Explore Esther Duflo through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets

We combine data from longitudinal surveys in seven low- and middle-income countries (plus the United States for comparison) to document that depressive symptoms among those aged 55 and above are prevalent in those countries and increase sharply with age. Depressive symptoms in one survey wave are associated with a greater decline in ability to carry out basic daily activities and a higher probability of death in the next wave. Using additional data from a panel survey we conducted in Tamil Nadu with a focus on elderly living alone, we document that social isolation, poverty, and physical health challenges are three of the leading correlates of depression. We discuss potential policy interventions in these three domains, including some results from our randomized control trials in the Tamil Nadu sample.

Documentation

Full Project Name: Women as Policy Makers: Evidence from a Randomized Policy Experiment in India, 1998-2002

Unique ID: 1

PIs: Lori Beaman, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, Petia Topalova

Location: Birbhum district in West Bengal and Udaipur district in Rajasthan, India

Sample: 265 village councils

Timeline: 2000 to 2002

Target Group: Civil servants, Men and boys, Rural population, Women and girls

Outcome of Interest: Citizen satisfaction, Elected official performance, Women’s/girls’ decision-making

Associated publications: https://www.povertyactionlab.org/sites/default/files/publications/65_Duflo_Women_as_Policy_Makers.pdf

More information: https://www.povertyactionlab.org/evaluation/impact-women-policymakers-public-goods-india

Dataverse: Chattopadhyay, Raghabednra; Duflo, Esther, 2007, “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India, 1998-2002”, https://doi.org/10.7910/DVN/2ENLN4, Harvard Dataverse, V4.

Section 10

This dataset was created on 2021-10-06 18:45:38.157 by merging multiple datasets together. The source datasets for this version were:

Women as Policy Makers in India Part A Baseline: womenpolicymakers_parta - Survey A completed at baseline

Women as Policy Makers in India:

Women as Policy Makers Part D Endline: womenpolicymakers_resurveyd - Part D completed at endline

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey instrument:

Section 4

Survey instrument:

Section 5

Survey instrument:

Section 6

Survey instrument:

Section 7

Survey instrument:

Section 8

This dataset was created on 2021-10-06 18:44:00.790 by merging multiple datasets together. The source datasets for this version were:

Women as Policy Makers in India Part B Baseline: womenpolicymakers_partb - Survey B completed at baseline

Women as Policy Makers in India Part C Baseline: womenpolicymakers_partc - Survey C completed at baseline

Women as Policy Makers in India Part D Baseline: womenpolicymakers_partd - Survey D completed at baseline

Women as Policy Makers Part A Endline: womenpolicymakers_resurveya - Survey A completed at endline

Section 9

Survey instrument:

We present results from six randomized control trials of an integrated approach to improve livelihoods among the very poor. The approach combines the transfer of a productive asset with consumption support, training, and coaching plus savings encouragement and health education and/or services. Results from the implementation of the same basic program, adapted to a wide variety of geographic and institutional contexts and with multiple implementing partners, show statistically significant cost-effective impacts on consumption (fueled mostly by increases in self-employment income) and psychosocial status of the targeted households. The impact on the poor households lasted at least a year after all implementation ended. It is possible to make sustainable improvements in the economic status of the poor with a relatively short-term intervention.

The data contains 5 different files, classified by topic. The file india_pov_c81_revise.dta contains variables on the number of dams in each district as well as information about the neighbouring districts. The data set also includes data on local poverty, such as a povertygap measure, the gini coefficient, mean per capita expenditure. The file india_ag_extend contains in addition, data on agricultural produc tion ( value, yield) for major crops and distinguishes between water-intensive and non-water-intensive crops. The file census.dta contains data on the population size and occupation. The file india_public_updown_doc.dta contains data on the availability of public goods such as water access, power facilities and road. The file malaria_code81.dta contains in addition a variable about malaria incidence.

