This document provides an overview of a study that estimates a dynamic agricultural production model using observed subjective distributions from farmers in Tanzania. The key points are:
1) The study measures farmers' subjective probability distributions over expected crop prices and yields at different points throughout the growing season. This allows the authors to relax rational expectations assumptions and incorporate observed expectations.
2) The model assumes crops grow according to a nested CES production function with labor, pesticides, and lagged output as inputs. Farmers are assumed to maximize expected profits subject to shocks modeled by subjective distributions.
3) Identification of the shock distributions gt(θt) is needed to estimate the model parameters but the study only observes subjective output
1. The document analyzes maternal beliefs about the technology of skill formation in children.
2. Objective estimates of the technology are obtained using data on skills, investments, and health conditions of children over time. These estimates find that investments have a statistically significant effect on skills.
3. Maternal beliefs about the technology are heterogeneous and may differ systematically from objective estimates if mothers are misinformed. Comparing beliefs to estimates could reveal if certain groups are misinformed.
This document analyzes the long-term effects of the 1832 Cherokee Land Lottery on wealth using census and other data. It finds that those who received land titles in the lottery on average had higher wealth levels in 1850 and 1860 compared to those who did not receive titles, even after controlling for other factors. The effects were larger for those matched to land closest to gold deposits discovered in the 1830s.
Hiring knowledge agents to spread information about a public health insurance scheme in rural India had a positive impact on villagers' knowledge about the scheme. The effect was entirely driven by agents on incentive pay contracts, who received bonuses based on villagers' knowledge levels. Improved knowledge, in turn, increased enrollment in the scheme. Social distance between agent and villager had a negative impact on knowledge transmission, but incentive pay canceled out this effect. The study used a randomized controlled trial to test these relationships, hiring different types of agents (fixed pay vs incentive pay) across villages.
1. The document analyzes maternal beliefs about the technology of skill formation in children.
2. Objective estimates of the technology are obtained using data on skills, investments, and health conditions of children over time. These estimates find that investments have a statistically significant effect on skills.
3. Maternal beliefs about the technology are heterogeneous and may differ systematically from objective estimates if mothers are misinformed. Comparing beliefs to estimates could reveal if certain groups are misinformed.
This document analyzes the long-term effects of the 1832 Cherokee Land Lottery on wealth using census and other data. It finds that those who received land titles in the lottery on average had higher wealth levels in 1850 and 1860 compared to those who did not receive titles, even after controlling for other factors. The effects were larger for those matched to land closest to gold deposits discovered in the 1830s.
Hiring knowledge agents to spread information about a public health insurance scheme in rural India had a positive impact on villagers' knowledge about the scheme. The effect was entirely driven by agents on incentive pay contracts, who received bonuses based on villagers' knowledge levels. Improved knowledge, in turn, increased enrollment in the scheme. Social distance between agent and villager had a negative impact on knowledge transmission, but incentive pay canceled out this effect. The study used a randomized controlled trial to test these relationships, hiring different types of agents (fixed pay vs incentive pay) across villages.
The document summarizes research on the quality of life insurance advice provided by agents in India. It finds that agents overwhelmingly recommend whole life policies over term life policies, even when term policies better meet customers' needs for risk coverage at low cost. Agents are motivated to recommend whole life due to higher commissions. The research tests whether the quality of advice improves when disclosure requirements make agency problems more transparent or when competition is increased. It uses an audit study approach with standardized customer profiles to evaluate agent recommendations across conditions.
The document presents a dynamic discrete choice model of demand for insecticide treated nets (ITNs) that accounts for time inconsistent preferences and unobserved heterogeneity. The model has three periods where agents make ITN purchase and retreatment decisions. Agents are either time consistent, "naive" time inconsistent, or "sophisticated" time inconsistent. The model is identified in two steps - first when types are directly observed using survey responses, and second when types are unobserved. Identification exploits variation from elicited beliefs about malaria risk. The model can point identify time preference parameters and utility functions up to a normalization.
This document summarizes a study on the long-term effects of teacher performance pay in India. The study conducted a 5-year randomized controlled trial across hundreds of schools in Andhra Pradesh. Schools were randomly assigned to receive either individual or group-based teacher incentives linked to student test scores, or to serve as controls. The study finds that students whose teachers received individual incentives scored significantly higher than controls on math and language tests after 5 years of exposure. They also scored higher on non-incentivized subjects. The study provides some of the longest-running and most robust evidence on performance pay for teachers.
The document summarizes a study that evaluated the impact of a business literacy intervention in rural Mexico. The intervention provided free 6-week business skills courses taught by professors and students to about 25 women entrepreneurs per class. The courses covered topics like accounting, pricing, taxes, and marketing. The study found that the training led to large, positive and significant effects on profits, revenues, and number of clients both in the short- and medium-run. There was also evidence of heterogeneous treatment effects. The results suggest the training improved accounting practices, lowered costs, increased mark-ups, and in some cases lowered prices.
Deloitte 2016 - EKF - Digital Customer Journey - conference material - f...Maciej Malesa
1) The document analyzes digital banking channels in Poland, including a study of customer journeys, functionality, and user experience across 15 major banks.
2) It finds that while banks have digitized early stages of the customer journey like information gathering and account opening, they have not fully supported financial goals through the relationship. Transactional banking is more digitized than credit products.
3) Banks fall into four categories based on their digital strategies - Cheetahs, Antelopes, Bears, and Turtles. Cheetahs offer the most functionality consistent with best practices but neglect user experience, while the mobile channel now provides a better user experience than web.
