metadata: authors: 0: author: Chen,Yutong
1: author: Cosar,Kerem
2: author: Ghose,Devaki
3: author: Mahendru,Shirish
4: author: Sekhri,Sheetal
count: India
docna: 0: docna: Gender-Specific Transportation Costs and
            Female Time Use : Evidence from India’s Pink Slip Program
docty: Policy Research Working Paper
abstracts: cdata!: Reducing gender-specific commuting
            barriers in developing countries has complex effects on
            women&#x2019;s labor dynamics. The paper studies a
            program that offers free bus rides for women in several
            Indian states (the Pink Slip program) using a synthetic
            difference-in-differences approach to shed light on labor
            supply and time use decisions of women. It observes
            decreased bus expenses and time saved on travel. Skilled
            employed women increase labor supply, while low-skilled
            married women shift focus to household chores. Unemployed
            women intensify job searches, yet overall employment rates
            remain unchanged. The findings highlight that alleviating
            commuting costs does not uniformly boost women&#x2019;s
            labor participation, as gender roles and societal norms
            continue to shape outcomes.
display_title: Gender-Specific Transportation Costs
            and Female Time Use : Evidence from India’s Pink Slip Program
listing_relative_url: /research/2024/02/34256684/gender-specific-transportation-costs-female-time-use-evidence-india’s-pink-slip-program
topicv3: Transport Economics m1326662 162,Gender and Economic Empowerment m1327622 911,Labor Supply m1504055 2751,Female Labor Market m1504222 2950
publishtoextweb_dt: 2024-02-12T00:00:00Z
dois: 10.1596/1813-9450-10701
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 83
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1947054
3: author_key: NA
4: author_key: 1345256
model_extractions: Policy Research Working Paper 10701

# Gender-Specific Transportation Costs and Female Time Use

Evidence from India’s Pink Slip Program _Yutong Chen Kerem Cosar Devaki Ghose Shirish Mahendru Sheetal Sekhri_ Development Economics Development Research Group February 2024
Policy Research Working Paper 10701

# **Abstract**

Reducing gender-specific commuting barriers in developing countries has complex effects on women’s labor dynamics. The paper studies a program that offers free bus rides for women in several Indian states (the Pink Slip program) using a synthetic difference-in-differences approach to shed light on labor supply and time use decisions of women. It observes decreased bus expenses and time saved on travel.

Skilled employed women increase labor supply, while lowskilled married women shift focus to household chores. Unemployed women intensify job searches, yet overall employment rates remain unchanged. The findings highlight that alleviating commuting costs does not uniformly boost women’s labor participation, as gender roles and societal norms continue to shape outcomes.

This paper is a product of the Development Research Group, Development Economics. 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 dghose@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
# **Gender-Specific Transportation Costs and Female Time Use: Evidence from India’s** **_Pink Slip_ Program**<sup>*</sup>

Yutong Chen<sup>†</sup> Kerem Cos ̧ar<sup>‡</sup> Devaki Ghose<sup>§</sup> Shirish Mahendru<sup>¶</sup> Sheetal Sekhri<sup>||</sup>

Originally published in the Policy Research Working Paper Series on _February 2024._ This version is updated on _May 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords** : Transport, Gender, Time-Use, Female Labor Force Participation **JEL codes** : J16, J22, R41

> *We thank the University of Virginia Department of Economics for financial support. For helpful comments and feedback, we thank S Anukriti and the World Bank Office of the Chief Economist, South Asia Region. The authors gratefully acknowledge support by the Umbrella Facility for Trade, which is funded by the governments of The Netherlands, Norway, Switzerland, Sweden and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are solely those of the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, or the Executive Directors or the countries they represent. All errors are our responsibility.

> † `yc3jk@virginia.edu` (University of Virginia)

> ‡ `kerem.cosar@virginia.edu` (University of Virginia, CEPR, CESifo, NBER)

> § `dghose@worldbank.org` (Development Economics Research Group, The World Bank)

> ¶ `shirish.mahendru@giz.de` (Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India)

> || `ssekhri@virginia.edu` (University of Virginia)
# **1 Introduction**

The gender disparity in commuting barriers is pervasive, affecting women in developing and developed nations. Recent OECD findings reveal a substantial gender commuting gap, with men averaging 33.4 minutes per day compared to women’s 21.9 minutes, indicating a 31.1% difference (OECD, 2016). Developing countries exhibit even more pronounced patterns; Indian women, on average, spend a mere 8 minutes daily on employment-related travel, compared to men’s 36 minutes.<sup>1</sup> ILO (2017) reports that lack of transportation decreases women’s probability of participating in the labor market by 16.5 percentage points among developing countries. Given the established inof barriers on women’s labor a natural arises: can fluence commuting supply, inquiry reducing these barriers influence women’s labor supply decisions? Whether policies directly addressing commuting costs would help increase female labor supply or not is far from obvious: social norms regarding household chores and traveling alone also significantly impact female labor supply, as evidenced by prior studies (Fanning Madden, 1981; Turner and Niemeier, 1997; Lee and McDonald, 2003; Abe, 2011; McQuaid and Chen, 2012).

Our study sheds light on the trade-offs women face when commuting cost is reduced by exploiting the rollout of a free busing scheme, the _Pink Slip Program_ , in two states of India. India is a pertinent setting for exploring this issue. There is an overwhelming gender gap in commuting time and modes used. The 2011 Census of India reveals that 30.2 % of women travel to work on foot, and only 24.6 % use any transportation, highlighting the limited access to transportation options for many women. In contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>2</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>3</sup> Several media

> 1India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 2World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 3The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

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reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>4</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 4The weights from the SDID approach are used in all survey data-based analyses at the individual level.

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in to the We scrutinize these nuanced effects three response policy change. along key dimensions: employment status, marital status, and skill levels. Opposing impacts on employed and unemployed women result in an overall null effect. Marital status and skill-level significantly influence time allocation, with skilled employed women leveraging commuting time savings to increase labor hours. Conversely, unmarried unemployed women intensify job searches, while short-term employment outcomes don’t reflect immediate positive shifts, emphasizing additional constraints to finding employment by women.

In contrast to skilled employed women, low-skilled married workers redirect commuting time towards household chores, reducing labor hours. We propose a model that implicates household reallocation of activities as a mechanism for this shift. The model illustrates that indivisible chores requiring discrete time can result in intra-household reallocation, where the re-optimization is influenced by the gender wage gap. In support of this theory, we observe that married men alter their time use reciprocally. Employed ones increased the labor supply, and the unemployed intensified their job searches. Hence, our main findings indicate that the free bus initiative benefits skilled and unmarried women but the low-skilled married women respond by reducing their labor and increasing their time devoted to household chores. Consistent with restrictive gender norms discussed by Jayachandran (2021) and Dinkelman and Ngai (2022), low-skill married women’s labor outcomes become worse, if anything. Without a deeper understanding of these countervailing effects on different populations of women, observing an overall null effect could lead to spurious policy recommendations. We argue that transportation subsidies to women, which might seem to have redistributive merit, do not necessarily help improve the labor market outcomes of all women. Only skilled employed women, who constitute a small share of the workforce, benefit from these types of programs in the short-run.

ir The second strand of literature we connect to analyzes the effects of reductions in commute times, often by providing transit subsidies in randomized control experiments, on job search and employment creation (Franklin, 2018; Abebe et al., 2016; Phillips, 2014; Moreno-Monroy and Posada, 2018). The dominant finding in this literature is that reductions in commuting costs increase job search intensity and employment. We demonstrate that social norms in countries can undermine the effects of that re- developing policies duce commuting costs, especially for women.

The rest of the is as follows. Section D describes the paper organized study setting. Section E describes the datasets. Section F outlines the to es- empirical methodology timate the impacts of reductions in commuting costs. Section H presents the results.

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Section J provides concluding remarks.

# **2 Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early response: just 20 days after the scheme’s launch, female daily ridership in DTC and cluster buses increased from 33% to 44%.<sup>5</sup>

Spurred by the good reception of the initiative in Delhi, on April 1 and May 7, 2021, Punjab and Tamil Nadu implemented free bus ride schemes for women in their states, allowing free travel in government-owned public buses.<sup>6</sup> From July 2021 to March 2022,

> 5https://shorturl.at/cAFMT, last accessed on May 13, 2024.

> 6In Punjab, these include PEPSU Road Transport Corporation (PRTC), PUNBUS, Punjab Roadways Buses, and City Bus Services, but did not include AC buses, Volvo Buses, and HVAC Buses (Express, 2021) In Tamil Nadu, the free ride scheme includes tickets for the Tamil Nadu State Transport Corporation (TNSTC) ordinary city buses.

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the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **3 Data**

## **3.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>7</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 7The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

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and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **3.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022). This survey is specifically designed to evaluate changes in bus usage among women in Delhi following the implementation of the _Pink Slip_ scheme. The survey collected responses from 2,025 women, of which 1,294 had been using the bus before the scheme’s introduction; we refer to this group as “continuous users.” Additionally, 231 respondents who began using buses after the scheme’s introduction are termed “new users.” The remaining 500 respondents who did not use buses before or after the scheme are labeled as “non-users.” The was selected at attractions and across sample randomly major generation points 6
Delhi, as shown in Appendix Figure C1.<sup>8</sup> Appendix Table D5 provides some summary statistics of the survey sample. Compared to non-users, bus users (both new and continuous) are predominantly aged between 31 and 40, more often homemakers, more likely to reside in households with a monthly income exceeding |20,000, and less inclined to travel for work. Given these differences, we constructed a comparable sample of nonusers, new users, and continuous users using propensity score matching. We match women on the following variables: age, occupation,<sup>9</sup> total average monthly household income and expenditure on travel, ownership of private vehicles, and driving knowledge.