This deposit provides replication data and code for the paper "Long-term Effects of the Targeting the Ultra Poor Program". It includes data from all five survey waves (baseline, 18 months, 3 years, 7 years, and 10 years) that track outcomes among treated and control households in a randomized controlled trial of the TUP program in West Bengal, India.

The promise of randomized controlled trials is that evidence gathered through the evaluation of a specific program helps us—possibly after several rounds of fine-tuning and multiple replications in different contexts—to inform policy. However, critics have pointed out that a potential constraint in this agenda is that results from small "proof-of-concept" studies run by nongovernment organizations may not apply to policies that can be implemented by governments on a large scale. After discussing the potential issues, this paper describes the journey from the original concept to the design and evaluation of scalable policy. We do so by evaluating a series of strategies that aim to integrate the nongovernment organization Pratham's "Teaching at the Right Level" methodology into elementary schools in India. The methodology consists of reorganizing instruction based on children's actual learning levels, rather than on a prescribed syllabus, and has previously been shown to be very effective when properly implemented. We present evidence from randomized controlled trials involving some designs that failed to produce impacts within the regular schooling system but still helped shape subsequent versions of the program. As a result of this process, two versions of the programs were developed that successfully raised children's learning levels using scalable models in government schools. We use this example to draw general lessons about using randomized control trials to design scalable policies.

This database contains data on the health histories of, and access to healthcare facilities for, individuals located in the Udaipur districts of Rajasthan, India. Data was collected at the household level, as well as at the individual level, separately for adults and children. Also, private and public healthcare facilities located in the area were also surveyed. Followup data, including additional baseline information collected in 2004-2005, endline data collected in 2007-2008, and other supplementary datsets (including a key for which locations received experimental treatments) are also included. The database also contains data used in "Improving immunization coverage in rural India: clustered randomized controlled evaluation of immunization campaigns with and without incentives." This data includes immunization history and household information for 5565 children, as well as supplemental information obtained from records kept at immunization camps. It also includes data obtained using the format from the National Family Health Survey conducted in India and data one household characteristics. For a full description of connections between the Health survey data and the Immunization survey data, please see the explanatory document included in the database.

The title of the project was: Improving Gujarat’s industrial pollution inspection standards

This repository contains data from the Tamil Nadu Aging Panel; specifically, it currently contains baseline and Wave 1 data collected on a sample of 6,294 elderly persons aged 55+ in Tamil Nadu, India from January 2019 to 2022. The data is part of an ongoing panel, which will be conducted in two additional waves through 2026. The panel is being conducted in collaboration with the government of Tamil Nadu, specifically the Department of Economics and Statistics (for survey assistance) and the Directorate of Public Health (for health measurements). The repository also includes i) data from a census conducted in 2018 from which the sample for the panel is drawn; and ii) data and replication code for the paper "Impacts of Cognitive Behavioral Therapy and Cash Transfers on Depression and Impairment of Older Persons Living Alone: A Randomized Trial in India," whose sample and much of the data came from the larger panel. Survey instruments for all data collections are included, and each subfolder contains a readme with further information on the data and materials within it. The Panel data (Baseline and Wave 1) and the replication data (CBT replication) can be merged using their unique identifiers. The Census data has been further anonymized to protect respondent privacy and cannot be merged with the above.