Dow Corning tasked a team with developing a model to forecast global silicone demand. The team analyzed industrial indicators and GDP data to build a regression model. The model forecasts demand within 1.4% error and consistently deviates from actual values by less than 15%. The team recommends continuous updates to the model as new data becomes available to improve accuracy.
Demand Planning Leadership Exchange: SAP APO DP Statistical Forecast Optimiza...Plan4Demand
866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com
If you are still using manual processes to support your demand planning cycles outside of APO, this Leadership Exchange is for you and your team. Join us to learn how to remove the burden of magnitude and get back on the track to leveraging your SAP APO DP to the fullest beginning with Statistical Forecast Optimization.
The session will focus on common issues and methods to maximize your implementation in order to really turbo-charge your Demand Planning. To do this, we’ll touch upon ways to simplify the process, which statistical models to use and when, and how to prioritize and manage by exception effectively for the long haul to evolve with your business.
A few key takeaways from this session include:
How to unclutter the process
Which Statistical Model to use & When
Tips for holistic optimization
Future design considerations
Check out this webinar on-demand at http://plan4demand.com/Video-SAP-APO-DP-Statistical-Forecast-Optimization
This document summarizes John Sneed's research on developing an earnings forecasting model based on theoretical factors rather than statistical selection of variables. It begins by describing Ou's existing model and its limitations in relying on statistical techniques without theoretical justification. It then discusses theories from economics literature on factors that could lead to differential profits across firms/industries: improper measurement of intangible capital like R&D/advertising, differential returns on such investments, and existence of market power. Based on these theories, Sneed develops a model incorporating variables like average R&D over 5 years, prior year's advertising, and 5-year average capital expenditures to test if it improves upon Ou's model.
The document discusses the design of an impact evaluation using an explanatory controlled before and after study design with multiple time points to collect quantitative and qualitative data to explain the results of a health insurance program called bHIP in Bangladesh. It considers two design options - enrolling all 16 districts over 5 years or enrolling 4 intervention and 4 control districts over 3 years - and discusses choosing indicators aligned with the program's theory of change.
The use of biodata for employee selection: Past research and future implicationsAndrea Dvorak
Summary of the article 'The use of biodata for employee selection: Past research and future implications.'
2009,human resource management review - human resource,no. 3,pp. 219-231,vol. 19
The document discusses different types of data and analytics. It defines data as facts and figures that can be used to draw conclusions. Both quantitative and qualitative data are described. Quantitative data includes numbers that can be objectively measured, while qualitative data includes descriptions that may be subjectively measured. The document also discusses descriptive analytics, which interprets historical data to understand problems; predictive analytics, which uses data to predict future outcomes; and prescriptive analytics, which recommends optimal actions by combining constraints with other analytics.
Data Quality Considerations for CECL MeasurementLibby Bierman
This webinar covers how institutions should be getting their data ready for the Current Expected Credit Loss Model, CECL, which will be the new standard for the ALLL or allowance for loan and lease losses.
Find out more at alll.com.
1) The document proposes four steps to make organizational decisions more robust and reduce the impact of cognitive biases: (1) identify high-risk decisions, (2) consider major uncertainties through sensitivity analysis and simulations, (3) consider alternative viewpoints to avoid confirmation bias, and (4) structure decision-making processes with pre-mortems, stage-gating, and devil's advocates.
2) Prediction markets and stage-gating with go/no-go decisions are proposed to involve employees, monitor projects iteratively, and insert points to reconsider projects.
3) Pre-mortems are suggested to legitimize dissent by hypothetically exploring why projects might fail before they start.
Evaluating the impact of trade liberalization on poverty with CGE/Micro-Simulation: a review of literature and an illustration with MIRAGE_HH (MIRAGE-Households)
Literature review: measurement of client outcomes in homelessness servicesMark Planigale
Explores a wide range of practical and theoretical issues relating to introduction of client outcomes measures in welfare / human service organisations, with a particular focus on the housing and homelessness assistance sector.
This document provides an overview of tools for theory of change analysis of environmental programs. It discusses how reconstructing a program's conceptual model, theory of change, and logical framework can provide clarity. The conceptual model should clearly define the intended impact, threats, and strategies. The theory of change should show outputs and outcomes with clear causal links and assumptions. The logical framework presents impacts, outcomes, and outputs in a table with indicators, baselines, and means of verification. Reconstructing these elements verifies and clarifies a program's underlying logic and assumptions.
The document summarizes research on the quality of life insurance advice provided by agents in India. It finds that agents overwhelmingly recommend whole life policies over term life policies, even when term policies better meet customers' needs for risk coverage at low cost. Agents are motivated to recommend whole life due to higher commissions. The research tests whether the quality of advice improves when disclosure requirements make agency problems more transparent or when competition is increased. It uses an audit study approach with standardized customer profiles to evaluate agent recommendations across conditions.
The document presents a dynamic discrete choice model of demand for insecticide treated nets (ITNs) that accounts for time inconsistent preferences and unobserved heterogeneity. The model has three periods where agents make ITN purchase and retreatment decisions. Agents are either time consistent, "naive" time inconsistent, or "sophisticated" time inconsistent. The model is identified in two steps - first when types are directly observed using survey responses, and second when types are unobserved. Identification exploits variation from elicited beliefs about malaria risk. The model can point identify time preference parameters and utility functions up to a normalization.
This document summarizes a study on the long-term effects of teacher performance pay in India. The study conducted a 5-year randomized controlled trial across hundreds of schools in Andhra Pradesh. Schools were randomly assigned to receive either individual or group-based teacher incentives linked to student test scores, or to serve as controls. The study finds that students whose teachers received individual incentives scored significantly higher than controls on math and language tests after 5 years of exposure. They also scored higher on non-incentivized subjects. The study provides some of the longest-running and most robust evidence on performance pay for teachers.
