Affirmative action in the form of reservations is a divisive and contentious topic of policy in India. In this paper, we aim to create a principled and data-driven model to design the reservations policy in India. We look at some arguments against current policy and try to resolve them. We use statistical modeling to create our new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status). RAMSES measures the multidimensional disadvantage faced by an individual as an "adjusted income", which attempts to calibrate the quantum of compensatory aid in the form of reservations for that individual to have a level playing field. We illustrate our model using a case study.
{"title":"An Alternative to India's Reservation Policy: A Unified Framework for Rigorous and Adaptive Measurement of Socio-Economic Status","authors":"Dhruv Sinha, Ojas Sahasrabudhe, Dhruv Agarwal, Debayan Gupta","doi":"10.1145/3378393.3402240","DOIUrl":"https://doi.org/10.1145/3378393.3402240","url":null,"abstract":"Affirmative action in the form of reservations is a divisive and contentious topic of policy in India. In this paper, we aim to create a principled and data-driven model to design the reservations policy in India. We look at some arguments against current policy and try to resolve them. We use statistical modeling to create our new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status). RAMSES measures the multidimensional disadvantage faced by an individual as an \"adjusted income\", which attempts to calibrate the quantum of compensatory aid in the form of reservations for that individual to have a level playing field. We illustrate our model using a case study.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125077800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matt Ziegler, Morgan Wack, Nancy Ingutia, Ian Muiruri, Nicholas Njogu, Kennedy Muriithi, William Njoroge, James Long, Kurtis Heimerl
Wildlife conservancies across the globe are increasingly recognizing their need to support their surrounding communities to sustainably operate. Rapidly shifting environmental and sociopolitical climates increasingly stress existing resource and service provisions, forcing wildlife conservancies to co-manage with local communities shared resources like water, wildlife, soil, pollinators, and security. This work presents a case study in Laikipia, Kenya on Ol Pejeta Conservancy's use of text-based technologies to provide services and build relationships with the many widely-dispersed communities on its borders. Through technology deployments, staff interviews, and community focus groups, we investigate a potential role for basic mobile phone services, like SMS and USSD, to help conservancy personnel disseminate accurate and timely information, gather community feedback, address grievances, and improve accountability. Our findings show that communication with locals requires intense and ongoing effort from conservancy staff. Partially successful deployments of phone services provide a proof-of-concept for their utility in community relations but highlight particular design challenges for wildlife conservancies; having critical needs for broad inclusive engagement; clear, deliberate communication; and careful trust-building.
{"title":"Can Phones Build Relationships?: A Case Study of a Kenyan Wildlife Conservancy's Community Development","authors":"Matt Ziegler, Morgan Wack, Nancy Ingutia, Ian Muiruri, Nicholas Njogu, Kennedy Muriithi, William Njoroge, James Long, Kurtis Heimerl","doi":"10.1145/3378393.3402279","DOIUrl":"https://doi.org/10.1145/3378393.3402279","url":null,"abstract":"Wildlife conservancies across the globe are increasingly recognizing their need to support their surrounding communities to sustainably operate. Rapidly shifting environmental and sociopolitical climates increasingly stress existing resource and service provisions, forcing wildlife conservancies to co-manage with local communities shared resources like water, wildlife, soil, pollinators, and security. This work presents a case study in Laikipia, Kenya on Ol Pejeta Conservancy's use of text-based technologies to provide services and build relationships with the many widely-dispersed communities on its borders. Through technology deployments, staff interviews, and community focus groups, we investigate a potential role for basic mobile phone services, like SMS and USSD, to help conservancy personnel disseminate accurate and timely information, gather community feedback, address grievances, and improve accountability. Our findings show that communication with locals requires intense and ongoing effort from conservancy staff. Partially successful deployments of phone services provide a proof-of-concept for their utility in community relations but highlight particular design challenges for wildlife conservancies; having critical needs for broad inclusive engagement; clear, deliberate communication; and careful trust-building.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
{"title":"RoadCare","authors":"Saurabh Tiwari, Ravi Bhandari, B. Raman","doi":"10.1145/3378393.3402284","DOIUrl":"https://doi.org/10.1145/3378393.3402284","url":null,"abstract":"Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the \"smoothest\" road route to a destination.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121432972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As disasters such as earthquakes and floods become more frequent and detrimental, it is increasingly important that water infrastructure resilience be strategically enhanced to support post-disaster functionality and recovery. In this paper, we focus on the problem of strategically building seismic-resilient pipe networks to ensure direct water supply to critical customers and certain proximity to water sources for residential areas, which we formalize as the Steiner Network Problem with Coverage Constraints. We provide complexity statements of the problem and present an efficient mixed-integer linear program encoding to solve the problem. We also investigate the problem of planning partial network installments to maximize efficiency over time and propose a fast and effective sequential planning algorithm to solve it. We evaluate our algorithms on synthetic water networks and also apply them to a case study on a water service zone in Los Angeles, which demonstrate the effectiveness of our methods for large-scale real-world applications.
