Jennifer Jarman, Robert M. Blackburn, Bradley Brooks and Esther Dermott (1999) 'Gender Differences at Work: International Variations in Occupational Segregation'
Sociological Research Online, vol. 4, no. 1, <http://www.socresonline.org.uk/4/1/jarman.html>
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Received: 08/10/98 Accepted: 29/03/99 Published: 31/3/99
|MM||Male Workers||Female Workers||F%*||#Occs||Year|
|Korea, Rep of||.308||10686000||6408000||37.49||44||1989|
*F% is the percentage of the labour force which is female.
where n is the number of occupations and , and y are the three parameters that are possible in the estimation equation.
However, it turned out that the estimate of y was approximately 1 in all the equations providing a good fit. We therefore dropped it from our estimating procedure and used the simpler equation.
We estimated the optimal values are = 0.60 and = 0.93.
Thus the final equation arrived at was
(For further details of the derivation of this formula see the Appendix).
|Korea, Rep of||.357||31||.308||32|
|Italy (.424)||Austria (.566)||Sweden (.683)|
|Poland (.500)||Bulgaria (.520)||Hungary (.583)|
|Japan (.443)||USA (.549)||Canada (.604)|
|Egypt (.455)||Tunisia (.540)||Angola (.658)|
LATIN AMERICA AND THE CARIBBEAN
|Haiti (.529)||Costa Rica (.677)|
ASIA AND THE PACIFIC
|E., S.E., and S. Asia|
|Malaysia (.343)||Rep. Of Korea (.357)||Hong Kong (.525)|
|Iran (.569)||Jordan (.618)||Kuwait (.655)|
Note: Romania, Senegal and China are excluded from this table; if included they would each have the lowest value for the relevant group.
Pattern 1 examples 1. Finland 1990 Count Midpoint 5871000 5.00 _______________________________________________ 2895000 15.00 ________________________ 954000 25.00 ________ 2444000 35.00 ____________________ 1035000 45.00 _________ 1270000 55.00 ___________ 1345000 65.00 ____________ 1698000 75.00 ______________ 1221000 85.00 ___________ 5949000 95.00 _______________________________________________ I....+....I....+....I....+....I....+....I....+....I 0 1280 2560 3840 5120 6400 Number of workers (thousands) 2. UK 1990 Count Midpoint 7058353 5.00 _____________________________________________ 2601457 15.00 _________________ 1582321 25.00 ___________ 2448438 35.00 ________________ 1088880 45.00 ________ 556013 55.00 ____ 2010840 65.00 ______________ 4908623 75.00 ________________________________ 573218 85.00 _____ 3140052 95.00 _____________________ I....+....I....+....I....+....I....+....I....+....I 0 1600 3200 4800 6400 8000 Number of workers (thousands)
Pattern 2 examples 1. Malaysia 1980 Count Midpoint 934474 5.00 ______________________________ 325783 15.00 ___________ 292564 25.00 __________ 1216912 35.00 _______________________________________ 1131809 45.00 ____________________________________ 126562 55.00 _____ 33503 65.00 __ 129749 75.00 _____ 0 85.00 _ 77319 95.00 ___ I....+....I....+....I....+....I....+....I....+....I 0 320 640 960 1280 1600 Number of workers (thousands) 2. Bahrain 1991 Count Midpoint 141997 5.00 _____________________________________________ 20527 15.00 _______ 11197 25.00 ____ 4759 35.00 __ 3568 45.00 __ 1323 55.00 _ 6025 65.00 ___ 4382 75.00 __ 17507 85.00 ______ 785 95.00 _ I....+....I....+....I....+....I....+....I....+....I 0 32 64 96 128 160 Number of workers (thousands)
Pattern 3 example Poland 1988 Count Midpoint 4364541 5.00 ______________________________________________ 1106794 15.00 _____________ 1019412 25.00 ____________ 676035 35.00 ________ 854910 45.00 __________ 4784504 55.00 ___________________________________________________ 562905 65.00 _______ 540594 75.00 _______ 2258429 85.00 _________________________ 2071794 95.00 _______________________ I....+....I....+....I....+....I....+....I....+....I 0 960 1920 2880 3840 4800 Number of workers (thousands)
Pattern 4 example Japan 1990 Count Midpoint 1526606 5.00 _________________________________________________ 557920 15.00 __________________ 501238 25.00 _________________ 204464 35.00 _______ 571956 45.00 ___________________ 1489590 55.00 ________________________________________________ 497026 65.00 _________________ 358734 75.00 ____________ 282456 85.00 __________ 183206 95.00 _______ I....+....I....+....I....+....I....+....I....+....I 0 320 640 960 1280 1600 Number of workers (thousands) Pattern 4/3 example France 1990 Count Midpoint 5416089 5.00 ___________________________________________ 1648091 15.00 ______________ 2083088 25.00 _________________ 2312713 35.00 ___________________ 1748341 45.00 _______________ 1782120 55.00 _______________ 1623309 65.00 ______________ 1114771 75.00 __________ 2212450 85.00 __________________ 2292002 95.00 ___________________ I....+....I....+....I....+....I....+....I....+....I 0 1280 2560 3840 5120 6400 Number of workers (thousands)Figure 5: Number of Workers by Level of Female Concentration (percentage of workers in an occupation who are women, with the range from 0 to 100 divided into 10 equal groups)
