Measuring Ethnic Labour Market Concentration and Segmentation

Measuring Ethnic Labour MarketConcentration and SegmentationQingfang Wang and Kavita Pandit

With the huge influx of immigrants into the United States in recent years, considerable

efforts have been devoted to describing the extent and variation of labour market

concentration across ethnic groups within or between regions. However, there is no

consensus among social scientists on how to measure and identify ethnic labour market

concentration patterns. The issues mainly include, firstly, how to define an employment

sector*as an industrial or an occupational sector; secondly, how to break down

employment categories; and thirdly the extent to which a job sector can be identified as

an ethnic-concentrated sector, that is, what index and what threshold value should be

used to define a ‘niche’ sector? Using the case of Chinese in the San Francisco

Consolidated Metropolitan Statistical Area, this paper demonstrates how different

choices could encourage different evalzuation and understanding of multi-ethnic urban

labour market segmentation processes.

Keywords: Ethnic Niche; Employment Sector; Chinese Immigrants; San Francisco

Ethnic labour market concentration, that is, where an ethnic group has a

predominant number of co-ethnic people in certain job sectors, has been drawing

more and more attention with the increasing number of immigrants. Considerable

efforts have been devoted to describing the extent and variation of labour market

concentration or segmentation across ethnic groups within or between regions (Ellis

and Wright 1999; Ettlinger and Kwon 1994; Logan et al. 1994, 2000, 2003; Waldinger

2001; Wang 2004; Wilson 1999; Wright and Ellis 2000). According to these studies, a

large number of ethnic minorities or immigrants are concentrated in job sectors with

low status and low pay. In contrast, native-born white Americans tend to be

concentrated in capital-intensive and lucrative jobs in white-collar and managerial

Qingfang Wang is Assistant Professor in the Department of Geography and Earth Science at the University of

North Carolina at Charlotte. Correspondence to: Dr Q. Wang, Dept of Geography and Earth Sciences, University

of North Carolina at Charlotte, Charlotte, NC 28223, USA. E-mail: [email protected]. Kavita Pandit is

Professor of Geography at the University of Georgia. Correspondence to: Prof. K. Pandit, Dept of Geography,

University of Georgia, Athens, GA 30605, USA. E-mail: [email protected]

ISSN 1369-183X print/ISSN 1469-9451 online/07/081227-26 # 2007 Taylor & Francis

DOI: 10.1080/13691830701614023

Journal of Ethnic and Migration Studies

Vol. 33, No. 8, November 2007, pp. 1227�1252

occupations. In addition to human capital, different socio-economic mechanisms are

believed to segment ethnic minorities or immigrants from ethnic-majority workers

(Hudson 2003; Waldinger 2001; Wang 2006; Wilson 2003).

Ethnic labour market segmentation exacerbates the socio-economic disadvantages

and inequalities of racial or ethnic minorities. For example, some empirical studies

demonstrate that the concentration of minority workers in certain occupations

depresses earnings of all workers in that occupation (Catanzarite 2003; Huffman and

Cohen 2004; Kmec 2003). Understanding the mechanism and outcomes of ethnic

labour market concentration and segmentation is critical to the issues of racial

inequality and the impacts of immigration and policy. However, the fundamental aim

of this analysis is to quantify the extent of ethnic labour market concentration or

‘niching’.

As the traditional residential and occupational gender segregation studies have

shown, measurement decisions have very real, substantive, and policy consequences

that potentially affect the scientific inferences of research (Brady 2003). James and

Taeuber (1985) also pointed out that choosing a measure of segregation on the basis

of one’s attraction to a certain interpretation will introduce arbitrariness into the

index selection procedure. A more serious problem is the tendency to use the

currently popular measure that will allow ‘the definition of segregation to flow from

one’s choice of a measure rather than the reverse’ (James and Taeuber 1985: 2). The

recent work by Semyonov et al. (2000) has also shown that conclusions on racial

composition and occupational segregation and inequality across American cities are

largely dependent on the measure used.

Unfortunately, there is no consensus on how to measure and identify ethnic labour

market concentration patterns. Compared with the decades-long debate in the fields

of residential and occupational gender segregation, the surprising absence of

discussion on measuring segmentation in ethnic labour market studies has become

more crucial than the methodological difficulty itself.

First of all, defining an employment sector*as an industrial or an occupational

sector*can produce different concentration patterns and understandings of such

patterns. The second issue is one of scale, and how to break down employment

categories. For decades the issue of scale has been plaguing inequality studies and

residential and occupational segregation (e.g. Hakim 1992; James and Taeuber 1985;

Semyonov et al. 2000; Watts 1997, 1998). There are few discussions on how different

scales of aggregation of industrial or occupational categories will result in different

concentration patterns. Finally, we need to determine the statistics to be used to

define an ethnic niche, i.e. the extent to which a job sector can be identified as an

ethnic-concentrated sector. Odds ratios, location quotients and representation

indexes are commonly used in the current literature. But there have been few

discussions on the difference between the indicators and on what threshold value

should be used to define a ‘niche’.

Given this background, our paper attempts to initiate a discussion about the

implications of how we measure ethnic labour market concentration and segregation in

1228 Q. Wang & K. Pandit

social science research. As discussed in occupational gender segregation studies, there is

no one index which can perfectly capture all the aspects of occupational segregation

(Hakim 1992; Watts 1997, 1998). Likewise, it would be a daunting task to develop a

‘standard’ method by which to measure ethnic labour market concentration due to the

multi-dimensional aspects of this phenomenon. However, understanding the con-

sequences of our methodological decisions in measuring ethnic labour market

concentration is vital to understanding the process of concentration itself. To this

end, our study discusses the different measures of ethnic labour market concentration

and segmentation and, using the case study of San Francisco’s Chinese population,

demonstrates how these measures affect interpretations of the urban labour market

segmentation process. In the following sections we first discuss the methodological

approaches of previous studies and then present the methodology and findings of our

own study.

Empirical Studies of Ethnic Labour Market Concentration

Scholars engaged in empirical studies of the labour market have to make a number of

decisions on how to denote the employment sector, the scale of aggregation of

employment categories, and the measures and threshold values to designate an

employment niche.

Employment Sector

Studies typically represent the types of job by the broad employment sector in which

the job is classified. Employment sectors can be defined with reference to either the

industry type (Ellis and Wright 1999; Logan et al. 1994, 2000, 2003; Razin and Light

1998) or occupation type (Richard 1998; Rosenfeld and Tienda 1999; Semyonov

et al. 2000; Waldinger 1994; Wright and Ellis 2000). The industrial classification

reflects the structural features of the local economy, i.e. whether workers are

employed in agricultural operations and natural resource exploitation, the produc-

tion of goods, or the provision of services. Particularly, usage of industry can capture

the main feature that most ethnic minorities have high concentration in the

production and/or provision of a good (Wilson 2003). Although this approach

accurately describes the structure of the economy, it suffers the disadvantage that

workers doing very similar types of work (e.g. clerical duties) can be classified into

different industrial sectors, depending on the nature of the firm. The occupational

classification of jobs, in contrast, focuses on the nature of the work and the skill

levels required. Given that both classifications provide useful information, a number

of authors use cross-classification of industrial and occupational sectors to capture

the variation of economic functions, wage outcomes, work settings, and technical

skills requirements of jobs (Ettlinger and Kwon 1994; Hudson 2003; Wilson 1999,

2003).

