This dataset is about books and is filtered where the author is Esther Duflo, featuring 2 columns: book, and publication date. The preview is ordered by publication date (descending).

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 dataset is about news and is filtered where the news title includes Esther Duflo, featuring 10 columns including classification, entities, keywords, news link, and news title. The preview is ordered by publication date (descending).

Ester Diuflo pranc Esther DufloE Duflo 2009 m Gimė 1972 m spalio 25 d 51 metai Paryžius PrancūzijaTėvas Michel DufloMoti

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 data set uses political reservations for women in India to study the impact of women's leadership on policy decisions. Using a dataset we collected on 265 village councils in West Bengal and Rajasthan, we compare the type of public goods provided in reserved and unreserved village's councils. Data sets based upon information provided by GP Pradhans, local villagers, and the 1991 Indian Census.

Massive Online Open Courses (MOOCs) present the potential to deliver high quality education to a large number of students. But they suffer from low completion rates. This paper identifies disorganization as a factor behind failure to complete a MOOC. Students who enroll one day late are 17 percentage points less likely to earn a certificate than students who enroll exactly on time. This reflects selection, but it does seem to be related to demographic characteristics, motivation to complete the course, or ability. This suggests that building in even more structure in the MOOC could be a factor in improving performance.

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:

Fayl Faylın tarixçəsi Faylın istifadəsi Faylın qlobal istifadəsi MetaməlumatlarSınaq göstərişi ölçüsü 536 599 piksel Dig

This data is the basis for the article: Banerjee, Abhijit V.; Banerji, Rukmini; Duflo, Esther; Glennerster, Rachel; and Khemani, Stuti: "Pitfalls of Participatory Programs: Evidence from a Randomized Evaluation in Education in India" in the American Economic Journal: Economic Policy.

Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.

Section 2

Description and codebook for subset of harmonized variables:

Section 3

Survey Instruments:

Section 10

No associated survey instrument

Section 5

Survey instruments:

Section 7

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Section 8

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Section 9

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Section 11

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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 6

Survey instruments:

Section 4

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 surv

This paper presents the results of two randomized experiments conducted in schools in urban India. A remedial education program hired young women to teach students lagging behind in basic literacy and numeracy skills. It increased average test scores of all children in treatment schools by 0.28 standard deviation, mostly due to large gains experienced by children at the bottom of the test-score distribution. A computer-assisted learning program focusing on math increased math scores by 0.47 standard deviation. One year after the programs were over, initial gains remained significant for targeted children, but they faded to about 0.10 standard deviation.

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.

Section 3

Survey Instrument:

Section 4

Survey Instrument:

Section 10

Survey Instrument:

Section 11

Survey Instrument:

Section 6

Survey Instrument:

Section 8

Survey Instrument:

Section 9

Survey Instrument:

Documentation

Guide to Datasets:

Full Project Name: Turning a Shove into a Nudge? A “Labeled Cash Transfer” for Education

PIs: Najy Benhassine, Florencia Devoto, Esther Duflo, Pascaline Dupas, Victor Pouliquen

Unique ID: 183

Location: Marrakech-Tensift-Al Haouz, Meknès-Tafilalet, l’Oriental, Souss-Massa-Draa, and Tadla-Azilal, Morocco

Sample: Households with children of primary school age from 636 communities

Timeline: 2008-2010

Target Group: Parents Rural population Students

Outcome of Interest: Dropout and graduation Enrollment and attendance Student learning

Published Papers:

More Information: https://www.povertyactionlab.org/evaluation/cash-transfers-education-morocco

Section 5

Survey Instrument:

Section 2

Description and codebook for subset of harmonized variables:

Section 12

Survey Instrument:

Section 7

Survey Instruments:

Section 13

This dataset was created on 2021-10-06 20:33:29.716 by merging multiple datasets together. The source datasets for this version were:

Morocco CCT Education Baseline Household: “cct_baseline_an.dta”: baseline household survey data

Morocco CCT Education Child Math Test Results: “cct_aser_an.dta”: data from tests in mathematics administered to one child per household during the endline household survey.

Morocco CCT Education Dropout Rate Correction: “cct_correction_dropout_date_an.dta”: database created to input corrections to the school visit data about dropout dates. When dropout dates were not consistent between two visits, data were manually checked to determine which data was the most accurate.

Morocco CCT Education Household Awareness: “cct_knowledge_households_year1_an.dta”: data from the awareness survey administered to a subset of households in 2009.

Morocco CCT Education Household Weights: “cct_hh_weights_an.dta”: Database with sampling weights for household surveys.

Morocco CCT Education School Prelim: “cct_preliminary_survey_an”: data from the school-level survey conducted in preparation for the study in 2008.

Morocco CCT Education School Strata: “cct_stratum_an.dta”: database at the school sector level with the stratum used for the randomization.

Morocco CCT Education School Visits: “cct_school_visits_an.dta”: Data from the 7 school visits done between 2008 and 2010.

Morocco CCT Education Tayssir Admin: “cct_tayssir_admin_data_an.dta”: Dataset with information on students enrolled in the Tayssir program, including number of days of absence by month and amount of transfer received by month.

Morocco CCT Education Teacher Awareness 1: “cct_knowledge_teachers_year1_an.dta”: data from awareness survey administered to a subset of teachers in 2009.

Morocco CCT Education Teacher Awareness 2: “cct_knowledge_teachers_year2_an.dta”: data from awareness survey administered to subset of teachers in 2010.

Morocco CCT Education Endline Household, Part 1: “cct_endline_an.dta”: endline household survey data First third of variables (1/3)

Morocco CCT Education Endline Household, Part 2: “cct_endline_an.dta”: endline household survey data Second third of variables (2/3)

Morocco CCT Education Endline Household, Part 3: “cct_endline_an.dta”: endline household survey data Final third of variables (3/3)

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

Connecting private dwellings to the water main is expensive and typically cannot be publicly financed. We show that households’ willingness to pay for a private connection is high when it can be purchased on credit, not because a connection improves health but because it increases the time available for leisure and reduces inter- and intra-household conflicts on water matters, leading to sustained improvements in well-being. Our results suggest that facilitating access to credit for households to finance lump sum quality-of life investments can significantly increase welfare, even if those investments do not result in any health or income gains.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.

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)

This package contains the dataset and code used in the study investigating whether intuitive and school-taught skills build on each other. According to large-scale surveys, most children and adolescents in India perform poorly in “abstract” arithmetic (i.e., the arithmetic operations typically taught in school). Yet, those employed in informal markets seem to perform relatively complex arithmetic operations mentally when handling transactions (e.g., to calculate amounts due or change). Is it possible to leverage the skills that these children already have to help them succeed in abstract arithmetic? We will conduct a study to address this question, by surveying children and adolescents selling in markets in and around Delhi in order to understand why they might succeed at “market” arithmetic but struggle with abstract arithmetic.