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was established in 1968 with an endowment from the Swedish central bank, the Riksbank. The award is administered by the Nobel Foundation, although it is not one of the Nobel Prizes, which were established in the will of Alfred Nobel. The award is chosen using the same method as the original Nobel Prizes and is awarded at the same ceremony as the prizes in Physics, Chemistry, Medicine and Literature every year in Stockholm, while The Nobel Peace Prize is awarded at a different ceremony in Oslo, Norway. Well known public figures who have received the award in the past include Milton Friedman of the University of Chicago in 1976 and Paul Krugman of Princeton University in 2008. The 2022 Prize The winners of the prize in 2022 were Ben Bernanke, Douglas Diamond and Philip Dybvig for "research on banks and financial crises". Ben Bernanke is most well known as the former chairman of the Federal Reserve, where he oversaw the response to the Global Financial Crisis, as well as for his academic work on the causes of the 1929 Wall Street Crash and The Great Depression, which he conducted largely at Princeton. Diamond and Dybvig are the authors of a formal theoretical model, the Diamond-Dybvig model, which pioneered the modelling of bank runs and financial panics. While the decision to award the prize to Bernanke, Diamond and Dybvig has been praised for recognizing their contributions to the theoretical and historical study of banking and its relation to financial panics, others have criticized the decision as their work is seen as ignoring the development of non-bank financial intermediaries, as well as criticizing Bernanke for his policy response to the financial crisis. Diamond is the University of Chicago's fourteenth recipient of the award, while Bernanke is Princeton's ninth and Dybvig is the Wahington University of St. Louis' second. The 2023 Prize The winner of the 2023 prize was Claudia Goldin of Harvard University for "having advanced our understanding of women's labor market outcomes". Goldin is only the third woman to receieve the Sveriges Riksbank prize - Elinor Ostrom being awarded in 2009 and Esther Duflo in 2019 - and the first woman to receive the prize as a sole winner. Goldin is an economic historian and a labor economist by training, whose work has focused on the historical development of women's participation in the labor market and the effects, both on the economy and on wider society, that this has had. Goldin's career-long research into these topics was summarised in her 2019 book Career and Family: Women's Century-Long Jouney toward Equity, which pointed to the unequal pressures which women and men haved faced when building careers over the past century, as women have had to balance their professional ambitions with their traditional role in the household. Goldin is Harvard's 11th recipient of the prize and the first since Micheal Kremer was a recipient in 2019, meaning that the Cambridge, Massauchussetts, based univerity is now only three prizes behind the University of Chicago. The 2024 Prize The winners of the 2024 Economics Nobel will be announced in October 2024. As with every other year, there will be intense speculation as to who will be awarded the prize. The Nobel acts as a signifier as to which sub-fields within the academic discipline of economics are most relevant to the wider world and as to which scholars have made the most cutting-edge contributions over their careers. In the past sub-fields such as Behavioral Economics (for which Richard Thaler was awarded the prize in 2017), Environmental Economics (for which William Nordhaus was awarded the prize in 2018), and the leaders of the "credibility revolution" in economics (for which Card, Angrist, and Imbens were awarded in 2021), received much wider public recognition and understanding of their findings after receiving the Nobel. The Nobel Committee do not release a shortlist of potential recipients, so as always we do not know who is in the running to be next year's recipient(s). From looking at past recipients and the state of current research in economics, however, we have a decent idea of candidates who are likely to win a Nobel in their career, if not in 2024. One contender who has yet to be awarded the prize is Daron Acemoglu of MIT and his co-authors, for their work on the long-run development of institutions which facilitate or hinder economic growth. Other likely nominations could be economists from the school of New Keynesian macroeconomics, such as Olivier Blanchard, Larry Summers and Gregory Mankiw, or economists who work on the issue of inequality, such as Thomas Piketty, Emmanuel Saez and Gabriel Zucman.

Documentation

Full Project Name: Impact of Female Leadership on Aspirations and Educational Attainment for Teenage Girls in India

Unique ID: 498

PIs: Lori Beaman, Esther Duflo, Rohini Pande, Petia Topalova

Location: Birbhum District, West Bengal, India

Sample: 495 villages

Timeline: 2006 to 2007

Target Group: Parents Men and boys Rural population Women and girls Youth

Outcome of Interest: Discrimination Enrollment and attendance Women’s/girls’ decision-making Self-esteem/self-efficacy Aspirations Gender attitudes and norms

Associated publications: http://science.sciencemag.org/content/335/6068/582

More information: https://www.povertyactionlab.org/evaluation/impact-female-leadership-aspirations-and-educational-attainment-teenage-girls-india

Dataverse: Lori Beaman; Raghabendra Chattopadhyay; Esther Duflo; Rohini Pande; Petia Topalova, 2012, “Powerful women and aspirations in India”, https://doi.org/10.7910/DVN/O3UKFO, Harvard Dataverse, V3.

