Women working in the software industry in India
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Transcript of Women working in the software industry in India
UNDERSTANDING THE EFFECTS OF TECHNOLOGY LIFE CYCLE MODEL
AND JOB AND LABOR QUEUES ON EMPLOYMENT OF WOMEN IN THE
INDIAN SOFTWARE INDUSTRY
By
MANI PANDE
B.A (Hons.), University of Delhi, 1992
M. A., Jawaharlal Nehru University, 1994
M.Phil., Jawaharlal Nehru University, 1997
--------------------------------------------------------
AN ABSTRACT OF A DISSERTATION
Submitted in partial fulfillment of the
Requirements for the degree
DOCTOR OF PHILOSOPHY
Department of Sociology, Anthropology and Social Work
College of Arts and Sciences
KANSAS STATE UNIVERSITY
Manhattan, Kansas
2004
Chapter One
Introduction
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Over the past several decades, the nation of India has developed into an important
location in the global software industry. Thousands of new jobs have been created as
multinational software firms have established Indian branch operations, and numerous
domestic start-up firms have been established to produce software. As part of this
process, an increasing number of Indian women have entered the labor market, received
educational training in software development, and obtained employment in the software
industry. It has been estimated that women comprise about 12 per cent of the total work
force (National Association of Software and Service Companies, NASSCOM, 2000).
Presently, little is known about the types of jobs being obtained by Indian women,
although initial evidence suggests that women are concentrated in low-skilled and low-
paying jobs in the software industry in India. This research is among the first studies to
shed light upon the status of women in the software industry in India. The research design
for the study is qualitative involving field research with in-depth, personal interviews of
female workers in the software industry. The research site is the city of New Delhi, India.
Female workers employed in the software industry were identified for participation in the
study through the use of snowball sampling.
This chapter begins by providing a brief overview of technology and skill-training
life cycle model, and examining whether it is applicable to computer technology. Next I
will examine the important factors that produce changes in the job and labor queues,
thereby providing opportunities to women to join certain types of occupations in the
Indian software industry. I will also provide a brief background of theories that have been
developed to understand the relationship between gender and technology. I will then
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proceed to provide the justification of research. Last, I will provide the organization of
the chapters of the dissertation.
Research Problem
It is a major contention of this study that the theory of job and labor queues as
given by Reskin and Roos (1990), and the theory of technology and skill life cycles as
given by Shanklin, Ryan and Flynn are applicable in understanding the positions of
female workers in the Indian software industry. Economists have argued that a new
technology, introduced slowly at first, becomes more widely accepted as intense and
heavily financed research and development efforts lead to better performance.
Eventually, it reaches a plateau of its performance limits. During the last stage, it
competes with a new technology until the superior technology wins and captures the
market (Ford and Ryan, 1981; Shanklin and Ryans, 1984).
The history of computer programming clearly illustrates that computer technology
has a technology life cycle. The development of the computer is intricately linked with
wartime needs. The first modern computer in the United States, Electronic Numerical
Indicator and Computer (ENIAC), was developed during World War II to calculate
ballistic missile trajectories. The British Colossus, completed in 1943, was used to
unscramble German radio transmissions. These early/first-generation computers were
clumsy machines, composed largely of electromechanical or electrical switches regulated
by vacuum tubes, making operations highly painstaking. A turning point in computer
technology was the introduction of the stored program/second-generation computers. It is
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my argument that it was at this stage we saw the introduction of a new technology –
stored program computers -- that competed with the old technology – first-generation or
vacuum computers – finally replacing it. The used of stored program technology made
using computers less painstaking and time-consuming because the instructions which
operated the machine were stored in the machine’s memory, along with the data to be
processed. This technology was further improved with the use of transistors during the
1950s. Transistors offered the benefits of speed, reduced size and enhanced reliability
(Kraft, 1977, Greenbaum, 1979, Donato, 1990). And finally the introduction of
microprocessors in the 1970s, which provided the capacity to put a computer on a chip,
led to the introduction of the Personal Computer or PC (Castells, 2000).
Along with changes in hardware, computer software technology has also
undergone tremendous change1. As computer software technology has matured there has
been a trend toward job fragmentation and deskilling (Kraft, 1977). During the early
stages, skills associated with software programming were highly specialized. For
example, the machine/first-generation languages involved putting a sequence of binary
numbers directly through simple toggle switches. The whole process was highly skilled,
tedious and time consuming. The second-generation languages also called assembly
languages made coding easier as they used easy identifiable codes called Mnemonics,
instead of numeric operation codes. Mnemonics are abbreviated versions of English
words that are easy to recognize (Computer Languages, 2002). The third generation
languages made coding less time consuming because the coders did not have to
1 The history of software is inter-related with the history of hardware. Several innovations in software had little or no impact until they could mesh with corresponding innovations in hardware (Ceruzzi, 1998).
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familiarize themselves with the internal architecture of the computer. Finally, coding was
deskilled with the introduction of fourth-generation languages during the 1990s that are
user-friendly enabling less technical people to become involved in the programming
process. For example, computer languages such as Visual Basic and HTML, or packages
like Dreamweaver and Flash, now allow novice programmers to develop software
programs or web pages (The History of Computer Programming, 2002). The fourth-
generation languages require few specialized skills as they employ a graphic user
interface (GUI) that is easy to understand, use and master.
It has been further argued that a skill-training life cycle evolves as the level of
demand and standardization of skill changes with the development of a technology. The
early stages of a technology, characterized by a high degree of product innovation, are
relatively skilled and labor intensive. However, as technologies mature, `standardization
and the expanded use and complexity of equipment permit a greater division of labor and
the subdivision of multifaceted tasks into more narrowly defined assignments’ (Flynn,
1993:16). Additionally, the availability of skill training and the mix of institutional
providers vary depending upon the phase of the technology. When a technology is new,
skill training is usually provided on the job through various programs at the workplace.
As with products, increased demand and standardization of skills permits their
‘production’ on a large scale away from R&D sites. During this stage skill training is
shifted to outside educational institutions, as employers cannot capture the return on
investments in general skills. Another reason for this shift is that it is easier to formalize
the training process by providing it in schools as demand for skills grow (Flynn, 1993).
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The early stages of computer technology, characterized by a high degree of
product innovation, were relatively skill and labor intensive. Also, highly skilled
professionals were required to operate first-generation computers because they were
cumbersome machines. . In the early phases of a technology, skill training is usually
provided on the job. During the early stage of computer technology, hardware
manufacturers provided in-house training to software workers. For example, IBM trained
its staff to provide in-house training courses to employees of companies using IBM
machines. In fact, the training acquired at ‘IBM school’ became so prestigious during the
1950s and early 1960s that government and private employers used the lure of IBM
training to recruit potential employees in what was a tight seller’s market in programming
(Kraft, 1977).
However, as computer technology matured, standardization led to a greater
division of labor and deskilling. But some tasks continued to be relatively highly skilled.
During the latter stages of technology, training is institutionalized. For example, training
of computer professionals in the US has been institutionalized in a three-tiered system:
research universities or schools of management, four-year engineering colleges and two-
year junior institutions (Kraft, 1977).
It is my belief that to understand the stimulus behind changes in job and labor
queues we can utilize, the concept of technology and skill training life cycle. Reskin and
Ross (1990) provided a model of job queues and labor queues to understand how women
are able to make inroads into occupations that were previously male-dominated. The two
authors argue that the most fruitful model sees occupational composition as a result of a
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dual-queuing process: labor queues order groups of workers in terms of their
attractiveness to employers, and job queues rank jobs in terms of attractiveness to
workers. The two theorists posited that queues are characterized by three structural
properties: the ordering of their elements (i.e. jobs and groups of workers), their shape
(the relative sizes of various elements – population subgroups in the labor queue and
occupations in the job queues), and the intensity of rankers’ preferences (whether or not
elements overlap). Reskin and Roos argued that changes in these three structural
properties provide some groups such as women with jobs that were formerly beyond their
reach. These considerations transform the labor queue into a gender queue resulting in
high level of sex segregation at the workplace.
By definition, the feminization of an occupation results from the disproportionate
recruitment or retention of women workers. Disparate retention of women workers is a
product of changes in the structural features of queues (Reskin and Roos, 1990). Reskin
and Roos have discussed in detail factors that can lead to transformations in job and labor
queues. At this juncture, I will be concerned only with factors that have led to changes in
job and labor queues in the Indian software industry.
The first factor that produces changes in the shape of the job queue is growth in
existing occupations as was witnessed during the 1970s in service sector occupations in
the United States. Job growth supports feminization as it can lead to a shortfall of male
workers (Reskin and Roos, 1990). In addition, growth is especially likely to prompt
employers to resort to women for jobs whose high entry requirements limit the number of
qualified prospects. In such circumstances, employers “reduce their hiring standards,
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recruit from the disadvantaged labor force, and provide additional training to raise the
productivity of the disadvantaged” (Doeringer and Piore, 1971: 165). In jobs that demand
hard-to-acquire credentials, rapid growth is likely to exhaust the supply of trained
workers from the preferred group (Reskin and Roos, 1990).
The growth of occupations has been an important reason for change in the job
queues in the Indian software industry as the industry witnessed phenomenal growth
during the last decade. Employment in the Indian software industry expanded in the late
1990s with the growth of software firms and software revenues. In 1996, it was
estimated that there were 140,000 software professionals in India. In 2000, the number
of software professionals was estimated at 410,000 (NASSCOM, 2002). Due to the
boom in outsourcing, the number of software professionals increased to 770,000 in 2003
(NASSCOM, 2004).
Most importantly, technological changes remitting from the life cycle of computer
technology further the division of labor, deskill jobs, or alter working conditions. This
leads to a reranking of occupations in the job queue by men, thereby allowing women to
fill these previously male-dominated occupations (Oppenheimer, 1970). As pointed out
earlier, coding was deskilled with the introduction of fourth-generation languages during
the 1990s that are user-friendly, enabling less technical people to become involved in the
programming process. In addition due to technological changes, new jobs such as
Webmaster and content manager have been created that are ranked low in occupational
hierarchy.
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It is hypothesized that the deskilling of some computer jobs and the creation of
new deskilled jobs are the primary reasons for the feminization of software-related
occupations that are ranked low in the occupational hierarchy. These jobs are not ranked
high in the occupational hierarchy as they are not highly skilled, and have low
occupational prestige. In fact, it has been found that IT occupations that experienced an
influx of women have witnessed a deterioration of rewards and working conditions
(Moghadam, 1997).
There is evidence that a sizeable portion of the work that is outsourced to Indian
software firms is neither technologically advanced nor critical to the business of the firms
outsourcing the work (Arora et al., 1999). Thus the argument that I am putting forward is
that the growth of software industry has created jobs that are ranked low in occupational
hierarchy, and women are more likely to occupy positions that are low in occupational
hierarchy. However, I would like to point out that after the study was conducted, the
latest data shows that increasingly highly skilled work is also being outsourced to India
(Kirplani, 2003). Additionally, the feminization of occupations occurs more rapidly in
small and high-turnover occupations (Reskin and Roos, 1990). In India, labor turnover in
the software industry was very high due to outmigration of members of the Indian labor
force, particularly to the US (Arora et al., 1999). At this juncture, I would like to point
out that outmigration to the US has been stemmed because the US government decreased
the quota for H1B visas issued to Indian citizens to 65,000 in 2003.
Another factor that leads to women making inroads into previously male-
dominated occupations is employers reranking of sexes in the labor queue (Reskin and
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Roos, 1990). Cultural change due to modernization, information from the mass media,
and the increasing labor force participation of women have led to a change in the mindset
of employers.
Colleges and universities have played an important role in changing the shape of
the labor queue as they provide women with increasing opportunities to acquire skills
(Reskin and Roos, 1990). The training sector in India has grown along with the software
industry. Many private, for-profit, institutes provide diplomas in computers (Arora et al.,
1999). In fact, women comprise about half of the student population in these institutes.
(Yee, 2000). This is in direct contrast to men who go to elite institutes such as the Indian
Institutes of Technology (IITs) to pursue a formal degree in engineering.
The growth of the software industry in India has reshaped the job queue by
creating thousands of computer-related jobs that have outstripped the supply of qualified
males. Women have benefited from the shortages as employers have had to resort to hire
women. Changes in computer technology, such as growth of PCs and introduction of
user-friendly computer languages, have led to the deskilling of some software jobs and
have transformed the structure of job queues. In addition, new jobs have been created
that are ranked low in the occupational hierarchy. The reranking of occupations in the job
queue has opened employment opportunities for women, primarily in the lower rungs of
the occupational hierarchy that were not available to them earlier. However, we must
remember that deskilling reinforces the gendered division of labor as women continue to
be concentrated in occupations that are ranked lower in the occupational hierarchy
(Hafkin and Taggart, 2001).
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The major reason for changes in the labor queue related to the technology life
cycle was that with the institutionalization of the training process, an increasing number
of women were able to acquire the requisite skills. In the early stages of computer
technology, training was provided in-house. Therefore, it was out of reach of most
women as opportunities were limited. It was only during the later stages of development
in computer technology that private educational institutions started providing training in
computer technology. This provided an opportunity for women to acquire the requisite
technical skills.
Background Research
In order to understand the reasons behind the concentration of women in low
skilled and low paying occupations, we need to understand the reasons behind the low
participation of women in the field of engineering. The last twenty years have seen the
development of an important field of research: feminist studies of technology. There are
primarily three main theories on the relationship between gender and technology: eco-
feminism, liberal feminism and technology as masculine culture. According to eco-
feminism, technology represents a way in which men try to dominate, and control both
nature and women (Van Zoonen, 1992).
The liberal feminists believe that technology, itself, is neutral; and, men and
women occupy different positions in relation to it. They argue that women are lagging
behind men in their understanding and use of technology because their potential is
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distorted by gender stereotyping. Women are forced to take on particular sex roles, which
conceal their true nature and capabilities (Grint and Gill, 1995).
The last 15 years has seen the emergence of a new field of research – technology
as masculine culture -- that provides a critique of both the eco-feminist and liberal
approach. This theory has challenged the view that women’s alienation from technology
resulted from lack of access to training and employment, and a result of sex stereotyping.
It also rejects the view that women’s absence from the technological domain could be
understood by the fact that there is a difference in the manner in which men and women
relate to the world. Instead the proponents of this theory argue that women’s alienation
from technology is a result of the historical and cultural construction of technology as
masculine (Faulkner and Arnold, 1985; Cockburn, 1985, 1986, 1991, 1992; Wacjman,
1991). These theorists argue that technical competence constitutes an important part of
masculine identity, and conversely a particular idea of masculinity has become central to
the definition of technology.
The theories provided a background to the culture of computing in educational
institutions and software companies. They helped me assess the stereotypes about women
and computers. The study did not find any evidence that women were afraid of
technology or computers. In fact, most respondents had a positive estimation of their
work as it was ‘technical in nature.’ However, most respondents pointed out that they
were discriminated at some points in their careers because of negative stereotypes about
women and computing.
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Justification of the Research
India provides a unique opportunity to analyze the utility of the technology and
skill training life cycle model, and the labor and job queue theory to understand the
position of women in the Indian software industry. India, a relative laggard among
developing nations, has witnessed tremendous growth in the software industry. The
country has become a major player in the global software industry as a key site for
software outsourcing. Despite these impressive achievements, the women in India are
lagging behind men. Although detailed data on the participation of women in the Indian
software industry are not currently available, preliminary evidence shows that women are
concentrated in low-paying and low-skilled jobs (Pande, 1997). This study is a much-
needed research required to fill the existing lacunae in the field of gender and technology
in the context of India.
Organization of the Dissertation
This chapter has introduced the study, giving a general background on the
theories of technology and skill training life cycle, and the labor and job queue theory. It
has also provided a brief overview of how these two theories can be combined to
examine the position of women in the Indian software industry. Chapter two provides a
comprehensive account of the reasons for India having emerged as a major contender in
the global software industry. It also provides a brief history of the Indian software
industry. It also discusses in detail the characteristics of the industry.
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Chapter three reviews the literature that pertains to technology and skill training
life cycles. It also discusses the process and product life cycles as all four of these cycles
are interlinked. The chapter also reviews the three feminist theories – eco-feminism,
liberal feminism and technology as masculine culture -- that are used to explain the low
participation of women in the field of engineering. It then examines which theory best
applies in understanding the position of women in computing. Lastly, it reviews the
literature on the job and labor queue theory, and discusses how changes in the two queues
can lead to feminization of previously male dominated occupations.
Chapter four examines whether or not the technology and skill training life cycle
model can be utilized to understand the life cycle of computer technology. It also
examines the reasons that have led to changes in the job and labor queues in the software
industry in India, thereby allowing women the opportunity to join the labor force.
Chapter five presents the methodology that I followed for conducting the fieldwork. It
discusses the research site, research design, methods of data collection and analysis.
Chapter six provides the analysis of work experiences of female software professionals,
and it also analyzes the opinions of women software professional about female-
dominated occupations such as teaching. Chapter seven discusses the skill-training
experiences of female software professionals. Chapter eight concludes the dissertation by
inductively drawing propositions from data that can be tested in further studies. It also
provides policy implications of the study.
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Chapter TwoThe Indian Software industry
Introduction
Over the past several decades, much attention has been focused on
understanding the effects of information technology on social development in
capitalist societies. There has been a flood of books, articles and media coverage
heralding the ‘information age’ (Gates, 1995; Grenier and Metes, 1995), the ‘digital
age’ (Birt, 1996), or the ‘network society’ (Castells, 2000), among other variations.
Beginning with the development of the mainframe computer and analog
telecommunications as independent technologies, followed by their convergence in
satellite systems, telephone systems, and computer networks, innovations in
information technology have escalated at an increasingly rapid pace. Waves of new
technological innovations have been prompted by the development of the PC, the
development of intra-organizational networking of PCs (e.g., client/server systems),
and the development of the Internet and other infrastructure for the global exchange
of digital information.
These waves of innovations have spawned the development of a large number
of new industries in the global economy. In turn, the vast wealth created by these
new industries has stimulated the development of new geographic regions that serve
as centers for the creation, design, production, and marketing of various types of
information technology-related products and services. These regions have obviously
not been limited to such areas in the United States as Silicon Valley, Boston’s Route
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128 region, or the Research Triangle, but have been extended globally to other
advanced industrial nations, and regions within developing nations as well. India is
one developing nation that has experienced rapid economic development as a result of
this process. The cities of Bangalore and Hyderabad have developed as important
regional production complexes in the global software industry. Other cities such as
Pune, New Delhi, Chennai, and Mumbai have also experienced significant software-
related growth.
The purpose of this chapter is to describe the development of the software
industry in India, identify factors that have influenced this process, and examine the
potential implications of this development for Indian society. The first section
outlines the early development of the software industry in India. The second section
describes important factors that have influenced the growth of the industry over the
past several decades. The third section describes the rapid growth that occurred in the
decade of the 1990s. Finally, several important implications of the development of
the software industry for India are discussed.
The Early Development of the Indian Software industry
The early development of India’s software industry is closely linked to the
adoption and use of mainframe computer hardware by Indian business, government,
and academic organizations. Until the mid 1960s, the vast majority of the software
used by these organizations was developed and provided by the multinational
companies (MNCs), e.g., IBM and ICL, who also manufactured and distributed the
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mainframe hardware (Birt, 1996). As in other computer-using nations, the expansion
of mainframe computer use in India made it increasingly difficult for the
multinational computer manufacturers to provide the full range of applications
software required to make efficient use of their mainframe hardware (Kaplinsky,
1987). As a result, Indian workers began to fill this void by developing specialized
software applications for mainframe systems. As the mid-1970s arrived, business,
government and academic users of mainframe computers relied both on imported
software that was bundled with mainframe hardware by MNC manufacturers, and a
small cadre of Indian software developers who developed software applications for
these machines.
In 1974, Tata Consultancy Service (TCS) became the first Indian firm to
export software. This was done in return for permission to import hardware. TCS
entered into a joint venture with the U.S. hardware company, Burroughs, that was
entitled Tata-Burroughs Ltd. Burroughs held a 40 percent equity share in the new
company. Around this time, the data processing divisions of other large Indian
companies began marketing the software they had developed in-house (Heeks, 1996).
In 1978, IBM closed its operations in India in response to a mandate from the
Indian government that the firm reduce the percentage of ownership in its Indian
operations to 40 per cent. This provided a further boost to the Indian software
industry. The public sector company, Computer Maintenance Corporation (CMC),
became the primary service provider for IBM equipment and software (Lakha, 1994);
and, a number of the 1,200 ex-IBM employees began setting up small software
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companies to provide services to local clients (Heeks, 1996). Thus, Indian firms
began to fill the void left by IBM.
At the onset of the 1980s, India’s nascent software industry was comprised of
a growing number of small and medium-sized firms that produced software
applications for mainframe hardware. However, the orientation of the industry began
to change as a result of the personal computing revolution underway in the United
States and other developed nations. In effort to expand the inflow of personal
computers (PCs) into India, the Indian government implemented the New Computer
Policy in 1984. In part, this policy initiative focused on eliminating or lowering
tariffs on imported computers and components, and encouraging investment in the
manufacture of PC hardware in India (Heeks, 1996).2 As a result, thousands of PCs
were imported into India. Moreover, several domestic companies were formed to
manufacture PCs and related hardware. Perhaps the most notable of the Indian PC
manufacturers was Wipro, Ltd., which later developed into one of the nation’s largest
software firms. The establishment and growth of personal computing transformed the
Indian software industry as demand for PC-based software applications prompted a
period of growth and development.
2 The New Computer Policy was passed in November 1984. It intended to liberalize and drive down prices. As part of the policy, the import duty on ordinary computers was halved and powerful computers were made duty free. The import duty on components was also reduced. Certain computers could be imported by actual users without the need of a government license. industrial licensing for private firms was relaxed. Large monopoly companies, with 40 per cent equity, were allowed into hardware production. Joint ventures with foreign firms were encouraged. Finally, excise duty on computers was removed.
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Factors Influencing the Contemporary Growth & Development of the Indian
Software industry
Since the mid 1980s, the Indian software industry has undergone extensive
growth, stimulated by the expansion of PC use, the growth of computer networking,
and the development of the Internet. This growth occurred at a particularly rapid
pace during the mid-to-late 1990s as MNCs increasingly outsourced software
development work from Indian firms and the Internet boom created rapid demand for
the development of Web-based software applications. In addition to these paths of
technological development, there were a number of factors that were critical in
facilitating this growth, leading to the development of India as a potential growth
center in the global software industry.
State Initiatives in Economic Policy
The Government of India has actively intervened in the development of the
Indian software industry through policy initiatives designed to promote exports,
provide infrastructure facilities, encourage foreign and domestic investment in the
industry, protect intellectual property, and establish the legality of electronic records
and digital information.
In 1986, the Policy on Computer Software Export, Software Development, and
Training was passed. This policy formally stated the Indian government’s
commitment to software development. A critical feature of this policy was that it
provided guidelines to promote software exports in effort that Indian software firms
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be able to capture a larger share of the global software market (Lakha, 1994). In
addition, this policy created more liberal conditions for foreign MNCs to operate in
India. Foreign MNCs were granted the right to have 100 percent equity in export-
oriented projects. One result of this policy change, for example, was that the U.S.
firm Texas Instruments established a software subsidiary in India in 1986 in which it
owned 100 percent equity (Dossani and Kenney, 2002).
In 1991, the Indian government established the Software Technology Park
(STPs) scheme. One facet of this program was to create state-of-the-art computer
facilities equipped with satellite links and dedicated earth stations for the global
transmission of digital information. This infrastructure could then be used by private
firms (domestic and foreign) and public sector organizations through applying to
become certified member units and form a Software Technology Park. An important
goal of the STP scheme is to attract firms with the capacity to provide single point
contract services for the development and export of software (Ministry of Information
Technology, 2000; Software Technology Parks of India, 2002).3
In order to provide incentives, software firms that become certified member
units of an STP are exempted from the payment of income tax until 2010, allowed to
import telematic equipment without custom duties, provided access to dedicated data
communication links and training facilities, and allowed less ‘red tape’ in securing
government approval. In addition, 100 percent foreign equity in ownership of
software firms in an STP is permitted (Ministry of Information Technology, 2000;
3 Single point contract services allow all stages of a software project to be completed in one location.
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Software Technology Parks of India, 2002). The first STP was established in
Bangalore. The number of STPs in India increased throughout the 1990s (see table
2.1). By 2002, 20 STPs had been established (Software Technology Parks of India,
2002). The vast majority of these are located in the southern half of the country.
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Table 2.1 Location of Software Technology Parks in India
City State Region
New Delhi Delhi North
Srinagar Jammu & Kashmir
North
Mohali Punjab North
Noida Uttar Pradesh
North
Jaipur Rajasthan North-west
Indore Madhya Pradesh Central
Gandhinagar Gujarat West
Calcutta West Bengal East
Bhubaneswar Orrisa East
Navi Mumbai Maharashtra West
Aurangabad Maharashtra West
Hyderabad Andhra Pradesh South
Vizag Andhra Pradesh South
Bangalore Karnataka South
Chennai Tamil Nadu South
Manipal Karnataka South
Pune Maharashtra West
Mysore Karnataka South
Thiruvananthapuram Kerala South
Guwahati Assam East
Source: Software Technology Parks of India (2002)
In addition to developing the STP scheme, the Indian government invested in
the development of a digital telecommunications infrastructure. During the 1990s,
the Nicent and Indonet computer networks were created to provide satellite
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telecommunications links to government and private users (Lakha, 1994).
Additionally, in 1992, an international satellite gateway was set up exclusively for
software exports (Software Technology Parks of India, 2002).
In effort to provide greater protection for the intellectual property of software
firms and ameliorate the piracy of software, the Indian government made
amendments to the National Copyright Law in 1994. The Copyright (Amendment)
Act of 1994 provided a formal definition of what constitutes a computer program,
made it illegal to distribute copyrighted software without proper authorization, and
delimited penalties for the infringement of copyrighted software (NASSCOM, 2000).
In effort to expand the provision of Internet services, the Indian government
passed the Internet Service Provider Policy in 1998. This policy mandated that
Internet service providers would not have to pay licensing fees for an initial five year
period. After five years, a nominal licensing fee of one Rupee ($ 0.02) would be
charged. After obtaining national security clearance, Internet service providers were
granted permission to set up international gateways, and to provide Internet services
over authorized cable television systems. Permission was also granted to state and
national electricity boards, and the railways, to lease excess telecommunications
capacity for data transmission (Department of Telecommunications, Government of
India, 2000).
Finally, in an effort to increase public confidence and elevate the use of online
transactions, digital communications, and other forms of online exchange, the Indian
government passed the Information Technology Bill in 2000. Intended in part to curb
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cyber crimes, the Information Technology Bill provides a legal framework so that
information is not denied legal effect, validity, or enforceability, solely on the
grounds that it is in the form of electronic records. The Bill mandates that unless
otherwise agreed, acceptance of a legal contract may be expressed by electronic
means of communication. (Ministry of Information Technology, 2000). Taken
together, the economic policy initiatives outlined above helped increase demand for
software, and provided incentives and conditions conducive to the growth of the
Indian software industry.
The Growth of Export Markets for Indian Software
As noted above, the initial export of Indian software occurred in the mid
1970s and concerned applications developed for mainframe hardware. Following
1981, the volume of Indian software exports began to steadily increase. This became
possible as Indian software developers developed a greater awareness of export
market opportunities, and the number of Indian workers with the skills to develop and
market software began to expand. As the domestic market for software stagnated in
the early 1980s, many small and medium-sized Indian software companies that had
been oriented toward the domestic market began to develop export markets for their
software (Heeks, 1996).
The export of Indian software gained further momentum with the PC
revolution, which elevated demand for new software applications. By 1985, revenue
from software exports had reached an estimated Rs. 30 crore ($6.6 million)
24
(NASSCOM, 2002). As noted above, the Policy on Computer Software Export,
Software Development and Training in 1986 provided further impetus with the
provision of official government guidelines for software exports (Lakha, 1994).
From the late 1980s onward, the emphasis on software exports by Indian firms
increased even more drastically. By the 1990s, many Indian firms that had initially
focused on producing hardware (e.g. Wipro, Ltd.), also started promoting the export
of software (Heeks, 1996).
The 1990s witnessed even further growth in the importance of software
exports to the Indian software industry. As noted above, Software Technology Parks
were created to provide single point contract services for the export of software.
Demand for software services was given a further impetus with the growing use of
‘outsourcing’ as a business strategy employed by MNCs and corporations around the
world (Heeks et al., 2000). With outsourcing, corporations sought to obtain software
services from independent software providers, rather than internally providing such
services. Outsourcing is typically done either to reduce overhead costs, realize cost
savings, and/or acquire software expertise, which a corporation does not have on-staff
(see Goe, 1991).
Facilitated by the STP infrastructure, Indian software firms became important
providers of software services outsourced by foreign corporations during the 1990s.
This led to a further expansion in export markets for software. For example, from
1991 to 1995, the value of Indian software exports increased from $164 million to
25
$485 million. The expanding stream of revenue from export markets has been critical
in financing the growth of the Indian software industry (NASSCOM, 2000).