The document summarizes a study that evaluated the impact of a business literacy intervention in rural Mexico. The intervention provided free 6-week business skills courses taught by professors and students to about 25 women entrepreneurs per class. The courses covered topics like accounting, pricing, taxes, and marketing. The study found that the training led to large, positive and significant effects on profits, revenues, and number of clients both in the short- and medium-run. There was also evidence of heterogeneous treatment effects. The results suggest the training improved accounting practices, lowered costs, increased mark-ups, and in some cases lowered prices.
Deloitte 2016 - EKF - Digital Customer Journey - conference material - f...Maciej Malesa
1) The document analyzes digital banking channels in Poland, including a study of customer journeys, functionality, and user experience across 15 major banks.
2) It finds that while banks have digitized early stages of the customer journey like information gathering and account opening, they have not fully supported financial goals through the relationship. Transactional banking is more digitized than credit products.
3) Banks fall into four categories based on their digital strategies - Cheetahs, Antelopes, Bears, and Turtles. Cheetahs offer the most functionality consistent with best practices but neglect user experience, while the mobile channel now provides a better user experience than web.
Dow Corning tasked a team with developing a model to forecast global silicone demand. The team analyzed industrial indicators and GDP data to build a regression model. The model forecasts demand within 1.4% error and consistently deviates from actual values by less than 15%. The team recommends continuous updates to the model as new data becomes available to improve accuracy.
Demand Planning Leadership Exchange: SAP APO DP Statistical Forecast Optimiza...Plan4Demand
866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com
If you are still using manual processes to support your demand planning cycles outside of APO, this Leadership Exchange is for you and your team. Join us to learn how to remove the burden of magnitude and get back on the track to leveraging your SAP APO DP to the fullest beginning with Statistical Forecast Optimization.
The session will focus on common issues and methods to maximize your implementation in order to really turbo-charge your Demand Planning. To do this, we’ll touch upon ways to simplify the process, which statistical models to use and when, and how to prioritize and manage by exception effectively for the long haul to evolve with your business.
A few key takeaways from this session include:
How to unclutter the process
Which Statistical Model to use & When
Tips for holistic optimization
Future design considerations
Check out this webinar on-demand at http://plan4demand.com/Video-SAP-APO-DP-Statistical-Forecast-Optimization
This document summarizes John Sneed's research on developing an earnings forecasting model based on theoretical factors rather than statistical selection of variables. It begins by describing Ou's existing model and its limitations in relying on statistical techniques without theoretical justification. It then discusses theories from economics literature on factors that could lead to differential profits across firms/industries: improper measurement of intangible capital like R&D/advertising, differential returns on such investments, and existence of market power. Based on these theories, Sneed develops a model incorporating variables like average R&D over 5 years, prior year's advertising, and 5-year average capital expenditures to test if it improves upon Ou's model.
The document discusses the design of an impact evaluation using an explanatory controlled before and after study design with multiple time points to collect quantitative and qualitative data to explain the results of a health insurance program called bHIP in Bangladesh. It considers two design options - enrolling all 16 districts over 5 years or enrolling 4 intervention and 4 control districts over 3 years - and discusses choosing indicators aligned with the program's theory of change.
The use of biodata for employee selection: Past research and future implicationsAndrea Dvorak
Summary of the article 'The use of biodata for employee selection: Past research and future implications.'
2009,human resource management review - human resource,no. 3,pp. 219-231,vol. 19
The document discusses different types of data and analytics. It defines data as facts and figures that can be used to draw conclusions. Both quantitative and qualitative data are described. Quantitative data includes numbers that can be objectively measured, while qualitative data includes descriptions that may be subjectively measured. The document also discusses descriptive analytics, which interprets historical data to understand problems; predictive analytics, which uses data to predict future outcomes; and prescriptive analytics, which recommends optimal actions by combining constraints with other analytics.
Data Quality Considerations for CECL MeasurementLibby Bierman
This webinar covers how institutions should be getting their data ready for the Current Expected Credit Loss Model, CECL, which will be the new standard for the ALLL or allowance for loan and lease losses.
Find out more at alll.com.
1) The document proposes four steps to make organizational decisions more robust and reduce the impact of cognitive biases: (1) identify high-risk decisions, (2) consider major uncertainties through sensitivity analysis and simulations, (3) consider alternative viewpoints to avoid confirmation bias, and (4) structure decision-making processes with pre-mortems, stage-gating, and devil's advocates.
2) Prediction markets and stage-gating with go/no-go decisions are proposed to involve employees, monitor projects iteratively, and insert points to reconsider projects.
3) Pre-mortems are suggested to legitimize dissent by hypothetically exploring why projects might fail before they start.
Evaluating the impact of trade liberalization on poverty with CGE/Micro-Simulation: a review of literature and an illustration with MIRAGE_HH (MIRAGE-Households)
Literature review: measurement of client outcomes in homelessness servicesMark Planigale
Explores a wide range of practical and theoretical issues relating to introduction of client outcomes measures in welfare / human service organisations, with a particular focus on the housing and homelessness assistance sector.
This document provides an overview of tools for theory of change analysis of environmental programs. It discusses how reconstructing a program's conceptual model, theory of change, and logical framework can provide clarity. The conceptual model should clearly define the intended impact, threats, and strategies. The theory of change should show outputs and outcomes with clear causal links and assumptions. The logical framework presents impacts, outcomes, and outputs in a table with indicators, baselines, and means of verification. Reconstructing these elements verifies and clarifies a program's underlying logic and assumptions.