Section 10

Survey instrument:

Section 11

Survey instrument:

Section 12

This dataset was created on 2021-10-06 18:52:27.489 by merging multiple datasets together. The source datasets for this version were:

Powerful Women in India Facilities Survey: Data collected from facilities survey on school facility quality, excluding the following sections: -Anganwadi -Math test -Reading test -School Details

Powerful Women in India Facilities Reading Test: Data collected from facilities survey on school facility quality only from the Reading Test section

Powerful Women in India Household Survey: Data collected from household survey, excluding section A1

Powerful Women in India Participatory Resource Appraisal: Data from the assessment of village resources through a participatory resource appraisal exercise

Powerful Women in India Pradhan Survey: Data from current and previous Pradhans and their spouses about economic condition and political activities

Powerful Women in India Pradhan Seats Reserved for Women: Data at community/village level regarding current and previous Pradhan seats

Powerful Women in India Teenager Survey: Data from teenagers interviewed (children aged 11-16 years)

Section 13

Survey instrument:

Section 14

Survey instrument:

Section 15

Survey instrument:

Section 16

Survey instrument:

Section 17

This dataset was created on 2021-10-06 20:34:42.626 by merging multiple datasets together. The source datasets for this version were:

Powerful Women in India Adult Survey: Adult survey data, excluding section F5 on education; Only one round of data collection

Powerful Women in India Adult Education: Adult survey data from section F5 on education; Only one round of data collection

Powerful Women in India:

Powerful Women in India Facilities Anganwadi: Data collected from facilities survey on school facility quality only from the Anganwadi section

Powerful Women in India Facilities Math Test: Data collected from facilities survey on school facility quality only from the Math Test section

Powerful Women in India Facilities School Details: Data collected from facilities survey on school facility quality only from the School Details section

Powerful Women in India Household Roster: Data collected from household survey section A1 - household roster

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey instruments:

Section 4

Survey instruments:

Section 5

Survey instrument:

Section 6

Survey instrument:

Section 7

Survey instruments:

Section 8

Survey instrument:

Section 9

Survey instruments:

Documentation

Guide to Datasets:

Full Project Name: Can Informational Campaigns Raise Awareness and Local Participation in Primary Education in India?

Unique ID: 43

PIs: Abhijit Banerjee, Rukmini Banerji, Esther Duflo, Rachel Glennerster, Stuti Khemani

Location: Jaunpur district in eastern Uttar Pradesh, India

Sample: Households and government schools in 280 villages

Timeline: 2005 to 2006

Target Group: Children Parents Primary schools Students Urban population

Outcome of Interest: Social service delivery Student learning

Intervention Type: Community participation Information

Research Papers: https://www.povertyactionlab.org/sites/default/files/publications/121-%20Pitfalls%20of%20Participatory%20Programs%20February%202010.pdf

More information: https://www.povertyactionlab.org/evaluation/can-informational-campaigns-raise-awareness-and-local-participation-primary-education

Dataverse: Duflo, Esther; Banerjee, Abhijit; Banerji, Rukmini; Glennerster, Rachel; Khemani, Stuti, 2009, “Pratham Information Project – Read India”, https://doi.org/10.7910/DVN/CHDLPN, Harvard Dataverse, V1.

Section 10

No associated survey instrument

Section 11

Survey instruments:

Section 12

This dataset was created on 2021-10-06 19:25:56.054 by merging multiple datasets together. The source datasets for this version were:

India Education Participation Child Test Panel: This data is tests of children’s reading and math ability. It was done both in school visits and during the household survey. The variable fromsurvey tells where the testing was done. Data from both the household survey and school visit testing has been merged together to form this dataset.