Educational Infrastructure
The growth of the Indian software industry has also been facilitated by the
development of an educational infrastructure with the capacity to produce the supply
of skilled workers needed to staff the expansion of the industry. The national culture
of India places a strong emphasis on educational achievement in math, science, or
engineering as a path to economic success, particularly among males. Moreover,
English is used as the primary instructional language. The Indian government has
been highly influential in the development of this labor supply through the
establishment of computer science programs and educational institutions to offer
these programs. In 1974, the Indian government established the computer science M.
Tech. degree at national and regional engineering colleges. This was followed by the
establishment of the computer science B.Tech. degree in 1977. These were the first
computer science degrees offered in India (Heeks, 1996).
In 1978, based upon the recommendations of the Rajaraman Committee, the
two degrees were expanded to new colleges. In 1982, two new degrees were
established -- a vocational three-year Masters in Computer Application, and a
Diploma in Computer Education (DCE) for computer science teachers. In 1984, the
26
Indian government established a one-year Diploma in Computer Applications (DCA)
for BSc graduates. The same year, the Sampath Committee was formed to plan for
future actions on electronics-related training (see table 2.2). This set into motion
procedures that have subsequently encouraged a continuous expansion and
development of the computer science curriculum offered by Indian universities and
colleges (Heeks, 1996). With the establishment of these programs, the supply of
Indian workers with training in software development increased.
Table 2.2 Computer Science Degrees Established by Indian Government
Degree Date Established
Computer Science M. Tech 1974
Computer Science B. Tech 1977
Master’s in Computer Application 1982
Diploma in Computer Education 1982
Diploma in Computer Applications 1984
Source: Heeks (1996)
A key component of the Indian educational infrastructure are the six Indian
Institutes of Technology (IITs)4, widely considered as being among the premier
universities in the world for technical education. In the 1960s, the IITs were
established by the Indian government in conjunction with the U.S.-based Ford
Foundation. Today, IIT graduates head some of the largest MNCs in the global
economy, including McKinsey & Co, United Airlines, and Bell Laboratories. A
4 The six engineering schools are located in Kharagpur, Kanpur, Mumbai, New Delhi, Chennai and Guwahati.
27
substantial number of successful entrepreneurs and venture capitalists working in
Silicon Valley in the United States are IIT graduates (see Saxenian, 1999).5 During
the late 1990s, these successful Indian professionals provided an important source of
knowledge and resources for friends and family working to expand and develop the
Indian software industry (Dossani and Kenney, 2002). Within India, IIT graduates
have risen to head many of India’s largest corporations, including India’s first
multinational firm, Indian Tobacco Company, and HCL, one of the largest
information technology firms (Ray and Jetley, 2000).
Admission into an IIT is restricted by an extremely rigorous entrance
examination. Only about two percent of the approximate 200,000 students who take
the examination each year are selected for admission (Ray and Jetley, 2000). As a
result of rigorous standards, the IITs produce global class workers with the
knowledge and skills necessary to establish and lead firms in many industries,
including software.6
In addition to government initiatives, a private infrastructure for software-
related training began to develop in the 1990s as the Indian software industry
expanded. A substantial number of private software training institutes were formed
5 According to a study conducted by Saxenian (1999), 40 percent of the business start-ups in Silicon Valley that were examined were founded (at least in part) by Indian entrepreneurs. Of these, half were founded by IIT graduates.6 In an article in Business Week, Kripalani et al. (1999) stated the following about IIT graduates: “Wall Street firms rely on Institute grads to devise the complex algorithms behind their derivatives strategies while big multinationals call on them to solve problems in new waysY. The rise of the IITians, as they are known, is a telling example of how global capitalism works today. The best companies draw on the best brains from around the world, and the result is a global class of worker: the highly educated, intensely ambitious college grad who seeks out a challenging career, even if it is thousand of miles from home. By rising to the top of Corporate America, these alumni lead all other Asians in their ability to reach the upper-echelons of world-class companies.”
28
as independent ventures. However, a growing number of software firms have also
established authorized training centers to provide certified courses in their specific
software technology. These firms include IBM Global Services, Oracle, Microsoft,
Adobe, Cadence, Lotus and Sony. During 1997-98, the revenue for private institutes
providing software training was estimated at $225 million (INFAC, Mumbai, 1998).
The Indian government recently announced the establishment of Indian
Institutes of Information Technology, modeled after the IITs. Many Indian
engineering colleges have increased their emphasis on information technology, which
has included the formation of new programs in IT management. A number of for-
profit, private sector initiatives have also been recently announced to provide
graduate training in computer science. These include a joint venture between
Mahindra Group and British Telecom, and another joint venture between Sterling
Infotech and Carnegie Mellon University (Arora et al., 1999; Overland, 2000).
It has been estimated that at the end of the 1990s, India was annually
graduating about 155,000 engineers and another 200,000 diploma holders from
private software training institutes. Of these, approximately 60,000 were being
employed by the information technology sector (Arora, et al., 1999). Taken together,
the graduates of the elite IITs, the graduates of India’s numerous engineering colleges
and other institutes of higher education, and the growing number of workers being
trained at the private software training institutes, have provided an extensive labor
pool for staffing the expansion of the software industry.
29
Low Barriers to Entry
While not unique to India, the ‘barriers to entry’ in the software industry are
relatively low. As the volume of Indian software exports began to expand, a large
number of new firms were able to enter the industry as the initial investment required
to start a software firm was small. Little more than office space and communication
facilities were required (Arora et al., 1999). For example, the Chief Executive Officer
of Infosys Ltd., N. R. Narayan Murthy, founded the company in 1981 with six other
software writers and $1000 pooled from household money largely controlled by their
wives (Karp, 1999). It has been contended that computer science graduates only need
to equip themselves with a PC and a couple of contracts to be a part of the local
information economy. Add a modem, and they quickly become global ‘infopreneurs’
(Heeks, 1999). In the late 1990s, the formation of software startups was facilitated by
the evolution and expansion of the Indian venture capital sector which began to
provide an important source of financial capital (Dossani and Kenney, 2002).
The firms that were early entrants into the Indian software industry were of
two types. The first type included established firms seeking to diversify into software.
This included computer hardware firms such as HCL and Wipro, as well as firms
with large in-house data processing and system integration facilities such as Larsen
Turbo. Other firms included BFL, Sonata, Satyam and Birla Horizons that were part
of large and medium-sized business houses.7 The other type of entrant was new start-
7 As noted above, another key early firm was Computer Maintenance Corporation (CMC) that focused on maintaining IBM computer systems after IBM left India. CMC has grown to over 2000 employees. It has the ability to develop and implement large and complex projects, especially for infrastructure systems.
30
ups. This included such firms as PCS, Datamatics, Infosys and Silverline
Technologies (Arora et al., 1999).
Low Wage Rates
A key factor that has contributed to the growth of the Indian software industry
through software outsourcing is that wage rates for Indian workers are relatively low
compared to rates for software workers in developed nations. In a study published in
1999, Arora et al. compared the salary ranges for Indian and U.S. workers across a
number of software-related occupations. At the highest level of earnings inequality,
the annual salaries of programmers in India were an estimated 7.1 per cent of the
salaries for programmers in the U.S. At the lowest level of earnings inequality, the
annual salaries of network administrators in India were an estimated 38.4% of the
salaries earned by their U.S. counterparts. Within the U.S., the highest salary ranges
in terms of absolute dollars were for database administrators and software developers.
The salaries of database administrators and software developers in India were
estimated to be 28.7% and 29.9% of their U.S. counterparts.8 These low wage rates,
in combination with the high levels of education, technical proficiency, and fluency in
English possessed by the Indian labor force, have served to elevate the Indian
software industry as a key source for outsourcing software services on a global basis.
8These figures were computed by converting the salary ranges listed by Arora et al. (1999) at an exchange rate of Rs 41.50/US$. The percentages were then calculated by taking the ratio of the midpoint of the salary range for an occupation in India to that of the salary range for the same occupation in the U.S.
31
The Structure and Prospects of the Software industry in India
As a result of the state initiatives in economic policy outlined above, the
growth of export markets and outsourcing of software services, the development of
the educational infrastructure, low barriers to entry, and low wage rates, the Indian
software industry experienced dramatic growth over the course of the 1990s. Precise
estimates of the number of firms comprising the Indian software industry are hard to
come by. However, available data suggests that the number of Indian software firms
expanded steadily over the 1990s. For example, the number of companies that are
members of NASSCOM increased from 94 in 1990-91, to 850 in 2000-01
(NASSCOM, 2002).
According to Arora et al. (1999), many firms entered the industry during, or
just before, the economic liberalization in 1991, and few have exited.9 The Indian
software industry is predominantly comprised of small and medium-sized firms.
Nearly one-fourth of software firms have sales of less than Rs 10 million (about $
25,000). However, the software market is dominated by a small tier of large firms
(Arora et al., 1999). For example, the top 25 firms in India with largest amounts of
software export revenue accounted for approximately 60 percent of software exports
in 2000-01 (NASSCOM, 2002).
According to Dataquest (a computer magazine published in India), Indian
software firms had captured 16 per cent of the global market in customized software
by 1996 (Dataquest, 1996). The majority of market leaders in the Indian software
9 The study was based on a survey of Indian software firms, field visits and interviews with industry participants, observers and US-based clients.
32
industry specialize in producing only software, with a few notable exceptions such as
Wipro and Satyam.10 This is in direct contrast to the early entrants in the industry,
who had close links with computer hardware development (Heeks, 1996).
Revenues generated by the Indian software industry expanded rapidly in the
late 1990s. During 2000-2001, the value of the software industry in India was
estimated at $8.26 billion. In comparison, the estimated value of the industry was
only $150 million a decade earlier in 1990-1991 (NASSCOM, 2002). During 1995-
2000, the compound annual growth rate for software industry revenue was 62.3
percent for export markets and 41 percent for domestic markets. In the year 2000, 260
of the Fortune 1000 companies outsourced software development work to Indian
firms (NASSCOM, 2002).
There is evidence that a sizeable portion of the work that is outsourced to
Indian software firms is neither technologically advanced nor critical to the business
of the firms outsourcing the work. For example, in a study of U.S. firms that
outsource work to the Indian software industry, Arora et al. (1999) found that more
sophisticated work tasks such as requirement analysis and high-level design were
typically done in-house or outsourced to U.S. consultants. Most projects outsourced
to Indian firms were found to be technologically undemanding and small in terms of
the man-months required to complete them. However, this was not found to be true
in all cases. Smaller U.S. firms in some industries (e.g., medical software) were
found to rely heavily upon Indian software firms.
10 As noted above, Wipro also manufactures PCs, while Satyam provides Internet services across the country.
33
Employment in the Indian software industry also expanded in the late 1990s
with the growth of software firms and software revenues. In 1996, it was estimated
that there were 140,000 software professionals in India. In 2000, the number of
software professionals was estimated at 410,000 (NASSCOM, 2002). Most of these
professionals have at least an undergraduate degree in engineering, mathematics, or
computer science, or a Masters in Computer Application degree, from an Indian
university or college. (Arora et al., 1999).
Available evidence suggests that there is a perceived difference in the
preferences of Indian software firms in hiring college graduates with training in these
fields, versus workers with degrees from the private software training institutes. In
the study conducted by Arora et al. (1999), few Indian software firms admitted to
hiring graduates from private training institutions. Firms believed that graduates of
the private software training institutes were not well suited for higher level tasks such
as software development. Rather, they were better suited for such tasks as providing
support and maintenance for back office operations, as well as lower-skilled services
such as medical transcription and claims processing for the insurance industry.11
Firms were also concerned that hiring graduates of the private training institutes
would send negative signals to customers about the quality of their software and
related services (Arora et al., 1999).
The Indian government and the software industry have actively attempted to
promote a reputation of quality by urging Indian software firms to acquire the
11 Although students at these centers spend about $ 700-750 to complete a computer course, they tend to get jobs that pay around $ 20 a month (see Joseph, 2000).
34
International Standards Organization (ISO) certification (Banerjee and Duflo,
forthcoming). A substantial number of Indian software firms have been able to
achieve ISO 90001 quality certification. However, these firms have been rewarded
primarily through greater volume of sales rather than higher price/cost margins
(Arora and Asundi, 1999).12
As the Indian software industry has grown over the past several decades,
regional software complexes (Storper and Walker 1989; Castells and Hall 1994;
Storper 1997) have developed as firms have clustered in specific areas of the nation.
These regional complexes have developed in the western and southern regions of
India. The spatial pattern of these complexes broadly mirrors the spatial distribution
of Indian engineering colleges.13 The city of Bangalore in Karnataka has developed
into the largest regional software complex. There are several factors attributed to
why Bangalore has developed into the analog of the ‘Silicon Valley’ of India:
Labor availability: Bangalore has an abundant supply of highly educated,
technically proficient labor that can be drawn from its research laboratories,
educational institutes and public sector firms.
Quality of life: Bangalore has mild climate and offers an active social life. For
example, it is the center of the Beer Drinkers Association of Information Technology
(BAIT), which brings together senior IT managers to discuss strategies, on the
12 ISO90001 of the International Standards Organization is the most popular quality certification in India. However, a few firms have opted for a quality certification process developed specifically for software called CMM- Capability Maturity Model (as cited in Arora and Asundi, 1999).13For example, data provided by Ramarao (1998), indicate that over two-thirds of the engineering colleges in India are located in the Southern and Western regions which possess around three quarters of the sanctioned capacity for the number of engineering students.
35
condition that they can drink a minimum of six mugs of beer in the evening.
Infrastructure: Bangalore offers a steady supply of power and water unlike
other Indian cities, which frequently experience power outages. It is also possible to
get around the city in few minutes, whereas it might take hours at peak time to get
around in other cities such as Mumbai (Heeks, 1998). While Bangalore is the largest
regional software complex in India, other important complexes have developed in
Hyderabad, Pune, Chennai, and Mumbai.
Future of the Indian software industry
The Indian software industry is expected to experience tremendous growth over
the first decade of the twenty-first century. According to NASSCOM, revenue for the
software industry in India is projected to grow to $ 87 billion in 2008. Software
exports are projected to grow to $50 billion in 2008. Thus, despite the huge amount
of growth projected for software export revenues, the share of total software revenue
accounted for by exports is expected to fall from 64.7 per cent in 2000 to 57.5 percent
in 2008. Domestic demand for software is expected to become much more important
to the Indian software industry as a result of the growing use of information
technology by the Indian government, industry, and citizens.
NASSCOM (2000) also predicts the total number of PCs in India to increase
from 4.3 million in 2000 to 20 million in 2008. The number of Internet users are
projected to increase from 3.2 million in 2000 to 100 million in 2008; and, the
number of Internet subscribers is expected to increase from 0.77 million in 2000 to 35
36
million in 2008. It remains to be seen how true to course these projections will be.
Currently, India rates low in Internet penetration compared to other Asian nations. In
2001, it was estimated that India had 1.8 million Internet users, which represented
only 0.3 percent of the adult population (Pearl, 2001).
It remains to be seen how true to course these projections will be. Over the
past several years, India has undergone its own version of the proverbial bursting of
the Internet bubble as many dot.com start-ups have gone out of business (Mukherjee
et al., 2000). Moreover, the U.S. recession in 2001 has cut demand for software
outsourcing from Indian firms. While the short term prospects for growth may be
diminished, it does appear reasonable (given past trends and the factors promoting
growth described in this paper) that the Indian software industry will experience
further growth as economic conditions change and the global economy re-enters an
expansionary phase. In turn, this growth portends to have important implications for
Indian society.
The overall magnitude of the benefits and economic returns provided to India
by the software industry will be determined, in part, by whether or not Indian
software firms can develop into globally competitive firms capable of producing
software products and services that are competitive with those of successful firms in
developed nations. There is evidence to suggest that some Indian firms may be able
to develop this capability. Indian firms have enjoyed some success in developing
software packages and products for the domestic market such as accounting packages
and word processing packages in Indian languages (Arora et al., 1999).
37
While outsourcing projects typically involved low-skilled work, Indian
firms have begun to take on domestic projects requiring higher level skills. For
example, the software required for the screen-based trading system of the Bombay
Stock Exchange and the reservation system of the Indian railways was developed
by the Indian software firm CMC (Arora et al., 1999).
In addition to these developments, some Indian software firms are becoming
less reliant on export markets for revenue, particularly revenue derived from
outsourcing by U.S. firms. For example, the two largest Indian software firms, Wipro
and Infosys, have both reduced their level of dependence on their biggest client, US-
based General Electric (GE). In 2000, GE accounted for 5 percent of Wipro’s sales
compared to 19 percent in 1998. Infosys experienced a similar reduction several
years earlier (Pesta and Ramakrishnan, 2001).
Wipro and Infosys have become the third and fourth largest companies in the
entire Indian economy. Together, the two companies account for about 10 percent of
India’s software exports and have a total shareholder value of around $13 billion
(Karp, 1999). Infosys was the first Indian company to be listed on the U.S.
NASDAQ stock exchange (Business Week, 1999).
Indian software firms have enjoyed limited success in exporting software
packages and products they have created (in comparison to exporting services).
Several of the largest software firms have been able to export a few packages such as
compilers and financial programs. Wipro, Ltd. was able to export thousands of copies
of its Instaplan project management package, in the late 1980s and early 1990s. This
38
was accomplished through forming an alliance with a U.S. marketing company to
help with program specification and design (Heeks, 1996). Beyond this, there are
currently few examples of Indian software packages becoming commercially
successful on a global basis.
A key factor that could facilitate the competitiveness of Indian software firms
is the ability of the nation to stem the outmigration of members of the best and
brightest of the Indian labor force, particularly to the U.S. (Sender, 2000). As noted
above, there are many successful professionals in Silicon Valley that are Indians
(Saxenian, 1999). The number of H1-B visas issued by the U.S. government to
Indians doubled between 1994 and 1996. Of the 65,000 H1-B visas issued in 1999,
20,000 were to Indians. As of October 2000, a total of 195,083 Indians have been
issued H1-B visas, and more than half have been issued green cards. Typical H1-B
applicants include architects, engineers, doctors, college teachers, programmers and
accountants. During 2000, the Clinton Administration approved S-2045 provisions
that increased the number of Indian visas to 195,000 annually for the next three years
(Ray, 2000). The Bush administration reduced the quota for H1B visas issued to
Indian citizens to 65,000 in 2003 (NASSCOM, 2003). While low wages undoubtedly
contribute to the outflow of highly educated workers from India, there is evidence
that some Indian software firms have begun to adopt U.S. style methods of
compensation in order to attract and retain workers. A case in point is Infosys. The
company has allotted 20 percent of its shares to employees through stock options,
creating 106 dollar-millionaires. Even a waiter in the Infosys conference room owns
39
shares of the company. In fact, 33 percent of Infosys 4,000 employees are rupee-
millionaires. The Infosys strategy has prompted other IT companies to begin to
provide stock options to their employees (Karp, 1999).
It is important not to overstate the importance or extent to which stock options are
being used by Indian software firms. The vast majority of Indians do not own or
trade stock. However, the diffusion of these compensation methods in the future to a
greater number of firms and industries could serve to change the Indian stratification
system. The professional and technical class of workers in the middle and upper-
middle segments of the income distribution would expand and have access to a larger
portion of the wealth being created by the Indian economy. In turn, this could
transform the nature of consumption, leading to a higher standard of living for these
segments of India’s working class.
In closing, the Indian software industry has experienced tremendous growth
over the past two decades and is on a developmental trajectory toward becoming an
important growth center in the global software industry. This growth has been
actively sought and promoted by the Indian government and the industry has become
a central lynchpin in the nation’s economy. The growth of the software industry has
the possibility of expanding the wealth of the nation and the standard of living
enjoyed by working class Indians. However, in order to accomplish these ends, it
appears necessary for the industry to further shift its focus away from low value-
added, outsourcing projects to high value-added software packages and services.
40
Chapter ThreeReview of literature
Introduction
Many feminist theorists have tried to understand the reasons behind the low
participation of women in the field of engineering (Panteli et al., 1997). For example,
despite women comprising half of the population in the United States, they constitute less
than 10 percent of the engineering work force (NSF, 1994). In addition, there are glaring
earning inequalities among men and women computer specialists. For example, during
the 1980s, women computer professionals earned 72 percent as much as their male
coworkers (Donato and Roos, 1987).
The last 20 years have seen the development of an important new field of
research: feminist studies of technology. I will begin with analyzing the three main
theories on the relationship between gender and technology – eco-feminism, liberal
feminism and technology as masculine culture. In order to establish the linkages between
technology and skill training life cycles and labor and gender queues, I will review the
literature on product, process, technology and skill life cycles. It is important to examine
all four of these life cycles as they are interlinked. The next section of this chapter will
provide a comprehensive review of theory of labor and job queues, and the factors that
lead to the feminization of previously male dominated occupations.
Eco-feminism
Proponents of the eco-feminist view of gender and technology contend that
technology represents a way in which men try to dominate and control both nature and
women. They argue that technology in its present form is a result of men’s desire to
41
dominate and exploit nature in the same way as they dominate and exploit women (Van
Zoonen, 1992). `We believe that the desire of men to control women is closely associated
with the desire to control, rather than to cooperate with nature – a philosophy which lies
behind modern scientific thinking’ (Zmroczek, et al.; 1987:121). Eco-feminists believe
that women are closer and more in tune with nature. As Susan Griffin (1984: 175) puts it:
We [women] can read bodies with our hands, read the earth, find water, trace gravity’s path. We know what grows and how to balance one thing against another … and even if … they [men] have transformed this earth, we say, the truth is, to this day, women still dream.
The eco-feminists believe that this closeness to nature is rooted in biology,
specifically women’s ability to give birth. They believe that women’s biology ‘has led to
specific way of knowing and experiencing the world, based on emotions, intuition and
spirituality. Eco-feminists call for celebration of female values which allegedly result
from this – pacifism and nurturance.’ (Grint and Gill, 1995: 5).
The main contribution of eco-feminism is that it highlights the patriarchal context
of the design and production of technology. It rightly points out that these social patterns
associated with technology are at odds of what is conceived of being feminine. However,
there are number of problems in the eco-feminist position with regard to the conception
of technology, gender and culture, which make it in the end neither an appealing nor an
empowering theory (Van Zoonen, 1992).
The essentialism of eco-feminism, its inability to deal with change, and its
reproduction of traditional ideas of femininity – albeit in a celebratory manner – have
been criticized. The critics point out that cross-cultural studies show that there is no
behavior that is inherently masculine or feminine; they are socially constructed categories
42
(Grint and Gill, 1995). It is also pointed out that the values eco-feminists ascribe to
women originate in women’s subordination. Particularly, it has been pointed out that eco-
feminism suffers from biological determinism. Lastly, eco-feminists conflate society and
technology. They assume that the patriarchal nature of technology can be read from the
patriarchal nature of society. This approach leaves no room for negotiation or resistance;
and, the only path open to feminists is that of rejection of technology and society (Van
Zonnen, 1992).
Liberal feminism
The liberal feminists believe that technology itself is neutral; and men and women
occupy different positions in relation to it. The liberal theorists argue that women are
lagging behind in their understanding and use of technology due to the roles they fulfill in
a sexist society. They point out that women and men are equal, sharing a basic humanity
and rationality. However, women’s potential is distorted by gender stereotyping.14
Women have been forced to take on particular sex roles (such as housewife and mother),
which have concealed their true nature and capabilities. Therefore, from this perspective
‘gender is conceived of as a system of representations, an ideology, which has been
overlaid on authentic, unspoiled and equal human beings.’ (Grint and Gill, 1995: 6). As
Van Zoonen (1992: 13) points out:
Liberal feminism assumes –with liberalism in general – that human beings distinguish themselves in their capacity for rationality and the exercise of rationality in public life. Humanity is located primarily in the mind, much
14 Related to this is the process of occupational sex-typing. It refers to the process by which certain occupations are designated as being primarily male or female. Sex-typing has produced a virtual bifurcation of the labor market into male and female sectors. Occupational sex-typing depresses women’s wages. This can be explained by the overcrowding hypothesis. According to this argument, sex-typing forces women into small and restricted number of occupations, while men have a wider range. This increases competition amongst women for these small number of jobs and lowers wages (Cohn; 1985).
43
less in the body. Consequently, liberal feminists do not conceive of physical differences between women and men as important, since both sexes share their mental capacity for rationality. In essence then, men and women are thought to be same in finding their fulfillment as human beings in the exercise of rationality in public life. However, rationality should be seen as a potential of human beings rather than a characteristic: a potential that will develop differently according to different social experiences. Given their relegation to the private sphere – a supposedly non-rational realm – women have not had, and probably will not have, the opportunity to develop their potential as human beings – assuming that this relegation does not change.
The significance that liberal feminists accord to gender varies. On one hand, it is
believed, that its effects are profound as women’s identity is guided through the process
of socialization into their sense of who they are and what they can expect. Others argue
that its effects are superficial as gender is seen as a set of stereotypes (Walby, 1990). To
overcome the pernicious effects of gender stereotyping on women’s relationships to
technologies, liberal feminists have formulated programs that will help women catch up
with men – such as information campaign to help women take up non-traditional careers,
affirmative action policies, etc. (Grint and Gill, 1995). However, these programs have
enjoyed limited success.
The theoretical perspective of liberal feminism is thought to be severely flawed.
Firstly, the critics argue that technology is never subjected to critical analysis (Karpf,
1987). The critics argue that ‘in liberal feminism in general, the society women live in
and the circumstances women should adapt to are taken for granted, neglecting
dimensions of power and divisions of class, ethnicity, sexuality etc. In such reasoning,
technology is thought of as independent factor affecting social relations without being
affected by them’ (Van Zoonen, 1992: 14).
44
The flip side of liberal feminism’s view of technology as neutral is the tendency
to see women as the problem. They argue that women need to overcome the effect of sex
stereotyping and adjust themselves to technology. The critics argue that liberal feminists
are preoccupied with changes that women are supposed to make and have left
masculinity unchallenged. ‘The male is treated as the norm, and women are supposed to
adopt masculine ways of relating to technology’ (Grint and Gill, 1995:7).
It is argued that liberal feminism is clearly underdeveloped. On the one hand, the
proponents of this approach present gender both as being profoundly important, as it is
the primary division in society. On the other hand, some argue that it has no impact on
technologies and other social products. ‘Its idea of a true and unspoiled human nature
which lies untouched behind the distortion of gender is difficult to maintain’ (Grint and
Gill, 1995: 7). Therefore, by recognizing the importance of processes of sex-role
stereotyping and socialization, liberal feminism acknowledges the influence of society on
an individual’s identity and ‘seems just a step away from the idea that identity is not
predetermined but socially constructed’ (Van Zoonen, 1992: 15). However, it does not
distinguish between those aspects of identity that are supposed to be natural and authentic
and those that are socially constructed. Finally, the assertion of liberal feminism that
gender is the primary division in society has led it to neglect other dimensions of power
such as race and class, and it has a tendency to ignore differences between women (Grint
and Gill, 1995).
45
Technology as masculine culture
The last 15 years has seen the emergence of research that provides a critique of
both the eco-feminist and liberal approach. This theory challenged the view that women’s
alienation from technology resulted from lack of access to training and employment, and
a result of sex-role stereotyping. It also rejected the view that women’s absence from the
technological domain could be understood by the fact that there exists a difference in the
manner in which women and men relate to the world (Grint and Gill, 1995). Instead it
argued that women’s alienation from technology is a result of the historical and cultural
construction of technology as masculine (Faulkner and Arnold, 1985; Cockburn, 1985,
1986, 1991, 1992; Wacjman, 1991). 15
The adherents of this approach view technology and masculinity as being
symbolically intertwined. They argue that technical competence constitutes an important
part of masculine identity, and conversely, ‘a particular idea of masculinity has become
central to our very definition of technology’ (Grint and Gill, 1995: 8). For example,
Wacjman (1991: 19) argues,
As with science, the very language of technology, its symbolism, is masculine. It is not simply a question of acquiring skills, because these skills are embedded in a culture of masculinity that is largely coterminous with the culture of technology. Both at school and in the workplace this culture is not compatible with femininity. Therefore, to enter this world, to learn its language, women have first to forsake their femininity.
From this perspective, technology is seen as more than simple artifacts or
hardware. Instead it is seen not as simply including things themselves, ‘but the physical
and mental know-how to make use of those things. Know-how is a resource that gives
15 Thomas Kuhn in The Structure of Scientific Revolutions (1970) had argued that scientific knowledge, like all other forms of knowledge, is affected at the most profound level by the society in which it is conducted.
46
those who possess it a degree of actual or potential power’ (Mckenzie and Wacjman,
1985: 22).
It is argued that one of the strengths of this theory is to locate the cultural
connection between masculinity and technology historically. This is a step in the right
direction away from the ahistoricism and essentialism of the other perspectives (Grint and
Gill; 1995). Women’s exclusion and alienation from technology is seen as a consequence
of changes, which occurred during the industrial revolution and the early development of
capitalism in the West. These theorists argue that prior to the industrial revolution,
women had more opportunities to acquire skills than they have in modern industrial
society. This was mainly because basic needs such as food, clothing, drinking and shelter
were taken care of within the family, and even manufacture was largely organized on a
domestic basis. Women assisted men at work within the household. However, all this
changed between the seventeenth and nineteenth century. This was mainly because of
the separation of private and public sphere, and move of manufacturing from the home to
factories (Wacjman, 1991). During the same period, ‘craft skills were challenged by the
introduction of machinery, and new skills emerged from the impetus of many inventions
and technical developments, which marked the industrial Revolution’ (Griffiths, 1985:
54).