Promise 2011: "Local Bias and its Impacts on the Performance of Parametric Es...CS, NcState
This document discusses local bias in parametric estimation models and its impact on model performance. It defines local bias as the deviation between parameters calibrated from local data versus general model defaults. An analysis of a software cost estimation model finds local bias varies between data groups and is positively correlated with decreased model accuracy and increased uncertainty, as measured by mean and variance of magnitude of relative error. The implications are that local bias should be identified and addressed to improve model evolution and balance accuracy versus stability.
This document summarizes an application of Bayesian analysis to forecast insurance loss payments. It begins with an overview of Bayesian methodology and credibility theory. It then presents a case study using a Bayesian hierarchical model to forecast loss reserves for workers' compensation claims data from 10 large insurance companies. Key steps included specifying the probability model, performing Markov chain Monte Carlo simulations for computation and inference, and checking model fit. Results showed the Bayesian model provided greater reserve estimates than traditional methods and accounted for uncertainty in long-term predictions.
This document discusses how companies can improve their sales and operations planning (S&OP) processes through predictive analytics, scenario planning, and risk management. It recommends that companies use digital modeling, simulation, and probabilistic predictive analytics to evaluate different scenarios and supply chain designs without experimenting on live operations. Incorporating risk management into S&OP allows companies to develop response plans for uncertain events and improve long-term sustainability and competitive advantage.
Governance of Risk in Public Policy - Nigel Gibbensmliebenrood
Modelling is useful for evidence-based policymaking in complex systems to evaluate options and costs/benefits. However, models have limitations and uncertainties that must be clearly communicated. Effective modelling requires constant dialogue between policymakers and modelers to ensure the appropriate question is answered and results are understood and usable. While modelling provides insights, it does not remove uncertainty, and results should not be presented as definitive facts.
1. Six Sigma is a set of techniques and tools for process improvement. It was introduced by Motorola in 1986 and involves identifying and removing the causes of defects and minimizing variability in manufacturing and business processes.
2. The Six Sigma approach follows the DMAIC model which stands for Define, Measure, Analyze, Improve and Control phases of a project. DFSS or DMADV approach is used for new product or service design.
3. Six Sigma defines different levels of belts that people take on - Champions, Master Black Belts, Black Belts, Green Belts and Yellow Belts to lead Six Sigma projects and implement process improvements.
Sequence-to-Sequence Modeling for Time SeriesArun Kejariwal
In this talk we overview Sequence-2-Sequence (S2S) and explore its early use cases. We walk the audience through how to leverage S2S modeling for several use cases, particularly with regard to real-time anomaly detection and forecasting.
With the current expected credit loss (CECL) model for the Allowance on the horizon, bankers will be asked to create future-looking methodologies that adjust for reasonable and supportable forecasts. Without adequate modeling experience, that can be a challenge for community banks and credit unions.
Watch the full webinar here: http://web.sageworks.com/forward-looking-alll-adjustments/
1. The document introduces Information Quality (InfoQ) and Practical Statistical Efficiency (PSE) as approaches to assess the quality and impact of research.
2. InfoQ involves evaluating eight dimensions of data quality: resolution, structure, integration, temporal relevance, sampling bias, chronology, concept operationalization, and communication/visualization. Each dimension is scored to calculate an overall InfoQ Score.
3. PSE evaluates how effectively research recommendations are implemented and their value over time. It assesses probability of implementation, time implemented, and other impact factors.
Cash and vouchers led to starkly different purchasing patterns. Voucher recipients purchased fewer types of items compared to cash recipients, indicating the voucher was extra-marginal for some food items. However, there were few differences in other outcomes like food security and asset ownership between the groups. Cash recipients were able to save more of the transfer value since vouchers could only be spent on certain goods. The voucher program also had higher costs for the implementing agency than cash transfers.
This study examines the stability of social, risk, and time preferences over multiple years using data from 2002, 2007, 2009, and 2010. The main findings are:
1. Risk preferences are not stable over time, while time preferences are highly stable.
2. Experimental measures of social preferences like altruism and trust show little stability over time.
3. There is some evidence that previous experimental outcomes can influence preferences in later experiments, such as being unlucky increasing later risk aversion or being paired with a generous partner increasing later generosity.
4. However, the impacts across experiments are small and the results should be interpreted cautiously due to sample attrition and differences in experimental designs over the years.
The study evaluated the impact of an alternative cash transfer program for education in Morocco that provided small, unlabeled cash transfers to fathers in poor communities. Over two years:
1. The unconditional cash transfers reduced the school dropout rate by 67-75% among children enrolled at baseline and increased school reentry by 85% among previous dropouts.
2. Adding attendance conditions did not provide additional educational gains compared to the unconditional transfers.
3. There was also little difference in impacts between transfers made to mothers versus fathers.
4. The program appeared to work in part by changing parents' perceptions of the returns to education and quality of local schools, without directly imposing conditions.
This document describes a study examining how increased access to mobile phones impacted small boat manufacturers in Kerala, India. The researchers conducted a census of 143 boat builders from 1997-2004, collecting data on output, prices, and boat quality. Prior to phones, builders only served local demand. Phones increased information sharing, allowing fishermen to learn about distant builders. This likely expanded each builder's effective market size. The study tests if this led more productive builders to grow and less productive ones to exit, increasing average firm size and productivity over time. The natural experiment from phone diffusion provides an opportunity to study these impacts.