Our study sample of the Delhi primary survey is the matched sample ( _n_ = 1, 290) consisting of 184 non-users, 182 new users, and 924 continuous users. Compared to the treated sample in the CPHS data, the matched sample is characterized by higher levels of education and annual household income. Approximately 68 % of the bus users in the matched sample hold a graduate degree, and 85 % of them report an annual household income of over |240,000.

**Perceptions of Buses** In Table A1, we compare perceptions of buses between non-users and users (panel A), as well as between new users and continuous users (panel B) in the post-scheme period. We examine the differences in their perceptions of five aspects: 1) affordability and availability of bus transit; 2) safety regarding accidents, crashes, threats, and thefts; 3) connectivity; 4) bus frequency, waiting time, travel duration, and unnecessary stops; 5) accessibility to bus stops. In panel A, we can see that non-users consistently give lower ratings across all five perspectives; they find bus travel less satisfactory across all five dimensions compared to users. They are particularly concerned about safety issues. The average rating concerning safety among non-users is 1.63, indicating a level of satisfaction that falls between highly unsatisfactory and unsatisfactory. In panel B, we find that compared to continuous users, and new users tend to give a lower rating to safety (0.13 points lower) but higher ratings to affordability and the availability of free commutes. Specifically, new users, on average, rate affordability of buses 0.19 points higher than continuous users, and this difference is statistically significant at a 1% level. These results suggest that when it comes to transportation, women prioritize not only safety concerns but also _affordability_ . Reducing costs in public transportation thus has the potential to encourage women to choose buses as a viable option for job search and

> 8The generation points are all major locations within the city where trips originate or are attracted to. These include major work areas, shopping districts, and schools.

> 9The occupation variable consists of the following categories: service, business, informal worker, daily wager, homemaker, and student.

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commuting.

## **3.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **4 Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0 otherwise. **1** ( _Time_ = _t_ ) takes the 8
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **5 Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **5.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in the treated states. Treated households report 10.637.2 log points lower expenses on all kinds of transport relative to control households after the program was implemented (Figure C1(a)). We also find that the share of transport expense in total expenditure decreases by 0.3-0.6 percentage points (Figure C1(b)). Consistently, we find a reduction in the expenditure on bus/train/ferries category (BTF) 9
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **5.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>10</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after the scheme’s launch. The remaining 45% spent between |1 and |1,000 on transportation. Specifically, among new bus users (see Figure C2), 82% spent more than |1,000 per month on transportation before the scheme, but this percentage decreased dramatically to 1% after the scheme was implemented. This 81% difference is also statistically significant at a 1% level. Furthermore, while none of the new bus users reported any spending on transportation before the scheme (the |0 category in Figure C2), the proportion increased to

> 10The survey inquires about the monthly travel expenditures of all respondents after the launch of the _Pink Slip Program_ in Delhi. However, it only asks new bus users about their monthly travel expenditures before the program’s launch, that is, only for the new bus users the survey records expenditures before and after the scheme.

10
53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **6 Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final analysis documents results for unemployed women.

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# **6.1 Time Use of Employed Women**

We estimate equation (7) for employed women, based on their current work status, and report the results in panel A of Table A3.<sup>11</sup> As shown in column 1, employed women in treated states spent less time traveling after the implementation of the free bus scheme compared to those in control states, which is consistent with potential substitution from slower modes of travel (cheaper but slower buses or walking) to faster buses. These travel time savings can be allocated to household chores, additional work, or a combination. Surprisingly, we find that it is primarily the former: hours spent on domestic tasks increased by 19.1% (column 2), with minimal change in the time spent working (column 3).<sup>12</sup> We then explore the potential heterogeneity of this pattern across employed women at various skill levels. We define low-skill women as individuals who have not received education beyond primary school, medium-skill women as those who have attended middle or secondary schools, and high-skill women as those who hold bachelor’s degrees or above.<sup>13</sup> We document the results by skill levels in panel B of Table A3.<sup>14</sup> Although all women save time on travel, employed women across various skill levels allocate this saved time to different activities. Specifically, employed low-skill women reduce their 11While the baseline results do not consider the flows in and out of employment, in Appendix Table D8, we present a matrix of women’s labor market status before and after the program. Among those employed in the post-treatment periods, 65.8% had also been employed before the program deployment. We thus also estimate equation (7) for women continuously employed during the study period to examine if the results are influenced by changes in employment status. The results are reported in Appendix Table D9. One can see that the estimated coefficients are similar to those in panel A of Table A3. In Appendix Table D10, we cluster the standard errors at the district level and the significance levels of the estimated coefficients are similar to those in panel A of Table A3 and Appendix Table D9.

> 12The result in working hours may prompt inquiries about wage effect. We, therefore, also use the InP section data to examine the impact on employed women’s wages and find a statistically insignificant increase in treated states compared to control states following the scheme’s implementation, as shown in column 1 of Appendix Table D11. However, it should be noted the wage results presented in Appendix Table D11 are only suggestive evidence. We observe wages for 64 % of the employed women featured in Table A3. Most of this attrition is not due to selection bias (i.e., respondents refusing to answer the income survey). Instead, when matching the PoI and the InP datasets, we find that over 70 % of the unmatched observations result from interviewers being unable to reach the household within the available fixed period.

> 13Goel (2017) and Thomas (2011) classify skill levels by education levels. Asuyama (2012) categorizes “people who were illiterate or have only received education below the primary level” as individuals with the lowest skill level. Mehrotra, Gandhi and Sahoo (2013) also group people who have below primary or only primary level of education as individuals with low level of skill. In this paper, we follow previous literature and group skill level by education level, setting individuals who have not received education or have only attended primary school as low-skill individuals.

> 14These results are based on women’s skill levels before the implementation of the scheme. As shown in Appendix Table D12, only minor changes in skill status occurred from the pre- to the post-period. We further verify this point in a regression in columns 2 and 4 in Appendix Table D13.

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work hours by 2 hours and increase their time spent on household activities by 1.1 hours, compared to high-skilled women.<sup>15</sup> We see a similar pattern for medium-skilled employed women, but the increase in time spent on household activities is 32 minutes less than their low-skilled counterparts. Conversely, employed high-skill women increased their working time by 1.1 hours after the scheme compared to their counterparts in control states.

Next, we explore whether marital status plays any role in inducing women to spend more time on household chores despite reduced travel times following the implementation of the free bus scheme. In India, as in many other developing countries, social norms ascribe child rearing, grocery shopping, and cooking as roles to women. These gender-based expectations could impact married and unmarried employed women differently.

We present the results on employed married women in panel A of Table A4. We find that employed married women saved 21.5 % more time commuting compared to their unmarried counterparts.<sup>16</sup> However, they reallocated this saved time to household chores while their time Since decisions about labor are reducing working. supply likely made at the household level, we also explore the impacts on the labor supply of employed men in the same households.

The results are reported in panel B of Table A4. Consistently, we observe the opposite pattern for employed married men residing in these households: They reduce their time devoted to household chores by 14.2% and increase their labor hours by 11.1%.

Next, given the richness of our data, we examine how the impacts on _employed married women_ vary by skill level. The results are reported in Table A5, which are similar to the results in panel B of Table A3. We observe that after the scheme’s implementation, lowskill (medium-skill) married women reduced their travel time by 21.0% (21.7%) and work time by 21.3% (9.9%), and shifted the saved time towards domestic tasks, resulting in a 37.1% (27.3%) increase. Meanwhile, high-skill married women did not make such a time

> 15We also decompose the wage effect by skill levels. As shown in column 2 of Appendix Table D11, employed low-skill women in treated states experienced an imprecise decrease in wages compared to high-skill women, relative to their counterparts in control states. As noted in footnote 33, the result is only suggestive.

> 16The current results are based on women’s contemporary marital status but do not change if we use the pre-period status. As shown in Appendix Table D14, around 95 % of women have the same status in the post-period as they do in the pre-period. In columns 1 and 3 of Appendix Table D13, we confirm that the program did not have any compositional effect on women’s marital status. We also examine the impact on wages for employed married women and present the results in column 3 of Appendix Table D11. As expected, compared to employed unmarried women, there is a decline in wages for employed married women following the scheme’s implementation. As noted previously in footnote 33, this piece of evidence is only suggestive.

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reallocation; instead, they used the saved travel time for work.

Our analysis thus suggests that the disproportionately large burden of household work on married women precludes them from benefiting in the labor market to either search for jobs or work more. These women instead use the time saved from commuting to help their spouses and men residing in the household increase their labor supply. Gender roles within households and bargaining power undermine the effects of the policy. In the next subsection, we provide a simple model of optimal time allocation within the household to formalize these mechanisms and to explicitly state the assumptions needed for it to be consistent with the empirical results obtained above.

