Semi-convergence of an Iterative Algorithm

An iterative method is introduced for solving noisy, ill-conditioned inverse problems. Analysis of the semi-convergence behavior identifies three error components - iteration error, noise error, and initial guess error. A derived expression explains how the three errors are related to each other relative to the number of iterations. The Standard Tikhonov regularization method is just the first iteration of the iterative method and the derived noise damping filter is a generalization of the Standard Tikhonov filter. The derived filter is a function two parameters, a regularization parameter and the iteration number parameter. The new method is tested on image reconstruction from projections simulated data set

Development, Technology Adoption, and Social Networks

Agriculture remains a key component of economic development, but the methodology for how development policies are determined has changed for developing countries. In the last decade, the focus of economic growth in developing countries has shifted from country-wide prescriptions to testable micro-development programs at the local level. As international development focuses in on local programs, social networks have been identified as a key component for their effective deployment. This dissertation analyzes the effects of a social network-based intervention. It contributes to the economics literature on identifying social network effects by implementing a randomized encouragement design to develop social capital, while simultaneously introducing a new method of development training. The program implemented here is comprised of two parts, and was conducted with female-headed households in rural Uganda, that were growing a relatively new cash crop, cotton. The first part conducted social network-based information games in 20 sample villages, in which each participant was trained in one aspect of cultivating cotton, and encouraged to attain a full set of knowledge on growing cotton through her assigned learning networks. They were presented with two different incentives schemes for accumulating information: competitive and team incentives. The second portion of the program paired the surveyed individuals at random with other game participants. These pairs were encouraged to develop team goals across the growing season and a time schedule for networking as well as update and share their learned information from the games on a regular basis. The estimated effects of the SNI, which comprise this dissertation, include both the effects from the information games and the effects of the mentored pairing; that is, the impact of acquiring one information point and one new link. I compare the effects of this program to a standard agricultural training program that was concurrently conducted during this research, in which extension agents taught the same information that was presented in the information games but with a traditional classroom-based teaching method. My games analysis shows that females learn more when presented with competitive incentives. The total number of learning points learned during competitive incentives first order stochastically dominates the total number of learning points learned during team incentives. However, for the dissemination of one specific information point, team incentives are better at ensuring that a unique information point reaches the entire group. Difference in difference estimates, controlling for the training program, show that the overall SNI program had significant effects on the average farmer, with diminishing returns for higher yielding farmers. I find that these average effects are comparable to the effects of the conventional training program, but at a fifth of the implementation cost. A closer examination shows that the SNI program has its most significant effect for farmers growing around the average output when the program was started in 2009 (100-200 kgs/acre), while the Training program has its greatest and most significant impact for those yielding above the average output in 2009. Therefore, the two programs are not necessarily substitutes in how they effect change. My research shows that a competitive incentive structure coupled with social network-based learning serves as an effective paradigm for improving outcomes for the poorest producers

The effects of school quality on fertility in a transition economy

This paper investigates the effects of school quality on fertility in a transition country. It aims to explain the slowing fertility and shrinking rural sector of a post Soviet country, Ukraine, through the decline in the quality of public services, in particular, school quality. It builds on earlier work of Rosenzweig (1982), which tests for the effects of a change in the price of child quality, measured here by school quality. Estimates from a generalized Poisson model of fertility show that school quality has a positive and significant effect on household fertility. Specifically, a 10 % increase in teacher quality is associated with a 3+% rise in fertility. This positive relationship between education and fertility distinguishes itself from the negative relationship that is commonly observed between these two factors. It also suggests that Ukraine should reconsider its population policies that are aimed at increasing fertility, from short term income transfers for rural families to long term investments into the quality and equality of their education system

Traversing the landscape of experimental power

We present an overview of the use of power calculations in experimental economics as well as other disciplines. We review the methodology proposed by the field of economics as well the pitfalls in failing to incorporate power calculations in lab and field experiments. We write this note to further draw attention to the issue, and to make a case that details of power calculations should be reported in experimental economics papers. This note should serve as a reference and overview to researchers in experimental economics on power calculations

As Good as the Networks They Keep?: Improving Outcomes through Weak Ties in Rural Uganda

We examine an intervention randomized at the village level in which female farmers invited to a single training session were randomly paired with farmers whom they did not know and encouraged to share new agricultural information throughout the growing season for a recently adopted cash crop. We show that the intervention significantly increased the productivity of all farmers except of those who were already in the highest quintile of productivity, and that there were significant spillovers in productivity to male farmers

As Good as the Networks They Keep?: Improving Outcomes through Weak Ties in Rural Uganda

We examine an intervention randomized at the village level in which female farmers invited to a single training session were randomly paired with farmers whom they did not know and encouraged to share new agricultural information throughout the growing season for a recently adopted cash crop. We show that the intervention significantly increased the productivity of all farmers except of those who were already in the highest quintile of productivity, and that there were significant spillovers in productivity to male farmers

How social structure shapes female competition throughout her lifetime

Many studies find a consistent gender gap in competitiveness where men are more likely to compete than women given the same level of ability. Using data from experiments with women ages 12 through 90 in matrilocal and patrilocal communities in rural Malawi, we show that this gender gap does not exist uniformly for all women nor across their whole lifetime. We first replicate three main findings from the gender and competition literature: (i) women are less likely to compete on average; and the gender gap differs by (ii) culture and by (iii) age. In a new finding, we show that the gender gap changes in a theoretically-predicted manner with motherhood status. We argue that these results, when combined, point to an overarching theory of gender and competition–one that is driven by environmental constraints that vary with age, fertility, and social structure. © 202

Correcting for Selection Bias in Learning-to-rank Systems

Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems. However, these data suffer from a number of biases that can result in poor performance for LTR systems. Recent methods for bias correction in such systems mostly focus on position bias, the fact that higher ranked results (e.g., top search engine results) are more likely to be clicked even if they are not the most relevant results given a user's query. Less attention has been paid to correcting for selection bias, which occurs because clicked documents are reflective of what documents have been shown to the user in the first place. Here, we propose new counterfactual approaches which adapt Heckman's two-stage method and accounts for selection and position bias in LTR systems. Our empirical evaluation shows that our proposed methods are much more robust to noise and have better accuracy compared to existing unbiased LTR algorithms, especially when there is moderate to no position bias.Comment: This paper appeared in The Web Conference (WWW'20), April 20-24, 2020, Taipei, Taiwa

Hierarchical Bayes models for daily rainfall time series at multiple locations from heterogenous data sources

We estimate a Hierarchical Bayesian models for daily rainfall that incorporates two novelties for estimating spatial and temporal correlations. We estimate the within site time series correlations for a particular rainfall site using multiple data sources at a given location, and we estimate the across site covariance in rainfall based on location distance. Previous rainfall models have captured cross site correlations as a functions of site specific distances, but not within site correlations across multiple data sources, and not both aspects simultaneously. Further, we incorporate information on the technology used (satellite versus rain gauge) in our estimations, which is also a novel addition. This methodology has far reaching applications in providing more accurate and complex weather insurance contracts based combining information from multiple data sources from a single site, a crucial improvement in the face of climate change. Secondly, the modeling extends to many other data contexts where multiple datasources exist for a given event or variable where both within and between series covariances can be estimated over time

Power(ful) Guidelines for Experimental Economists

Statistical power is an important detail to consider in the design phase of any experiment. This paper serves as a reference for experimental economists on power calculations. We synthesize many of the questions and issues frequently brought up regarding power calculations and the literature that surrounds that. We provide practical coded examples and tools available for calculating power, and suggest when and how to report power calculations in published studies