India Education Participation Household Survey Child Panel: This survey was done of households, asking information on the household, parents, children, and schools. This also includes testing of children’s math and reading ability (see section “Child Testing”). It was determined that the school ID variable in “householdsurveyschool.tab” and both the school ID and teacher ID “householdsurveychild.tab” were unreliable and/or incomplete and therefore were dropped.

India Education Participation Household Survey Panel: This survey was done of households, asking information on the household, parents, children, and schools. This also includes testing of children’s math and reading ability (see section “Child Testing”). It was determined that the school ID variable in “householdsurveyschool.tab” and both the school ID and teacher ID “householdsurveychild.tab” were unreliable and/or incomplete and therefore were dropped.

India Education Participation Household Survey School Panel: This survey was done of households, asking information on the household, parents, children, and schools. This also includes testing of children’s math and reading ability (see section “Child Testing”). It was determined that the school ID variable in “householdsurveyschool.tab” and both the school ID and teacher ID “householdsurveychild.tab” were unreliable and/or incomplete and therefore were dropped.

India Education Participation School Observation Panel: This is a form that surveyors fill out as they observe the school.

India Education Participation School Survey Panel: These are questions asked to school supervisors, including information about the school and each of the teachers. The variable for teacher ID was determined to be incomplete and therefore was dropped

India Education Participation School Survey Teacher Panel: These are questions asked to school supervisors, including information about the school and each of the teachers. The variable for teacher ID was determined to be incomplete and therefore was dropped

India Education Participation VEC Member Turnover: This is administrative data that gives information about whether members of the VEC were members in the baseline and midline. There is no corresponding questionnaire. The data is organized as follows: For individuals in the baseline (surveyround = 1), the variable futmember is an indicator variable for whether the individual continues as a member of the VEC at the time of the midline. The variable surveyround takes the value of 0 if the individual does not continue as a member of the VEC at the time of the midline. Similarly, for individuals in the midline (surveyround = 2), the variable prevmember is an indicator for whether the individual in the midline was also a member of the VEC in the period of the baseline (and 0 otherwise). There are no observations for which both

Abstract (en): Microfinance institutions have started to bundle their basic loans with other financial services, such as health insurance. Using a randomized control trial in Karnataka, India, we evaluate the impact on loan renewal from mandating the purchase of actuarially-fair health insurance covering hospitalization and maternity expenses. Bundling loans with insurance led to a 16 percentage points (23 percent) increase in drop-out from microfinance, as many clients preferred to give up microfinance than pay higher interest rates and receive insurance. In a Pyrrhic victory, the total absence of demand for health insurance led to there being no adverse selection in insurance enrollment.

Context

The Nobel Prize is a set of annual international awards bestowed by Swedish and Norwegian institutions in recognition of academic, cultural, or scientific advances.

In 2019, 14 people were awarded with the Nobel Prize. Of these, only 1 was female (Esther Duflo, in the Economy category), representing 7% of the total in this year. And that makes it a pretty ordinary year, since the overall average is 0.47 women per year.

In the last 20 years, there have been more female Nobel laureates than in the first 90 years. However, comparing percentage values of the first decade (1900-1909) to the last decade (2010-2019), the inscrease was from 5 to 10%. It is progressing in the right direction, but maybe not fast enough!

Content

This dataset is about women in Nobel prize from 1901 to 2019. It was obtained by simply extracting data from the Wikipedia pages about Nobel Prize.

Acknowledgements

I thank Iaroslava Mizai, my my inspiration.

Inspiration

My inspiration was Iaroslava Mizai, as well as all women and their great achievements, whether recognized or not.