Women were excluded from the new skills that developed during the industrial
Revolution. The previous male domination of fields such as carpentry and iron working
translated into male domination in new skills of patternmaker, iron founder, turner, fitter,
wheelwright, etc. These skills became male-dominated and are still so today. In this
manner, the industrial Revolution and the rise of factory-based manufacture led to a rigid
47
division of labor along gender lines, and women who became industrial laborers found
themselves segregated in low paying and least skilled jobs (Griffiths, 1985). Capitalism is
viewed essentially as a male creation, where men migrated into controlling roles, both as
capitalists and workers in the new industrial enterprise, and women filled the spaces that
they left behind (Faulkner and Arnold, 1985).
Women were excluded from technology at another level during this period.
During the industrial Revolution, there was a very close relationship between invention
and entrepreneurship. This meant that typically, an individual would have an idea for an
invention, and would either put up the capital to exploit the idea or seek a wealthy
partner16. However, due to the large amount of capital that was needed, women rarely
acted as entrepreneurs in their own right. It was only in 1882 that the Married Women’s
Property Act gave English women legal possession and control of property independently
of their husband. Additionally, women were denied access to education, and specifically
to the theoretical grounding in mathematics and mechanics upon which most innovations
were based. As Griffiths (1985: 56) point out:
As business activities expanded and were moved out of home, middle-class men increasingly left their wives to a life of enforced leisure. This change in social roles was reflected in the education given to daughters and sons, girls learning accomplishments (such as fancy needlework and piano-playing) whilst boys received an academic education. Accomplishments, it need hardly be said, were not the most appropriate foundation for participation in the world of inventor-entrepreneur.
These writers view the introduction of capitalism and the ensuing shifts in the
division of labor as playing a decisive role in the exclusion of women from knowledge
16 Some women were able to overcome hurdles and make important contributions. Mary Somerville was a very distinguished mathematician, and Lady Lovelace is credited with the early ideas of computer programming. It is also argued that in horticultural and agrarian societies women invented tools such as the hoe, the scratch plow, grafting, hand pollination and early irrigation. However, the significance of these inventions was never acknowledged (Wacjman, 1991).
48
and practices that constitute technology (Griffiths, 1985). Technologies, which emerged
during the industrial Revolution, or ‘capitalist technologies,’ were characterized as more
masculine than previous technologies. These theorists argue that the maleness of
technology effectively excluded women from new sources of invention. ‘In this way,
women were denied access to the new skills and knowledge created by the technological
dynamism of capitalism’ (Faulkner and Arnold, 1985: 48).
In other formulations, the development of capitalism is seen as consolidating,
rather than originating, power differences between the sexes and their relationship to
technology (Cockburn, 1985; McNeil, 1987). Cockburn17 (1986) argues that capital
applied new technology to class advantage thereby revolutionizing the forces of
production and wresting back control of production from skilled workers, increasing
productivity and maximizing profit. Along with this, men appropriated and sequestered
the technological sphere, extending their tenure over each new phase at the expense of
women. Therefore, she believed that as capitalism developed, already existing power
differences between men and women were given a new articulation in relation to class
differences, so that women lost as both women and workers.
Machinery offered men as a sex opportunities that were not open to women. Already certain technologies of which men had exclusive tenure had a special significance in production; now they took on an amplified importance. Those who had traditionally worked the materials from which the tools were made would now adapt their skills to the new machine age. What capital needed in place of smiths and wrights were ‘mechanics’ and ‘engineers.’ It was only men, inevitably, who had the tradition, the confidence and in may cases also the transferable skills to make the leap (Cockburn, 1985: 29-30).
17 Cockburn carried out extensive field work to understand the impact of technology on gender. She studied clothing, ware-house, radiography and computer programming firms. She carried out empirical research between 1982 to 1984. The case studies included observation combined with interview and postal questionnaires. She interviewed approximately 200 people, all involved in one way or another with the technologies.
49
Cockburn further argues that machinery that was developed for the new factories was
designed by men, and reflected male power and capitalist domination. She points out that
male craft workers actively resisted the entry of women in new spheres of production,
denying them membership into unions that might have given them some bargaining
power (Cockburn, 1985). Women were denied the opportunity ‘to enter and defend jobs
deemed skilled’ (Faulkner and Arnold, 1985:8), and were forced into those jobs
considered unskilled and were accorded less pay.
It has been pointed out that there is a dialectical relationship between women and
skill such that women are concentrated in jobs that are deemed as unskilled. Even those
occupations in which women constitute a majority come to be seen as relatively less
skilled than those dominated by men. Skill is not some ‘objectively identifiable quantity,
but rather is an ideological category, one over which women were (and continue to be)
denied the rights of contestation. (Grint and Gill, 1995 :9).
Feminist researchers stress that the exclusion of women from technology is as
much a feature of contemporary society as it was in the earlier stages of capitalism. These
writers point out that technology18 still remains a black box to women. They argue that
women are concentrated in jobs that teach them nothing about the internal structures and
processes of the equipment on which they work. They argue that women’s relationship to
technology has become less interactive (Cockburn, 1985; Wacjman, 1992). ‘There
appears to be general law that women are found in jobs where they may press the button
18 Feminist writers have pointed out that the very definition of technology is biased. We tend to think of technology in terms of industrial machinery and cars, ignoring other aspects of technology that affect our everyday life. This emphasis on technologies dominated by men conspires to diminish the significance of women’s technologies such as horticulture, cooking, childcare. This reproduces the stereotype of women as being technologically ignorant and incapable (Wacjman, 1992).
50
to achieve normal output, but not in jobs that meddle with the works where they could be
called upon to intervene in the mechanism itself’ (Cockburn, 1986: 79).
Those who argue that technology is intimately related to masculine culture point
out that the effects of this are profound. They argue that technologies are not neutral
artifacts, which would be the same if they were produced by either men or women, but
rather objects that ‘bear the imprimatur of their social context’ (Karpf, 1987: 162) –
including the gender relations which constitute the context. Theorists have argued that
work on social shaping of technology has highlighted the manner in which military,
industrial, national and class interests shape the design of a vast number of technological
artifacts. Feminists point out that interests of a deeply gendered character determine the
way in which technologies are shaped.
The effect of male control of technology – and women’s exclusion and alienation from it – is that the technologies produced for use by women may be highly inappropriate to women’s needs and even pernicious (e.g. the Pill), as well as embodying male ideologies of how women should live. What passes for women’s control is mainly a mirage of the market – the exercise of preference (within financial, geographical, cultural and time constraints) and with the negative sanction of refusing to buy, although as consumers women have also to some extent resisted and modified technologies offered to them (Karpf, 1987: 159).
It is argued that technology is gendered (Cockburn, 1986, 1992; Wacjman, 1992).
Technological competence ‘correlates strongly with masculinity and incompetence with
femininity’ (Cockburn, 1986: 78). As a result of the culture of the context in which they
are produced technologies come to embody ‘patriarchal values’ (Wacjman, 1991: 17).
They can thus be seen as a sign of women’s oppression. Moreover, once constituted, they
can be the source of this oppression. It is this double aspect of technology – both sign and
source of oppression – Cockburn has termed this the circuit of technology (Cockburn,
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1992). Therefore, technology ‘is constituted by, but also helps to constitute social
relations’ (Karpf, 1987: 12).
Another key concept used by those who see technology as ‘masculine culture’ is
identity. Masculinity, it is assumed, is partly constructed through the notion of technical
competence: ‘It is evident that men identify with technology and through their
identification with technology men form bonds with one another. Women rarely appear
in these stories, except as wives at home providing the backdrop against which the men
freely pursue their great projects’ (Wacjman, 1991: 141). In contrast, the belief that
women lack technical competence is not merely a sex-stereotype but has ‘indeed become
part of feminine gender identity’ (Wacjman, 1991: 155).
Identity is seen as an important mechanism through which the seemingly
association between masculinity and gender gets reproduced. In fact, it is pointed out that
‘doing gender’ creates differences between girls and boys, women and men, differences
that are not natural (West and Zimmerman, 1998). Cockburn (1992) further argues that
gender is ‘more of doing than being.’ From this perspective, Wacjman (1991) argues that
most of the programs designed to encourage female participation in technical fields failed
as women actively resisted technology because of the implications for their feminine
identity.
The theory of technology as masculine culture also has a number of problems and
tensions. Firstly, the essentialism that characterizes radical and eco-feminist writings has
proved difficult to eradicate, even from the works of those authors that disavow it. The
notion of fundamental difference between men’s and women’s values underlies much
work in this vein (Grint and Gill, 1995). For example, Faulkner and Arnold (1985) argue
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that technology is alienating to women because the goals embodied in it are not
necessarily women’s goals. They point that military technology does not embody
women’s values. The proponents of this theory also do not subject to critical analysis the
notion of women’s goals. They adhere to the view point that women as a group share
specific interest and goals, which the eco-feminist dub as anti-militaristic and pacifist.
Even men as a group have self-interest, which are inimical to women’s interest (Grint and
Gill, 1995).
Secondly, it is pointed out that there is a tension regarding ideology. On the one
hand, the gendering of technology is said to have little to do with the prevalence of male
subjects (and the absence of female ones) in its design per se, but is attributed to a larger
structure such as masculinity and patriarchy. On the other hand, it is argued that actual
embodied males act in their own ‘male interests.’ This implies that the presence of
women would, in fact, make a difference – that women would design different
technologies. In the first version, a notion of ideology is implicitly being mobilized.
However, in the second version, men are depicted as acting in their own male interests.
Therefore the nature of the relationship between the ideology of masculinity and actual
human subjects is not addressed (Grint and Gill, 1995).
The critics also point out that there is a problem with the notion of patriarchy.
They point out that there is confusion about the extent to which men’s interests and the
interests of capital can be conflated. Ruling-class and working-class men are given an
identity of interests, and treated as a homogenous group whose technologies alienate and
oppress women (Grint and Gill, 1995).
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Lastly, it is pointed out that there is a tendency towards a ‘kind of functionalism.’
The critics point out that ‘in stressing the performative aspects of the gender-technology
relation, the arguments become functionalist, explaining women’s and men’s relationship
to technology only in terms of its functions for gender identity’ (Grint and Gill, 1995:
16). For example, Cockburn has argued that women may resist technology because it is
stereotyped as masculine. Thus for a woman entering a technological field often means
forsaking femininity. According to the critics, the problem with this approach is that what
it means to act as a man or a woman within the context of technology is answered in
advance. This does not leave any place to change or challenge, and no theoretically
principled way to deal with situations in which women engage in behavior defined as
masculine and vice versa. The critics argue that the theory presents a bleak and
tautological picture of the gender-technology relation. ‘Male use of technology
communicates power and control….. The whole realm of technology and the
communication around it reinforces ideas of women’s powerlessness’ (Benston,
1992:41).
Women and computers
Now I will examine how computing is closely associated with masculine identity,
and how women use rejection of computers to assert something about themselves as
women. Feminists argue that computer technology is gendered (Turkle, 1984, 1988,
1990; Perry and Greber, 1990; Frissen, 1992; Kirkup, 1992). Firstly, the development of
computer technology is closely linked with wartime needs. It has been pointed out that
military represents ‘our cultures definition of masculinity in its clearest form’ (Perry and
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Greber, 1990: 85). 19 The British colossus, completed in December 1943, was a decoding
machine used to unscramble German radio transmissions. Although it was unable to run
stored programs, it was similar to a modern computer in all other respects. In the United
States, the Ballistic Research Laboratory and Professors from the Moore School of
Engineering collaborated to design ENIAC, which assisted in the computation of ballistic
tables (Perry and Gerber, 1990).
It has been pointed out that the manner in which PCs were manufactured and
marketed ensured that computers were gendered (Haddon, 1988). Kirkup (1992) argues
that during the 1970s computers in the UK were marketed towards a male hobbyist,
especially an electronics hobbyist, as you could do little with it apart from learning about
computing. The next step was the development of computer games.
It was no accident that most of these early games were military simulations of some kind; they reflected the major software research and development of the time. Warfare simulation games are at least as old as chess, but computer games are new in that they stimulate not only the tactics but the sight and sound of a ‘kill.’ …. By the mid-1980s one of the most important variables connected with ownership of a micro-computer was having an 11-14 year old boy in the family (Kirkup, 1992: 272).
One of the most interesting theoretical positions on the gendering of computers is
that of Sherry Turkle (1984, 1990). She synthesizes feminist epistemology,20 object
19 In a related study, Edwards (1990) argues that the identification of military interests with masculine gender definition has affected the production and use of computers. He points out that women comprise a noticeable percentage of personnel in military and computer science. However, he suggests that women’s presence in these bastions of masculinity might threaten masculinity as a political institution. He sees the increasing ‘militarization of computers’ and the corresponding ‘computerization of military’ – each sector reinforcing the other – as attempts to buttress the prevailing social order.20 Her definition of epistemology is derived from the works of Piaget (1950). For Piaget, epistemologie genetique was to eschew inquiry into the true nature of knowledge in favor of a comparative study of the diverse nature of different kinds of knowledge. However, she points out that she differs from Piaget in an important respect. ‘Where he saw diverse forms of knowledge in terms of stages to a finite end point of formal reason, we see different approaches to knowledge as styles, each equally valid on its own terms’ (Turkle and Papert, 1990: 129).
55
relations theory,21 and cognitive science. She argues that the present social construction
of computer use encourages a particular style of thinking, which is not only repressive for
many women, but restricts the potential of computers. In her first major work, Turkle
(1984) studied the manner in which children and adults relate to computers and use them
as tools in their work. She identified two different styles to computer programming
among children learning to program in the LOGO language at a private school – hard
mastery that employs a linear style that depends on planning, advance conceptualization
and precise technical skills and soft mastery that relies on a less structured system of
gradual evolution, interaction and intuition.
Hard mastery is the imposition of will over the machine through the implementation of the plan. A program is the instrument of premeditated control. Getting the program to work is more like getting “to say one’s piece” than allowing ideas to emerge in the give-and-take of conversation…. [T]he goal is always getting the program to realize the plan. Soft mastery is more interactive… Hard mastery is the mastery of the planner, the engineer, soft mastery is the mastery of the artist: try this, wait for a response, try something else, let the overall shape emerge from an interaction with the medium. It is more like a conversation than a monologue (Turkle, 1984: 104-105).
In her later works, she elaborated her categories, and now she distinguishes
between a formal analytic approach (rather than hard mastery) and bricolage (rather than
soft mastery). She has borrowed the term bricolage from French anthropologist Claude
Levi-Strauss. He had used the term to make a distinction between western science and the
science of ‘preliterate societies.’ The former is the science of the abstract, the latter is the
21 The object relations school has been particularly influential in the feminist conceptualizations of science. This theory argues that mechanisms through which men and women model themselves and their relations to the world are different. To acquire his masculine identity, the boy must reject and deny his former dependencies, attachments and identifications with his mother (Wacjman, 1991). This theory was also used by Keller (1983) to argue that men and women have different cognitive skills. She believed that as a male distinguishes himself from his mother, he learns to distinguish between himself and the other, and subject and object. She believes that as most scientists are men this male mind set about detachment and mastery has been written into the norms and practices of modern science.
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science of the concrete. She argues that like the bricoleur, the soft master ‘works with a
set of concrete elements’ (Turkle, 1988: 105). She believes that although the bricoleur
works with a closed set of materials, the results of combining elements can lead to
surprising and new results.
Bricoleurs are also like writers who do not use an outline but start with one idea, associate to another, and find a connection with a third. In the end, an essay ‘grown’ through a negotiation and association is not necessarily any less elegant or easy to read than one filled in from an outline, just as the final program produced by the bricoleur can be as elegant and organized as one written with a top-down approach (Turkle and Papert, 1990: 40).
Turkle argues that society has much to gain from valuing and encouraging
bricoleurs. Upon the basis of empirical research, Turkle argues that women (and some
men) are alienated from the computer because the computer culture imposes a particular
correct style of interaction, based on a formal top-down approach, in which problem is
dissected into separate parts and solved by designing sets of modular solutions (Turkle
and Papert, 1990).
Turkle made an important contribution to understand the alienation of women,
namely the different approach that women have towards programming. Empirical studies
have identified other reasons for low participation of women in the computing. It has
been found that women in engineering have to portray a professional image, which for an
engineer is male, to survive in engineering (Hacker, 1982; Carter and Kirkup, 1990;
Frissen, 1992; Valentile, 1992; Tijdens, 1997; Panteli et al., 1997, 1999). Although the
number of women in the computer industry has increased over the years, women continue
to be concentrated in lower levels of the IT industry. This concentration is seen as the
outcome of the male-dominated and masculine nature of computing work (Panteli et al.,
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1997). Most women are not encouraged to study computing in school. As popular belief
would have it, especially among computer scientists and educational institutions that have
few female students, women are afraid of science and computers. Even the media
portrays an image of a computer scientist as ‘being someone else – different from us.’
This image helps reproduce a certain computer scientist -- the compulsive programmer or
the computer nerd -- which ensures a low recruitment of women in the profession
(Ramussen, 1992). Empirical research has shown, however, that a significant proportion
of women in the IT industry display similar aspirations as men. In fact, a significant
proportion of women rate responsibility, interest, and challenge at work even higher than
their male colleagues (Panteli et al., 1999). Family, culture, upbringing, and especially
the presence of an engineer father or brother play an important role in women choosing
engineering as a profession (Carter and Kirkup, 1990).
Males employ different strategies to try and maintain their control in engineering.
Most women engineers point out that at all levels of education, the people who make life
difficult for girls and women breaking into areas that are traditionally male preserves are
male students (Carter and Kirkup, 1990). Computer programmers form sub-cultures and
women are perceived as being on the outside (Hapnes and Sorensen, 1992). Hacker
(1982) showed that men studying engineering employed different strategies to ridicule
women in their field.
Women are not easily accepted as colleagues by men in the field. Many engineering magazines are liberally sprinkled with advertisements portraying women draped suggestively over one piece of equipment or another. The Iowa Engineer, published at Iowa State University, came complete with a centerfold ‘E-girl of the Month’ and a dirty joke page. At scientific/engineering conventions, attractive women in bunny suits staff merchandise booths; a tape measure on a scantily clad model illustrates the benefits of the metric system. A university fair booth promotes
58
agricultural engineering with a leather mini-skirted mannequin, a sign on its rear reading, ‘Ag Engineering, for a BROAD education’ (Hacker, 1982: 343).
Women in engineering face an uphill task to be accepted in a male dominated
profession. Linn (1987) provides an interesting example to explain men’s attitude
towards women, and understand gender and technology stereotypes. She says that in
explaining the absence of women in high-status jobs, one of her friends offered an
explanation: ‘Women don’t get offered jobs because they have babies’ (Linn, 1987: 127).
The career identities of women engineers involve managing cultural contradictions. In
managing motherhood and career expectations, some women even decide to remain child
free, thus resisting the cultural demands which associated femininity with motherhood
(Evetts, 1994). In order to fit in a male-dominated profession, women even have to limit
their options at work to allow themselves time at home (Carter and Kirkup, 1990).
Women are left out of informal groups that provide opportunities to negotiate a
rewarding career strategy in software (Tierney, 1992).
Life-Cycle Model Approach
Although the field of gender and technology has flourished in the last 20 years,
not many feminist theorists have analyzed the fact that even technologies have a life
cycle. In fact, the relationship between gender and technology is not static, but occurs
within the context of the dynamics of technological change. The life cycle of technology
has an effect on division of labor between sexes because the skill associated with any
technology changes as the technology progresses in its life cycle.
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Analyses of the effects of technological change on skills and employment ‘are
usually cast in a static framework that involves a series of discrete jumps from one
technology to another – each with unique skills’ (Flynn, 1993: 9). In direct contrast, the
life cycle model of technological change argues in favor of sequential development paths
– from birth to growth, maturity and eventually stability or decline. Technology life
cycles – primarily technology and skill training life cycles – could logically account for
women’s inroads into previously male-dominated professions.
The Product Life Cycle Approach
The concepts of product, process and technology life cycles were formulated to
maximize results on product sales. The formulation of an S-shaped growth curve for
products and ideas is credited to French sociologist Gabriel Tarde in a work published in
1890. Dean (1950) coined the term `product life cycle’ to refer to phases of development
of an individual product. He argued that throughout the life cycle of a product continual
changes occur in promotional and price elasticity, and in costs of production and
distribution. These require adjustments in price policy (Dean 1950).
The most popular version of the product life cycle postulates that all successful
products pass through four recognizable phases. The first stage is market development --
a new product is introduced in the market before there is proved demand for it, and often
when it is still being technically improved (see Figure 3.1). The second stage is market
growth or takeoff stage. During this phase, demand accelerates and the total market
expands. The third stage is market maturity – demand levels off and replacement
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demands become an increasing share of product sales. In the final stage – market decline
– sales drift downwards as product begins to lose consumer appeal (Levitt, 1965).
Figure 3.1: Product Life Cycles
I II III IV
Introduction Growth Maturity Stability/decline
Sales
Phase
Adapted from: Flynn, Patricia M. 1993. Technology Life Cycles and Human Resources. Lanham: University Press of America.
The Process Life Cycle
Coincident with changes in the nature and demand of product are changes in the
production processes designed to produce them. With the growth of markets for a
product, the locus of change and innovation shifts from the product to production process
required to produce the product (Abernathy 1978). In effort to reduce costs, the
production process becomes standardized and routinized as a ‘best practice’ method is
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identified and refined. Typically this involves further automation and the use of
specialized machinery and equipment, which has the consequence of making the product
changes expensive. Capital-intensive, mass-production techniques replace small-batch
production as products mature and as competitive advantage increasingly becomes a
function of cost minimization (Flynn, 1993: 11).
The Technology Life Cycle
Economists have pointed out that changes in technologies are often the stimulus
behind changes in the product and production process. In fact, technologies in industries
such as consumer electronics, automobiles, shoe manufacturing, mining equipment and
air conditioning have their own life cycles. A new technology, introduced slowly at first,
becomes more widely accepted as intense and heavily financed research and development
efforts lead to better performance. Eventually, it plateaus as it reaches its performance
limits. During the last stage, it competes with a new technology until the superior
technology wins and captures the market (Ford and Ryan, 1981; Shanklin and Ryans,
1984).
There is no one-to-one relationship among technologies and products and
production processes – several product and process life cycles may evolve with the
development of a single technology. In fact, technological evolution may herald changes
in products and in production processes. For example, as a technology matures,
uncertainty about its capabilities and limitations declines, and products and production
processes become more standardized. Rapid product innovation accompanies the earliest
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phases of technology development, whereas process innovation peaks in the latter phases
as product design stabilizes (Flynn, 1993).
Other aspects of technological change can also affect the timing and shape of
process and product life cycles. Rapid technological change can increase uncertainty and
hinder the moves towards standardization of products. It can also shorten the effective
lives of products (Flynn, 1993).
The Skill-training Life cycle
Economists have pointed out that a skill-training life cycle evolves as the level of
demand and standardization of skills changes with the development of a technology. The
early stages of a technology, characterized by high degree of product innovation, are
relatively skill and labor intensive. However, as technologies mature, standardization and
increased use of equipment leads to a greater division of labor. More importantly it leads
to ‘the subdivision of multifaceted tasks into more narrowly defined assignments’ (Flynn,
1993:16).
As tasks become ‘deskilled,’ the workers’ skills, experience, and the independent decisions making become less important. The tasks of semi-skilled operatives, for example, often shift to monitoring and control of the equipment. In addition, product assembly can be done by low-skilled and unskilled workers who concentrate on a very limited number of specific tasks. Once embodied in the work force, skills are transferred to the production equipment (Flynn, 1993:16-17).
Thus the technology life cycle models suggests that the development and
introduction of new technology generates relatively high-skill professional and technical
needs. As a technology matures, some jobs such as repair and maintenance will continue
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to be relatively highly-skilled. But mostly standardization and mass-production
techniques cause deskilling of a wide range of tasks. In its extreme form, deskilling can
lead to the elimination of certain tasks (Flynn, 1993).
The availability of skill training and the mix of institutional providers vary
depending upon the phase of the technology. When a technology is new, skill training is
usually provided on the job through various programs at the workplace. In the early phase
of technology, scientific and engineering personnel design and create experimental
products. Subsequently, they teach others the skills associated with the new technology
(Flynn, 1984).
As with products, increased demand and standardization of skills permits their
‘production’ on a large scale away from R&D sites. During this stage skill training is
shifted to schools as employers cannot capture the return on investments in general skills.
Another reason for the shifts is that as demand for skills grow, it is easier to formalize the
training process by providing it in schools. Types of training provided are diverse
depending upon the mission, funding arrangements and decision making process in
schools. Initially, training is offered by schools and colleges that are oriented towards
meeting the needs of the employers. But as the demands for the skill matures, training is
widely diffused among educational and training institutions. After a technology peaks
and demand for it declines, training becomes less available at the various educational and
training institutions. Once the technology becomes obsolete, the locus of skill training
may shift back to the firm as it seeks to fill its relatively short-term, skill replacement
needs (Flynn, 1993).
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Labor Queues and Job Queues
It is a major contention of this study that as technology progresses, bringing about
changes in the skill-training life cycle, the labor and job queues associated with it change
providing an opportunity to women to fill positions that were previously beyond their
reach.
Ever since Lester Thurow (1969) put forward the labor market queue theory to
explain poverty amongst African-Americans in the United States, social scientists have
increasingly used the theory to explain occupational segregation. According to Thurow,
unemployment amongst African-Americans was higher because employers ranked them
lower in the labor queue than white Americans.
Employers choose their workers from as far up the queue as possible, but as the demand for labor expands, the dividing line between employed and unemployed shifts closer to the lower end. If a subgroup of the labor force is concentrated at the lower end for either objective or subjective reasons, the subgroup’s employment situation will be sensitive to the aggregate level of demand for labor. If a subgroup is concentrated at the top of the queue, changes in aggregate demand, unless they are very large, will have little effect on its situation, and it will remain employed (Thurow, 1969: 48-49).
Although Thurow and his successors concentrated on labor queues, later theorists
argued that along with labor queue, there is a job queue that represents workers ranking
of jobs. Rotella (1981) and Strober (1984) and her colleagues recognized the importance
of job queues, although they did not designate them as such. Rotella examined the
feminization of clerical work, while Strober and her colleagues argued that society grants
men first choice of jobs and that men select the most attractive ones available. Therefore,
it is argued that the best jobs go to the most preferred workers, less attractive jobs go to
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workers lower in the labor queue, bottom ranked workers may go jobless, and the worst
jobs remain unfilled (Thurow, 1972: 73).
Reskin and Roos (1990) reformulated the theory of labor and job queues to
explain women’s inroads into male occupations. They argued that occupational
composition is a result of a dual-queuing process: ‘labor queues order groups of workers
in terms of their attractiveness to employers, and job queues rank jobs in terms of their
attractiveness to workers’ (Reskin and Roos, 1990:29).
The two theorists posited that queues can be characterized by three structural
properties: the ordering of their elements (i.e. jobs and groups of workers), their shape
(the relative sizes of various elements – population subgroups in the labor queue and
occupations in the job queues), and the intensity of rankers’ preferences (whether or not
elements overlap). All queues are composed of ordered elements (occupations, jobs,
subgroups of workers), and their ordering dictates where each workers ends up. The
absolute and relative numbers of ‘elements in a queue determine its shape.’ The number
of prospective workers in each subgroup in a labor market determine the shape of the
labor queue. Similarly, the number of jobs at each level determine the shape of the job
queue. Panel A and B of Figure 3.2 show how the shapes of labor and job queues can
vary while the order remains constant. This variation influences the access of workers to
preferred occupations and each occupations chances of recruiting workers from each
subgroup. For example in a society with few preferred workers (A2) and few desirable
jobs (B2) preferred workers will monopolize the preferable jobs. However, if there is a
mismatch in the relative number of jobs and workers at the same level some workers will
get better or worse jobs as compared to others in their groups. For example, if preferred
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jobs outnumber highly ranked workers, as characterized in A2 and B1, employers will fill
jobs with workers from lower in the labor queue than usual. In contrast, if the job queue
has more less preferred jobs (as in B2), only the highest ranked workers will get the
desirable jobs, and those ranked in the middle of the labor queue will have to settle for
less.
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Panel A: Hypothetical labor queues ordered by sex for predominantly male and female-typed jobs.A1 A2
Panel B: Hypothetical job queues ordered by nonmanual-manual work for predominantly nonmanual and predominantly manual occupation structure
B1 B2
Figure 3.2: Variation in the shape of job and labor queue. Adapted from Reskin, Barbara and Patricia Roos (1990). Job Queues, Gender Queues. Philadelphia: Temple University Press.
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Nonmanual
Manual
Nonmanual
Manual
Men
Women
Men
Women
The third property of queues is the intensity of raters’ preferences. For, some
employers group membership is of paramount importance in ordering the labor queue. If
group membership is of utmost importance, employers prefer employees from the
preferred group regardless of their qualification (Reskin and Roos, 1990). Figure 3.3
illustrates variations in the intensity of raters’ preference with respect to the sex of the
employee in three hypothetical labor queues. Panel A illustrates a situation where group
membership is of utmost importance – the space between the sexes shows that employers
prefer hiring lowest-ranked males as compared to the highest-ranked females. Panel B
depicts a weak aversion to females as employers show preference for moderately
qualified women over men with low qualifications, and highly qualified women over
moderately qualified men. Panel C illustrates an intermediate situation in which
employers prefer average men over average women, but will hire talented women over
mediocre men.