This document discusses a new approach to measuring the impact of foreign labor on native employment. It presents two natural experiments using data on H-2A visa workers and unemployment insurance records from North Carolina farms. The results section analyzes the effect of the recession on job referrals and native labor supply, finding that higher unemployment led to more job referrals but lower native employment, suggesting native workers withdrew from the labor market during economic downturns.
The document discusses increasing girls' enrollment in secondary schools in India. It notes that the gender gap in education is more pronounced in Bihar, with girls' enrollment dropping off sharply at age 14 when transitioning to secondary schools. Distance to secondary schools is a major barrier, with enrollment declining as distance increases. The authors propose exploring cost-effective, scalable alternatives to expanding access beyond the default approach of school construction, such as providing bicycles.
This paper examines middleman margins and the impact of providing price information to potato farmers in West Bengal, India. It finds that middlemen earn very large margins of 50-90% of farmgate prices on average. When farmers were provided daily wholesale market price information, it had no average effect on prices received but increased volatility, consistent with a bargaining model. This suggests neither risk-sharing nor asymmetric information play a major role in middlemen margins. The key cause of high margins appears to be the market power of middlemen in the hierarchical potato marketing system.
This document summarizes a study on daily income targets and labor supply among 257 Kenyan bicycle taxi drivers. The study collected detailed daily logs from participants over several months, including whether they had a cash need that day and if so, the amount. The results suggest that drivers (1) have cash needs that vary substantially and put them off until the last day, (2) work more on high-need days, and (3) are more likely to quit after reaching their target for the day. Providing unexpected cash payments did not affect labor supply. The study aims to better understand how individuals without formal work arrangements motivate themselves to meet daily income targets.
This study evaluates a performance incentive program in Mexican high schools aimed at improving mathematics achievement. It provided monetary incentives to students (T1), teachers (T2), or both (T3) based on scores on curriculum-aligned mathematics tests. The program was implemented over three school years in 88 schools randomly assigned to treatment or control groups. Initial results found positive effects on test scores, with the largest effects in the treatment combining incentives for both students and teachers.
1) The document describes a field experiment that randomized offers of index insurance to agricultural households in India to study the interaction between formal insurance and informal risk-sharing networks.
2) It finds that the presence of strong informal risk-sharing networks through castes/jatis reduces demand for formal insurance, and that basis risk, where payouts do not perfectly correlate with losses, also reduces demand.
3) However, informal and formal insurance can be complements when basis risk is high, as both provide partial coverage against different risks. The study uses detailed survey and rainfall data on castes/jatis to characterize their risk-sharing practices.
The document discusses a study on the impact of female property rights on suicide rates in India. It notes that women's ability to inherit property is restricted in many societies, including in India. The study uses variation in property rights for women generated by state amendments to inheritance laws and land reforms. It develops a model of intra-household bargaining incorporating conflict and finds that better property rights for women are associated with a decrease in the gender difference in suicide rates but an increase in overall male and female suicide rates, possibly due to increased intra-household conflict from challenging traditional gender roles.
The document analyzes how pro-poor growth, or reducing poverty, will impact global energy demand. It develops an economic model showing that as household income grows over time in a nonlinear way, ownership of durable goods like refrigerators also increases nonlinearly. The model predicts that the pace of income growth matters, with more uneven income growth leading to higher durable ownership. Analysis of a Mexican anti-poverty program confirms the model's predictions, showing that households receiving more uneven transfers over time were more likely to acquire refrigerators. The findings suggest that projections of energy demand need to account for how rapidly populations rise out of poverty to avoid underestimating future demand.
The document describes an experiment conducted in Malawi to test if job referral networks disadvantage women. The experiment found that when people could refer either men or women, only 30% of referrals were women, compared to 38% of initial applicants. However, when people could only refer one gender, men and women referred at similar rates regardless of the gender they were restricted to. This suggests social incentives rather than differences in productivity lead referral networks to disadvantage women.
This document summarizes a study evaluating Mexico's Hábitat program, which aims to improve infrastructure and quality of life in marginalized urban areas. The study uses a randomized saturation design where municipalities were randomly assigned a treatment fraction, and polygons within municipalities were then randomly assigned as treatment or control. Over 9,700 households were surveyed at baseline in 2009 and follow-up in 2012 to evaluate impacts on infrastructure access, health, social capital, and other outcomes. The randomized design at multiple levels aims to quantify program impacts while accounting for potential spillovers between treatment and control areas.
This document summarizes research on the relationship between medieval trade and religious tolerance in South Asia. The key findings are:
1) Medieval ports in South Asia that benefited from overseas trade centuries ago experienced significantly less Hindu-Muslim violence over the last 200 years compared to other towns, despite being more ethnically diverse.
2) This effect is attributed to the exogenous and non-replicable complementarities between Hindu and Muslim communities that arose from medieval trade, not other factors like wealth, geography, or institutions alone.
3) The research provides evidence that exogenous changes generating robust inter-ethnic complementarities can have a lasting positive impact on peaceful coexistence in ethnically diverse societies.
The paper analyzes the price effects of cash versus in-kind transfers using data from a randomized experiment in Mexico. It finds that:
1) Prices for goods decline more for in-kind transfers than for cash transfers, with the difference in price effects being statistically significant.
2) The price effects are larger in more remote villages, consistent with those villages having more closed economies and less competition.
3) The results provide empirical evidence that in-kind transfers can have larger impacts on prices than cash transfers due to introducing supply as well as demand for goods in local markets.