Pattern 5 examples 1. Korea 1989 Count Midpoint 2479000 5.00 ____________________ 1008000 15.00 _________ 1560000 25.00 _____________ 3992000 35.00 ________________________________ 5093000 45.00 _________________________________________ 463000 55.00 _____ 1824000 65.00 _______________ 0 75.00 _ 684000 85.00 ______ 0 95.00 _ I....+....I....+....I....+....I....+....I....+....I 0 1280 2560 3840 5120 6400 Number of workers (thousands) 2. Romania 1990 Count Midpoint 118344 5.00 __ 623276 15.00 ______ 245282 25.00 ___ 5367925 35.00 ___________________________________________ 157684 45.00 __ 247254 55.00 ___ 409492 65.00 ____ 681629 75.00 ______ 54772 85.00 _ 0 95.00 _ I....+....I....+....I....+....I....+....I....+....I 0 1280 2560 3840 5120 6400 Number of workers (thousands)
Japan 1990 Occupation Female percentage of workers Professional, tech. 40 __________________________________ Clerical & services 58 _________________________________________________ Sales 37 ________________________________ Manufacturing, etc. 28 _________________________ Administration 9 ________ Agriculture, etc. 45 ______________________________________ I.........I.........I.........I.........I.........I 0 12 24 36 48 60 Mauritius 1990 Occupation Female percentage of workers Professional, tech. 35 _____________________________________________ Clerical & services 37 _______________________________________________ Sales 27 ___________________________________ Manufacturing, etc. 29 ______________________________________ Administration 17 ______________________ Agriculture, etc. 27 ___________________________________ I.........I.........I.........I.........I.........I 0 8 16 24 32 40 Norway 1990 Occupation Female percentage of workers Professional, tech. 57 ____________________________________ Clerical & services 76 ________________________________________________ Sales 54 __________________________________ Manufacturing, etc. 15 ________ Administration 31 __________________ Agriculture, etc. 27 _________________ I.........I.........I.........I.........I.........I 0 16 32 48 64 80 Sweden 1991 Occupation Female percentage of workers Professional, tech. 63 ___________________________________________ Clerical & services 73 _________________________________________________ Sales 55 ______________________________________ Manufacturing, etc. 18 _____________ Administration 34 _______________________ Agriculture, etc. 24 _________________ I.........I.........I.........I.........I.........I 0 15 30 45 60 75
An important consideration is the consistency of detail within occupational groups. Some areas of employment may have been more finely broken down than others so that, for example, there may be several forms of service work identified but relatively fewer classifications for manufacturing work. This has implications for the measured levels of gender concentration and segregation. In this respect, it has frequently been observed that women tend to be clustered in a small number of occupations, in contrast to men who are spread across a much larger number. While this pattern may reflect disparities in the employment opportunities of women and men, it has often been suggested that it is partly a product of the occupational classification scheme. Since occupations that are predominantly male have received more attention from researchers and government statistical organizations, they have tended to be classified more finely. In contrast, women's occupations have tended to be classified in large umbrella categories. Attempts to construct an interpretation of gender inequalities in employment based on the more restricted distribution of the female labour force relative to that of the male labour force, such as discussions around the 'crowding' of women into a limited number of occupations, would clearly need to be tempered by an awareness of this problem. This aspect of classification may also help to explain why there has been no discussion of the 'crowding' of men, despite the very high male concentrations in some occupations. The striking version of this problem in the present data is the extremely high proportion of the workforce in a single occupational group - ranging from 59% to 67% - in three countries: Senegal, China and Romania. Inevitably all three show low levels of segregation, but it is doubtful whether these results are meaningful in comparison to others.
'Segregation' is used as a general term to include a number of different data patterns. All of these patterns relate to the distribution of men and women in the labour force, and all are of interest in the study of gender inequality. However, they are essentially of two distinct kinds (discussed below), and each kind carries with it its own set of methodological issues. One of these kinds of data patterns is always referred to as segregation, and we shall keep to this practice. Sometimes the second kind is also called segregation, but in order to be more precise in the analysis and discussion or gender patterns in employment, we argue that it is more appropriate to refer to it as concentration.
A situation of total segregation would exist if all occupations were staffed exclusively by one sex or the other - that is, there were no occupations in which both men and women were employed. In some countries there are many single-sex occupations (particularly where the female share of the labour force is low) but there are also a substantial number of occupations where both men and women are employed. Nevertheless, all occupations are gendered in that they are, to differing degrees, predominantly male or predominantly female. Segregation refers to the extent to which this pattern occurs - the extent to which the sex distribution across occupations approaches total segregation. This may be conceptualised as the relationship between the gendering of occupations and the gender of workers. There would be no relationship, that is no segregation, if the mix of women and men in each occupation were the same. In practice this does not happen and there is always some degree of segregation. While there is a tendency for people to think of segregation as meaning total segregation, in fact there is a range of values that are relevant, and the level can vary - at least in principle - from zero segregation to total segregation.
The most widely used conception is the gender distribution within a particular occupation. Usually it is measured as the percentage of workers in the occupation who are women. This is a measure of the concentration of women in the occupation, and similarly the concentration of men is measured by the male percentage of workers in the occupation. For example, if 80 women and 20 men are employed in a particular occupation, the female concentration is 80% and the male concentration is 20%. While the usual practice is to measure the concentration of women, the male concentration is readily deducible from this.
The main point of interest is often the extent to which occupations are dominated by one sex, perhaps by identifying the two or three occupations with the highest concentrations of women, or the proportions of occupations employing each sex exclusively. From a broader perspective, the interest may lie in the general pattern of polarisation into predominantly female and male occupations. Although concentration typically measures the gender composition of an occupation, it can equally well apply to a group of occupations, an industry or a section of the labour force such as part-time workers. Whatever the type of grouping, there is a distinct level of concentration for each, for example, administration will have one value and agriculture another.
To some extent the percentage of women in an occupation depends on the extent of the employment of women in the labour force; the more women there are in employment, the more there are likely to be in any particular occupation. To take account of this, the female percentage in an occupation is sometimes divided by the female percentage of the employed labour force. This ratio is often referred to as the over-representation or under-representation of women in an occupation (according to whether its value is greater or less than one). This is not really a different conception of concentration, but rather a modification derived from the original measure.
Another way of examining concentration, which provides a useful overview, is to look at the distribution of workers across occupations with different degrees of female (or male) concentration. All of the occupations are grouped by their percentage female, then the number of workers can be plotted for levels of concentration (say at 10% intervals), showing the extent to which workers are employed in predominantly male, female, or mixed occupations.
In estimating the regression equation we were concerned to take account of differences due to changes over time as well as differences across countries. Our historical data, taken mainly from the UK, might suggest a national bias. However, the method is not really affected in this way, and so far as we could judge there was no problem.
Using these data we derived the most appropriate equation to express the relation between the number of occupations and the expected values of MM. The equation should have the following constraints: MM = 0 for no segregation, which necessarily occurs when the number of occupations = 1; MM = 1 for complete segregation, which necessarily occurs when the number of occupations equals the number of people in the employed population; and MM increases as the number occupations increases. The second criterion could not be met perfectly by our estimating equation, because it is dependent on the actual number of workers in each labour force. We therefore chose an asymptotic approach to a value of 1 as the number of occupations approaches infinity, while the estimated value is very close to unity for the number of workers in all of the countries in our data. Since the number of occupations in even the most detailed occupational scheme is far short of the number of workers, for example, Bahrain in the ILO data set (1050 occupations), the function serves precisely as intended.
Once we had the appropriate estimating equation we needed to choose a particular number of occupations on which to standardise. We chose 200 occupations as this was towards the middle of the range of sizes for the national data sets, and because it represents a level where estimates are reliable. For low numbers of occupations, any increase in numbers has a large effect, but at 200 occupations this is no longer so; therefore any small errors in the equation can have little effect.
Beyond these basic constraints, the criteria on which the parameter values for the weighting formula were chosen are as follows:
1) A high and significant correlation fitting the data to our equation. We used the non-linear regression function in the SPSS for Windows software programme, and achieved a value of R2 of .6088.