Journal of Ethnic and Migration Studies 1229

Scale of Aggregation

The problem of ‘scale’ has long haunted residential segregation and inequality

studies. The basic problem is that analyses at different scales for a single area can

produce different results regarding the extent of the segregation (Egan et al. 1998;

James and Taeuber 1985; Massey and Denton 1988; White 1983; Wong 1997). Studies

of labour market segmentation are confronted by the same problem, the main

difference being that the ‘scale’ represents the level at which an employment sector is

aggregated* ‘sectoral’*rather than the size of a ‘spatial’ unit. Yet because the issue

of sectoral aggregation/disaggregation is not explicitly spatial, there is a tendency to

overlook how the level of aggregation/disaggregation of an employment sector*the

‘scale’*influences conclusions about which sectors serve as ethnic niches.

In general, a highly detailed disaggregating of the labour market will allow

researchers to identify precise and narrowly-defined job sectors in which ethnic

workers are concentrated. However, detailed categorisations run the risk of producing

large numbers of sparsely populated job categories because ethnic minorities and

immigrants make up only a small proportion of the labour force in an area. Detailed

categorisations are therefore particularly problematic for analysing ethnic niches in

small labour markets and for small ethnic groups.

Even in labour markets that have large enough numbers of ethnic workers, a high

level of disaggregation presents a problem in that the researcher is dealing with

hundreds of job categories only a few of which emerge as ethnic niches. Many

researchers choose to report the ‘largest’ ethnic niches or the number of niche sectors.

Although the niche categories will be more exactly defined, it becomes difficult to get

a broad view of ethnic labour market patterns (in other words, seeing the trees but

missing the forest).

In practice, most researchers opt to work with more aggregated employment

sectors. Yet, there is a wide variation in just how many employment categories are

considered. Logan et al. (1994, 2000), for example, employed a 47-category industry

scheme when studying the ethnic labour market in major US metropolitan areas. In

their studies of immigrant employment distribution in Los Angeles, Ellis and Wright

(1999) used as few as 15 industrial sectors and 14 occupation categories (Wright and

Ellis 2000). Obviously, the number of categories increases when researchers use a

cross-classification of industrial and occupational sectors. For example, Ettlinger and

Kwon (1994) ended up with 370 categories when they cross-classified 10 industrial

and 37 occupational groups in their study of Asian immigrants in New York and Los

Angeles. Wilson (2003) ended up with over 900 categories (48 industries�19

occupations) when comparing the spatial variation of ethnic niches nationwide in the

US. In contrast, Hudson (2003) had only 36 cross-classified groups (6 industries�6

occupations) for her study of the Atlanta Metropolitan Area.

From these studies it is clear that, ultimately, the choice of how many employment

sectors to use is based on the specific research questions of each individual study and

the size of the local labour market. What is less clear, however, is the sensitivity of the


results to the level of aggregation/disaggregation used by the authors. This is one of

the questions that our study seeks to answer.

Different Indices

Scholars have used a number of different measures to assess the concentration of

ethnic minorities in an employment sector. These include the odds ratio (Logan et al.

1994), the representation index (Rosenfeld and Tienda 1999) and the location

quotient (Wright and Ellis 2000). Although similar in concept, i.e. all the indices seek

to identify the sectors in which the concentration of an ethnic minority is

disproportionately high, there are subtle differences in how each index measures

this concentration.

The odds ratio (OR) is the ratio of two odds, an odd referring to the number

of occurrences of a particular event divided by the number of non-events. In the case

of ethnic concentration in the labour market, the numerator of the OR is the ratio of

ethnic workers E in a sector i to ethnic workers working in all other sectors, t-i, of the

economy (i.e. the odds of working in a particular occupation for a particular group,

illustrated by Ei/Et�i). The denominator represents the same ratio for all other (non-

ethnic) workers O (i.e. Oi/Ot� i). The values of odds ratio range from 0 (for the

employment sector where there are no workers from ethnic group E) to infinite (for

the employment sector where all the workers come from the same ethnic group E). If

ORB1, it suggests that ethnic group E is less concentrated in sector i when compared

to other ethnic group members. If OR�1, it suggests that ethnic group E has the

same degree of concentration as other ethnic group members. If OR�1, it suggests

that ethnic group E is more concentrated than other ethnic group members in sector

i. The higher the value of OR, the higher degree of concentration for ethnic group E

in sector i.

The representation index (RI), in contrast, is a ratio of two probabilities rather

than two odds. The numerator of RI is the share of ethnic workers in a particular

sector (i.e. probability represented by Ei/Et). The denominator represents the same

share for all other workers (i.e. Oi/Ot). Similar to the odds ratio, the values of RI

range from 0 to infinite and the higher value represents a higher degree of

concentration.

To illustrate the relationship between OR and RI, We use PE to represent the

probability of ethnic group E working in an employment sector and PO to represent

the probability of non-group E members working in the same sector. Then

RI�PE

PO

(1); and

OR�PE

1 � PE= PO

1 � PO

which can be derived intoPE

PO

�1 � PO

1 � PE

(2)


Where 05PE51 and 05PO51. Therefore, the relationship between RI and OR will

be determined by the value of1 � PO

1 � PE

in equation 2. Theoritically we have the

following three conditions:

if PE�PO, OR�RI;

if PE�PO, OR�RI;

if PEBPO, ORBRI.

The comparison between OR and RI suggests that OR is more sensitive to the

change of PE and PO. But the more similar the concentration patterns of an ethnic

group to other members in the labour market, the smaller the difference between OR

and RI. When the number of the employment sector is significantly large, the

difference between these two indices is very small. Figure 1 gives the contrast between

three indices.1 The difference between OR and RI can hardly be detected from the

graph.

The location quotient (LQ) technique is commonly utilised in regional economic

base analysis (Flegg et al. 1995; Isserman 1977) to determine if a region has a smaller

or greater share of an industry in comparison with a reference economy. The method

is transferred to the labour market study to determine whether a particular sector has

a greater or lesser share of an ethnic group compared to a reference employment

sector. Similar to the RI, the numerator is given by the share of a particular ethnic

group working in an employment sector (Ei/Et, which equals to PE). The

denominator of the LQ represent the percentage of total labour force in the

metropolitan area working in the same employment sector (i.e. (Ei�Oi)/(Et�Ot)). If

LQ�1, it suggests the ethnic group of interest is more concentrated in sector i when

compared to the share of this sector in the local labour market as a whole. The values

of LQ range from 0 to infinite, with higher values indicating higher concentration.

Mathematically, the values of OR and RI are determined by the sectoral

distribution of the ethnic group of interest and other ethnic groups. However, the

value of LQ is determined by not only the sectoral distribution of the ethnic group,

but also the share of the specific sector in the entire local economy. When the number

of sectors is sufficiently large, the share of each sector is going to be significantly small

and the value of the LQ is highly determined by the sectoral distribution of the ethnic

group. When we set the share of each sector fixed, the changes of LQ are much less

sensitive to the sectoral changes of different ethnic groups. That is the reason why, in

Figure 1a, both OR and RI change dramatically with a modest change of LQ, when

the share of ethnic group members changes in the employment sector while the share

of the sector is set fixed. The simulation also suggests that, when the share of ethnic

group in an employment sector is less than 50 per cent (50 per cent is uncommonly

high for most ethnic minorities in most US metropolitan areas), the difference of

three indices is negligible.


