In this section, I present the results from the empirical analysis. In Section 3.7.1, I discuss general trends in employment by gender, before I turn to analyzing the
3.7.2. In order to study the relative importance of within and between industry changes, the changes in the dissimilarity index are decomposed into sex and industry mix components in Section 3.7.3. Motivated partly by the observation that the changes in gender segregation over time are driven by industries that do mainly employ medium and low skilled workers, I study the trends in segregation by level of education in Section 3.8.
3.7.1 General employment trends by gender
As mentioned in Section 3.6, the labor force participation rate of Norwegian females started to increase rapidly in the 1970s. This had an enormous impact on the Norwegian work force. Table 3.1 shows that the work force, as defined in Section 3.4, increased by about 820,000 individuals between 1970 and 2009 and about 77 percent of these ”new” workers were female. The female share of the work force increased from 24 percent in 1970, to 34.9 percent in 1980 and 44.5 percent in 1990, where after it slowly increased to 47.1 percent in 2009, as shown in Table 3.1.
The large inflow of female workers affected the industry composition of the Norwegian economy, as shown in Table 3.2. Manufacturing was initially the largest industry sector but its share of total employment declined steadily throughout the period. Agriculture, construction, transportation and wholesale and retail trade also decreased in relative size. Public sector industries, and especially health and social work, increased, as did the employment share in real estate, renting and business activities. Employment shares by gender are plotted in Figure 3.3.
It shows that manufacturing, construction and wholesale and retail trade have been the most important industries employing males, and that health and social work has been the most important employer of females. Over time, however, the the male share in manufacturing has decreased, and males have become more evenly distributed over industries. Females, on the other hand, have become more concentrated in health and social work over time, and in 2009, almost 40 percent of all employed women were working in the health and social work sector.
The female employment share increased in most industries as shown in Figure 3.4. Interestingly, however, the female employment share did not change in the two most segregated sectors, construction and health and social work, where the female share has remained below ten percent and above 80 percent, respectively.
This analysis already suggests that there have been many changes in the
3.7.2 Trends in segregation
The overall trend in gender segregation, as expressed by the dissimilarity index, is depicted in Figure 3.5. The dissimilarity index is computed at three different levels of aggregation. I focus on the most detailed level of data (3-digit level in 1970-1980 and 5-digit level in 1986-1998 and 1996-2009) and use the more aggregated data as sensitivity checks.
Given the large changes in the female labor force participation and industry composition discussed in the previous section, the level of industrial segregation has been surprisingly stable. Using industry data reported at the 5-digit level, the dissimilarity index varied around 50 percent, meaning that in order to obtain an equal distribution of males and females within industries, about 50 percent of all workers should change industries. Between 1970 and 1980, segregation decreased when using 3-digit level data. In the 1980s, there was a decrease in the dissimilarity index measured at all levels of segregation, followed by a number of years characterized by stability. From the mid 1990s, there were signs of increasing levels of segregation, although the increase was very small. These results are in line with those of Jensberg et al. (2012), who concluded that the period 1990-2010 was characterized by stability, and that if anything industrial segregation was on the rise.
3.7.3 Decomposition of trends
The advantage of expressing the level of segregation as one single number is that it is easy to interpret, and trends are easy to overview. The downside is that a lot of information is lost. Based on Figure 3.5, one would conclude that there was very little change in the level of segregation between 1970 and 2009. But from Section 3.5, we know that the dissimilarity index is the sum of within industry composition of males and females and the industry mix in the economy. From Section 3.6, we also know that there have been significant changes both in the gender composition within sectors and in the industry composition. In this section,
relationship between these two forces, and I study which industries contributed to the changes in segregation. I split the data into four periods: 1970-1980, 1980-1990, 1990-1998 and 1999-2009.
In addition, I apply a method that was introduced by Hakim (1993), which is helpful in studying whether the changes in segregation over time stem from male or female dominated industries, or both. More specifically, I divide industries into three categories based on the female share of employment in each industry. An industry is defined as male (female) dominated if its female share of employment is more than 10 percentage points lower (higher) the overall female share of employment. In 2009, for example, the female share of total employment was 47.7 percent. Then it follows that industries with a female share below 37.7 percent were defined as male dominated, while industries with a female share above 57.7 percent were female dominated. The rest were classified as integrated industries.
