metadata: authors: 0: author: Stokenberga,Aiga
1: author: Ivarsson Molina,Linda Ellin Maria
2: author: Fulponi,Juan Ignacio
3: author: Dominguez Gonzalez,Karla
count: Argentina
docna: 0: docna: Leveraging Big Data to Understand Women’s
            Mobility in Buenos Aires
docty: Policy Research Working Paper
theme: Road Safety,Disaster Preparedness,Mitigation,Disaster Response and Recovery,Gender,Human Development and Gender,ICT,ICT Solutions,Public Transport,Environment and Natural Resource Management,Private Sector Development,Climate change,Urban and Rural Development,Adaptation,Disaster Risk Management,Urban Development
lndinstr: 0: lndinstr: Investment Project Financing
lndinstr_key: 1353855
abstracts: cdata!: While the travelers&#x2019; gender
            has not been a central consideration driving urban mobility
            planning, increasing evidence points to
            gender-differentiated mobility preferences and behaviors.
            This paper explores this topic in the context of the Buenos
            Aires Metropolitan Area, aiming to identify policy relevant
            differences between the mobility of women and men. It does
            so by leveraging mobile phone&#x2013;based data,
            combined with existing household travel survey data and an
            original large-scale interception survey implemented in late
            2021 and early 2022. The paper provides descriptive analysis
            of key spatial and temporal mobility patterns as well as
            implements statistical analysis to identify whether gender
            represented a key determinant of mode choice in the context
            of the pandemic. The analysis finds that women in the Buenos
            Aires Metropolitan Area travel less, tend to have shorter
            individual trips, and are much more likely than men to
            travel during off-peak hours, including due to
            disproportionately taking on &#x201c;care
            mobility&#x201d; responsibilities. In terms of mode
            choices, women represent the majority of public transport
            users and are more likely than men to say they would cycle.
            However, women&#x2019;s share among public transport
            users and their actual cycling and walking appear to be
            spatially correlated with, respectively, the availability of
            public transport services and dedicated, safe
            infrastructure. The travel responses to the pandemic
            documented in the original survey data also suggest that
            women are more likely than men to switch from public
            transport to private motorized modes as soon as their
            incomes allow them to unless appropriate health safety
            measures are put in place.
display_title: Leveraging Big Data to Understand
            Women’s Mobility in Buenos Aires
listing_relative_url: /research/2024/01/34234978/leveraging-big-data-understand-women’s-mobility-buenos-aires
topicv3: Transport m1326661 135,Big Data m1327333 661,Transport Policy m1327402 1826,Gender m1327621 615
publishtoextweb_dt: 2024-01-09T00:00:00Z
dois: 10.1596/1813-9450-10662
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 14
author_keys: 0: author_key: 963564
1: author_key: 1947959
2: author_key: 2238429
3: author_key: 1310440
model_extractions: Policy Research Working Paper 10662 Leveraging Big Data to Understand Women’s Mobility in Buenos Aires _Aiga Stokenberga_ _Ellin Ivarsson Juan Ignacio Fulponi Karla Dominguez Gonzalez_ Transport Global Practice January 2024
Policy Research Working Paper 10662

# **Abstract**

While the travelers’ gender has not been a central consideration driving urban mobility planning, increasing evidence points to gender-differentiated mobility preferences and behaviors. This paper explores this topic in the context of the Buenos Aires Metropolitan Area, aiming to identify policy relevant differences between the mobility of women and men. It does so by leveraging mobile phone–based data, combined with existing household travel survey data and an original large-scale interception survey implemented in late 2021 and early 2022. The paper provides descriptive analysis of key spatial and temporal mobility patterns as well as implements statistical analysis to identify whether gender represented a key determinant of mode choice in the context of the pandemic. The analysis finds that women in the Buenos Aires Metropolitan Area travel less, tend to have shorter individual trips, and are much more likely than men to travel during off-peak hours, including due to disproportionately taking on “care mobility” responsibilities. In terms of mode choices, women represent the majority of public transport users and are more likely than men to say they would cycle. However, women’s share among public transport users and their actual cycling and walking appear to be spatially correlated with, respectively, the availability of public transport services and dedicated, safe infrastructure. The travel responses to the pandemic documented in the original survey data also suggest that women are more likely than men to switch from public transport to private motorized modes as soon as their incomes allow them to unless appropriate health safety measures are put in place.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at astokenberga@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Leveraging Big Data to Understand Women’s Mobility in Buenos Aires