By definition, ethnic concentration emphasises the concept of ‘niche’ versus ‘non-

niche’, ethnic groups versus ‘other’ ethnic group, no matter what the local economic

structure (which is reflected by the share of employment sectors).2 Therefore we

generally favour the choice of the odds ratio or representation index than location

quotients. An important feature of the odds ratio also lies in its statistical significance:

the natural logarithm of the odds ratio is asymptotically normal (see Figure 1b for the

simulated distribution of natural log form of three indicators; for more discussion see

also Agresti 2002). Using a metric of log odds ratios, the statistical tests of odds ratio

can provide hard evidence for the significance of the differential distribution of

0

20

40

60

80

100

1 9 17 25 33 41 49 57 65 73 81 89 97

Percentage

Val

ue

of

Th

ree

Ind

icat

ors

OR

RI

LQ

-6

-4

-2

0

2

4

6

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentage

Val

ue

of

Ln

(In

dic

ato

r)

ln(OR)

ln(RI)

ln(LQ)

Figure 1. Simulated changes of OR, RI, and LQ with increase in sectoral distribution

(a) Changes in values of indicators

(b) Natural logarithm values of three indicators


groups over selected job sectors (Rosenfeld and Tienda 1999: 101�2). The empirical

studies using the log odds ratios to examine the labour market concentration process

also provide consistent practice in ethnic labour market studies (Hudson 2003; Wang

2006; Wilson 2003).

Identifying Ethnic Niches

As we saw above, rising values of the OR, RI, and LQ suggest that a particular

employment sector disproportionately attracts ethnic minority workers. However, at

what point should one identify a sector as an ethnic niche sector? Scholars have

chosen, relatively arbitrarily, particular threshold values of the OR, RI or LQ above

which a sector is deemed an ethnic niche. Threshold values used in past studies

range from 1.2 (Hudson 2003), 1.5 (Logan et al. 2003), to 2 (Ettlinger and Kwon

1994).

Another complicating issue is that, often, sectors with very few workers overall may

show very high values on the chosen index if, in relative terms, a high number of

those are ethnic workers. This is closely related to the issue of aggregation discussed

earlier. If the labour market is broken down into relatively few sectors, i.e.

employment sectors are defined in highly aggregated terms, there will be sufficient

numbers of workers*non-ethnic and ethnic*in all sectors and it may not be

necessary to look at this issue. However, if highly disaggregated labour market

categories are utilised, there is a risk that a given sector may report a high odds ratio

(or other index) but have very few ethnic workers in absolute terms. Then, we have to

ask, should a sector with just a handful of ethnic workers be classified as an ethnic

niche? If not, what is the minimum number of ethnic workers who need to reside in a

sector for it to be considered a niche?

An increasing number of studies now set a restriction on the minimum number of

ethnic workers necessary to qualify as an ethnic niche (given, of course, that the

sector meets the necessary level for the concentration index). This number has varied

from 300 to 500 (Allen and Turner 1997; Wilson 1999, 2003). However it should be

quickly apparent that setting an absolute value for this minimum restriction is

problematic, since the number of ethnic workers in a given sector varies according to

the size of the labour market and the total ethnic labour force, and the level of

aggregation of the employment sectors. Choosing a fixed number as threshold level a

priori is risky because, if it is too high, it may miss counting ethnic niches; set too low,

it will cause some sectors with extremely small numbers of workers to be classified as

ethnic niches.

Wang’s (2006) recent study stipulated that an ethnic niche has to have at least 50 per

cent of the average number of ethnic workers across all employment sectors. In her

case study of the San Francisco Metropolitan Area, the average number of Asian

workers in an employment sector is 1,337 workers (a total of 556,097 Asian workers

divided by 416 sectors). So, an Asian niche must then have at least 668 (50 per cent of

1,337) Asian workers. A Hispanic niche likewise should be calculated by the average


size of the Hispanic labour force in the study area. Fifty per cent is still arbitrary;

however, unlike an absolute restriction, the relative restriction considers the size of the

local labour market, the size of the ethnic labour force, and how employment sectors

are broken down. Therefore, this strategy is particularly useful in a multi-ethnic labour

market context.

The previous discussion should make it clear, then, that the identification of ethnic

niches is influenced by the way in which employment sectors are defined, the level of

aggregation of employment categories, the concentration index used, and the

threshold value and minimum worker restriction applied to the sector. What is

important to ascertain is how and to what extent variation in these different factors

influences the results. In the following section, we detail how we will use data for

Chinese workers in the San Francisco Metropolitan Area to assess how the definition

of Chinese employment niches in that labour market is influenced by these various

factors, and how different measures could produce different interpretations of the

labour market segmentation process.

Data and Methodology

Data used for this study are drawn from the 5% Integrated Public Use Microdata

Samples (Ruggles et al. 2004) for the San Francisco Consolidated Metropolitan

Statistical Area (CMSA), chosen mainly because this area has a large and ethnically

diverse labour market. In 2000, non-Hispanic whites comprised only 50.6 per cent of

the total metropolitan population, while blacks, Hispanics and Asians constituted,

respectively, 7.8, 19.7, and 20.4 per cent of the total population.

Due to the complexity and variation within the broadly defined racial/ethnic

groups (for a discussion of the ambiguous categorical usage of race and ethnic

identification, see Hamilton and Form 2003), we chose to focus on a subset of the

Asian population*the Chinese. According to the ‘race’ category in the 2000 US

Census, the Chinese are those who identified their ancestral origins as mainland

China, Hong Kong or Taiwan. The Chinese represent one of the largest immigrant

ethnic groups in the United States, which in practical terms ensures sufficient

numbers for a labour market study such as this one. San Francisco’s Chinese

population is historically concentrated in one of the oldest and biggest Chinatowns in

the United States, working in restaurants, laundries, garment factories, gift and

jewellery shops (Wong 1998). In the latter part of the twentieth century, however, San

Francisco witnessed a dramatic growth of high-tech industries, in which Chinese

engineers and computer scientists played an important role (Saxenian 1999).

Consequently, Chinese workers can be found across the spectrum of the labour

market. Given the size and diversity of San Francisco’s Chinese population, we

decided to use this group as the case study to examine the issues associated with the

identification of ethnic niche sectors in the labour market.3

We conducted analyses for employment sectors defined in three ways: industry

(IND), occupation (OCC), and a combination of industry and occupation (CROSS).


According to the US 2000 Census, ‘industry’ refers to the economic sector and work

setting, while ‘occupation’ refers to a worker’s specific technical function. A worker

is classified in that industry or occupation in which s/he earned the most money; if

the worker was not sure about this, s/he was classified in the sector in which s/he spent

the most time. The Census’s three-digit-code system provides the most detailed

industrial and occupational breakdown of the employed labour force, and yields

256 civilian industrial sectors and 468 occupations.

For each type of employment classification, we conducted analyses with

disaggregated and aggregated sectoral categories. For analyses of IND we used the

256 categories at the disaggregated and 19 at the aggregated level. In the case of OCC,

we used 468 categories at the disaggregated level and 24 at the aggregated. The

aggregation for both industrial sectors and occupations is based on the similarity of

different types, using the two-digit code provided by the US Census. Disaggregated

and aggregated schemes were also defined for CROSS: instead of using the unwieldy

number of 256�468 categories (leading to a staggering 119,808 sectors, many with

‘structural zeros’),4 we used the combination of 19 industries and 24 occupations for

a total of 418 CROSS sectors after dropping 38 structural zero sectors. This number

of categories is close to the number of occupational categories at the disaggregated

level. For the aggregated CROSS analysis, we used the six major industries (extractive,

transformative, distributive, producer services, social services, and personal services;

see also Hudson 2003 for a discussion) and 24 major occupations, giving a total of

144 sectors.