The document discusses subsidies for malaria treatment. It notes that malaria kills over 1 million people per year. A new effective drug called ACT is available but unaffordable for most. A program called AMFm aims to reduce ACT prices via subsidies to improve access and fight drug resistance, but there is a risk of overtreatment which could waste money and contribute to drug resistance. The paper aims to study how to balance improved access and targeting of subsidies for ACT malaria treatment.
This document summarizes a study that evaluated the impact of a mobile phone-based agricultural advice and information service called Avaaj Otalo (AO) on cotton farmers in Gujarat, India. The study used a randomized controlled trial with 1,200 farmers randomized to either receive AO, AO plus physical extension sessions, or serve as pure controls. The study examined the impact on sources of information, agricultural knowledge, and farming practices. It also analyzed peer effects and information sharing. The results of the study will provide insights into the effectiveness of using mobile phones to deliver agricultural extension services and the diffusion of information among farmers.
1. Introduction Data Model Identification Estimation Results
Estimation of a Dynamic Agricultural Production
Model with Observed, Subjective Distributions
Brian Dillon
Cornell University
and Harvard Kennedy School
August 30, 2012
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
2. Introduction Data Model Identification Estimation Results
Motivation: crop production
To grow crops, farmers solve a dynamic resource allocation problem
The problem is not unlike many other dynamic choice problems:
portfolio management, inventory management, human capital
investment
The solution to this problem can involve delay of some choices,
distribution of activities across time, and updating of expectations
as new information arrives
Between-farmer variation in expectations clearly matters (Gin´,
e
Townsend, Vickery 2008)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
3. Introduction Data Model Identification Estimation Results
What if we measure expectations?
Early literature in agricultural economics (Bessler and Moore 1979;
Eales 1990)
Manski (2004) makes the case for measuring expectations
Nyarko and Schotter (2002) show that there is a big difference
between observed and estimated expectations
Delavande et al (2010) review the recent development literature
that uses subjective probabilities
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
4. Introduction Data Model Identification Estimation Results
What we get from measuring expectations
Two contributions to the estimation of dynamic choice models:
1. Allow us to relax the rational expectations assumptions that
are standard for these models (Wolpin 1987; Rust 1987, 1997;
Fafchamps 1993)
2. There is a lot of information in a subjective distribution over
an endogenous outcome
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
5. Introduction Data Model Identification Estimation Results
Why go through a structural exercise?
Apart from the pure value of estimating a less restricted
production function...
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
6. Introduction Data Model Identification Estimation Results
Why go through a structural exercise?
Apart from the pure value of estimating a less restricted
production function...
Production elasticities tell us something about resilience of the
production process to shocks
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
7. Introduction Data Model Identification Estimation Results
Why go through a structural exercise?
Apart from the pure value of estimating a less restricted
production function...
Production elasticities tell us something about resilience of the
production process to shocks
What we know about shocks already largely deals with
• Consumption/asset smoothing (Townsend 1994, Morduch
1995, Hoddinott 2006, Barrett and Carter 2006, Jacoby and
Skoufias 1998, Fafchamps et al 1998)
• Human capital (Hoddinott and Kinsey 2001, Aguilar and
Vicarelli 2012)
• Two papers look at how farmers move labor across time,
within a season: Fafchamps (1993) and Kochar (1999)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
8. Introduction Data Model Identification Estimation Results
Why go through a structural exercise?
And we can also simulate important, relevant policies:
1. Insurance
2. Forward contracting
3. Improvements in information delivery
4. Changes in input supply
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
9. Introduction Data Model Identification Estimation Results
Contributions of this paper
We use a sequence of observed inputs, price expectations, and
yield expectations to estimate an agricultural production function
Methodological contributions:
1. Develop a general method for estimating dynamic choice
models with observed subjective distributions
2. Show how counterfactual choice data (“How much pesticide
did you want to apply last week?”) can be used in estimation
Substantive contributions:
1. Recover estimates of all elasticities of substitution between
inputs (within and across periods)
2. Simulate the impact of insurance, forward contracting, and
information provision policies
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
10. Introduction Data Model Identification Estimation Results
Plan of the talk
• Data set
• Model basics
• Identification of shock densities
• Estimation
• Results
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
11. Introduction Data Model Identification Estimation Results
Data set
• 195 cotton farmers in 15 villages in NW Tanzania
• Face-to-face agriculture and LSMS surveys conducted in
summer 2009 and summer 2010
• From September 2009 - June 2010: investment, time use,
shocks, agricultural input and output, and other data
gathered every 3 weeks
• High frequency interviews also gathered subjective probability
distributions over end-of-season prices and yields, and
qualitative distributions over pest pressure and rainfall at
various points throughout the year
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
12. Introduction Data Model Identification Estimation Results
Measuring subjective distributions
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Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
13. Introduction Data Model Identification Estimation Results
Evolution of subjective price distributions
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of stones 2
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2 3
1 2 Bin number
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Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
14. Introduction Data Model Identification Estimation Results
Evolution of subjective yield distributions
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Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
15. Introduction Data Model Identification Estimation Results
Smoothing of distributions
Let
• xi be a response vector
• d ∈ RN+1 be the interval boundaries
• z be the random variable in question
• k be the number of counters
We fit a four parameter beta CDF, Gi (z | a, b, ρ, κ), by solving:
N j 2
m=1 xj
(ai , bi , ρi , κi ) = arg inf − G (dj+1 | a, b, ρ, κ)
a,b,ρ,κ k
j=1
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
16. Introduction Data Model Identification Estimation Results
Sample summary statistics
Mean sd Min Max
Household size (people) 8.33 3.90 2 23
Dependency ratio* 1.31 0.85 0 5.5
Head age 46.85 14.69 20 100
Head is male (%) 85.0 - - -
Years of education (HH head) 4.19 3.46 0 11
Radios 0.83 0.71 0 4
Bicycles 1.19 1.00 0 10
Dairy cattle 1.33 2.84 0 20
Non-dairy cattle 3.87 7.89 0 60
Goats 5.27 8.05 0 50
Sheep 1.67 3.74 0 30
Total acres 9.67 11.03 1 82
Number of plots 2.71 1.17 1 7
Number of crops grown 3.45 1.26 1 8
Labor expenditure (TSH) 78,248 139,485 0 1,020,000
Fertilizer expenditure (TSH) 21,149 81,359 0 715,000
Animal labor expenditure (TSH) 33,497 92,724 0 750,000
Transport expenditure (TSH) 10,333 20,049 0 144,000
Other cultivation expenditure (TSH) 6,929 15,817 0 100,000
Total cultivation expenditure (TSH) 150,156 254,863 0 1,514,700
Notes: author's calculation from survey data; cultivation data refers to 2008-2009 cultivation of all
crops; 1 USD ! 1 400 TSH; *Dependency ratio is number of persons aged < 15 or aged > 65 divided
!"1,400
by number aged between 15 and 65.