{"title":"Enhancing Seismic Resilience of Water Pipe Networks","authors":"Taoan Huang, B. Dilkina","doi":"10.1145/3378393.3402246","DOIUrl":"https://doi.org/10.1145/3378393.3402246","url":null,"abstract":"As disasters such as earthquakes and floods become more frequent and detrimental, it is increasingly important that water infrastructure resilience be strategically enhanced to support post-disaster functionality and recovery. In this paper, we focus on the problem of strategically building seismic-resilient pipe networks to ensure direct water supply to critical customers and certain proximity to water sources for residential areas, which we formalize as the Steiner Network Problem with Coverage Constraints. We provide complexity statements of the problem and present an efficient mixed-integer linear program encoding to solve the problem. We also investigate the problem of planning partial network installments to maximize efficiency over time and propose a fast and effective sequential planning algorithm to solve it. We evaluate our algorithms on synthetic water networks and also apply them to a case study on a water service zone in Los Angeles, which demonstrate the effectiveness of our methods for large-scale real-world applications.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124083624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the age of social media, disasters and epidemics usher not only devastation and affliction in the physical world, but also prompt a deluge of information, opinions, prognoses and advice to billions of internet users. The coronavirus epidemic of 2019-2020, or COVID-19, is no exception, with the World Health Organization warning of a possible 'infodemic' of fake news. In this study, we examine the alternative narratives around the coronavirus outbreak through advertisements promoted on Facebook, the largest social media platform in the US. Using the new Facebook Ads Library, we discover advertisers from public health and non-profit sectors, alongside those from news media, politics, and business, incorporating coronavirus into their messaging and agenda. We find the virus used in political attacks, donation solicitations, business promotion, stock market advice, and animal rights campaigning. Among these, we find several instances of possible misinformation, ranging from bioweapons conspiracy theories to unverifiable claims by politicians, to the sale of face masks which may not necessarily protect the wearer. As we make the dataset available to the community, we hope the advertising domain will become an important part of quality control for public health communication and public discourse in general.
{"title":"COVID-19 on Facebook Ads: Competing Agendas around a Public Health Crisis","authors":"Yelena Mejova, Kyriaki Kalimeri","doi":"10.1145/3378393.3402241","DOIUrl":"https://doi.org/10.1145/3378393.3402241","url":null,"abstract":"In the age of social media, disasters and epidemics usher not only devastation and affliction in the physical world, but also prompt a deluge of information, opinions, prognoses and advice to billions of internet users. The coronavirus epidemic of 2019-2020, or COVID-19, is no exception, with the World Health Organization warning of a possible 'infodemic' of fake news. In this study, we examine the alternative narratives around the coronavirus outbreak through advertisements promoted on Facebook, the largest social media platform in the US. Using the new Facebook Ads Library, we discover advertisers from public health and non-profit sectors, alongside those from news media, politics, and business, incorporating coronavirus into their messaging and agenda. We find the virus used in political attacks, donation solicitations, business promotion, stock market advice, and animal rights campaigning. Among these, we find several instances of possible misinformation, ranging from bioweapons conspiracy theories to unverifiable claims by politicians, to the sale of face masks which may not necessarily protect the wearer. As we make the dataset available to the community, we hope the advertising domain will become an important part of quality control for public health communication and public discourse in general.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126003560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Milusheva, R. Marty, Guadalupe Bedoya, Elizabeth Resor, Sarah Williams, Arianna Legovini
UPDATED---June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies.