As discussed earlier, we chose to use the odds ratio (OR) to measure ethnic

concentration in the labour market. Since one of our goals was to assess the

sensitivity of the results to different threshold values of the odds ratio, we conducted

separate analyses using threshold values of 1.2, 1.5 and 2.0 for the odds ratio. These

values fall in the range used by previous studies.

We then determined the changes caused by different levels of minimum worker

restriction. The issue is particularly pertinent to the disaggregated analyses because,

with the rise in the number of sectors, the number of workers in each sector becomes

significantly reduced. Consistent with our earlier discussion, we chose to use a

percentage minimum restriction rather than an absolute number. We first calculated

the average size of each employment sector for the Chinese, which is given by the

total number of the Chinese population divided by the total number of employment

sectors. Then we tested the effect of varying the minimum worker restriction from

0 to 100 per cent of the average size.

Finally, we undertook a regression analysis to test the extent to which the

probability of working in a niche sector is affected by the manner in which we define

the niches. This step was particularly important to us since the majority of studies

that identify ethnic niches are focused on testing hypotheses of the concentration

process. Our final step, therefore, sheds light on the sensitivity of these analyses to

niche definitions.


Findings and Discussion

Effects of Sectoral Definition and Aggregation Levels

Tables 1 and 2 present the distributions of Chinese workers in San Francisco across,

respectively, aggregated industrial and aggregated occupational categories. Using

the odds ratio greater or equal to 1.2, there are four industrial niche sectors

(Manufacturing, Finance and Insurance, Professional and Scientific, and Accom-

modation and Food Services) and six occupational niche sectors (Financial,

Computer and Mathematical, Architecture and Engineering, Life/Physical/Social

Science, Food Preparation/Serving, and Production). In general, these categories are

broad but reveal the dualistic nature of Chinese engagement in the workforce: they

work in sectors that require high levels of formal education and certification as well as

low-end service jobs.

Table 3 presents the odds ratios for the Chinese presence in cross-classified job

categories also defined in aggregated terms (6 industrial sectors�24 occupations).

Odds ratios greater than 1.2 are shown in bold, indicating that the corresponding

occupation�industry combination is a niche sector. Given that there are far more

categories in this scheme than in the separate industry and occupational analyses, it is

not surprising to see that as many as 30 niche sectors emerge. The results provide a

much more detailed and nuanced picture. For example, whereas the occupational

analysis indicated that Computer and Mathematical occupations represented an

important niche for the Chinese, Table 3 shows that this is true in all industries except

Table 1. Distribution of Chinese workers by aggregated industrial sector

Industrial sectorsNumber of

Chinese workers in sector% of all Chineseworkers in sector

Oddsratio

Agriculture, forestry, fishing and hunting 413 0.18 0.2Mining 150 0.07 0.9Utilities 1,189 0.52 0.8Construction 6,657 2.90 0.5Manufacturing 55,253 24.06 1.9Wholesale trade 8,351 3.64 1.1Retail trade 21,468 9.35 0.8Transportation/warehouse 8,840 3.85 0.9Information/communication 11,581 5.04 1.1Finance and insurance 15,530 6.76 1.3Real estate, rental and leasing 3,641 1.59 0.8Professional and scientific 28,745 12.52 1.2Management 4,149 1.81 0.4Education, 13,024 5.67 0.7Healthcare, social service 17,180 7.48 0.8Arts, entertainment, recreation, 1,713 0.75 0.4Accommodation and food services 18,515 8.06 1.5Other services 6,557 2.86 0.6Public administration 6,676 2.91 0.8

Note : Entries in bold represent Chinese niche sectors based on the odds ratio �1.2 criterion.


Personal Services. Some other cross-classified sectors emerge as large niches even

though their associated industry and occupation do not emerge as niches by

themselves (e.g. business operation specialists in extractive industries, or healthcare-

related occupations in transformative and distributive industries).

Niche sector patterns become much more complex when we move to disaggregated

categories. Because the full results are difficult to display (given the large number of

categories), Table 4 presents only the five largest niche sectors*based on the number

of workers in the sector*that emerge when we used the detailed industrial,

occupational and cross-classified categories (note that we used the more stringent

OR�1.5 criterion here, given that a much larger number of sectors met the 1.2

threshold criterion). There is a striking correspondence that emerges between the

industrial and the occupational niche sectors. For example the electronic and

computer-related manufacturing industry is the largest industrial niche, while the

computer and software-engineering occupation leads the occupational niches. The

one disjuncture, however, is that, whereas restaurants and food services are a major

niche industry, there is no corresponding food service occupation in the top five

occupational niches. Conversely, the occupation of sewing-machine operators

(associated with garment production) emerges as a strong niche occupation

(OR�21.5) but is not captured in the industrial classification.

Table 2. Distribution of Chinese workers by aggregated occupational sector

Occupational sectorNumber of workers Percentage of workers

in sectorOddsratio

Management 25,801 11.24 0.9Business operations specialists 5,289 2.30 0.7Financial specialist 12,125 5.28 2.1Computer and mathematical 26,668 11.61 2.4Architecture and engineering 19,677 8.57 2.7Life, physical, and social science 5,432 2.37 1.5Community and social services 1,629 0.71 0.5Legal 1,398 0.61 0.4Education, training and library 7,455 3.25 0.6Arts, design, entertainment, sports, media 4,947 2.15 0.7Healthcare practitioners and technical 9,552 4.16 1.1Healthcare support 2,013 0.88 0.6Protective services 1,261 0.55 0.3Food preparation/serving 12,901 5.62 1.4Building- and grounds-cleaning/maintenance 3,775 1.64 0.5Personal care and service 3,754 1.63 0.6Sale 20,846 9.08 0.8Office and administrative support 31,112 13.55 0.9Farming, fishing and forestry 165 0.07 0.1Construction trade 4,163 1.81 0.4Extraction workers 0 0.00 0.0Installation, maintenance and repair workers 4,217 1.84 0.6Production 20,538 8.94 1.6Transportation and material-moving 4,914 2.14 0.5



Once again, the cross-classified sector analysis yields greater insights into the

industrial and occupational niche patterns. For instance, as Table 4 shows, Chinese

engagement in computer and mathematical occupations is particularly significant

in the Professional, Scientific and Manufacturing industries. Such insights are

Table 3. Odds ratios for Chinese worker presence in cross-classified

industry�occupation sectors

Industry/Occupation Extractive Transformative DistributiveProducerservices

Socialservices

Personalservices

Management 0.7 1.1 1.1 0.9 0.5 1.1Business operations

specialists5.4 0.8 0.7 0.7 0.8 0.4

Financial specialist 0.0 2.5 3.1 1.8 2.4 1.5Computer and

mathematical2.1 2.8 2.3 2.1 1.8 0.9

Architecture andengineering

0.0 3.0 2.8 2.0 1.8 1.5

Life, physical and socialsciences

1.5 2.5 0.9 1.5 1.1 0.5

Community and socialservices

dropped dropped 0.0 0.3 0.6 0.5

Legal 0.0 0.4 0.6 0.4 0.3 0.7Education, training

and librarydropped 0.9 0.2 0.9 0.6 0.7

Arts, design,entertainment,sports, media

0.0 1.5 0.8 0.7 0.8 0.3

Healthcarepractitioners andtechnical

0.0 1.2 2.7 0.3 1.1 0.8

Healthcare support dropped 5.0 1.5 0.4 0.6 0.0Protective services 0.0 0.0 0.6 0.3 0.4 0.1Food preparation and