# **6.2 A Model of Intra-Household Time Allocation**

The reallocation of household chores from men to women, along with the reduction in labor supply among married women–specifically those with low and medium skills– could be driven by two mechanisms related to household-level decision-making aimed at maximizing joint utility. First, there may be a discrete shift from men to women in household chores that require a discrete amount of time. For example, a long commute by mothers may have previously made it optimal for the father to take their kids to school, perhaps on a bus. After the roll-out of free bus rides for women, this responsibility may be reallocated to the mother. The gender wage gap in India increases the likelihood of such re-optimization at the household level, as men typically earn more than women within the same household.<sup>17</sup> A second mechanism could be the nature of work typically performed by women, which, compared to men, is less likely to be full-time with a fixed wage.<sup>18</sup> Let’s consider the following example of part-time work: a low-skilled woman employed as a house cleaner, who must commute to a neighborhood with high socioeconomic status. Before the free rides program, she has a time-consuming commute on foot. This leads to an

> 17We use the PoI section data to identify husbands and wives, and the InP section data to determine individuals’ average monthly wages between January and March 2021, the three months prior to the scheme’s implementation. After matching data from these two sections, 21% of the observations did not respond to the income survey, raising concerns about potential selection bias. However, over 70% of those who failed to report income did so because interviewers were unable to reach the households during the designated time span. Among the matched sample with income observations, about 99% of the households reported the husband’s wage as being higher than or equal to the wife’s. In these households, husbands’ wages are, on average, twice as high as those of their wives.

> 18Fletcher, Pande and Moore (2017) show that the types of jobs Indian women report wanting vary by age, but are primarily of a part-time nature, reflecting the demands of other household responsibilities, particularly in the context of marriage and childbearing. 73% of women willing to take a job prefer regular, part-time work while only 22% report wanting regular, full-time work; the remaining 5% want a mixture of only occasional full or part-time work.

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indivisible labor supply, where she finds it optimal to stay in the work neighborhood for the entire day to clean multiple homes. However, when buses become free and the travel time between her residential and work neighborhoods decreases, she finds it optimal to skip cleaning one of the houses she previously cleaned. Instead, she takes a bus back to her neighborhood to run a household errand during the day, then returns to the work neighborhood for another job. This reallocation of time simultaneously reduces both her work and travel hours while increasing the time she spends on household chores.

We now describe a simple model of optimal time allocation within the household to formalize this mechanism. Consider a different-sex household in which the man works salaried so that his income _Im_ does not depend on hours worked. The woman, on the other hand, has a divisible labor supply at an hourly wage of _w f_ where the _f_ subscript stands in for _female_ . The total female time endowment is _E_ hours per day. There is an household chore _H_ hours. It is an indivisible such as indispensable requiring activity, grocery shopping, which needs to be performed by either the man or the woman. The disutility of the chore is _dm_ and _d f_ for the man and woman, respectively.

The household maximizes joint utility by allocating the chore to one of its members, captured by an indicator function **1** _H_ that takes the value of one if it is performed by the woman, and zero otherwise: Note that, in most households in India, household work will be shared by both men and women with the bulk of the work done women. The choice of either men or women by performing household work is done for simplicity for exposition and the results hold when household work can be shared. Household surplus is increasing in total income net of the disutility from the household chores. Total income depends on hours worked by the woman, which is the residual left after commuting _C_ hours and taking care of _H_ if she is responsible for the household chores: where the constraint captures the subsistence threshold of income _<u>w</u>_ that the household needs to attain. We assume that male salary alone is not high enough to meet subsis15
tence, i.e., _Im <_ _<u>w</u>_ , so that the woman needs to work regardless of _C_ subject to the time constraint _E − C −_ **1** _H · H ≥_ 0. We assume that the commute time _C ∈{Cslow_ , _C f ast}_ depends on whether the woman takes a slow or fast mode of transport, requiring a higher or lower travel time, _Cslow > C f ast_ , respectively. Therefore, there is a trade-off in female time allocation between work versus commuting. We assume that before the program offering free bus rides to women is rolled out, the slower and more time-consuming commute is chosen. We do not model a man’s commuting as his salary does not depend on hours worked.

Next, we back out from the model the restrictions on the parameters needed for the decisions to be with our results. If these restrictions are optimal compatible empirical plausible, the model offers a useful lens through which these results can be interpreted. Recall that our objective is to rationalize the decrease in labor supply and the increase in household chores for women after the introduction of free bus rides. In our model, this corresponds to **1** _H_ = 0 when _C_ = _Cslow_ , and **1** _H_ = 1 when _C_ = _C f ast_ . Assuming that fast commuting by free buses leaves enough time for the woman to work even if she undertakes the household chore, i.e., _E − C f ast − H >_ 0, the latter implies For these assumptions to be compatible with non-negative wages, gender-specific disutility of chores needs to satisfy _dm > d f_ . Moreover, man’s disutility has to be high enough so that hourly low-skilled female wages cannot compensate for it within the household. This is a plausible assumption for a low-skilled household in the Indian context.

However, in the absence of another restriction, _dm − d f > w f_ would imply that household work would be allocated to the woman even before the program when _C_ = _Cslow_ . To rationalize **1** _H_ = 0 when _C_ = _Cslow_ , it must be the case that doing both the household chores and working—which necessitates commuting—violates the time constraint of the woman: _E − Cslow − H <_ 0. Hence, the two constraints faced by the household—to perform _H_ and attain subsistence within the time endowment—leave no choice but for the man to do household chores and the woman to work. When commuting time drops, however, they find it optimal to reallocate the household work to the woman. For this to reduce her market work hours, model parameters have to satisfy That is, household chores are more time-consuming than the time saving from the free 16
and faster commuting.

To sum three are to rationalize the estimated reallocation up, assumptions sufficient of chores within the household: a more precarious labor force status for women (captured by the low hourly wage compared to the fixed male salary), the indivisibility of a high enough time required for essential household work, and asymmetric genderspecific disutility for these chores. All of these are likely to apply to households with less educated, low-wage women, consistent with empirical evidence as documented above and in recent literature (Fletcher et al., 2017).

# **6.3 Impact on the Time Use of Unemployed Women**

We now shift our focus to examining unemployed women who are actively seeking jobs. First, we estimate equation (7) for these women, classifying them based on their current work status, and present the results in columns 1 and 2 of Table A6.<sup>19</sup> We find that unemployed women in treated states spend more time traveling and, concurrently, less time on domestic duties compared to their counterparts in control states following the implementation of the _Pink Slip_ scheme. Such a shift implies a potential increase in the time these women allocate to job search.

We further investigate whether the changes are driven by the intensive margin or the extensive margin. To do so, we estimate equation (7) for women who have the same preand post-period labor market status–unemployed, and report the results in columns 3 and 4 of Table A6.<sup>20</sup> We that the effects are more for women who are find pronounced consistently unemployed, suggesting that it is these always-unemployed women who are most responsive to the free bus tickets. Therefore, the changes are mainly driven by the intensive margin. Additionally, we estimate equation (6) for travel time for both currently unemployed women (Appendix Figure C2(a)) and always-unemployed women (Appendix Figure C2(b)). In both figures, we observe that unemployed women in treated states spent more time traveling after the scheme’s implementation compared to those in the control states.

Similarly, we explore whether the time usage pattern for unemployed women holds across skill levels and whether married unemployed women also benefit. As shown in Appendix Table D16, the increase in travel time observed in column 1 of Table A6 is

> 19In Appendix Table D15, we cluster the standard errors at the district level. The significance levels of the estimated coefficients are similar to those in Table A6.

> 20In Appendix Table D8, we present a matrix of women’s labor market status before and after the program. Among those who were unemployed in the post-treatment periods, 58.3% were without employment in the pre-period.

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driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **7 Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme, which is 15.7% of the control mean. We also present the corresponding event study graph in Figure C3, which shows that this marginal increase persisted in the post-implementation periods. The estimated coefficients for time periods 1 and 2 after the policy are statistically significant at 10% and 15% levels, respectively. In period 3, the magnitude remains unchanged, though it is imprecise. These findings indicate that more women are moving from being out of the labor market to actively looking for jobs, although they are not more likely to find employment in this short-run period.

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**Evidence from the Delhi Primary Survey** The results on female time use so far are based on the CPHS data which does not separate travel times into purpose of travel (i.e., commuting to work vs non-work related trips). The Delhi Survey, on the other hand, reports the purpose of travel and the average travel distance for each respondent. While this data does not report employment status, the survey answer ‘ _travel for work_ ’ is meant to capture travel _to work_ in the case of employed women and travel _to search_ for work in the case of unemployed women.

We use the Delhi data to compare the purpose of travel and the average travel distance between new and continuous users of bus transit in the post-implementation periods. We acknowledge that the new users are induced into using the buses because they are free and hence are selected relative to incumbent users. The comparison group of continuous users is matched on observable characteristics to the new users as discussed in Section E.2. While this does not address the selection issue completely, this comparison is still revealing and useful. With this disclaimer in mind, we estimate the following regression: where _Yi_ is a dummy variable for travel purposes which is equal to one if it is for work or a dummy variable for average travel distance via buses which is equal to one if the average distance exceeds 10 kilometers. _I_ new users is a dummy variable set to one for new users and zero for continuous users. _X_ is a vector of control variables including an individual’s marital status and education level. The results are reported in Table A8.

In column 1, we find that new bus users are 6.8% more likely to travel for work than continuous users. The estimated coefficient decreases slightly to 6.6% after conditioning on marital status and education. We then restrict the matched sample to users whose travel purpose is work, which includes travel to work or job search depending on unreported employment status. In columns 3 and 4, we observe that, on average, new users are 18.2%-19.8% more likely to travel longer than 10 kilometers compared to continuous users. These results provide suggestive evidence that free passes enable women to travel greater distances, potentially allowing them to work in more remote jobs or facilitating their job search efforts.