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
17. Introduction Data Model Identification Estimation Results
Model assumptions
Important:
1. Farmers are dynamically consistent (will relax, if we have time)
2. Independence of shocks across time
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
18. Introduction Data Model Identification Estimation Results
Model assumptions
Important:
1. Farmers are dynamically consistent (will relax, if we have time)
2. Independence of shocks across time
Less fundamental:
1. Separable household model
2. Risk-neutral maximization of expected plot-level profits
3. All forms of labor are interchangeable
4. No binding credit constraints
5. Functional form choices
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
19. Introduction Data Model Identification Estimation Results
Crop evolution
Expanding on Fafchamps (1993), crops grow according to:
yi0 = φi Ai e θi0
yi1 = h1 (yi0 , li1 , pi1 )e θi1
yi2 = h2 (yi1 , li2 , pi2 )e θi2
yi = h3 (yi2 , li3 , pi3 )e θi3
where θit ∼ git (θit ) for t = 0, . . . , 3
Ai is acreage
φi is a plot-specific yield shifter
li and pi are labor and pesticides
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
20. Introduction Data Model Identification Estimation Results
Crop evolution cont.
We use nested CES functions:
1
γ γ γ
h1 (y0 , l1 , p1 | α1 , α2 , γ) = [α1 y0 + α2 l1 + (1 − α1 − α2 )p1 ] γ
1
δ δ δ δ
h2 (y1 , l2 , p2 | β1 , β2 , δ) = β1 y1 + β2 l2 + (1 − β1 − β2 )p2
1
ω ω ω
h3 (y2 , l3 , p3 | κ1 , κ2 , ω) = [κ1 y2 + κ2 l3 + (1 − κ1 − κ2 )p3 ] ω
Which gives us 9 production parameters to estimate:
• Share parameters (α1 , α2 , β1 , β2 , κ1 , κ2 )
• Transformed elasticity parameters (γ, δ, ω)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
21. Introduction Data Model Identification Estimation Results
Farmer’s objective function
From the viewpoint of the first period:
δ
γ γ γ
max E [qc ]Eθi1 θi2 θi3 κ1 β1 α1 yi0 +α2 li1 +(1−α1 −α2 )pi1 γ
e δθi1
li1 ,pi1
ω
δ
∗δ ∗δ ∗ω
+ β2 li2 + (1 − β1 − β2 )pi2 e ωθi2 + κ2 li3
1 3
ω
∗ω
+ (1 − κ1 − κ2 )pi3 e θi3 − (ql lit + qp pit )
t=1
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
22. Introduction Data Model Identification Estimation Results
Identification of gt (θt )
We need measures of gt (θt ) in order to proceed
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
23. Introduction Data Model Identification Estimation Results
Identification of gt (θt )
We need measures of gt (θt ) in order to proceed
Nested fixed point method (Rust 1987): iterate between guesses of
production parameters and gt parameters until convergence
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
24. Introduction Data Model Identification Estimation Results
Identification of gt (θt )
We need measures of gt (θt ) in order to proceed
Nested fixed point method (Rust 1987): iterate between guesses of
production parameters and gt parameters until convergence
But we only observe subjective output distributions
Ψ0 (y ), . . . , Ψ3 (y )
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
25. Introduction Data Model Identification Estimation Results
Identification of gt (θt )
We need measures of gt (θt ) in order to proceed
Nested fixed point method (Rust 1987): iterate between guesses of
production parameters and gt parameters until convergence
But we only observe subjective output distributions
Ψ0 (y ), . . . , Ψ3 (y )
We can use those to directly estimate gt (θt ), within the context of
the model
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
26. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
y reported
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
27. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
!3 realized
y reported
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
28. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l3 , p3) chosen
!3 realized
y reported
"3(y) reported
incl:
g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
29. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l3 , p3) chosen
!2 realized !3 realized
y reported
"3(y) reported
incl:
g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
30. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l2 , p2) chosen (l3 , p3) chosen
!2 realized !3 realized
y reported
"2(y) reported
incl:
g2(!2) "3(y) reported
g3(!3) incl:
(l3* , p3*) g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
31. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l2 , p2) chosen (l3 , p3) chosen
!1 realized !2 realized !3 realized
y reported
"2(y) reported
incl:
g2(!2) "3(y) reported
g3(!3) incl:
(l3* , p3*) g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
32. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l1 , p1) chosen (l2 , p2) chosen (l3 , p3) chosen
!1 realized !2 realized !3 realized
y reported
"1(y) reported
incl:
g1(!1) "2(y) reported
g2(!2) incl:
g3(!3) g2(!2) "3(y) reported