serving0.0 0.7 1.3 0.0 0.6 1.5

Building-/grounds-cleaning/maintenance

0.0 0.9 0.7 0.3 0.6 0.8

Personal care andservices

0.0 0.0 0.7 0.5 0.6 0.6

Sales 1.4 0.8 0.8 0.8 0.8 1.1Office/administrative

support1.3 0.8 1.1 0.9 0.9 0.7

Farming, fishing andforestry

0.1 0.3 0.3 0.0 0.0 0.0

Construction trade 0.0 0.4 0.3 0.2 0.5 0.7Extraction work 0.0 0.0 dropped 0.0 0.0 droppedInstallation,

maintenance, andrepair work

0.0 0.7 0.6 0.7 0.6 0.5

Production 0.9 1.8 1.1 0.6 0.4 1.6Transportation and

material-moving0.2 0.7 0.4 0.3 0.5 0.8



particularly valuable in studies that compare niche sectors across different ethnic

groups because a particular industry or occupation may serve as a niche for more

than one group. For example, analyses not reported in this study show that both

Hispanic workers and non-Hispanic white workers in San Francisco are highly

concentrated in the management industry. However, when we use cross-classified

sectors it becomes clear that each group occupies a very different occupational space

in the same industry: white workers are concentrated in professional and manage-

ment occupations while Hispanic workers are concentrated in occupations such as

building, moving, protective services and ground cleaning. By simply focusing on

industrial niches, in such a case, a researcher could miss the bigger picture of labour

market segmentation.

Table 5 provides a useful summary of the overall effects of aggregation (and the

effects of varying the odds ratio threshold, discussed in the following section).

Focusing for now only on the results for the OR�1.2, we can see that the number of

niches varies enormously between aggregated and disaggregated schemes. This

highlights how problematic it can be to compare the results of ethnic labour market

concentration patterns derived from different studies without alluding to the level of

aggregation/disaggregation used in their sectoral classifications. The table suggests that

Table 4. The five largest Chinese niches and the number of workers engaged in them for

disaggregated industrial, occupational and cross-classified sectors

Sector Ethnic nicheNo. of Chinese

workers in sector% of all Chineseworkers in sector

Oddsratio

IND Electronic component and productmanufacturing

19,297 8.4 2.2

Restaurants and other food services 15,099 6.6 1.6Computer systems design and relatedservices

11,741 5.1 1.7

Computer and peripheral equipmentmanufacturing

10,065 4.4 2.4

Banking and related activities 5,628 2.5 1.7OCC Computer software engineers 13,989 6.1 3.2

Accountants and auditors 9,002 3.9 2.5Electrical and electronics engineers 5,096 2.2 3.8Sewing-machine operators 4,649 2.0 21.5Miscellaneous engineers, includingagricultural and biomedical

4,292 1.9 3.3

CROSS Manufacturing�production 16,487 7.1 1.9Manufacturing�architecture andengineering

11,862 5.2 3.1

Accommodations and food services�food preparation/serving

11,661 5.1 1.5

Professional, scientist�computer andmathematical

8,537 3.1 2.0

Manufacturing�computer andmathematical

7,535 3.3 2.8

Note : Niches identified using OR�1.5 criterion.


Table 5. Chinese employment niches and their size by odds ratio threshold for aggregated and disaggregated industrial, occupational and

cross-classified sectors

Odds ratio threshold

1.2 1.5 2.0

Sector and level ofaggregation

Total number ofsectors underclassification

Sectors classifiedas Chinese niches

% Chineseworkers in niches


% Chineseworkers in niches


% of Chineseworkers in niches

INDAggregated 19 4 51.41 2 32.12 0 0Disaggregated 256 65 60.06 39 41.94 17 19.04

OCCAggregated 24 6 42.39 5 36.77 3 25.46Disaggregated 468 132 54.08 87 40.02 50 24.47

CROSSAggregated 138 30 41.38 28 41.04 14 22.50Disaggregated 418 104 55.24 78 44.67 49 22.84

Journ

al

ofE

thn

ica

nd

Migra

tionS

tud

ies1

24

1

there is somewhat less variation across aggregation levels in the number of workers

who are classified as working in niche sectors. However, the variation is not

insignificant and the same lessons hold.

Effect of Varying the Threshold Values of the Odds Ratio

Both the number of niches and of workers in niche sectors decrease dramatically as

the threshold value of the odds ratio is increased (Table 5 and Figure 2a and b).

Whereas there were four Chinese industrial niches identified at the aggregated level at

an odds ratio of 1.2, when the threshold value is increased to 2.0 there are no niche

sectors to be found (this is also clear from Table 1). In the case of occupational niches,

the major Chinese niche*food preparation and serving*is lost when the threshold

value of the odds ratio goes up. The rising threshold values have a much greater

dramatic impact when using aggregated sectoral categories simply because there are

fewer categories to begin with, and the ‘disappearance’ of a particular sector as a

niche involves much larger numbers of workers.

Overall, it becomes clear that any picture we derive of the ethnic segmentation of

the labour market will depend crucially on the odds ratio we apply to define niches.

Low odds ratio criteria will exaggerate the extent to which ethnic groups are

clustering in particular sectors, while high odds ratio criteria may lose valuable

information on existing ethnic niches (although we can have a high level of

confidence in those niches that are identified).

Effects of Varying the Minimum Worker Restriction

The analyses so far did not impose any minimum size restriction for identifying a

niche: a sector was designated as an ethnic niche simply based on the odds ratio

criterion. If, however, a minimum size restriction is instituted, the number of niches

identified drops significantly. Figure 3 illustrates how the number of niches falls as the

minimum worker restriction is increased. The most rapid declines in the niche count

are evidenced between no size restrictions to a size restriction of 50 per cent of the

average sector size. The percentage of niche workers also decreases with a rising

minimum restriction, but the decline is somewhat more modest. This is not entirely

surprising because the size restriction weeds out the very small sectors with few

workers.

The effect of applying various minimum worker restrictions on the top niche

sectors identified is illustrated in Table 6. The table clearly shows that there is a

significant effect: as increasingly stringent minimum worker restrictions are imposed,

smaller sectors with high odds ratios are dropped in favour of larger sectors whose

odds ratios are lower (but still meet the odds ratio threshold). For example, under the

industrial classification with no minimum restriction, ‘Knitting Mills’ is identified as

the third most significant sector (with an odds ratio close to 12.0). However, since

there are only 200 workers in this industrial sector in the sample, this sector drops out


when the minimum restriction is raised to 50 per cent (or approximately 450

workers) and clothing accessories/manufacturing takes its place. However with a

further increase in the minimum restriction, this industrial niche, too, drops out and

is replaced by dry cleaning and laundry services, characterised by a lower odds ratio

0

20

40

60

80

100

120

140

IND OCC CROSS

Category

Nu

mb

er o

f N

ich

es

1.2

1.5

2

0

10

20

30

40

50

60

70

1.2 1.5 2

Odds Ratio

Per

cen

t o

f N

ich

e W

ork

ers

IND

OCC

CROSS

Figure 2. Effects of varying the threshold values of the odds ratios for disaggregated

industrial, occupational and cross-classified sectors

(a) Changes in the total number of niche sectors

(b) Changes in the share of Chinese workers employed in ethnic niches


but with significantly more workers. Similar shifts are seen in occupational and most

particularly in the cross-classified category (where one of the sectors identified as a

niche under the 0 per cent restriction has only 2 workers!)