# **8 Conclusion**

In the face of a literature that costs often re- large documenting increasing commuting duce women’s work hours and labor force participation, there is an increased policy 19
attention to decreasing the commuting barriers for women. Using the roll-out of free bus services for women in several states of India under the _Pink Slip Program_ , this paper evaluates the consequences of a reduction in commuting costs on women’s time use, including work hours, travel times, and time on household work, as well as their labor force participation.

Employing a synthetic difference-in-differences approach, we find that household expenditures on buses fall and travel time for women employed before the program decreases, implying that demand for bus transportation by women is elastic. We also show, however, that reducing commuting costs alone may not be enough for women to increase work hours or participate more in the labor market. Heterogeneous responses may reflect the re-optimization of agents with varying demographic status: free buses do incentivize unmarried, skilled, and unemployed women to increase labor force participation and search more for Skilled and unmarried women who are intensively jobs. already employed increase their work hours. But low-skilled and married women use the time saved from commuting using free buses to do more household work, replacing some of the household work done by their spouses, who in turn increase their work hours. These results indicate the potential importance of household-level decision-making and intrahousehold bargaining power in determining gender norms that impede some women from taking advantage of the free bus scheme to increase their labor supply.

Our findings have important policy implications: If the goal of providing free transportation to women is to improve women’s labor market outcomes, only a small share of the workforce–primarily skilled unmarried women–benefits from this policy in the immediate short-run.

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**Turner, Tracy and Debbie Niemeier** , “Travel to work and household responsibility: new evidence,” _Transportation_ , 1997, _24_ , 397–419.

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# **Tables**

Table 1: User Perceptions of Buses & _Pink Slip_ Scheme

|||(1)||(2)|t-test|
|---|---|---|---|---|---|
|Variable|N|Mean/SD|N|Mean/SD|(1)-(2)/SE|
|_Panel A. Nonusers vs Users_||||||
||N|onusers||Users|Difference|
|Affordability and availability of|184|3.196|1,106|3.914|-0.718***|
|free commute||(0.786)||(0.820)|(0.065)|
|Safety against accidents, crashes,<br>threats, and thefts|184|1.630<br>(0.640)|1,106|4.052<br>(0.878)|-2.422***<br>(0.068)|
|Connectivity|184|2.772<br>(0.798)|1,106|4.046<br>(0.919)|-1.274***<br>(0.072)|
|Bus frequency, waiting time, travel<br>time, and unnecessary stops|184|2.484<br>(0.992)|1,106|3.238<br>(1.197)|-0.754***<br>(0.093)|
|Accessibility to bus stops|184|2.424<br>(1.053)|1,106|3.167<br>(1.091)|-0.743***<br>(0.086)|
|_Panel B. New users vs continuous users_||||||
||N|ew users|Contin|uous users|Difference|
|Affordability and availability of|182|4.071|924|3.883|0.189***|
|free commute||(0.828)||(0.816)|(0.066)|
|Safety against accidents, crashes,|182|3.945|924|4.073|-0.128*|
|<br>threats, and thefts||(0.884)||(0.876)|(0.071)|
|Connectivity|182|4.187|924|4.018|0.168**|
|||(0.878)||(0.924)|(0.074)|
|Bus frequency, waiting time, travel|182|3.214|924|3.242|-0.028|
|<br>time, and unnecessary stops||(1.177)||(1.201)|(0.097)|
|Accessibility to bus stops|182|3.132|924|3.174|-0.042|
|||(1.048)||(1.100)|(0.089)|

_<u>Notes:</u>_ Respondents evaluate their perception of a particular aspect of buses on a scale ranging from 1 (highly unsatisfactory) to 5 (highly satisfactory). In the last column, we test the differences between treated and control areas using a t-test with equal variance. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

27
Table 2: The Impact on Household Transportation Expenditures

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Ln(Transport)|Ln(BTF)|Transport/Expenditure|BTF/Expenditure|BTF/Transport|
|Treat_×_ Post|-0.197**|-0.801***|-0.004**|-0.003***|-0.066***|
||(0.084)|(0.201)|(0.001)|(0.001)|(0.016)|
|Control Mean (Level)|349.97|100.56|0.03|0.01|0.28|
|_R_<sup>2</sup>|0.62|0.68|0.67|0.64|0.62|
|No. of HHs|22,791|22,791|22,791|22,791|22,791|
|N|150,233|150,233|150,233|150,233|150,233|
|HH FE|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|

_<u>Notes:</u>_ “Expenditure” = Total monthly household expenditure. “Transport” = Total monthly household expenditure on transport. “BTF” = Monthly household expenditure on daily bus/train/ferry fare. We conduct analysis on the sample of households who are also represented in the individual data. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) in columns 1 and 2 are in Rupees. Standard errors are clustered at the state level. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

Table 3: The Impact on Time Use for Employed Women

||(1)|(2)|(3)|
|---|---|---|---|
|Ln|(Travel Time)|Ln(Time for HH)|Ln(Time for Work)|
|_Panel A. Employed Women_||||
|Treat_×_ Post|-0.098***<br>(0.011)|0.191***<br>(0.053)|0.014<br>(0.051)|
|Control Mean (Level)|0.60|3.01|6.72|
|_R_<sup>2</sup>|0.52|0.53|0.43|
|_Panel B. Employed Women By Skill_||||
|Treat_×_ Post|-0.076|-0.049|0.161*|
||(0.070)|(0.060)|(0.085)|
|Treat_×_ Post_×_ Low-Skill|-0.065|0.377***|-0.292**|
||(0.059)|(0.072)|(0.095)|
|Treat_×_ Post_×_ Medium-Skill|-0.002|0.199***|-0.100|
||(0.076)|(0.040)|(0.084)|
|Control Mean (Level)|0.73|2.98|6.94|
|_R_<sup>2</sup>|0.52|0.54|0.43|
|Individual FE|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|No. of Individuals|2,916|2,916|2,916|
|N|9,906|9,906|9,906|

_<u>Notes:</u>_ We restrict the sample to women who are employed. In this table, we categorize women by their current employment status. The omitted group is the employed high-skilled women who have bachelor’s degrees or above in panel B. “Travel Time” = Time spent on travel. “Time for HH” = Time spent on household activities. “Time for Work” = Time spent on work done for the employer. “Low-Skill” = Women who went to primary schools or received no education. “Medium-Skill” = Women who went to middle school, secondary schools, or higher secondary schools. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is hours per day and is the average for employed women in pre-periods and control states in panel A and is the average for employed high-skill women in control states during pre-periods in panel B. Standard errors are clustered at the state level. + is p _<_ 0.15, * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

28
Table 4: The Impact on Time Use for Employed Women and Employed Men

||(1)|(2)|(3)|
|---|---|---|---|
||Ln(Travel Time)|Ln(Time for HH)|Ln(Time for Work)|
|_Panel A. Employed Women_||||
|Treat_×_ Post_×_ Married|-0.215***<br>(0.026)|0.196**<br>(0.059)|-0.248***<br>(0.061)|
|Control Mean (Level)|0.70|2.69|7.21|
|_R_<sup>2</sup>|0.52|0.54|0.43|
|No. of Individuals|2,916|2,916|2,916|
|N|9,906|9,906|9,906|
|_Panel B. Employed Men_||||
|Treat_×_ Post_×_ Married|-0.010<br>(0.037)|-0.142**<br>(0.054)|0.111***<br>(0.029)|
|Control Mean (Level)|0.79|1.51|7.57|
|_R_<sup>2</sup>|0.52|0.71|0.30|
|No. of Individuals|1,867|1,867|1,867|
|N|7,318|7,318|7,318|
|Individual FE|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

_<u>Notes:</u>_ In panel A, we restrict the sample to employed women; and in panel B, we restrict the sample to employed men from _households_ with employed women (the sample in panel A of Table A4). In this table, we categorize individuals by their current employment status. A (wo)man is unmarried if (s)he is divorced, unmarried, or widowed. In panel A, the omitted group is employed unmarried women; and in panel B, the omitted group is employed unmarried men. “Travel Time” = Time spent on travel. “Time for HH” = Time spent on household activities. “Time for Work” = Time spent on work done for the employer. “Married” = A dummy takes the value of one if a (wo)man is married and zero if the (wo)man is divorced, unmarried, or widowed. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is hours per day. The control mean is the average for employed unmarried women in control states during pre-periods in panel A and is the average for employed unmarried men in control states during pre-periods in panel B. Standard errors are clustered at the state level. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

29
Table 5: The Impact on Time Use for Employed Married Women by Skill

||(1)|(2)|(3)|
|---|---|---|---|
||Ln(Travel Time)|Ln(Time for HH)|Ln(Time for Work)|
|Treat_×_ Post (TP)|-0.202*|0.021|0.185|
||(0.103)|(0.098)|(0.153)|
|Treat_×_ Post_×_ Low-Skill (TPL)|-0.007|0.350***|-0.398*|
||(0.093)|(0.099)|(0.176)|
|Treat_×_ Post_×_ Medium-Skill (TPM)|-0.014|0.252***|-0.284+|
||(0.119)|(0.059)|(0.177)|
|**TP+TPL**|-0.210***|0.371***|-0.213**|
|p-value|(0.000)|(0.000)|(0.048)|
|**TP+TPM**|-0.217***|0.273***|-0.099**|
|p-value|(0.000)|(0.002)|(0.047)|
|Individual FE|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Control Mean (Level)|0.62|3.48|6.68|
|_R_<sup>2</sup>|0.53|0.54|0.43|
|No. of Individuals|1,602|1,602|1,602|
|N|5,038|5,038|5,038|

_<u>Notes:</u>_ We restrict the sample to employed married women. The omitted group is the high-skill women who have bachelor’s degrees or above. “Low-Skill” = Women who went to primary schools or received no education. “MediumSkill” = Women who went to middle school, secondary schools, or higher secondary schools. “Time for HH” = Time spent on household activities. “Travel Time” = Time spent on travel. “Time for Work” = Time spent on work done for the employer. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is hours per day. The control mean is the average for employed high-skill married women in pre-periods and control states. Standard errors are clustered at the state level. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

Table 6: The Impact on Time Use for Unemployed Women

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Unemploy|ed Women|Always Unem|ployed Women|
||Ln(Travel Time)|Ln(Time for HH)|Ln(Travel Time)|Ln(Time for HH)|
|Treat_×_ Post|0.102<sup>+</sup><br>(0.056)|-0.437<sup>+</sup><br>(0.249)|0.144***<br>(0.042)|-0.596**<br>(0.236)|
|Individual FE|Yes|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Control Mean (Level)|0.24|2.58|0.27|1.82|
|_R_<sup>2</sup>|0.64|0.75|0.99|0.73|
|No. of Individuals|1,570|1,570|776|776|
|N|5,636|5,636|3,197|3,197|

_<u>Notes:</u>_ We restrict the sample to women who are unemployed but actively seeking employment. In columns 1-2, we categorize women by their current employment status; and in columns 3-4, the employment status of women remains constant over time. “Travel Time” = Time spent on travel. “Time for HH” = Time spent on household activities. “Time for Work” = Time spent on work done for the employer. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is hours per day and is the average for women in pre-periods and control states. Standard errors are clustered at the state level. + is p _<_ 0.15, * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

30
Table 7: The Impact on Labor Market Status for Women

||(1)|(2)|(3)|
|---|---|---|---|
||in Labor Mkt.|Employed|Job Search (Not Employed)|
|Treat_×_ Post|0.003|-0.017|0.011<sup>+</sup>|
||(0.011)|(0.016)|(0.006)|
|Control Mean (Level)|0.14|0.51|0.07|
|_R_<sup>2</sup>|0.81|0.89|0.78|
|No. of Individuals|43,855|4,537|41,347|
|N|189,668|16,007|177,657|
|Individual FE|Yes|Yes|Yes|
|Wave FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|

_<u>Notes:</u>_ “in Labor Mkt.” = A dummy takes the value of one if a member is employed or is unemployed but is looking for a job; it takes the value of zero if a member is unemployed and is neither willing nor looking for a job. “Job Search (Not Employed)” = A dummy takes the value of one if a woman is searching for jobs and zero if a woman is out of the labor market. “Employed” = A dummy takes the value of one if a woman is employed and zero if a woman is unemployed and is looking for a job. All regressions include individual, quarter/wave, and year-fixed effects and control for the average number of daily new confirmed cases as a share of the population. The control mean (level) is the proportion of women belonging to a specific status in pre-periods and control states. Standard errors are clustered at the state level. + is p _<_ 0.15, * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

Table 8: Travel Purpose and Distance: New vs. Continuous Users

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||||Travel P|urpose: Work|
||Travel f|or Work|Travel Distan|ce (Bus)_>_ 10 km|
|New users|0.068*|0.066*|0.182***|0.198***|
||(0.040)|(0.039)|(0.054)|(0.057)|
|Other Controls|No|Yes|No|Yes|
|Control Mean|0.44|0.44|0.52|0.52|
|_R_<sup>2</sup>|0.00|0.07|0.02|0.06|
|N|1,106|1,106|496|496|

_<u>Notes:</u>_ We conduct regression analysis on the matched sample. In columns 1-2, we include all new users and continuous users; in columns 3-4, we restrict the sample to users whose travel purpose is work, which includes both work and job search, in the post-scheme period. “Travel for work” means the users’ main travel purpose is work. “Travel Distance (Bus) _>_ 10 km” is a dummy variable which equal to one if a user’s average travel distance by buses after the introduction of the free bus scheme is over 10 kilometers and zero otherwise. Other controls include an individual’s marital status and education level. Standard errors are clustered at the user level. * is p _<_ 0.1, ** is p _<_ 0.05, and *** is p _<_ 0.01.

31
# **APPENDIX**

# **A Data**

## **A.1 Construction of a State Panel for Synthetic Difference-in-Difference**

States that implemented the _Pink Slip program_ could exhibit dissimilarities from other states, including neighboring ones. To mitigate this concern, we utilize a synthetic difference-in-differences approach (SDID) proposed by Arkhangelsky et al. (2021). The SDID method re-weights the control units to align their time trends with those of the treated units and subsequently applies a DID analysis to the re-weighted panel. That is, this method establishes a synthetic counterfactual state for causal estimation. Since our treatment occurs at the state level rather than the household (or individual) level, we aggregate our household-level (or individual) data to the state level and then match states and their pre-trends using a set of variables discussed below We use six variables for the household-level data. The variable “size group of household” is a categorical variable that includes groups such as one member, three members, eight to ten members, and more than 15 members. It is based on the number of members in a household. The variable “age group of household” is a categorical variable that includes groups such as households dominated by children, households dominated by grown-ups, and balanced households with seniors. The variable “occupation group of household” is a categorical variable that includes groups such as wage laborers, selfemployed professionals, entrepreneurs, and farmers. The occupation group of a household is based on the distribution of members of a household by the nature of their occupation. The CMIE classifies households into different groups based on certain rules and not just based on the occupation of the head of the household. The variable “education group of household” is a categorical variable that includes groups such as all graduates, graduates majority, and some literates. The education group of a household is based on the distribution of members of a household by their education level. The variable “gender” is a categorical variable that includes groups such as only males, only females, female majority, and balanced. The last variable we use is the average new confirmed daily cases as a share of the state population. For each categorical variable, we then generate corresponding dummy variables, and these dummy variables after aggregating to the state level can be interpreted as the share of households having a certain 32
characteristic. For example, one of the dummy variables created from “gender” is the share of households only having male members.

We use nine variables for the individual-level data: Hindu and age, religion (e.g., Muslim), caste (e.g., upper caste, scheduled castes, and scheduled tribes), discipline (e.g., law and medicine), marital status (e.g., married, unmarried, and divorced), literacy, and education level (e.g., primary school and middle school). Except for age, all other variables are categorical variables. Similarly, we generate the corresponding dummy variables before aggregating them to the state level. Again, we also use the average new confirmed daily cases as a share of the state population.

Using this approach, we compare the outcomes of treated states with that of a weighted combination of control states, the “synthetic” treated states without the free bus ride scheme, which has similar pre-treatment trends as the treated group. The weights applied in our regression for the household-level analysis and individual-level analysis are defined as follows: Household-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Household Weight _it_ , (4) Individual-Level: Weight _ijt_ = State Weight _j ×_ Time _t ×_ Individual Weight _it_ . (5) In equations (9) and (10), “State Weight _j_ ” and “Time _t_ ” are the state unit weights and time weights derived from SDID, respectively. “Household Weight _it_ ” is the sampling weight of a household in the CPHS data. “Individual Weight _it_ ” is the sampling weight of a member of a sample household in the CPHS data.

# **A.2 Delhi Primary Survey**

**Travel Purpose & Distance** The survey inquires about respondents’ primary travel the a selection of work purpose during post-scheme period, offering options including (including work and job search), education, healthcare, shopping, religion, leisure, pick/drop off, and others. Additionally, the survey collects information regarding users’ travel distances and travel distances buses. It should be average average specifically by noted that the survey only asked new users but not continuous users about their average travel distance prior to the implementation of the scheme.

33
# **B Additional Figures**

[OT1]fontenc [letterpaper]geometry verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in format booktabs [sc]mathpazo pdflscape graphicx verbatim multicol lscape caption color subcaption adjustbox tfrupee [bottom]footmisc Gender-Specific Transportation Costs and Female Time Use: Evidence from India’s _Pink Slip_ Program<sup>21</sup> Yutong Chen `yc3jk@virginia.edu` (University of Virginia) Kerem Cos ̧ar `kerem.cosar@virginia.edu` (University of Virginia, CEPR, CESifo, NBER) Devaki Ghose `dghose@worldbank.org` (Development Economics Research Group, The World Bank) Shirish Mahendru `shirish.mahendru@giz.de` (Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India) Sheetal Sekhri `ssekhri@virginia.edu` (University of Virginia) May 16, 2024

## **Abstract**

Reducing gender-specific commuting barriers in developing countries has complex effects on women’s labor dynamics. We study a program that offers free bus rides for women in several Indian states (the _Pink Slip_ program) using a synthetic difference-in-differences approach to shed light on labor supply and time use decisions of women. We observe decreased bus expenses and time saved on travel. Skilled employed women increase labor supply, while low-skilled married women shift focus to household chores. Unemployed women intensify job searches, yet overall employment rates remain unchanged. Our findings highlight that alleviating commuting costs does not uniformly boost women’s labor participation, as gender roles and societal norms continue to shape outcomes.

**Keywords** : Transport, Gender, Time-Use, Female Labor Force Participation **JEL codes** : J16, J22, R41

> 21We thank the University of Virginia Department of Economics for financial support. For helpful comments and feedback, we thank S Anukriti and the World Bank Office of the Chief Economist, South Asia Region. The authors gratefully acknowledge support by the Umbrella Facility for Trade, which is funded by the governments of The Netherlands, Norway, Switzerland, Sweden and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are solely those of the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, or the Executive Directors or the countries they represent. All errors are our responsibility.
# **C Introduction**

The gender disparity in commuting barriers is pervasive, affecting women in developing and developed nations. Recent OECD findings reveal a substantial gender commuting gap, with men averaging 33.4 minutes per day compared to women’s 21.9 minutes, indicating a 31.1% difference (OECD, 2016). Developing countries exhibit even more pronounced patterns; Indian women, on average, spend a mere 8 minutes daily on employment-related travel, compared to men’s 36 minutes.<sup>22</sup> ILO (2017) reports that lack of transportation decreases women’s probability of participating in the labor market by 16.5 percentage points among developing countries. Given the established inof barriers on women’s labor a natural arises: can fluence commuting supply, inquiry reducing these barriers influence women’s labor supply decisions? Whether policies directly addressing commuting costs would help increase female labor supply or not is far from obvious: social norms regarding household chores and traveling alone also significantly impact female labor supply, as evidenced by prior studies (Fanning Madden, 1981; Turner and Niemeier, 1997; Lee and McDonald, 2003; Abe, 2011; McQuaid and Chen, 2012).

Our study sheds light on the trade-offs women face when commuting cost is reduced by exploiting the rollout of a free busing scheme, the _Pink Slip Program_ , in two states of India. India is a pertinent setting for exploring this issue. There is an overwhelming gender gap in commuting time and modes used. The 2011 Census of India reveals that 30.2 % of women travel to work on foot, and only 24.6 % use any transportation, highlighting the limited access to transportation options for many women. In contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>23</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>24</sup> Several media

> 22India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 23World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 24The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

1
reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>25</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category that includes buses, was reduced for households with women in treated states compared to control states. Findings from the Delhi survey corroborate these results at the individual level. New female users of buses report negligible transportation costs after the start of the policy as opposed to their substantial expenditures on other modes of transport before switching to buses.

We then examine if women in the treated areas change the time spent on traveling and, if they do, the potential reallocation of that time to other margins of time use, such as household chores and labor Diverse outcomes women supply. emerge among

> 25The weights from the SDID approach are used in all survey data-based analyses at the individual level.

2
in to the We scrutinize these nuanced effects three response policy change. along key dimensions: employment status, marital status, and skill levels. Opposing impacts on employed and unemployed women result in an overall null effect. Marital status and skill-level significantly influence time allocation, with skilled employed women leveraging commuting time savings to increase labor hours. Conversely, unmarried unemployed women intensify job searches, while short-term employment outcomes don’t reflect immediate positive shifts, emphasizing additional constraints to finding employment by women.

In contrast to skilled employed women, low-skilled married workers redirect commuting time towards household chores, reducing labor hours. We propose a model that implicates household reallocation of activities as a mechanism for this shift. The model illustrates that indivisible chores requiring discrete time can result in intra-household reallocation, where the re-optimization is influenced by the gender wage gap. In support of this theory, we observe that married men alter their time use reciprocally. Employed ones increased the labor supply, and the unemployed intensified their job searches. Hence, our main findings indicate that the free bus initiative benefits skilled and unmarried women but the low-skilled married women respond by reducing their labor and increasing their time devoted to household chores. Consistent with restrictive gender norms discussed by Jayachandran (2021) and Dinkelman and Ngai (2022), low-skill married women’s labor outcomes become worse, if anything. Without a deeper understanding of these countervailing effects on different populations of women, observing an overall null effect could lead to spurious policy recommendations. We argue that transportation subsidies to women, which might seem to have redistributive merit, do not necessarily help improve the labor market outcomes of all women. Only skilled employed women, who constitute a small share of the workforce, benefit from these types of programs in the short-run.

ir The second strand of literature we connect to analyzes the effects of reductions in commute times, often by providing transit subsidies in randomized control experiments, on job search and employment creation (Franklin, 2018; Abebe et al., 2016; Phillips, 2014; Moreno-Monroy and Posada, 2018). The dominant finding in this literature is that reductions in commuting costs increase job search intensity and employment. We demonstrate that social norms in countries can undermine the effects of that re- developing policies duce commuting costs, especially for women.

The rest of the is as follows. Section D describes the paper organized study setting. Section E describes the datasets. Section F outlines the to es- empirical methodology timate the impacts of reductions in commuting costs. Section H presents the results.

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Section J provides concluding remarks.

# **D Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early response: just 20 days after the scheme’s launch, female daily ridership in DTC and cluster buses increased from 33% to 44%.<sup>26</sup>

Spurred by the good reception of the initiative in Delhi, on April 1 and May 7, 2021, Punjab and Tamil Nadu implemented free bus ride schemes for women in their states, allowing free travel in government-owned public buses.<sup>27</sup> From July 2021 to March 2022,

> 26https://shorturl.at/cAFMT, last accessed on May 13, 2024.

> 27In Punjab, these include PEPSU Road Transport Corporation (PRTC), PUNBUS, Punjab Roadways Buses, and City Bus Services, but did not include AC buses, Volvo Buses, and HVAC Buses (Express, 2021) In Tamil Nadu, the free ride scheme includes tickets for the Tamil Nadu State Transport Corporation (TNSTC) ordinary city buses.

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the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **E Data**

## **E.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>28</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 28The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

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and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **E.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022). This survey is specifically designed to evaluate changes in bus usage among women in Delhi following the implementation of the _Pink Slip_ scheme. The survey collected responses from 2,025 women, of which 1,294 had been using the bus before the scheme’s introduction; we refer to this group as “continuous users.” Additionally, 231 respondents who began using buses after the scheme’s introduction are termed “new users.” The remaining 500 respondents who did not use buses before or after the scheme are labeled as “non-users.” The was selected at attractions and across sample randomly major generation points 6
Delhi, as shown in Appendix Figure C1.<sup>29</sup> Appendix Table D5 provides some summary statistics of the survey sample. Compared to non-users, bus users (both new and continuous) are predominantly aged between 31 and 40, more often homemakers, more likely to reside in households with a monthly income exceeding |20,000, and less inclined to travel for work. Given these differences, we constructed a comparable sample of nonusers, new users, and continuous users using propensity score matching. We match women on the following variables: age, occupation,<sup>30</sup> total average monthly household income and expenditure on travel, ownership of private vehicles, and driving knowledge.

Our study sample of the Delhi primary survey is the matched sample ( _n_ = 1, 290) consisting of 184 non-users, 182 new users, and 924 continuous users. Compared to the treated sample in the CPHS data, the matched sample is characterized by higher levels of education and annual household income. Approximately 68 % of the bus users in the matched sample hold a graduate degree, and 85 % of them report an annual household income of over |240,000.

**Perceptions of Buses** In Table A1, we compare perceptions of buses between non-users and users (panel A), as well as between new users and continuous users (panel B) in the post-scheme period. We examine the differences in their perceptions of five aspects: 1) affordability and availability of bus transit; 2) safety regarding accidents, crashes, threats, and thefts; 3) connectivity; 4) bus frequency, waiting time, travel duration, and unnecessary stops; 5) accessibility to bus stops. In panel A, we can see that non-users consistently give lower ratings across all five perspectives; they find bus travel less satisfactory across all five dimensions compared to users. They are particularly concerned about safety issues. The average rating concerning safety among non-users is 1.63, indicating a level of satisfaction that falls between highly unsatisfactory and unsatisfactory. In panel B, we find that compared to continuous users, and new users tend to give a lower rating to safety (0.13 points lower) but higher ratings to affordability and the availability of free commutes. Specifically, new users, on average, rate affordability of buses 0.19 points higher than continuous users, and this difference is statistically significant at a 1% level. These results suggest that when it comes to transportation, women prioritize not only safety concerns but also _affordability_ . Reducing costs in public transportation thus has the potential to encourage women to choose buses as a viable option for job search and

> 29The generation points are all major locations within the city where trips originate or are attracted to. These include major work areas, shopping districts, and schools.

> 30The occupation variable consists of the following categories: service, business, informal worker, daily wager, homemaker, and student.

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commuting.

## **E.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **F Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0 otherwise. **1** ( _Time_ = _t_ ) takes the 8
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **G Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **G.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in the treated states. Treated households report 10.637.2 log points lower expenses on all kinds of transport relative to control households after the program was implemented (Figure C1(a)). We also find that the share of transport expense in total expenditure decreases by 0.3-0.6 percentage points (Figure C1(b)). Consistently, we find a reduction in the expenditure on bus/train/ferries category (BTF) 9
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **G.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>31</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after the scheme’s launch. The remaining 45% spent between |1 and |1,000 on transportation. Specifically, among new bus users (see Figure C2), 82% spent more than |1,000 per month on transportation before the scheme, but this percentage decreased dramatically to 1% after the scheme was implemented. This 81% difference is also statistically significant at a 1% level. Furthermore, while none of the new bus users reported any spending on transportation before the scheme (the |0 category in Figure C2), the proportion increased to

> 31The survey inquires about the monthly travel expenditures of all respondents after the launch of the _Pink Slip Program_ in Delhi. However, it only asks new bus users about their monthly travel expenditures before the program’s launch, that is, only for the new bus users the survey records expenditures before and after the scheme.