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Women Women Men Women Men MenLow Moderate Low High Moderate High
Women Men Women Men Women MenLow Low Moderate Moderate High High
Women Women WomenLow Moderate High
Men Men MenLow Moderate High
Level of qualification
Level of qualification
Level of qualification
Panel B: Sex group membership is a minor consideration to rankers.
Panel C: Sex group membership is an intermediate consideration to rankers
Figure 3.3: Variation in the shape of job and labor queue. Adapted from Reskin, Barbara and Patricia Roos (1990). Job Queues, Gender Queues. Philadelphia: Temple University Press.
70
Panel A: Sex group membership is an overriding consideration to rankers.
They further argued that ‘changes in size – of subgroup of workers or of various
occupations – that create a mismatch between the number of workers at some level in a
labor queue and the number of jobs in the corresponding level of the job queue can lead
occupations’ composition to change’ (Reskin and Roos 1990:34).
According to Reskin and Roos, four factors transform a labor queue into a gender
queue. First, sex labels that characterize jobs as “women’s” and “men’s” influence hiring
as it leads employers’ to prefer one sex over another. Second, employers’ difficulty in
identifying productive workers can make them resort to proxies such as educational
attainment, experience and group membership. Sex often influences hiring because of
stereotypical beliefs such as men are more productive in ‘male’ jobs as they are more
rational, stronger and more mechanically adept and so forth. Third, some employers do
not hire women as they worried about the negative responses of male workers. They
believe that this might affect productivity, raise labor cost by increasing turnover or men
might demand higher wages for working with women. Fourth, some employers are not
compelled to minimize wages. Therefore, they are not interested in hiring women
employees who could be paid less. Finally, some employers’ willingly accept higher
wages as the price for favoring women.
Reskin and Roos (1990) argue that men and women use similar criteria to rank
jobs in the job queue despite anecdotal claims that women place a premium on jobs that
are compatible with child care and men value blue-collar occupations to confirm their
masculinity. Most workers use criteria such as income, social standing, autonomy, job
security, congenial working conditions and chances of advancement to rank jobs in the
job queue.
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Explaining Women’s Inroads Into Male Occupations
By definition, the feminization of an occupation results from the disproportionate
recruitment or retention of women workers. Disparate recruitment or retention of women
workers is a product of changes in the structural features of queues (how employers order
workers and how workers rank jobs, and the intensity of either group’s preference)
(Reskin and Roos, 1990). The first factor that results in the change of the shape of the job
queue is growth in occupations as was witnessed during the 1970s in service sector jobs
in the United States. In fact, the economic boom of the 1990s led to further increases in
service sector occupations. Although occupational growth can open male occupations to
women, it does so only after it has exhausted the supply of candidates from the preferred
groups (Oppenheimer, 1970). Growth is especially likely to prompt employers to resort
to women for jobs whose high entry requirements limit the number of qualified prospects.
In jobs that demand hard-to-acquire credentials, rapid growth is likely to exhaust the
supply of trained workers from the preferred group. For example, as the computer
industry expanded in the US, demand for system analysts skyrocketed, and
approximately 64,000 men and 31,000 women found work. This almost doubled
employment in this occupation (Reskin and Roos, 1990). Given women’s lower position
in the labor queue, they are among the ‘disadvantaged’ who benefit from shortages. As
Oppenheimer (1970: 99-100) points out:
The continuously growing demand in an industrializing society for workers with a fairly high level of general education plus some special skills has resulted in a chronic shortage of “middle quality” labor. … It is the occupations in a poorer position to compete for such labor that tend to utilize female labor. Once recourse has been made to female labor to provide quality labor at a low price, employers tend to get used to
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relatively well-educated workers who have been working for much less than men who have received a comparable education. To substitute men to any considerable extent would require either a rise in the price paid for labor or a decline in the quality of labor, or both.
Additionally, the feminization of occupations occurs more rapidly in small and
high-turn over occupations. For example, the insurance industry, which employed fewer
than 100,000 people in 1970 and retained workers for about five years, flipped from
being overwhelmingly male to female dominated within a decade. In occupations in
which a policy-setting body controls the labor market, anticipated labor shortages often
stimulate actions to increase the pool of qualified workers. Increasing educational
participation may mean lowering admission standards or admitting groups formerly
excluded (Reskin and Roos, 1990).
According to Reskin and Roos, many occupations witnessed male worker
shortages in the 1970s not because they grew dramatically but because their rewards and
working conditions deteriorated. This leads to men to rerank the occupations in the job
queue. Often technological change that furthers the division of labor, deskills work, or
alters working conditions, can set the stage for occupational decline in the job queue.
This can lead to a reranking of occupations in the job queue by men. Jobs change when
employers transform the technology of production -- for example, from handwork to
operating a machine. Even reorganization of work -- for example, dividing the
production of an item into several stages, so that workers make pieces of a product rather
than the whole item – sets the stage for occupational decline (Oppenheimer, 1970). The
computer industry provides an example of technological change altering an occupation’s
sex composition.
73
Early computers had no operating systems, so programmers performed craftlike tasks: for example, rewiring circuits each time they ran a program. The development of early operating systems, allowed programmers to store common tasks in machine memory, freeing them for higher- level projects. However, as the computer workforce grew, managers sought to contain labor costs by separating from programming two new occupations: system analysts, who designed information systems; and coders, who translated programs into computer codes and entered data. The industry constructed the latter jobs as clerical and filled them predominantly with women. System analysis was initially men’s work; only in the 1970s did it begin to feminize (Reskin and Roos, 1990: 43).
Other factors that led to reranking of occupations in the job queues were declining
earnings and benefits, declining job security, occupational prestige and mobility
opportunities at work and changes in occupations’ skill mix (Reskin and Roos, 1990).
Another factor that leads to women making inroads into previously male
occupations is employers’ reranking of the sexes in the labor queues. According to the
queuing perspective, there are four reasons employers might have for advancing women
ahead of men: (1) they believed that productivity or cost differentials between the sexes
have changed; (2) their aversion towards women or preference for men declines or
disappears; (3) the cost for indulging preference increases; (4) new rankers entered the
profession who did not support male preference (Reskin and Roos, 1990).
Throughout this century, the shape of the labor queue has steadily changed as
women’s share in the labor force has increased. Employers’ increasing need for women
for desirable jobs helped stimulate their growing availability. Most women learned about
the existence of previously male-dominated jobs while doing sex-typical jobs. For some
feminizing occupations, women learned of opportunities and mastered the jobs’
responsibilities as consumers or homemakers. For example, women tried their hand at
real estate sales after selling or buying a home. Colleges and universities played an
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important role as they exposed women to opportunities in customarily male professions.
They also provided a pathway into a few nonprofessional occupations as well. However,
the key question is still unanswered: Why do women prefer jobs that men have rejected
in favor of greener pastures? The answer is simple: because they are preferable to most
female occupations because they have higher wages and have better working conditions.
However, the relative advantages drop slightly once these occupations are identified as
those employing disproportionate number of women (Reskin and Roos, 1990).
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Chapter FourTheoretical Perspective
Introduction
Many feminist theorists have argued that technological change leads to deskilling
of jobs thus providing women an opportunity to fill these positions. For example, Reskin
and Roos (1994) argued that technological change sets the stage for occupational decline
leading to a reranking of occupations in job queues, thus facilitating entry of women in
previously male-dominated occupations. However, most theorists have not discussed
whether the life cycle of technology has an effect on the employment of women. This
chapter primarily aims to examine whether we can utilize the life cycle model approach,
specifically, the technology and skill-training life cycles, to throw additional light on
labor and job queue theory as given by Reskin and Roos. I will synthesize the two
approaches to develop an analytical framework for understanding the concentration of
women in lower positions in the occupational hierarchy in the software industry in India.
Initial evidence from the Indian software industry will be used to examine whether the
theoretical framework may be appropriate for this task. Additional evidence will be
provided on the labor force participation of women in the United States in the last century
to corroborate the theoretical arguments.
As pointed out earlier, economists have argued that most technologies in
industries such as consumer electronics, automobiles, shoe manufacturing, etc., have their
own life cycles of development. Once introduced, a technology is improved over time,
and eventually replaced by a new innovation. Along with these technology cycles, a skill-
training life cycle evolves as the level of demand and standardization of skills changes. In
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the case of computer technology, there is a definite pattern to technology and skill-
training life cycles. The early stages of computer technology, characterized by a high
degree of product innovation, were relatively skill and labor intensive. However, as
computer technology matured, standardization led to a greater division of labor or
deskilling. But some task continued to be relatively highly skilled.
I shall examine the stage of the life cycle of computers22 in which deskilling
occurred, thereby allowing for changes in the labor and job queues to facilitate the entry
of women in previously male dominated occupations. The first section of the chapter
discusses the different stages of the computer technology life cycle and the involvement
of women programmers in each stage. The second section discusses how technological
change has led to changes in the job and labor queues. The third section of the chapter
discusses how we can synthesize the two approaches. The last section discusses the
implications of my research.
The Life Cycle of Computers and Women Programmers
The development of the computer is intricately linked with wartime needs. The
first modern computer in the United States, ENIAC, was a World War II project funded
largely by the military. The technology was developed primarily to calculate ballistic
missile trajectories. The British colossus, completed in December 1943, was a single-
purpose decoding machine designed to unscramble German radio transmissions (Perry
and Gerber, 1990).
22 Computer technology does not represent a single technology; it consists of a set of technologies. I am primarily interested in the life cycle of Personal Computers, and programming languages.
77
ENIAC was a giant collection of registers and was very clumsy, making
operation highly painstaking. According to the skill-training life cycle, the early stages of
a technology are relatively skill and labor intensive. This is true for computer technology
as highly skilled professionals were required to operate these computers because
instructions could not be stored in the machine’s memory. As Kraft (1977: 23) points out:
By modern standards they were exceedingly clumsy devices, composed largely of electromechanical or electrical switches regulated by vacuum tubes. Quantities to be calculated by the machines were fed into them on paper or magnetic tape or punched cards. Changes in the operating “program” were made by adjusting a given combination of switches and physically rearranging special self-contained circuits called “plug boards.” … The entire cumbersome process was known as “external programming” since the instructions could be changed only by physically moving the plug boards, control switches, etc. … In addition, a given set of instructions – the program – had to be absolutely complete before the machine could do so much as add a column of numbers. ... Finally – and this was the most tedious and time-consuming of all – the program contained on tape or cards had to be entered in a form the machine was able to accept and act on.
It is argued that the availability of skill training depends upon the phase of
technology. In the early phases of a technology, skill training is usually provided on the
job. During the early stage of computer technology, hardware manufacturers provided in-
house training to software workers. For example, IBM trained its staff to provide in-
house training courses to employees of companies using IBM machines. In fact, the
training acquired at ‘IBM school’ became so prestigious during the 1950s and early
1960s that government and private employers used the lure of IBM training to recruit
potential employees in what was a tight seller’s market in programming (Kraft, 1977).
During this stage, some women, who were college graduates with backgrounds in
math or science, were hired to do hand calculations and develop software to predict
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ballistic trajectories. Women were hired because it was assumed that programming would
be like clerical work. However, when employers realized that programming was more
complex, involving abstract logic, mathematics and knowledge of electrical circuitry,
they started hiring more men and the sex composition of programming shifted (Kraft,
1977). In fact, some theorists argue that women have often been involved in the early
stages of a technical field, but once the field has stabilized and demonstrated its
intellectual and financial potential, women are excluded (Perry and Gerber, 1990).
A turning point in computer technology was the introduction of the stored
program computers during the 1940s. This stage marks a break from the earlier
technology. It was at this stage the new and superior technology competed with the old
one and replaced it (See Figure 4.1).
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Figure 4.1: Computer Hardware Technology Life Cycle Model
--- stored computing technologylimit of first generationcomputertechnology
product &processperformance
Technology B
Technology AOnset of effort
1940 1970 Years
Adapted from: Richard N. Foster, “A Call for Vision in Managing Technology,” McKinsey Quarterly Summer, 1982, McKinsey and Company Inc
The introduction of stored computers made using computers less painstaking and
time-consuming because the instructions, which operated the machine, were stored in the
machine’s memory along with the data to be processed. A hardware control unit within
the main body of the computer decoded the stored instructions for the computer to
process the data (Kraft, 1977; Greenbaum, 1979; Donato, 1994). As Ceruzzi (1998: 84)
points out:
With a stored-program computer, a sequence of instructions that would be needed more than once could be stored on a tape. When a particular problem required that sequence, the computer could read that tape, store the sequence in memory, and insert the sequence into the proper place(s) in the program. By building up a library of sequences covering the most frequently used operations of a computer, a programmer could write a
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sophisticated and complex program without constant recourse to the binary codes that directed the machine.
However, during this stage, all programming was not simplified. For some
programmers, the job became more elaborate and tedious. The stored computer made
programming simpler for some programmers only because ‘others now had to do a much
more involved and time-consuming kind of programming’ (Kraft, 1977; Greenbaum,
1979; Donato, 1994).
It has been pointed out that technological change may herald changes in products
and production processes. The introduction of stored computers led to change in the
process life cycle with the use of transistors in the production process. Transistors offered
the benefits of speed, reduced size, and enhanced reliability – important advantages that
finally led to the acceptance of the computers in the business community. Finally,
vacuum-tube, or first-generation computers, were replaced by solid-state transistor
hardware, or second generation computers, in 1958 (Donato, 1994). Further business
expansion, and the success of early models of computers during the 1960s, forced the
computer industry to manufacture cheaper and more reliable components for computers.
Thus, the introduction of new technology also led to the manufacture of new and better
products. The IBM computer company introduced the integrated circuit component
family of machines that added further speed and reliability to computers (Greenbaum,
1979).
The introduction of stored-program computers made the task of training software
workers comparatively easier. The training of computer professionals in the US has been
institutionalized in a three-tiered system: research universities or schools of management,
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four-year engineering colleges and two-year junior institutions. The junior colleges
prepare the least skilled programmers and primarily provide vocational training. Four-
year colleges train more skilled programmers, teaching students to design and write
programs rather than simply code. Elite institutes train highly skilled programmers, such
as system analysts, who design entire computer systems or languages (Kraft, 1977;
Donato, 1994).
Another turning point in the technology life cycle of computers was the advent of
the microprocessor in 1971, which provided the capacity to put a computer on a chip. The
introduction of the PC23 was particularly important for changes in software technology as
it was followed by a step towards user-friendly computing software in 1983. We can
characterize the introduction of the PC a result of the product life cycles that began with
second-generation computers. It does not mark a break from the earlier technology as it is
also based on the concept of stored programs.
The microprocessor was used as the basis of design for Apple I and then of Apple
II, the first commercially successful microcomputer, or PC, in 1976. Apple Computers,
which was started by two college dropouts in the garage of their parent’s home, had sales
of $ 583 million by 1982, ‘ushering the age of diffusion of computer power.’ Following
the success of Apple, IBM introduced its own version of the microcomputer, the Personal
Computer, in 1981 (Castells, 2000). The operating system used in IBM PCs in the early
1980s was called Disk Operating System or DOS. It was a mainstream operating system
until it was replaced by Windows 95, which is also based on DOS (Ceruzzi, 1998).
23 After the introduction of the Apple microcomputer, IBM launched its own version of microcomputer that was given the name—Personal Computer (PC).
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Apple’s Macintosh, which was launched in 1984, was the first computer that
introduced icon-based user-interface technology. A fundamental condition for the
diffusion of microcomputers was fulfilled with the development of new software adapted
to their operation. The role of Microsoft, the computer software giant, is very important
as it was the company that provided user-friendly software that was preloaded for free by
PC vendors (Castells, 2000).
During the 1970s and 1980s, employment in computer occupations witnessed a
rapid growth in the United States. Women moved into computer occupations in large
numbers (Donato and Roos, 1987). However, the growth did not benefit women as they
were concentrated in lower paying jobs. For example, in 1981, the earnings of a female
programmer averaged 70 percent of male programmers (Kraft and Dubnoff, 1983).
Although women benefited monetarily from working in computer occupations relative to
other women, their status was not comparable to similarly employed men.
In the last two decades of the twentieth century, increasing chip power resulted in
dramatic enhancement of microcomputing power.
Since the mid-1980s, microcomputers cannot be conceived of in isolation: they perform in networks, with increasing mobility, on the basis of portable computers. This extraordinary versatility, and the capacity to add memory and processing capacity by sharing computing power in an electronic network, decisively shifted the computer age in the 1990s from centralized data storage and processing to networked, interactive computer power-sharing. Not only did the whole technological system change, but its social and organizational interactions as well (Castells, 2000).
Along with changes in computer hardware, software technology has also
undergone tremendous change. The first computer programming language, Short Code,
was developed in the late 1940s. It belongs to the category of machine/first-generation
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languages as it involved putting a sequence of binary numbers directly through simple
toggle switches. The whole process was tedious and time consuming (The History of
Programming Languages, 2002)
Unlike first-generation/ machine languages, the second-generation languages also
called assembly languages use easy identifiable operation codes called Mnemonics,
instead of numeric operation codes. Mnemonics are abbreviated versions of English
words that are easy to recognize (Computer Languages, 2002). The third-generation
languages that were developed in the 1950s made coding less time consuming and less
expensive (Backus, 1981). A defining feature of these languages was that programmers
did not have to familiarize themselves with the internal architecture of the computer. The
earliest third-generation language, FORmula TRANslating system or FORTRAN, was
introduced in 1957. The language was designed at IBM for scientific computing. Since
FORTRAN did not suit business needs of that time, COBOL or Common Business
Oriented Language was developed in 1959. COBOL became one of the first languages to
be standardized to a point where the same program could run on different computers from
different vendors and produce the same results (Ceruzzi, 1998).
Another computer language that combined the features of COBOL, FORTRAN
and ALGOL24, was Pascal.25 Developed in 1968, Pascal was used for artificial
intelligence research (Wirth, 1996). Pascal as a programming language lost its popularity
with the growth of C. Dennis Ritchie developed C in 1972 at Bell Labs. C was developed
for UNIX systems. It is very similar to Pascal, but uses pointers26 extensively. It is fast
24 Developed mainly in Europe between 1958 and 1960, ALGOL, was proposed as a more rigorous alternative to FORTRAN. It was intended from the start to be independent of any particular hardware configuration, unlike the original FORTRAN (Ceruzzi, 1998).25 Developer of Pascal, named the language after French philosopher and mathematician, who in 1642 designed one of the first gadgets that might be truly called a digital calculator (Wirth, 1996)26 Pointers are addresses to the memory location of the computer, and they make programming simpler.
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and powerful, but it is hard to read. During the 1970s and 1980s, a new programming
method, known as object oriented programming or OOPS27 was developed. Using OOPs
technology, C++ was developed in 1983. It was designed to organize the raw power of C
using OOPs. It is most often used in simulations such as games. With the growing
popularity of interactive TV in the early 1990s, Sun Microsystems started developing a
new portable language. However, after interactive TV failed, they shifted their focus to
the World Wide Web, and developed a new language for the Internet that came to be
known as Java. It is truly a ‘language of tomorrow’ as it object-oriented and implements
advanced techniques such as true probability of code and garbage collection. While
the earlier languages such FORTRAN and COBOL were intended for only one purpose,
the languages of today such as C, C++ and Java can be used for any purpose (The
History of Programming Languages, 2002).
The growth of PCs allowed for the deskilling of programming languages as it led
to the widespread diffusion of computers, and provided the opportunity for more persons
to learn computer languages. The role of hobbyists is very important in the diffusion of
computers as they made conscious efforts to popularize computers among other amateurs
(Ceruzzi, 1998). During the 1990s, fourth-generation languages were developed that were
user-friendly and enabled less technical people to become involved in the programming
process. These are also termed non-procedural languages because the programmer says
what needs to be done rather than how to do it (Computer Languages, 2002). Most of
these programming languages are easy to learn and use. For example, the development of
27 Objects are pieces of data that can be packaged and manipulated by the programmer.
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computer languages such as Visual Basic (VB)28 and HTML29, or packages like
Dreamweaver and Flash, now allow novice programmers to develop software programs
or Web pages. They require few specialized skills as they employ a graphic user interface
(GUI) that is easy to understand, use, and master. Since they are easy to master, they are
ranked low in the software occupational hierarchy, both by workers and employers.
The life-cycle of programming languages can be illustrated similarly like
computer hardware technology life-cycle model (see figure 4.2). The
second-generation/assembly languages replaced first-generation/machine languages
during the late 1940s because of superior technology. They in turn were replaced by
third-generation languages as they made coding less time consuming. During the 1990s,
fourth-generation languages were developed; however, they have not replaced third-
generation languages. Instead the introduction of fourth-generation languages has led to
division of programming languages into two categories – whereas third-generation
languages such as Java, C and C++ are used to do ‘high-end’ programming, fourth-
generation languages are used to do low-skilled programming.
28 Visual Basic is based on the BASIC language developed in 1964. BASIC is a very limited language and was designed for non-computer science people. VB is used to create simple interfaces to other Microsoft products such as Excel and Access without a lot of code, although it is possible to create full applications with it.29 The CERN team, which is credited with the invention of the Internet, created a format for hypertext documents that they named hypertext markup language (HTML). It lets computers adapt their specific languages within this shared format.
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Figure 4.2: Programming Languages Life-Cycle Model
Onset ofProduct Limit of Fourth generation& Second generationProcess Onset of thirdPerform- Limit of generationance First
generation
1940 1945 1955 1990 Time
Fig 4.2: Data Derived from Ceruzzi (1998) and Computer Languages (2002)
The Feminization of Software Programming in India
The introduction of non-procedural languages has allowed for the feminization of
occupations that employ these technologies in India. It is a major contention of this study
that the feminization of software occupations has taken place only in certain jobs,
specifically those that employ languages that are developed in the later stage of the
software technology life cycle.
Traditionally, computer programmers have been divided into three categories:
coders, programmers and system analysts. As the most skilled programmers, system
analysts design whole complex data processing systems rather than parts of a larger
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program like programmers. Programmers solve data-processing problems through
designing, writing and debugging. Coders are like clerical workers and they translate
programs into computer languages (Kraft, 1977). For this study, I will use this
classification, and also determine what kind of programming languages are used by each
group.
Women comprise about 12 per cent of the total workforce in the Indian software
industry (NASSCOM, 2000). It is believed that the disparate recruitment or retention of
female workers is a product of changes in the structural features of job and labor queues
(how employers order workers and how workers rank jobs, and the intensity of either
group’s preference). I will begin by examining the factors that have led to changes in the
job queue.
It was pointed out earlier that the first factor that produces change in a job queue
is job growth in an occupation. Job growth supports feminization as it can lead to a
shortfall of male workers. In fact, evidence from the United States has shown that sex
segregation across industries dropped the most during the 1970s in the fastest growing
occupations (Fields and Wolf, 1989). The Indian software industry witnessed
phenomenal growth during the last decade. During 2000-2001, the value of the software
industry in India was estimated at $8.26 billion. In comparison, the estimated value of
the industry was only $150 million a decade earlier (1990). In addition, employment in
the Indian software industry also expanded in the late 1990s with the growth of software
firms and software revenues. In 1996, it was estimated that there were 140,000 software
professionals in India. By 2000, the number of software professionals had jumped to
410,000 (NASSCOM, 2002).
88
It must be kept in mind that not all the jobs that have been created are ranked high
in the occupational hierarchy. In fact, there is evidence that a sizeable portion of the work
that is outsourced to Indian software firms is neither technologically advanced nor critical
to the business of the firms outsourcing the work (Arora et al., 1999). As pointed out
earlier, after the study was conducted, there is evidence that shows that increasingly
skilled work is being outsourced to India (Kirplain et al, 2003). The argument that I am
putting forward is that the growth of the Indian software industry has led to the
feminization of certain types of jobs within the software industry. This is attributable to a
shortfall of male workers for these jobs because they prefer occupations that are ranked
higher in the occupational hierarchy as they are more skilled.
It has been further argued that in high-turnover occupations, shortages can quickly
assume crisis proportions. As a result, the feminization of such occupations occurs more
rapidly (Reskin and Roos, 1990). In India, labor turnover in the software industry was
very high due to the outmigration of members of the best and brightest of the Indian labor
force, particularly to the U.S at the time of the study. As noted in a previous chapter,
Indian workers, particularly IIT graduates, are among the most successful professionals
in Silicon Valley (Saxenian, 1999). Most managers of Indian software firms see attrition
as a major problem as there have been many cases where the entire project team left after
the first six months (Arora et al., 1999). This has led to a shortage of male labor, thereby
providing the possibility for women to fill the positions abandoned by men. However,
there has been a change in this trend in the last two years as the number of engineers
migrating from India to the US has dropped substantially due to the cut in work visas
issued to Indian citizens.
89
Reskin and Roos (1990) found that most occupations that experienced a shortage
of male workers during the 1970s not only witnessed a dramatic growth of jobs, but also
a deterioration of rewards and working conditions relative to other occupations for which
male workers qualified. As a result they became less attractive to male workers. These
jobs are not ranked high in the occupational hierarchy as they are not highly skilled and
have low occupational prestige. As was pointed out earlier, it is easy to master and use
these technologies. Thus it is likely that employers cannot attract and retain enough
qualified male workers in these occupations. Therefore, they turn to women to fill these
positions.
Factors that have likely made jobs unattractive to men is that the entry of large
number of women in these occupations has led to a drop in salaries, status, and working
conditions. As Moghadam (1997: 14) warns:
As computer-based skills become more commonplace, and as the need for more workers to use them in a greater variety of ways grows, more women will be again recruited. But this will be at a lower wage because these will be no longer considered specialist skills, merely something that women can do.
It should also be pointed out that women’s entry into the labor force in jobs
related to information technology, albeit in entry level secretarial positions or
manufacturing jobs, brings many women disposable income for the first time, raises their
status in their own eyes, and frequently leads to their desire for more training and
upgrading of skills (Hafkin and Taggart, 2001). Initial evidence from India suggests that
women working in the software industry have high self-esteem as they have more income
in low skilled occupations in the software industry than they did in other feminized
occupations such as teaching (Yee, 2000).
90
Another factor that leads to women making inroads into previously male
occupation is employers’ reranking of sexes in the labor queues. Cultural change due to
modernization, information from the mass media, and increasing labor force participation
of women in India have led to a change in mindset of employers. Many no longer believe
that there are productivity or cost differentials between the sexes.
Now I will examine the changes in the labor queue that have led to women
making inroads into certain occupations within the software industry. Colleges and
universities have played an important role in changing the composition of the labor
queue. The growth of private, for- profit institutes has provided women an opportunity to
acquire skills that could enable their entry into the software industry. The training sector
in India has grown along with the software industry. In 1997-98, revenues were estimated
at Rs 8.56 billion (about $226 million), up from Rs 6.6 billion for the previous year. As
pointed out earlier, women comprise about 50 per cent of total student enrollment in
private institutes. However, the general perception is that graduates of these institutes are
not suited for software development. The increase in certification courses such as
Microsoft Certified Software Engineer/Developer (MCSE/D), Certified Novell Engineer
(CNE), and IBM Net Professional certification has also enlarged the pool of workers. In
fact, many graduates of private institutes acquire these certifications in order to place
them more favorably in the job market (Arora et al., 1999).
Synthesis
The history of computer programming clearly illustrates that computer technology
has a technology and skill-training life cycle. According to the technology life cycle, a
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technology, once introduced, is improved overtime, and eventually replaced by a new
innovation. This is true for computers as they were introduced in the late 1940s to satisfy
wartime needs. These early/first-generation computers were clumsy machines, composed
largely of electromechanical or electrical switches regulated by vacuum tubes, making
operations highly painstaking. A turning point in computer technology was the
introduction of the stored program/second-generation computers. This made using
computers less painstaking and time-consuming because the instructions, which operated
the machine, were stored in the machine’s memory along with the data to be processed.
This technology was further improved with the use of transistors during the 1950s.
Transistors offered the benefits of speed, reduced size and enhanced reliability (Kraft,
1977, Greenbaum, 1979, Donato, 1994). And, finally, the introduction of
microprocessors in the 1970s, which provided the capacity to put a computer on a chip,
led to the introduction of the PC (Castells, 2000).
Along with changes in hardware, computer software technology has also
undergone tremendous change. As computer software technology has matured there has
been a trend towards job fragmentation and deskilling (Kraft, 1977). During the early
stages, skills associated with software programming were highly specialized. For
example, the machine/first-generation languages involved inputting a sequence of binary
numbers directly through simple toggle switches. The whole process was highly skilled,
tedious and time consuming. The second-generation languages also called assembly
languages made coding easier as they used easy identifiable codes called Mnemonics,
instead of numeric operation codes. Mnemonics are abbreviated versions of English
words that are easy to recognize. The third generation languages made coding less time
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consuming because the coders did not have to familiarize themselves with the internal
architecture of the computer. Finally, coding was deskilled with the introduction of
fourth-generation languages during the 1990s that are user-friendly enabling less
technical people to become involved in the programming process (Computer Languages,
2002).