Although our focus is on sectors that are found to be significant at different

minimum worker levels, it is useful to examine the sectors that get dropped as the

minimum restriction is raised. For example, the cut-and-sew clothing manufacturing

industry is very ‘small’ in terms of the number of engaged workers (only 77), but its

odds ratio is very high at 44.2. Even if this sector drops out at more stringent

minimum-worker restriction levels, it is clear that the clothing manufacturing

industry in San Francisco is most definitely a Chinese-dominated sector. This is

consistent with the long history of clothing manufacture in the San Francisco Bay

area (Wong 1998). Thus, by paying special attention to the sectors with a very high

odds ratio (even if they contain small numbers of workers), we stand to learn

something about the distinctive historical, cultural and economic circumstances of

particular ethnic groups.

Regression Analysis

So far, our focus has been on the criteria by which ethnic employment niches are

identified, and our analyses have confirmed that the choice of concentration index,

aggregation levels, threshold values and minimum-worker restrictions all influence

which sectors emerge as niches. In most studies, however, the identification of ethnic

niches is but the first step in understanding the processes by which ethnic niches are

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100

Restriction as Percentage of Average Size

Nu

mb

er o

f N

ich

es

IND

OCC

CROSS

Figure 3. Relationship between the number of niche sectors identified and the

restriction level of minimum workers in the sector for industrial, occupational and

cross-classified sectors

Note: The threshold value of the odds ratio is held constant at 1.5


Table 6. The top three Iidustrial, occupational and cross-classified niches under different minimum-worker restrictions

Minimum-worker restriction level

0% 50% 100%

Top 3 niches No. ofworkers

Oddsratio


Oddsratio


Oddsratio

Industrial sectorsCut-and-sew clothing manu-

facturing4,208 14.3 Cut-and-sew clothing man-

ufacturing4,208 14.3 Cut-and-sew clothing

manufacturing4,208 14.3

Textile product mills exceptcarpets and rugs

1,299 13.0 Textile product mills exceptcarpets and rugs

1,299 13.0 Textile product mills exceptcarpets and rugs

1,299 13.0

Knitting mills 203 11.9 Clothing accessories/ otherclothing manufacturing

682 8.8 Dry-cleaning and laundryservices

1,214 2.5

Occupational sectorsTextile knitting & weaving

machine setters, operators& tenders

77 44.2 Sewing-machine operators 4,649 21.5 Sewing-machine operators 4,649 21.5

Sewing-machine operators 4,649 21.5 Tailors, dressmakers andsewers

613 5.9 Tailors, dressmakers andsewers

613 5.9

Tailors, dressmakers andsewers

613 5.9 Pharmacists 1,251 5.2 Pharmacists 1,251 5.2

Cross-classified sectorsFinance & insurance�arts,

design, entertainment,sports & media

228 22.2 Transportation &warehouse�financialspecialists

326 4.4 Manufacturing�architecture & engineering

11,862 3.1

Agriculture, forestry, fishingand hunting�businessoperations specialists

2 5.6 Transportation &warehouse�financialspecialists

316 3.4 Wholesale trade�computerand mathematical

595 3.1

Mining�businessoperations specialists

26 5.1 Wholesale trade�financialspecialists

430 3.3 Public administration�financial specialists

1,078 2.9

Journ

al

ofE

thn

ica

nd

Migra

tionS

tud

ies1

24

5

created. So a question of interest to us was: To what extent is our understanding of

the niching process affected by how we identify niches?

To explore this issue, we began with a widely used logistic regression model to

explain the probability of working in a niche sector:

Ln [(P�1)/(1�P�1)]�a�bX (3)

where the left side is the natural logarithmic form of the probability of working in

ethnic niches versus non-niches, X is the matrix of independent variables predicting

the probability of niche employment, and b is the set of parameters. The independent

variables included in our analysis are those that have been widely used in previous

studies (Hudson 2003; Portes and Jensen 1989; Portes and Sensenbrenner 1993; Razin

and Light 1998; Wang 2004), and include age, sex, marital status, English fluency,

education, length of residence in the United States, whether self-employed, and size of

family.

The objective of our analysis was to determine to what extent the significance of

the independent variables in our model changed as we varied our criteria for

identifying a niche sector. We were confronted, however, with a daunting number of

possible scenarios since we could explore changes due to variation in sectoral

definitions, aggregation levels, odds ratio thresholds, and minimum-worker restric-

tions. Keeping in mind that the present analysis is only to explore and illustrate the

effects of varying niche identification criteria (rather than providing a thorough and

comprehensive analysis of the phenomenon), we only explored the effects of varying

the minimum-worker restriction levels (0, 50 and 100 per cent) for disaggregated

industrial, occupational, and cross-classified niches. These are the dimensions along

which studies of employment niches vary the most and provide a useful illustration.

Regression results using, once again, data for Chinese workers in San Francisco, are

presented in Table 7. Several observations can be made. The results show that being

younger, male, married, foreign-born, not fluent in English, having a bachelor’s

degree, and not being self-employed increases the probability of a Chinese worker

being engaged in a niche sector job.

What is particularly interesting is that these results, i.e. the signs of the parameters,

are very stable across the different sectoral classifications and minimum-worker

restriction levels. Indeed at first glance, it suggests that we need not worry too much

about how we define niches since the predictor variables behave remarkably

consistently.

However, with closer inspection, it is clear that there are important differences to

be noted across the sectors/restriction levels. Looking across sectors, for example, the

results show that Chinese workers with poor English exhibit twice the likelihood of

working in ethnic niches than fluent English speakers under the cross-classified sector

results, but only 1.3 times the likelihood if we look at the industrial sector results

(true regardless of the minimum-worker restriction level). Similarly, when we use the

occupational niche classification, the likelihood of working in ethnic niches for those


Table 7. Regression results (dependent variables� probability of working in industrial, occupational and cross-classified niches) using 0%,

50% and 100% minimum-worker restriction levels

Industrial niches Occupational niches Cross-classified niches

0 50 100 0 50 100 0 50 100

Age �0.012***(.9882)

�0.013***(.9875)

�0.014***(.9865)

�0.010***(.9900)

�0.010***(.9904)

�0.011***(.9887)

�0.011***(.9894)

�0.012***(0.9877)

�0.014***(.9865)

Female �0.237***(.7892)

�0.253***(.7766)

�0.311***(.7327)

�0.305***(.7373)

�0.306***(.7363)

�0.332***(.7176)

�0.385***(0.6801)

�0.395***(.6740)

�0.413***(.6615)

Married 0.359***(1.4315)

0.357***(1.4291)

0.316***(1.3711)

0.304***(1.3552)

0.312***(1.3656)

0.331***(1.3928)

0.303***(1.3540)

0.300***(1.3495)

0.306***(1.3582)

Poor English 0.353***(1.4239)

0.326***(1.3851)

0.257***(1.2928)

0.478***(1.6127)

0.483***(1.6214)

0.477***(1.6118)

0.626***(1.8706)

0.678***(1.9704)

0.718***(2.0512)

Degree 0.294***(1.3422)

0.298***(1.3474)

0.367***(1.4432)

1.115***(3.0500)

1.129***(3.0925)

1.150***(3.1580)

0.793***(2.2100)

0.798***(2.2216)

0.761***(2.1413)

Foreign-born 0.749***(2.1147)

0.761***(2.1408)

0.817***(2.2634)

0.664***(1.9417)

0.692***(1.9973)

0.718***(2.0500)

0.755***(1.3572)

0.739***(2.0930)

0.742***(2.0997)