Aiga Stokenberga,<sup>1</sup> Ellin Ivarsson, Juan Ignacio Fulponi, Karla Dominguez Gonzalez JEL: R40, R41, R42 Keywords: Gender, mobility, urban transport, big data

> 1Corresponding author: <u>astokenberga@worldbank.org.</u> The authors would like to acknowledge the technical contributions to the study provided by Javier Burrieza, Luis Willumsen, María Fernanda Ortiz, Miguel Picornell, and Ricardo Herranz (CDR data analysis) and by Javier Morales Sarriera, Lucila Lavezzolo, Rodrigo Hernan Martinez, Maximiliano Daniel Roca, and Roberto Agosta (implementation of interception survey with current car and motorcycle users). The authors are also grateful for the contributions to the study provided by the Secretariat of Transportation and Public Works of the City of Buenos Aires. The authors would like to acknowledge the generous funding received from the Quality Infrastructure Investment Partnership (QII).
# **1. INTRODUCTION AND REVIEW OF THE LITERATURE**

**Transport planning should not be gender neutral** (Dominguez Gonzalez _et al_ ., 2020). Even if transport related improvements seem to benefit women and men equally, women and men experience transport and infrastructure differently in terms of affordability, acceptability, and availability (Alam _et al_ ., 2022). Lack of safe transport can reduce women’s access to economic opportunities (International Labor Organization, 2017). For example, global evidence suggests that the lack of proper infrastructure heavily influences women’s decision to cycle (Garrard _et al_ ., 2008), contributing to gender inequity in access to jobs and other opportunities in parts of the urban areas where such infrastructure is unavailable. In addition, there is a recognition that there are other, non-transport-related barriers that can constrain women’s mobility, such as social norms regarding gender, unequal division of household responsibilities, and other more personal elements influencing women’s agency, such as self-efficacy and aspirations (Dominguez Gonzalez _et al_ ., 2020). Unfortunately, regardless of these differences, transport planning has mainly focused on unidirectional travel from home to work, paying less attention to, for instance, shorter trips associated with “mobility of care”, which are typically carried out by women (Sánchez de Madariaga, 2013).

**Gender influences the decision to travel in the first place and, specifically, mode choices and trip patterns.** It was not until the last decade that data on mobility in Latin America started to be genderdisaggregated in origin-destination surveys, for instance, in Chile, Colombia, Uruguay, and Mexico City. Evidence on women’s mobility from Latin America seems to resemble that from some developed countries in terms of modal choice, distance, and travel purpose. Data shows that women travel shorter distances, have more complex multimodal trips, and walk and rely on public transport more than men. Women have less access to cars and motorcycles, while the lack of proper infrastructure widens the gender gap in the usage of bicycles. Existing data from San Jose, Costa Rica, and Mexico City, Mexico, support these findings. Gender differences are also prevalent in terms of travel time. Except for Bogota (Colombia) and Montevideo (Uruguay), cities in Latin America show longer travel times for men, suggesting that men travel longer distances and that women tend to prefer to, or are forced to, work from home or close to home, being constrained in their access to economic opportunities (Perez, 2019).

**Women’s mobility constraints can be disproportionally affected by external shocks.** The available evidence on the impact of the COVID-19 pandemic on mobility reveals that it affected women’s mobility through less economic capacity for transport and less time available for journeys (González-Sánchez _et al_ ., 2021). The pandemic and the related restrictions mainly impacted the economic activities where women have higher representation, such as the hospitality industry and low-paid and informal jobs, which led women to either stop working altogether or turn to part-time jobs. At the same time, women’s mobility seems to have been higher during the pandemic than men’s, as they are disproportionately more employed in sectors that require physical presence such as health and social services. Based on mobile data analysis from Barcelona, low-income people, the majority of them women, were not able to reduce their travel due to being employed as frontline workers (Mejía-Dorantes _et al_ ., 2021). This “mandatory” travel that particularly affected women also includes household related activities (e.g., medical visits, shopping), which increased during the pandemic. In addition, women bearing higher responsibilities at home, derived, for instance, from home schooling, had even less time for traveling, with this effect being even more predominant for single parents (González-Sánchez _et al_ ., 2021) _._
**The current paper studies recent mobility patterns in the Buenos Aires Metropolitan Area (** **_AMBA for its Spanish acronym_ ) – the largest urbanized area in Argentina and one of the largest in Latin America – to propose specific areas of transport policy that can target women’s travel needs.** To do so, the paper leverages _big data_ and complementary data to identify transport patterns that are distinct between men and women, including in terms of the spatial patterns, distances traveled, the temporal distribution of trips throughout the day, and mode choices. Second, the paper analyzes the gender-differentiated changes in mobility patterns associated with the pandemic, a significant shock that affected both transport supply and travel preferences, some of which have persisted even three years later. Understanding these gender-differentiated responses is important for developing policy solutions that can steer the mobility choices towards more sustainable modes if a similar shock were to occur in the future.

# **2. METHODOLOGY**

**The analysis takes advantage of a number of innovative data sources and methodologies that can provide complementary insights.** These include, first and foremost, Call Detail Record (CDR) – mobile phone – data, that are increasingly recognized as a valuable resource for transport planning. CDR data are observed for every call/text made or received with a timestamp and GPS coordinates of the tower from where the call is made. Such data allows measuring mobility across entire cities or even countries at very fine spatial and temporal levels. The analysis in the current paper is based on CDR data obtained for three different periods (October 2019, October 2020, and October 2021) from a major mobile phone service operator in Argentina, representing 37 percent of the national market, with over 8 million unique subscribers. The methodology fused CDR data with data from public transport validations (the SUBTE smart card in Buenos Aires) and publicly available household mobility survey data from 2010/2018 - _Encuestas de Movilidad Domiciliaria_ (ENMODO). In addition, the analysis incorporates data from a largescale interception survey and a stated-preference survey with private motorized transport users across AMBA implemented by the study team, respectively, in November-December 2021 and February- March 2022.

**The mobility indicators are calculated for personal mobility travel, filtering out from the origindestination (OD) matrices the trips associated with professional mobility (e.g., taxi drivers)** . The methodology for identifying professional mobility and overall methodology for calculating OD matrices generally follows other studies that have leveraged CDR data for mobility analysis (see, e.g. Bachir _et al_ ., 2019; Bayir _et al_ ., 2010). The analysis is based on a total of 2,010 travel zones across the metropolitan area, of which 534 are in central Buenos Aires (Autonomous City of Buenos Aires, or CABA) and 1,476 in the Province of Buenos Aires (PBA). The CDR data sourced from the mobile phone operator was processed into OD matrices by following a verification process and developing algorithms that disaggregate the matrices into different modes (i.e., non-motorized, public transport, and private motorized), segment them into trip purposes (i.e., home-based trips to work, home-based trips to other activities, and nonhome based trips), and by gender and age (using anonymized client data available from the mobile operator, with gender-specific expansion factors applied based on the available household travel survey data for AMBA).

**The assessment of the mobility patterns distinguishes by socioeconomic (income) group.** This is done based on the spatial association of the identified travelers’ home locations and the socioeconomic
indicators available for the specific neighborhood blocs, specifically, the share of households with “unsatisfied basic needs” ( _Necesidades Básicas Insatisfechas_ , NBI) available from the National Institute of Statistics and Census of Argentina (INDEC) at the census radius level for most of the study area. Additional details on the methodology used and the broader set of results obtained are available in Stokenberga _et al_ . (2023).

**Complementing the CDR-based analysis, the study team implemented two sets of field surveys in late 2021 and early 2022** . These included an overall interception survey with 20,306 car and motorcycle users to better understand the overall motivations for private vehicle use (including the considerations associated with the COVID-19 pandemic), followed by a phone-based stated preference survey with 600 car and motorcycle users to identify their willingness to switch to alternative modes depending on factors such as hypothetical changes in travel times and costs. This data collection effort was conducted in close collaboration with the Government or the City of Buenos Aires and was informed by its parallel data collection activities (e.g., vehicle counts at pre-determined points across the urban area). The survey randomly intercepted drivers of motorized vehicles that stopped at 25 predetermined points (service stations and parking lots), and the final sampling ensured that the survey sample meets predetermined (based on previous travel surveys) quotas of women and men that travel by car or by motorcycle and that live in CABA versus PBA. The sampling was adjusted to the approximate volume of traffic at the mentioned points and considered peak and off-peak hours based on data provided by the Secretariat of Transportation and Public Works of CABA.

**Based on the collected data, the study team analyzed overall differences in mobility behaviors and stated preferences** between male and female car and motorcycle users and implemented regression analysis to estimate the importance of various personal and trip attributes (including gender) in determining the odds of switching to private modes only since the start of the pandemic. Specifically, the analysis applies a logistic regression model with the dependent variable being a binary yes/no of whether the person is a new user of private motorized modes as compared to the pre-pandemic period.

**Finally, where relevant, the analysis highlights results based on the most recent public household travel survey** for the Buenos Aires metropolitan region (ENMODO 2018), whose official results were published by the government in 2022.

# **3. MAIN FINDINGS**

**The first key finding of the analysis is that women in AMBA tend to travel less, even though their trips may be organized into more complex trip chains with multiple trip purposes and stops** . According to the CDR-based analysis, in 2021, men in AMBA made about 19.3 million trips per day, compared to 18.8 million trips by women. However, while the total number of trips by men versus women was higher in the younger age groups (up to 44 years of age), among the older age groups the number of trips made by women was higher, especially in the >64 age group where women were estimated to make 2.1 million daily trips compared to 1.7 million among men (Figure 1). Of course, the _total_ trips may not represent the gender specificity of travel patterns perfectly given that the number of men and women traveling differs in the first place – especially in the older age group the number of women in AMBA significantly exceeds
the number of men. In terms of trip generation _rates_ – or number of trips made per person<sup>2</sup> per day – men in AMBA are estimated to make an average of 2.63 trips per day each, compared to 2.42 trips among women. The gender imbalance in trip generation rates is consistently observed across all age groups and is the largest in the eldest category, where men make about 18 percent more trips per day per person compared to women.

**_Figure 1_** _: Total trips and trip generation rates on a weekday in October 2021, by gender and age_

<!-- Start of picture text --> A. Total trips per day B. Trip generation rate<br>Men Women Men Women 3.2<br>5,000,000 2.9<br>2.7<br>2.5 2.6<br>4,000,000 2.4 2.3 2.4<br>2.2 2.2<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br><18 18-28 29-44 45-63 >64 <18 18-28 29-44 45-63 >64<br><!-- End of picture text -->

# **Secondly, the CDR analysis also suggests that women have shorter individual trips compared to men.**

About 66 percent of the trips made by women in AMBA according to the October 2021 data are shorter than 5 kilometers, compared to 61 percent of men’s trips. The higher distances traveled by men compared to women persist across the income spectrum although the difference declines somewhat at the higher income deciles. The average trip distance differences between men and women are also relatively consistent across travel modes as reported in the ENMODO 2018 household travel survey although are more distinct in the case of travel by car or by public transport than in the case of the non-motorized modes. Similarly, data from a survey implemented by the team in AMBA with current private motorized vehicle users broadly corroborates this pattern: 9.2 percent of women have commutes or other types of “typical” trips that last less than 10 minutes, compared to 5.2 percent of men.

# **Gender norms may play a role in women’s travel decisions in AMBA and, specifically, the distances**

**traveled** . Even if the analyzed data does not explicitly ask questions about intrahousehold resource allocation between women and men, from the ENMODO 2018 household travel survey it can be inferred that gender roles contribute to defining women’s decisions of when are how far to travel. The more family members there are in the household, the farther men seem to travel, possibly to access better economic opportunities, while for women with one family member the distance decreases once they add an additional one.

**Thirdly, public transport appears to serve more women than men, especially in northern AMBA.** Women constitute the majority of public transport users, with the total number of daily public transport trips made by women (4.07 million) in October 2021 being 13 percent higher than those made by men (3.59 million), and public transport represents a greater share in women’s overall mobility compared to men’s

> 2 There are a total of 7.34 million men and 7.75 million women in AMBA. The largest difference in population size by gender is in the older age group (64 and older), with nearly 990,000 women but only about 650,000 men.
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**The greater reduction in mobility among women than men during the pandemic was partly due to women’s greater reliance on public transport that saw its services reduced for several months** . However, the disproportionately greater shift away from public transport among women – with a drop in the number of trips of about 76 percent compared to October 2019, against 69 percent among men (Figure 6) – was also driven by their greater aversion to the risk of exposure to the virus, which was a bigger concern when using public transport compared to other modes. For example, the survey implemented by the team with current car and motorized vehicle users suggests that about 12 percent of them had previously used another mode for that same trip (11 percent of men and nearly 17 percent of women), and about 77 percent of these had previously used public transport. When asked about the main factors determining mode choice, the risk of COVID-19 was much more salient for women than men, with over 40 percent of women naming it the single most important factor determining travel choices, compared to less than 32 percent of men. On the other hand, comfort and travel time were more likely to be named as the most important travel attributes by men than women, while the importance of travel cost is roughly similar for men and women, with 12 percent of women and 11 percent of men ranking it as the most important attribute.

**_Figure 6:_** _Change in the number of trips in October 2020 vs. October 2019 (%), by mode and gender_

<!-- Start of picture text --> NMT Private motorized Public transport Total<br>10<br>0<br>Women<br>-10<br>-20 Men<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br><!-- End of picture text -->

**Similarly, a statistical model developed using the collected survey data suggests that the odds of becoming a** **_new_ user of private motorized vehicles just since the onset of pandemic were significantly higher for women – by about 31 percent compared to men** . The estimated odds of being a new user of a car/van or motorcycle are also higher for people with at least some university education and people who had at least some type of change in their occupation status (e.g., work becoming remote or hybrid). The odds decrease with the person’s age, meaning that the older the respondent, the more likely he or she was already using the private motorized vehicle already before the pandemic as opposed to becoming a new user. The odds of recently switching to private motorized modes are also higher if the start of the trip is in CABA rather than in PBA, likely explained by the higher financial ability of households residing in CABA. As people already before the pandemic relied disproportionately more on private motorized modes to go on trips for ad-hoc reasons such as recreation or to see a doctor, the odds of _switching_ to those modes since the start of the pandemic were lower compared to other (more regular) types of trips such as to work or school. The results are also intuitive regarding the statistical association with the person’s stated most important travel attributes: the odds of becoming a new user of private motorized modes are
statistically significantly higher if the person stated the risk of contracting the virus as the single most important travel attribute.

**_Table 1_** _: Factors statistically associated with becoming a new user of private motorized modes since the start of the pandemic (among people who currently use private motorized modes)_

|**Log likelihood = -6838.1909**|**Num**<br>|**ber of obs. = 1**<br>|**8,269**<br>|
|---|---|---|---|
||**Odds Ratio**|**Std. Err.**|**P>|z|**|
|**Gender(1=male)**|0.6856|0.0375|0.000***|
|**Age **|0.9851|0.0072|0.000 ***|
|**Education(1=some university)**|1.4394|0.0718|0.000***|
|**Change in employment status sincepre-pandemic**||||
|**Became virtual**|1.6548|0.1160|0.000 ***|
|**Became hybrid**|1.4794|0.0904|0.000 ***|
|**Became unemployed/stopped working**|2.2180|0.1941|0.000 ***|
|**Changedjobs**|2.5602|0.1991|0.000 ***|
|**No response**|1.4359|0.1542|0.001 **|
|**Trip start location(1=CABA)**|1.2763|0.0736|0.000 ***|
|**Recreation trip (1=yes)**|0.7013|0.0779|0.001 **|
|**Social trip (1=yes)**|0.7988|0.0671|0.008 **|
|**Health trip (1=yes)**|0.5445|0.0746|0.000 ***|
|**Most important factor affecting travel choices**||||
|**Comfort**|0.9519|0.0793|0.554|
|**Travel time**|1.0112|0.0826|0.892|
|**Risk of COVID-19 infection**|1.3797|0.1084|0.000 ***|
|**No response**|0.2017|0.2059|0.117|
|**Constant**|0.2035|0.0247|0.000 ***|

_Note: Constant estimates baseline odds_

**The decline in public transport use during the pandemic was particularly steep among higher income women** (and men) who could reduce their travel more because of higher job flexibility and opportunities to tele-work or study from home to stay safe. Meanwhile, lower-income residents had to keep traveling to get to work. Compared to low-income women, high-income women were also more likely to increase travel by walking and cycling, at least partly due to the concentration of higher income groups in CABA where non-motorized transport infrastructure is better – similarly to patterns found in other major urbanized areas such as Barcelona (Mejía-Dorantes _et al_ ., 2021) _._

# **4. DISCUSSION AND CONCLUSION**

**Transport planning and investment decisions rarely take into consideration gender-based mobility needs, especially at high spatial and temporal resolution.** Therefore, many publicly funded transport projects do not benefit everyone equally. In particular, they generally tend to benefit the fraction of the population – typically men from medium and high-income backgrounds (Department for Transport, 2022) – who travel long distances and use highways or more expensive rail journeys to access job opportunities further afield.
**Understanding how women’s travel patterns differ, due to greater overall risk aversion (Blais and Weber, 2001) and sensitivity to safety issues (Akar, 2013) as well as lower access to personal vehicles (Duchene, 2011), among other factors, is important for the purposes of transport system planning.** Moreover, it is important to know how these differences play out at high spatial and temporal detail. Leveraging new sources of data can allow transport planners to understand the aspects in which mobility is the least gender neutral and act on this knowledge to make transport systems work for all. _Big data_ – high volume and frequency data generated by mobile phones, applications, and similar sources – can become a core part of the decision-making toolkit. It can improve the focus of the “gender lens” that planners apply to designing transport solutions and thus ensure that they truly serve everyone. Further, taking the gender-based differences in mobility patterns into consideration during transport project appraisal and economic analysis has the potential to transform how the investment decisions are being made and enhance their impact.

**The results presented in this paper confirm that women and men in AMBA have different travel patterns and needs** , and also during the recent pandemic showed differences in how their travel behaviors changed. Women in AMBA travel less, tend to have shorter individual trips, and are much more likely than men to travel during off-peak hours, including due to disproportionately taking on “care mobility” responsibilities. In terms of mode choices, women represent the majority of public transport users and are more likely than men to say they would cycle. However, women’s share among public transport users and their actual cycling and walking appear to be spatially correlated with, respectively, the availability of public transport services and dedicated, safe infrastructure. As demonstrated by their modal choices during the recent pandemic, women in AMBA also appear to be more concerned with health safety than men and therefore more likely than men to switch from public transport to private motorized modes as soon as their incomes allow them to unless appropriate health safety measures are put in place.

**Several policy recommendations emerge in response to the paper’s findings for better targeting mobility solutions to AMBA’s women’s needs.** An increased focus on micro-mobility could be particularly effective at serving the mobility needs of women, given their shorter average travel distances and greater presupposition for cycling if adequate and safe infrastructure were available. Similarly, women’s tendency to travel during off-peak hours has implications for public transport route planning and schedules, to ensure that women’s travel needs during the off-peak hours are adequately met. For example, on-demand public transport services may be a solution to efficiently address women’s travel needs during the offpeak hours. Increasingly mixed land use planning and transit-oriented development, including generation of more affordable housing near transit stations, can be an opportunity for bringing services closer to women. Similarly, companies could make transport arrangements for employees to reduce their time burden – which tends to be particularly high for women. Finally, the observed gender-differentiated mobility responses to the pandemic suggest that, while women in AMBA still represent the majority of public transport users, they are more likely to switch to private motorized modes in the presence of a significant public health shock. These lessons learned from the pandemic can be put to use to design a more sustainable and gender-inclusive public transport system to prepare for the eventuality of a future event of similar magnitude.