{"title":"Can crowdsourcing create the missing crash data?","authors":"S. Milusheva, R. Marty, Guadalupe Bedoya, Elizabeth Resor, Sarah Williams, Arianna Legovini","doi":"10.1145/3378393.3402264","DOIUrl":"https://doi.org/10.1145/3378393.3402264","url":null,"abstract":"UPDATED---June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133822626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ICTD is a field with a long history of interventionist research in a broad set of domains, including health, agriculture, education, and civics. A common thread between many of these interventions is that they addressed the knowledge and actions of practitioners who were engaged in development activities in their contexts. In this paper, I survey the past literature of ICTD interventions targeting practitioners to identify a common typology that spans domain and context. I use Lave and Wenger's Communities of Practice (CoP) theory as a way to understand the situated and social aspects of practice and describe how ICTD interventions have often engaged with such communities. I discuss how a CoP lens may intersect with other theoretical lenses in ICTD and related fields, specifically around concepts of agency, intrinsic motivation, amplification, and sustainability. I describe how such intersections may inform future interventionist research in the Global South.
{"title":"Practitioners and ICTD: Communities of Practice Theory in Technology Interventionism","authors":"Anthony Poon","doi":"10.1145/3378393.3402271","DOIUrl":"https://doi.org/10.1145/3378393.3402271","url":null,"abstract":"ICTD is a field with a long history of interventionist research in a broad set of domains, including health, agriculture, education, and civics. A common thread between many of these interventions is that they addressed the knowledge and actions of practitioners who were engaged in development activities in their contexts. In this paper, I survey the past literature of ICTD interventions targeting practitioners to identify a common typology that spans domain and context. I use Lave and Wenger's Communities of Practice (CoP) theory as a way to understand the situated and social aspects of practice and describe how ICTD interventions have often engaged with such communities. I discuss how a CoP lens may intersect with other theoretical lenses in ICTD and related fields, specifically around concepts of agency, intrinsic motivation, amplification, and sustainability. I describe how such intersections may inform future interventionist research in the Global South.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129731190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manasvini Sethuraman, Rebecca E. Grinter, E. Zegura
With over 6 million articles and 38 million editors, English Wikipedia is one of the largest peer produced and edited encyclopedias on the Internet. Some attribute this success to the peer production process which allows many to contribute. However, not all articles are created equally, or evolve similarly, in terms of quality and amount of attention they receive from the editing community. We examine pages with geotagged content in English Wikipedia in four categories, places with Indigenous majorities (of any size), Rural places, Urban Clusters, and Urban areas. We find significant differences in quality and editor attention for articles about places with Native American majorities, as compared to other places.
{"title":"Approaches to Understanding Indigenous Content Production on Wikipedia","authors":"Manasvini Sethuraman, Rebecca E. Grinter, E. Zegura","doi":"10.1145/3378393.3402249","DOIUrl":"https://doi.org/10.1145/3378393.3402249","url":null,"abstract":"With over 6 million articles and 38 million editors, English Wikipedia is one of the largest peer produced and edited encyclopedias on the Internet. Some attribute this success to the peer production process which allows many to contribute. However, not all articles are created equally, or evolve similarly, in terms of quality and amount of attention they receive from the editing community. We examine pages with geotagged content in English Wikipedia in four categories, places with Indigenous majorities (of any size), Rural places, Urban Clusters, and Urban areas. We find significant differences in quality and editor attention for articles about places with Native American majorities, as compared to other places.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125116926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arpita Biswas, M. Kołczyńska, Saana Rantanen, Polina Rozenshtein
Fairness and bias in automated decision-making gain importance as the prevalence of algorithms increases in different areas of social life. This paper contributes to the discussion of algorithmic fairness with a crowdsourced vignette survey on recidivism risk assessment, which we compare to previous studies on this topic and to predictions of an automated recidivism risk tool. We use the case of the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) and the Broward County dataset of pre-trial defendants as a data source and for purposes of comparability with the earlier analysis. In our survey, each respondent assessed recidivism risk for a set of vignettes describing real defendants, where each set was balanced with regard to the defendants' race and re-offender status. The survey ensured a 50: 50 ratio of black and white respondents. We found that predictions in our survey---while less accurate---were considerably more fair in terms of equalized odds than previous surveys. We attribute it to the differences in survey design: using the balanced set of vignettes and not providing feedback after responding to each vignette. We also analyzed the performance and fairness of predictions by race of respondent and defendant. We found that both white and black respondents tend to favor defendants of their own race, but the magnitude of the effect is relatively small. In addition to the survey, we train two statistical models, one trained with balanced data and other with unbalanced data. We observe that the model trained on balanced data is substantially more fair and possess less in-group bias.