Recentimmigrant

�0.041 �0.059 �0.057 0.01 0.03 0.043 0.145*(1.1558)

0.191**(1.2108)

0.211**(1.2355)

Self-employed

�0.704***(.4946)

�0.720***(.4868)

�0.750***(0.4722)

�0.543***(0.5809)

�0.538***(.5842)

�0.663***(0.5154)

�0.664***0.5146

�0.866***(.4208)

�0.795***(.4514)

Family size 0.01 0.006 �0.004 �0.006 �0.008 �0.008 �0.002 �0.003 0Constant �0.78*** �0.76*** �0.83*** �1.25*** �1.38*** �1.42*** �0.91*** �0.94*** �0.99***

�2Log �7228.8 �7204.1 �7056.3 �6923.3 �6829.8 �6701.1 �7139.9 �7037.7 �6973.2chi2 510.062 510.489 538.217 962.011 972.22 1022.889 853.947 900.006 872.734r2_p 0.034 0.034 0.037 0.065 0.066 0.071 0.056 0.06 0.059N 11000 11000 11000 11000 11000 11000 11000 11000 11000

Notes : *pB0.05; ** pB0.01; *** pB0.001; numbers in parenthesis are odds ratios between the odds of working in ethnic versus the odds of not working, which is the

exponential form of the coefficient [odds ratio�exp (b)]; odds ratios in all analyses kept constant at 1.5.

Journ

al

ofE

thn

ica

nd

Migra

tionS

tud

ies1

24

7

Chinese with a bachelor’s degree is more than twice that when we use the industrial

niche classification (keeping the minimum-worker classification the same, viz. 3.16

vs.1.44). Immigrating into the US after 1995 has no effect if we use IND and OCC,

but its effect becomes pretty robust when we use the cross-classified sector analyses

(especially at high levels of minimum-worker restriction). These results suggest that

the role of factors like demographic characteristics, human capital and ethnic

resources may vary significantly depending on how we define ethnic niches. In other

words, different sectoral classifications are likely to support or work against particular

theoretical hypotheses.

The variations in the model parameters due to changes in minimum restriction

are much more modest than those seen when sectoral definitions change. This is

not surprising given that only a relatively small number of workers are dropped as

the restriction increases. However, in combination with sectoral definition, the

effect of varying the minimum restriction can be somewhat significant. For

example, when all other conditions are held constant, having a bachelor’s degree

increases the chance of working in a Chinese niche by a factor of 1.15 when using

the CROSS and 100 per cent restriction. But the coefficient of the same variable is

only 0.29 when using IND without a minimum restriction. The difference can be

crucial in terms of how we understand the ‘niching’ process.

Figure 4 graphs the predicted probabilities of working in a Chinese niche for

workers who are female, married, with good English, no bachelor’s degree, not

foreign-born, and not self-employed, and with other conditions held at their mean

levels. The figure shows that the probabilities were the highest under the industrial

niche classifications. As expected, rising minimum restrictions lowered the

predicted probabilities. This figure clearly captures a part of the overall point

0.1

0.15

0.2

0.25

0.3

IND OCC CROSS

Category

Pro

bab

ility 0

50%

100%

Figure 4. Predicted probability of working in ethnic niches for assumed type of workers


that we are making in this paper: the probability of working in an ethnic niche

sector depends crucially on the way in which the sectors are defined, the level of

aggregation/disaggregation of jobs, the threshold value of the odds ratio, and the

minimum-worker restriction imposed.

Conclusions

The rapid growth of the foreign-born population in the US over the past few decades

has generated an enormous interest in ethnic labour markets. There has been a

particular interest in the processes contributing to the formation of ‘ethnic niches’, i.e.

job sectors dominated by a particular ethnic group. This study was based on the

premise that the way in which we measure and identify ethnic labour market niches

influences how we understand the phenomenon itself. Data for Chinese workers in

San Francisco were used to explore the implications of different concentration

measures. Our findings can be summarised as follows.

The manner in which employment sectors are defined and the level of

disaggregation of job categories greatly influence the number of niches that are

derived and the share of the ethnic population that is engaged in niche sectors. There

are clear differences based on whether sectors are defined using industrial,

occupational, or cross-classified criteria. Our results suggest that it is most

appropriate for researchers to make a priori decisions on sectoral classifications

and aggregation levels based on their particular research questions. Industrial sectors,

for example, may be more useful when examining the structural change of the

economy, whereas occupational sectors may be appropriate when a study focuses on

the nature of work and skill levels. In terms of aggregation, with greater

disaggregation there is a rise in the number of niche sectors identified and also an

increase in the share of the ethnic population classified as working in ethnic niches.

Detailed categorisation is appropriate when the size of the metropolitan area or the

ethnic group is sufficiently large. However, when dealing with small ethnic groups, a

detailed scheme leads to numerous empty cells in the analysis, and it becomes

preferable to work with a smaller number of categories. Therefore, it cautions the

comparison between different places simply by the number of niche sectors and

workers.

With regard to the concentration measures and their threshold levels, the literature

suggests that there are no significant differences in using the odds ratio, the

representation index or the location quotient, mainly because most ethnic minorities

share relatively small or local labour markets. We favour the odds ratio because of its

statistical significance and conceptual emphasis on the comparison between different

ethnic groups. In terms of the threshold value, 1.5 is the most widely used value in the

literature, although 1.2 and 2 were used occasionally. Generally speaking, a lower

threshold value of an odds ratio will be more appropriate for highly aggregated

employment sectors, because values set a more stringent criterion in defining an

ethnic niche. While most studies emphasise the large niche sectors measured by the


number of engaged populations, the sectors with the biggest odds ratios were often

overlooked. Therefore, examining sectors with extremely high odds ratios may

provide important information on the ‘ethnic domination’ in specific labour market

sectors.

A third important issue is the ‘minimum restriction’, i.e. the minimum number of

ethnic workers who need to be in a sector in order for it to be classified as an ethnic

niche. This is particularly important when using highly disaggregated job categories

which could yield very sparsely populated sectors yet with high odds ratios. Our

study suggests that a relative minimum restriction is preferable to using an absolute

number such as 300 or 500, which appeared in previous studies. A relative minimum

restriction is expressed as a percentage of the average number of ethnic workers across

all employment sectors, and allows the analysis to be sensitive to the size of the labour

market and of the particular ethnic group, and the level of disaggregation. When the

ethnic group is small, the level at which we set this minimum restriction percentage

does not make much difference, since the average size of the group across sectors is

fairly small. However, when the ethnic group is large, the level at which we set the

minimum restriction has a significant impact on the number of niches identified. We

prefer 50 per cent or below for most ethnic minority workers when using detailed

employment sectors, because high percentage restriction seems too rigorous at the

detailed level. Again, looking into the sectors with extremely high odds ratios but

dropped due to the minimum restriction is useful.

Finally, all the factors discussed above can be expected to contribute to how we

understand labour market concentration patterns and the factors that influence them.

We examined this through logistic regression in which the probability of working in a

niche sector (dependent variable) was expressed as a function of a number of

personal and family attributes, and the models were run using different sectoral

definitions, levels of aggregation, and minimum-worker levels. We found that,

although the qualitative nature of the results did not change under the different

scenarios (i.e. the signs of the predictors remained the same), the size of the various

coefficients could vary considerably. Indeed, it would be possible for researchers to

find support for a particular hypothesis regarding ethnic niches by simply tinkering

with the criteria used to define niches. Of course we do not suggest that there be a

single, fixed definition for measuring ethnic labour market concentration. However

the sensitivity of findings to the manner in which we define niches must be

recognised. We hope this study will make scholars even more aware of how the

technical choices they make in defining niche sectors are fundamentally linked to the

theoretical results they derive.