Along with the technology life cycle, a skill-training life cycle has also evolved.
According to the skill-training life cycle, the early stages of a technology are relatively
skill and labor intensive. The early stages of computer technology, characterized by a
high degree of product innovation, were relatively skill and labor intensive. In the early
phases of a technology, skill training is usually provided on the job. During the early
stage of computer technology, hardware manufacturers provided in-house training to
software workers. During the latter stages of technology, training is institutionalized. For
example, training of computer professionals in the US has been institutionalized in a
three-tiered system: research universities or schools of management, four-year
engineering colleges and two-year junior institutions.
It can be seen clearly that progress in the computer technology life cycle led to
developments, such as introduction of PCs that were important for popularization of
computers. As all aspects of social life were computerized, there was an explosion in
demand for computer professionals. The expansion of jobs in computer-related
occupations led to changes in job queues as demand for computer professionals
outstripped the availability of qualified men. Women benefited from labor shortages as
employers had to resort to hiring women, thus providing them opportunities that were
earlier out of their reach.
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Most importantly, technological changes remitting from the life cycle of computer
technology furthered the division of labor, deskilled jobs, or altered working conditions,
leading to a reranking of occupations in the job queue by men, thus allowing women to
fill these previously male-dominated occupations (Oppenheimer, 1970). As pointed out
earlier, coding was deskilled with the introduction of fourth-generation languages during
the 1990s that are user-friendly, enabling less technically proficient people to become
involved in the programming process. In addition, due to technological changes, new jobs
such as Webmaster and content manager have been created that are ranked low in
occupational hierarchy within the software industry. The technological changes led to
changes in the structure of job queues as men reranked these jobs lower in the job queue.
This provided opportunities to women that were previously beyond their reach. However,
we must remember that deskilling reinforces the gendered division of labor as women
continue to be concentrated in occupations employing ‘point and click’ technologies that
are ranked lower in the occupational hierarchy within the software industry (Hafkin and
Taggart, 2001).
The progress of the computer skill-training life cycle led to changes in the labor
queue. In the early stages of computer technology, training was provided in-house. For
example, IBM provided training in computing to a limited number of employees. This
training was out of reach of most women as opportunities were limited. In addition, the
masculine character of computing kept them from pursuing a formal degree in computer
engineering. Therefore, the process of acquiring training was gendered, and programming
occupations became sex-typed. It was only during the later stages of computer
technology that educational institutions started providing training in computer
94
technology. With the institutionalization of the training process, women were able to
receive such training and jobs in programming occupations. Thus, education provided a
pathway into computer-related occupations. The proliferation of computer courses in
community colleges and for-profit private institutes has allowed women to enter the
software industry without acquiring a formal computer engineering degree. However, this
has contributed to occupational segregation in the software industry as women are able to
acquire only those jobs that are ranked low in the occupational hierarchy.
Research Questions
Following from the discussion, this research will examine the work experiences of
women employed in the Indian software industry. It will primarily focus on the content of
the work of female software professionals, the technology involved in their work, and
the process by which they have developed work skills. Additionally, the jobs of women
software professionals will be compared to jobs in other sectors of the economy,
specifically teaching, which is a female-dominated occupation in India. It is expected that
theories of job and labor queues and theories of technology and skill-training life cycle
will provide insight into what one should expect to find in relation to these issues.
The research will be guided by the three general research questions:
Indian women who are involved in programming jobs that involve the use of technology in the early stages of technology life cycle will have different work experiences than women employed in programming jobs that involve the use of technology in the later stages of its life cycle.
In the preceding discussion, it was pointed out that as a technology progresses in
its life cycle, it becomes deskilled. Economists have pointed out that a skill-training life
95
cycle evolves as the level of demand and standardization of skills changes with the
development of technology. The early stages of a technology are relatively skill and labor
intensive. However, as a technology matures, standardization leads to a greater division
of labor. Most importantly, it leads to deskilling (Flynn, 1993). Therefore, it is my
argument that women working in programming jobs that involve the use of technology in
its later stages will have different work experiences as their work is deskilled compared
with women employed in programming jobs that use technology in its early stages.
Indian women who are involved in programming jobs that involve the use of technology in the early stages of technology life cycle will have different skill-training experiences than women employed in programming jobs in the later stages of the technology life cycle.
As pointed out earlier, the availability of skill training and mix of institutional
providers vary depending upon the phase of technology. In the early stages, skill training
is usually provided on the job through various programs at the workplace. Increased
demand and standardization of skills permits their production on a large scale away from
R&D sites. During this stage skill training is shifted to schools. Thus, skill training is
easily available when a technology is in its later stage (Flynn, 1993). For example, many
private-for-profit institutes in India provide training in fourth-generation or procedural
languages. Women comprise about 50 percent of the student population in these
institutes. Thus women will have different educational or skill training experiences
depending upon the technology, they use at work.
Indian women who are employed in the software industry will perceive their jobs as more desirable compared to other opportunities such as teaching available to them.
Reskin and Roos have argued that women prefer jobs that men have rejected
96
because they are preferable to most female occupations because they have higher wages
and better working conditions. However, the relative advantages drop slightly once these
occupations are identified as those employing disproportionate number of women
(Reskin and Roos, 1990).
Implications of the research
The proposed research will uncover the complexities of social composition, and
life experiences of women software professionals in India. Specifically, the significance
of the study may be delineated at various levels. Firstly, the study will be part of the
much-needed empirical research in the area of gender, science, and technology in the
Indian context. Secondly, a closer scrutiny of national level data shows the absence of
data on women software professionals. This will be a modest attempt to collect some
base line data. Thirdly, the study will provide a new perspective in theory on women and
technology as it links the concept of technology and skill-training life cycle to that of job
queues and gender queues. Fourthly, the research will be a genuine attempt to bridge the
gaps and transverse the contemporary feminist field by charting a passage between India
and the USA. Lastly, the suggestions made by the study in terms of changes in cultural
and educational structure can be used by policy makers to increase participation of
women in engineering.
97
Chapter FiveMethodology
The study required field research as the growth of the software industry in India is
a recent phenomenon, and quantitative data are not easily available. The Indian
government presently does not have detailed data on women software professionals. The
first section of the chapter discusses the research site, the second section deals with the
research design that was employed for the study, and the last section deals with the
interview procedures employed and the questions that this study will answer.
Research site
The research site for the project was the city of New Delhi. It is the capital of
India, and the second largest city in the country (See Figure 5.1). I chose New Delhi as
the research site for the study as it is the only city in north India with a sizeable number
of software companies. Another reason for selecting New Delhi was that it is my
hometown. I am familiar with the city, its culture, language and people. Most
importantly, I chose to conduct research in New Delhi since I worked in the city as a
journalist for five years, I had contacts that were very helpful for me in identifying and
selecting respondents for participation.
As in the case of Bangalore, the reasons for New Delhi becoming a major software
center in the northern part of the country are:
Labor availability: Skilled labor is easily available as there are many prestigious
engineering colleges in and around New Delhi, especially IIT, Delhi, Delhi
College of Engineering and Delhi Institute of Technology. In addition the city
98
has many polytechnic colleges and for-profit institutes that provide training in
software technologies.
Style of Life: Most software professionals hailing from north India like to settle in
New Delhi because it is the largest city in the region. There are many
opportunities for career enhancement available in the city.
Infrastructure: The government has set up a Software Technology Parks in New
Delhi and Noida, a suburb of New Delhi. The parks contains facilities such as
direct satellite uplinks to facilitate the export of software. Many software
companies have also started development centers in Gurgaon, another suburb of
New Delhi because of availability of good communication facilities.
99
Figure 5.1: Location of New Delhi in India
100
Female software professionals from eleven companies were interviewed for the
study. The companies included two telecom giant multinational companies (MNCs), one
Indian software company writing software for the telecom sector, one of the largest
Indian computer companies specializing in hardware, one software company writing
software for newspapers, and five Internet companies – out of which two provided online
infotainment, one was an online job site, one made web pages for clients and one was an
online newspaper.
The companies varied in size with the smallest having only seven employees and
the largest 2000. At the time of the interviews, only four companies had laid off
employees on the account of the recession that was ongoing at the time when the
interviews were conducted (See Table 5.1 for further details). Only five companies
provided additional job training to the employees. The courses offered ranged from
training on new technologies to teaching yoga (See Table 5.1 for further details).
101
Table 5.1: Profile of Companies 30
Name of company
Area Number of employees
TrainingProvided
Employees terminated
Softpro American
telecom MNC
1600 Yes Yes
Systec German
telecom MNC
1500 No No
Jobs.com Internet 175 Yes No
TCM Hardware 2000 No No
MOI Infotainment 50 No No
Digital.com Infotainment 60 Yes Yes
Hungama Internet 20 No Yes
Plus software Software 50 No Yes
Projectpro Web service 60 No No
Second Inc Internet 7 Yes No
Netplus Software 250 Yes Yes
30 The names of the companies have been changed to maintain confidentiality.
102
Research Design
The study employed qualitative methodology involving field research with in-
depth, personal interviews of female workers in the software industry. The study was
conducted over a period of three months in the summer of 2002. Women software
professionals using programming languages developed at both the early and later stages
of the technology life cycle were interviewed. The study revealed that systems analysts
and programmers used programming languages that were developed at the early stages
of the software technology life cycle. On the other hand, the coders and non-technical
professionals used programming languages that were developed at the later stages of the
software technology life cycle. The non-technical professionals were also interviewed as
they shed additional light on the use of technologies and work environment in the
software companies.
Respondent Selection
I employed snowball sampling for identifying women software professionals. In
total, 27 female software professionals were interviewed for the study. This included ten
professionals using early stage programming languages – one systems analyst, eight
programmers and one non-technical professional. Seventeen software women
professionals were using late stage programming language -- thirteen coders and four
non-technical professionals (See Tables 5.2 and 5.3 for further details). Thus a
correlation was found between the life cycle of programming languages and occupational
classification. Out of the five non-technical professionals, one was a senior level manager
in a German MNC, one was the Chief Executive Office (CEO) of an Internet company
103
and, three were correspondents. However, all of them were technically proficient and
used programming languages at work.
Systems analysts, programmers and coders were found to be using different
programming languages that were developed at different stages of the software
technology life-cycle. As hypothesized, both systems analysts and programmers
employed programming languages (example, C and C++) that were developed in the
early stages of the software technology life cycle. The operating systems used for
programming included Linux, Unix, Solaris, Windows NT etc.
Some women programmers used highly specialized software along with early
stage programming languages. For example, programmers, working with the German
multinational company, used highly specialized software, System Application and
Product (SAP), for office automation. Monica, who worked as a programmer, explained:
After getting the standard SAP product…. We study our customers needs, …we try to map them. If you have already got the processes in SAP then we do not change it. If it is not there then we try and change it to some extent. For changing we use Apache language. It is a combination of SQL and C language.”
Most coders used programming languages that were developed in the later stages
of the technology life cycle such as HTML or packages such as Dreamweaver, Adobe
Photoshop, Fireworks and Illustrator. In addition, some women coders were found to use
web technologies such as ASP, which have also been developed in the later stages of the
technology life cycle. Women coders responsible for maintaining websites that had huge
databases, in addition to using languages such as HTML, used database management
tools such as Oracle, FoxPro and SQL.
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Only one Internet company had developed an in-house content management
system that made the work of coders even simpler as the data automatically transferred to
the web page. The company consequently had to hire less staff. They had only two
people working in technical capacity. Explaining the work, Sudha, a coder, said: “The
software directly does File Transfer Protocol, whenever news come, it is put into the
software, and it automatically goes to the web page.”
105
Table 5.2: Stages of Programming Languages for Systems Analysts & Programmers
Name Occupational
classification
Language Stage of language in
technology life cycle
Sarita Systems Analyst C, C++ Early (third
generation)
Indrani Programmer C, C++, Rational
Rose
Early (third
generation)
Monica Programmer SAP Early (third
generation)
Niharika Programmer C Early (third
generation)
Shirina Programmer C++ Early (third
generation)
Ritika Programmer C, C++ Early (third
generation)
Nishita Programmer BASIC Early (second
generation)
Milli Programmer C, C++ Early (third
generation)
Shalini Programmer SAP Early (third
generation)
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Table 5.3: Stages of Programming Languages for Coders & Non-Technical
Professionals
Name Occupational
classification
Language Stage of language in
technology life cycle
Maitrayee Coder Visual Basic,
Inhouse lang
Late (fourth
Generation)
Mekhla Coder Photoshop, Flash, Illustrator Late (fourth
Generation)
Meena Coder HTML, ASP Late (fourth
Generation)
Krishna Coder Photoshop, Illustrator,
Dreamweaver, Frontpage,
HTML, Fireworks
Late (fourth
Generation)
Malika Coder Visual Basic Late (fourth
Generation)
Neela Coder HTML Late (fourth
Generation)
Sheela Coder Dreamweaver, Photoshop Late (fourth
Generation)
Ayesha Coder Photoshop, Quark, Freehand Late (fourth
Generation)
Preeya Coder Lotus Notes Late (fourth
107
Generation)
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Table 5.3: Stages of Programming Languages (contd.)
Name Occupational
classification
Language Stage of language in
technology life cycle
Sudha Coder ASP, Photoshop, Frontpage Late (fourth
Generation)
Divya Coder Photoshop, Illustrator, HTML,
Dreamweaver, Flash
Late (fourth
Generation)
Swati Coder Photoshop, Illustrator, Flash,
HTML
Late (fourth
Generation)
Manjula Coder ,Dreamweaver, HTML Late (fourth
Generation)
Sucharu Non-tech SAP Early (third
Generation)
Aprajita Non-tech Quark Express, Dreamweaver,
Mindtree, Photoshop
Late (fourth
Generation)
Aishwarya Non-tech Frontpage, Netscape Late (fourth
Generation)
Vishaka Non-tech Dreamweaver, Vineyard Late (fourth
Generation)
Yukti Non-tech Vineyard, Dreamweaver,
Photoshop
Late (fourth
Generation)
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Names and email addresses of female software professionals were compiled from
personal contacts. Emails were then sent to these professionals telling them about the
study and seeking their permission to include them in the research. I also asked them if
they would be able to give me references of some of their friends working in the software
industry. All of them replied promptly, and agreed to be part of the study.
After reaching New Delhi, I called these respondents, and set up a time and place
for the interviews. I took references of other respondents from these female professionals,
and interviewed them.
Data Collection
Semi-structured and personal interviews were used as the primary method for data
collection. I used a semi-structured interview schedule with open-ended questions (see
Appendix A), but also pursued other topics as they arose. For example, when I
interviewed the non-technical professionals, I asked them why and how did they learn
programming languages, although it was not part of their work profile.
The use of semi-structured interviews ensured that all essential topics were covered in
the interview, and that specific data were obtained. Moreover, the use of open-ended
questions allowed women software professionals to freely express their views on the
research questions and provide deeper insights into their life experiences. In addition, I
followed the approach of active interviewing that provided an environment conducive to
the production of the range and complexity of meanings that shall address the relevant
issues. As Holstein and Gubrium (1995: 17) point out:
Active interviewers ….. converse with their respondents in such a way that alternate considerations are brought into play. They may suggest
110
orientations to, and linkages between, diverse aspects of respondents’ experience, adumbrating – even inviting – interpretations that make use of particular resources, connections and outlooks. Interviewers may explore incompletely articulated aspects of experience, encouraging respondents to develop topics in ways relevant to their own experience.
The approach of active interviewing proved to be very helpful for me as some of
the respondents were unable to express their views or understand the questions. For
example, when I asked the respondents if they could describe to me the characteristics of
their work, they were unable to comprehend the question. I told them I wanted to know if
their work involved any job rotation, how much control did they have over their work,
did they have to work in team or were they satisfied their salary and work profile. Once I
explained the question more specifically, most respondents gave me elaborate responses
to the questions. While interviewing, a systems analyst and the CEO of the Indian
software company, I encouraged them to develop topics that were relevant to their
experience. They both described to me experiences of women working in senior level
positions. For example, the systems analyst told me that ‘it was very lonely at the top for
women.’ Because of her seniority, men felt intimidated.
Confidentiality of any information elicited was assured to each respondent in
order to secure participation. I promised them that their names will not appear in any
presentations and published reports resulting from this research. Confidentiality is also
very important to elicit an honest response. As Lofland and Lofland (1995) have pointed
out, a guarantee of confidentiality for the people being researched is viewed as an
essential technique for ‘getting in.’ Moreover, once entrée has been accomplished, it is
viewed as a sacred trust.
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The interviews were conducted in English and Hindi. Each interview lasted
between 30 and 60 minutes. Interviews were recorded with the permission of the
respondents, and later transcribed for analysis. Only two respondents did not agree for the
interview to be recorded. I took notes on my laptop for both of these interviews.
Respondents had the right to terminate the interview. They also had control over the tape-
recorder (i.e. they could ask the tape-recorder to be turned off). One of the respondents
terminated the interview in the middle as the senior partner of the company thought that
the interview was taking more time than anticipated. I had contacted one of the partners
of the firm seeking permission to interview females working in the company. He granted
me permission, and asked me to come in the evening to the office to interview the female
software professionals. He instructed the two respondents not to let me tape the
conversation. The interview took longer than expected because I had to take notes as I
was not granted the permission to record the interview. This made the boss angry, and he
had the interview terminated. I asked him to let me continue, but he was unyielding.
I had planned to conduct interviews in the homes, or outside their place of
employment in order to ensure that the respondents were in a comfortable surrounding
and felt free to express their views. However, I conducted only two interviews at a
restaurant. The others were conducted in conference rooms at the place of work. But most
respondents expressed their views freely as the interviews were still conducted in quiet
settings away from prying eyes. Some respondents did not want to be interviewed at
home as they stayed in the office until 8-10 pm at night. They thought that it was more
convenient to be interviewed in the office during the lunch break. Some of the interviews
were set up by their bosses, and they preferred the interviews to be conducted in the
112
office. The human resource department of the American multinational company set up
the interviews with the software professionals after I gave them a letter stating that the
study was being done primarily for research purposes, and the names of the respondents
and the company would never be mentioned (See Appendix B).
The interviews began as an informal talk with me giving a brief introduction
about myself and the goals of the study (See Appendix A). Most respondents were
amused to be part of the study as in India it is not common to conduct research. However,
after I explained the goal of the study, the interviewees expressed their happiness in being
selected for the study.
Data Analysis
While I was still in the field, I tried to transcribe interviews in the same week as
they were conducted. Initially I managed to do it, and it helped me to refine my interview
schedule. Though I could not do this as the research progressed and more interviews
accumulated, I made it a point to hear the transcripts of each interview in the night on the
day it was conducted.
The analysis of the interviews took place in two stages: (a) initial coding
of themes and issues, (b) coding with NUD*IST31. While I was in the field,
listening to the tapes and reviewing my field notes helped me to formulate some
of the very first themes guiding this analysis. At this early stage, these categories
remained open for revision. (Ely, 1984; Lofland and Lofland, 1984).
31 NUD*IST: an acronym for non-numeric unstructured data index searching and theorizing. This is a computer package that assists in qualitative data analysis.
113
I transcribed only five interviews simultaneously during my data collection
process. The rest were transcribed after my return to the United States. My first step
toward analysis of the interview data was to draw a small chart for each of my questions.
It was simpler for factual information like age, marital status, salary, designation at work,
years of experience The process was more complicated for subjective questions.
My initial themes were various sections of my questions in the interview
schedule: educational background, nature of work and technology, issues related to
gender, nature of company, and future perceptions. This open coding helped me bring
themes to the surface from inside the data. The themes are at a low level of abstraction
and come from researcher’s initial questions, previous literature, terms used in the social
setting, or new thoughts stimulated through immersion in the data. As Schatzman and
Strauss (1973; 121) have pointed out, a researcher needs to see abstract concepts in
concrete data and move back and forth between abstract concepts and specific details:
Novices occasionally, if not characteristically, bog down in their attempts to utilize substantive levers [i.e., concepts of a discipline] because they view them as real forms. Experienced researchers and scholars more often see through these abstract devices to the ordinary, empirical realities they represent; they are thereby capable of considerable conceptual mobility. Thus, we urge the novice in analysis to convert relatively inert abstractions into stories – even with plots.
After constructing these themes, I looked for sub-themes or sub-categories with
each. For example, for the theme on gender, the initial sub-categories were: staying late
at work, managing family and work, treatment of female employees. Similarly, for the
question on nature of work and technology, I had categories such as programming
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languages, job rotation, control over work, perception about jobs. Drawing these tables or
charts helped me in analyzing the nature of relationship between nature of work and
programming languages used, work experiences of female employees in software
companies. While I was almost midway in my analysis, I started using NUD*IST. I
coded free nodes first. I created 34 free node32 categories (See Appendix C). After
creating these nodes, I coded all the relevant information pertaining to all these
categories. I then created the index tree33 (See Appendix C). The index tree basically
helped in creating a mental map of the entire data analysis. Categories and themes that
seemed “free” and unrelated found a connection and place in the analysis scheme. For
example, using index trees was really helpful in identifying factors that affected the
educational choice of the respondents. There were some categories such as sense of
security or lack of guidance that seemed unrelated initially, but using index trees helped
me identify them as factors that influence educational choices of respondents.
To make analysis of the data simple around the research questions, I made two
index trees. I constructed the first index tree to code the data on the research questions
about work experiences of female software professionals, and work experiences of
female software professionals compared to female-dominated occupations. I constructed
the second index tree to code data for the research question on the skill-training
experiences of female software professionals (see Appendix C).
32 Nodes are places for storing ideas and collecting coding. Free nodes are non-hierarchical.33 Index Tree nodes include categories and sub-categories.
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Chapter SixWork Experiences of Female Software Professionals
Software technology has a skill-training life cycle as the level of skills change
with the development of the technology. The early stage programming languages are
highly skilled and labor intensive. However, the later stage programming languages,
which are also referred as procedural languages, are deskilled. Thus the stage of
programming language has an effect on the type of work that software professionals do.
This chapter compares the work experiences of women software professionals with
reference to the stages of programming languages.
Although traditionally, computer programmers have been classified into three
types: coders, programmers and systems analysts (Kraft, 1977). The categorization of
software professionals as given by Kraft does not shed light on the kind of technologies
used by the female software professionals at work. For this study, I found that systems
analysts and programmers were using early stage programming languages, and coders
and non-technical professionals used later stage programming languages. The first
section of the chapter deals with different criteria that are used in the study to rank jobs in
the job queue. The second part of the chapter discusses work experiences of women
software professionals. The last section compares work in the software industry with
teaching.
Ranking of Jobs in the Job Queue
It is important to understand the ranking of jobs in the job queue as workers rank
jobs in job queues in terms of their attractiveness. This ranking, in addition with rankings
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in the labor queue, determine which jobs are available for which group of workers. For
example, best jobs go to the most preferred workers and less attractive jobs go to less
preferred workers. For this study, I have used the following criteria’s to determine the
worker’s ranking of jobs in the job queue: income, autonomy, working conditions and
chances of advancement. In addition, I have asked the respondent their perceptions about
their own job, and also of those jobs that are ranked higher and lower in the job queue as
compared to their own.
Ranking of Jobs Based on Salary
Women software professionals using early stage programming languages, on
average, earned about Rs 28,000 ($560) per month (See Table 6.1). Amongst them, the
only systems analyst interviewed refused to tell her salary. The programmers earned
about Rs 25,000 ($500) per month (See Table 6.2 for further details) Women
programmers working with multinational companies had much higher salaries (Rs 30,000
/ $ 600 per month) compared to women working with Indian software companies (Rs
10,000 / $ 200 per month). The only non-technical professional using early stage
programming language earned Rs 55,000 ($510) per month.
Women software professionals using later stage programming languages earned
about Rs 18,000 ($360) per month. This difference in salary was found despite the fact
that women software professionals using early and later stage programming languages
had the same number of years of experience (three years).
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Table: 6.1 Salary and Experience Based on Stage of Programming Language
Programming Language Average Salary Average Experience
Early Rs 28,000 ($560) 3 years
Late Rs 18,000 ($360) 3 years
Among software professionals using later stage programming languages, coders
earned about Rs 12,000 ($ 240) per month and non-technical professionals earned Rs
24,000 ($480) per month (See Table 6.2 for further details). A wide disparity was seen in
the salary of coders, with the highest salary being Rs 35,000 ($700) per month and the
lowest Rs 5,500 ($110).
Even women working in the software companies in non-technical capacities
earned almost double the amount earned by coders -- about Rs 24,000 ($500) per month.
However, the average experience of non-technical professionals was about five years –
about two years more than that of coders. Amongst the non-technical professionals, the
CEO of the Indian company earned the highest salary -- about Rs 100,000 ($2,000) per
month. (For details see Table 6.2).
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Table 6.2: Salary and Experience Based on Occupational Classification
Occupation Average Salary Average Experience
Programmers Rs 25,000 ($500) 2 years
Coders Rs 12,000 ($240) 3 years
Systems Analysts Did not tell 13 years
Non-technical
professionals
Rs 24,000 ($500) 5 years
Ranking of Jobs and Salary Satisfaction
Out of ten software professionals using early stage programming language, six
were satisfied with their salary. This included five programmers and the only non-
technical software professional. The systems analyst was not satisfied with her salary.
However, she was quick to point out that she was not being discriminated on the basis of
sex, and was earning as much as the men in the same position in the occupational
hierarchy.
Women software professionals whose salaries had been cut back due to the
recession at the time of the study were dissatisfied with their salary. Some felt that the
companies were using the recession as an excuse to exploit employees. As Milli, a
software engineer, pointed out:
I think that people are not satisfied with their salary. After the recession, companies have taken advantage of it. They have exploited the employees as they know that the market conditions are poor. I don’t think the current scenario is bad.
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Nine out of seventeen software professionals using late stage programming
languages were satisfied with their salary. This included seven coders and two non-
technical professionals. Despite having lower salaries, about half of the coders seemed
satisfied with their salary. Seven coders out of a total of thirteen said that they were
satisfied with their salary. They pointed out that the salary was satisfactory keeping their
experience in mind. “I am satisfied because with my experience it is okay,” said Krishna,
a coder. Only one coder pointed out that although the salary was satisfactory, the
workload was too much in relation to it. As Ayesha, a web designer, said: “I did not mind
the salary, but I did mind the workload.”
Most non-technical professionals were also satisfied with their salary. Three out
of five said that their salary was satisfactory taking their experience into account. Only
two said that they were not satisfied as ‘some extra money is always welcome.’
Job Characteristics of Female Software Workers
Nature of work
The nature of the work of women software professionals using early stage
programming language differed significantly from those using late stage programming
language. The work of those using early stage languages was more challenging, skilled
and diverse. For example, the systems analyst, interviewed for the study, was in-charge
of an entire team. Her work involved management and technology development. She was
not only providing technical guidance to team members, but was also in-charge of
financial management, coordination, resource generation and marketing. Describing her
work, Sarita, the systems analyst pointed out:
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We have four divisions in our organization, and I am heading one of the divisions – switching and routing. I am heading a team of about 40 people. I am working on all state of art technologies, and I feel very proud about it. Most of the work involves project management, financing, resourcing, coordination and team management, revenue generation, coordination with marketing. As a profit incentive head, I am responsible for revenue generation, and also responsible for resources and batches. It is overall responsibility so most of the work is about that, plus I have to look for new business and I have to update my section with new technology, new projects, new skills, new papers, new proposals. It is quite challenging.
The work of programmers was also skilled and diverse. They were involved in
development of software and packages at all the stages of its life cycle. For example,
Indrani, a software engineer with the American multinational company, explained:
Responsibility of a particular module34 is given to me, including the entire software life cycle that starts with the requirement analysis. We start with design, once the design phase is complete, we have a coding phase then system testing, system integration and system integration testing.
In addition, the nature of the work of programmers changed with each project
requirement. “My work has been very diverse throughout. I am not restricted to any
particular project. For the moment it is for voice-over. Tomorrow, it can be something
else,” said Nishita, a programmer.
The programmers had to keep themselves acquainted with the emerging
technologies. It was primarily their task to master these technologies on their own
depending upon project requirements, although sometimes companies provided them
some form of training. For example, Indrani, added, “… We have to get acquainted with
new technologies depending upon project requirements.”
The work of the only non-technical professional using an early stage
programming language was also skilled and diverse. On being asked to describe the
34 A module is part of the project that performs a particular function.
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nature of her work, Sucharu said, “My work involves interacting with customers,
understanding their needs, mapping it on the system, which is the ERP (enterprise
resource planning). We do interfacing with the client and the software. Major work is
understanding the customer’s needs and mapping it on the software.”
In contrast, the work of women software professionals using late stage
programming languages was limited as they were primarily responsible for making and
maintaining websites. On being asked to describe the nature of the current job, most of
them pointed out that their work involved making html web pages, uploading them and
doing the coding to maintain the website. For example, describing her work, Mekhla, a
coder said: “I make changes in the web pages, if the editorial staff has some technical
difficulties like web pages are not uploading, I have to do that….. We have to make
special packages also. For example, I made one for the soccer world cup. We have to do
maintenance and uploading of pages.”