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53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **H Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final analysis documents results for unemployed women.

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# **H.1 Time Use of Employed Women**

We estimate equation (7) for employed women, based on their current work status, and report the results in panel A of Table A3.<sup>32</sup> As shown in column 1, employed women in treated states spent less time traveling after the implementation of the free bus scheme compared to those in control states, which is consistent with potential substitution from slower modes of travel (cheaper but slower buses or walking) to faster buses. These travel time savings can be allocated to household chores, additional work, or a combination. Surprisingly, we find that it is primarily the former: hours spent on domestic tasks increased by 19.1% (column 2), with minimal change in the time spent working (column 3).<sup>33</sup> We then explore the potential heterogeneity of this pattern across employed women at various skill levels. We define low-skill women as individuals who have not received education beyond primary school, medium-skill women as those who have attended middle or secondary schools, and high-skill women as those who hold bachelor’s degrees or above.<sup>34</sup> We document the results by skill levels in panel B of Table A3.<sup>35</sup> Although all women save time on travel, employed women across various skill levels allocate this saved time to different activities. Specifically, employed low-skill women reduce their 32While the baseline results do not consider the flows in and out of employment, in Appendix Table D8, we present a matrix of women’s labor market status before and after the program. Among those employed in the post-treatment periods, 65.8% had also been employed before the program deployment. We thus also estimate equation (7) for women continuously employed during the study period to examine if the results are influenced by changes in employment status. The results are reported in Appendix Table D9. One can see that the estimated coefficients are similar to those in panel A of Table A3. In Appendix Table D10, we cluster the standard errors at the district level and the significance levels of the estimated coefficients are similar to those in panel A of Table A3 and Appendix Table D9.

> 33The result in working hours may prompt inquiries about wage effect. We, therefore, also use the InP section data to examine the impact on employed women’s wages and find a statistically insignificant increase in treated states compared to control states following the scheme’s implementation, as shown in column 1 of Appendix Table D11. However, it should be noted the wage results presented in Appendix Table D11 are only suggestive evidence. We observe wages for 64 % of the employed women featured in Table A3. Most of this attrition is not due to selection bias (i.e., respondents refusing to answer the income survey). Instead, when matching the PoI and the InP datasets, we find that over 70 % of the unmatched observations result from interviewers being unable to reach the household within the available fixed period.

> 34Goel (2017) and Thomas (2011) classify skill levels by education levels. Asuyama (2012) categorizes “people who were illiterate or have only received education below the primary level” as individuals with the lowest skill level. Mehrotra, Gandhi and Sahoo (2013) also group people who have below primary or only primary level of education as individuals with low level of skill. In this paper, we follow previous literature and group skill level by education level, setting individuals who have not received education or have only attended primary school as low-skill individuals.

> 35These results are based on women’s skill levels before the implementation of the scheme. As shown in Appendix Table D12, only minor changes in skill status occurred from the pre- to the post-period. We further verify this point in a regression in columns 2 and 4 in Appendix Table D13.

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work hours by 2 hours and increase their time spent on household activities by 1.1 hours, compared to high-skilled women.<sup>36</sup> We see a similar pattern for medium-skilled employed women, but the increase in time spent on household activities is 32 minutes less than their low-skilled counterparts. Conversely, employed high-skill women increased their working time by 1.1 hours after the scheme compared to their counterparts in control states.

Next, we explore whether marital status plays any role in inducing women to spend more time on household chores despite reduced travel times following the implementation of the free bus scheme. In India, as in many other developing countries, social norms ascribe child rearing, grocery shopping, and cooking as roles to women. These gender-based expectations could impact married and unmarried employed women differently.

We present the results on employed married women in panel A of Table A4. We find that employed married women saved 21.5 % more time commuting compared to their unmarried counterparts.<sup>37</sup> However, they reallocated this saved time to household chores while their time Since decisions about labor are reducing working. supply likely made at the household level, we also explore the impacts on the labor supply of employed men in the same households.

The results are reported in panel B of Table A4. Consistently, we observe the opposite pattern for employed married men residing in these households: They reduce their time devoted to household chores by 14.2% and increase their labor hours by 11.1%.

Next, given the richness of our data, we examine how the impacts on _employed married women_ vary by skill level. The results are reported in Table A5, which are similar to the results in panel B of Table A3. We observe that after the scheme’s implementation, lowskill (medium-skill) married women reduced their travel time by 21.0% (21.7%) and work time by 21.3% (9.9%), and shifted the saved time towards domestic tasks, resulting in a 37.1% (27.3%) increase. Meanwhile, high-skill married women did not make such a time

> 36We also decompose the wage effect by skill levels. As shown in column 2 of Appendix Table D11, employed low-skill women in treated states experienced an imprecise decrease in wages compared to high-skill women, relative to their counterparts in control states. As noted in footnote 33, the result is only suggestive.

> 37The current results are based on women’s contemporary marital status but do not change if we use the pre-period status. As shown in Appendix Table D14, around 95 % of women have the same status in the post-period as they do in the pre-period. In columns 1 and 3 of Appendix Table D13, we confirm that the program did not have any compositional effect on women’s marital status. We also examine the impact on wages for employed married women and present the results in column 3 of Appendix Table D11. As expected, compared to employed unmarried women, there is a decline in wages for employed married women following the scheme’s implementation. As noted previously in footnote 33, this piece of evidence is only suggestive.

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reallocation; instead, they used the saved travel time for work.

Our analysis thus suggests that the disproportionately large burden of household work on married women precludes them from benefiting in the labor market to either search for jobs or work more. These women instead use the time saved from commuting to help their spouses and men residing in the household increase their labor supply. Gender roles within households and bargaining power undermine the effects of the policy. In the next subsection, we provide a simple model of optimal time allocation within the household to formalize these mechanisms and to explicitly state the assumptions needed for it to be consistent with the empirical results obtained above.

# **H.2 A Model of Intra-Household Time Allocation**

The reallocation of household chores from men to women, along with the reduction in labor supply among married women–specifically those with low and medium skills– could be driven by two mechanisms related to household-level decision-making aimed at maximizing joint utility. First, there may be a discrete shift from men to women in household chores that require a discrete amount of time. For example, a long commute by mothers may have previously made it optimal for the father to take their kids to school, perhaps on a bus. After the roll-out of free bus rides for women, this responsibility may be reallocated to the mother. The gender wage gap in India increases the likelihood of such re-optimization at the household level, as men typically earn more than women within the same household.<sup>38</sup> A second mechanism could be the nature of work typically performed by women, which, compared to men, is less likely to be full-time with a fixed wage.<sup>39</sup> Let’s consider the following example of part-time work: a low-skilled woman employed as a house cleaner, who must commute to a neighborhood with high socioeconomic status. Before the free rides program, she has a time-consuming commute on foot. This leads to an

> 38We use the PoI section data to identify husbands and wives, and the InP section data to determine individuals’ average monthly wages between January and March 2021, the three months prior to the scheme’s implementation. After matching data from these two sections, 21% of the observations did not respond to the income survey, raising concerns about potential selection bias. However, over 70% of those who failed to report income did so because interviewers were unable to reach the households during the designated time span. Among the matched sample with income observations, about 99% of the households reported the husband’s wage as being higher than or equal to the wife’s. In these households, husbands’ wages are, on average, twice as high as those of their wives.

> 39Fletcher, Pande and Moore (2017) show that the types of jobs Indian women report wanting vary by age, but are primarily of a part-time nature, reflecting the demands of other household responsibilities, particularly in the context of marriage and childbearing. 73% of women willing to take a job prefer regular, part-time work while only 22% report wanting regular, full-time work; the remaining 5% want a mixture of only occasional full or part-time work.

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indivisible labor supply, where she finds it optimal to stay in the work neighborhood for the entire day to clean multiple homes. However, when buses become free and the travel time between her residential and work neighborhoods decreases, she finds it optimal to skip cleaning one of the houses she previously cleaned. Instead, she takes a bus back to her neighborhood to run a household errand during the day, then returns to the work neighborhood for another job. This reallocation of time simultaneously reduces both her work and travel hours while increasing the time she spends on household chores.

We now describe a simple model of optimal time allocation within the household to formalize this mechanism. Consider a different-sex household in which the man works salaried so that his income _Im_ does not depend on hours worked. The woman, on the other hand, has a divisible labor supply at an hourly wage of _w f_ where the _f_ subscript stands in for _female_ . The total female time endowment is _E_ hours per day. There is an household chore _H_ hours. It is an indivisible such as indispensable requiring activity, grocery shopping, which needs to be performed by either the man or the woman. The disutility of the chore is _dm_ and _d f_ for the man and woman, respectively.