Abstract (en): We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers' attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children's test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies. (JEL I21, J31, J45, O15)

Documentation

Guide to datasets:

Full Project Name: Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya

PIs: Esther Duflo, Pascaline Dupas, Michael Kremer

Unique ID: 39

Location: Western Province, Kenya

Sample: 210 primary schools

Timeline: 2005 to 2007

Target Group: Children Parents Primary schools Students Teachers

Outcome of Interest: Student learning Provider performance

Intervention Type: Community participation Incentives

Dataverse: Duflo, Esther; Dupas, Pascaline; Kremer, Michael, 2011, “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya”, https://doi.org/10.7910/DVN/LWFH9U, Harvard Dataverse, V2,

Associated publications: https://www.povertyactionlab.org/sites/default/files/publications/39%20Peer%20Effects%2C%20Teacher%20Incentives%2C%20and%20the%20Impact%20of%20Tracking%20Project.pdfhttps://web.stanford.edu/~pdupas/DDK_ETP.pdf

More information:https://www.povertyactionlab.org/evaluation/peer-effects-pupil-teacher-ratios-and-teacher-incentives-kenya

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey instrument:

Section 4

Survey instrument:

Section 5

Survey instrument:

Section 6

Survey instrument:

Section 7

Survey instrument:

Section 8

This dataset was created on 2021-10-06 19:28:05.196 by merging multiple datasets together. The source datasets for this version were:

Kenya ETP Student Test Baseline: Modified from: Student_test_data.dta (one observation per student). It includes baseline characteristics of the students and the “treatment” dummies – whether the school was sampled for “Tracking”, whether the student was assigned to the Contract Teacher, etc. Can be matched with students' test scores at both the endline (fall 2006) and long‐term follow‐up (fall 2007) tests via *pupilid*

Kenya ETP Student Test Endline: Modified from: Student_test_data.dta (one observation per student). It includes baseline characteristics of the students , their test scores at the endline (fall 2006), and the “treatment” dummies – whether the school was sampled for “Tracking”, whether the student was assigned to the Contract Teacher, etc. Can be matched with students' long‐term follow‐up (fall 2007) tests via *pupilid*

Kenya ETP Student Test Long Term Followup: Modified from: Student_test_data.dta (one observation per student). It includes baseline characteristics of the students , their test scores at the long‐term follow‐up (fall 2007), and the “treatment” dummies – whether the school was sampled for “Tracking”, whether the student was assigned to the Contract Teacher, etc. Can be matched with students' endline (fall 2006) tests via *pupilid*

Kenya ETP Student Attendance: “Student_pres_data.dta” is the dataset that contains the presence data for students (presence during surprise visits organized by research team). It is in long format (multiple observations per student, each observation corresponding to a student‐visit). The dataset also includes the baseline characteristics of the students, and the “treatment” dummies.

Kenya ETP Teacher Attendance: “Teacher_pres_data.dta” is the dataset that contains the presence data for teachers (presence during surprise visits organized by research team). It is in long format (multiple observations per teacher, each observation corresponding to a student‐visit). The dataset also includes the baseline characteristics of the teachers, and the “treatment” dummies.

We examine how participation in a microfinance program diffuses through social networks. We collected detailed demographic and social network data in 43 villages in South India before microfinance was introduced in those villages and then tracked eventual participation. We exploit exogenous variation in the importance (in a network sense) of the people who were first informed about the program, "the injection points". Microfinance participation is higher when the injection points have higher eigenvector centrality. We estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and non-participants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants, but that information passing by non-participants is still substantial and significant, accounting for roughly a third of informedness and participation. We also find that, conditioned on being informed, an individual's decision is not significantly affected by the participation of her acquaintances.

Context

Global and regional probability of dying among children aged 5-14 (10q5) and number of deaths by UNICEF Regions
Estimates generated by the UN Inter-agency Group for Child Mortality Estimation (UN IGME) in 2019
downloaded from http://www.childmortality.org
Notes:
10q5 is the probability of dying between age 5 and 14 expressed per 1 000 children aged 5
Lower and Upper refer to the lower bound and upper bound of 90% uncertainty intervals.
Regional classifications refer to the UNICEF's regional classification.