In addition, I found that the Indian company specializing in computer hardware
employed only women as customer support engineers. The women were either working
at the customer’s site or at the office providing solutions for hardware problems faced by
the customer, coordinating between engineers in the field. In order to manage their
diverse tasks, the women software professionals used Lotus Notes software. This
software helped them do database management, file sharing, word processing etc.
Describing the nature of her work, Sudha, said;
We provide technical support to field engineers and customers. The company gives me technical material to read, and if the field engineers require help as we are dealing with computer hardware… They call us up and tell us that they are facing this problem. Because of our experience we come to know that for this particular problem, this is the troubleshooting method, so we guide our engineers.
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Like the coders, the technical work that most non-technical professionals did was
not skilled or challenging. They primarily maintained and created web pages. For
example, the CEO of the Indian company said that when she started the company, apart
from management, she used to do technical work such as making web pages etc.
Describing her work, she said, “I am heading a unit that is into search and head hunting.
The first four years, I set up the entire company so I handled everything from marketing
to making web pages.”
Team Work
Most respondents said that they had to work in teams, irrespective of whether they
were using early or later stage programming languages. The interviewees pointed out that
the work was divided between team members depending upon a person’s area of
specialization. Describing her work, Niharika, a software engineer said:
Work is divided equally among the members. We have a ten member group, and within that we have five two member teams. The project manager or technical leader introduces us to the requirement of the client, and then the team members divide the work and set a time frame to finish it. We divide the work amongst ourselves depending upon our specializations.
On being asked, how she assigns work to the team members working under her, the
systems analyst explained:
I have program managers for each of the projects. Currently I have four projects under me. I do broad level planning for them, while day-to-day planning is done by program managers. But I keep a day-to-day tab on status because in most cases program managers need help in making decisions. I do not interfere in day-to-day activities, but I do help them out when I realize that some things are going wrong. For example, I became late for our interview today because there was an urgency. We are working on a project, and it ran into some problems. The program
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manager took some decisions that caused a problem in our program. I called the program manager back from lunch, redesigned the strategy and redistributed work.
The CEO of the Indian software company also said that she gives a free hand to
her employees to carry their day-to-day responsibilities and just maintains checks on
them. She explained, “I have a team of about 40-45 people working with me. I have to
ensure that all the people at different levels are doing their work well. I do not maintain
strict tight controls on them, I just make regular checks.”
Even the coders pointed out that team work was important in order to ensure that
work is done in an orderly fashion. Describing her work, Sheela, a coder, said:
Most of time we have to work in teams. If we have to design a microsite. A microsite is an independent channel by itself within the entire site. For example, we did a microsite on the prisoners of war. We assign different modules to each person. Each person completes the task assigned to him or her. Team work is very important. We have to interact with each other to ensure smooth flow of work”
Control Over Work
Most interviewees believed that they had substantial control over their work
irrespective of whether they were using early or late stage programming languages.
However, they pointed out that their work is always supervised, and they can always
consult the team leader or head of the department if they face a problem. As explained by
two programmers:
You have to make decisions yourself, but your project manager is always there to review your work, and if he does not like it, he will tell that it should be done in another way. He gives you an opportunity to rethink the problem.
The team leader gives you the problem and you are supposed to find the solution. You can discuss the solution with him, and if he agrees you can
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go ahead with it. ….. You are not bound by one particular method. You are given work, you do it, you can take any method, just discuss it with your team leader. Once you reach a consensus you can go ahead.
The interviewees attributed flexibility at work to various reasons such as the work
culture, the size of the organization, and the nature of job. For example, Manjula,
working as a web developer in a company that employed only six people, pointed out that
the organization was so small that everyone’s voice was heard. “We are a small firm, and
we communicate easily and talk about your problems.”
Another web designer, Mekhla, said that she had control over her work because
she was the only technical person in the entire team. “Nobody understands coding so we
have to handle everything. The work is simple, and it has to be done in HTML. However,
the other team members do not know the language so they have to consult me with their
problems.”
Some female software professionals pointed out that at times their work is
governed by factors that are not in their control. For example, Ayesha, a coder said:
“Production can really affect your final outcome….. Sometimes you might design
something, and the printers might not agree with you or they might not have paper to
print for the technique that you have used.”
Job Rotation
The work of most women software professionals using early stage programming
languages involved multi-tasking. This included the systems analyst, six programmers
and the non-technical professional. For example, the systems analyst interviewed for the
study pointed out that she was not only responsible for projects technically, but her work
involved financial management, coordination, resource generation and marketing. Out of
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eight programmers, only two said that they had never seen a change in their job profile.
Most programmers pointed out that their work involved multi-tasking. They had to work
on different projects at the same time, and they also worked with different teams on these
projects. Explained one interviewee: “Depending upon the project requirements, the work
changes. I might be designing something else tomorrow. Everybody is skilled in most
things. I have been shifted from project to project.”
Nine out of seventeen women software professionals using late stage
programming language said that their work involved job rotation. This included seven
coders and two non-technical professionals. The coders whose work did not involve job
rotation seemed dismayed. Expressing her disappointment Sudha, a coder said: “Job
rotation should be there, but it is not there. If the company sends me to a customer site, I
would learn more and give better guidance to the field engineers. But it has not happened
as yet.”
Another coder pointed out that there was not much scope for job rotation as she
was working in an online infotainment company, which employed only two persons in a
technical capacity. “I do not think that job rotation is possible in this company. It is not a
software company, where you have different projects every three-four month. Here we
work on only one project. You just try and improve the quality of your work.”
The Job Mobility of Women and Promotion
About half of respondents – three using early stage programming language and
twelve later stage programming language -- pointed out that females in their company
had not received a promotion recently. On being asked if females had received
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promotions in the company in the last year, two interviewees said they did not know as
they had recently joined the company. Most interviewees blamed the ongoing recession
at the time of the study for lack of upward mobility in the company and did not think that
the sex of an employee had a role to play in promotion. For example, Vishaka, working
in a non-technical capacity said, “These days we are having retention so there are no
chances for promotion. If a woman deserves promotion, I am sure that she will get it, it
is not that they will prefer male members above her.” Others also pointed out that their
designation had changed. However, they did not receive a hike in their salary.
On being asked what it takes for women to move up the occupational ladder, the
systems analyst believed that women at high level positions in a company have to deal
with ‘games men play’ as the women are a minority in numbers. She explained:
You need to cope with games and politics that go on in the organization. You cannot lose your nerves and get disheartened by the games men play. You have to be smart and keep your eyes and ears open. You should know how to deal with day- to-day situations.
The interviewees, irrespective of the stage of programming language used
believed that women need to work hard, be technically competent, knowledgeable, and
disciplined in order to move up the occupational ladder. For example, Maitrayee, a coder,
said: “….. You should give your full attention to your work, work hard, put 100 percent
effort and commitment. Everyday if you are ready to work with commitment then you are
able to move up the occupational ladder.” However, a small minority believed that
females might sometimes need to go an extra step in order to move up the occupational
ladder. As explained by Krishna, a programmer:
She has to be confident, technically sound, speak up and not to get bogged
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down by other people. She has to be competent. She might have to force her way out at times in order to show that she is equivalent to men. Men are naturally aggressive, but women are not. However, it is required at times. Sometimes you need to be strong and bold.
However, most respondents believed that a change is needed in the attitudes of
both companies and people in order to ensure than men and women have the same
opportunities. “We need to change this attitude that women are the weaker sex,” said
Sucharu, a non-technical professional. Others believed that companies should make
special provisions for women to ensure that they are more comfortable in their work
setting. For example Milli, a software engineer explained:
I think the companies need to make an extra effort. They should keep this in the mind that if they send a woman out of town to do some work, they have to make better arrangements for her. For example, they have to arrange a single room for her. They do not have to do this for a man as they are able to adjust in any kind of environment. But that does not go for a woman. I think they need to make all these efforts to push women ahead. They should realize that women are equally good, and even if they go out of town they can handle things better.
Many interviewees pointed out that women need to strike a balance between
home and work in order to be successful. As two interviewees pointed out:
If a woman is married and has children, it is important that she manages her time better because as the situation is in India, cities are not safe. She has to finish her work early. Many of us prefer to come early rather than stay late. We do not mind coming around seven or eight in the morning, and be out of the office by seven or maximum eight pm. A certain degree of discipline and time management is required. I do not think that women are weaker, maybe a little in Math. But I feel that has more to do in the process in which you are groomed, since childhood one thing is inculcated in you that at the end of the day it is the home front that you have to manage. I think that a girl child grows up instinctively thinking like that.
I think lot has to come from women. One of the facts of life is that women get pregnant, and that plays a very important role in their career course.
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People expect that once when you have a baby, you will take time off, and would like to go home early. Either you should be extra efficient, and show them what needs to be done so they are not worried. Women tend to move with their husband. It is very unfair because people think that if a man moves it is good for his career, whereas for a woman it is only for her husband. It is always perceived that her decision will be related to a lot of other decisions. This is the fact of life that we have to live with. These are the two most important things that we discuss when we hire a woman. This is something that at societal level we need to figure out. At an individual level, if a woman is really good and if she can command respect then no one has any problems. There are enough women reaching the top, but it is a tough drive, you need to have a supportive family, you need to be independent to take your own decisions.
Describing the different role expectations from both the sexes and the effect they
can have on a woman’s career, Niharika, a software engineer added:
I think attitude, determination, family support is required for a woman to move up in her career. A woman needs extra as you are expected to do more as far as your family is concerned. If you are married, even if you reach home at 10 at night, you are expected to cook. Men generally do not do all this work.
The analysis of the results show that jobs that involve the use of early stage
programming languages are ranked higher in the job queue than those that involve the use
of later stage programming languages. The study arrives at the given conclusion because
women software professionals using early stage programming languages on an average
have higher salaries – they earn Rs 10,000 ($200) per month more than those using later
stage programming languages. The work of professionals using early stage programming
language was found to be more skilled and challenging as compared to that of those using
later stage programming languages. However, the research did not find discernible
differences between autonomy at work, opportunities for team work, job rotation and
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occupational advancement amongst software professionals in reference to the stage of
programming language.
Ranking of Jobs based on Self-perception
An analysis of data shows that number of years of work experience has a
correlation with the perception of software workers toward their jobs. Most women
software professionals using early stage programming language categorized their position
as high, middle or low depending upon their years of work experience. The systems
analyst, who had 13 years of work experience described her position as a high level
position as she was the head of the routing and switching division, and reported directly
to the President of the company.
All programmers described their jobs as low or middle level depending upon the
number of years of work experience. Women programmers with three or more years of
work experience believed that their jobs were in the middle of the occupational hierarchy
of the company. Only one programmer with three years of experience said that her
designation was of an entry level person. However, the nature of work was more like a
middle level employee in the company. “In terms of salary and designation, I am a low
level employee. However, with resource crunch, I am doing the work of a higher level
employee as I have got high level authorizations,” she pointed out. The rest of the
programmers with less than three years of experience believed that their jobs were at the
bottom of the occupational hierarchy. The field of study did not seem to have an effect on
the self-perception of workers. (For further details see Table 6.3). Although, one
programmer, who did not have an engineering degree, and had only two years of
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experience, pointed out that her work was that of a middle level employee. But her
colleagues in the same company, who had engineering degrees and similar job
experience, believed that they were low level employees. The only non-technical
professional, using early stage programming language, categorized herself as a middle
level employee and she had been working for six years.
Table 6.3: Educational, Experience and Perception of Women Professionals Using Early
Stage Programming Language
Name Field of study Occupation Experience Perceived level
Sarita Engineering Systems analyst 13 years High
Indrani Engineering Programmer 1 year Low
Monica Engineering Programmer 3 years Low
Niharika Engineering Programmer 2 years Low
Shirina Engineering Programmer 3 years Middle
Ritika Engineering Programmer 5 years Middle
Nishita Non-engineering Programmer 2 years Middle
Milli Non-engineering Programmer One-and-half Low
Shalini Non-engineering Programmer 5 years Middle
Sucharu Non-engineering Non-techncial 6 years Middle
On the other hand, most women software professionals using late stage
programming languages had a very positive estimation of their work. Twelve of them
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described their jobs as a middle level position, three described it as a high level position,
one described it as a low level position, and one explained that there were no strict
hierarchies in the company (See Table 6.4).
The study found that the coders in particular have a higher self perception about
their work, irrespective of their work experience (See Table 6.4 for further details). The
range of experience varied from six months to nine years, and did not seem to have a
correlation with the self-perception of work. For example, a coder with six months of
experience described her work as being high level, whereas another coder with nine years
of experience described her position as middle level. Even the coders with an engineering
background had a positive estimation of their work. They described their work as middle
and high level, despite the fact that they had much less work experience. (See Table 6.4).
In contrast to the coders, the self-perception of non-technical professionals using
late stage programming languages was correlated with the number of years of experience.
Women professionals with work experience ranging between two and five years said that
they were middle level employees. The CEO of the Indian software company described
herself as the ‘dominant person’ in the company. The only non-technical professional
with less than two years of experience said that she was a low level employee as she was
working at the entry level position, and did not have any prior work experience. (See
Table 6.4).
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Table 6.4: Education, Experience & Perception of Women Software
Professionals Using Late Stage Programming Language
Name Field of Study Occupation Experience Perceived Level
Maitrayee Engineering Coder 2 years Middle
Mekhla Engineering Coder 10 months High
Meena Non-engineering Coder 3 years Middle
Krishna Non-engineering Coder One& half yrs Middle
Malika Non-engineering Coder 3 years Middle
Neela Non-engineering Coder 4 years No hierarchy
Sheela Non-engineering Coder 2 years Middle
Ayesha Non-engineering Coder 2 years Middle
Preeya Non-engineering Coder 9 years Middle
Sudha Non-engineering Coder 2 years Middle
Divya Non-engineering Coder 4 years Middle
Swati Non-engineering Coder 6 months High
Manjula Non-engineering Coder 2 years Middle
Aprajita Non-engineering Non-technical 4 1/2 years Middle
Aishwarya Non-engineering Non-technical 12 years High
Vishaka Non-engineering Non-technical Six months Low
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The analysis of the results clearly shows that coders have a higher self-perception
of their work as compared with other software professionals despite the fact that their
work is less skilled, challenging and diverse. This might suggest that taking their
educational background and work experience into account, coding provides them with
avenues such as job rotation, autonomy at work and occupational advancement that might
not have been available to them otherwise. One of the other popular alternates available
to them would be teaching which most respondents believed was ‘not creative or
challenging’ and was not well paid.
Perception of Jobs ranked Higher in the Job Queue
Most software professionals, irrespective of the programming language used, had
similar opinions about jobs that were ranked higher to them in the occupational hierarchy.
Most believed that the jobs that were ranked higher in the occupational hierarchy were
less technical and required more managerial work. They pointed out that the work
required decision making, interacting with clients, supervision, allocation of tasks,
guiding juniors, managing and coordinating between team members. As two
programmers explained:
They also do programming, but more of management. We are into core programming, they guide us and do programming also. I think it is 50-50 programming and management.
Each module has one technical leader, who has various kinds of responsibilities, not programming alone. His responsibility is more towards the managerial side. His job is to manage this small group of people under him. Above a technical leader, we have project manager. … The project manager looks after the management of several projects.
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They believed that a software professional should have more experience and
knowledge in order to be able to fill these positions. As Shalini, who worked as a
programmer with a multinational company, explained:
You need lot of experience. They are really good in their respective fields, and we should also try and become like that. In order to achieve the higher positions, you need to be a champion in your field, and you should have more than eight to ten years of experience.
The respondents pointed out that along with extra responsibilities, there is more
stress and tension as they are answerable to higher management in the company. Despite
this most software professionals aspired for jobs that are ranked higher in the
occupational hierarchy. As Sheela, who was working with a web company, explained,
“The higher level jobs require more managerial skills. Developers are at the lowest level
then you have team leaders. The work of team leaders is more about management, and it
requires a lot of experience. And, I would like to be a manager.”
Perception of Jobs Ranked Lower in Job Queue
Most women software professionals using early stage programming languages
believed that those using late stage programming languages were ranked lower in the
occupational hierarchy. They believed that the work of those using late stage
programming languages was not very technically challenging. The women programmers
said that the work was monotonous and could be learned by doing a six-month diploma,
while one needed to have a formal engineering degree in order to learn programming. As
Shirina, who was working with an Indian software company explained.
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If you are doing programming, and working on core languages such as C and C++, you will be considered a middle level programmer. But if you are working on HTML and doing web designing then you will be considered a lower level programmer. Actually, everybody wants to aim higher. Right now I am a middle level so I do not want to do those kind of jobs. The work is not like what we are doing; they do not work on core technologies; they use HTML which any layman can do. By doing a course from NIIT for five months, you can do that programming. But if you want to go for a middle and high level job you need an engineering degree.
The women software professionals using early stage programming languages
pointed out that their work was more satisfying, and did not want to do web work that
involves the use of late stage programming languages. As Shirina explained:
They are working for web all the time. It is a different kind of line so if one is interested in website designing then it is okay to pursue that career. Since I am involved in programming, I am interested in learning more in this field instead of doing that job.
Another problem that professionals using early stage programming languages had
with the work of those using late stage was that it was less autonomous, more sedentary.
For example, Monica, said: “Sometimes, I am happy that I do not have to do that kind of
work…. Those jobs are like that all the time someone is fiddling with your work. I am the
kind of person, who does not like any kind of disturbance when I am working.”
In addition, the programmers and systems analyst pointed out that they did not
think very highly of jobs involving web designing because the salary was much lower in
these jobs. On being asked about what she thought about jobs that were ranked in the
occupational hierarchy, Sudha, an engineer said: “They are okay, I do not say that these
are bad positions. The coders are satisfied with their work, but they are not satisfied with
their salary. Although the pressure is much more than what should be there.”
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On the contrary, women software professionals using late stage programming
languages argued that their jobs were creative, and positions that only require cutting and
pasting were ranked below them in the occupational hierarchy. As Divya a coder said: “It
is very boring and monotonous work as it only involves copying things from here and
there, and not applying any creativity. You are just copying work that has been done
already. I also did that kind of work when I started my career.”
Some coders believed that those who do not have much work experience are
ranked lower in the occupational hierarchy because they are still honing their technical
skills. As Krishna pointed out:
The beginners are ranked lower in the occupational hierarchy. They have about two years of experience, and they are still in a learning phase. Their work is very simple and basic. They learn from us. For example, if I am doing some implementation work, they assist me in it. They come and watch me and then do it themselves later.
The coders also pointed out that they also learned from the new recruits. For
example, Manjula said, “The new recruits provide us fresh outlook. They can act as our
mentors. They can learn from us and we can learn from them, it is a process of mutual
exchange.”
Most women software professionals using early stage programming languages
ranked those jobs that involved the use of late stage languages lower in the job queue.
The professionals using late stage programming languages, particularly the coders,
ranked those jobs that were ‘less creative’ lower in the job queue. However, all software
professionals agreed that jobs that were ranked higher in the job queue involved
management.
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Women and Work Experience
Sex Composition of Jobs
All women software professionals using early stage programming language apart
from the non-technical professionals, pointed out that their peers were predominantly
men. The only systems analyst, who was heading one of the technical departments, said
that all the other department heads were men. All the eight programmers said that their
peers were predominantly men.
However, most women software professionals using later stage programming
languages pointed out that men were not a majority in numbers at their level. For
example, seven coders said that there was an equal mix of men and women in their job.
Four coders and four non-technical professionals pointed out that women outnumbered
men at their level. But two women coders, who provided technical assistance to field
engineers, said that there were more men in their field than women. This was primarily
because the company hired only men as field engineers while women were hired for desk
jobs. Their job profile included providing technical assistance to field engineers, and
listening to the customer complaints over the telephone.
Among the non-technical software professionals, four said that their profession
had more women. On comparing her work to the other women in the department,
Vishaka a non-technical professional said: “I feel happy here as there are many women in
my department. I feel as far as the nature of the job is concerned, I cannot compare as
each individual is doing their own work. Hard work and sincerity makes all the
difference.” The CEO of the software company said that the other partner who had more
shares than her in the company was a man.
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All programmers pointed out that there were fewer female employees in their
department as compared to other departments such as management in the companies. As
Niharika, a software engineer pointed out: “I have been working for eleven months. In
my team there is no other woman. However, there are lots of women working in non-
technical departments in the company. They all joined at the same time as me, and they
are my friends.”
The interviewees also tended to compare their work technically with other female
employees of the company. The systems analyst pointed out that she was the only female
in a senior management position so her work was very different from all the other female
employees. “I am the only female head of a department. I hope my juniors are able to
achieve what I have in the near future.”
When asked how her work compares with that of other women in the company,
Shirina, a software engineer explained:
My work is very different. I am working in a technical capacity, and most other women are in the administrative section The work is absolutely different, no doubt everyone’s work is good. But we have to devote more time and have to have more knowledge.
The interviewees, when comparing their work with the other women in the
company, tended to have a better self-assessment of their work under conditions where
most of the other women were working in a non-technical capacity in the company. As
two respondents working in an online infotainment company pointed out:
I think my work is more satisfying….I am very happy doing what I am doing. I would not like to be an editor. Most of the women are editors…. I like my work because it is technical.
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My work is very technical, it requires coding, surfing the net and picking up stories. The work of most other female employees is totally content oriented. It is like journalism. I prefer my work as I am not into writing.
Treatment of Female Employees
Most interviewees, irrespective of the stage of programming language used,
believed that the sex of the employee did not have an effect on the manner in they were
treated. However, when asked if women are treated differently by their bosses, the
systems analyst answered that it is a possibility as ‘men sometimes have different
attitudes towards women.’ Only three programmers believed that there was differential
treatment on the basis of sex. As one programmer explained:
If you are going abroad, and if you have to choose between man and woman, there is a leaning towards sending the male….. In this industry, you are expected to be professional, you might have to travel abroad. …..Sometimes, if your immediate supervisor, is a female then it might hurt the ego of some males if you have a female leading the group.
One programmer argued that women have certain advantages that other male
employees do not enjoy. “If the boss is a man, and a mistake is done by a girl, she will
get a scolding in a much politer way than a male employee,” she explained.
Three coders also believed that female employees are treated differently by their
superiors. Explaining the differential treatment of male and female employees, Malika, a
coder, pointed out:
People have this mentality that girls are unable to do certain things and are different. One day I told my boss that I want to go out in the field. He told me that in our organization, we want the girls to be in the office because it is more comfortable for them. If you are doing marketing then you have to go out as it is the requirement of your job. But if you are in this department, we have field engineers who work in the field more efficiently then it is not advisable. You can work easily by sitting in office.
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According to the respondents, male bonding has a significant effect on the work
environment, and treatment of female employees. One interviewee, Ayesha, described
her experiences:
….Male bonding is always there. I faced it sometimes when I was working in production, and had to print my work. All the printers were men, and they will never let you know what is happening. They always treat you as if you do not know anything, and somehow you end up feeling that you do not know anything yourself. They will just tell you we will do the work. They act as if they are doing a favor. Once I had a fight with them. Another woman employee and I had gone for some work. This fellow kept on saying that he will do it, but he had no intention of doing it. We figured that out. But if you are pushy and clearer and little stern, then you can get your work done.
Interestingly, all the five non-technical professionals believed that the sex of the
employee had no effect on the manner in which they were treated by their superiors.
Sucharu, a manager in the MNC explained: ‘We have a very young crowd since this is
the software division. Lot of people are out of college and they are freshers so I have not
seen anything.’
Staying Late
Most respondents, irrespective of the stage of programming language used,
pointed out that staying late in the office was a major problem for female employees as
they had to strike a balance between work and home; and females faced issues of threat to
their safety. As Monica a software engineer pointed out: “One of my friends in the
telecommunication department wanted to leave early as her son was ill. However, her
boss did not let her go home early, and he also made her come to work on a Saturday and
Sunday.” The female respondents believed that staying late at work leads to problems at
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home as family members fear for the safety of women. As elaborated by Indrani, a
software engineer:
Staying late was difficult for my parents to accept initially, but they know that the industry works like this. But I know that it will be definitely difficult for me to stay late later in my life. I am not married right now so managing a family does not pose a problem. But the thought remains in my mind that later I might have to juggle work and home. I leave my home at 8 am, and go back at 8 at night after twelve hours of work. If you have to stay late then you might have to stay for 2-3 hours depending upon the kind of work that you are handling at that time so it is difficult.
Comparison of Working in Software Industry and Teaching
For most women software professionals in India, there are very few other
opportunities that are available to them in a tight labor market. One of the most common
alternate is teaching.35 Teaching has benefits such as short working hours and holidays.
However, teachers in India are paid a very low salary.
On the other hand, working as a software professional involves working long
hours and staying late in the office. On an average, women software professionals, who
were interviewed for the study, worked for about 12 hours everyday – coming to office at
8 AM and leaving at 8 PM. They often had to stay back late in the office to meet
deadlines and sometimes even work on weekends. The long and late hours pose problems
specifically for women as most cities in India are not safe for women at night. This is
particularly true if women have to use public transportation. There are many reported
cases of molestation36 and even rape37 of women traveling alone in bus or train. Working
35 Women comprise 29 percent of teachers at the primary school level in India. This number drops to 22 percent at the university level. However, only 54% of women are literate as compared to 76% of men in India. www.ibe.unesco.org/International/ Databanks/Dossiers/sindia.htm - 42k36 In 1997, 34,937 cases of molestation were reported in India. This was an increase from 32,479 reported cases in 1996. http://www.hsph.harvard.edu/Organizations/healthnet/SAsia/resources/resourcesframe.html37 In the year 2002, 403 cases of rape were reported in New Delhi. This represented an increase of 9.4% from the last year. However, the police solved only 344 cases, and arrested 544 persons in 2004. Most of
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long hours also does not leave enough time to women professionals to devote to their
families.
Most respondents believed that striking a balance between family and work was
the biggest hurdle facing women software professionals as they often had to keep long
hours at work, stay back late, and were occasionally expected to travel. Despite this
problem, most interviewees did not want to pursue female-dominated profession such as
teaching38 that gave more time to look after the family. Only four software professionals
– three using early stage and one using later programming languages -- pointed out that
they liked teaching, and might consider it as an option at a later stage. This included the
systems analyst, two programmers and one coder.
On being asked whether she would like to pursue another occupation, the systems
analyst said that she would like to retire early, and take up a less stressful occupation
such as being a school principal. However, she did not want to teach students.
I would like to be a principal in a good school. I might take up that job when I retire or when I am in my 50s, when I want to get a cool job. My job is very demanding so later in my life I would like to take up a job that is less stressful. I would like to take a job of principal not that of teacher. I have a daughter, and it becomes difficult for me to manage family and work. I have put her in day care. I have kept a servant for doing the household work so I am able to dedicate more time to my daughter when I am at home.
Most interviewees believed that teaching provided ample time to women to be
able to meet their domestic responsibilities. However, they believed that the problem was
the rapes in India are committed by men known to the victim such as neighbors, fathers and uncles.http://www.hindustantimes.com/news/611_0,001300540000.htm38 In India, teaching is the only popular female-dominated profession in the middle class. Women from the middle class look down upon other female dominated professions such as nursing and secretarial work. Therefore, the study asked the respondents to compare their profession only to teaching as most of the interviewees hailed from a middle class background.
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the salary of teachers is comparatively less and the work environment is not good. As
pointed out by one programmer:
Sometimes that I think, teaching is a good option. It gives you some form of financial freedom as you have a source income, you can look after your family. You have the same hours as your children. You are back when they come back home from school. But once you have worked in a corporate environment, you do not want to go back to teaching. Salary and work environment are a major concern.
Some respondents argued that teaching like medicine was suitable for women as it
involved care and nurturing, qualities that women possess. ‘There are some professions
for which you need to have specific qualities. For example, doctors have to provide care,
and therefore medicine is considered favorable for women. I might like to become a
teacher myself. I consider it a good profession like being a lecturer,’ said a programmer.
Teaching appeased some women software professionals due to the flexibility of
work hours. They argued that teaching is a good option, after you get married and have
children. The interviewees pointed out that they might pursue teaching when their
children are very young. As one programmer elaborated:
Teaching is good for girls after you are married. In software companies you have to stay back late, and you have so much pressure on your mind. At this moment, I do not think that I would be able to devote my time for my family. Teaching is a nine to five job, even there you enhance your knowledge. It is not a dull job. I might think about it myself at a later stage. I might take it up for two-three years when I have my children, and then go back to software.
Many women software professionals using later stage programming languages
argued that teaching was not a ‘satisfying’ occupation as it is not creative or challenging.
They believed that web designing or coding was more creative as it involved using logic.
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In fact, the respondents believed that teaching was very boring, repetitive and sedentary.
Some respondents went as far as to say that teaching ‘does not involve using the brain.’
Expressing their opinion, two coders said:
Teaching is a good job for the society. I worked as a teacher for some time. I felt that I was doing a good job for the society, but at the end of the day I felt that I was more important. I was not satisfied. I wanted to do something more creative or something in which I could use my brain. Teaching after a time becomes sedentary. It becomes repetitive because after some time you know by heart what you have to teach, be it at the school or college level. Even if the syllabus changes, it does not change that much. Maybe people feel a lot of satisfaction in teaching, but I do not.
….I am not fond of teaching at all. I want to do work that is not only creative, but uses the brain all the time, more logic based. My work is more fun as teaching is repetitive and monotonous over the years as the course curriculum is always the same.
Only one software professional using later stage programming languages said that
she wanted to pursue teaching later in her life so she could devote more time to her
family and children. However, she believed that teaching as a profession had a set of
problems such as boredom and no opportunities for occupational advancement. On being
asked what she thought about female-dominated occupations she said:
Teaching is fine, but I do not like it. Teaching does not get much respect, though it is good for married women, as you have time for both your family and your job. It will be better if you are in a good family where you get 100% support, and you do not have to do much housework. The other option is that you get into an easy job such as teaching so you can look after the family. Maybe after marriage, I might like to get into a professional college as a lecturer. My parents have always wanted me to join college as a teacher. But in teaching, there is no personal growth. It is only theoretical knowledge nothing practical.
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Only one software professional had previous teaching experience. The CEO of the
Indian company had held a visiting teaching position at a management institute in the
past. However, she said that she did not like to teach as she ‘got bored of giving the same
explanations and examples again and again.’
Conclusion
On the basis of the findings, we can argue that the jobs that involve the use of
early stage programming languages are ranked higher in the job queue. One of the key
reasons behind this ranking is the skilled nature of these jobs in comparison to those that
involve the use of later stage programming languages. These findings are supported by
the skill-training life-cycles that hypothesizes that early stages of technology are highly
skilled and later stages lead to subdivision of multifaceted tasks into narrowly defined
assignments. The research also found that women were employed in higher numbers in
jobs that involved the use of late stage programming languages as compared to early
stage. This finding is supported by Reskin and Roos that women are more likely to fill
positions that are ranked lower in the job queue.
In addition, the analysis of the results clearly shows that women software
professionals perceive their jobs as more desirable as compared to teaching. This despite
the fact that most women are segregated within the software industry in low skilled and
low paying jobs. As pointed out earlier, Reskin and Roos had also hypothesized that
women prefer jobs that men have rejected for greener pastures because these jobs are
preferable to most female occupations. Thus women prefer jobs that are ranked lower in
the job queue in the software industry to traditional female occupations such as teaching.
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They prefer working in the software industry to teaching even though they acknowledge
that teaching has some rewards that are not available to them such as shorter working
hours, more time for the family and children and holidays.
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Chapter SevenSkill-Training Experiences of Female Software Workers
Skill- training of women software professionals occurs at three levels – formal
education, training at technical institutes, and job training. The kind of skill-training
acquired influences the job opportunities available to women software professionals. The
first section of the chapter discusses the educational background of female software
professionals in India. This includes both formal education and training at technical
institutes. The last section of the chapter discusses the job training experiences of women
software professionals.
Educational Background
Educational background plays an important role in influencing the opportunities
available to the software professionals. Women software professionals, who had an
undergraduate39 engineering degree, appear to be in an advantageous position in the job
market as compared to those who did not have the degree. This is particularly the case in
India as the society places a lot of emphasis on education, especially the study of
Mathematics and Sciences; and, persons skilled in these subjects are provided higher
status40. In fact, it appears that those with an engineering degree are regarded as more
competent in the industry.
39 For this study, I have used the terminology that is used in the US. I am referring to a Bachelors degree as an undergraduate and Masters as a graduate degree. However, in India, a Bachelors degree is referred to as graduate and Masters as postgraduate degree.40 The cultural fascination for Mathematics and Sciences can be seen from the fact that there is considerable pride in the country that the ‘zero’ and the place value system of Arabic numerals were invented in India (‘We taught the world how to count’).
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In this study, out of the 27 respondents, only eight had an undergraduate
engineering degree. Six of them were using early stage programming languages. This
included five programmers and the systems analyst. Two women engineers working as
coders used later stage programming languages.
Educational Qualifications of Women Software Professionals Using Early Stage
Programming Languages
Out of ten software professionals using early stage programming languages, only
six had an undergraduate engineering degree. This included the systems analyst and five
programmers. None of the software professionals with an engineering degree had any
additional qualification such as a computer diploma, or certification course, or a graduate
degree.
Among the three programmers who did not have an undergraduate engineering
degree, two had completed a Masters in Computer Application (MCA41). One
programmer had done a graduate diploma in computer science42, and she was also
pursuing an MCA (See Table 7.1). This suggests that an engineering degree provides an
edge in acquiring a more technically challenging job. Moreover, the only route to break
the glass ceiling available to those who do not have an undergraduate engineering degree
is to do a graduate degree in computer science such as an MCA. The only non-technical
professional using early stage programming language had studied commerce at the
undergraduate level and completed an MBA.
41 MCA is a graduate degree, and is offered to those who do not have an undergraduate engineering degree.42 Private teaching institutes offer different types of diplomas in computer science, ranging from six months to two years.
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Table 7.1: Educational Qualification of Professionals Using Early Stage Langs.
Name Occupation Undergraduate
Degree
Graduate
Degree
Additional
Qualification
Sarita Systems Analyst BE in Electrical
Engineering
None None
Indrani Programmer Bachelors in Information
Tech.
None None
Monica Programmer BE in Computer Science None None
Niharika Programmer Bachelors in Information
Tech.
None None
Shirina Programmer Btech in Comupter
Science
None None
Ritika Programmer Btech in Comupter43
Science
None None
Nishita Programmer BSC in Electronics MCA None
Milli Programmer Bachelors in commerce MCA None
Shalini Programmer BA in English Enrolled in
MCA
Diploma in
computer
science
Sucharu Non-technical Bachelors in Economics MBA None
43 In India, engineering colleges provide both bachelors in engineering and bachelors in technology degrees. The name of the degree varies upon the name of the institution from which it is acquired. For example, a student graduating from IIT gets a BTech degree, whereas a student graduating from Roorkee College of Engineering (which is ranked number two in India) gets a BE degree.
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Educational Qualification of Women Software Professionals Using Later Stage
Programming Languages
Women software professionals using later stage programming languages had
varied educational degrees, especially at the undergraduate level. The
commerce/business44 stream was found to be the most popular degree at the
undergraduate level. In comparison to women using early stage languages, they had
graduate degrees and other additional qualifications such as diplomas in computer
science. A comprehensive analysis of the educational background is given below.
Out of the seventeen women software professionals using later stage
programming language, only two coders had an undergraduate engineering degree.
However, they did not have any additional qualifications. Six software professionals
using later stage programming languages had an undergraduate degree in
commerce/business, and they were all working as coders. Among others, three had a
Bachelor’s Degree in Science; four had completed Bachelor’s in Arts. Additionally, one
had studied graphic design at the undergraduate level. Out of seventeen women software
professionals using later stage programming language, five had a graduate degree and
three others were pursuing a graduate degree at the time of the study. Among the coders,
seven had done a diploma in computer science (See Table 7.2).
Among the non-technical professionals using later stage programming languages
none had an engineering or computer science background. However, despite this lack of
technical education, they were well versed with most of the technologies used in their
companies. Virtually all pointed out that had learned them on the job.
44 At the undergraduate level, the degree is popularly known as BCom.
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Table 7.2: Educational Qualification of Professionals Using Later Stage Lang.
Name Undergraduate
Degree
Occup Graduate
Degree
Additional
Qualification
Maitrayee Btech in Electronics Coder None None
Mekhla BE in Electronics Coder None None
Meena BCom Coder MCom Diploma in German
Krishna BCom Coder Pursuing
MA in
English
None
Malika BSC in Math Coder None Diploma in electronics
Neela BA Pass Coder MA in
French
Diploma in computers
Sheela BSC Coder None Diploma in computers
Ayesha Bachelors in Graphic
Designing
Coder None None
Preeya BCom Coder None None
Sudha BCom Coder None Diploma in computers
Divya BSc Coder None Diploma in computers
Swati BCom Coder Pursuing
MBA
Diploma in computers
Manjula BCom Coder None Diploma in computers
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Table 7.2: Educational Qualification (Contd)
Name Occup Undergraduate
Degree
Graduate Degree Additional
Qualification
Aprajita Non-tech BA in English MA in English None
Aishwarya Non-tech BA in
Statistics
MBA None
Vishaka Non-tech BA in English Doing MA in
English
None
Yukti Non-tech BA in English MA in English Diploma in
Journalism
The Role of Math and Physics in Educational Choices
Engineering Degree and Liking Math and Physics
An analysis of the transcripts shows that liking Math and Physics as subjects was
important for opting for an engineering degree. Excelling in these subjects at the school
level provided the strong foundation needed to compete in the rigorous and competitive
entrance tests for engineering colleges. All the women software professionals using early
stage programming languages said that they liked studying Math in school. However, two
pointed out that they did not like studying Physics as ‘it was difficult.’ This included one
programmer and the non-technical professionals. “We felt that since it was a computer
science degree, Physics was a real burden,” said the programmer. The software
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professionals believed that Math and Physics provided them with an edge that they
needed to pursue an engineering degree. One interviewee explained:
I was extremely comfortable with Math and Physics. This was one of the reasons why I opted for engineering. When I went to college, it was very rare for women to study engineering degree. A lot of people used to ask why do you want to study engineering. They used to feel jealous, but we used to get praise also as we were very few women who had got through a good engineering college. I really enjoyed that period.
Non-engineering Degree and not Liking Math and Physics
About half of women software professionals using later stage programming
languages did not like studying Math and Physics. Out of seventeen professionals, nine
said they hated studying Math and Physics. However, the two coders with an engineering
degree liked both the subjects. Most professionals who studied commerce in college had
to study Math at the college level. In fact, some of them decided to do a pass course in
commerce as Mathematics was compulsory for an honors degree.45 Amongst the four
non-technical professionals, only one liked Math and one liked Physics. As one
interviewee, explained her fear of Math:
Oh, my god….. I was neither good or bad, I was an average student, but I felt that it should have been taught to us in a different manner. We should not have been scared or frightened of Math. In fact, Math for me was a psychological issue. Every time before the exam I used to fall ill. I would start running high temperature. I was average in Physics. I never wanted to pursue it.
Working in the IT industry seemed to have changed their perception about the
importance of the two subjects. Many interviewees pointed out that their fear of Math in
school was completely unfounded. In fact, they expressed a desire to study the subject 45 In India at the undergraduate level, a student has a choice of doing a Pass course or an honors degree. An honors degree is more valued as it is more comprehensive and rigorous. For example, a student doing Bachelors in Commerce (hons) in University of Delhi has to study Mathematics, whereas in pass course a student does not need to study the subject.
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again. “I like logic. I wish I could go back to school and do it all over again.” They also
believed that the subjects were important, especially when working in the IT industry.
When I used to study things such as calculus, I used to think how and where in the world it will help me, but it does come into use. You notice now being in this field, it is helpful. We used to say that it is useless. But no it is not, you do get to utilize it.
Reasons for Choosing Engineering as a Field of Study
Engineering Choice and Sense of Security
After finishing high school, many respondents were slightly unsure about their
future prospects. However, those that chose to study engineering, irrespective of the kind
of programming language used reported that it provided them with a sense of security
about their careers. They believed that it would be easy for them to get a job if they had
an engineering degree. In India, a lot of emphasis is placed on studying for a
“professional” degree (example, engineering or medicine) as opposed to a Bachelors in
Arts or Sciences because it provides better job prospects in a tight labor market.
On being asked why they chose engineering as a field of study, the interviewees
pointed out that it provided them with a sense of security about their future career.
Explaining the reasons for studying engineering, Indrani, a software programmer, said:
After you finish class XII, you have a feeling of insecurity. Therefore you want to get hold of a professional degree instead of doing a normal Bachelors course such as Chemistry (Hons). It gives you a sense of security that after four year years I will be having a job in hand. If I want to go for further studies now that is my own choice. After class XII, you are thinking what you want to do, if you do graduation after XII then again you are faced with decisions about what you want to do. If you get hold of a professional degree then your line is fixed.
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The respondents further added that they studied computer science amongst all
fields in engineering because it was one of the most sought after fields when they went to
college because the computer industry was booming around the globe. “I choose to study
computer science as it was very highly paid field at that time. It was at a peak amongst all
other engineering fields.”
Engineering Choice and Presence of Engineer
As pointed out earlier, family, culture, upbringing, and especially the presence of
an engineer father or brother play an important role in women choosing engineering as a
profession (Carter and Kirkup, 1990). The findings from this study are consistent in that
the presence of an engineer in the family was found to play an important role in a
woman’s decision to study engineering. Respondents, whose father’s were also
engineers, pointed out that their father’s encouraged and motivated them to study
engineering. In addition, their father’s also taught them at home to provide a solid
foundation in subjects such as Math and Physics.
Out of eight interviewees, who had an engineering degree, four believed that the
presence of an engineer in the family, especially their father, affected their decision to
study engineering. As two interviewees put it:
My father is an engineer. My younger brother is studying engineering. My father’s presence influenced my career choice. Since we were kids, I used to say that I would become an engineer. Our father provided us support, he helped us strengthen our concepts. We never went for tuitions. He taught us instead….He provided us guidance.
My father encouraged us a lot in our studies and career. We are three sisters so he always wanted us to be different, to study and excel. When he thought that I was more interested in math, he encouraged me to study engineering. He took me to his office where he was working as an engineer. I saw the kind of work he did, and I thought that I wanted to do something similar.
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Reasons for Choosing Non-engineering Field
Non-engineering Choice and Lack of Guidance
The interviewees working in technical capacities – programmers and coders, who
did not have an engineering degree irrespective of the programming language used at
work felt constrained in the computer industry by the lack of an engineering degree. They
said that they were unable to pursue an engineering degree because they did not have
proper guidance or were unable to clear the entrance examinations. As Malika, a coder,
pointed out, “I was not aware of many things. Since I am the eldest child, my parents did
not have much idea about my career so I choose an institute randomly rather than go for
an engineering degree.” However, the non-technical professionals did not believe that not
having an engineering degree had an effect on their career.
Explaining why she did not pursue an engineering degree, Nishita, a software
engineer said:
I had plans for pursuing a BTech46, but I did not get the right branch in engineering so instead of reattempting after one year, I thought a Masters degree would be a better idea. That way I could achieve two things. I could get a specialization in electronics and a Masters in computers.
Non-engineering Choice and Interest in Management
As pointed out earlier, the commerce/business stream appeared to be a very
popular educational choice among the women software professionals using later stage
programming languages. Many of the respondents studied commerce due to their interest
in pursuing a management degree. The interviewees pointed out that they studied
commerce as they liked subjects such as commerce, finance, accounting etc. However,
when they did a course in computers, they liked it so much that they decided to pursue it
46 B Tech is equivalent to Bachelor of Science in Computers in the US.
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as a career. As two interviewees said,
I always wanted to go into the management stream…. Due to this interest, I opted for BCom (hons). Due to some personal reasons, I was unable to go out of the home to do coaching for an MBA so I was unable to clear the entrance examination. I then did a diploma course in computers while doing graduation and that interested me in computers so I gave the entrance exam for MCA.
I was a commerce student in school. I was not into pure sciences so I decided to continue with commerce in college. I thought this would keep my options open rather than studying arts. In school, I liked commerce—accounts etc so I continued with commerce. Meanwhile, I joined a small computer course and since I liked it I kept extending for three years.
Non-engineering Degree and Parental Pressure
Interestingly, some women software professionals using later stage programming
languages pointed out that they studied commerce and then moved on to computer
science because their parents wanted them to do so. It should be noted that in India,
studying commerce became very popular during the early 1990's. However, when the
computer industry boomed during the late 1990s and jobs in the IT industry increased,
many students shifted to studying computers. Parents put pressure on their daughters to
study commerce as they thought that they would be able to get jobs after finishing the
degree. As one interviewee explained:
I was not interested in computers or commerce either. I decided to study commerce because I was not independent enough to make my own decisions. I just did what was suggested to me by my parents. They thought that the commerce line was good for a girl, and everyone was studying commerce at that time. They think that the computer line is also okay for a girl as it is a desk job.
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Limitations at Work Due To Having a Non-engineering Degree
As pointed out earlier, respondents without an engineering degree irrespective of
the programming language used felt the lack of the degree restricted their choices in the
job market. For example, out of eleven coders who did not have an undergraduate
engineering degree, six believed that not having the degree limited their opportunities for
advancement in the jobs. Many of them were working towards a graduate degree in
computer science or wanted to do it in order to overcome this shortcoming. They
believed that although they were as competent as engineers, they were discriminated at
their jobs because of this deficiency. As Malika explained:
Yes, our opportunities are limited. Therefore, I am doing a BE in electronics and communication. My five papers are left, so hopefully I will complete my degree. First, if you have a BE degree, you enter an organization at a different level. If you just have a diploma, then a different level is assigned to you. There are differences in salary. Even if you are working at the same level as an engineer, less opportunities will be given to you. Your bosses think that you are not intelligent. They think you are not competent and efficient. They think about you in a negative manner.
The biggest hurdle faced by non-engineers was that big software companies were
unwilling to hire them or even call them for an interview. Thus they were confined to
working in smaller companies,where the salary was less and working conditions and
benefits were not at par with the bigger companies. For example, one coder, pointed out,
Most top firms – their basic requirement is that you must have an engineering degree. Maybe they might consider you because you have work experience or they might call you for an interview because someone knows you. The first block is if you do not have a BE or BTech degree, they won’t even you call you for an interview.
The only avenue for those without an engineering degree to overcome the
shortcoming in their education was to do an MCA. Among women software professionals
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using early stage programming languages, who did not have an engineering degree, two
had finished an MCA, and one was studying towards the degree at the time of the
interview. “Most of the reputed companies go for BE or MCA. That is why I joined this
MCA course just to overcome the deficiency in my education. I applied once at Infosys.
They send me an email saying that I must first complete my MCA and then apply for a
job.”
However, women working in non-technical capacities did not believe that a lack
of an engineering degree hindered them in any way, even though they were expected to
be technically proficient. For example, Sucharu, elaborated:
It is not necessary that I need an engineering background… I do not need to know core technical stuff. I have to have some kind of technical knowledge, but that comes with experience.
Effect of Educational Background on College Experiences
Women software professionals using different stage of programming languages at
work had different college experiences. This was primarily because women software
professionals using later stage programming languages had studied mostly in an all girls
college, or coeducational colleges with a large female student population. In comparison,
those who were using early stage programming languages studied in engineering colleges
where there were only a handful of female students. Most women software professionals
using later stage programming languages had pleasant college memories. They believed
that their gender did not affect their college experience. As put by one worker, “I had fun
in college. I never studied. Our gender did not matter.”
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However as most women using early stage programming languages studied in
colleges where girls were few in numbers they tended to have a limited number of friends
during their college years. As put by one engineer, “We were only three girls, out of a
class of twenty. This meant that we had a very restricted number of friends. You had to
be friendly to these girls. You had to share your experiences, and work with them.” They
were sometimes harassed by their male peers as they were a minority in number. As
Shirina, an engineer, pointed out:
I had a hard time in college because we were four girls out of a class of thirty. The ragging in first year was horrible. We used to be sitting in the class, concentrating on our studies, and guys used to throw chalk at us. It was really embarrassing, and frustrating.
Staying late was a problem in college also. Many female students did not avail of
the laboratory facilities provided to them at the college because they did not want to stay
late. As one interviewee explained:
Sometimes you cannot stay late at college. If you are supposed to do an assignment which involves staying late in college, you will be one of the first people to buy a PC at home. During exam time, you have a lot of pressure in completing assignments that require staying late in college labs. This was one of the reason why I bought a PC.
While some engineering students believed that teachers were nicer to female
students as they thought that they were more sincere, most pointed out that teachers had
certain negative stereotypes about girls studying engineering. As Nishita pointed out:
Some of the colleagues and teachers had the impression that girls take up science only because they feel like or are influenced by their parents. Secondly, they do not attribute as much intelligence in us as they do in boys. The fact that I came first throughout my college was always questioned. People always raised their eyebrows.
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Desire For Further Studies
Most respondents, irrespective of the programming language used, wanted to
study further. Future education held a promise of exciting new job opportunities, an
increasing knowledge base and diversifying into a new field. Out of twenty-seven
interviewees only three did not want to study further. Out of ten women software
professionals using early stage programming languages, six wanted to study further.
They wanted to study for a graduate degree in computer science such as a Masters in
Computer Application or a Masters in Technology or an MBA. The interviewees who
wanted to study computer science believed that a graduate degree would give them a
competitive edge in a tight job market. “I want to do M.Tech. I do not think that a
Bachelors can make me move forward,” explained one programmer.
The women software professionals using early stage programming languages
believed that a Master’s degree would not only work to their advantage in finding new
and better jobs, but it would also help in moving up the occupational ladder in the
company. Software programmers pointed out that they have to be abreast with the latest
technologies, and a graduate degree can help them increase their knowledge base. As
Ritika explained:
I plan to go for an MS... I feel I need to study more because when I was doing my B.Tech. my understanding of the subject was very less. I have covered a lot in the last five years. If I read about a concept right now, I am able to implement it, which I was not able to do while as a student.
The interviewees pointed out that they wanted to pursue an MBA degree as they
wanted to get into the management side in the near future. The only systems analyst in
the study also wanted to do an MBA as she thought that a formal training in management
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might be helpful for her as her work involved management of software projects and team
members.
Most women software professionals using later stage programming languages
also wanted to study further, but they had very varied interests. Out of twelve coders
asked the question about further studies47, only two did not want to study further. Two
wanted to do an MBA, and three wanted to pursue an MCA. The two coders with an
engineering degree wanted to study computer science further. One wanted to do a
diploma in networking and the other wanted to do an MTech degree. Of the remainder,
one wanted to do a diploma in electronics; one wanted to do a graduate degree in graphic
designing; and, one wanted to pursue wildlife studies. None of the non-technical
professionals using later stage programming languages expressed any desire to pursue
further studies in computer science. Apart from the CEO, who did not want to study
further, all wanted to pursue a degree in their own field.
The interviewees who said they wanted to do an MCA hoped the degree would
open new opportunities for them as not having a formal engineering/ computer science
degree was a major obstacle to them securing more technically challenging and higher
paid jobs. Explaining her reasons for wanting to do an MCA, Manjula, a coder said:
I have a commerce degree. It is a stumbling block in me getting a good job in a software company. If you do not have a B.E. or B.Tech., you are required to have a Post Graduate degree in computer science.
As pointed out earlier, two coders pointed out that they wished to pursue an MBA
degree. The two respondents wanted to pursue an MBA degree due to interest in the
subject. Both of them had an undergraduate commerce degree. The two engineers
47 I was unable to get the response of one coder on this question as the interview was terminated before it could be completed by her supervisor.
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working as coders wanted to study computer science further as they did not like the kind
of work they were doing, and thought another degree will help them get a job as a
software programmer. As put by one of these workers:
I have completed CISCO certification for network associate. Next, I will be going for Solaris administration. This is the second level of training for networking. I am really interested in networking. I am doing it to upgrade myself and keep me updated because I did not see any personal growth within the company, especially in my job.
Interestingly, only one coder wanted to quit the field of computers, and move on
to the field of wildlife studies. As pointed out earlier, she explained that her parents had
forced her to move into the field of computer science, although she was not interested.
She pointed out:
I want to do my PhD in wildlife conservation. When I go to websites, I see the jobs that are available in the field area, not the desk jobs. They ask for PhDs in wildlife conservation. I do not have that training. This has always been my first priority. It is just that after college, I was not that independent that I could make my decision. I just did what was suggested to me by my parents. I thought that I would establish myself first in the field of computer science and then make the shift. I am independent enough to make a decision now.
Effect of Education on the Future
Most respondents, irrespective of the programming language used, who wished to
study further believed that their future job prospects would be affected by whether they
would be able to acquire higher education. The female software workers who wanted to
pursue an MBA, hoped that the degree would help them move into a management job. “I
hope I am in a management position in five years from now. Not at a very senior level,
but maybe a senior consultant engineer,” said Monica, a software programmer.
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The women software professionals wanting to pursue an MCA hoped that the
degree would give them the kind of break that they were looking for in the computer
industry. “I hope I get a programming job, and become a team leader in the near future,”
said one coder. Others believed that a graduate degree would help them in doing path-
breaking research in computers. “I hope I will be doing more in-depth development work.
We get so many opportunities so hopefully, I will be pulling up some new technologies,”
said Krishna.
Many respondents expressed the desire of starting their own companies after
finishing their higher studies. Said one Coder:
I want to start my own studio, and I hope it is working well. I don’t expect to have a really big company, just a small company that is just doing well. I will be happy doing what I do right now. I do not want to lead a team of people as I am happy doing the kind of work that I am doing right now because I am satisfied with my work. I want to be in the same position, but with bit more money.
Job Training
Work experience is a critical part of the skill-training experience of women
software professionals. On being asked how they learned the skills required for their
work, most respondents, irrespective of the kind of programming language used believed
that training on the job was crucial, although educational institutions provided a basic
foundation. As two interviewees explained:
I learned the basic skills in college. We were taught programming and the basics of programming languages. But then there are several concepts that we need to know while actually making a professional software. This you learn on your job….. We know basic tools, but there are always new things that are added to Java, new libraries, if you need them you have to study them here. But if you know the basics, grasping is very easy. It is a part of our job so it is nothing that we have to do extra.
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I did a six month intensive training course at Self-combat. It is a web designing institution. After completing my training, I worked there for 1 ½ yrs then I came here. Creativity comes through practice. Institutes just provide guidance.
Six companies in the study provided training courses to the employees. As one of
the respondents working with the American MNC explained:
Yes, we have several in-house training programs organized every month. There is a training calendar that is posted every month on our Intranet wherein we are provided information about technical training being conducted by people both from outside and within the company. Our manager approves the training, and then we can attend it. We have several extracurricular training also such as art of living classes – yoga and all. We also have employeeship training, where they teach us what it means to be the employee of this company.
Interestingly, the Indian company, specializing in hardware, provided training
only to the male engineers working at customer sites, and did not provide training to the
female employees, who provided technical assistance over the telephone. Said one female
engineer, ‘Sometimes, the company provides training, but it is only for field engineers. It
is not for girls who sit in the office.’
All the non-technical professionals – two MBAs and three correspondents -- as
part of job training learned programming languages, although it was not formally part of
their work profile. They pointed out that they picked up languages at work in order to
make their work easy.
I do technical work myself in order to ensure that my work gets done on time. If my content is being held up only due to technology…. Because the content has to be fed, and program has to run it, so I learn how to run the program. So whenever I finish the article, I upload it myself on the
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website rather than wait for someone. My ultimate aim is that whatever I am supposed to do is done on time. So whatever hurdles have come, I have learned to improve or resolve them even if it means learning some programming language.
Conclusion
The analysis of the data clearly shows that the majority of the women software
professionals do not have an engineering degree, and instead, studied for a diploma in
computer in community/polytechnic colleges or for-profit private institutes. According to
the skill training and technology life cycle, education of technologies at the later stages in
the life-cycle is more dispersed, and easily available. The analysis of data showed that
majority of the female software professionals, specifically coders, were using
technologies that had developed in the later stages of the software technology life-cycle,
and training in these technologies is provided at community/polytechnic colleges or for-
profit private institutes. We can also see that the shape of the labor queue has also
changed as the number of women entering the software industry is increasing because the
training is widely dispersed. Women are able to enter the computer industry without
acquiring a formal computer engineering degree. However, this has contributed to gender
segregation within the software industry as the majority of women are able to acquire
only those jobs that are ranked low in the occupational hierarchy.
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Chapter EightDiscussion and Conclusions
Over the last decade, an increasing number of women have joined the software
industry in India. Most women in India still work in female-dominated occupations such
as teaching and nursing. However, the rapid growth of the software industry in India
since the early 1990s has provided women with opportunities that were not available to
them earlier. Many women have acquired computer training in private for-profit
institutes, and started working in the software industry. The number of women studying
engineering has also increased over the years.48 Women comprise about 12 percent of the
labor force in the IT industry. However, this study suggests that within the software
industry women are concentrated in jobs that are ranked lower in the job queue – jobs
using programming languages that have developed at later stages of the software
technology life cycle. The primary objective of this chapter is to inductively construct
elements of a theoretical model of the dual queue and technology and skill-training life-
cycle theory that explains the effect of skill training and technology life cycle on the
structural properties of the queues, specifically the ranking of jobs in the job queue and
shape of the labor queue.
The Effect of Skill Training Life-cycle on Ranking of Occupations in the Job Queue
As I wrote in Chapter Three, Reskin and Roos (1990) reformulated the theory of
labor and job queues to explain women’s inroads into previously male-dominated
occupations. The two theorists posit that workers rank jobs in job queues in terms of their
48 Currently, only three percent of men and one percent of women in India have a college education. Therefore, women comprise 33 percent of the total student population in colleges and universities. This sex ratio is found for most fields of study except (1) engineering and commerce – where women account for a much smaller proportion of students, (2) education – where women account for 50 percent of the students. http://www.census.gov/ipc/prod/wid-9801.pdf
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attractiveness, and employers rank workers in terms of their attractiveness. Reskin and
Roos point out technological change that elaborates the division of labor, deskilled work
or altered working conditions sets the stage for occupational decline. This leads to men’s
reranking of occupations in the job queue, providing women with opportunities to fill the
deskilled positions. According to them, the computer industry provides an example of
technological change altering an occupation’s sex composition. However, the queue
theory as given by Reskin and Roos provides a static analysis of the link between
technological change and reranking of occupations in the job queue. It does not answer
the question: when does technological change occur?
Economists have pointed out that a technology has a life cycle that provides the
stimulus for changes in the product and production process. A new technology is
introduced slowly at first. It becomes more widely accepted as research leads to better
performance. Eventually, it plateaus as it reaches it performance limit, and is replaced by
a superior technology (Ford and Ryan, 1981: Shanklin and Ryans, 1984). Along with the
technology life cycle, a skill-training life cycle also evolves. The early stages of a
technology are relatively skill and labor intensive. However, as technologies mature,
standardization leads to subdivision of multifaceted tasks into more narrowly defined
assignments (Flynn, 1993).
It is a major contention of the study that reranking of occupations in the job queue
occurs at a later stage in the technology life cycle due to changes in the division of labor
and the deskilling of work. As illustrated in Chapter Four, computer technology has a
technology and skill-training life cycle. Computers were introduced in the 1940s to
satisfy wartime needs. The early/first generation computers were clumsy machines. They
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had no operating systems, and highly skilled programmers were required to operate them
because one had to rewire the circuits each time to run a program. On the other hand,
operating the second generation computers was less painstaking because the instructions
to operate the machines were stored in the machine’s memory along with the data to
operate them (Kraft, 1977, Greenbaum, 1979, Donato, 1994).
Along with changes in the hardware, computer software technology has also
evolved. As computer software technology has matured, there has been a trend towards
job fragmentation and deskilling (Kraft, 1977). The early generation languages – first,
second and third – are highly skilled as a programmer needs to specify how a particular
function has to be performed. However, the later – fourth -- generation languages are
deskilled as the programmer says what needs to be done rather than how to do it
(Computer Languages, 2002).
According to Reskin and Roos, workers try to maximize income, social standing,
autonomy, job standing and chances of occupational advancement. They rank jobs on the
basis of the characteristics accordingly. The analysis of the data found that occupations
that involve the use of later stage programming languages are ranked lower in the job
queues, and those involving the use of early stage programming languages are ranked
higher in the job queues. On average, women software professionals using early stage
programming languages earned Rs 10,000 ($200) more than those using later stage
programming languages, although they had the same number of years of experience.
Despite this most women using later stage programming languages were satisfied with
their salary.
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As hypothesized by the skill training life cycle theory, the work of women
software professionals using early languages was highly skilled and multifaceted. The
systems analyst was in-charge of an entire team. The programmers were involved in the
development of the software at various stages of the technology life cycle. The work of
women software professionals using later stage programming languages, particularly the
coders, was more narrowly defined. For example, the coders were only responsible for
making and maintaining websites. The research did not find wide differences between
women software professionals using programming languages that were developed at
different stages of the technology life cycle regarding autonomy, chances of occupational
advancement, and opportunities for job rotation and working in teams.
The study had asked all the respondents where they believed that their job fit in
the occupational hierarchy in the company because Reskin and Roos point out that
workers rank occupations in the job queue in terms of their attractiveness. The data found
that there was a correlation between ranking of jobs in the occupational hierarchy and
numbers of years of work experience among women software professionals using early
stage languages. However, women software professionals using later stage languages said
that their jobs were ranked high in the occupational hierarchy even when they had few
months of experience.
It can be concluded from the data that women working in higher status
occupations have a different logic for evaluating their jobs in the job queue to women in
low status occupations. Women in high status occupations have more objective criteria
for assessing their status in the job queue. For example, they believe that status is
correlated to the number of years of experience. It is my belief that women working in
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low status occupations have a higher self-perception about their jobs as they believed that
their job was highly skilled. Indian society places a lot of emphasis on the study of
Science and Math, and those who excel in these subjects or do ‘technical’ work are
awarded higher status in society. Coding also provides women software professionals
with avenues such as opportunities for occupational advancement, autonomy and team
work that might not have been available to them. A popular career choice for women in
India is teaching which does not provide these benefits. Coding is also a good option for
some of the women as there are not many job opportunities available to women with only
an undergraduate degree in commerce or arts. As I showed in Chapter 7, out of seventeen
women software professionals working in jobs that involved the use of later stage
programming languages, fifteen had a bachelor’s degree in arts or commerce.
The declining rewards in jobs that involve the use of later stage programming
languages make them unattractive to men. This means that employers are unable to find
qualified men to fill the positions. They often turn to women for whom jobs in the
software industry represent a step up compared to other choices available to them such as
teaching. The study found that women are highly represented in jobs that involve the use
of later stage programming languages. Most coders and non-technical professionals said
that within their companies either women were a majority or there was an equal mix of
men and women in their kind of jobs. In contrast, all women software professionals using
early stage programming languages said that their peers were predominantly men.
An interesting finding of the study was that women professionals working in non-
technical capacities were also proficient in programming languages. Most of them had
learned these languages on the job. The salary of non-technical professionals ($500 per
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month) was almost twice that of coders ($240). This shows that one of the avenues
available to women coders was to move into management or editing if they are working
for infotainment companies. However, most coders had high estimation of their jobs as
compared to the non-technical professionals as their work was more technical in nature.
They believed that their work was highly ‘creative and challenging.’
As I had pointed out in Chapter Four, job growth in occupations has led to
changes in the job queue in India. Job growth supports feminization as it can lead to a
shortfall of male workers. Employment in the Indian software industry expanded during
the last ten years. In 1996, it was estimated that there were 140,000 software
professionals in India. By 2000, the number of software professionals rose to 410,000.
Due to outsourcing of software work to India, there has been a dramatic increase in the
number of software professionals in India in the recent years. According to NASSCOM,
the Indian software industry employed 770,000 software professionals in 2003. However,
not all the jobs that are created are highly skilled. In the1990s, the work that was
outsourced to India was neither technologically advanced nor critical to firms who
outsourced it (Arora et al., 1990). Although there is a shift from this trend, much of the
work being outsourced to India is still not highly skilled. Women have benefited from the
expansion in the software industry as rapid growth has exhausted the supply of trained
workers from the preferred group – men.
Reskin and Roos (1990) point that the job queue changes more rapidly in high-
turnover occupations. As I pointed in Chapter Four, in the 1990s, due to the IT boom in
the US, the labor turnover in the software industry was very high due to outmigration of
skilled workers, particularly to the US. The US government has decreased the number of
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H1B visas issued to Indians from 195,000 in 2000 to 65,000 in 2003. The number of
software professionals migrating to the US annually has decreased, but the numbers are
still high (NASSCOM, 2004).
According to Reskin and Roos (1990), occupations that witness a shortage of
male workers experience a deterioration of rewards and working conditions. This leads to
the reranking of the occupation in the job queue, ultimately leading to rapid feminization
of the occupation. This research supports this proposition. As I pointed out in chapter Six,
occupations that involve the use of later stage programming languages are female
dominated, and are lower paid and deskilled.
In summary, we can argue that technological change remitting from the skill-
training life cycle has led to an increased division of labor, and the deskilling of jobs
involving the use of later stage programming languages within the software industry.
Therefore the jobs that involve the use of later stage programming languages are ranked
lower in the job queue because they are deskilled and have low salary. These findings
allow for the inductive construction of the following testable hypothesis:
H1: Programming jobs that employ technologies that are in the early stages of
their life cycle will be highly skilled, and ranked higher in the job queue
On the other hand, the jobs that involve the use of early stage programming languages
are highly skilled. As a result, they are better paid and have better working condition.
This is turn means that they are ranked higher in the job queue. This brings us to the next
testable hypothesis:
H2: Programming jobs that employ technologies that are in the later stages of
their life cycle will be deskilled, and ranked lower in the job queue.
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Since jobs that involve the use of early stage programming languages will be
ranked higher in the job queue, the preferred group in the labor queue will fill these
positions. As employers rank men higher than women in the labor queue men are
more likely to be employed in jobs that involve the use of early stage programming
languages. This leads us to our next testable hypothesis:
H3: Programming jobs that employ technologies that are in the early stages of
their life cycle will be male-typed.
Men rank jobs involving the use of later stage programming language lower in
the job queue because of declining rewards such as salary and the nature of work.
Employers are unable to attract and retain enough qualified male employees for these
positions. Thus the employers have turned to women to fill the positions, and such
jobs represent a step up for most women. However, this creates and reinforces gender
segregation within the software industry as women are increasingly being employed
in jobs that involve the use of later stage programming languages. Thus the declining
attractiveness of jobs that involve the use of later stage programming languages has
increased women’s representation in these jobs. In addition, the growth of
occupations in the software industry, particularly those employing later stage
programming languages, high labor turnover, and deterioration of rewards in
occupations employing later stage programming languages have supported the
feminization of these occupations. The preceding findings allow for the inductive
construction of the following testable proposition:
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H4: Programming jobs that employ technologies that are in the later stages of
their life cycle will employ a disproportionate number of women.
The Effect of Skill Training Life Cycle on the Labor Queue
Reskin and Roos (1990) believe that colleges and universities helped change the
shape of labor queue as education offered women pathways into previously male
dominated occupations. For example, they point out that the proliferation of real estate
courses in community colleges allowed women to sidestep the requirement that brokers,
almost all men, sponsor would-be sales agent. However, they leave one question
unanswered: At what stage of the technology life cycle will women be able to avail
educational opportunities?
As given in Chapter Three, the skill-training life cycle theory points out that the
availability of skill training and the mix of the institutional providers vary depending
upon the phase of technology. When a technology is new, skill-training is usually
provided on the job through various programs at the workplace. At this stage, scientific
and engineering personnel design and create products, and teach others the skills
associated with the new technology. During the later stage, skill training is shifted to
schools as employers cannot capture the return on investments. As the demand for the
skill matures, training is widely diffused among educational institutions (Flynn, 1993).
The training of computer professionals in the US has been institutionalized in a
three-tiered system: research universities or schools of management, four year
engineering colleges, and two year junior institutions. The junior colleges provide
vocational training; four-year colleges teach students to design and write programs rather
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than simply code; and, elite institutions train highly skilled programmers who design
entire computer systems or languages (Kraft, 1977; Donato, 1994).
The foregoing discussion is applicable to India also. India also has three kinds of
institutions providing training in computers: research universities such as the IITs that
train highly skilled programmers such as systems analysts, who design entire computer
systems or languages; four year engineering colleges that teach students to design and
write programs; and, for-profit private institutes that provide vocational training in later
stage programming languages.
Women comprise the majority of the student population at vocational training
institutes (Yee, 2000). However, the number of women studying engineering is much
smaller compared to males. This research found that out of 27 respondents, only eight
had an engineering degree. None of the respondents with an engineering degree had any
additional qualifications. Eight respondents had studied for a diploma in computers at
vocational colleges. The women software professionals with a vocational degree were
more likely to work as coders using later stage programming languages at work. Out of
13 coders, seven had a diploma in computers. This clearly shows that the proliferation of
computer courses in vocational colleges has provided women with a pathway into the
software industry.
Cultural changes are also reshaping the labor queue. As Indian society moves into
the 21st century, traditional values about women and their place in the society are being
challenged. Women are increasingly joining the labor force because they have a desire to
work. An increasing number of Indian women believe that work defines their self-
identity. Even parents encourage women to join the labor force, although they prefer if
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the girls work in ‘traditional’ occupations. The role of mass media is also important as it
no longer portrays the stereotyped image of an Indian woman – a woman who stays at
home and cares for the family. A successful Indian woman is often portrayed as someone
who can juggle both home and work. Thus these cultural changes are increasing the
number of women seeking higher education, and vocational colleges fill the demand of
this ever increasing group.
In fact, women prefer working in the software industry as compared to traditional
female occupations such as teaching. This is the case, even though teaching has benefits
such as shorter working hours and holidays. Most software professionals believe that
working in the software industry allows them to be ‘creative, and independent,’ and
teaching is ‘boring and repetitive.’
In a nutshell, we can argue that educational opportunities are more easily
available to women when a technology has matured, and is deskilled, as training at this
stage is dispersed. This changes the shape of the labor queue as the number of women
acquiring training increases. The shape of the labor queue has changed in India as the
proliferation of for-profit-private institutes has made training in computers easily
accessible to women. In fact, women comprise 50 percent of the student population at
these institutes (Yee, 2000). Women are able to bypass the requirement that is required to
join the software industry – having an engineering degree. This brings us to the following
testable propositions:
H5: Training for programming occupations will be more diffused in the later
stages of the technology life cycle.
178
H6: Women are more likely to acquire training in computers at vocational
colleges.
Reskin and Roos (1990) believe that employer’s reranking of sexes in the labor
queue can provide women with opportunities in previously male dominated occupations.
As the data shows some companies like to hire women for desk jobs in the software
industry. For example, I found that one of the Indian hardware giants hired only women
to interact with clients on the telephone, and only men to go to the field. Women were
hired for the positions as they possess ‘people skills.’ The basic rule of economics applies
here: reciprocity between demand and supply. Employers’ increasing need for women for
desirable jobs helped to stimulate their growing availability.
Queue and Life Cycle Theory
In conclusion, we can argue that the employers rank men higher in the job queue
than women in the software industry as men are more likely to have an engineering
degree. Men are also ranked higher as they do not face problems such as staying late in
the office or managing family and work. Occupations that involve the use of early stage
programming languages are ranked higher than those that employ later stage languages as
they are highly rewarded and skilled (see Figure 8.1). The ranking of occupations is
affected by the stage of the programming languages used. Programming languages that
are in the early stage of their life cycle are highly ranked as they are more skilled than
those that are in the latter stage of their life cycle. Women have been able to make
inroads into occupations that involve the use of later stage programming languages as
women and later stage programming languages are ranked lower in the queue. This
179
shows that within the software industry in India gender segregation is being reinforced as
women are being segregated into occupations that involve the use of later stage
programming languages.
Therefore this research has tried to extend the theory of job and labor queues by
making a connection with the technology life cycle model theory. Although Reskin and
Roos discuss the effect of technological change on the shape of job and labor queue, they
provide a static model of analysis. This research shows that the stage of the technology
leads to the changes in the job and labor queues. This study shows that ranking in the job
queue is determined by the stage of the technology as technologies that are in the early
stage of their life cycle are highly skilled and therefore ranked higher. Whereas
technologies that are in the later stage of their life cycle are deskilled and jobs involving
their use are ranked lower in the job queue. This research also shows that the stage of the
technology affects the shape of labor queue as educational opportunities are more
dispersed when the technology is in its later stage. Thus more people are able to acquire
training and join the labor queue.
180
Figure 8.1: Illustration of the Labor and Job Queue in the Software industry
in India
Change in nature of tech
Women and Computing
As I pointed out in Chapter Three, feminist theorists argue that computer
technology is gendered (Turkle, 1984, 1988, 1990; Perry and Gerber, 1990; Frissen,
1992; Kirkup, 1992). First, the development of computer technology is closely linked
with wartime needs. Second, the manner in which PCs are manufactured and marketed
ensures that computers are gendered. Although the theory of masculine culture argues
that masculinity and technology are intertwined, and technical competence is an integral
part of masculine identity, I did not find evidence that women were afraid of computers
or technology. On the contrary, women software professionals were comfortable working
with computers. They expressed satisfaction with their work, and believed that their work
was better than that of other women working in the company as it was ‘technical.’
However, most respondents who did not study Math or Physics in school pointed out that
unlike men they were not encouraged to study these subjects. The respondents said that
181
Men
Women
Early
Late
they were told that it was ‘not necessary’ for them to study these subjects as they were
women. Therefore, there is some evidence that there is a dialectical relationship between
masculinity and technology. This evidence is further supported by the fact that most
respondents pointed out that men in engineering formed cliques to keep women out of the
inner circles as they believe that masculinity and technology are coterminous. These facts
also support the liberal feminist perspective that women’s potential is distorted by gender
stereotyping. The analysis of the data showed that most women believed that their fear of
Math and Science in school was unfounded. They pointed out that they should not have
been afraid of these subjects as excelling in them would have worked in their benefit in
the software industry.
But most women, irrespective of their educational background, had high career
aspirations. They wanted to study further and move up the occupational ladder. However,
they did acknowledge that they faced problems in their quest that men did not have to
encounter such as managing family and staying late in the office. This highlights the role
of patriarchy in influencing the work experiences of women software professionals.
Women are still disproportionately responsible for housework, despite working outside
the home. Most respondents feared for their safety therefore they did not want to stay late
in the office. This clearly shows that women are still victims of violence. However, the
three theories on gender and technology do not address these issues. Although the theory
of technology as masculine culture, discusses the cultural context of technology, it does
not deal with specific problems that women face on a daily basis because of living in a
patriarchal society.
182
This study found evidence that family, culture, upbringing and especially the
presence of an engineer in the family played an important role in women choosing
engineering as a profession. Out of eight engineers interviewed, four believed that the
presence of an engineer in the family made them choose engineering as a field of study.
The data also shows that when women challenge traditional notions of masculinity such
as studying engineering, they face severe resistance from their male peers. Most women
software professionals with an engineering degree pointed out that males, particularly
their peers in schools, made life difficult for them. This shows that although they might
have the same aspirations as men, women face difficulties when they try and break into
traditionally male-dominated occupations such as engineering.
The study also found evidence that women would like special programs as
espoused by the liberal feminist for women to help them ‘catch up.’ For example, some
respondents pointed out that the government should have quota for women in engineering
colleges to increase their representation. All respondents said that companies should
provide special provisions for women so that they can manage family and work such as
provide day care facilities, ability to telecommute, and transportation for women to go
back home in case they stay late in the office.
Policy Implications
I would like to conclude the dissertation by making some policy implications to
increase the representation of women in the software industry, and ensure that women are
not segregated in low-paying and low skilled occupations. I believe that this objective can
be reached by bringing about change at three levels:
183
(1) Structural: There is a need to bring about structural change as the patriarchal
nature of Indian society influences educational and career choices of men and women. In
Indian society, parents place a lot of pressure on boys to study Math and Physics,
whereas girls are not encouraged to study these subjects. In order to encourage girls to
study Math and Physics, change has to be brought about at the level of family, mass
media and schools. Parents should encourage their daughters also to excel in Math and
Science. Having a strong background in Math and Science is necessary since school
provides a strong foundation that is required to compete in entrance tests to engineering
colleges. Studying engineering provides women software professionals opportunities to
work as systems analyst and programmers. The role of schools and teachers in studying
Math and Physics is also important as girls can be encouraged at this level. Schools
should provide an atmosphere conducive to women for studying subjects that have been
traditionally labeled as ‘male-dominated.’ The schools should make sure that male peers
do not harass girls in engineering colleges. Even teachers need to be sensitized to the
needs of women in engineering.
In order to increase the representation of women in the software industry, Indian
companies should provide facilities to women software professionals to meet their
specific needs. The companies should provide women the option of working from home.
They should provide transportation to women employees if they have to stay late in the
office in order to meet deadlines. The companies should follow a policy of affirmative
action to recruit women. In the US, women’s representation among systems analysts
grew in part because employers were pressured to hire women. For example, IBM
recruited its first female programmers by advertising under Help Wanted in the women’s
184
section of the New York Times (Donato, 1990).
(2) Action/ Agency: Change cannot be brought about without striving for it. Women
should also work towards finding a place in the software industry. They should work
hard, dedicate time and effort to work in order to move up the occupational ladder.
(3) Ideological: There is a need to bring about change in the value systems of the society
regarding education of women, and their place in the labor force and home. In a country
where two-thirds of women are illiterate, there is a need to teach the society that the
education of the girl child is important. There is a need to bring about an ideological shift
in the society regarding the place of women in the household and labor force. Many
parents believe that girls don’t need to study as they will not have to work when they
grow up because their husbands will take care of them. This kind of thinking needs to be
challenged. Indian society needs to be taught that education of girls is equally important
and women need to earn a living in order to be independent. Indian society’s ideology
regarding women’s place in home needs to be challenged. Society’s cultural thinking that
women are solely responsible for housework needs to be shaken. It is important that men
understand that they are as much responsible for housework as the women. It seems like
an uphill task, but change cannot occur overnight.
Future Research
To test the hypotheses given in the earlier sections of this chapter, I would do a
comparative study between women and men software professionals in India. Due to
constraints of time and money, my dissertation research was limited only to 27 women
software professionals. I would do a study that compares the job and skill-training
experiences of men and women software professionals. I would employ a survey using
185
quantitative methods to test the hypotheses that have inductively constructed through this
study.
186
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Appendix
Appendix A
Initial Contact
Hello, may I speak with -------------------. (If she is not there or cannot come to the
phone, ask “when would be the best time that I could reach her.” Try to determine a time
to call back. If she does not live there, ask if the person would know where you could
contact them. If they do not know, terminate with. “I am sorry to have bothered you,
good-by.” If she comes on the phone, continue in the following manner.)
My name is Mani Pande. I am a PhD student at Kansas State University. I am
attempting to collect data for my doctoral dissertation on the role of women in the Indian
software industry. I believe that you are employed as a computer programmer in the IT
industry. Is that correct? I would like to interview you, as part of my research. The
interview will take about 45 minutes, and will focus on issues dealing with your job and
educational background.
I want to assure you that any answers you provide to interview questions will be
kept anonymous and confidential, and will never be identified with your name. The data
collected will be used to complete my doctoral dissertation. Can I set up time to interview
you? Could we conduct the interview at your home or another place where you feel
comfortable?
196
Interview Schedule
A. About myself:
Hello, my name is Mani Pande, and I am a PhD student at Kansas State University.
As I told you over the telephone, I wish to interview you for the research that I am
conducting for my doctoral dissertation on the role of women in the Indian software
industry. Again, I want to assure you that any answers you provide to interview
questions will be kept anonymous and confidential, and will never be identified with
your name. Before I ask you any questions, would you like to know something about
me, and the work I am doing?
Can I record the interview?
B. Factual and background information:
(1) Name
(2) Age
(3) Marital status
(4) Current Designation at work
(5) Years of experience
(6) Name of company
(7) Salary (in Rupees)
C. Open-ended opinion based questions:
(1) Questions on educational background:
1-What is your educational background?
2-What is the highest degree you have earned?
3-If college, what field is your degree in?
4-Which college or university did you attend?
197
Questions on education for those who have a formal engineering degree:
1-What interested you in pursuing a formal engineering degree rather than some other
degree?
2-Is anyone else also an engineer in your family? If yes, did the presence of another
engineer influence your career choice? How?
3-While you were in college, did the fact that you were a woman in engineering have
a bearing on your experience? If yes, how?
4-How did you feel about taking Math and Physics classes as part of your college
degree?
Questions on education for those who do not have a formal engineering degree:
1-What interested you in pursung the degree you did as opposed to a formal
engineering degree?
2- How did you feel about taking Math and Physics classes as part of your college
degree?
3- While you were in college, did the fact that you were a woman have a bearing on
your experience? If yes, why?
4 - Do you think the fact that you do not have a formal degree in engineering limits
your opportunities for advancement in your job? Please explain your answer.
(2) Questions on nature of work and technology:
1- What is the nature of your current job?
2- How many people in your company are in the same job or position as yours? Are
they predominantly men or women or, is there close to an equal mix of men and
women?
3- What software do you use?
4- How did you learn the skills required for this job?
5- Please describe the characteristics of your work?
a- Do you have to work in teams?
b- Do you have control over your work?
c- Does your work involve job rotation?
d- Is your salary satisfactory?
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6- Where does your job fit in the occupational hierarchy of your company?
7- What are your perceptions about jobs that are ranked higher than your job?
8- What are your perceptions about jobs that are ranked lower than your job?
(3) Questions on gender
1 -How would you compare your work experience with those of other women in
the company?
2- How do your work experiences compare with those of men in your company?
3 -What do you think it would take a woman to move up in your company?
(4) Questions on nature of company:
1- What kind of products or services are provided by your company?
2- To the best of your knowledge, how many employees work at your company?
3- Do you think that male and female employees are treated differently by the bosses
in your company? Please explain your answer.
a- Were any promotions received by women employees?
b- Does your company provide additional training?
4- In the recent recession, were any employees terminated by your company? If yes,
would you say that the majority of those terminated were female employees?
Male employees? Or, was there roughly an equal mix of male and female
employees terminated?
(5) Questions about future perceptions:
1-Where do you see yourselves five years from now?
2-Would you like to earn some other related degree? If yes, why?
3- If you had a choice, would you have prefer to work in another occupation? Please
explain your answer.
a- For example, would you like to work in female dominated occupations such as
teaching?
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4- What do you think can be done to enhance the opportunities for advancement for
female workers in the software industry?
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Appendix B
Human Resource Manager
Systec Software
New Delhi
July 12, 2002
Dear Sir/madam,
I am a PhD student in Sociology at Kansas State University. I am
conducting research on female software professionals in India. The research examines the
work and educational experiences of female software workers. For the purpose of my
study, I would like to interview women working in the software division of your
company. I would like to assure you that the interviews are only for research purpose.
The information collected will be kept confidential, and not published without the
permission of the company. The name of the interviewees and the company will not be
revealed.
Thanking You.
2050 Jardine Drive, #28 Yours Sincerely,
Manhattan, KS, 66502
USA
Mani Pande
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Appendix C
Free Nodes
(F 1) //Free Nodes/marital status
(F 2) //Free Nodes/education
(F 3) //Free Nodes/engineering choice
(F 4) //Free Nodes/college experience
(F 5) //Free Nodes/math and physics
(F 6) //Free Nodes/engineering limitation
(F 7) //Free Nodes/nature of job
(F 8) //Free Nodes/numbers
(F 9) //Free Nodes/software
(F 10) //Free Nodes/skill training
(F 11) //Free Nodes/team work
(F 12) //Free Nodes/control over work
(F 13) //Free Nodes/job rotation
(F 14) //Free Nodes/salary satisfied
(F 15) //Free Nodes/job rank
(F 16) //Free Nodes/lower occupation
(F 17) //Free Nodes/work & women
(F 18) //Free Nodes/work & men
(F 19) //Free Nodes/women moving
(F 20) //Free Nodes/company products
(F 21) //Free Nodes/number in company
(F 22) //Free Nodes/treatment
(F 23) //Free Nodes/women promotion
(F 24) //Free Nodes/additional training
(F 25) //Free Nodes/recession
(F 26) //Free Nodes/five years
(F 27) //Free Nodes/another degree
(F 28) //Free Nodes/higher jobs
202
(F 29) //Free Nodes/another occupation
(F 30) //Free Nodes/enhance opportunity
(F 31) //Free Nodes/nontechnical
(F 32) //Free Nodes/supervise
(F 33) //Free Nodes/nonengineering choice
(F 34) //Free Nodes/experience
*** This node has no children.
203
Tree Nodes
Skill-training experiences of female software professionals
(1 1 1) /Education/Engineering/Reasons for choosing degree
(1 1 1 1) /Education/Engineering/Reasons for choosing degree/Like Math and Physics
(1 1 1 2) /Education/Engineering/Reasons for choosing degree/Sense of security
(1 1 1 3) /Education/Engineering/Reasons for choosing degree/Presence of engineer
(1 1 2) /Education/Engineering/College Experience
(1 1 3) /Education/Engineering/Higher studies
(1 1 3 1) /Education/Engineering/Higher studies/Future plan
(1 2) /Education/Nonengineering
(1 2 1) /Education/Nonengineering/Reasons for choosing degree
(1 2 1 1) /Education/Nonengineering/Reasons for choosing degree/Did not like Math
and Physics
(1 2 1 2) /Education/Nonengineering/Reasons for choosing degree/Struggles in school
(1 2 1 3) /Education/Nonengineering/Reasons for choosing degree/Interest in
management
(1 2 1 4) /Education/Nonengineering/Reasons for choosing degree/Parental pressure
(1 2 2) /Education/Nonengineering/College experience
(1 2 4) /Education/Nonengineering/Limitations
(1 2 4 1) /Education/Nonengineering/Limitations/Discrimination
(1 2 4 2) /Education/Nonengineering/Limitations/Higher studies
(1 2 4 2 1) /Education/Nonengineering/Limitations/Higher studies/Future plans
204
Work Experiences of female software professionals
(2) /Work
(2 1) /Work/Programming Language
(2 2) /Work/Ranking in job queue
(2 2 1) /Work/Ranking in job queue/Salary
(2 2 1 1) /Work/Ranking in job queue/Salary/Salary satisfaction
(2 2 2) /Work/Ranking in job queue/Job characteristics
(2 2 2 1) /Work/Ranking in job queue/Job characteristics/Nature of work
(2 2 2 2) /Work/Ranking in job queue/Job characteristics/Control over work
(2 2 2 3) /Work/Ranking in job queue/Job characteristics/Job rotation
(2 2 2 4) /Work/Ranking in job queue/Job characteristics/Mobility
(2 2 2 4 1) /Work/Ranking in job queue/Job characteristics/Mobility/Staying late
(2 2 2 4 2) /Work/Ranking in job queue/Job characteristics/Mobility/Balancing
family
(2 2 3) /Work/Ranking in job queue/Perceptions
(2 2 3 1) /Work/Ranking in job queue/Perceptions/Higher job perception
(2 2 3 2) /Work/Ranking in job queue/Perceptions/Lower job perception
(2 2 3 3) /Work/Ranking in job queue/Perceptions/Self-perception
(2 3) /Work/Women's work experience
(2 3 1) /Work/Women's work experience/Sex composition
(2 4) /Work/Comparison with female occupations
205