The household maximizes joint utility by allocating the chore to one of its members, captured by an indicator function **1** _H_ that takes the value of one if it is performed by the woman, and zero otherwise: Note that, in most households in India, household work will be shared by both men and women with the bulk of the work done women. The choice of either men or women by performing household work is done for simplicity for exposition and the results hold when household work can be shared. Household surplus is increasing in total income net of the disutility from the household chores. Total income depends on hours worked by the woman, which is the residual left after commuting _C_ hours and taking care of _H_ if she is responsible for the household chores: where the constraint captures the subsistence threshold of income _<u>w</u>_ that the household needs to attain. We assume that male salary alone is not high enough to meet subsis15
tence, i.e., _Im <_ _<u>w</u>_ , so that the woman needs to work regardless of _C_ subject to the time constraint _E − C −_ **1** _H · H ≥_ 0. We assume that the commute time _C ∈{Cslow_ , _C f ast}_ depends on whether the woman takes a slow or fast mode of transport, requiring a higher or lower travel time, _Cslow > C f ast_ , respectively. Therefore, there is a trade-off in female time allocation between work versus commuting. We assume that before the program offering free bus rides to women is rolled out, the slower and more time-consuming commute is chosen. We do not model a man’s commuting as his salary does not depend on hours worked.

Next, we back out from the model the restrictions on the parameters needed for the decisions to be with our results. If these restrictions are optimal compatible empirical plausible, the model offers a useful lens through which these results can be interpreted. Recall that our objective is to rationalize the decrease in labor supply and the increase in household chores for women after the introduction of free bus rides. In our model, this corresponds to **1** _H_ = 0 when _C_ = _Cslow_ , and **1** _H_ = 1 when _C_ = _C f ast_ . Assuming that fast commuting by free buses leaves enough time for the woman to work even if she undertakes the household chore, i.e., _E − C f ast − H >_ 0, the latter implies For these assumptions to be compatible with non-negative wages, gender-specific disutility of chores needs to satisfy _dm > d f_ . Moreover, man’s disutility has to be high enough so that hourly low-skilled female wages cannot compensate for it within the household. This is a plausible assumption for a low-skilled household in the Indian context.

However, in the absence of another restriction, _dm − d f > w f_ would imply that household work would be allocated to the woman even before the program when _C_ = _Cslow_ . To rationalize **1** _H_ = 0 when _C_ = _Cslow_ , it must be the case that doing both the household chores and working—which necessitates commuting—violates the time constraint of the woman: _E − Cslow − H <_ 0. Hence, the two constraints faced by the household—to perform _H_ and attain subsistence within the time endowment—leave no choice but for the man to do household chores and the woman to work. When commuting time drops, however, they find it optimal to reallocate the household work to the woman. For this to reduce her market work hours, model parameters have to satisfy That is, household chores are more time-consuming than the time saving from the free 16
and faster commuting.

To sum three are to rationalize the estimated reallocation up, assumptions sufficient of chores within the household: a more precarious labor force status for women (captured by the low hourly wage compared to the fixed male salary), the indivisibility of a high enough time required for essential household work, and asymmetric genderspecific disutility for these chores. All of these are likely to apply to households with less educated, low-wage women, consistent with empirical evidence as documented above and in recent literature (Fletcher et al., 2017).

# **H.3 Impact on the Time Use of Unemployed Women**

We now shift our focus to examining unemployed women who are actively seeking jobs. First, we estimate equation (7) for these women, classifying them based on their current work status, and present the results in columns 1 and 2 of Table A6.<sup>40</sup> We find that unemployed women in treated states spend more time traveling and, concurrently, less time on domestic duties compared to their counterparts in control states following the implementation of the _Pink Slip_ scheme. Such a shift implies a potential increase in the time these women allocate to job search.

We further investigate whether the changes are driven by the intensive margin or the extensive margin. To do so, we estimate equation (7) for women who have the same preand post-period labor market status–unemployed, and report the results in columns 3 and 4 of Table A6.<sup>41</sup> We that the effects are more for women who are find pronounced consistently unemployed, suggesting that it is these always-unemployed women who are most responsive to the free bus tickets. Therefore, the changes are mainly driven by the intensive margin. Additionally, we estimate equation (6) for travel time for both currently unemployed women (Appendix Figure C2(a)) and always-unemployed women (Appendix Figure C2(b)). In both figures, we observe that unemployed women in treated states spent more time traveling after the scheme’s implementation compared to those in the control states.

Similarly, we explore whether the time usage pattern for unemployed women holds across skill levels and whether married unemployed women also benefit. As shown in Appendix Table D16, the increase in travel time observed in column 1 of Table A6 is

> 40In Appendix Table D15, we cluster the standard errors at the district level. The significance levels of the estimated coefficients are similar to those in Table A6.

> 41In Appendix Table D8, we present a matrix of women’s labor market status before and after the program. Among those who were unemployed in the post-treatment periods, 58.3% were without employment in the pre-period.

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driven women and unmarried women. by high-skill unemployed unemployed Specifically, we observe a null effect (0.02 with _p >_ 0.10) on travel time and an insignificant effect on time spent on household chores (-0.127 with _p >_ 0.10) for married unemployed women, suggesting the persistence of entrenched gender norms.

# **I Impact on Employment and Job Search**

In the previous section, we documented that the _Pink Slip_ scheme affects the amount of time that employed women devote to work, varying by their skill level: increasing for high-skilled and decreasing for low-skilled women. By definition, time spent on work is not applicable for unemployed women. Yet, they display a similar pattern: highskilled unemployed women increase travel time, whereas low-skilled women decrease it. Considering that a portion of this travel time may involve searching for job, the _Pink Slip_ scheme could potentially impact women’s labor market outcomes. While we are unable to directly measure travel time spent on job searches in the CPHS data, we could infer the extent to which these searches may impact labor market outcomes based on observed changes in participation, employment or unemployment. We now present these results, followed additional evidence on the and distance of travel from the Delhi by purpose Primary Survey.

**Evidence from the CPHS Data** We estimate equation (7) to investigate whether women are more likely to be in the labor market, more likely to be employed, and more likely to switch from being out of the labor market to actively searching for jobs after the _Pink Slip_ scheme. The results are in columns 1 to 3 of Table A7. We no reported find statistically or economically significant impact on being in the labor market or being employed. However, there is a marginal increase in the job search for women. Women who were previously out of the labor market are 1.1% ( _p <_ 0.15) more likely to start searching for jobs after the scheme, which is 15.7% of the control mean. We also present the corresponding event study graph in Figure C3, which shows that this marginal increase persisted in the post-implementation periods. The estimated coefficients for time periods 1 and 2 after the policy are statistically significant at 10% and 15% levels, respectively. In period 3, the magnitude remains unchanged, though it is imprecise. These findings indicate that more women are moving from being out of the labor market to actively looking for jobs, although they are not more likely to find employment in this short-run period.

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**Evidence from the Delhi Primary Survey** The results on female time use so far are based on the CPHS data which does not separate travel times into purpose of travel (i.e., commuting to work vs non-work related trips). The Delhi Survey, on the other hand, reports the purpose of travel and the average travel distance for each respondent. While this data does not report employment status, the survey answer ‘ _travel for work_ ’ is meant to capture travel _to work_ in the case of employed women and travel _to search_ for work in the case of unemployed women.

We use the Delhi data to compare the purpose of travel and the average travel distance between new and continuous users of bus transit in the post-implementation periods. We acknowledge that the new users are induced into using the buses because they are free and hence are selected relative to incumbent users. The comparison group of continuous users is matched on observable characteristics to the new users as discussed in Section E.2. While this does not address the selection issue completely, this comparison is still revealing and useful. With this disclaimer in mind, we estimate the following regression: where _Yi_ is a dummy variable for travel purposes which is equal to one if it is for work or a dummy variable for average travel distance via buses which is equal to one if the average distance exceeds 10 kilometers. _I_ new users is a dummy variable set to one for new users and zero for continuous users. _X_ is a vector of control variables including an individual’s marital status and education level. The results are reported in Table A8.

In column 1, we find that new bus users are 6.8% more likely to travel for work than continuous users. The estimated coefficient decreases slightly to 6.6% after conditioning on marital status and education. We then restrict the matched sample to users whose travel purpose is work, which includes travel to work or job search depending on unreported employment status. In columns 3 and 4, we observe that, on average, new users are 18.2%-19.8% more likely to travel longer than 10 kilometers compared to continuous users. These results provide suggestive evidence that free passes enable women to travel greater distances, potentially allowing them to work in more remote jobs or facilitating their job search efforts.

# **J Conclusion**

In the face of a literature that costs often re- large documenting increasing commuting duce women’s work hours and labor force participation, there is an increased policy 19
attention to decreasing the commuting barriers for women. Using the roll-out of free bus services for women in several states of India under the _Pink Slip Program_ , this paper evaluates the consequences of a reduction in commuting costs on women’s time use, including work hours, travel times, and time on household work, as well as their labor force participation.

Employing a synthetic difference-in-differences approach, we find that household expenditures on buses fall and travel time for women employed before the program decreases, implying that demand for bus transportation by women is elastic. We also show, however, that reducing commuting costs alone may not be enough for women to increase work hours or participate more in the labor market. Heterogeneous responses may reflect the re-optimization of agents with varying demographic status: free buses do incentivize unmarried, skilled, and unemployed women to increase labor force participation and search more for Skilled and unmarried women who are intensively jobs. already employed increase their work hours. But low-skilled and married women use the time saved from commuting using free buses to do more household work, replacing some of the household work done by their spouses, who in turn increase their work hours. These results indicate the potential importance of household-level decision-making and intrahousehold bargaining power in determining gender norms that impede some women from taking advantage of the free bus scheme to increase their labor supply.

Our findings have important policy implications: If the goal of providing free transportation to women is to improve women’s labor market outcomes, only a small share of the workforce–primarily skilled unmarried women–benefits from this policy in the immediate short-run.

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