(l2* , p2*) g3(!3) incl:
(l3* , p3*) (l3* , p3*) g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
33. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l1 , p1) chosen (l2 , p2) chosen (l3 , p3) chosen
!0 realized !1 realized !2 realized !3 realized
y reported
"1(y) reported
incl:
g1(!1) "2(y) reported
g2(!2) incl:
g3(!3) g2(!2) "3(y) reported
(l2* , p2*) g3(!3) incl:
(l3* , p3*) (l3* , p3*) g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
34. Introduction Data Model Identification Estimation Results
Timeline of decisions, realizations, and data collection
(l1 , p1) chosen (l2 , p2) chosen (l3 , p3) chosen
!0 realized !1 realized !2 realized !3 realized
"0(y) reported
incl: y reported
g0(!0) "1(y) reported
g1(!1) incl:
g2(!2) g1(!1) "2(y) reported
g3(!3) g2(!2) incl:
(l1* , p1*) g3(!3) g2(!2) "3(y) reported
(l2* , p2*) (l2* , p2*) g3(!3) incl:
(l3* , p3*) (l3* , p3*) (l3* , p3*) g3(!3)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
35. Introduction Data Model Identification Estimation Results
Identification of g3 (θ3 )
Taking the normalization E [e θt ] = 1 for all t:
Pr[y < Y ] = Pr E [y |Ω3 ]e θ3 < Y
Y
= Pr θ3 ≤ ln
E [y |Ω3 ]
where Ω3 is the period 3 information set
⇒ g3 (θ3 ) is constructed by transforming ψ3 (y )
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
36. Introduction Data Model Identification Estimation Results
Key proposition (summarized)
Proposition
If h = H(θ1 , θ2 ) is a function of two random variables, and
1. We know densities fh (h) and fθ2 (θ2 )
2. H is monotonic in θ1
then we can consistently estimate fθ1 (θ1 ) by taking repeated draws
from fh and fθ2
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
37. Introduction Data Model Identification Estimation Results
Identification of g2 (θ2 )
∗ ∗
Plugging (l3 , p3 ) into the definition of output allows us to write
output from the period 2 perspective as:
y= H2 φ, α1 , α2 , β1 , β2 , κ1 , κ2 , γ, δ, ω;
A, l1 , p1 , l2 , p2 ; ql , qp , E [qc ]; θ0 , θ1 e θ2 e θ3
∞
And E [y |Ω2 ] = −∞ y ψ2 (y )dy = H2 (·)
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
38. Introduction Data Model Identification Estimation Results
Identification of g2 (θ2 ) cont.
This gives a method for numerically estimating g1 (θ1 ) using
repeated draws from ψ1 (y ) and g2 (θ2 )
M
1 ym
Pr[θ2 < Θ2 ] = I ln − θ3m ≤ Θ1
M E [y |Ω2 ]
m=1
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
39. Introduction Data Model Identification Estimation Results
Estimation of θt and φ
Given any guess of the parameters, we find the realized values of
the shocks:
• θ0 , θ1 , θ2 come from FOC of the farmer’s decision problem
• θ3 comes from realized output y and ψ3 (θ3 )
Lastly
∞
ˆ −∞ y ψ0 (y )dy
φ=
A
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
40. Introduction Data Model Identification Estimation Results
Likelihood function
Then the joint likelihood for the observed inputs, output and
distributions is:
L(α1 , α2 , β1 , β2 , κ1 , κ2 , γ, δ, ω |
P
i i i i
φ, A, l, p, y , ql , qp , E [qc ], θ0 , θ1 = gi0 (θ0 )gi1 (θ1 )gi2 (θ2 )gi3 (θ3 )
i=1
We maximize the log likelihood over the 9 production parameters
and: α1 , α2 , β1 , β2 , κ1 , κ2 , γ, δ, ω
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
41. Introduction Data Model Identification Estimation Results
Results: shock densities
Summary statistics for gt (θt )
Variable Mean s.d.
!0 lower bound -2.95 1.86
!0 upper bound 2.43 1.70
E[!0] -0.14 0.61
!1 lower bound -2.49 1.84
!1 upper bound 2.01 1.48
E[!01] -0.01 0.58
!2 lower bound -4.19 1.4807*
!2 upper bound 3.19 1.4807*
E[!2] -2.35 1.17
N 212 212
*SD of !2 upper and lower bounds is constant by
construction, because both reflect variation in acreage
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
42. Introduction Data Model Identification Estimation Results
Conclusion
Separation of output equation into its dynamic and stochastic
components is not a necessary condition for this to work
But monotonicity in θt is necessary
Observation of shock densities reduces number of parameters to be
estimated
But it also increases the pressure on the functional form, because
the error variance does not adjust to increase the contribution of
very low probability parameter contributions to the likelihood
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
43. Introduction Data Model Identification Estimation Results
Where things stand...
Ongoing work on this paper involves:
1. Embedding the farmer’s problem in a utility framework
2. Comparing results with those from the nested fixed point
method
3. Interpretation and simulations
4. Relaxing the dynamic consistency assumption?
→ could use data on counterfactual, optimal pesticide
application
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O
44. Introduction Data Model Identification Estimation Results
Thanks!
Brian Dillon Estimation of a Dynamic Agricultural Production Model with O