2) It was evident that the two outliers on the upper tail of the ILO data, Finland and Bahrain, had high levels of segregation even when the number of occupations was standardised. We were concerned that the standardization equation should not excessively deflate the coefficients estimated for 200 occupations in the countries. This meant we were not looking for the best fitting regression curve; we were balancing the smallest reduction in the correlation coefficient against the least reduction in the standardised values for these countries, given that the other criteria for the choice of the formula parameters were met.
3) As a check we estimated equations with and without the value of 1 for a huge number of occupations, and with the exclusion of the two outliers with large numbers of occupations. With the number of occupations tending to infinity, the value of R2 increased but the equation was unaffected. Dropping the outliers produced a percentage change of about 1 in the value of R2 for the unchanged equation.
4) Since the British Census provided good quality data for 4 different sized occupational groupings, we looked for consistency across their respective standardized measures of MM. Because the estimates based on larger numbers of occupations are less subject to error, we gave particular attention to the two values based on 371 and 77 occupations; the estimates for the grouping with 9 occupations were somewhat out of line with the others, confirming our belief that this grouping was comprised of too few occupations to yield a reliable measure, and so this did not affect our solution. We also had French data with two levels of occupational classification, which provided a further check on our estimates. The way occupations are grouped is bound to have some effect, so that it is not possible to get identical results when we standardise for different groupings. However, it emerged that the estimates were pretty consistent.
If data had been available it might have been worth checking on more countries. Nevertheless, the regression curve is very stable, in the sense that modest variations in the parameters have very little effect, so it is most unlikely that further tests would have had any significant consequence.
2 The terms 'segregation' and 'concentration' are used to differentiate different types of data patterns. Some analysts refer to all of these in an undifferentiated way as 'segregation'; however, we find this confusing. We use the term 'segregation' when we are referring to the tendency for men and women to be employed in different occupations across an entire spectrum of occupations. 'Concentration' refers to the representation of one sex within one or a group of occupations. For a further discussion and definitions, see Appendix 'Key Concepts'.
3 The book by Anker and Hein (1986) is an important contribution in this area, including case studies and macro level data for Cyprus, the city of Lucknow in India, the city of Colombo in Sri Lanka, the city of Accra-Tema in Ghana, Mauritius, and the city of Lima, Peru. Their focus, however, is primarily on urban employment.
4 This assessment of the literature is based on extensive searches in databases such as SOCIOFILE, and BIDS. Material written in languages not referenced by these sources will not be represented.
5 Some of these reasons include lack of importance placed on informal sector activity by governments, gender bias in the construction of official categories, a narrow focus and definition of economic activity as geared solely to the production of goods and services as commodities rather than as contributions to livelihood, the unwillingness of respondents to make unofficial economic activity visible to government officials. There have been some interesting efforts, however, to improve the sensitivity of official statistics with respect to the informal sector (e.g. Anker and Anker, 1989; Anker and Dixon-Mueller, 1988; Anker et al, 1987). While it has become a convention in feminist circles to work towards making women's work more visible, especially to government and development agencies, it may be the case that this would have a disempowering effect as it may simply increase the opportunities for state control which may not always be positive for the women concerned (See Ferguson, 1994, for a thought-provoking discussion of the effects of extending government power, although he does not discuss gender issues at great length).
6 As already noted some countries have even fewer categories (e.g. Mexico with 17), but these are regarded as having too few categories for useful analysis.
7 It is difficult to predict whether an unemployed worker will find employment within the same occupation or not.
8 See Appendix, 'Segregation', for a discussion of choice of measure.
9 Thus, if MMni is the observed value of MM in country i where the data set has n occupations and MMnE is the expected value for n occupations, we estimate MM200i = MM200E x MMni / MMnE.
10We should note that this meets three basic criteria: MME = 0 when n = 1; MME increases as n increases; and MME 1 as n . The third criterion here is not precisely what is required, but the difference is negligible, as explained in the Appendix.
11 Bahrain's economy is unusual because of its heavy reliance on migrant workers. Only 42% of its labour force is composed of citizens of Bahrain (Bahrain Human Rights Organisation (1995) www.iae.dtu/u/d946801/bhro2.htm).
12 We use the concepts of 'vertical' and 'horizontal' in their usual mathematical sense, rather than the unusual sense sometimes employed in segregation research. Most confusing in other segregation research is the use of the term 'horizontal' segregation to apply to what is actually the resultant of mathematically vertical and horizontal components (e.g. Hakim 1979, Moore 1985, Rubery and Fagan 1995).
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