1970-1980
Between 1970 and 1980, the dissimilarity index decreased by 0.48 percentage points. Table 3.3 shows that the decrease was the sum of two opposing forces.
On the one hand, the negative sex component suggests that the within industry change in gender composition alone would have led to a 2.07 percentage point decrease in segregation in this period had the industry composition remained at its 1970 level. On the other hand, the industry mix component was positive, meaning that the relative employment share of segregated industries increased, thus increasing segregation by 1.59 percentage points.
In Table 3.4, I investigate the impact of different industries on the dissimilarity index, following Bertaux (1991). I calculate the sex and industry mix components separately for all industries and aggregate the industry specific components to the 1-digit level to facilitate the display of the results. Most sex components were negative, indicating that males and females became more equally distributed within industries, either because an originally male dominated industry experi-enced faster female employment growth than in the overall economy, or because a female dominated industry experienced slower growth than average growth in female employment. There were, however, two exceptions: the agriculture, hunting and forestry sector became more male dominated, while the wholesale and retail trade sector became even more female dominated, increasing segregation by 1.41 and 0.80 percentage points, respectively.
largest negative industry components because they were all fairly segregated industries that decreased in relative size.
The industry specific sex components are plotted against the industry mix components in Figure 3.6, which allows me to identify the industries that contributed the most to the changes in the dissimilarity index. The figure confirms the findings in Table 3.4, but also further highlights that the gender distribution became more equal in most industries, as only three industries (agriculture, hunting, fishing and forestry, retail of food beverages and tobacco and food manufacturing) had significantly positive sex components, i.e., became more segregated.
In Table 3.5, I study whether the changes in segregation came from changes in male or female employment, or both. Following Hakim (1993) and Blau et al.
(1998), all industries were divided into male dominated, integrated and female dominated industries based on their gender composition as discussed above. By holding the category of each industry fixed over time (within subperiod) in Panel A, it is possible to track the flows of workers between the three categories. In other words, by tracking the flow of male and female workers between male dominated, integrated and female dominated industries between 1970 and 1980 we can learn about the underlying changes in the sex component. Similarly, the change in the distribution of total employment is informative of the industry mix component. In Panel B, the industries are re-categorized based on their current year gender composition and this shows how the reallocation of workers affected the categorization of industries. The distribution of industries is tabulated in Panel C.
Panel A suggests that the negative sex component in Table 3.5 is driven by male employment. Males moved from male dominated industries, such as agriculture, fishing and manufacturing to integrated and female dominated industries. Women, on the other hand, became more concentrated in female dominated industries, which increased segregation. Panel B suggests that some initially integrated industries became male dominated as a consequence of the inflow of male workers and outflow of female workers.
3.3 was driven mostly by employment growth in female dominated industries. In Panel B, there are some signs of polarization, meaning that the employment shares in female and male dominated industries increased at the expense of integrated industries.
1980-1990
Between 1980 and 1990, the dissimilarity index decreased by 3.4 percentage points.
Table 3.3 shows that this was the result of men and women becoming more equally distributed within industries, and of relative employment growth in integrated industries. In particular, Table 3.4 indicates that reductions in employment in male dominated industries like agriculture and manufacturing, and female dominated wholesale and retail trade had a lowering impact on segregation, and that employment growth in the health and welfare sector did not have as strong an impact on segregation in the 1980s as in the 1970s. The within industry changes in gender composition were very similar to those in the 1970s, with two exceptions:
the female employment share in the agriculture sector increased (while total employment decreased), leading to less segregation, while public administration became more female dominated, leading to an increase in segregation.
Figure 3.7 shows that local government administration had a particularly large impact on segregation, as it became more female dominated and increased in relative size. Expansions of child care and municipal social service offices, which were highly female dominated, and national defence, which was male dominated, also put upward pressure on segregation. At the other extreme, downsizing in male dominated industries such as agriculture, fishing, shipbuilding, ocean transport and construction helped to decrease segregation. Ocean transport became less male dominated partly because downsizing mostly affected male workers, and general somatic hospitals became slightly less female dominated, which helped to reduce segregation.