{"title":"The Role of In-Group Bias and Balanced Data: A Comparison of Human and Machine Recidivism Risk Predictions","authors":"Arpita Biswas, M. Kołczyńska, Saana Rantanen, Polina Rozenshtein","doi":"10.1145/3378393.3402507","DOIUrl":"https://doi.org/10.1145/3378393.3402507","url":null,"abstract":"Fairness and bias in automated decision-making gain importance as the prevalence of algorithms increases in different areas of social life. This paper contributes to the discussion of algorithmic fairness with a crowdsourced vignette survey on recidivism risk assessment, which we compare to previous studies on this topic and to predictions of an automated recidivism risk tool. We use the case of the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) and the Broward County dataset of pre-trial defendants as a data source and for purposes of comparability with the earlier analysis. In our survey, each respondent assessed recidivism risk for a set of vignettes describing real defendants, where each set was balanced with regard to the defendants' race and re-offender status. The survey ensured a 50: 50 ratio of black and white respondents. We found that predictions in our survey---while less accurate---were considerably more fair in terms of equalized odds than previous surveys. We attribute it to the differences in survey design: using the balanced set of vignettes and not providing feedback after responding to each vignette. We also analyzed the performance and fairness of predictions by race of respondent and defendant. We found that both white and black respondents tend to favor defendants of their own race, but the magnitude of the effect is relatively small. In addition to the survey, we train two statistical models, one trained with balanced data and other with unbalanced data. We observe that the model trained on balanced data is substantially more fair and possess less in-group bias.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the impact of urban infrastructure investment on female labour force participation is studied for different income groups, namely, the poor, the lower class, and the middle class. In particular, the effect of government spending on water supply/sanitation, buses, and basic household services, under the Jawaharlal Nehru National Urban Renewal Mission (JNNURM) on female labour force participation is assessed. A probit regression model, over a pooled cross sectional dataset at the district level is used for the analysis. Female labour force participation is the categorical dependent variable, while government spending on the infrastructure scheme is the main explanatory variable. The results indicate that investment on water supply and sanitation facilities in a district has a positive effect on female labour force participation among middle class women, but not on lower class women. The expenditure on dwellings, and basic services, discourages female labour force participation among poor women. The paper throws light on the role of class in determining the effect of urban infrastructure programs on female labour force participation. It shows that the poor women are not driven into labour force due to lesser burden of household chores in the presence of better water supply, and sanitation facilities, and the urban housing programs alienate poor women from job opportunities, while encouraging middle class women to participate in the labour force due to the creation of a slum free, and supposedly safe environment.
{"title":"The effect of urban infrastructure development on female labour force participation among the poor and middle class in India","authors":"Ahalya Ramanathan, S. Paul","doi":"10.1145/3378393.3402234","DOIUrl":"https://doi.org/10.1145/3378393.3402234","url":null,"abstract":"In this paper, the impact of urban infrastructure investment on female labour force participation is studied for different income groups, namely, the poor, the lower class, and the middle class. In particular, the effect of government spending on water supply/sanitation, buses, and basic household services, under the Jawaharlal Nehru National Urban Renewal Mission (JNNURM) on female labour force participation is assessed. A probit regression model, over a pooled cross sectional dataset at the district level is used for the analysis. Female labour force participation is the categorical dependent variable, while government spending on the infrastructure scheme is the main explanatory variable. The results indicate that investment on water supply and sanitation facilities in a district has a positive effect on female labour force participation among middle class women, but not on lower class women. The expenditure on dwellings, and basic services, discourages female labour force participation among poor women. The paper throws light on the role of class in determining the effect of urban infrastructure programs on female labour force participation. It shows that the poor women are not driven into labour force due to lesser burden of household chores in the presence of better water supply, and sanitation facilities, and the urban housing programs alienate poor women from job opportunities, while encouraging middle class women to participate in the labour force due to the creation of a slum free, and supposedly safe environment.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：