that whatever the political class looks like today, it’s supposed to be of you, and for you — and, in fact, it needs you.

Source: Electoral candidates shouldn’t need white-collar backgrounds

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ICYMI: Conservative MPs laugh at Amarjeet Sohi’s past as city bus driver

2017/02/22 Leave a comment

So ironic, if not hypocritical, as the Conservatives, in government or opposition, always take aim at “elites.”

(My recent analysis of Senate appointments confirmed that senators appointed by PM Trudeau have more “elite” backgrounds than those by former PM Harper – Diversity in the Senate – Policy Options):

A federal cabinet minister who learned to deal with the public while driving an Edmonton transit bus was laughed at this week in the House of Commons, apparently for that very reason.

Amarjeet Sohi, the minister of infrastructure and Liberal MP for Edmonton Mill Woods, rose in the house Tuesday to speak about transportation.

He began his remarks by acknowledging that as a former transit driver he was especially shocked to learn that a bus driver in Winnipeg had been stabbed to death earlier in the day.

“Mr. Speaker, as a former bus driver, I want to convey our thoughts and prayers,” Sohi said.

On the video recording of the proceedings of the House of Commons, loud laughter could be heard coming from the opposition benches.

Sohi’s colleagues on the Liberal side could be seen shaking their heads in disbelief.

“What I heard was laughter,” Sohi said Wednesday during an interview from Ottawa.

The former transit bus driver went on to serve two terms on Edmonton city council, before winning a seat as a Liberal member of Parliament in the 2015 federal election.

“I take pride in my background,” Sohi said.

“I think it does demonstrate a streak of elitist attitude in the Conservative party, where maybe they don’t appreciate we have working-class people in Parliament in the Liberal government who are making a difference in the lives of Canadians.”

Adam Vaughan, a Liberal MP from Toronto, raised a point of order in the House on Wednesday and asked that the laughter be “withdrawn,” which would strike it from the record.

“This is offensive to the values of this House, to the values of Canadians and the diversity of all of us,” Vaughan said.

But Conservative House leader Candice Bergen refused.

“There’s all kinds of laughter that occurs here,” Bergen responded in the House. “So we absolutely respect and honour all of the jobs that we’ve done, and the experience we bring to this house.”

Sohi said he wasn’t personally upset by the laughter, but he thought Bergen’s statement fell short of what was required.

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Why Are Immigrants More Likely to Concentrate in Certain Industries? – The Atlantic

2015/10/07 Leave a comment

Interesting case studies of the importance and impact of bonding capital within immigrant communities (enclaves) and link to occupations:

In the U.S. one might notice a curious concentration when it comes to jobs—certain ethnicities dominate certain industries. Greek immigrants are more likely to run restaurants than immigrants from other countries, and Koreans more likely to run dry-cleaning shops. Yemeni immigrants are 75 times more likely than immigrants of other ethnicities to own grocery stores, and Gujarati-speaking Indians are 108 times more likely to run motels.

Specialization among ethnic minorities, immigrant or not, isn’t new: It’s happened with Jewish merchants during Medieval times and with the Chinese in the laundry industry in 1920s California. Why does it happen so often? A recent report from the National Bureau of Economic Research attempts to explain this phenomenon.

William R. Kerr and Martin Mandorff, the paper’s authors, found that the social insulation of immigrant communities plays a big role in creating business pipelines into industries where previous generations have already found success. The trend is most common among groups that have tight-knit networks and in industries that lend themselves to self-employment. A variable that decreases the likelihood of ethnic concentration is when an job requires extensive licensing, certification, or education within the U.S, since many immigrants will have difficulty getting those bonafides.

The authors find that the way that immigrants socialize is especially relevant to the heavy concentration of immigrant-owned businesses in very specific industries. Immigrants often cluster, both geographically and socially, with those who are similar to them. Many arrive and stay with family or friends, and still others choose to move to a community with familiar customs and language. Staying within the same communities—and marrying within them—is most common among groups that are small and less assimilated.

This proximity can have important ramifications when it comes to how and where these groups find employment. Socializing—everything from religious to recreational activities—involves hanging out with people from a similar country or region. This can result in a transfer of jobs and skills to new immigrants that make them more likely to continue working in a certain industry, be it driving a taxi or cooking in a restaurant.

And the effects of that can multiply when played off the predisposition toward entrepreneurship that exists among specific immigrant groups. For instance, 45 percent of Korean men are self-employed compared to 15 percent of the male immigrant population overall. This tendency toward self-employment means that not only are owners are willing and able to hire fellow immigrants for their businesses, but also that there’s the ability to create an intergenerational trajectory, where owners are able to pass their business down to their children and grandchildren, continuing the job-clustering effect.

These same social connections can provide a sort of informal mentorship. In their research, the economists found that in 17 out of 25 case studies of immigrant groups, the industries where ethnic groups displayed the greatest concentration of entrepreneurship were also the industries where they displayed the greatest concentration of overall employment. That’s because the clustering around specific industries isn’t just helpful for finding work—it’s helpful for learning how to buy and run your own business as well. The relationships forged in these tight knit communities are especially helpful for existing and aspiring entrepreneurs, who can pick up important tips on starting and maintaining a business from those in the community who have navigated challenges like taxes, startup capital, and inspections. And when it comes to self employment, such advice and support is critical, and may give some groups a huge advantage.

Source: Why Are Immigrants More Likely to Concentrate in Certain Industries? – The Atlantic

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