Table 3.5 shows that similarly to the 1970s, male employment increased in female dominated and integrated industries at the expense of male dominated industries. Among female workers, employment increased in integrated industries, and decreased in both male and female dominated industries. Total employment increased in integrated and female dominated industries, but a comparison with the numbers of Panel B suggests that the gender composition of some initially integrated industries changed to being either male or female dominated in this
1998. Table 3.3 shows that the increase was driven by changes in the industry mix (+1.03), as the sex component was very small (-0.16).
The small sex component was partly explained by the fact that the industry specific sex components were smaller in absolute size than in previous periods (notice the difference in the scale on the axes in Figures 3.6 to 3.9). In other words, there was a general tendency towards smaller within industry changes in gender composition. The fact that the components were smaller could be related to the slower increase in the female labor force participation rate. Still, some industries stand out. First, the sex component of the health and welfare sector was only -0.32, which was largely explained by a slower defeminization of public service sectors, such as child care activities and social welfare services for the aged (see also Figure 3.8). Fast female employment growth in (primary) education also increased segregation. Local government administration and municipal social service offices decreased in relative size in the 1990s, and the female share of employment decreased in these industries, resulting in negative sex and industry mix components. The further expansion of child care and social welfare services for the elderly, which mostly employed females, continued in the 1990s, putting upward pressure on segregation. The only private sector industry that stand out in Figure 3.8 is telecommunications. The employment share in telecommunications decreased while the female employment share decreased, resulting in a rather large positive sex component.
In the 1990s the flows of male and female workers between integrated, male and female industries were smaller than in previous decades, but the directions of the worker flows were essentially the same as in earlier periods. Both male and female employment decreased in male dominated industries and increased in female dominated industries, suggesting that male workers moved in a way that reduced segregation while the opposite was true for female workers. The total employment numbers suggest that the positive industry component was driven by employment in female dominated industries.
Between 1999 and 2009, the dissimilarity index increased by 1.10 percentage points. The decomposition in Table 3.3 shows that the sex component was negative and the industry mix component was positive, but the relative size of the two components changed compared to earlier periods. Within industry changes contributed less to reducing segregation (-0.61), while employment growth in segregated industries contributed more to the change in segregation (+1.70).
In Table 3.4 and Figure 3.9, where the industry specific components are displayed, we see that most sex and industry mix components were small in absolute size compared to earlier periods. Many industry mix components in Figure 3.9 were positive, but it was mainly employment growth in child care activities and nursing and caring for the aged that drove the increase in segregation. Feminization of primary and secondary education also continued in the 2000s. This period was characterized by good economic conditions in Norway, and as a consequence the construction industry expanded while its female employment share decreased, resulting in a rather large and positive impact on gender segregation.
A few additional points in Figure 3.9 are worth mentioning. The expansion of child care was the main force driving the increase in the dissimilarity index in this period, although the female employment share in the sector decreased somewhat. While not labelled in the figure, the female employment share in telecommunications (hidden behind primary and lower secondary schools) decreased sharply in this period, which contributed to an increase in the dissimilarity index. At the other end of the sex component scale, we find national post activities, which went from female dominated to gender balanced as the employment share decreased. Provision of personnel went from being female dominated to slightly male dominated.
Table 3.5 shows a pattern that is very similar to previous periods, although in 1999-2009 both male dominated and integrated industries lost workers to female dominated industries. The changes were, however, quite small. Comparing Panels A and B, it looks as if some of the initially female dominated industries became integrated and some of the initially integrated industries became male dominated as a consequence of the worker flows.
composition played an important role in the development of gender segregation over time. The expansion of the health and welfare sector was very important in driving segregation, as it absorbed almost 50 percent of all female labor market entrants since 1970, and employed almost 40 percent of all female workers in 2009.
The impact of the health and welfare sector on segregation was at its largest in the 1970s, but it also affected the level of segregation considerably after 1990. The expansion of child care services and care for the aged and disabled were the main the drivers of between sector segregation over time.
The story of segregation in the Norwegian labor market is primarily a story about female workers, but men have also played an important role. Especially in the 1980s, downsizing of male dominated industries, such as agriculture and manufacturing, counteracted the upward pressure that the expansion of female dominated service industries put on segregation. In later years, male employment increased in business activities, and to some extent also to mining and quarrying and construction, which lead to more segregation as these industries are male dominated.