Notes

[1] To look at the changes in the values of each indicator with the increase in sectoral

distribution, the number of total labour forces in each sector are set fixed and the share of the

ethnic group in each sector changes from 1 to 99 per cent.


[2] We are not saying that local economic structure is not important. In fact, ethnic labour

market concentration studies should be examined under local economic contexts. However,

separating the measuring/defining ‘concentration’ from other effects will help us to better

understand both.

[3] For the sake of simplicity, we restricted our sample to those Chinese workers employed in

civilian job sectors, aged between 16 and 64; we did not differentiate between self-employed,

public- or private-sector workers.

[4] The structural zero sectors are those that have no workers in them. In our case, they mainly

comprise of Community and Social Services occupations and the Extraction industry. The

detailed sectors are available from the authors.

References

Agresti, A. (2002) Categorical Data Analysis, New York: Wiley (2nd edition).

Allen, J. and Turner, E. (1997) The Ethnic Quilt: Population Diversity in Southern California.

Northridge: California State University, Center for Geographical Studies.

Brady, D. (2003) ‘Rethinking the sociological measurement of poverty’, Social Forces, 81(3): 715�52.

Catanzarite, L. (2003) ‘Race�gender composition and occupational pay degradation’, Social

Problems, 50(1): 14�37.

Egan, K.L., Anderton, D.L. and Weber, E. (1998) ‘Relative spatial concerntration among minorities:

addressing errors in measurement’, Social Forces, 76(3): 1115�21.

Ellis, M. and Wright, R. (1999) ‘The industrial division of labour among immigrants and internal

migrants to the Los Angeles economy’, International Migration Review, 33(1): 26�54.

Ettlinger, N. and Kwon, S. (1994) ‘Comparative analysis of US urban labour markets: Asian

immigrant groups in New York and Los Angeles’, Tijdschrift voor Economische en Sociale

Geografie, 85(5): 417�33.

Flegg, A.T., Webber, C.D. and Elliott, M.V. (1995) ‘On the appropriate use of location quotients in

generating regional input�output tables’, Regional Studies, 29(6): 547�61.

Hakim, C. (1992) ‘Explaining trends in occupational segregation: the measurement, causes, and

consequences of the sexual division of labour’, European Sociological Review, 8(2): 127�52.

Hamilton, R.F. and Form, W.H. (2003) ‘Categorical usages and complex realities: race, ethnicity,

and religion in the United States’, Social Forces, 81(3): 693�714.

Hudson, M. (2003) ‘Modeling the probability of niche employment: exploring workforce

segmentation in metropolitan Atlanta’, Urban Geography, 23(6): 528�59.

Huffman, M.L. and Cohen, P.N. (2004) ‘Racial wage inequality: job segregation and devaluation

across US labour markets’, American Journal of Sociology, 109(4): 902�36.

Isserman, A. (1977) ‘The location approach to estimating regional economic impacts’, Journal of the

American Institute of Planners, 43(1): 33�41.

James, D.R. and Taeuber, K.E. (1985) ‘Measures of segregation’, Sociological Methodology, 15(1):

1�32.

Kmec, J.A. (2003) ‘Minority job concentration and wages’, Social Problems, 50(1): 38�59.

Logan, J., Alba, R. and McNulty, T. (1994) ‘Ethnic economies in metropolitan region: Miami and

beyond’, Social Forces, 72(3): 691�724.

Logan, J.R., Alba, R., Dill, M. and Zhou, M. (2000) ‘Ethnic segmentation in the American

metropolis: increasing divergence in economic incorporation, 1980�1990’, International

Migration Review, 34(1): 98�132.

Logan, J., Alba, R. and Stults, B. (2003) ‘Enclaves and entrepreneurs: assessing the payoff for

immigrants and minorities’, International Migration Review, 37(2): 344�88.

Massey, D. and Denton, N. (1988) ‘The dimensions of residential segregation’, Social Forces, 67(2):

281�315.


Portes, A. and Jensen, L. (1989) ‘The enclave and the entrants: patterns of ethnic enterprise in

Miami before and after Mariel’, American Sociological Review, 54(6): 929�49.

Portes, A. and Sensenbrenner, J. (1993) ‘Embeddedness and immigration: notes on the social

determinants of economic action’, American Journal of Sociology, 98(6): 1320�50.

Razin, E. and Light, I. (1998) ‘The income consequences of ethnic entrepreneurial concentrations’,

Urban Geography, 19(6): 554�76.

Richard, D. (1998) ‘A joint model of residential and employment location in urban areas’, Journal of

Urban Economics, 44(2): 197�215.

Rosenfeld, M. and Tienda, M. (1999) ‘Mexican immigration, occupational niches, and labour-

market competition: evidence from Los Angeles, Chicago, and Atlanta, 1970�1990’, in Bean,

F.D. and Bell-Rose, S. (eds) Immigration and Opportunity. New York: Russell Sage

Foundation, 64�105.

Ruggles, S., Sobek, M., Alexander, T., Fitch, C.A., Goeken, R., Hall, P.K., King, M. and Ronnander,

C. (2004) Integrated Public Use Microdata Series: Version 3.0. Minneapolis: Minnesota

Population Center.

Saxenian, A. (1999) Silicon Valley’s New Immigrant Entrepreneurs. San Francisco: Public Policy

Institute of California.

Semyonov, M., Haberfeld, Y., Cohen, Y. and Lewin-Epstein, N. (2000) ‘Racial composition and

occupational segregation and inequality across American cities’, Social Science Research,

29(2): 175�87.

Waldinger, R. (1994) ‘The making of an immigrant niche’, International Migration Review, 28(1):

3�30.

Waldinger, R. (2001) Strangers at the Gates: New Immigrants in Urban America. Berkeley: University

of California Press.

Wang, Q. (2004) ‘Asians’ concentration in the US urban labour market: a disaggregated study’,

Population, Space and Place, 10(6): 479�94.

Wang, Q. (2006) ‘Linking home to work: ethnic labour market concentration in the San Francisco

CMSA’, Urban Geography, 27(1): 72�92.

Watts, M.J. (1997) ‘The measurement of occupational gender segregation’, Journal of the Royal

Statistical Society (Series A), 160(1): 141�5.

Watts, M.J. (1998) ‘The analysis of sex segregation: when is index measurement not index

measurement?’, Demography, 35(4): 505�8.

White, M.J. (1983) ‘The measurement of spatial segregation’, American Journal of Sociology, 88(5):

1008�18.

Wilson, F. (1999) ‘Ethnic concentration and labour-market opportunities’, in Bean, F.D. and Bell-

Rose, S. (eds) Immigration and Opportunity. New York: Russell Sage Foundation, 106�40.

Wilson, F. (2003) ‘Ethnic niching and metropolitan labour markets’, Social Science Research, 32(3):

429�66.

Wong, B.P. (1998) Ethnicity and Entrepreneurship: The New Chinese Immigrants in the San Francisco

Bay Area. Boston: Allyn and Bacon.

Wong, D. (1997) ‘Spatial dependency of segregation of indices’, The Canadian Geographer, 41(2):

128�36.

Wright, R. and Ellis, M. (2000) ‘The ethnic and gender division of labour compared among

immigrants to Los Angeles’, International Journal of Urban and Regional Research, 24(4):

583�601.


Documents

Measuring Ethnic Labour Market Concentration and Segmentation