Content

Child Mortality Estimates. Last update: 19 September 2019. Contact: childmortality@unicef.org

For further details please refer to http://data.unicef.org/regionalclassifications/

Acknowledgements

http://www.childmortality.org

Photo by Heather Mount on Unsplash

Inspiration

Abhijit Banerjee, Esther Duflo and Michael Kremer were awarded the Nobel Prize in Economics 2019, for their "experimental approach to alleviating global poverty." With their new approach to getting reliable answers on the best ways to combat global poverty, maybe some children's lives could be saved.

Documentation

Full Project Name: Happiness on Tap: Piped Water Adoption in Urban Morocco

PIs: Florencia Devoto, Esther Duflo, Pascaline Dupas, William Parienté, Vincent Pons

Unique ID: 93

Location: Tangiers, Morocco

Sample: 1,000 home owners in urban areas

Timeline: 2007 - 2008

Target Group: Urban population

Outcome of Interest: Citizen satisfaction, Diarrhea

Guide to Datasets:

Published Papers:

More Information: https://www.povertyactionlab.org/evaluation/household-water-connections-tangier-morocco

Section 10

Survey instrument:

Section 11

Survey instrument:

Section 12

This dataset was created on 2021-10-06 18:54:24.290 by merging multiple datasets together. The source datasets for this version were:

Morocco Water Access:

Morocco Water Access Household Distance to Tap: distances_price_zones_anl : Contains information about the distance to the closest public tap and the pricing schedule for the BSI connection

Morocco Water Access Endline Household, Part 1: endline_ACD_hhid_anl : household survey data at endline from section A, C, and D in the survey instrument

Morocco Water Access Household Treatment Spillover: spillovers_anl : Contains information about the share of treatment households within 20 or 50 meters radius. Also contains information about whether households had gotten connected to the grid by August 2009

Morocco Water Access Baseline Illness Diary: suivimaladies_decembre07_corr_anl : illness diary data from December 2007

Morocco Water Access Baseline Household: baseline_menage_hhid_anl : baseline household survey

Morocco Water Access Endline Household, Part 2: endline_BDEFKLM_hhid_anl : household data at endline from survey sections B, D, E, F, K, L, and M

Section 13

Survey instrument:

Section 14

Survey instrument:

Section 15

Survey instrument:

Section 16

This dataset was created on 2021-10-06 20:34:05.138 by merging multiple datasets together. The source datasets for this version were:

Morocco Water Access Baseline Household Roster: baseline_roster_hhid_anl : household roster from baseline survey

Morocco Water Access:

Morocco Water Access Endline Presence of E. Coli: endline_colis_anl : presence of e. coli in household water supply at endline

Morocco Water Access Treatment/ Control Groups: groupe_connexion_anl : Dataset with information about who is treatment and who is control, and whether those in the treatment groups got connected to the grid, and if so the date of the connection

Morocco Water Access Baseline School Diary: scolarisation_baseline_anl : children's school diary data at baseline

Morocco Water Access Midline 1 Illness Diary: suivimaladies_mai08_corr_anl : illness diary data from first followup after baseline in May 2008

Morocco Water Access Midline 2 Illness Diary: suivimaladies_aout08_corr_anl : illness diary data from second followup in August 2008

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey instrument:

Section 4

Survey instrument:

Section 5

Survey instrument:

Section 6

Survey instrument:

Section 7

This dataset was created on 2021-10-06 18:53:18.211 by merging multiple datasets together. The source datasets for this version were:

Morocco Water Access Baseline Age-Gender Reference: sexe_age_ref_anl : reference dataset for sex and age of each household member at baseline

Morocco Water Access Endline Education: education_endline_anl : data on whether children in household were registered for school at endline

Morocco Water Access Endline School Diary: scolarisation_endline_anl : children's school diary data at endline

Morocco Water Access Endline Illness Diary: suivimaladies_novembre08_corr_anl : illness diary data from endline November 2008

Section 8

Survey instrument:

Section 9

Survey instrument: