By Akshima Tejas Ghate Presented at Transforming Transportation 2017

1. www.TransformingTransportation.org
Increasing formal employment of women in Indian cities –
Implications for mobility and low carbon transport
Akshima Tejas Ghate
Presented at Transforming Transportation 2017

2. Increasing formal employment of
women in Indian cities –
Implications for mobility and low carbon transport
Akshima Tejas Ghate
Fellow, The Energy and Resources Institute (TERI), New Delhi
PhD Scholar, Indian Institute of Technology (IIT), Delhi
Presentation @ Transforming Transportation 2017
January 12, 2017, Washington DC

3. Mobility in Indian cities is changing
Connaught Place, Delhi, late 1990s Connaught Place, Delhi, 2010
Sources of images: http://www.trekearth.com/gallery/Asia/India/East/West_Bengal/Delhi/photo1452143.htm
http://media.gettyimages.com/photos/massive-traffic-jam-can-be-seen-at-the-connaught-place-area-in-new-picture-id91548677 2

4. Mobility in Indian cities is changing
* All India numbers; Remaining numbers are for urban areas (sample of cities) Sources of data: CSTEP and IUT (2014); Ministry of Road Transport and Highways, Govt. of India (Various Years) 3
2007 2011
Per Capita Trip Rate 1.06 1.20
Motorized Trip length 6.0 km 8.2 km
Walk and NMT share 45% 36%
Public transport share 20% 16%
Cars and two wheelers* 81 million 121 million
Almost everything about mobility in Indian Cities is changing…
And my hypothesis is that this change is being shaped by the
gendered changes in mobility, especially the changing
mobility of women in Indian cities

5. Gendered changes in mobility – trends observed
internationally
Global experience telling us that the mobility of women is increasing and
changing while that of men is almost stagnant, same and even declining
64.8
87.1
74.4
86.9
Women Men
Daily travel time (minutes) - Paris region
1976-77 2001-02
Dupont and Krakutovski (2011)
Access to cars, driving licenses among women is increasing (Fan, 2015; Hurez and
Richer, 2014; Kalter et al., 2011; Polk, 2009) 4
Key driver of this change –
Increasing employment
of women

6. Growth in employment of women faster than men
Source of data in graph: Census of India (2011 and 2001) 5
13%
4%
107%
44%
34%
63%
30%
173%
80%
69%
Greater Mumbai Delhi Bangalore Pune Urban India (Total)
Decadal growth rate (2001 to 2011) - Main workers
Males Females

7. Implications for future mobility, especially low-carbon
transportation goals
6
Females’ travel patterns, in general, have been found to be more sustainable and
low carbon than that of males’ (Hanson, 2010; Kronsell et al., 2014; Polk, 2009, 2003)
But, is by choice?
Need to-
- go beyond the established finding that women’s travel patterns are
more low-carbon than men
- Understand the impact of employment on mobility choices that women
make and the reasons for it

8. My research focus
Research focuses on understanding -
• Changes in mobility of women on account of their engagement in economic activities
• Differences in travel behaviour of employed women and men
• Differences in travel-related CO2 emissions of women and men
Source of data in graph: Census of India (2011 and 2001 ) Sources of images: http://smartcity.eletsonline.com/wp-content/uploads/2014/12/Transport-Outside.jpg, http://images.indianexpress.com/2014/11/helmet-drive-759.jpg
Delhi Pune
39%
21%
12%
55%
17%
21%
Share of households
owning TWs
Share of households
owning cars
Female WFPR
Delhi UA
Pune UA
Case study cities
7

9. Research approach
Clear learning from literature –
Important to consider the activity patterns, time use and travel
in conjunction in gender and mobility studies
Activity-based travel behaviour research approach
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10. Model structure
Data requirement -
- Single day activity travel diary (for a working/week day)
- Sample size – 1200 each in both cities
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11. Key Findings
Secondary and primary data analysis
10

12. 23%
36%
20%
9%
6%
3% 3%
29%
34%
19%
9%
4% 3% 2%
0-1 2-5 6-10 11-20 21-30 31-50 51+
21%
35%
79%
65%
FINDINGS – ‘Work from Home’ Vs. ‘Travel to Work’
Trip length km
- Share of women working from home higher
- Share of very short trips (0-1 km) higher amongst women
Analysis for urban India; Source of data: Census of India (2011)
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13. FINDINGS – Work trip lengths
T-Test results
- Null hypothesis rejected
- Statistically significant
difference in work trip
lengths between females and
males
Analysis for urban India; Source of data: Census of India (2011) 12

14. FINDINGS – Modes used to commute to work
27%
45%
20%
4%
24%
11%
5%
5%
4%
6%
14%
22%
6%
5%
PT – 34%
PT – 24%Personal modes – 29%
Personal modes – 16%
Analysis for urban India; Source of data: Census of India (2011)
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15. FINDINGS – Modes used by females to commute to
work in case study cities
Source of data: Census of India (2011)
Delhi
Pune
37%
20%
2%
11%
24%
37%
6%
7%
4%
5%
24%
18%
2%
2%
Females
Males
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32%
26%
2%
12%
5%
19%
18%
12%
5%
2%
32%
25%
5%
3%
Females
Males

16. FINDINGS – Correlation between increase in female
employment and personal mobility
Growth in Cars + TWs and female workforce - Strong
positive correlation (0.95)
Correlation stronger in case of TWs(0.94) than cars (0.27)
0
20000
40000
60000
80000
100000
120000
140000
1981 1991 2001 2011
Numbers(inthousands)
Growth - Female workers, Cars, TWs, Cars+TWs
Main workers_female Cars Two wheelers Cars+ TWs
0
20000
40000
60000
80000
100000
1991 2001 2011
Numbers(inthousands)
Growth - Female workers,
Female Driving licenses
Main workers_female Driving licenses_female
Growth in female workforce and driving
licenses (females) - Perfect positive
correlation (1.0)
r = 1
r = 0.95
r = 0.94
r = 0.27
15Source of data: Census of India (2011); Ministry of Road Transport and Highways, Govt. of India (Various years)

17. Recommendations
16

18. Changes in
social
structures,
especially
gendered
changes
Population
growth
Urbanization
Economic
growth
Income
growth
Conventional
perspective
New
perspective
If we are serious about sustainable and low carbon mobility, then we
need to go beyond the conventional mobility approach that is
insensitive to gendered changes occurring in mobility
Need a Gendered perspective to mobility
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19. What does this imply? (1)
In the SHORT TERM –
Focus on augmenting & improving walking and public transport infrastructure
and services to cater to specific mobility needs of women
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Walking infrastructure - Availability, Security, Universal accessibility
Public transport services - Security to and at stops, In-vehicle security
- Scheduling and routing of services
- Universal accessibility
- Women-only services
Intermediate public transport - Shared mobility/ride-hailing/women-only services
- Use of technology for ensuring security

20. What does this imply? (2)
In the SHORT TERM –
Focus on soft policies that encourage increased female participation in labour
force and give them options to choose low-carbon mobility modes
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- Flexible working hours
- Work from Home
- Mobility solutions provided by employers
- Regular services
- Services for odd-hour travel
- Child-care facilities at/near workplace

21. In the MEDIUM & LONG TERM –
Relook at transport planning methodologies and models to capture
gendered changes/differences in mobility
- Move away from-
- single purpose trips to multi-purpose trips
- over emphasis on Journey to Work
- Consider-
- interactions between activities, time use and travel
- gendered disaggregation of travel variables
- scheduling of trips
- Trip sharing and escorting in households
- gendered constraints related to access to personal vehicles, allocation of household travel
budget, time poverty of women
What does this imply? (3)
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22. Adopt a gendered approach to mobility to enable
achievement of sustainable and low carbon mobility goals
Thank you
Acknowledgement – Lee Schipper Memorial Scholarship for facilitating and supporting my research
akshima@teri.res.in
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